1- 888- 266- 6909

www. transcri pti onet c. com

FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

REEVALUATION OF

SCIENTIFIC ISSUES ASSOCIATED WITH CHLORPYRIFOS 

HEALTH EFFECT

DOCKET NUMBER: EPA-HQ-OPP-2012-0040

FIFRA SAP WEB SITE 

http://www.epa.gov/scipoly/sap/

OPP Docket Telephone: (703) 305-5805

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL

ONE POTOMAC YARD (SOUTH BUILDING)

2777 SOUTH CRYSTAL DRIVE

ARLINGTON, VIRGINIA 22202

APRIL 10TH – 12TH , 2012
Page 2

1 DR. FRED JENKINS: Good morning again

2 everyone. I want to welcome everyone to this FIFRA Scientific

3 Advisory Panel meeting. The topic of this meeting is

4 Chlorpyrifos health effects. My name is Fred Jenkins, and I am

5 the designated federal official for this meeting. 

6 Before we get started, I just wanted to take a

7 couple of minutes to go over a few administrative items. As the

8 DFO, I serve as the liaison between the panel and the agency. I

9 am responsible for insuring that provisions of the Federal

10 Advisory Committee are met.

11 I want to extend my thanks to this entire

12 Panel for agreeing to serve in this meeting. I want to extend

13 my thanks to the public for attending. 

14 The FIFRA SAP is a Federal Advisory Committee

15 that provided independent scientific peer review and advice to

16 the Agency on pesticide issues. It is important to note that

17 the Panel only provides advice and recommendations to EPA, and

18 all regulatory decision making and implementation authority

19 remains with the Agency. We have worked with appropriate agency

20 officials to ensure that all appropriate ethics regulations are

21 satisfied for this meeting.

22 Panel Members have been provided the
Page 3

1 provisions of the Federal Conflict of Interest Laws, and each

2 participant has filed a financial disclosure report. I, along

3 with our Deputy Ethics Officer and in consultation with the

4 Office of General Counsel, have reviewed these reports to ensure

5 that all ethics requirements are met. 

6 This meeting provides an opportunity for the

7 public to comment. We will have several people who will be

8 speaking during the public comment period. If you have not made

9 prior arrangements and you wish to make a public comment today,

10 please let me know or someone else in our SAP Staff Office. 

11 Everyone in our Staff Office is seated right here to my right. 

12 Let any of us know, and we will make sure that we accommodate

13 time for you to speak. 

14 Without having made prior arrangements, we ask

15 that you please limit your comments to 5 minutes. There is a

16 Public Docket for his meeting. The Docket number is listed on

17 the Agenda. All background materials and other related

18 documents are available in the Docket. I will work to ensure

19 that the EPA slide presentations will be available very soon,

20 hopefully today on the Docket as well.

21 At this point, I want to introduce and extend

22 my thanks to Dr. Kenneth Portier, who is the Chair of the FIFRA
Page 4

1 SAP and also the Chair of this meeting. Thank you, everyone. 

2 DR. KENNETH PORTIER: Thank you and good

3 morning. I want to extend my appreciation to the Panel for all

4 of you being here and taking part in this meeting. At this

5 point, I would like to introduce the Panel. 

6 I am Ken Portier. I am a Biostatistician and

7 Managing Director of the Statistics and Evaluation Center at the

8 American Cancer Society in Atlanta, and we will start with Dr.

9 Pope. 

10 DR. CAREY POPE: I am Carey Pope. I am

11 Professor of Toxicology from Oklahoma State University and the

12 Center for Veterinary Health Sciences. 

13 DR. STEPHEN KLAINE: I am Steve Klaine,

14 Director of the Institute of Environmental Toxicology at Clemson

15 University, and I am a member of the permanent Panel.

16 DR. MARTHA SANDY: I am Martha Sandy. I am a

17 Toxicologist with the Office of Environmental Health Hazard

18 Assessment, part of California EPA and I am member of the

19 permanent Panel. 

20 DR. HEATHER YOUNG: I am Heather Young,

21 Associate Professor of Epidemiology of George Washington

22 University School Public Health and Health Services.
Page 5

1 DR. CHARLES VORHEES: Hi, I am Charles

2 Vorhees. I am Professor of Pediatrics at the University of

3 Cincinnati. I am a Neuroscientist by training, and I am in the

4 division of Pediatric Neurology at Children's Hospital. 

5 DR. JOHN VENA: Good morning, I am John Vena. 

6 I am UGA Foundation Professor and Public Health at the

7 University of Georgia College of Public Health, and I am an

8 Environmental Epidemiologist. I am Head of the Department of

9 Epidemiology and Biostatistics. I am Member of the Chartered

10 Science Advisory Board of EPA. 

11 DR. ALVIN TERRY: Alvin Terry, Professor of

12 Pharmacology and Toxicology at Georgia Health Sciences

13 University.

14 DR. BARRY RYAN: Good morning, my name is

15 Barry Ryan. I am a Professor in the Department of Environmental

16 Health at Rollins School of Public Health of Emory University in

17 Atlanta. 

18 DR. WILLIAM POPENDORF: Morning, I am Will

19 Popendorf, Emeritus Professor of Industrial Hygiene, Utah State

20 University.

21 DR. BETTE MEEK: I am Bette Meek, Associate

22 Director in Chemical Risk Assessment at McLaughlin Center,
Page 6

1 University of Ottawa with a background in Regulatory Risk

2 Assessments. 

3 DR. CHENSHENG LU: Good morning, Alex Lu, I am

4 Associate Professor for Harvard School of Public Health.

5 DR. STELLA KOUTROS: Hi, I am Stella Koutros. 

6 I am an Epidemiologist in the Occupational Environmental

7 Epidemiology Branch in the Division of Cancer Epidemiology and

8 Genetics at the National Cancer Institute.

9 DR. GAYLIA JEAN HARRY: I am Jean Harry, Head

10 of the Neurotoxicology Group at the National Institute of

11 Environmental Health Sciences. 

12 DR. ELLEN GOLD: I am Ellen Gold. I am

13 Professor and Chair of the Department of Public Health Sciences

14 at the University of California-Davis. 

15 DR. ABBY COLLIER: I am Abby Collier,

16 Associate Professor of Pharmacology at the University of Hawaii

17 School of Medicine.

18 DR. RUSSELL CARR: Russell Carr, Associate

19 Professor of Toxicology at the College of Veterinary Medicine,

20 Mississippi State University.

21 DR. BRIAN BUCKLEY: Brian Buckley, Executive

22 Director of Laboratories at the Environmental and Occupational
Page 7

1 Health Sciences Institute, Analytical Chemist and Graduate

2 Faculty Member of the School Public Health Environmental

3 Sciences and Joint Graduate Program of Toxicology for Rutgers

4 University. 

5 DR. MICHAEL ASCHNER: I am Michael Aschner. I

6 am Professor of Pediatrics and Pharmacology at Vanderbilt

7 University Medical Center.

8 DR. KENNETH PORTIER: Thank you very much. As

9 you can see, we have a good Panel of experienced experts. I

10 think most of them, except for 2, have been on a previous SAP,

11 so they know what this is all about. 

12 We have a pretty full agenda for the next

13 couple of days. By my count, we have 16 issue questions that

14 EPA wishes the panel to discuss. We are going to move forward

15 with the presentations by EPA. It will start with an

16 introduction by Dr. Bradbury. 

17 DR. STEVEN BRADBURY: Thank you, Dr. Portier. 

18 Welcome to all the members of the Panel. As I mentioned at many

19 SAP Meetings, I first want to start off by thanking you all for

20 the effort you will be putting in, already have put in, in

21 preparing for the meeting this week and the efforts you will be

22 undertaking during the course of the week and of course as you
Page 8

1 prepare your report. 

2 The SAP, Science Advisory Panel, is a really

3 instrumental component to our overall program. The Science

4 Advisory Panel, if you will, is Chartered through our statute. 

5 The Federal Insecticide, Fungicide, and Rodenticide Act sets up

6 the Scientific Advisory Panel as a Panel of independent

7 scientific experts that can give the agency advice on

8 challenging scientific issues that underlie our decision making.

9

10 Clearly, as many of you know from previous

11 experiences on the SAP that the science is the foundation to the

12 decisions that we make. Science, transparency and rule of law

13 are the 3 themes that our administrator feels are very important

14 in the day-to-day work of our agency. The Science Advisory

15 Panel is an important component to our overall efforts to ensure

16 that we are bringing forward the best available science and the

17 best insights we can to the decisions that we have to make. 

18 Sometimes, the Science Advisory Panels are

19 focusing on broad methodological issues in certain areas of

20 science, from ecological issues to human health, and the merging

21 of the two. Sometimes, we need your advice on a specific

22 chemical as we are taking a look at chlorpyrifos over the next
Page 9

1 week.

2 I think as you will see in our charge

3 questions and in the documents that we have provided, there are

4 some broad cross-cutting themes that come forward in the

5 specific examples of chlorpyrifos. So, we are looking forward

6 to getting your advice about some of the issues specific to

7 chlorpyrifos, and expecting we will see some other principals or

8 concepts or ideas that will come through your review that can be

9 applicable more broadly. 

10 So, the challenges that we will be talking

11 about over the course of the week, I think reflect some of the

12 concepts that we have been working with you in different SAPs

13 over the last several years.

14 In February 2010, we had an SAP that was what

15 we called our framework document that we prepared that was

16 taking a look at the NRC Report of 2007 in terms of toxicology

17 testing in the 21st Century, and beginning to shape some ideas

18 with you in terms of how do we take a look at the concept of

19 adverse outcome pathways or toxicity pathways as they are called

20 in the NRC Report. 

21 How do we start looking at experimental

22 toxicology information and begin to integrate it with
Page 10

1 epidemiological information to ensure that we are using the best

2 available information and start to think about how do you

3 integrate that information. How do you develop a hypothesis or

4 hypotheses around toxicity pathways and integrate that

5 information from experimental toxicology information to

6 epidemiological information. 

7 That framework and the concepts in the

8 framework we tried to carry forth in the chlorpyrifos document

9 that you have been working on. We have also used some of those

10 concepts and some series of atrazine SAP reviews that we have

11 had over the last year or so, again trying to understand

12 epidemiological information, how to best use that in the context

13 of experimental information. How do you generate hypotheses

14 thinking about adverse outcome pathways as a way of putting

15 together our risk assessments.

16 In the NRC Report of 2007, we also brought

17 those concepts to the SAP, I think it was last May or at least

18 spring or summer of 2011. A specific SAP around 21st Century

19 Toxicology, if you will, and adverse outcome pathways and how to

20 start to think about the evolution of that technology, and how

21 to start using that kind of information, and if not information,

22 that thought process in a way we undertake risk assessments. 
Page 11

1 You will see some of those themes, I think, I hope, in the paper

2 that you have been reading. In some, it is reflected in many of

3 the charge questions that you will be working on with us. 

4 I think one of the challenges in the concept

5 of the toxicity pathway looking both at experimental information

6 and epidemiological information is trying to understand

7 perturbations as the NRC 2007 Report talks about. As you look

8 at a toxicity pathway, the ability to measure perturbations in a

9 pathway and with the new technology, it is becoming increasingly

10 easier, if you will, to measure perturbations in systems. 

11 I think the challenge, and some of the

12 challenge in the science that we are bringing forth to you, is

13 how to interpret those perturbations in terms of whether or not

14 there is enough perturbation that you have knocked the system

15 off its normal pathways and put it on a trajectory toward a

16 toxicity pathway or an adverse outcome. 

17 You will see in some of the charge questions

18 that we are trying to get at that concept. Within that concept,

19 issues of dosimetry come up. Dosimetry, you will see, is a

20 major theme both in terms of understanding how much of a dose

21 does it take to cause a perturbation in terms of magnitude,

22 frequency, duration of that exposure. How do you link those
Page 12

1 different steps in a toxicity pathway together to move from a

2 hypothesis to starting to formulate causal relationships? In

3 some of our charge questions, I think you will see we are trying

4 to get some advice or some help on that. 

5 Some of the advances in the concepts around

6 adverse outcome pathways and toxicology testing in the 21st

7 Century, in some ways it is new, the technology is new, but in

8 some way the concepts is kind of old in terms of mode of

9 action. How do you start to put together hypotheses in a risk

10 assessment as well as in the research laboratory to start to put

11 together information to tell a story? In the world of risk

12 assessment, telling a story would make sense. It understandably

13 has always been a significant challenge as many as you know in

14 your careers. 

15 One of the documents that we rely on in the

16 pesticide program, but in the agency as a whole, is the agency's

17 risk characterization handbook, which talks about the importance

18 of a risk assessment and a risk characterization component of a

19 risk assessment being transparent, clear, consistent and

20 reasonable, TCCR. If a risk characterization is putting the

21 information well, it should be transparent, it should be clear.

22 Information in there should be consistent not only within the
Page 13

1 risk assessment, but consistent with other risk assessments that

2 may be tackling similar issues, and it is reasonable.

3 Let me just touch a little bit upon some of

4 the concepts in the Risk Characterization Handbook. I think

5 those themes will be helpful as we all reflect on that as we go

6 through the charge questions, and maybe ask clarifying questions

7 in our presentations.

8 So in the context of transparency, and that is

9 really the foundation to ensure that you have a clear and

10 consistent reasonable risk assessment, some of the issues or

11 some of the concepts in the Agency's Risk Characterization

12 Handbook that it stresses are that in risk characterization,

13 assumption should be clearly articulated. Knowing what the

14 assumptions are should not be implicit, they should be explicit.

15 The impacts of those assumptions should be clearly articulated

16 in the risk characterization. 

17 What is the influence of different assumptions

18 in the conclusions that are coming together in the risk

19 characterization? The use of extrapolation was to be clearly

20 articulated in a risk characterization. I think issues of

21 exposure in particular for this specific case study before us is

22 an important aspect of the extrapolations or the assumptions
Page 14

1 that it will have to bring forward into this risk assessment,

2 both from the experimental toxicology data as well as the

3 epidemiological information, and how are we going to extrapolate

4 from the empirical exposure measurements that we have, either in

5 an in vitro system or through measurements from subjects in the

6 epidemiological study and extrapolate that to kind of exposure

7 scenarios we expect this chemical to have in the environment.

8 Again, there is not a right answer. It is

9 knowing the different approaches that can be employed, what the

10 assumptions are in those different approaches and how that needs

11 to be interpreted in terms of the uncertainty as we go forward. 

12

13 The use of models and measurements clearly

14 articulating how the different choices of models or empirical

15 information can influence the characterization of the risk. 

16 Making sure that there have been plausible

17 alternatives that has been exercised. Usually, there is more

18 than one way to try to tackle a problem. It is not so much to

19 find the perfect solution, but to make sure that we are looking

20 at reasonable approaches, reasonable plausible ways of

21 interpreting the data and articulating that clearly. 

22 Obviously, there will be some gaps in the
Page 15

1 information and understanding what the implications of those

2 gaps may be in our overall decision making. Not all gaps are

3 created equal, and understanding how different gaps can

4 influence our overall conclusion would be very helpful and part

5 of the concept of transparency.

6 Clarity, of course, is very important. I

7 think the word sort of speaks for itself, but our conclusions

8 and our thoughts should not be obscured. It should be very

9 clear, very obvious, what our thinking is in our documents and

10 any advice that we can get from you and ensuring as we go

11 forward that there is not obscure conclusions or obscure

12 thinking going on in the document that it is clear.

13 Consistency; certainly you would want to be

14 consistent in following Agency Guidelines or basic approaches

15 that we use. But I think it will be very helpful in this peer

16 review in terms of consistency is helping us think about this

17 specific risk assessment and the context of risk assessments

18 that may be similar in terms of the kinds of information or the

19 kinds of challenges that are before us in terms of

20 epidemiological knowledge bases or experimental knowledge bases.

21 As we look at this body of information, how does that body of

22 information compare to other kinds of compounds or maybe similar
Page 16

1 kinds of challenges that have been addressed and how were those

2 tackled, and is there some connectivity between this specific

3 issue and some issues that we maybe have faced in the past in

4 the Agency or other parts of the Government or may face in the

5 future for that matter. 

6 Reasonably, I will just touch on a few

7 concepts there, but certainly we want to ensure that there is a

8 clear logic to how we are putting together our concepts. Here I

9 think the ideas of the adverse outcome pathway, the toxicity

10 pathway that the NRC report of 2007 articulates, give us a

11 foundation or a game plan or an approach to ensure that we are

12 putting together our thoughts in a logical and consistent

13 manner. 

14 If this is happening at one level of

15 biological organization, what are the concepts in terms of

16 dosimetry or biological systems that give us confidence that the

17 next step in biological organization could be in play. What is

18 that linkage that is going on and what is the information that

19 connects those linkages? How does the experimental information

20 and the epidemiological information give us insights into

21 putting together various adverse outcome pathways and

22 understanding what they mean, what the uncertainties are, what
Page 17

1 some of the assumptions are as we try to understand the

2 strengths or limitations of different hypothesis-based adverse

3 outcome pathways in our risk assessment. 

4 I think another aspect of reasonableness in

5 our deliberations and the feedback we can get from you is in the

6 context of generalizability. As we take a look at the

7 epidemiological information or take a look at the experimental

8 information and we are putting that together and making

9 inferences from that information, all that knowledge is coming

10 from and experimental system. They are basically models for

11 everybody in the country. Empirical data is a model

12 essentially, just a physical model. Sometimes there are

13 mathematical models.

14 Understanding the generalizability of the

15 various models of information or mathematical models or studies

16 that are models, physical models trying to mimic reality, giving

17 us a sense of how generalizable the data is before us will be

18 very important too. An important part of the reasonable concept

19 in the Agency's Risk Characterization Handbook.

20 As we go forward through the charge questions,

21 you are going to see that we are asking you for advice on many

22 principals around transparency, clarity, consistency,
Page 18

1 reasonableness, using the adverse outcome pathway, the NRC

2 Report of 2007 as sort of a framework to be getting our thoughts

3 together. We will be asking you to give us advice on different

4 hypotheses, if you will, in this risk assessment, and the

5 strengths and limitations of those different risk hypotheses as

6 we put them together as we move forward with your advice. 

7 That will be very helpful because as with this

8 chemical, as with most chemicals, there usually is not one right

9 answer that pops up. There are usually lots of different ideas

10 and different concepts that need to come together, not all

11 created equal necessarily, but understanding the strength, the

12 limitations, the uncertainties associated with this information

13 as it comes together is really helpful for the Agency in terms

14 of the risk managers making their decisions. 

15 We make decisions everyday in the face of

16 uncertainty. I have been in the Agency for 30 some years. I do

17 not think we have ever made a decision with absolute certainty,

18 or ever made a decision with 100% certainty. So, we are dealing

19 with uncertainty all the time. What is helpful is to get

20 insight as to the nature of the uncertainty. Big uncertainty,

21 little uncertainty, uncertainty that has this impact, this

22 uncertainty has that impact, so that we can put it together and
Page 19

1 understand the nature of the uncertainty and how that

2 uncertainty could influence our decision making in terms of our

3 risk management activities. 

4 So, transparency, clarity, consistency,

5 reasonableness is what we hope we can offer and try to offer in

6 our document, and hope to get that kind of feedback from you as

7 we go forward over the next week. 

8 So with that, if it is okay with the Chair, I

9 would like to introduce Karen Whitby, who is the Acting Division

10 Director for the Health Effects Division in our Pesticide

11 Program to give a few opening comments. We will proceed from

12 there if it is okay Dr. Portier.

13 MS. KAREN WHITBY: Thank you Steve. As Steve

14 said, my name is Karen Whitby and I am serving as the Acting

15 Director of the Health Effects Division in the Office of

16 Pesticide Programs. I welcome the Panel and reiterate Steve's

17 appreciation for their time and efforts. Your feedback is an

18 important component of improving the science foundation of our

19 regulatory decisions. 

20 We understand the large amount of material

21 that you have been asked to consider and appreciate your

22 commitment to high quality science. I acknowledge the entire
Page 20

1 OPP Chlorpyrifos Team, but particularly the efforts of Anna

2 Lowit, Aaron Niman, Carol Christensen, Chester Rodriguez, Joel

3 Wolf, Jeff Dawson, Tom Myers, Wade Ritten, and many others that

4 have also contributed in significant ways. 

5 I recognize Rebecca Zubo from the Office of

6 Children's Health Protection for her contributions as well. I

7 extend my sincere gratitude to the ORD Team, Ginger Moser, Bill

8 Mundy, Stephanie Padilla and Danelle Lobdell. Both Ginger and

9 Bill will be speaking today. I also appreciate the written

10 comments from Marsha Morgan and Cecelia Tan. 

11 Our colleagues at ORD are vital to this

12 effort, and we truly appreciate their support, time and talents.

13 The Agency's Risk Characterization Handbook describes concepts

14 of transparency, clarity, consistency and reasonableness as key

15 to scientifically supportable risk assessment. 

16 Our commitment to transparency, clarity and

17 consistency is reflected by OPP's development of a draft

18 framework for integrating epidemiology with other lines of

19 scientific evidence. This framework describes the steps for

20 analysis and evaluation of data from multiple areas, such as

21 adverse outcome pathways and modes of action, in vivo outcomes

22 from experimental toxicology studies and epidemiology studies. 
Page 21

1 The framework further describes the use of the

2 Agency's mode of action framework and modified Bradford-Hill

3 Criteria to bring all of this information together in a

4 systematic way into a weight of the evidence analysis. 

5 For chlorpyrifos, we are at the stage where we

6 have completed detailed analysis of specific scientific areas,

7 but have not integrated across lines of evidence. Before we can

8 perform the integration, we are looking for your feedback on our

9 current analysis. It is important to ensure that the scientific

10 analysis of each area is strong and supportable before we begin

11 the difficult challenge of integrating across lines of evidence.

12 With that in mind, we are not asking the panel

13 to perform the weight of evidence for us. Rather, we are asking

14 that you provide review of our interpretation of individual

15 lines of evidence along with the relative strengths and

16 uncertainties. 

17 It would also be helpful to hear your advice

18 on alternative hypotheses or additional scientific

19 considerations we have not included in the white paper. The

20 concepts of reasonableness and ranges of risk and uncertainty

21 are described in the agency's Risk Characterization Handbook. 

22 Similarly, the NRC's Science and Decisions Report recommends the
Page 22

1 agency consider alternate hypotheses and alternate approaches in

2 the risk assessment process. 

3 Consistent with this, we request that in your

4 deliberations you consider a variety of options and importantly,

5 the relative strength and uncertainty associated with them. 

6 This kind of feedback will assist the agency in evaluating the

7 ranges of risk and uncertainty. 

8 With this, I conclude my introductory remarks.

9 We look forward to your thoughtful deliberations over the next

10 few days. Our next speaker is Anna Lowit. 

11 DR. KENNETH PORTIER: Before we go to Dr.

12 Lowit, are there any questions from the Panel for Dr. Bradbury

13 or Dr. Whitby? 

14 One of the things that came to mind as you

15 were talking is does the handbook address the issue of how you

16 handle alternate but reasonable AOPs for the same process? I

17 mean, that is part of what this white paper is all about, right,

18 kind of looking through all the adverse outcome pathways that

19 chlorpyrifos may presume. So, is the handbook to that stage yet

20 that talks about, well I have 5 competing and reasonable and

21 consistent and clear pathways, what am I going to do with that? 

22
Page 23

1 DR. STEVEN BRADBURY: Well since the Risk

2 Characterization Handbook was written probably 10 years ago,

3 roughly, the NRC Report was not there yet. So, some of this

4 vocabulary, toxicity pathways or adverse outcome pathways, did

5 not exist. My thoughts on the document, though, are that it is

6 compatible or it is consistent with, or anticipatory of the kind

7 of issues we have now when we talk about adverse outcome

8 pathways. The Risk Characterization Handbook talks about taking

9 a look at plausible reasonable alternatives to the information

10 that you may have in front of you in a risk assessment.

11 NRC 2007 did not create the realization that

12 sometimes you can look at a body of information and formulate

13 different mode of action hypotheses, or think about the fact

14 that the chemical could have multiple initiating events, and if

15 you can use the words back then in terms of how it may start a

16 mode of action. The Bradford-Hill Criteria had been around for

17 quite a while, right Dr. Meek? 

18 So, the Risk Characterization Handbook, I

19 think is broader than that. I think it encompasses some of the

20 challenges that we have seen chlorpyrifos or other risk

21 assessments coming up. I think what the NRC report does is

22 perhaps provide us some vocabulary. I realize we are still kind
Page 24

1 of debating what an adverse outcome pathway is, but I think

2 there is some clarity around that. 

3 I think the NRC report of 2009 helps provide

4 some framework and a way to integrate different kinds of

5 information, but consistent with the concept the Risk

6 Characterization Handbook is laying out; which is take a look at

7 alternative ways of looking at the information. 

8 What are the strengths and limitations of that

9 information? How close do you think you are to causal

10 relationships, quantitative causal relationships, where are you

11 more at the other extreme; got some good information to form a

12 hypothesis, but you may not have the information that you have

13 to really test that hypothesis. But you can see where to go in

14 terms of the kind of information that may be needed to take the

15 hypothesis and start to put more oomph behind sort of the

16 linkages in the different levels of biological organization. 

17 So the Characterization Handbook does not

18 capture vocabulary 2007 and 2008. In my opinion, it captures a

19 way of thinking that is very compatible with the new kind of

20 information we are starting to get. 

21 DR. KENNETH PORTIER: Okay, thank you. Dr.

22 Lowit, please introduce yourself as well.
Page 25

1 DR. ANNA LOWIT: My name is Anna Lowit. I am

2 a Senior Scientist in the Immediate Office of the Health Effects

3 Division in the Office of Pesticide Programs. One of my roles

4 as a Senior Scientist is the Technical Lead for the chlorpyrifos

5 team. On behalf of the entire team, which spans 3 offices, the

6 Pesticide Program, Multiple Division, the Office of Research and

7 Development and with help from the Children's Health Protection

8 Office, we would like to again thank each and every one of you

9 for being here, for all of the work that you have done to get to

10 this point. There is a lot of work after we all walk out as you

11 know. It takes a lot of time and talent to put these meetings

12 together. Without you, this would not happen and we genuinely

13 appreciate everything and your contribution. 

14 So, you have heard our themes for the day,

15 transparency, clarity, reasonableness, consistency, adverse

16 outcome pathways. You are going to see those themes, integrated

17 across multiple lines of evidence. You are going to hear those

18 themes over and over and over again today and over the next

19 couple of days. These are good starting principles and good

20 organizing principles. We are going to keep coming back to them

21 because they are probably the best place to start. This is a

22 very complicated case, and to begin with some solid,
Page 26

1 transparent, clear principles, it is a good place.

2 So if we just begin, to restate, there are a

3 couple of NRC reports that really provide the foundation for a

4 lot of the thinking that went into the white paper that each and

5 every one of you have. Particularly under the Toxicity Testing

6 in the 21st Century. We think about adverse outcome pathways as

7 a beginning point to organize information, to think about key

8 events and how they link together, but also how to bring

9 information across multiple lines of evidence. 

10 So, you bring in in vitro studies,

11 biomonitoring studies, epidemiology studies, to bring it all in

12 a way that begins to make sense together by integrating across

13 all these multiple lines of evidence. You are also going to

14 hear us talking about thinking about disruption of homeostasis

15 leading to adverse outcomes. 

16 As Dr. Bradbury mentioned, science is getting

17 better and better at measuring things smaller and smaller and

18 earlier and earlier in the process. The challenge is to take

19 those things and to make sense of them to think about how they

20 link together and what is required to disrupt homeostasis and

21 eventually lead to an adverse outcome. 

22 From the Science and Decision Point document,
Page 27

1 it is important as an Agency to think about doing improved

2 technical analysis, including not only mode of action, but

3 pharmacokinetics thinking about biomonitoring, how to use that

4 kind of information. 

5 Science and Decisions recommends that we do a

6 better job of evaluating alternative interpretations and

7 alternative approaches. You certainly heard of those themes

8 from Dr. Bradbury and from Karen Whitby. 

9 I think the last theme that you may not have

10 heard about yet this morning is this idea that risk assessment

11 needs to be aligned with the needs of risk managers, and the

12 idea that problem formulation is the best place to start. As I

13 will go through in a few minutes, we have actually been thinking

14 about problem formulation for chlorpyrifos for several years

15 now. We are doing this entire analysis and this assessment in a

16 very step-wise systematic approach. 

17 So, we will just start at the beginning and

18 give you a sense of the regulatory history of chlorpyrifos

19 because there is quite a bit there.

20 It was first registered in 1965 as an OP. 

21 There was a long history of use. In June of 2000, Dow, the

22 technical registrant, entered into an agreement with the Agency
Page 28

1 to eliminate and phase out nearly all of the residential uses. 

2 At that same time, there was some agricultural mitigation on

3 some key crops and some mitigation on workers. Chlorpyrifos was

4 the Registration Eligibility Decision in 2006, which would have

5 come at the same time as the OPQ Mode of Assessment. 

6 About a year later, a petition came in from

7 the National Resources Defense Counsel and the Pesticide Action

8 Network of North America to revoke all the tolerance and cancel

9 the registrations. 

10 In 2008, we came before this panel with an

11 initial new literature review focused on many of the same topics

12 that we will talk about today. I think of that 2008 SAP as our

13 first take at problem formulation of looking at the new science

14 available since 2000 on infants, children and pregnant women in

15 the laboratory, but also from epidemiology studies. 

16 In that review, some of you were there. We

17 did an extensive literature review of all of the available

18 cholinesterase data at the time. We did an extensive review of

19 metabolic ontogeny of the potential for young children to be

20 more sensitive based on pharmacokinetics. We did a review of

21 paraoxonase as a potential mediator of sensitivity. We also did

22 a review of the epidemiology at the time, and the available data
Page 29

1 on experimental toxicology in animals for long-term effects. 

2 Many of those same themes appear in this current issue paper.

3 In 2009, we also did an SAP on looking at

4 approaches for semi-volatile pesticides. Why that is relevant

5 is because it is part of the petition from NRDC. 

6 In 2010 as you have heard already, we brought

7 a draft framework for incorporating human epidemiology and

8 incident data into human health risk assessment. I will go into

9 that a little bit more in a few minutes, but it really begins

10 the conversation about using the concepts from the NRC 2007 and

11 2009 to integrate lines of evidence.

12 So to bring the regulatory context into

13 present day, currently the pesticide office is undergoing the

14 registration review process. For chlorpyrifos, this opened in

15 March of 2009, which in English means that we put out a plan of

16 how we were going to do the assessment along with the schedule. 

17 What that means is over the course of several years, we will be

18 doing review and analysis and different stages of risk

19 assessment in hopes of having all of that completed in 2015. 

20 As part of that process, we put out a risk

21 assessment in July of last year. The Docket number is there in

22 case you do not have it already. If you have seen that
Page 30

1 assessment, the focus of that assessment is entirely on the

2 cholinesterase inhibiting potential of chlorpyrifos. Based on

3 the recommendations from the SAP in 2008 and based on our own

4 independent review of the data, cholinesterase data continues to

5 be the most sensitive dose response data available for deriving

6 quantitative points of departure. That is the focus of that

7 July assessment. That assessment also states very clearly that

8 we are continuing our review and our analysis of the

9 epidemiology data by monitoring data and experimental toxicology

10 studies with not only the data that we looked at in 2008, but

11 things have come out since that time. 

12 So a little bit more detail about 2008.

13 Actually I covered that. So for brevity, I am just going to

14 keep going. So, in 2008, some of the key recommendations that

15 bring us to today were what I just said, was that the

16 cholinesterase data remains the most appropriate data for

17 deriving points of departure. That is risk assessment jargon. 

18 In English what that means is that the cholinesterase data

19 provides the most sensitive data for bringing the starting point

20 for extrapolation for human health risk. 

21 We have lots of data from many sources. We

22 have root specific data, so we have oral data, we have dermal,
Page 31

1 we have inhalation and why that is important is because the

2 exposure to chlorpyrifos can be to all of those routes. Route

3 specific is going to be the best way to go. We have life stage

4 specific data. So, we have things from pregnant rats. We have

5 newborn rats. We have rat data from PND11, which is close to

6 what you would think of as a human newborn all the way up to

7 adult rats of many different durations. 

8 There is a large database that you will hear

9 from Ginger Moser today on in vivo rodent studies and the

10 long-term effects from early life exposure. We did an extensive

11 review of that in 2008, and many of the same themes you will

12 hear today. There is limited dose response data, which makes

13 that data challenging for deriving points of departure. 

14 Across the board generally, you see that only

15 doses of about 1 mg/kg or greater demonstrate early effects on

16 behavior. You will see in a few minutes that the points of

17 departure we have from cholinesterase are lower than that. 

18 In 2008, the recommendations from the SAP were

19 that the epidemiology studies supported the idea that

20 chlorpyrifos likely plays a role in the findings, particularly

21 out of the Columbia Cohort, but that there are challenges of

22 using the data from Columbia for deriving points of departure. 
Page 32

1 For example, they only measured the biomarkers at one point in

2 time, which makes it difficult to match that one point in time

3 to windows of susceptibility. 

4 In another issue that we have a charge

5 question about is the idea that you cannot exclude the

6 possibility that you have combinations of effects of

7 chlorpyrifos with other cholinesterase inhibiting pesticides,

8 particularly diazinon propoxur. 

9 A couple of years ago, we brought to this

10 Panel a draft framework from incorporating epidemiology into our

11 assessments. Some of you served on that Panel. We appreciate

12 you coming back. But if you have not seen that document or were

13 not part of that peer review, just to hit the highlights, that

14 framework focuses largely on the concepts from the NRC in 2007

15 and 2009, and relies heavily on the Agency's mode of action

16 framework with the modified Bradford-Hill Criteria. 

17 So, there is really nothing in that framework

18 that is new or different. It is really based on standard

19 practice in epidemiology, toxicology and risk assessment. The

20 idea is to bring everything together in one place to think about

21 transparency, clarity and also this idea of consistency that we

22 are using the same organizing principles in all of our
Page 33

1 assessments. 

2 What we like about the framework and the

3 Bradford-Hill Criteria and the idea of the AOP concept is it

4 provides not only a foundation for consistency and transparency,

5 but also flexibility because you can bring in lots of different

6 information. It is a nice way to weigh information in a very

7 transparent way. 

8 So for those of you not familiar with the

9 Bradford-Hill Criteria, I will just go through it quickly just

10 to make sure that we are all on the same page. This is the

11 modified version used in the Agency's Mode of Action Framework.

12 You begin with a postulated mode of action, or

13 I think what we will call today adverse outcome pathways. You

14 think about a series of key events in that path to an outcome. 

15 So, we will talk about initiating events and the events that

16 would be disrupted, perturbations that would disrupt homeostasis

17 leading to adverse outcome pathways, new jargon for the same

18 ideas. 

19 You think about the experimental support for

20 that, concordance of dose response for key events and temporal

21 relationships for key events. You might hear us talk about

22 causality and the idea that we are linking events from A to B to
Page 34

1 C. This is the idea that we are talking about that A happens

2 before B before C before D, and that there is a reasonable

3 logical dose response relationship between them. Remember, dose

4 response is important for our purposes. So, we are always

5 coming back to dose response. 

6 As we put things together, think about

7 biological plausibility, how the ideas come together and make

8 sense, and the strengths and the specificity and the consistency

9 of that. Certainly uncertainties is one of the big words we

10 will talk about a lot today. 

11 So the next figure, the adverse outcome

12 pathway: this is a slide that we have been showing quite a bit

13 out of the framework. It is adapted from the NRC report

14 overlaid with some circles to give you a sense of where

15 different kinds of information come together. It is a nice

16 picture of how complicated it can get. If you step back from

17 it, almost all that information exists for chlorpyrifos. The

18 integration across multiple lines is certainly not an easy task.

19 Okay, this is too much animation for me, so I am just going to

20 go to the end.

21 So, a figure straight out of the NRC from

22 2007, this is where we like to take that same adverse outcome
Page 35

1 pathway and put it in a different idea that you have the normal

2 function, and then you have events that would occur that would

3 take you away from that normal function. Certainly one of the

4 big challenges here is to think about how much and when

5 perturbation from homeostasis does it require to bring you down

6 into adverse outcomes. It is when, it is how much, it is what

7 life stage. Bill will show this again in a few minutes, so I

8 will not go too much into that. 

9 So, bring it to grounding. So in the July

10 assessment, one of the questions that we had before the meeting,

11 so I slipped this slide in, was about how we would derive points

12 of departure. Dose response is important. The quality of the

13 dose response is important, the route is important. The life

14 stage is important. 

15 Cholinesterase data continued to be the most

16 robust source of all of that information. There are many

17 studies with multiple doses that go from very low dose to very

18 high dose. We have a strong sense of the dose response for that

19 for multiple routes and multiple life stages. We can use that

20 life stage and route specific information as a very strong

21 confident starting point for extrapolating human risk. 

22 In 2008, we performed what we thought was a
Page 36

1 pretty comprehensive, I think we call it just about every

2 dataset out there, not only had it been developed by individuals

3 in academia but also from Dow and other companies. We have

4 supplemented that since then with newest data, not only from the

5 literature, but some state of the art studies developed by Dow.

6 We have used a benchmark dose approach, which

7 means we are doing empirical modeling of that data using a

8 decreasing exponential model, which has been reviewed by this

9 panel 3 or 4, if not more times depending on how you count. It

10 is an incredibly transparent, robust approach that we have used

11 for many cholinesterase inhibiting compounds by this point. 

12 So, the points of departure in our assessment

13 and the numbers are not that important, but they do provide a

14 point of comparison as we think about other datasets that you

15 will hear over the next few days. There is a fairly brand new,

16 what we call a comparative cholinesterase study for

17 chlorpyrifos.

18 What that means it that it is a specially

19 designed study to directly compare the relative sensitivity of

20 adults and juvenile rats, and the juvenile rats are aged PND11

21 to most closely match that of human birth. There is a small

22 amount of sensitivity that shows up in that study, which
Page 37

1 provides the point of departure in the juvenile rats. That

2 makes sense because juvenile animals tend to be more sensitive

3 because of their immature metabolic system. 

4 With respect to repeated chronic exposure from

5 oral exposure, the datasets out of pregnant rats provides the

6 most sensitive data, also using a benchmark dose for approach. 

7 So a lot of what we are going to talk about

8 today comes around 2 central ideas and 2 central science

9 questions. The first is more qualitative. We will think about

10 it in that way that qualitatively, what are the lines of

11 evidence and the relative strength and uncertainties of those

12 lines of evidence that suggest the possibility that chlorpyrifos

13 exposure may or may not result in long-term neurodevelopmental

14 effects from early life exposure.

15 The second question is more quantitative, and

16 we like to think about it that way, that the lines of evidence

17 and the strengths and the uncertainties associated with that

18 evidence that the adverse effects from the first question could

19 be attributed to doses lower than those, which elicit 10%

20 cholinesterase inhibition and the 10% cholinesterase inhibition

21 is our response level we use in regulatory decision making. 

22 Compared to 2008, some of the things we will
Page 38

1 talk about are the same. Some of them are quite different. 

2 Either way, we have gone into a lot of depth on some topics. 

3 Certainly, cholinesterase inhibition is a key area. We have

4 done, with help from our colleagues at ORD, a great deal of

5 digging into the mechanistic data, looking at plausible

6 hypotheses and modes of action adverse outcome pathways. 

7 We have done a deeper analysis of adverse

8 outcomes in rodents, thinking about neurodevelopmental effects

9 on particularly behavior and cognition. We have done a much

10 deeper and more thorough analysis of the epidemiology studies

11 from the 3 major children's cohorts, from not only Columbia, but

12 CHAMACOS and Mt. Sinai. That also includes some of the newer

13 studies. 

14 We have done, compared to 2008, a much more

15 detailed evaluation of available biomonitoring data,

16 particularly urinary data from multiple populations across the

17 U.S. We are thinking about the available physiologically-base

18 pharmacokinetic models that exist and alternative approaches

19 that are maybe less sophisticated that could help us interpret

20 biomonitoring data so that we can begin to make connections

21 across datasets. 

22 So this slide I will bring back later. I will
Page 39

1 not talk about it too much, but it is a nice,

2 pull-it-all-together, it is a plot out of the NRC that brings

3 all of the major components together to think about how to do

4 risk characterization. It is largely in line with the AOP

5 concept as you begin with the idea of compounds and metabolites,

6 whether those are active metabolites or not. Then you have this

7 whole idea of modes of action of adverse outcome pathways coming

8 together in a dose response analysis. 

9 What I like about this figure is it brings

10 together this idea that you are calibrating in vitro and human

11 dosimetry together in one place. I think that is really one of

12 the biggest challenges we have the chlorpyrifos is to really

13 bring in line and to make sense of not only in vitro data, but

14 epidemiology data and animal toxicology data all together. 

15 Calibration is sort of the key areas. We have epidemiology

16 studies, we certainly have lots of urinary biomarker data. 

17 So this one does not show too well on the

18 screen, but it is directly out of the issue paper. We put

19 together this organizing framework for the chlorpyrifos just

20 almost for ourselves to keep everything straight, so that we

21 know who we are looking at, what the populations are. It is

22 important to think about how they are exposed, because certainly
Page 40

1 infants are exposed in a very different way than is the fetus

2 and is mom. 

3 We have multiple AOPs that we are tracking. 

4 We have cholinesterase inhibition that we know quite a bit about

5 and that we have a lot of experience with. Then, we have

6 multiple hypotheses for the neurodevelopmental outcomes.

7 It is important to think about critical

8 duration from a risk assessment point of view. Are they single

9 day events, are these chronic events or is it more complicated

10 than that. We have windows of susceptibility that may have

11 different durations. Certainly what the adverse outcome related

12 to those are, is it cholinergic toxicity or is it some sort of

13 neurodevelopmental outcome. 

14 With that, I think the last slide is just the

15 outline of today. So, we are done with the introduction. I

16 will be passing the microphone in a few minutes over to Bill

17 Mundy. He is going to give the presentation. Stephanie Padilla

18 is also at the table. She was a major contributor to that

19 section. Ginger Moser was also a contributor to that piece of

20 the document. 

21 After the break, I think Ginger Moser will be

22 giving the presentation on the Effects in in vivo Studies in the
Page 41

1 Developing Brain, followed by Carol Christensen who is going to

2 talk extensively about the epidemiology cohort. Lieutenant

3 Aaron Niman along with Chester Rodriguez developed the chapter

4 on biomonitoring and interpretive approaches. You will hear

5 from Lieutenant Niman on that. I will finish it up at the end

6 to bring all of the themes back together and talk about the

7 charge questions and where we are headed next. 

8 DR. KENNETH PORTIER: Okay, at this point, we

9 open it up to any clarifying questions from the Panel. I just

10 remind you if you have questions, state your name and use the

11 microphone. Anyone? Dr. Harry.

12 DR. JEAN HARRY: So, just a concept as you are

13 going through and you define periods of exposures and then you

14 also define, looking at the complicated graph at the end, you

15 also define susceptible windows of exposure. You are also now

16 talking about disruption of homeostasis. So, you have a lot of

17 concepts within those 3 statements. 

18 Just before we get into it, I would like to be

19 clear about how are you thinking about animal studies that do a

20 low-level exposure across a long time period, which may actually

21 catch your windows of susceptibility, but may not represent

22 disruption of homeostasis during that susceptibility? Is that
Page 42

1 making any sense? 

2 DR. ANNA LOWIT: I was following you until the

3 last phrase. I will speak more at a high level and I do not

4 want to get into discussion, but I think certainly you have hit

5 on one of the challenges of this whole enterprise that you have

6 lots of studies. I think Ginger's presentation particularly

7 will highlight that you have lots of studies with lots of

8 different dosing regimens, lots of different labs doing lots of

9 different, very interesting things. They are not always in line

10 with each other. They have different hypotheses about why they

11 are doing different things. 

12 The challenge is really to bring all of that

13 together and looking at it from multiple points of view, almost

14 a 3-dimensional, multidimensional look of what are the strengths

15 of those dosing regimens, where are the uncertainties, where can

16 you bring in information from other sources to focus what that

17 window of susceptibility may have been, where is the uncertainty

18 and strength in doing that, and bringing this multidimensional

19 thing together will help us understand where those uncertainties

20 and strengths lie, and help us define in our risk assessment,

21 what are the critical durations if you think about it as ranges.

22 I think I will just stop with that. 
Page 43

1 DR. KENNETH PORTIER: Any additional

2 questions? Okay, I think we will move on. Dr. Mundy. I am

3 sorry, I will put my glasses on here and get this right. That

4 is what I get for reading all the way over.

5 DR. WILLIAM MUNDY: That is okay, I can barely

6 see the screen. My name is Bill Mundy. I am a

7 Neurotoxicologist in the Office of Research and Development. I

8 have laboratories. I work in our laboratories down in research,

9 Triangle Park, North Carolina. I am going to spend the next 20

10 minutes or so summarizing some of the data that suggests there

11 are multiple biologically plausible pathways by which

12 chlorpyrifos and its metabolites could result in developmental

13 neurotoxicity. 

14 My talk is going to focus on those pathways

15 that are not associated with the inhibition of

16 acetylcholinesterase. 

17 So here is an outline of my talk. I am going

18 to start out giving you a little bit more detailed overview of

19 the adverse outcome pathway concept. I will give a definition,

20 talk about how it can be used to place data that is derived at

21 different levels of biological organization in the context. I

22 will give an example of an AOP that has been developed for a
Page 44

1 chemical that causes neurotoxicity. I will talk a little bit

2 about the criteria you would use to evaluate how well data in

3 the literature would support an adverse outcome pathway. 

4 I am then going to go on and give you an

5 overview of the potential pathways or the data that exists that

6 suggests there are potential pathways for chlorpyrifos to result

7 in adverse neurodevelopmental outcomes. I will give a specific

8 example for the pathway of neurite outgrowth. I will talk about

9 where the data currently exists where chlorpyrifos and these

10 metabolites have effects at different levels of biological

11 organization, and then talk just a little bit about how these

12 pathways relate to the presence or absence of inhibition of

13 acetylcholinesterase. 

14 So here is our working definition of an

15 adverse outcome pathway. So, an adverse outcome pathway or an

16 AOP, is a conceptual framework that portrays existing knowledge

17 concerning the linkage between a direct molecular initiating

18 event and an adverse outcome at a level of biological

19 organization relevant to risk assessment.

20 So as we have already mentioned, this AOP

21 concept incorporates concepts that we normally think about such

22 as mechanism of action, which we think about as being a detailed
Page 45

1 understanding of the molecular basis of the toxic effect in the

2 concept of mode of action, which we think about as the likely

3 key or critical events that lead to the ultimate toxic outcome. 

4

5 So this next slide has a diagram of a generic

6 adverse outcome pathway. As you can see, it explicitly lays out

7 the steps between the molecular initiating event and an adverse

8 outcome of concern to a risk assessment. Note that this adverse

9 outcome can be at the level of the individual, so for example,

10 development of cancer; or at the level of a population, so there

11 is loss of a species and an ecosystem.

12 So what this does is explicitly lays out the

13 key events that occur at increasing levels in biological

14 organization. You have the first interaction of the chemical

15 with the cell with molecules in the cell as the molecular

16 initiating event. This interaction may be something like the

17 receptor ligand interaction or bindings to macromolecules in the

18 cell, DNA binding, protein oxidation for example. 

19 This effectiveness at the molecular level

20 would then lead to effects at the cellular level. For example,

21 gene activation, increased levels of protein expression, altered

22 signaling, you would have changes in cell structure or function.
Page 46

1 These changes at the cellular level would then lead to changes

2 at the tissue or organ level, such as altered tissue

3 development, altered tissue function. These changes at the

4 level of the tissue organ would ultimately lead to the effect in

5 the individual or the population, which we would consider

6 adverse. 

7 So by thinking of toxicity in terms of adverse

8 outcome pathway, you get a number of things. First, it sort of

9 forces you to explicitly propose a set of biologically plausible

10 linkages between your molecular initiating event and your

11 adverse outcome.

12 As an experimental toxicologist, I think Steve

13 mentioned this, it is like forcing you to actually lay out your

14 working hypothesis at all levels of biological organization. 

15 When you do that, it allows you to place data that is collected

16 at different levels of biological organization into a context

17 with which you can evaluate the relationship between these

18 different levels of organization. 

19 Finally, as we all know, most chemicals be

20 they a pharmaceutical chemical, an industrial chemical, an

21 environmental contaminant, have more than one site of action or

22 mechanism of action. By having an explicitly laid out adverse
Page 47

1 outcome pathway, it helps you to focus on those chemical

2 mechanisms that are related to this specific adverse outcome

3 that you are interested in or you regulate. 

4 I would also like to talk about the adverse

5 outcome pathway in terms of the toxicity pathway concept that we

6 have already heard mentioned. So, most of you know the toxicity

7 pathway concept was laid out in the NRC report, Toxicity Testing

8 in the 21st Century. The toxicity pathway is a normal cellular

9 response pathway as outlined there, which is then perturbed by

10 the introduction of a chemical stressor, which we would call a

11 molecular initiating event.

12 Now, this introduction of the chemical would

13 perturb the cellular pathway away from normal. So you have a

14 perturbed function or structure of the cell. Now depending upon

15 the degree of perturbation, so depending upon factors like dose

16 or time of exposure, the cell may have adaptive responses that

17 return the cell back to its normal cellular response pathway, if

18 that degree of perturbation is greater. So thinking about

19 things like an increase in dose or an increase in time of

20 exposure, you overcome those adaptive responses. 

21 The cell now continues down a maladaptive

22 response to cell injury, cell dysfunction and ultimately leading
Page 48

1 to the adverse outcome. 

2 So, the toxicity pathway, molecular initiating

3 event the perturbed cellular response can be seen as the first 2

4 steps in an adverse outcome pathway. 

5 So, I would like to briefly talk about this

6 example of an adverse outcome pathway with a neurotoxicity of

7 domoic acid. Now, this was a case study that was put together

8 to understand how adverse outcome pathways could be used as part

9 of a Pellston Workshop. This case study has actually been

10 published by Watanabe et al, which is referenced in your Draft

11 Issue Paper. What they have done is listed explicit molecular

12 initiating event, key events at increasing levels of biological

13 organization and related them to an adverse outcome. 

14 So, domoic acid is an algal toxin. It has a

15 structure of excitatory amino acid. Both in humans, wildlife

16 and experimental animals, it has been shown to produce memory

17 loss, seizures and ultimately death. So, based on this

18 structure of domoic acid, they propose the following pathway. 

19 Domoic acid acts as an excitatory amino acid,

20 and the molecular initiating event would be binding to and

21 activating glutamate receptors in the brain, specifically the

22 kainate receptor. The 3D structure of kainate structure is
Page 49

1 shown below. Activation of the kainate receptor increases eye

2 influx, causes neuronal depolarization, and ultimately at the

3 level of the cell results in disruption of normal intracellular

4 calcium levels. 

5 At the cellular level, you can see that

6 increasing levels of domoic acid cause an increase in the amount

7 or level of intracellular calcium. What you probably cannot see

8 in that lower graph is increase in intracellular calcium as

9 associated with increasing cell death. 

10 Now because these kainate receptors are

11 expressed primarily in the hippocampus, the organ level response

12 is an in hippocampal cell death. Below there, you can see some

13 photomicrographs of the pyramidal layer of the hippocampus. On

14 the left side, it is from a control animal, and on the right

15 side is from an animal treated with domoic acid. I think even

16 in these small pictures, you can see there is a large amount of

17 cell loss in the treated animal. 

18 Now because hippocampal circuitry is important

19 for learning and memory, disruption of hippocampal circuitry by

20 the loss of these cells in the pyramidal layer leads to memory

21 loss. The data there shows impaired performance on a memory

22 task by a rat treated with domoic acid. 
Page 50

1 So, what this case study shows is that you can

2 explicitly lay out an adverse outcome pathway, stating plausible

3 biological connections at increasing levels of biological

4 complexity and support that with data from the literature. 

5 I would like to continue to use this example

6 to talk about some of the criteria you would use to evaluate how

7 well data supports an adverse outcome pathway. If you think

8 about plausibility, there needs to be an excepted theoretical

9 basis for the biological connections you are proposing. In the

10 case of domoic acid, there is a huge toxicology and

11 neurobiological literature on excitatory toxicity and the

12 relationship between excitotoxicity and behavioral changes. 

13 There should be a temporal relationship.

14 So, effects that occur at a lower level of

15 biological organizations should occur prior to effects that

16 occur at the next. There are a number of examples of this for

17 domoic acid, but one is we do not see behavioral changes in the

18 animal before you see significant cell loss in the hippocampus. 

19

20 Of course with any study of toxicology, dose

21 response is important. In terms of the adverse outcome pathway,

22 doses that cause a significant effect at a lower level of
Page 51

1 biological organization should be less than or about the same as

2 doses that would cause a change at the next level. In the case

3 of domoic acid, if you look at the concentrations that activate

4 the kainate receptor, they occur at concentrations that are

5 lower than or about the same as the concentrations that increase

6 intracellular calcium.

7 The other important issue to think about in

8 dose responses is the adaptive response. At what concentration

9 do you see an effect at one level of biological organization

10 that overcomes any adaptive response at that level lead to an

11 effect at the next level? 

12 I know that it is hard to see, but if you look

13 at that calcium dysregulation graph, you can see at the lower

14 level of domoic acid, calcium goes up rather significantly. 

15 Over the time course, which is shown there, which is about 20 or

16 30 minutes, it starts to come back down to baseline. So, the

17 cell can buffer at increases in intracellular calcium. Indeed

18 at that level of domoic acid, you do not see very much cell

19 death. However, at the next level of domoic acid, you see a

20 large increase in intracellular calcium, and it does not return

21 to baseline. In fact, you have overwhelmed the calcium

22 buffering capabilities of the cell, and now get cell death and
Page 52

1 subsequent loss of cells in the hippocampus. So, dose response

2 is critical. 

3 Finally, you want to think about

4 experimentally verifying the relationship between the effects at

5 each level of biological organization. So, altering the effect

6 at one level should alter the effect at the next. There are

7 lots of examples in the literature for domoic acid. If you use

8 an antagonist at the kainate receptor in the presence of domoic

9 acid, you block the increase in intracellular calcium. If you

10 buffer intracellular or chelate intracellular calcium in the

11 cell, you block the cell death, experimentally verifying these

12 links between different levels of organization. 

13 So I would like to move on and talk about some

14 of the potential biological pathways that could lead to

15 neurodevelopmental toxicity through chlorpyrifos or its

16 metabolites. So, this is just an overview showing that for

17 chlorpyrifos metabolites, data exists at a number of levels of

18 biological organization. What we have done is taken this data

19 and sort of arbitrarily named some domains based on the

20 molecular or mechanism under which this data falls. 

21 Each one of these domains, I do not have time

22 to go into it but it is in the draft report, has a biologically
Page 53

1 plausible connection between disrupting the domain and the

2 pathway and resulting in an adverse neurodevelopmental outcome. 

3 These domains including disrupting the morphogenic properties of

4 the acetylcholinesterase protein, directly interacting with

5 cholinergic receptors, perturbing the endocannabinoid system,

6 increasing the amount of reactive oxygen species, perturbing the

7 serotonergic system and binding to tubulin or

8 microtubule-associated proteins, or MAPs.

9 So, I think you can see there is at least some

10 data at some level of biological organization for each one of

11 these proposed domains or pathways. I think one thing that is

12 obvious fairly quickly is that there is not a complete set of

13 data at every level of biological organization for anyone of

14 these pathways. For example for the reactive oxygen species

15 pathway or domain, there is no proposed molecular initiating

16 event, although there is data both in vitro and in vivo showing

17 that there is increase in reactive oxygen species at the

18 cellular level, and also data at the level of the CNS showing an

19 in vivo stress response genes to reactive oxygen species. 

20 The other point I would like to make here is

21 my opinion is that one reason that there is not a complete set

22 of data for any one of these is that in reality, these domains
Page 54

1 have not been explicitly proposed as adverse outcome pathways. 

2 Without that framework, the appropriate data has not been

3 generated to test that pathway. 

4 So, what we have done in this slide is

5 actually propose a potential adverse outcome pathway based on

6 the morphogenic properties of acetylcholinesterase. We have

7 proposed a molecular initiating event, key events at the

8 cellular level and organ level, and an adverse outcome of

9 concern to risk assessment. This pathway is based on the

10 neurobiological literature showing that the acetylcholinesterase

11 protein can act as a morphogen and control cell growth in the

12 developing nervous system. 

13 In this pathway, the molecular initiating

14 event is binding to and altering the morphogenic properties of

15 the acetylcholinesterase protein. Alteration of the

16 acetylcholinesterase morphological part of the protein would

17 then alter neurite outgrowth, so the outgrowth of axons and

18 dendrites at the cellular level. This effect on neurite growth

19 at the cellular level would perturb the normal connectivity or

20 normal development of the connections in the developing nervous

21 system. This impaired neural connectivity would then cause

22 impairment in cognition in the whole organism. 
Page 55

1 So, there is data for both chlorpyrifos,

2 chlorpyrifos oxon and trichloropyridinol both in vitro and in

3 vivo at several levels of biological organization. As you know,

4 both chlorpyrifos and chlorpyrifos oxon can bind to the

5 esteratic site of acetylcholinesterase. That has been proposed

6 to result in a conformational change that alters the morphogenic

7 properties of the protein. In addition, chlorpyrifos oxon

8 combines with the peripheral anionic site of the protein, also

9 proposed to alter its morphogenic properties. At the level of

10 the cellular response, there is data in vitro showing that

11 chlorpyrifos, chlorpyrifos oxon and trichloropyridinol can

12 decrease neurite outgrowth in vitro, in some cases at levels

13 that do not inhibit acetylcholinesterase, but also at levels

14 that do inhibit acetylcholinesterase. In vivo, there is data

15 showing the chlorpyrifos oxon can decrease axon outgrowth. 

16 However, this data is only shown in the presence of inhibition

17 of acetylcholinesterase. 

18 So, this was our attempt to place some of the

19 data at different levels of biological organization in the

20 literature into a biologically plausible adverse outcome

21 pathway. This slide gives a little bit more detail on some of

22 the data that exists at different levels of biological
Page 56

1 organization. So, if you look at the morphogenic properties of

2 acetylcholinesterase, as I just said, the molecular initiating

3 event would be binding to acetylcholinesterase and altering that

4 morphogenic site. 

5 There is data showing that that leads to

6 decreased or altered neurite outgrowth at the cellular level in

7 at least one piece of data showing that that leads to change in

8 axon length at the level of the central nervous system.

9 For cholinergic receptors, a molecular

10 initiating event would be binding to and activating the M2

11 subtype of the muscarinic receptor. At the cellular level, this

12 leads to a decrease in production of cyclic AMP. 

13 For perturbation of the endocannabinoid

14 system, we have data suggesting an initiating event in binding

15 to and inhibiting the CB1 receptor and also inhibition of a

16 downstream enzyme, a fatty acid hydrolase. 

17 For reactive oxygen species, there is no

18 proposed molecular initiating event, but there is data showing

19 increased reactive oxygen species formation at the cellular

20 level both in vitro and in vivo. At the organ level, there is

21 data showing that this increase in reactive oxygen species leads

22 to an increase in expression of oxidative stress response genes.
Page 57

1 For the serotonergic system, again there is no

2 explicit molecular initiating event, but there is data at the

3 cellular level showing that there are changes to serotonin and

4 serotonin signaling. These changes in serotonin signaling are

5 also observed in the central nervous system and different brain

6 regions at the organ level. This has been shown to result in

7 changes in serotonergically mediated behavior, both in the basal

8 response and after drug challenges.

9 Finally for tubulin and microtubule-associated

10 proteins, the molecular initiating event would be binding to

11 these 2 proteins. This leads to a change at the cellular level

12 of impairing axonal transport. 

13 So, I would like to sort of finish up here by

14 showing some data that suggests that these pathways may be

15 initiated at levels or doses that do not necessarily inhibit

16 acetylcholinesterase. I have given four examples here, where

17 acetylcholinesterase activity was assessed in the same system

18 comparably. These examples all have concentration or dose

19 response relationships. There is a significant response in the

20 absence of significant inhibition of acetylcholinesterase. 

21 So, the first example is in vitro example

22 using primary neuronal cultures, again showing a decrease in
Page 58

1 neurite outgrowth and the absence of significant inhibition of

2 acetylcholinesterase. 

3 The second example looks at neonatal exposure

4 to the rat, chlorpyrifos showing an increase in oxidative stress

5 response genes at levels that did not inhibit

6 acetylcholinesterase in the brain. 

7 The third example again is in vitro looking at

8 primary neuronal cultures. It was shown that there can be

9 inhibition of axonal transport in these cultures in the absence

10 of acetylcholinesterase inhibition. 

11 The last example again is perinatal exposure

12 to the rat, showing changes in serotonergic tone in the adult

13 CNS in the absence of cholinesterase inhibition. In this

14 particular case, acetylcholinesterase activity was measured in a

15 separate experiment using identical exposure protocol.

16 So, just an overall summary of the data for

17 multiple plausible events induced by chlorpyrifos and its

18 metabolite: So in terms of plausibility, there is data

19 suggesting multiple biologically plausible initiating events

20 and/or downstream events. However, as I mentioned, few of these

21 have been explicitly outlined as adverse outcome pathways and

22 few of them have been studied as such. 
Page 59

1 In terms of temporal relationship, almost all

2 of these studies, the data show there is a temporal relationship

3 between exposure and effects at increasing levels of biological

4 organization. In terms of dose response, there is limited data

5 showing dose response relationships across increasing levels of

6 biological organization. However, as I just showed in the last

7 slide, there is evidence for effects, which are either similarly

8 sensitive or more sensitive to chlorpyrifos or its metabolites

9 in the inhibition of acetylcholinesterase. 

10 Finally, in terms of experimental

11 verification, there is limited data showing experimentally a

12 verification between an effect at one level of biological

13 organization and the next. I think that is the last slide. 

14 DR. KENNETH PORTIER: At this point, we open

15 up the questions from the panel. I think Dr. Aschner wanted to

16 ask one way early in your presentation, so he is probably going

17 to ask you to flip back to a previous slide. 

18 DR. MICHAEL ASCHNER: Michael Aschner, hi

19 Bill. Thanks for the presentation. I have a question. Maybe

20 we can go back to the linear path. It is like the second slide.

21 It is more of a framework for the panel, but are we supposed to

22 think in those terms, is this sort of the way that you would
Page 60

1 like us to think about chlorpyrifos or any other chemical? 

2 Because the way that I envision things, and we

3 can argue about chlorpyrifos is that there are many chemicals

4 that do not work this way where you cannot go to this linear

5 pathway. I can envision chemicals which affect the third box

6 from the left right away, skipping the first two. When we are

7 done here, are we supposed to say one, two, three, four, five,

8 six happen in a sequence? I do not think we can use this as a

9 framework. Even if it works for chlorpyrifos, and I am not sure

10 it will. It does not work with many, many other chemicals, so

11 it may put the agency into some - I mean you want consistency

12 between different compounds. With this kind of framework, it is

13 not going to work. 

14 DR. WILLIAM MUNDY: I cannot answer what we

15 want the Panel to do. Maybe Anna can describe that. I will say

16 that this is obviously a very simplified linear flow. Adverse

17 outcome pathways may have multiple initiating events leading to

18 a common downstream event, or conversely there may be an initial

19 initiating event that leads to multiple adverse effects. 

20 So, as we know, biology is not linear, it is

21 three-dimensional or four-dimensional if you include time. But

22 the concept is really linking an effect at one level to the next
Page 61

1 level. I would challenge you to show me a compound that does

2 not interact with a molecular level yet causes an effect.

3 DR. MICHAEL ASCHNER: Sugar.

4 DR. WILLIAM MUNDY: Sugar is a nutrient. 

5 There is a huge metabolic pathway for sugar.

6 DR. MICHAEL ASCHNER: Osmotic shock with

7 sodium chloride. 

8 DR. WILLIAM MUNDY: Sodium chloride will start

9 affecting calcium fluxes. 

10 DR. MICHAEL ASCHNER: Sodium chloride will

11 skip DNA binding, protein oxidation, receptor ligand

12 interactions, gene activation, protein production, alter

13 signaling. It is going to go straight into the third box if

14 there is any. 

15 DR. WILLIAM MUNDY: Those are examples. There

16 are obviously innumerable initiating events. That is one of the

17 problems if we start talking about adverse outcome pathways is,

18 what is the initiating event. We have not identified all of

19 those yet, but I think this is a more general discussion that

20 may not answer your question. I do not know if Steve wants to

21 talk about how we want the panel to think about adverse outcome

22 pathways. 
Page 62

1 DR. STEVEN BRADBURY: You should think as your

2 mind takes you into challenges. I am not trying to put you in a

3 box, but I think it is fair to say that not only EPA but NIH and

4 FDA are trying to use this concept as we think about risk

5 assessments and work in our research labs to help put

6 information together. 

7 As Bill said, biological systems are

8 inherently nonlinear. It is hard to write and talk in nonlinear

9 ways, some do better than others. I think the NRC Panel is just

10 trying to help us think about putting some thoughts together,

11 some of the events that link to change in an inherently

12 nonlinear system. 

13 With regard to an initiating event, I am

14 trying to think of a way a chemical does not interact with

15 something to cause a change at a tissue level. It covalently

16 binds to something. It non-covalently binds to something. It

17 starts redox cycling or it does not, but I think the idea of the

18 NRC report is that a chemical interacts with something at that

19 level. What that interaction would be, those are just some

20 examples. These are not going to inclusive of all of the

21 examples, by way a xenobiotic may, or nutrient may, interact

22 with the system. 
Page 63

1 I think what we are curious about is if a

2 chemical could cause something at a higher level of biological

3 organization. I think the thought of the NRC Panel was that

4 something must have happened at a lower level of biological

5 organization. If we are having changes at a population level

6 and reproductive failure in birds due to cholinesterase

7 inhibition, which you might be able to see, something probably

8 happened that leads to a change in fecundity in the females. 

9 To what degree can you start to piece together

10 that, realizing there is going to be multiple pathways

11 interacting with each other and it is going to be a nonlinear

12 relationship, but a way to try to organize our thoughts. If you

13 all come up with a different way of organizing the thinking

14 around this data, that would be helpful. 

15 In the context of our charge questions, we are

16 trying to get some advice building on previous SAPs and how to

17 use this concept to organize our thoughts. If it cannot be

18 done, it cannot be done. I would be curious to hear why, but

19 then what would be the alternative way to try to link

20 information at different levels of biological organization

21 together to try to put a story together. We understand the

22 strengths and limitations of this clearly.
Page 64

1 DR. CHARLES VORHEES: I have a lot less

2 complicated question. So, I am just interested you talked

3 about these various effects and these hypothesized mechanism

4 studies, and you talked about them as some of them occurring at

5 doses below which cholinesterase inhibition occurs. Could you

6 give us a little feel for what those doses are and how far

7 removed they are from cholinesterase inhibition levels? 

8 DR. WILLIAM MUNDY: Yes, I did not give

9 details. A lot of them are in the draft document. Some of the

10 in vitro cases, there may be a 100 fold less than inhibiting

11 acetylcholinesterase. In the in vivo situations, Stephanie? 

12 Not as much. So, the examples I gave without giving the details

13 of the levels were showing in the same system that there was at

14 least not significant change in acetylcholinesterase with a

15 statistically significant change in the outcome.

16 That relationship does depend on the system,

17 whether be it in vitro or in vivo. Frankly, there is really not

18 that much data surprisingly where researchers have done a

19 careful dose response relationship between a molecular event,

20 cholinesterase inhibition, in the next level of event. That is

21 where I think getting toxicologists to use this adverse outcome

22 pathway as a concept, as a framework. It allows you to think
Page 65

1 about the experiment you need to do to answer that question. It

2 can range from less than 10 fold to over 100 fold in some of the

3 in vitro studies. 

4 DR. WILLIAM POPENDORF: My reading of it, I

5 guess I did not quite grasp that linkage and it is done on a

6 molar basis, right? How is that done in terms of then going

7 from a dose that is typically not measured at a molar level

8 within a cell or to a cell to the in vitro studies that you are

9 talking about? 

10 DR. WILLIAM MUNDY: Sure, that is a really

11 good question. In some cases, that is part of the difficulty in

12 developing those experiments, especially going from an effect

13 that goes from an in vitro prep, no matter what level of

14 biologic organization is at, from an in vitro to an in vivo. 

15 So, some of the examples you would use as looking at an in vivo

16 dose, estimating a molar concentration in the blood or brain. 

17 What we did not talk about at all here is the extrapolation

18 issues and how you extrapolate quantitatively between levels of

19 biological organization. 

20 So, within in vitro experiments or without in

21 vivo experiments, you can make the direct comparison. But you

22 are right, going from an in vitro at a molar basis, in vivo to a
Page 66

1 dose basis, what you would at the first level do is try to

2 convert that dose to a blood or a target tissue concentration in

3 a molar basis. Of course, with better pharmacokinetics, you may

4 be able to get that quantitative relationship down to a finer

5 degree. That is a very excellent point, though.

6 DR. WILLIAM POPENDORF: That is basically what

7 you did to derive, when you say 100 fold, you have done that on

8 those examples? 

9 DR. WILLIAM MUNDY: So, the 100 fold was

10 actually measuring neurite outgrowth, for example, and

11 acetylcholinesterase in the same system at the same time. So,

12 it was purely a molar basis. 

13 DR. RUSSELL CARR: In an in vitro system, it

14 is real easy to make that jump between the molecular target and

15 the cholinesterase inhibition. However, in an in vivo system,

16 unless you really have a well-established dose response as to

17 over time, you cannot make a claim that there is no

18 cholinesterase inhibition when you just take one time sample. 

19 That is an issue with a lot of the studies that they just depend

20 on one time sample, and say that there is nothing there when we

21 know in developing animals, their cholinesterase activity

22 recovers so fast because they are growing, a lot of de novo
Page 67

1 synthesis. So, if you miss that window where you may have had

2 40% or 50% inhibition, it may have recovered by the time you

3 measured it if you did not select a correct time. 

4 DR. WILLIAM MUNDY: I agree. 

5 DR. KENNETH PORTIER: Any additional

6 questions? As I was looking at this, I was trying to think

7 where do you include things like promoters, chemicals that

8 actually act as promoters rather than direct toxicants? I do

9 not know if I see it in this one or maybe in the next slide as a

10 modifier of homeostasis, right? 

11 DR. WILLIAM MUNDY: Yes, I think the whole

12 idea of the toxicity pathway concept is not that there has to be

13 a toxic effect, but indeed that you are just perturbing a normal

14 pathway. So in that case, any pathway could be a toxicity

15 pathway if you are perturbing it from going to its normal

16 function.

17 I think that would include the concept of a

18 promoter. The idea would be though, you need to show that

19 perturbation at that particular level of biological organization

20 overcomes any adaptive response and leads to an adverse effect

21 at the next level. So does it respond to a chemical that is

22 actually an adaptive response of increasing proliferation to
Page 68

1 replace damaged cells actually overcompensate and lead to cell

2 proliferation and tumor at the next level? So, you could have

3 normal pathways that are perturbed outside of normal to a degree

4 at least, to an effect at the next level. I think that is

5 pretty inclusive in my opinion. 

6 DR. KENNETH PORTIER: Any other questions? 

7 Otherwise, we are going to go ahead and take a 15 minute break

8 at this point. I am going by this clock over here, definitely

9 not that one over there. This one says 10:37, so we will return

10 in 15 minutes. 

11 (WHEREUPON, a recess was taken)

12 DR. KENNETH PORTIER: So during the break, I

13 am sure there were a lot of new questions that came up, but we

14 will have a couple of more opportunities to ask those questions.

15 At this point, I am going to turn back to EPA for the next

16 presentation, Dr. Moser. 

17 DR. GINGER MOSER: Okay, good morning. I am

18 Ginger Moser. I am a Neurotoxicologist with ORD down in the

19 North Carolina Research Triangle Labs. What I am going to talk

20 about is to go over the animal studies, looking at chlorpyrifos

21 effects on the developing brain. 

22 By way of introduction, I want to start out
Page 69

1 with the discussions that we had in 2008 with the SAP back then.

2 They reviewed the animal studies and concluded that independent

3 of the time of exposure, whether it be gestation or postnatal,

4 dose of 1 mg/kg and greater demonstrated significant effects on

5 behavior. They did express 2 areas of concern, being the

6 inconsistent behavioral effects and the lack of dose response of

7 data on cholinesterase inhibition. At that time, they

8 recommended that we review the critical events at the time of

9 the exposure and targeted end points. What we had done back

10 then was only look at the effects of 1 mg/kg. 

11 So, what we have done in this current review

12 is to look at the behavioral changes in terms of the domains of

13 neurological function, what type of neurobehavioral effects are

14 being altered. We wanted again to try to look at critical

15 exposure periods and of course, the primary thing to look at is

16 brain cholinesterase inhibition during the critical periods. 

17 So, in the document you saw a targeted review

18 of the papers that have come out since 2008. We wanted to look

19 at those papers carefully in terms of what new information they

20 gave and how well they integrated with the existing database. 

21 What I am going to try to cover today is that full database and

22 not just focus on those more current papers. 
Page 70

1 So, the existing literature for chlorpyrifos

2 is really kind of a moving target. There was actually another

3 paper that came out just within 2 weeks ago, bringing the total

4 papers up to 26. They come from 11 different laboratories. So,

5 not all of these studies are being conducted at 1 or 2 places.

6 There were 9 papers that have come out since

7 2008 adding 2 new laboratories. I should note one of the papers

8 that came out since 2008 is in Chinese, so it was kind of

9 difficult to evaluate that one, even though I do have an

10 informal translation of that paper. I am not going to include

11 that data. 

12 So, in the document, we focused our review on

13 the studies where they were dosing the animals at any time up to

14 weaning. We have only looked at the papers where the animals

15 were assessed after weaning, because we wanted to look at more

16 long term impacts. We did not want to have the confound of

17 concurrent cholinesterase inhibition. We only looked at the

18 papers where they had lower doses because we wanted to avoid a

19 lot of cholinesterase inhibition and the toxicity that is

20 involved with that. We also focused on the rodent studies. 

21 That left us with 20 papers from 9 different

22 laboratories. Of those 20 papers, there were 25 actual studies
Page 71

1 because several of the papers looked at different exposure

2 periods within the same paper. So, there is a lot of

3 variability in these 25 studies. They include differences in

4 the exposure paradigms. In fact, there are 10 gestational

5 studies that have 7 different dosing periods, 13 postnatal

6 studies with 5 different dosing periods, and then they have 2

7 perinatal studies, each of which have different dosing periods. 

8 So, it is next to impossible to really compare across the

9 studies that have the same dosing period. 

10 There are 4 different routes of

11 administration, 4 different vehicles that have been used. One

12 of the vehicles that is widely used is DMSO. That has been a

13 concern. It was a concern of the 2008 SAP because it does

14 change the pharmacokinetics of the chemical somewhat. The dose

15 selections, about half of the studies actually only used 1 dose.

16 A few of the other papers have looked at either 2, 3 or 5

17 doses. As just as far as the species go, about half of the

18 studies have been done in rats and about half in mice now. 

19 So, as I am going through this, I want to just

20 kind of keep in mind the guidelines that the agency has issued

21 for neurotoxicity risk assessment. Concerning developmental

22 neurotoxic studies, the conclusions and the guidelines for
Page 72

1 interpretation were the developmental effects in animals do

2 indicate the potential for altered neurobehavioral development

3 in humans, and that the specific types of developmental effects

4 that are seen in the animal studies may or may not be the same

5 as those seen in the humans. You can kind of keep that in mind

6 as we are going through the animal studies and as we go through

7 the human studies this afternoon. 

8 So, what I wanted to do is go through the

9 studies, summarize the studies in terms of the different

10 functional domains that are being measured, give an overview of

11 the outcomes and some representative data in a couple of places,

12 and then try to give an indication of the strengths and the

13 limitations of some of those studies. 

14 So, for cognition, there were quite a few

15 studies that have looked at measures of cognition. The spacial

16 learning and memory has been looked at using a radial arm maze,

17 using a morris water maze and then a foraging task in a radial

18 arm maze. Eight out of the 9 studies where they have used these

19 tests have reported altered function. Most of those studies

20 show deficit in cognition or impaired cognition, but a few have

21 actually shown improvement. I will show an example of that in a

22 minute. 
Page 73

1 These 9 studies include gestational and early

2 postnatal exposures. The effects have been seen at 1 mg/kg per

3 day and above. Maybe at 0.3 mg/kg per day, but that was one

4 study that has some other issues with it. In fact, the 2008 SAP

5 had a lot of concerns with that study and felt like it really

6 should not be considered. The lowest no effect level that has

7 been reported, and this is in a newer paper, is 0.2 mg/kg per

8 day. In that study, they did use the water maze. 

9 There have been five studies that looked at

10 alternation learning in a T maze, all of which showed no effect.

11 There were two studies that looked at classical conditioning

12 using a passive avoidance, and all of those showed no effect. 

13 These tests that have been used are all

14 validated and accepted and widely used in the literature with

15 the only exception being the foraging task, which is a novel

16 task. In that paper, it was presented as an alternative to the

17 radial arm maze. The morris water maze has of course been used

18 for two or three decades. There have been some replications

19 using this test within one laboratory, two different studies

20 that I will show in a minute. 

21 The radial arm maze has been used across

22 several laboratories, but unfortunately they have used different
Page 74

1 procedures and different equipment. The gender differences that

2 have been reported differ in terms of the affect and also in the

3 control values. 

4 With cognitive tests, it is important to have

5 measurements of the activity level or any kind of motor

6 disturbances because this can confound the performance. So, six

7 of the studies actually did look at some measure of activity. 

8 Out of those 6 studies, 5 did not show changes in the activity

9 levels. 

10 There was dose response seen in a little over

11 half of the studies where they used two or more doses. Of

12 course, again, a lot of the studies only used one dose. 

13 Sometimes the effect size was small, sometimes not. Sometimes

14 the differences were transient and sometimes they were

15 persistent. 

16 This is an example of the morris water maze. 

17 These are two papers that came from the same laboratory. They

18 had multiple studies within these papers looking at mice that

19 had been exposed only during gestation from GD9 through 18. 

20 What is being measured here is the latency for

21 the animal to find a hidden platform in this large tank of water

22 that is a little bit dark, hopefully you can see that as a
Page 75

1 morris water maze. So, over time, the animals learned this

2 pretty easily. You can see a learning curve where the latency

3 decreases in the controls. 

4 In this study, they looked at four different

5 doses. They reported effects at 1 and 3 mg/kg per day, but not

6 at the higher doses, the 5 and the 10. The graphic quality here

7 is just really difficult for me to see. It is very difficult to

8 pick out the different symbols, but right there, the red arrow

9 points to the 3 mg/kg dose group. You can see that it is higher

10 than the controls, which I think is that right there. They did

11 report no changes in swim speed, which is an important measure

12 in this test. 

13 Now the purpose of these papers was to look at

14 potential treatments or attenuation of the chlorpyrifos effects

15 by using either nicotine or stem cell transplantation. So,

16 ignoring all that data, we only wanted to focus on the

17 chlorpyrifos alone group. In three more groups of animals, they

18 looked at 3 mg/kg of chlorpyrifos and the learning curves are

19 indicated by the red arrows. In all cases in all three of these

20 studies, there was slower learning that was taking place in the

21 treated animals compared to control. Again, in all studies,

22 there were no changes in swim speed. 
Page 76

1 These were some studies using the radial arm

2 maze. In this case, they came from the same laboratory. They

3 are using rats and they are using the radial arm maze shown

4 there where some of the arms are baited, so the animals runs

5 down the arms to get the food. Some of the arms are not baited.

6 A reference memory error is when the rat goes into an arm that

7 never has food in it. 

8 Again with training, the control animals

9 learned this. Their number or errors go down. Both slides were

10 seeing PND 1 through 4 exposure at 1 mg/kg only. They had no

11 dose response here. What they reported in both of these studies

12 were increased errors in the males, but decrease, which means

13 improvement, in the females. 

14 This is the data for the males here. The

15 controls are learning the task and the treated animals are

16 learning it, but they learn it slower. The females of the

17 treated animals tend to learn it more quickly. The authors

18 state that in both cases, there was a significant overall effect

19 of treatment, but not a significant treatment by time

20 interaction, so they concluded that even this change was

21 significant across training, even though it looks like here at

22 the end of training, these data points are closer together. 
Page 77

1 In this other study, you see the same pattern

2 with the females where the treated animals are improved in terms

3 of their learning, and the males show a higher number of errors,

4 but only at the beginning of training. In this case, they said

5 it was significantly different only at the first trial. The

6 overall story here with the difference in the sexes was same in

7 both of these studies, but the actual values looked a little bit

8 different.

9 The next domain is the anxiety, emotion effect

10 type of domain. Anxiety is measured with elevated plus mazes

11 and a light/dark box. In seven out of the eight studies, there

12 were reported altered behaviors, again either with gestational

13 or postnatal exposures, and all the effects were seen at 1 mg/kg

14 per day and above. 

15 There were a couple of studies that looked at

16 novelty and chocolate preference, which is preference for a

17 novel substance, the two studies that looked at novelty

18 exploration showed no effect, but there was a change in

19 chocolate preference, but this was only in one study. There has

20 only been one study that looked at despair using a forced swim

21 test, and there was no effect. 

22 Now again, these tests are validated, they are
Page 78

1 well accepted and used in the literature, but the specific end

2 points within the tests have not been consistently measured or

3 altered across the studies. Even within the same laboratory and

4 across studies, there have been different procedures, different

5 end points that they look at, which may contribute to some of

6 the differences. There are also differences in the direction of

7 change. 

8 In the mouse studies, there were six mouse

9 studies, three showed increased anxiety and three showed

10 decreased anxiety. There were only two rat studies, one of

11 which showed decreased anxiety. As with the cognitive test,

12 activity measurements can confound the measurement in these

13 studies, and six of the studies did look at activity levels and

14 five of them reported no differences. There was only dose

15 response in one out of the four studies seen where they used two

16 or more doses. Again, sometimes the effect sizes were small,

17 sometimes they were not.

18 This study shows a couple of replications

19 looking at anxiety using the elevated plus maze or the

20 light/dark box. Both of these tests capitalize on the rodent's

21 preference for the dark and/or the enclosed size of the

22 chambers. 
Page 79

1 In this first study, they looked at anxiety in

2 mice that had been exposed during gestation and lactationally. 

3 They only looked at females. They reported increased anxiety. 

4 This is the measurement with the light/dark box. The effect was

5 only seen at 1 mg/kg. It is a pretty clear difference between

6 the 1 mg and the low and the high dose. In the elevated plus

7 maze, they also saw a change in the time of the open arms, which

8 is indicative of increased anxiety. This was again only in the

9 1 mg dose. 

10 Now, these were litter mates. They were not

11 the same animals that were tested. They were litter mates, but

12 it showed replication within the same study of the same type of

13 effect using two different tests. 

14 Now, this other study looked at mice using

15 just the elevated plus maze. These were mice that were exposed

16 during the last postnatal period. Here we have a nice dose

17 response and the percent of time spent in the open arms, which

18 indicates decreased anxiety only at the high dose, even though

19 the 1 mg dose tended to be kind of falling in between the

20 controls and the high dose. So, there were changes in both of

21 these studies, but the direction of change was different. 

22 The next domain that has actually had quite a
Page 80

1 few studies now is looking at social and maternal behaviors. 

2 The most studies have looked at the same sex social

3 interactions, so these are male/male and female/female

4 interactions. Six out of 10 studies have reported altered

5 behaviors, again as across either gestational or postnatal

6 exposures. 

7 It has been pretty consistent that the social

8 interactions are increased, but with increased agonistic or

9 aggressive responses, which is only seen in the males. Five

10 studies have looked at maternal behaviors, although there are

11 some differences in how they actually did these maternal

12 behavior studies. They did show changes in four out of the five

13 studies. 

14 There are several of the studies that have

15 consistently reported decreased reactions to an intruder, where

16 an intruder male is placed in with the female, but then there

17 are increased or decreased behaviors towards the pups. It

18 should be noted that all of these studies have come from the

19 same laboratory and have all been conducted in mice, but the end

20 points have not been standardized across those studies within

21 the same laboratory. In some cases, there have just been one or

22 just a few significant changes out of a fair number of behaviors
Page 81

1 that are measured. 

2 It should also be noted that test of social

3 behavior has not been widely used in developmental neurotoxicity

4 studies, although they have been recommended to be studied more.

5 They are used a lot in other area of research, but not as much

6 in developmental neurotoxic. 

7 The motor activity domain is another one where

8 there have been quite a few studies that have looked at this. 

9 Fourteen studies have looked at spontaneous or exploratory

10 behavior. This includes generalized locomotion and rearing. 

11 Basically, the tests have been conducted in figure-8 chambers or

12 open fields. Eight out of the 14 studies have reported changes

13 in the activity levels with either gestational or postnatal

14 exposures. 

15 Now, the rat studies have been pretty mixed

16 with one study showing increased, two showing decreased and two

17 showing no effect on activity. Those have all been with

18 postnatal exposures. There has been no effect reported at all

19 with rats with gestational or perinatal exposures. It has been

20 more consistent in that increased activity has been seen in all

21 three of the studies where they looked at this in mice. That is

22 gestational and postnatally. 
Page 82

1 Now, habituation is the change in activity

2 over time. This is different from the total overall activity

3 levels. There have only been six studies where they have

4 actually analyzed habituation. Out of those six, three have

5 reported alterations in this pattern. There have been different

6 statistical approaches across the laboratories, so it is more

7 difficult to make any kind of comparisons on this end point. 

8 Overall, the motor activity is accepted, widely used in

9 toxicology, in pharmacology and psychology, so this is widely

10 used behavior.

11 And then to just touch on the neuromotor

12 domain, the last one, this is very limited. There have only

13 been three studies where they have even looked at this. There

14 have been two studies that show no change in the auditory

15 startle response, and one study that reported a change in other

16 measures of neuromuscular performance. Again, with so few

17 studies, there is not much that could be said there. 

18 Now, an important thing is to look at the

19 cholinesterase inhibition in either the pup or the fetus during

20 an exposure to try to relate this to the studies where they see

21 behavioral changes. What I have shown here are the studies

22 where they looked at cholinesterase inhibition at 1 mg/kg given
Page 83

1 either once or repeatedly. 

2 As you notice, this dose is the one that

3 produced effects in almost all the domains. When 1 mg/kg is

4 given gestationally, there have only been two studies that

5 looked at the fetal brain cholinesterase inhibition. In both

6 studies, it showed no inhibition either 4 hours after the dose

7 to the dam on GD20, or 24 hours after the dose to the dam. But

8 there is no information on the fetal cholinesterase inhibition

9 at any shorter times after dosing or whether there has been any

10 inhibition on any other days. GD20 was the only day that has

11 been looked at. 

12 When you look at the early postnatal studies

13 where the pup is directly exposed between PND 1 to 4, you do see

14 inhibition in the pup brain, whether it be given 1, 2 or 4 hours

15 after dosing. If you give it on PND 1 just a single dose, you

16 do see inhibition at 2 and 4 hours, but when it is given from

17 PND 1 to 4 and then look at them on PND 4, you see inhibition at

18 1 hour, but not 4 hours. This shows the faster recovery, which

19 it is known that the very young pups recovery very quickly. 

20 As you can see, both of these studies in rats

21 and mice show inhibition at 1 mg/kg. There have been no studies

22 that have looked at potential inhibition at any lower doses at
Page 84

1 this time exposure period. When you look at the later exposure

2 period, PND 11 through 14, one study showed inhibition that was

3 in rats, one study showed no inhibition in mice. 

4 So, there are a lot of potential sources of

5 variability in these outcomes that you can see have been

6 somewhat inconsistent, although there are some consistencies. 

7 These include the differences in experimental design that have

8 already given some information about procedural equipment

9 differences, lack of a dose response, differences in species and

10 strains, route of exposure, differences in the ages, the periods

11 of exposure and the differences when they are tested as adults. 

12

13 There are differences in control values in

14 some cases and differences in the treatment effects, and that

15 they are not always exactly reproduced across studies. There

16 are differences in whether there might be brain cholinesterase

17 inhibition in the pup. It looks like there probably is not. At

18 least the gestational studies show that there is not fetal

19 inhibition, but postnatally clearly there is. There are

20 differences in the interpretations that have to do with

21 statistical analyses. 

22 In some of the studies, the method of analyses
Page 85

1 and also the acceptance criteria are not consistently applied. 

2 As I have mentioned, there are a lot of cases where the effect

3 size is fairly small, which could represent either subtle

4 effects or they could be false positives. There is also the

5 potential for false negatives, either by having conservative

6 acceptance criteria, which was especially the case in one paper,

7 or by having underpowered study and not having a great enough

8 sample size to really be able to pick up an effect. Then of

9 course, there is a publication bias against studies of negative

10 data, which can also influence interpretations. 

11 So to summarize, it is clear that there are

12 multiple studies and laboratories now that have reported changes

13 in various aspects of neurobehavioral function in the adult

14 rodents following developmental exposure. These effects are

15 seen long after the chemical exposure for the most part. If

16 there is any cholinesterase inhibition, it has recovered by the

17 time the rodents are tested. 

18 These behavioral alterations show some

19 consistency if you look at the broad domains of neurological

20 function, the most consistent effects being in cognition,

21 especially with spacial learning and memory with being changes

22 in anxiety, even though the direction of that change is
Page 86

1 different, and in social behavior. As I say, the differences in

2 direction and the magnitude of change and sometimes differences

3 in the gender specificity, there are differences in the

4 procedures, etc., that really impact the comparability and make

5 it very difficult to compare directly across studies. 

6 There does not seem to be any clear evidence

7 of a critical period of exposure. These effects are seen pretty

8 much whether the exposure is during gestation or during

9 postnatal periods. There is not a large difference in species

10 between the rats and the mice. Basically, the new publications

11 support the previous analyses in that the behavioral effects

12 were again seen at doses of 1 mg/kg per day and above, and that

13 again there is still not sufficient dose response data for

14 effects relative to cholinesterase inhibition. 

15 There is just not enough data to be able to

16 definitively conclude the relative sensitivity between these

17 developmental outcomes and the cholinesterase inhibition because

18 there have just not been enough studies that have looked at

19 dosed below 1 mg/kg and actually measured cholinesterase

20 inhibition at those time points. 

21 So what we really need are more robust dose

22 response and time course data to really be able to be sure that
Page 87

1 you are not just missing a critical time of cholinesterase

2 inhibition in the pups. There needs to be more systematic

3 replications of the exposure periods. With that, I can try to

4 answer some questions. 

5 DR. KENNETH PORTIER: So we open it up to any

6 questions from the Panel? Dr. Harry? 

7 DR. JEAN HARRY: Ginger on point of

8 clarification with all the work you went through going through

9 all of these papers, I have a few questions. I am just going to

10 put them out to save time. One was you talked about the morris

11 water maze and its use in neurosciences as an established model.

12 Most of the studies you appeared to look at were only looking

13 at the acquisition and we are not following the extended

14 protocols for the morris water maze.

15 DR. GINGER MOSER: In that they did not do

16 probe trials, if that is what you mean.

17 DR. JEAN HARRY: Or reversal or any other

18 types 

19 DR. GINGER MOSER: No.

20 DR. JEAN HARRY: They just did the initial

21 acquisition? 

22 DR. GINGER MOSER: These studies that were
Page 88

1 done that I showed only looked at the acquisition, learning with

2 several trials a day over a week and did not do probe trials. 

3 The Chinese paper actually does do a probe trial, but again, it

4 is a very informal translation, so it is difficult to make

5 positive statements of that. 

6 DR. JEAN HARRY: So continuing with that, I

7 guess there are just a couple more. One is the differences in

8 the age that the animals are tested. That can vary especially

9 when you are doing the anxiety, when you are doing the locomotor

10 activity, there are some ages that the data is close to

11 irrelevant because you get counts of five depending on the

12 apparatus that you are using. I was wondering how that crossed.

13

14 I was also wondering when you started to talk

15 about one needing the robust data, and this is the first time I

16 have heard things come out about the studies in mice. So, now

17 you are considering mice versus the rat studies. Is there

18 consistent data to tell you from these papers the housing

19 conditions postnatally of the mice, because they are often

20 handled differently than rats where females are group-housed,

21 males in an appropriate setting are singly housed due to

22 aggression, but that is going to significantly impact these
Page 89

1 behavioral aspects. 

2 Is there any consistency that you are seeing

3 in the literature about them maintaining that, or is it the

4 standard situation where everything is too expensive, so we are

5 going to group house the males and hope they do not fight? 

6 DR. GINGER MOSER: So actually I think that is

7 two questions. One is about the motor activity. We only looked

8 at motor activity tests that were conducted in post weaning

9 animals. So, we were not looking at the pre-weaning where there

10 are the lower motor activity counts. I think the very earliest

11 that motor activity was assessed was about 4 to 6 weeks of age

12 and sometimes older. To look across the age of testing, it just

13 is all over the place. So again, I did not even attempt to pull

14 that into the analyses because it is just too varied. Although

15 within one lab, they tended to test them at the same time. 

16 As far as the housing conditions, there is

17 just simply not the information in there to say. One would

18 assume or one would hope that within the same laboratory, they

19 would house them the same way across studies. It is not always

20 the case though, but the information is just not there. 

21 Definitely, it can affect, especially the social behaviors of

22 the mice and looking at the conspecific behaviors of the same
Page 90

1 sex. There is just not the information there to be able to make

2 those evaluations one way or the other. 

3 DR. KENNETH PORTIER: Any additional Dr.

4 Popendorf.

5 DR. WILLIAM POPENDORF: I have a question kind

6 of on the other side on the cholinesterase side. I am not sure

7 how familiar you are with it, but you know that is kind of one

8 of those tests that has a lot of nuances that makes measurements

9 and small changes very tricky, shall we say. I wondered has

10 anyone looked at the methods that were used on those particular

11 studies of cholinesterase and sort of evaluated their

12 reliability and the ability of those studies to detect small

13 changes. 

14 DR. GINGER MOSER: Well again, there is not a

15 lot of information given on these assays because for the most

16 part, these studies are behavioral people that are doing the

17 studies that do not give a lot of information. I have looked at

18 them. They just cite the Ellman method. If you are lucky, it

19 says what the substrate concentration was. For the most part,

20 the substrate concentration, the acetylcholine concentration is

21 sufficient in the range of 0.8 to 1mL. Otherwise, there is not

22 much detail given. They do not really have a great track record
Page 91

1 in doing these assays. 

2 In some cases, it is just almost a side

3 comment, saying we did this as pilot experiments. That is what

4 one laboratory tended to do a lot, the one that did a lot of the

5 mice and no control values given or anything like that. It is

6 very hard to do a critical review of the way they did the assay

7 because there is just not the data there. 

8 The good thing is that with chlorpyrifos, of

9 course as an OP, so the inhibition is relatively stable as

10 opposed to say a carbamate. Hopefully, some minor differences

11 and deletions whatever would impact the assay that much. 

12 DR. KENNETH PORTIER: Anyone else? Oh, Dr.

13 Vorhees.

14 DR. CHARLES VORHEES: So, I am going to ask

15 you a question that I hope you take right. So, the agency

16 understood these deficiencies in the design of the developmental

17 neurobehavioral studies for a long time trying to look at the

18 literature and determine the relationship to cholinesterase

19 inhibition. So within the agency, you understand these issues

20 better than anyone. So, why isn't the agency undertaken to do a

21 more definitive test internally to address these gaps in the

22 literature? 
Page 92

1 DR. GINGER MOSER: Well that is a fun one to

2 answer. In the past, we have conducted some studies internally,

3 especially for some of the acute cholinesterase inhibition of

4 various ages. But as you probably know, Hurl's focus on the

5 different research efforts has changed over the years. Just

6 like, I think the funding agencies are not as interested in

7 funding this kind of work anymore, Hurl is looking at other

8 aspects of research. With limited resources, which we have very

9 limited resources, we just cannot do everything. That has been

10 how it has ended up being. Clearly, there has been no really

11 definitive studies that have been done here. 

12 DR. JEAN HARRY: Not that hard of a

13 question for you. So, on the next one as you are looking at the

14 academic literature versus somewhat of a control guideline

15 driven study, could you clarify and point out your finding of

16 power in the academic? I mean if we are being asked to sort of

17 evaluate how you are looking at all of the data, so that is why

18 I am asking for the point of clarification. 

19 You have certain types of controls that can

20 run in a definitive or at least one more step in a definitive

21 type study, but yet in many times, they are limited in the

22 extensiveness that their behavioral analysis can go through. 
Page 93

1 So, they stop at an acquisition versus probe test or that type

2 of thing. Your academic studies, if they are done on a

3 mechanistic base or they really want to go after something, they

4 could go deeper into the behavior and they could go further in

5 assessing them, which is something that is not often capable of

6 being done when you have an N of 10 and males and females and

7 four to six dose groups, you really cannot handle all of that.

8 Are you seeing that the studies that are

9 coming from that academia environment are providing you data

10 that are letting you take things further or do they seem to be

11 stopping at a screening type level? 

12 DR. GINGER MOSER: It kind of depends. There

13 are studies that I did not cover, but then they go on with the

14 same animals, do challenges with specific pharmacological probes

15 and that sort of thing. With the goal of trying to address

16 certain hypotheses about changes in this neurotransmitter system

17 versus this one, as far as this went, we were just trying to

18 focus on what the neurobehavioral outcomes were themselves. But

19 there have not been a lot of those studies overall out of the 25

20 studies. A lot of them are more just this is what we get. 

21 In some cases, for instance with the foraging

22 one, it was almost more of this is a new test that we are
Page 94

1 proposing, and here we are doing it with chlorpyrifos. With the

2 morris water maze data that I showed you, they were more focused

3 on the looking at stem cell transplantation and the effect of

4 that and being able to attenuate the effects of chlorpyrifos. 

5 So, the purposes are somewhat different across the studies. 

6 In some cases, they will mention, make

7 statements about sample sizes that, you know, in one of 10 we

8 have found in our experience to be sufficient to detect effects

9 of blah, blah, blah. That has not happened very often either. 

10 So, it is a different kind of mindset than say a guideline. 

11 There is the one study that was conducted

12 purely by the test guideline, had a lot of information about the

13 power of the studies and the statistical analyses and correction

14 for multiple testing, etc. So, it varies basically across the

15 studies. Academic labs, when they are reporting a lot of this

16 stuff, just typically do not give the kind of details that we

17 would like to have for making those really critical assessments

18 of the data. Just like the housing information, we do not know.

19

20 In some cases, there may only be eight animals

21 per dose group. I would say that for a lot of behaviors, eight

22 animals may not be enough. That is why I was saying some of the
Page 95

1 negative data may be more an issue of power rather than being

2 negative data. If they do not show the data in graphs or give

3 your data in tabular form, you cannot really assess that. A lot

4 of times, they do not. They will only show the significant

5 effects and say other things were not significant. I will leave

6 it at that. It does make things, like what you are trying to

7 assess much harder to do when you are looking at cross studies.

8 DR. CHARLES VORHEES: I would like you to just

9 comment on what appears to me to be a disconnect between the

10 issues of cholinesterase inhibition, which is as I understand

11 cholinesterase inhibition and there are people here with a lot

12 more expertise than I have, but it appears that the regulatory

13 action is based on an acute short-term effect and what the

14 neurodevelopmental studies do are they are looking really for

15 whether or not there is some effect that is a permanent, long

16 term, maybe even irreversible effect. 

17 I am having trouble with the question what is

18 the connection between -- because it does not seem to me, while

19 I can understand cholinesterase inhibition as a marker for

20 setting some safety standard, it does not seem to relate at all

21 to the long-term irreversible effect, which is apparently there

22 because I think the EPI studies, if I am understanding them
Page 96

1 properly, show some long-term effects and the animal

2 developmental studies show long-term effects. So, can you help

3 me pull that together. 

4 DR. ANNA LOVIT: I think that is more of a

5 risk assessment question, so I will take a stab. There are

6 multiple parts to this. I am going to go on for a minute or 2. 

7 The first piece is that we are interested in all durations of

8 exposure. In people, whether it is through the food or through

9 work or activities in the field are exposed, can be exposed to

10 chlorpyrifos from a single-day event or from repeated days of

11 from repeated years of exposure. 

12 So, we are interested in all durations,

13 certainly not just the acute effects. Cholinesterase inhibition

14 is a key event in a pathway of toxicity, if we go back to our

15 AOP concept, leading to cholinergic toxicity. If you think of

16 cholinesterase in a vision as the initiating event to

17 cholinergic toxicity, by protecting for that key event, that

18 initiating event, in other words using a point of departure for

19 which there is very little inhibition of cholinesterase, we

20 prevent cholinergic toxicity. That is the idea of using a 10%

21 inhibition in cholinesterase to prevent for cholinergic

22 toxicity. That is whether it is an acute event or a very
Page 97

1 long-term very low level exposure like you may experience, let's

2 say in a worker kind of activities. 

3 The bulk of what we have talked about so far

4 today and the bulk of what it is in the white paper and what you

5 heard from both Dr. Moser and Dr. Mundy would be a different set

6 of adverse outcomes. So, this would be a different set of

7 adverse outcomes related to neurodevelopmental effects unlikely

8 to have much to do with inhibition of cholinesterase. The

9 protein acetylcholinesterase may have a big role. I think that

10 is one of plausible hypotheses on the table, but the inhibition

11 leading to cholinergic toxicity would be a different pathway, a

12 different adverse outcome. 

13 If you go back to my last slide before the

14 outline with the issue paper of our conceptual framework for

15 this whole analysis, it is keeping all of the moving parts

16 separate, thinking about the dose response for cholinesterase

17 inhibition leading the cholinergic toxicity, and how that

18 relates in a quantitative dose response way to those initiating

19 events or other key events or adverse outcomes that are related

20 to neurodevelopmental. That is the fundamental question that we

21 are incrementally getting to. 

22 What we are asking of all of you is to help us
Page 98

1 ensure that our review of the pieces that form that ultimate

2 question are sound, our reviews are sound, the science that is

3 in those reviews is sound with thinking about alternative points

4 of view and how the strengths and the weaknesses of all those

5 alternatives come together as we work to those conclusions. 

6 DR. KENNETH PORTIER: Dr. Vena? 

7 DR. JOHN VENA: One question, well two

8 questions. One is you mentioned in your summary slide the

9 behavioral alterations and, of course, the consistent effects

10 you mentioned, cognition, anxiety and social behavior. In your

11 opinion, where is the strongest evidence in which of those

12 domains? So the second question is what time period of exposure

13 in the animal evidence appears to be most important, the

14 gestational, the perinatal or the postnatal? 

15 DR. GINGER MOSER: I think the second question

16 is the easiest to answer. Across all of these studies, there is

17 no critical window of exposure that has emerged. You can get

18 cognitive effects when they are exposed gestationally. You can

19 get it when they are exposed postnatally. Nothing has fallen

20 out that shows any critical window of exposure. 

21 As far as the first part of it, I think there

22 are several ways to answer that. If you just look at the most
Page 99

1 consistent findings as being where you actually get significant

2 effects that when the data are shown, you kind of agree with the

3 facts and the tests were done correctly and that there are tests

4 that are well accepted and used in neuroscience, I would

5 probably have to say either that the cognition and then the

6 anxiety, but again you have the differences in the direction of

7 change on those end points. 

8 For instance, there have been some hypotheses

9 as to why that might be. For the radial arm maze where you see

10 the improved performance in the females and worsened performance

11 in the males, the authors said that that was evidence of

12 endocrine changes or endocrine effects, even though there was no

13 data that has ever been presented to support that hypothesis in

14 any way. So, we just kind of have to take it as that, just a

15 hypothesis. 

16 There has never really been any hypothesis to

17 explain the differences in the anxiety level increased versus

18 decreases, but anxiety is one of these tests that you really

19 have to be careful with what you are doing to try to get the

20 exact same effects over and over. Some of the differences could

21 be subtle differences. 

22 For example, whether the animal is put into
Page 100

1 the dark part of the box to begin with, or put into the light

2 side of the box to begin with. That can influence the actual

3 outcome. When I went through all these papers, there were

4 differences, even though it is in the same lab. Sometimes they

5 would start in the light side and sometimes they would start in

6 the dark side. 

7 So, you cannot explain it away in terms of

8 that. It was not completely clear, but those are the kinds of

9 factors that probably do weigh in on those outcomes. Some of

10 the other changes, like I said the motor activity, it is really

11 all over the place. The increases, decreases, no effect. When

12 you see that much increases, decreases, no effect, you kind of

13 start to wonder is there something specific going on that we

14 have not captured. I am not sure if that answered your question

15 or not, but yeah. 

16 DR. CHARLES VORHEES: So, one of the things I

17 would just like to point out I think kind of fits with what you

18 are saying, and that is that a lot of these tests that are being

19 used to evaluate chlorpyrifos or other toxins or possible toxins

20 have been taking out of the neuroscience literature, and their

21 intended purposes when they were developed were very different

22 than using them in a toxicological screen. 
Page 101

1 Tests of effect are a perfect example. They

2 are being taken in a way out of context and used as if they can

3 be used as toxicological outcome measures. There is really no

4 proof that I know of that they can be transplanted in that way. 

5 I would say the same is true of social tests. They have a

6 particular application in neuroscience as models of human

7 disorders where you are giving explicit neurotoxins or you are

8 giving drugs or you are giving genetic changes. They work on

9 those contexts.

10 Whether or not they translate very well into

11 this setting is very hard to say. When you look at those social

12 interaction tests that you mentioned, it is very difficult to

13 know how to interpret that after exposure to chlorpyrifos. Does

14 that mean anything the same as it means in an autism model? I

15 am not so sure. It is very difficult to know what it means when

16 transplanted without validation stuff. 

17 DR. KENNETH PORTIER: Dr. Pope? 

18 DR. CAREY POPE: Ginger, on your next to last

19 slide and in your response to Dr. Vena, you talk of most

20 consistent effects. Dr. Bradbury and others have brought up

21 this word consistency. I am just wondering in the context of

22 all the end points that are studied, does that mean that there
Page 102

1 are changes or it is consistent that there are changes that are

2 the same between studies? 

3 DR. GINGER MOSER: At the broader level, I

4 would say consistent in terms of changes. Narrowing it down a

5 little bit, consistent across studies in some cases for example

6 where I showed you with the morris water maze and they did 4

7 different studies and got pretty much the same effects. But as

8 anybody knows if you do the same study over and over, you are

9 not going to get the exact same effect, at least not in any of

10 the studies I have ever seen you get the exact same effect. So,

11 I would not expect them to replicate exactly unless everything

12 was done exactly the same and even then, you just have

13 differences in animals. When you get into behavioral type

14 changes, a lot of things can affect it. Even the set of animals

15 that get in from the vendor can have an effect. 

16 So overall, the consistency I mean is more

17 that there are effects and that for the most part, there is

18 consistency when they are replicated, like within the same lab

19 or if they are looking at the same end points. That is what I

20 was thinking of when I was talking about consistency. 

21 DR. CAREY POPE: I would not, of course,

22 expect to see the exact same results from a behavior or a
Page 103

1 biochemical study, but different directional effects. To me, I

2 have to wonder about the word consistency if they are going in

3 different directions. 

4 DR. GINGER MOSER: That is a little bit more

5 where you have to give it a little bit more consideration. Are

6 the changes that take place in the different directions, you

7 know -- the change in activity for anxiety for example is that

8 just because these are random effects that happen to be

9 significant, which some people could interpret it that way, or

10 is it because if you look closely enough, you will find the

11 reason? 

12 I did look at this, things like which side

13 they started testing the animals in. There are all these other

14 things that are not reported. They do not report the light

15 level in the room. They do not report things like how the

16 animals are placed or how it is defined whether they are

17 entering into one side versus the other. Some of these very

18 specific details are not in the papers, but they can impact the

19 outcome. 

20 So, I do not want to say that those findings

21 are inconsistent, but it would just be nice to have more

22 information to be able to evaluate the differences, I guess. 
Page 104

1 DR. KENNETH PORTIER: Dr. Harry? 

2 DR. JEAN HARRY: So Ginger, based upon that, I

3 just want to get your thinking on something. In the guideline

4 recommendations, it says in your choice of a learning and memory

5 task is to choose it based upon if you have information. Based

6 upon information of the compound and what you think would be,

7 basically use your intelligence and do not just pull cubical A

8 off and put it for every compound. So, that crosses over to

9 what was just mentioned as a number of these tests have been

10 pulled off. 

11 While they are using neuroscience, and very

12 few of them actually have a neural substrate basis for the

13 behavior, but there are a number that do like the morris water

14 maze has condition place preference, a number of those, but they

15 have been taken over and used. I guess we do have a lot of data

16 in the morris water maze, but morris water maze looks at

17 something in learning and memory that is very different than

18 what a radial arm maze looks at and how the learning and memory

19 are conducted. 

20 What I was interested in was the very last

21 thing in your summary, which was that you need a robust dose

22 response and time course data. I think that that might come
Page 105

1 back to some of the questions that have been asked. I would

2 sort of like to know for clarification what you might have

3 pulled out of there. 

4 Is there data in the literature that if you

5 were going to go and do that, if anybody was going to go do that

6 study to let you know, is there an end point that raises itself

7 as something that might give you a robust end point that you

8 could hope from that change you could get a dose response? Or

9 are we still looking for that functional outcome test

10 measurement that would give us that robust data? 

11 DR. GINGER MOSER: I think we are still

12 looking to some extent because we do not know, for example, what

13 the neural substrate might be for these behavioral changes. We

14 do not know if it is just generalized or what. So, that goes

15 back to the AOP thoughts about what could actually be causing

16 these effects. We could be talking about just differences in

17 neuronal connectivity. Why would that happen? That is what we

18 do not know. 

19 Because we do not know that part of it, it is

20 a lot harder to say, well since we know that there is a change

21 in this, then this test measure is a good evaluator of that. We

22 do not know the reason, so it is harder to pull up to use
Page 106

1 justifications like that to decide what kind of test we are

2 going to do. 

3 I think that most of these tests actually are

4 used because that is what the laboratory is good at doing. They

5 have seen other effects with other chemicals on these tests, so

6 they want to see whether chlorpyrifos affects these tests as

7 well. Some of the anxiety and especially some of the social

8 behaviors have been based actually, the more recent ones, have

9 been based on some of the EPI studies, maybe not with

10 chlorpyrifos, but with other EPI studies that are beginning to

11 suggest that chemical exposures during development at some point

12 may be causing some changes in social behavior of the kids or

13 anxiety levels of the kids. 

14 So the later studies have been using those

15 tests because of that. A lot of these more recent studies have

16 been looking at the social behaviors and some of the anxiety. 

17 You kind of have to look at the timeline in what is being looked

18 at. I am not sure if that was everything you were asking or

19 not. I just did want to clarify what I am talking about. 

20 The dose response, we really need to be able

21 to understand brain cholinesterase inhibition after exposure. 

22 It may be a very short time after exposure, and the animals have
Page 107

1 recovered by an hour. If that happens day after day, that could

2 actually produce some long-term changes. We do not have the

3 data to be able to make that assessment. 

4 First of all, you need to look at the

5 cholinesterase data. You need to end up with doses that you

6 know do not inhibit cholinesterase and bracket that, and then

7 look at the neurobehavioral outcomes of those doses. We just do

8 not have that full information that we could just follow the

9 whole way through. Even combining all the studies together just

10 does not give us that. 

11 DR. KENNETH PORTIER: Dr. Lowit? 

12 DR. ANNA LOWIT: Yes, I appreciate that

13 exchange. That was a nice exchange. I just want to, I think as

14 we progress through the afternoon and into the charge questions,

15 there are a lot of very neat questions here. 

16 There are many, many experiments that you can

17 contemplate in your mind to run, and decades-long work you could

18 do in the laboratory. I just would like to remind all of you to

19 think of the time scale that the agency makes decision in light

20 of uncertainty and pulls in information. This is what we do

21 every day. 

22 The time is going to come around 2013 or 2014
Page 108

1 or whatever the date was, a couple of years from now, that we

2 will need to make another regulatory safety findings on

3 chlorpyrifos. As you think about your deliberations, if you can

4 think about it in multiple levels that they were looking for the

5 strengths and weaknesses of existing information, but were also

6 looking to think about given the strengths and the limitations,

7 how would you think about filling holes. Despite that, we need

8 to know how those strengths and limitations today can be weighed

9 against each other so that we can go back and do a weight of the

10 evidence and think across multiple lines of evidence. 

11 I think these exchanges of what could be done

12 and how it could be done are very important for the totality of

13 the science around chlorpyrifos, but I also just want to

14 remember that there are some very grounded questions that given

15 today's science, where are we, and how best to use today's

16 science. 

17 DR. KENNETH PORTIER: This is Ken Portier. 

18 One last question. How many of these 26 papers were new from

19 2008 to present? You did not mention that. 

20 DR. GINGER MOSER: There are nine since 2008

21 and two new labs.

22 DR. KENNETH PORTIER: Did any of those nine
Page 109

1 replicate previous studies or are they all new? You see, that

2 is one of the issues that I have been kind of having with the

3 neurobehavioral. They do not really replicate. They keep

4 pushing an edge. They do not go back and help us establish or

5 validate.

6 DR. GINGER MOSER: The newer studies do not as

7 much. Some of the earlier studies, for example from Duke, tend

8 to replicate each other. They would use the same testing

9 procedures, but then they would add their pharmacological

10 challenge with the serotonin agent or with the cholinergic agent

11 or whatever, but the first part of the testing would be the same

12 and they did replicate those effects. 

13 For things like the radial arm maze with the

14 different gender differences and the direction of the cognitive

15 effects, that was replicated three times within the Duke lab. 

16 More recently, one of the more recent papers did replicate the

17 same finding at a different laboratory. So yeah, in some cases

18 they did, but in other cases, the newer studies would report one

19 set of data and then the next study would say okay given that,

20 we are going to now look at this. They did not replicate that. 

21 They just went on to the other thing. 

22 Basically, the answer is sometimes they do and
Page 110

1 sometimes they do not. The Duke lab was good about it. I talk

2 about the Duke lab and the Italian lab, because they are the two

3 that have the most papers. The Italian lab really has not tried

4 to replicate their own data as many times as the Duke lab did. 

5 The Italian lab tended to do different things. They are

6 actually the more recent papers. 

7 DR. KENNETH PORTIER: Dr. Meek? 

8 DR. BETTE MEEK: Ginger another question. I

9 tend to think of weight of evidence in relation to data sets

10 across chemicals. So, have you looked at the results of similar

11 studies for other chemicals? 

12 DR. GINGER MOSER: Well the simple answer is

13 no. There are studies looking at developmental outcomes of

14 other OPs for example, but they are far fewer. As far as

15 pesticides go, chlorpyrifos has the bulk of the literature. So,

16 out of those few studies for other OPs, most of them do not have

17 the same end points. So, it makes it even harder to kind of

18 make any kind of comparisons. The only exception that I can

19 think of and again, I have not done a literature search, so I do

20 not know, but I do know that the Duke lab has looked at diazinon

21 and they find some similar outcomes, not sure whether the Duke

22 lab has done others. 
Page 111

1 One or two of the other studies have also

2 looked at parathion or methyl parathion, one of the more recent

3 studies that I only talked about the chlorpyrifos data, they

4 also looked at methyl parathion I think it was. There were some

5 differences and some similarities in the effects of the two

6 chemicals. 

7 As far as doing an overall literature

8 evaluation, no, we have not done that. I can say there is just

9 not nearly the amount of data and it would be very hard to make

10 comparisons of the chemicals. 

11 DR. KENNETH PORTIER: It is noon, and I think

12 what we are going to do is break for lunch and send you out with

13 the rest of the office workers to forage. We will be back at 1

14 o'clock and begin our discussion on the epidemiology data, thank

15 you. 

16

17

18

19

20 (WHEREUPON a break was taken for lunch)DR.

21 KENNETH PORTIER: So we are ready to move on with our discussion

22 of epidemiology. And I guess Dr. Christensen, are you ready? 
Page 112

1 Go for it.

2 

3 DR. CAROL CHRISTENSEN: Good afternoon. My

4 name is Carol Christensen. I am an epidemiologist with the

5 Pesticide Program Office and at this time we will present our

6 review of children's health epidemiology cohort studies. 

7 I will note that the presentation at this time

8 will track closely with section 4 of the draft white paper as

9 well as Appendixes 4-6 of that draft white paper. And I believe

10 we have left ample time at the conclusion of the presentation

11 for any clarifying questions on either the oral presentation

12 today or the written material. 

13 So as it has been mentioned this morning, in

14 September of 2008, EPA presented its targeted review of these

15 three children health cohort studies, the database available at

16 that time. The FIFRA SAP at that time concluded that while

17 chlorpyrifos likely played a role in the adverse

18 neurodevelopmental outcomes, it cannot conclude that

19 chlorpyrifos was the sole contributor due in part to the fact

20 that participants were exposed to several other chemicals and

21 pesticides, including other OPs. 

22 The panel further noted that the epidemiology
Page 113

1 study should not be considered as a basis for the

2 characterization of the point of departure due in part to the

3 lack of a biologically plausible mode of action, which is of

4 course our topic of discussion this week as well, in addition to

5 exposure uncertainties that were present in the database at that

6 time. 

7 The panel also recommended some supplementary

8 analyses it presented in Appendix 4 of the material, but was

9 clear to note that these analyses would help clarify the results

10 and interpretation of the observational studies for use and risk

11 assessment, but not likely change the overall inference from

12 that database at that time.

13 Though today we update and expand our targeted

14 review of these three children health cohort studies, we have

15 included new etiologic papers. About one year ago this month,

16 researchers from Egypt, the three respective children's health

17 cohort studies, simultaneously published their results of

18 prenatal exposure in relation to Intelligence Quotient in

19 school-age children.

20 In addition, there are new methodological

21 papers, and evaluation of socioeconomic status is a potential

22 confounder using hierarchical regression techniques as well as
Page 114

1 some exposure characterization and validation work, which go to

2 further clarifying and in some ways reducing uncertainties

3 around exposure measurement methodology particularly in the

4 Columbia University cohort. The supplemental analyses to which

5 I just referred have been completed by the principal

6 investigators with the Columbia cohort. Those have been

7 published in full as Appendix 4 of the draft material. 

8 In addition, today's presentation and the

9 written material you have been provided is informed by our 2010

10 draft framework for the analysis and incorporation of

11 epidemiology research and to the risk assessment process. So

12 overall, at this time we are asking for the panel's guidance

13 regarding the qualitative and quantitative use of these data in

14 our risk assessment process.

15 So the three cohorts are briefly denoted here.

16

17 The Columbia Children's Environmental Health

18 Study cohort recruited participants between 1998 and 2002. The

19 study is located in New York City. Authors restricted

20 enrollment to women who self-identified as Dominican or

21 African-American decent. There are about, give or take,

22 three-hundred mother-infant pairs enrolled within this cohort. 
Page 115

1 Notably, the Columbia cohort researchers were

2 the only ones to measure chlorpyrifos parent compound directly

3 as a measure of exposure in their analytical search. As I have

4 just briefly alluded to, researchers with this cohort also

5 performed a validation study, which we will get into in some

6 detail. 

7 The Mt. Sinai Children Growth and Development

8 cohort, also located in New York City, enrolled participants in

9 a similar timeframe, between 1998 and 2001. Similarly a

10 multi-ethnic cohort comprised of African-American, Hispanic and

11 about 20 percent of the cohort self-identified as Caucasian.

12 In contrast to the Columbia cohort, these

13 researches measured nonspecific biomarkers of chlorpyrifos

14 exposure; TPCy, as you will hear about a little bit further from

15 my colleague Lieutenant Niman, is a nonspecific biomarker of

16 chlorpyrifos. Chlorpyrifos, chlorpyrifos methyl as well

17 triclopyr, so three different pesticides could possibly result

18 in a urinary concentration of TPCy as well as environmental

19 exposure to the degredate itself.

20 Dialkyl phosphates are nonspecific measures of

21 OPs. There are several different OPs that could result in DAP

22 concentration in urine. But I will note that for each of the
Page 116

1 investigations that measured DAPs, they further broke out that

2 sub-class into both diethyl and dimethyl phosphate, and it is

3 the diethyl phosphate that is the potential result of

4 chlorpyrifos exposure.

5 The University of California at Berkley

6 designed the CHAMACOS cohort, Children and Mothers of workers in

7 Salinas Valley, recruited participants between 1999 and 2000,

8 with some additional data collection through 2002. 

9 So this cohort in contrast to the two located

10 in New York City is comprised of Mexican-American, agricultural

11 farm worker populations. So these participants either report

12 that they themselves have been employed in agricultural work or

13 they reside with an individual who is employed in agricultural

14 work.

15 Similar to the Mt. Sinai cohort in their

16 analytic investigations they have measured TPCy and DAPs as a

17 biomarker of chlorpyrifos and/or organophosphate exposure. And

18 both the CHAMACOS and the Mt. Sinai cohort measured PON1 in

19 relation to the neurodevelopmental outcomes as well as an

20 interaction with the pesticide exposure.

21 So these three perspective birth cohorts were

22 designed to examine environmental exposures and adverse health
Page 117

1 outcomes among children. But for our purposes today, of course,

2 we are focusing on those evaluations looking at chlorpyrifos

3 and/or organophosphate exposure in relation to birth outcomes

4 and neurodevelopment. Each cohort enrolled women about midway

5 through pregnancy and followed the children of those pregnancies

6 over time, making multiple measures of health outcomes. So

7 there is clearly a longitudinal component. These are

8 longitudinal birth cohort studies.

9 And we note that the Children's Environmental

10 Health and Disease Prevention research centers through which

11 these studies are performed are funded in part by EPA and NIEHS.

12 So looking across these cohorts, of course,

13 eligibility criteria are specific within each cohort. But

14 looking across these cohorts I have noted some general

15 characteristics of eligibility. 

16 Women were recruited when they were seeking

17 prenatal care prior to the midpoint in pregnancy. The CHAMACOS

18 cohort, I believe, enrolled women up through 26 weeks of

19 pregnancy. The New York City cohort there was a residency

20 requirement and participants had indicated their intent to

21 deliver at participating study hospitals for the purpose of

22 medical record linkage and other information exchange.
Page 118

1 To isolate the effect of chlorpyrifos or

2 organophosphate exposure on these neurodevelopmental outcomes,

3 the researchers restricted enrollment to those who did not

4 report having other comorbidities or health risk factors that

5 could also adversely affect neurodevelopment. So participants

6 were not included if they reported history of diabetes,

7 hypertension, known HIV or other birth defects. Similarly it is

8 a no smoking population, very low alcohol use as well as no

9 reported illegal drug use, which was confirmed by biomarker. 

10

11 So overall, these three cohorts represent

12 multi-ethnic studied populations. On average, across the three

13 cohorts, the average age at first birth is somewhat young, about

14 25 years of age; and again, given the restriction criteria, low

15 prevalence of risky health behaviors and comorbidities that

16 could also adversely affect neurodevelopment. 

17 So the next several slides go over some common

18 characteristics of study design, method and analyses. Of

19 course, exposure assessment is a key component to understanding

20 both the validity of the epidemiology studies as well as

21 possible use in risk assessment. Across these three cohorts we

22 have differing exposure profiles, different pathways, and
Page 119

1 different routes. The intercity populations may have been

2 exposed to chlorpyrifos through residential use of the pesticide

3 in the home environment prior to the period of the voluntary

4 cancellation of pesticide products for in-home use. As we noted

5 in the written material, each of these cohorts sort of span the

6 time period in which the products were removed form the

7 residential marketplace. 

8 Within the CHAMACOS cohort, of course,

9 participants could have been exposed in occupational type of

10 setting, so inhalation or dermal exposure is possible. 

11 Unintentional oral exposure; take-home exposure is another

12 potential route.

13 So across the cohorts of multiple measures,

14 biomarkers of exposure as well as environmental measures of

15 exposure were assessed. Again, it's noted that the Columbia

16 cohort is the only one to have measure chlorpyrifos directly.

17 Both Mt. Sinai and CHAMACOS measure TCPy and

18 DAPs within a validation study that we'll get into. TCPy in

19 meconium was measured. Meconium is considered to be an

20 integrative biomarker of exposure to the fetus weeks 13 through

21 delivery, so it's somewhat of a longer term biomarker of fetal

22 exposure.
Page 120

1 Uniquely, within the CHAMACOS cohort,

2 researchers measured both acetyl- and butyrylcholinesterase as a

3 third marker of organophosphate exposure, and each of the

4 cohorts administered a self-report questionnaire regarding

5 pesticide use overall in occupational characteristics.

6 So as we move to consider the quality of

7 exposure assessment and uncertainties in exposure assessment

8 which are key to the deliberations this week, it's useful I

9 think, as suggested by David Savitz, to consider kind of what's

10 an ideal measure as compared to an actual measure.

11 So considering the research question of

12 interest, that prenatal chlorpyrifos or organophosphate exposure

13 in relation to adverse neurodevelopmental outcomes in childhood,

14 an ideal measure may be an estimate of external exposure during

15 a known critical window of susceptibility. And as we heard this

16 morning from Dr. Moser, there's some lack of clarity as to what

17 exactly that critical window of susceptibility may be.

18 Repeat samples would also be ideal; a better

19 characterization of the periodicity or variability of exposure;

20 the variation in exposure within an individual over time, which

21 may influence exposure response relation. And also to be

22 accurately and precisely reflective of the true rank of the
Page 121

1 categorization of actual exposure within the study population,

2 and ideally this would also include the timing of sample

3 collection with the application of the pesticide or with the

4 ingestion event.

5 So you know, with any epidemiology study --

6 not just these children's health cohorts -- an ideal measure is

7 often unattainable so actual measures include a one-time or, in

8 the case of CHAMACOS, an average of two urine samples taken over

9 the latter period of pregnancy. 

10 For the most part, measures were taken in the

11 third trimester, both the etiologic measures as well as the

12 validation measures with the exception of meconium, and specific

13 and non-specific biomarkers of chlorpyrifos were currently used.

14 So sort of using that as a rubric to think

15 about sources of uncertainty or measurement error, that's why I

16 delineated here within these three cohort study sources of

17 measurement error: so the difference between external exposure

18 and internal dose, the use of the single measure, and the lack

19 of clarity around some of the temporal and spatial patterns of

20 exposure; the lack of ability to measure during a critical

21 window, although it's likely because the third trimester is a

22 period of rapid brain growth and development of the fetus that
Page 122

1 the exposures during the third trimester possibly play a role in

2 neurodevelopmental outcomes. Missing information is common

3 across epidemiology studies. Authors used several methods to

4 impute when available. And of course, laboratory errors are a

5 source of overall measurement error.

6 But with the exception of the measurement of

7 urinary TCPy in relation to fetal growth and development in the

8 Mt. Sinai study, each of these biomarkers were measured at CDC

9 laboratories with extensive QA/QC measures published.

10 So since the time of the last Panel review,

11 researchers with the Columbia cohort have published about some

12 validation work, the Whyatt studies from 2007 and 2009. 

13 But in forming that work was some basic data

14 collection performed at enrollment, specifically questionnaire

15 data from that cohort indicating that 85% of the participants

16 report use of a pesticide at least once during pregnancy and a

17 significant proportion reporting pesticide use at least monthly

18 during the period of the pregnancy.

19 So there is some evidence from self-report

20 questionnaire data that prior to the time period of the

21 volunteer cancellation within the Columbia cohort that there is

22 consistent and relatively use of pesticide.
Page 123

1 Information external to this cohort, the study

2 authors reported that the New York City Housing Authority -- and

3 I think I failed to note when I was discussing cohort

4 characteristics, a majority of the participants in the Columbia

5 cohort reside in rental housing units managed by the New York

6 City Housing Authority, so public housing dwellings.

7 So the New York City Housing Authority

8 reported that use of chlorpyrifos in the residential setting. 

9 Again, this is the period prior to the cancellation of these

10 pesticide products in the home environment were actually greater

11 than any other county in the state of New York, which would

12 include the agricultural areas. So again, there's information

13 that exposure and prevalence was high.

14 So to further address some of the exposure

15 uncertainties present in the methodologies, and again, went

16 ahead and performed an exposure validation study. It collected

17 several different environmental as well as biological measures

18 of exposure. In Whyatt et al., 2007, both the personal air

19 monitoring -- so sort of a backpack apparatus, which was worn by

20 participants for a 48-hour period -- as well as stationary

21 two-week integrated air monitoring units were in the home. I

22 think they were located adjacent to the bed in the home
Page 124

1 environment.

2 So these air concentration measures were

3 taken, and from those data, we understand that the

4 intra-individual variability was much lower than the

5 inter-individual variability. So it indicates that at least

6 with air measurements, there is some stability in exposure

7 within an individual over time.

8 However, we must note that this validation

9 study was performed between 2001 and 2004, which was right at

10 the period of the voluntary cancellation, so exposures were

11 rapidly declining. And as we noted in the paper, the extent to

12 which these validation results sort of reflect that pre-exposure

13 period is still in the area of uncertainty.

14 With regard to biomarkers or biological

15 matrices in which exposures were measured within this validation

16 study, authors measured repeat maternal urine samples. Meconium

17 is fecal matter that collects in the intestines of the

18 developing fetus beginning around week 13 and 14 and expelled

19 right around the time of delivery.

20 So TCPy in meconium was also measured, as well

21 as neonatal urine. So the purpose of the validation study, of

22 course, was to both evaluate other kind of biomarkers, which
Page 125

1 could be, you know, valid measures of exposure as well as

2 reporting of the correlations of different biomarkers which

3 reflect, importantly, different windows of exposure.

4 So this is just a qualitative kind of

5 schematic of the gestational period indicating when the

6 biomarkers were collected. Again, meconium considered to be

7 reflective of an exposure period, generally speaking, the last

8 two trimesters of pregnancy. The personal backpack air monitors

9 were worn during week 32 for a 48-hour period, and the repeat

10 urine samples and the integrated air monitoring stations were

11 implemented week 32 through delivery, so the last eight weeks of

12 pregnancy.

13 And importantly, in that paper, authors

14 reported a moderate but significant correlation, and this is the

15 table repeated. So importantly, from this table six of the

16 Whyatt publication, we see that there is a moderate -- not high,

17 a moderate but statistically significant correlation between the

18 one-time measure of chlorpyrifos in umbilical cord blood and

19 TCPy levels in meconium, which of course reflect that longer

20 period of exposure; so lending some evidence that that one-time

21 measure of exposure within the Columbia study may accurately

22 reflect the ranking or the categorization of participants with
Page 126

1 respect to prenatal chlorpyrifos exposure at least within

2 two-to-three-fold as suggested by study authors.

3 So this is new information available to the

4 agency and to the Panel since the time of our last

5 deliberations. The exposure measures within these studies are

6 generally very good and, you know, greater than you would see in

7 other types of environmental epidemiology studies.

8 With the aid of the validation work that I

9 just detailed, we have the ability to characterize uncertainty

10 to a large extent. However, there are uncertainties that still

11 remain. Of course, within the analytic studies, we will have a

12 one- or two-time measure of exposure. Again, the validation

13 study which I just detailed offers some suggestion that that

14 one-time measure is a relatively good way to rank participants

15 with respect to prenatal chlorpyrifos exposure.

16 And we still lack clarity as to the

17 periodicity or the variability of exposure within an individual

18 over time. But the repeat urine samples in women during the

19 last eight weeks of pregnancy clearly show that there is a high

20 degree of individual variability using TCPy and the urine.

21 And as we noted, the prevalence of pesticide

22 exposure is generally thought to be high among these cohorts,
Page 127

1 particularly the time prior to the cancellation. But as we hear

2 in greater detail in the subsequent presentation, the median

3 concentration of TCPy in the urine is certainly within the range

4 of the general population across each of these cohort studies,

5 so this may indicate a difference between actual and measured

6 exposure.

7 So moving on to confounding bias and other

8 important characteristics of epidemiology studies to consider

9 when evaluating both the accuracy in the epidemiology inference

10 made as well as use in risk assessment. Across these studies,

11 authors used several methods to control for confounding,

12 including some very clear restriction or enrollment criteria; so

13 controlling through variability within the comparison groups

14 through design.

15 Extensive information or data collection was

16 performed, including information concerning race, education,

17 income, housing, quality, those vary individual-level variables

18 were considered for inclusion in statistical modeling. And with

19 respect to socioeconomic status, which is generally considered

20 to be a challenging type of variable to measure in any

21 epidemiology study, not just in these birth cohort studies. 

22 Within the Columbia cohort, again, researchers performed
Page 128

1 methodological work on the role of SES.

2 So methods used to identify confounding

3 variables; we generally found them to be kind of traditional and

4 established methods based upon both biological and statistical

5 evidence. 

6 There were some instances in which formal

7 forward and backward step-wise selection procedures were used,

8 but generally speaking, it was kind of testing model stability

9 in whether risk estimates changed appreciably with and without

10 the inclusion of a confounding variable. And overall, we found

11 that model selection was based on Parsimony, given the sample

12 size and the question on the table.

13 So general confounding factors; of course,

14 demographic, health-related, other environmental exposures to

15 which these participants may have been exposed is also key. 

16 Across the studies, authors measured PAH; measures of

17 environmental tobacco smoke or secondary smoke; organochlorines,

18 particularly within the CHAMACOS cohort within which a majority

19 of participants reported that a non-U.S. country as their

20 country of origin, particularly Mexico. And information about

21 the postnatal environment was also collected.

22 But over the next few slides, we'll go over
Page 129

1 some of the specific types of environmental chemicals that have

2 been evaluated for a potential confounding bias as well as SES.

3 So this is just a very basic slide on kind of

4 when is a confounder a confounder? Of course, not every

5 variable could be a confounding variable. Confounders must be

6 causally related to the outcome, must be statistically

7 associated with the exposure. And not only statistically

8 associated, in work outside of these three cohorts, we know that

9 associations have to be moderately high to have some potential

10 confounding effect. So of course, blood lead and methylmercury

11 are two known neurotoxicants.

12 But we know from these studies that the

13 correlation between these factors and the mean exposure prenatal

14 chlorpyrifos and/or organophosphate exposure is low. Each of

15 the cohorts evaluated prenatal lead as a potential confounder,

16 and none of them found it to be a confounder. So we do have

17 that information to add to our consideration of these data.

18 Concerning other organophosphate pesticides,

19 the table presented in Appendix 4 was produced. It's an

20 unpublished table, but it was produced as a result of suggested

21 analyses by the SAP in 2008.

22 So from this table, the question had arisen as
Page 130

1 to the effect of both diazinon and propoxur as two other

2 organophosphate pesticides as potential confounding variables in

3 the relation with neurodevelopment.

4 So in these unpublished analyses, again,

5 presented in Appendix 4, we see that with the addition of

6 diazinon and propoxur, the magnitude and the direction of the

7 association is comparable with and without these variables and

8 suggesting that, at least with respect to mental development

9 index on the Bayley Scale, these variables are not strong

10 confounders.

11 So with respect to socioeconomic status,

12 Lovasi et al., in 2010, produced results of the multi-level

13 analysis considering individual as well as group-level type of

14 characteristics in the modeling of, again, this very kind of

15 broad, multi-level characteristic which we refer to as

16 socioeconomic status.

17 Outside of these cohorts and across

18 epidemiology in general, there's lots of literature about the

19 challenge of appropriately modeling factors around SES. So it

20 was illustrative for the researchers with the Columbia

21 University cohort to produce these data.

22 So within their assessment, Lovasi et al., in
Page 131

1 2010, Table Number 2 -- I apologize for the very small font on

2 this replicated table, but I'm only trying to show that when you

3 consider both these kind of group level variables, it's kind of

4 the idea that there could be aspects, in this case, of

5 socioeconomic status which are very difficult to measure --

6 possibly even immeasurable -- but that can be best reflected by

7 a decision to be part of a certain kind of group or just by

8 virtue of the fact that you are part of a certain group.

9 So in considering both group-level variables

10 and individual-level variables, we see in this, again, very

11 small font that the estimates for chlorpyrifos across those

12 different models, the magnitude and the direction is very

13 similar, indicating that socioeconomic status using these

14 methods is well-accounted for as a potential confounder.

15 So just a few general notes on statistical

16 analyses before we get into study results; overall, looking

17 across these several different studies published within the

18 three prospective birth cohorts, we found statistical modeling

19 decisions to be appropriate to the research question,

20 characteristics of the data and so on.

21 Sample size, we believe, is adequate to assess

22 the main effects proposed by study authors but possibly
Page 132

1 underpowered for the evaluation of interaction or effect

2 modification. And in many instances, sensitivity analyses were

3 performed to evaluate different modeling options, different

4 transformations of the data, different statistical models with

5 and without confounders, et cetera.

6 So over the next several slides, we'll go over

7 the combined results of the three cohorts with respect to fetal

8 growth and infant and childhood neurodevelopment.

9 The fetal growth data, again, were presented

10 to the panel in 2008; presented now in our written material in

11 some greater detail than at that time, again, with the benefit

12 of our draft framework for evaluating incorporating epidemiology

13 into risk assessment.

14 To evaluate fetal growth characteristics,

15 authors again hypothesized this link a priori. Each of the

16 study authors performed medical record linkage to identify the

17 outcome, birth weight, birth length, et cetera.

18 So across these studies, we see a mixed

19 picture and certainly a set of inconsistent results with

20 expected fetal growth and other birth characteristics within the

21 Columbia cohort.

22 Again, Columbia cohort measured chlorpyrifos'
Page 133

1 parent compound directly. Those authors reported an inverse

2 association with both birth length and birth weight; so the

3 greater the chlorpyrifos exposure, the less the measure of birth

4 length and birth weight.

5 But looking across to the Mt. Sinai cohort

6 which measured TCPy in maternal urine as a biomarker of

7 exposure, those authors reported no effect. And surprisingly,

8 within the CHAMACOS cohort, authors reported a non-significant

9 and slight, but yet seemingly positive, association.

10 So across the studies, three different

11 results. Some have suggested this could be due to differences

12 in the measure of exposure.

13 With regard to head circumference and

14 gestational age, not a compelling set of results and generally

15 no findings; but some suggestion of an inverse relation with

16 head circumference within the Mt. Sinai cohort and gestational

17 age within the CHAMACOS cohort but only in the presence of a

18 PON1 genotype or phenotype. The Columbia folks did not measure

19 head circumference or gestational age. So overall, a mixed

20 picture across these cohorts using, again, different biomarkers

21 of exposure.

22 To evaluate neonatal neurodevelopment, authors
Page 134

1 with two of the three cohorts used the Brazelton Neonatal

2 Behavioral Assessment tool, the goal of which was really to

3 assess the newborn and provide information to parents and

4 caregivers as to the best way to, you know, care for that

5 infant.

6 There are 28 behavioral and 18 primitive

7 reflex domains included within the Brazelton index. They

8 included things like, for example, range of state, which is, you

9 know, we all know that some infants are a little more irritable

10 or excitable than other infants, and the extent to which those

11 kinds of responses could be related to primitive or innate and

12 neurological functioning as evaluated within this tool. The

13 tool was assessed by a trained neonatologist, administered in a

14 hospital setting within several days of delivery.

15 So the Mt. Sinai cohort and the CHAMACOS

16 cohort both measured DAPs; again, that non-specific biomarker of

17 OP exposure. We see some evidence of consistent results. Again

18 using a similar measure of exposure with respect to number of

19 abnormal reflexes, the two cohorts reported positive

20 associations, i.e., the greater the concentration of DAPs in

21 maternal urine, the greater the odds of an increased number of

22 abnormal reflexes; and I believe it's a two or more of the 18
Page 135

1 abnormal reflexes.

2 Similarly, there's a consistent but no finding

3 across the two cohorts with respect to the other behavioral

4 domains assessment of Brazelton index. Overall, the Brazelton

5 tool is used extensively in both clinical and research settings

6 and is found to be a good measure of this outcome.

7 The characterization of the relation with DAPs

8 as opposed to chlorpyrifos is, of course, a limitation. With

9 regard to infant neurodevelopment, each of the three cohorts

10 made this measure using the Bayley Scales. 

11 Bayley Scales make two measures of mental

12 development index and the psychomotor development index. The

13 Bayley Scales sort of get at infant kind of curiosity or how the

14 infant interacts with the world to how the infant reacts to the

15 world measured on these two scales.

16 Across the cohorts, this tool was utilized

17 between 6 and 36 months. It's thought to have high reliability

18 and validity. The interrater reliability coefficients are

19 around 0.85, so that's pretty good. But it's considered to be a

20 screening tool to assess infants that might be at need for early

21 intervention.

22 So in combination with the Bayley Scale,
Page 136

1 authors also utilized the Child Behavior Checklist designed to

2 assess social competence and behavioral problems. It's based

3 upon a 99-item questionnaire, administered to the mom by a

4 trained psychologist.

5 The HOME tool, the Home Observation

6 Measurement of the Environment, was also used across the three

7 cohorts. The nature of the stimulating environment is thought

8 to be a key confounder in this relation, so it's measured across

9 the three cohorts.

10 So here, similarly see a somewhat mixed

11 picture; some areas of consistency, but still a mixed picture

12 within the Columbia cohort, which again measure chlorpyrifos in

13 cord blood. We see some evidence of increasing risk over time

14 or at the 36-month time point inverse associations with both the

15 mental and the psychomotor development scale identified. So the

16 greater the chlorpyrifos exposure, the lower the score; and a

17 lower score, in this case, is indicative of an adverse effect.

18 We see similarly within the CHAMACOS cohort a

19 non-significant inverse association measured at six months

20 becoming statistically significant at 24 months. But within the

21 Mt. Sinai cohort, that association is attenuated over time, so

22 that is an area of inconsistency.
Page 137

1 Overall, the effects were seen with MDI, or

2 the Mental Development Index, more than the psychomotor

3 development index. And importantly, the measure of TCPy was

4 universally not associated with either MDI or PDI within any of

5 the timeframes in which these are measured.

6 Attention problems again were measured by two

7 of the three cohorts with a combination of maternal report and

8 direct observation. Participants were scaled using DSVM ADHD or

9 ADHD-like symptoms and assessed between three and five years of

10 age.

11 Within the Columbia cohort, positive

12 associations were identified at 36 months for both pervasive

13 developmental disorder, which is distinct from ADHD but similar;

14 and the CHAMACOS cohort reported, again, positive effects at 24

15 months and then at five years.

16 We know that the sensitivity of identifying

17 attention or hyperactivity-related issues at 36 months across

18 the literature has noted it to be limited; but having said that,

19 there is some evidence of consistent positive associations. So

20 in this case, positive would again mean greater exposure is

21 greater risk of these types of attentional issues.

22 And lastly, intelligence measures. As I noted
Page 138

1 about a year ago, each of the three cohort authors

2 simultaneously published their evaluation of prenatal exposure

3 and intelligence measured in school-age children, each of which

4 used the Wechsler Scale for measuring intelligence, which has

5 four components and an overall full scale IQ measure.

6 This tool has been standardized in U.S.

7 populations and has norms in both English and Spanish speaking

8 subpopulations. We note that the developers of the Wechsler

9 Scale have identified cultural bias as a possible limitation to

10 their tool.

11 But as we indicated in the written material,

12 we believe that the somewhat homogeneous composition of the

13 studied populations with respect to race and ethnicity would

14 somehow ameliorate the potential effect of this bias somewhat.

15 So overall, across the three cohorts, again

16 measuring chlorpyrifos in the non-specific measure, we see some

17 evidence of consistent results. Each cohort reported inverse

18 associations, i.e., the greater the measure of prenatal

19 exposure, the lower the measure of intelligence.

20 Notably, each cohort identified effects with

21 working memory as a subcomponent of the Wechsler tool. These

22 studies took place again within school-age children, average age
Page 139

1 of seven years. So over that period of time in this

2 longitudinal birth cohort study, there were some issues with

3 dropout. And in some instances the number of missing tests is

4 large, but the authors were able to show that on measured

5 variables. Those included and those excluded from that study

6 were comparable.

7 So having reviewed some study methods and

8 analysis issues as well as summarizing these results, we'll go

9 on to our evaluation and synthesis formed by our Draft

10 Framework.

11 So overall, there are several strengths

12 looking across the studies: measures of outcome again used, or

13 tools used in both the clinical and in a research setting and

14 validated in several different subpopulations and with different

15 primary languages spoken in the home; exposure measures of an

16 extensive, which include the validation work performed by

17 researchers with the Columbia cohort.

18 In many cases, important confounding variables

19 were measured and modeled when appropriate. Again, it appeared

20 to be an emphasis on parsimony and statistical model selection. 

21 And the comparability of study design, i.e., the prospective

22 cohort design across the cohorts as well as other similarities
Page 140

1 such as the similar use of outcome assessment tools aid

2 comparability between the groups. Study design reduces many

3 potential sources of bias.

4 Limitations have to do with both some issues

5 around exposure measure, especially when we consider these data

6 for use in risk assessment, as well as other factors around

7 confounding. There could be unknown factors, unmeasured

8 factors, which could correlate with chlorpyrifos exposure and be

9 potentially associated with neurodevelopmental outcomes.

10 However, we note those have not been

11 hypothesized yet in the literature; and among those that have a

12 known or potential confounding bias -- those that have been

13 measured -- there's lack of evidence to explain the positive

14 associations that have been observed.

15 Additionally, residual confounding in the form

16 of SES is a potential; however, the additional work performed by

17 researchers within Columbia, the hierarchical regression paper

18 suggests that this variable, given the restriction criteria, the

19 similarity of comparison groups through design as well as the

20 ability to model on both an individual and group level reduce

21 the potential. And there's limited discussion of the postnatal

22 environment across these cohorts as limitations.
Page 141

1 So some issues on study design; looking again

2 across the three cohorts, each of these three studies was based

3 on a priori hypotheses, other information in the published

4 literature. So we believe that the potential for multiple

5 comparison or artifactual observations are somewhat limited due

6 to these well-founded a priori hypotheses.

7 Statistical modeling, we found again to be

8 appropriate, although we did note that they were discussed in

9 varying degrees of detail across the publications. Imputation

10 of missing data, we found also to be appropriate and useful to

11 enhance statistical power and methods of imputation differed

12 across the different study groups but were sufficiently

13 explained.

14 The selection criteria, as I noted at the

15 beginning of the presentation, were extensive. It enhances

16 internal validity, again, the comparability of the groups on

17 factors relating to race, ethnicity, and income. However, it

18 could reduce the generalizability of these data as these are

19 relatively narrowly focused study populations.

20 The high prevalence of exposure; I think each

21 of these populations were identified because both the

22 residential and occupational exposure is typically higher than
Page 142

1 in the general population.

2 Enhances study power or the ability to

3 identify an effect if an effect exists; however, we do note that

4 there's likely limitation in study power with respect to

5 evaluating interactions.

6 Statistical analysis and grouping; we found

7 overall, several methods were used, motivated by sample size and

8 study hypothesis. Generally, the grouping and the

9 categorization, we found to be straightforward. The use of

10 sensitivity analyses to evaluate, for example, different

11 transformations of data. Inclusions of different variables in

12 the model, we also found to be appropriate and well-discussed.

13 Regarding exposure measurement, as noted

14 earlier, the use of the different biomarkers of chlorpyrifos

15 exposure somewhat hinder the direct comparability of these

16 results. Again, others have suggested that heterogeneity in

17 risk estimates could be due to differences in biomarker

18 measurement.

19 The use of one-time or, in the case of

20 CHAMACOS, the average of the two exposure markers during the

21 third trimester of exposure again do not inform the temporal and

22 spatial patterns of exposure, which could have occurred during a
Page 143

1 critical window of exposure.

2 And the generalizability, as Dr. Bradbury

3 mentioned earlier this morning, is of key interest to us. We

4 know that while the populations are somewhat narrowly defined --

5 and we'll hear a little bit more about this in a moment -- the

6 urinary concentration of TCPy is within the range of the general

7 U.S. population.

8 With regard to outcome measures, again,

9 standard tools utilized similarly across the cohorts; tools that

10 emanate from both the clinical and in a research setting. 

11 Quality assurance and control, we found to be adequate looking

12 across these studies.

13 Limitations in contrast to the fetal growth

14 and development outcomes; assessment of neurodevelopment is

15 somewhat more subjective.

16 But overall, we feel that the potential for

17 non-differential error is likely greater than for differential

18 measurement error in the assessment of these outcomes.

19 Just to summarize, for confounding bias,

20 multiple methods of control were utilized. Multiple measures of

21 potentially positive confounding factors were made including

22 other environmental exposures -- lead and mercury as well as
Page 144

1 SES. Overall, we thought there was a lack of examination of

2 interaction or heterogeneity by other exposures with the

3 prenatal chlorpyrifos or organophosphate measurement.

4 Other sources of bias which may impact

5 epidemiological inference: selection bias. As I noted, these

6 are birth cohort studies perspective in nature. At seven years,

7 the retention rate for participants was moderately high in the

8 60 to 80% range. When available, comparison of those included

9 and excluded from various evaluations were found to be

10 comparable on many important factors but the extent to which

11 they're comparable and unmeasured factors, of course, is

12 unknown, is an uncertainty.

13 Again, the potential for non-differential

14 bias, we believe, is greater than for differential bias; given

15 both a study design, the fact that these exposures were measured

16 at birth prior to either the identification or the measurement

17 of these outcomes. And with respect to outcome ascertainment,

18 for the most part, researchers or psychometricians or study

19 staff that implemented these neurodevelopment assessment tools

20 was blinded to exposure status. 

21 In most, but not all cases, this was mentioned

22 in the publications. And also, each of these cohorts measured
Page 145

1 various environmental exposures, not just the chlorpyrifos or

2 the organophosphate exposure, so that would also reduce the

3 potential for differential measurement error in the assessment

4 of the outcome.

5 So moving toward our conclusions;

6 synthesizing. Kind of again going back to this Bradford Hill

7 type of considerations: considering the strength of the

8 observations identified. There were some strikingly large odds

9 ratios noted across these studies but not all. We believe that

10 in instances in which moderate or no association was observed it

11 could be due to non-differential measurement error or other

12 factors.

13 There is some evidence of exposure-response in

14 the studies, which had more than two categories or who measured

15 exposure on a variable.

16 As we noted in the risk section, there is some

17 consistency across the multiple cohorts that looked at similar

18 neurodevelopmental outcomes, particularly with respect to the

19 number of abnormal reflexes, with respect to the mental

20 development index component of the Bayley Scale, and with

21 respect to IQ measurement and particularly the measurement of

22 working memory.
Page 146

1 Again, these prospective studies are shielded

2 from many sources of potential bias that you encounter in an

3 epidemiology study. The measurement of exposure in-utero

4 assures that exposures preceded these effects being measured in

5 the neonatal time period or the infant time period or in

6 school-age children.

7 Biological plausibility; this, of course, is a

8 key interest in other areas of our draft paper. While generally

9 plausible, still lacks specific detail surrounding biologically

10 plausible mode of action. This is discussed extensively

11 elsewhere.

12 And with respect to the sort of criteria or

13 the consideration of cessation of exposure, in the Whyatt

14 evaluations in both 2004 and 2005, authors note that prior to

15 the voluntary cancellation of chlorpyrifos from in home

16 environment, there was that inverse association that we noted

17 with the characteristics with birth weight and birth length.

18 However, subsequent to that voluntary

19 cancellation, that association was no longer identified. That

20 could be perceived as sort of in support of a cessation

21 consideration or criteria, for lack of a better word, but we

22 note in our written material that there's -- and authors note as
Page 147

1 well in the supplemental analyses provided in Appendix 4 -- that

2 there's probably limited statistical power to make that

3 evaluation. The studies were not really designed for that

4 purpose.

5 So in summation, we believe that the current

6 database strengthens the conclusion of the 2008 SAP and our

7 conclusion at that time that chlorpyrifos likely plays a role in

8 neurodevelopmental outcomes observed.

9 Given the study conduct, design, and methods,

10 as we've detailed today and in our written material, we believe

11 that it is more likely that risk estimates have been under

12 rather than over-estimated and that this information, of course,

13 will continue to be evaluated and integrated with other lines of

14 information over time.

15 So that's a lot of information in about an

16 hour. I'll stop my rapid-fire delivery and ask if there are any

17 questions.

18 DR. KENNETH PORTIER: And we'll all take a

19 deep breath here, integrate that, and then I'll open it up for

20 questions. Does anybody want to begin? Yes, Dr. Buckley and

21 then Dr. Vena.

22 DR. BRIAN BUCKLEY: Do you have any idea how
Page 148

1 much of the data was imputed in these studies, or more or less

2 from study to study what percent of the data, if you will?

3 DR. CAROL CHRISTENSEN: So I guess the short

4 answer would be no, I can't give you a number kind of off the

5 top of my head. In some instances, it was significant. If I

6 recall correctly, within the Rauh 2011 publication looking at

7 prenatal chlorpyrifos exposure and IQ, there was about 40% of

8 those exposure data that were below the limit of detection.

9 DR. STELLA KOUTROS: I'm sorry. I don't

10 remember it that way, so you might want to just double-check

11 that. I was under the impression that the imputation was for

12 the confounding variables and that was minimal, not for the

13 exposure.

14 DR. CAROL CHRISTENSEN: I'd be happy to double

15 check that.

16 DR. STELLA KOUTROS: Yeah, maybe there was

17 also for some of the exposure, but I think that, from what I

18 recall, the imputation of the confounders was mostly what the

19 imputation was referring to.

20 DR. CAROL CHRISTENSEN: Okay. I can clarify

21 and get back --

22 DR. BRIAN BUCKLEY: Can I follow up then and
Page 149

1 ask how they dealt with non-detects?

2 DR. CAROL CHRISTENSEN: Across the studies,

3 they dealt with non-detects and using different methods from a

4 standard kind of LOD divided by the square root of two to an

5 imputation, you know, kind of considering the distribution of

6 the data above the limit of detection. So it varied.

7 DR. JOHN VENA: Just a few points of

8 clarification, in your presentation in, I guess, slide 26; you

9 said that head circumference was not studied in the Columbia. 

10 Is that correct? But on Table 10, you say it's null, head

11 circumference is null.

12 DR. CAROL CHRISTENSEN: Thank you for bringing

13 that up to allow me to clarify: there were some formatting

14 errors within this table. In fact, to my recollection, it was

15 not measured. That was an error that there was a null in that

16 cell.

17 DR. JOHN VENA: Right. Because you say in the

18 text that Whyatt, 2004 did not report an association with head

19 circumference, so I wasn't sure if that was that they didn't

20 find it or they didn't study it.

21 DR. CAROL CHRISTENSEN: I believe it's they

22 didn't study it, and that again was an error in Table 10. So
Page 150

1 thanks for the chance to clarify.

2 DR. JOHN VENA: Okay. And also, you mentioned

3 in these tables and in your presentation that the Mt. Sinai

4 study was TCPy, but --

5 DR. CAROL CHRISTENSEN: Only for fetal growth

6 and development. And I think for the remainder of the -- sorry,

7 I'll let you continue.

8 DR. JOHN VENA: Yeah, I know. But -- okay. 

9 So TCPy was just up through gestational age. Okay. But then in

10 the next table, you say DAPs on -- let's see -- slide 28. You

11 say chlorpyrifos was for -- okay, that's the Columbia. But the

12 Mt. Sinai then was the -- was DAPs that looked at abnormal

13 reflexes and neurodevelopment. They didn't --

14 DR. CAROL CHRISTENSEN: That's correct.

15 DR. JOHN VENA: They didn't look at TCPy. I

16 was curious why they didn't do that. Why they --

17 DR. CAROL CHRISTENSEN: I can't answer for the

18 study authors. I do know that only for the evaluation of fetal

19 growth and development did they use TCPy, and in that

20 measurement of TCPy, they did not use the CDC laboratories; I

21 think they used their own internal laboratory for that

22 measurement.
Page 151

1 I could -- you know, I could conjecture as to

2 why they went another way, but --

3 DR. JOHN VENA: Okay. I'm just trying to get

4 all this stuff straight.

5 DR. CAROL CHRISTENSEN: Yeah, it's a lot of

6 details.

7 DR. JOHN VENA: And then the University of

8 California Berkeley cohort in the table -- in your chart 28, you

9 say it's DAPs; but in the Table 10, you say TCPy.

10 DR. CAROL CHRISTENSEN: Yeah. Again, thank

11 you for the opportunity to clarify. There are some formatting

12 errors on that Table 10. That should be DAPs.

13 DR. JOHN VENA: That should be DAPs.

14 DR. CAROL CHRISTENSEN: Yeah.

15 DR. JOHN VENA: Can we get a corrected table?

16 DR. CAROL CHRISTENSEN: Sure.

17 DR. JOHN VENA: Because it's just --

18 DR. CAROL CHRISTENSEN: Confusing.

19 DR. JOHN VENA: Just trying to keep it

20 straight, that's all.

21 DR. CAROL CHRISTENSEN: Sure.

22 DR. JOHN VENA: Confusing, okay. Because
Page 152

1 they're different measures, they're different, they mean

2 different things, and so it's important to understand which

3 measure they're using for which outcome and how you can compare

4 across studies and all of that stuff. 

5 DR. CAROL CHRISTENSEN: Yeah. Absolutely.

6 DR. JOHN VENA: Okay. And the other question

7 I had was about ADHD.

8 DR. CAROL CHRISTENSEN: Yeah.

9 DR. JOHN VENA: In the table, you don't say

10 that Columbia looked at it; but then in your slide, you said

11 they did at 36 months. The only thing on the table with ADHD

12 was listed under Berkeley study.

13 DR. CAROL CHRISTENSEN: They did. Within the

14 Rauh 2006 publication, they measured ADHD or ADHD-like symptoms

15 as measured at 36 months.

16 DR. JOHN VENA: Okay. I'll have to re-read

17 the summaries of those studies. Thanks.

18 DR. CAROL CHRISTENSEN: Yeah.

19 DR. KENNETH PORTIER: I think we sent you an

20 email asking for that Table 10 update maybe for the Panel by

21 tomorrow morning.

22 DR. CAROL CHRISTENSEN: Absolutely. Yeah.
Page 153

1 DR. KENNETH PORTIER: So okay. Up next is Dr.

2 Collier.

3 DR. ABBY COLLIER: Just a point of

4 clarification, is Agency in slides 13 and 14 saying that

5 meconium is a suitable reservoir to measure exposure over

6 pregnancy as opposed to doing earlier studies? Is that sort of

7 what you're implying?

8 DR. CAROL CHRISTENSEN: I'm sorry. I missed

9 the other clause: as opposed to --

10 DR. ABBY COLLIER: Are you saying that as

11 opposed to, say, taking blood samples earlier in pregnancy that

12 you can use meconium at term and it sort of counts as a

13 reservoir for the chlorpyrifos? So are you using it as a marker

14 tissue for exposure over pregnancy -- the meconium?

15 DR. CAROL CHRISTENSEN: I'm relaying that the

16 study authors in that validation work reported moderate yet

17 significant correlation between meconium and the cord blood

18 measure at delivery. And in fact, other studies outside of

19 these cohorts are supporting that meconium might be a valid

20 biomarker of exposure.

21 DR. ABBY COLLIER: Thank you.

22 DR. KENNETH PORTIER: Dr. Popendorf, I think,
Page 154

1 is next.

2 DR. WILLIAM POPENDORF: Yes, thank you. I've

3 got a few questions, but let me just follow up on that one. 

4 These other studies are in human populations as well or are

5 there animal studies on meconium?

6 DR. CAROL CHRISTENSEN: Oh, on meconium. 

7 Yeah, I cannot speak to the animal studies; I was referring to

8 other biomarker validation work that I've seen. I've not

9 extensively evaluated those data for this purpose, but part of

10 my general knowledge, I've seen kind of a growing consensus that

11 meconium is looking like a decent biomarker of exposure in

12 general.

13 DR. WILLIAM POPENDORF: Okay. One of my

14 questions had to do with a miscellaneous population in

15 characterizing it. It's agricultural, which I guess it is; but

16 it also said there was only like maybe 10 percent, as I recall,

17 of the pesticides used in that area were chlorpyrifos. No

18 indication what the other ones were -- other ones that might

19 have similar biomarkers in particular, if they had DAP in their

20 tests?

21 DR. CAROL CHRISTENSEN: If I recall correctly,

22 yes, other OP --
Page 155

1 DR. ANNA LOWIT: The investigators have

2 provided us with that list, and if you'd like to see that list,

3 we can find it. It would take a little bit of time. But if you

4 think the other two that form the TCP would be chlorpyrophos

5 methyl, which is predominantly used on wheat, correct? 

6 DR. ANNA LOWIT: Historic Grains. And we're

7 talking about Southern California, so it's unlikely that

8 chlorpyrophos methyl is used. And the other one would be

9 triclopyr, and that one, we don't know; we'd have to look up.

10 The CHAMACOS group has given us a list of the

11 other OPs and their use because it's California; you can get the

12 use from California during the period of the recruitment. But

13 it would take a little digging, but if you want it, we could

14 have it by tomorrow.

15 DR. WILLIAM POPENDORF: I guess I would like

16 to see that because they use both TCPy and DAP, and I guess I'd

17 have to look to see what -- or let's say, eventually looked at

18 and just to get a sense of, you know, what was the agricultural

19 component or you implied that it was and -- I'm not so sure that

20 it is, but that would be good.

21 So you got the recruitment 

22 Oh, slide 19, I guess. Because I wondered in
Page 156

1 the text as well and you kind of went through this, in terms of

2 assessing confounding biases, I wasn't clear what exactly you

3 were looking at there in terms of what was correlating with what

4 to qualify or not qualify as a confounding bias. Maybe you

5 could just clarify it in that, Carol, are you saying that for

6 instance, the chlorpyrifos exposure in something else is

7 correlated and that would determine whether it's a confounder?

8 DR. CAROL CHRISTENSEN: So right. So my point

9 in this slide was to note, particularly for those who don't have

10 a background in epidemiology, that, you know, often we hear,

11 "Oh, such-and-such could be a confounder."

12 But there are actually very specific criteria

13 which must be met in order for confounding bias to occur; one of

14 which is that that confounding variable -- and I'm using lead

15 and mercury as an example on this slide -- must be causally

16 related to the outcome. So we know that lead and methylmercury

17 are known neurotoxicants, so that criteria are satisfied.

18 It's also key or vital that the confounder in

19 this, again lead and mercury, must be statistically associated

20 with the main exposure, in this case chlorpyrifos. And I was

21 emphasizing within this little box at the bottom left that not

22 only must it be correlated, but methodological work outside of
Page 157

1 these cohort studies suggest that that correlation must be

2 moderate, not just a correlation, but a somewhat moderate or

3 significant correlation to have a potential confounding bias.

4 And so in the case of lead and methylmercury,

5 as reported by study authors, those correlations are either very

6 low; below 10 percent, for example. Within the Columbia cohort,

7 the correlation is actually -0.08, so it's very, very low

8 correlation between the confounder and the exposure.

9 And when authors included lead in the model,

10 there was no change in the effect estimates. So each of the

11 three cohorts measured prenatal lead; each of the three cohorts

12 evaluated prenatal lead as a potential confounding variable

13 across these studies, and none of them included blood lead as a

14 confounding variable.

15 DR. WILLIAM POPENDORF: So if I followed that,

16 right, you not only look for correlations between the two

17 exposures but between, for instance, lead and the outcome. 

18 DR. CAROL CHRISTENSEN: Correct.

19 DR. WILLIAM POPENDORF: Because I'm wondering

20 in the case where chlorpyrifos is accounting for maybe 10

21 percent of the effect, you could see a non-correlation in two

22 exposure variables but still have a significant effect of, let's
Page 158

1 say, lead independent of --

2 DR. CAROL CHRISTENSEN: Correct.

3 DR. WILLIAM POPENDORF: Yeah.

4 DR. CAROL CHRISTENSEN: That's absolutely

5 correct.

6 DR. WILLIAM POPENDORF: But you did look at

7 both to disqualify it as a confounder?

8 DR. CAROL CHRISTENSEN: That's correct.

9 DR. WILLIAM POPENDORF: That wasn't clear. 

10 Thank you.

11 DR. CAROL CHRISTENSEN: Okay.

12 DR. KENNETH PORTIER: Next. Next, I have Dr.

13 Carr; then Dr. Ryan.

14 DR. RUSSELL CARR: Hello. On slide 34, using

15 the Wechsler, Rauh 2011 said that in the Columbia cohort that

16 lower IQs and lower working memory associated with chlorpyrifos

17 exposure, and they did, in their model, they eliminated

18 polycyclic aromatic hydrocarbons and other compounds.

19 In another publication by that same group,

20 they found that PAHs cause attention deficit syndrome in the

21 same cohort of children, but yet they put in there that

22 chlorpyrifos played no role in that.
Page 159

1 Physiologically, you can't separate attention

2 of a child from IQ. If he has a poor attention, his IQ's going

3 to be lower; much like the same if he has a poor attention he's

4 not going to do working memory very well. And I'm trying to

5 wrap my head around how you can say one chemical affects this

6 process in the brain, and another one affects this, when those

7 two processes are linked really strongly.

8 DR. CAROL CHRISTENSEN: Okay. So I'll start

9 with, right, both within the Columbia cohort, there's been

10 evaluations of both chlorpyrifos and PAH. And within the PAH

11 paper on attention, it was determined that chlorpyrifos and PAH

12 are independent predictors of that outcome. They both predict

13 that outcome.

14 So you could have a multi -- you know, there's

15 --

16 DR. RUSSELL CARR: So they both create

17 attention deficit syndrome -- I mean, the problem, the attention

18 problems?

19 DR. CAROL CHRISTENSEN: There's evidence of an

20 association with that outcome with both chlorpyrifos and PAH.

21 DR. RUSSELL CARR: Independently of one

22 another.
Page 160

1 DR. CAROL CHRISTENSEN: Independently of one

2 another, and they've tested for confounding within both.

3 DR. KENNETH PORTIER: Let me just follow up:

4 does that also mean -- when you say independent, it means if you

5 put them both in multiple regressions, they're both significant?

6 Or do you mean you put one in, associate it, you put another

7 one in? What did they mean by independently of each other? I'm

8 just trying to --

9 DR. CAROL CHRISTENSEN: They're both

10 independent predictors of that outcome, so chlorpyrifos in the

11 presence of PAH within the statistical model would be predictive

12 of that outcome. PAH in the presence of chlorpyrifos, you know,

13 when you add both of them together, it depends on what else is

14 in the model.

15 I mean, I would suspect that, yes, when

16 they're both in there, you can see -- right. I just know that

17 they're separately and independently predictive of that outcome.

18 Now your second part of your question with

19 respect to the Wechsler tool, I'm not a child psychologist. I

20 don't administer these exams, but I do know from my reading

21 about the Wechsler tool is that you can assess intelligence in a

22 child who has attention issues.
Page 161

1 DR. RUSSELL CARR: But if you know going in

2 that child has ADHD and has very strong indication, but is that

3 mentioned in these methods that they go in and say, "Okay. This

4 child is showing symptoms of attention deficit disorder,"

5 because a lot of times when they administer the Wechsler and

6 they understand that, they realize that his IQ's probably going

7 to be about 10 points higher. 

8 I mean is that in the methodology or this

9 paper that they actually considered that these children had

10 attention deficit disorder before they administered an IQ exam?

11 DR. CAROL CHRISTENSEN: I do not recall in

12 that a mention of that within the methods section that they

13 separately administer these tests differently among kids who

14 have known or reported ADHD or are medicated for ADHD. I would

15 have to go back and double-check that. I don't recall it off

16 the top of my head.

17 DR. RUSSELL CARR: Okay.

18 DR. KENNETH PORTIER: Next is Dr. Ryan.

19 DR. BARRY RYAN: Yes, I'd just like to explore

20 a little bit this exposure validation study, and I guess it's

21 the slides beginning 11, 12, 13, 14, and those areas. And I'm

22 particularly interested in slide number -- I guess it is 12
Page 162

1 where we talk about maternal urine in the last trimester and

2 then the figure that goes after that is a kind of a depiction of

3 the time course of the validation study here.

4 DR. CAROL CHRISTENSEN: Okay.

5 DR. BARRY RYAN: I just want to clarify that

6 the data that are in the table after that -- I'm sorry to keep

7 on having you jump through the slides here, but the data in the

8 table after that where it says, "First prenatal, second

9 prenatal, third prenatal," and so on, that represents only from

10 week 32 on?

11 DR. CAROL CHRISTENSEN: That is correct.

12 DR. BARRY RYAN: Okay. And so the fourth

13 prenatal visit, the reason the end is dropped is probably

14 because several women went in and delivered their children in

15 there. So the temporal variability on that really is only over

16 a relatively short period of time.

17 DR. CAROL CHRISTENSEN: Over an eight-week --

18 DR. BARRY RYAN: An eight-week period.

19 DR. CAROL CHRISTENSEN: Up to a maximum of an

20 eight-week timeframe. I think the average was 6.25 weeks.

21 DR. BARRY RYAN: I'm sorry. I missed the

22 average of -- I missed the number.
Page 163

1 DR. CAROL CHRISTENSEN: Maximum of an

2 eight-week timeframe. I think the average across this

3 validation study population was an evaluation period of about

4 6.25 weeks.

5 DR. BARRY RYAN: Okay, but so something on the

6 order of six weeks?

7 DR. CAROL CHRISTENSEN: Yes, up to eight.

8 DR. BARRY RYAN: And the correlation there,

9 obviously it's statistically significant and you lose some of

10 the significance at the forefront because, again, it's

11 relatively small. But those numbers are still pretty small, and

12 there's a lot of stuff that's not accounted for by knowing that

13 this is the relationship between TCPy and maternal urine and

14 whatever it is we're looking at -- meconium. Is that right?

15 TCPy levels in meconium and chlorpyrifos in

16 maternal and umbilical cord blood, right?

17 DR. CAROL CHRISTENSEN: Right. These are

18 merely correlation coefficients; these are not adjusted

19 estimates. And yes, we acknowledge that these are moderate

20 correlations. They're not high correlations.

21 DR. BARRY RYAN: And again, the meconium, you

22 think, integrates from week 14, 13 -- I can't remember.
Page 164

1 DR. CAROL CHRISTENSEN: I've heard different

2 and read different things -- 13 or 14, yeah.

3 DR. BARRY RYAN: Somewhere in that range,

4 though. So we're looking at something that was really collected

5 at the very end of pregnancy correlated with something that's

6 been integrated over the entire pregnancy -- well, not the

7 entire, but two-thirds of the pregnancy.

8 DR. CAROL CHRISTENSEN: Right. And that is

9 the gist of this slide, yes.

10 DR. BARRY RYAN: Okay. That's all. I just

11 wanted clarification on that. Thank you.

12 DR. KENNETH PORTIER: Dr. Vena.

13 DR. JOHN VENA: It's not my area, but I don't

14 think ADHD and IQ are along the same domain. It seems to me

15 that -- I'm not sure what the basis for your comment is -- that

16 somebody who has ADHD has a lower IQ. I mean, what's the

17 evidence for that?

18 DR. RUSSELL CARR: I'm not saying they have a

19 lower IQ; I'm saying their scores on a test could possibly be

20 lower.

21 DR. JOHN VENA: Again, I don't know, but I'm

22 not sure if that's the case. I'm not.
Page 165

1 DR. RUSSELL CARR: I mean, that was my

2 understanding.

3 DR. KENNETH PORTIER: Dr. Young.

4 DR. HEATHER YOUNG: If you check that -- the

5 paper that you're talking about, they do acknowledge that child

6 behavior issues can affect the score on the working memory

7 index. And so they did a supplementary analysis that looked at

8 that and took it into account. So it's in the Rauh paper that

9 you can see they did acknowledge that, and they did something to

10 look at that.

11 DR. KENNETH PORTIER: And this is why we have

12 a big Panel. Dr. Vena again.

13 DR. JOHN VENA: Thanks for that clarification.

14 And I did go back and look at the summary in Appendix 6 of

15 Whyatt 2004, and it does say on page 4 in this first paragraph:

16 "Head circumference was not significantly associated with

17 maternal personal air or cord blood chlorpyrifos levels." So in

18 fact, they did look at head circumference. And so the table's

19 right; it is null -- Table 10.

20 DR. CAROL CHRISTENSEN: Thanks for that

21 clarification. I'll make sure that that's accurate when I

22 distribute that table tomorrow.
Page 166

1 DR. JOHN VENA: Right.

2 DR. KENNETH PORTIER: Dr. Harry.

3 DR. GAYLIA JEAN HARRY: So I have a question

4 for clarification, just as we're trying to see across these

5 studies is what you're asking us to look at. So I've

6 administered the Wechsler, but I really don't know the Bayley

7 Scores that you're looking at. But there are different things

8 that would come in at the 36 months that you would not be able

9 to detect at 24 months because of the developmental aspect of

10 it.

11 And I was wondering if you could provide the

12 Panel, in general, some information on what you might expect to

13 see changing between 24 and 36 because you've got -- you've got

14 a couple studies suggesting you've got an inverse relationship

15 at 24 months, but then you've got the Columbia study that says

16 you don't see any inverse association 'til 36 months.

17 Could you help us out with what some of those

18 pivotal changes might be between the 24 and the 36 months of

19 changes you would be detecting, things you are able to measure,

20 at 36 months that you cannot measure at 24 that change over?

21 DR. CAROL CHRISTENSEN: Right. Again, I think

22 my best answer to that question -- and then I would have to go
Page 167

1 back into looking at materials published by those who developed

2 the Bayley Scale -- but my best answer to that question would

3 be, you know, certainly as children develop, motor coordination

4 is always increasing over time.

5 My understanding of the Bayley is it's, you

6 know, sort of looking at the same time of the child's kind of

7 interest and attention and interest in exploring the world and

8 interacting with that world as a way to develop mental capacity

9 and cognition, combined with the ability to explore and

10 experiment with the world, i.e., the psychomotor issues.

11 So as, you know, young children age, those two

12 things come closer together; and uncommonly those differences,

13 if they do occur, are more notable. So I think my simple answer

14 would be that over time, if deficits are occurring, it's going

15 to be a little bit easier to identify those deficits.

16 But more specific than that, I would have to

17 go back to the references concerning the Bayley Scales.

18 DR. KENNETH PORTIER: Dr. Aschner.

19 DR. MICHAEL ASCHNER: Quick question: in

20 looking at the nutritional data of the different cohorts, is

21 there anything there about fish consumption between -- any

22 differences between the three? Some generalized comments about
Page 168

1 that?

2 DR. CAROL CHRISTENSEN: About fish

3 consumption. Overall dietary information was not reported

4 within these published evaluations, and we're going with what is

5 available in the published peer review literature. Through

6 personal communication with the principal investigators, we had

7 asked that question around methylmercury exposure and fish

8 consumption; and overall qualitatively, my understanding is that

9 it's low.

10 In fact, the correlation with methylmercury

11 and chlorpyrifos is also low. I believe, at least with respect

12 to the Columbia cohort, for which I have some informal

13 information, fish consumption is not high.

14 DR. MICHAEL ASCHNER: I was just wondering if

15 in the Berkeley area, it was higher; I realize in New York, it's

16 probably pretty low.

17 DR. CAROL CHRISTENSEN: Pretty low. I have no

18 information to share about that.

19 DR. KENNETH PORTIER: Dr. Popendorf.

20 DR. WILLIAM POPENDORF: Yeah, I've been

21 looking for the information, but I just had one question. I

22 might as well just ask you than trying to find it in the text. 
Page 169

1 But slide 28, the Brazelton outcomes -- the two positives;

2 refresh me: was it just total numbers of measures that were

3 positively associated, or was there particular measures within

4 that?

5 DR. CAROL CHRISTENSEN: It was total numbers. 

6 So that positive association is referring to increasing urinary

7 concentration of DAPs in moms, measured in the third trimester,

8 were positively correlated with the odds of having two or more

9 abnormal reflexes measured.

10 But I think it's three or more in CHAMACOS and

11 two or more in Mt. Sinai. One is two and one is three, but it

12 is two or more abnormal reflexes out of the 18 measured.

13 DR. KENNETH PORTIER: Dr. Pope.

14 DR. CAREY POPE: Yeah, there was some mention

15 about TCPy levels earlier and where they're coming from methyl

16 parathion or wheat and triclopyr.

17 DR. ANNA LOWIT: Chlorpyrophos methyl.

18 DR. CAREY POPE: Methyl chlorphyrophos, right.

19 And triclopyr, there was a question about. It's, as far as I

20 know, used on broadleaf weeds, and it's about the -- according

21 to this group's work -- it's about the seventh as far as amount

22 of pesticide use for industry and commercial purposes. And
Page 170

1 roughly speaking, it's anywhere from a quarter to a half of the

2 amount of pesticide used compared to chlorpyrifos.

3 So yeah, that is a substantial, you know,

4 pesticide use. It may not be used as broadly across multiple

5 purposes or multiple sites compared to chlorpyrifos as far as

6 amount. It's quite substantial.

7 DR. ANNA LOWIT: Well, certainly right now, we

8 can't confirm the numbers that you just gave, but you are

9 accurate that triclopyr is in residential products and can be

10 bought by individuals at your local store as a herbicide.

11 DR. CAREY POPE: I have a ten-gallon container

12 at home. I use it personally.

13 DR. ANNA LOWIT: We're in the process of

14 getting the Ag use right now. We've sent some emails out, so we

15 won't confirm the numbers, but certainly it is a residential

16 product.

17 DR. CAREY POPE: Yeah.

18 DR. KENNETH PORTIER: Dr. Young.

19 DR. HEATHER YOUNG: Carol, can I just confirm

20 slide 20? None of that's published, right -- the stuff where

21 they look at the confounding with the other pesticides?

22 DR. CAROL CHRISTENSEN: That's correct.
Page 171

1 DR. HEATHER YOUNG: Oh, okay.

2 DR. CAROL CHRISTENSEN: It's replicated in

3 full in your Appendix 4, but it is unpublished.

4 DR. HEATHER YOUNG: Okay. But it's not

5 published. Okay.

6 DR. CAROL CHRISTENSEN: Correct.

7 DR. HEATHER YOUNG: Thank you.

8 DR. KENNETH PORTIER: Dr. Lu.

9 DR. CHENSHENG LU: Just a quick question: so

10 in addition to DAP and TCPy, what other OP -- what other

11 pesticide data you have received from all the three studies? 

12 Not just pesticide data; like, metabolite data. In addition to

13 DAP, like, do you have MDA -- the metabolite for Malathion?

14 DR. CAROL CHRISTENSEN: Oh, outside. I

15 believe Malathion was measured in the Mt. Sinai cohort.

16 DR. CHENSHENG LU: But not across all three

17 studies?

18 DR. CAROL CHRISTENSEN: Not across -- for

19 Malathion specifically, not across all three studies, no.

20 In addition to diazinon and propoxur, I think

21 there were other OPs measured by Columbia. Diazinon and

22 propoxur and chlorpyrifos were the most frequently detected
Page 172

1 within the cohorts, so those were the ones that they considered

2 for their model adjustment; but there were other measures made,

3 and I can get you that list.

4 DR. KENNETH PORTIER: Okay. I think we've

5 exhausted that. Why don't we move on then to discussion of

6 biomonitoring? Mr. Niman?

7 MR. AARON NIMAN: Good afternoon. My name's

8 Aaron Niman. I'm an industrial hygienist with the Office of

9 Pesticide Programs, Health Effects Division. My presentation

10 today focuses on Section 5 of the issue paper, which provided a

11 review of chlorpyrifos biomarkers of exposure; and then

12 presented a TCPy biomonitoring study comparison; then discuss

13 evaluation of the relationship between urinary TCPy levels and

14 acetylcholinesterase and human populations.

15 And then finally, it discussed risk-based

16 biomonitoring interpretation options currently being evaluated

17 by the Agency to further incorporate biomonitoring data into the

18 risk assessment process.

19 So my presentation today is basically going to

20 follow the outline of the Section 5 of the issue paper.

21 Just to begin with, chlorpyrifos biomarkers of

22 exposure. There's fairly extensive literature on chlorpyrifos
Page 173

1 biomonitoring, some of which was discussed in the previous

2 presentation, but the most common biomarkers of exposure are the

3 urinary metabolites, TCPy, and the non-specific dialkylphosphate

4 metabolites DEP and DETP.

5 In addition to those, chlorpyrifos has been

6 directly measured in biologic media, including blood and other

7 biologic media such as meconium.

8 I won't go into a lot of detail, since a lot

9 of this was just discussed in the last presentation. But

10 certainly in terms of scientific considerations about evaluating

11 and interpreting biomonitoring results with specificity in

12 issues related to longitude and variability are particularly

13 important.

14 And as it's been mentioned, both DEP and DETP

15 are considered non-specific biomarkers because they are

16 metabolites of multiple OP pesticides. And similarly, TCPy is

17 generally considered more specific than the dialkylphosphates,

18 but it is a metabolite of a couple different parent compounds as

19 well. So there are specificity considerations.

20 In addition to just the metabolic profile of

21 the compounds, additional considerations, both these -- all of

22 these biomarkers are also environmental degradates, so they're
Page 174

1 present in environmental media, whether it's dust or food

2 samples.

3 So when evaluating, in this case exposure to

4 chlorpyrifos, if you're measuring TCPy, although TCPy is because

5 of chlorpyrifos exposure, you'll tend to overestimate exposures

6 if you don't account for the direct exposure to TCPy.

7 In terms of longitudinal variability, most of

8 the literature suggests that there's fairly high day-to-day

9 variation within a person and their levels of both the DAPs and

10 TCPy. This is likely due to the fact that these compounds have

11 fairly short biologic half-life, and the nature of exposure

12 particularly outside of occupation, the populations tends to be

13 fairly intermittent. So for that reason, it's not surprising

14 that you'd see fairly high variability.

15 It's been suggested, because of the

16 variability that's been observed, it's difficult to quantify

17 pesticide exposure in the typical risk assessment metric,

18 exposure or intake per day over an extended duration of time.

19 However, it's also been suggested that,

20 particularly for the purposes of epidemiology where the goal's

21 either to assign broad exposure categories or simply rank

22 exposure, that the spot-urine measurements can be suitable for
Page 175

1 those purposes.

2 And then some of the validation work discussed

3 in regard to the Columbia cohort adds some credibility to that;

4 particularly spot-urine samples can be used for the purposes of

5 exposure assessment in epidemiology.

6 Moving onto blood biomarkers, a benefit, or a

7 strength, of direct measurement of chlorpyrifos in blood is a

8 direct measure of the internal dose of chlorpyrifos. However,

9 in general, blood measurement seems to be much less common in

10 the literature. Part of this probably has to do with it require

11 more sensitive analytical methods. 

12 In addition, it also places a greater burden

13 on study participants, so there might just be fewer

14 opportunities to collect blood to measure chlorpyrifos.

15 An additional interpretation consideration is

16 that given the rapid half-life of chlorpyrifos, particularly in

17 blood, it represents a narrower period of exposure even relative

18 to urinary biomarkers.

19 I'm now going to transition to the TCPy

20 biomonitoring study comparison presented in the paper, and the

21 main goal of this was to compare summary results reported in the

22 literature focusing on TCPy urinary concentrations, both in the
Page 176

1 general U.S. population and targeted study populations, which

2 includes the three children's health cohorts.

3 And this comparison was intended to be fairly

4 broad in nature and just essentially provide a frame of

5 reference for the three children's health cohorts. And it's

6 notable there are some key differences, and all the studies

7 considered that we didn't want to take the comparison too far.

8 Unique differences related to the time and

9 relative to the residential phase-out in addition to unique

10 study populations, whether it is differences in age, location,

11 setting, which might influence the dominant route of exposure,

12 for example.

13 A number of these studies have specific

14 research objectives, so the goal is to provide sort of a broad

15 base comparison, not do an "apples to apples" comparison.

16 In terms of studies that were selected, of

17 course we considered NHANES to get general population estimates.

18 We also included observational studies conducted by EPA's

19 Office of Research and Development.

20 In addition to those also included a subset of

21 studies that have been performed by Dow. These are

22 observational studies of individuals applying pesticides both in
Page 177

1 occupational and residential use scenarios. And the subset are

2 intended to provide both the high and the low for occupational

3 and residential use scenarios because there have been much more

4 studies that have been submitted to the Agency.

5 In addition to that included studies from the

6 peer-reviewed literature, many of which that have previously

7 been reviewed and evaluated by the Agency in the 2008 SAP. And

8 there's a fairly comprehensive review by Eaton et al., 2008.

9 This is just a summary of the studies. I

10 won't go into any detail; it's more for your reference if you

11 have any questions at the end of the presentation. But I'll

12 just emphasize the studies, in terms of age, had different study

13 populations, and included different collection periods.

14 This first figure was presented in Section 5

15 of the issue paper, and it's a summary of TCPy urinary levels in

16 child study populations.

17 All the way to the left are the reference

18 ranges based off of summary statistics reported by CDCs in their

19 exposure report. And these ranges reflect the 5th and 95th

20 percentile -- between the 5th and 95th percentiles of urinary

21 TCPy levels in 6- to 11-year-olds.

22 And again, we made general conclusions in the
Page 178

1 issue paper just looking across various studies, the ranges seem

2 somewhat similar, although some studies did note higher median

3 levels, for example, in a number of the ORD studies under Egeghy

4 et al., which included all the ORD observational studies.

5 Moving onto the study comparison of adult

6 populations. Again, all the way on the left includes the study

7 results from NHANEs, and again, this time it's adults ages 20 to

8 59. And all the way to the right, we've highlighted the results

9 from the three children's health cohorts. I will narrow in on

10 the children's health cohorts in the next slide, but I just

11 wanted to make the point where the Dow studies fit in. And

12 again, these were studies where they monitored individuals

13 applying pesticides in both occupational and residential use

14 scenarios.

15 And particularly, the high end were observed

16 to be quite a bit higher than the levels observed in the general

17 U.S. population.

18 Narrowing in on the three children's health

19 cohorts in comparison to NHANES, again, in comparison of the

20 entire distribution, the distributions seemed somewhat similar. 

21 There were comparison of median levels; they were reported to be

22 slightly higher both in the CHAMACOS and Mt. Sinai cohorts, and
Page 179

1 slightly lower in the Columbia cohort.

2 And these findings have been noted by the

3 investigators in some of their publications, and they're noted

4 in the discussion section of Section 5.

5 We also noted that in regard to the Columbia

6 paper, in contrast to the other cohorts, some of that urinary

7 TCPy data was collected during the residential phase-out, which

8 might provide some sort of explanation to why median TCPy levels

9 were lower than NHANES'.

10 I'll now move on to the evaluation of the

11 relationship between urinary TCPy levels and

12 acetylcholinesterase inhibition in human populations. Just a

13 quick overview on monitoring of acetylcholinesterase.

14 The main biomarkers are red blood cell

15 acetylcholinesterase and plasma butyrylcholinesterase. Red

16 blood cell acetylcholinesterase is considered an early marker of

17 potential chlorpyrifos toxicity, and plasma

18 butyrylcholinesterase has been shown to be inhibited at lower

19 levels of exposure than red blood cell acetylcholinesterase.

20 And it's considered a more sensitive biomarker

21 for purposes of evaluating potential physiological change or

22 exposure to chlorpyrifos and other OP pesticides, but its
Page 180

1 toxicological relevance is still unclear.

2 In terms of previous Agency findings, at the

3 2008 SAP, the Agency did evaluate the literature on the

4 association between TCPy and acetylcholinesterase activity in

5 human populations largely based on a number of

6 registrant-submitted studies as well as other studies in the

7 literature.

8 And the main conclusions at that SAP is that

9 there were some particular individuals within studies where

10 inhibition was noted, but it was difficult to draw definitive

11 conclusions about the overall relationship between urinary TCPy

12 and acetylcholinesterase inhibition. And this is largely due to

13 the large variability in response that was observed in those

14 studies.

15 Since the 2008 SAP, there've been a couple

16 more recent publications that have done a fairly nice job of

17 evaluating the association between urinary TCPy and

18 acetylcholinesterase inhibition. 

19 The first of these, Garabrant et al., 2009,

20 was a study that focused on manufacturing facility workers that

21 are involved in the production of chlorpyrifos and a comparison

22 of their TCPy and acetylcholinesterase activity relative to a
Page 181

1 reference group of other unexposed chemical manufacturing

2 workers.

3 The second study, Farahat et al., was a

4 longitudinal study involving cotton field workers in Egypt who

5 were involved in chlorpyrifos application.

6 Just a note about these studies: there was --

7 when you look at the results in the publications, it's pretty

8 clear that these are occupational level exposures. In

9 comparison with the creatinine-adjusted results reported by CDC,

10 general population urinary concentrations range from about 1.5

11 to roughly 10 micrograms TCPy per gram creatinine.

12 In contrast, if you look at the exposed

13 populations in both of these studies, they range anywhere from

14 an order of magnitude to three orders of magnitude higher than

15 the NHANES urinary concentrations.

16 This figure wasn't presented in the paper. We

17 focused on the main findings, but we thought it would be useful.

18 These figures were not included in the actual

19 issue paper, but we thought it would be useful to present some

20 of the data, and this just shows the results of the Garabrant

21 study evaluating the association between TCPy in both red blood

22 cell and plasma acetylcholinesterase activity.
Page 182

1 As you can see, the figure on the left, they

2 reported no statistical significant association between urinary

3 TCPy and red blood cell acetylcholinesterase. And the figure on

4 the right, they noted an inflection point at which they observed

5 a statistically significant dose response, and that was based on

6 the more sensitive biomarker, plasma butyrylcholinesterase.

7 This second figure is from the Farahat et al.,

8 2011 study. And interestingly, they did observe a statistically

9 significant association between urinary TCPy and red blood cell

10 acetylcholinesterase.

11 It's probably notable that the levels of

12 exposure or the urinary levels observed by Farahat et al. are

13 even higher than the levels observed in Garabrant's. That might

14 be part of the reason they were able to observe that

15 statistically significant dose response relationship.

16 So the main findings that are summarized in

17 the issue paper is that Garabrant did not observe a

18 statistically significant association between TCPy and red blood

19 cell acetylcholinesterase, but they did observe a significant

20 association between TCPy and plasma butyrylcholinesterase above

21 a cutpoint of 110 micrograms TCPy per gram creatinine.

22 In contrast, the Farahat et al. study did note
Page 183

1 a statistically significant association above an inflection

2 point or a cutpoint of around 3,100 micrograms TCPy per gram

3 creatinine.

4 It also noted consistency between both the

5 studies with regard to the association between urinary TCPy and

6 butyrylcholinesterase. In particular, both observed the

7 inflection point or cutpoint at around 110, 114 micrograms TCPy

8 per gram creatinine.

9 Moving on to the interpretation options being

10 evaluated by the Agency. In the issue paper, we outlined

11 several different qualitative, semi-quantitative, and

12 quantitative interpretation options that vary in their level of

13 sophistication and how they would be incorporated into risk

14 assessment.

15 Regardless of the interpretation approach,

16 there are some key challenges, many of which have been noted

17 before related to the specificity and issues related to

18 longitudinal variability.

19 And additional consideration or challenges is

20 the toxicological relevance of the biomarker. And the TCPy, for

21 example, it is formed as part of the detoxification of

22 chlorpyrifos and it can occur in the absence of the
Page 184

1 chlorpyrifos-oxon, which is the toxic form of chlorpyrifos.

2 And also particularly environmental levels,

3 the relationship between urinary TCPy and acetylcholinesterase

4 is currently unclear. So these are additional considerations

5 when trying to make sense of biomonitoring data.

6 In terms of the interpretation approaches, the

7 most general approach outlined in the issue paper are the

8 qualitative approaches, which we just characterized as a

9 non-comparative and comparative approach. In both of these

10 approaches, the goal would be to evaluate biomonitoring data

11 without coming up with a quantitative estimate of chlorpyrifos

12 exposure.

13 Some of the analysis I presented in the first

14 half of this presentation is probably an example of a

15 non-comparative approach where the goal is to provide general

16 comparisons and trends in exposure across studies, but there's

17 no direct link to chlorpyrifos, particular routes, or health

18 outcome.

19 The second approach would be a comparative

20 approach where it would be biomonitoring data would be compared

21 to controlled human studies, and the study that is probably most

22 suitable for doing this type of comparative approach is the
Page 185

1 Nolan et al. study in which they administered a single dose of

2 chlorpyrifos to human adult volunteers.

3 However, there are some uncertainties and only

4 fairly general comparisons could be made using this approach.

5 The next approaches outlined in the paper are

6 semi-quantitative approaches, the first of which is a screening

7 approach that makes the use of biomonitoring equivalence. And

8 the idea or the goal of this approach would be to estimate the

9 biomarker level that's likely associated with the regulatory

10 exposure guidelines, such as a reference dose.

11 And the general methodology that would be used

12 would be to incorporate relevant pharmacokinetic information

13 with forward dosimetry methods to convert a reference dose or

14 other regulatory guideline into a metric that can be directly

15 compared with biomonitoring data.

16 At this point, there's a fair amount of

17 uncertainty using this approach, so we've proposed it mostly as

18 a screening approach to characterize or understand results in

19 biomonitoring studies.

20 The next semi-quantitative approach is a

21 reverse dosimetry approach, and this would be used to estimate

22 -- actually estimate chlorpyrifos exposure in terms of intake
Page 186

1 per day so that it can be compared to a regulatory screening

2 value.

3 The Mage et al., 2004 is commonly cited in the

4 literature, and it's example of a reverse dosimetry method that

5 incorporates creatinine-adjusted urinary levels to estimate

6 daily exposure.

7 It also has considerations that would have to

8 be evaluated relative to -- it lacks route-specific information,

9 so assumptions would have to be made as well as considerations

10 related to biomarker specificity.

11 The final approach outlined in the paper is a

12 fully quantitative approach where risk would be evaluated using

13 exposure linked physiologically based pharmacokinetic model. In

14 terms of PBPK models, the preferred quantitative approach would

15 be to use a fully calibrated and evaluated PBPK model.

16 And this schematic here just shows the

17 Timchalk et al., 2002 model, which was most recently presented

18 at a 2011 SAP.

19 There are several groups currently working on

20 the chlorpyrifos PBPK modeling, all of which is based off of the

21 Timchalk et al., 2002 model, which has been refined since it was

22 originally published back in 2002.
Page 187

1 And as I mentioned, it has recently been

2 evaluated by the SAP. Some Panel members here, I believe, were

3 part of that Panel. 

4 And some of the key limitations that were

5 noted during that SAP is that the model parameters that go into

6 the PBPK model are based on a typical adult human, so as a

7 result it does not incorporate life-stage changes related to

8 either pregnancy or childhood development.

9 And the model also only simulates oral

10 exposure to chlorpyrifos with an emphasis on dietary exposure. 

11 As a result, it does not simulate either dermal or inhalation

12 exposure, which may be relevant in some circumstances.

13 So just in summary, on the various

14 interpretation options, in the paper we've outlined several

15 approaches to analyze biomonitoring data associated with

16 chlorpyrifos exposure.

17 Each approach has its limitations that need to

18 be carefully considered for any type of interpretation. And at

19 this SAP, we're soliciting recommendations regarding the

20 strengths and limitations of approaches, as well as alternative

21 approaches not yet considered by the Agency.

22 With that, I'll take any questions.
Page 188

1 DR. KENETTH PORTIER: Okay. Any questions? 

2 Dr. Buckley.

3 DR. BRIAN BUCKLEY: Brian Buckley. I have a

4 few questions. First is that for most of the studies that we've

5 looked at all sort of ended around 2004 with a couple exceptions

6 related to far more populations and such.

7 The concentrations for non-occupationally

8 exposed or indirectly occupationally exposed were in the

9 ballpark of that 1 to 10, you know, ppb or so. Any idea what

10 the general populous might look like today or yesterday or last

11 week?

12 MR. AARON NIMAN: We've actually -- we've been

13 waiting for (Maurice) and (Haynes).

14 DR. BRIAN BUCKLEY: Maurice's got it.

15 MR. AARON NIMAN: Yeah. And there's been a

16 delay in getting that published. I know we're hoping -- I think

17 actually later, towards the end of this year -- to get more

18 recent urinary TCPy data for the general pop, so we would like

19 to do that.

20 DR. BRIAN BUCKLEY: It hasn't been the same

21 since Dana left, huh?

22 MR. AARON NIMAN: Yeah.
Page 189

1 DR. BRIAN BUCKLEY: Next question: How

2 important do you think it is to correct for creatinine with

3 these measurements, and Barr and Angerer paper suggesting that

4 that may not be the best way to go. How do you sort of rectify

5 that?

6 MR. AARON NIMAN: I guess ideally, it's nice

7 to present things both ways, both creatinine and

8 volume-adjusted. I know because of differences in excretion of

9 creatinine between like, demographic groups, if you're trying to

10 do a comparison between kids and adults, some of those

11 differences could be because of differences in the amount of

12 creatinine that they excrete in urine. And same if you're

13 comparing Caucasian and African American populations.

14 So I think the ideal: present both ways, just

15 understand that some of the differences could be because of

16 creatinine.

17 DR. BRIAN BUCKLEY: So do you think that it

18 may be better to, within a population, sort of limit or to

19 present creatinine corrected within a subgroup or a

20 subpopulation as being more effective and across populations

21 that may not have the same sort of strength? Is that a fair

22 assessment?
Page 190

1 MR. AARON NIMAN: Yeah, I think that's

2 reasonable.

3 DR. BRIAN BUCKLEY: Okay. The other question

4 that I have is, as we try to link exposure to dose to effect,

5 all of the exposure in all of the models that you've taken into

6 account all occur at the biomarker level with not so much link

7 to the potential exposure based on the air particulate and the

8 dust and the soil and all those other guys that we have a lot of

9 data on.

10 Is there a reason you don't want to reach out

11 to that next level and try and -- you know, especially in terms

12 of risk characterization -- go the media upon which people are

13 exposed and incorporate that in your model?

14 MR. AARON NIMAN: Yeah, I think certainly,

15 there's a lot of multimedia studies that are -- certainly,

16 there's a lot of data out there, whether it's dietary exposure

17 or levels of chlorpyrifos residue, in dietary samples. A lot of

18 the ORD work -- they've taken multimedia samples and they've

19 tried to predict urinary levels with those.

20 Certainly that data out there, and I think

21 there's definitely opportunities to look at the multimedia

22 concentrations and try to go from exposure to internal dose.
Page 191

1 Ideally, I think you'd look at both and they

2 would tell a consistent story; but if not, understand where the

3 differences might lie.

4 DR. BRIAN BUCKLEY: I would think that if

5 you're trying to assess the alternate pathways of exposure other

6 than just dietary ingestion, inhalation, dermal absorption,

7 things like that, those other media concentrations would play a

8 much bigger role in assessing where the potential pathway is.

9 MR. AARON NIMAN: This might be better for the

10 discussion tomorrow, but the way you can link the information at

11 least qualitatively is you can, for example, use a reverse

12 dosimetry method and then to try to characterize that, look at

13 the multimedia concentrations and say, "Even though we can't

14 make a direct connection between a particular route, when we

15 look at the different routes of exposure, we would expect this

16 one to dominate."

17 DR. KENNETH PORTIER: Dr. Lowit.

18 DR. ANNA LOWIT: Yeah. So I think you have

19 hit on one of the big areas of uncertainty that we'd really like

20 to get some feedback on. Thinking about whether it's reverse

21 dosimetry or dose reconstruction, that's largely what you're

22 getting at, dose reconstruction.
Page 192

1 There are a variety of ways you can do that. 

2 So in our July 2011 risk assessment, as in also what would have

3 been done for 2000 or 2003, the Agency does extensive exposure

4 assessment in food and water and estimates of worker populations

5 for different activities.

6 So those assessments already exist within the

7 public docket. What we have not done is to link to the media,

8 which you're talking about, and then to do either dose

9 reconstruction and get to urinary biomarker or vice versa, go

10 the other way.

11 One of the big challenges that we'd like to

12 hear some feedback; around the uncertainties is this idea of

13 route-specific metabolism and uptake, that you get different

14 tissue dosimetry across different routes, keeping in mind that

15 chlorpyrifos generally has to be activated to the oxon.

16 And so you have to think about bypassing liver

17 metabolism, site-specific metabolism to the oxon, all those

18 things. It gets very complicated quickly. And so while we talk

19 about it, thinking about the strengths and the uncertainties

20 associated with all these existing procedures.

21 This is an important question for us because

22 it's an area where we have to go. And so as you think about the
Page 193

1 range of possibilities, as Lieutenant Niman described, of what I

2 describe as hand waving being the -- largely what we're doing

3 right now up to various levels of sophistication including some

4 of the proposals that Dow has put together that they presented

5 to the SAP in 2011 -- heavily sophisticated, fully

6 compartmentalized PBPK modeling to exposure assessment, all the

7 bells and whistles.

8 Those are largely research efforts and they

9 have great promise, but as we talked about earlier, we're in a

10 position to make regulatory decisions on a real-time basis in

11 the next couple of years.

12 So as you think about these issues, think

13 about where the science is headed and where we can encourage

14 people to go in that direction of what we can do now and where

15 the uncertainties would be and what those considerations might

16 be.

17 DR. KENNETH PORTIER: Dr. Collier.

18 DR. ABBY COLLIER: I have a question similar

19 to Dr. Buckley about integrating these approaches. We heard a

20 lot in the previous section about pregnancy, development,

21 neonatal; and in this section, we've heard a lot about adult

22 pharmacokinetics.
Page 194

1 And while it's a lovely model that you've got,

2 it's not a pediatric PK model and they are very, very different.

3 And I was just wondering if you could comment moving forward on

4 how you plan on addressing that because kids aren't small

5 adults.

6 MR. CHESTER RODRIGUEZ: Chester Rodriguez,

7 EPA. As you know, we're not proposing to use the PBPK model

8 that's been developed for the typical adult human. You know, we

9 just brought it up as a potential approach. But yeah, the model

10 has not been developed to include different life stages at this

11 point.

12 DR. ANNA LOWIT: So thank you, Chester. So

13 in line with my comments a minute ago, the ideal preferred

14 situation would be that we would have available to us today a

15 fully compartmentalized PBPK model for a pregnant woman, even

16 that would age of the child. And that would include food. We

17 could do food; we could do water; we could do air; and we can do

18 dermal.

19 And that's a little bit of a pipe dream. It's

20 certainly in the next two or three years. So I think one of the

21 things I would really encourage you to do is to think about all

22 of the approaches that are available and what the limitations of
Page 195

1 those would be for extrapolating adults to human, a non-pregnant

2 adult to a pregnant female.

3 Where are those uncertainties and where are

4 they qualitative and where are they quantitative; and what those

5 uncertainties are, where are the strengths in those and how we

6 can use those strengths and limitations to get at some of the

7 answers to these really difficult questions?

8 DR. ABBY COLLIER: Well, I would like to

9 follow up on that and say that, yeah, pregnancy is kind of a

10 pipe dream for everyone at the moment, but there are nice

11 pediatric PK models out there at the moment. Simcyp Paediatric

12 is lovely. I mean, they're not cheap, but they're lovely.

13 And so we need to sort of go a little bit

14 beyond our comfort zones to say, "If it exists, can we use it?" 

15 Thanks.

16 DR. KENNETH PORTIER: I think you want you to

17 hold that concept for our discussion, too, and bring it back up

18 again. Dr. Buckley.

19 DR. BRIAN BUCKLEY: Yeah, to follow up again,

20 it would seem to me that dose reconstruction is a lot easier to

21 accomplish at this stage of the game than a more complicated

22 PBPK model. And if you're going to use a validation potentially
Page 196

1 for your PBPK model, dose reconstruction might not be a bad way

2 to go in the sense that you're going to close the loop at that

3 point in time and characterize the whole issue.

4 I mean, in terms of dose reconstruction, BPA

5 and others already have models as to how much dirt kids eat and

6 how much we breathe and all that other good stuff. And you have

7 good studies out there that have measured the air and the water

8 and all that other good stuff. So it would just seem to me that

9 to not include dose reconstruction as a tool at this point in

10 time leaves a powerful tool out of the toolbox.

11 DR. KENNETH PORTIER: Clearly, the Panel's

12 ready to go on this questions. Dr. Popendorf.

13 DR. WILLIAM POPENDORF: Yes, just a couple of

14 things that I guess I missed going through on page 5, those

15 other data, the Dow data. My question's really on the Dow data.

16 Was all that Dow data then either during application or shortly

17 thereafter?

18 MR. AARON NIMAN: So there were observational

19 studies where at the start of the study, there was no known

20 exposure to chlorpyrifos or they may have had background

21 environmental levels of exposure. And during that period, they

22 collected, for example, baseline acetylcholinesterase activity
Page 197

1 measurements.

2 And then there was a period, whether it was an

3 occupational or residential pesticide, they performed some sort

4 of activity where they applied a pesticide and then their

5 urinary measurements were collected at time points after that.

6 DR. WILLIAM POPENDORF: Okay. So I guess, you

7 know, those were applicators then basically or possibly mixed or

8 loaders, but occupational.

9 MR. AARON NIMAN: Yeah.

10 DR. WILLIAM POPENDORF: They are. Okay. I

11 wanted to make sure. And my other question's on the Garabrant

12 and Farahat results, the PBPK modeling. What model was that

13 that generated those?

14 MR. CHESTER RODRIGUEZ: Okay. So they used

15 the same model that is the same schematic. Yeah. So the way

16 they approached this is that they ran the model for different

17 doses of chlorpyrifos to steady state to find out or to assess

18 the relationship between acetylcholinesterase activity and the

19 biomarker, you know, TCPy.

20 So it's more of a prediction of the dose

21 response behavior, or can they match that to the data they

22 collected in the study.
Page 198

1 DR. WILLIAM POPENDORF: Steady state. When

2 you say that, I'm thinking that like, that usually would take

3 several days if it's urgent, so is that a defect, I mean, within

4 the model context?

5 MR. CHESTER RODRIGUEZ: Right. So, you know,

6 that's what's nice about models. You can simulate just a bunch

7 of scenarios. But basically, you know, are they running the

8 model based on constant exposure and at a steady state? Because

9 if you don't do that, then are you going to have this episodic

10 behavior. So that's why they had to do it that way.

11 DR. KENNETH PORTIER: Did I see another

12 question? Oh, Dr. Lu.

13 DR. CHENSHENG LU: Oh, just a question about

14 these two studies they introduced 2009, 2011. What is their

15 analytical methods in terms of comparing to the NHANES method? 

16 It seems like, as I can recall, that they have a very high limit

17 detection. Is that true?

18 MR. AARON NIMAN: We can look into the

19 analytical method. I don't know offhand.

20 DR. KENNETH PORTIER: Dr. Vena.

21 DR. JOHN VENA: Could I just have a

22 clarification that these studies use DAP, which uses a
Page 199

1 combination of a multiple metabolites DMP, DEP. You know, your

2 tables, you had the DEP and DETP, which is part of the DEP, so I

3 guess I'm trying to get at is, I'm not sure, but I have to

4 reread these, but how they looked at all of these DAP, total

5 DAP, and then they used DMP and DEP and some of the models.

6 And so I guess is they found some associations

7 with some of these metabolites and some with others. So really,

8 what is considered the best? You know, if you're using this

9 kind of estimate, what's the best estimate to use for

10 chlorpyrifos? Is it the total DAP? Is it the DMP? Is it the

11 DEP or, you know, what?

12 Just maybe kind of explain that for me what

13 your take is on all that.

14 MR. AARON NIMAN: Now you're talking about the

15 epi studies previously presented or the study summary

16 comparisons here?

17 DR. JOHN VENA: Sorry, the epi studies that

18 are part of this use these different measures, and so they're

19 summarized in the literature in terms of these combinations of

20 these biomarkers that they use.

21 MR. AARON NIMAN: I'm sorry. I guess I'm

22 still a little confused. They're just the relative strengths of
Page 200

1 the different biomarkers. Can you repeat the question?

2 DR. JOHN VENA: If you're going to use any of

3 these measures, what's the best measure to use to look at

4 chlorpyrifos? You know, is it total DAP, which is -- here they

5 use -- this defines the sum of my molar of six DAP metabolites. 

6 DMP metabolite was a sum of molar concentrations of three. 

7 Dimethylphosphate only and dimethylphosphate metabolite level is

8 a sum of molar concentrations of three dimethylphosphate

9 metabolites.

10 And so they use all of these, and it looks

11 like they used all of these in their models and looked at all of

12 them: DAP, DMP, DEP.

13 MR. AARON NIMAN: I guess an ideal situation,

14 direct measurement of the parent compound would be preferred

15 because there's less uncertainty because it's this actual

16 measurement of the parent compound. Relative to that, I would

17 say that TCPy is probably preferable because it has fewer parent

18 compounds that are metabolites.

19 And then DEP and DETP, but there'll be

20 specific biomarkers.

21 DR. JOHN VENA: It's helpful. So in terms of

22 hierarchy, in terms of thinking about these, in terms of getting
Page 201

1 at what kind of exposure misclassification there is, et cetera,

2 to think about these different measures that they use as

3 estimates of exposure.

4 DR. KENNETH PORTIER: Dr. Pope.

5 DR. CAREY POPE: Yeah. Dr. Vena, so as far as

6 the DMP versus DEP, if you're thinking about chlorpyrifos, it is

7 ethyl-containing chemical. So dimethylphosphate wouldn't be a

8 particularly good one, if you're interested in chlorpyrifos

9 exposures. Diethyl would be much better.

10 DR. KENNETH PORTIER: I think at this point,

11 we'll move on to Dr. Lowit and a summarization of everything,

12 after which we'll take a break.

13 DR. ANNA LOWIT: Go and do a little musical

14 chairs, so okay. Carol Christensen is going to come back up to

15 the table to join me in case there are any other clarification

16 questions.

17 So I'm going to not jibber-jabber too long but

18 come back to our major things from this morning just to bring us

19 full circle. We've heard a lot of exciting, very technical,

20 very detailed presentations over the late morning and early

21 afternoon.

22 Just bring us back to first principles and to
Page 202

1 talk about what's in our Charge questions and the kinds of

2 feedback that will be most helpful from all of you to help us

3 move on in the next steps of our analysis.

4 And I'll take a minute briefly and talk about

5 what we mean by "weight of the evidence" from a risk assessment

6 point of view, how to bring information together, and what does

7 it mean to be transparent and clear.

8 So if we start back at organizing principles

9 and the ideas that we started the morning with, it goes back to

10 the Risk Characterization Handbook, the NRC reports from the

11 Toxicity Testing in the 21st Century, and Science and Decisions.

12

13 They collectively provide the conceptual

14 foundation for the ideas that we've been talking about: being

15 transparent; having everything out on the table for everyone to

16 see so that people understand how information was brought

17 together and conclusions were made; how you take information

18 across multiple lines of evidence, whether it's in vitro

19 studies, in vivo studies, epidemiology, other biomonitoring.

20 Thinking about adverse outcome pathways and

21 modes of action and key events leading to adverse outcomes and

22 how to make causal linkages across those key events.
Page 203

1 Thinking about, as science and technology get

2 more and more sophisticated, we can measure more and more

3 sensitive, smaller things. But those don't necessarily mean

4 adverse outcome; they can just mean change. And so how to

5 interpret disruptions, perturbations from homeostasis and when

6 we're on a path to an adverse outcome.

7 These are all important concepts as we think

8 about putting all this chlorpyrifos information together in a

9 coherent story that makes sense.

10 So just to remind you, we've been on a very

11 step-wise, methodical systematic approach starting in 2008 with

12 an initial -- what was really a problem formulation -- no, we

13 didn't call it that at the time -- which was a preliminary

14 review of chlorpyrifos and many of the issues that we're talking

15 about here.

16 In 2010, we put together a draft framework for

17 incorporating epidemiology in our risk assessments. We're

18 gaining more and more experience with that framework. We've

19 done a lot of work with atrazine, and so here we're thinking

20 about using this for chlorpyrifos as we move forward.

21 So in 2011, in July of last year, we put out a

22 risk a preliminary risk assessment that focused on
Page 204

1 cholinesterase inhibiting potential of the compound based on the

2 most sensitive dose response data available for driving points

3 of departure.

4 That assessment explicitly states that there's

5 a plan to conduct a full weight of evidence evaluation, and that

6 evaluation is where we're headed right now.

7 So the analysis that we've done for this SAP,

8 for this white paper, largely builds on the 2008 and the risk

9 assessment and the public comments we received from that with a

10 lot of new data and far more in-depth analysis on some critical

11 areas such as epidemiology, biomonitoring, and adverse outcome

12 pathways.

13 And what we're going to be looking for from

14 all of you on our charge questions is really a consideration of

15 the strengths and uncertainties associated with all these lines

16 of evidence. And we'd like for you to think about this in both

17 qualitative terms and quantitative terms.

18 So go back to a slide I used a few minutes

19 ago, a few hours ago, a slide right out of the NRC from 2007

20 that really puts together all the pieces in a risk

21 characterization. But what I've done on the next slide is

22 overlay this same figure with the chlorpyrifos situation.
Page 205

1 So as you move from the left side of the

2 figure, the parent compound, of course, is chlorpyrifos, but we

3 have to always keep in our mind that the oxon metabolite is the

4 active metabolite for cholinesterase inhibition. 

5 And there's still some lack of clarity,

6 whether it would be the oxon or the parent that would be

7 associated with neurodevelopmental outcomes. And of course, we

8 spent a lot of time talking about TCPy today and the challenges

9 interpreting all of that data.

10 Inside the big blue circle is this idea of

11 Mode of Action and adverse outcome pathways; that we think about

12 perturbations and key events and causal linkages, and taking

13 information from in vitro studies and in vivo studies and

14 putting them together in a pathway.

15 So we're tracking two different health

16 outcomes: one is acetylcholinesterase inhibition leading to

17 cholinergic toxicity, and the other one being the biological

18 plausibility and the hypotheses around neurodevelopmental

19 outcomes.

20 So what I really wanted to highlight here is

21 more the right side of the figure where we have information from

22 the children's cohorts. The Columbia cohort provides the most
Page 206

1 robust information because it's specific to chlorpyrifos.

2 We also have TCPy measures from a variety of

3 populations. Some of them, you can tease out exposure to

4 chlorpyrifos; some others, it's much more difficult to do. And

5 we also have, as Dr. Buckley pointed out, we have a great deal

6 of information on exposure from different media. We also have

7 exposure assessments performed by our own assessors from the

8 Registration Eligibility Decision, and from the 2007 risk

9 assessment.

10 All those things come together, and the real

11 question is the calibration of the in vitro, the modes of

12 action, and across the human dosimetry. All of these things

13 lead to these questions, these quantitative and qualitative

14 questions about linking and about dosimetry.

15 So weight of the evidence; what do we mean by

16 that? So if you don't have experience with a lot of panels or

17 dealing with the Agency, we'll just take a minute to explain to

18 you what we mean by that and how we would go about it.

19 So in the Draft Framework for incorporating

20 epi, it's really founded on several things: the Bradford Hill

21 criteria, the Agency's Mode of Action framework, which is also

22 built on a strong international basis from many years of review
Page 207

1 and contribution. We have the NRC reports from 2007 and 2009.

2 But what we want to do in weight of the

3 evidence analysis is to characterize the extent to which data

4 support a hypothesis, and particularly thinking about

5 dose-response assessment. That involves a number of steps, some

6 of which we're talking about here.

7 We're assembling data, which we've done quite

8 a bit of over the last few years. We've evaluated that data for

9 quality and relevance, and we have done that and now we're

10 asking you to give us feedback on how well we've done and what

11 more we need to do.

12 And then the next step would be followed by

13 integration across different lines of evidence, and certainly

14 we're moving in that direction.

15 Transparency. We've heard a lot about

16 transparency starting this morning. Transparency is important,

17 if not vital, to this process. Because we're talking about a

18 very complicated situation across many lines of evidence, it's

19 very important to be clear and transparent about what

20 information is used, how it's used, and how it's evaluated and

21 how things are brought together; how we've weighed information

22 in certain directions; how we've thought about the strengths and
Page 208

1 the limitations; and how we've thought about how those

2 limitations and those uncertainties affect the outcome.

3 And this is one of the reasons we talk about

4 ranges and options, because if we think about ranges and options

5 in a weight of the evidence analysis, we can get a good picture

6 of the totality of the decisions and where we are strong and

7 where we are weak.

8 So we've been through a lot of presentations

9 today, everything from adverse outcome pathways to effects on

10 the developing brain and epidemiology and biomonitoring, and I

11 certainly won't take time to go through all of that.

12 But there are two key scientific questions

13 we're really focusing on with this SAP that I'd like you to keep

14 in the back of your mind. The first one is largely a

15 qualitative one around: what are the strengths and the

16 limitations of the different signs of evidence that suggest the

17 potential for chlorpyrifos result in long-term

18 neurodevelopmental effects.

19 And the second one is more of a quantitative

20 question: Given the first one, to what extent do these adverse

21 effects may or may not be attributed to doses lower than those

22 elicit 10 percent inhibition of acetylcholinesterase, which is
Page 209

1 typically our benchmark for regulatory decision-making.

2 So if we just pull the most salient pieces of

3 information across the presentations you've heard, let's start

4 with the qualitative evidence around long-term effects from

5 early-life exposure.

6 So very broadly speaking, there are things

7 from three lines of evidence: the mechanistic studies that we

8 heard about from Dr. Mundy; the in vivo studies that Ginger told

9 us about; and epidemiology that we heard about after lunch.

10 So in Charge question 2.1 will be our

11 qualitative consideration of the mechanistic studies and the

12 adverse outcome pathways.

13 It is our preliminary conclusion that an

14 adverse outcome pathway has not been firmly established for

15 neurodevelopmental outcomes, but we do acknowledge that there's

16 a growing body of literature that does demonstrate that

17 chlorpyrifos or its oxon are biologically active on processes

18 that affect the developing brain.

19 With respect to the in vivo experimental

20 studies, we have a qualitative question we're going to ask for

21 feedback on, which is 3.1.

22 And certainly, we heard from Dr. Moser this
Page 210

1 morning that there is difficult interpretation because you have

2 some studies going up and some studies going down. 

3 But from a broad, higher-level consideration,

4 multiple studies and multiple labs report changes in

5 neurobehavioral function in adults following developmental

6 exposure, and those are generally long after the chemical

7 exposure has ended and any inhibition that may have existed has

8 long gone.

9 Consistency is a challenge when looking at

10 these studies, but there are a few domains for which there is

11 more consistency than the others, and I think we certainly heard

12 about cognition and anxiety some earlier today.

13 So from the epidemiology studies, if we look

14 at it from a qualitative point of view, I think question 4.1 is

15 about the quality of our analysis. Question 4.2 is about

16 thinking about the role that chlorpyrifos may or may not have

17 played in the neurodevelopmental outcomes, particularly those

18 reported by Columbia.

19 And as we heard from Dr. Christensen, there's

20 consistency in some outcomes and not others, but particularly

21 notable are the abnormal reflexes across a couple of the

22 cohorts, the ADHD-like behavior across some of the cohorts, and
Page 211

1 the MDI and the more recent IQ paper across all three cohorts.

2 So from a qualitative point of view, our

3 preliminary conclusion is take all this information together,

4 and I've summarized rings and rings of information in a matter

5 of 120 seconds, I think; that they do together support a

6 preliminary conclusion. 

7 The exposure to chlorpyrifos may result in

8 adverse neurodevelopmental outcomes with an important caveat

9 that the conditions for which those occur are still unclear and

10 uncertain.

11 And that lack of clarity and that uncertainty

12 leads us to question number 2, around the more quantitative

13 issues. And so this is a fairly grounded risk assessment point

14 of view kind of question.

15 Our typical practices develop points of

16 departure based on 10 percent inhibition of acetylcholinesterase

17 from dose-response studies, and then that would be our starting

18 point for extrapolation. So for example, if you had a rat

19 study, you'd then extrapolate to humans and then to sensitive

20 humans.

21 So we have the same three lines of evidence:

22 the mechanistic and the adverse outcome pathway studies; the in
Page 212

1 vivo experimental tox studies; and the epidemiology.

2 So in charge question 2.2, we'll ask you to

3 give us some feedback about what is known or not known about the

4 relative sensitivity from the AOP studies around these end

5 points and the cholinesterase inhibition.

6 And we certainly have noted in our

7 presentation from Dr. Mundy, and it's noted on this slide, this

8 in our minds remains an area of uncertainty.

9 In charge question 3.2, we'll ask for some

10 similar feedback from the in vivo experimental toxicology

11 studies. As you heard from Dr. Moser, a dose of one milligram

12 per kilogram per day or even higher is a very common dose. In

13 many of these studies, it's a common comparison point, as you

14 saw Dr. Moser use across domains. It's a common place to think

15 about what there is from these studies compared to the domains?

16 And it is notable that our points of departure

17 are based on the animal studies are lower or the same,

18 certainly. They're certainly all lower than the one milligram

19 per kilogram.

20 In a more recent study, by an author's name

21 I'm not going to try to pronounce, does show one of the lowest

22 NOELs in these studies of 0.2. And it is notable that our
Page 213

1 acute, single-dose endpoint is about the same as that, the 0.2,

2 and that our chronic end point is about an order of magnitude

3 lower.

4 So as we move on to think about the

5 epidemiology studies, we do ask you to think about in -- let me

6 take a step back.

7 In 2008, the SAP was fairly clear about the

8 uncertainties in the Columbia study and that those made it

9 challenging to direct points of departure from the Columbia

10 epidemiology study. And we largely remain in concurrence with

11 that, and we see a proposal to continue to use the animal study.

12 But all that said, it's important to look at

13 the Columbia study in particular and think about what the levels

14 reported in that study. We're going to try to make sense of

15 them. So the upper tertile reported in the Rauh in the 2006

16 study, which would be the first of the neurodevelopmental

17 outcome papers.

18 The upper tertile is 6.17 picogram per gram

19 higher and what you see in this table is that with the

20 cancellation of chlorpyrifos, there is a fairly rapid decline of

21 -- if you look in the top three rows of this table -- there's a

22 very rapid decline of the mean chlorpyrifos and cord blood
Page 214

1 across that time. Once the voluntary cancellation occurred, the

2 in-home use, either from the city or from the actual homeowners

3 or from the residents in the rental properties, declined

4 rapidly; so that 6.17 is not a magic number, but certainly a

5 point to think about for purposes of comparison.

6 So the question remains: the degree to which

7 levels and the picogram per gram in blood are they likely to

8 elicit acetylcholinesterase inhibition?

9 And our preliminary thought on that is most

10 likely not, but there's limited evidence to get you to that

11 point, and that's one of the reasons we're looking for some good

12 feedback on not only reverse dosimetry but dose reconstruction

13 to help put some meat around some of these evaluations.

14 So one of the best places of -- one of the

15 only places of information where you get chlorpyrifos and

16 cholinesterase inhibition is a deliberate dosing study in adult

17 males done back in the eighties. And just to make sure that you

18 know that this study was reviewed by the Human Studies Review

19 Board and found to be both ethically and scientifically

20 conducted. And so because of that, we use it in our

21 evaluations.

22 That study has two components: one is an oral
Page 215

1 dose, and one is a dermal dose. And it's notable that neither

2 dose nor the men in the study, none of the men exhibited red

3 blood cell cholinesterase inhibition. But you did see in the

4 oral dose a good amount of plasma.

5 But as Lieutenant Niman indicated, plasma

6 butyrylcholinesterase, the degree in which this is a

7 toxicologically relevant endpoint is still unclear.

8 But the peak blood levels from that study in

9 the oral study are worth comparing to the Columbia. But before

10 I show you the comparison, I want to just make sure it's on the

11 table that this is a very tenuous comparison because we're

12 actually comparing apples and oranges. We have males in a

13 clinic versus pregnant females in their real life, the males

14 getting a single dose whereas the women are probably exposed

15 daily in their homes.

16 You have a very controlled, clinical setting

17 where the people could be monitored, and there's a time course

18 -- they took their blood every few hours and looked at their

19 time course -- versus a real-world exposure where you're getting

20 multiple pathways. People are living their life.

21 And in the Nolan study, you have an oral dose,

22 and they can control for the other routes; but in the real
Page 216

1 world, that's not the way it happens. So you have to be careful

2 with the comparison, but it's still important.

3 So something happened since yesterday. That

4 red arrow got moved between computers. The red arrow should be

5 somewhere between the 0.1 and the 1, not between the 1 and the

6 10. But either way, the point is still the same.

7 If you can look on the x-axis, this is a plot

8 on that Dow -- I let them know I was going to use this plot. 

9 This is an adaptation of Figure 40 from their paper from the

10 2011 -- I guess that should be 2011 -- the PBPK paper that was

11 reviewed by this panel. What's important is that the units on

12 the x-axis are picogram per gram so that we can match units from

13 single oral dosing to what's in the Columbia.

14 So the Nolan error should be between the 0.1

15 and the 1, and that tracks over to something in the order of

16 approximately 10,000 picogram per gram. And keep in mind, with

17 the Columbia cohort, we're talking about things in the order of

18 10 or less, largely below the limit of detection.

19 For most of the women, a 6 or higher in the

20 upper-tertile. So the degree to which the men in the Nolan

21 study did not exhibit any red blood cell cholinesterase in the

22 order of 10,000 picogram per gram; yet it seems to us that the
Page 217

1 likelihood at 6 is also quite low.

2 But there are some uncertainties with that

3 because the 6 picogram per gram in the upper-tertile, in and of

4 itself, needs to be interpreted with some care because we're

5 unaware of the time of application within the apartments, so we

6 certainly have not matched blood levels in the cord blood or the

7 women to the time of application. So we do not know in any way

8 when those applications were made.

9 We also don't know the critical windows of

10 susceptibility -- that applications occurred during those

11 critical windows or the degree to which chlorpyrifos exposure

12 occurred in those windows. Given that there's still a lack of

13 clarity around the adverse outcome pathway, it's challenging to

14 think about critical windows.

15 We also don't know the contribution of other

16 cholinesterase-inhibiting pesticides to the idea of

17 cholinesterase inhibition. We know that diazinon and propoxur

18 were detected at fairly high rates in the same women in this

19 cohort.

20 And if you also go back to one of the early

21 Whyatt papers, it's 2003 through 2004 -- I'm not sure which one

22 -- there's a list of other cholinesterase-inhibiting pesticides
Page 218

1 that were detected in the blood of the women. For example,

2 something in the order of a quarter to a third of them had

3 measurable levels of 3-Hydroxycarbufuran, and some of you know

4 that carbufuran is extremely potent cholinesterase inhibitor.

5 So this is an area of uncertainty with respect

6 to thinking about whether or not exposure to multiple chemicals

7 could elicit 10% inhibition.

8 And we're certainly looking for your feedback

9 on all of these issues. They're multi-factorial issues across

10 multiple lines of information. This is a key area for us to

11 think about: interpreting the Columbia data; interpreting the

12 other chemicals; interpreting across the in vitro to the in

13 vivo, and not only the in vivo animal, but interpreting all the

14 way up to the human.

15 So charge questions 5.3 and 5.4 begin to get

16 at approaches for thinking about reverse dosimetry and also dose

17 reconstruction. And there was an exchange with Dr. Buckley

18 where I think I really laid out the sorts of things we're

19 looking for in this question.

20 There are a number of research groups pursuing

21 these same questions. There is Dr. Lu and his group is doing

22 some of this work. Dale Hattis, who's a collaborator with the
Page 219

1 Columbia group is also pursuing some of this work. Dow is

2 pursuing some of it and it was to some degree presented at the

3 SAP a year or so ago.

4 There are less sophisticated ways of doing

5 this kind of thing. And it's important that we get a sense of

6 the strengths and the limitations of all of those not only in

7 the immediate term, but in the long term of where the science is

8 going so we have a sense of what we can do now and where the

9 science may be heading or where we can encourage the science to

10 go.

11 This is also an area where we might want to

12 think about ranges because each of these approaches may provide

13 a different answer and how to understand those different

14 answers, given their own limitations and strengths.

15 So charge question 6.0 is about really

16 thinking about it at a high level. Thinking about pulling out

17 of the details, out of the weeds, at the 1,000- to 10,000-foot

18 level of thinking about all of the multi-dimensional plausible

19 responses across dose, across species, across route.

20 To think about the overall uncertainty and the

21 variability and the strengths and the limitations across the

22 totality, this is consistent with the advice from the NRC. It's
Page 220

1 also consistent with the Agency's Risk Characterization

2 Handbook.

3 I think that may be the last one. So just to

4 end, obviously we're committed to transparency. We're bringing

5 you the issue paper, and we're asking you some very difficult

6 questions and hoping to get some excellent feedback.

7 We'll be taking all the feedback that we get

8 and moving to the next steps of bringing all these lines of

9 information together in a weight of the evidence to do a

10 Bradford Hill criteria analysis to look at ranges of risk and

11 different levels of uncertainty.

12 And I think that's my last slide.

13 DR. KENNETH PORTIER: Any clarifying questions

14 from the panel? I guess that's why we scheduled through Friday,

15 right? There's a lot of questions.

16 Not seeing any questions. We're going to go

17 ahead and take a break just for the Panel and the audience. We

18 have scheduled for this afternoon two groups of public

19 commenters. 

20 Before I do that, Dr. Lowit, are your staff

21 going to be here tomorrow morning if there are additional

22 questions that come up to the Panel that, you know, if need be
Page 221

1 we can bring some of you up after the public comments section

2 tomorrow for some final clarifying questions?

3 DR. ANNA LOWIT: Yeah, our entire team will be

4 here tomorrow.

5 DR. KENNETH PORTIER: Okay. Good. Dr.

6 Popendorf?

7 DR. WILLIAM POPENDORF: I guess I would like a

8 little clarification on that one slide, 21. The data in the

9 right column, both what's in the brackets and how did those

10 values by year -- what is that? What are you saying here, by

11 year or groups of years, in some cases -- the rows? Are those

12 different publications? It just wasn't clear what that data

13 represented.

14 DR. CAROL CHRISTENSEN: Sure. So slide number

15 21 was showing within -- yes, within the Whyatt 2003 and 2004

16 publications -- I think more in 2003, they delineate average or

17 mean chlorpyrifos concentration in cord blood.

18 DR. WILLIAM POPENDORF: Cord blood.

19 DR. CAROL CHRISTENSEN: By year. So within

20 those births that took place in 1999, in which there were 87,

21 the mean concentration of chlorpyrifos in cord blood is noted on

22 the right-hand side, 5.0 picogram per gram, and that's a
Page 222

1 standard deviation. 

2 DR. WILLIAM POPENDORF: Standard deviation?

3 DR. CAROL CHRISTENSEN: I mean, sorry. I'm

4 95% confidence-bound. Sorry.

5 So similarly for your 2000 and 2001, the 1998

6 to 2002 is the overall average, so this is the cohort sample

7 size used for the analytical evaluation, so it's 1999 through

8 2002 summed together, so the end there is 314 with a mean

9 chlorpyrifos concentration in cord blood of 4.0 picogram per

10 gram.

11 DR. WILLIAM POPENDORF: 1999 through 2002?

12 DR. CAROL CHRISTENSEN: Right. And then

13 moving to the Whyatt 2009 validation paper, the summation of

14 years between 2001 to 2004, as I mentioned, this work was done

15 right around the time of the voluntary cancellations. 

16 And this was a validation study inclusive of a

17 proportion of the total cohorts, but it wasn't everybody in the

18 cohort. It was a subset of the cohort for which these

19 additional environmental and biological exposure measures were

20 made.

21 So within that cohort, the average was below

22 the limit of detection at that point in time, looking across
Page 223

1 those three years. And I reported the 95th percentile as 2.5

2 picogram per gram of chlorpyrifos in cord blood.

3 DR. WILLIAM POPENDORF: Our printed versions

4 aren't as clear as that, I can tell. I was trying to help. 

5 Thank you.

6 DR. KENNETH PORTIER: Okay. We'll go ahead

7 and take a 15-minute break because it's a fairly large group. 

8 We'll meet again at 10 to 4:00. That's the good news. The bad

9 news is we're going to be here until at least 6:00, so prepare

10 for another about two-and-a-half hour presentation.

11 (WHEREUPON, a recess was taken.)

12 DR. KENNETH PORTIER: At this point, we've

13 finished with the formal EPA presentations and we're moving into

14 the public commenters. And as a public meeting, I'm reminding

15 everyone that any material presented before the Panel will be

16 part of the public docket. 

17 There was a copy of a research paper in draft

18 form that was distributed. We've pulled those back right now

19 because we need to double-check on copyright issues and whether

20 we can put that in the public docket or how we'll manage that. 

21 But we'll work on that. Hopefully, you'll get the paper. Dr.

22 Pope's already read it, so there's nothing I can I can do about
Page 224

1 that.

2 We've talked to Dow, and they've agreed to

3 kind of cut their presentation, half this afternoon and half

4 tomorrow morning. So we're hoping to break somewhere around the

5 5:30, 5:45 time period.

6 Our first public presenter is Dr.

7 Rhomberg, please identify yourself and move forward.

8 DR. LORENZ RHOMBERG: Thank you. Yes, I am

9 Lorenz Rhomberg. I'm a principal at Gradient, which is an

10 environmental consulting firm in Cambridge, Massachusetts.

11 My opportunity to be here is sponsored by Dow

12 AgroSciences, but I'm actually going to be talking about some

13 interests of mine that have gone on for a long time on the

14 fairly more general question about weight of evidence and

15 questions of incorporating human and animal data together.

16 I really appreciate the Panel for giving me

17 this time, and I hope you will indulge me for awhile while I

18 talk about something that isn't really about chlorpyrifos as

19 such, but rather about the larger questions that we heard about

20 in this morning's presentation from EPA about the implications

21 for the chlorpyrifos case for how you do some of these larger

22 questions of incorporating epidemiology and doing weight of
Page 225

1 evidence, merging multiple lines of evidence from animal and

2 humans and so on and so forth.

3 So I'm going to be just addressing that wider

4 question in the hope that putting the framework on the table

5 will help further discussion.

6 Well, we have epi, and we have tox here. And

7 of course, the advantage of epi is that it's relevant,

8 real-world stuff; but on the other hand, it's observational. 

9 The things are uncontrolled, and it's difficult to measure

10 exposures and to isolate individual potential causes. Because

11 it's observational, we're stuck with the complexity of the real

12 world.

13 On the other hand, in toxicology and Mode of

14 Action studies, we can control things and we can look at one

15 causal element at a time with a systematic approach. But it's

16 indirect, and we have to somehow infer the role for that in the

17 larger inference that we want to make about humans.

18 So there's really role for both of these. 

19 It's not that one should trump the other. We really want to

20 find a way to merge these and both bring them to bear, and

21 that's what my comments are going to be about.

22 There is a paper -- and this one is open
Page 226

1 access so it's in the docket, but if you come across it however,

2 you're free to read it and pass it around -- called

3 Hypothesis-Based Weight of Evidence Evaluation of the

4 Neurodevelopmental Effects of Chlorpyrifos by me and some of my

5 Gradient co-authors from Critical Reviews in Toxicology.

6 We submitted it with our comments. It should

7 be in the docket, and it's available for you to look at.

8 This paper does several things. It discusses,

9 first of all, this, what we're calling, hypothesis-based weight

10 of evidence approach as a method for combining human, animal,

11 and Mode of Action data as a methodology in itself. And then it

12 uses chlorpyrifos as an example for some of that.

13 It also goes on to make some comments about

14 the EPA framework for merging human and animal data and

15 comparing that with our method.

16 And then it goes on, as I say, to actually

17 apply this to the case of chlorpyrifos. That third part, I'm

18 not going to be talking about today, but Dr. Julie Goodman will

19 be talking about it later on from this paper.

20 So what is this hypothesis-based weight of

21 evidence? Well, this is the name that we've given to an

22 approach that we've taken to really try to grapple with some of
Page 227

1 the questions that you have put on the table here and have tried

2 to grapple with.

3 How, in the end, do we really weigh evidence? 

4 How do we bring together these different lines of animal and

5 human evidence and put them together? How do we work through

6 the implications of inconsistencies among studies, of further

7 assumptions that need to be made, of ambiguities in the studies

8 and so on, in order to do a weight of evidence?

9 We talk about weight of evidence all the time,

10 and in the end the question about how to make the leap between

11 some data that you have on the table and how do you make the

12 judgments about it has always been sort of problematic.

13 The theme of consistency -- so transparency

14 has been struck several times here, and I think that one way to

15 explain what we're trying to do with this hypothesis-based

16 weight of evidence is to lay out the inferential basis for why

17 you think you're getting your conclusions from the data so that

18 they can be discussed as scientific matters and that we can

19 somehow unpack that kind of leap of judgment so that we can

20 understand it. It's very much in the aid of transparency. I

21 just wanted to mention that.

22 And in addition to this paper of chlorpyrifos,
Page 228

1 the first one listed up here, we also have some other papers

2 that have been published recently.

3 We also have some other papers: one on

4 methanol carcinogenicity; one on dioxin thyroid hormone

5 perturbation, all done by the same kind of method and also

6 setting out further elements of the methodology; formaldehyde as

7 a leukemogen has some commonality with chlorpyrifos in that it's

8 a case that should sound familiar. 

9 We have epidemiology studies that are mixed. 

10 Some are positive; some are not. The ones that are positive

11 have to be analyzed several different ways with different dose

12 measures to find the positive effects.

13 And then we have real questions from the

14 animal data about whether those effects are plausible in humans,

15 and that's the thing to be sorted out. There's some

16 commonality. As I said, the question's on the table. And then

17 we have naphthalene carcinogenicity, the first one in the series

18 that really laid out a lot of our methodology.

19 And I was glad to see that this was cited as a

20 good model of how to weight of evidence in EPA's proposed

21 Endocrine Disruptor Screening Program Weight of Evidence

22 Guidance. They cited it as a favorable example of a good way to
Page 229

1 approach this question. In their own weight of evidence

2 processes in there actually showed a lot of the same kind of

3 thinking.

4 So what we have now is this draft framework

5 for incorporating epidemiologic evidence from EPA, and I'm going

6 to make a few quick comments on it.

7 First of all, it really focuses on the human

8 data and their usefulness. In a way, it's sort of how to bring

9 the human data in rather than talking about the toxicology data

10 that much in themselves.

11 It reviews strengths and weaknesses and

12 interpretation issues of many of the common study designs. In a

13 way, it's sort of a primer of the ways you should think about

14 these things.

15 It really encourages a systematic review of

16 the human data. That's something that's really to be

17 encouraged, and we would hardly endorse.

18 It encourages this "multiple lines" of

19 evidence thinking to include animal and Mode of Action evidence

20 using the extended Bradford Hill criteria, and the human

21 relevance framework is a way to do that.

22 Now with all that said, in the end, it doesn't
Page 230

1 really though say that much about exactly how you should

2 accomplish this integration. And in fact, that very question is

3 the question that's sort of on the table here for chlorpyrifos.

4 And that's what I wanted to address, simply

5 about: how do we actually go about this integration question? 

6 And we'll say however that the, what we're calling now, the

7 draft white paper, I guess, actually does much better than this

8 than the Guidance that it's supposed to be following.

9 The white paper that you've seen actually

10 points to the questions in a very productive way, I think, and I

11 was very happy to see that and I think it's a very good start on

12 this question. I'm trying to offer some help for how to go a

13 little bit farther with it.

14 So what are these Bradford Hill postulates

15 that we are now extending? Here are the familiar Bradford Hill

16 postulates. He called them postulates, not criteria. I won't

17 go over them; they're familiar to all epidemiologists.

18 But the point I want to make is that these are

19 guides for thinking through observational data where we don't

20 really have the benefit of isolating causes very well. And so

21 these are patterns that you should see in data when there is a

22 causality present, and some of the ones that are really
Page 231

1 important here are the idea of consistency and specificity of

2 effects that we talked about before and so on.

3 The question now is: if we were going to take

4 these and extend them, how would we do that? And that's what

5 this EPA guidance does; it makes the extended Bradford Hill

6 criteria, and now the question is -- we add a Mode of Action and

7 the key event steps and the human relevance of the animal data

8 via common Mode of Action can be evaluated in order to bring

9 those in.

10 The question now really comes: where among

11 Bradford Hill's areas -- his postulates or his criteria -- does

12 the animal data usually really come into the question? Well, in

13 some ways, in lots of places, but two in particular.

14 I'll, first of all, say that sometimes people

15 say, "Oh, experiment is one of the criteria," right? What he

16 was really talking about there was an intervention in the human

17 data. It was taking the handle off the Broad Street pump and

18 stopping the cholera epidemic.

19 So that's really not experiment in the sense

20 of animal experiments. Where animal experimental work really

21 comes in is in biological plausibility because we are now

22 invoking biological causes for the patterns that we see in
Page 232

1 epidemiology because epidemiology can mostly see patterns of

2 associations. The underlying modes and mechanisms of action of

3 that can be backed up with this kind of mechanistic work that we

4 can do with animals.

5 And also in some sense, consistency in that we

6 are saying that these animal data are relevant because they are

7 observing something about those biological properties that we

8 can observe directly in the animals that ought to be common to

9 the humans. And so in some sense, there's a consistency between

10 the effects and animals and humans, and that's the main places

11 where the animal data would come in.

12 I will say that we really should remember that

13 these postulates are not a checklist; we don't say, "We've got

14 some of this, we've got some of this, and therefore, there we

15 go." I did once get told by somebody at a regulatory agency

16 that will remain unnamed, "Oh, we don't really need plausibility

17 because we have enough of the other criteria already."

18 Yeah, that's not the right attitude, I don't

19 think, because there aren't measures of evidence in themselves. 

20 They're rather, in Bradford Hill's own words, "guides to

21 thinking through the data." What does he mean by that, thinking

22 through the data?
Page 233

1 Here, if you read down past the criteria, in

2 that 1965 paper is a really important quotation. He says what

3 the fundamental question is is this: "Is there any other way to

4 explain the set of facts before us, is there any other answer

5 equally or more likely than cause and effect?"

6 A set of facts now is all the epi studies; the

7 positive effects and the negative effects, the Mode of Action

8 and animal studies and all the other potential explanations for

9 what we have. 

10 So what he's saying is we have this array out

11 outcomes that we have in the studies among the human studies for

12 different endpoints and different dose measures. We've got the

13 animal studies and so on. Somehow those have to have

14 explanations. One of those is the common causal thread that we

15 are trying to assess with these criteria and these evaluations.

16 But the other possibility is that they have

17 other reasons other than that causality. Instead of thinking of

18 weight of evidence as a bathroom scale -- how much do we put on

19 it and how heavy does it come down -- he's saying, "We should

20 look at weight of evidence as a two-pan balance."

21 So on the one hand, you have sort of that

22 story that you would make with a causal explanation, and then
Page 234

1 the other story says, "How else would you explain the same set

2 of facts, which are in fact in front of us and needs some kind

3 of explanation if we denied that causal element of the chemical

4 at hand.

5 It's weighing those alternative ways of

6 explaining those facts that gives you the weight of evidence. 

7 That's what he meant by thinking through, and that's how I think

8 he intended his criteria to be used.

9 That's really the basis that I try to work out

10 in hypothesis-based weight of evidence, so I see this proposal

11 on my part being, one, very consistent with Bradford Hill

12 criteria -- extended Bradford Hill criteria, Mode of Action

13 human relevance framework, and the EPA's guidelines as far as

14 they go. And I'm trying to suggest ways to maybe bring them a

15 little bit farther.

16 So the question is: how much does evidence

17 weigh? We have to weigh this evidence. How much does evidence

18 weigh? What process do we have for judging how compelling it is

19 and how to trade off among apparent contradictions in the data

20 -- effects that weren't consistent from one study to another,

21 things that seem to happen here but not there?

22 So my contention is that evidence has no
Page 235

1 meaning except in relation to specific hypotheses; otherwise,

2 it's just data. The difference between data and evidence is

3 that you're bringing those data to bear on a question.

4 So what we should do is we should organize the

5 weight of evidence around evaluating specific hypotheses against

6 the data. So what we want to do is we want to articulate:

7 what's the proposed basis for inferring that some phenomenon

8 that we've seen in an animal study or in a human study also

9 applies to our target population?

10 What is it that we think that those things

11 have in common so that one constitutes evidence for the other? 

12 What is it? We sort of say, "Oh, yeah, we use animal evidence

13 as evidence because we do it all the time. That's what we do

14 and that's convention."

15 But there's a scientific reason behind it and

16 it is worthwhile going back to that scientific reason, realizing

17 that what it is is it's not just an extrapolation from the one

18 study to the humans or to the human target populations. It's

19 really a generalization. There's something in common that they

20 have that underlies the response here and the response that

21 you're worried about in humans that suggests one provides

22 evidence for another. That's why it's evidence.
Page 236

1 So if it's a generalization, then the question

2 arises: where else should it apply? Not just in this study and

3 in our target population, but where else in our data should we

4 see it and do we really see it?

5 That's why we have to look at all the data,

6 the positive, the negatives, and reconcile that with that

7 proposal about what's being in common.

8 If there are inconsistencies, how should they

9 be explained? There are always inconsistencies, especially in

10 epidemiologic data. We know that's why we don't just rely on

11 one study. But there's got to be some kind of reason, known or

12 unknown, for those inconsistencies. And so we have to have

13 plausible reasons for why things that don't agree shouldn't be

14 counted as refutations of our idea, but it rather can be

15 accommodated in.

16 What's being relegated to error, chance,

17 happenstance? And if this generalization isn't true, this basis

18 for using these data as evidence, how else would we explain the

19 result if this is not causing such-and-so in humans. We've got

20 this animal study, and it's got a positive, what does that mean?

21 Is it an animal-specific effect? How are we going to explain

22 that?
Page 237

1 So we're here about different but related

2 endpoints because that really comes in here with the idea of

3 domains of effect in the neurotoxicity with what kinds of

4 effects in the epi studies should be considered to be supporting

5 one another, even if they're different endpoints.

6 How do we decide when different endpoints

7 support one another? In this framework, it's important to

8 articulate the commonality of what the different endpoints are

9 taken to represent as the very manifestations of the same

10 underlying thing. And then you have to sort of further specify

11 what accounts for the variation and how that comes out in this

12 study versus that study. "It's because males are different from

13 females; it's because of the shadows in this end of the maze,"

14 or whatever like that.

15 What accounts for the failures of some

16 manifestations to appear in some cases? Is it specificity? Is

17 it the conduct of the study, the way the endpoint was measured

18 or whatever?

19 And what other consequences of this

20 hypothesized common process ought to be seen, and do we, in

21 fact, see them? That's a way of putting our reasoning on the

22 table and testing it against all of the data and not just
Page 238

1 saying, "Is this something that's consistent with our idea?"

2 So if you define endpoints too broadly, you

3 may be gaining some positive effects by saying in this broader

4 definition of what constitutes an endpoint, we now have more

5 things that are considered positive. But you also got more

6 negatives of all the places that those things didn't happen, or

7 you've got more cases of where you have to explain why it ended

8 up differently this way, then that way.

9 A lot of these explanations could be

10 constructed, but if you're -- and assuming them without data,

11 you're sort of adding to the burden of your hypothesis with

12 things not in evidence, to use the legal term.

13 So what we really might want to do in the end

14 is create competing accounts of the data, and I'm using accounts

15 sort of as a technical term here -- an articulated set of

16 proposed explanations for all of the observations.

17 We've got Bradford Hills' set of facts out on

18 the table. All of the results that we have, we have to account

19 for them somehow. One possibility is that a lot of them or all

20 of them have to do with some common underlying causal effect of

21 the chemical. That's the one on the left, and that would say,

22 "These findings indicate some human risk."
Page 239

1 And they have to lay out the reasonings why,

2 how we resolved the contradictions in those studies, and how

3 reasonable are the things we've had to assume to do those

4 resolutions.

5 On the other hand, on the other pan of the

6 balance, we'd say, "If these results don't indicate some kind of

7 effect in humans," then we have to say, "what are the reasons

8 not?" It's also an assertion, and it also has to be as an

9 assertion tested against the data. It's the countervailing

10 assertion because of the opposite assertion.

11 The reasons why not -- how the findings that

12 we have are otherwise explained. If it's not caused by

13 chlorpyrifos, why is there this effect that looks like something

14 that was associated with the chlorpyrifos in appearance? And

15 are those assumptions reasonable?

16 And it's balancing those two that we have to

17 deal with. So we've got these two epi -- we've got these epi

18 studies of chlorpyrifos. And these are sort of in a way, in a

19 nutshell, the alternative accounts for them.

20 On the one hand, on the left there, you say,

21 "Well, there are some specific endpoints here, and we're going

22 to say those associations that we see really are true positives
Page 240

1 and they're indicative of an effect of chlorpyrifos." And then

2 the things that don't agree, the ones that are in the opposite

3 direction, or the nulls or something like that have to have some

4 reasons for why they didn't happen as well.

5 If this is a real generalization, it should

6 happen in all the cases and not just on Tuesdays but not Fridays

7 or in this study but not that study. There has to be some

8 reasons for why it didn't appear universally. And we put our

9 reasoning for that or our tentative explanations on the table

10 and evaluate it.

11 On the other hand, we could say, "Well, maybe

12 those specific findings are the null ones are the true ones and

13 there really is no effect, and it's the positives in those

14 studies that are really the aberrations.

15 And we say, "What is the reasoning by which we

16 would see those as aberrations? Confounding factors, residual

17 confounding hasn't been eliminated, other kinds of questions and

18 measures and so on and so forth." And we have to adjudicate

19 between those.

20 That's what you're doing when you're being an

21 epidemiologist and just looking at the epi data. What happens

22 when you introduce the animal data in-between here?
Page 241

1 Now what I'm suggesting is that Mode of Action

2 and plausibility of the effects is what the animal data have to

3 tell you about how you shift the balance there. So it's not

4 simply that you're taking -- are there familiar endpoints among

5 the human data and the animal data that somehow seem to be

6 reminiscent of one another.

7 It's not just that, "Oh, the animal data

8 provides some sort of weight. We put that weight in the hat

9 along with the human conclusion." We're not merging conclusions

10 here; we're actually merging at the level of underlying

11 explanations in terms of Mode of Action, Steps in Mode of

12 Action, dose response for Mode of Action as we understand it,

13 and indeed, these adverse outcome pathways that is the

14 organizing principal in the EPA guidelines for doing this that

15 so much was said about.

16 So the questions here really are: do these

17 animal data corroborate the specific endpoints? Is any kind of

18 specificity or lack of specificity explained by that? We

19 hypothesize common mammalian Mode of Action to actually say this

20 underlines the animal effect and the human effect.

21 And when we do it from a dose response point

22 of view, does that make sense? We can identify Mode of Action,
Page 242

1 and then we can ask is this plausibly operating at

2 subcholinesterase inhibition levels. This is plausibly

3 accounting for the effects that we see in the humans to help us

4 tip the balance there between those two alternative ways of

5 looking at the human data.

6 We're not looking at hard proof here; we're

7 looking at trying to see how far we're leaning towards one way

8 or the other. That's what we're doing with weight of evidence. 

9 We're not trying to prove or disprove; we're trying to say,

10 "Where in the poles between these alternative interpretations

11 should we say the bulk of the evidence lies?"

12 I guess my own view is with the size of those

13 arrows, it's fairly clear: I think that the human data are not

14 particularly compelling in themselves, and that the real

15 question about how are you going to get effects in those humans

16 given the high doses that animals need to do the kinds of Mode

17 of Action elements that are necessary would sort of tip you

18 towards that idea of saying that, really, the human data aren't

19 showing us something.

20 But that's my view, and we'll have a lot of

21 discussion of that as we come along. I will say, just a couple

22 of points here, that there are two ways of sort of looking some
Page 243

1 of these things I think need to be distinguished. One is

2 chlorpyrifos inhibition as a Mode of Action itself -- in other

3 words, cholinergic effects. And that would be an important

4 thing to work through from this point of view in those

5 possibilities.

6 We also have the possibility that chlorpyrifos

7 is causing things that are not strictly cholinergic effects

8 themselves, but nonetheless don't happen at significant levels

9 far below the levels at which acetylcholinesterase inhibition

10 occurs.

11 It's a separable question, and it could be

12 that the evidence on whether effects can be seen in the absence

13 of cholinesterase inhibition can say whether there are such

14 things as non-cholinergic effects. But if there are, that

15 doesn't say that they happen at all other doses. That has a

16 dose response level, too.

17 We heard in the adverse outcome pathway

18 discussion this morning about how the doses that are necessary

19 to do the first steps are smaller than the doses that are

20 necessary to do the subsequent steps at the higher levels of

21 organization and so on and so forth.

22 And so the real question is: given the doses
Page 244

1 that are necessary and the animals are given the doses that we

2 have available to humans, is that a plausible kind of thing to

3 say that those kinds of things are happening? Dose response is

4 key.

5 So my final slide here is this: this is the

6 seven steps that we call our hypothesis-based approach. We

7 would like to suggest this as good practice that could be

8 followed. I know the writing is tiny. I just wanted to use the

9 table from our paper, so I epitomized it on the right-hand side

10 there.

11 First step: systematic review and evaluate

12 study quality. That's very important. EPA has done that. 

13 Systematic review, of course, has to have standardized criteria

14 for evaluation, so you don't just find the good points of the

15 studies that you like and note the bad points of the studies

16 that you don't like. You have to define those criteria ahead of

17 time, and that's important.

18 Then you look for consistency, specificity,

19 and so on within the epi and also within the tox. Consistency

20 among studies, because after all, if these effects are real,

21 they ought to happen regularly; and if they don't, it's not that

22 explanations for why that isn't can't be had, but you have to
Page 245

1 put them on the table. It's part of your reasoning. The degree

2 that you can make ad hoc assumptions in order to work out, one

3 of these things are not refuting assumptions, adds to the burden

4 of what you're trying to explain without data and that, in some

5 sense, weakens the weight of evidence.

6 And then we articulate the logic for why these

7 data constitute evidence. Why is it that we think that those

8 phenomena are telling us something about the target population? 

9 And so then we can evaluate those generalizations across animals

10 and humans and say, "How do they actually match up with respect

11 to the data we have?"

12 So we evaluate the hypotheses is step four. 

13 In step five, we really look for this commonality of causes

14 across epi and tox as an underlying sort of Mode of Action

15 adverse outcome pathway level. And then we formulate these

16 competing accounts, the two pans of the balance.

17 If this really is a causative effect, here's

18 how we explain the things that it would explain; and here's how

19 the way we explain the discrepancies. If there isn't that

20 causative effect, here's how we explain all the things that look

21 like they were pointing in that direction that are, in some

22 sense, false.
Page 246

1 And by weighing those two things, we can come

2 to conclusions and also identify data that could help us tip us

3 one way or the other.

4 I think it's important to realize here that

5 quantitative evaluation is really -- even when you're doing this

6 in a hazard identification mode, you're really looking for

7 hazard; you're looking for the qualitative question of whether

8 this can do something.

9 The quantitative aspects of dose response of

10 the Mode of Action really are important part in deciding whether

11 you actually have the evidence for that kind of qualitative

12 process happening at the doses that we have.

13 A separate question: then using those data to

14 set a human safety level, but the quantitative stuff actually

15 does come into the dose response in this.

16 And so with that, I will finish and hope that

17 you find these comments useful for your further deliberation. 

18 Thank you.

19 DR. KENNETH PORTIER: Any questions from the

20 Panel? I'm not seeing any. Thank you very much.

21 DR. LORENZ RHOMBERG: Thank you.

22 DR. KENNETH PORTIER: I can't read the writing
Page 247

1 either here or here, so you're saying this is in your

2 publication, though? Dr. Rhomberg? You said this table on the

3 left is in your publication referenced at the beginning?

4 DR. LORENZ RHOMBERG: Right.

5 DR. KENNETH PORTIER: Okay. And your

6 publication is probably one of the things on the docket that was

7 submitted.

8 DR. LORENZ RHOMBERG: Submitted with public

9 comments. There's a link to it, and it's an open access paper,

10 so anyone with the link is free to go download the paper and

11 read it. Thank you.

12 DR. KENNETH PORTIER: Okay. Thank you. Next,

13 we're going to move on to the Dow Public Comments, and I guess

14 Dr. Juberg, you're going to be kind of coordinating this?

15 DR. DALAND JUBERG: Yes, I will be. I'll be

16 doing much as Dr. Lowit was, serving as the beginning and end

17 for the Dow presentations, and I agreed with Dr. Portier and Mr.

18 Jenkins. We'll cut the Dow AgroSciences presentation in half. 

19 We'll focus on a couple epidemiology presentations today and

20 then conclude tomorrow with a couple that relate back to animal

21 and exposure.

22 So let me first begin. I'm going to offer a
Page 248

1 few opening remarks. I'm Daland Juberg with Dow AgroSciences. 

2 By title, I'm the North American leader of the Human Health

3 Group within the R&D organization. By training, I'm a

4 toxicologist and environmental health scientist. I've been the

5 principal toxicologist for chlorpyrifos since 2006 with the

6 company.

7 As a team, let me just explain; we have

8 toxicologists, fate and metabolism, ecotoxicologists, so forth. 

9 We brought some of those with us today, specifically to hone in

10 on a few of the charge questions that you've been tasked with

11 looking at.

12 And before I go too much further, let me, one,

13 thank Dr. Portier and the Panel for giving us this time. I know

14 this is a long day. We're very appreciative. I'd like to thank

15 the Environmental Protection Agency, the Office of Pesticide

16 Programs, and Dr. Bradbury. And particularly, I'd like to thank

17 the whole HED team, we know, as they do, that this is a

18 voluminous literature to work through. 

19 These are very vexing, challenging questions,

20 pushing the science on risk assessment these days, so we're

21 appreciative of the attention to deep science, and that's what

22 we're here to do briefly is to help you all with presentations
Page 249

1 and with submitted comments that have been given to you in the

2 docket and otherwise for assistance with some of these very

3 challenging scientific questions.

4 So purpose today -- broadly, and it gets back

5 to some of what Dr. Bradbury was indicating because we're ardent

6 advocates of deep science -- to bring an awareness of recent

7 investigations and study data for consideration in risk

8 assessment. And these will involve some studies that we've

9 done, some independent reviews, and some even what I call

10 pushing the science on the risk side; using scientific

11 principles including causality, scientific methodology,

12 integration of hazard and exposure, and weight of evidence; in

13 addition, to provide perspective on chlorpyrifos exposure,

14 epidemiology, neurodevelopment, and utility of in vitro studies.

15 Over the years, as the primary registrant of

16 this molecule, we've done many, many studies, but what we're

17 going to do is try to target a few today with some additional

18 perspectives that at some of the Charge questions that are for

19 you all.

20 We've been on this journey as well, and you

21 know, we're well-aware of some of the challenging literature

22 that's been developed; the academic studies; the epidemiology
Page 250

1 studies that exist over the last four to five years.

2 We've been fortunate to participate in a

3 number of these panels and thank the SAP and the EPA for

4 allowing us to participate. So what we're going to do just

5 briefly is, you know, I want to illustrate that we take science

6 in different directions.

7 Sometimes, it's the conduct of a new study

8 where appropriate; oftentimes, it's pushing science in terms of

9 risk approaches; oftentimes, we have the need to recruit and

10 have independent reviews and individuals look at data aside from

11 us. That's a very important part of it. So I'm just, through

12 this slide, highlighting a few of those.

13 A couple of years ago, we conducted a

14 comparative cholinesterase study. This is, as Dr. Lu had

15 indicated, a very common and consistent study that the agency

16 has used for assessing points of departure and ultimately risk. 

17

18 This study, in this case, has provided some

19 data in EPA's recent risk assessment and, you know, all cases, I

20 would say with all these journeys, we continue to publish this

21 literature. These are two that are in publication right now. 

22 I'm the fellow that did agree to pull this one
Page 251

1 back. I did have authority to give it to you, but it looks a

2 little uncorrected proof. I said, "Fine, we'll give this to you

3 another way." 

4 So these two publications relate to that

5 particular study. One was entitled Cholinesterase Inhibition

6 and Toxicokinetics in Immature and Adult Rats After Acute or

7 Repeated Exposures to CPF or CPFoxon. This was just accepted

8 this week, and that's why we don't have that.

9 And the second one was a modeling approach

10 after that entitled Acetylcholinesterase Inhibition Dose

11 Response Modeling for Chlorpyrifos and Chlorpyrifos-oxon. So to

12 the extent those data help you and the publications help you and

13 the publications help you in your deliberations, we can get

14 those to you.

15 A second push here, has been the subject of

16 some thought today, was the recent SAP last year, February, the

17 PBPK linked modeling. This was an initiative that we undertook

18 a number of years ago where we basically said, "We're going to

19 combine exposure data and modeling with effects data and

20 modeling."

21 And this was the subject of an SAP, which we

22 appreciate. We appreciate the Agency's attention and
Page 252

1 willingness to devote time to that. I'm encouraged and we

2 continue to push those kinds of data. 

3 I will note that within the Society of

4 Toxicology -- some of which you are familiar with --that work

5 this year garnered highlighted by the best paper awarded by the

6 mechanism section was also highlighted by the risk assessment,

7 specially sectioned. So it's gotten some notoriety. We

8 continue to think that, as was alluded to by the HED and a

9 scientist this morning, that there's a need at least to continue

10 to explore those approaches.

11 I would draw your attention to some outside

12 efforts that we've had done for us. These are independent

13 publications on neurodevelopment, exposures, and weight of

14 evidence for epidemiology. These have all been published. I

15 think they've been given to you, the Eaton, et al., Li, et al.,

16 Pruiett, et al. papers.

17 Traditionally, Dow longstanding, we're one of

18 the few industrial labs that still has a tox lab, and we've are

19 some of the first to have been integrating continually now

20 kinetic data into all of our tox testing.

21 And so what this allows us to do is, point

22 number four, improved animal kinetic data in conjunction with
Page 253

1 cholinesterase effects. This allows a direct comparison of

2 exposure and effects and strengthens those models of PBPK that

3 we have.

4 And then finally, my colleague Dr. Carol Burns

5 will talk further about this, just recently this past year, she

6 conducted and led another study of manufacturing exposures with

7 resultant of inhibition of plasma or RBC cholinesterase

8 observations.

9 And this again, the point here is that the

10 more data we collect, the more consistent the science becomes. 

11 And again, this becomes very consistent with what some of the

12 models are predicting.

13 This is just one slide, and then I'll move on

14 to who our presenters are today. This is just an example of

15 that recent CCA Study, or Comparative Cholinesterase, that the

16 Agency has used in part for deriving new points of departure. 

17 And there was a comment this morning about

18 attention and need to protect both acute and repeated exposures.

19 I would say that this study has been used for some of that. I

20 would draw your attention to that when you have a point of

21 departure, there's been much talk around the one milligram per

22 kilogram per day, but clearly, there's need to go more
Page 254

1 conservative when you're doing risk assessment for regulatory

2 decision-making. Those levels tend to be 0.36 and, I think,

3 0.03 milligrams per kilogram per day.

4 But then importantly, it's important for us to

5 realize that there are safety factors or additional factors put

6 beyond that that allow us to then protect Human Health. So the

7 levels do drop down a bit; hence, that's what the acute PAD and

8 chronic PAD are. Ultimately, those are the ones that are

9 proposed.

10 So what I'd like to do now is basically

11 introduce two of our presenters for today and one for tomorrow. 

12

13 Dr. Carol Burns will speak about epidemiology

14 regarding children's health. She is with The Dow Chemical

15 Company in Midland, Michigan. Dow AgroSciences physically is in

16 Indianapolis, Indiana, so we're separated. That's where we are.

17 She will address charge questions -- I won't read them to you

18 -- but she's going to cover a few of them, as you'll see.

19 She will be followed by Dr. Julie Goodman of

20 Gradient. She will talk further about the specific paper that

21 Dr. Rhomberg mentioned entitled Hypothesis-Based Weight of

22 Evidence Evaluation of Chlorpyrifos and Neurodevelopmental
Page 255

1 Effects.

2 Tomorrow, we'll begin with Dr. Michael

3 Bartels, who will shed light on use of a validated PBPK/PD model

4 for chlorpyrifos to correlate in vitro substrate concentrations

5 with in vivo systemic exposures.

6 Dr. Paul Price is here. He will be here. 

7 He's not prepared to make comments, but if there are comments

8 that come up about the modeling, please feel free to ask him. 

9 And I would encourage you to engage these presenters. We're

10 here to help you all and to shed light on data perspectives that

11 we can as we continue on this journey.

12 So with that, I'll turn it over to Dr. Burns,

13 unless there are questions for me.

14 DR. KENNETH PORTIER: Any questions from the

15 Panel? Okay. Good. Thank you.

16 DR. CAROL BURNS: Thank you for letting me

17 talk today. It's late in the afternoon; I have lots of

18 pictures, so hopefully we'll stay awake and we'll go through

19 these points. Oh, thank you. Ah, thank you.

20 I'm an epidemiologist at The Dow Chemical

21 Company. I've been at Dow 17 years now. The mission of our

22 department is product support and responsible care, so we do
Page 256

1 studies of our employees and studies of people who use our

2 products, and present at panels like this.

3 So today, I'll cover a few of the Charge

4 questions. For epidemiology for me, exposure is integrated

5 within the hazard assessment as well, so they're sort of

6 intermingled. Talking about the role of chlorpyrifos, I don't

7 believe is confirmed in the health outcomes.

8 And so you see the Charge question is in

9 parentheses afterwards, so you can keep track if you're assigned

10 to one of those questions.

11 The presumed chlorpyrifos exposures based on

12 urinary TCPy are dominated with co-exposure to dietary and

13 preformed environmental TCPy.

14 Chlorpyrifos blood exposure in the Columbia

15 study is pretty low, and it is below the current revised Limit

16 of Quantitation by the 2010 CDC method.

17 And blood chlorpyrifos levels in the Columbia

18 study are well below what would be expected for plasma and red

19 blood cell cholinesterase inhibition.

20 So I'll talk about each of those points in

21 detail.

22 So first of all, let's talk about the role of
Page 257

1 chlorpyrifos in the epi studies. Now Dr. Christensen talked a

2 lot about confounding and bias and misclassification, and I

3 thought she did a good job, so I'm not going to cover that.

4 But I will talk about, today, looking for

5 replication across multiple studies. And I think it's important

6 to me that the Draft Framework said that it would place higher

7 confidence in results that were replicated or reproduced from

8 multiple studies.

9 And because of its use of chlorpyrifos in

10 blood, Columbia has the most robust exposure metrics. So I'm

11 going to start with the Columbia data and give an example of

12 looking for replication across that using birth weight and birth

13 length.

14 As Dr. Juberg commented, there are some formal

15 reviews that have been written and, you know, many, many pages,

16 but I would recommend that you look through those, looking at

17 the discussions on confounding and bias and so forth.

18 To start out though, I think it's important to

19 talk about exposure, and there were some questions earlier about

20 what is the best biomarker of exposure for chlorpyrifos. And I

21 have these sort of in hierarchical order of what might be the

22 most specific, and that would be chlorpyrifos in blood.
Page 258

1 And then going down, we have the TCPy in

2 urine, which has some limitations, primarily because it's also

3 present in food and the environment.

4 Next is the diethylphosphates, and I think

5 it's important to know that 10 of the registered OPs metabolize

6 to the DEPs, whereas the dimethylphosphates are not a biomarker

7 for chlorpyrifos.

8 And lastly, I think this is important to

9 remember that the DAPs, as a group, are not specific and they

10 could represent one or many of the 27 registered

11 organophosphates.

12 Dr. Christensen and I think had similar ways

13 of presenting the data. So this is a piece of Table 10 from

14 page 59. Again, just looking at birth length and birth weight

15 because both of those outcomes were statistically significant in

16 the Columbia study.

17 As you look across, you see that Mt. Sinai

18 study was null for birth weight and birth length, and the UC

19 Berkeley study was also null or not significant for those

20 endpoints.

21 Some improvements or considerations for this

22 type of summary is -- the differentiation between a
Page 259

1 non-significant and null is not entirely clear to me. And as I

2 pointed out before, the DEPs are much more specific and

3 appropriate to evaluate chlorpyrifos than the DAPs. 

4 And I think it's important to realize that

5 other analyses were done in these studies and to somehow

6 characterize the number of comparisons that are made with, for

7 example, cholinesterase and PON1, and the -- that there have

8 been two additional studies looking at birth weight besides

9 these three cohorts.

10 So this is how a revised table might look. 

11 And so you see there in this column, we have our Columbia data

12 that we've seen before. But for Mt. Sinai, remember that in

13 addition to TCPy, they had analysis for DEP, PON1, and plasma

14 cholinesterase. So they did eight analyses using some or many

15 of these, and ten for birth weight, all of which were not

16 statistically significant.

17 Berkeley had several analyses as well,

18 numbering up to nine analyses. None was statistically

19 significant.

20 And then we have two new studies: one in New

21 Jersey where they did look at chlorpyrifos in blood and using

22 analysis of maternal serum and cord serum, found no statistical
Page 260

1 association.

2 And lastly, the Ag Health Study, looking at

3 women who reported ever using chlorpyrifos; found no association

4 for birth weight. They did not test for birth length.

5 So in conclusion, just using these two

6 outcomes, these results have not been replicated in four

7 studies.

8 And getting back to the specificity of

9 exposure; comparing the DAPs to TCPy in urine and chlorpyrifos

10 in blood. Here's an example of how you might come to different

11 conclusions based on what specific biomarker you're using.

12 I'm sorry. These three slides I added just

13 after the break, so these won't be in your printed copies. So I

14 apologize for the confusion.

15 So here, we see the Rauh 2006 data for the

16 PDD, pervasive developmental disorder. Indeed, the association

17 is -- the PDD increases with increasing chlorpyrifos; and the

18 DAPs have a similar increase with increasing DAPs.

19 However, it's important to remember that TCPy

20 in urine was not statistically significant, even though the

21 direction was not reported.

22 The DMP, which is not a marker of
Page 261

1 chlorpyrifos, was increased with increasing DMPs. But the DEP,

2 which is more specific to chlorpyrifos than the general DAP, was

3 in the opposite direction.

4 So in conclusion, you get a different

5 conclusion if you're looking at TCPy in urine and DEP than just

6 cord blood and DAPs. And similar is true with the recent papers

7 on IQ. Again, chlorpyrifos in that blood was statistically

8 significant. This is from CHAMACOS, I think. This is the

9 California data.

10 They did not report any results for their TCPy

11 in urine. And the DAP was significant; DMP, which is not a

12 marker of chlorpyrifos, was significant; and the DEP was not

13 statistically significant.

14 So keeping this in mind, I created a table

15 with all the statistically significant hypotheses generated by

16 Columbia, thinking that Columbia uses blood. It's probably the

17 most robust. So how well do the other four studies support

18 these conclusions?

19 And only one study found in it the p value was

20 equal to 0.05. None of the other studies that evaluated these

21 endpoints found a statistically significant association

22 confirming Columbia's.
Page 262

1 Now let's switch gears and talk about exposure

2 for a little bit and comparing TCPy in urine to chlorpyrifos in

3 blood. And there's been some talk already about biomonitoring

4 alone does not provide duration, source, or timing of exposure. 

5 And we know that presence alone does not indicate an adverse

6 effect.

7 And it's important to remember that these epi

8 studies didn't collect behavior on or around the time when they

9 collected the urines. And so it's really difficult to speculate

10 prior exposure levels without additional data about how, when,

11 or why any of these women were exposed.

12 But we do know that the observed range is

13 consistent across general population studies. So let's look at

14 how this might fall out. And this is adapted from Figure 5 that

15 Lieutenant Niman put together, but I'm more pictorially

16 oriented, so I did it just a little bit different.

17 So here's our NHANES data, and these data are

18 years before and after the change in urban use patterns. And

19 it's interesting to me that we don't see a drop in the median of

20 the general population for urinary TCPy.

21 Now these next studies here, these next four,

22 are the Columbia study, the Mt. Sinai, and the UC Berkeley
Page 263

1 study, for which we have urinary TCPy data.

2 And this New Jersey study, this is Barr et

3 al., which has also looked at birth weight and found no

4 association. So you see, even though some of the means are a

5 little bit higher or lower from one city to another, for the

6 most part, most of the participants are within the shaded area,

7 which is the NHANES range.

8 And the last study was the Ag Health Study,

9 and we don't have biomonitoring data on all those participants

10 of that analysis, but we might consider that those women might

11 be similar to agricultural spouses, which is represented by the

12 Alexander's spouses study here.

13 So you see they're slightly higher than NHANES

14 with a few outliers of women that were applying chlorpyrifos.

15 Conversely, the Ag Health Study women, since

16 they reported using chlorpyrifos, might be considered an

17 applicator. The Thomas study is actually information -- urinary

18 biomonitoring from Ag Health Study participants -- a sample of

19 them. I think it was about 30. And the Alexander study is

20 applicators as well.

21 So it's possible that the Ag Health Study

22 women in the birth weight paper may have been comparable to
Page 264

1 applicators or spouses. And so this is how those data might

2 look.

3 The problem with urinary TCPy at the levels

4 consistent with the U.S. population is that it doesn't

5 adequately quantify exposure.

6 And on this one, I disagree that TCPy is

7 valuable in epidemiology studies below this point. Can you

8 really differentiate, say, three from eight at this level to say

9 someone is higher or lower exposed? And the reason for that is

10 that most TCPy in population studies is ingestion of the

11 metabolite.

12 And Dr. Lu's study, looking at kids who ate an

13 organic diet, really demonstrated that well; that the organic

14 diet kids had very, very low urinary TCPy compared to more

15 traditional diet.

16 Recently, the study by Morgan et al. confirmed

17 that urinary TCPy does not reliably quantify exposure to

18 chlorpyrifos.

19 And then most recently, we have two studies

20 that collected urine and blood at the same time, and that study

21 was the Whyatt 2009 validation study and concluded that TCPy and

22 chlorpyrifos were not correlated.
Page 265

1 And then the recent study that we did on our

2 Dow employees; we had 16 unexposed employees. Again, we

3 collected blood and urine on the same day, and 80% of those with

4 detectable TCPy did not have any detectable chlorpyrifos in

5 their blood.

6 This is just from the Morgan paper to

7 demonstrate that when they collected data on TCPy and

8 chlorpyrifos in food, air, soil, and surfaces, you can see that

9 10 times the levels -- the absorbed dose was due to the TCP.

10 However, if we want to look at higher levels

11 of TCPy in urine, the biomonitoring studies of the applicators

12 inform very well on behaviors, and this is coming from the

13 Alexander and Thomas studies on applicators.

14 They found activities like wearing gloves, how

15 much you apply, the type of tractor, if you have a breakdown,

16 all that type of activity is predictive of the urinary levels. 

17 And interestingly, the study of Egyptian

18 applicators who had very high exposures confirmed that the

19 behaviors and exposures that they had are inconsistent with the

20 practices in the U.S. I grew up in Nebraska, and I don't know a

21 single farmer who applies barefoot.

22 And then the manufacturing employees; these
Page 266

1 workers are protected under OSHA, and that includes a monthly

2 monitoring of cholinesterase.

3 And I think we're all on the same page here. 

4 We've seen this slide before. So this is the Garabrant study. 

5 So these are workers -- these are data that were collected in

6 '99 and 2000, and these exposures were related to job

7 activities.

8 And I think it's remarkable that the predicted

9 dose response for cholinesterase in Midland, Michigan workers,

10 men and women working in a manufacturing plant, identified an

11 inflection point for cholinesterase at 110 micrograms per gram

12 creatinine. And the Egyptian applicators study concluded the

13 same. I think that's pretty interesting.

14 So here, let's look at the same data that we

15 saw before. We have our NHANES people over here; our three

16 cohorts: the New Jersey cohort, and then some applicator

17 studies. And I've added over here the Garabrant data, the

18 Egyptian Farahat applicator data. And then these are the

19 results from the recent Burns study in which we collected data

20 from manufacturers in May.

21 And we see here the shaded area is consistent

22 with NHANES; the blue data is the inflection point for plasma
Page 267

1 cholinesterase; and the green line is the inflection point from

2 the Farahat study for acetylcholinesterase inhibition.

3 And what we see is that the studies that

4 collected cholinesterase, we can see that we would not expect to

5 have plasma cholinesterase in these population studies down

6 here. And indeed, both Mt. Sinai and CHAMACOS collected

7 cholinesterase data.

8 The recent study of our current workers, even

9 though this line goes above the predicted for plasma

10 cholinesterase, none of our employees had depressed

11 cholinesterase at 20 percent.

12 So now, we've been talking about urine, let's

13 look at blood levels. And there are two additional studies that

14 have blood levels if I wish to compare the Columbia data. 

15 First, let's look at the Columbia data. There was a lot of

16 discussion about the blood levels over time, and these results

17 -- these ranges are children born in '99, children born in 2000

18 and so forth.

19 So you can see that, indeed, exposure levels

20 appear to decline by year. This, slide 20, is the current Limit

21 of Quantitation for the 2010 CDC method. And we know from the

22 validation study that TCPy levels, at least in these last three
Page 268

1 years, are consistent with the U.S. population levels if not

2 lower and that no cholinesterase depression is plausible for

3 these later years.

4 Now on a log scale, I've circled those four

5 years, so you can see where they fit. And then the New Jersey

6 study, we have maternal serum and cord serum. We can see,

7 again, those levels are within U.S. -- the Columbia data and

8 below the current Limit of Quantitation.

9 And then the study by Berman took place in

10 Israel, and they used the newer methods, so their lower level is

11 half of the Limit of Quantitation.

12 So where does that fit with our estimation for

13 cholinesterase? Here are those studies again, and this is the

14 level predicted for 10% plasma cholinesterase. This is the

15 range predicted for acetylcholinesterase. And this is where the

16 animal birth weight studies would fall.

17 So you can see this is 100 times higher than

18 these levels over here.

19 So I'm summary, there are statistically

20 significant effects reported by Columbia, but they aren't

21 replicated in the other cohort studies. Urinary TCPy at the

22 population level is a poor exposure metric for epi studies,
Page 269

1 other than saying you're above or below.

2 Chlorpyrifos exposure in the Columbia study

3 are low and below the 2010 CDC revised Limit of Quantitation. 

4 Blood levels in the Columbia study are well below expected

5 plasma cholinesterase and red blood cell acetylcholinesterase

6 inhibition.

7 I can answer questions, or we can have Julie

8 talk and do a Q&A after.

9 DR. KENNETH PORTIER: And we'll open it up for

10 questions right now. Anyone? Dr. Popendorf.

11 DR. WILLIAM POPENDORF: Yes, I just had a

12 couple there, but that last slide -- the one before that, you

13 were talking about ChE and the AChE. Those two bars, those

14 represent a 10 percent range, is that --

15 DR. CAROL BURNS: 10 percent range.

16 DR. WILLIAM POPENDORF: For both of them,

17 okay. The other question on your recent work on the

18 applicators, do they involve --

19 DR. CAROL BURNS: Manufacturers.

20 DR. WILLIAM POPENDORF: Oh, manufacturers. 

21 Was most of their exposure dermal?

22 DR. CAROL BURNS: I don't know.
Page 270

1 DR. WILLIAM POPENDORF: You haven't -- or

2 could you --

3 DR. CAROL BURNS: We didn't --

4 DR. WILLIAM POPENDORF: I don't know if you --

5 DR. CAROL BURNS: Well, just briefly, there's

6 about 50 people in the plant, so we had volunteers. So I had

7 eight individuals from the saran plant and I think 30 from the

8 chlorpyrifos plant. And they came in on Monday and provided

9 urine, blood, and saliva, and so presumably, they had no

10 exposure over the weekend. And then the next day, on Tuesday,

11 came in, provided another blood, urine, and saliva.

12 And so on day two, that was the day that I

13 showed.

14 DR. WILLIAM POPENDORF: Sounds good.

15 DR. CAROL BURNS: So I didn't collect if it

16 was dermal or oral. That was all speculation.

17 DR. WILLIAM POPENDORF: Okay.

18 DR. BRIAN BUCKLEY: If I could follow up. 

19 What was the detection limit for the methods for your worker

20 study?

21 DR. CAROL BURNS: Mike wrote those down for

22 you. So the TCPy in urine, that limit of detection was two
Page 271

1 parts per billion. And we did that analysis in-house, and just

2 for clarification, the Garabrant study, that was done at Dow as

3 well and that Limit of Quantitation was one to two compared to

4 NHANES, which was 0.4, and then the blood was 20.

5 And Mike, did you send that out or did you do

6 that?

7 DR. MICHAEL BARTELS: Mike Bartels, Dow

8 Chemical. The TCP methods across the various studies were quite

9 comparable, one to two parts per billion. The chlorpyrifos

10 method used for the new Burns 2012 study was comparable to the

11 newer CDC-published method. In fact, that was the method used

12 for the analysis that was done in-house, and that was 20 parts

13 per trillion in blood.

14 DR. KENNETH PORTIER: Dr. Vorhees.

15 DR. CHARLES VORHEES: So I'm trying to

16 understand sort of the bigger picture that you're driving at. 

17 So we heard a presentation just a few minutes ago that says,

18 "Well, if you're going to weigh hypotheses, what is your

19 alternate hypothesis to explain that human epi neurobehavioral

20 data?"

21 DR. CAROL BURNS: What I see is that we have a

22 study that it looks like the kids born in '99, 2000 may be
Page 272

1 different than the kids born in 2000, 2001. And is it due to

2 their levels of chlorpyrifos, or is it something else that those

3 levels of chlorpyrifos are related to? I mean, why does someone

4 apply chlorpyrifos in their home and someone else doesn't?

5 I think those are issues of confounding and

6 bias in this classification, so that's why I look for

7 replication. Are we seeing the other studies done in different

8 states, different methods, different investigators? Are they

9 coming to the same conclusion on the same endpoints? I don't

10 have a reason for why those aren't the same, but it's not. 

11 What we're not seeing is every other story

12 that's consistent isn't consistent with those stories.

13 DR. CHARLES VORHEES: So am I hearing you

14 right? Are you saying that there may be a relationship in the

15 early data where the levels are higher but not later?

16 DR. CAROL BURNS: No, I'm saying that the

17 relationship isn't due to chlorpyrifos.

18 DR. CHARLES VORHEES: Just some unknown?

19 DR. CAROL BURNS: Mm-hmm.

20 DR. CHARLES VORHEES: And you don't have a

21 hypothesis about what that unknown is?

22 DR. CAROL BURNS: Mm-hm.
Page 273

1 DR. KENNETH PORTIER: Dr. Lu?

2 DR. CHENSHENG LU: Could you tell us under

3 what circumstances that CDC need to revise their old LOQ for the

4 blood level of chlorpyrifos in 2010?

5 DR. CAROL BURNS: I don't know under the

6 circumstances about their method, no.

7 DR. CHENSHENG LU: Oh, because it sounds very

8 unusual, and I'm not aware of that.

9 DR. CAROL BURNS: Maybe Dr. Bartels has, as an

10 analytical chemist.

11 DR. MICHAEL BARTELS: This is a newer method. 

12 It's not specifically superseding the prior method, but it's the

13 newest method that's been published by the same laboratory. It

14 incorporates analysis of more classes of pesticides for a

15 broader survey of exposure to chemicals. The detection limit is

16 20-fold higher with half the sample size, so it's not

17 necessarily superseding, but it is the latest method out of that

18 laboratory.

19 DR. CHENSHENG LU: So my understanding is that

20 they add a new chemical to the method that eventually drive the

21 LOQ higher.

22 DR. MICHAEL BARTELS: They've probably listed
Page 274

1 a newer method for the same analysis of chlorpyrifos in blood,

2 and detection limit is 20-fold higher than the prior method.

3 DR. KENNETH PORTIER: Dr. Harry.

4 DR. GAYLIA JEAN HARRY: So I think I have the

5 same baffled look that Dr. Vorhees has because one of the things

6 that had struck me about that Columbia study when it was being

7 initially looked at was somewhat of a power of within that

8 cohort where there was exposure, and then you took the compound

9 off of the market.

10 So it almost gave you that great experimental

11 control that you never would have gotten otherwise, and where

12 I'm having a little trouble is if you don't have an alternative

13 -- I mean, right now it looks like I have exposure. I found in,

14 I mean, your slide number 5, I think, one of your slides were

15 showing that you had a difference in birth length and birth

16 weight that was inverse, but then that was no longer present

17 after 2000.

18 So that one interpretation could be that you

19 lost the effect and that one of the known things that you knew

20 happened was the loss of exposure to the compound that seemed to

21 correlate with the loss of an effect.

22 So unless there is an alternative, given that
Page 275

1 that is a defined cohort within a population, you know, within a

2 set population and not different ones, that's actually pretty

3 strong evidence that there may be something happening with the

4 chlorpyrifos exposure.

5 DR. CAROL BURNS: Yeah. Here's the data, and

6 here they did analysis these two years by birth weight and this

7 was statistically significant with birth weight. These years

8 were not. Now you wouldn't expect this to be statistically

9 significant because you have no heterogeneity in exposure.

10 The question that I have, and the 2008 SAP

11 asked in Appendix 4 I think it is -- one of the appendices -- is

12 the question is: do these kids have higher birth weight than

13 these kids? That's the question.

14 It's not is this statistically associated with

15 birth weight; it's as a group has these kids with lower birth

16 weight, is the effect now gone? And as I interpreted it in the

17 answer in the appendix was they said, "Well, we weren't designed

18 to do it, and we don't have enough statistical power."

19 Now in another paper, in the paper looking at

20 the MDI/PDI, again they used statistical testing to say, "Over

21 these years, looking at statistical significance." I had a

22 slide on this in 2008, but I don't have it this time.
Page 276

1 If you look at the PDI scores of the children

2 born in '99, 2000, and 2001, the scores do not improve. The

3 scores of the children born in 2001, if you look at the Rauh

4 pediatrics paper 2007, the scores of the children in 2001 are

5 not better than the scores of the kids in 1999.

6 So even though the authors say that, you know,

7 the study was not designed to analyze in that way when they have

8 presented it. They aren't showing a rebound of any effects in

9 the kids when they do analysis by levels. They don't do

10 analysis by years to see if there's a rebound, looking at the

11 natural experiment that they have; that there's no more exposure

12 in these later years.

13 But all of the problems that might exist in a

14 New York City housing development would still exist.

15 That's my alternative hypothesis. But, you

16 know, I don't want to criticize the Columbia study. It's a

17 strong study. They did good analysis. It's a complicated

18 scenario. I can't even begin to imagine. But what I do know is

19 these levels of chlorpyrifos are very low and, you know, they're

20 comparing low versus very low versus extremely low.

21 DR. BRIAN BUCKLEY: If I could follow up on

22 Dr. Lu's question. Do you have any reason to doubt those
Page 277

1 original CDC values even though they've changed their method

2 last year or two years ago?

3 DR. CAROL BURNS: The Limit of Quantitation

4 below 15 is, can they really tell a difference between 3 and 8? 

5 I don't know.

6 DR. BRIAN BUCKLEY: But they had published

7 those methods previous as well, right?

8 DR. CAROL BURNS: They had.

9 DR. BRIAN BUCKLEY: And those methods had been

10 used quite a bit by CDC, right?

11 DR. CAROL BURNS: Only for this study and the

12 Barr study. I haven't seen them used elsewhere.

13 DR. BRIAN BUCKLEY: Which Barr study, there

14 were a couple?

15 DR. CAROL BURNS: The New Jersey study.

16 DR. BRIAN BUCKLEY: There were a couple New

17 Jersey Barr studies.

18 DR. CAROL BURNS: Yeah, but it's the same

19 data.

20 DR. BRIAN BUCKLEY: It is.

21 DR. CAROL BURNS: Barr and Yan. Yeah.

22 DR. BRIAN BUCKLEY: But you don't have any
Page 278

1 reason to doubt the values for this?

2 DR. CAROL BURNS: I don't have any reason that

3 why -- I don't know. Hi, Mike.

4 DR. KENNETH PORTIER: Dr. Bartels.

5 DR. MICHAEL BARTELS: Mike Bartels from Dow

6 Chemical. I didn't give a critique on that method. It wasn't

7 part of our discussions planned for today, but since the topic

8 was brought up, I do have some concerns about the methodology.

9 After speaking with the laboratory and

10 critically reviewing the method as published and additional data

11 that I received, my opinion is that the method was not validated

12 below 15 parts per trillion. In analytical methodology, if you

13 want to measure something down to X level, you fortify the

14 matrix down to X level, and you show you can measure it

15 appropriately.

16 In the published method, the blood was

17 fortified down to 15 parts per trillion and not below. It was

18 good solvent recovery or solvent standards that showed linearity

19 and sensitivity and solvents, but in the presence of blood

20 matrix, the method was only valid down to 15 parts per trillion.

21 There were different quality controls run on

22 multiple days showing about 20 percent RSD, which is good, but
Page 279

1 the reality is 20 percent RCM 15 can give you an error of five

2 or six parts per billion at that low level. There were no

3 measurements done below that.

4 As far as I know, there were no replicated

5 analyses of any of the individual samples, especially the high

6 exposure samples. There were no field fortifications on the

7 samples to show that there was not any contamination at the

8 place of collection or between collection and transport to the

9 analytical lab, which was New York City to Atlanta.

10 There were also no storage stability samples

11 with the samples to verify stability, so that summary of my

12 critique is in the 2008 docket. I can go over that again later

13 tomorrow when I come back up, if you wish.

14 DR. KENNETH PORTIER: You can go read it in

15 the docket.

16 DR. CAROL BURNS: You can find it.

17 DR. KENNETH PORTIER: Yeah, it's in the

18 docket. Dr. Vena.

19 DR. JOHN VENA: The figures up here, you said,

20 were maternal blood because --

21 DR. MICHAEL BARTELS: Those are cord blood

22 data.
Page 280

1 DR. CAROL BURNS: Yeah, that's the cord blood

2 from --

3 DR. JOHN VENA: It's the cord blood, because

4 that's what they --

5 DR. CAROL BURNS: Yeah, and these data came

6 from Eaton's publication, which he got the ranges from.

7 DR. JOHN VENA: Right. So what we're talking

8 about here is not maternal blood then. These are the cord blood

9 samples because that's what they use in their paper to correlate

10 with the birth weight and the other outcomes, right? Am I

11 correct?

12 DR. CAROL BURNS: Right, except for a few when

13 they didn't have cord or maternal, and they imputed -- I don't

14 know - 17 percent or so.

15 DR. MICHAEL BARTELS: There was about 20

16 percent of the cord blood samples that were not taken or

17 measured, so those levels were imputed from the overall

18 relationship of cord to maternal blood ratios. And there's an

19 equation for that in one or two of the Columbia papers.

20 DR. JOHN VENA: I guess part of my head was

21 comparing to all these other datasets. You said these are cord

22 blood samples that we're using, which is a different measure
Page 281

1 than the blood samples that you're measuring in adults. I'm not

2 sure you can really compare them. It's a different metric. 

3 It's a whole different scenario.

4 DR. CAROL BURNS: Another thing that Larry

5 Needham had written about was controlling for blood lipids, and

6 that was one thing that we collected in our recent manufacturing

7 study to see if, indeed, lipid levels were changing our

8 interpretation of exposure. I think there's still some

9 interpretation and some work to be done about that role and how

10 well cord blood versus maternal blood tells you the story.

11 I think we see here there's a difference in

12 the cord blood in the New Jersey study versus the maternal. 

13 It's a different range.

14 Does your head hurt now?

15 DR. WILLIAM POPENDORF: In the New Jersey

16 study, for instance, it's merely my question: is there any other

17 study that looked at cord blood than these three epi studies? 

18 That's all.

19 DR. CAROL BURNS: Not that I'm aware of.

20 DR. KENNETH PORTIER: For the record, that was

21 Dr. Popendorf.

22 Okay. I think we'll go ahead and move on,
Page 282

1 given the lateness of the hour. Dr. Goodman is next. Dr. Julie

2 Goodman.

3 See, even our pointers are giving up, right? 

4 I said, even our pointers are giving up at this point.

5 DR. JULIE GOODMAN: Should I get started? 

6 Well, thank you very much for the opportunity to speak today. 

7 I'm Julie Goodman, a principal at Gradient, which is an

8 environmental consulting firm in Cambridge.

9 I have a master's in epidemiology, a Ph.D. and

10 board certification in toxicology, and I'm also on adjunct

11 faculty at the Harvard School of Public Health, where I teach a

12 graduate class on research synthesis and data analysis.

13 And today, I'm going to be talking about

14 hypothesis-based weight of evidence evaluation of chlorpyrifos

15 and neurodevelopmental effects. This is a study we conducted

16 independently with funding from Dow.

17 I'm primarily going to be focused on Question

18 4, which is about how you integrate the epidemiology data into a

19 risk assessment.

20 And I'm going to start by describing our

21 approach, and I know you still can't read this slide, but you

22 saw it before. But the idea was I would go through each step. 
Page 283

1 I can't go through the specifics; they're in this 82-page paper

2 we did. I hope you all -- it's on the docket -- have time to

3 read it because the specifics are really what make a weight of

4 evidence analysis.

5 But I just want to show you that the framework

6 and the thought process so you can see how we arrived at our

7 conclusions. And then I will show how we can then apply this to

8 answer questions about how to integrate epidemiology data into

9 the chlorpyrifos risk assessment.

10 Step one is to systematically review

11 individual studies relevant to the causal question at hand and

12 separately for each line of evidence; so separately for

13 epidemiology, toxicology, and mechanistic studies.

14 So for example, for the epidemiology studies,

15 we looked at the cohorts that evaluated neurodevelopmental

16 effects in children -- the Columbia, Mt. Sinai, Berkeley, and

17 the New Jersey cohorts -- and then we did the same for the

18 animal and mechanistic studies.

19 Step two. As opposed to many reviews, which

20 look study by study, step two is actually looking endpoint by

21 endpoint. So for every study that looked at newborn head

22 circumference, we evaluated all of these studies and all of the
Page 284

1 ways that these studies looked at newborn head circumference;

2 meaning if they looked at it using different exposure metrics or

3 different statistical analyses, we tried to consider everything.

4 We did the same for infant neurobehavior,

5 cognitive and motor development, child behavioral outcomes.

6 And then we essentially looked at all the

7 factors that could influence how one interprets the epidemiology

8 data. For chlorpyrifos, exposure assessment plays a huge role. 

9 In terms of issues with the specificity of the exposure metric

10 to precise or accurate the exposure metric was; issues with

11 exposure misclassification.

12 Also, similar issues with the outcome

13 assessment: how precise and accurate it was, when it was

14 assessed relative to when exposure was measured. Also, the

15 clinical significance, this was brought up earlier today by EPA.

16 When we're using that framework that talks testing in the 21st

17 century, the concern is really adverse effects, so we want to

18 understand: are we just looking at homeostatic processes or are

19 we looking at actual adverse effects?

20 In terms of epi, we also look at the

21 confounding and bias, whether we see exposure response. And

22 then again, statistical analyses; all of these studies have
Page 285

1 many, many statistical analyses. And so there's not only a

2 concern for the multiple comparisons issue, but also are you

3 seeing similar results when you do analyses in different ways,

4 and if not what does that tell you about causation?

5 We then do a similar analysis for the animal

6 studies, again looking endpoint by endpoint. We looked at

7 social and maternal behavior, emotion and anxiety, motor

8 function and cognitive function.

9 Considering similar types of issues, many of

10 which Dr. Moser brought up earlier today, such as was DMSO used

11 as a vehicle? Were the controls appropriate?

12 Also looking at factors like consistency of

13 outcomes across studies, dose-response, and again, this

14 biological significance of response. We'll be looking at

15 homeostasis, and we'll be looking at actual adverse responses.

16 And then similarly for the mechanistic data,

17 looking at neuronal differentiation, oxidative stress,

18 cAMP-related cell signaling, serotonergic dysfunction. And

19 looking at factors that affect not only the interpretation of

20 these results, but the applicability of these results to humans.

21 Once this is done, we've sort of gotten the

22 lay of the land of what's out there regarding these data. We
Page 286

1 identify the hypotheses, and I think this is really where

2 hypothesis-based weight of evidence varies from a lot of other

3 weight of evidence frameworks in that we're not just testing the

4 hypothesis.

5 Does chlorpyrifos induce adverse

6 neurodevelopmental effects at doses blow those that protect

7 against acetylcholinesterase inhibition?

8 We're also evaluating an alternative

9 hypothesis that chlorpyrifos induces neurodevelopmental effects

10 only at doses above which inhibit acetylcholinesterase. And the

11 reason why this is important: I think a lot of people feel that

12 that second hypothesis is kind of implied; that if your data

13 support the first, they're not going to support the second.

14 But really, the idea is, is you have to

15 evaluate the data with respect to all possible hypotheses and

16 see -- as I'll go through in this talk -- is it more likely they

17 support this hypothesis of causation, or is it more likely that

18 they support this hypothesis of some alternative cause or

19 alternative explanation?

20 And in some cases, you have enough information

21 that you can really judge that one is really most likely to be

22 the truth, and that's the one we should hang our hat on.
Page 287

1 The first way we evaluate these hypotheses is

2 within the silos within each line of evidence. So we first

3 looked at the epidemiology data, and after considering all the

4 factors I discussed, we concluded that factors other than

5 chlorpyrifos likely cause the reported outcomes or the observed

6 associations are statistical anomalies.

7 With regard to the animal toxicology data, we

8 concluded that most observed effects occurred at doses that did

9 not induce systemic toxicity but were high enough to inhibit

10 acetylcholinesterase.

11 And that regarding the mechanistic studies,

12 that mechanisms in vitro below exposures that cause

13 acetylcholinesterase inhibition may be attributable to study

14 limitations -- and I believe Dr. Bartels is going to be talking

15 about that tomorrow -- but also that no mechanism has been shown

16 in vivo in the absence of acetylcholinesterase inhibition.

17 So that brings us to step five, which is

18 really bringing it altogether, looking at the lines of evidence

19 holistically. And so I tried to, you know, summarize pages and

20 pages in one table. I hope I've done it justice, but

21 essentially on the left, these are the human data. Here's the

22 animal data. And essentially, based on Eaton's paper, in the
Page 288

1 two -- the Columbia and the Berkeley cohorts -- he estimated

2 exposure of about seven to eight nanograms per kilogram per day

3 and rain levels at 0.33 to 3.3 nM.

4 But then when you go to the animal studies,

5 you're seeing plasma cholinesterase inhibition at 100 micrograms

6 per kilograms per day, and then the LOAEL for

7 acetylcholinesterase inhibition in rat pups at 1 milligram per

8 kilogram per day.

9 So we have a margin of exposure of 10 to

10 100,000, based on these metrics. And then if you go, you know,

11 looking at brain concentrations in rat pups exposed to the LOAEL

12 for acetylcholinesterase inhibition, it estimated brain

13 concentration of 0.5 to 4.6 micromolar.

14 Looking at the minimum dose in all but three

15 of the in vitro studies, we have a dose of one micromolar. And

16 here, we see a margin of exposure of 1,000.

17 So the take-home here is that we have these

18 exposures and the epidemiology studies that are at least

19 1,000-fold lower than exposures that appear to be causing

20 effects in animal studies.

21 How do we account for this? So step six is

22 about evaluating the alternative accounts. So we see that
Page 289

1 there's two possible ways to account for the data: the first is

2 that the epidemiology evidence is sufficiently compelling that

3 even though it is inconsistent with evidence from animals

4 exposed to much higher doses, there must be some proposed

5 mechanism that must be right regarding low-dose

6 neurodevelopmental effects. That's one way to account for the

7 data that I just showed you in that table.

8 Another way is to say that these doubts about

9 these mechanisms at doses below those that inhibit

10 acetylcholinesterase have merit and that apparent associations

11 from epidemiology studies don't indicate causation, and these

12 associations must be attributable to something else, for

13 example, bias or confounding or exposure measurement error.

14 And I just want to bring up here, a question

15 was asked of Dr. Burns, you know, what's the alternative? If

16 you're saying it's not chlorpyrifos, what could it be? 

17 Obviously if we could, I mean, answer that, we wouldn't be here

18 today, right? I mean, yeah, I guess the point I want to bring

19 home is that you're not looking at this epidemiology data in a

20 vacuum. You have to consider the animal and the mechanistic

21 data, and how does that inform your results?

22 So it's not just that, "Well, there's
Page 290

1 inconsistencies in the epi, and we need to figure out how to

2 explain it." It's we need to figure out how to explain it in

3 light of the animal and the mechanistic data. And essentially,

4 in order for that first account to be true, you have to come up

5 with many, many more ad hoc assumptions about what's occurring

6 than if you accept the second account in that it just seems that

7 there's far fewer ad hoc assumptions you have to make for the

8 data to support that account.

9 And so because of that, that led us to the

10 conclusion that that is the most likely way to account for the

11 data in front of us and essentially led us to our conclusion

12 that a causal association between chlorpyrifos and

13 neurodevelopmental effects in the absence of

14 acetylcholinesterase inhibition is not plausible for humans and

15 that the few statistically significant associations observed in

16 the epi studies are likely attributed to some alternative

17 explanations.

18 And in terms of next steps, the idea is to

19 then apply this to a chlorpyrifos risk assessment. So now that

20 I've briefly gone over our framework and evaluation, I want to

21 talk about how we can apply this to looking at the end risk

22 assessment and specifically at the Charge questions.
Page 291

1 So Charge question 4.1 asks about the

2 sufficiency, clarity, and quality of the review. Section 4.0 is

3 laid out in such a way that it's very consistent with many

4 weight of evidence frameworks in that it discusses the cohorts

5 first, and then the endpoints; so endpoint-by-endpoint analysis,

6 like I talked about before and then also the strengths and

7 limitations.

8 And this is a very good approach, but I think

9 this discussion would really benefit from a more complete

10 discussion of several things, including the fact that there were

11 multiple comparisons in studies.

12 This discussion often missed that they were

13 evaluations using different exposure metrics, some with more or

14 less specificity in that they weren't always consistent or that

15 statistical methods weren't always results from different

16 statistical methods. And just to bring to your attention, one,

17 in the Rauh study, chlorpyrifos is looked at as a continuous

18 variable in terms of looking at associations with MDI and PDI,

19 and there was no association found.

20 And then it was looked at in quartiles, and

21 there was an association between the lowest quartile and the

22 highest, but there was no dose response in that the middle
Page 292

1 quartiles were, I believe, higher than the lowest and the

2 highest.

3 And a lack of dose response is evidence

4 against causation, you could say. Then the data were divided in

5 sort of the highest quartile versus the other three combined,

6 and that's when you see statistical significance.

7 So it is possible that that's the truth, so to

8 speak, that there really is an association, and you really only

9 see it that way. But the only way to know that is to take a

10 step back and evaluate all these statistical analyses and

11 determine if that's so, and that hasn't been done.

12 Another very positive thing had to do with --

13 the draft issue paper discusses factors that could have led to

14 an underestimation or an overestimation of effects, but there

15 was really an emphasis on those that could have underestimated

16 effects, and I think it would benefit from a more balanced

17 discussion.

18 For example, a common misconception is that

19 non-differential misclassification will always bias towards the

20 null. This is the case if you're comparing two groups. But if

21 you have more than two groups, it could bias results in any

22 direction, and so we really don't know how that's going to
Page 293

1 affect results.

2 And another issue is that even, I mean, as Dr.

3 Burns said, the study design is, I mean, it's excellent in terms

4 of epidemiology studies. But if there are issues, if you don't

5 actually know what exposures are, for example, or if you have

6 lots of exposures around the limit of detection that could shift

7 people in one group or another.

8 Even though your study design is laid out,

9 that might be enough to push you into the direction of saying,

10 "We don't really know what these results mean in terms of

11 causation."

12 And then probably another thing that I think

13 would benefit -- the draft document does a very good job at sort

14 of saying, "How do the epidemiology inform our interpretation of

15 the toxicokinetic studies?" But it doesn't go the other way. 

16 There's no discussion of how the results of the toxicokinetic

17 studies inform how we interpret the epi studies, and I think the

18 document would really benefit from that discussion.

19 And then finally, as I said, in Section 4,

20 there is an endpoint-by-endpoint discussion, but I think a more

21 detailed evaluation of this both in the tables and the text

22 would be helpful. Right now in the appendices, there's a very
Page 294

1 detailed table, study by study.

2 But I just want to take a minute to talk about

3 how doing this will help this evaluation. So I've copied here

4 Table 10 from the draft issue paper and was very encouraged to

5 see this. This is very in line with our weight of evidence in

6 that you're looking weight endpoint by endpoint. Each row is a

7 different endpoint, each column or the major columns are the

8 studies, and then the minor columns -- if you want to call it

9 that -- are different exposure metrics.

10 And so this really helps you get an idea of:

11 are you seeing consistency across studies? But this table would

12 be much more compelling if all of the exposure metrics were

13 listed, or at least the more compelling ones. So for example,

14 in the Mt. Sinai and the Berkeley cohorts, the DAPs is listed,

15 but not DEP and DEP is more specific to chlorpyrifos.

16 Another issue is that there's no definition of

17 when a result is put as null and when it's put as either inverse

18 or positive but not significant. I didn't look too much into

19 it, but I did find one case where the confidence interval for a

20 no was actually tighter than the confidence interval for a

21 positive non-significant. If you're going to do it this way,

22 you really need to be consistent.
Page 295

1 One issue with doing it this way, of course,

2 though is you have no idea of the strength of the association. 

3 Because, of course, if you really want to see consistency, you

4 could have nulls findings across the board, but if they're all

5 in the same direction, that might indicate there's something

6 going on, and you can't get that information from this kind of

7 table.

8 And another issue is, I talked about before

9 when multiple statistical analyses were conducted in the same

10 study, that's not really reflected in this table.

11 So I just discussed these points, so I'll go

12 onto the next slide.

13 We're going to stick with Table 10, which I

14 feel like my big issue is just that -- again, that the null

15 doesn't really tell you anything about strength of association,

16 but this might be a way that you could see a grand overview.

17 I just took that box. These are just the MDI

18 and PDI results. But the idea here is I put in columns for the

19 DEPs, and what happens is, is for example, if you look at MDI at

20 24 months in Table 10, all you see is this inverse and positive

21 association with the DAPs.

22 But then when you add the DAPs, you see that
Page 296

1 you have null results. So with the more specific biomarker,

2 you're not seeing any association. So I think spreading it out

3 like that is really helpful. And also stretching it out in

4 terms of MDI and PDI were kind of crammed together, and it was,

5 I found, a little hard to interpret as opposed to having kind of

6 as little as possible in one row so you can really -- you're not

7 going to mix up different endpoints.

8 But as I said, having the null and not showing

9 how many statistics per paper doesn't really get a weight of

10 evidence, so in here, I've just taken a piece of Table 5 from

11 our analysis. So this is just showing the MDI scores, but what

12 we've done is, you know, we have for all the cohorts that looked

13 at MDI scores, we have this study. We have all the different

14 ways that MDI scores were looked at so, you know, for example,

15 in the Berkeley study, it was looked at by maternal urinary TCPy

16 with, you know, less than the median detected or greater.

17 It was looked at by maternal urinary DEPs and

18 then concurrent child urinary DEPs, and then, of course, by

19 different ages. And then you can look down the column where it

20 says the estimate to get an idea of the strength of association

21 and then the confidence interval and the p-value to see what's

22 going on. And just to make it easier for this talk, I've boxed
Page 297

1 in red the two statistically significantly associations.

2 So it's pretty clear here that, yes, there was

3 a statistically significant association, but it's among a sea of

4 null associations.

5 And you can see this is a huge table, and this

6 is only part of a table and there's lots of endpoints, so it's a

7 very tedious process, but I think it's really the best way to

8 understand for each endpoint what do all the data say?

9 And you need to take it a step further. I

10 just grabbed a few bullets. This isn't really a complete

11 evaluation, but factors you have to look at. Well, in terms of

12 the MDI and the PDI evaluation in these statistically

13 significant associations, the mean score is in the high exposure

14 group. We're actually within the normal range. They weren't

15 adverse. There was also a very modest difference between low

16 and high, so even though it was statistically significant, it

17 wasn't necessarily biologically relevant. There weren't any

18 multiple comparison adjustments in the epi studies, no evidence

19 for exposure response.

20 Some factors were considered, we felt that

21 confounding by certain exposures and SES weren't completely

22 adequately controlled. 
Page 298

1 And then also, again, you're not looking at

2 these data in a vacuum. I realize that these measure different

3 factors than the WISC-IV scores, but there's still all -- I

4 mean, if the idea is, is there's some sort of similar Mode of

5 Action that's getting at this, you want to at least understand

6 is it consistent with other endpoints? So would you expect if

7 there's effects on MDI and PDI, do you think there should be

8 affects on WISC-IV? I mean, maybe you do, maybe you don't, but

9 you -- that's something you need to think about and come to a

10 conclusion about that.

11 And then of course, finally, an association is

12 not consistent or coherent with a toxicology and mechanistic

13 data. So you have to consider that. 

14 All right. So moving onto Charge question

15 4.2, and my response is much shorter, I promise.

16 This is a question about the preliminary

17 qualitative conclusion that chlorpyrifos likely played a role in

18 the neurodevelopmental outcomes in the epidemiology studies. 

19 And I would say that EPA's conclusion would be more compelling

20 if there was a comparison to determine whether the causal

21 hypothesis or some other explanation is or are better supported

22 by the data, if there was a more complete assessment of the
Page 299

1 impact of studying limitations, a more detailed assessment of

2 discordant results both within and across studies among the same

3 and related endpoints, like I just showed for MDI, and then a

4 discussion of how results of toxicology and mechanistic studies

5 affect the interpretation of epidemiology results.

6 And it's my opinion, and my colleagues, when

7 we did our hypothesis-based weight of evidence analysis, by

8 going through that exercise, we felt that the evidence indicated

9 that effects and exposures below those that cause

10 acetylcholinesterase inhibition are just not plausible.

11 So I would say we disagree with this

12 conclusion off EPA for Question 4.2.

13 And finally, regarding Question 4.3., the

14 Agency proposed that an acetylcholinesterase inhibition data

15 from lavatory animals is the most appropriate data to use for

16 the dose-response modeling and the depravation of points of

17 departure.

18 And we agree with this conclusion, and I did

19 just want to bring up that in the preamble, there was an

20 implication that because some effects are observed in the

21 studies, there must be some mechanism by which they occur. And

22 it also mentioned that there are limitations in the exposure
Page 300

1 assessments in the epi studies that affect their use and risk

2 assessment, and I would argue that for the same reason why you

3 shouldn't be using these data in a risk assessment, this also

4 affects how you interpret the epidemiology data themselves.

5 You have to decide if these data are robust

6 enough to use, and if they are, you could use them for both. 

7 But if they're not, they shouldn't be used for either. And

8 again, there is evidence from the animal and mechanistic studies

9 that calls into question the plausibility of the effects of the

10 low exposures.

11 So again, this supports evidence that

12 acetylcholinesterase inhibition is the most appropriate. And I

13 know I said finally, but I forgot about 4.3 being this is really

14 finally -- and essentially, this is a question asking for

15 approaches to incorporate epi data into the Grow pretty fast

16 risk assessment. And I think epi data should be incorporated in

17 the same way as any other kind of data. You evaluate it for

18 quality, for robustness, what does it all mean. And

19 essentially, we think this should be done using some kind of

20 weight of evidence analysis where you compare whether the data

21 more strongly support a hypothesis of causation or some other

22 alternative explanation for apparent associations.
Page 301

1 And as I've said several times, it's our

2 conclusion that a hypothesis-based weight of evidence analysis

3 indicates that a causal association between chlorpyrifos

4 exposure and neurodevelopmental effects in the absence of an

5 acetylcholinesterase inhibition is not plausible for humans.

6 This can inform the hazard characterization in

7 that we've evaluated this data, and we've determined that it

8 doesn't indicate there'll be hazards below those that cause

9 acetylcholinesterase inhibition and essentially informs the

10 dose-response as well because we're confirming the use of that

11 for the dose-response analysis and point of departure.

12 And then finally, as we discussed, you cannot

13 determine precise exposures from these studies, but it does give

14 us sort of a sense of a range of human exposures and sort of a

15 comparison of human exposures versus those in the animal and the

16 in vitro studies.

17 So in that sense, it can give us an idea of

18 margins of exposure, which can then, you know, lend support to

19 whether we're using the right endpoint of acetylcholinesterase

20 inhibition.

21 So that is our 82 pages in 25 minutes, and I'm

22 happy to take any questions. Thanks.
Page 302

1 DR. KENNETH PORTIER: Dr. Vorhees.

2 DR. CHARLES VORHEES: So maybe I missed it

3 because you went through this one part pretty fast, but correct

4 me if I'm mistaken, it seemed like you swept all the little

5 animal studies aside kind of quickly and focused on the human

6 data. Did I understand you right that you just don't regard

7 them as reliable in providing useful information?

8 DR. JULIE GOODMAN: No, I didn't --

9 DR. CHARLES VORHEES: What is your take on it

10 other than sort of they're irrelevant?

11 DR. JULIE GOODMAN: No, there's no data are

12 irrelevant. I think that's kind of a key is that we evaluate

13 everything. It's just whether or not they support or don't

14 support the hypothesis.

15 And I think yeah, I didn't spend time talking

16 on it today because it seemed like a lot of the focus of this

17 draft issue paper and the Charge questions were highly

18 incorporated for genealogy, so I wanted to spend my time talking

19 about that.

20 But we do talk a lot about the animal data,

21 and Dr. Lee's going to be talking about it a lot tomorrow. I'm

22 wondering if it's better to wait until she talks because she'll
Page 303

1 be giving a more in-depth talk about those data. But we

2 certainly didn't just sweep them under the rug. We did as a --

3 DR. CHARLES VORHEES: So you have a view on

4 that.

5 DR. JULIE GOODMAN: Yes, yes.

6 DR. CHARLES VORHEES: And that is?

7 DR. JULIE GOODMAN: Oh, my view is that

8 basically that the overall evidence does not indicate that neuro

9 -- how do I phrase this? It's like neurodevelopmental effects

10 occur below those doses that causes acetylcholinesterase

11 inhibition.

12 DR. CHARLES VORHEES: But you don't dispute

13 that there are effects? There are neurobehavioral effects at

14 some level of exposure. You're just arguing that you don't see

15 an effect below the level at which cholinesterase inhibition

16 occurs. That's all you're trying to say.

17 DR. JULIE GOODMAN: Yes.

18 DR. CHARLES VORHEES: Okay. So you accept the

19 findings in the studies at above the level at which

20 cholinesterase inhibition occurs as real, true effects.

21 DR. JULIE GOODMAN: Yeah, I don't want to

22 speak to specific studies right now. I'm not really prepared to
Page 304

1 talk about that, but I think that it's -- yeah, I'm not

2 dismissing those studies at all. I mean, really, what I was

3 focused on was whether we see effects below those that cause

4 acetylcholinesterase inhibition.

5 DR. KENNETH PORTIER: Dr. Harry.

6 DR. GAYLIA JEAN HARRY: Yeah, it was a

7 whirlwind presentation, so if you may have cut across something

8 and we thought that you, you know, sort of trimmed it aside,

9 we'll take that into account.

10 But when you were going back, and I guess

11 there's a caution that I'll raise to you in presenting this as

12 you did, okay? I need to set it within that framework. 

13 When you got to the table that you were

14 putting in and putting all of the different effects -- Table 5

15 -- you made a comment that you had these two endpoints, and they

16 frame of no effects is the way that it was sort of worded, okay?

17

18 And I just want to put caution in there

19 because the different things you're looking at, they're not 12

20 months and 24 months and 18 months on a test is not the same

21 thing as what that 36-month test is going to be because there's

22 a developmental dynamics that's happening, and you may not see
Page 305

1 the effect until that aspect of the development of the organism

2 has gotten to that component so it will manifest as a deficit.

3 DR. JULIE GOODMAN: That's a very good point,

4 right.

5 DR. GAYLIA JEAN HARRY: Just keep that in mind

6 with your presentation of the data because it's not a lot of

7 endpoints at one age that would represent lots of different

8 things. It represents a developmental process.

9 The other one, and I guess I'm a little

10 concerned about this and I'm not real sure you have an answer,

11 but it seemed to come across in one of the other presentations

12 from the public is that I think it was in 2008 maybe where we

13 proposed the caution that be cholinesterase inhibition could be

14 used as a mode of action to look to see if it could be used for

15 the regulatory aspect.

16 But we cautioned the Agency that that may not

17 be the only affect that these types of compounds may have on

18 development. That really hasn't been presented by the Agency

19 right now, and yet the presentations that have come from Dow

20 have potentially opened up a very big can of worms of now coming

21 back to say, "Is there something else we should look at on

22 development that does not come back to the cholinesterase
Page 306

1 inhibition that we have looked at about trying to regulate this

2 compound?"

3 So it's sort of implied. No, actually, it's

4 very clearly stated that you were saying that you didn't think

5 that there would be an effect that would be under a level that

6 you had cholinesterase inhibition. And I think there's some

7 animal data out there that suggests that you can get adverse

8 effects, and I don't know how well that's been reviewed.

9 So I'm now at a quandary of being asked to

10 look at data to say, "How do you pull all this together if

11 you're looking at a mode of action?" And now there are

12 arguments that that may be the only mode of action or no, now

13 there are other modes of action we should take into

14 consideration.

15 So I wasn't even going to bring it up in any

16 of the things during the week, but the two public presentations

17 have now raised that as a possible concern that you may open a

18 can of worms you don't want to open in that there may be other

19 mechanisms that we need to take into consideration when we look

20 at the developmental aspects of things.

21 And I don't know whether you guys want to

22 address it or you want to sort of reword a few things in there.
Page 307

1 DR. JULIE GOODMAN: I mean, one thing I

2 actually chose the wording of the hypothesis to not say that

3 that was actually the mode of action was through

4 acetylcholinesterase. So in essence, the hypothesis is just --

5 it's almost like that could be used as a marker.

6 So even if it's a totally different mechanism,

7 we can use that to say, "If exposures are below us, those at --

8 that inhibit acetylcholinesterase -- we're below levels of

9 concern." That's what I was getting at.

10 Daland, do you want to answer that?

11 DR. DALAND JUBERG: No, I don't want to

12 answer. I just wanted to say that's the reason we're at a

13 challenging point here because what you'll hear tomorrow, I

14 think, will put into perspective some of the in vitro dose

15 levels as with Dr. Abby Li, some of the attention on the other

16 side of the equation, the animal studies and so forth.

17 So if it isn't addressed tomorrow, please, I

18 mean, it's in our minds now, and let's -- yeah, let's raise this

19 further and talk about it.

20 DR. KENNETH PORTIER: Dr. Vorhees.

21 DR. CHARLES VORHEES: I'd like the chemists

22 and the toxicologists to help inform an ignorant neuroscientist
Page 308

1 about something that I'm not understanding. So when you're

2 measuring these metabolites, how is it that you're getting

3 measurements below the limit of the detection? Can you just

4 explain to me? How do you have valid data? In most essays I

5 know, if it's below the limit of detection, you don't use those

6 numbers even if your analytical instrument generates a number.

7 So I'm finding this very difficult to try to

8 comprehend.

9 DR. MICHAEL BARTELS: I agree with you. 

10 That's my simple answer. In fully comprehensive methodology,

11 that's used for residue analyses according to very concise EPA

12 guideline. You might have a limit of detection, a range, and

13 then a Limit of Quantitation, where you can actually assign a

14 number to the sample.

15 The method, as it's written -- and I would

16 encourage you to look at the Barr 2002 paper. It's -- I'm not

17 sure if it's open access, but you can probably get access or we

18 can present some details of it again tomorrow.

19 They did an extrapolation of their lowest

20 fortification level at 15 parts per trillion and showed that

21 they had good linearity of the solvent standards down to the

22 instrument detection limit where they could see it in solvents
Page 309

1 -- down to one part per trillion or a little below.

2 It wasn't validation data presented within the

3 method paper to show they can look and measure it in blood

4 accurately.

5 DR. CHARLES VORHEES: So even if you

6 extrapolate down below your limit of detection because you find

7 that it's linear, but what's the data down in that range then

8 mean?

9 DR. MICHAEL BARTELS: If you're talking about

10 a different molecule in a different matrix, say, you know,

11 benzene and dirt or something. If you're below your limit of

12 quantitation, but you can see it and you're above what you've

13 defined as the limit of detection -- if you defined it in your

14 method, that means you can see it and you can say it's there but

15 you can't put a number to it. You can't say what the

16 concentration is in this sample.

17 DR. CHARLES VORHEES: So as I understood it,

18 the CDC limit of detection was 20.

19 DR. MICHAEL BARTELS: The one used for the

20 Columbia study samples was one part per trillion. The method

21 used by the CDC lab for the more recent Berman study was 20

22 parts per trillion, which is the 20/10 paper, I think. It's the
Page 310

1 Israel --

2 DR. JOHN VENA: He's talking about the

3 Columbia data.

4 DR. MICHAEL BARTELS: Correct. The earlier

5 work. 

6 DR. JOHN VENA: It's stated, published, limit

7 of detection is one.

8 DR. MICHAEL BARTELS: Right. My opinion is it

9 was validated to 15.

10 DR. KENNETH PORTIER: Dr. Ryan, we're also

11 running up against my deadline here, and I'm seeing the panel

12 sitting back here, which means we're losing engagement at this

13 point.

14 Before I forget, Dr. Ryan, I wanted to ask Dr.

15 Juberg, as team leader, whether Dr. Burns and Dr. Goodman will

16 be available in the morning to continue discussions as the panel

17 kind of thinks through more of these things?

18 DR. DALAND JUBERG: They absolutely will, yes.

19 DR. KENNETH PORTIER: Dr. Ryan.

20 DR. BARRY RYAN: I think it was you that

21 pointed out that the new method, the 2010 data that we've been

22 talking about, the 2010 limited detection we've been talking
Page 311

1 about. Will it include the number of other compounds?

2 DR. MICHAEL BARTELS: Yes, some other classes.

3 I don't have all of them in my head. Yes, but that's how the

4 authors their new method: it's more multifunctional.

5 DR. BARRY RYAN: It would not be the least bit

6 surprising to be that the limit of detection would go up for a

7 particular compound if you are monitoring 20 different things. 

8 You have to give up something to get something else,

9 essentially, on that. And it wouldn't surprise me if there were

10 a factory of ten different between the limit of detection for a

11 single analysis, and one that looked at 20 different compounds. 

12 Would you agree with that?

13 DR. MICHAEL BARTELS: Yes, however the method

14 used in the earlier study was also a multi-compound assay. They

15 had 20 or more analytes in the earlier 2002 method.

16 DR. BARRY RYAN: Were they the same compounds?

17 DR. MICHAEL BARTELS: No, in different

18 classes.

19 DR. BARRY RYAN: Were they in the same matrix?

20 DR. MICHAEL BARTELS: Yes. Well, yes. When

21 we talk about blood, actually, all these studies were done with

22 serum or plasma.
Page 312

1 DR. BARRY RYAN: Okay.

2 DR. KENNETH PORTIER: Dr. Koutros, we haven't

3 heard from you in a while.

4 DR. STELLA KOUTROS: Yes, I wanted to say I

5 know this comment really isn't in line with what we've been

6 talking about in the last 15 or 20 minutes. But given that we

7 just spoke about all of the integration of the epidemiology

8 results, I wanted to make a comment on that.

9 I appreciate the inclusion of adding the

10 various biomarkers to the tables that were presented. I think

11 that's actually a really useful thing. But I also wanted to say

12 that there are still some limitations, I think, in the tables

13 that were presented and it's hard to really look at the epi

14 results in a table that just says, "Null, null, null, inverse,

15 inverse."

16 So I think what we probably have to talk about

17 with respect to the epi results is why are interpretations

18 different, not what all the results show. So for example, you

19 gave a, you know, example about these statistical analyses and

20 why you thought there was an issue with that. I didn't really

21 understand that at all.

22 So I think we have to really be on sort of the
Page 313

1 tables. I know we have to synthesize data, but it's really sort

2 of the specifics of our interpretation, I think, that are more

3 important.

4 DR. JULIE GOODMAN: And I would agree with

5 that. My concern is that, the idea is that, you know, what I've

6 been hearing not necessarily today, but just in general is that

7 we have these three cohorts and we consistently see results.

8 We found in our analysis that the results

9 really consistent, and the only way to really judge whether they

10 are -- I mean, you might disagree, but the only way to judge

11 whether they are consistent or not is to lay them all out in

12 that way, and they're not really laid out in a way that you can

13 make that judgment.

14 And so that's sort of why I'm proposing to

15 look at it that way. And conveniently, they have all been laid

16 out for you in our paper so, you know, you're welcome to look at

17 that.

18 And maybe it isn't the best way or you could

19 come up with another way to do it, but that's just my concern. 

20 I want to make sure that it's all on the table so you can really

21 evaluate whether there's consistency or not.

22 DR. KENNETH PORTIER: Dr. Lu.
Page 314

1 DR. CHENSHENG LU: I guess if there's

2 opportunity for tomorrow morning, I would not keep the Panel

3 here after six. So I would rather defer my question to tomorrow

4 morning.

5 DR. KENNETH PORTIER: And I appreciate that. 

6 I think what we will do is break at this point. We start again

7 tomorrow morning at -- is it 9:00?

8 SPEAKER: Yeah.

9 DR. KENNETH PORTIER: I've got to double-check

10 my agenda here. So tomorrow morning at 9:00, we will reconvene,

11 and we'll see you then.

12 (WHEREUPON, the MEETING recessed for the day

13 at 6:00 p.m.)

14

15

16

17

18

19

20

21

22
Page 315

1 DR. KENNETH PORTIER: Welcome to the second

2 day of this FIFRA Scientific Advisory Panel Meeting on

3 Scientific issues associated with Chlorpyrifos Health Effects. 

4 I'm Ken Portier, the Chair of the SAP and

5 Chairman of this session, and I welcome you to the continued

6 discussion. We're going to continue this morning with a public

7 presentation by Dow. But before we do this we're going to

8 reintroduce the panel, as we normally do on our second day. 

9 We'll go around and introduce everyone.

10 As I said, I'm Ken Portier. I'm a

11 biostatistician and managing director of the Statistics and

12 Evaluation Center at the American Cancer Society.

13 DR. CAREY POPE: Good morning. My name is

14 Carey Pope. I'm a professor of toxicology at Oklahoma State

15 University.

16 DR. STEPHEN KLAINE: Good morning. I'm Steve

17 Klaine. I'm a professor at Clemson University and I direct the

18 Institute of Environmental Toxicology.

19 DR. MARTHA SANDY: I'm Martha Sandy. I'm a

20 toxicologist with the Office of Environmental Health Hazard

21 Assessment Cal/EPA. 

22 DR. HEATHER YOUNG: Heather Young,
Page 316

1 epidemiologist, George Washington University School of Public

2 Health and Health Services.

3 DR. CHARLES VORHEES: Charles Vorhees,

4 professor of pediatric in pediatric neurology at the University

5 of Cincinnati and Children's Hospital.

6 DR. JOHN VENA: John Vena, professor of

7 epidemiology at the University of Georgia, College of Public

8 Health.

9 DR. ALVIN TERRY: Alvin Terry, a professor of

10 pharmacology and toxicology at Georgia Health Sciences

11 University. 

12 DR. BARRY RYAN: Barry Ryan, professor in

13 environmental health at Rollins School of Public Health at Emory

14 University.

15 DR. WILLIAM POPENDORF: Will Popendorf,

16 Emeritus professor in Industrial Hygiene at Utah State

17 University.

18 DR. BETTE MEEK: And Bette Meek, associate

19 director in chemical risk assessment at McLaughlin Center,

20 University of Ottawa. 

21 DR. CHENSHENG LU: Good morning. Alex Lu. 

22 I'm an associate professor from Harvard School of Public Health.
Page 317

1 DR. JEAN HARRY: Jean Harry, neurotoxicologist

2 at NIHS.

3 DR. ELLEN GOLD: Ellen Gold. I'm professor

4 and Chair of the Department of Public Health Sciences at the

5 School of Medicine at UC Davis.

6 DR. ABBY COLLIER: Abby Collier, associate

7 professor of pharmacology at the University of Hawaii, School of

8 Medicine.

9 DR. RUSSELL CARR: Russell Carr, associate

10 professor of toxicology, MSU State University College of

11 Veterinary Medicine.

12 DR. BRIAN BUCKLEY: Brian Buckley, executive

13 director of laboratories, the Environmental and Occupational

14 Health Sciences Institute, Rutgers University and a member of

15 the graduate faculty of Environmental Science, School of Public

16 Health and Toxicology. 

17 DR. MICHAEL ASCHNER: Michael Aschner, I'm

18 professor of pediatrics and pharmacology at Vanderbilt

19 University Medical Center.

20 DR. KENNETH PORTIER: Thank you all. Before

21 we get started, I'd ask Dr. Jenkins here if he's got any opening

22 business remarks.
Page 318

1 DR. FRED JENKINS: Good morning, everyone. 

2 I'm Fred Jenkins, designated Federal Official for this meeting. 

3 This morning is the last opportunity for public comments. Our

4 public comments will be closing after this morning. 

5 If you are here and scheduled to give a public

6 comment, particularly after Dow's presentations, please see me

7 so we can have your presentation uploaded to the computer - me

8 or someone else on the SAP staff. Thank you. 

9 DR. KENNETH PORTIER: Thank you very much. 

10 I'll remind everyone, this is a public meeting and it's being

11 recorded and I ask the panel and all the speakers to identify

12 themselves and to use the mic. At this point, I'll turn to Dr.

13 Juberg for any opening remarks in this continuing public

14 presentation from Dow. Dr. Juberg.

15 DR. DALAND JUBERG: Thank you, Dr. Portier and

16 members of the panel. We appreciate the opportunity to be here

17 again before you and thank you for your time. We have one more

18 Dow presentation. I'll have some concluding remarks, but there

19 will be another presentation in between those. I did have just

20 a couple of clarifying points from yesterday.

21 Any questions on pre and post exposure,

22 relative to the voluntary withdrawal and how those may have
Page 319

1 shifted or affected scores of various magnitude and types. Dr.

2 Carol Burns has a couple of slides. We're not requesting time

3 for those, but if that's clarification for you all, please make

4 that request.

5 Similarly, Dr. Paul Price, who's not up here,

6 we haven't requested time. He's got some, you know, expansion

7 of overall exposure between Columbia, all the way up to other

8 metrics. If Dr. Bartles doesn't address those today, I would

9 encourage you -- we have those available at your disposal if

10 you're wanting those. 

11 And then finally, I would just encourage,

12 we've submitted comments over the course of many years and we

13 have some of the Sentinel publications or the Eaton Lee and

14 Pruitt papers. I know it's been a very challenging four to six

15 to eight weeks to get all this under your belt. If there's

16 nothing else, I would encourage to read some of those, along

17 with our comments or some questions yesterday raised that I

18 think some of the points we have, from our perspectives, have

19 given insight or input on. So I would encourage you to look at

20 those materials.

21 Finally, all our speakers remain available

22 today. Again, for any clarifying points that you may have. 
Page 320

1 With that, I would like to turn it, at this point, over to Dr.

2 Michael Bartles. 

3 DR. MICHAEL BARTLES: Good morning. Thank you

4 for giving me the opportunity to speak today. My name is Dr.

5 Michael Bartles. A little background on my technical expertise:

6 I'm a scientist at Dow Chemical Toxicology Laboratory. I've

7 worked in the area of metabolism and pharmacokinetics and

8 biomonitoring method development for a number of years. 

9 I've published on biomonitoring methods for

10 the TCBY metabolite of chlorpyrifos, chlorpyrifos and the oxon

11 metabolite in blood and other tissues. I've collaborated with

12 the EPA on cross-validation of biomonitoring methods and I've

13 also worked with the CDC on biomonitoring methods for other

14 molecules.

15 What I'm going to speak about today is the use

16 of the physiological base kinetic model that was presented in

17 last year's SAP to help estimate in vitro exposures from some of

18 the in vitro substrate concentrations used in the many

19 publications that have been discussed and are on the topic of

20 chlorpyrifos non-cholinergic effects.

21 My talk is responsive to Charge Questions 1

22 and 2.2. Chlorpyrifos is well known to inhibit the activity of
Page 321

1 numerous esterases in mammals. An inhibition of cholinesterase

2 is the most sensitive biomarker of chlorpyrifos exposure and

3 biological effects. 

4 The goal of the evaluation I'm discussing

5 today is to show that the estimates in vivo exposures derived

6 from in vitro assays of non-cholinergic effects are generally at

7 or above levels known to cause cholinesterase inhibition.

8 To start off, I'll go over a little chemistry.

9 Chlorpyrifos shown at the top of the slide is known to be

10 highly metabolized, especially by the oral route through an

11 activated intermediate to give primarily the trichlorpyridinol

12 metabolite and the corresponding diethyl phosphates. This

13 metabolite is a variety of conjugates. It is the primary

14 urinary biomarker used in most exposure study estimates. 

15 Chlorpyrifos itself and the oxon metabolite

16 are only minor metabolites in blood. What's circulating is

17 generally greater than 99 percent trichlorpyridinol. The trace

18 levels of oxon metabolite that are formed are generally rapidly

19 hydrolyzed. And this metabolite is also responsible for

20 phosphorylation of using residue of cholinesterase, resulting in

21 cholinesterase inhibition.

22 The high metabolism of chlorpyrifos has been
Page 322

1 shown to be consistent across a number of animal studies and

2 also across a serious of doses it's been shown that above about

3 a milligram per kilogram, there's a more substantial saturation

4 of this metabolism. 

5 What we have in the Y-axis of this graph is

6 the ratio of the TCPy to chlorpyrifos in blood. And as I said

7 before, certainly at the lower doses, the chlorpyrifos -- the

8 TCP of the chlorpyrifos ratio is generally 100 or higher. Above

9 a milligram, it tends to drop, most in young animals, adult

10 animals. Most of these are old studies. There are some data,

11 also, from an inhalation study we recently conducted.

12 Back to the oxon, as I mentioned before, the

13 low levels of oxon that are formed are known to phosphorylate,

14 both albuterol and acetylcholinesterases. This metabolite has

15 also been shown in a variety of numerous recent publications to

16 chemically react with a number of different amino acid sites on

17 a variety of different proteins. And this is probably not a

18 complete list, but these are some of the representative recent

19 indentified targets of various proteins at various sites. So

20 overall, what this tells us is that the oxon metabolite, if is

21 present, can react with a variety of proteins. 

22 Now, numerous biological effects have been
Page 323

1 reported for both chlorpyrifos and the oxon, following in vitro

2 incubations with cells, homogenates, purified proteins. There

3 are several exampling points are accidental outgrowth,

4 cytotoxicity, cholinesterase inhibition, even genomic response

5 changes.

6 Most in vitro studies have been conducted with

7 the oxon only or have shown that the oxon is substantially more

8 active than chlorpyrifos. This summary here is consistent with

9 the fact that the chlorpyrifos oxon is quite reactive with

10 protein and may elicit its effects through that mechanism. 

11 To properly assess how these in vitro effects

12 relate to human health relevance, it's important to try and

13 correlate what these in vitro or substrate exposures are to the

14 various preparations, with in vivo exposure levels.

15 To this estimation, we used a validated,

16 physiological-based kinetic model with pharmacodynamic end

17 points that was well-discussed and reviewed by the 2011 SAP. 

18 This model accounts for saturable processes

19 like absorption, metabolism. It accommodates life stages from a

20 few weeks old through adulthood. Also, it can be used in a

21 Monte Carlo fashion, incorporate variations in physiology and

22 metabolism for assessment of exposure across populations. The
Page 324

1 cartoon of this model design, which was presented by the EPA

2 yesterday, as shown here, fairly classic. The original adult

3 model has multiple compartments for both chlorpyrifos and the

4 oxon for tissue distribution; metabolism of the deformed

5 trichlorpyridinol that occurs from both chemicals and is

6 excreted out of the -- in the model as urinary elimination in a

7 one compartment fashion. The model also includes parameters for

8 estimation of inhibition and regeneration of esterases. 

9 Beyond the first 2002 version of this model,

10 additional modifications have been made. In summary, what was

11 presented last year was a life stage version of this model in

12 which the physiology across all tissue compartments was scaled

13 with age and body weight. The fat compartment is also varied,

14 based on population, distribution, which indirectly affected the

15 slowly perfused compartment volume. Blood to volume also were

16 scaled.

17 Metabolism rates in brain, liver, and blood

18 were also varied, based on tissue size in brain and liver and

19 logged normally distributed across the range that's been

20 identified and compared to the literature values. 

21 In addition, PAN1 hydrolysis activity in the

22 blood was scaled with both age and blood volume and varied,
Page 325

1 based on log distributions. More details on these are in last

2 year's report, which is available in the docket and a paper by

3 Smith 2011. 

4 The output of this model is what we're using

5 for this evaluation I'm discussing today. What we have here is

6 a graph which shows in the top two bands the estimates of

7 chlorpyrifos blood levels, following a single oral dose of

8 chlorpyrifos in micromolar concentrations against dose. These

9 are population estimates with simulated groups of 5,000

10 individuals, nephron physiology and metabolism. 

11 The blue data represent predicted blood levels

12 from 30-year-old adults. The corresponding green band here

13 represents the chlorpyrifos levels in a three-year-old

14 population. These top two bands are for chlorpyrifos. The

15 bottom two bands are predicted levels of chlorpyrifos oxon

16 metabolite in blood; again, in blue for adults and in young

17 children in green. 

18 You can see here an increase in the scattering

19 and a slight super linearity in the kinetics, about a

20 half-milligram or higher, per kilogram dose. This is consistent

21 with the saturation of metabolism that I pointed earlier from

22 several of our animal studies.
Page 326

1 As points of comparison, in addition to the

2 micromolar concentrations, we present several of these dose

3 level concentrations in terms of nanograms per gram for

4 comparison to existing biomonitoring cohort data. 3.5 nanograms

5 per gram is the equivalent to .01 micromolar and the only order

6 of magnitude lower is the 3.5 picogram per gram concentrations. 

7

8 As presented by the EPA yesterday, the Nolan

9 biomonitoring set had blood levels in this range, consistent

10 with a half-milligram per dose administered to those human

11 volunteers. The Burns 2012 data has blood levels a little off

12 the chart here, but in this range, following along the axis, and

13 the one human cohort study that measured blood levels. In the

14 Columbia experiments, it had blood levels off the graph here,

15 about two or three orders of magnitude below the lowest level

16 presented on this graph.

17 Now, utilizing this graph to interpret in

18 vitro data requires that we get some estimate of chlorpyrifos or

19 cholinesterase inhibition. There have been a lot of discussions

20 and presentations on what the accrued and repeat cholinesterase

21 inhibition points of departure are. The model output, which is

22 a little conservative here, shows 10 percent inhibition for RBC
Page 327

1 cholinesterase at about .1 milligrams per kilogram, and brain at

2 about .5. 

3 So for the remaining use of this graph, I have

4 a gold bar indicating cholinesterase inhibition range and

5 starting at .1 milligram and going higher. The inhibition

6 curves are based on the right Y-axis, which is cholinesterase

7 inhibition from 100 to zero percent of control. 

8 Okay. So if we take a look at a selection of

9 recent in vitro biological effects data where people looked at,

10 in the top three lines, cholinesterase inhibition -- excuse me

11 -- and the in vitro assays, or the bottom lines are various in

12 vitro, generally a cellular end points, often dealing with

13 neuronal cells. 

14 You can see that I've presented these data in

15 terms of test material and no effect concentration for the in

16 vitro end point for both chlorpyrifos, in the left two columns,

17 and the oxon on the right. 

18 I also categorized the test material exposures

19 based on the presence or absence of serum in the culture media. 

20 Initially, what you're going to observe from these data are that

21 most of assays have been conducted with either the oxon or have

22 shown that the oxon is substantially more active than the parent
Page 328

1 chlorpyrifos. Generally, several orders of magnitude lower

2 effect levels. 

3 Also, in the case of this data set and most of

4 the publications, there are generally an absence of serum,

5 certainly in the neuronal culture, incubations. And we

6 generally see lower effect levels in the absence of serums,

7 often in the low animal concentrations versus micromolar effect

8 levels in the presence of serum.

9 Now, if we take one example of cholinesterase

10 inhibition in the first publication and these eight references

11 are the first eight references in my reference slide at the end

12 of my presentation. 

13 If we take the first example of cholinesterase

14 inhibition in the presence of serum at one to five micromolar

15 for a no effect concentration, go back to our predicted graph of

16 blood levels and assume that immediate concentrations are

17 synonymous with blood concentrations, in terms of exposure to

18 the tissues their selves, you can see that one to five

19 micromolar on the Y-axis in a simple read-across at the top of

20 prediction of oxon levels in the population, which would sort of

21 be the 95th percentile sensitive subpopulations. 

22 That one micromolar oxon concentration in
Page 329

1 vitro correlates to a 10 milligram per kilogram does in vivo. 

2 And the five, correspondently, it's off this graph, but well

3 above 10 milligram per kilogram. This is certainly above any

4 cholinesterase points of departure that have been measured in

5 animal studies or the few human studies where they've shown

6 cholinesterase inhibition. And again, these bands are

7 chlorpyrifos and then the bottom one is the oxon, which is being

8 used for this read-across. 

9 Doing this kind of read-across can give us an

10 estimate of what in vivo exposures are relative to in vitro

11 concentrations, but there are some confounders that have to be

12 taken into consideration when utilizing this kind of approach. 

13 As I mentioned earlier, the protein is often

14 absent from some of the culture media. Since chlorpyrifos is

15 highly protein-bound, greater than 99 percent, these no effect

16 levels can represent 100 percent above availability, so to

17 speak, relative to the one percent that's available out of

18 blood.

19 So for the purposes of the read-across that

20 I'm going and presenting after this, there's a 100-fold

21 correction for serum-free culture medium experiments. For

22 example, 10 nanomolar serum-free would be equivalent to 1,000
Page 330

1 nanomolar in blood.

2 Second point that DMSO is often used as a

3 vehicle for in vitro media incubations. This is a very useful

4 solvent. It works well and it is used quite widely in a lot of

5 in vitro study types. The problem with it is it's not an inert

6 solvent. It does have pharmacological activity. The

7 concentrations used that can range from .1 to .5 percent of

8 total media volume are comparable to in vivo dose levels of

9 1,000 to 5,000 milligrams per kilogram, assuming a volume

10 distribution of 1. These concentrations are not outside the

11 range of acute tox effects for this solvent in rodents, which

12 have been reported in a review in the past to range from 4,000

13 to 20,000 milligrams per kilogram. 

14 Finally, some of the end points, while very

15 sensitive may not be specific. Specifically, one assay that's

16 on the bottom of the table I'm using, utilizing an ELISA-based

17 assay to look at increases in phosphorylation of the CREB

18 protein. This phosphorylased version of the protein, which is

19 measured by an ELISA assay, could potentially have been

20 confounded by the fact the chlorpyrifos sites on itself has been

21 just shown to chemically react with a wide variety of protein

22 and therefore, the diethylphosphorylated protein, which has been
Page 331

1 reported in vivo and in vitro, could've cross-reacted with a

2 native phosphorylation for this protein and given some

3 artifactual results. 

4 So with these confounders in mind, if we go

5 back to the table that we had, using read-across approach that I

6 did for the first line, for the other examples on the table, we

7 see that most of this data correspond back to in vivo exposures

8 of 10 milligrams per kilogram or something in the milligram per

9 kilogram range. 

10 There are a few examples that are lower than

11 that, that when we classify or qualify these data with the fact

12 that some of them use DMSO as vehicles. We can see that some of

13 the lower effect levels were when DMSO is used as a solvent in

14 the medium and this may have compromised the effect levels. We

15 don't know that yet because we don't have corresponding data

16 with other solvents in that test system. We do know that DMSO

17 can synergistically enhance cholinesterase effects in vivo, and

18 it's been shown to have effects on membrane permeability for

19 chemicals.

20 In addition, I mentioned that phosphorylation

21 of the CREB protein, which the authors report a fairly low

22 effect level could potentially been compromised by
Page 332

1 cross-reactivity in the end point of the enzyme-based assay.

2 Regardless of the qualifiers, what we tend to

3 see then that most estimates of in vivo exposures from these in

4 vitro media substrate concentrations are at or above the levels

5 required for cholinesterase inhibition for in vitro and in vivo.

6 So beyond this extrapolation, if we take one

7 example from axonal growth in Reference 4 here, look at effect

8 levels that are in the lower milligram per kilogram range, we

9 can see that some of these effects are, although reported at

10 lower levels, in vitro, actually have higher effect levels, in

11 vivo, in some type of in vivo blood system. The example I chose

12 there, Yang, et al. (2008) reported a decrease in axonal growth

13 and in vitro substrate concentrations in picomolar

14 concentration, 1 to 10 picomolar of the oxon.

15 As a point of reference, these authors found

16 the cholinesterase inhibition was 10-fold higher than the axonal

17 effect at 10 to 100 nanomolar. When this group evaluated these

18 type of effects in vivo in the zebra fish, which is a nice model

19 of in vivo animal with little metabolic capability, they showed

20 that the oxon-based cholinesterase inhibition was substantially

21 higher in vivo than in vitro; it was 10 to 30 nanomolar versus

22 .01 to .1 in vitro. The neuronal effects were also
Page 333

1 substantially higher at 10 to 1,000 nanomolar in vivo in the

2 zebra fish model versus 10 to 30 nanomolar for the

3 cholinesterase inhibition.

4 So in summary, there are numerous in vitro

5 studies that report non-cholinergic effects from chlorpyrifos

6 and the oxon. The effect levels are generally and substantially

7 lower with the oxon metabolite, consistent with the chemical

8 reactivity of this material with numerous proteins. 

9 There's another good review of this topic

10 Flasco, et al. last year. Estimates of in vivo exposures from a

11 variety of in vitro data can be determined with a validated life

12 stage-based, physiological-based kinetic model with

13 pharmacodynamic end points. Utilizing this approach to estimate

14 in vivo exposures from in vitro substrate concentration show

15 that nearly all the in vitro experimental conditions were

16 conducted at levels that were at or above levels that caused

17 cholinesterase inhibition in vivo.

18 Comparisons of some studies have shown that

19 there are few non-cholinergic effects seen in vivo or at higher

20 exposure levels than those reported in vitro. And as a result

21 of this evaluation, we agree with the agency that the

22 cholinesterase inhibition seen in animals and human is still the
Page 334

1 best biomarker of cholinergic and non-cholinergic effects to be

2 used for risk assessment purposes. And as I mentioned, on the

3 last slide I have References 1 through 8, which are out of the

4 table and additional references beyond that. 

5 Questions?

6 DR. KENNETH PORTIER: We would open it up to

7 any questions from the panel. Dr. Collier.

8 DR. ABBY COLLIER: Hi, Abby Collier. May I

9 have two points of clarification, please?

10 The first one is on your second slide where

11 you showed the metabolic pathways. Can you comment on which

12 conjugation enzymes are more important? 

13 Is it sulfonation or glucuronidation? Do you

14 know?

15 DR. MICHAEL BARTLES: Both are farmed. I want

16 to say the glucuronide is a little more prevalent, but since

17 it's known that there are individual differences between those

18 when different laboratories have done biomonitoring of the TCP

19 metabolite, they've used enzymes that are mixtures of both

20 glucuronidase and sulfatase, or in our case, we've done acid

21 hydrolysis and have shown complete cleavage of both conjugates

22 under the conditions used. 
Page 335

1 DR. ABBY COLLIER: And my question relates to

2 your variability in your model. And you said that you varied

3 PAN1 with age, blood volume, and volume of distributions,

4 basically, so you can vary the PAN1. Can you also do that for

5 your P450 divisions and have you looked at that? 

6 Because as you probably well know, the P450s

7 are not always present at birth either, and they vary up to a

8 couple of years of age. So for the young kids that might be

9 important. 

10 DR. MICHAEL BARTLES: I'd refer you

11 specifically to the Smith paper, which is one of my references

12 in the SAP report, which is in the docket from last year. Both

13 PAN1 and the CYPs were varied with age and with physiology and

14 there's a very good description of that. We also did a

15 comparison of our experimental results with literature data for

16 CYP variations for this type of metabolism for organophosphates.

17

18 DR. ALVIN TERRY: So I think one point of

19 clarification in the culture studies where DMSO was utilized,

20 it's also in the control samples. So making the argument that

21 there's a big DMSO effect wouldn't be confirmed in the study

22 where the controls didn't show any effect.
Page 336

1 DR. MICHAEL BARTLES: I agree that the

2 controls were generally negative in the end points measured;

3 however, there's a big body of literature on the effects of DMSO

4 in vivo and in vitro used in culture media or as a vehicle in

5 vivo for a variety of administration routes.

6 There have been several reported studies where

7 people have shown synergistic enhancement of a cholinesterase

8 inhibition with DMSO used as a vehicle. There are also two sort

9 of sets of experiments either within a paper or within two

10 papers for in vitro systems where the authors had discussed DMSO

11 altering the effects of the system. One is an ROS paper by

12 Jeter, out of our laboratory and then there was another

13 discussion by Prendergast, where they compared their data to an

14 earlier reference that had lower no effect levels and had DMSO

15 in their culture systems. 

16 So there is a precedent for both in vitro and

17 in vivo with this test material. I agree that the controls

18 within the experimental designs have been negative. Some of the

19 effects seen in vivo, though, occur slightly after some of the

20 in vivo sacrifice times. We have a whole separate response

21 we've given to the EPA in the past, and I would be happy to talk

22 about it further offline.
Page 337

1 DR. PORTIER: Dr. Harry.

2 DR. JEAN HARRY: Jean Harry. So the approach

3 is usually taken if you do use DMSO and you find an effecting

4 culture, is to determine whether your effect is DMSO dependent

5 because if you do open the cell membrane, you allow greater

6 permeability in there.

7 In the studies that you were outlining and

8 that you've looked at, were here efforts made to see if those

9 effects were indeed DMSO dependent? You use a different solvent

10 if you did or did or did not use it in serum. I mean, neuronal

11 cells don't see serum. So that creates another whole different

12 problem in these things. Did you look to see whether these some

13 of these specs were truly DMSO dependent or just sort of assume

14 that it might've been a DMSO confounder?

15 DR. MICHAEL BARTLES: Right now we're

16 mostly assuming it. There haven't been a lot of control

17 experiments where did with and without. Most of the studies use

18 that as a test material and the controls were negative. If it

19 enhances membrane permeability for test material, though, if the

20 test material is not present in the control, it's not a full

21 evaluation of the changes to the cell.

22 DR. KENNETH PORTIER: Dr. Buckley.
Page 338

1 DR. BRIAN BUCKLEY: How does the model account

2 for interspecies difference of the glucuronidation and sulfation

3 as rat models or rats themselves, tend to sulfate more than

4 humans?

5 DR. MICHAEL BARTLES: Let me go back to the

6 cartoon here. As published in 2002, this is an interspecies

7 PBPK model. As I mentioned earlier, formation of the

8 trichlorpyridinol occurs from both the chlorpyrifos by direct

9 dearylation and by hydrolysis of the oxon. 

10 The TCP, once formed, as a free phenol is

11 assumed to be virtually all conjugated and excreted as one or

12 the other. There are no specific rates for conjugation. 

13 There's just an elimination once formed. And then the methods

14 used to measure TCPy, again, like I've stated, either for

15 enzyme-based hydrolysis or chemical hydrolysis has been

16 validated to show that there are complete hydrolysis of either

17 conjugate.

18 DR. BRIAN BUCKLEY: But if the conjugation

19 rate was rapid with respect in one species, say, rat versus

20 human, then the oxon concentration would naturally be less in

21 the rat than the human, correct?

22 DR. MICHAEL BARTLES: The oxon concentration?
Page 339

1 DR. BRIAN BUCKLEY: Sure. If you follow a

2 pathway and you're grabbing that TCPy concentration as rapidly

3 as it forms, then the oxon is going to change the TCPy more

4 rapidly, correct?

5 I mean, that's chemically collaborative,

6 right? You start pulling from one end, you're going to pull the

7 formation from the other, correct?

8 DR. MICHAEL BARTLES: Well, it depends on

9 which ends the step rate is limiting, oxon hydrolysis or TCP

10 conjugation. 

11 DR. BRIAN BUCKLEY: Right. And what I'm

12 saying is if TCP conjugation is not a rate limiting step, then

13 the oxon will form TCPy potentially more rapidly, correct? It's

14 a concentration that TCPy will continue to decrease as its

15 conjugated. 

16 DR. MICHAEL BARTLES: Which department are you

17 talking about, blood or urine?

18 DR. BRIAN BUCKLEY: Blood at this point in

19 time, yeah. 

20 DR. MICHAEL BARTLES: Well, yeah. The blood

21 concentrations will drop as the material gets more highly

22 conjugated. It'll eliminate in a different fashion.
Page 340

1 DR. BRIAN BUCKLEY: So if there's a different

2 kinetic pathway between the TCPy conjugation in a rat and a

3 human, then it would be a more protective effect because the

4 oxon concentrations at any given time would potentially be less

5 for a given dose, correct?

6 DR. MICHAEL BARTLES: Right. But when we've

7 measured oxon hydrolysis rates, we've done that in the absence

8 of conjugation. We've looked either in liver or blood

9 preparations. So those enzymatic rates aren't relevant for

10 looking interspecies differences in hydrolysis. And we have

11 seen those, and those are incorporated into the model.

12 DR. CAREY POPE: Mike, the point about the P

13 CREB phosphorylation, is it possible like an adduct? I mean,

14 that also sounds, potentially, kind of important if it is

15 adducting CREB as opposed to modifying its phosphorylation.

16 DR. MICHAEL BARTLES: Oh. The fact that there

17 could be chemical modification to proteins. Yes, that's been

18 well-discussed in the literature. The importance is at what

19 concentration that does that occur because as I described in the

20 slide back here, the oxon metabolite, if present, will react

21 with different proteins. We've shown that metabolism of the

22 chlorpyrifos itself, to either the TCP or oxon is
Page 341

1 concentration-dependent and saturates it higher animal level

2 toxicity study dose levels. You have much higher rates of

3 metabolism, 2 TCPy and hydrolysis rates of oxon to TCPy is the

4 in vivo dose levels get lower. 

5 The use of a physiological-based kinetic model

6 will let us predict tissue dosimetry of the chlorpyrifos and on

7 the oxon. So I think the important useful take-home from this

8 estimation evaluation is that if the oxon is eliciting

9 cholinergic and non-cholinergic effects through presence of

10 oxon, then the cholinesterase inhibition in the tissues is a

11 good biomarker for oxon activity against other end points in

12 that tissue.

13 DR. ABBY COLLIER: Abby Collier. Just to

14 follow-up on Dr. Buckley's comment, point of clarification,

15 you're not scaling the conjugative metabolism at with age then. 

16 Yes or no?

17 DR. MICHAEL BARTLES: The conjugation rates of

18 TCPy, no, I do not think we are.

19 DR. ABBY COLLIER: You're not scaling

20 clearance with age. Okay. Thank you.

21 DR. KENNETH PORTIER: Last chance. No? Thank

22 you very much then, Dr. Bartles. The next public commenter is
Page 342

1 Dr. Li from Exponent. 

2 Dr. Li, I think we have copies of your slides

3 and then two other articles; is that right?

4 DR. ABBY LI: Yes, there was a paper that --

5 DR. KENNETH PORTIER: Press the mic, please. 

6 DR. ABBY LI: Oh.

7 DR. KENNETH PORTIER: Thank you.

8 DR. ABBY LI: Yes. There's a paper that was

9 recently published that we got permission from the publisher to

10 distribute here. I think they're waiting for my slides to come

11 up.

12 The other thing is that I distributed updated

13 comments. My colleague, Dr. Carole Kimmel, is a co-author on

14 our comments. She has been able to have the time to have her

15 input and these are the updated comments. 

16 Okay. I might need some training here. Thank

17 you very much for allowing me to participate in this SAP. I'm

18 going to focus on Charge Questions 1 through 3, with an emphasis

19 on the in vivo neurodevelopmental studies and risk assessment. 

20 I'm been asked by Dow AgroScience to make independent comments,

21 and these oral comments have been developed by Dr. Carole Kimmel

22 and me. We are both from Exponent Health Sciences. 
Page 343

1 Dr. Carole Kimmel is an expert on

2 developmental and reprotoxicology and risk assessment. She was

3 previously a senior scientist at EPA, working in this area and

4 she was a pioneer in participating in the conduct of

5 developmental neural studies that formed the basis for the EPA

6 Guidelines. 

7 I've had many years of experience conducting

8 and devaluating adult in developmental neurotoxicity studies and

9 have a lot of experience with pesticide risk assessment and I

10 was an author on the NCR's Tox 21 Report.

11 So today, what I'd like to do is first

12 summarize what we believe are the key risk assessment issues;

13 show you some data on cholinesterase inhibition important to

14 question of relative sensitivity with non-cholinergic modes of

15 action. 

16 To respond to the Charge Questions 1 through

17 3, with examples of issues to consider. And what I hope to do

18 is to provide a different perspective than that which was

19 already presented in the EPA Science Advisory Panel Issue paper.

20 And I think Dr. Ginger Moser did an excellent job reviewing the

21 data. I'd like to not show the same data that she has shown. I

22 just want to make it clear that we agree with much of what's in
Page 344

1 there and what we're trying to do is pick out those things that

2 we might help you to see the data from a different angle. And

3 finally, to end my talk with some concluding remarks. 

4 There are relatively few studies with dose

5 response data on both cholinesterase inhibition and neural

6 behavioral effects and neurodevelopmental effects with

7 non-cholinergic end points. But you can take a look at the

8 entire data set and there's pretty good evidence that there's

9 greater sensitivity of RBC or brain cholinesterase inhibition in

10 the dams or pups, compared to that for neural behavioral and

11 neurodevelopmental effects. 

12 When we evaluate the data, as I will describe

13 later, we come to the conclusion that the lower L, N-O-A-E-L --

14 that means an adverse effect level for neurobehavioral effects

15 is around one milligram per kilogram per day. And the BMDL10

16 for cholinesterase inhibition is .03. So our overall conclusion

17 is that cholinesterase inhibition is the most sensitive end

18 point and protective of other effects. 

19 So now I'm going to show you some of the data.

20 I purposely picked some of the data from the labs that are

21 spending the most time evaluating non-cholinergic modes of

22 action and hypothesize that these might be independent of
Page 345

1 cholinesterase inhibition. These, you have to go back into the

2 early literature where some of these cholinesterase inhibition

3 studies were conducted because they weren't conducted

4 concurrently in the later studies.

5 So here you can see at PND1, you get 80

6 percent brain cholinesterase inhibition, two hours after dosing,

7 with one milligram per kilogram per day, SC. That's

8 subcutaneous DMSO. By four hours, this drops by half to 40

9 percent. So the detection of cholinesterase inhibition depends

10 on the time of sacrifice after dosing.

11 You can see here that 24 hours after dosing at

12 one milligram per kilogram per day from PND, 1 through 4, you

13 get 20 percent cholinesterase inhibition. This means that we're

14 getting significant brain cholinesterase inhibition, despite the

15 24-hour time point. And you can imagine what might happen at

16 earlier time points.

17 There's very little data on cholinesterase

18 inhibition after gestational exposure in the fetus, but there's

19 one study that showed that at two milligrams per kilogram per

20 day, and this is 24 hours after the dose, you get 20 percent

21 brain cholinesterase inhibition. 

22 So you have to ask the question, if you looked
Page 346

1 at it earlier, would it be a good bet that you might see

2 something at one, under the conditions that they're dosing?

3 So when you take a look at the entire data

4 set, though, this is just a few of the studies that are reported

5 in EPA'S Table 4. And this highlights that the most sensitive

6 cholinesterase inhibition, BMDL is .03. This data looks at

7 pups, fetus -- it's just a lot of data there that they can pick

8 out this .03 as being more sensitive than all the other ends

9 points.

10 So now let me move right into the charge

11 questions. Please comment on the Agency's preliminary

12 conclusion that cholinesterase data remain the most robust

13 source of data for deriving points of departure for

14 chlorpyrifos.

15 We agree with this conclusion. For

16 cholinesterase inhibition, you have the advantage of many

17 studies, as I've described earlier, multiple dose levels and

18 really good dose response data that's amenable to quantitative

19 modeling, and then you have many oral studies at different ages,

20 which is the most relevant for the general population that gets

21 its exposure primarily for dietary exposures. 

22 In the neurodevelopmental studies, especially
Page 347

1 the non-cholinergic data, most of the studies have one or two

2 dose levels. The effects are often just at one dose level or

3 not dose-related. So not amenable to quantitative modeling, and

4 you're pretty much left with having to say you have an effect

5 level at one milligram per kilogram per day and derive points of

6 departure from that. 

7 A point that I think is really important is

8 that the majority of the mechanistic studies are subcutaneous

9 DMSO. I think that -- well, we -- Carole and I -- believe that

10 the route of administration is very relevant for deriving points

11 of departure. 

12 Now, don't get me wrong; I understand that for

13 mechanistic studies and academic research, there are many

14 legitimate reasons for different routes of administration, but

15 when you're deriving the POD and looking at these studies and

16 seeing whether you're going to use these studies for risk

17 assessment, route of exposure is extremely important. 

18 For example, one of the reasons for using SC

19 has been stated that it avoids the potential confounds of

20 different rates of gastrointestinal absorption and first past

21 effects on bioavailability. However, first past effects on

22 bioavailability in GI absorption are, indeed, very relevant for
Page 348

1 deriving safe levels of exposure to humans.

2 So we know that SC versus oral route does

3 impact toxicology and pharmacokinetics. In a side-by-side

4 comparison by Carr and Nale, they found that there's greater

5 brain cholinesterase inhibition with SC route of exposure and

6 that you get decreased body weights in pups with SC DMSO, but

7 you don't see that with the oral. 

8 Smith, et al., more recently, has data and

9 using that with pharmacokinetic analysis, found that SC prolongs

10 brain cholinesterase inhibition versus oral, due to slower

11 release from injection site depot and localized brain

12 metabolism. 

13 Their issue is not only with the route of

14 exposure, but the DMSO, which is a potential confounder for

15 determination of cellular and pharmacologic effects. This is

16 going to be very important as we move towards Tox 21 with end

17 points that are more subtle and precursor events. 

18 Two EPA scientists found in the 1980s that

19 DMSO in developmental exposures produced endocrine and

20 neurochemical changes and they cautioned the readers in hope

21 that their examples would discourage its continued use in

22 toxicological studies.
Page 349

1 So we have hints -- and these are not

2 side-by-side experiments, but these are a few of the behavioral

3 studies or results that actually was repeated in more than one

4 study. In this particular case, it's improved performance on

5 radial arm maze in the females. 

6 So in this example, you can see that you get

7 an effect at one milligram per kilogram per day. It was the

8 only one dose level tested. When you give it orally, there's no

9 effect at one milligram per kilogram per day. In fact, that was

10 a much stronger study because it had three dose levels and a

11 number of other design features that made this a stronger study.

12

13 So when you are asking the question for risk

14 assessment, the one on the right is the most relevant one for

15 use for risk assessment and there was not an effect at one

16 milligram per kilogram per day using oral route of exposure.

17 Another general comparison that's notable and

18 of interest is increased male aggressive response that was

19 measured by the same lab in two different studies. In the first

20 earlier study, they used DMSO and they found effects that one in

21 three milligram per kilogram, but then they saw the light and

22 they changed to peanut oil in all subsequent studies and they
Page 350

1 found no effect at one milligram per kilogram, and really very

2 subtle marginal effects at three milligram per kilogram per day.

3 So the next question is please comment on the

4 Agency's preliminary conclusion that although there are multiple

5 biological possible hypotheses, the mechanistic experimental tox

6 data do not yet support a coherent set of key events.

7 Dr. Mundy did an excellent job describing

8 this. We agree with their conclusions. There's a dearth of

9 studies designed to test the hypothesis regarding causal

10 relationships between perturbations and functional changes. 

11 I'd like to point your attention to the

12 Exponent comments that Carole and I put together that gives an

13 example for serotonergic end points that describe some of the

14 additional issues with these non-cholinergic modes of action. 

15 The next question is please comment on the

16 degree with which these studies suggest that end points relevant

17 to evaluating neurodevelopmental outcomes may or may not be more

18 sensitive. 

19 As I said, there are few studies that did this

20 concurrently, but there's enough data to come to the conclusion

21 that these neurodevelopmental effects are reported in one to six

22 milligram per kilogram. And we know that these doses causes
Page 351

1 acetylcholinesterase inhibition at those dose levels and that

2 these neurodevelopmental effects are occurring at doses that are

3 higher than the lowest BMDL10.

4 Question 2.2 continued, it's a very long

5 question. I only have time to focus on the issue of what is the

6 kind of magnitude and duration or perturbations that you need to

7 start worrying about that might lead to adverse effects later. 

8 I'd like to address that a little bit, but before doing that I'd

9 like to point out that whether perturbations are key events

10 leading to adverse effects on the developing nervous system

11 simply can't be answered with the current data.

12 Part of the problem we have is that we are

13 hampered by the lack of replication of studies using exactly the

14 same experimental conditions. The example that I gave you

15 showed that even in the hands of the same laboratory, they're

16 not able to repeat the same results exactly, and that raises a

17 lot of questions about the different modes of action. 

18 Now I would like to address the question of

19 magnitude. There is no magic number that you can apply across

20 the board. The biological significance of percent change -- and

21 I talk about this because the majority of the non-cholinergic

22 data that's published out there expresses the data, graphically,
Page 352

1 in percent change. That depends on the reliability and

2 variability of the control values for the specific brain area

3 and age of animal.

4 So in this particular case, in adults, they

5 saw a 50 percent decrease in dopamine hippocampus levels after

6 developmental exposure. That's huge. And that's something to

7 be concerned about. But the first thing that we need to do is

8 take a look at the control dopamine levels. 

9 In this particular case, compared to the other

10 regions, it's really very low. It's 1.2 to 1.3 nanogram per

11 region. That is unreliably low. The same authors in other

12 studies report that they don't evaluate that because it was

13 unreliably low. Thus, the 50 percent decrease is not

14 biologically meaningful and yet this paper is out and published

15 and there are many other papers that have cited this as evidence

16 of an alternative mode of action effecting dopamine levels.

17 So the other thing to take a look at -- and

18 these are just examples -- I think we need to apply to every end

19 point is to consider when the time point was taken, and when

20 they are taken at earlier time points to recognize that

21 concurrent control values are important, but they may be

22 changing rapidly at young ages and that impacts how you
Page 353

1 interpret what a percent change means. 

2 So whether effects are adverse should consider

3 historical control values. I encourage the EPA to seek and

4 request those data so that they can better evaluate whether

5 these findings are truly adverse.

6 Question 3.1: Please comment on the degree to

7 which these studies -- and it says behavioral -- support the

8 qualitative conclusion that chlorpyrifos exposure may result in

9 long-term adverse effects.

10 There is evidence of long-term effects on the

11 developing nervous system and they have been reported and we

12 believe that they happen around the 3 to 5 milligrams per

13 kilogram per dose level. 

14 The findings of these multiple studies are not

15 consistent with each other and we agree with the EPA that the

16 behavioral data do not provide evidence for a specific profile

17 of effects. Based on the criteria I will describe next, we

18 believe that the threshold is somewhere around one milligram per

19 kilogram per day with more convincing effects up at three.

20 I do this evaluation of studies because this

21 addresses the question: "Please comment on the scientific

22 quality and robustness of the animal neurodevelopmental
Page 354

1 studies."

2 Now there are more than 60 mammalian in vivo

3 neurodevelopmental papers, and they all have varying results for

4 particular outcomes that could be related to a number of

5 important variables. You need to keep that in mind when I'm

6 giving you these examples in trying to provide a different

7 perspective. 

8 None of these studies are done side-by-side,

9 so there's always some change in the experiment, as Dr. Moser

10 pointed out, that makes it hard to really make complete

11 confident comparisons. 

12 What one approach would be to take all of

13 these data and say I can't make heads or tails of this. I'm

14 just going to identify the lowest dose level at which any

15 alteration is found in any parameter and that's actually what we

16 did in the paper, Lee, et al., where we tried to provide the

17 tables -- I tried to put it in way that I was hoping that if the

18 authors, themselves saw the table, they would think that it

19 accurately reflected what they reported in their paper. But the

20 second approach is to evaluate the utility of those studies for

21 risk assessment purposes and to assess whether the pattern of

22 findings is consistent with the adverse effect. And that's what
Page 355

1 we tried to do in the text of that paper. 

2 This requires critical evaluation of the

3 methods and results for purposes of setting safe levels of

4 exposure, not just how well do they contribute to the

5 mechanistic research, which all of these papers really do a

6 great job doing. Now we're asking a different question for risk

7 assessment purpose.

8 This also requires us to evaluate the patterns

9 of outcome with respect to study design, methods and procedures,

10 consistency of alterations within cross studies, absence and

11 presence of findings, and dose response patterns. 

12 So for the neurobehavioral studies, here's an

13 example of some of the stronger studies or how we regarded a

14 study to be stronger. We considered a study to be stronger if

15 it had an oral route of exposure. We did not ignore the

16 subcutaneous studies, but if they had similar effects then the

17 oral study, if it was a strong study on the basis of the other

18 parameters, would break the tie.

19 A number of dose levels, two to three is like,

20 minimal in my regard. I mean, you need to have at least two to

21 actually get a sense of a dose response. The sample size for

22 behavior, we considered a stronger study to be 8 to 10. There
Page 356

1 were several that were up in the 20s. The experimental unit for

2 developmental studies needs to be in the liver, especially for

3 gestational and early post-natal exposures. 

4 We looked at the behavioral methods, including

5 the control for environmental influences and we carefully

6 evaluated how they derived the statistical analysis and

7 sometimes the data was evaluated more than one way. Part of the

8 evaluation requires an assessment when they transformed the data

9 to ask the question how well do they actually reflect the

10 original data?

11 Let me give you one example of why this is

12 important to look at the data. Here is an example of motor

13 activity over a 60-minute period and each of the -- is there --

14 okay. So the X-axis is every five-minute time bend. You can

15 see a nice habituation curve for control one and five milligram

16 per kilogram. I've looked at a lot of this kind of data. This

17 is really good data and I don't see an effect at one and five

18 milligram per kilogram. 

19 In fact, when they did the repeated measures

20 analysis of variance they did not get a dose effect. There was

21 no statistically significant effect in the over mean activity,

22 but they did get a dose times time interaction. And in many of
Page 357

1 the other papers they would report the post-tox group

2 comparisons on each bend and there was nothing there. So I

3 would simply say that no statistical significance was reported.

4 However, when the data was analyzed using a

5 linear trend, which is basically described as the slope of the

6 decrease in activity over consecutive five-minute blocks, they

7 get, in effect, at one and five milligrams per kilogram and I

8 believe that this effect, based on linear trend, is not an

9 adverse finding. 

10 So that may be why we come to a different

11 conclusion than what the EPA's report does because we looked at

12 this data and made some judgments as to whether this finding was

13 actually adverse.

14 The other thing that we think is important,

15 especially for the studies where there's only one dose level or

16 no consistent dose response relationship, it's important to take

17 a look at the available historical and positive control data to

18 assess the reliability of the methods and variability of the

19 control behavior. 

20 So here's an example that I'd like to present

21 that puts a different perspective on data. Unfortunately, the

22 pointer doesn't work, but what you can see here is this is
Page 358

1 radial arm maze. On the Y-axis is the errors, and the X-axis

2 are trials. There are different trial blocks. 

3 Yes. Okay. So here are the males. Here are

4 the females. You can see here -- and I'm going to focus on the

5 first trial block because it's fairly representative of the rest

6 of the data. If you look at the first trial block, you've got

7 about eight across the board for a control low and high dose

8 level. There's no effect in the males. 

9 For the females, for control, you have around

10 eight and then you have at one milligram per kilogram it goes up

11 to 13 and then hovering around 10.

12 So this would be considered an adverse effect

13 where at one milligram per kilogram you have an increase in

14 errors at the low dose but not the high dose. I was interested

15 in this because there were a number of other studies that did

16 not show an inverted U-shaped curve dose response curve. Not

17 only that, because I evaluated all the other studies from that

18 same laboratory, they actually made a big point in all the other

19 studies where they tried to show that chlorpyrifos disrupts the

20 sectoral differentiation of the brain. 

21 They make the point that ordinarily, in their

22 lab, there's a male difference compared to females. That
Page 359

1 females do worse and that in fact, the other hypothesis that

2 wasn't considered is that maybe what's causing this is that the

3 females are unusually low in this particular control group. In

4 fact, if you take a look at the first time bends, here's the

5 Study B that I just showed you with the controls: males and

6 females are comparable. And Study B for one milligram per

7 kilogram, you have this normal sex difference that they got in

8 two other historical controls. 

9 So in fact, if you look at this one milligram

10 per kilogram, this is an effect at 13 that is right about,

11 smack-dab where the other studies are. So this raises a

12 question of whether this non-monotonic change in learning in

13 memory is a real adverse effect.

14 So the next question is please comment on the

15 scientific quality and robustness of the animal

16 neurodevelopmental studies.

17 We agree that the total database is robust,

18 but some, but not all studies and findings are robust. So if

19 all the findings are treated equally, then the scientific

20 quality and robustness of the risk assessment is compromised. 

21 Here's an example: In this study -- this is

22 developmental reflexes -- in these two studies happen to be the
Page 360

1 studies that epidemiologists love to cite as demonstrating

2 biological plausibility for abnormal reflexes that they think

3 there's an association in their cohorts.

4 These two studies are probably the weakest

5 studies in terms of providing that biological plausibility

6 because in the first case, it's conducted up at 25 milligrams

7 per kilogram, which is a really big dose. And in the other

8 case, there's only one dose level in this experimental design is

9 especially weak in that you don't even have information on the

10 number of sample size there are. And there were two pups per

11 set from an unstated number of litters.

12 Whereas you have other studies that are much

13 stronger that evaluated this data across three dose levels, used

14 oral doses that tell you exactly what the number of litters they

15 have in the sample size and they have no effects.

16 So the conclusion, looking at that data, is

17 that there's no effect on developmental reflexes rather than

18 picking out one study that is the weakest study and claiming

19 that that show biological plausibility. You go on and see the

20 other data, and for the mouse, there's, again, a pattern of lack

21 of effect in better studies that have actual dose response data,

22 and we conclude that there's no effect up to about six to ten
Page 361

1 milligrams per kilogram.

2 Here's another example of the importance of

3 comparing behavioral methods, and this is just to show that

4 motor activity is not all the same. You can have a home cage

5 one hour activity and figure eight maze is one of the better

6 motor activity tests. Then you have this open field,

7 five-minute number of line crossing, and in this particular

8 study, they don't describe the criteria for what makes them

9 decide that that line was crossed, as compared to other really

10 well, you know, good behavioral studies that give you more

11 confidence. They will say something like, well, we considered

12 it when the two paws crossed or something. It just gives you a

13 greater sense that they had control over what they were looking

14 at. So if you get an effect here but you don't get an effect

15 here, then these should have greater weight. 

16 Here's a third example: Evaluation of pattern

17 and magnitude of effect. Here's a study in which you have

18 errors in the first eight visits on a radial arm maze and you

19 have the average errors at Week 1, Week 2, Week 3, Week 4. 

20 First, you need to note that there's a very small range of

21 errors. Here's the axis from two to four. I want you to

22 concentrate on and focus on control versus the low dose because
Page 362

1 I am saying that I think that at one, we don't have an adverse

2 effect.

3 So here's controls, and it's the closed

4 circles, and the one milligram per kilogram tracks just right on

5 top of it until the fourth week there's this strange uptick. So

6 the question is, is that an adverse effect? We conclude that

7 that's not an adverse effect because we're talking about 2.7

8 versus three errors. 

9 In fact, this group actually expresses the

10 data in a different way. And when they did this, the data

11 actually was really very nice. For the purposes of comparing

12 with another OP, they were able to show really nice dose

13 response data. What they did here is they took a 16-day average

14 and what they found, statistically, this is the control low,

15 mid, and high doses, is that statistically, they only got

16 effects at the highest dose. And the text says they've got

17 effects at the mid and high dose. 

18 So taken together, we believe that the no

19 effect level, for the purposes of risk assessment would be one

20 milligram per kilogram in this particular case. I just wanted

21 to show you that this is the data that shows what I showed you

22 just previously. You have these errors in the males. You have
Page 363

1 females with improvement. You get nice dose response curves, no

2 inverted U-shaped curves here. 

3 I showed you this slide earlier and this shows

4 that with SC DMSO, you get effects at one. At oral, you get no

5 effects at one milligram per kilogram and this one breaks the

6 tie for us, given the design of the studies, and that's how we

7 come to a different conclusion that perhaps, EPA came to.

8 So the final slide is: "Please comment on the

9 degree to which studies that measure cholinesterase inhibition

10 provide more sensitive end points the end points measured in the

11 experimental neurodevelopmental studies.

12 Well, I hope that all the data I showed you

13 for the cholinergic end points convinced you, but here is the

14 bottom line for risk assessment. You've got the BMDL10 at .03. 

15 Regardless of your point of view of whether one is a no observed

16 adverse effect level, there are clearly effects that are

17 reported at one, up until six for a neurodevelopmental. 

18 This comes from a paper that is in press that

19 Carole and I published together, focused on birth outcomes and

20 developmental effects. You can see here at five milligrams per

21 kilogram, that's the low L for maternal toxicity. You're

22 getting quite obvious signs of autonomic effects of
Page 364

1 cholinesterase inhibition. 

2 So here you've got one through 12. You've got

3 the BMDL10 and we agree with the Agency that that is protective

4 of the effects on developmental effects in the animals. When

5 you ask the question of biological plausibility, you have to

6 reconcile the problem that these effects are being seen at doses

7 that are orders of magnitude higher than what might be estimated

8 for human exposure. And these are all in milligrams per

9 kilogram because that's how I think of the world. 

10 This is my last slide, and my concluding

11 remarks are based on our evaluation of both the SC and oral

12 studies. We believe that the low L, NOAEL for neurobehavioral

13 outcomes is 1) that the specificity of direction of finding the

14 dose response pattern and the attention to methodologic issues

15 should play a prominent role in evaluating the weight of

16 evidence. And regardless of the point of view on

17 neurobehavioral outcomes, cholinesterase inhibition is indeed

18 the most sensitive objective and reproducible end point on which

19 to base hazard and risk assessment. Thank you very much.

20 DR. KENNETH PORTIER: Thank you. That was

21 very clear. I even understood it.

22 DR. ABBY LI: Oh, good.
Page 365

1 DR. KENNETH PORTIER: Let's see if the panel

2 did. Any questions? Let's start with Dr. Aschner.

3 DR. MICHAEL ASCHNER: Michael Aschner. Thanks

4 very much, Abby. I have a couple of questions or comments. The

5 first one is can you comment a little bit more about what you

6 define as maternal toxicity in the study that just came out? I

7 think it was in your last slide.

8 DR. ABBY LI: Oh, okay. Yeah, they saw

9 tremors. You know, that sort of thing for the maternal

10 toxicity. This is the Morrison, et al. (2000) paper.

11 MR. MICHAEL ASCHNER: My second question is,

12 again, I'm coming back to your last slides. The three criteria

13 that you have, in terms of toxicity related to dose are, you

14 know, pretty much putting a bullet into somebody's head,

15 maternal weight -- I mean, fetal weight.

16 DR. ABBY LI: Oh. Yeah, I'm sorry about that.

17 This is just what I could conveniently get out of another paper

18 that was focused on those end points. So that's just what

19 happened to be in that particular paper.

20 I'm trying to make the point here, based on

21 the tox, that you have lots of neurodevelopmental effects at

22 this dose. That's where the doses are that the papers are
Page 366

1 reporting it. I was just trying to put a picture up that

2 allowed you to compare this with the others on this log basis.

3 MR. MICHAEL ASCHNER: My other question would

4 be then, do you feel that there is any information between the

5 .03 and the 1 that might be useful? Or do you think that --

6 DR. ABBY LI: There's none. Dr. Moser did a

7 really good job explaining that. 

8 DR. MICHAEL ASCHNER: Okay.

9 DR. ABBY LI: I think that's the reason why --

10 is it the Billauer study?

11 Yeah. This is one study that I think looked

12 at anxiety, if remember correctly. It's not one of the

13 strongest studies, but it's the only one that you have an effect

14 at one milligram per kilogram and an NOAEL at .02. But I want

15 to point out that you have a Morrison paper that is actually

16 very nice, in terms of learning and memory. They use a spatial

17 delayed alternation test, really nice beautiful data. They got

18 no effects at all. And that study is not used because they

19 didn't get an effect at one, but maybe it should be, in terms of

20 weight of evidence. 

21 DR. KENNETH PORTIER: Anyone else? Dr. Harry.

22 DR. JEAN HARRY: Jean Harry. Nice
Page 367

1 presentation. Thanks for pulling that together. I have a

2 couple of questions for clarification of how you guys are

3 looking at this. 

4 When you were going through the first note on

5 slide -- just as a point for you -- on slide, I can't even tell

6 the number. The one about the DMSO vehicle study. 

7 DR. ABBY LI: Yeah.

8 DR. JEAN HARRY: You don't need to go back to

9 it. Your investigators there were not at EPA, they were at

10 NIHS.

11 DR. ABBY LI: Oh. Thank you. I'm sorry. 

12 I thought Plaque was one of them. I thought he was an EPA

13 scientist. 

14 DR. JEAN HARRY: That's when he was at NIHS.

15 DR. ABBY LI: Oh. Okay.

16 DR. JEAN HARRY: You were asking about

17 historical data and historical controls. 

18 DR. ABBY LI: Which slide are we talking

19 about?

20 DR. JEAN HARRY: Just as a general concept. 

21 That you were talking about the need for, when you look at a

22 percent change or you look at that type of aspect, you need to
Page 368

1 look at the historical controls, which, I agree, you need to

2 know what your control values are like, but how are you viewing

3 that recommendation you're making for historical controls?

4 Because sometimes that becomes a very stagnant

5 data set and not a living, moving target which it really has to

6 be.

7 DR. ABBY LI: Well, I mean, I think that

8 this -- so I think what you're saying is that you can get this

9 huge historical control data. It may not be relevant for the

10 particular experiment. Is that what you're trying to say?

11 DR. JEAN HARRY: Well, it might be something

12 that you got two years earlier, but it now doesn't apply with

13 exactly the person that's running the assay now, exactly the

14 enzymes that are being done now. That type of thing. 

15 So how are you looking at -- what are you

16 considering a historical control base at which to compare to? 

17 DR. ABBY LI: Well, what I'm encouraging

18 the Agency to do when they're dealing with non-cholinergic modes

19 of action, especially these subtle kind of perturbations and

20 effects, to ask for the control values and ask them to give you

21 the data where it was conducted under identical conditions. I

22 think that's really important.
Page 369

1 DR. JEAN HARRY: Like, within a two-year

2 period of time or something like that.

3 DR. ABBY LI: Oh, okay. Yeah, that would

4 be fine. Right. But that sense really makes a difference. And

5 I'm sure you, as an investigator, understand that, especially

6 for some of these end points that are being evaluated.

7 DR. JEAN HARRY: So if you come back to that

8 example, if you go to Example Number 3.

9 DR. ABBY LI: Can you tell me the slide

10 number, please?

11 DR. JEAN HARRY: If I can see it. It's

12 40-some. I'm going to guess 46. There we go. 

13 So where was a point where you were sort of

14 talking about that and you were talking about the magnitude of

15 the effect and that fact that it is, I don't know, a

16 half-an-error difference, which is true. But if you come back

17 to this and you look at this slide and you ask about your

18 control data, do you have an issue with this Figure that

19 potentially your control animals did not show learning in this

20 procedure?

21 DR. ABBY LI: Yes.

22 DR. JEAN HARRY: Okay.
Page 370

1 DR. ABBY LI: But it is what it is. The point

2 is that the Agency has all this data out there. They have to

3 make a call. I don't think you should dismiss any study. I'm

4 just thinking that you should take a very close look at the

5 data. Like I was saying, that's why I was looking at the left

6 panel here because it was really nice data. 

7 DR. JEAN HARRY: So in this basis, you would

8 raise an issue that in looking at your control data that if your

9 controls do not show learning and memory, then therefore it's

10 very difficult to determine --

11 DR. ABBY LI: Well, it changes my level of

12 concern for the outcome and it starts becoming judgment, whether

13 you're going to call it an adverse effect. What would you call

14 it, Jean?

15 DR. JEAN HARRY: I would call it an invalid

16 study.

17 DR. ABBY LI: Okay. But I don't think that's

18 completely fair. I don't think it's completely fair because,

19 you know, I think you'd just want to make the best use of that

20 data. I think this was interesting and there was just a

21 consistent pattern that I thought was -- they did many with

22 another OP and they did the comparisons. It was just very nice
Page 371

1 data.

2 DR. JEAN HARRY: There would be lots of other

3 things I would ask about this data before I would accept it in

4 --

5 DR. ABBY LI: Well, then you're tougher than I

6 am. Okay. My point is that you've got to have that toughness

7 when you're looking at the literature or else you're going to

8 end up with saying I don't see any pattern because you're

9 accepting everything as equal and you have all the noise. So

10 you should get rid of the noise and focus on whether you have

11 patterns on the good studies.

12 DR. JEAN HARRY: Now, I have one other point

13 of clarification from you, which is just more your opinion of

14 how you're looking at this. 

15 So you've gone through this and you gave a few

16 studies. In the study that you considered the best stud to make

17 the decisions on -- again, you want a number.

18 DR. ABBY LI: Yeah. So this was just trying

19 to show you that, you know, there was this nice pattern here. I

20 mean, I agree that it's kind of an equivocal effect given all

21 the other things going on in the study, but that also shapes how

22 you view how big of an effect there is on learning and memory.
Page 372

1 DR. JEAN HARRY: What I was going to ask you

2 about that is that's one study with nan of 20. What you've

3 raised in some of your concerns is how consistent are you

4 getting your studies and whether you could replicate them.

5 Now, I realize a nan of 20 puts it within sort

6 of the guideline recommendations of animals, but how would you

7 view it if the same lab had run a cohort of an nan of 10 and had

8 run it as a replication study, so two cohorts of 10?

9 DR. ABBY LI: Mm-hmm.

10 DR. JEAN HARRY: Let's say the first one they

11 found no effect; the second one they did. I mean, we all know

12 that happens, especially when you look at it that way, but yet

13 when you're talking about your historical controls, you could

14 potentially give a replicate study designed in such as way that

15 you have two replicates that's fully handled, just a time point

16 out, greater weight than one study where you sorted through 20

17 animals in of all these doses to try to get that much type of

18 work done. How do you view that?

19 DR. ABBY LI: Oh. So you have a replicate

20 design. You see one in one replicate. You don't see one in the

21 other.

22 DR. JEAN HARRY: Just in general, would you
Page 373

1 view a replicate study as stronger data?

2 DR. ABBY LI: Well, in a replicate study, I

3 would consider that you designed it upfront as a replicate

4 design. So I feel that you should do the statistics like you

5 intended a priori.

6 DR. JEAN HARRY: Yes.

7 DR. ABBY LI: And evaluate the study and see

8 what you get first and not try to pick and choose the data

9 afterwards.

10 DR. JEAN HARRY: No. That's not what I'm

11 asking. I'm asking would you view that if you set your design

12 up, not as running 20 all at one time --

13 DR. ABBY LI: And just with sets of 10.

14 DR. JEAN HARRY: -- but that you set it up as

15 a replicate study of 10. So you've designed it that way and you

16 analyzed it that way, is there one you would have more

17 confidence in than the other?

18 DR. ABBY LI: Whether a replicate design is

19 better than not a replicate design?

20 DR. JEAN HARRY: Mm-hmm.

21 DR. ABBY LI: Okay. So here's my experience. 

22 The more you -- it's a balance. In fact, I struggled with this
Page 374

1 when I was first trying to decide how to do the

2 neurodevelopmental guidelines, right. I ended of thinking

3 replicate design is nice because you can maybe control the time

4 of testing and all that kind of thing, but you end up spreading

5 the study out across many days, which has the other type of

6 error that comes up with. 

7 So I think that a replicate design can appear

8 to be better, but is not necessarily. It really depends. I

9 don't think there's a straight answer on that.

10 DR. KENNETH PORTIER: Dr. Terry.

11 DR. ALVIN TERRY: Yes. I'd like you to go

12 back to the slide just before this one. And maybe I'll sort of

13 defend this graph a little bit from both of your criticisms. 

14 First of all, if I'm understanding this

15 correctly, this is probably 11 Graph from the radial arm maze. 

16 DR. ABBY LI: I can't hear you. I'm sorry.

17 DR. ALVIN TERRY: Okay. I just want to make

18 sure this graph isn't misinterpreted. From my understanding,

19 this is working memory errors averaged by week. So a controlled

20 set of animals is going to reach an asymptotic level of working

21 memory errors within a week. So you're not going to see an

22 acquisition curve like you would a water maze or something of
Page 375

1 this nature.

2 DR. ABBY LI: I think there's like, if I

3 remember correctly, four trials. So it's still on the low side

4 for errors.

5 DR. ALVIN TERRY: I don't remember how many

6 arms this particular maze is. Is this an eight-arm, 12-arm,

7 16-arm?

8 DR. ABBY LI: I have to look that up. I think

9 it's a 12-arm.

10 DR. ALVIN TERRY: So if you're thinking about

11 an asymptotic level performance in controls, you know, three

12 errors would probably be very reasonable. So you're looking at,

13 over the course of several weeks, the very first week controls

14 can reach asymptotic levels. So they're not going to have very

15 many errors. 

16 So I think it's a misinterpretation to say

17 there's not any evidence of acquisition there and that there's

18 not a difference between the control and the chlorpyrifos

19 treated.

20 DR. ABBY LI: Well, I mean, I do think that it

21 is valid to say that you've really got a very narrow range of

22 errors.
Page 376

1 DR. ALVIN TERRY: And that's just the manner

2 of this task.

3 DR. ABBY LI: I mean, I showed you the other

4 study on the other radial arm maze that they were up at eight to

5 13, right? This one.

6 DR. ALVIN TERRY: Yes. And you're looking at

7 Sessions 1 through 3. It depends on whether you add the

8 sessions.

9 DR. ABBY LI: So one through three is

10 equivalent to Week 1.

11 DR. ALVIN TERRY: One through three?

12 DR. ABBY LI: Well, I think it's wonderful

13 that I got the EPA SAP behaviorists talking. So I'm going to

14 let Vorhees talk and I'll be quiet.

15 DR. CHARLES VORHEES: Charles Vorhees. This

16 is a 16-arm. So you can't compare them very easily.

17 DR. ABBY LI: Okay.

18 DR. CHARLES VORHEES: Did you look at the --

19 and this is addressed to Dr. Carr -- did you look at the

20 learning within sessions on your data that Dr. Li is showing

21 there? That's your data, right?

22 DR. CARR: Yes, it's my data. And I will be
Page 377

1 honest. My graduate student did this, and when he brought it to

2 me I was concerned. But there was a small amount of learning,

3 not as much as I would've expected, but it does appear, you

4 know, the way he presented it -- it doesn't appear that there

5 is, but there was.

6 DR. CHARLES VORHEES: So within Week 1, was

7 there a learning curve?

8 DR. CARR: A slight learning curve, yes. it

9 wasn't as strong as I would've expected, but it was present.

10 DR. CHARLES VORHEES: Okay. Do you have any

11 idea of why that was?

12 DR. CARR: I cannot tell you that.

13 DR. ABBY LI: I have a speculation.

14 DR. CHARLES VORHEES: Sometimes it's just

15 nature of the beast when you do these and -- and to touch back

16 on how you do them, sometimes the manpower dictates that you

17 spread it across the top. So you can't set up 20 animals and

18 run them in a 12-arm radial maze together. It's really

19 difficult to do that.

20 DR. ABBY LI: You know, there are so many

21 different factors, right? What I tried to look at in my tables,

22 I tried to look at this but I have no evidence. I tried to say
Page 378

1 well, could it be the way they were depriving food, right?

2 So in this particular study, it's started out

3 with a 90 percent deprivation. There was a good reason for

4 doing that because they didn't want to interfere too much with

5 causing problems on top of trying to look at the effect of

6 chemical. There could be -- we don't know, but that's the data.

7 Well, I would be interested. Would you agree

8 that in that particular case maybe the NOAEL is one?

9 DR. CHARLES VORHEES: In that particular case,

10 yes.

11 DR. ABBY LI: Okay. I didn't do too badly. 

12 Thank you. So this is the kind of thing. It shows up on the

13 EPA table as an effect, but in my paper, it shows up as an

14 effect in the lab in the table, but in my analysis, I say that

15 that's an NOAEL of one. And that's how I can come to a

16 different conclusion than someone else by looking at the data,

17 critically.

18 DR. RUSSELL CARR: This is Dr. Carr. I would

19 also like to say that improvement in Week 4, I would never ever

20 say that chlorpyrifos improved performance in a radial arm maze.

21 So therefore, I would say, Dr. Li, that's possibly not a true

22 effect.
Page 379

1 DR. KENNETH PORTIER: This is Ken Portier. 

2 Dr. Li, you obviously looked very closely at all these studies

3 and the analyses. I was just wondering if any of them used

4 anything like a Bayesian, an empirical Bayesian model approach

5 to analysis because a lot of what you're talking about here is

6 kind of incorporating expectation into this analysis. 

7 So the expectation, you wouldn't see

8 improvement in the fourth week, and yet you did see it. So a

9 Bayesian or an empirical Bayesian analysis would have shrunk

10 that estimator close to the -- and it would've shown up as not

11 significant. So there are new statistical analysis approaches

12 that help you incorporate that thinking. And we've seen that

13 before other panels, it's just I haven't hear the "Bayes" word

14 in a day in a half. I had to bring it up, clearly. I just

15 wondered.

16 DR. ABBY LI: No. That has not been applied. 

17 At the most, you know, I was hoping to see maybe some kind of

18 principal component analysis in some of the social behavior

19 stuff rather than a T-test for each of the individual behaviors,

20 right?

21 DR. KENNETH PORTIER: So looking for some kind

22 of latent underlying structure that would lead to these --
Page 380

1 DR. ABBY LI: You know, like some of the

2 social behaviors that Dr. Moser presented, you know, you get a

3 lot of effects at one milligram per kilogram, but if you look at

4 the data, you're looking at eight to ten, even more at different

5 kinds of parameters. And you look at duration and frequency of

6 each one, you're going to get a hit. 

7 So that's why we're saying if you look

8 carefully at the statistics, you look carefully at the pattern,

9 I'm not that impressed in some of the social behavior. 

10 DR. KENNETH PORTIER: Any additional

11 questions? Seeing none. Thank you very much, Dr. Li.

12 DR. ABBY LI: You're welcome. Thank you.

13 DR. KENNETH PORTIER: At this point, Dr.

14 Juberg is going to have closing remarks. 

15 DR. DALAND JUBERG: Yes. I will be very

16 brief. Hopefully we haven't overextended our welcome. I know

17 you all have precious time to do a lot of work. I just wanted

18 to go back -- I think I have a couple of slides. To reiterate

19 Dr. Bradbury's recommendations that we go after transparency,

20 clarity, consistency and reasonableness, I think these are not

21 only tenets of process, but also as guiding principles in

22 conjunction with the evaluation of all the animal and human data
Page 381

1 that you have seen, that you'll continue to see. So I just want

2 to break this into four points, really. I'm just going to read

3 these to you.

4 Based on what you've heard, it's our

5 conclusions that new toxicological data and advancements in

6 PB/PK/PD modeling has increased the reliability and confidence

7 in the current risk assessment for chlorpyrifos. 

8 Secondly, laboratory data reporting adverse

9 outcomes from non-cholinergic perturbations are not anchored

10 with a biologically plausible mode of action relevant to humans

11 at exposures below those causing cholinesterase inhibition. 

12 And finally, third, recent independent reviews

13 which integrate human animal data have concluded that protection

14 against cholinesterase inhibition is protected against

15 neurobehavioral and neurodevelopmental effects. And finally,

16 critical epidemiological analysis have considered hazard,

17 exposure and relevant biomarkers and does not support a causal

18 role for chlorpyrifos in reported adverse outcomes in humans.

19 So with that, I'm going to adjourned and our

20 panelists will remain if you have any further questions for

21 them. Dr. Portier, thank you very much and Dr. Jenkins as well

22 and to the panel.
Page 382

1 DR. KENNETH PORTIER: Don't leave. Don't

2 leave. There might be some questions here. Dr. Lu.

3 DR. CHENSHENG LU: So I meant to ask this

4 question yesterday, but I figured there was going to be some

5 information coming up this morning. The question is directly to

6 your conclusion. 

7 The first conclusion here that the Dow team,

8 you know, has raised this concern over and over again in terms

9 of seeing some new behavioral effect but no cholinesterase

10 inhibition is not a plausible conclusion in terms of at certain

11 levels this causes harm.

12 I have two concerns. One is that we're

13 talking about two different outcomes; one is acute house

14 outcome, which is acetylcholinesterase inhibitions and then the

15 other one is a chronic house outcome, which is

16 non-cholinesterase or neural behavior development. So I don't

17 know how you can conclude that this two things has to have some

18 kind of relationship in order to make a conclusive evidence that

19 level is a concern.

20 Many chemicals, if not all of them, behave

21 very differently in terms of acute versus chronic. For example,

22 if kids ingest a bolas of lead, obviously these kids are going
Page 383

1 to have heavy metal poising symptoms right away, but not

2 necessarily mean that this kid will instantaneously become low

3 IQ here. But whereas, if the kid is exposed to very low level

4 of lead over a period of time, obviously it's going to impair

5 his or her IQ.

6 So I don't see -- there has to be a link

7 between these two measures. You know, pasteurized solvent, you

8 mentioned, chemical does have these kinds of two different

9 behaviors. So I kind of disagree that these two things has to

10 be linked in a way, you know, identifying the association

11 whatsoever. I just don't -- I disagree, but maybe you have

12 different insight on this or the panel has different insight.

13 DR. DALAND JUBERG: No. Just to say that, you

14 know, for regulatory risk assessment, we are doing studies on

15 both acute and chronic durations of exposure. Cholinesterase

16 inhibition in both situations appears to be the most sensitive

17 marker. And through a weight of evidence evaluation, much of

18 what you heard from a lot of scientists, we're not seeing that

19 these effects are occurring for either duration below those

20 doses that inhibit cholinesterase inhibition. So that seems to

21 still be a critical sensitive marker that seems to be protective

22 against other neurobehavioral outcomes, regardless of duration.
Page 384

1 DR. CHENSHENG LU: But at the moment when we

2 measure cholinesterase inhibition, that reflect acute response

3 to the exposure. Cholinesterase has been used in an

4 occupational setting many, many years ago. The only reason that

5 we're looking at cholinesterase inhibition in farmers is because

6 we want to document the exposure either for today or for the

7 workweek. 

8 If the measurement was not correct, according

9 to the protocol, then that number would be rejected, according

10 to either California or Washington State protocol because that

11 number does not reflect acute response to the exposure. 

12 So again, that's my point that when we look at

13 acetylcholinesterase, we look at it as an acute response, not

14 necessarily chronic low-level exposures. Disagree that there

15 has to be a link unless there is data. And that's why I

16 remember one of the slides that Dr. Li showed that there are

17 very few studies show the link between acetylcholinesterase and

18 neural behavior. To me, I think that's obvious because we're

19 talking about two different outcomes. How could you link these

20 two different outcomes together in a scientific way?

21 DR. DALAND JUBERG: I guess the only other

22 think I would add is that is in many reasons why the chronic
Page 385

1 referenced dose and point of departure is lower than the acute

2 by a couple of orders of magnitude. And then on top of that,

3 for what we're trying to do here, and regulatory risk assessment

4 protection of public health, that's why in addition to those,

5 you've got additional factors below that that we're

6 incorporating to maybe address any remaining uncertainty. 

7 Yeah, we can talk about frank effect levels

8 and then no effect levels but then moving beyond that, that's

9 where, you know, to account for some of those remaining

10 uncertainties. There's always going to be uncertainty in

11 science. We bring in other additional factors. I may not have

12 adequately addressed it.

13 DR. KENNETH PORTIER: Dr. Goodman, you want to

14 join in on this?

15 DR. JULIE GOODMAN: I just wanted to add that

16 the issue is that you have these associations in epidemiology

17 studies that are occurring at exposures or is a magnitude lower

18 than acetylcholinesterase inhibition. So you may have a point

19 that what's happening, these neural behavioral effects might not

20 be through that pathway, but the point is we need to understand,

21 do we think that those associations are indicative of causation

22 or due to some alternative explanation? 
Page 386

1 And the fact that when you do this weight of

2 evidence analysis and look at all the data together that you're

3 not seeing any of these effects at exposures below that when you

4 have that biomarker of acetylcholinesterase inhibition indicates

5 that those effects -- the reported associations in epidemiology

6 studies are not likely indicative of causation.

7 DR. KENNETH PORTIER: One last comment, Alex,

8 and then we're dying for a break here.

9 DR. CHENSHENG LU: Okay. So I guess my

10 comments are actually simpler than what was just described. I'm

11 talking about two boxes; one is acute toxicity and one is

12 chronic. In this case, prolapsus. I say for many other

13 chemicals as well. There's no breach cross over these two boxes

14 because if you look at outcome, they're very different. So how

15 can you just argue that there has to be some sort of association

16 between acetylcholinesterase inhibition versus smaller head size

17 or shorter baby body length?

18 I don't see the connection unless there's a

19 defined issue that one is constant to the other. But at this

20 moment, I don't think that's the case. I used lead as example. 

21 I think there are many, many chemicals that actually behave very

22 differently, not what you said in the Caucasian, because that's
Page 387

1 not what I'm saying. I'm just in a very simple box model.

2 DR. KENNETH PORTIER: Yeah, that's good. You

3 know, I'm sure you'll be able to bring this up again so the

4 panel discusses it. At this point we're going to take a

5 15-minute break. 

6 And the remaining public commenters, please

7 make sure that your material is loaded if you're going to use

8 slides at this point. And if there any additional public

9 commenters who have not registered, this is the last call. 

10 Please meet with Fred Jenkins up front here. We'll meet back at

11 11:00 by this clock.

12 (WHEREUPON a brief recess was taken)

13 DR. KENNETH PORTIER: Okay. Let's reconvene. 

14 At this point, we're going to continue with the public comment

15 section. Our next public commenter is Jennifer Sass with the

16 NRDC. And by previous arrangement, she's going to be addressing

17 the panel via the telephone. 

18 We agree that this is not a good precedent

19 that we want to set for the panel for having people address the

20 panel via technology. We like to see them face-to-face, but in

21 this case it was unavoidable and it was done prearranged. So

22 we're not setting a new precedence here; we're just making one
Page 388

1 exception. So Dr. Sass.

2 DR. JENNIFER SASS: Hello. Thank you very

3 much. Can the panel hear me clearly?

4 DR. KENNETH PORTIER: Yes. We can hear you

5 very clear.

6 DR. JENNIFER SASS: Okay. I have prepared

7 comments and was hoping to be there to circulate them to you. 

8 Thank you very much, EPA, for accommodating this. I wasn't able

9 to be with you today, unfortunately. Although I know you've all

10 seen me enough, but I'm only tiresome in person. 

11 I will be reading through my comments, which

12 I've circulated electronically. Thank you very much to the SAP

13 for giving your service to this important assessment of

14 chlorpyrifos. A decade ago, EPA took the important step towards

15 protecting human health by canceling inborn uses of

16 chlorpyrifos, and as a result, children today are not being

17 exposed to these high and unsafe levels from many of the

18 household uses of chlorpyrifos.

19 Unfortunately, though, chlorpyrifos is still

20 widely used in agriculture at about 10 million pounds annually,

21 and this puts rural populations, including farm workers and

22 their families and bystanders that live or work or go to school
Page 389

1 near fields at risk, as well as continues to have chlorpyrifos

2 residue on food, putting consumers at risk, generally.

3 My organization, NRDC, petitioned the EPA in

4 2007 to cancel the remaining uses of chlorpyrifos. As part of

5 the settlement with EPA, EPA was scheduled to release an updated

6 assessment of its health risk by June 2011. That deadline has

7 now passed. So we're asking the EPA to use its authority and

8 its obligation to protect human health, especially mothers,

9 infants, and children from chlorpyrifos by canceling remaining

10 uses.

11 My responses to the questions are here. For

12 Question 1, that was a comment on whether cholinesterase was the

13 most robust source of data for deriving a part of departure. 

14 I'll refer to the SAP's expertise, except to

15 say that cholinesterase inhibition may or may not be the most

16 robust source of data. I don't know the answer, but I do know

17 that it's not the only source of data regarding mechanism of

18 toxicity or end point of concern.

19 The SAP can recommend that EPA adjust for

20 potential risks of non-cholinergic toxic effects, through either

21 additional safety factors or by quantitatively adding the risks

22 together for multiple toxic pathways. This was an approach that
Page 390

1 is similar to the one used by the EPA integrated risk

2 information, or IRIS program, to calculate an acceptable level

3 of exposure, the trichloroethylene, TCE. In that most recent or

4 final assessment, they considered multiple end points of concern

5 together. 

6 Question 2 points out that there are multiple

7 biologically plausible hypotheses, but mechanistic data is

8 inadequate to support alternate modes of action or adverse

9 outcome pathways. 

10 NRDC would like to suggest that EPA has not

11 actually shown that it can protect all end points of concern,

12 using its current end point selection and methodology. It is a

13 matter of weight of evidence versus weight of speculation. 

14 EPA is speculating with inadequate evidence

15 that by setting its point of departure based on a 10 percent

16 inhibition of cholinesterase, it is protective of all adverse

17 end points; however -- and I quote NIEHS scientists -- "Health

18 protective assumptions are the default, must always be the

19 approach, unless informed otherwise by multiple

20 utility-consistent, adequately powered studies covering the full

21 range of human exposures."

22 Dr. Lorenzo Tomatis, former director of the
Page 391

1 International Agency for Research on Cancer said, and I quote,

2 "The issue is not singularly the adequacy of mechanistic or

3 epidemiologic evidence but certainty for protecting public

4 health."

5 The NAS 2009 report by the National Academies,

6 titled Science and Decisions, Advancing Risk Assessments,

7 otherwise known as the Silver Book, recommended that when

8 information is missing or unreliable that the agencies use

9 scientifically-based default assumptions that protect health

10 rather than waiting for more data. And then this way agencies

11 could speed up chemical assessments and decision-making

12 processes while still being protective of health.

13 The NAS Committee concluded in their report,

14 and I quote, "Established defaults need to be maintained for the

15 steps and the risk assessment that require inferences." NAS

16 recommended that EPA and other agencies update their defaults

17 and assumptions, including where they are unstated or implicit

18 assumptions, replace these with explicit ones.

19 For example, for this question EPA does not

20 have data on other mechanisms of action and is therefore,

21 essentially quantitatively ignoring that potential contribution

22 to risk. SAP could recommend that EPA acquire additional
Page 392

1 uncertainty factors where it has inadequate data to describe

2 alternate mechanisms of toxicity or adverse outcome pathways.

3 Question 3 is regarding the neurodevelopmental

4 data from laboratory animals. NRDC believes that the rodent

5 data do support the conclusion that prenatal or early postnatal

6 chlorpyrifos exposure may result in long-term adverse effects. 

7 And sadly, this is also supported by the epidemiologic studies

8 that you will review in children who are exposed prenatally.

9 The SAP could recommend that EPA revoke all

10 tolerances and cancel remaining uses of chlorpyrifos in light of

11 these unsafe continued uses. EPA has the authority and, in

12 fact, the obligation to protect human health and the environment

13 from continued exposures to this unnecessary and unsafe

14 pesticide. 

15 Question 4 is regarding epidemiology. EPA has

16 identified new epidemiologic studies published in the last year

17 that demonstrate lasting impairments in children exposed,

18 prenatally to organophosphates, including chlorpyrifos. 

19 Particularly the Chimariko study, which is

20 rural in agriculture populations, provides real world evidence

21 that EPA's current restrictions or label mitigations are not

22 working. EPA rarely actually has data to test whether its label
Page 393

1 restrictions are providing effective mitigation and they're

2 effectively protecting people in the environment. But here with

3 this study, we actually have evidence in real world that the

4 mitigations are not protected enough and that children are being

5 exposed and suffering lasting consequences.

6 SAP could recommend that EPA consider these

7 data as evidence that continued use of chlorpyrifos presents an

8 unacceptable risk to expose populations, particularly where

9 prenatal exposures occur.

10 Question 5 is regarding biomonitoring and

11 exposure data. I'm going to move a little off the actual

12 question, but to bring up some important exposure and

13 biomonitoring data. Residential bystanders' post-application

14 inhalation exposures are too high. EPA's own analyses of some

15 of the volatilization for exposures for residential bystanders

16 indicates an end quarter of the acute ambient air concentrations

17 resulted in risk estimates that exceeded EPA's level of concern.

18

19 Over half of the acute application site air

20 concentration resulted in risk estimates of concern. And some

21 of the spray drift risk estimates calculated by EPA were also

22 exceeding levels of concern, particularly for acute adults and
Page 394

1 children inhalation exposures from ground mosquito treatment.

2 These data point strongly to the need to

3 assess both drift and volatilization as a significant pathway of

4 exposure for people. In some cases this pathway alone exceeds

5 level of concern, according to EPA's own analyses, and

6 therefore, presents an unacceptable risk. 

7 SAP could recommend that EPA consider these

8 data as evidence that continued use of chlorpyrifos presents an

9 unacceptable risk. The drinking water assessments that EPA

10 conducted in 2011, presented in a preliminary health risk

11 assessment proved that drinking water alone exceeded many of

12 EPA's risk estimates. I presented a Table from EPA's own

13 assessment -- I haven't altered it in any way -- in my comments

14 on page 5. I've highlighted all of the areas where the

15 exposures exceed EPA'S acceptable limit. 

16 If EPA had actually used the 10 times stpA

17 factor to protect children instead of no stpA factor, all of

18 these actually would've been 10 times higher and all scenarios

19 for all populations at all application rates would have exceeded

20 the Apad. So SAP could recommend that EPA consider the data as

21 evidence that continued use of chlorpyrifos is unacceptably high

22 from drinking water alone.
Page 395

1 There's also evidence that occupational

2 exposures are unsafe. EPA's own assessment reports that many of

3 the exposure scenarios, 350 exposure scenarios for workers

4 exceeded acceptable limits. Some, even when the full level of

5 personal protective equipment was used. And importantly, it is

6 important to remember that occupational exposures are not just

7 to adult workers, it is also farm workers that are under age and

8 pregnant women who work in fields as well.

9 In conclusion, I'd like to suggest that SAP

10 could recommend that EPA finalize its policy to protect workers

11 and children of workers in agriculture fields to provide a

12 measure of protection for these young farm workers and their

13 families. This policy was announced in a Federal Register in

14 2009 by EPA, but has been held up and has not been finalized

15 yet. 

16 I'm happy to answer any questions. Thank you.

17 DR. KENNETH PORTIER: Thank you. Do we have

18 any questions from the panel for Dr. Sass?

19 Dr. Sass's written comments were in the

20 docket; is that correct? 

21 DR. FRED JENKINS: Yes. 

22 DR. KENNETH PORTIER: Yes. We'll distribute
Page 396

1 hard copies for you so that you can look at that. Thank you

2 very much, Dr. Sass. It doesn't seem to be any questions here.

3 DR. JENNIFER SASS: Thank you.

4 DR. KENNETH PORTIER: So the next public

5 commenter before the panel is Dr. Dale Hattis from Clark

6 University. Dale.

7 DR. DALE HATTIS: Yes. I must preface my

8 remarks by saying that these are my personal views and are not

9 representative of Clark University, my EPA funders, or even my

10 collaborators on the project.

11 As you may know from the EPA background

12 materials, and my colleague has been working for a number of

13 years, funded by an EPA STAR Grant on chlorpyrifos dosimetry in

14 people. 

15 The aim of this is to bridge a gap in the

16 epidemiological literature between the biomarkers used in the

17 epidemiological studies, in particular, blood chlorpyrifos and

18 the chronic oral and inhalation doses that EPA will need to

19 consider if it wants to use the epidemiology rather than the

20 animal experiments as a basis for the determination of

21 tolerances in RfDs and RfCs. 

22 Slide 2. Next slide. Oh, I do it. Okay. 
Page 397

1 Let's see. This discussion has two parts; first, and the more

2 extensive part, will be talking about this dosimetry issue,

3 where I can sort of abbreviate by shorthand of saying the

4 dosimetry ratio. That is the ratio of chronic dose, either by

5 inhalation or ingestion to the blood level. So all of the PBPK

6 modeling and et cetera, and another analysis are designed to

7 give some insight into that ratio. We're way below in these

8 experiments, in these observations, levels where saturation is a

9 problem. 

10 So it's reasonable to take this, more or less,

11 as a linear function, the amount of blood chlorpyrifos

12 reflecting at least some measure of the chronic doses. And

13 these doses, at least to the extent of the air levels, are

14 relatively consistent within houses, within apartments for the

15 Columbia study. 

16 The dosimetry discussion involves developing a

17 prior, and this is at least a Bayesian inspired analysis, if not

18 a true Bayesian analysis, from a bounding of the possible range

19 of this dosimetry ratio, external dose to internal dose, based

20 upon the data that are internal to the Columbia study that was

21 shared with me.

22 The second part is a PBPK-based uncertainty
Page 398

1 distribution, varying a couple of key parameters: the metabolism

2 rate and the tissue blood partition coefficients. And then I'll

3 do something like a Bayesian combination of these, although you

4 should have the caveat that I'll give you the formula, but it's

5 not exactly Bayes theorem. 

6 After that, I'll talk about how one might

7 interpret the Columbia study results for reduced short-term

8 memory and IQ in terms of RfD and RfC type standards. So there

9 are risk management considerations in defining what a benchmark

10 dose -- that benchmark response is in this context, and these

11 are beyond where a techy like me usually ventures, but in order

12 to show these implications, I have to make assumptions about

13 what our reasonable range of choices might be and then we'll

14 conclude with implications of some alternative benchmark

15 response definitions for lower confidence limits on associated

16 inhalation and oral intake studies using the central estimates

17 for the dosimetry ratio and the lower confidence limits on the

18 doses, or upper confidence limits on the potency indicated by

19 the Columbia epidemiologic study of the neurological indices in

20 relation to the blood levels measured in the mothers.

21 I'm first going to talk about the bounding

22 calculation from the Columbia data themselves. I defined a
Page 399

1 lower limit as the observed ratio of measured air intake for the

2 individual women projected to the birth date of the babies

3 because there are secular trends in all these biomarkers. So

4 basically there was some lag time between the measurement of the

5 air levels and the actual birth of the babies. 

6 So I at least took into account the decline

7 expected in that period for the average of the whole population.

8 Basically, that gives rise to a dosimetry ratio of about half a

9 nanogram per kilogram a day, per picogram per gram of

10 chlorpyrifos in the blood. For an upper limit I also had

11 measurements in the same women of the excretion rate of TCP,

12 normalized creatinine, which I could then interpret in terms of

13 a daily excretion rate. And this gives a much larger -- so

14 basically, assuming that some of the TCP might've come from

15 other sources, this represents an upper limit on the possible

16 dose that chlorpyrifos that people could have -- that the

17 mothers could have gotten.

18 We know the lower limit is, you know, of the

19 order of half a nanogram per kilogram per day. The upper limit

20 calculated in this way is something like 55, 54 nanogram per

21 kilogram per day, per picogram per day. So that sets some

22 reasonable limits. I decided that I wouldn't consider those as
Page 400

1 absolute limits, that there's some chance that the real value

2 could be outside that, so I assigned subjective one percent to

3 99 percent confidence limits to these bounds and that results in

4 a geometric mean, a log normal distribution with a geometric

5 mean of about 5.5, a very large geometric segregation of 2.43. 

6 And that's what that distribution looks like in sort of

7 anti-logs, you know, unlogged space. If I plotted the log, it

8 would be symmetrical. 

9 The PBPK analysis of the dosimetry ratio

10 starts with the Timchalk model results. The Timchalk model was

11 published, originally, in 2002. He presented results in SAP

12 hearings in 2008 that implied a ratio of about 40 nanogram per

13 kilogram per day for ingestion exposure and 26-fold less for

14 inhalation exposure, giving a ratio or about 1.5 in the terms

15 that I had prior being .5 to 50.

16 The Timchalk metabolism rate estimates,

17 however, were derived -- the large difference between, in the

18 Timchalk model, expectations for oral versus inhalation exposure

19 come from a huge first pass effect. That is, essentially, stuff

20 that's ingested has to be passed through the liver before it

21 reaches the rest of the systemic circulation. And in the case

22 of the original rat-derived metabolism rates, that ratio is, you
Page 401

1 know, something like 99 percent of the material. So that

2 creates a big difference from the inhalation, which gets the

3 systemic circulation more directly and only a quarter of the

4 blood volume get though the liver with each per minutes. 

5 So essentially, that creates this huge

6 difference between the oral and the inhalation route of

7 exposure, in terms of its interpretation, in terms of blood

8 level.

9 Our uncertainty analysis of the adapted model

10 takes into account uncertainties in both metabolisms. I had

11 updated metabolism rate information in people. So this is

12 people who died, but they had various ages that -- you know, we

13 had a total of 30 people worth of recent information published

14 by Smith, I believe. I took from that the five adult women as

15 the basis for our thing. But there's uncertainty because there

16 is variation from person-to-person. There's uncertainty in that

17 average, calculated from only five people. So I took into

18 account the statistical confidence limits expected from that

19 information. 

20 Also, the people who died and gave rise to

21 these liver microsome measurements weren't known to be pregnant.

22 So there's at least potential for there to be a systematic
Page 402

1 difference between pregnant people and non-pregnant people in

2 their liver metabolism rate. So I found a substantial

3 compilation of -- well, it was seven different drugs where the

4 clearance values have been measured, in vivo, both for the

5 pregnant people and the non-pregnant people. And that

6 difference was usually approximately two-fold, with confidence

7 limits ranging from, more or less, equal to about four-fold

8 larger. So I incorporated that uncertainty as well, into the

9 overall analysis of how uncertain I should be about these

10 recalibrated PBPK measurements. 

11 It's important to note that in calibrating the

12 model at this stage, I have not used the results from the

13 Kisicki and Nolan, et al. direct administration studies because

14 of EPA concerns for the informed consent procedures. EPA

15 considers that it is barred from using the Kisicki data, and

16 therefore, to make it potentially useful to EPA, I haven't done

17 that. But I will do that later before, just to add another

18 point of comparison that can give perspective on the likely

19 uncertainties.

20 So here's what the results come out to be. 

21 The best estimate projections are in the upper left. For

22 ingestion, I've basically got a central estimate of about 188
Page 403

1 ratio of nanogram per kilogram day, per picogram per gram of

2 chlorpyrifos in blood. Inhalation is projected to be about six

3 or seven times more efficient in producing blood level, compared

4 to the ingestion where the central estimate is only 28. And you

5 can see that one standard deviation, high or low, or one

6 centered, high or low, in both the metabolism parameter and the

7 tissue blood partition coefficients as derived from -- has

8 modest, you know, effects on the overall results.

9 So basically, I used these results with the

10 uncertainties in these two dimensions to construct an overall

11 stratified random sample of the overall distribution. So this

12 is the distribution that I get from that process of permuting

13 both of those parameters. 

14 Now, there are other uncertainties. I

15 probably should be more uncertain than I have represented here

16 about the metabolism rate because there could be differences

17 between the functioning of the enzymes in human liver and those

18 that are inferred from the in vitro microsome measurements

19 combined with the amount of microsomal protein per gram of

20 litter and things and that sort. So there's probably more

21 uncertainty than I have mentioned in these PBPK model results,

22 but that's what I've got so far. 
Page 404

1 So what you see here is something that instead

2 of the prior, which had a central estimate of about 5.5, this is

3 a central estimate of approximately 27 or a bit more, and it

4 spreads out between 20 and 40 in ways that are interested. So

5 the formula for combining the two distributions is a posterior

6 probability that the inhalation dose to blood level is

7 essentially the product -- the probability that's in any

8 interval of the dosimetry factor -- measured in log space

9 usually -- is a product of product of the probability that the

10 value is in that interval of the prior. The probability that

11 it's in that interval times the calibrated model, divided by the

12 sum of all of such products. So essentially, this is

13 integration over all of the reasonable values of the thing.

14 So what I get when I do this combination is

15 this very much resembles the model projection because the model

16 projection was considerably narrower in its distribution. So

17 that heavily influences this result with a central estimate of

18 about 26. And I think I've got the statistic. 

19 So the updated -- with the geometric mean of

20 26 and 2.5 percent to 95 percent confidence levels, something

21 like 20.5 to 33.5 nanogram per kilogram/day inhaled, per

22 picogram per gram in maternal blood. Using the central estimate
Page 405

1 for the ingestion to inhalation ratio, you get something like

2 175 nanogram per kilogram per day, per picogram in maternal

3 blood.

4 So all of that stuff goes to how we use, how

5 we can convert the dosimetry findings, which are in terms of

6 either maternal or child blood, core blood in the Columbia

7 epidemiology study to doses that EPA could potentially use in

8 its determination of ADIs and RfCs and RfDs. Essentially, the

9 ultimate result -- okay. 

10 Now, we'll turn to the Columbia

11 neurodevelopmental findings. Essentially, what they developed

12 was a series of progression relationships controlling for other

13 confounders of the relationship to either the working memory

14 index or full scale IQ. 

15 The working memory index was the kind of

16 parameter that gave the most clearly positive results,

17 essentially, with basically, they related the national log

18 algorithm of the working memory index with a particular

19 coefficient to the blood chlorpyrifos levels with a P-value of

20 less than .01. Full scale IQ was also positive, but at a much

21 more marginal P-value of .05 or so. 

22 So basically, now that I have these
Page 406

1 relationships I can then go on to then translate those into

2 external ingestion or inhalation exposure levels. Most of the

3 exposure in the Columbia cohort, might well have been

4 inhalation. 

5 So possible risk management choice; we need to

6 talk a little bit about possible risk management choices for

7 defining a benchmark dose. One possibility is that you would

8 have a 10 percent excess risk of being below the first

9 percentile of the index. Both IQ and the working memory index

10 are constructed similarly to have a population mean of 100 in a

11 standard deviation of 15, so that the first percentile

12 corresponds to a reading of about 65.

13 So the question is okay, if you want to have a

14 10 percent excess risk as your benchmark, raising the incidence

15 of scores as low as that from one percent to 1.1 percent, how

16 much do you need to shift the population mean in order to get

17 that result? The Columbia findings are in terms of,

18 essentially, population means or population geometric means.

19 Essentially, to do that requires a shift of

20 overall distribution of about .54 index points. If you want to

21 take another possible definition of the benchmark response, that

22 is, beyond the fifth percentile -- that's the 75.3 or so, on the
Page 407

1 index -- and you want to again, use this as the benchmark dose,

2 a 10 percent excess risk from five percent to 5.5 percent would

3 require a shift in the mean of about .7 index points. I'm going

4 to present some results based upon this second criteria.

5 Now, there's another possible way in which you

6 could value individual shifts in IQ for risk management

7 considerations and that is to say, okay, well, we know that IQ,

8 and probably related neurological functions, are important for

9 long-term worker productivity and therefore, lifetime income. 

10 So Gross, in conjunction with Joel Schwartz and others at

11 Harvard did a study, or published in a study in about 2002 that

12 suggested that each index point reduction, in the context of

13 lead, was estimated to be associated with a loss of about two

14 percent in lifetime income for 2-year-olds, or about $14,500 in

15 2002. 

16 So let's say that we wanted to consider that

17 you wouldn't want to suffer more than $100 per exposed person

18 damaged for a lifetime income. If you want to do that, then of

19 course, you need to limit the population shift to less than

20 about 100th of an IQ point, which is a pretty stringent type of

21 standard.

22 So anyhow, that's another basis that you might
Page 408

1 consider for risk management. Still, there are people who would

2 want to be more stringent than that because $100 of lifetime

3 income might not be. Although it wouldn't be detectable, it

4 might not trivial be either when considered as a population

5 average and there might be variation among people as well.

6 So what does that all mean for the overall

7 numbers and how do they compare with the RfDs/RfCs derived from

8 the animal studies? 

9 I have made a study of the animal studies, but

10 my recollection is that you come out with a population aggregate

11 dose of about 300 nanograms per kilogram/day for the ultimate

12 ingestion. Now, this is an inhalation intake. As you see, the

13 inhalation dose that's associated with .7-point change in the

14 index, if you consider that as your benchmark, that comes out to

15 be about 18 nanogram per kilogram/day. If you then apply a

16 traditional uncertainty factor, you get down to 1.8 nanogram per

17 kilogram/day. And if you want to apply the FQPA factor in

18 addition, it gets down to .18. If you want to just accept your

19 benchmark of 100th of an IQ-point standard, that gets you to

20 about .26 nanogram per kilogram/day by inhalation.

21 Now, this is not for IQ, this is working

22 memory. There's a similar, but only a little less stringent
Page 409

1 effect for the IQ. So this is now the same working memory end

2 point and the ingestion in an ingestion index. This comes down

3 to, you know, with all the calculations, either something

4 between 1 and 12 nanogram per kilogram/day for the .7 IQ point,

5 7-point shift and 1.8 for the ingestion.

6 So these are now similar calculations for the

7 full-scale IQ measurements. Suffice it to say that these

8 numbers -- the points of departure are comparable to the EPA's

9 ultimate population aggregate dose target, but if you then go on

10 to the 300, I think it would be on that scale. But then if you

11 go on to apply the uncertainty factors to that, you get much

12 lower. Or if you adopt the .01-point change in the index

13 standard, then you get too much lower values, all of which may

14 suggest that if you believe the Columbia results and the other

15 epidemiological findings that are somewhat parallel to that,

16 that there's some indication that the usual procedure to base

17 your standard on the 10 percent benchmark inhibition in the

18 animal studies is not picking up the effects of dose levels that

19 are needed -- that may impair neurodevelopmental of higher

20 neurological functions in people. And I would be happy to

21 expand on any of these suggestions if the panel has questions. 

22 DR. KENNETH PORTIER: Thank you, Dale. Any
Page 410

1 questions? Dr. Collier.

2 DR. ABBY COLLIER: Abby Collier. Hi, Dale.

3 DR. DALE HATTIS: Hi.

4 DR. ABBY COLLIER: I ask this question mostly

5 out of interest, but I think it is relevant for the panel. When

6 you built your pregnancy model, did you include metabolic

7 contribution from the placenta?

8 The reason I ask is because from the Bisphenol

9 A literature, we have a lot of people starting to say that

10 clearance across the placenta may be predicative.

11 DR. DALE HATTIS: I didn't. I didn't put that

12 in. Basically, there's a convention PBPK modeling to put all

13 the metabolism in the liver. It's reasonable to possibly do

14 that, but I didn't have any separate measurements. What I did

15 have was these ratios of whole body clearance for pregnant women

16 versus non-pregnant women. So that should implicitly include

17 that kind of conjugates, but the model itself didn't have that. 

18 Were you to put a fair amount of metabolism outside the liver,

19 it would decrease the difference between an ingestion and

20 inhalation pathway, but it might not change the inhalation much.

21 It would change mainly the ingestion numbers, I think. 

22 DR. KENNETH PORTIER: Dr. Lu.
Page 411

1 DR. CHENSHENG LU: Just for clarification,

2 when you mentioned external dose, you're really referring to

3 inhalation dose?

4 DR. DALE HATTIS: Well, inhalation exposure or

5 absorption through the gut. So I haven't got an absorption

6 factor in there. So I'm basically saying that the -- basically,

7 what I do in the model is take the stuff that I call

8 ingestion-absorbed dose and send it directly into the artery

9 feeding the liver. So that it all has to go to the liver before

10 getting anywhere else.

11 DR. CHENSHENG LU: All right. So I think this

12 external dose, the terminology seems very confusing if you also

13 take into account the absorption through the injection, then

14 obviously that's not external anymore.

15 DR. DALE HATTIS: Injection?

16 DR. CHENSHENG LU: Yeah, the oral injection. 

17 The absorption taking place in the gut.

18 DR. DALE HATTIS: I think, and I stand to be

19 corrected on this, that chlorpyrifos is relatively well

20 absorbed. That there's not a whole lot of loss through the

21 feces, but if that's wrong, then that would differ -- that would

22 cause you to adjust upward, slightly, the ingestion dose.
Page 412

1 DR. CHENSHENG LU: So the other question is

2 that your model built on the moment at age 7 when the IQ test

3 was taken place --

4 DR. DALE HATTIS: Well, the dose response

5 relationship is based on that.

6 DR. CHENSHENG LU: Right. So would this model

7 be generalized to other age groups? Say, for example, I mean, I

8 look at your model and it seems like there's no time lag time in

9 the models. So what happens is if the same kid that was tested

10 at age 7, have a certain number of IQ knowing the

11 neurodevelopmental level. 

12 Would your model be robust enough when the

13 kids become 11 years old?

14 DR. DALE HATTIS: The blood measurements were

15 all made at the time of birth, but we're measuring the

16 implications for neurodevelopment at age 7, which is when you

17 can measure and other things more robustly; although, they

18 measured other things earlier in the same populations, the

19 Bailey Scales and things like that.

20 DR. CHENSHENG LU: But my point is the basis on

21 what you provide here that if the same kids -- we measure the

22 same kids' IQ at age 9, the outcome would be the same as age 7.
Page 413

1 DR. DALE HATTIS: It might be. The

2 measurements don't yet exist, but I think that by the time

3 you've got a difference at age 7, you know, it's reasonable to

4 presume that that's a predicator of the lifetime

5 neurodevelopmental scores. We won't know that for some decades,

6 I think.

7 DR. KENNETH PORTIER: Any additional

8 questions? Dr. Pope.

9 DR. CAREY POPE: Early on in your presentation

10 you said something about the metabolism of chlorpyrifos appeared

11 to be somewhat lower in humans liver samples --

12 DR. DALE HATTIS: Than rats.

13 DR. CAREY POPE: -- than lab rats. With the

14 rat, it's kind of easy to control what the animal is consuming

15 and to see states and stuff like that. What kind of control on

16 the human tissues do you have as far as tobacco or alcohol

17 consumption or disease or cause of death?

18 DR. DALE HATTIS: Done. The experimenters

19 harvested the liver, essentially, early after death, but didn't

20 further characterize -- they characterized the -- and this is

21 actually stuff that went into some Dow modeling as well, later

22 -- but it did -- they characterized the age and the gender of
Page 414

1 the people but not prior exposures. 

2 Basically, it's catch if catch can, you know,

3 for those kinds of studies. I think there may well have been a

4 listing of cause of death in the paper and I don't remember. 

5 Many of the subjects for these kinds of studies, they try to

6 favor head injury victims and things like that, but it's

7 whatever they died of.

8 DR. CAREY POPE: What about the variability if

9 you were comparing -- and this is not my area -- but the

10 variability of kind of metabolic rates and some of the processes

11 you're looking at in human samples versus the rodent tissues?

12 DR. DALE HATTIS: I've got observations that

13 can be analyzed that way and I haven't done it. My guess is

14 that humans are more variable as a general matter because

15 they're not genetically uniform and they're not uniform in their

16 exposures that can induce liver enzymes.

17 DR. KENNETH PORTIER: Dr. Popendorf.

18 DR. WILLIAM POPENDORF: Just one comment and

19 then a question. As far as the oral absorption, it's 70

20 percent, based on the Nolan data. It's a small difference. 

21 DR. DALE HATTIS: Okay.

22 DR. WILLIAM POPENDORF: I'm just trying to
Page 415

1 figure out here, just to get a clear idea as to how these

2 numbers were derived in the last, you know 13, 14. 

3 What's the relationship between, like for

4 instance, the inhalation dosage you got here and the airborne

5 concentrations that were measured in Columbia data?

6 DR. DALE HATTIS: The air concentrations in

7 the Columbia study -- let me go and look that up.

8 DR. KENNETH PORTIER: Dale, use your mic a

9 little better too, so people in the back can hear.

10 DR. DALE HATTIS: Oh, sorry. I've got to look

11 it up in order to answer your question fairly.

12 DR. WILLIAM POPENDORF: Well, I'm thinking

13 conceptually, not quantitatively. You referred early on to the

14 fact that the Columbia's study, when they were looking at air

15 concentrations, they were decreasing over time. I presume you

16 meant over the years.

17 DR. DALE HATTIS: Yeah. Right. For example,

18 for 110 samples that were collected in 1999 were the women

19 before they gave birth. The average air levels were 6.91 -- oh,

20 wait a minute. That's the average blood levels. The blood

21 level was about 6.91 for 1999. Let me go look at the air

22 levels.
Page 416

1 The average air levels in 1999 were 3.4. 

2 Well, that's the air intake that I calculated by applying a

3 breathing rate from average pregnant people. For 1999, that

4 left about an average of 3.4 nanogram per kilogram/day. For

5 2000, I got 2.2; for 2001, I got 2.63; and then for 2002 and

6 2003, I got 1 and 1.2. so there's a trend there. And in 1998,

7 based on only four samples, I got 10.

8 So there is a secular trend toward reduction

9 over the years, partly as a result, most likely of EPA's phased

10 reduction and the slow outgassing phase of the residential uses

11 and probably the relatively slow outgassing of the chlorpyrifos

12 absorbed on surfaces. 

13 DR. WILLIAM POPENDORF: So I guess I'm --

14 DR. DALE HATTIS: So now we're talking about

15 -- in terms of nanogram per kilogram/day, we're talking about

16 two or three as the range of observed levels in the early years.

17 Now, that's ingestion. So let's go back a slide.

18 DR. WILLIAM POPENDORF: Yeah.

19 DR. DALE HATTIS: This is the inhalation doses

20 for the IQ effect. The estimated inhalation dose to produce the

21 .7 change is 31. The inhalation dose to do the .01-point change

22 in the index was .4. So the .4 is close to 8-fold less than the
Page 417

1 levels that went into the study. The 31 is of the order of

2 10-fold more than the average air level.

3 DR. WILLIAM POPENDORF: And that's within, I

4 guess, the range of the measurements, a lot of variability to

5 incur that. 

6 DR. DALE HATTIS: Yeah. Oh, yeah. There's

7 quite a lot of variability. I'm just basically using the

8 averages in a particular year as the way of washing out some of

9 that.

10 DR. KENNETH PORTIER: Dr. Popendorf, can I

11 recommend that may you go to lunch with Dale and kind of work

12 through it. We'll leave it open. We'll come back after lunch. 

13 It's 12:05. We're going to go ahead and take our one-hour lunch

14 break. Yes, Dr. Vena?

15 DR. JOHN VENA: I just have one question. It

16 looks like you're trying to say that you looked at air

17 distribution and you developed and you looked at the modeling

18 for the maternal blood. Are these all maternal blood or is this

19 core blood?

20 DR. DALE HATTIS: Yeah. There's a strong

21 correlation between the maternal -- it's actually quite, and not

22 very much difference, like seven or eight percent difference, on
Page 418

1 average, for the maternal blood and a child blood, which is a

2 huge surprise because maternal blood has much more fat in it

3 than the core blood, So that the concentrations that are

4 observed are comparable. 

5 So I should make the additional correction for

6 what the average ratio is of child to maternal and I didn't do

7 that yet in these calculation. I expected to, but I ran out of

8 time. Nevertheless, it's one of the most puzzling aspects of

9 this modeling that there is not a bigger difference because

10 there is a several fold difference in the amount of fat in core

11 blood plasma versus maternal plasma.

12 DR. JOHN VENA: So these final slides are

13 maternal blood?

14 DR. DALE HATTIS: In terms of maternal blood,

15 but applied with less than perfect accuracy to the core blood

16 measurements. So an additional correction should be made to

17 make that they're on the same basis, but the differences won't

18 be very large.

19 DR. KENNETH PORTIER: I'm going to give

20 the panel lunch to think about this. Dale, are you going to be

21 here after lunch?

22 DR. DALE HATTIS: I'll come back.
Page 419

1 DR. KENNETH PORTIER: So we'll leave the open

2 comment period open until right after lunch. We'll entertain a

3 couple more questions if you think of them, as the panel thinks

4 of clarifying questions from any of the public commenters and

5 then right after that we're close the public comment period and

6 EPA will come back to the table to read the questions but we'll

7 open up for any clarifying questions that panel may have

8 developed over night that they want to ask EPA. At that point

9 we'll close the general questions and move onto our panel

10 discussions. So we're going to try to get back here at 1:00, by

11 this clock. It gives us 55 minutes. We'll probably let it

12 slide five minutes, but let's try to be back by 1:00. Thank

13 you.

14 (Whereupon, a luncheon recess was taken.)

15 * * * * *

16 

17 DR. KENNETH PORTIER: I know the panel

18 was very productive over lunch; that is why I did not want to

19 rush them back because they were working on it and now the

20 memory stick floating around; they are getting organized.

21 Continuing with the public comment section, I

22 wanted to ask Dr. Popendorf if he had gotten his question
Page 420

1 clarified and if he had something he wanted to potentially share

2 with the panel from his discussions with Dr. Hattis. 

3 DR. WILLIAM POPENDORF: Thank you, yes, we did

4 get my questions answered and I think I will have some comments

5 based on that when we talk about PBPK modelling. 

6 DR. KENNETH PORTIER: Are there any additional

7 questions from the panel for Dr. Hattis? No. Dale I hope you

8 had a good lunch. Thank you. Are there any additional

9 clarifying questions for the other public commenters? This is

10 your last chance here, although it is never the last chance; we

11 end up bringing them back anyway. In theory this is the last

12 chance. Dr. Harry.

13 DR. JEAN HARRY: So there was a mention that

14 there was a slide or data available on the differences in the

15 Columbia Cohort and the before and the after, that was mentioned

16 that you had. Yeah.

17 DR. KENNETH PORTIER: If you can provide that

18 to Fred here he can have that table printed out. The staff over

19 here at the table can take care of it and then we will get that

20 distributed. You did not have a question about it; you just

21 wanted that to look at, right? Dr. Harry?

22 DR. JEAN HARRY: Well, I do not know if I have
Page 421

1 a question about it until I look at it, sorry. But, no I mean

2 we had asked that question yesterday and they were saying they

3 would provide that data today and it was mentioned so it would

4 be nice to have it.

5 DR. KENNETH PORTIER: Yes, Dr. Popendorf. Two

6 questions.

7 DR. WILLIAM POPENDORF: Two questions, yes. I

8 don't know if Dr. Sass is still available, but one of my

9 questions was related to Table 5.

10 DR. KENNETH PORTIER: Yes, we would have to

11 call her.

12 DR. WILLIAM POPENDORF: Okay, the other

13 question, I'm not quite sure it is probably directed generally

14 towards EPA or anyone else that could. I'm sort of looking to

15 see if there were any estimates of ingestion of chlorpyrifos

16 through foods? Market basket type data done by survey type work

17 would not necessarily; I mean certainly it is related to these

18 cohorts that we are looking at but I did not actually see that

19 type of data in materials presented.

20 DR. KENNETH PORTIER: Well let me just ask

21 first a question from the public commenters. If anybody wanted

22 to comment on that question, you know, DOW if they have got any
Page 422

1 data or any experts, because I really want to close off the

2 public comment section. 

3 I do not think there are any more questions. 

4 I think at this point we are going to close the public comment

5 section and open up back to EPA for comments. I know they had a

6 couple of things they promised the panel yesterday that they

7 wanted to mention to us and then they can ask your question, so

8 we will turn it over to Dr. Lowit. 

9 DR. ANNA LOWIT: This is Anna Lowit. We do

10 have a couple of things we had committed to chasing down over

11 night that we have followed up on and we are loading the slides

12 now, but in the meantime I think we can answer Dr. Popendorf's

13 question fairly quickly. 

14 DR. JEFF DAWSON: For the record, Jeff Dawson;

15 I'm an exposer scientist in the Health Effects division and Dr.

16 Popendorf in the paper there is an appendix with different dose

17 estimates that we incorporated in there for -- it is at the very

18 end, related to exposer scenarios and I think there is one in

19 there for dietary estimates of dose, so if you want to look at

20 that. It is attachment 1 or Appendix 1; it is a table.

21 DR. KENNETH PORTIER: Dr. Young.

22 DR. HEATHER YOUNG: I just wanted to follow
Page 423

1 up. There is an epi paper too by Lu et al. that talk about

2 dietary exposer from OP sources. I do not think it was included

3 in this but it is referenced in one of the other papers. 

4 DR. KENNETH PORTIER: Any additional questions

5 for EPA, that came up? Dr. Pope.

6 DR. CAREY POPE: This is for Dr. Christensen. 

7 Yesterday, I think Dr. Vena had brought up the idea of

8 questioning the dialkylphosphate and what DMP and DEP and DETP

9 represented or what the importance of those was. 

10 In your presentation you gave some of the

11 later behavioral changes from the epi studies showing the APS;

12 and I think one of the other presenters also brought this up and

13 suggested there was not kind of some correlation between DEPS

14 and some of the behavioral endpoints.

15 Can you kind of clarify that for me? 

16 Specifically addressing the diethyl phosphate correlation with

17 some of the epi findings?

18 DR. CAROL CHRISTENSEN: Sure, Carol

19 Christensen, EPA. I think on the first issue we have some

20 information to present as to the potential parent compounds for

21 the different DAP in DAP subclass groups, and Jeff Dawson will

22 be presenting that.
Page 424

1 Concerning the epi studies, as I noted in my

2 presentation yesterday, although I sort of tabularly depicted

3 the results in terms of the DAP each of the respective

4 investigations that used this nonspecific measure of OP exposure

5 as their analytic tool in the epi studies broke out DEP and DMP.

6 Chlorpyrifos exposure would not go to DMP as was discussed

7 yesterday. Chlorpyrifos plus other OPs may go to diethyl

8 phosphate.

9 So regarding your latter point as to

10 clarifying what results were attributable to DEP versus DAP

11 versus DMP, that information should be pretty clearly laid out

12 in Appendix 5 of our materials. It is not laid out in Table #10

13 as we know. 

14 But there were instances in which the DAP

15 association appeared to be driven by DMP as noted yesterday. 

16 But there were other situations in which the DEP association you

17 could theorize that that was driving the DAP. So it varied and

18 I cannot recall off of the top of my head kind of which went

19 where but it should be in Appendix 5.

20 DR. CAREY POPE: Particularly for your

21 presentation on that slide 34 do you have any feeling about

22 whether DMP is driving those correlations?
Page 425

1 DR. KENNETH PORTIER: A slide on Intelligence

2 Measures.

3 DR. CAROL CHRISTENSEN: Yes I do, in fact I

4 was just looking at that as I was updating Table 10. If I'm

5 recalling correctly, for the CHAMACOS cohort in which I have DAP

6 listed. The DEP, the beta coefficient representing the inverse

7 association between DEP and these various measures of

8 intelligence were at greater magnitude, i.e., a greater inverse

9 association with the DEP then with the DMP in that instance. It

10 is notated in the paper clearly, but that is my recollection

11 having looked at that recently. You are talking about the

12 intelligence measures, yes.

13 DR. KENNETH PORTIER: Okay, Dr. Dawson. or Dr.

14 Lowit, you want to introduce this?

15 DR. ANNA LOWIT: Yes, I will just give you a

16 sense of what we have done. We have four small things to follow

17 up on from either yesterday afternoon or from this morning.

18 Jeff Dawson will cover the first two. The

19 first one is just to make sure there is clarity around the limit

20 of detection, limit of quantitation for the Columbia study. The

21 next one is the issue of the DAPs and which OPs contribute to

22 DMPs and DAPs. 
Page 426

1 And then I will turn it over to Bill Mundy,

2 who has one clarification on Averse Outcome Pathways and then I

3 will finish with one small issue around the Johnson paper that

4 was discussed earlier. 

5 DR. JEFF DAWSON: Thank you. Again, Jeff

6 Dawson, from the Health Effects division. Yesterday there was

7 some discussion about the analytical methodology related to cord

8 blood analysis and we did some checking between the end of

9 yesterday and now to put some clarity around it as Dr. Lowit

10 indicated. So this was a published method Barr et al., 2002 and

11 we could potentially make that paper available for you to look

12 at in more detail if you desire.

13 In Table 4 of Barr, she reported an LOD of 1

14 pg/g with a very high R2. Then we did a quick overview of this

15 paper and had some conversations with the chemist in our

16 analytical lab as well and they felt pretty comfortable with the

17 methodology; they believe it is a sophisticated and sensitive

18 method. And it was really designed to get at low levels and we

19 estimate that the limit of quantification would be around 3

20 pg/g.

21 We also had some communications with Dr.

22 Whyatt at Columbia, and they felt confident in the method as
Page 427

1 well.

2 And the table there on the bottom is just a

3 representation of the years that the data was collected and the

4 ranges of the measure in cord blood. So you can see if you look

5 at 3 pg/g is the limit of quantification, or compare them to

6 that or the limit of detection of 1 pg/g. Any questions?

7 DR. KENNETH PORTIER: Dr. Buckley?

8 DR. BRIAN BUCKLEY: In the meantime I spoke to

9 Barr without the et al., but I found out the difference between

10 the two methodologies; and basically the second was the more

11 recent, the 2010 which is also a Barr paper, was at a difference

12 suite of pesticides; and the compromised conditions that they

13 needed for that secondary suite of pesticides is one of the

14 contributing factors.

15 They also had a slight difference in

16 methodology in the sense there is smaller injection volume and

17 smaller blood volume, but they did not feel the need to get any

18 lower because they had enough sensitivity, which is why they

19 were able to get away with smaller blood volume, et cetera. But

20 the principle difference between the two methodologies between

21 '02 and 2010 was the difference in the pesticide suite that was

22 analysed in '02 versus 2010.
Page 428

1 DR. KENNETH PORTIER: Any additional

2 questions? Thank you. 

3 DR. JEFF DAWSON: The next slide talks about

4 the origin of the different alkyl phosphates, so we just copied

5 this slide directly from the NHANES, a forth report, the link is

6 there on the bottom.

7 There are six basic categories of metabolites.

8 They are all considered a dialkylphosphate, so when we talk

9 about DAP it really represents all six of those compounds.

10 Then the DEPs are the ones outlined with the

11 red box around them and as you go down the different columns you

12 can see what pesticides contributed or you would have a

13 metabolite of that pesticide ending up as a DEP. So just to add

14 some clarity and we can make copies of this so you can have it

15 for your records. 

16 You can see chlorpyrifos and diazinon are on

17 the list that contributes to DEPs; they are pretty broadly used

18 in agriculture. Several of the others are more limited in their

19 use so you would not expect probably as wide as of exposure kind

20 of contribution in the general population for example to those

21 different pesticides. 

22 DR. KENNETH PORTIER: Any questions on this? 
Page 429

1 And we would like a copy of that to pass it around. Dr.

2 Popendorf. 

3 DR. WILLIAM POPENDORF: Just kind of going a

4 little beyond that but I also, that would relate perhaps to, did

5 you come up with the list of the pesticides used in Monterey

6 County, because that will relate to this, thank you.

7 DR. JEFF DAWSON: Next slide, so this was a

8 slide that was provided to us from Brenda Eskenazi, one of the

9 investigators on CHAMACOS; so they went through and created this

10 list as part of their data collection. And you can see in their

11 cohort, the ones who contribute to DEPs, predominately used are

12 diazinon, chlorpyrifos and disulfoton. 

13 DR. KENNETH PORTIER: Any questions on this

14 one? And additional questions? Yes.

15 DR. STELLA KOUTROS: I forget, did they report

16 on the correlations with these, on chlorpyrifos?

17 DR. CAROL CHRISTENSEN: So the CHAMACOS

18 cohort, they measured DAPs and they measured the diethyl and the

19 dimethyl separately. So you are referring to the correlation

20 between diazinon and chlorpyrifos, and disulfoton? They would

21 not have had that information.

22 DR. STELLA KOUTROS: Was there a different
Page 430

1 study that had some other measures of certain pesticides? I do

2 not recall which one it was.

3 DR. CAROL CHRISTENSEN: The Columbia study

4 measured out the parent compounds.

5 DR. KENNETH PORTIER: Any additional questions?

6 DR. JEFF DAWSON: There was also some

7 discussion yesterday about sources of TCPy and as we heard

8 yesterday chlorpyrifos, chlorpyrifos methyl, which are both

9 organophosphate insecticide, and triclopyr which is a selective

10 herbicide.

11 Just a little perspective on how they are

12 used. Chlorpyrifos is a broad use insecticide, a lot of crops

13 and it is also still registered for a number of nine crop uses,

14 for example, in ornamentals and industrial sites. 

15 Chlorpyrifos methyl is much more limited in

16 use. It is used predominately as an insecticide for grain

17 storage. So if you were going to be exposed to that it would be

18 through the diet pretty much is the prevalent source.

19 And then triclopyr, the only agricultural

20 commodity you can find is upon rice as an herbicide. It is also

21 available on home-use products and it is used for controlling

22 range land in certain forestry areas where they have done
Page 431

1 cutting to keep it manageable to go in and replant.

2 Last night we look through the data for the

3 California pesticide use reporting to see how much was used in

4 the CHAMACOS area. We used Monterey County which is in Salinas

5 Valley, as an indicator and I'm not sure if that covers the

6 entire Salinas Valley or not. 

7 But triclopyr, you can see over the years 1998

8 2002, around 3000 - 4000 pounds were used and that is dwarfed

9 by the amount of chlorpyrifos that is used in that area during

10 that timeframe and it is 50 100 and some thousand pounds per

11 year. We think that spike is from some certain pest outbreak or

12 something over that year. I also looked last night at the 2007 

13 2009 data for chlorpyrifos and it is kind of in the same range. 

14

15 Just to give you an idea so you can end up

16 with the relative contributors potentially to TCP, and I will

17 pass them along. 

18 DR. WILLIAM MUNDY: This is just to clarify

19 some of the terminology I used yesterday. Terms of mode of

20 action or adverse outcome pathways, that could be alternatives

21 to cholinesterase inhibition. In the tables I have listed them

22 as domains, but this is just to be clear that those domains
Page 432

1 could be considered as potential alternative modes of action. 

2 So we had six of them listed; they are now on here just called

3 pathways, so just to clarify the terminology. 

4 DR. KENNETH PORTIER: Any questions on this? 

5 Dr. Harry.

6 DR. JEAN HARRY: This saves me bringing it up

7 later. What I wanted to know is exactly what are you defining

8 as morphogenic? 

9 DR. WILLIAM MUNDY: So acetylcholinesterases

10 (AChE) during early periods of development can control cell

11 differentiation and cell growth, neuronal growth. So a

12 morphogenic role would be altering the morphology of the cell.

13 DR. JEAN HARRY: It might have just been how I

14 was reading it in the document; it sort of went through

15 self-proliferation being a component of that.

16 DR. WILLIAM MUNDY: That is a potential

17 component. The adverse outcome pathway that we proposed, as an

18 example, focused on neurogenic -- the neurite outgrowth that

19 could be controlled by cholinesterases.

20 DR. JEAN HARRY: The reason I was asking is

21 just probably more for clarification as you get down the road

22 when you start using the term exactly what does it mean because,
Page 433

1 as you know, if cells are proliferating, they are not going to

2 differentiate; if they are differentiating they are not going to

3 be proliferating. But yet if you are taking both of those, in a

4 way you might just want to set a very define definition, or at

5 least an explanation for within the Agency, how you are going to

6 be using that term because it can start to take a life of its

7 own. 

8 And if you cluster the things together, you

9 might get data that you would be interpreting as being

10 contradictory but it might be complimentary with lots of people

11 that are not in the field that would know the area of how they

12 might be interpreting it. So it was just a point of

13 clarification you might want to think about doing.

14 DR. WILLIAM MUNDY: Sure, thanks. I should

15 have defined morphogen. 

16 DR. MICHAEL ASCHNER: It is just a comment and

17 I do not know your view on this Bill, but I would actually, Russ

18 (phonetic) to me would not be necessarily -- it is more of a

19 response to alterations in any of the other rather than an

20 initiating event.

21 DR. WILLIAM MUNDY: That is why in the

22 literature there is no initiating event proposed but it has been
Page 434

1 suggested that the reactive oxygen species levels that are shown

2 to be increased after response to chlorpyrifos or the oxon could

3 be responsible for further downstream effects on

4 neurodevelopmental processes.

5 So, again, that is why I did not call them

6 pathways initially but domains under which the data were

7 falling.

8 DR. KENNETH PORTIER: Any additional questions?

9 Dr. Vena.

10 DR. JOHN VENA: These events as well as the

11 domains, are these related to exposure to chlorpyrifos a parent

12 compound or all the additional metabolites that occurs, the ones

13 that we just went through on the table, and TCPy? 

14 DR. WILLIAM MUNDY: The three metabolites that

15 have been associated with one or more of these events are

16 chlorpyrifos the oxon and the TCP, in more limited cases, not to

17 my knowledge the DAPs.

18 DR. ANNA LOWIT: And one little issue just to

19 make sure there is clarity and accuracy in the report. We heard

20 a fairly lengthy detailed presentation from Dr. Li and I think

21 largely she concurred with the preliminary conclusions of the

22 Agency. 
Page 435

1 But there was a good bit of discussion about a

2 paper out of Russell Carr's lab, a Johnson et al., paper. And

3 we just want to ensure that the discussion on that paper is

4 accurate as it is in the paper and that the appropriate

5 statistics are considered in the report because we are unsure

6 that the conversation accurately reflected the statistics that

7 were done on the report. If we can just ensure that if this

8 Johnson paper gets a lot of play in the report, that it is

9 accurate.

10 I think what is more important is that with

11 the analysis done by the Agency and the analysis done by Dr.

12 Lee, as none of those studies were being proposed for points of

13 departure. Any one individual point of data, any one study, is

14 not necessarily any more or less important than any other given

15 the nature of that database. So we would like to ensure that

16 one study for which got a lot of discussion is not weighted

17 heavily against an entire database of 20 some odd other studies.

18 DR. KENNETH PORTIER: Dr. Carr.

19 DR. RUSSELL CARR: Based on what I said this

20 morning, I was a little confused. I got my sexes mixed up just

21 trying to recall the paper from memory. Our lowest effect level

22 was a 1 mg/kg and it was in male rats. In female rats was where
Page 436

1 we saw a little bit of the opposite effect but not significant. 

2 But the statistics that were in the paper do match the data as

3 Anna said.

4 DR. KENNETH PORTIER: And that completes EPA

5 presentation, one last call for clarifying questions before I

6 close the presentation part of the meeting.

7 DR. ANNA LOWIT: Just to make sure Dr. Portier

8 that we circulated the revised version of Table 10 just to make

9 sure everyone got it.

10 DR. KENNETH PORTIER: That was handed out over

11 lunch. At this point I'm going to go ahead and close the public

12 presentations part of the meeting and we are going to move

13 forward with the panel discussion of the issues put before the

14 panel by EPA.

15 So, we release the EPA staffers and Dr. Lowit,

16 are you going to be reading the questions this afternoon?

17 DR. ANNA LOWIT: Everyone is running away. I

18 will read the first one and then I will start delegating all the

19 others. Of course they are all very long.

20 Question 1.0. It is well established that

21 AChE inhibition is the primary mode of action/adverse outcome

22 pathway for OPs, like chlorpyrifos. Because AChE inhibition is
Page 437

1 the initiating event for this mode of action/adverse outcome

2 pathway (AOP), using AChE inhibition as a regulatory endpoint is

3 protective of downstream cholinergic effects. Moreover,

4 historically, given the sensitivity of AChE inhibition data for

5 OPs, these data have been considered to be protective of other

6 potential toxicities and/or modes of action for OPs.

7 In 2008, the Agency performed a comprehensive

8 review of the available AChE data from multiple life stages. 

9 This review has been supplemented with the newest studies. 

10 Consistent with the recommendations from the 2008 SAP, the

11 Agency believes that AChE data remain the most robust dose

12 response data for deriving points of departure in in vivo

13 experimental toxicology studies with laboratory animals. 

14 Please comment on the Agency's preliminary

15 conclusion that AChE data remain the most robust source of data

16 for deriving points of departure for chlorpyrifos. 

17 Please include a discussion of the strengths

18 and uncertainties of this preliminary conclusion.

19 DR. KENNETH PORTIER: And we have asked Dr.

20 Carr to lead off the discussion this afternoon. Dr. Carr.

21 DR. RUSSELL CARR: I agree with the Agency's

22 conclusion that ChE data remains the most robust source of data
Page 438

1 for deriving points of departure for chlorpyrifos. This is

2 based on observation that all negative effects reported on in

3 vivo developmental exposure to chlorpyrifos have been

4 accompanied by acetylcholinesterase inhibition. 

5 And those studies that measured ACh activity

6 at an appropriate time point following the administration of

7 chlorpyrifos did not use a confounding potentially toxic vehicle

8 that is DMSO or utilize an exposer paradigm that would allow it

9 to be useful to determine the point of departure. 

10 While this being said, there are candidates

11 that could replace acetylcholinesterase as a more sensitive

12 indicator but at this juncture those have not been fully

13 validated and their alteration has not been determined to result

14 in a measureable neurotoxic response. 

15 DR. KENNETH PORTIER: Thank you. Next, Dr.

16 Collier.

17 DR. ABBY COLLIER: I agree with Dr. Carr and I

18 have only one thing to add, which is that I think uncertainties

19 relating to life stage levels of blood cholinesterase in human

20 exists and that we have to be sure that we are comparing apples

21 with apples if we are going to use cholinesterase as an

22 endpoint. 
Page 439

1 DR. KENNETH PORTIER: Thank you. Dr. Pope.

2 DR. CAREY POPE: I agree, I think the question

3 asked for strengths or uncertainties. let me expand a little

4 bit. I think there are a huge number of strengths in using

5 cholinesterase dose response data for deriving points of

6 departure and so far I have not seen anything else that fits

7 this. So obviously, to me, it is the most robust set of data

8 for doing so. If you put that together, since 1940's the

9 mechanism of toxicity has been acetylcholinesterase inhibition

10 demonstrated in multitudes of studies over the years. 

11 SAP panels have discussed this and kind of

12 keep concurring, so I think it is undeniable and most extensive

13 database available for evaluating neurotoxicity in developmental

14 neurotoxicity and applicable for determining points of

15 departure. I stop there. 

16 DR. KENNETH PORTIER: Dr. Popendorf.

17 DR. WILLIAM POPENDORF: I also agree with the

18 previous speakers by and large and I think the only thing that I

19 would add would be in terms of other candidates. I think if you

20 consider the metabolites we have discussed, there are a lot of

21 problems with most of them and I think the only other viable

22 potential candidate would be the actual chlorpyrifos itself in
Page 440

1 blood for instance. That is not a good substitute, which is why

2 I agree with the basic premise that acetylcholinesterase is the

3 best available.

4 DR. KENNETH PORTIER: At this point we will

5 open it up to anyone else on the panel who wants to comment on

6 this question. Or anyone who wants to add. Dr. Carr.

7 DR. RUSSELL CARR: I just want to go ahead and

8 add this for the record.

9 There has been discussion about using DMSO as

10 a solvent, which I have concerns with. There are some

11 additional papers that have occurred since the 2008 SAP. In

12 2009 Hanslick et al., was basically looking at neuronal

13 apoptosis following developmental exposure to DMSO and on PND 7

14 a single dose of as low as .3 mills per kilogram increase the

15 incidents of neuronal apoptosis and that was DMS alone.

16 Also there has been several studies in zebra

17 fish to demonstrate that DMSO has a neurotoxic effect, which

18 alter behavior following acute exposure but more importantly in,

19 I think it was 2-TN on Turner et al., basically found that if

20 you could expose zebra fish embryos to levels as low as .0025

21 percent, which is 25 ml/l. It induces gene expression in those

22 embryos. 
Page 441

1 And that kind of suggests that if you put DMSO

2 into a developing system, it does have the potential to interact

3 on its own and, in addition, probably could potentiate the

4 effect of other chemicals in that system.

5 DR. KENNETH PORTIER: Any additional comments?

6 Dr. Anna Lowit does that look like an accepted answer. I will

7 not even attempt to summarize this stuff.

8 DR. ANNA LOWIT: It is pretty clear actually,

9 so yes.

10 DR. KENNETH PORTIER: We are going to move on

11 then to Question 2.1.

12 LIEUTENANT AARON NIMAN: Question 2.l. As

13 discussed in Section 3.2.1, although there are numerous

14 mechanistic studies in the scientific literature, the research

15 on different hypotheses does not provide sufficient data to

16 establish causal linkages among different levels of biological

17 organization to show how effects lead to adversity. 

18 As such, a mode of action or adverse outcome

19 pathway leading to effects on the developing brain cannot be

20 established at this time. Moreover, although multiple

21 biologically plausible hypotheses are being pursued by

22 researchers, based on the current state of the science, no one
Page 442

1 pathway has sufficient data to be considered more credible than

2 the others. 

3 Please comment on the Agency's preliminary

4 conclusion that although there are multiple biologically

5 plausible hypotheses being evaluated by research scientists, the

6 mechanistic experimental toxicology data does not yet support a

7 coherent set of key events in a mode of action/adverse outcome

8 pathway.

9 DR. KENNETH PORTIER: Dr. Harry.

10 DR. JEAN HARRY: I think we all pretty much

11 agree your answer to this is yes. We agree with the Agency. I

12 would like to say I think the Agency has done an excellent job

13 of pulling together the data, not only just on chlorpyrifos but

14 with the processes of the mechanism, the potential for there to

15 be ChE activities other that the normal way of thinking about

16 them, the cellular responses that could be associate, and to

17 really sort of set up the fact that there may be a diverse role

18 during development and possibly during other life stages of the

19 organism.

20 I really think it is an excellent put together

21 package. A lot of work went into it. A lot of expert thought

22 process went into it.
Page 443

1 What I found interesting as I was reading

2 through it I sort of got to the point and said, "This is

3 starting to look like all the old lead literature or the mercury

4 literature where there are a lot of little pieces but yet have

5 not really identified from a lot of those little pieces to be

6 able to have a pathway to stay with." 

7 So I think that it seems like what you have in

8 the literature and what you put forward very well is that you

9 have got the starting point of some studies that if they are

10 expanded pass a descriptive stage by the investigators, that you

11 may start to look at some bases for other dynamics of how they

12 may be happening, how you may be having developmental effect but

13 right now they are very speculative on any sort of mechanism and

14 your data is not really there to identify another pathway or

15 even something that you do not know that is not secondary to an

16 already known mechanism process mode of action.

17 I think the Agency laid out very well that

18 there really needs to be that next step follow-through on some

19 of those findings but that it is not there yet. But I think

20 that some of that in the way that they worded the report really

21 nicely demonstrated that to be able to get there the benefit is

22 going to come from not just looking at chlorpyrifos alone but at
Page 444

1 looking at a lot of the genetic and pharmacological data that

2 may start to support some of the dynamics.

3 So I think that is a long way of saying yes I

4 agree with you but I agree with you on the basis of the Agency

5 really laid out well an open and unbiased looking at what the

6 data was out there and I thought they really did a very good job

7 of pulling it together, of possible mechanisms and other aspects

8 of expanding outside just limited data framework for

9 chlorpyrifos. So I think there is a strong basis for my yes, I

10 agree with the Agency. 

11 DR. KENNETH PORTIER: Dr. Carr. 

12 DR. RUSSELL CARR: I concur with Dr. Harry.

13 DR. KENNETH PORTIER: This could be really

14 easy. Dr. Collier.

15 DR. ABBY COLLIER: I agree with Dr. Harry and

16 Dr. Carr and I have nothing to add.

17 DR. KENNETH PORTIER: Dr. Pope.

18 DR. CAREY POPE: Yes, me too.

19 DR. KENNETH PORTIER: Well what is all of this

20 stuff written on your screen? Dr. Terry.

21 DR. ALVIN TERRY: Maybe I will be the

22 troublemaker here. In essence I also agree with Dr. Harry's
Page 445

1 conclusion but when I was reading the white paper and thinking

2 about this I guess all in context and looking at that statement

3 these data do not yet support a coherent set of key events.

4 Then I look on page 36, the other report when

5 discussions of lead, meth, mercury and ethanol are being sort of

6 summarized even today with thousands of published papers on

7 these accepted developmental neural toxicants no coherent

8 adverse outcome pathway can be constructed.

9 So when I'm reading all this I sort of felt

10 like I was painted into a corner that it is very difficult to

11 meet these criteria and if you look at domoic acid, the real

12 idealized pathway, very very few times we ever encounter a

13 compound like that. So having said that it seems like a very

14 difficult thing for me to do but as it stands right now looking

15 at all the literature, I would also agree with the central

16 premise.

17 DR. KENNETH PORTIER: I will open it up to the

18 rest of the panel, any additional comments? Going twice. Okay. 

19 So those were the easy questions, alright? And I think that is

20 another pretty clear short response. Let's go on to Question

21 2.2.

22 LIEUTENANT AARON NIMAN: Question 2.2. 
Page 446

1 Although a mode of action/adverse outcome pathway has not been

2 established, qualitatively, the growing body of mechanistic

3 studies does demonstrate that chlorpyrifos and/or its oxon are

4 biologically active on a number of processes that affect the

5 developing brain. 

6 Some mechanistic studies provide evidence of

7 possible effects which are similarly sensitive or more sensitive

8 than AChE inhibition (e.g., neurite outgrowth, binding to

9 muscarinic receptors, axonal transport; serotonergic nervous

10 system development). Some of these comparisons must be

11 considered with caution since the amount of change in the in

12 vitro systems required to elicit an adverse effect in vivo is

13 unknown. Moreover, extrapolation from in vitro perturbations to

14 in vivo effects has not been established, which introduces

15 additional uncertainties. 

16 Given the doses/concentrations evaluated in

17 the in vitro and in vivo mechanism studies, please comment on

18 the degree to which these studies suggest that endpoints

19 relevant to evaluating potential neurodevelopmental outcomes may

20 or may not be more sensitive than AChE inhibition. 

21 Please include in your comments a discussion

22 of the strengths and uncertainties. 
Page 447

1 Please also include in your comments a

2 discussion of the scientific understanding of dose response

3 relationships for biological perturbations and the magnitude,

4 frequency and/or duration of such perturbations that can lead to

5 adverse effects at higher levels of biological organization to

6 support characterization of the likelihood of adverse outcomes

7 in a human health risk assessment.

8 DR. KENNETH PORTIER: Yes, it is a lot. The

9 only thing I would point out, I think on the second line you

10 said "does not" and it reads does, right? So it does

11 demonstrate chlorpyrifos and/or its oxon are biologically

12 active. DR. Harry.

13 DR. JEAN HARRY: The longer the question the

14 longer the answers. I think this is where we are not getting to

15 just the yes but you have asked a lot of questions here and I

16 think it is trying to filter through to give you back the

17 comments that you really want.

18 While it is not exactly in the question, I may

19 do these in bullet forms. A lot within the document was asking

20 that you were expecting eventually for these things to be linked

21 to a functional or structural change. And I think again you

22 need to come back and define what you are asking for from that
Page 448

1 because are you asking for eventually any of these in vitro

2 systems to be able to translate over to a behavioral

3 manifestation? Is that what you are taking as a functional or

4 do they have to translate over into a histopatholgical change

5 that you can see within an H&E stain on a guideline study? 

6 That might be expecting way too much because

7 they are relatively crude endpoints. So I think there just

8 needs to be a definition of exactly what your expectations are

9 going to be for it to be translated in your mind over, because

10 the functional change could end up being a change in

11 self-physiology; that is as much function as a behavioral

12 manifestation.

13 A structural change could be a structural

14 configuration change of a synapse and that is as much as a cell

15 depth change could be. So it is going to be within that

16 framework where you are starting to look at what your

17 requirements will be for that.

18 Again, like I said, this is sort of coming in

19 bullets because there are lots of questions here and hopefully

20 when we write this up it will be much more integrated for you. 

21 I think some of your other concerns are that you are looking at

22 whether something is more sensitive or not and there seems to be
Page 449

1 this concern about whether any of the in vitro methods could

2 manifest and give you the dose response that you are looking

3 for.

4 Some issues were raised as in some studies you

5 would find nothing at a couple of doses or you might find

6 something at two doses but no does response.

7 I think you are going to have to look at the

8 systems that people are using and look outside of their further

9 sensitivity within either a genetic component or at a

10 pharmacological component because you may not get those same

11 type of linear dose response that you really want to see to be

12 able to do your point of departure from. These types of studies

13 may never provide that type of data. They just may not be

14 sensitive enough to do it yet as in the type of numbers it is

15 giving you. 

16 So I'm not real sure that you are going to get

17 a number of the things that you are going to get magnitude dose

18 response, you are going to get that nicely laid out like you are

19 getting with ChE inhibition at times. 

20 I think the other thing that was not brought

21 out in any of the papers in trying to evaluate those

22 concentration in vivo, in vitro, any type of translation there
Page 450

1 is the fact that in all of the in vitro systems you are looking

2 at a cell in isolation and that is not the way a cell is going

3 to be in vivo.

4 So you can be looking at a neuron in isolation

5 but that is not the way it is going to exist in the brain. I'll

6 take the example of looking at neural stem cells or progenitor

7 cell populations. There is some data that is available on those

8 as to whether it is stimulating proliferation, those aspects,

9 but we know that the niche that those cells live in tightly

10 regulates those cells within the brain.

11 So looking at them in isolation is going to be

12 another problem for you to try to do the translation over

13 because it may be that trying to recapitulate in vitro what you

14 see in vivo is going to be very very difficult. And I know that

15 there is a lot of effort of saying that, "If I look at a neural

16 stem cell it will give me a more current development." But it

17 is a cell in isolation and even when you look at the

18 differentiation the distribution of the cell that the stem cell

19 will differentiate into is not recapitulating what you would

20 have in the brain.

21 Not real sure I'm answering your question but

22 it is more concerns of considerations as you are looking at
Page 451

1 that. Again, I found the document excellent in going through

2 some of the underlying biology even for the in vitro systems. 

3 It did not seem to me that they were being dismissed; it seem to

4 me that the Agency was really looking at them to see what type

5 of data that they could provide them and I think that that is

6 going to be exactly how you are going to have to look at that

7 data.

8 And in models that you develop for looking at

9 any other compound to look at how do you take the in vitro data,

10 again the linear model for the domoic acid is like the one

11 clean-cut model. I think you can probably find a couple of

12 other models out there that you could look at to see how they

13 follow out, that there is enough data available, but it is going

14 to be a rare occasion that you are going to be able to go in

15 those box sequences. You are going to have to look at how a

16 number of different modifying events will change those.

17 You were asking about the magnitude or

18 frequency, it is going to be somewhat difficult to try to relate

19 in vitro to in vivo. Again, as I said I am not sure you are

20 going to find the dose response but if you think about it the

21 reason that you go in vitro is you are going in there because

22 you are trying to ask a very specific question as to how it
Page 452

1 relates to your hypothesis of a mechanism that may be affected.

2 And you might go in and do in essence a dose

3 response looking at what you might find and equivalent to a

4 functional observational battery you do in the adults. It is

5 that first tier observation, "What do I see at any dose." And

6 then you come back in and when you find something you hope that

7 at that point that you can address that deeper. You find an

8 effect; you can go after the effect. What is the mechanism

9 underlying the effect.

10 I think where the data falls short for the

11 Agency is that it has done the descriptive and it has not gone

12 further into detail in mechanism and then once it has identified

13 a mechanism, has not necessarily gone back to do your dose

14 response to see if that mechanism could hold up to a dose

15 response effect. Although on the other end, some of these

16 studies in the future may offer you that robustness that Dr.

17 Moser was asking for, that you need a robust endpoint at which

18 to be looking at.

19 So as it comes back I'm not real sure that I

20 would set a demand of these studies that they have to show a

21 dose response. Nor would I set a demand that they have to show

22 a very robust pronounce severity of response because it can be
Page 453

1 subtle changes that can happen. So it is going to be putting a

2 whole package together of all of the individual in vitro data

3 and how does one aspect support the other.

4 I think the other aspect is when you are

5 talking about, again, staying on the relationship across the

6 exposure and the biological outcome and will that cross over to

7 what would happen in vivo and to maybe a human health event. I

8 think one of the things that you need to look for or hopefully

9 that investigators will provide you is to actually take their

10 data and translate from in vitro to in vivo. 

11 So if you have something that is implying that

12 you have an alteration in neurite outgrowth as a developmental

13 component in a dish, there are ways that you can design that

14 next study to say does this happen in the developing animal. It

15 is not going to be a standard study but it will be a study

16 designed to do that. So you can see if these effects translate

17 over. If they have got any predictive validity at all to help

18 you out that will help you both with chlorpyrifos but would also

19 help you in the long run about whether you would look at these

20 things if they have any predictive validity for developmental

21 neurotoxic that are not.

22 I think the other aspect; one of the other
Page 454

1 areas that were shown to be affective was in the area of axonal

2 transport. There is a lot of information in chemical exposure

3 and alteration and axonal transport at which you could take what

4 may be found in vitro and test that hypothesis again to whether

5 that predicts what would happen in vivo with a very targeted

6 approach. 

7 I guess if we come down to all of that is I

8 think you have got a lot of data out there but I do not think it

9 is getting to the point of where you can use it because the

10 investigators are not taking it where you need to have it taken.

11 And to do that predictive validity so you can know which one of

12 these in vitro tests would actually be a good one to predict

13 that you would have a developmental outcome in any way, shape,

14 or form.

15 If you think I have talked long, I know the

16 others have got longer and more specific comments that they want

17 to go after. 

18 DR. KENNETH PORTIER: And with that hopefully

19 Dr. Carr will not say ditto, right? Dr. Carr.

20 DR. RUSSELL CARR: Basically I agree with Dr.

21 Harry. I think that the uncertainties we face in this situation

22 are enormous. A lot of the problem with the in vitro studies is
Page 455

1 they are looking for basically a molecular target where we are

2 talking about a chemical that has an established molecular

3 target that could modify things downstream that will have

4 effects in a different manner for which we really cannot find

5 looking at it in in vitro system because you know in the central

6 nervous systems there are so many different systems connected

7 that one can communicate with others and disrupt one and you can

8 have effects in the other.

9 And so as we progress from in vitro to in vivo

10 we are going to have to start trying to utilize our PBK

11 knowledge to try to get an estimate of target site and dosage

12 because that is what is important here is target site and

13 dosage. Will we get enough of the active compound at the target

14 site to cause a biological perturbation; and then how can we

15 measure that such that we can use it for human risk assessment? 

16 And I think that is probably our biggest difficult step there is

17 moving from these mechanistic studies into human risk assessment

18 because like Dr. Terry mentioned with lead and other things we

19 are still looking for that magic box in those but we do know

20 those are developmental toxicants. 

21 I was very impressed with the paper. I was

22 very impressed with the amount of detail that went into it and I
Page 456

1 think that we are hopefully moving in the right direction and I

2 do hope that we can start taking more advantage of the

3 technologies that we have especially with the PBK model and

4 where we can actually try to estimate and connect what we think

5 we find in a plate what actually occurs in the body.

6 DR. KENNETH PORTIER: Dr. Collier.

7 DR. ABBY COLLIER: Well, I agree with Dr.

8 Harry and Dr. Carr. I have a few additional comments. I really

9 do believe that the oxon in protein adducts seem to be a

10 potentially important pathway and the oxon seem to have an

11 incredibly rapid half-life despite its relatively high affinity

12 for ChE.

13 The law of averages indicates that sooner or

14 later it is going to bind to another cellular component and I

15 really think the criticality of both components can mediate the

16 magnitude of the effects so I think the oxon should not be

17 discounted but clearly the weight of the evidence is that ChE

18 inhibition is what is going on.

19 You asked about a scientific understanding of

20 dosage response relationships for biological perturbations. My

21 understanding is that PK and PD should not be uncoupled too far,

22 that PK can inform PD because pharmacokinetic differences can
Page 457

1 affect active site concentrations and, therefore, can affect

2 pharmacodynamics effects.

3 And to this end, I think TCPy and DETP

4 conjugation by the sulfotransferases and glucanotransferase is

5 actually an important thing to consider.

6 According to Dr. Bartels' submission from DOW,

7 the glucuronides and the sulfonates are equally prevalent but we

8 do not know the relative affinities. And I would remind you

9 that since you are interested in pregnancy and pediatrics, human

10 fetuses, a UGT null as far as activity goes, until at or around

11 the time of birth. Therefore, the glucuronidation is being done

12 by the maternal liver and the placenta and we are just about the

13 only mammal where the fetus does not have UGT active in the

14 liver.

15 And so what that means is that despite the

16 acetylcholinesterase abducting oxon not really getting

17 conjugated if glucuronidation is the right limiting path, then

18 other metabolites could accumulate to the un-toxic levels or you

19 could interfere with your own biomarker analysis and cause

20 errors in your dosimetry calculations. 

21 I do think that the strengths of the

22 approaches that have been taken so far are that much is known
Page 458

1 about the P450s and PON1 and has been integrated into the

2 analyses that have been done, and I think that is really

3 fantastic. I think some of the efforts toward PB/PK and

4 PB/PK/PD modeling have been really well presented and really

5 thoughtfully done, especially the attempts by Dr. Hattis with

6 the pregnancy model. We all talk about that as something we

7 would like to see and I see you are moving forward with it so

8 that is fantastic.

9 I think the weaknesses are that there is a

10 lack of knowledge of conjugation in humans with chlorpyrifos;

11 Phase II is really high capacity. sulfotransferase and UGTs are

12 really high capacity and we keep talking about that chlorpyrifos

13 is a high extraction drug and high face past metabolism; and

14 despite that P450s are important, I really think that

15 conjugation may be playing a role in this.

16 The other weakness that I seem to keep coming

17 back to is the species differences and sulfotransferases. So

18 some of the UGTs particularly the 2B family, but many of the

19 sulfotransferases non-orthologous between rodents and humans. 

20 And I think this might explain why Dr. Hattis said when he was

21 modeling rat clearance data it was very different to what he

22 started doing with human clearance data. 
Page 459

1 So too much reliance on a rodent model without

2 reference to whether or not the enzymes we are looking at are

3 really truly representative of the human situation can cause

4 problems and it could start interfering with your ability to

5 look at your endpoints I think. 

6 So those are mostly my points about PK and

7 about metabolism. I do also think that pregnancy is a very

8 specific state and it is not just a case of changes in blood

9 flow or changes in volume of distribution. We also need to

10 start considering that there may actually be metabolic changes

11 specific to pregnancy as well as related to steroid metabolism. 

12

13 And finally, specific developmental periods in

14 which perturbation may occur in the human still appear to be

15 ill-defined, and also to some extent in rodent species. One

16 discussion that I had with somebody else was that "What is a

17 postnatal day 10, meaning birth." And I have to agree with

18 that; that if we have really good data saying when developmental

19 periods arise then we can have better translation of results to

20 human given that much of these data cannot be generated in

21 humans. But I think that there is a consensus at a broad level

22 at the moment that we are studying the right developmental
Page 460

1 periods if we are looking at pregnancies and pediatrics.

2 So those are what I think are the weaknesses

3 at the moment when you ask about the scientific understanding of

4 dose response relationship and the magnitude and frequency or

5 the duration of these effects.

6 However, I really would like to congratulate

7 you because what we have seen is some really positive movement

8 in a direction that is very difficult to study, so thank you.

9 DR. KENNETH PORTIER: Thank you. Dr. Pope.

10 DR. CAREY POPE: So changes in neurite

11 outgrowth or dendritic spine expansion on transport of these

12 other cellular responses that have been reported in sub-studies

13 with chlorpyrifos or chlorpyrifos oxon exposure in vitro,

14 sometimes report at level remarkably low in the picomolar range,

15 studies by Shu et al. and some of Pam Lyon's (phonetic) work. 

16 But being an in vivo person I tend to think

17 that these kinds of comparisons must be made with caution. For

18 example, adding chlorpyrifos oxon directly to some hippocampal

19 neurons in vitro in two Shu culture mediums is kind of

20 unrealistic for how the oxon could potentially reach one of

21 those neurons. 

22 The more simplified or isolated and biological
Page 461

1 system is, the less macromolecules around that system and

2 available for interacting with the toxin. And many detoxifying

3 binding proteins are obviously not included in these kinds of in

4 vitro conditions potentially modifying the interaction of the

5 chemical with the cell. 

6 Kind of similarly, if an acetylcholinesterase

7 in a disrupted tissue is looked at for its sensitivity to

8 chlorpyrifos oxon, it may show a certain level of sensitivity. 

9 If you pull that enzyme or any other sensitive enzyme out of

10 that tissue and study it in idle isolation, you are going to see

11 it is a lot more sensitive to chlorpyrifos oxon based on

12 concentration of the chemical. 

13 Such effects of tissue components in the

14 relative potency of these chemical makes, for me, extrapolation

15 of in vitro effects to an in vivo setting a very difficult one. 

16 In general, the collective data suggested changes in neural

17 developmental endpoints required relatively higher exposure in

18 the in vivo animal models than in a number of these in vitro

19 preps.

20 An important factor in these types of in vivo

21 studies, which has been raised by the panel previously, is the

22 time that acetylcholinesterase is evaluated. In particularly in
Page 462

1 developing animals, the timing may be even more critical to make

2 sure that you are looking at the right time and there is a

3 window of opportunity that could be very short and that kind of

4 definition of that opportunity to see inhibition has to be

5 demonstrated under some conditions. For example, with higher

6 exposure levels to make sure that if there is inhibition in a

7 particular study then you are going to see it. To me

8 neurobehavioral studies that are conducted without

9 acetylcholinesterase tagging along should be avoided.

10 While neurobehavioral changes following early

11 chlorpyrifos exposure may not be potentially causally related to

12 acetylcholinesterase inhibition, I think it is reasonable to

13 conclude with the existing data that we have got, that

14 neural-developmental disruptions following chlorpyrifos in

15 general occurs at levels of exposure at, near or around those

16 associated with significant inhibition.

17 DR. KENNETH PORTIER: Thank you. Dr. Terry.

18 DR. ALVIN TERRY: I just have a few comments. 

19 I would first like to mention I agree with Dr. Harry and others

20 about how well written this white paper was in covering all

21 these potential mechanisms. I would like to add one area that

22 was not covered and that is the potential of chlorpyrifos and
Page 463

1 the oxon affecting growth factor receptor levels.

2 I know the Slotkin group has shown effects on

3 FGF in neonatal animals I believe. Dr. Carr published a paper a

4 few years ago and I think it was in neonates also showing

5 effects on nerve growth factors, and then those were in neonate.

6 And our group has shown effects on nerve growth factor related

7 receptors. So I would like to see that added or at least

8 thought about.

9 Just a few other comments and certainly I have

10 never been a regulatory person so I do not envy being in your

11 shoes when it comes to making these kinds of decisions. And I

12 would be very remised to make any regulatory decisions based on

13 in vitro data. 

14 And I agree with Dr. Pope and others that

15 extrapolating from in vitro studies all the way to whole animal

16 and human is very problematic. But I do think they can educate

17 us on potential mechanisms other than cholinesterase that are

18 very important and you, in the paper, have certainly highlighted

19 some of those.

20 I do agree with the idea that most of the

21 studies in vitro tend to look at fairly high concentration, but

22 this is not always the case, whether it be the parent compound
Page 464

1 or the oxon. And so if you look at some of the neurite

2 outgrowth, that is at least three or four of those they are

3 looking at either, you know, low nanomolar levels of the oxon or

4 low micromolar levels of the parent compound in many cases you

5 do not detect cholinesterase inhibition at those levels, at

6 least we do not. 

7 One of the things that also was not mentioned

8 in the white paper is effects of mitochondrial morphology in

9 transporting oxon. So that is something we have seen its levels

10 as low as five nanomolar of oxon -- this is in vitro again --

11 primary cultures, and as low as a micromolar of the parent

12 compound. So that is another of the mitochondrial morphology

13 issue would be another one to add.

14 And then if you think about all of this in

15 context, and this was brought up earlier about potential

16 covalent modification of multiple proteins other than a

17 cholinesterase and many of you know that the Lockridge group has

18 done a lot of this work. 

19 If you think about covalent modification of

20 things like kinesin tubulin and the effect in tubulin

21 polymerization, you know, motor proteins involved in axonal

22 transport, these are fundamental processes that could affect a
Page 465

1 lot of downstream pathway. 

2 And again getting back to the -- you will

3 never fit the domoic acid paradigm to this type of thing, but

4 those are not trivial effects I would argue and I think that

5 this should be stated again. 

6 So collectively you look at things, neurite

7 outgrowth, mitochondrial morphology, covalent modification of

8 proteins that are involved in axonal transport, and on and on,

9 collectively this is a lot of stuff and I think that it should

10 not be trivialized by just saying that in vitro studies do not

11 predict what happens in the real world. And I think that is

12 pretty much what I had to say. 

13 DR. KENNETH PORTIER: Thank you, anybody want

14 to add anything? Dr. Harry.

15 DR. JEAN HARRY: I guess the one thing I would

16 like to add is that in looking at the whole body of data that is

17 out there I think the Agency is taking a very conservative and

18 safe approach by going with looking at the ChE inhibition for

19 regulating and determining risk. I think you have got a good

20 basis for that; you have got really good data for that.

21 You have got a lot of things that are

22 happening in vitro and I guess I'm just going to raise this one
Page 466

1 caution because it does come back up in thinking about the lead

2 and the mercury and a number of other compounds. Is that, if I

3 have a compound that is going to affect neurite outgrowth and I

4 have a compound that is going to affect axonal transport and I

5 have a compound that is going to affect cross linking of other

6 proteins and I have a compound that is going to affect neural

7 stem cells, if I'm seeing this at even the levels that are about

8 what you see the inhibition happening in vivo, I would expect

9 that I would have a really messed up animal.

10 So we see a lot of these processes that are

11 maintained, they are critical to every cell of the brain yet we

12 usually find some sort of specificity within at least pathology

13 or within a functional outcome. And that has always been where

14 the disconnect has been between neural toxicants in vitro and in

15 vivo. Is that if you get these generalized responses, how do

16 you adjust those now to specificities that can happen in vivo.

17 So that sort of plagued us through a lot of

18 compounds over the years is that type of pivot point and I think

19 until we have some really good studies that target something and

20 say, "If I do a neurite outgrowth study and I do it this way in

21 vitro, I can look at that compound and this dose and I can

22 predict that this is what I would see in vivo and I can show
Page 467

1 it."

2 Until you have that data you really just have

3 a lot of experiments looking at different endpoints that are

4 affecting directly in a dish, not necessarily translating over

5 in vivo, at least within a range where human health risk

6 assessment is concerned. Having done both in vivo and in vitro

7 it is like they each serve a purpose but you use the data to the

8 best benefit that you can.

9 DR. KENNETH PORTIER: Any additional comments?

10 Dr. Aschner.

11 DR. MICHAEL ASCHNER: What I would like to do

12 is actually to break it into the individual points; I may be a

13 little bit redundant. The first question is essentially what is

14 the degree to which these studies, in vitro and in vivo,

15 evaluating potential neurodevelopmental outcomes and may or may

16 not be more sensitive than AChE inhibition. 

17 I think the answer for that is that we really

18 do not know. I think these studies are generally predictive and

19 they can be used to come up with some ideas in terms of

20 mechanistic effects that might be taking place but again I would

21 be very cautious and it has been said before in terms of how you

22 extrapolate from the in vitro to the in vivo because it is a
Page 468

1 sort of reductionist kind of approach.

2 I'm also not sure how you compare cell

3 differentiation if you collect cells from an animal on day one

4 and you look at the axonal growth for example, how you would

5 equate that with exposure that might be occurring later on in a

6 completely different system of neurotransmitters. So again you

7 might get some hints in terms of some of the mechanisms that

8 might take place but how these translate to the in vivo system I

9 think would have to be followed up.

10 One thing that I did not find in the document,

11 and I want to actually reiterated what was said before. It is a

12 wonderfully written document and I commend you on that but I

13 think what was missing for me in terms of what is the next step

14 is that we do have actually a lot of techniques that can be

15 incorporated to look at some of these issues that you are

16 raising in the in vivo animals. 

17 There is wonderful imaging types of systems

18 where you can look at cell differentiation, migrations, and up

19 to genesis and none of that has been done. I do not think

20 anybody is willing to pay for it but I think the kinds of

21 questions that you are asking might be derived from these types

22 of studies.
Page 469

1 In terms of question number two, please

2 include in your comments a discussion of the strengths and

3 uncertainties. I think I have touched upon that. In terms of

4 the third question, it says dose response and again when you do

5 dose response in vitro it is obviously nice to see that you get

6 one in the first place but not all the dose responses are always

7 linear or curbed; they can be "U" shaped. And that again raises

8 different issues in terms of how you are going to extrapolate

9 the data from these findings.

10 The kind of dose response that you will get

11 might also depend on the composition of the cells that you have

12 in a plate so if you do these types of studies only with neurons

13 you may get one type of effect. If you do it in the presence of

14 microglia and astrocytes, the effects may go away. And once you

15 get to all these complicated issues and once you incorporate it

16 into the dish, it is very hard to take these data and say X

17 happens in vitro and have some certainty or whatever percent

18 certainty you may assign to it, in terms of this is going to

19 happen in vivo as well.

20 So I agree pretty much with everything that

21 was said. I agree with the presentation of Dr. Mundy I think in

22 terms of biological organization, which is question four, this
Page 470

1 can be very informative in terms of what is happening but in

2 terms of coming up with a risk assessment, I think there are so

3 many shortcomings and I can go on and on, that one would have to

4 look at all these data very cautiously before making any

5 decisions.

6 DR. KENNETH PORTIER: Thank you. Any

7 additional comments? I think that was pretty clear. What I'm

8 going to do right now is we are going to go ahead and take our

9 15 minutes midafternoon break.

10 I would like to see briefly Question 3

11 discussants in the break room. I'm hoping we can maybe get

12 through all of Question 3 today; it has three parts to it and we

13 had one part scheduled for tomorrow. I'm hoping we can just go

14 ahead and tackle that today. Thank you.

15 BREAK 

16 DR. KENNETH PORTIER: I think our game plan is

17 we are going to try to get through all of Question 3 today. We

18 had scheduled part of Question 3 to be tomorrow morning but I

19 think it would be nice if we could kind of wrap up the Question

20 3 discussion and then start fresh tomorrow morning with the EPI

21 Question 4. And I understand Question 3 is probably going to

22 take the rest of the afternoon just so everybody knows.
Page 471

1 So I think we wrapped up Question 2.2, no more

2 questions, so why don't we go ahead and read Question 3.1.

3 DR. ANNA LOWIT: Dr. Portier, we have had a

4 request at the break for a document. I just want to make sure

5 that while we are chasing it down behind the scenes that we are

6 chasing down the right thing. Dr. Sandy asked that we find the

7 EPA review for the Marson et al., 2000. We will have a review

8 of the guideline version of the same study but not Marson, which

9 would be a shorter version of the guideline study. So we can

10 find you the review of the guideline study but it will not be

11 Marson per se although it is the same information.

12 DR. MARTHA SANDY: This is the study that has

13 the lowest BMDL for inhibition of acetylcholinesterase according

14 to one of the tables, I guess on page 19 or 20 of your

15 background documents. We are just looking for a description a

16 little bit more description of that study and its study design

17 for measuring the time points of dosing and measuring AChE.

18 DR. ANNA LOWIT: Okay, I'm glad I clarified

19 because that is different from what I was just asked for. Okay,

20 we will find that.

21 DR. MARTHA SANDY: Thank you.

22 DR. KENNETH PORTIER: Thank you. 
Page 472

1 DR. GINGER MOSER: Question 3.1. As discussed

2 in Section 3.2.2, the experimental toxicology data in laboratory

3 rodents show neurobehavioral effects following developmental

4 exposure with changes in a number of neurological domains. In

5 2008, the SAP agreed to this preliminary conclusion, and the

6 nine additional studies available since 2008 add further

7 support. 

8 Please comment on the degree to which these

9 studies show changes in a number of neurological domains and

10 support the qualitative conclusion that chlorpyrifos exposure

11 during gestation and/or early post-natal period may result in

12 long-term adverse effects on the developing nervous system. 

13 What evidence does or does not support this conclusion? 

14 Please also include in your comments a

15 discussion of the strengths and uncertainties. 

16 Please also include in your comments a

17 discussion of the scientific understanding of dose response

18 relationships for biological perturbations and the magnitude,

19 frequency and/or duration of such perturbations that can lead to

20 adverse effects at higher levels of biological organization to

21 support characterization of the likelihood of adverse outcomes

22 in a human health risk assessment as articulated in NRC 2007.
Page 473

1 DR. KENNETH PORTIER: So that looks like five

2 questions. Dr. Vorhees.

3 DR. CHARLES VORHEES: That is a very

4 complicated question to which I have a somewhat complicated

5 answer. It seems to me in looking this question over and

6 reviewing the studies involved, it seems to me, unless others

7 see it differently, that they are asking for an independent

8 evaluation of the strengths and weaknesses of those 21 studies

9 that are cited in Appendix 3. 

10 Now, in order to do that, I'm not going to go

11 through every study and bore you with the design and the

12 findings because if I did that we would not finish even today. 

13 What I would like to do, however, is step through some of the

14 studies to the extent of bringing up some issues that I do not

15 think that the Agency highlighted as potential concerns in some

16 of these studies. To just bring to your attention issues that a

17 developmental behavior neuroscientist would think about as

18 issues in these kind of study design.

19 So, I'll take them kind of in the order that

20 Appendix 3 lays them out just for convenience. So that I can do

21 this systematically, although many of the studies in the EPA

22 table appear more than once I will not do them more than once.
Page 474

1 There is an Abou-Donia study that I just would

2 like to point out a few things about. They used a supplier

3 supplied timed pregnant animal shipped from the supplier for

4 doing a prenatal study. Now, I have worked for the last 15

5 years with a stress HPA access, a neuroscientist, and I can just

6 tell you that I think that is a really bad experimental decision

7 because prenatal stress is known to have long-term effects on

8 behavior. 

9 Now, the defense of that would be, well you

10 are ordering in all the animals as timed pregnant but how do you

11 know that the compound does not interact with the stressor,

12 because there will be no control within the study that would

13 allow you to determine that since the controls would be exposed

14 to the vehicle, and I'm learning a lot about the bad things

15 about DSMO but aside from that, I think it is a serious problem

16 and it arises over and over in this particular literature.

17 Another thing I would like to point out in

18 this study, and this is a problem that you see in a number of

19 these studies and I think we have alluded to it and I would just

20 like to emphasis it here, that people who worked in

21 developmental animal studies, multi-verse species animals for

22 many years know. and Dr. Moser brought it up yesterday. Is that
Page 475

1 it is very important that you look at litter. You look at the

2 unit analysis as it sometime is called by litter, and in this

3 study they only have five litters in the principle treated

4 group; it actually has multiple treated groups. And I think

5 that is a major problem. This study is seriously deficient in

6 sample size. 

7 In addition to that they did something which

8 is another, I think, big problem in developmental studies, and

9 this has been talked about in the literature for many years I

10 always refer people back to the Holsen and Pearce study that

11 looked at this and simulated it, and that is the problem of

12 oversampling within a litter. And this study severely

13 oversamples the number of offspring that are evaluated per

14 litter. 

15 So when you take the combination of a small

16 number of litters per group and you oversample the number of

17 offspring per litter, what you do is you set yourself up; the

18 risk of type one errors goes way up as Dr. Holsen simulations

19 show the more you do that. And I do not know why but in this

20 whole field we have been going around and around on this for

21 years and yet the lesson does not seen to be learned as

22 indicated by the fact that this study is 2006; and this
Page 476

1 information has been known to those of us who do research in

2 this area since, I think it has been settled since the 70's but

3 that is maybe just my view of it. 

4 But I know all agencies' regulatory studies

5 recognize this issue and set things up so that those are not

6 problems. So when you get on to, for example, the Icengole

7 study you run into that same sort of problem; they used timed

8 pregnant animals shipped in. 

9 Another thing that they do and this particular

10 laboratory does, which is a completely unproven method, and that

11 is what they do is shortly after birth they randomize the pups

12 among the dams. I think the goal of that is to try to randomize

13 genetic and intrauterine effects. We know that air variance

14 clusters within litters. They are trying to break that

15 association so that they do not have to pay much attention to

16 the litter variable as they go along.

17 And this laboratory does this and they not

18 only will randomize one shortly after birth, they will randomize

19 every three days. And I think their goal is to try to break

20 that genetic or maybe epigenetic intrauterine effect. The

21 problem is you do not change the dam factor. So, the dam factor

22 is still going to produce a clustering effect as the animals are
Page 477

1 reared. 

2 There is not a single published scientific

3 paper that I know of where anyone has actually tested, whether

4 or that procedure breaks litter clustering. Nobody wants to do

5 that study because it is a giant pain in the neck, for which you

6 will get a very ho-hum answer. So, nobody does it. It is a

7 completely unproven method and I think it is a problem. 

8 I will just point out another problem that I

9 do not know whether other people would be sensitive to, but it

10 bothered me. They run a test called spontaneous alternation in

11 this experiment. I do not know how many of you know what his

12 test is about, but the concept behind is that if you put a rat

13 in a T maze with no reinforcement whatsoever, they will go down

14 and they will choose an arm. Then, you put them back in at the

15 beginning. What will tend to happen about 67 percent of time on

16 that second trial, they will choose the alternate arm. That

17 indicates that they remember the arm they went down, and are now

18 interested in exploring the other choice. 

19 You run this test. The idea behind the test

20 is to run it under low anxiety conditions. It is a novel

21 environment already. The goal is not to increase anxiety to a

22 higher level. This laboratory runs this test in an elevated T
Page 478

1 maze. I have never seen this before. What you are basically

2 doing is running the elevated plus maze as a T maze. 

3 So, for those of you who are not into this

4 that may not seem like you are like, wow, who cares, believe me,

5 this is not the way you want to run it. It is counter to one of

6 the basic premises of the test of what you are trying to

7 measure. You are not trying to measure anxiety. You are trying

8 to measure memory. If you increase the anxiety level to a high

9 level, you can potentially interfere with what you are trying to

10 measure. I would also point out that every finding in this

11 study, whether statistically significant or not, is in fact

12 quite small. 

13 Another thing I would like to bring up about

14 the radial arm maze, which I think is a very serious problem in

15 this whole literature. It is not just the chlorpyrifos

16 literature; it is really a problem throughout neuroscience. I

17 had to put is so bluntly, but I think a lot of people who run

18 the radial arm maze do not really know what they are doing. 

19 So, the problem that you have is that you have

20 to think what you are trying to measure, short-term memory. How

21 do you measure short-term memory? Short-term memory is a

22 phenomenon that depends on recent events and it decays very
Page 479

1 rapidly over time, like trying to remember a telephone number. 

2 In a rat, you put him in the radial maze, the

3 idea is that all the bated arms, they have to go down that arm

4 and remember that they just visited it so that they do not go

5 back and visit it again having forgotten that they already went

6 down that arm. 

7 It is absolutely essential in such a test that

8 you have a delay between their first arm choice and their

9 second, third, fourth arm choice. If you do not have a delay in

10 between, rodents are just like people. They figure out the

11 simplest way to solve the problem without relying on short-term

12 memory, because that is harder to do than it is to just learn a

13 phenomenon called chaining. In chaining, the rat learns a very

14 simple strategy to defeat the test. 

15 All I have to do to maximize the number of

16 rewards I get, which is what the rat wants, it to go out of each

17 arm and turn right. If I turn right on every option that I get,

18 I will go around the maze and collect the maximum number of

19 reinforcements, and I do not have to use any memory whatsoever. 

20 All I have to learn is a habit. Habit learning is much easier

21 than relying on short-term memory.

22 None of these experiments do the appropriate
Page 480

1 controls to rule out that what they are really measuring is

2 habit formation rather than short-term memory. I will come

3 later to the one study that actually seems to have controlled

4 for it. When we come to it, we will come to the conclusions

5 that it reached. 

6 There is another study, I do not know if I can

7 pronounce the authors' names. It is done in mice. It is kind

8 of an interesting study. It is Billauer-Haimovitch, and there

9 were some good features in this study. The fostered some of the

10 litters and they did not foster other litters. Then, they

11 looked to see whether fostering or not fostering made any

12 difference. They report without showing the data that it did

13 not make a difference. Then they pooled the data. 

14 I would like to comment on an issue that comes

15 up in the morris water maze. There are a number of things, and

16 Dr. Harry has alluded to some of these. One of the things that

17 people do not often pay very much attention to, this is spacial

18 task. The idea is to measure spacial mapping, which is a

19 hippocampally-dependent form of learning and is homologous in

20 humans to declarative memory. Declarative memory is memory for

21 people, places, things and events. 

22 What becomes essential if you are going to use
Page 481

1 this as a rodent version of declarative memory is that you have

2 a spacial component that is really spacial. What these people

3 do is they use a very tiny maze. It is 87 cm in diameter. For

4 mice, the standard size that is considered appropriate for mice

5 is 122 cm. Not only that, you can use a small maze if you want

6 to adjust the size of the goal proportionately. But what you do

7 not want to do is use a small maze and a very big target,

8 because then it is not very difficult. If the platform is large

9 and the maze is small, the animal is going to find it rather

10 easily. There is no much demand on hippocampal place cells. 

11 So, what they do is use a pretty large

12 platform and a pretty small maze. It produces a search area

13 ratio of 74 to 1. 74 to 1 is not an adequate test of spacial

14 mapping. We need ratios that are above 200 to 1. Many mazes

15 are 300 to 1 or 400 to 1. So, it is just not a good test for

16 what they were trying to get at.

17 A strength of this paper is that they have 3

18 separate experiments in there. They find an effect each time,

19 but they use latency. We know that latency in the morris maze

20 can be confounded by a number of factors, but obviously swim

21 speed is one of them. Now, they measured swim speed and they

22 say that there is no effect. That is good, but they did not
Page 482

1 report path length. We know path length is the principle index

2 of spacial mapping, and they do not report it. They also did

3 not do a probe trial, which is if you are going to say you are

4 looking at spacial learning and reference memory, you have to do

5 a probe trial or you have not measured it. They also say they

6 ran the qued version as a control procedure, but they do not

7 tell us what they found. So it is another example.

8 The next study is by them as well, Turgeman,

9 and they use the same maze, have the same issues. So I will not

10 belabor that one as if I have no belabored these. So, this will

11 get easier as I go along. 

12 Laviola did kind of an interesting thing. 

13 They tested and they used reeler knockout mice. They bread

14 hets. They took out of each litter the knockouts and the wild

15 types and tested them. So, it ends up being kind of a

16 complicated study. But my problem with the study is not that

17 they used reelers, because I thought that was sort of an

18 interesting idea given what we know about reelers and early

19 brain development, but they do some very strange statistical

20 things. 

21 One of the things that is very strange is to

22 test animals, and for example, by the way they test the axon in
Page 483

1 this study, it is odd in that way. But when you do a

2 statistical analysis on 45 minute locomotor data and you do not

3 find an effect and you go in and just say, well I am going to

4 look at the last 25 minutes because I want to, I just sort of

5 becomes rather strange. That is exactly what they do. They

6 just say well, it looked to us like there was an effect in the

7 last 25 minutes, so we will do a separate additional analysis on

8 the last 25 minutes. Because that is significant, is everything

9 okay? 

10 They did not do their subjects by litter, so

11 it has that potential confound in it. In fact, they did not

12 appear to use sex as a factor in the analysis. This study is

13 just full of problems in the design, in the way the study was

14 conducted and in the analysis. So, it did not leave me with a

15 lot of confidence. 

16 There is a whole series of studies by an

17 Italian group, Venerosi in 2010 as one example. They use an

18 interesting statistical approach. What they tell you is that

19 they ran most of the outcome data by analysis of variance, which

20 would appear to be pretty appropriate, but in some cases, they

21 did not run the analysis of variance. They simply used a Tookie

22 test selectively without an ANOVA finding, without a significant
Page 484

1 F test. 

2 Well, I guess I do not have a problem if you

3 can justify doing selective testing by some, say a priori

4 prediction. When you get to the results, you should at least

5 tell us which of the data are in fact significant by ANOVA

6 followed by Tookie and which are significant by Tookie without

7 ANOVA. They do not tell you. 

8 Haviland study I found to be a problematic

9 study. There is no mention of litters that litters were used. 

10 There is no mention of the number of litters taken from each

11 litter. There is no mention of how sex was handled in the

12 design. The results are not well described statistically. They

13 are done in a very sort of sweeping general sort of style. 

14 There is no mention of how they do pair-wise comparisons. They

15 do mention it for the thyroid assay, but they do not mention it

16 for the behavioral outcomes, whether the use the Fisher LSD for

17 the behavior, but it would have been nice if they had said so. 

18 Alright, enough.

19 The Levin studies with Dr. Slotkin have an

20 interesting feature. They report that they use this randomized

21 litter. Even though they are randomizing the pups in the litter

22 to break litter factor, they still go ahead and select one out
Page 485

1 of each artificial litter, one male and one female, for testing.

2 I do not quite understand the logic there. It seems a little

3 circular. So, I am going to break the litter effect, but I am

4 going to treat the litter effect as though it is important. But

5 they say that they use analysis of variants set at 0.05 to look

6 at main effects, but at interactions they are going to look at

7 anything that is P less than 10%. 

8 I do not understand that and I do not

9 understand the rationale for it, but their biostatistician is

10 here. If they want to defend this, you know, I will let them. 

11 They use an animal in their studies that concerns me. They do

12 not use the most common and well-understood animal for learning

13 and memory tests, Sprague Dawley from Charles River or Harlan. 

14 They use the Zivic-Miller Sprague Dawley. If you go into the

15 older experimental psychology literature on learning and memory

16 and you look at the learning of the Zivic-Miller Sprague Dawley.

17 If you go into the older experimental psychology literature on

18 learning and memory and you look at the learning of the

19 Zivic-Miller Sprague Dawley, what you find is, it is the worst

20 performer of all the stocks of Sprague Dawley rats. It is

21 particularly bad in shock avoidance tasks, which means it is a

22 terrible in active avoidance. It is great in passive avoidance
Page 486

1 because when they get shocked, they will not move. 

2 So, when you take an oddball animal and you

3 use it in a setting like this, you produce data that are then

4 difficult to fit in, I think, with other studies in the

5 literature. It is interesting to me that eventually they

6 dropped in some of their later studies, they stopped using the

7 Zivic-Miller. I do not know why, but I could guess. 

8 We talked about some of the difficulties in

9 the Levin and Slotkin studies about dose response. I will not

10 go back over those. So, that covers the dam study and its

11 essential features because the same design considerations are

12 used. I thought that the Aldridge study was one of the stronger

13 studies because of the use of ketanserin. I think when they use

14 ketanserin as a challenge to look at 5-HT2 receptor involvement,

15 they were actually onto something. I hope at some point they

16 choose to follow that up. 

17 The Ricceri study is very similar to the other

18 studies from the Italian group. One of the things about several

19 of the Italian studies is they do a extremely complex design, in

20 which they have 3 prenatal groups crossed by 3 postnatal groups

21 producing a 9 group study. My experience with large, and I

22 guess a lot of us have tried those, is you start losing control
Page 487

1 over the study. There are practical problems with trying to do

2 extensive behavioral analyses, keep all other factors constant

3 when you have the enormous number of animals going through your

4 protocol. You inevitably end up with large numbers of

5 interaction terms in your analysis of variance that become very

6 difficult to sort. They really have some major problems in

7 sorting these data.

8 I will just say that when it comes to the

9 Johnson study, again, I think that that study would benefit from

10 control over in the radial arm maze for the phenomenon of

11 chaining. I am not saying that chaining actually happened in

12 their animals, but without some description or assurance that

13 chaining did not happen, it remains an open question. 

14 I think in the Carr study of 2001, one of the

15 concerns there in that design is the number of litters they used

16 was relatively modest. They did very slightly over sample per

17 litter. They took 2 per litter per sex in order to get up to

18 their sample size that they wanted of 10 per sex per treatment

19 group. It is a little shy on the ends.

20 The Jett study is an interesting study. It

21 could have been a very important study, but I think the

22 experimenters made some mistakes. What made it interesting is
Page 488

1 that they tested 2 doses, 0.3 which is an area where we would

2 like to have some data, and a high dose of 7. Unfortunately, I

3 do not understand their exposure protocol, which is postnatal

4 day 7, 11 and 15. It is not clear and there is no justification

5 for how those days were chosen. 

6 They also test in the morris water maze at 24 to 28 days. That

7 is not an optimal time to look at spacial learning. Spacial

8 learning is just merging at that stage of development. 

9 Some labs do not get very good learning at

10 that age because it is not a fully developed capacity. Their

11 statistics are not described properly. They leave a lot to be

12 guessed at. The statistical model is unspecified. They do no

13 report path length data for the morris maze. They report that

14 there were no differences on Q trials, but they do not show us

15 any data. They report that there are no effects on speed. That

16 is good. 

17 Their sample looks okay when you just glance

18 at it, 10 males and females in the control group, 10 and 9 in

19 the low dose and 9 and 8 in the high dose, but you do not know

20 how many litters that came from. Then, you get to this very

21 problematic sentence that it could have been selected from as

22 little as 2 litters. You know, it is hard to know, but it does
Page 489

1 not look promising. 

2 I think that the strongest design study in

3 this whole group is the Morrison study because it was done, I am

4 guessing, as a guideline kind of study. So, the sample sizes

5 are bigger and it is a nice feature that they include a 0.3, a 1

6 and a 5 mg/kg group. They expose for, what I would suggest, is

7 a more reasonable exposure time. They do not try to just pick

8 out of the air random developmental periods to do their

9 exposure. They expose from implantation to postnatal day 10. I

10 would fault them for giving the postnatal exposure to the Dams. 

11 There are several studies that do that. I know regulatory

12 guideline studies do this. I strongly disagree with it. I do

13 not think it is an appropriate design. They seldom document how

14 much of the compound is getting through lactation to the pups. 

15 I think it is a poor choice, and I have said so before in other

16 contexts, and I have written about this. Nobody listens. 

17 Another thing they do that statistically, they

18 do sort of an interesting thing. They know they are going to be

19 doing a lot of ANOVAs, so to try to control for that, they set a

20 higher P value of 0.02. That is kind of an interesting way to

21 try to protect against false positives. They are very strict

22 about it. They get a number of 0.03 and 0.05 effects, and they
Page 490

1 just say they are not significant. Now, one could look at those

2 and say well maybe there is something there, but they basically

3 do not get anything. 

4 This study is largely null, but one of the

5 strengths of it is they used a delayed spacial alternation test.

6 One of the nice things that they did is they actually tested

7 different delays between trial 1 and trial 2, actually testing

8 working memory. They actually did working memory as you are

9 supposed to do working memory. So, they actually do not find a

10 working memory effect. It remains an open question. I think in

11 the animal studies whether there are working memory deficits, it

12 sort of seems like there are from the radial maze, but the

13 radial maze, they way they have been run, have some problems. 

14 The study that ran a strong paradigm, at least for working

15 memory, does not find an effect. You cannot exactly compare

16 delayed spacial alternation to radial arm, so there is always

17 that. By and large, this looked like a strong study. 

18 Now I am going to sort of get to my bottom

19 line. First, I would just like to say that there are many

20 effects seen in these studies. Whatever their deficiencies,

21 there are many long-term effects documented at doses in the

22 range of 1 to 5 mg/kg per day. It has already been shown to us
Page 491

1 ad nauseam that these doses significantly inhibit

2 cholinesterase. Let's just make that clear right there. 

3 There are only 3 studies that I could identify

4 of the 21 that used doses below 1 mg/kg. Two used 0.3 and 1

5 used 0.2. Those studies are the Jett study, the Braquenier

6 study and the Morrison study. Of these, 2 found no effects at

7 that lower dose and only 1, the Jett study which is deeply

8 flawed, found an effect at 0.3. Because that study contains, I

9 think among the most serious methodological flaws, it is very

10 difficult to know whether any weight should be put on it or not.

11 So, I do not think that the studies and the data from the Jett

12 study should be relied upon. It would require a replication in

13 a properly designed and analyzed study. In the absence of that,

14 I think that study has to be heavily discounted. 

15 I think in the remaining studies, 1 mg/kg and

16 above, there are many difficult interpretational and

17 methodological limitations. There are many inconsistencies as

18 been discussed across the different tests. Some of the tests

19 that are used transplanted, as I said yesterday from

20 neuroscience, are transplanted without apparent full

21 appreciation for what they were designed to do. 

22 For example, an anxiety test picked up and put
Page 492

1 down into a neurotoxicology setting. What does it mean if an

2 animal show an increase or a decrease in anxiety? Which one of

3 those is a toxic manifestation? Which is bad for us? I mean,

4 we know that in human beings, high anxiety is detrimental to

5 performance and so is low. People perform best in a mid range

6 of moderate anxiety. Is that true for rodents? Well, we think

7 it is. So, if you give them a test of anxiety and they are up a

8 bit or they are down a bit, but they are not extremely high or

9 extremely low, just because it is significantly different from

10 baseline, is that a toxicological end point just because it is

11 an end point that has changed? I do not know, but I can tell

12 you that there is no data that I know of that establishes that

13 it is a neurotoxic outcome. 

14 I think the same should be said about the

15 social tests. They are very interesting and they have a place

16 in neuroscience. As I think I alluded to, they have a place in

17 autism modeling where the disorder that you are trying to model

18 is a social disorder. If you are knocking out a candidate gene

19 in looking for a social deficit because you believe that you

20 knocked a gene out that is important to social behavior, and in

21 that context, you can interpret a change in anxiety, I think,

22 within the context in an appropriate way.
Page 493

1 But, how do you move that over into a

2 toxicological setting? I think the cognitive test for that

3 reason. I do not think that many people would say that impaired

4 learning is a good thing. When you have impaired learning, you

5 do not have the same interpretational issues that you have with

6 social interactions. If a Dam goes out to her pups and licks

7 them more or less, I do not know which of those is the toxic

8 outcome, and that is a problem to understanding a lot of these

9 data. A lot of the Italian data are like that. 

10 Question 3.1. I think the evidence support

11 that developmental neurobehavioral study, show an adverse

12 effects of CPF exposure, they show long-term effects. The

13 strengths and weaknesses of these studies are outlined above,

14 and are the principle findings and the study designs that are

15 independently summarized in the things I have said and in the

16 EPA table. Adequately summarize the key features of these

17 studies. In general, the studies fall into 2 major categories,

18 those that have adequate group sizes, in which control for

19 litter effects by sampling only 1 offspring per litter

20 stratified by sex, or only barely oversampled per litter by

21 using 2 of each sex per litter. It think those are in the

22 acceptable range. Analyze the data using appropriate
Page 494

1 statistical models, ANOVA models with factors of group and sex,

2 optimally using litters of blocking factor and a completely

3 randomized block design, in which treatment and sex are fixed

4 effects within the blocks. Using repeated measure factors where

5 you have trials, days or intervals when you have a repeated

6 measure on the same subject. 

7 Then, I think there is another set of studies

8 that failed to control for litter effects. They tested too few

9 litters. They were underpowered. They were very prone to type

10 1 errors. They should be significantly discounted in

11 determining the weight of evidence for or against

12 neurobehavioral effects for developmental exposure to CPF. 

13 Some of the studies include appropriate

14 downstream statistical analyses and some did not. In general,

15 most studies appropriately use factorial ANOVA or ANOVA models

16 with follow-up methods, although the follow-up methods vary

17 quite widely. Some of the models used further step down slice

18 techniques or simple effect ANOVAs and some do not. I think

19 that can be an issue. 

20 I think in this literature, some of the a

21 posteriori techniques that are used are, my guess I could say

22 is, sort of out of date. I think Fisher's LSD test has been
Page 495

1 discussed in many statistical textbooks for its limitations are.

2 It can be used when you only have 3 groups, but above 3 groups,

3 it does not control for Alpha and therefore is not recommended. 

4

5 I have gone over, I think, the significant

6 problems with the interpretation of some of the learning and

7 memory tests. I think that these are by and large appropriate

8 methods. The radial arm maze and the morris water maze are

9 appropriate tests for inclusion in studies of this kind, but the

10 caveat is of course when they are done appropriately with

11 appropriate controls, appropriate size mazes, appropriate

12 trials, appropriate safe guards for confounding factors, the use

13 of appropriate additional methods like Dr. Harry mentioned

14 yesterday. One of the things that you would want to do is do a

15 reversal phase. Reversal is a very fundamental aspect of

16 spacial mapping that is neglected in most of these studies. 

17 Dose response. Although a number of

18 experiments reviewed here and had 2 or 3 doses, linear or even

19 curvilinear or even monotonic dose response relationships were

20 frequently not seen. This was clearly troubling to the agency

21 and certainly troubled to me as a reviewer. It is difficult to

22 understand some of these effects. We can conjecture
Page 496

1 explanations as has gone on in the biological literature forever

2 about curvilinear, inverted U-shaped curves, but do we have any

3 evidence that that happens with this particular compound? I do

4 not actually know of any such evidence. I did not see any. I

5 did not see much discussion of the issue in these papers either,

6 though I have to say I discounted authors' discussion sections

7 rather heavily as I was just mostly focused on their methods and

8 results. 

9 Persistence of effects. One of the aspect of

10 my review of these papers to me stands out. That is that at the

11 doses of 1 mg and above, there are many studies which find

12 long-term effects. I do not think that there is any evidence to

13 suggest that these effects stem in any way from

14 acetylcholinesterase inhibition, but they are there. What the

15 mechanism is, is entirely unclear. Many of these studies

16 exposure prenatally, test the offspring as juveniles,

17 adolescents or even adults, weeks and months after the exposure

18 is over when there is no more cholinesterase inhibition, and

19 these effects still show up. They show up in a large number of

20 studies. It is hard to discount that those are all type 1

21 errors. So, I think there is something going on there. I think

22 the agency comes to that same conclusion, but what it is, is a
Page 497

1 very difficult thing to pin down. 

2 So, it does look like that there is a

3 developmental vulnerability to this compound based on these

4 data. It would be interesting of someone would figure out what

5 that was. Should I stop right there before I go to 3.2? 

6 DR. KENNETH PORTIER: Let's let some of the

7 other panelists figure out if there is anything left unsaid. 

8 That was very good though. I was thinking about the statistical

9 issues you brought up. I tend to concur. There is no reason to

10 use and unprotected Fisher's test, unless what you are really

11 doing is searching for significance. You want a bunch of

12 significant effects, so you have something to report in the

13 journal, but it is really not supported anymore without doing

14 comparison-wide control, which is what you do not have. Dr.

15 Aschner, I think you are next.

16 DR. MICHAEL ASCHNER: Michael Aschner. There

17 is not much that I could say after Chuck. I am not really in a

18 position to evaluate the studies individually, but I think what

19 I just heard actually helps me to explain or understand better

20 why there are such big discrepancies between the studies. You

21 know, I always have difficulty when same doses are used in the

22 same genders, and you get bidirectional effects. The assertion
Page 498

1 that no matter direction the effect occurs is indicative that

2 there is some effect, which seems to be the EPA conclusion, and

3 something that did bother me. 

4 I guess when you take into account all of

5 these flaws in all of these studies, that might explain actually

6 why they are getting at times inconsistent results. I do agree

7 with what Chuck said about persistent effects. I am not sure

8 though, that any of these animal studies actually have been

9 carried out far enough. I would think that there are probably

10 some other domains that need to be looked at. 

11 We heard earlier today for example that

12 dopaminergic systems might be affected. A lot of these systems,

13 other neurotransmitter systems, have a tremendous redundancy in

14 terms of the clinical manifestations of the deficits that occur

15 within these systems. For example, with a dopaminergic system,

16 if you wipe out 60% or 80% of the neurons, you would not expect

17 to see any effects that manifest clinically or behaviorally in

18 these animals. If you do these studies early on after the

19 exposure, postnatal months 2 and 3, it might be that many of

20 these effects are still unmasked because they do not show up

21 until a much later life stage periods. 

22 In terms of the doses, I think some of these
Page 499

1 studies have been done at different doses, very few of them

2 actually. It comes back to some of the other issues that we

3 mentioned earlier about, I think Dr. Lu mentioned them. It is

4 very hard to equate these studies in terms of the exposures,

5 because many of them use subcutaneous exposures. Others use

6 oral exposures. We know very little about how much of these

7 compounds are transferred to the pups under the different

8 conditions. We did not see anything about this. 

9 My overall conclusion is that indeed there are

10 some effects that seem to persist and there are some effects,

11 whether they occur at doses that are lower than the 1 mg/kg per

12 day, I do not think I could say with any high level of

13 confidence, but I think clearly some of the effects are

14 persistent. They do occur and I will close with that. There is

15 not really much else that I could add to what Chuck said before.

16

17 DR. KENNETH PORTIER: Thank you. Dr. Carr? 

18 DR. RUSSELL CARR: I will have to concur with

19 Dr. Aschner that there is not much else I can say, except to

20 speak to that I agree with the point that this chemical does

21 exert long-term effects and it tends to be that everybody stops

22 at 1 mg/kg. At that dose and above, we do see effects. Below
Page 500

1 that dose, so far there have been no effects observed, but that

2 kind of speaks to what the does response relationship, it may

3 possibly, that U-shaped dosage curve may start to occur at that

4 low dose where maybe effects at that low dose are not indicative

5 or similar to the effects at the higher doses because it is such

6 a low dose. 

7 So until we can figure out what we can test to

8 pick up that, then we are kind of stuck in a quandary of are the

9 low doses really having an effect, because the effects may be

10 totally different than what we are used to observing at the

11 higher doses. 

12 DR. KENNETH PORTIER: Dr. Harry? 

13 DR. JEAN HARRY: Jean Harry. So, I totally

14 appreciate everything you went through, for going through those

15 papers. I think it really represents how peer review data does

16 not necessarily mean that it is a correct study. I think that

17 that was a great presentation about going through, but also

18 coming into the fact that a lot of things are falling out, even

19 in the face of some fatal flaws in the studies that need to be

20 considered. 

21 I want to support the caution that was raised

22 for starting to integrate in some new test methods into
Page 501

1 toxicology, the social behavior I am sure is coming over because

2 of the autism work. That is a difficult assessment. I have

3 done it. You can skew it in a lot of different ways. It is

4 maybe applicable to the animal models for autism, but even that

5 is somewhat in question. It is about all there is there, so it

6 relies on that. The anxiety question, again, that is an

7 elevated T maze and it is talking about looking at changes that

8 would happen because they would be responsive to antianxiety

9 drugs. So, is it anxiety or is it a behavior that is responsive

10 to an antianxiety pharmacological agent. Again, we do not

11 really know. 

12 So, I think investigators are going to start

13 pulling those in because they are coming up in the literature

14 more and more. How you interpret them, what they mean, is it

15 good or bad? If you have 3 contacts with a stranger mouse, is

16 that worse than having 6 contacts with a stranger mouse? I

17 mean, we really do not know those answers. I think trying to

18 translate that over into the toxicology field needs to be done

19 with extreme caution. 

20 The learning and memory is a different

21 component. If we have a deficit in learning, whether it is an

22 attention deficit, as long as we are not just measuring a
Page 502

1 performance and motor deficit rather than a learning and memory

2 deficit, do those studies and they are done correctly, we can

3 interpret what would be adverse there. 

4 So, I think overall, the data suggests there

5 is something there that is happening at 1 and above. Whether it

6 is due to cholinesterase inhibition, we really do not know that.

7 It is associated with it, and that gives you a good marker. I

8 think that is pretty much it. I agree with everybody else's

9 comments that have been made. 

10 DR. KENNETH PORTIER: This is Ken Portier. I

11 have a question, something that Dr. Aschner said about long-term

12 effects. I wonder, Dr. Vorhees, can you expand a little bit

13 more on long-term, what does it mean in these animals for

14 long-term effects on learning and memory? Did these studies

15 really address long-term, or this deficits that show up in a

16 juvenile that may or may not be something that affects an adult?

17 If I am making sense.

18 DR. CHARLES VORHEES: It makes sense. There

19 is virtually no data in this set where anyone tried doing the

20 same learning tests at 2 different ages to try to answer that

21 question, so I cannot really say that there is anything here

22 that speaks to it. With other neurotoxins, it has been done and
Page 503

1 people can demonstrate the effects that show up during say,

2 adolescences, it will appear then in adulthood and a few of us

3 have gone out and tested again at a year, which in a rat is a

4 pretty long interval and middle age. Yeah, those effects with a

5 real neurotoxin, will show up again. Whether they would show up

6 in this case, I do not know. 

7 Let me just add a comment. I do think that

8 the issue that I did not emphasize but others have emphasized

9 that I think is real important is this route of administration

10 for this compound. I mean, that was not something I was

11 particularly sensitive to coming from the world of giving drugs

12 where you give them subcutaneously. 

13 In fact, with this compound, it seems to be

14 for a lot of reasons, the route is going to play a very

15 important role in how various tissues are going to see the

16 compound. Of course, the brain being the one I am particularly

17 interested in, how much it is going to see it, how long it is

18 going to see it, are critical things. Of course, I mentioned

19 the vehicle used. I think there is now, I am hearing since I

20 have been here, some serious concerns about potential

21 interactions between the compound and the vehicle used in the

22 DMSO studies. 
Page 504

1 There are studies in here that use oil, which

2 seems like a better way to go. There are some of those that are

3 pretty well designed, in which we found some long-term effects. 

4 There are a number that you might now discount because of the

5 DMSO to some extent.

6 DR. KENNETH PORTIER: Yes, Dr. Aschner?

7 DR. MICHAEL ASCHNER: Michael Aschner. I

8 agree with what Chuck has said. I think the early

9 manifestations in terms of behavior would probably persist into

10 adulthood. The point that I was trying to make is that there

11 might be some behaviors, and correct me if I am wrong on that,

12 that you might not pick up at 2 or 3 months of age, which may

13 show up later on in life. So, that may not obviously occur with

14 lower doses. It is just that they take much longer to manifest

15 because of redundancies within those neurotransmitter systems. 

16 That has not been looked at. 

17 DR. KENNETH PORTIER: This is Ken Portier. 

18 Yes, I noticed that none of these are multigen studies for

19 example. So, maternal behavior in an exposed child that then

20 moves on to try to breed, it could affect that kind of behavior.

21 We have not seen any of that when we do see that in some other

22 studies. Dr. Carr, did you want to jump in here? I thought you
Page 505

1 were getting ready.

2 DR. CHARLES VORHEES: Can I comment on that? 

3 I think most of the literature shows that in rodents, cognitive

4 capacity in rodents is crystalized by 60 to 90 days.

5 DR. KENNETH PORTIER: Dr. Harry? 

6 DR. JEAN HARRY: Jean Harry. I would like to

7 say something about route of administration. While I think it

8 is important when you are doing your final assessment for risk

9 on the route, if you are doing that initial study or you are

10 trying to test something to see if it is possible, I would not

11 automatically rule out the subcutaneous route. But if you did

12 find something in the subcutaneous, which would be more likely,

13 then to try an oral route, but I do not think I would

14 automatically dismiss it. Just put it within the framework with

15 what know about route of exposure if at all possible. I am not

16 sure I would toss out papers that did that.

17 DR. CHARLES VORHEES: I do not know that I

18 would toss them out entirely, but I would say that we know from

19 the ethenol literature that you really cannot give ethanol

20 subcutaneously. If you do, you will get spurious findings all

21 over the place. I do think with every drug or agent that you

22 test, you really do have to be sensitive to the route of
Page 506

1 administration as an issue. How it modulates your

2 interpretation is another thing, but we know that you can give

3 it wrong sometimes and really skew the findings.

4 DR. KENNETH PORTIER: Dr. Carr? 

5 DR. RUSSELL CARR: I think there also is

6 another issue where your vehicle comes into play, how it behaves

7 in that subcutaneous route. We had a paper a few years ago

8 where we did oil subcutaneous and oil oral, and they were really

9 similar as far as cholinesterase inhibition, same dosages, but

10 DMS was totally different. 

11 DR. KENNETH PORTIER: Dr. Pope? 

12 DR. CAREY POPE: Yes, I guess also whether you

13 want to ignore, so to speak, studies that used a subcutaneous

14 route for example. I am thinking somebody was showing data that

15 suggest that oral dosing is not the only thing going on here. I

16 mean, a kid could get exposed by dermal dosing and possibly, I

17 think Dale Hattis was saying inhalation exposures may be

18 relevant. So, that oral doses is important, but it is not the

19 only thing. Subcutaneous may capture some of these other

20 routes. 

21 DR. CHARLES VORHEES: Well that is a good

22 point. I am glad you reminded me of it because it seems to me
Page 507

1 that one of things that would be valuable in this area is to

2 have an inhalation study where someone looked at neurobehavioral

3 outcomes after inhalation exposures. There are many inhalation

4 tox labs that could do such a study. If they would just

5 collaborate with someone who does the neurobehavioral, I think

6 you could get a very interesting and maybe useful study out of

7 something.

8 DR. KENNETH PORTIER: That was Dr. Vorhees. 

9 My only issue was that every time I have seen the animal

10 inhalation studies, it seems like they are so controlled and

11 stressful for the animal, I do not know if anything you would

12 get would be a stress response. Dr. Terry? 

13 DR. ALVIN TERRY: Dr. Alvin Terry. So, I had

14 a few questions for Dr. Vorhees. I heard a lot of weaknesses

15 and not many strengths when it comes to these behavioral studies

16 that were overviewed. One criticism with a water maze study

17 that I did not feel that was particularly valid is the issue of

18 not reporting path lengths if they reported latency and swim

19 speeds because path length is factored in to swim speed. So, it

20 is centimeters traveled per second that was one thing. And then

21 with the radial arm maze study and this issue of chaining, and I

22 did not review these papers individually, so do you know for a
Page 508

1 fact if the ones you reviewed did not have a central hub so that

2 was prevented with the chaining effect? 

3 DR. CHARLES VORHEES: Charles Vorhees. They

4 have a hub, but they did not confine them to the hub between

5 trials. Not one of these studies discussed confinement. So you

6 have to have that delay. There is published data that showed

7 that if you do not do that, you do not get a working memory.

8 DR. ALVIN TERRY: Okay, are these the Levin

9 papers or, which? 

10 DR. CHARLES VORHEES: Nobody who used the

11 radial maze included a delay interval in the central region.

12 DR. ALVIN TERRY: One argument I would make

13 there, and it is not semantics, but if an animal adopts a

14 chaining strategy and you see in acquisition over time, there is

15 still a lot for me to learn. It stops the strategy. It is a

16 form of learning. If the 2 groups are different, then they are

17 different.

18 DR. CHARLES VORHEES: It is habit learning. I

19 am just trying to point out that they probably are not measuring

20 working memory. They are probably measuring habit learning. 

21 DR. ALVIN TERRY: Some other kind of learning.

22 DR. CHARLES VORHEES: I am not saying there is
Page 509

1 not an effect there. I was trying to actually say there is an

2 effect there. I just would not describe it as a working memory

3 effect. Unless someone is willing to go in and rule out this

4 alternate interpretation that it could be habit, that is what I

5 am saying. 

6 Now, to your morris maze, and I did try to

7 point out that several of these papers have stated that there is

8 no swim speed effect, but another control that you need in that

9 situation is the probe trial. It seems to be that if you do not

10 demonstrate the animals shows adequate spacial bias, you really

11 have not gone all the way to showing that it is a reference

12 memory deficit. So, they might show that there is the spacial

13 learning deficit, but they have not shown a reference memory

14 deficit. Some of these authors are a little broad stroke in the

15 way the talk about what they found. So, I am just faulting them

16 for that. There are studies in here that are strong. I guess I

17 over emphasize the negative probably. That is what reviewers

18 tell me. 

19 DR. ALVIN TERRY: I thought I was on NI Study

20 Section for a minute there. Another question I had too about

21 the overview of the radial arm maze studies, and you mentioned

22 working memory errors, which is the typical wind shift version,
Page 510

1 I know that on some occasions, and I am not speaking

2 specifically to these studies because I do not know and you

3 could clarify, if this is a 12 arm or 16 arm maze and they have

4 some arms bated and some not bated. It breaks the chain, so

5 that would not be necessarily a valid argument, or are they

6 reporting always just working memory errors or are they also

7 report reference memory errors in some of these studies? 

8 DR. CHARLES VORHEES: Charles Vorhees. Most

9 of those studies do report that there is some increase. Well,

10 some of the studies got a significant effect on reference memory

11 errors and that is true. It is very difficult to know if an

12 animal was using a training strategy. It is very difficult to

13 know whether a reference memory, for example, what is the cost

14 to the animal to go down the wrong arm and make a reference

15 memory error. What is that cost? The very next arm probability

16 wise is going to be a bated arm. It really costs the animal

17 very little to do down the unbaited arm, even if they know it is

18 unbaited, it does not really cost them. The way rats move

19 through these mazes, it costs them probably 6 to 10 seconds to

20 go down there. 

21 Without the confinement procedure, chaining

22 could be going on even in the reference memory errors, and you
Page 511

1 would not know it unless you also say video tape the animals and

2 watch them for the pattern that they are using. That is

3 something that they should be doing. If you are not going to

4 confine them in some sort of system that closes gates or

5 something, then what you should be doing instead is videotaping

6 them. 

7 The Barnes maze is an example. The maze is

8 very similar. What the Barnes maze scoring, which is done all

9 pretty much manually, looks at is the strategy that the animal

10 uses because they do the same chaining thing in the Barnes maze,

11 which is an open arena with a lot of holes. They have to find

12 the hole that has the escape. So, it is very similar

13 conceptually. What you see is that animals go through stages,

14 but when you finally get to spacial learning, they go from the

15 start straight to the goal. 

16 So, what you want to see is a strategy in

17 which the animal demonstrates that they are going to arms and

18 not repeating, but they are not going sequentially. That is

19 what you need to see. These studies are just mute on that

20 point. There is no way to tell whether they did or did not

21 because the investigators, I do not think, ran those analyses. 

22 So, you have an interpretational issue and I
Page 512

1 do not know how to resolve it based on what is presented. 

2 DR. KENNETH PORTIER: I think Dr. Lowit would

3 like to jump in on this. 

4 DR. ANNA LOWIT: I did have one question for

5 thought. It occurred to me yesterday listening to the

6 conversation. There are times when we will go to investigators

7 of academic research in any area, whether it is tox studies or

8 exposures studies or epidemiology studies. We do it on a

9 regular basis. We contact the authors and we ask them questions

10 about their study. Sometimes we get a lot of information. Some

11 people do not give us a lot of information. It is a personal

12 choice that. 

13 This body of data is not one of those areas

14 where we have done that where we have gotten on the phone and we

15 have gotten emails and we started bugging people about answering

16 our questions or providing additional details. Listening to

17 you, I am getting the sense of some of the critiques, some

18 information may exist in some of these laboratories that help

19 produce some of these critiques. It sort of came back to my

20 mind that this may be an area that we would have to wait the

21 resources to do that given the conclusions of the panel. We

22 would sort of have to wait how much effort that would take and
Page 513

1 what would be the value added to the analysis. It is certainly

2 something that we could be something that we could think about. 

3 Certainly, if there were certain studies or particular studies

4 that seem to be lack in their details. Let's say they may have

5 promise or may have interesting information but are lacking in

6 details, if there are a handful of those, you can certainly

7 suggest. That is something that we could think about is

8 pursuing talking to the investigators and getting additional

9 details. 

10 DR. KENNETH PORTIER: I would start by asking

11 how many Dams. I really appreciated what he was saying because

12 I have done these kinds of reviews before. It is very

13 frustrating to read the methods. They will not even tell you

14 how many Dams were used in the study. That is critical to all

15 the analysis. If I had to ask one question that would be the

16 first one I would ask. It drives a lot. It tells you a lot

17 about the study. Dr. Harry? 

18 DR. JEAN HARRY: Jean Harry. Just based upon

19 that comment and I do not know if anybody around the table could

20 do it or just depose it, but there is a number of neuroscience

21 journals that now have, and they are sharing this list. Journal

22 of Neurochemistry is one that does it. They have had so much
Page 514

1 trouble in some of the behavioral studies of people being able

2 to replicate findings. I get a lot of this is probably coming

3 up much more in the transgenic animals, that they actually have

4 before you submit a paper. They have a list of everything they

5 want to know about the experimental design. You have to include

6 it before the Journal will even consider the paper. It is

7 almost a check off. It is things like litter. It is things

8 like chaining. It is these types of things that what are the

9 critical features that have to be mentioned so that somebody

10 could replicate it. A number of them are putting it in their

11 supplemental information so it is not taking up a lot of the

12 Journal, but it is accessible for reproducibility. 

13 I have yet to see that come up in a tox

14 journal, but yet it seems to be the area that needs that

15 information so much. It might be something you could propose to

16 some of the journals or editors or bring up at SOT, is it

17 something that any of the tox journals would consider following

18 through on as some of the neuroscience journals are doing. 

19 There is a precedent for basically demanding a certain type of

20 information prior to even a manuscript being considered. 

21 DR. KENNETH PORTIER: Okay, he promised me it

22 would be at least an hour, and it was a little over an hour,
Page 515

1 thank you. Any additional comments on this question before we

2 move to question 3.2, which has 2 parts? Yes, Dr. Vena.

3 DR. JOHN VENA: I am not sure after listening

4 to all of this, if I have a good sense of the last question in

5 the charge question. What it the bottom line in terms of the

6 accumulated evidence of the animal studies, and is there any

7 sense of a dose response relationship for these perturbations in

8 the magnitude? I do not know if I clearly heard that.

9 DR. CHARLES VORHEES: Maybe I did not make

10 that very clear. The dilemma is that there are a few studies,

11 which have dose response relationships. There are many studies

12 that do not show dose response. There are many studies that

13 would look like they are dose response, but they have so many

14 methodological issues it is not clear whether you want to put

15 much weight on them. So, I am a little torn about the

16 interpretation on that. 

17 I would say that it is quite unclear to me

18 whether there is a linear or anywhere close to linear dose

19 response for neurobehavioral effects. I just do not see going

20 across studies that that looks very strong. I really appreciate

21 the other panel members commenting on this because I found this

22 a very difficult issue to try to deal with in these 21 studies
Page 516

1 because it is so problematic here. I cannot explain why someone

2 would get an effect at 1 and not at 5, or they would get an

3 effect at 1 and 3 and then not at 5 mg/kg. It is very difficult

4 to make sense out of to me. 

5 DR. KENNETH PORTIER: Dr. Carr? 

6 DR. RUSSELL CARR: I concur in the fact that

7 there are select studies that do show dose response, but then I

8 think the methodological problems come into play sometimes. I

9 do not think right now that there is a clear understanding of

10 whether or not there is or not a dose response. It is like

11 Chuck said, if you are not getting effects at 5 and you are

12 getting effects at 1 in one study and the next study is the

13 reverse, it is really difficult to make a clearcut across the

14 board this is what is happening.

15 DR. KENNETH PORTIER: This is Ken Portier. I

16 get the feeling here that there is just not a confidence in

17 replicability of that dose response at this point. So, you see

18 some hints, but it is not to that point where you feel that if

19 somebody replicated that study they would get exactly that same

20 dose response until we get to that point. It may just be a

21 function of just not enough studies, not enough good studies. 

22 Dr. Lu? 
Page 517

1 DR. CHENSHENG LU: Maybe the rodents are not

2 an ideal model for this type of testing. I mean, people use

3 primates for neurobehavioral testing on lead and other metals. 

4 I am not promoting primate research, but with this discussion

5 about those inconsistencies, so it kind of leads to the

6 question, would the agencies further consider data from rodent

7 models in this neurobehavioral testing and just completely not

8 using it.

9 DR. KENNETH PORTIER: Okay, I think we have

10 kind of done this one. Why don't we move onto question 3.2,

11 part 8. 

12 DR. GINGER MOSER: Question 3.2 much of which

13 has been discussed. The dose response data in the in vivo

14 experimental neurodevelopmental toxicity studies are not

15 amenable to empirical dose response modeling. As many studies

16 use only 1 or 2 doses, and in some cases the lower dose but not

17 the higher dose level produce significant effects. Many studies

18 report effects at a dose of 1 mg/kg per day. A dose that

19 produces some amount of brain cholinesterase inhibition when

20 given directly to the pups postnatally, but may or may not alter

21 fetal brain cholinesterase activity given to the Dams

22 gestationally, 1 study Braquenier et al., 2010, using lower
Page 518

1 doses administered to the Dam on GD 15 to lactational day 14

2 reported a NOEL of 0.2 mg/kg per day. Comparing the NOEL of 0.2

3 mg/kg per day to a repeated dosing acetylcholinesterase

4 inhibition, BMDL10 of 0.03 mg/kg per day suggests that

5 cholinesterase inhibition is a sensitive and protective

6 endpoint. Please comment on the scientific quality and

7 robustness of the animal neurodevelopmental toxicity studies. 

8 Please comment on the degree to which studies that measure

9 acetylcholinesterase inhibition and those that measure

10 neurodevelopmental outcomes could be integrated to evaluate

11 whether points of departure based on 10% acetylcholinesterase

12 inhibition provide more sensitive endpoints than endpoints

13 measured in the experimental neurodevelopmental studies. Please

14 include in your comments of consideration the strengths and

15 uncertainties associated with this assessment. 

16 DR. KENNETH PORTIER: Dr. Vorhees, it is up to

17 you whether you take these separately or as a group.

18 DR. CHARLES VORHEES: Okay, I think it might

19 make more sense to break them apart and have the Panel comment. 

20 I promise this is going to get shorter as we do along. 

21 So, this may not be the most satisfactory

22 answer, but here goes. So the quality of the studies in this
Page 519

1 category varies. There are some of high quality. Overall, the

2 studies by Slotkin and Levin, and those of Carr and the one by

3 Morrison, and several of the Ricceri and Venerosi studies are

4 the better ones because they generally use multiple doses. They

5 had adequate sample size. They controlled for litter effects,

6 some in a little bit of unorthodox ways, and they in general

7 used pretty sound behavioral methods. They used appropriate

8 statistical methods to analyze their data. Because all of these

9 studies find some long-term neurobehavioral effects, these data

10 collectively may be regarded as a robust set of findings, but

11 only at doses of 1 mg/kg and above.

12 The data are mute or negative concerning

13 affects below 1 mg/kg of CPF. Among these studies however,

14 there are some concerns. In the Slotkin and Levin experiments,

15 the use of commercially supplied time pregnant animals is a

16 concern as was the use of Zivic-Miller Sprague Dawley's and a

17 number of their studies, some of their statistical methods. So

18 for that reason, there are a few doubts about some of their

19 findings. 

20 As people have talked about before, they find

21 some inverse findings in males and females, for example in the

22 radial arm maze. That is sort of a puzzling effect. For
Page 520

1 studies I think with regulatory implications, preferable to use

2 mainstream stock such as the Charles River Sprague Dawley or

3 Harlan as I mentioned before. 

4 There were some concerns about what is being

5 measured in the radial arm mazes. Not that there is not a

6 finding there. I acknowledge there is a finding. I think that

7 the question is what is the finding? What does it mean? It is

8 a working memory or reference memory effect as the authors

9 present it as, or is it habit formation? 

10 The weight of evidence for the neurobehavioral

11 studies is that no effects are reliably or convincingly

12 demonstrated below 1 mg/kg per day. 

13 DR. KENNETH PORTIER: Dr. Aschner? 

14 DR. MICHAEL ASCHNER: I agree with everything

15 that was said. The only comment that I have to offer here is

16 that perhaps the only way that we might get at some of these

17 issues is to do a cross laboratory study. Given the lack of

18 doses in most of these studies and the other deficiencies, if we

19 could come up with a unifying design for experiments to be

20 conducted in those laboratories that are considered to be the

21 leading laboratories in this area, then perhaps doing these

22 studies carefully designed taking care of all of these issues
Page 521

1 methodological issues, dosing issues, if we came up with a

2 series of studies that can be done on 3 or 4 labs and then look

3 at the data, we might get some answers as to how reliable those

4 findings are. I think with what we have right now, there are so

5 many uncertainties that we really do not know. 

6 DR. KENNETH PORTIER: Dr. Carr? 

7 DR. RUSSELL CARR: I will just concur to the

8 previous statements. I find that I agree that anything below 1

9 mg/kg per day, we do not see anything. I think probably future

10 studies need to maybe focus on those dosages a little bit more,

11 although we do run the problem, as Ginger said that other day,

12 of not wanting to publish negative data. There may be a lot of

13 negative data out there, effects at those dosages that did not

14 make it to the literature because it was negative data. That is

15 just the way it is.

16 DR. KENNETH PORTIER: Dr. Harry? 

17 DR. JEAN HARRY: I really have nothing to add

18 other than the comments that have just been made that any future

19 efforts should try to focus on the 1 and below, because you know

20 what is happening above that, link the 2 together and really

21 just do a really good study, but that is hoping that the

22 investigators out there will do that. 
Page 522

1 DR. KENNETH PORTIER: Anyone else? Dr.

2 Vorhees, they pull out in this statement this one 2010 study

3 Braquenier, I did not hear you say anything particular about

4 that one? What was your finding on that particular study? If

5 you could go back one slide just so he can see.

6 DR. CHARLES VORHEES: I can go back and talk

7 about that one.

8 DR. KENNETH PORTIER: Just because they kind

9 of pointed it out, I thought maybe you had looked at that one

10 particularly. I did not get a feeling for whether, I guess I

11 need a clear reading, is that a good study or a poor study or an

12 in between study? 

13 DR. CHARLES VORHEES: Okay, this is a mouse

14 study in CD1 mice. They use 0.2, 1 and 5 mg/kg administered on

15 E14 to P14 as I have it. Again, they give it postnatally to the

16 mother, not directly to the pups. It is a pretty carefully done

17 study. They call their litters to a standard size. The

18 stratify for sex using a random selection. They find effects,

19 but the effects are not dose dependent. That was one of the

20 things about the effects that they found. They also probed

21 rather hard on effects, which I think we would question whether

22 or not you would do follow-up analyses on. For example, they
Page 523

1 said well, they saw a trend in the light dark test in side

2 preference at 0.08. They said well that is sufficient to go on

3 and dot a posteriori testing using Dunnetts. I do not think I

4 would do that. They made quite a bit out of an elevated plus

5 maze finding where the trend was at point less than 10. They

6 went ahead and did Dunnetts tests anyway saying that they saw

7 significantly reduced time in open in these mice at a

8 significant level in Dunnetts, even though the ANOVA was at

9 0.10.

10 So, I think this study has some problems. I

11 mean, I think my reading of what the EPA was saying was that

12 there are effects reported in the paper at 1 mg and there are no

13 effects at that dose 0.2. That is what they are saying, well

14 that is then the no effect level. I do not think that the study

15 is very strong in some of its features. The study design was

16 okay, but they did some, can I use this word, squirrelly things

17 statistically, some sort of unorthodox things that I am not sure

18 are easily defended. Yet if you accept their interpretation,

19 there are findings. Again, I do not know what the elevated plus

20 maze means in this context. I do not know what the light dark

21 test means in this context. Those are the tests. There is also

22 locomotor test. They did not find anything there. 
Page 524

1 All the effects are marginal. I think that is

2 a generous description of the findings. 

3 DR. KENNETH PORTIER: Dr. Carr, you were going

4 to add something? No? Okay. Dr. Lowit? 

5 DR. ANNA LOWIT: I am sensing a slowing of the

6 comment that had a clarification. Before you close, and I do

7 not think you are ready, but before you close, I have heard a

8 lot of details on certainly a subset of the poorer quality. We

9 have talked quite a bit about dose to the extent that that is

10 possible. But, there was a short exchange that I did not feel

11 like it came full circle about the age of the testing. There

12 was a comment from 1 or 2, and I do not remember who the

13 individuals were, about the onset of behaviors later than the

14 testing happened. We just want clarity around what those ideas

15 and what the science behind those ideas or if this just creative

16 thinking or if there is science behind behaviors that would

17 begin or brain development that would occur I think up to age

18 60, where you are testing, let's say age, 50 if that is a

19 systematic problem or the relationship. I guess I beg a little

20 conversation around the age of the testing versus the age of the

21 onset of the behavior, if that makes any sense at all.

22 DR. KENNETH PORTIER: Can we come back to
Page 525

1 that? Because we still have B to do. I think that is where we

2 were going to move next is to the 3.1B. If you could click the

3 slide one more just so people can remember what we are talking

4 about in B. I will ask Dr. Vorhees. 

5 DR. CHARLES VORHEES: Let me just try to wrap

6 that one up. There is that last sentence. I guess I really did

7 not really address as Dr. Vena pointed out, which is part of 3.2

8 is asking us to compare, comparing the NOEL of 0.2 to a repeated

9 measuring of acetylcholinesterase inhibition, BMDL10 of 0.03

10 suggests that it is the most sensitive. I guess they are asking

11 do we agree with that. I would say yes. That is my read of

12 that. Just to make sure that that is clear, my understanding of

13 that is the answer to that is yes. Ready to go on? Okay. 

14 So, B is about doses at which 10%

15 acetylcholinesterase inhibition was assessed compared to the

16 neurobehavioral effects after CPF. Of the neurobehavioral

17 effects reported in the reviewed experiments that assessed

18 acetylcholinesterase inhibition, I could find no studies that

19 were identified that should affects on behavior below 10%

20 inhibition, which was approximately, and this very approximate. 

21 I am just using what is presented in those papers, not drawing

22 on other data on that the Agency has, I think, drawn on. In
Page 526

1 those behavioral papers using approximately 1 mg/kg. Doses

2 below 1 mg/kg do not show convincing evidence as I understand it

3 of neurobehavioral effects combined with significant inhibition

4 of acetylcholinesterase. 

5 So, that is about as far as I can comment on

6 that issue. I found myself sort of constrained in the

7 behavioral studies because many of them do not do the

8 acetylcholinesterase in the same study. So, there were very few

9 that did, but it looks like you do not get any behavioral

10 effects below 1 mg/kg. There is a dose at which you do get

11 acetylcholinesterase inhibition. 

12 So, by inference, you can say that nothing

13 below that is going to produce as far as we know today, a

14 neurobehavioral effect. At least, nobody has been able to show

15 it with the 3 studies that have a lower dose. In those 3

16 studies, 2 are negative, 1 is positive and seriously flawed. 

17 That is the dilemma. When you get down to those doses, that

18 literature is so thin that it is very difficult to draw much in

19 the way of inferences, at least for me. I just could not make

20 strong statements about such a very tiny data set. 

21 We have, I would bet, there are probably at

22 least 1000 studies now on candidate genes for autism spectrum
Page 527

1 disorder, behavioral studies in mice. Do we know what the

2 mechanism of autism is? Are we close? I think not. So, here

3 you have 21 studies on a compound. 

4 I think one has to think about this in

5 perspective. This is a very small data set. The fact that

6 there are inconsistencies in it, when I look back at the lead

7 literature when it was starting, the fetal alcohol literature

8 when it was at this stage of development, methamphetamines,

9 cocaine, go down the list of prenatal thought to be established

10 developmental neurotoxins, and they were a mess in the early

11 going. I do not find that terribly surprising. People got

12 better and better at doing experiments, at looking at the right

13 things as time went on. People made mistakes and they went back

14 and they persisted. There were more and more perspective

15 epidemiological studies and then very gradually over about 25

16 years for cocaine it took before the data began to really come

17 into some kind of convergence. I do not know that the animal

18 data on this compound are any more close to what we know about

19 alcohol, lead, methylmercury, methamphetamine, cocaine. 

20 So, we are trying to look at this from the

21 larger sort of perspective. This is a very tiny data set. When

22 you are looking at the low dose, it is even tinier 3 studies. 
Page 528

1 So, I do not feel like I can make strong statements, just that

2 there is not anything persuasive below 1 mg.

3 DR. KENNETH PORTIER: Dr. Aschner? 

4 DR. MICHAEL ASCHNER: I agree. There is

5 nothing below 1 mg/kg per day. So, I cannot add much to this. 

6 Since Chuck started talking about some of the things that can be

7 done, again I think there are a lot of genetic issues that have

8 not been considered in any of these studies. There are

9 different variants of acetylcholinesterase, the R type and the S

10 type, which might affect synaptic plasticity if we talk about

11 development. If you consider other issues, I have not seen a

12 single study that has done stereology, looking at point volume,

13 for example. I mean, there is a whole laundry list and I know

14 it is unrealistic on my part to suggest those kinds of studies. 

15 Again, to reiterate what Chuck said in terms of our knowledge, I

16 think the number of studies that have looked at many of these

17 mechanisms, mode of actions and outcomes, and specifically

18 related them to cholinesterase inhibition, we can count them in

19 one hand, the careful studies. 

20 DR. KENNETH PORTIER: Dr. Carr? 

21 DR. RUSSELL CARR: I really cannot add much

22 more to that. I mean, Chuck pointed out the obvious. It is
Page 529

1 very small. It is very new. As we progress, hopefully it will

2 get stronger and we can do a better job at this. 

3 DR. KENNETH PORTIER: Dr. Harry? 

4 DR. JEAN HARRY: I think in answering directly

5 to your question, which pretty much says if you do the point of

6 departure based upon the cholinesterase inhibition, not only is

7 it more sensitive endpoint, but it is more reliable endpoint in

8 which you can get consistency across a lot of your studies, so

9 much more than the neurobehavioral endpoints are. 

10 DR. KENNETH PORTIER: We are going to throw in

11 a ringer, Dr. Popendorf.

12 DR. WILLIAM POPENDORF: Good description. I

13 agree particularly with the last statement in terms of

14 cholinesterase being an important historic measure in something

15 that we find to be very reliable. The other endpoints would be

16 based on dose. Then we get into issues of different animal

17 systems. I think we are just opening an can of worms should we

18 get to the point where we have data or an endpoint that we could

19 justify below. I am just going to comment on the value of that

20 PBBK, as I think that will come up numerous times. As we get

21 into the levels below 10% or using the measure other than

22 acetylcholinesterase, I think that PBBK type model is going to
Page 530

1 be a virtually essential tool to relate dose to outcome, and

2 particularly the dose to the target site and facilitate the

3 linkages that we need between animals and again, going back a

4 step further to the in vitro and in vivo tests. So, overall, I

5 agree with everything that has been said. 

6 DR. KENNETH PORTIER: Anyone else? Dr. Harry?

7

8 DR. JEAN HARRY: This did not have to do with

9 this question, but I did want to mention that we asked to

10 discuss a point.

11 DR. KENNETH PORTIER: We will come back to

12 that question. Anyone else on this part B? Yes, Dr. Buckley? 

13 DR. BRIAN BUCKLEY: I just have a question. I

14 am not hearing sort of strong feelings about the values below 1.

15 Does that mean there just are not enough studies does at this

16 point in time to make that definitive statement so we really

17 want to leave that as sort of an unknown classification as

18 opposes to weakly no effect? I am trying to get a feel from

19 everybody. 

20 DR. CHARLES VORHEES: I think there is just

21 not enough data. So, I think that the studies just do not speak

22 to this issue. The 2 studies that seem pretty well done do not
Page 531

1 find anything. So, to the extent that you would want to put

2 weight on 2 studies showing no effects, that is what we have. 

3 That is my sense is 2 studies is kind of inadequate. Proving

4 that null is hard enough and of course never can be proven

5 really, but 2 studies? That is really terrible. 

6 DR. RUSSELL CARR: At least, may I add that we

7 also maybe we have not seen the effects yet. Maybe it is there.

8 We just have not found it. Right now as far as what we know,

9 we have not observed the effects.

10 DR. KENNETH PORTIER: I think at this point,

11 let's come back to Dr. Lowit's question about the long-term

12 behaviors versus the age of testing. Dr. Harry, did you want to

13 start that discussion? 

14 DR. JEAN HARRY: I can start a part of it. 

15 One would be to address it as a question. On the last paper

16 that was looking at the mice and, as I do not remember, looking

17 at the light dark place preference and the elevated T maze, so

18 in mice, the light dark place preference is often considered an

19 equivalent in the adolescent, in the very early postweaning

20 animal to the elevated T maze in the adult. So there are

21 certain tests that would be applicable to use in those young

22 animals, and others that you really need to take up to be about
Page 532

1 50 to 60 days of age to get a fully integrated system if you

2 were going to test for alterations within it. I have to default

3 on some other timing, and I do not remember the rat data. I am

4 much more knowledgeable of the mouse data now developmentally. 

5 You were saying that for learning and memory,

6 for cognitive functioning that you would pick up on a morris

7 water maze or a radial arm maze type thing, you should pretty

8 much have it crystalized by about 60 days of age. 

9 DR. CHARLES VORHEES: So, it is not going to

10 change after that. It is fully formed. It is at full capacity.

11 It is not going to get better if you test them at 180 days. In

12 rodents when you get past a certain age, then you start getting

13 into the aging issue and performance starts to deteriorate

14 again. They usually have to get out to 18 months to 2 years to

15 start seeing that. I am not sure what Dr. Lowit is driving

16 toward here. 

17 DR. JEAN HARRY: I think one of her questions

18 was if you tested the animals at day 50, are you now testing

19 them a little too young? You are not testing at the appropriate

20 time. Are there cautions about age of testing that they should

21 be looking at in some of these data sets.

22 DR. ANNA LOWIT: There was an exchange, I do
Page 533

1 not remember who the individuals were, around the maturation and

2 the testing. I just wanted to ensure that what we are talking

3 about are true uncertainties and no speculation. The

4 uncertainty is based in good science and not just throwing out

5 ideas for the sake of throwing out ideas. I wanted to close

6 that piece.

7 DR. MICHAEL ASCHNER: It may be something that

8 I mentioned in terms of dopaminergic system where I said if you

9 lose 80% or 90% functional capacity, you will see some effects. 

10 Those may not manifest until much later on in life, not

11 specifically the behavioral changes. This comes back to what

12 Dr. Carr said. I do not think these kinds of domains have even

13 been looked at. So, I am not commenting specifically about the

14 behavior. What Dr. Vorhees says if you see something at 60

15 days, you will probably see it at 90 days. I accept that, but I

16 am saying that if you have either functional deficits, which are

17 motor related, if you tested 60 days and it is related to the

18 dopaminergic system, you might not see them until the animals

19 are 18 months old.

20 DR. CHARLES VORHEES: So, cognitive effects, I

21 think the other thing that may have triggered that thought that

22 you had, which I was talking about some concerns about one of
Page 534

1 the morris water maze tests where they tested them at 24 to 28

2 days. We know that spacial learning and reference memory are

3 not fully developed at that age. By the time you get to 60 days

4 of age, it is. 

5 When you are doing developmental testing, you

6 just have to be sensitive to the fact that that particular type

7 of learning and memory is not fully mature at that age. There

8 might be things that you would learn at those ages that maybe

9 you would benefit from that you would not see when they are 60

10 days of age. But it is also possible that an effect that you

11 see at 24 to 28 days will in fact disappear when the animals are

12 older. It can go either way. 

13 So, I do not mean to throw that out to

14 disparage the fact that they were testing at that age. I am

15 just saying that it is unclear that that kind of an effect, just

16 as if in children you tend to do Bayleys at a certain age. 

17 Bayleys are not very predictive of waist data and why is that? 

18 They have partial prediction, WAIS, Wechsler Adult Intelligence

19 Scale. So, how do you interpret that? 

20 It is a developmental marker that has some

21 cognitive components in it. Because children's intelligence is

22 not crystalized until later, it can change because that is still
Page 535

1 a developing function. I am just trying to say that the same

2 thing goes on in animals that developmental psychologists I

3 think would tell you about the Brazelton to the Bayley, from the

4 Bayley to the WPPSI, from the WPPSI to the WISC, and from the

5 WISC to WAIS. They have various predictive correlations, but

6 the earlier ones are the poorest predictors of the adult ones. 

7 DR. KENNETH PORTIER: Dr. Harry? 

8 DR. JEAN HARRY: So I think the one comment

9 that Dr. Aschner was making was we also know that in a number of

10 different types of studies that sometimes you do not see things,

11 especially at low dose effects, you may not see things unless

12 the animal or organism is challenged in some way. That could be

13 represented either on your pharmacological challenges that you

14 have seen done. Other people have proposed that that challenge

15 could come in with aging. As the process of aging, as the

16 process of the dynamics that happen within the brain and the

17 stress that is put on is over time that you may start to unmask

18 changes as a function of aging, not necessarily the young adult,

19 but as aging. It is a continuum up and down. You may unmask

20 different things at different times. 

21 DR. KENNETH PORTIER: In my comments earlier,

22 that is kind of what I was trying to get at. I was trying t
Page 536

1 elicit that kind of discussion that some things may not show up

2 early. A deficit may only show up later. I was thinking in

3 terms of some of the other studies that I have seen, not

4 chlorpyrifos, but some of the others for example, where you see

5 gender switching very early that could have implications for the

6 whole life cycle of the animal that may not show up very clearly

7 early on, but it will be obvious later on. That is all that I

8 was trying to get at. 

9 Any additional comments on question 3,

10 neurodevelopmental discussion? Yes, Dr. Pope? 

11 DR. CAREY POPE: So, if I understood what you

12 were saying Dr. Vorhees, that there were 3 studies that actually

13 go down into below the 1 from the experimental animal

14 neurobehavioral studies, yes 2 negative and 1 positive. The 1

15 positive you said there are a lot of difficulties with the

16 design. But, did you mention that one of these also was a

17 well-conducted study that looked at delays in response to kind

18 of focus more on working memory? The Morrison study? 

19 DR. CHARLES VORHEES: The Morrison study did

20 not find an effect.

21 DR. CAREY POPE: Was that also the study that

22 you said was kind of enforcing POO2 cutoffs? 
Page 537

1 DR CHARLES VORHEES: Yes, absolutely that is

2 true.

3 DR. CAREY POPE: Just for fun, were any of

4 those findings in that study between 0.02 and 0.05 show

5 statistical significance?

6 DR. CHARLES VORHEES: I do not remember off

7 the top of my head exactly what. I have to find that one. I do

8 not have these memorized, not my area. Well for example, he

9 found a point 0.03 effect on acoustic startle response latency,

10 not amplitude. The principle measure in that test is in fact

11 amplitude not latency. I do not know what it means. He found

12 some delays in developmental landmarks. Vaginal patency was

13 delayed in the high dose group, pinna detachment and preputial

14 separation were delayed at 0.03 and 0.05, but they did not reach

15 the 0.02 cutoff, so he dismissed those. I have nothing on the

16 spacial delayed alternation test of working memory at any level.

17 So, those are sort of physical landmark developmental delays

18 more than anything. The startle effect, I would not put much

19 weight on. He discusses that at some length, which is why he

20 does not think that that is a particularly important

21 observation. I sort of buy his argument that a change in

22 startle latency without a change in amplitude is of questionable
Page 538

1 significance biological meaning. Did that answer your question?

2

3 DR. CAREY POPE: Yes.

4 DR. GINGER MOSER: Just to add one thing about

5 that with the Morrison paper. Just to point out that that

6 delayed spacial alternation was done at weaning. As we have

7 already heard, weaning may be a bit early for some of these

8 spacial tasks. The adult testing was done in the same animals. 

9 So, it is unfortunate in a way because that means that adult

10 animals were not tested in that task naEve. That is a confound

11 that we have no idea how that would have come out otherwise. I

12 think that was an important thing to point out with that paper. 

13

14 DR. CHARLES VORHEES: I agree. I think that

15 is worth pointing out, but I would defend it in the sense that

16 while they would not be naEve to the task, the decay function

17 that you see with progressively longer delays intervals, should

18 map pretty much the same in the adult as in the juvenile animal.

19 I do not think that you disrupt the fundamental integrity of

20 that test because of its nature. You do not really disrupt it

21 by testing an animal multiple times. That is one of those tests

22 that you can do multiple times. You should still get the same
Page 539

1 decay function each time you do it. The curve will not look

2 exactly the same as they have more practice, but it will look

3 largely the same because the working memory does not show large

4 practice effects. It is inherent in its nature that it does not

5 show large practice effects. 

6 DR. KENNETH PORTIER: I think we have run out

7 of juice here. Again, the Panel is sitting back. They are not

8 engaged anymore. I think we are to the point where we have kind

9 of worried the neurodevelopmental data to death. We have to

10 apologize to the epidemiologist for having to sit through it,

11 but that is the nature of this panel. You learn a lot, but you

12 spend a lot of time sitting waiting for your turn. Your turn

13 will come tomorrow morning. 

14 At this point, I think we are going to end the

15 Panel deliberations for today. We will start again tomorrow

16 morning at 9 o'clock with the question 4 issues on epidemiology.

17 Okay? 

18 

19 DR. KENNETH PORTIER: So Good morning. This

20 is day 3 of the FIFRA Scientific Advisory Panel Meeting on

21 scientific issues associated with chlorpyrifos health effects. 

22 I am Ken Portier, Chair of the FIFRA SAP and Chair of this
Page 540

1 session, and we are in the middle of the panel discussion on

2 the agency questions. As we normally do, we are not going to

3 go around the room and introduce all the panel again. By this

4 point, everybody in the audience and everybody here are good

5 friends. We all know each other. So we are going to move

6 forward.

7 Before we start with question 4, I wanted to

8 double check to make sure if anybody had any leftover issues

9 with question 3. And I know that Dr. Vorhees wanted to make a

10 comment about some of his material. Dr. Vorhees?

11 DR. CHARLES VORHEES: After we adjourned

12 yesterday, Dr. Moser was kind enough to bring to my attention

13 an error I made, so I just want to correct it for the official

14 record. In the Javelin study I said that they did not analyze

15 the data by litter, but in fact, they did. So I just want to

16 make that clarification. Thanks.

17 DR. KENNETH PORTIER: So any additional

18 clarifying remarks from the panel? Okay. It looks like we are

19 ready to go on and talk about the epidemiology question, which

20 should be fun. Dr. Christensen?

21 DR. CAROL CHRISTENSEN: Carol Christensen. I

22 will read question 4.1. Section 4 in appendixes 5 and 6
Page 541

1 provide the agency's review of the available epidemiology

2 studies from the Columbia Mothers and Newborn study, the Mount

3 Sinai Child Development Study and the Center for Health

4 Assessment of Mothers and Children of Salinas, or the CHAMACOS

5 Study.

6 Consistent with the 2008 SAP recommendations

7 the agency has considered information offered in each of these

8 three cohort investigations. However, EPA acknowledges the

9 privacy of the Columbia Cohort data for the purposes of

10 informing risk assessment because researchers measure

11 chlorpyrifos parent compound directly. 

12 Please comment on the sufficiency, clarity

13 and quality of the agency's epidemiology review as contained in

14 Section 4 in appendixes 5 and 6 of the draft issue paper with

15 respect to identifying major strengths and limitations of each

16 study. 

17 DR. KENNETH PORTIER: Thank you. And our

18 lead discussant is Dr. Gold.

19 DR. ELLEN GOLD: Good morning, Ellen Gold. 

20 So the panel believes that the epidemiology section of the

21 draft issue paper is very well written clear, accurate and

22 fairly complete. We commend the agency on the inclusion of the
Page 542

1 epidemiologic evidence and its risk assessment and its staff on

2 the thorough review of the epidemiology literature and putting

3 their epidemiology review in the context of the modified

4 Bradford Hill criteria, as recommended by the 2010 SAP. In

5 addition to reviewing the potential for selection and

6 information biases in each of the studies reviewed.

7 Epidemiologic studies such as these are very

8 difficult to conduct, because they require large numbers of

9 mothers and infants, or children to be followed longitudinally

10 for an extended period with extensive data collection at

11 regular intervals to ascertain exposure measures, data on

12 potential confounders and assessment of health outcomes, all of

13 which can change over time. 

14 These studies are logistically difficult to

15 implement and require great commitment and potentially large

16 burden in terms of time and effort on the part of the study

17 participants over a lengthy period of time, often with little

18 or no specific benefit or return to themselves.

19 So the agency's review adequately summarizes

20 the challenges and scientific contributions of each of these

21 studies, so I am just going to hit the high points of what we

22 agree with and some additions that we would suggest. 
Page 543

1 So, all three studies have the significant

2 strength of being longitudinal prospect of designs. The most

3 effective design for establishing the temporal sequence in

4 relating exposure to health outcomes, specifically in these

5 studies relating exposure measures obtained prenatally or at

6 delivery to outcomes measured at six months up to nine years of

7 age.

8 Regarding the exposure assessment as the

9 draft paper points out, only the study by the Columbia

10 investigators measured a biomarker of the parent compound in

11 cord blood as well as in mother's milk, and conducted an

12 exposure validation study and looked at correlations of cord

13 blood measures with mother's blood and meconium and used the

14 cord blood as the measure of exposure in relationship to scores

15 on standard neurodevelopmental test batteries for children of

16 ages seven years in the 2011 publication, at early time periods

17 for the earlier publications.

18 We agree with that agency that because this

19 study included the most specific exposure measure, particular

20 attention should be focused on its results. We would disagree

21 with some of the comments that were made the first day or so

22 that, because the observed associations occurred at level below
Page 544

1 those required for acetylcholinesterase inhibition, results

2 should be discounted. Instead, we believe that these findings

3 come from well-designed and conducted study, and thus, instead

4 suggest that the mechanism may be other than

5 acetylcholinesterase inhibition. 

6 Furthermore, the very large effect measures

7 observed in this study for the relation of exposure to

8 attention deficits reduced birth weight prior to the voluntary

9 withdrawals of chlorpyrifos, reduced mental and psychomotor

10 development and reduced IQ are unlikely to be due to important

11 misclassification, or bias in assessing exposure or to

12 uncontrolled confounding.

13 The two other studies used nonspecific

14 metabolites as markers of exposure, although all three studies

15 used TCPy, which is in relationship to the exposure, and then

16 looked at it in relationship to the outcomes. So this is a

17 better measure than the DAPs, the other indirect biomarker, but

18 it does have some limitations.

19 So we thought that the potential

20 misclassification of exposure might be more explicitly stated,

21 and the value of using TCPy as a biomarker also hinges on the

22 mode of action, which is not really well established with
Page 545

1 certainty yet.

2 As the epidemiology review appropriately

3 notes, all three studies were strengthened and decided by using

4 biomarkers of exposure instead of relying on self reports,

5 which would be more likely to result in much greater

6 misclassification of exposure. All three studies also use

7 similar standard validated measures of non-verbal and general

8 intelligence, behavior and home environment, which enhance the

9 quality of the outcome assessments and the ability to assess

10 the consistency of the findings regarding these outcomes across

11 studies.

12 In addition, as appropriately noted in the

13 agency's review, all three studies collected extensive data on

14 potential confounding variables, used appropriate

15 multi-variable statistical methods to control for confounding

16 effects of socioeconomic factors, lifestyle and behavioral

17 factors as well as additional environmental exposures, and

18 conducted sensitivity analyses to determine if assumptions made

19 about missing data, for example, were appropriate. Those are

20 all strengths.

21 One concern is that, in general, the sample

22 sizes of the three studies were only moderately large, ranging
Page 546

1 from just over a hundred to slightly under five hundred,

2 depending on which subset of the data mothers and children was

3 being analyzed.

4 So the epidemiology review correctly notes

5 that the modest sample sizes were a limitation in having

6 sufficient statistical power to detect as statistically

7 significant possible modest relations of exposure to outcomes

8 or interactions with other variables. But the limited sample

9 sizes were possibly one of the important limitations of these

10 studies, which is reflected in the wide confidence intervals

11 for some of the effect measures -- and I don't think enough was

12 made of this -- of moderate or large increases in exposure to

13 see statistically significant affects, in IQ for example.

14 However, some evidence of interaction with

15 paraoxonase 1, either genotype of phenotype was provided in

16 some of the studies, and some examinations of interactions with

17 other exposures were presented and summarized in the review.

18 So two items that might be added to the

19 review are, first of all, in that table 10 which we thought was

20 a good table -- this is in light of some of the comments that

21 were made -- replacing the null and positive non-significant

22 with actual point estimates in 95 percent confidence intervals.
Page 547

1 For effect estimates, to the extent possible, we realize that

2 there's a lot of detail there and you will have to pick and

3 chose a little bit, but we think that would be more

4 informative. 

5 And also, secondly, noting in the text of

6 other interactions, such as with the sex of the child,

7 gestational age at the measurement of exposure, length of

8 breast-feeding, use of alcohol, et cetera, were not

9 consistently described in the three studies. And in most

10 cases, the sample sizes were inadequate to have sufficient

11 statistical power to detect meaningful effect modification as

12 statistically significant.

13 And this later point is important, because it

14 means that the potential for stronger associations or larger

15 affects in subgroups with potentially enhanced susceptibility

16 could not be or were not adequately examined or reported.

17 In addition, the review in the draft paper

18 mentions that the restriction of some of the study samples by

19 race, ethnicity or to low-risk pregnancy, such as non-smokers

20 or women without comorbidities reduce the potential for

21 confounding, which is a plus, but also reduce the generalized

22 ability of the results. 
Page 548

1 And perhaps the review did not sufficiently

2 emphasize that this limitation also meant that affect

3 modification by race or ethnicity, or other risk factors could

4 not be examined with these study sample restrictions, or the

5 sample sizes in general were inadequate to examine such

6 interactions. So differential affects for subgroups with other

7 risk factors or characteristics really could not be determined.

8 The review also examined potential for a

9 misclassification and bias in each of the studies and mentions

10 that the likelihood is that any such misclassification, or

11 biases that operated were non-differential and thus likely to

12 result in an under estimation of the affect. And for example,

13 for chlorpyrifos exposures, the assessment of that was unlikely

14 to be differential with respect to the outcomes that were

15 measured, and thus would have resulted in bias to the null or

16 under estimation of affect measures. 

17 Similarly, although not explicitly stated in

18 the publications, it was unlikely that those who were assessing

19 the outcomes using standard instruments knew the exposure

20 levels of the participants, which could have biased their

21 assessments. Thus, again, the misclassification of outcomes

22 could have occurred but were likely to be non-differential,
Page 549

1 with respect to the exposure levels, and thus were likely to

2 have resulted in bias to the null or underestimation of

3 effects.

4 We also thought that we ought to respond to

5 the issue that was raised about multiple comparisons that came

6 up in the public comments. We feel it is important to note

7 that all the comparisons made in the three studies were

8 hypothesis-driven and dealt with related outcomes rather than

9 reflecting "fishing expeditions" that would have been likely to

10 result in significant findings by chance, due to multiple

11 comparisons. So, we feel that the multiple comparisons issue

12 is not an important concern regarding the findings of these

13 three studies over the years.

14 We also felt that the agency's review

15 reflects the author's views from the three studies that among

16 the statistically significant effects seen most appeared to

17 have a linear relationship with exposure and have no evidence

18 of threshold. But when we looked at some of the graphs and

19 other results presented in some of the papers, it would appear

20 that this point actually requires some further data and

21 examination.

22 I will just say, when I was at graduate
Page 550

1 school, my biostatistician and faculty said, look at the data. 

2 And when you look at the data, for example, the graphs in the

3 Columbia study seemed to suggest no threshold for the effect on

4 working memory but do suggest a threshold for full-scale IQ.

5 In the California sample, the graphs in the

6 most recent paper suggest a drop in IQ that begins usually in

7 the second quintile. It depends which outcome they were

8 looking at, and then doesn't seem to get worse with increasing

9 dose. So with increasing quintiles of exposure, this would

10 also be suggestive of a threshold effect. And the graphs in

11 the recent publication from the Mount Sinai Study seem to

12 indicate no threshold when using tertiles, but the confidence

13 in the roles are quite wide. 

14 So the review appropriately notes that, due

15 to the modest sample sizes, statistical power may have been

16 inadequate to detect departures from linearity with logged

17 transformed exposures or outcomes. It might also be mentioned

18 that the modest sample sizes limited statistical power to

19 really assess dose response adequately, which is one of the key

20 criteria promulgated by Bradford Hill.

21 And also, through the quartiles -- see, their

22 tertiles or quintiles weren't comparable across study, so it
Page 551

1 makes comparing them a little bit difficult. 

2 In conjunction with the modified Bradford

3 Hill criteria, the review states that the biologically

4 plausible role for chlorpyrifos in relationship to adverse

5 neurodevelopmental outcomes is believed to involve inhibition

6 of acetylcholinesterase. While this is a reasonable

7 assumption, both papers by Rauh and the one by Bouchard, recent

8 ones, note that neurodevelopmental deficits associated with

9 prenatal and early postnatal exposure to chlorpyrifos in

10 rodents were seen at exposure levels well below the threshold

11 for systemic toxicity caused by cholinesterase inhibition in

12 the brain. And they both note that other mechanisms should be

13 considered involving disruption of neural cell development,

14 neural transmitter symptoms, synaptic formation in different

15 brain regions and others may play roles.

16 In addition, the observed effect modification

17 also suggests other mechanisms, including oxidative stress and

18 lipid peroxidation. The mode of action is discussed in more

19 detail elsewhere in the draft paper, but these additional

20 potential mechanisms might be added in the epidemiology portion

21 of the review. And as noted before, just because the

22 significant effects are observed at exposure levels below which
Page 552

1 acetylcholinesterase inhibition occurs does not mean that the

2 effects are not real, but rather that the mechanisms in humans

3 are likely to be different, perhaps, other than

4 acetylcholinesterase inhibition. So we think that more

5 mechanistic work needs to be done to clarify this issue. And

6 that is all I have. I will stop there.

7 DR. KENNETH PORTIER: Thank you very much. 

8 That was actually very clear. I was able to follow everything.

9 That was good. Dr. Koutros?

10 DR. STELLA KOUTROS: Hi. I concur with all

11 the points that Dr. Gold made, but I also wanted to add an

12 additional comment about the analysis of effect modification by

13 PON1. Although we have noted the limited statistical power to

14 detect interactions, I still think there is significant added

15 value of these findings. It is not really a common thing to

16 have so much functional information for a gene or a set of

17 variants and to know that the exposures of interest are

18 metabolized through this pathway. So the fact that there are

19 several observable associations that were modified by PON1

20 bolsters the finding that the given exposures that we are

21 looking at are related to the outcomes assessed, in my opinion.

22 DR. KENNETH PORTIER: Good point. Dr. Lu?
Page 553

1 DR. CHENSHENG LU: I have to say that this is

2 probably the most hard-working panel that I ever attend. We

3 meet at lunch, dinner, and last night we exchanged emails by 11

4 o'clock. So the other girl has stated pretty much summarize

5 our comments. So I have nothing to add.

6 DR. KENNETH PORTIER: Dr. Vena?

7 DR. JOHN VENA: Yes. I'd like to commend Dr.

8 Gold for integrating all of the comments from each member of

9 the team, so I think she really nicely summarized our

10 deliberations. I would also like to just mention that I

11 thought Appendix 6, provided by EPA -- the detailed review of

12 each of the studies -- was really well done. I thought that it

13 was really helpful in going through each of the studies and

14 then going back to the review paper to really kind of

15 understand what were all of the strengths and limitations.

16 We did note in our review that there were

17 some areas though that maybe were in the appendix that were not

18 in the text of the actual document, and so we made

19 recommendations about where those additional things could be

20 added to clarify the issues. So I agree that the review is

21 clearly written, generally accurate, provides very thorough

22 review in the context of the criteria. And we have made notes
Page 554

1 where those additions would really help clarify the issues.

2 DR. KENNETH PORTIER: Dr. Young?

3 DR. HEATHER YOUNG: I concur with the rest of

4 the panel. Again, I just want to commend the EPA on the

5 thoroughness of the review. I have been on several of these

6 panels and this is the best review that I have seen yet. And

7 so you all did a wonderful job summarizing all of the

8 literature. And a lot of it was very detailed literature, so I

9 think it was very well done.

10 DR. KENNETH PORTIER: So, Dr. Carr?

11 DR. RUSSELL CARR: I have two points. The

12 first is the rodent data reported in the introduction of the

13 Rauh and Bouchard paper. This all comes from one laboratory. 

14 And the dosages they used in those studies were never below 1

15 mg per kg, and they claim that it was non-cholinesterase

16 inhibiting but we know, in fact, that dosages of 1 mg per kg

17 will lead to cholinesterase inhibition. They did not

18 adequately measure cholinesterase inhibition in a lot of those

19 papers.

20 The second thing I have is, in the 2011 Rauh

21 paper, they found decreased IQ in 7-year-olds from the Columbia

22 cohort, and the paper was very well written. They accounted
Page 555

1 mathematically for possible confounders such as PAHs and

2 environmental tobacco smoke. 

3 However, if you go back to 2009, in that same

4 cohort of kids they found decreased IQ and working memory

5 scores in five-year olds and they attributed that to exposure

6 to polycyclic aromatic hydrocarbons. And again, they did

7 mathematical calculations and said it wasn't due to

8 interactions with the chlorpyrifos that it was due to PAHs. 

9 I am at a quandary here, because I have got

10 the same cohort of kids who had decreased IQ at 5-years-olds

11 and decreased IQ at 7-year-olds, but they are being caused by

12 different things. I can understand how you can mathematically

13 account for these things in any model. And the more you know

14 about what you are working with, the stronger the model

15 becomes. But the brain, especially the developing brain, is

16 very dynamic, is very intricate. There is so much that we

17 don't know. And I am just having a hard time biologically

18 separating out that PAHs are present; chlorpyrifos is present,

19 but yet they are having the same effect simultaneously through

20 two different routes and no interactions. And basically, that

21 is what these papers say, is that I just cannot biologically

22 separate that. 
Page 556

1 I mean, you can do it mathematically and

2 statistically but, as far as a biological function, if those

3 two compounds are there and they both claim that they both

4 caused decreased IQ, how can you separate that? I mean, I just

5 want to just state that that is a quandary for me right now.

6 DR. KENNETH PORTIER: Dr. Gold?

7 DR. ELLEN GOLD: I would have to go back and

8 check the paper, in particular. I mean I looked carefully at

9 the more recent one and less carefully at the older one. So

10 setting aside for a moment the biological piece -- we can come

11 back to that, but mathematically, I think that when they are

12 adjusting one for the other, they can see independent effects. 

13 So it is like saying what happens when you look only at one and

14 when you look only at the other and when you look at both.

15 And then what they don not see is what we

16 call enhancement or interaction or in fact modification, when

17 both are there together. So they are seeing independent

18 effects of each. 

19 So I would have to go back and check the

20 older paper about the PAHs, because that one I actually do not

21 recall. 

22 In the more recent one, though, what they are
Page 557

1 saying is this was an independent effect of chlorpyrifos,

2 independent of the PAHs, because when they adjusted for it and

3 they checked for effect modification they didn't see it, so

4 they saw an independent effect. Biologically, it's a separate

5 issue with it. Probably you are more qualified to comment on

6 it than I am.

7 DR. RUSSELL CARR: Dr. Carr. I understand

8 mathematically, you have to separate that. But even with that

9 assessment, is, they selected out the chlorpyrifos kids for

10 kids who had residues of chlorpyrifos in their umbilical cord. 

11 They selected the PAH by kids who had residues of PAHs, or

12 through environmental monitoring or PAH adducts in their

13 umbilical cords.

14 There is a potential for a kid who has a low

15 IQ to fall in both those categories. And so, if at 5 years old

16 he falls into the PAH category and they claim that it is

17 through the PAHs, then he possibly could fall into the

18 chlorpyrifos category because he is still living in the same

19 environment exposed to all the same things. And so that is one

20 thing.

21 I understand that mathematically they can

22 correct for that, but just the dynamics of the situation
Page 558

1 puzzles me a little bit.

2 DR. ELLEN GOLD: So I think what we can say

3 is, there are kids who have only one or the other exposure or

4 who have both exposures. And that could be different at

5 different time points. Okay? And mathematically, what you are

6 saying is when they measured it, what did they see when they

7 only had one or when they only had the other or when they had

8 both. 

9 DR. RUSSELL CARR: Well the exposure occurred

10 prenatally. And so the authors did not identify if the kids --

11 we include just the chlorpyrifos kids but we excluded the kids

12 that had the PAHs exposure, or that they include just the PAH

13 kids and excluded them if they had a chlorpyrifos exposure. I

14 mean, you understand, those kids were exposed prenatally and

15 that the environmental air monitoring picked up both chemicals.

16 DR. ELLEN GOLD: But not in all of them. 

17 That is my point, is that some of them are going to have one or

18 the other and some will have both.

19 DR. RUSSELL CARR: But we do not know that. 

20 That was not differentiated in any of these papers whether they

21 used that as criteria for selecting those children. I mean,

22 some of those kids could have been exposed to both of them.
Page 559

1 DR. ELLEN GOLD: Yes. 

2 DR. RUSSELL CARR: So that kind could have

3 fallen into that low IQ group and then picked out in the PAH

4 paper as having a low IQ to the exposure of PAHs, but then turn

5 around and picked out in the chlorpyrifos paper as having the

6 low IQ due to exposure to chlorpyrifos just because he has a

7 low IQ and was exposed to both. 

8 I mean, I understand what you are saying, but

9 because it wasn't delineated, I don't see how we can just say,

10 "You got a low IQ at 5-year-old and it's due to your exposure

11 of PAHs, but your IQ is still low at 7, but now it's due to

12 your exposure to chlorpyrifos.

13 DR. KENNETH PORTIER: Dr. Carr, I think you

14 got it backwards though, because you are looking from the

15 outcome back. I am thinking of this as a biostatistician. And

16 the analysts knows the cord blood level for both of these

17 chemicals, all right? So when they looked at 5-year-olds and

18 they analyzed the effect as a function of that two-by-two table

19 of plus or minus/plus or minus chlorpyrifos and a PAH they were

20 able to show that the combination didn't add. But if you

21 looked at plus/minus chlorpyrifos there was an effect

22 plus/minus PAHs, there is some effect and no interaction.
Page 560

1 And then when they did it again at seven the

2 probably found the same thing, but now it's stronger with the

3 chlorpyrifos than the PAH side. The PAH change was probably

4 stronger at five. I think that is what I hear you saying in

5 that analysis.

6 DR. RUSSELL CARR: And I understand that. 

7 But still, how can you separate the two? If you know that at

8 five, this one caused it, how can the effects at five due to

9 that one causing a low IQ not still be present at seven? 

10 Biologically you can't do that.

11 DR. JOHN VENA: I don't know if they are

12 saying it's not there. 

13 DR. RUSSELL CARR: They said it's not

14 correlated.

15 DR. JOHN VENA: Well they don't have to be

16 correlated. It's just that in the 7-year-olds the effect was

17 bigger -- 

18 DR. RUSSELL CARR: Due to chlorpyrifos.

19 DR. JOHN VENA: -- on the chlorpyrifos side. 

20 Did they actually say there isn't a PAH effect?

21 DR. RUSSELL CARR: They said the effect

22 wasn't correlated, that the correlation was not significant
Page 561

1 between the PAHs and the chlorpyrifos. They are independent.

2 DR. JOHN VENA: Yeah, they are independent

3 effects. That is what they found. That is in their model.

4 DR. RUSSELL CARR: See, my point is -- I

5 understand that, but biologically they are not. 

6 DR. JOHN VENA: It doesn't mean that both

7 could be related to the outcome. It's just that each one

8 individually is related to the outcome. But there was no

9 interaction.

10 DR. RUSSELL CARR: So there is an

11 interaction. So they are having separate biological effects

12 leading to the same outcome.

13 DR. KENNETH PORTIER: Dr. Young?

14 DR. HEATHER YOUNG: I mean, I think it's

15 important to remember we are talking about an EPI study, so we

16 are looking at associations. They are not saying there is a

17 causal pathway here. And so it is reasonable to believe that

18 there could be an association independently with PAHs, with

19 these neurodevelopmental outcomes, and also chlorpyrifos plays

20 a role. 

21 And so, I think -- this is kind of leading

22 into 4.2 -- but the agency's conclusion wasn't that
Page 562

1 chlorpyrifos was the sole actor but that it plays a role. And

2 so, I mean, I find it reasonable that there could be a whole

3 mix of stuff that these kids are exposed to that may have

4 independent effects on these neurodevelopmental outcomes. And

5 so it's not saying, "Okay, we are pointing to one thing at age

6 5 and now we are saying it's another thing at age 7. We are

7 just saying that there were associations and they showed up at

8 these different time points because of the way that the kids

9 fell into the tertiles or the quartiles, however they were

10 analyzed at those time points.

11 DR. KENNETH PORTIER: I think we are going to

12 probably continue this discussion in the next session, but --

13 Dr. Harry?

14 DR. JEAN HARRY: Jean Harry. So I have a

15 little concern with the strength of the statement you read. So

16 you read from what's going to be in the reports so that is why

17 I am responding to it. And in trying to associate the effects

18 that we are seeing in non-enzyme inhibition activity -- and I

19 don't really think that you can say that. 

20 When you went through the mechanistic data

21 and we went through the animal data, I mean we raised enough

22 issues in the fact that you have to look at that at very
Page 563

1 specific times to really get a handle on when that really

2 happens, and that's going to be very difficult in a human study

3 to do.

4 The issue was the strength in saying that

5 these effects are likely due to non-cholinesterase inhibition

6 effects, and I don't think that can be said. And I think, if

7 it is written in that format, it is inconsistent with

8 yesterday's conversation of how we were looking at the

9 mechanistic studies, their possibilities. 

10 And I think what's interesting is, a number

11 of things in the report raised that, as we are understanding

12 more and more about the cholinergic system during development,

13 we are realizing that a lot of these effects that we think are

14 specific to development may also still be related to that

15 transmitter activity. So we are getting a better understanding

16 of the system and I don't think we can rule it out quite that

17 much.

18 So, I guess, bottom line -- I think the

19 strength of that statement is inconsistent with the discussion

20 that we had yesterday.

21 DR. KENNETH PORTIER: Dr. Gold, did you want

22 to --
Page 564

1 DR. ELLEN GOLD: I thought I understood, but

2 now I'm not sure I do. All I think we are saying is don't rule

3 out the possibility that there might be another mechanism. I

4 think that is all we are saying.

5 DR. JEAN HARRY: That wording is fine. The

6 wording as it was written was a lot stronger and that is what I

7 was concerned about. 

8 DR. KENNETH PORTIER: The other thing I

9 thought I heard you say is that, as our understanding of

10 mechanisms increase, when we expect to see certain effects show

11 up might be incorporated in when we test for them in the

12 epidemiology. I mean I thought you were saying some of the Epi

13 testing may be occurring at the wrong time based on maybe our

14 understanding of how the cholinergic system develops. Dr.

15 Harry?

16 DR. JEAN HARRY: I'm not sure that it's going

17 to drive when we should test in the children, but I think what

18 we may find is, the specific roles that we have given to this

19 enzyme, we may learn that it does more than that or activities.

20 The oxon may do more. 

21 So we have to keep in mind that we are

22 limiting our knowledge-base, of the enzyme of where we were a
Page 565

1 number of years ago and we may actually be starting to learn

2 that it does more in the subtly. So that association may stay

3 there in many more things than we think it is, right now. 

4 That is speculative, but I think it does put that caution in of

5 there is no association between the two simply because you

6 can't measure something at the time you chose to try to measure

7 it. 

8 You know you have got the issue of timing, as

9 we have raised quite often, but we have also got the issue that

10 we may start understanding that this enzyme is actually doing a

11 lot more than what we credited it for doing 10 years ago.

12 DR. KENNETH PORTIER: Dr. Pope?

13 DR. CAREY POPE: This is Carey Pope. I agree

14 with some of the points that Dr. Harry is making, but I think

15 Dr. Gold, in her comments used the words "well below those

16 eliciting systemic toxicity or something along those lines, not

17 that these were well below those elicit in the

18 acetylcholinesterase inhibition. And to me there is a

19 distinction there in wording that you could -- in my mind, you

20 can have some degree of acetylcholinesterase inhibition in some

21 systems, not saying in all systems, without eliciting systemic

22 toxicity. So those things need to be kept kind of separate.
Page 566

1 DR. KENNETH PORTIER: Dr. Popendorf?

2 DR. WILLIAM POPENDORF: Yes. I have two

3 additional comments. One is related to the misclassification

4 that was talked about in the review paper and you mentioned in

5 your report. 

6 I am concerned -- and I don't have particular

7 references to point to, but the issue paper in particular

8 consistently quotes R values. And my interest is always on

9 R-squared. And its importance, so the R-squared explains the

10 fraction of a variance attributable to the outcome, and this

11 gong to come out in some other comments as well, but most of

12 the significant R-squared values are really very small. We are

13 talking in the order of .1, little less, little more. That

14 means 90 percent of the variance of the outcomes is

15 attributable something else.

16 So I am little concerned on the one hand of

17 causation in the likely statement, and I appreciate as well. 

18 But in terms of misclassification, my concern here is that if

19 you have an R-squared of .1 and you have misclassification,

20 what effect would that have on the result? 

21 I think the comments about the effect is

22 always going to be an underestimate, would be true if you had a
Page 567

1 very strong association, really only had one direction to go. 

2 But if you've got an R-squared of .1, misclassification has

3 almost as likely effect to go in positive as a negative

4 direction. So I would like some comment on that.

5 DR. ELLEN GOLD: If I can comment on that. I

6 think, again, we are getting a little bit into 4.2. But what I

7 will say is two things. One is, in epidemiology, what they

8 have reported a lot of are odds ratios, which is the typical

9 measure, and most of these are much stronger than 1.

10 I mean, some of them are on the order of 3,

11 4, 5, 6, and those kids of odds ratios, it would be very

12 difficult for misclassification to overcome. So that's point

13 number one.

14 And point number two is, I think

15 neurodevelopment, like most outcomes of chronic conditions that

16 we look at don't have one attributable factor that is

17 associated with it that we call the multifactorial. And so

18 it's not uncommon to see that one particular factor doesn't

19 explain all of the variance or even a lot of the variance,

20 because there are lots of things that affect neurodevelopment

21 just like there are lots of things that affect a lot of other

22 health outcomes that we look at.
Page 568

1 But I think it would be unlikely that the

2 odds ratios -- and I think Dr. Young has done a report on these

3 in her section -- the magnitude that we are seeing would be

4 unlikely to be overcome, even if you discovered some -- and

5 undoubtedly there is some misclassification, undoubtedly. It

6 always happens. But I think in this case, it would be unlikely

7 to overcome the magnitude, the associations that are seen and

8 reported. 

9 DR. WILLIAM POPENDORF: I mean I am more of

10 the regression type person in the environment because my second

11 point is also related to that, but I am more of a regression

12 portion, so I take your point on the odds ratio that that may

13 be true. I don't appreciate that as much. But on the

14 regressions, I don't see the same strength there.

15 DR. ELLEN GOLD: Actually it is quite similar

16 because the odds ratio come from regressions. And in many of

17 these papers, the affects are using data values, and so the

18 betas are quite strong. And, again, I think I would make the

19 same comment about unlikely to be overcome by

20 misclassification. 

21 And again, the misclassification is probably

22 in one direction so that these are likely to be underestimates.
Page 569

1 I mean, it could be in both directions but the most likely is

2 that it's -- is it non-differential misclassification, which

3 would tend to bias things towards the null.

4 DR. WILLIAM POPENDORF: If it's a two-way

5 classification, it would have probably a different effect than

6 if it had multiple different classifications, in which it would

7 fall and then things get a little more complicated, but maybe

8 we can have some further discussion as that develops.

9 My second point, I guess, kind of reflects my

10 first in being an environmental oriented person. I'm more

11 comfortable with looking at values and not sort of changes and

12 nonparametric. 

13 And particularly, in looking at the data

14 subsequent to 2001, you get a lot more nondetext (phonetic). 

15 And I would just suggest that there are ways of dealing with

16 nondetext that use the environmental measures more directly

17 than nonparametric tests that, at least some people would be

18 more comfortable with. That was addressed in the July 2010 SAP

19 meetings. Some methods were presented. 

20 There was some article that was used, one by

21 Richardson. There was an Epi referenced Richardson and Company

22 2003 is another reference where they are accounting. And
Page 570

1 basically you can do regression on missing data. In fact, you

2 can even do it if you have no data at all. If it is all

3 nondetext, you can set some upper bound limits using that, and

4 that just makes some recommendations that -- I think there are

5 more useful tests that could be done on some of those data with

6 missing values.

7 DR. KENNETH PORTIER: Dr. Sandy?

8 DR. MARTHA SANDY: I just have a question for

9 Dr. Harry. When you were talking about the things that we may

10 learn about the activity of the acetylcholinesterase enzyme, as

11 I read the draft background paper and listened to the

12 discussions, I understood that it was this protein. Not the

13 enzymatic activity, but the protein in neurodevelopment may

14 have other functions that are distinct from its enzymatic

15 function. Is that correct? And were you also suggesting that

16 the enzymatic activity might do other things that we don't

17 know?

18 DR. JEAN HARRY: So basically, what you said

19 is correct. But what I was thinking in a sense, that you've

20 got the protein but you've also got the enzymatic activity,

21 which is going to drive potentially the amount of the protein

22 and the activity, so they are linked in a dynamic fashion. But
Page 571

1 in a sense, we appear to be learning more about what the system

2 can contribute to the development of the brain, and that may be

3 a combination of -- I mean, it's going to be a synthesis of the

4 protein degradation, the protein level of the protein. So I

5 think we are sort of talking in the same way.

6 Basically, that system, as we learn more

7 about that, we may learn more about what might happen if we

8 start to alter the level of the available protein to the system

9 during critical times of development.

10 We seem to be learning more about it as our

11 interest have been geared toward it. And I think, with the

12 interest and all the Alzheimer's diseased-based drugs that they

13 are trying to do, we may actually learn much more about this

14 than we would have before.

15 DR. KENNETH PORTIER: I think what I'd like

16 to do is just go on to 4.2 and we can kind of continue the

17 discussion on the agency's take on the chlorpyrifos. So why

18 don't we go ahead and read question 4.2?

19 DR. JEAN HARRY: Similar to the initial

20 conclusions from 2008, the agency has preliminarily concluded

21 that, qualitatively, chlorpyrifos likely played a role in the

22 neurodevelopmental outcomes reported in the epidemiologic
Page 572

1 studies. 

2 And that information available since 2008

3 including both new epidemiologic investigations as well as

4 epidemiologic method's papers strengthens this conclusion. 

5 Please comment on the agency's preliminary qualitative

6 conclusion that chlorpyrifos likely played a in the

7 neurodevelopment outcomes observed in the epidemiology studies.

8 

9 Please include in your comments a discussion

10 on the strengths and uncertainties associated with this

11 preliminary conclusion. 

12 DR. KENNETH PORTIER: Dr. Young?

13 DR. HEATHER YOUNG: So, overall, the panel

14 reiterates the 2008 SAP's conclusion and the agency's

15 concurrence with the statement that chlorpyrifos likely plays a

16 role in neurodevelopmental outcomes in the three cohorts.

17 The qualitative conclusions of the

18 epidemiology review seems quite well-justified. The panel

19 agrees with the agency that, although exposures to other

20 acetylcholinesterase, inhibiting compounds cannot be ruled out

21 as contributing to neurodevelopmental outcomes and the

22 potential combination and/or added effects of these compounds
Page 573

1 does not rule out the role of chlorpyrifos. However, it should

2 be noted that it cannot be stated that chlorpyrifos is the sole

3 contributor to the observed outcomes. 

4 With regard to the strengths of the three

5 studies, specifically they all have perspective longitudinal

6 designs which permit clear indication of a temporal

7 relationship of the exposure to the adverse neurodevelopmental

8 outcomes. 

9 There is relative consistency of findings in

10 different populations but using similar standardized exposure

11 and outcome measures, strength of the associations, use of

12 objective measure of exposure and standardized validated

13 measures of outcomes, controlled multiple confounding

14 variables, suggestion of a dose response effect, minimization

15 in bias in assessing outcomes and exposure the likelihood that

16 the basis is a misclassification of exposures and outcomes

17 resulted in a bias of the null, an underestimation of the

18 effect. And then there were attempts to investigate genetic

19 and phenotypic effect modification and dose response effects.

20 We also went through and did an analysis of

21 some of those points. Some of these details were previously

22 presented in the 2008 SAP report, but we have reiterated them
Page 574

1 here so that we have all of our Epi evidence in one place. 

2 It should be noted that the studies published

3 since 2008 have continued to show associates between the

4 neurodevelopmental outcomes and potential exposure to

5 chlorpyrifos and have only strengthened the available Epi

6 evidence. 

7 Recently, analysts have looked at

8 neurodevelopmental outcomes in older children and addressed

9 some of the issues of confounding by a socioeconomic status of

10 other pesticides, and issues of exposure measurement

11 validation. 

12 So if we look at strength of association we

13 focused mainly on the Columbia cohort, as this cohort

14 specifically measures the chlorpyrifos directly from the cord

15 blood, and therefore had the most robust exposure measurement.

16 Although the results from the other cohorts

17 are useful, even if you would decide to negate those studies

18 due to nonspecific exposure measurement, the Columbia cohort

19 provides a number of strong associations.

20 We see effects as early as fetal growth and

21 continue to see them through early childhood with the recent

22 evidence of neurodevelopmental effects seen at age 7. 
Page 575

1 To summarize with fetal growth, there were

2 statistically significant deficits in birth weight and birth

3 length with infant neurodevelopmental, statistically

4 significant and very strong deficits on the Bayley Psychomotor

5 Development Index at three years when comparing high to low

6 exposure groups. And in a more recent analysis, that these

7 decrements persisted even when they adjusted for the group in

8 individual level by socioeconomic variables, which was one of

9 the concerns that we had in 2008. 

10 Earlier studies also saw strong increased

11 odds of mental delay 2.4 in psychomotor delay of almost 5 at

12 age 3 when comparing high to low exposure groups. One of the

13 concerns in 2008 was that would these associations possibly go

14 away when we controlled for diazinon and propoxur exposures. 

15 In Appendix 4 there is an unpublished analysis that show that

16 the chlorpyrifos still showed significant increased odds. In

17 fact, the measures were stronger when you controlled for the

18 diazinon and propoxur exposures. 

19 Looking at attention problems, the Columbia

20 Cohort showed extremely large odds ratios 11 for attention

21 disorder, 6.5 for ADHD and 5.4 for PDD when comparing high to

22 low chlorpyrifos exposure groups. So these are really large
Page 576

1 and not likely to be affected by any sort of residual

2 confounding.

3 And then looking at the more recent study

4 with intelligent measures we saw significant decreases of 1.4

5 percent in full scale IQ and approximately 3 percent in working

6 memory among 7-year-olds for each standard deviation increase

7 in chlorpyrifos exposure. 

8 And the interesting thing to note is that

9 they did sensitive analyses and the results persisted even when

10 they looked at only those with detectable chlorpyrifos levels,

11 so getting rid of the ones where they imputed the values below

12 the LOD. In addition, they found no evidence of mediation by

13 child behavior when they looked at the working memory

14 instrument.

15 When looking at consistency of association,

16 we compared the results from the Berkeley and Mount Sinai

17 Cohorts and found that there were several areas in which they

18 were consistent with or supportive of the conclusions from the

19 Columbia Cohort.

20 It should be noted again that the Berkeley

21 and Mount Sinai Cohorts did not replicate the results on fetal

22 growth that we see in the Columbia Cohort. Although they are
Page 577

1 similar in composition and design, they did not measure

2 specific chlorpyrifos exposure, but instead used nonspecific

3 measures. But the internal validity of the cohorts gives us

4 confidence in the consistency of the results.

5 With the neonatal neurodevelopmental, this

6 was not measured in the Columbia study, but we saw consistently

7 in both the Berkeley and Mount Sinai Cohorts that there were

8 increased abnormal reflexes in the neonates associated with

9 maternal DAPS. 

10 In infant natal development in the Mount

11 Sinai Cohort, we found that prenatal DAP was significantly

12 associated with deficits in the Bayley MDI at 12 months among

13 Blacks and Hispanics. And notably, the association was

14 enhanced among children that had maternal carriers of the

15 PON1QR and RRR, so the fast metabolizers.

16 The Berkeley Cohort also showed significant

17 decreases in MDI at 24 months associated with increased

18 prenatal and infant urinary DAP measures, and also showed

19 evidence of poor MDI scores at two years among those children

20 that had the PON1 allele, so similar results to what the Mount

21 Sinai Cohort showed.

22 The Berkeley Cohort, with regard to attention
Page 578

1 problems showed total urinate prenatal and postnatal DAP

2 measures associated with significantly increased odds of PDD at

3 age 2. And in addition, prenatal DAP was associated with ADHD

4 in the child behavior, and checklist problems at 5 years.

5 Child concentrations at DEP were also

6 adversely associated with a composite measure of attention in a

7 recent study by Marks, et al.

8 With regard to intelligent measures, the

9 Berkeley Cohort showed a similar effect to what we saw in

10 Columbia, which was a significant deficit of 7 points in full

11 scale IQ among the 7-year-olds when comparing with highest

12 quintile child maternal DAP to the lowest level.

13 The Mount Sinai Cohort showed slight but not

14 significant decrease in full scale IQ perceptual reasoning and

15 working memory that was associated with prenatal maternal

16 urinary DAP in 6- to 9-year-olds.

17 They also showed increased prenatal and

18 maternal urinary DAP associated with decreases in perceptual

19 reasoning and maternal QQ characters. And this association did

20 show a monotonic trend.

21 One more point that I want to make and it's

22 about the crude exposure response relationship. We thought
Page 579

1 this was notable that when you looked at the pre/post

2 cancelation analysis in the Columbia Cohort -- they had not

3 meant to test this and it was a unique natural experiment that

4 occurred.

5 And when thinking in Epi terms, the

6 effectiveness of a prevention measure is generally when you

7 remove the potential exposure, the outcome goes away and you

8 see the exposure measure is decreased. And we thought that it

9 was unique in this study that it does argue for a crude dose

10 response relationship because the study show that post

11 cancellation, there was a significant reduction in chlorpyrifos

12 and maternal personal air samples when you looked at the pre

13 and post cancellation levels. 

14 And then we also see an improvement in the

15 3-year MDI and PDI scores looking at pre and post cancellation,

16 so that you see an association in the pre-cancellation group

17 and no association in the post cancellation group. So although

18 it wasn't designed to test that, it does provide evidence of a

19 crude dose response relationship.

20 With regard to the uncertainties, we said

21 that you should note that the relatively modest sample sizes

22 limit the statistical power to detect some meaningful
Page 580

1 differences that may be statistically significant, also limits

2 the ability to look at effect modification by rate of ethnicity

3 in some of the genetic characteristics.

4 Relatively moderate to large exposure

5 differences were needed to see significant effects due to the

6 modest sample sizes.

7 There is a lack of clarity regarding the

8 linear dose response relationship instead of the potential

9 threshold effect. The other concern was use of a single or

10 average sample in the Berkeley Cohort for exposure.

11 Whyatt did note in the 2009 paper that there

12 were moderate but significant correlations between Meconium and

13 quart of maternal blood in average urine TCPy but it is still

14 unclear the representative of the single point of exposure. 

15 Obviously time varying exposures with the ability to define

16 cumulative exposures would have been preferable.

17 Another uncertainty is lack of a specificity

18 of a window of affect and potential for misclassification of

19 individual exposure measures. External generalized ability of

20 the cohorts, given their unique racial ethnic and socioeconomic

21 characteristics is another uncertainty. However, it should be

22 noted that their exposures are within the range of those seen
Page 581

1 in NHANES, and that kind of gets into another question later

2 on.

3 Questions about the biologic plausibility are

4 also an uncertainty due to the lack of clarity on the mechanism

5 of action, particularly at the low exposure level seen in the

6 cohorts and limited and mixed animal studies showing there are

7 developmental effects.

8 So in conclusion, although the three studies

9 were not exactly comparable, more similarities than

10 discrepancies were found across them. And the panel concluded

11 that the additional literature since the 2008 SAP continues to

12 support and strengthens the evidence for the conclusion that

13 chlorpyrifos plays a likely role in the adverse effects in

14 child neurodevelopment. 

15 And I believe there was some late night

16 comments that some of my other discussants had, so I will leave

17 those for them because I didn't get them incorporated. 

18 DR. KENNETH PORTIER: Thank you. Dr. Gold,

19 do you want to add anything?

20 DR. ELLEN GOLD: No. Dr. Young has

21 incorporated all of my comments. I have nothing to add.

22 DR. KENNETH PORTIER: Dr. Harry?
Page 582

1 DR. JEAN HARRY: I will safely say no. I

2 concur with the discussions that have happened. I think,

3 between the last two we've covered, we've covered this one

4 well.

5 DR. KENNETH PORTIER: Dr. Koutros?

6 DR. STELLA KOUTROS: I also agree with all

7 the points made by Dr. Young. I did make some comments, which

8 she was referring to late last night. I think she did cover

9 some of the major points, so I don't really want to go into too

10 much detail about some of the things I said. I think we can

11 add it to the text later.

12 But I did want to make a comment about one

13 aspect of a confounding assessment that I found particularly

14 compelling in the data. The author's made efforts to present

15 data on co-exposures to several top candidate environmental

16 exposures that could have accounted for the observed

17 associations like lead and mercury and PCBs and environmental

18 tobacco smoke, PHs, organochlorine pesticides and some other OP

19 insecticides. 

20 So the authors showed that none of the

21 exposures likely explain the observed associations. This sort

22 of directly speaks to the it-must-be-something-else argument
Page 583

1 that was made the other day, I think. 

2 So I think we have to remember that, in

3 order for the observed effect to be attributable to something

4 else that it would have to be related to chlorpyrifos exposure.

5 So the authors did not show that to be the case, or not

6 necessarily a chlorpyrifos exposure in the metabolites of

7 interest, as well, that were explored in the other studies. 

8 But also from my own experience in the

9 agricultural setting, we find, where many pesticides are

10 applied at the same time, we have found low to moderate

11 correlations between pesticides, so this suggests that it's not

12 unreasonable to consider the effects of the each pesticide

13 independent. Not that, that is true in all settings, but I

14 think this also leads me to believe that confounding due to

15 some of these other chemicals is unlikely. And so, I think

16 that's it.

17 DR. KENNETH PORTIER: Dr. Vena?

18 DR. JOHN VENA: I concur with what was said. 

19 I don't have anything to add right now.

20 DR. KENNETH PORTIER: Dr. Lu?

21 DR. CHENSHENG LU: Well again, kudos to Dr.

22 Young. I think my email stamp is 10:04 p.m. We finalized that
Page 584

1 version that Dr. Young has reported. 

2 I have one thing to add to this comment. 

3 Again, I think the group concur with agency's preliminary

4 conclusion that chlorpyrifos is likely to play a role. 

5 However, in order to prove or add confidence to this

6 preliminary conclusion, I would suggest to the agency to look

7 at other pesticide measurements in the data. That's why I

8 asked the agency yesterday -- or Tuesday that, in addition to

9 chlorpyrifos or TCPy, what else pesticide data do you have,

10 especially from Columbia study.

11 So for example, if you look at this pre and

12 post phase-out period and you see the changes from association

13 to no association to chlorpyrifos residue in cord blood

14 samples, do you see the same trend for, say for example

15 malathion or diazinon or the other way around, and that

16 analyses will somehow strengthen or weaken your preliminary

17 conclusion whether chlorpyrifos is likely to play an important

18 role. And I think that in essence should be very easy to do,

19 but I think definitely worth it to sort of double check your

20 conclusion using other data.

21 DR. KENNETH PORTIER: Dr. Koutros? 

22 DR. STELLA KOUTROS: Dr. Lu and I had a short
Page 585

1 discussion about that this morning and I think what he is

2 suggesting is that we always have this question in Epi that it

3 could be something else. This happens all the time, and so

4 there is a huge burden on us to try and show that these other

5 exposures are not causing the effect.

6 So additional analyses that I think he was

7 suggesting, and I really don't recall from the study what they

8 did -- although they did do some of this work -- if there is

9 any other measured pesticide exposure, it would be very easy to

10 put them in a model at the same time and see if the effects of

11 chlorpyrifos remain. But again, I don't recall what

12 measurements were from what particular study, so I don't know

13 if that makes it a little bit more clear, and not necessarily

14 just -- I mean, I don't really think just looking at malathion

15 and saying that -- you know, just whatever they have measured. 

16 Of course, I wouldn't suspect you would want to do a new

17 analysis just to look at that, or a new biomarker analysis just

18 to look at that. 

19 But this actually made me wonder -- you know,

20 I wasn't at the last SAP, so it sounded, from some of the

21 material that I read, that there had been some communication

22 with the cohort authors in the past. And I am not sure what
Page 586

1 available biological samples are still there and how, if at

2 all, they've considered pulling any of their data. So I didn't

3 know if anybody knew anything about that.

4 DR. KENNETH PORTIER: Dr. Aschner?

5 DR. MICHAEL ASCHNER: I mean, if you did this

6 actually and you showed for example that you're using a lot

7 more malathion, I mean would you be able to conclude that

8 malathion improves the IQ? I mean I'm having problem with this

9 kind of -- 

10 DR. STELLA KOUTROS: Can you repeat the

11 question?

12 DR. MICHAEL ASCHNER: Yeah. If, at the same

13 time, you had a tremendous increase in the amount of malathion

14 that is being used post the removal of chlorpyrifos, would your

15 conclusion be that malathion increases the IQ?

16 DR. STELLA KOUTROS: I'm not sure exactly

17 what you are driving at but -- go ahead.

18 DR. CHENSHENG LU: I guess you have to make

19 the comparison at pre and post phase-out. Yes; if, say for

20 example, during the phase-out period, malathion use this

21 increase by 50 percent and then all show up in the cord blood

22 sample, and that -- yes, maybe that's the case. And that's
Page 587

1 what I'm saying, that if you need to look at this additional

2 analysis and ask themselves whether chlorpyrifos is likely to

3 play a role.

4 DR. KENNETH PORTIER: Dr. Koutros?

5 DR. STELLA KOUTROS: So, in order for

6 whatever this is to play a role, it would have to be related to

7 prenatal chlorpyrifos exposure, which I think is highly

8 unlikely. But I do think a limitation of the studies, and

9 actually one of the major potential confounders that could be

10 at play is postnatal exposure, which is likely to be related to

11 chlorpyrifos and may also be related to the outcome. So that,

12 I think, is unknown and probably will be unknown.

13 DR. KENNETH PORTIER: Dr. Popendorf?

14 DR. WILLIAM POPENDORF: I've got a statement

15 and a half, I suppose, but one would be about the weaknesses

16 that I'm going to talk about in a the future response with the

17 DAP and TCP as indices of exposure. But the other is,

18 certainly I think that the strength of that crude dose response

19 is very good for the Columbia data, and I think there is future

20 opportunities to see more differences as you have more

21 separation in time.

22 Again, it seems like the -- the one that I'm
Page 588

1 looking at, the environmental residues were certainly still

2 there in the 2002, 2003, maybe even 2004 timeframe. So the

3 more time that separates them, I think, the more opportunities

4 there will be with that data or that type of study to look for

5 effects, opportunities to detect differences.

6 DR. KENNETH PORTIER: That's only if they are

7 continuing to recruit into the cohort, right? They are not

8 doing that anymore. The cohort stops. So that would only be

9 if you were getting some new children into the cohort today at

10 extremely low level.

11 DR. WILLIAM POPENDORF: I would certainly

12 except that. And I guess I'm sort of potentially encouraging

13 that type of study to be reinitiated with all the similarities

14 and measurements, cohort or socioeconomic factors, et cetera.

15 DR. KENNETH PORTIER: Dr. Terry?

16 DR. ALVIN TERRY: This sort of goes back to

17 question 4.1. And I was sort of sitting here scratching my

18 head about a comment or two that Dr. Harry made and I wanted to

19 get clarification, and it relates to her asking Dr. Gold to

20 sort of tone down the comments about the possibility that these

21 detected neurobehavioral effects were non-cholinesterase

22 related. 
Page 589

1 And I'm thinking about some of the

2 presentations yesterday. I think it was one of the public

3 presentations, and correct me if I'm wrong; in the Columbia

4 cohort, the only real measurements of chlorpyrifos were in cord

5 blood and they were something only worth pg/g per mil, very low

6 but detectable. So why is it not a fair statement to suggest

7 that these effects may be non-cholinesterase related? I don't

8 know of anyone who has ever detected cholinesterase inhibition

9 with a pg/g per mil concentration. Did I misinterpret what you

10 said?

11 DR. JEAN HARRY: I wasn't digging heals into

12 the sand. What I was cautioning about was in the wording. And

13 I think it's just going to -- to back and look, because it was

14 stated that in the absence of clinical science, again, you're

15 at lower levels. 

16 It almost seemed to take us right back to

17 say, "Well, if it's not that then it's got to be due to all

18 these other things that we know happened during development." 

19 And I just thought that the wording of that paragraph -- and

20 again, it was the subtleties of the wording was inconsistent

21 with the discussion that we had around the table yesterday. 

22 Not that there are not potential effects, but since we cannot
Page 590

1 say timing, we cannot say when during gestation -- I mean we

2 really can't say that what you're looking at is not due to that

3 activity.

4 Such a strong paragraph that took a quote out

5 of a paper in the discussion, which was great, I mean, it sort

6 of set it up. And I think that that issue needs to be there. 

7 But in order to outgrow other types of things that -- to take

8 the mechanistic studies and bring them over as possibility of

9 what might be causing that I think is inconsistent with their

10 discussion yesterday that sort of says, "Yes, we have a lot of

11 things there but there is a lot of questions in the in vitro

12 data about applying them over."

13 So, in a way it's a matter of semantics of

14 the wording of that paragraph, not that you have to rule out,

15 that other things are happening. It's a consideration. It was

16 just more in the editorial style of the paragraph.

17 DR. KENNETH PORTIER: Dr. Gold once again.

18 DR. ELLEN GOLD: I just have a quick comment.

19 I did go back and look at the paragraph, and it just says that

20 these other things may play a role, so I hope that addresses

21 the concern. But the other part of it was a direct quote from

22 the article by the authors, which we can removed. But I'm just
Page 591

1 saying that these other things should be considered as maybe

2 playing a role.

3 DR. JEAN HARRY: It might be the direct quote

4 coming into the discussion because some of that is speculative.

5 And when you bring it over into a report like this it gives it

6 a little more credibility.

7 DR. KENNETH PORTIER: The way I heard this is

8 that -- what you're saying is it doesn't take cholinergic

9 effects off the table. It makes them less likely and increases

10 the likelihood that we need to look at some of these other

11 mechanisms. And I think you were worried that it was taking

12 cholinergic effects off the table, and I thought that's what

13 you were getting at. A bunch of hands went up. I think Dr.

14 Vena, and then -- 

15 DR. JOHN VENA: I would just recommend taking

16 the quote out and just paraphrasing what they meant to say. I

17 agree it may be better.

18 DR. KENNETH PORTIER: I see a lot of heads

19 shaking. Dr. Sandy?

20 DR. MARTHA SANDY: And I'll just say that

21 question 4.3(a) is going to get to this discussion again, so

22 stay tuned. 
Page 592

1 DR. KENNETH PORTIER: Dr. Koutros?

2 DR. STELLA KOUTROS: I'm sorry to ask again,

3 but has any pooling of data or samples been considered? Does

4 anybody know?

5 DR. CAROL CHRISTENSEN: I can respond for the

6 sake of clarity. I believe those efforts are ongoing. I don't

7 know what stage they are ongoing but I had asked that question

8 and those efforts are underway to do some type of pooling

9 analysis across the three with biological samples, but I don't

10 know a lot of details about it.

11 DR. KENNETH PORTIER: So it was not reported

12 before the panel. It's not in any material and you missed it.

13 DR. STELLA KOUTROS: No. Yeah, I just wanted

14 to make the point that that would be really useful to see that

15 data because -- I mean, I don't know if it's pooling of data or

16 pooling of biological samples for a new effort, but either way,

17 it could add some additional clarity having a bigger sample

18 size to look at some of these questions.

19 DR. KENNETH PORTIER: I think that's not an

20 unreasonable recommendation for the panel to include that says

21 that kind of pooling would be the logical next step for the

22 analysis to move forward, and hopefully you'll get that in the
Page 593

1 report. 

2 I have 10:15. I think it's a good time to

3 take a break before we move onto questions 4.3, which is two

4 parts. Dr. Vena, some closing remarks?

5 DR. JOHN VENA: Yeah. Just one point of

6 clarification for Alex Lu that -- in the write-up, she did have

7 that, describing Whyatt and Rauh's recent report of the control

8 for diazinon and propoxur exposures and dichlorpyrifos still

9 showed significant odds ratios. So there was some stuff that

10 was done to control for that but I don't think they had

11 malathion in there though, but I'm not sure.

12 DR. KENNETH PORTIER: Let's go ahead and take

13 a 15-minute break. We'll be back 10:30 by this clock.

14 (BREAK)

15 DR. KENNETH PORTIER: Unless someone has

16 pressing issues on 4.2, I'd like to go ahead and move on to

17 question 4.3. It's continued discussion of the Epi stuff, so I

18 don't think it limits the panel's conversation. It just brings

19 in another dimension. So why don't we read 4.3? It's a long

20 one.

21 DR. CAROL CHRISTENSEN: Question 4.3. As

22 discussing question 2.0, a mode of action or adverse outcome
Page 594

1 pathway has not yet been fully elucidated for the potential

2 neurodevelopmental outcomes as a result of prenatal

3 chlorpyrifos exposure. 

4 Although this does not undermine the

5 qualitative interpretation of these studies and the preliminary

6 conclusions stated above, the identification of a dose response

7 for neurodevelopmental effects based upon a mode of action is

8 not possible. Further, given the urine and cord blood sampling

9 frequency in the study, there is a large degree of uncertainty

10 in estimating absolute exposure response relationships, as

11 opposed to establishing relative exposure groups for evaluating

12 associations.

13 With respect to dose response, critical

14 durations of exposures and windows of susceptibility are

15 unknown. In 2008, the SAP cautioned against using the Columbia

16 Cohort data for deriving points of departure for risk

17 assessment due, in part, to only measuring biomarkers at one

18 point in time and because they could not exclude the

19 possibility that effects seen were due to chlorpyrifos in

20 combination with other pesticides.

21 In 2008, the SAP advised against using data

22 from the epidemiology studies for deriving POD due to
Page 595

1 limitations and exposure for the purpose of risk assessment. 

2 I'm just going to go on to Part A. Due to

3 the limitations of exposure assessment performed in the

4 epidemiologic investigations, for the purpose of quantitative

5 risk assessment, the agency has concluded that the

6 epidemiologic data are not sufficient for deriving points of

7 departure for quantitative risk assessment.

8 The agency proposes that acetylcholinesterase

9 inhibition data from lab animals remains the most appropriate

10 data to use for dose response modeling and the derivation of

11 points of departure.

12 Please comments on the scientific evidence

13 that does and does not support this conclusion, as well as the

14 strengths and limitations of the evidence.

15 DR. KENNETH PORTIER: Dr. Sandy, would you

16 start the discussion?

17 DR. MARTHA SANDY: Sure. I would like to

18 commend the EPA. I thought the draft document very well

19 summarized the data and laid it out. I'd like to start by

20 describing the multiple lines of evidence that suggests that

21 adverse effects may be attributed to doses lower than those

22 which illicit 10 percent inhibition of acetylcholinesterase.
Page 596

1 There are evidence that comes from the

2 epidemiological studies that we've just heard about. These are

3 the three longitudinal children's cohorts. These studies

4 indicate that chlorpyrifos likely play a role in

5 neurodevelopmental outcomes. The Columbia study has the most

6 direct measure of exposure for chlorpyrifos, measuring the

7 compound and cord in material blood.

8 Overall, the levels of chlorpyrifos measured

9 in this study were within the range of levels measured in the

10 general U.S. population at that time, based on the NHANES data.

11 So they are not exceptionally high levels.

12 And while we do not have data on

13 acetylcholinesterase inhibition in either the Columbia study

14 participants or the NHANES participants, we would not

15 anticipate that acetylcholinesterase inhibition would be

16 associated at these levels of chlorpyrifos.

17 Specifically as noted in the draft document,

18 effects seen in the Rauh, et al 2006 paper reported

19 chlorpyrifos, the main level at 6.07 peak-o-grams per gram of

20 blood. And these peak-o-grams per gram blood levels are

21 unlikely to illicit acetylcholinesterase inhibition based on

22 the Noland 1984 study in male adults where they see peak levels
Page 597

1 of chlorpyrifos in the 10 to 30 ng per mil level that results

2 in inhibition of acetylcholinesterase.

3 Evidence also comes from the Animal

4 Neurodevelopmental Studies. And I'll refer back to the

5 discussion we had yesterday of the neurobehavioral studies that

6 were the focus of the discussion for charge question three and

7 the conclusion that there were only three of those

8 neurobehavioral studies that evaluated doses below the 1 mg/kg

9 and also assessed acetylcholinesterase inhibition where two of

10 those studies were negative and one was positive but flawed, as

11 discussed yesterday.

12 But as discussed in Dr. Mundi's (Phonetic)

13 presentation and in the draft document, there are additional

14 neurodevelopmental studies conducted in vivo. There's two

15 additional studies in rates that reported effects at doses

16 below those in which acetylcholinesterase inhibition was

17 detected. One study was the Ray, et al 2010 study that

18 reported an increase in oxidative stress genes in the CNS. No

19 effect was seen on acetylcholinesterase inhibition in this

20 study. Specifically, they dosed the animals by gavage. They

21 were 7-day-old rat pups and they got doses of either 0.1, 0.51

22 or 2 mg/kg. And they saw changes in rat or brain gene
Page 598

1 expression. And among the changes they saw that oxidative

2 stress genes were affected by this treatment.

3 As noted on page 30 of the draft document, it

4 has been suggested that oxidative stress can result in

5 disregulation of signaling pathways, controlling no progenitor

6 cell function. So the study is suggestive of an effect that

7 needs to be investigated further.

8 The second study is a rat study that has

9 reported effects on serotonergic tone of the adult CNS with no

10 effect on the adult CNS with no effect on the

11 acetylcholinesterase inhibition. But this study administered

12 chlorpyrifos in DMSO and the acetylcholinesterase activity was

13 measured 24 hours after the last dose, so there are doubts

14 about this finding and whether it was seen at a level where you

15 didn't have inhibition. So we've got one additional study from

16 those discussed yesterday in vivo in animals.

17 There is also evidence that comes from

18 several in vitro mechanistic studies that have been summarized

19 in this draft document that EPA provided us that demonstrate a

20 variety of effects at the molecular and cellular levels. In

21 addition to the effect summarized in the draft document, there

22 are additional studies looking at other effects, as mentioned
Page 599

1 yesterday by Dr. Terry, such as the effects of chlorpyrifos and

2 their growth factor and mitochondrial morphology.

3 Many of these effects have been observed at

4 very low concentrations in vitro. Levels below which

5 acetylcholinesterase inhibition would be predicted in a couple

6 of cases they actually tried to measure it in the in vitro

7 system and didn't see any inhibition.

8 So these lines of evidence suggests that

9 chlorpyrifos at levels below which acetylcholinesterase

10 inhibition occurs can effect neurodevelopmental. And they

11 indicate that the use of acetylcholinesterase inhibition may

12 not be the most appropriate data for dose response modeling and

13 derivation of a point of departure for chlorpyrifos.

14 I urge the EPA to work to use the

15 epidemiology studies to inform the dose response assessment

16 with chlorpyrifos, perhaps put some bounds on the possible

17 concentration responses observed. I suggest that using the

18 Columbia cohort, since that cohort had the measures of the

19 parent compound chlorpyrifos admittedly at one time point

20 birth, measuring it in the cord blood and the maternal blood. 

21 But I encourage the EPA to work with folks that are doing

22 pharmacokinetic modeling, as we heard from Dr. Hattis and from
Page 600

1 others, to predict exposure levels and get some sense of the

2 levels that were experience in utero, and again, put some

3 bounds on the concentration responses that you might expect.

4 I do understand that the animal data that we

5 do have for dose response is the best at this time and it's for

6 another endpoint. It's for inhibition of acetylcholinesterase.

7 Some of the limitations of using those data include the data

8 that are being proposed for the points of departure in the rat.

9 It's based on a repeat dosing study in pregnant rats. It's

10 based on measurement of red blood cells acetylcholinesterase

11 activity at a single point in time. 

12 Four to five hours after that last dose was

13 administered, the dose -- so I'm not sure how certain we are

14 that that time point, four to five hours after the last dose is

15 an optimal time point for measuring acetylcholinesterase

16 inhibition. The draft EPA document on page 17 says that the

17 time of peak effect is typically within one to several hours

18 after dosing. 

19 The dosing in this study was from gestation

20 day 6 to 20, and so we have to think about how confident we are

21 that inhibition of acetylcholinesterase had reached steady

22 state in this experiment. These were pregnant dams. 
Page 601

1 Looking across the other studies that the EPA

2 has reviewed, it looks like we can have some confidence that

3 the inhibition had reached steady state. Since a companion

4 studied that dosed dams from gestation day 6 through lactation

5 date 10 gave a similar BMDL 10 for the red blood cells

6 acetylcholinesterase inhibition.

7 We have to ask how confident we are that the

8 dose response for acetylcholinesterase inhibition in the

9 pregnant rat is predictive of acetylcholinesterase inhibition

10 in the human fetus, so that's another unknown.

11 So, going back to the Columbia human cohort,

12 I think there are some strengths in the exposure assessment

13 that was done, as discussed in previous parts of this question

14 4. It's rare to have that much information, actual measurement

15 of air, levels of chlorpyrifos of blood, levels of

16 chlorpyrifos, levels of metabolite TCPy in different

17 substrates, meconium and urine. So I really encourage the EPA

18 to use that information to come up with some assessment of

19 integrated exposure and look at that. And I'll turn it over to

20 my fellow discussants.

21 DR. CHENSHENG LU: Again, Dr. Sandy's email

22 stem at 10:54 p.m. last night. But I concur with what she said
Page 602

1 in terms of these specific questions. I do have something to

2 add in question 4.3 in terms of what we can do.

3 Basically I agree that, so far, the data from

4 the three studies is not sufficient to be used to come up with

5 the point of departure, mainly because those studies are more

6 focused on measuring the health outcome than doing the exposure

7 assessment.

8 DR. KENNETH PORTIER: Dr. Popendorf?

9 DR. WILLIAM POPENDORF: And I think I overall

10 concur with the general comments that are being made. I will

11 touch on the timing issue in a human study at a later question,

12 which also affects that in steady state. My sense is that it

13 might not be as important the timing of the blood sample. It

14 might not be as important as the single dose. And I also

15 concur with the value of the PBPK. That, I think, keeps coming

16 up to be used in a lot of different ways, and I certainly

17 encourage that in addition to the animal studies.

18 One other point that I'll make again at a

19 later question, but the reliability of cholinesterase

20 measurement, in particularly trying to detect small changes in

21 any animal, cholinesterase is a challenge. And the

22 methodologies that are used to -- the issue is one of
Page 603

1 repeatability of the analytic measure from run to run or day to

2 day, and that has been well known in human studies. There's no

3 reason that it doesn't apply to animal studies as well. 

4 And that issue really hasn't been discussed

5 in any form here within the issue paper. I'll bring that up

6 again later in another human-related question. But it just

7 kind of brings into question the overall reliability needs to

8 be looked at carefully of small changes in cholinesterase, even

9 in animal data.

10 DR. KENNETH PORTIER: Any additional comments

11 from the panel? Dr. Collier?

12 DR. ABBY COLLIER: I caution agency about

13 using rat and mouse data for points of departure based on

14 acetylcholinesterase inhibition, using that to extrapolate to

15 human neonates in fetuses and I'm going to tell you why. 

16 The cytochromes p450 that are absolutely

17 required to create the active oxon includes CYPs 182384, 2C19

18 and 2C9, and there's a 2B isoform in there as well. I'm not

19 certain of which is the dominant pathway. 2C19 and 2C9 are

20 highly polymorphic in humans but may not have the same

21 polymorphisms in rats and mice.

22 Secondly, there are different homologs and
Page 604

1 orthologs in the rats compared to the humans, and so the

2 relative amounts of metabolism you may receive from a rodent

3 species compared to a human neonate are going to be different.

4 Thirdly, in human neonates and/or fetuses,

5 some of these CYPs are not present at birth or in the fetus and

6 they rise after birth; notably, CYP 1A2, but there are others. 

7 Whereas, we know that they are present in the fetal mouse and

8 rat. So if you go right back to the top of this metabolic

9 pathway from chlorpyrifos activating to the oxon, which is the

10 oxon that everybody's talking about with acetylcholinesterase

11 inhibition. The activation of the oxon may be significantly

12 different, what you would get from a mouse or rat to what you

13 see in human studies. 

14 Having said that, I'm well aware, personally

15 and professionally, of how hard it is to do these studies in

16 human fetal and neonatal and pediatric tissue. But given the

17 trouble with this type of extrapolation, there's a lot of good

18 data out there as to when these turn out. And I do refer you

19 to the work from Professor Ron Hines' lab. I refer you to the

20 work from Professor J. Stephen Leeder's lab that has been

21 looking at the ontogeny of these cytochromes p450. 

22 But I think there's been so much emphasis on
Page 605

1 acetylcholinesterase inhibition. And when you look at the

2 metabolic pathway for activation, the cytochrome p450 come

3 first to activate and make the oxon. And there may not have

4 been enough consideration of that with respect to inter-species

5 extrapolation. Thank you.

6 DR. KENNETH PORTIER: Want to make sure we

7 get that to Dr. Sandy to include in the minutes. Any

8 additional comments? I just wanted to kind of make sure I

9 understood what you're basically saying. So in terms of

10 answering the specific question that EPA ask, you're saying

11 that the animal data is only one part of the data that they

12 need to look at. It is not the only part of data. 

13 You're suggesting the Epi data can inform the

14 dose response assessment that PK modeling can inform, and that

15 all of those, in addition to understanding some of the

16 biochemistry, is going to be necessary to establish a points of

17 departure. Is that pretty much a summary?

18 DR. ABBY COLLIER: Yes, with the addition

19 that -- I'm saying there's multiple lines of evidence

20 suggesting there are effects that are likely to occur below the

21 level of acetylcholinesterase inhibition, therefore those rat

22 data on acetylcholinesterase inhibition -- well the best data
Page 606

1 available to do quantitative dose response assessment are not

2 directly -- we have a lot of uncertainty in linking them to a

3 conclusion that that will be protective against what we are

4 observing in the Epi studies. And I'm suggesting that they

5 really work hard to use those epidemiological studies in

6 exposure concentration response associations to develop some

7 dose response or bounding on how well that rat data, what

8 confidence you're have that that's going to be in the same

9 range.

10 DR. KENNETH PORTIER: So Dr. Christensen or

11 Dr. Lowit, do you understand the panel's answer on this, and

12 the additional clarification question? I'll give you the

13 opportunity here because I'm hearing they don't necessarily

14 agree with you on this particular statement.

15 DR. ANNA LOWIT: I guess, when I put my light

16 on it -- because I had heard Dr. Sandy a little bit different

17 than I think maybe the way that you heard Dr. Sandy, and so I

18 think maybe it's worth clarifying to make sure the way I heard

19 Dr. Sandy's suggestion, and I thought it was largely a very

20 good one -- that, largely consistent with what was said

21 yesterday, with respect to the dose response data and the

22 reliability of that data, the rat cholinesterase data is still
Page 607

1 the best starting point, for example.

2 But if you remember from a couple of days

3 ago, several of the EPA speakers talked about thinking about

4 ranges of risk and ranges of uncertainty in areas of strength

5 and uncertainty. And what I heard Dr. Sandy suggest was to

6 take those other datasets and to use them in a bounding

7 analysis to get a handle on what those bounds of uncertainty

8 and ranges of risk that would help characterize the relative

9 confidence in that cholinesterase data that would provide that

10 reliable starting point, and that's the way I heard it. And it

11 sounds like you heard it a little bit differently, so I think

12 maybe a little bit more conversation about that would help.

13 DR. MARTHA SANDY: In essence, I think what

14 you just said is correct. I'm acknowledging that it's

15 difficult to do dose response on anything but the animal data. 

16 But I'm not sure the response that you're assessing is truly

17 protective. And that's why I'm asking for the use of the Epi

18 to do the bounding to get some sense of -- so it's all about

19 how we phrase this, but to get some level of how much

20 uncertainty there is in using that rat data with a different

21 endpoint; is it protective against the neurodevelopmental

22 effects that we are seeing.
Page 608

1 DR. KENNETH PORTIER: Dr. Lu?

2 DR. CHENSHENG LU: I would suggest Dr.

3 Portier wait until we finish 4.3(b) before making any

4 conclusion. I think there are some comments for 4.3(b) which I

5 think is more relevant to the whole question of 4.3, instead of

6 just 4.3(a).

7 DR. KENNETH PORTIER: Good suggestion. So

8 let's move on to 4.3(b) and see what we find.

9 DR. CAROL CHRISTENSEN: Question 4.3(b): The

10 agency does, however, believe that the epidemiologic data are

11 useful to informing other key aspects of the chlorpyrifos risk

12 assessment, including hazard characterization, exposure

13 characterization and quantitative uncertainty characterization

14 analysis. 

15 Please suggest approaches and/or analyses for

16 potentially using the epidemiology data in different parts of

17 the chlorpyrifos risk assessment, including those noted above. 

18 Some of these may also be covered in question 5.4 below.

19 DR. KENNETH PORTIER: And Dr. Meek has been

20 very quiet in the corner there and now she has her chance to

21 address the panel.

22 DR. BETTE MEEK: Thanks very much. So,
Page 609

1 firstly, because I haven't said very much today, I wanted to

2 recognize the contribution of the agency, particularly in the

3 contribution of the framework for integrative analysis to

4 evaluate multiple lines of evidence in the context of

5 understanding of the AOP/MOA. I think it's excellent construct

6 by which to think about weight of evidence. And of course,

7 it's an excellent construct to think about weight of evidence

8 in the context as we move forward to incorporate increasingly

9 varied types of information from evolving technologies.

10 So in relation to the epidemiological data

11 informing the key aspects of the chlorpyrifos risk assessment,

12 I'd like to expand this somewhat beyond hazard

13 characterization, exposure characterization and quantitative

14 uncertainty characterization analysis because the

15 epidemiological data do provide information relative also to

16 dose response, though again, not necessarily as the basis for

17 the point of departure, but I think that they do inform us.

18 First of all, in relation to exposure

19 characterization, I think that the environmental monitoring and

20 biomonitoring data in the epidemiological studies contribute to

21 the overall database on estimation of exposure, including

22 particularly population variability, and to some degree,
Page 610

1 inter-individual variability. But again, to the extent that

2 those cohorts weren't very large, they at least provide us some

3 information on those aspects.

4 In particular, they provide insight into

5 perhaps more generalizable observations on temporal trends in

6 exposure of the general population following the impact of

7 withdrawal of the domestic uses of chlorpyrifos, and I think

8 that's an important contribution. I think they also provide

9 unique opportunity to investigate the relationship between

10 environmental levels and biomonitoring, since for some studies

11 there're both types of data available.

12 And I guess, to some degree they can also

13 provide some insight into the suitability of the various

14 biomarkers as measures of short and/or long-term exposure to

15 chlorpyrifos.

16 So in relation to the hazard

17 characterization, clearly, the epidemiological data contributes

18 strongly to an evolving database on potential hazard in humans,

19 and that's critical. They have and continue to contribute to

20 hypothesis generation for targeted investigations of

21 developmental mental toxicity in animal studies. And to some

22 degree, they confirm expectations concerning potentially
Page 611

1 susceptible subgroups based on mode of action; so the PON1

2 genotypes. They do provide some information on the extent of

3 impact of other factors which, in combination with

4 chlorpyrifos, may impact on observed effects.

5 An emulation of the quantitative uncertainty

6 characterization analysis; again, I wanted to address this

7 particularly in the context of dose response analysis.

8 So EPA has concluded that the current

9 epidemiological database strengthens the 2008 conclusion that

10 chlorpyrifos likely plays a role in the observed adverse

11 effects on child neurodevelopmental, specifically those

12 reported by Columbia University. I think that we've heard that

13 as a recurring theme this morning in the discussions of the

14 epidemiological data.

15 So given that this is the case, it seems

16 important then to maximally utilize the available data on dose

17 response from these studies, again, as Dr. Sandy indicated, to

18 at least bound referenced doses developed on the basis of

19 animal data.

20 And I'm encouraged by, for example, the work

21 that Dr. Hattis is doing, and also the developments in the

22 physiologically based pharmacokinetic modeling. 
Page 612

1 I wanted to add something here though. In

2 addition, though, given the potential significance of the

3 observations in the epidemiological studies, I think it's also

4 clearly desirable to consider the potential, at least

5 semi-quantitative impact of factors of study design

6 interpretation to bound the dose response relationship from the

7 human studies. And probably, there needs to be systematic

8 consideration, again, at least semi-quantitatively of the

9 potential impact on reported dose response analysis of exposure

10 measurement or outcome ascertainment in impact to confounding

11 variables and statistical analysis.

12 We heard this morning that, in fact, clearly

13 -- you know, we can't attribute the observed effects solely to

14 chlorpyrifos, but what can we do additionally to understand

15 what's the potential contribution from other pesticides? I

16 think that's really key. And that's the end of what I have to

17 say, and I'd invite my co-discussants to address the question.

18 DR. KENNETH PORTIER: Dr. Lu?

19 DR. CHENSHENG LU: So my point of view for

20 this question is that, Epi data, especially Columbia data

21 actually offer a great opportunity for the agency to derive to

22 the POD for neurodevelopmental effects in infants and to
Page 613

1 determine whether the chlorpyrifos level that they were

2 measured in the, the cord blood sample, could inhibit either

3 red blood cells or acetylcholinesterase. And I think this can

4 be easily done in either EPA laboratory or other laboratories

5 as well. And even better if EPA can use the already published

6 PBPK models specifically for cord blood. And then use those

7 levels that Columbia study measured and can extrapolate to see

8 whether there could be a range of chlorpyrifos level during the

9 pregnancy over the period of 9 months. So you actually can

10 have a range of the chlorpyrifos level in cord blood samples

11 and then see whether those levels actually inhibit or not

12 inhibit to the extent that we can measure.

13 So why this is important? I think the

14 results of the laboratory testing will answer a critical

15 question that I raised yesterday after The Dow Group, their

16 presentation in terms of whether there is an association

17 between chlorpyrifos and neurodevelopmental effect in the

18 absence of acetylcholinesterase inhibition is plausible or not

19 for humans.

20 And to answer this question, since it has

21 been raised by the Dow research team over and over again, I

22 think the easiest and most readable answer would be to look
Page 614

1 into this Columbia data.

2 So my opinion is that this is two separate

3 observations in which the cholinesterase results from acute

4 exposure scenario, whereas the neurobehavior is likely to be

5 caused by chronic low levels of chlorpyrifos exposure.

6 The Columbia study along with the other two

7 studies were all designed to examine the neurobehavior outcome

8 from in utero or chronic chlorpyrifos exposures, not just acute

9 response from the recent event. 

10 However, I think the cord blood data would be

11 ideal to test this hypothesis, whether my personal opinion is

12 valid. Thank you.

13 DR. KENNETH PORTIER: Dr. Sandy?

14 DR. MARTHA SANDY: As I indicated in the

15 previous question, the levels of chlorpyrifos measured in the

16 cord blood in the Columbia Cohort are within the range of what

17 was observed in children and the general population in NHANES

18 at the time. And we have no indication that the general

19 population was experiencing any inhibition of

20 acetylcholinesterase. 

21 So I would start with the premise that,

22 likely, these children and their mothers were not experiencing
Page 615

1 inhibition of acetylcholinesterase inhibition. They were

2 exposed to fairly low levels.

3 The additional work proposed by Dr. Lu to use

4 pharmacokinetic modeling and look at that and actually do some

5 tests would be helpful in confirming that assumption.

6 But given the severity of the effects that

7 are being observed in these three longitudinal cohorts in euro

8 exposure to chlorpyrifos, effects seen from birth throughout

9 age 7, especially the IQ deficits, I would suggest that the EPA

10 consider using -- if they must rely on, for a point of

11 departure, the rat data on inhibition of acetylcholinesterase

12 for point of departure, I would seriously recommend considering

13 retaining the 10-fold FQPA factor because of all the

14 uncertainty in vulnerable populations, other exposures that

15 have occurred in these cohorts, as well as to just the polation

16 in general that may contribute to lower levels of

17 acetylcholinesterase activity and vulnerability of the system

18 of the no developmental system to the action of chlorpyrifos.

19 DR. KENNETH PORTIER: Dr. Young?

20 DR. HEATHER YOUNG: I concur with everything

21 that's already been said. I just want to reiterate that I

22 think it's important to view that Epi data at least for some
Page 616

1 sort of bounding. I realize it's not there and it's not the

2 best data for the point of departure at this point, but at

3 least to start to us it similar to what Dr. Hattis was showing

4 yesterday and at least some of the human data is then being

5 used in the PBPK models and in doing some bounding with it.

6 DR. KENNETH PORTIER: Anyone else on the

7 panel has a comment on this question? Dr. Popendorf?

8 DR. WILLIAM POPENDORF: Just an observation,

9 following up on Dr. Lu's suggestion of looking at cord blood; I

10 was looking at the relationships between cord blood and

11 maternal blood, and there was a discrepancy that I haven't

12 really investigated the origin. 

13 Whyatt in all 2004 showed a correlation

14 regression model, basically. And if you run the median values

15 you'll see a difference of about a 50 percent difference

16 between cord and maternal. Whyatt 2005 shows mean values that

17 are within 10 percent of each other, closer to five percent,

18 almost equal. And I don't know why that discrepancy is. I

19 know within the PBPK model, they use one assumption. Other

20 people use a different assumption. So I think that particular

21 point needs to be investigated and clarified for uses between

22 material and cord, in particular.
Page 617

1 DR. KENNETH PORTIER: Anyone else? Dr.

2 Christensen?

3 DR. CAROL CHRISTENSEN: Just on that point

4 between the Whyatt 2004 paper and the 2009 paper; you meant

5 2009 or 2005?

6 DR. WILLIAM POPENDORF: 2005.

7 DR. CAROL CHRISTENSEN: So between 2004 and

8 2005?

9 DR. WILLIAM POPENDORF: Right, two

10 publications. It could be 9 but I wrote 4 on my note.

11 DR. CAROL CHRISTENSEN: Okay. I will look

12 into that. I was just going to note they are slightly

13 different subsets of the population in that validation work

14 performed in Whyatt 2009 than that was performed in the earlier

15 paper, which could result in that discrepancy.

16 DR. WILLIAM POPENDORF: Right. Which could

17 explain the difference. But then you have a question of why is

18 there a difference and how are you going to use those two for

19 future extrapolations. 

20 DR. CAROL CHRISTENSEN: Okay. We'll note

21 that.

22 DR. KENNETH PORTIER: Dr. Lowit, Dr.
Page 618

1 Christensen, I heard a number of approaches for analysis and

2 suggestions for initial analysis to this question, so I think

3 it's adequately answered. Your comments?

4 DR. ANNA LOWIT: No. I think that, for the

5 most part it's very clear and largely inline of what I think we

6 were thinking. 

7 I'd just like to comment on -- if we can keep

8 the conversation about the strengths and the weaknesses of the

9 science and where the uncertainties are and where there is

10 ability to quantify those uncertainties and where they are

11 largely qualitative, and using bounding kind of things to get a

12 handle on the magnitude of that, that is largely the most

13 useful thing that we can do, that you can do to help us take

14 the next steps in our analysis of putting all the pieces

15 together.

16 Because, if you remember, as Dr. Popendorf

17 pointed out yesterday, you have actually only seen a slice of

18 what is a very complicated, very large risk assessment that

19 includes exposure assessment for multiple pathways in different

20 groups of populations throughout the country.

21 So as we get further into these media

22 questions about uncertainty, if we can focus on the science and
Page 619

1 less so on the policy, that's the most helpful thing for us.

2 DR. KENNETH PORTIER: So what I'll do is ask

3 Dr. Meek and Dr. Sandy to kind of make sure that A and B are

4 separated, because A deals with the uncertainties and the

5 scientific evidence, and B kind of addresses the additional

6 analyses and moving forward kind of things. So we'll just kind

7 of try to make sure in our report that we have those as well

8 separated as we can get them. Okay? Any additional comments

9 on all of question 4? Dr. Lu?

10 DR. CHENSHENG LU: Well I understood what the

11 agency mentioned about what Dr. Lowit just talked about. But

12 my trouble is that, even as of today, there's very few new data

13 in this field. And for the new data that we discussed

14 yesterday in terms of neurobehavior in Rodan (phonetic) study,

15 actually added a vision of uncertainty to this overall

16 discussion. So I don't know how we are going to improve this

17 subject without better data, and we cannot just look at those

18 data over and over again hoping that there is going to be some

19 new insight coming out from the same dataset. Again, the new

20 insight doesn't help because the animal testing for the

21 neurobehavior. So I think this is something very fundamental

22 for the agency to think about it. That's how I would look at
Page 620

1 it.

2 DR. KENNETH PORTIER: And I think, in

3 question 4.1 there's a lot of opportunity to talk about data

4 needs because there is strengths and limitations discussed

5 there. And my notes indicate there are a number of places

6 where we indicated some data needs that would help clarify. 

7 Because you asked the issue of clarification and there are a

8 lot of places where it's not very clear, and additional data,

9 additional analysis will pay off on that. Dr. Lowit?

10 ANNA LOWIT: And, yeah, the chlorpyrifos is

11 certainly one of those chemicals where the -- I'm sure their

12 publications have come out in the last week since we've been

13 sitting here that the science is constantly evolving and very

14 rapidly. I think at this point people are really getting

15 smarter and smarter doing these things.

16 So to the extent that we -- there is a forum

17 to encourage people to be moving in certain directions, I would

18 concur with that, as long as there is a balance between that

19 and understanding that we've got some very difficult decisions

20 to make and real time for which a lot of new data is not inform

21 too much. So if you can just balance those two needs.

22 DR. KENNETH PORTIER: Dr. Sandy?
Page 621

1 DR. MARTHA SANDY: Just a follow-up on that. 

2 It seems that we are spending a lot of time trying to figure

3 out, based on just a few studies that measured

4 neurodevelopmental effects and acetylcholinesterase one or two

5 or three, it would be great if EPA can encourage somehow or

6 fund or conduct themselves studies on key observed effects in

7 whatever system you need, and at the same time, do a nice dose

8 response and look at acetylcholinesterase inhibition. That

9 would probably not take more than six months to actually do the

10 work, and that would greatly improve the database, I believe. 

11 This is for the animal in vivo studies.

12 DR. KENNETH PORTIER: I'd like to see that in

13 4.1. Okay. Any additional comments? That was a good

14 discussion, I thought. I'd like to move on with question 5.1

15 and begin the discussion on exposure profile and biomonitoring

16 research with the recognition that we will try to break around

17 noon or 12:15, so we have about an hour to begin that

18 discussion with 5.1.

19 LIEUTENANT AARON NIMAN: Section 5 of the

20 draft issue paper presents an overview of the principle

21 chlorpyrifos biomarkers and the comparison of biomonitoring

22 studies that measured urinary TCPy levels in a range of study
Page 622

1 populations involving both the general population and

2 potentially vulnerable populations, including children, workers

3 and farm families.

4 Please comment on the degree to which the

5 agency identified the primary chlorpyrifos biomarkers of

6 exposure. Appropriately discuss the strengths and limitations

7 of such biomarkers and how the strengths and limitations effect

8 the interpretation of chlorpyrifos biomonitoring data. 

9 DR. KENNETH PORTIER: Thank you. And Dr.

10 Buckley tells me that this group has been working very hard, so

11 I expect a nice coherent report at this point. 

12 DR. BRIAN BUCKLEY: You are going to

13 definitely get a report. The coherency is in the eye of the

14 beholder. I will tell you that what I have is both a

15 distillation of the comments that we share with one another as

16 well as some individual comments that were forwarded to me

17 directly. So with that in mind, I'm going to read the comments

18 that I have so far.

19 Considering the availability of standard

20 methods, the specificity of the biomarker, the number of

21 laboratories capable of making the measurement in the relative

22 concentration levels, the first choice for a biomarker would be
Page 623

1 chlorpyrifos in blood, followed by TCPy in urine and then DETP,

2 DEP in urine. The total DAPs as DMP and DEP are not selective

3 enough to be a true biomarker for chlorpyrifos, although it may

4 be more appropriate in a risk consideration or calculation

5 model because all of the acetylcholinesterase inhibiting

6 chemical should be considered together when evaluating risk. 

7

8 The panel recognizes the inability of urinary

9 TCPy to distinguish between exposures of chlorpyrifos,

10 chlorpyrifos-methyl and trichlopyr as well as direct exposure

11 to TCPy as a chlorpyrifos degradate. It is more selective,

12 however, than the DAPs and currently is the most selected for

13 urinary metabolites, but questions remain.

14 There should be more emphasis placed on the

15 direct intake of the environmental degradate TCPy mainly

16 present in foods. 

17 As early as the late 1990s, the Ryan Group

18 (phonetic) had identified an anomaly in the amount of TCPy

19 found in urine was substantially greater than the measured

20 intake of chlorpyrifos. This work has continued with papers by

21 Morgan et al, Wilson et al, Lu et al, indicating the presence

22 of degradates in foods. 
Page 624

1 Ryan et al have continued this work with a

2 recent PhD student focusing on the kinetics of this process. 

3 While this work has not yet been published, and the other works

4 are mentioned, insufficient emphasis has been placed on

5 observation, putting into question the utility of urinary TCPy

6 as a useful measure of exposure to the parent compound.

7 The panel also recognizes the ability to

8 measure acetylcholinesterase and butyrylcholinesterase and

9 their inhibition as biomarkers of exposure, but they are even

10 less specific than the DAPs. They are, however, more

11 indicative of potential health risk and rather, are more than

12 just a biomarker of exposure.

13 In addition, the ability to measure the

14 anilities is likely to vary widely from laboratory to

15 laboratory and they are difficult to calibrate, as has already

16 been noted by Dr. Popendorf. I just want a point of my own

17 personal comment. 

18 While these measurements are key potentially

19 within the biomarker -- not necessarily the biomarker, but the

20 folks doing the research on the in vivo and in vitro studies,

21 their ability to be compared from laboratory raises some

22 doubts, especially for very small changes because of the
Page 625

1 difficulty in calibration. So when we consider

2 acetylcholinesterase and butyrylcholinesterase inhibition and

3 you're trying to compare a study to study, there is a lot of

4 uncertainty, I think, that's included in that comparison. And

5 they should probably be evaluated for changes after an exposure

6 more within a laboratory than across laboratories or from study

7 to study.

8 Full consideration of conjunctive metabolism

9 will assist in correct biomarker use, including interpretation

10 of biomonitoring data and appropriate dissymmetry to with,

11 despite the acetylcholinesterase adducting axon, won't be

12 conjugated if glucuronidation or sulfonation are rate limiting

13 or ontogenetically deficient. 

14 And other metabolites may accumulate due to

15 the inadequacy of the metabolism. This includes the potential

16 for error in biomarker analysis and may cause error in

17 dissymmetry, estimations or recalculations due to overlarge

18 estimations of exposure in the fetus or child.

19 In the future, more selective biomarkers are

20 expected, including direct measurement of the oxon in blood, as

21 well as adducts in both blood and urine. These adducts include

22 proteins, glucuronides, et cetera. 
Page 626

1 While the oxon is very short-lived, we

2 believe it is possible to measure it in the blood and

3 represents the true species that presents risk. It is also

4 exclusive to exposure to chlorpyrifos as opposed to the

5 chlorpyrifos degradate TCPy. 

6 When evaluating any of the other biomarkers

7 in blood, the EPA will need to consider that some of these

8 biomarkers will differ in concentrations between cord blood and

9 material blood as they have different lipophilicity.

10 There also needs to be more emphasis placed

11 on urinary metabolites, glucuronides and sulfates. And in

12 discussions with Dr. Collier, we both agree there has not been

13 significant effort to look at either of the glucuronide or

14 sulfate metabolites. And I believe the primary reason is that

15 these metabolites have only recently been evaluated both from a

16 physiological and analytical perspective, which, from the

17 analytical perspective, they just haven't been looked at until

18 recently when we've had to do it in some of the other

19 compounds.

20 Measuring multiple metabolites simultaneously

21 and then taking ratios of metabolites, such as TCPy to DETP

22 should provide greater discrimination between exposure to
Page 627

1 chlorpyrifos rather than its degradate product. It may even be

2 possible to do a source of apportionment and separate degradate

3 exposure from exposure to the active.

4 Okay. Other considerations. In the

5 presentation, the agency suggested that meconium could be used

6 as a biomarker for fetal chlorpyrifos exposure throughout

7 pregnancy. This is not suggested to be a good idea for the

8 following reasons: 1) no studies in chlorpyrifos and amniotic

9 fluid have been performed; 2) the diffusion and/or transport of

10 chlorpyrifos across the placenta in either direction is

11 unknown. But since its rather fat soluble, equilibrium within

12 the maternal serum might be postulated. 

13 This does not seem to be the case with at

14 least one umbilical maternal serum study presented at this

15 meeting, which is beyond 2010; 3) metabolism across the

16 placenta is unknown; 4) the contribution of umbilical tissue,

17 including any adducting of cord tissue, for example, by the

18 oxon is likewise unknown. Fetal and maternal blood flow,

19 umbilical cord tissue consists of a polymatrix, which is made

20 up of uco-polysaccharides. It contains one vein to carry

21 oxygenated blood and nutrients to the fetus and two arteries

22 carrying the oxygenated blood and fetal waste back to the
Page 628

1 placenta. These vessels are lined with endothelia. 

2 Occasionally, only two vessels, one vein and

3 one artery are present in the umbilical cord, but this is not

4 necessarily related to fetal defects or complications of

5 pregnancy. 

6 Based on these characteristics and the very

7 fat soluble natural chlorpyrifos, and it is relatively fat

8 soluble metabolites, the umbilical cord would not be expected

9 to function as a good reservoir of these. However, since it is

10 so well profused, umbilical tissue may be expected to be a

11 target for oxon binding.

12 5) Meconium being comprised mostly of

13 intestinal epithelia, lanugo, mucus, amniotic fluid, biowater

14 is reasonably hydrophilic and thus should be also considered a

15 poor matrix or reservoir for chlorpyrifos and other fat soluble

16 xenobiotics to accumulate.

17 It may be marginally better for the

18 metabolites, TCPy and DETP, but these molecules would also have

19 some net flow in the power cellular pathway. 

20 6) While placental studies are undetermined

21 which is the limitation of data available to the agency,

22 several important points in this respect are already known from
Page 629

1 elicit drug literature and should be considered in light of

2 attempting the same type of monitoring for chlorpyrifos or

3 other antibiotics. 

4 For example, antipuradine (phonetic), an

5 amphetamine derivative, is used in placental profusion

6 experiments as a marker of pure diffusive transport with

7 affectively no barrier. In contrast, coke, cocaine and

8 cotinine showing differing slightly less fat soluble profiles. 

9 For example, cocaine is transferred across the placental at

10 only 80 percent the rate of antipurine. And some studies have

11 suggested that the placenta acts as a depo (phonetic) for

12 cocaine accumulation preventing transfer to the fetus.

13 Additionally, previous studies have indicated

14 that while nicotine, again highly fat soluble, is transferred

15 into the fetal compartment up to five times the concentration

16 in maternal blood, cotinine concentrations in the fetal

17 compartment were considerably lower than corresponding maternal

18 serum levels.

19 Again, it has been suggested that cotinine

20 adducts, the placenta, preventing equilibrium of the

21 concentrations between the maternal and fetal systems. These

22 systems support the need for greater consideration of the
Page 630

1 transplacental characteristics of chlorpyrifos. And since

2 placental characteristics change drastically by term, leaky

3 after about 36 weeks, placental studies need to consider each

4 trimester in one trimester after eight weeks when placenta is

5 profused, since prior to eight weeks, only active transported

6 diffusion of the placenta can occur due to villi (phonetic)

7 being blocked.

8 Such studies would be ideally recommended

9 before extrapolating fetal exposure and may be included as part

10 of the longitudinal study of pregnancy. And we all agree that

11 longitudinal studies of pregnancy are key in terms of

12 determining what the fetal exposure is going to be overall. 

13 That's something that was discussed at great detail and

14 represents a significant data gap.

15 These points highlight the uncertainty of

16 using meconium as a measure of exposure over the course of

17 pregnancy at this time. And that's it.

18 DR. KENNETH PORTIER: Thank you. Dr.

19 Collier?

20 DR. ABBY COLLIER: I'd like to thank Dr.

21 Buckley for incorporating on my comments with those at the risk

22 of the panel, and I have nothing further to add.
Page 631

1 DR. KENNETH PORTIER: Dr. Lu?

2 DR. CHENSHENG LU: To be consistent, I got

3 the last draft on Dr. Buckley at 9:45 p.m. last night. I have

4 nothing to add.

5 DR. KENNETH PORTIER: Dr. Popendorf?

6 DR. WILLIAM POPENDORF: Well I agree in a

7 broad sense with that, but I will provide some comments with

8 regard to the two middle priorities of the urinary biomarkers. 

9 Whether TCPy is better than DETP, there's pluses and minuses

10 for both of them, and I think there's logic to put the sequence

11 as he did. 

12 TCPy, probably it's biggest strength is that

13 it has a lot of historical value. It's used in other studies

14 so much. But I think the other data that was talked about

15 through the actual amounts of TCPy in the environment, the

16 potential for direct dosing with TCPy is actually

17 quantitatively a lot bigger than the errors or uncertainties

18 associated with DEP. So, if I had the choice, I would put DEP

19 as the second and TCPy as the third. But I think there's no

20 question that chlorpyrifos in blood is number and DAP is number

21 4.

22 DR. KENNETH PORTIER: Thank you. Hopefully
Page 632

1 we can kind of capture that in the write-up. Dr. Ryan Barry?

2 DR. BARRY RYAN: I'd just like to add to my

3 support to what Dr. Popendorf just said that, you know, the

4 ordering of TCPy versus the explicit DAPs can be problematic. 

5 You know. Clearly chlorpyrifos in serum is the way to go. And

6 after that it's a little bit troublesome on what to pick next. 

7 I mean TCPy had some other problems associated with it. The

8 dietary intake is one that we mentioned in this. So what the

9 ordering is, I don't think is all that critical. We got the

10 first one; we got the last one. The two guys in the middle, we

11 are not quite so sure about. 

12 There was one other point that I wanted to

13 make that I got sidetracked on. Oh, yeah, the longitudinal

14 studies. I just want to reiterate and add some extra emphasis

15 to the need for these longitudinal investigations during the

16 pregnancy itself.

17 What we are seeing really is 1 point, maybe 2

18 points over the course of pregnancy where a measurement of some

19 kind is made, whether it's cord blood, whether it's maternal,

20 whether it's urine, whatever it might be. And of course, we

21 are well aware that chlorpyrifos, in particular, and OPs in

22 general have a biological half-life that's measured in days,
Page 633

1 not months or years. So if we just happen to hit it, great, we

2 did a good job; we got what we wanted. But there's really no

3 way of knowing if we've gotten that unless we go through and do

4 a longitudinal investigation.

5 And I challenge the women around here to tell

6 me whether it's likely that we could get women to participate

7 throughout their entire pregnancy and give us urine samples and

8 blood samples, say on a two-day cycle or something like that,

9 which is really kind of what we need. Now, more realistically,

10 if we could look at something on the order of a week perhaps

11 would be much more likely to hit any exposures that might be

12 there.

13 It could very well be that we measure a cord

14 blood level or meconium level -- notwithstanding what Dr.

15 Collier is pointing out to us -- or whatever that indicates

16 relatively low levels of exposure, but these may very well be

17 intricative over the entire pregnancy. While one large hit at

18 one time versus a very low level throughout the entire time

19 period could give an entirely different effect, especially if

20 it were at some critical point during the neurodevelopmental

21 progress of a fetus. So I just wanted to add my extra impetus

22 on the longitudinal investigation type idea. That concludes my
Page 634

1 remarks.

2 DR. KENNETH PORTIER: Dr. Collier?

3 DR. ABBY COLLIER: In response to Dr. Ryan,

4 I'm not speaking for the women on the panel. I am speaking as

5 an investigator that works in pregnancy as well as pediatrics. 

6

7 Yes, it is possible to run trials where women

8 are not going to stay on a two-day cycle. We'll give you blood

9 and urine but we'll give you specifically urine across the

10 course of the pregnancy. However, we tend to think about this

11 as an individual-based kind of thing, and it doesn't have to be

12 that way.

13 If you want to think about using like a

14 pharmacokinetic reference point, there's two stage, classical

15 pharmacokinetics where you take medical samples from one

16 person. But instead, we use a population-based study. So if

17 we can collect amniotic fluid and fetal hair and placenta all

18 the way across gestation from women who deliver early or from

19 women who -- all the way across gestation all the way up to

20 term. Then we can develop a population profile within the

21 population we are studying. And those are very, very feasible

22 studies. In fact, I know of a couple that are going on right
Page 635

1 now in Hawaii in our OBGYN department.

2 So one of the things that I see with using

3 the epidemiology studies as a primary reference point is that

4 Epi studies are fantastic and they tell us a lot about our

5 population. And when we start to talk about some of the more

6 basic science mechanisms we need to use a more basic science

7 approach, like a longitudinal study that's repeatedly sampled. 

8

9 And people get stuck on the idea we

10 repeatedly sample a single person. But if we take a

11 population-based approach it can still be reasonably accurate

12 within what its designed to do. Thank you.

13 DR. KENNETH PORTIER: Dr. Buckley?

14 DR. BRIAN BUCKLEY: Yeah. I just want to

15 respond to Dr. Ryan's comments, and actually Dr. Popendorf's

16 too. I completely agree at this stage of the game that the

17 difference between chlorpyrifos in blood as a biomarker and any

18 of the other urinary biomarkers is a huge, huge difference.

19 I would, however, like to re-emphasize a

20 point that I made just in a glancing fashion. I do think

21 there's potential when you start looking at the ratios of some

22 of these guys, DETP to TCPy, and see if we can get some sort of
Page 636

1 relationship representing a true exposure to chlorpyrifos

2 versus a secondary exposure to TCPy. Because if you're just

3 exposed to TCPy you shouldn't produce DETP. Otherwise, you

4 should have about, essentially, a one to one ratio. And if we

5 can capture both of those, we have the potential, all of a

6 sudden, with things we are already measuring to now do a much

7 better job at differentiating exposure to TCPy versus

8 chlorpyrifos directly itself. So I think there's some promise

9 there.

10 I don't think there's been any studies done

11 it yet. To be honest and reflective of something else, I don't

12 know how much future studies there will be done on it. I don't

13 see this as a hot area at this point in time, and development

14 of new biomarkers may be secondary.

15 Hopefully, there are enough studies out there

16 that this is one of the things that they could tack on without

17 too much trouble. And we'll talk about some of those other

18 studies when we talk about 5.1(b).

19 DR. KENNETH PORTIER: Dr. Ryan?

20 DR. BARRY RYAN: I would just like to add my

21 support to what you've just said, Dr. Buckley, regarding

22 getting both of those at the same time. I think the sum of
Page 637

1 those two together is far superior to individual things. We

2 would get information about how much TCPy is coming into the

3 body as a degradator in the environment from some other way by

4 looking at exactly that, and I think that's an excellent

5 suggestion you've made.

6 DR. KENNETH PORTIER: One of the questions

7 asked was commenting on the Whyatt paper on the agency's

8 discussion on these biomarkers, and I'm not quite sure I heard

9 the panel's assessment of that. Was that job done well? I

10 mean, I heard a lot of things you suggested but I didn't know

11 if that was critical or supportive, and I need something a

12 little more direct there.

13 DR. BRIAN BUCKLEY: I think that the white

14 paper did a good job describing the principle biomarkers out

15 there and their limitations. I absolutely agree with that. I

16 don't think they placed enough emphasis on looking toward blood

17 measurements of chlorpyrifos. I think that that's something

18 that we as discussants emphasize more greatly and I understand

19 the reason. There weren't so many labs that could do that

20 measure versus the number that could do the measurement of

21 TCPy, so most of what they had to work with in terms of the

22 agency has been based on TCPy measurements and urine as the
Page 638

1 principle urinary metabolite.

2 And because most of their discussion focused

3 on existing papers and the weight of what was out there, I

4 think they did a reasonable job at covering the bulk of the

5 literature. But it's the use of the one or two papers where

6 they're starting to measure chlorpyrifos in blood should get

7 more emphasis in terms of a true biomarker of exposure.

8 And while they discussed the confounders of

9 the degradate TCBy in the environment, perhaps more focus

10 should've been paid upon that as a serious confounder as

11 opposed to just a recognition that it was one.

12 DR. KENNETH PORTIER: Kind of like to see

13 that assessment in there as well. Dr. Gold?

14 DR. ELLEN GOLD: I'd like to go back to the

15 point about the longitudinal studies during pregnancy, if I

16 could for a moment. There already are existing studies. The

17 National Children's Study, for example, is recruiting women

18 before they become pregnant and following them with a number of

19 biomarkers and certainly collecting samples and

20 neurodevelopmental measures of outcome as well. 

21 And we too have studies ongoing in our

22 department for women who become pregnant and have had a prior
Page 639

1 child, say, with autism. And they've got three different

2 groups. They're not just the high-risk women, but other women

3 to compare them to, and doing serial measures through pregnancy

4 with neurodevelopmental outcome measures.

5 And so, tapping into some of those existing

6 data sources, where maybe all that we needed would be measuring

7 the biomarkers that you're interested in because you probably

8 have a lot of the other data that you would need. So I would

9 encourage the agency perhaps to look into those possibilities

10 because, I think without too much more work, you could answer

11 some other questions you're interested in. Again, I'm not

12 speaking for women at the table though.

13 DR. KENNETH PORTIER: Dr. Vena, do you want

14 to speak for women at the table?

15 DR. JOHN VENA: No. Just to clarify, Brian

16 what you were saying in the last comment about TCPy being a

17 confounder of the measurement of exposure, but I don't see it

18 as that. I see it as a complimentary biomarker of the exposure

19 to the parent compound, so I don't see it as a confounder but

20 as an important exposure measure. Because, what I'm trying to

21 grasp is the metabolism of the parent compound, how quickly it

22 reacts -- you know, we've talked about those flowcharts and
Page 640

1 where it goes. 

2 So when you go to measure these point

3 measurements, it's a snapshot of what's there, so the exposure

4 was there. It's already metabolized. So TCPy gives us a

5 measure of the -- so it seems to me that you have to have both,

6 that you have to have a parent compound and you have to have

7 metabolite, because at one point in time, the parent compound

8 is already metabolized and it's gone to the next phase. So I

9 don't see it as a confounder. It's part of the measure of the

10 total exposure pathway, it seems to me.

11 DR. BRIAN BUCKLEY: The problem is, though,

12 that TCPy is generated in the environment before the ingestion

13 process. So the food that you're eating may contain

14 chlorpyrifos. It may also contain TCPy directly. There's no

15 metabolism done at that point in time. The oxon is not formed

16 in the body. Potentially there's no damage done with the TCPy

17 ingestion at that point in time.

18 So, while you're measuring a total

19 concentration of TCPy, it's two components or that which have

20 done damage or potentially done damage through the oxon

21 metabolism pathway, and that which is degraded in the

22 environment before it's been ingested or inhaled or any other
Page 641

1 exposure.

2 DR. JOHN VENA: The question I have though is

3 that -- and the reliance on the parent compound and the oxon is

4 that that's what causes the acetylcholinesterase inhibition. 

5 But now, what we are talking about is not

6 just acetylcholinesterase inhibition, we are talking about

7 potentially other effects related to whatever exposure

8 parameters are related to the parent compound. 

9 So, I guess my question is, is the metabolite

10 -- if we are thinking about the mode of action and all these

11 other effects that aren't related to the acetylcholinesterase

12 inhibition -- is it potentially related to the TCPy?

13 DR. BRIAN BUCKLEY: Right. But the TCPy

14 doesn't represent the parent compound. Now is TCPy in and of

15 itself a potential toxicant, for lack of a better term -- and I

16 don't know that anyone studied that, per se. With all the

17 literature out there, there's got to be some -- someone has

18 looked at TCPy as opposed to just the chlorpyrifos.

19 But because the exposure is not to the parent

20 but to the TCPy directly, unless there's a link between TCPy

21 and the toxicological endpoints that we've discussed, it's

22 exposure doesn't make up part of the risk characterization. I
Page 642

1 wouldn't think so anyway.

2 DR. JOHN VENA: So the proportion of TCPy in

3 urine -- what proportion would be related to chlorpyrifos

4 related to the other? I guess that's the big question. Seems

5 to me that the majority of it would be related to the parent

6 chlorpyrifos versus these other pesticides. So the key is, if

7 you do have measurements, like what a couple of folks have

8 said, if you do have measurements of everything at the same

9 time, then which I think the Columbia study does is, maybe

10 you'll have a sense of how much of that TCPy is in measure and

11 how sensitive it is to give you a measure of the exposure of

12 the parent compound and as metabolites. That's just my

13 question. I'm not an exposure assessment person but I'm just

14 trying to get my head around this though.

15 DR. BRIAN BUCKLEY: I'm going to turn that

16 question to Dr. Ryan and Dr. Lu, who have actually done these

17 studies and can tell you with greater certainty.

18 

19 DR. KENNETH PORTIER: Yeah. I saw Dr. Ryan

20 and Dr. Popendorf kind of shaking their heads and -- "No, no,

21 wait --." So one of you guys have to jump in here. Dr. Ryan?

22 DR. BARRY RYAN: I'll jump off first. The
Page 643

1 paper that we did in the late 1990s suggested, as much as 90

2 percent of the TCPy that was excreted in urine came from

3 someplace else. That is, if we took into account all of the

4 chlorpyrifos that went into someone's body and we only came up

5 with 10 percent of what came out the back end, I think that's a

6 very, very high estimate.

7 On the other hand, if it's even a third or

8 equal amounts, that would be a significant problem. Those are

9 the type of numbers I've got. But Dr. Lu has done some work in

10 this area. Perhaps he has more recent data. I am currently

11 doing some work in this area but I don't have the data analyzed

12 at this point, so I can't give the numbers for that.

13 DR. KENNETH PORTIER: Dr. Lu?

14 DR. CHENSHENG LU: I was afraid to be

15 considered as kind of self-promoting our paper, but since

16 everybody point to me so I can talk about this. So we did

17 identify this problem way back in early 2000. 

18 So we did a study in the laboratory in terms

19 of buying a couple of juices from the supermarket and we take

20 the sample to analyze the so-called environmental degradation

21 portion of the metabolite in the juice. And then we also

22 fortified the juice with known amount of chlorpyrifos and we
Page 644

1 left the juice in the refrigerator for three days and five

2 days, and then we take juice sample and sent it to the same

3 laboratory for analysis of the degradation, the additional

4 added to it.

5 So anyway, in brief, we found that no matter

6 how long you leave the fortified juice sample in the

7 refrigerator, you always get 30 to 35 percent degradation. 

8 It's not a hundred percent. It's not one percent. It's always

9 30, 35 percent degradation. Meaning that there could be just

10 30, 35 percent of TCPy in the environment that actually

11 degraded itself. And the rest of the 65 still remain as a

12 parent compound.

13 So how significant that is? We use a PBPK

14 model, basically it's on a thing called ERDEM, which was peer

15 reviewed all through the SAPs many years ago for carbaryl for

16 the chlorpyrifos. And as you know that chlorpyrifos has

17 abundant data that we can create some kind of PBPK model with

18 great confidence.

19 So we used a another study which is designed

20 to look into the multi-exposure pathway and try to compare the

21 estimated TCPy level to the TCPy that we physically measured in

22 the urine that collect from the same kids in the study. Guess
Page 645

1 what? The PBPK output, in terms of estimated TCPy, also 35

2 percent higher than we physically measured in the urine sample.

3

4 So I thought that we answered the question in

5 terms of what is the degree of the environmental TCPy and how

6 was that attributed to the dose estimate. So we quickly wrote

7 a paper and published in 2010.

8 DR. KENNETH PORTIER: Okay. I guess that

9 answers that question. Any additional issues? Dr. Pope?

10 DR. CAREY POPE: Dr. Lu, that last comment

11 you made, or set of comments, I was confused whether you were

12 talking about the amount of degradation that you were measuring

13 or the amount of degradate that you were measuring?

14 DR. CHENSHENG LU: I thought that's the same

15 thing.

16 DR. CAREY POPE: You were talking about

17 putting chlorpyrifos in juice and waiting various times and

18 measuring. Were you measuring the breakdown chlorpyrifos or

19 attempting to into TCPy under those conditions, or just kind of

20 like what is the level of the degradate in the juice?

21 DR. CHENSHENG LU: Oh, we measured total

22 amount of TCPy in the juice.
Page 646

1 DR. CAREY POPE: Uh-huh. Were you expecting

2 to see that chlorpyrifos was producing TCPy during those times?

3 DR. CHENSHENG LU: Well that's what we

4 suspect. The exposure data suggest that there is a known

5 amount of TCPy is degradated itself in the environment or in

6 food, and that's why we went to investigate that magnitude.

7 DR. KENNETH PORTIER: Dr. Ryan?

8 DR. BARRY RYAN: Just to address that same

9 issue. I have a doctorial student who just finished up and her

10 work was to look at various different types of foods and look

11 at both at the same time.

12 We spiked chlorpyrifos and a number of other

13 things into the food, measured their degradation of the

14 chlorpyrifos and the simultaneous increase in TCPy. So we are

15 actually getting some of the kinetics back out, and our studies

16 were done over a period of about two weeks in relatively simple

17 matrices ranging from water up to orange juice, was probably

18 the most complex matrix we have.

19 We are also looking at data in other types of

20 food, looking at the degradate versus a parent compound for a

21 series of these things. Those data, you know, they're not

22 published at this point. She's finished her doctorate so
Page 647

1 she'll write the papers over the next couple of months and

2 we'll submit them, but they're not available at this point.

3 DR. CAREY POPE: It's kind of cyclical to me

4 thinking like that, but yesterday someone presented some data

5 about TCP being used in the field as opposed to the relative

6 amounts of chlorpyrifos. Yes. And it looked like really good

7 data, I mean, down to the pounds in a particular -- was at

8 Selena Valley (phonetic) or -- that's very informative data. 

9 And the information I was commenting on was

10 from an EPA publication 2011 that is more general and less down

11 to the pounds. But it talks about triclopyr relative to

12 chlorpyrifos. This is the group work of 2011. 

13 And they talk about roughly 17 to 44 percent

14 on the nation basis of relative amounts of triclopyr compared

15 to chlorpyrifos. So while it looks like it's predominantly

16 chlorpyrifos in that dataset, around the country it looks like

17 it's much more chlorpyr relative to chlorpyrifos being used. 

18 So this doesn't address degradation in food

19 products, but just the application of triclopyr nationwide and

20 other places besides Selena Valley. 

21 DR. KENNETH PORTIER: Dr. Lu?

22 DR. CHENSHENG LU: Since we are on this
Page 648

1 subject, although it kind of, somewhat, deviates what we are

2 supposed to discuss, but I just want to emphasize or alert the

3 panel, is that, even though the presence of environmental

4 degradate TCPy or DEPs has been talked about for a long time,

5 but we don't really have a systematic way to investigate it.

6 And I have to say this; there is a great

7 possibility -- I'm not discounting other investigator's papers

8 that's published, but I have to alert that there is a great

9 possibility -- when you are using the same method aiming to

10 analyze either chlorpyrifos or other organophosphate there is a

11 chance that an analytical method itself could degrade the

12 parent compound to either TCPy or DEP. So that's why there is

13 some discrepancy reported in the literature in terms of one

14 paper published that is 95 percent of the degradation product

15 in the samples. But the questions is that they never reported

16 whether the analytical method itself degrade the parent

17 compound in the process of laboratory analysis.

18 So in our juice study, we actually spent

19 almost a month to develop a method specifically for that

20 purpose. And we have a great confidence that the method itself

21 will not degrade parent compound to TCPy in the laboratory. So

22 I think, again, there is a void in the science in terms of
Page 649

1 whether the data that's reported are true or valid.

2 DR. KENNETH PORTIER: We are handing out the

3 slides that Dr. Dawson presented yesterday and I think that

4 includes the data. Dr. Buckley?

5 DR. BRIAN BUCKLEY: Just to address Dr. Lu's

6 last comment. One would hope that the analytical laboratory

7 would do the necessary spikes to ensure that they don't get

8 compound degradation during sample preparation. There are ways

9 to safeguard, but all labs don't do that. You are absolutely

10 right. And that safeguard should be in place in any analysis

11 to make sure that the degradation product is not the one that's

12 actually measured or created, more importantly, during the

13 sample preparation process.

14 DR. KENNETH PORTIER: Dr. Sandy?

15 DR. MARTHA SANDY: Can I ask a question since

16 we are on to degradation? Is it anything known about the

17 stability of DEP in samples?

18 DR. KENNETH PORTIER: Who's going to jump in

19 on this one? Dr. Buckley?

20 DR. BRIAN BUCKLEY: I'll offer and say that,

21 all of these compounds have sort of -- I hate to call it this,

22 but sort of platforms stability. A lot has to do with how the
Page 650

1 samples are maintained throughout the process. 

2 Photo degradates are always possible. 

3 Thermal degradates are always possible. DETP/DEP are sort of

4 that next plane of stability that we think, as long as the

5 samples are reasonably well-preserved you're not going to have

6 an issue in terms of degradation.

7 Having said that, I can make almost anything

8 degrade just by treating it improperly, and you never know. 

9 Dr. Popendorf, I think, is probably going to offer comments.

10 DR. WILLIAM POPENDORF: It was kind of an

11 elaboration on the question because I think you were referring

12 to -- I know my interest was just -- once it's formed in the

13 environment, particularly if it's on surfaces and such, how

14 stable would it be there? Not in a sample but just in the

15 environment, home environment, workplace environment, possibly

16 even in an agricultural environment. 

17 DR. BRIAN BUCKLEY: Are we talking about TCPy

18 or DETP?

19 DR. WILLIAM POPENDORF: It could be both. 

20 But I guess TCPy in particular, yeah.

21 DR. BRIAN BUCKLEY: I think TCPy is pretty

22 stable in the environment relative to other environmental --
Page 651

1 it's more stable obviously than chlorpyrifos.

2 DR. KENNETH PORTIER: Than DEP?

3 DR. BRIAN BUCKLEY: DETP, I'm not aware of

4 great environmental concentrations of this stuff. It's not the

5 degradate product, as I understand it. It's a metabolite,

6 which is a different process.

7 DR. KENNETH PORTIER: Dr. Sandy?

8 DR. MARTHA SANDY: My question was

9 specifically stability of DEP in urine.

10 DR. BRIAN BUCKLEY: It's one of the stable

11 metabolites that we monitor pretty routinely. We try and

12 preserve all samples because they, like everything else, things

13 do degrade. But it is one of the more stable metabolites that

14 we measure in urine.

15 DR. KENNETH PORTIER: Dr. Lu?

16 DR. CHENSHENG LU: I think one of the three

17 cohort studies actually attempt to measure the stability of

18 DEPs in their urine sample. And either they publish in the

19 paper or make a presentation. I will not come up just without

20 any evidence. I would look for it.

21 But, again, going back to the degradation

22 issue; I'm afraid that this magnitude of environmental
Page 652

1 degradation of organophosphate pesticide has been blown out of

2 proportion.

3 Remember, the chemical will not degrade by

4 themselves spontaneously. It has to be either through

5 enzymatic metabolites or through hydrolysis in this case. It

6 has to be one or both. It's almost impossible, say, you put a

7 chlorpyrifos in the air and then you would just degrade it by

8 itself. If that's the case, then what's the use of this

9 chlorpyrifos for insect control? 

10 Again, that's what I'm saying. I have great

11 doubt in seeing degradation in the air sample. Because if

12 that's the case, then why we use chlorpyrifos to kill mosquitos

13 and so on. 

14 Again, I'm not saying that our paper is a

15 bible in this subject. But I think that magnitude, in terms of

16 how much organophosphate can be degraded in the environment has

17 been brought out of proportion.

18 DR. KENNETH PORTIER: Dr. Buckley.

19 DR. BRIAN BUCKLEY: To follow-up with Dr. Lu

20 and place emphasis again, I agree. And we haven't looked at

21 food products as much as anything else. But, you know, if it's

22 35 percent we can count for, great. But when we did work back
Page 653

1 in the 90s on application of the dorms, we were measuring

2 chlorpyrifos readmission weeks after application, so it had not

3 degraded to an extent that it was beyond measurement. In fact,

4 it was quite stable and could be measured. So post

5 application, it is stable for weeks. 

6 Having said that, you know, I don't know what

7 the relative contributions of dose are, although it sounds like

8 35 percent, roughly, is what you might get from an ingestion.

9 DR. KENNETH PORTIER: And I think you finally

10 came back to Dr. Vena's original question which had to do with

11 intake, proportional intake.

12 DR. JOHN VENA: Yes, but also I think it was

13 a really good discussion to put into context the ranking of

14 these biomarkers and how they'll be used and how they can be

15 used in the context of these risk assessment procedures, et

16 cetera, so this was helpful.

17 DR. KENNETH PORTIER: Okay. I think at this

18 point, we are going to go ahead and break for lunch. It's noon

19 by this clock. We'll break for an hour. I'm not finished with

20 this question. We'll come back and make sure we've got all the

21 finished ideas before we move on to 5.2.

22 (Lunch Break)
Page 654

1 DR. KENNETH PORTIER: Let's continue the

2 discussion on Question 5, Exposure Profile and Biomonitoring

3 Research. I want to remind everybody who's dialed in, it turns

4 out that we can't mute you out. You have to mute yourself. So

5 please use *6 at this point to mute the line so we don't hear

6 your breathing in the background. Thank you.

7 Let's see. Dr. Buckley, you said you had a

8 comment you wanted to make on 5.1a, as a continuation of the

9 discussion?

10 DR. BRIAN BUCKLEY: Yes. As a continuation

11 of the discussion, I was asked about the degradation rates of

12 the two potential degradates, DETP and chlorpyrifos in the

13 environment. And I said I didn't recall any real studies on

14 those and did a literature of lunch rather than eat lunch, and

15 did not find anybody who studied the degradation of the

16 degradates. 

17 So if we are able to use ratios of say, DETP

18 to TCPy, we are going to have to have a better understanding of

19 the degradation rates of the degradates in the environment. 

20 And looking at the molecules, I mean, I would believe there

21 would be an inherent difference in those two, which would lend

22 itself still, to potential use of the ratios to at least, you
Page 655

1 know, predict exposure to the active as opposed to the

2 degradate. But it's a more convoluted process at this point in

3 time.

4 DR. KENNETH PORTIER: Thank you, any

5 additional comments on 5.1a? Dr. Popendorf, you had mentioned

6 something?

7 DR. WILLIAM POPENDORF: Yes, just to clarify

8 when we were discussing the cholinesterase methods and their

9 variability. We will provide some references on that.

10 DR. KENNETH PORTIER: And then during the

11 lunch break, Dr. Lu had a one page from an Environmental Health

12 Perspectives paper copied out with some diagrams? Dr. Lu, did

13 you want to say anything about this?

14 DR. CHENSHENG LU: All this is pertaining to

15 question 5.2.

16 DR. KENNETH PORTIER: Then we'll get on to

17 that. Okay. So why don't we move on to 5.1B? Go ahead and

18 read the question. 

19 LIEUTENANT AARON NIMAN: Section 5 of the

20 draft issue paper compares biomonitoring findings from the

21 three children's health cohorts with other major observational

22 exposures studies in the United States. Based on comparison
Page 656

1 with NHANES 2001-2002, median TCPy levels in the CHAMACOS and

2 Mount Sinai cohorts were slightly higher than the general

3 population. It should be noted that exposures experienced by

4 CHAMACOS and Mount Sinai cohorts overlapped the start of the

5 residential chlorpyrifos phase-out. By contrast, median TCPy

6 levels in the Columbia cohort, for which sampling occurred when

7 chlorpyrifos use should have rapidly declined due to voluntary

8 cancellation, were slightly lower than the levels measured by

9 NHANES in the general population. Please comment on the

10 adequacy of the Agency's comparison for the purposes of

11 evaluating chlorpyrifos exposure levels in the three children's

12 health cohorts. Are there any additional biomonitoring studies

13 that should be included in the Agency's comparison?

14 DR. KENNETH PORTIER: So Dr. Buckley, I see

15 two questions here. A comment and anything additional.

16 DR. BRIAN BUCKLEY: The studies discussed in

17 this section are the best available and by consequence we

18 believe that the EPA did a good job, at least evaluating those

19 studies, and as a starting point for their criticality, or

20 critical analysis of the larger risk component. They do,

21 however, look at specific types of exposure: agriculturally

22 based exposure, and exposures in city dwelling units likely
Page 657

1 treated for insects on a regular basis.

2 Further they span a range of times when

3 chlorpyrifos use was ubiquitous through the phase out of indoor

4 uses of the insecticide. Because of this, there are

5 inconsistencies in the data, but these inconsistencies really

6 are indicative of change of use patterns. Current use in

7 indoor settings is dominated by parathyroid's, rather than

8 chlorpyrifos.

9 Agricultural settings, however, are still

10 likely to see large exposures to chlorpyrifos. And that's

11 important for a lot of the considerations of future

12 populations, and evaluations of concentrations and exposure

13 risk.

14 There appears to be inconsistent recognition

15 in this change, especially in light of the comparison with

16 group norms, for example, the NHANES studies. 1999-2000

17 NHANES, will indicate higher exposures to chlorpyrifos in

18 residential settings than later data. But that should be to no

19 one's surprise. 

20 Now with that in mind, we believe the next

21 NHANES data set may be the most important, and is likely to

22 reflect the decrease in exposure caused by the voluntary
Page 658

1 removal from the home market. If the levels progress in a

2 manner similar to those predicted by the decrease demonstrated

3 in the Columbia study, the risk from chlorpyrifos may decline

4 as rapidly.

5 Even if this is true, chlorpyrifos is a model

6 compound for a risk paradigm that includes EPI Dose

7 reconstruction, PBPK modeling and exposure dose symmetry,

8 requires a much broader consideration of the studies. The

9 agency seemed to concentrate on studies that include a reported

10 health outcome and we wonder why these studies were the

11 principal focus, as many studies provide data on exposure and

12 dose, but weren't the principal focus.

13 For example, to be protective the agency

14 should consider the NHEXAS study, because it represents the

15 highest level at exposure and may contribute, or continue to

16 represent direct or indirect agricultural exposure. And this

17 sort of speaks to some of the general impressions that we got,

18 which was chlorpyrifos may be a model compound in the sense

19 that there is so much data available, relatively speaking.

20 Now, clearly we spent the last day and a

21 half, two days decrying how much data we don't have at this

22 point in time. But relatively speaking, there are few
Page 659

1 environmental agents that we have more data on. So if you're

2 going to use all of the tools in the toolbox, the Epi data, the

3 PBPK data, et cetera, et cetera, chlorpyrifos is not a bad

4 model compound to work with.

5 But because of that we think that you should

6 use as much data as possible and not just focus on the data

7 that's reflective of studies that provide outcomes for human

8 health. And by that I mean, you've got data -- many studies

9 that have measurement of environmental matrices, as well as

10 urinary concentrations of the metabolites. 

11 And if you're going to do a dose

12 reconstruction or the dose symmetry model based on those -- and

13 we think that that's important -- then those studies should be

14 included too.

15 Many of the studies listed in the draft

16 paper, but not directly discussed should be considered when

17 establishing dose and subsequently risk. The New Jersey

18 studies where cord blood measurements were used as the

19 principal sample type is important, because there's likely to

20 be a desirable biomarker and studies more frequently in the

21 future.

22 Farm worker studies are important, because
Page 660

1 the farmer -- because their families are likely to be the ones

2 of the remaining populations that see significant exposure. 

3 Again, to be validated by the next round of NHANES data. So

4 you may see the general population across the country drop in

5 terms of their metabolite level, but the farm worker families,

6 or families near agricultural areas may remain high.

7 The CPS study by Lu and CPPAES studies are

8 important, because they provide data on multiple exposure

9 vehicles media and will be especially useful in does

10 reconstruction. Dose reconstruction will be paramount in

11 validating PBPK models to be subsequently discussed. Among the

12 current the studies that are those that look at both the

13 urinary concentration and the media where the exposure is

14 likely to occur, will provide the best models for closing the

15 knowledge gap between exposure and dose. And studies where

16 urine was collected within one half-life after a fresh exposure

17 may provide the most useful information.

18 Knowing when the exposure -- or knowing when

19 they application of the pesticide occurred, is going to be key

20 in any PBPK modeling, in any exposure dose reconstruction, et

21 cetera, because you at least know the degradation rate of the

22 pesticide; you know potentially the PBPK behavior. 
Page 661

1 Although not ready for this report, studies

2 are now underway that are longitudinal in design affording a

3 better understanding of actual exposure profiles when compared

4 to cross sectional approaches. Due to the short biological

5 half-lives of the metabolites of chlorpyrifos in the body, a

6 spot check of a relatively small number of people may not give

7 a good indication of the fraction of individual's exposure at

8 some important level over the longest period. Say a year. 

9 Only a longitudinal investigation can get at this important

10 data.

11 Several new studies have been completed and

12 will be published in the near future that are of interest to

13 this group. The Children's Pesticide Exposure Study, lead by

14 Dr. Alex Lu of Harvard School of Public Health, focuses on

15 dietary intake of children and the related pesticide exposure.

16 The Children Exposure to Environmental

17 Pesticides, by Dr. Barry Ryan of Emory University, evaluates

18 the utility of biomarkers of pesticide exposures. DAPs and

19 pesticide specific markers of OPI and parathyroid exposure's

20 environmental levels measured in soil, house dust, and food. 

21 Once again, getting back to the notion that we need that whole

22 loop, not just what's in the body, but what the environment is
Page 662

1 that's providing those exposures. And that will help us do the

2 dose reconstruction.

3 Dr. Stanebar (phonetic) and Barry Ryan of

4 Emory University and Dr. Tapanawan (phonetic) of Chiang Mai

5 University of Northern Thailand, examined pesticide biomarker

6 concentrations in pregnant mothers and similar markers in their

7 newborn children. Multiple measurements in both urine and

8 serum had been made throughout pregnancy, giving a better

9 longitudinal picture of exposure.

10 Several smaller investigations are underway,

11 designed to evaluate direct intake of pesticide degradates and

12 to evaluate the kinetics of the degradation process in the

13 environment, including food. That's going to be key in

14 understand whether, or not the degradates themselves can be

15 used as biomarkers.

16 Data quality of some of the studies have been

17 called into question due to the apparent changes in the limits

18 of the detection associated with two methods used to evaluate

19 serum chlorpyrifos concentrations, as developed by the CDC. 

20 The questions arise due to misunderstandings of the methods. 

21 There's an apparent 20-fold difference in the

22 LODs (phonetic) between the two methods, and this can be
Page 663

1 accounted for in three ways. First, the newer method uses a

2 sample size of one half as large and the older method injects

3 twice as much, thereby accounting for a factor of four

4 differences in LODs. 

5 Second, although both methods are

6 multi-contaminant, the newer method spans a much larger range

7 of anolyte polarities. In order to obtain adequate recoveries

8 for some of the less polar compounds, there's likely some

9 sacrifice in sensitivity towards the more polar compounds, such

10 as chlorpyrifos.

11 The reason we are bringing this in is, as we

12 start to evaluate exposure data and dose reconstruction, the

13 ability to evaluate study to study and concentration to

14 concentration, and studies where you have lots of non-detects

15 versus lots of measured concentrations, are all going to be

16 dependant upon and understanding of the methods used to perform

17 the analysis.

18 And it's those cross validations in comparing

19 studies to study that are going to give you the most

20 information in terms of whether, or not a biomarker is

21 effective based on its ability to be quantified, or the percent

22 detects.
Page 664

1 DR. KENNETH PORTIER: Thank you. Dr.

2 Collier?

3 DR. ABBY COLLIER: I have nothing further to

4 add at this time. Dr. Buckley summarized what we discussed

5 nicely.

6 DR. KENNETH PORTIER: Dr. Lu?

7 DR. CHENSHENG LU: I have nothing to add

8 either. Thanks.

9 DR. KENNETH PORTIER: Dr. Ryan?

10 DR. BARRY RYAN: All I would like to add is,

11 Dr. Gold brought up earlier today a National Children's study

12 as being one that may be providing useful information. The

13 only problem I have with a National Children's study is we are

14 not quite sure what kinds of things are going to be collected

15 in that study in terms of biomarkers, environmental samples and

16 so on.

17 If indeed all of the things that we want to

18 have collected are collected, I think that'll be an excellent

19 study to follow. We'll see the kids from pregnancy until age

20 21 and that is really what we want. We want that type of

21 longitudinal investigation. Unfortunately, I think in the

22 National Children's study the current protocols call for really
Page 665

1 snap shots at relatively, widely, spaced time spans over the

2 lifetime of the child.

3 DR. KENNETH PORTIER: Anyone else want to add

4 to this? So Dr. Buckley, as you right this up do you envision

5 a priority on these additional studies, where EPA might get the

6 most bang for their buck out of participation? Can you see

7 adding that in?

8 DR. BRIAN BUCKLEY: We'll certainly make an

9 attempt. I think both the studies that we -- well, I think

10 NHANES is going to be paramount obviously. It will tell us

11 where our benchmarks are. But in terms of understanding the

12 mechanism and looking for more of the criteria that we are

13 looking for, I think the studies that we outlined -- both by

14 Dr. Ryan and Dr. Lu -- are the types of studies that will

15 provide a great deal of information. 

16 Whether or not EPA decides to take on dose

17 reconstruction as a validation tool -- and we recommend that

18 they do -- they may have a lot of the studies that they need

19 for that. But I do believe the studies by Dr. Ryan and Dr. Lu

20 are both going to provide a lot more of the information, but

21 we'll have that reflected in a report.

22 DR. KENNETH PORTIER: Any additional
Page 666

1 comments? Dr. Lowit do you have any -- I mean I thought this

2 was --

3 DR. ANNA LOWIT: No. That was a very clear

4 answer. Thank you.

5 DR. KENNETH PORTIER: Very good. Now we get

6 to move on to question 5.2. 

7 LIEUTENANT AARON NIMAN: In Section 5.0 of

8 the draft issue paper, the agency summarized the 2008

9 preliminary findings on the association between urinary TCPy

10 levels and acetylcholinesterase and butyrylcholinesterase

11 inhibition and discussed two recent studies involving

12 manufacturing workers in the U.S. and Egypt.

13 Please comment on the scientific quality of

14 these studies and their findings. Please include a discussion

15 of their strengths and limitations. Please comment on the

16 strengths and limitations of the evidence from this research to

17 show an association between TCPy and acetylcholinesterase and

18 butyrylcholinesterase inhibition at exposure levels experienced

19 by occupational populations.

20 DR. KENNETH PORTIER: Dr. Popendorf?

21 DR. WILLIAM POPENDORF: Though I don't have

22 my comments as finally written as some of the prior
Page 667

1 respondents, but I think I have a lot of things to say. And

2 I'd like to start with the Farahat study before going on to the

3 Garabrant study. 

4 Both of these were occupational exposure

5 studies, observational in nature. I think, from their positive

6 side, both of them had a relatively large number of

7 participants which, for an observational type study of this

8 nature -- although compared to the Epi studies, I'm sure that

9 would present a different perspective, because we are talking

10 about 38 in the Farahat study in Egypt and 53 exposed in the

11 manufacturing study. 

12 And I think from a broad perspective, these

13 two studies provide very good justification for the PBPK

14 models. That's probably their biggest value, because I think

15 they are both exposures that are well beyond those that are

16 applicable to the children and home exposures that we've been

17 talking about.

18 So what I'd like to do is just walk through

19 some of the strengths and most of the weaknesses of the two

20 studies. If I had a graph, I think there's a lot of parallels

21 of their strengths and weaknesses. 

22 So again, I'll begin with the Farahat study
Page 668

1 which involved 14 chlorpyrifos applicators. These are hand

2 applicators walking through cotton fields applying

3 chlorpyrifos, along with 24, sort of what they call engineers

4 and technicians that are also getting exposed by not directly

5 handling it. 

6 On the plus side, they were getting high

7 exposures, which is easier to measure. And I think that they

8 are also fresh chlorpyrifos, which probably means low levels of

9 background TCPy, which would eliminate some of that

10 contribution.

11 The doses were high enough to illicit

12 acetylcholinesterase responses. I mean, half of these folks --

13 there is a 210 article as well, which is a little more

14 descriptive of the process that these -- you know, half of the

15 applicators were walking through these fields barefooted. It's

16 okay to do in cotton, I guess, but we certainly wouldn't -- you

17 know, provide some high levels of exposures; the foliage in

18 front of them and walking through the wet foliage, so you can

19 imagine the levels.

20 On the downside, that particular study had

21 high levels of pre-exposure background of both TCPy -- and they

22 took their cholinesterase measures before the official spring
Page 669

1 season, but the high background of TCPy, which is about five

2 times the level that we are going to see in the manufacturing

3 workers means that there was a lot of prior exposure, which

4 maybe has some pluses. They may have been closer to steady

5 state than you might've otherwise expected because they were

6 working for roughly two weeks, anywhere from 9 to 17 days of

7 application.

8 The urine samples ended up being mostly

9 morning voids, which some of the Dr. Lu's results suggest that

10 that's not the best time to collect a sample. But again,

11 they're probably in steady state, so maybe that's not as bad as

12 some of the other researchers have found.

13 They took four blood samples within 30 days,

14 but there was no unexposed control group for the blood sampling

15 work. So the concerns about the run-to-run analyses of

16 cholinesterase that we mentioned earlier should've affected

17 these people.

18 On the other hands, the cholinesterase

19 results, some of these people had very high exposures and

20 supposedly, looking at TCPy versus both butyrylcholinesterase

21 and acetylcholinesterase, fairly good correlations. And

22 interestingly, they did not report change in cholinesterase. 
Page 670

1 They reported actual cholinesterase activities. Probably

2 reflects some of their baseline. They didn't really discuss

3 why they didn't show change. They just showed valued compared

4 to sort of the normal, if you will, of what they described as

5 their background range. And they had one person down to 90

6 percent ACH inhibition or below. The rest of the group

7 reported no symptoms, which is interesting.

8 Nonetheless, those results did fit the --

9 well, they didn't use the PBPK but they did have a similar

10 pattern. In fact, the handout that we got -- I think Dr. Lu's

11 going to talk about -- you can see the bimodal relationship

12 with an inflection point that he'll address.

13 And perhaps not surprisingly, both the TCPy

14 and butyrylcholinesterase remain high for another 8 to 10 days,

15 as far as they track these people afterward. Maybe that's not

16 too surprising for those levels of exposure. And again, it

17 might reflect continuing exposures outside of that study.

18 The other large plus that I mentioned in the

19 beginning is that the pattern of both butyrylcholinesterase and

20 acetylcholinesterase match the kinds of expectations that you

21 would expect from the PBPK model and I think that's very, very

22 encouraging.
Page 671

1 So overall it's interesting. If you

2 understand what Lawrence of Arabia, he liked the desert because

3 it's clean. And I wouldn't call this a clean study but it's

4 interesting.

5 The Garabrant study was a 2009, again,

6 observational in nature. 53 manufacturers of chlorpyrifos, 60

7 controls saran (phonetic) manufacturers studied over one year

8 sort of as an opportunity of convenience with an ongoing

9 occupational health monitoring.

10 The background chlorpyrifos, again, contained

11 high TCPy values because those workers were already being

12 exposed. And in those cases, their exposures were high enough

13 to elicit a change in butyrylcholinesterase but not in terms of

14 acetylcholinesterase.

15 One of the weaknesses of that study was their

16 baseline blood values were supposedly pre-exposure since these

17 were employees in a plant that -- they didn't state how long

18 they gave the age of the participants, but not how long they'd

19 been working there, so there's no telling how long those

20 pre-exposure blood samples were taken. It could've been years.

21 They really didn't have any other pre-exposure opportunities

22 in that study.
Page 672

1 Urine samples, again, morning; it's really

2 only three samples collected. The urine samples and blood

3 samples weren't exactly matched. And they chose urine samples

4 that matched within seven days of the blood sample being taken,

5 and so there's definitely some temporal disconnect between

6 urine and bloods. The bloods were being collected as part of

7 an ongoing occupational health survey.

8 Again, there was no indication that, although

9 they had that referenced population, the blood samples were not

10 being used to matched their laboratory methods. And if you

11 look at the variance and their cholinesterase of the controls,

12 they were seeing some very high variations in

13 acetylcholinesterase indicative of the method. 

14 Again, the fact that they weren't using it to

15 control -- and what one does in the older studies -- and

16 somehow we seem to know about that 20/30 years ago, but have

17 forgotten that if you're going to do this kind of study, you

18 use controls that are unexposed and don't even live in the same

19 area, don't work in the same area. You collect their blood,

20 analyze it intermixed with the exposed group, and you'll see

21 the day-to-day or run-to-run changes in the cholinesterase. 

22 And you adjust the values of your exposed group to reflect the
Page 673

1 changes in the unexposed. And that's how it should be done,

2 and most of these studies don't do that, which is okay. You

3 know. It's designed for a clinical diagnostic tool and it

4 works fine for that. But when you're trying to measure 10

5 percent or less, it gets somewhat unreliable. 

6 The other negative was there method for

7 butyrylcholinesterase. That was the only real change that they

8 found in terms of cholinesterase inhibition was on

9 butyrylcholinesterase side. And they were using a -- I think

10 that was a -- yeah, they were using a proprietary kit for that

11 and, again, had a lot of variation.

12 The ongoing occupational health program, a

13 person would be -- there would be at least some investigation. 

14 They would be withdrawn from exposure, beginning at the 20

15 percent butyrylcholinesterase inhibition. So during the year's

16 time, 43 percent of the exposed group were pulled out from

17 that, but 13 percent of the saran manufacturers also had a 20

18 percent butyrylcholinesterase response. That's really -- and I

19 think mostly indicative of the method and its variability. From

20 a business case perspective, I think they really should look at

21 what they're doing. They're probably a good portion of the 43

22 percent. The exposed may not be really showing a real change
Page 674

1 either.

2 But again, the butyrylcholinesterase

3 inflexion point, very similar to the Farahat study. The

4 Garabrant study used the PBPK, the Timchalk PBPK model. The

5 model pretty much paralleled the results and they both had an

6 inflection point at the same, roughly 110 micrograms of TCPy

7 per gram of urine adjusted for creatinine. So taken together,

8 they both really do support that PBPK model. 

9 Beyond that, I think there are some

10 significant methodologic issues if you try to use it too

11 quantitatively. But the overall pattern -- again, I mentioned

12 PBPK several times before. I think it supports the whole chain

13 of events from in vivo, in vitro back on up to these types of

14 human exposures.

15 DR. KENNETH PORTIER: Okay. Dr. Buckley?

16 DR. BRIAN BUCKLEY: I agree with everything

17 that Dr. Popendorf said and I'm just going to add a few

18 comments and observations about the studies.

19 I think that this is as close as we'd be able

20 to get to a human study mimicking the 1984 study. We couldn't

21 do that anymore where people are just voluntarily ingesting

22 chlorpyrifos. So in terms of in starting a PBPK model, you go
Page 675

1 from -- at least the first time this season or whatever, you go

2 from little or no exposure to this massive exposure and then

3 it's how your body deals with it.

4 I caution, though, two things. First, the

5 Egyptian study. The concentrations were so much higher -- well

6 they were higher enough than the U.S. study that I'm not sure

7 how relevant they are to a lot of the risks that we are

8 considering here. These are folks that had a huge exposure. 

9 And I don't think there's a lot of discrepancy about health

10 effects above that acetylcholinesterase inhibition line. 

11 I think that, primarily, where all of the

12 discussion is centered is in that region below, and whether or

13 not there are health effects or risks that need to be

14 considered. 

15 I also will caution that both of these

16 studies, I think, are probably appropriate for PBPK models of

17 adult men. And I don't think that we can create one PBPK model

18 and have it applicable to all of the populations that we are

19 considering when we are considering risks. And we talk a

20 little bit further about this in 5.4. But there are vulnerable

21 populations that a PBPK model derived for adult farm workers,

22 or whatever, probably not appropriate for small children or
Page 676

1 pregnant women. And I think that that's something that needs

2 to be noted, that one size probably does not fit all in terms

3 of the PBPK models.

4 DR. KENNETH PORTIER: Dr. Collier?

5 DR. ABBY COLLIER: I agree with Dr. Popendorf

6 and also Dr. Buckley. And he took the words right out of my

7 mouth about one-size fits all models, so thank you.

8 DR. KENNETH PORTIER: Dr. Lu?

9 DR. CHENSHENG LU: So that study from the

10 Garabrant Study 2009. So I think this is well-documented in

11 the literature as well, is that every time we look at the

12 acetylcholinesterase or butyrylcholinesterase data we have to

13 make sure that they are using the right assay, because the

14 whole assay is very sensitive to many, many factors.

15 I remember a couple of years ago we had an

16 SAP involving Dr. Barry Wilson from UC Davis, and he actually

17 have a very animated section, in terms of whole assay issues. 

18 So this 2009 study used assay that's fairly

19 new. It's in the laboratory-based assay that don't really

20 reference in terms of has been replicated or not or is a

21 commonly used assay. But based on the description, it looks

22 pretty new. So I have no way to judge whether the quality of
Page 677

1 the cholinesterase enzyme activity is good or bad. 

2 But if you look at 2011 of Farahat's study,

3 they used battery powered test kit, which is considered

4 non-valid measurement. But we'll go to there later.

5 So for the 2009 study, assuming the quality

6 is good, the data that's presented in the paper actually

7 deserve further close examination. And I'm actually talking

8 about these two graphs here on slide 16. So on your right hand

9 side, this is the acetylcholinesterase and this is the

10 butyrylcholinesterase.

11 So again, I want to highlight the variance of

12 the measurement. If you look at both acetylcholinesterase and

13 butyl, it varies from 140 percent inhibition to 70 percent

14 inhibition, so that kind of tells you. But if you kind of draw

15 the line and facilitate by the PBPK model -- actually if you

16 look at acetylcholinesterase, doesn't matter how high the TCPy

17 is measured in the urine. There's essentially no change of the

18 acetylcholinesterase of this wide range of TCPy from, I would

19 say 0.1 to 1000. Whereas, if you look at butyrylcholinesterase

20 you would start seeing the inverse correlation in which TCPy

21 increase from 100 and above, and you see a linear degration

22 inhibition of butyrylcholinesterase.
Page 678

1 So what does this mean? So one question

2 that's put out to the panel is whether acetylcholinesterase is

3 the most sensitive biomarker for organophosphate pesticide

4 exposure. Well obviously the data shows that's not the case. 

5 Right?

6 So what happened, actually butyl got

7 inhibited but not acetylcholinesterase. I think this is some

8 kind of a mechanism that has not yet been either identified or

9 categorized.

10 We just happened to have an animal study that

11 actually sort of have a similar hypothesis of this, and I can

12 kind of put it out here -- although the data has not been peer

13 reviewed yet -- is that, it's very likely that when you have

14 exposure to, say, chlorpyrifos, once the chlorpyrifos is

15 absorbed in the body, it will interact with butyl first because

16 butyl is a boundant. It's in the plasma. It can quickly bound

17 to chlorpyrifos and got inhibited. And whatever is left, the

18 residual will go into the secondary compartment, which is

19 acetylcholinesterase, in a central nerve system. And then it

20 will interact with enzyme and got inhibited. I'm not saying

21 that this is true, but I mean, the speculation is, if this is

22 true, then we are talking about totally different trajectory in
Page 679

1 terms of how we are going to look at in vitro

2 acetylcholinesterase as the mode of action without any

3 butyrylcholinesterase enzyme inhibition data? So that's what I

4 take from the 2009 study.

5 And in terms of the 2011 study, in addition

6 to the quality of the cholinesterase measurement, I really want

7 to have this showed in front of the panel because some may not

8 have the access to the paper.

9 So this full graph shows before, during and

10 after application event. If you just use your naked eye

11 examination, those relationships are actually non-changed. It

12 doesn't matter if it's before, during or after application.

13 So the question is how good is this data,

14 especially knowing that by looking at the TCPy measurement in

15 the urine, some of the people in the study actually is exposed

16 to extremely high level chlorpyrifos. I don't even see that

17 kind of number before; 5000 microgram per gram of creatinine

18 adjustment. I don't even know if there's any literature that

19 documents this kind of level. But if you look at

20 cholinesterase enzyme inhibition it barely changed, to some

21 degree. So again, I question their cholinesterase enzyme

22 measurements.
Page 680

1 I mean, no doubt that this taking place in

2 Egypt, they don't have that kind of facility, but again, I

3 don't think that we should use this data from this study for

4 further analysis. 

5 And something that's really concerning is, if

6 you read the paper carefully -- I don't know how they passed

7 this, but there's a couple of workers, their

8 acetylcholinesterase enzyme inhibition actually reached to

9 almost 90 percent. I don't know how these workers survived. 

10 90 percent of acetylcholinesterase inhibition will lead to

11 death right away. But this paper got published without any IRB

12 (phonetic) challenge.

13 So, I guess the reason they have this such

14 high inhibition of acetylcholinesterase is the poor quality of

15 the measurement, is the poor quality of a test kit. But

16 otherwise, I think that person or the couple of persons have

17 this low, almost zero -- I mean some of the butyl is actually

18 zero. I don't know how those people can survive. So that's

19 just my comment.

20 DR. KENNETH PORTIER: So, for the record, Dr.

21 Lu was referring to the slides for RBC cholinesterase and

22 plasma butyl cholinesterase in the Niman presentation before
Page 681

1 the panel.

2 DR. CHENSHENG LU: Slide number 16 and 17.

3 DR. KENNETH PORTIER: Right, slide 16 and 17.

4 And I don't know if you're going to want to include that in

5 the report but at least we should make reference to that, and

6 then make reference to the Farahat paper page 804.

7 DR. BARRY RYAN: I just have two very minor

8 points, additional point on this topic and then I'll shut up. 

9 Dr. Buckley, pointed out that we are talking about men

10 exclusively in this. I would like to just add to the point

11 that these are actually healthy men. And men overall may have

12 a different effect, and certainly, overall, populations could

13 have different effects as well.

14 And secondly, I think Dr. Lu just pointed out

15 more clearly to me and said more concisely, but I think that

16 the exposure levels that are being shown here are occupational

17 type levels, and there may be other pathways that are -- I

18 don't want to say saturated, but are run-through with respect

19 to this.

20 And what Dr. Lu was just saying regarding

21 butyrylcholinesterase inhibition versus acetylcholinesterase

22 inhibition kind of supports that. That's all I have on this.
Page 682

1 DR. KENNETH PORTIER: Dr. Vena and then Dr.

2 Terry.

3 DR. JOHN VENA: Just to add that, in the

4 description here it does say that the Garabrant paper had 22

5 percent female study participants in the study population and

6 26 percent in the referenced groups, female. 

7 But for the most part, I agree that this is

8 generalizable to adult men. And the doses in the populations

9 really aren't relevant for generalized ability to many of the

10 issues that we've been discussing here, which is the

11 reproductive development effects and exposure to pregnant

12 ladies and children. 

13 So although I guess it is informative, I

14 guess the reason this question was asked is informative to the

15 bigger issues of acetyl transfer cholinesterase inhibition and

16 what doses create those effects.

17 DR. KENNETH PORTIER: Dr. Terry?

18 DR. ALVIN TERRY: Yes. I have one thing to

19 clarify. It wouldn't change the overall interpretation of

20 whether the Farahat study is worth utilizing or not, but there

21 was the issue mentioned about 90 percent acetylcholinesterase

22 inhibition.
Page 683

1 We routinely do studies in animals where you,

2 if you exposure animals intermittently, overtime you can easily

3 approach 90 cholinesterase in this and see no cholinergic

4 signs. So I just wanted to clarify that. I'm sorry, couldn't

5 hear? Okay. 

6 Just wanted to clarify one point, and that

7 was the statements you made about someone having cholinesterase

8 inhibition approaching 90 percent and not having symptoms. If

9 you are exposed intermittently -- and I'm basing this on adult

10 rodent models -- you can routinely approach 90 percent

11 cholinesterase inhibition without seeing any cholinergic signs.

12 But this is a different type obviously, so that's rodents,

13 adult rodents in its intermitted exposure overtime.

14 DR. CHENSHENG LU: Just to clarify, is it

15 butyrylcholinesterase or acetylcholinesterase at the 90 percent

16 inhibition level?

17 DR. ALVIN TERRY: Either or both.

18 DR. WILLIAM POPENDORF: If you look back and

19 some of the old Graub data (phonetic) from the 40s and early

20 50s, there was one particular series where they took people

21 down below, it was like down to one percent active in a 99

22 percent inhibition. I don't remember the timeframe but they
Page 684

1 took them down over some period of time. They've got

2 decreasing levels of activity. They're down to 99 percent

3 inhibition. If you do it slow enough, they didn't have

4 symptoms either, so it is possible to do that with people as

5 well.

6 DR. KENNETH PORTIER: Dr. Pope?

7 DR. CAREY POPE: Yeah, just to add to that. 

8 We don't even have to do it intermittently. We can do it with

9 a single acute dose with chlorpyrifos given subcutaneously. 

10 Their brain cholinesterase will be dropped to 90 percent

11 inhibition with very few signs of severe cholinergic signs.

12 DR. KENNETH PORTIER: Dr. Carr?

13 DR. RUSSELL CARR: What Dr. Pope said.

14 DR. KENNETH PORTIER: Dr. Lu.

15 DR. CHENSHENG LU: I just want to clarify

16 this. I mean, this is hard to read the data because they don't

17 provide detailed information. But if you look at these two

18 graphs at the bottom of slide 17, one shows almost the end of

19 application. The other shows this 14-day post-application. 

20 And over that 14-day period, I assume that those low numbers

21 from the same individual -- which I cannot assure because the

22 paper does not say so -- but if those are the same persons, how
Page 685

1 can those persons survive 14 days with acetylcholinesterase

2 inhibition at a 90 percent level? I agree that animal testing

3 you could see nothing instantaneously. But what if you sustain

4 that level of inhibition at 14 days? 

5 So again, what I'm trying to say in here is

6 that there are some unanswered questions associated with these

7 two studies. I think the Egyptian study has more problems than

8 the 2009 study. 

9 But again, this kind of identifies something

10 in terms of -- this could be true because, remember,

11 acetylcholinesterase can be overly inhibited become H. So this

12 could be the H symptoms, which we don't really see that in the

13 previous literature. But also, just bear in mind that

14 acetylcholinesterase can also reactivate it. So that's why I'm

15 saying this does not really answer many questions that I have.

16 DR. KENNETH PORTIER: Dr. Popendorf?

17 DR. WILLIAM POPENDORF: I was just going to

18 comment on butyrylcholinesterase side. You know the PBPK model

19 actually -- I can't explain the mechanics of it but they do

20 predict that the butyrylcholinesterase would be inhibited well

21 before the acetylcholinesterase side. So I don't think that

22 should be too surprising. 
Page 686

1 But on the other hand, we've not really

2 discussed the value of acetylcholinesterase. Clearly it's

3 getting inhibited more early. Should it be considered as an

4 alternative biomarker, basically? Perhaps this has been

5 discussed in literature but it hasn't been discussed here.

6 DR. RUSSELL CARR: There's a lot of

7 individual variability with butyrylcholinesterase, and so it's

8 not as baseline stable as red blood cell cholinesterase is in

9 humans. And there is age-dependent increases in

10 butyrylcholinesterase, and so the variability kind of limited

11 it out. We kind of hashed that out in 2008 that it was just

12 too variable to be depended upon. It is a very good marker of

13 exposure because it's more sensitive than red blood cell

14 cholinesterase but it is just too variable.

15 DR. KENNETH PORTIER: Well one of the

16 questions I have, I guess, also a question here about the

17 association is the existence of this breakpoint/inflection

18 point in the relationship, as you can kind of see it in the

19 handout, the Farahat paper or in the EPA presentations. And I

20 wondered, do you think this fairly represents this kind of

21 inhibition and then kind of a point at which the rate of

22 inhibition increases? That kind of quantitative association,
Page 687

1 is that that reasonable? I didn't hear anybody talking to

2 that. Dr. Popendorf?

3 DR. WILLIAM POPENDORF: I mean, for blood

4 RBC, basically, is what you're looking at, and there are two

5 mechanisms that would account for that. One is the temporal

6 reversion, the disassociation, if you will, of the

7 phosphorylation of that enzyme which occurs over a short period

8 of time at a fairly high rate and something I think for

9 chlorpyrifos, if a third or so of the initial inhibition is

10 released in the first 24 hours. And then, of course, you're

11 getting replacement RBCs. 

12 So up to the point between the plasma acting

13 as sort of a buffer, if you will, for some of that dose, and

14 then those other two mechanisms replacing the RBC, you see

15 almost no change until those two mechanisms are sort of

16 overwhelmed by the dose; and then you're going to see that

17 drop-off. So I think there are some fairly, fairly well

18 understood mechanisms. 

19 I mean, the figure 3 of the Garabrant study

20 is basically a plot of just the results of the Timchalk model,

21 and it shows the plasma, RBC and brain CHE all with those

22 breakpoints. So it's predictable and it matches, so I think
Page 688

1 the mechanisms are fairly well understood.

2 DR. KENNETH PORTIER: Dr. Lu?

3 DR. CHENSHENG LU: So the inflation point

4 would determine basis on butyrylcholinesterase measurement. IN

5 the 2011 study, I don't think that butyrylcholinesterase

6 measurements are valid. Just this is on the graph. Whereas,

7 on the 2009 study, the butyrylcholinesterase seems okay. But

8 again, at that level, we don't see any acetylcholinesterase

9 inhibition. So what is the implication of this inflection

10 point, for butyrylcholinesterase related to

11 acetylcholinesterase when there is essentially no change? 

12 So I think, you know, inflection point is a

13 good idea but, you know, you're talking about a non-targeted

14 cholinesterase enzyme activity and try to relate it to more

15 specific, has toxicological meaning, which is

16 acetylcholinesterase which we don't see any change. So what is

17 good about this inflection point? 

18 DR. KENNETH PORTIER: Don't ask me. Dr.

19 Popendorf?

20 DR. WILLIAM POPENDORF: I was just thinking. 

21 When you say good you mean informative or beneficial? What do

22 you mean by good? 
Page 689

1 DR. CHENSHENG LU: Well good means that you

2 can find an inhibition at level start going up, exposure level,

3 to kind of give an indication in terms of, at what exposure

4 level you start seeing butyrylcholinesterase inhibited. But my

5 point is that, yes, based on the 2009 study there's a clear

6 distinction between non-inhibited versus inhibit. But if you

7 look at acetylcholinesterase measurement from the same person,

8 there's no information whatsoever what should be the inflection

9 point. But we all use acetylcholinesterase as a toxicological

10 endpoint, not butyrylcholinesterase, not just a variation

11 perspective, also it's very general. There's no specific

12 toxicological implication if butyrylcholinesterase are

13 inhibited.

14 DR. WILLIAM POPENDORF: So I think you're

15 saying that it's not informative to know that there is an

16 inflection. And I think if you look at that data -- I mean, I

17 agree with you on that point. If you look at the figure 5 of

18 that paper to reflect Dr. Buckley's point, I mean some of the

19 people at just about that inflection point, they're up 80

20 percent above their baselines. I mean, if you didn't have a

21 line there you might almost think the inflection is going the

22 other way. But, I think, obviously that line was generated not
Page 690

1 just at random. There's physiologic reasons for that

2 happening. But, yeah, I think I agree that it's not helpful to

3 know that. Okay. It happens. We don't know how to interpret

4 it.

5 DR. KENNETH PORTIER: Any additional

6 comments? Dr. Lowit, was that a clear enough -- I think

7 they'll be able to write this up.

8 DR. ANNA LOWIT: Yes. Thank you.

9 DR. KENNETH PORTIER: Okay. Why don't we go

10 on to 5.3, which at least has a short description. I don't

11 know if the discussion is going to be short. 

12 LIEUTENANT AARON NIMAN: Question 5.3: 

13 Several approaches ranging from qualitative to the most

14 sophisticated PBPK/PD modeling approach were introduced as

15 potential options for analyzing the chlorpyrifos biomonitoring

16 data. Please comment on the strengths and limitations of these

17 approaches. In addition, please suggest, if appropriate,

18 alternative approaches or analyses not identified by the

19 agency.

20 DR. KENNETH PORTIER: Dr. Meek?

21 DR. BETTE MEEK: Thanks. Well, I really like

22 the manner in which the agency's presented the options for
Page 691

1 interpreting biomonitoring data, ranging from qualitative,

2 non-comparative looking at trends or comparative, taking into

3 consideration controlled human studies to semi-quantitative

4 approaches estimating biomarker levels associated with

5 regulatory exposure guidelines or estimating exposure from

6 biomarker levels using reverse dosimeter PBPK model.

7 I think that presentation of a number of

8 options in an increasing data informed construct of this nature

9 has the potential to maximize the use of biomonitoring data for

10 different applications, accounting additionally for factors

11 contributing to variability and external exposure estimates.

12 But depending upon the quality, the

13 availability and specific nature of the biomonitoring data and

14 the intent of its use, the question is really, I think, what

15 degree of uncertainty is acceptable for the intended purpose

16 and in what population.

17 So those approaches are necessarily dependant

18 on the extent of the data available on toxicokinetics relevant

19 to the population subset of interest and mode of action and

20 their integration, so with the Cadillac being the PBPK/PD

21 model. But again, that needs to be fit for purpose and needs

22 to be carefully evaluated.
Page 692

1 So the framing by the agency I think permits

2 flexible use of biomonitoring data in this context, including

3 that for chlorpyrifos. But for example, if the objective is

4 media-specific assessment or management, there's a need for

5 dose reconstruction or reverse dosimetry from the biomonitoring

6 data.

7 As a minimum, currently, I think the

8 biomonitoring data on chlorpyrifos should be helpful in ground

9 truthing the total external exposure estimates under various

10 use conditions, which are necessarily based on many more

11 assumptions, such as activity patterns and intakes in

12 concentrations in various media.

13 I'd also like to encourage the agency to

14 seriously consider the development of a value of kin to a

15 biomonitoring equivalent concurrently with the derivation of a

16 referenced dose in the conduct of the assessment. 

17 So biomonitoring equivalent is a calculated

18 level of a biomarker associated with exposure consistent with

19 health protective guidance values for the general population. 

20 This would provide a valuable addition for interpretation of

21 the biomonitoring data in a public health risk context. It

22 would also draw efficiently on the existing process for review
Page 693

1 and consultation for the regulatory assessment. So BEs are

2 based on similar considerations as the reference dose but

3 incorporating very few additional considerations.

4 In relation to the PBPK model, I think that's

5 been discussed extensively in a previous SAP and I won't go

6 into that in any detail. Again, a PBPK model, which is fit for

7 purpose and well verified would serve to reduce a lot of the

8 uncertainty associated with some of the issues that we are

9 struggling with, and I'll leave it at that.

10 DR. KENNETH PORTIER: Dr. Buckley?

11 DR. BRIAN BUCKLEY: One of the observations I

12 made about these models is they are very much going to be

13 data-dependent. In other words, the more data you have, the

14 more you'll be able to progress through qualitative into

15 quantitative approximations of dose. And I think that the

16 further you can go from qualitative assessment to quantitative

17 assessment the better the overall risk assessment is going to

18 be in the long run. So the more data you have, the better your

19 opportunity to work with things.

20 But there are two things that I want to point

21 out, and the first being much more important. The ability to

22 use the quantitative model is only going to be as good as the
Page 694

1 PBPK model. And I've discussed before the fact that the single

2 PBPK model that we have is probably not appropriate across all

3 populations, and especially not true for the vulnerable

4 populations that we are going to consider. So those are things

5 we -- and we discussed this a little bit more on Question 5.4

6 as well -- that the quality of the PBPK model should be

7 assessed directly.

8 I am also going to recommend, both here and

9 in 5.4, that we utilize all of the exposure media data; the

10 soil, the air, the water, the food, et cetera, for creating a

11 dose reconstruction or a potential dose reconstruction and

12 utilizing that information to validate the PBPK model. In

13 other words, if you know approximately what the concentration

14 that someone would've been exposed to -- based on the media and

15 based on all of the things that we discussed the other day

16 about how much dirt do kids eat and how much do we breath and

17 drink and put our hands on tables, et cetera -- if you can

18 estimate the dose, you should be able to pull that through your

19 PBPK model at that point in time and derivate at a theoretical

20 concentration of metabolites in urine. Then you can compare

21 those concentrations. You have lots of studies to work with at

22 that point in time. 
Page 695

1 It's not just the Columbia study and the

2 Mount Sinai and the CHAMACOS study. You have all of those

3 other studies that didn't measure outcomes to work with. So

4 there's a lot of data there that you can potentially validate

5 your PBPK model. And we are going to discuss this a little bit

6 more in 5.4 but I think that's key.

7 On a minor note, the semi-quantitative

8 information utilizing creatinine corrected urine. We don't do

9 creatinine correction in our laboratory for a number of

10 reasons, but one of them being that the creatinine that's

11 excreted from individual to individual differs greatly based on

12 lean body mass and other things, and it was discussed real well

13 on a paper by Dr. Barr (phonetic).

14 But more importantly, we utilize a specific

15 gravity. And I think it's better for comparing study to study

16 because it more accurately reflects the dilution, if you will,

17 of the urine because it represents total dissolve solids rather

18 than just a single component of creatinine. Those are my

19 observations.

20 DR. KENNETH PORTIER: Dr. Collier?

21 DR. ABBY COLLIER: I'd like to agree with Dr.

22 Meek about the quality of the data that agency has available to
Page 696

1 it. And I have some very specific recommendations in terms of

2 your PBPK modeling. 

3 When we use models we have a saying; junk in

4 equals junk out, and I really do think that we are not putting

5 junk into these models. It's very apparent over the last few

6 days that agency has deemed really high quality data available

7 to them, especially from the biomonitoring data. So therefore,

8 the development of these models shouldn't present as much of a

9 challenge as it would be if the field went as well developed.

10 The presenter from Dell, Dr. Bartells

11 (phonetic), indicated that life-stage specific modeling was

12 available to the M3 via PBPK/PD method. However, when

13 questioned directly, agency replied that they do not scale for

14 pediatrics.

15 In adult PK or PBPK modeling, we can simply

16 use an allometric scaling variant such as a three quarter

17 power, and that does give some sort of an indication of what's

18 going on with children.

19 Alternatively, Wang's (phonetic) modification

20 of the Dubois and Dubois method can be used, and it's slightly

21 more physiological and could easily be applied to adult PBPK

22 data to try and get close to what we would see with children. 
Page 697

1 However, this is a very simplistic way of looking at improving

2 prediction for pediatric populations.

3 Another point is, when developing these

4 models, it's come to my attention over the course of the last

5 few days that chlorpyrifos is highly, highly protein bound. 

6 And a consideration in the models is that plasma proteins in

7 children and in pregnancy differ drastically. So in developing

8 such models, that's going to be very important.

9 That may be least important for TCPy or DETP

10 if these are endpoints rather than chlorpyrifos itself,

11 although I'm not aware of the unbound fraction. I'm not aware

12 of what the percentages of those are.

13 A sophisticated PBPK model for children

14 already exists. Okay? It belongs to Simsit (phonetic), which

15 is a company in Sheffield, England. I am aware that it's

16 expensive, but it is a very good model and it is flexible to

17 allow input.

18 Although, as was mentioned earlier by agency,

19 developing a pregnancy PK or PBPK model, it's kind of

20 challenging and ambitious. I was extremely gratified to see

21 what Dr. Hattis has been doing. I thought that was very

22 exciting and potentially very useful.
Page 698

1 It is suggested for several of the reasons

2 that I mentioned earlier, and reasons mentioned in 5.1, that a

3 pregnancy model is developed as a multicompartment model with a

4 fetal compartment including the fetus, the amniotic SEC/fluid

5 and the placenta are modeled as a separate unit. I acknowledge

6 that this will affect the outcomes from the data as presented

7 by Dr. Hattis, especially for oral exposure, but less so

8 effective against inhalational.

9 However, to give you an example of why this

10 may be important, at term the placenta is profused to around

11 about 600 mils per minute, which means the mother's blood is

12 exchanged to the placenta as little as every six to eight

13 minutes before it presents an extremely important cardiac

14 output and extremely important part of the total metabolic

15 profile.

16 Furthermore, the average surface area of a

17 placenta at term is around about 11 meters squared. So we're

18 talking about a very large, very rapidly profused and exchanged

19 area. And most of the CYPs as well as the glucuronyl

20 transferase, sulfotransferases that metabolized chlorpyrifos

21 have been found in the placenta. So based on that, as a

22 speculative argument, I suggest that it is considered that the
Page 699

1 fetal compartment is a separate compartment in a model that's

2 developed.

3 Alternatively, if it can be shown that the

4 placenta is not sufficiently metabolic or that changes from the

5 fetal compartment is not sufficiently important, then you can

6 leave the placenta in its compartment as part of liver

7 metabolism, thus simplifying the model.

8 One thing that I think I raised earlier in

9 panel discussions is that this is an increasing interest not

10 just for pesticides. There are several other compounds of

11 interest that EPA will be looking at where this has come up

12 over and over again. So the utility of a pregnancy specific

13 model for toxicokinetics will not just be useful in respect

14 with FIFRA, but other things the agency may be asked or desire

15 to look at. Thank you.

16 DR. KENNETH PORTIER: Dr. Lu?

17 DR. CHENSHENG LU: I look over this question

18 many times. I thought that the question should be framed this

19 way, in terms of how can we utilize sophisticated PBPK/PD model

20 to analyze chlorpyrifos exposure data so we can recreate a

21 situation in which, under what exposure scenario, that lead to

22 certain levels of chlorpyrifos residue in the cord blood
Page 700

1 samples, because that's how PBPK has been utilized in terms of

2 estimating or predicting urinary concentration based on what

3 exposure scenario.

4 So the way that the agency asked me this

5 question, I'm confused. But I guess I sort of like interpreted

6 in a way that, how can we use biomonitoring data in the PBPK

7 model in a sense that we can lead to further knowledge? So I'm

8 thinking about reverse dosimetry, which is possible. And I

9 would say that there will be a paper published in General

10 Toxicology in 2012 that basically expands the PBPK model and

11 create a verse dosimetry, which actually use chlorpyrifos TCPy

12 data as input and then back calculate and estimate what would

13 be the original exposure, either injection dose or inhalation. 

14 I guess that's what the agency was asking.

15 But, I think we also have a complete SAP

16 either a year ago or two years ago that kind of delegate this

17 subject. So I think I will encourage the agency to look into

18 that report as well.

19 I think the most important thing about the

20 PBPK/PD model approach is that, there are several models out

21 there that we can utilize. But keep in mind that models have

22 to be validated using human data, not just animal data. Again,
Page 701

1 I point out butyrylcholinesterase versus acetylcholinesterase. 

2

3 There's a huge gap that we don't know, in

4 terms of in animals. So if you don't have human data that you

5 can utilize and validate in the PBPK model, you always had to

6 kind of ask yourself how good the model is. But I do believe

7 that the PBPK or PD model is the way to go in terms of the

8 future regulatory activity. Again, I would like to know under

9 what kind of exposure scenario that the Columbia kids or the

10 mother has such levels of chlorpyrifos in their cord blood. Is

11 it predominately from oral injection from food or is it from

12 inhalation of the contaminated indoor air as a result of a pass

13 application. Those questions can be answered by the PBPK

14 simulation.

15 In terms of PD model, I think that could be

16 complicated. Listening to what Dr. Morris' deliberation

17 yesterday, it seems like there's really no good animal data

18 that you can use to create a PD model that gives you some kind

19 of degree of confidence that we can actually link exposure to

20 some sort of neurobehaviors, and that part I think need to be

21 further developed. But overall, I would say that they'll be

22 more advantage than limitations in terms of using PBPK/PD model
Page 702

1 in this specific subject.

2 DR. KENNETH PORTIER: Dr. Ryan?

3 DR. BARRY RYAN: I just had two minor

4 comments on this section, but my two comments are much more

5 eloquently displayed and talked about by, first, Dr. Buckley

6 and then Dr. Collier, so I will refrain from taking the time of

7 the committee here for that.

8 DR. KENNETH PORTIER: Dr. Meek, you know, as

9 I was reading and rereading the question, the word analyzing

10 the biomonitoring data -- I kept saying what do they mean by

11 using a PBPK model to analyze data? I always think of a model

12 as a way of kind of describing the data. But to me, analyzing

13 is looking at relationships and associations. And what are the

14 other approaches that they're talking about? I mean we've

15 concentrated on the PBPK/PD models, but do you get a feeling

16 for what other approaches are? Did you cover that in your --

17 it's been so long since you did your presentation. I need to

18 come back to the beginning to make sure we address the whole

19 statement here.

20 DR. BETTE MEEK: I mean, I introduced what

21 the other approaches were. And in my view, I think that every

22 single one of them has value, depending upon the context in
Page 703

1 which you're using this. And I think of this in a risk

2 assessment context. 

3 So, depending upon the level of

4 sophistication of the analysis that you're doing, you can use

5 the biomonitoring data, for example, to ground truth your

6 external exposure estimates. You can use it to potentially, if

7 you have -- and again, it depends very much on the data that

8 you have available as well. So again, there isn't a specific

9 -- I don't understand what the specific application is here and

10 so I think there's been a little bit of confusion. That's why

11 people have interpreted that slightly differently. For

12 example, if we're talking about the PBPK model, how does that

13 relate to the biomonitoring data? And I was thinking of it

14 more on a broader risk assessment context as to how you can use

15 the biomonitoring data. 

16 But what I'm encouraged about is the fact

17 that the agency is looking into several approaches to using the

18 biomonitoring data maximally in risk assessments.

19 DR. KENNETH PORTIER: So you completed that

20 first sentence by saying, for analyzing the biomonitoring data

21 for risk assessment, and you're focusing on that. And I wanted

22 to get back to Dr. Lowit and see is that exactly what you had
Page 704

1 in mind in this question? I wanted to make sure we are going

2 in the right direction here.

3 DR. ANNA LOWIT: Well, if we can maybe push

4 it just a touch, because the issue brought up, I just left

5 myself a note about. 

6 So Bette, as she says, introduced the topic

7 and there are a variety of approaches, and each in its own

8 context has strengths and limitations. What we haven't heard

9 outside of the pregnancy gestational PBPK modeling is the

10 specifics about the strengths and the limitations of the

11 alternatives. 

12 So I just relooked at my slides. Just to

13 give you sort of a grounded example, there needs to be a risk

14 mitigation decision in the order of 2015, which means the

15 science analysis would need to be done a year'ish before that,

16 and that's less than two years from now, and so, to run the

17 experiments, the build the model, run some more experiments,

18 probably, have a gestational model thoroughly reviewed and

19 published and then available to the agency to do a full-blown

20 risk assessment in less than two years. 

21 I'm all for big goals. I would beg you to --

22 and I agree with the idea of encouraging that science. I would
Page 705

1 request that alternative, maybe a little bit less sophisticated

2 approaches, get some more detailed discussion. That would also

3 be very helpful.

4 DR. KENNETH PORTIER: Dr. Collier?

5 DR. ABBY COLLIER: I'd just like to reiterate

6 that before I launched into a tirade about PBPK models, I did

7 very directly say that you can take data from a simple PK model

8 or even an adult PBPK model and use an allometric approach or

9 even a partially physiologically-based approach, which is what,

10 adding a couple of math equations if you want to be really

11 blunt about it. And it's really approximate. But according to

12 Anderson 2010 and Anderson in Hull (phonetic) for 2009, it's

13 actually not a bad approach for pediatrics. So that's a simple

14 way of getting it faster if the agency is concerned about

15 timing.

16 DR. ANNA LOWIT: That's an excellent example.

17 And then I would ask if we could take the next step further. 

18 You know, that's a great example. That's something that can be

19 done relatively quickly. If you could help us understand where

20 those uncertainties would be, you know, because this is really

21 about uncertainty analysis. If we were going to do a less than

22 gold standard analysis, where would the uncertainties be and
Page 706

1 what would be the direction of those uncertainties? And would

2 there be a way to get a handle on the quantitative? Are we

3 talking order of magnitude, just a few fold? Is it up? Is it

4 down? That kind of sense would be very helpful, or a direction

5 of where to go to find that, that kind of specifics would be

6 very, very helpful.

7 DR. ABBY COLLIER: How long is a piece of

8 strength? So, the reason that we are moving from PK analysis

9 to PBPK is not because we like playing with computers. It's

10 because it's more sophisticated. But along with sophistication

11 comes time, energy and things.

12 But the basic PK model is relying on

13 something like three quarter power. They do have a lot of

14 uncertainty in it. And the one thing I think that's difficult

15 with that type of approach is that you are going to end up

16 missing some of your most vulnerable populations. So it's the

17 same in that you're going to catch your most of the people, but

18 it depends on what agency is really, really concerned about. 

19 And we're talking about vulnerable populations anyway;

20 pregnancy pediatrics.

21 The thing with the more blunt the model, the

22 more likely you are to miss subtleties where toxicity is
Page 707

1 important, and that's as much guidance as I can give you

2 without sitting here and trying to build a model for you on the

3 back of an envelop.

4 DR. KENNETH PORTIER: Dr. Lu?

5 DR. CHENSHENG LU: So I think, in my brain, I

6 think I have things cut out for agency to do for the next year

7 or two. I think that might be very interesting. So let's use

8 Columbia study as example. 

9 Knowing that, what we believe is the indoor

10 chlorpyrifos in the air contribute the majority of exposures,

11 right? That's why you don't see anything after the phase-out. 

12

13 So each agency can use whatever the PBPK

14 models have in the literature and then select one that you

15 think that by focusing on inhalation exposure will be the most

16 ideal and reliable pathway. And then the PBPK model estimate

17 of urine concentration would be somewhat in the range of -- and

18 you measure in the maternal urine. 

19 Then you can use that PBPK model and convert

20 it to a verse dosimetry, in terms of whether the level that you

21 see in the cord blood would be correlated with the indoor air

22 concentration that was used as input parameters. And then you
Page 708

1 can shut down the inhalation components because there is a

2 phase-out period and see whether the reverse dosimetry would

3 adequately describe what happened after the inhalation

4 component got taken away from the model, and see whether you

5 can get the same or close to the same level of the chlorpyrifos

6 in cord blood. 

7 And if you're satisfied with the verse model,

8 I think you have accomplished a great deal in terms of

9 sensitivity analysis, because you can see how far off that

10 manipulation in terms of inhalation-yes verses inhalation-no,

11 and then to determine whether you're satisfied with the

12 sensitivity analysis.

13 But again we talk about three cohort study. 

14 But Columbia tend to stand out as the most valuable to this

15 whole exercise because they have indoor air concentration

16 measured both before and after the phase-out. And that could

17 become a very important parameter for PBPK model exercise.

18 The PBPK model we published in 2010 rely on a

19 couple of measurements that we have on the food injection. 

20 Without that food injection, as we state in the paper, that the

21 model actually grossly underestimate the true exposure. So I

22 think Columbia study may behave [sic] the same pattern that the
Page 709

1 study that we did way back in 1998.

2 DR. KENNETH PORTIER: Dr. Buckley?

3 DR. BRIAN BUCKLEY: I really want to echo

4 what Dr. Lu just said. That's exactly what I was referring to

5 in terms of using the exposure media data to validated your

6 PBPK model. And no more studies need to be done. It's not the

7 only study that you have both media data for and urine

8 excretion. 

9 So if you have a rugged model you can move it

10 past the Columbia study NHEXAS. You can move it into all sorts

11 of other studies where you have environmental media and you

12 have urinary output. And it shouldn't be a three-year project,

13 you know? That should be something that you can accomplish

14 fairly quickly and I encourage you to do it.

15 But I think Dr. Lu sort of gave you a

16 step-by-step-by-step as to how to valid that PK model, that's

17 exactly what I was referring to in terms of utilizing the

18 concentrations and the media to validate your exposure

19 estimate, and therefore, your PBPK model.

20 DR. KENNETH PORTIER: So I want to remind Dr.

21 Lu, Dr. Buckley and Dr. Collier, make those notes and pass them

22 on. And Dr. Meek then has the fun task of incorporating this
Page 710

1 trying to answer that question. That's kind of why I asked the

2 question, is, I felt that we concentrated too far on the PBPK

3 side and not on the simple side. Any additional comments to go

4 in here? 

5 DR. ANNA LOWIT: Can I just push one more

6 question?

7 DR. KENNETH PORTIER: Sure.

8 DR. ANNA LOWIT: I'm sorry to keep being a

9 pain, but this one is really, really important. So I love what

10 I heard. I think that's excellent. But what I didn't hear is

11 that one last connection. 

12 So, in theory, what you are proposing is to

13 take something of one of the existing models, whether it's the

14 Timchalk or Lu's adaption or the Hattis. All of these are good

15 efforts but incomplete in their own pieces. 

16 To take one of those or all of them or any of

17 them and to do these analysis -- and we are still going to be

18 at the end and then they left with that, sort of

19 elephant-in-the-room uncertainty of what about the fetal

20 compartment, and how do we get a handle on the direction of

21 that uncertainty? Are we over? Are we under? Does it wash

22 itself out by doing multiple approaches or does the bounding
Page 711

1 give you a handle on that? 

2 DR. KENNETH PORTIER: Dr. Lowit is looking at

3 Dr. Carr and Dr. Collier for some insight here and they are

4 kind of shaking their heads like, "Okay, you're opening a big

5 box of --"

6 DR. ABBY COLLIER: Dr. Collier was staring at

7 Dr. Carr going, "Save me." I'm dying to tell you something

8 that you won't like to hear. If you really want to sort out

9 what's going on in the fetal compartment you need to study

10 what's happening in the placenta and you need to see what's

11 going on potentially as well in the amniotic fluid.

12 It doesn't have to be a giant, you know,

13 follow a thousand women over pregnancy, but you need some basic

14 estimates to start building your model. There are people

15 doing studies like this. There are labs that have them up and

16 running. They are not studying chlorpyrifos. And to find out

17 what's going on in the fetal compartment you need to understand

18 the flow. It's more of a flow issue than a metabolism issue, I

19 think, at this stage, so that you can find out whether or not

20 you've got equilibration between the mom and the fetus, because

21 that's not at all clear from the umbilical cord bloods and the

22 maternal serum bloods.
Page 712

1 So whether or not you've got equilibration,

2 which makes everybody's life a lot easier, including Dr.

3 Hattis' model, or whether or not there's something else going

4 on, in which case you need that separate compartment. So it's

5 potentially more about metabolism at this stage. That's how I

6 would answer that question.

7 DR. KENNETH PORTIER: Dr. Carr, do you have

8 anything to add to this? Dr. Carr says no. Dr. Vena?

9 DR. JOHN VENA: Just one question. Did any

10 of the studies collect placenta or save any of the placenta?

11 DR. KENNETH PORTIER: Dr. Christensen?

12 DR. CAROL CHRISTENSEN: Not to my knowledge. 

13 But CHAMACOS study did make a measure in amniotic fluid though.

14 DR. JOHN VENA: You may want to check with

15 them because we did a study in Buffalo. There was 100

16 perspective pregnancies and there was one guy that we had that

17 did collect some placenta, a small group. But I know there are

18 other studies throughout the nation where they've collected

19 placenta. I would be surprised that they would not have

20 attempted at least a subset to get some placenta.

21 DR. KENNETH PORTIER: I think Dr. Lu and then

22 Dr. Collier.
Page 713

1 DR. CHENSHENG LU: I agree there is a

2 gigantic elephant in the room. But I think, in terms of a

3 fetal compartment, I don't think that's an uncertainty issue. 

4 It's a scientific issue because the science has not advanced to

5 that level yet. 

6 But if we can get a good hold on the

7 chlorpyrifos level in cord blood, I think it's a huge

8 accomplishment. If the agency can describe

9 pharmacokinetically, in terms of how chlorpyrifos levels change

10 in the cord blood over that nine months of pregnancy period,

11 that would lead to observable neurobehavioral change. I think

12 that's a huge accomplishment in terms of the rest of the --

13 like, for example, placenta. You know. We don't even know

14 should we collect the whole placenta or portions of the

15 placenta. Which portion of placenta should we collect, and so

16 on and so forth.

17 We have a colleague that -- she actually

18 collects her own placenta, and she says it's a pain in the neck

19 to collect the whole thing. And where are you going to store

20 it, right? So again, I don't think that's a sensitivity issue.

21 I think it's more like a science issue that we don't really

22 have that kind of capability yet. So I don't even thing that
Page 714

1 we should stop right here. 

2 As far as I can tell, if I can understand the

3 pharmacokinetics or the dynamics of chlorpyrifos levels in cord

4 blood, I would be very satisfied with this whole exercise.

5 DR. KENNETH PORTIER: Dr. Collier?

6 DR. ABBY COLLIER: So, to follow up on that,

7 Dr. Lu, how do you think it gets into the cord blood? Cord

8 blood is a fetal exposure. It gets across the placenta. So if

9 we can come up with some sort of equation that describes it

10 without actually studying the placenta, that's fine. I mean,

11 that's really good.

12 I guess I should probably tell you this. 

13 The University of Hawaii has a placenta bank with 15,000 DNA

14 samples. And it is eight and a half thousand mother/child

15 peers with DNA. And we don't have placenta for all of them,

16 but we do have placenta for about, I'm going to say 5,000 of

17 them, as well as amnion and chorion rolls. We don't have

18 amniotic fluid, and I'm the director of the biorepository.

19 And that's actually been running for nine

20 years now, so I think we may have missed the chlorpyrifos

21 cut-off, is the problem. It's that the placentas that we do

22 have, you wouldn't be able to necessarily do an environmental
Page 715

1 study on it because I think it started in '02, maybe, maybe

2 '03. But if you had a scientist that was running those

3 studies, that may be something.

4 So people are talking about how would you

5 collect them and where would you store them, those are some

6 issues that we've solved. I just maybe should mention that. 

7 And we're not the only one. There are other researches. I

8 mean Roberto Ramirez (phonetic) has an amniotic fluid bank,

9 although it's for his private research. It's not a

10 biorepository. And there are several researchers nationwide

11 that have these types of repositories but they are primarily

12 for their own research and they don't make them available as a

13 repository, so they do exist though.

14 DR. KENNETH PORTIER: Dr. Sandy?

15 DR. MARTHA SANDY: This is not quite as

16 exciting as what we just heard. But I'm thinking, because it

17 is so difficult to figure out what the pharmacokinetics are in

18 the fetus and in the fetal compartment, can we step back and

19 say, if we can just get a better handle on the maternal

20 exposure? Because really that's what you would want in the end

21 control for, how much of whatever substance we're worried

22 about, in this case chlorpyrifos, is in the air and the diet in
Page 716

1 the water, et cetera. What's a maternal exposure that results

2 in effects in the fetus? So we still need some way to model

3 better and predict over that 9-month time course based on one

4 sample in cord blood and maternal blood what the exposures

5 were. But I'll throw that out as another alternative.

6 DR. KENNETH PORTIER: Dr. Buckley?

7 DR. BRIAN BUCKLEY: I think you know this but

8 the New Jersey study did do pesticides in cord blood, so

9 certainly you have some of the data that potentially you could

10 work with.

11 DR. KENNETH PORTIER: I think what I'd like

12 to do at this point is kind of take our 15-minute mid afternoon

13 break. We'll come back and continue to discuss this before we

14 go to 5.4. I see wheels turning, but maybe a little bit of

15 coffee might speed those wheels up. We'll return, let's just

16 say 10 till 3:00.

17 (BREAK)

18 DR. KENNETH PORTIER: As I expected, coffee

19 works wonders, and we've gotten a lot of good conversation

20 going on. I know Dr. Meek has talked with Dr. Lowit about

21 restating the question so we know exactly what EPA wants us to

22 answer. And I think there was a little bit of confusion on the
Page 717

1 question. But before we get to that -- because Dr. Lowit spent

2 so much time talking. She hadn't had a chance to take her

3 break. While she's out of the room I'm going to ask Dr.

4 Collier, who met with Dr. Lu and they've come up with another

5 topic to discuss under this. Dr. Collier?

6 DR. ABBY COLLIER: It's not so much another

7 topic. In talking with Dr. Lu, Dr. Lowit was really concerned

8 about how to move forward in a timely manner with a minimum

9 level of uncertainty. And Dr. Lu and I sort of talked about

10 how building these sophisticated models take a long time. They

11 are very intensive.

12 And we think that, perhaps the best way to

13 move forward is to take the studies you have where you have

14 maternal sera and fetal cord blood and to analyze those with

15 respect to coming up with an equation that describes those

16 studies, because they are all slightly different.

17 Looking at the data that was presented and

18 the data that we looked over before we arrived, there is a

19 consensus. And so an equation could be built that models

20 between those five or six studies, and that would give you your

21 first basic idea of what the transfer to the fetus is. 

22 And as an initial starting point, I think
Page 718

1 that would be very valuable and it would meet the objectives

2 that you need to meet in the manner that you need to meet it,

3 as long as the understanding was that it's a starting point. 

4 Alex, do you want to comment?

5 DR. CHENSHENG LU: The only thing I would

6 like to add too, which might introduce another layer of

7 uncertainty, is that, because when we measure cord blood we

8 measure at the birth, so we don't know what happened in the

9 previous month. So if the interest is to look into this whole

10 integrated exposure over that period of nine months then we do

11 need to consider whether there will be a reliable PBPK model

12 for just cord blood itself, independent from the chemical of

13 interest, so we can extrapolate the level that we measure at

14 birth in cord blood and to whatever month of your interest, the

15 second month or third month. But knowing that, that could

16 introduce additional uncertainty.

17 But I think, you know, we agree that this

18 might be the most immediate solution to tackle these issues

19 without waiting for the new data or for new models.

20 DR. KENNETH PORTIER: So the kind of model

21 you're talking about is more an empirical one, right? A

22 regression model, not a mechanistic model, right? So the first
Page 719

1 link would be making that relationship between cord blood,

2 maternal blood. Dr. Meek, did you learn?

3 DR. BETTE MEEK: No, that's fine. I think

4 the discussion has evolved since that.

5 DR. KENNETH PORTIER: And Dr. Lowit, we

6 decided to solve the problem while you were not in the room. 

7 Typically that works the best. No. I wanted to ask if you had

8 any additional questions on this question, insights. I think

9 we've tried to answer the question here. At the break, I got

10 the feeling from most of the panel that the ultimate answer is

11 still going to be an appropriate PBPK model, eventually. To

12 answer the kind of questions that you need you're going to need

13 to be -- and that's the message that we gave in 2008 and

14 probably the same one in 2011 -- I forget when the last

15 chlorpyrifos -- so the panel is at least consistent in it's

16 messaging. That may not help you in the short-term but I think

17 in the long-term we still feel that there's a lot of value in

18 doing the right kind of integration of these biomonitoring data

19 through a knowledge-build physiologically-based

20 pharmacokinetic, pharmacodynamic model.

21 DR. ANNA LOWIT: I appreciate the added

22 addition, and I appreciate you letting me push you a little bit
Page 720

1 to try to understand our needs, so thank you.

2 DR. KENNETH PORTIER: So any additional

3 comments on question 5.3? So why don't we move on to 5.4,

4 which is another long one?

5 LT. AARON NIMAN: Characterization of

6 chlorpyrifos exposure experienced by women in the Columbia

7 cohort, particularly during the pre-cancellation period,

8 remains an important uncertainty in using these data and

9 quantitative risk assessment. Exposure levels in the range

10 measured in the cord blood data from the epidemiology studies,

11 pg/g plasma are probably low enough that it is unlikely that

12 the cohort mothers would experience acetylcholinesterase

13 inhibition at the time of delivery. 

14 However, the biomonitoring data were taken

15 after birth and not necessarily associated in time with an

16 application in chlorpyrifos. As such, the actual of such

17 exposure, particularly during any critical windows of

18 susceptibility is not known, and a better understanding of the

19 range of possible exposures and the degree to which they may or

20 may not have elicited inhibition of acetylcholinesterase

21 remains a key scientific question.

22 In light of panel discussions of questions
Page 721

1 4.3 and 5.3, please suggest approaches and/or analyses which

2 would inform the understanding of the degree to which exposure

3 levels experienced by the Columbia Cohort participants may or

4 may not have been below doses which result in 10 percent

5 inhibition of acetylcholinesterase in the most sensitive life

6 stages.

7 Please discuss the strengths and

8 uncertainties associated with such analyses. Please include in

9 your discussions approaches involving chlorpyrifos and its

10 metabolites, and also chlorpyrifos plus other

11 acetylcholinesterase inhibiting pesticides, for example,

12 propoxur diazinon, which the cohort participants were exposed

13 to.

14 DR. KENNETH PORTIER: Dr. Buckley, not an

15 easy set of questions.

16 DR. BRIAN BUCKLEY: Well as per my other

17 questions, I have a script but I'm going to deviate from it

18 almost immediately because of the discussion we just finished

19 having.

20 I believe that PBPK modeling is one way to go

21 about it. And even if it's something as simple as a single

22 compartment, half life-based PBPK model, you can get a rough
Page 722

1 idea of what mom might've been exposed to.

2 You can certainly look at some of the

3 equation modifications that Dr. Collier was talking about to

4 get an estimate of what trial was exposed to. But in order to

5 really get an idea of what the overall process is, especially

6 since chlorpyrifos has such a short half-life, this is where

7 you really need to do dose reconstruction. 

8 This is where you really need the information

9 about when was the application made with respect to when was

10 the samples taken. And then how many times the applications

11 was made over the course of the pregnancy and how much was

12 applied. That's the type of dose reconstruction that's going

13 to give you as much a feel for whether or not the infant or the

14 fetus was at risk as trying to back calculate a dose or a fetal

15 cord blood concentration at birth projecting six and half or

16 seven months ago. Okay?

17 So if you have a rough idea of approximate

18 does versus cord blood concentration, then you can reconstruct

19 dose based on the surveys of the people and how often they

20 applied the pesticides, et cetera, et cetera. All right? But

21 you're going to need both, and it's the dose reconstruction

22 that's going to tell you whether or not there was true risk
Page 723

1 back in the day from the time it was applied.

2 And that's off the cuff comments based on the

3 discussions we just had. Now I'm going to go through the

4 comments that were put together by the rest of the panelists

5 here.

6 It's important to realize the short half-life

7 of chlorpyrifos and its metabolites in the body, and it calls

8 into question any spot data that might be used. 

9 Large cross-sectional investigations may

10 catch some exposures but did not put them into context. And

11 longitudinal investigations clearly are going to be your best

12 source of information in constructing a model.

13 Understanding the limitations to the data

14 within the context of a PK/PD model, having the potential to

15 estimate dose at a given time since exposure may provide some

16 information. But once again, go back to the dose

17 reconstruction.

18 Information that's been required for

19 reasonable estimation of maximum dose would be whether the

20 exposure dose was a steady-state or a bolus, and approximately

21 how long after the bolus exposure was the sample collected. 

22 Once again, the best way to validate your PK/PD or any model
Page 724

1 that you chose.

2 With a very simple one-compartment model and

3 time after exposure, reasonable estimate of the maximum dose

4 delivered to mom and whether or not acetylcholinesterase

5 inhibition threshold was reached, but a more sophisticated PBPK

6 model may provide even better data assuming that PBPK model is

7 realistic for the fetus. Okay?

8 Previous panelists have noted that the

9 decided lack of realistic PK/PD models for chlorpyrifos exist. 

10 You've gotten some more feedback as to better models, so

11 hopefully you'll find one that's more effective. 

12 And effective PBPK model is one that is

13 applicable to target populations such as pregnant women and

14 small children. A lot of this stuff has been discussed so I'm

15 going to bypass most of these comments because we've covered

16 them already.

17 Many of the studies discussed in 5.1 provide

18 data on the concentration of chlorpyrifos and media. And

19 market basket data exist. These are the primary tools for

20 generating an effective exposure assessment and subsequent

21 reconstruction of potential dose. It's a further to what Dr.

22 Lu said earlier on and what I reiterated. But dose
Page 725

1 reconstruction can be used to evaluate the efficacy of the

2 PK/PD model as well as to predict how much the exposure was

3 over the course of the pregnancy. Once again, most of these

4 comments we've already offered, so I'm not going to re-read

5 them.

6 Now, the effect of mixtures of chlorpyrifos

7 and diazinon, chlorpyrifos and propoxur, chlorpyrifos, propoxur

8 and diazinon have not previously been considered. 

9 There's no easy way to approach this. PBPK

10 models are tough enough with one component systems. We are

11 just starting to get a feel for competitive inhibitions or

12 additive effects or both. And like all mixtures, both

13 constructive and destructive interferences can occur.

14 Questions will have to be addressed: Do they

15 affect each other's half-lives and distributions in clearance

16 through the metabolic competition? Are there net

17 acetylcholinesterase effects additive or multiplicative? Do

18 they share mechanistic pathways? To address this, we recommend

19 further studies describing 5.3 to improve estimates of effects

20 when mixtures of antibiotics are used as compared to single

21 agents.

22 Probably not in enough time for you to get
Page 726

1 your stuff done because you are looking at a two-year window. 

2 Mixture studies are difficult to do but there are mixture data

3 out there that might be used for at least modifying your PBPK

4 models if you think that the competitive pathways exist along a

5 single metabolic pathway. All right? If they utilize the same

6 enzymes for metabolism, you might be able to modify your PBPK

7 to say two similar pesticides will actually compete for

8 enzymatic metabolic processes and you won't have the same

9 thing, or they may actually enhance one another in terms of

10 their net effect. So both of which you're going to have to

11 deal with data where people are studying the mixtures.

12 Using currently available data other than

13 improving in silico PK or PK/PD approaches, again, described in

14 5.3, I'm not sure there is more the agency can do to reanalyze

15 or transform the available data into meaningful studies.

16 Once again, more discussion that will be

17 included in the report that I don't need to cover here, but

18 I'll just bullet point reiterating the need to potentially

19 separate a 10 percent acetylcholinesterase inhibitory effect

20 with any sort of other negative health outcomes should probably

21 be considered, or should definitely be considered. They don't

22 necessarily have to be tied to one another.
Page 727

1 The 10 percent marker might just be a nice

2 threshold upon which we've done things in the past. We should

3 look to other potential biomarkers, other potential outcomes as

4 drivers for creating risk assessment. And I think that that's

5 about it.

6 DR. KENNETH PORTIER: Dr. Collier?

7 DR. ABBY COLLIER: I agree with Dr. Buckley.

8 DR. KENNETH PORTIER: Dr. Lu?

9 DR. CHENSHENG LU: So just to continue what I

10 respond to the previous question, and also looking at this

11 graph, is that, no matter what you're going to do, you won't

12 see 10 percent inhibition of acetylcholinesterase in a Columbus

13 study just because, at the level of 100 micrograms of TCPy per

14 gram of creatinine, the acetylcholinesterase still remains

15 uninhibited.

16 Sure, this is et al. data. So let's put

17 safety factor at 100 to the newborn babies. We're talking

18 about 10 micrograms of TCPy and the urine still don't inhibit

19 acetylcholinesterase and that 10 micrograms of TCPy happen to

20 be the 95th percentile of the general population in this

21 country.

22 So again, I want to emphasize, just because
Page 728

1 of this question, and also question number 6, to separate this

2 whole regulatory activity by chronic and acute. I still

3 believe that we are talking about two different things and it

4 cannot be mixed together as one thing. You have to have an

5 acute endpoint. You have to have a chronic endpoint. We are

6 talking about two different things you cannot mix together.

7 You know, the study that's recently published

8 sort of reinforce my belief that you won't see 10 percent of

9 acetylcholinesterase inhibition at the level that people

10 exposed to. It doesn't matter if it's the adults or the kids. 

11

12 DR. KENNETH PORTIER: Dr. Ryan?

13 DR. BARRY RYAN: The points I wanted to raise

14 with respect to this question had been brought up in other

15 questions, so I really have nothing to add at this point. Dr.

16 Buckley summarized our points of view here. The group has

17 worked together a little bit on this anyway, and I think we

18 should just move on.

19 DR. KENNETH PORTIER: Any additional

20 comments? Yes, Dr. Carr?

21 DR. RUSSELL CARR: The last part of it where

22 it talks about the mixture of propoxur and diazinon, and it
Page 729

1 says which of the cohorts participants were exposed to too,

2 also, what we've got to consider there too is everything else

3 they were exposed to. I mean, you've got PAHs in there which

4 could balance the HR receptor, which could modify P450,

5 manipulations of the organophosphates. You've got even

6 piperonyl butoxide. And those had been listed in the

7 epidemiology studies as leading to neurobehavioral, the

8 deficits in these children in this cohort.

9 And so, you know, we just got to make sure

10 that we don't just narrow the focus on the OPs right here

11 because we've got a lot of other actors here that could

12 potentially come into play, and I know that's really difficult

13 to handle. And I don't really have an answer but I just think

14 we need to just keep that in the back of our mind.

15 DR. KENNETH PORTIER: Do we have any

16 conservative assumptions that can be made with regard to the

17 mixture issue that would kind of simplify it down to

18 chlorpyrifos equivalents, especially for the

19 acetylcholinesterase inhibition outcome? Dr. Buckley?

20 DR. BRIAN BUCKLEY: I've only recently delved

21 into the whole mixture scenario. 

22 I don't think additives are protective
Page 730

1 enough, but it probably gets you to the middle of the road, in

2 terms of overall. And it's where people generally start. But

3 the reality is, until you get in and study, you know, you don't

4 know what's going to be more than additive at this stage of the

5 game.

6 At the same, this is a potential for being

7 less than additive as well. You know. If you just take the

8 concentration of the two and add the effects, that's somewhat

9 protective. But there are certainly opportunities, as we

10 discussed earlier on -- you know, DMSO (phonetic) not a big

11 thing. Chlorpyrifos has it's known, but if DMSO makes

12 chlorpyrifos much more bioavailable because it opens up

13 transport mechanisms all of a sudden, you've got more than an

14 additive effect at that point in time.

15 And we'll continue to study more of these

16 things with time. But up until now, people have been somewhat

17 afraid to delve into -- well I shouldn't say that. People have

18 been cautions in their approach to dealing with mixtures just

19 because it's tough enough to study one component at a time

20 versus the other. It's not a recommendation but they can start

21 with additive effects and see what that does.

22 DR. KENNETH PORTIER: Dr. Lu?
Page 731

1 DR. CHENSHENG LU: I think the mixture is

2 kind of a complicated subject. But I think in this case, not

3 just the Columbia study, I think the NHANES study and our study

4 as well, chlorpyrifos is frequently detected in the samples,

5 whereas diazinon or other pesticides -- the frequency of

6 detection is much, much lower. So in terms of the defect from

7 the mixture of pesticides, it's very easy to tease out

8 diazinon. And other pesticides because the frequency of

9 detection is somewhere around 10 percent, whereas chlorpyrifos

10 is somewhere 70/80 percent.

11 DR. KENNETH PORTIER: Dr. Pope?

12 DR. CAREY POPE: Dr. Lu, is the frequency of

13 the detection based on the detectability of the diazinon, for

14 example, or is it because of the levels of the diazinon

15 relative to chlorpyrifos? The table handed out by EPA just

16 this morning showed that, at least in the slimmest value,

17 there's like more than twice as much diazinon being used there

18 than chlorpyrifos.

19 DR. CHENSHENG LU: I don't exactly know that

20 they use these patterns, but I think diazinon is commonly used

21 in agriculture where, before the phase-out, chlorpyrifos is

22 commonly used in the residential setting, the pest control and
Page 732

1 so on. 

2 I think you can look at the NHANES data

3 somewhat. I agree that that's what I remember, that frequency

4 detection for each pesticide used in those studies are

5 dramatically different compared to chlorpyrifos.

6 DR. KENNETH PORTIER: Dr. Popendorf?

7 DR. WILLIAM POPENDORF: I've got a couple of

8 comments. Just looking recently in the Whyatt 2007, actually

9 air concentrations diazinon were about four times the air

10 concentration of chlorpyrifos, in terms of mean values. It's

11 an odd-looking distribution but the actual concentrations are

12 quite a bit higher.

13 My other comment though is combining sort of

14 Dr. Buckley's and Dr. Lu's comments about using blood and

15 modeling. And we don't really know what the last application

16 was, but we not only have blood so we could do what you

17 suggested Dr. Lu but we also have the array of air

18 concentrations. And one could chose the highest value there as

19 with the highest value in blood and find out does the highest

20 blood value produce cholinesterase; does the highest air value

21 give you a dose that would produce cholinesterase.

22 And one can make the assumption or argument
Page 733

1 that the highest air value may represent in fact the most

2 recent application.

3 DR. KENNETH PORTIER: Dr. Dawson?

4 DR. JEFF DAWSON: Just a little clarity about

5 chlorpyrifos use. Definitely, at the time it was in the

6 residential market, this was and still is a major agricultural

7 use pesticide kind of broadly used on a variety of crowds, so

8 millions of pounds per year.

9 DR. KENNETH PORTIER: Dr. Lu?

10 DR. CHENSHENG LU: Just to clarify Dr.

11 Popendorf's comment; we're talking about frequency of

12 detection, not concentration, in terms of chlorpyrifos and

13 diazinon. There's a frequency of detection.

14 DR. WILLIAM POPENDORF: I think they were

15 actually detected very close to -- 

16 DR. KENNETH PORTIER: One is in blood and one

17 is in air.

18 DR. WILLIAM POPENDORF: I'm talking air,

19 yeah.

20 DR. KENNETH PORTIER: You're talking air --

21 are you talking air or are you talking blood?

22 DR. CHENSHENG LU: I'm talking about
Page 734

1 concentration of urine, metabolites. 

2 DR. WILLIAM POPENDORF: Okay. I'm talking

3 air, in terms of dose.

4 DR. CHENSHENG LU: Okay. 

5 DR. WILLIAM POPENDORF: Environment versus --

6

7 DR. KENNETH PORTIER: So media matters here. 

8 Media matters. Dr. Buckley?

9 DR. BRIAN BUCKLEY: Yeah. I just want to

10 re-emphasize what Dr. Popendorf said but to bring it back

11 again. The environmental media, their concentrations can be

12 used to extrapolate or to project a max dose. And I would

13 encourage you to use more than just air, but use the market

14 basket survey information to estimate the contributions from

15 diet, the dust. Anything else that you have matrix on, you use

16 them all to reconstruct that dose. And that should give you an

17 idea of what the max concentration was that the fetus might be

18 exposed to.

19 DR. KENNETH PORTIER: Yeah. Dr. Buckley, I

20 think that was the major topic of the 2011 panel where we

21 looked at linkage between the exposure modeling and the PBPK,

22 right? I think we did that. Was that 2010?
Page 735

1 DR. ANNA LOWIT: Just food though, just food.

2 DR. KENNETH PORTIER: Oh, just food, that's

3 right. Just the food part. But I think they've been working

4 on that.

5 DR. BRIAN BUCKLEY: Well it seems like a lot

6 of the models are based primarily on an air exposure route and

7 inhalation root, solely. And really, you've got to sum all the

8 potential exposures.

9 DR. KENNETH PORTIER: Jeff Dawson?

10 DR. JEFF DAWSON: Just one other thing. I

11 think this mentioned this yesterday, but in Appendix 1 of the

12 document we gave a flavor of that. We took some of the

13 scenarios of our risk assessment and put them in as a few

14 representative ones to try to illustrate how we do that

15 routinely in risk assessment, if you want to look at that.

16 DR. KENNETH PORTIER: Dr. Sandy?

17 DR. MARTHA SANDY: Yeah. And just to follow

18 up on what you said, Dr. Portier, it was February 2011 that the

19 panel looked at the PBPK/PD model. And one of the comments

20 that was made by the panel at that time was you do need to take

21 into account all routes of exposure, including the

22 hand-to-mouth pathway, inhalation diet and others demo -- 
Page 736

1 DR. KENNETH PORTIER: And barefoot in the

2 house. Any additional comments on this question? Dr. Lowit?

3 DR. ANNA LOWIT: I think we are good. 

4 Thanks.

5 DR. KENNETH PORTIER: So Dr. Buckley, you got

6 all the notes of all the discussion, right? Good. 

7 DR. ANNA LOWIT: So we're moving on to

8 question 6?

9 DR. KENNETH PORTIER: Do you want us to take

10 a break while you guys reassess? 

11 DR. ANNA LOWIT: No. 

12 DR. KENNETH PORTIER: I think we are ready to

13 go.

14 DR. ANNA LOWIT: At the break we congressed,

15 and I think the things that we heard didn't change our

16 thoughts. So before we read question 6, Dr. Vicky Dallarco,

17 Senior Advisor to our office director is just going to sort of

18 lay out where we think what we've heard sort of at the

19 thousandth, 10 thousandth foot level in order to setup question

20 6.

21 DR. VICKY DALLARCO: Thank you, Anna. Yeah. 

22 We were huddled during the breaking making sure that we had our
Page 737

1 head around everything that was being said, and the discussion

2 has been excellent. And over the last couple of days, you have

3 really helped us in pointing out strengths and limitations and

4 uncertainties around the lines of evidence. 

5 But the analysis that we'll do at this

6 compound is going to be unique for us, and so clarity is really

7 important. So we want to make sure we understand what you're

8 saying, that's why we want to repeat it and make a couple of

9 points. And what you're actually doing around these different

10 questions, you're laying down our roadmap and we want to make

11 sure we understand that roadmap.

12 And so the points I'm going to make will get

13 at some of the comments that we made today. So what we heard

14 yesterday is, the rat is not an invalid model to use for a

15 point of departure. But we heard today, there is uncertainty

16 around that model.

17 And we also heard the recommendation that, to

18 look at the epidemiology in a quantitative way or a

19 semi-quantitative way to characterize the uncertainty around

20 the rat or the rat points of departure. And so, as we do that,

21 Anna has emphasized in the remarks that she's made how

22 important it is to understand as we look at the different
Page 738

1 approaches that you're recommending -- like the idea approach

2 to do that is that PK model for this susceptible population

3 that we don't have. 

4 But we heard some other approaches that we

5 could do that sort of comparison dose reconstruction. But as

6 we look at these different approaches, and some of the

7 uncertainties and limitations you've been discussing need to be

8 put in the context of these approaches as we do these

9 comparisons or this bounding. I actually thinks this helps

10 sets up 6, so you want to make sure that you have uncertainty

11 here with this rat model. You're using the human information

12 or the Epi data to try to characterize that, and with these

13 different approaches, to have a clear understanding of, well,

14 what are the assumptions we're invoking as we do that. What

15 are the uncertainties, because that's important to understand. 

16 In other words, you're not invoking so many assumptions. You

17 have so much uncertainty here in trying to characterize the

18 uncertainty there. 

19 So that clarity, kind of bringing that

20 together will be helpful as you talk about the approaches in

21 how we do this. And when we look at the observational findings

22 in the epidemiology and where we see neurobehavioral effects in
Page 739

1 the rodents, cholinesterase inhibition, if you look at that,

2 we're talking around four orders of magnitude difference. And

3 we understand that rats and humans, the dynamic process of

4 neurodevelopment is different in terms of timing. We

5 understand there're pharmacokinetic differences. You've done a

6 very good job in pointing that out, but it would be helpful if

7 you could provide some perspective about how the combined

8 physiology between the rat and human could account for so many

9 orders of magnitude difference to try to put that in a

10 biological context or some biological plausibility around that.

11 Or maybe some of these approaches that you're suggesting that

12 we do can help maybe narrow that margin down a little bit so

13 it's more plausible, more reasonable. So anyway, that's

14 basically some of the points we heard and some of the

15 connections. And I don't know if anybody wants to make any

16 comments about that, but did I get it right?

17 DR. KENNETH PORTIER: Dr. Meek?

18 DR. BETTE MEEK: Yeah. I think it's a

19 question not just of the biology as well, Vicky, but it's why I

20 was pushing this morning to have some bounding, some

21 semi-quantitative bounding on the estimates from the

22 epidemiological studies. And the way that that's traditionally
Page 740

1 done is that people who have a lot of experience in the area

2 have some idea of how much impact one of these confounding

3 variables might have, whether it's a two-fold or 10-fold or -- 

4 I was asked myself, for example, to look at

5 uncertainty in the context of replacing the kinds of default

6 assumptions that we normally use with kinetic and dynamic data

7 for adjustment factors. And initially I said, "Well, I don't

8 think I can easily do that." And then when I thought about it,

9 I could because I had a pretty good idea of, you know, when we

10 replace the default assumptions with relevant kinetic and

11 dynamic data what the bounds on that were. You know. Was it

12 10, 100, 1000? 

13 So I would challenge particularly the

14 epidemiologist to think a little bit about what these

15 uncertainties that we have in the epidemiological studies. For

16 example, there could be exposure to other pesticides. But does

17 that explain a forward or magnitude difference? I mean there

18 needs to be some semi-quantitative thinking in that direction,

19 so that's kind of what I was trying to get at this morning.

20 DR. KENNETH PORTIER: Anyone else want to

21 react? Dr. Buckley?

22 DR. BRIAN BUCKLEY: Yes. Something you said
Page 741

1 about not having a PBPK model. I know that we described one

2 for kids, and I thought we had described at least one or two

3 for adults. But more than anything else, what have been said

4 now by three or four or five panelists, if you have a mechanism

5 to validate your PBPK model, whichever one you chose. And we

6 can't stress that enough in terms of importance with whether or

7 not you want to utilize a PBPK model that you use the dose

8 reconstruction to figure out whether or not the multiple

9 studies that have environmental media and urine values, that

10 you have the two components you need to decide whether or not

11 the PBPK model that you want to use is valid.

12 Now, we can argue about the fetal component

13 and we can argue about whether or not the kid PBPK model is the

14 one that you want to go with. But having the ability to

15 validate is paramount in assessing whether or not a PBPK model

16 can be utilized.

17 DR. KENNETH PORTIER: I was sitting there

18 thinking. I think we're all kind of processing. Dr. Collier?

19 

20 DR. ABBY COLLIER: So one of the things that

21 I struggle with when you're talking about four orders of

22 magnitude, and I've sort of been thinking about it the last few
Page 742

1 days, is the way in which animal data are used to extrapolate

2 to a whole in vivo person.

3 And if you think about it from like a

4 pharmacology drug development paradigm, we don't skip in vitro

5 human, like metabolic studies and things. And perhaps some of

6 the orders of magnitude are coming from -- interspecies

7 extrapolation is well-recognized and has its uses, but in some

8 cases I can be far, far out. And I'm wondering if, for

9 chlorpyrifos, not so much the behavioral data which I cannot

10 speak to, but more for things like the metabolic data, if we're

11 not really missing something here, because we're trying to jump

12 too far from your rat metabolism into a human outcome, and I do

13 wonder if that's where some of your variability comes from, and

14 I've been thinking about that pretty much since I got here. 

15 That's the suggestion that I can offer.

16 DR. KENNETH PORTIER: Dr. Harry?

17 DR. JEAN HARRY: So, totally different

18 comment than anything that's been said. But one of the things

19 you asked was, you were asking about how do you sort of deal

20 with this animal difference to the human? And the thing that

21 popped in my head is that, we could say timing. We could say

22 length of time that it takes for the brain to develop is
Page 743

1 shorter in one. But the problem is we are looking at the

2 rodent brain as a rodent version of the human brain and there

3 are a number of differences. I mean there are a number of

4 things that are the same. There's the different regional

5 differences and the structural comparisons, the different

6 nuclei of neurons, the transmitters that control different

7 things. So there's a lot of things that are very similar.

8 But just as we mentioned in the in vitro

9 study, if you start to take out a neuro and you look at it in

10 isolation, it is going to perform, more than likely, very

11 differently than when you look at it in its glee environment

12 around it. 

13 So that made me think, you know, one of the

14 different things, is, most of the things that you're looking at

15 were intercepted as looking at a neuronal component. And in a

16 rodent, the neurons and glee and atrocities are sort of split

17 about 50/50. When you get to the human it's a much higher

18 proportion of the cells within the brain or the atrocities, not

19 the neurons.

20 So, while you've got this whole dynamic --

21 and we have the timing -- we also have the fact that the brain

22 is not just a smaller version of the human brain, which is
Page 744

1 another whole dynamics that you wish you didn't have to go

2 into. But if we do, and being a glee person, if we continue in

3 the way that we look at things on a neuronal-centric type

4 thing, we lose a large proportion of the cells within the human

5 brain that have very dynamic roles. You know. The neurons are

6 not going to work unless the atrocities are there. They have

7 all the transmitters, all the receptors. You know. So there

8 is a whole world out there that really hasn't gotten into any

9 of these interpretations in even the in vitro models. So it's

10 not helping you any but it was one thing that you asked that

11 sort of popped in mind. That is something we now know within

12 the last few years that's very different between the rodent

13 brain in the human brain.

14 DR. KENNETH PORTIER: Dr. Popendorf?

15 DR. WILLIAM POPENDORF: Just following up on

16 that a bit. The other day, yesterday perhaps, someone

17 mentioned using primates as an example and I would imagine that

18 would be a step in the right direction. Of course, we are

19 adding years to your solution here, but I would be curious to

20 follow-up. I mean how much more similar would a primate brain

21 be than a rodent brain?

22 DR. JEAN HARRY: A lot more similar. And a
Page 745

1 lot of people have been proposing things like the African green

2 monkey or a marmoset. So in a way it's almost what aspect of

3 brain development you want to look at that might be closer and

4 the distribution. So you don't always have to go to a higher

5 order of the non-human primate. There are some other small

6 primates that you could look at in a much more reasonable

7 fashion, and at which there are colonies around the country

8 that you could use those -- 

9 There was like an African green monkey colony

10 at Wake Forest quite a while ago, but those are being

11 incorporated a lot more into some of the neurodegenerative

12 disease models and things like that. So I think they are

13 starting to find that there is a lot of power by looking at

14 something in the middle versus a Reeses or a rat. There are

15 lots of things in the middle that could work.

16 DR. KENNETH PORTIER: Dr. Buckley?

17 DR. BRIAN BUCKLEY: One other thing, getting

18 back to your floaters and magnitude; Dr. Lu said this with the

19 last question but I want to re-emphasize. Don't underestimate

20 the power of low-does long-term versus acute exposure. Big

21 difference, especially over the developmental timeframe.

22 DR. KENNETH PORTIER: I've been thinking
Page 746

1 about that too. I don't know enough about the developmental,

2 but I always wonder if one acute event at the right time could

3 actually be potentiated and produce this big output, that full

4 order of magnitude could be that one exposure, but we don't

5 know what that time is. I haven't heard anybody say when in

6 the developmental stage would we expect to see a perturbation

7 that would impact IQ at 7 years old. That would be nice to

8 know but I don't think anybody knows that. Dr. Harry?

9 DR. JEAN HARRY: So, I don't know if you want

10 to step back into it, but I guess if you go back and look in

11 the LED (phonetic) literature. And I know over the many years

12 of sitting on this panel at different things there's a lot of

13 concern about the time for hand-to-mouth, so the exposure --

14 but if you look at some of the LED literature and -- I don't

15 know if the Mercury does it or not, but the LED somewhat

16 identifies age three as being very, very sensitive to

17 perturbations that come in. And that's probably where some of

18 the animal studies have been modified to try to look at

19 postnatal day 10 in an animal, thinking that you might be

20 catching about that age 3 in timing, to say if I disrupt

21 homeostasis by this chemical on-board at this critical time,

22 would I potentially have a long-term effect. And I think in
Page 747

1 the developmental biology field, you're going to find people

2 believe that that can be true. You could have that one little

3 episode.

4 And if you look at some of the drug of abuse

5 literature you could probably follow and get supportive data on

6 that, that one exposure just at that time, whether it's in the

7 last trimester or second trimester when you know things are

8 happening, that if you disrupt that, everything builds upon the

9 time before. So you can have this huge accelerated and

10 augmented type effect that you may not see if you have low dose

11 exposure that's not enough to reach a threshold, and the system

12 can change its homeostatic balance so it develops properly. 

13 Everything is sort of delayed together.

14 DR. KENNETH PORTIER: I think now we're ready

15 to go to question 6. 

16 DR. ANNA LOWIT: The 2009 NRC Report, Science

17 and Decisions focused on improving the technical analysis

18 through the development and use of scientific knowledge and

19 information to promote more accurate characterizations of risk,

20 and thus improving the utility of risk assessment for risk

21 management decisions.

22 The NRC Report also pointed out that
Page 748

1 regulatory risk assessment does not routinely approach public

2 health and environmental problems by arraying a wide range of

3 options for dealing with them. 

4 In the case of chlorpyrifos, in light of the

5 questions, discussions of question 1 through 5, please provide

6 guidance for assessing and presenting the range of plausible

7 responses at given doses in the effect of the overall

8 uncertainty and variability around that range.

9 DR. KENNETH PORTIER: Dr. Meek?

10 DR. BETTE MEEK: Well this is a probably a

11 continuation of the discussion we've initiated. 

12 So as I've indicated, in response to another

13 question, I really think it's important to maximally utilizing

14 the available data and dose response from the epidemiological

15 studies as a basis at least to bound referenced doses developed

16 in the basis of points of departure from animal data. And

17 again to the extent possible, we are going to have to

18 characterize around those different estimates our degree of

19 uncertainty. And again, that's why I was kind of pushing for

20 at least some semi-quantitative bounding of the dose response

21 relationship from the human studies based on the impact of the

22 uncertainties.
Page 749

1 This would perhaps clarify the basis for a

2 conclusion that the uncertainties associated with one or other

3 are similar, hugely different. But it's a missing part of the

4 equation from my perspective, currently, as to how we should be

5 using the data on dose response in relation to predicted

6 outcome here.

7 So I think we lose important information in

8 collapsing a huge database on dose response to even the single

9 or a couple of points of departure. So, for example, for risk

10 management purposes, it would be helpful to characterize the

11 nature of the risk above the reference dose. And for that

12 reason, I think that it's helpful to present an array of dose

13 response data for different types of effect, again, bounded by

14 their relative uncertainty.

15 To me, it's much more informative and

16 presents the point of departure for the referenced dose in the

17 context of the range of effects reported in the broader

18 database. 

19 I'm hopeful, as well, that it will also

20 promote our relying on more certain rather than the most

21 conservative data in every case. And as a minimum, I think

22 it's helpful in communicating the relative degree of protection
Page 750

1 provided by the selected point of departure. So I think

2 there's been some confusion in some of the discussion around

3 the table about whether we are confusing acute and chronic

4 effects here or whether we are selecting the point of departure

5 on the basis of it's expected to be protective for other types

6 of effects.

7 So since this last question is rather broad,

8 I wanted to raise one other issue, and it relates to

9 comparability within the agency across compounds, because since

10 I tend to think of weight of evidence in this context -- and it

11 was an issue that was raised by Steve on the first day, so by

12 way of comparison with other datasets.

13 So part of the value of the framework for

14 integrative analysis to evaluate multiple lines of evidence is

15 to enable us to draw inference on weight of evidence from the

16 totality of the data. 

17 And so, for example, in the document it's

18 stated that there is a number of known developmentally

19 neurotoxic chemicals with well established relationships

20 between exposure and neurological disorders in humans for which

21 a definitive mode of action has not been established. For

22 example, lead, methyl, mercury and ethanol.
Page 751

1 So it seem to me that it might at least be

2 helpful as a basis for characterizing our confidence in the

3 results of the epidemiological studies for this dataset to

4 compare the nature of the extent of the data as a basis to

5 estimate dose response in humans with that of other substances,

6 such as lead and mercury. So then you get at least a relative

7 since of how confident you are in this dataset compared to

8 other datasets that you've used for similar applications in the

9 agency. And that's all that I had.

10 DR. KENNETH PORTIER: Dr. Lu?

11 DR. CHENSHENG LU: NRC like to draw big

12 pictures and make recommendations. I was hoping that they

13 would use chlorpyrifos as their case study to see whether they

14 can come up with this kind of recommendation or not, which is

15 very challenging. 

16 But regardless, though, previously NRC

17 published a book that led to the passage of Food Quality

18 Protection Act. So this may lead to something even more

19 significant than just food quality protection that -- I don't

20 know.

21 But again, I want to re-emphasize my strong

22 belief that, in the case of chlorpyrifos, I would suggest the
Page 752

1 agency to separate the endpoint from chronic to acute.

2 Mode of action is obviously a legitimate

3 approach, looking into the toxicological endpoint. But knowing

4 the animal data and the recent human study, it's obvious that

5 in terms of acute and chronic does not match.

6 The human study 2009 indicated that you have

7 to reach certain amounts of exposure in which ordinary people

8 will never experience, will have some sort of measurable

9 acetylcholinesterase inhibition. However, we also see the

10 outcome from the Columbia Study before and after the phase-out

11 of the chlorpyrifos that those adverse neurobehavioral outcomes

12 disappear. 

13 I mean, one of the questions we're asking is

14 whether it's true for chlorpyrifos to likely play a role in

15 those adverse neurobehaviors, if EPA can conduct further

16 analysis to confirm with a certain degree of confidence that

17 that's the case.

18 Now the question is that, whether those

19 neurobehavioral outcomes will be meaningful to, in terms of

20 reflecting on acetylcholinesterase inhibition. Again, question

21 number 5, I'd also say, for sure that you will not see any

22 inhibition, again, because of the study that's recently
Page 753

1 published. That leads to my belief that this is two different

2 things.

3 So in terms of point of departure, it's

4 almost in evidence that agency needs to come up with two points

5 of departure, one for the acute, one for the chronic and focus

6 on neurobehavior. That's what I think will lead to a better

7 outcome. Thank you.

8 DR. KENNETH PORTIER: Dr. Sandy?

9 DR. MARTHA SANDY: I'm going to focus on some

10 broader issues related to the risk characterization. This

11 Lanson (phonetic) decisions report is recommending that, as you

12 do a risk assessment you take into account vulnerability of the

13 populations, so vulnerability factors and background exposures,

14 as well as mode of action. 

15 And as we have discussed over the last few

16 days, we know that there are no developmental effects that are

17 seen in the epidemiology studies, and the odds ratios measures

18 of effect are pretty strong, so, as it was discussed, we think

19 those are real effects. The levels at which they were seen

20 appear to be levels that are not outside the range of levels of

21 chlorpyrifos measured in the U.S. population based on NHANES

22 data, so there are low level effects.
Page 754

1 We have discussed the pharmacokinetic

2 modeling needs to somehow come to a good estimate of what the

3 exposure might've been during that prenatal period. But as we

4 do the risk assessment, you're going to need to discuss in the

5 risk characterization the uncertainty that would be due to -- I

6 would say the major uncertainty as whether protecting against

7 acetylcholinesterase inhibition is going to protect against

8 neurodevelopment effects.

9 Another major uncertainty is whether the dose

10 response data for acetylcholinesterase inhibition in pregnant

11 rats is relevant to pregnant humans and their fetus. Then of

12 course there are other uncertainties. 

13 Variability would be discussed in terms of

14 differences in biologic susceptibility, such as differences in

15 health and disease status, genetics. And here we have talked

16 about the metabolic polymorphisms related to chlorpyrifos

17 metabolism. And life-stage, of course, is another important

18 variability. So we don't know what the critical window is

19 during the prenatal period for exposure to cause these adverse

20 effects. It might be the entire period. It might be a certain

21 period. So in looking at a cohort study and comparing one

22 cohort to another, you might have some variability in the dose
Page 755

1 response estimates from that.

2 And then there's background exposures that

3 could be other exposures to chlorpyrifos if we are looking, for

4 example, at the Columbia study. And because we have air

5 measures but we don't have dietary measures or other sources,

6 in the dose reconstruction, there might be some variability due

7 to, I guess that would be uncertainty, because we don't have

8 definitive information and estimates of what each route

9 contributed. 

10 Another source of background exposure could

11 be, as we have just discussed, exposures to other chemicals

12 that might compete for metabolic enzymes interfering with other

13 stages in the proposed adverse outcome pathway. We don't

14 really have adverse outcome pathways either. There is

15 uncertainty there too. 

16 There is a lot to be discussed in the risk

17 characterization, depending upon what you actually use to

18 derive your point of departure and how you come to your health

19 protective number, and these are just some of the things that

20 should be covered.

21 DR. KENNETH PORTIER: Dr. Sandy, one of the

22 things that I think you missed is that, at least in this case,
Page 756

1 we have the epidemiology in what we probably all agree is the

2 most susceptible population, in the sense that it's done

3 pregnant women who have likelihood of higher exposure.

4 DR. MARTHA SANDY: I am aware of that. I

5 don't know where the agency is going -- what information

6 they're going to use to derive at dose response and a point of

7 departure. Right now they are proposing to use the rat data,

8 so I'm bringing up that.

9 And in discuss just in risk characterization,

10 you have a number you're going to discuss is it protective. 

11 You're going to say that we think the most vulnerable

12 population is likely the fetus. And hopefully you'll be able

13 to say with some confidence you think you're protecting the

14 most vulnerable population. I'm just saying these are factors

15 to be discussed in the risk characterization.

16 DR. KENNETH PORTIER: Anyone else would like

17 to add to this discussion? It's kind of a broad question, so

18 anything you say kind of can get in here. It's your

19 opportunity. Dr. Vena?

20 DR. JOHN VENA: It seems to me that, at the

21 beginning when you approached this, you said that you wanted to

22 take each of these buckets, these pieces of information that
Page 757

1 you've collected and that we've talked about separately. And

2 now the task is the putting it together and saying, okay,

3 what's the full picture of this and how can we interpret the

4 plausible outcomes in humans, and what the epidemiology says in

5 the context of the quality of the exposure measurements, the

6 dose response measurements that we have, and in the human

7 studies, what the animal evidence tells us with regard to what

8 those potential exposure levels are or could be and how they're

9 related to those outcomes in animals. And given that, what are

10 the potential measures of risk. You said utility of the risk

11 assessment for risk assessment decision, is then to put into

12 context the epidemiology relative to all the other stuff.

13 And I think you'd have a very difficult job

14 because of the inconsistencies between some of what the

15 epidemiology says, the issue of whether it's an acute or a

16 chronic exposure, and in particular, what's the mode of action.

17 

18 You know. So to me, putting into context

19 what the Epi means relative to the mode of action and the fact

20 that it's likely, the findings appear to be not at doses that

21 are related to the mode of action that you're seeing mostly in

22 the animals and the acetylcholinesterase inhibition. 
Page 758

1 So you're in a tough spot, I think. But

2 somehow, you have to do that with regard to taking the

3 epidemiology and thinking about it with your best available

4 evidence from each one of those buckets. And so I think each

5 of these points provided you some very specific guidance into

6 how to kind of gage that uncertainty, what's the variability

7 around those uncertainties and then putting these things in the

8 context with regard to making decisions about what's the best

9 thing we can do to protect folks who are exposed at the levels

10 that are in the environment and that maybe the NHANES will tell

11 us what the current exposures are of some of these compounds,

12 et cetera. So that's my view in terms of -- I'm not sure if

13 that's what you're trying to get at with this question.

14 DR. KENNETH PORTIER: As I read it again, I

15 guess it was something Dr. Meek said about the -- once you're

16 all said and one, your point of departure probably needs to be

17 stated in something that the public health community can react

18 against. So instead of as an internal dose, you really want a

19 biomonitoring level below which you wouldn't want to keep the

20 population.

21 And I think part of what we're hearing here

22 is them saying that it's nice to talk about a point of
Page 759

1 departure that talks about cholinesterase level in the blood of

2 a pregnant woman, but that's not going to help the public

3 health people really protect -- you need to bring it back out,

4 right? Sometimes we don't see those kinds of issues. Dr.

5 Vena?

6 DR. JOHN VENA: Just one thing to add also is

7 that, there is also some information in the epidemiology that

8 says there may be susceptible subgroups based on either their

9 genetic background or makeup or ability to metabolize things,

10 et cetera, so that also has to be considered as part of this as

11 well, in terms of thinking about the variability around those

12 dose ranges, et cetera.

13 DR. KENNETH PORTIER: Dr. Lu?

14 DR. CHENSHENG LU: Well the one thing I look

15 at the question and I say that it has to be public health

16 implications. So the question is, for example, I can rephrase

17 this question and say, "Well, which one has more public health

18 implications? The acute inhibition of acetylcholinesterase or

19 the neurobehavior outcome that we've seen in Columbia study?" 

20 So obviously I will say I don't know. But most people would

21 say, "It's the Columbia Study outcome that has more public

22 health implication than the acetylcholinesterase inhibition." 
Page 760

1 So again, I think somewhere -- I couldn't

2 remember which question. I make a suggestion that taking the

3 data from Columbia study, in terms of what are the levels of

4 chlorpyrifos residues in cord blood samples, and use those

5 numbers to do a study to see whether those levels can cause any

6 acetylcholinesterase inhibition. And I think EPA already sort

7 of said it's highly unlikely it would have any inhibition at

8 all, right? So again, that kind of paved the road into this

9 so-called separation of POD from chronic to acute. Because,

10 again, you don't see inhibition under those levels, but those

11 levels does cause neurobehavior outcome. And that's my

12 justification into this POD from acute to chronic. I think I

13 can justify this.

14 And until we have a better picture in terms

15 of how to do this, I wouldn't worry about variability or

16 uncertainty issues because, depending upon how you're going to

17 look into the separated POD, you don't even know what kind of

18 variation you're going to bring into this whole exercise.

19 But I think the concept has to be agreed upon

20 not just the panel, but the agency in terms of looking at this

21 cholinesterase inhibit chemicals, do we need a POD look into

22 acute versus chronic.
Page 761

1 DR. KENNETH PORTIER: Dr. Buckley?

2 DR. BRIAN BUCKLEY: In dealing with

3 uncertainty, one thing that I hope didn't get glossed over,

4 because it was only addressed in the very last question, which

5 absolutely can't discount the role of multiple exposures to

6 multiple chemicals and their potential effects. So whenever

7 you decide where that protective line is and with the brain

8 trust here, we're not going to give you a definitive number. 

9 But keep in mind that some of the effects that you may have

10 seen may have been because of additive effects or even worse

11 with some of the other co-exposures. And it's just something

12 that you need to keep in mind when you decide on what

13 uncertainties are going to be utilized in a protective fashion

14 versus which are just uncertainties.

15 DR. KENNETH PORTIER: I think we've squeezed

16 the panel dry at this point. Any additional comments, Dr.

17 Lowit? Typically what we do at the end of the meeting like

18 this is we kind of do a go-around-the-room. If there are any

19 additional issues or thoughts that you'd like to possibly get

20 down on paper, when we do our final report, we certainly are

21 going to address the six sets of questions that EPA has put

22 before the panel and we have a lot of material for that.
Page 762

1 But sometimes issues are raised that we just

2 didn't get to that you'd like to at least put down at the end

3 of the report and say, "Oh, maybe EPA should be thinking about

4 this as well." So at this point, we'll just kind of go around

5 the room and see if there's any additional issues for the next

6 SAP. And I'll pick on Dr. Meek here. She's still warm from

7 the last question.

8 DR. BETTE MEEK: So, I actually have one, and

9 this may exist and it may just be that I haven't seen this in

10 the documentation for this meeting. But it would be extremely

11 helpful at the outset of discussions of weight of evidence on

12 particular types of effects to have a priori (phonetic)

13 criteria for judging the adequacy of the studies so that those

14 would be stated at the front end, because it would facilitate

15 input, I think, of panel members, others in term of impacting

16 on the discussion of all of the studies, as opposed to looking

17 at each individual studies. 

18 In any case, I recognize well that not all

19 factors can be taken into consideration in that manner, but it

20 would at least be useful as a starting point.

21 DR. KENNETH PORTIER: So this gets back to

22 the issue of, you know, the report should at least say how many
Page 763

1 dams did they use, sample size, little things like that that

2 help us understand the design and be able to replicate this

3 study.

4 DR. BETTE MEEK: In fact, it came up in some

5 of Jean's comments about, you know, these have been specified,

6 for example, for publication in terms of actually just the data

7 that needs to be presented to evaluate the adequacy of the

8 study. And you weight some of those factors more than others

9 if you are evaluating for risk assessment purposes, so I think

10 we need to be a bit explicit about that up front.

11 DR. KENNETH PORTIER: Dr. Popendorf?

12 DR. WILLIAM POPENDORF: Well there was one

13 thing that -- I guess this is my chance to add -- as I was

14 going through talking about the Garabrant study in particular. 

15 I sort of paused and I'm trying to read my notes, but I was

16 making the comparisons in the field study. The chlorpyrifos

17 was fresh and we can make some reasonable assumptions that

18 there wasn't TCPy in the environment. In the Garabrant Study

19 it was an in-factory typesetting, so there is a very good

20 chance that there was residual TCPy in the facility to which

21 they were exposed and contributing to that TCPy, which just

22 adds one more uncertainty to that study.
Page 764

1 Other than that, I mean, I'm thinking that

2 overall, you know, you've presented a lot of information. 

3 We've talked about a lot of things that leave a lot of -- and

4 we can appreciate the uncertainty with which you're dealing. 

5 And if you conclude that at the end there is not enough

6 evidence to judge that there was effects from this population,

7 I hope that it wouldn't go away as an issue.

8 There may not be enough evidence, but it

9 doesn't mean that it doesn't happen. And that's part of your

10 role as the EPA, is to protect us all from this. So I for one

11 appreciate the continuing interest and continuing to ask the

12 questions that you're asking us.

13 DR. KENNETH PORTIER: Dr. Ryan?

14 DR. BARRY RYAN: While I've made a number of

15 suggestions along the way of modification or new studies that

16 should be put together, or would actually add to this, I think

17 the EPA has done an excellent job putting together this report.

18 And what is out there is what is out there, and they've done a

19 very good job of reviewing that. 

20 I would still like to say there are more

21 things to be done. I think we've all said in our papers

22 before, more research is needed, and I think this is a
Page 765

1 continuing interest of all of us here. The work is good. 

2 There's more to be done.

3 DR. KENNETH PORTIER: Dr. Terry?

4 DR. ALVIN TERRY: There was this one thing I

5 remember in discussions of Dr. Vorhees the other day that just

6 sort of came back to my head, that we've seen very detailed

7 critiques of the epidemiology of the rodent neurobehavioral

8 data, the in vitro data. But what we didn't hear from was a

9 neuropsychologist to critique the neuropsych data that's being

10 used in this epidemiology.

11 I think between now and the next couple of

12 years, it would be nice to hear from -- or at least, you would

13 probably want to hear from someone that -- 

14 DR. KENNETH PORTIER: Dr. Vena?

15 DR. JOHN VENA: I just want to say that it

16 was a pleasure to meet everyone here and work with you. And

17 this is my first time in this group and SAP and I learned a

18 lot. Thank you.

19 DR. KENNETH PORTIER: Dr. Vorhees?

20 DR. CHARLES VORHEES: Well I'd echo that. I

21 did learn a lot myself. I was very impressed by the review of

22 the epidemiological evidence, the Columbia study in particular,
Page 766

1 and what the epidemiologist had to say about it. And I was

2 impressed by the magnitude of some of those effects.

3 And it seems to me, while I didn't particular

4 find that the animal data was very helpful, it seems like the

5 human Epi studies are very helpful. And so it seems to me that

6 those could be relied upon, at least that was my take on it, to

7 provide a great deal of guidance in how to set some kind of

8 uncertainty or protection level. So thank you.

9 DR. KENNETH PORTIER: Dr. Young? 

10 DR. HEATHER YOUNG: I'm just going to say

11 ditto Dr. Vorhees. It's exactly what I wanted to say.

12 DR. KENNETH PORTIER: Dr. Sandy?

13 DR. MARTHA SANDY: I agree with that. And

14 I'd add that the EPA consider getting some more animal data on

15 dose response, reiterating what I've said before, looking at

16 acetylcholinesterase inhibition in low-dose studies. We're

17 also looking for some of the stronger neurodevelopment effects

18 in animals, if you feel that you need to use the animal data as

19 well as the Epi data.

20 DR. STEVE CLANEY: I just want to echo the

21 fine job by the agency. And hopefully this panel will help set

22 a path forward. It seems like a lot of ground to cover.
Page 767

1 DR. KENNETH PORTIER: Dr. Pope?

2 DR. CAREY POPE: Well I guess I'm a little

3 overwhelmed over the last few days thinking about the problems

4 with behavioral studies, animal studies and the problems with

5 all studies, I guess.

6 I mean, from an extreme biologist

7 perspective, you'd like to think that you can kind of confine

8 variables and just look at the effect of one chemical, for

9 example. But as we've discussed, I mean even under these kinds

10 of conditions there are things that can go wrong that you can't

11 think about ahead of time.

12 And not being an epidemiologist and kind of

13 listening to people talk about these studies, I mean there are

14 all kinds of ways you can poke holes at places and think about,

15 well, was this an interacting factor or not? Was this covered?

16 Was this controlled for? And I think, the totality of it is

17 that, no matter what the dataset is, there's always going to be

18 some holes that are very unclear. And so the four orders of

19 magnitude difference in responses, I mean that's a big hole to

20 cover for me.

21 And the only thing that keeps coming back to

22 me is this idea of the single point analysis from the Epi study
Page 768

1 it's in, and Dr. Lu's recurrent idea about chronic versus acute

2 and whether there -- I mean, there can obviously be a chronic

3 response from an acute insult, particularly in a developing

4 system.

5 So there's lots to think about. I'm glad I'm

6 not a risk assessor working for EPA.

7 DR. KENNETH PORTIER: Why don't we go to Dr.

8 Lu?

9 DR. CHENSHENG LU: I kind of wonder sometimes

10 what if those three studies all have the same protocol. What

11 if they all measure chlorpyrifos in cord blood? The discussion

12 would be very different in this SAP. And that kind of

13 highlights the deficiency in terms of -- you know, it takes a

14 lot of resources and effort to conduct first cohort study. And

15 I don't even know when will be the next time that we're going

16 to have another three brand new cohort study plan. 

17 So the last hope is the National Children's

18 Study. However, according to Dr. Ryan, the outlook is not very

19 optimistic at this, based on our discussion. And also, I

20 understand that National Children's Study is a multiagency

21 study. But I do hope that EPA would exercise their inference

22 at the study design level at this that the study should take
Page 769

1 multiple samples, looking at long-term exposure and so on and

2 so forth. Otherwise, I think we're wasting another perfect

3 opportunity to tackle issues of not just chlorpyrifos but other

4 environmental chemical issues related to health outcome.

5 DR. KENNETH PORTIER: Dr. Koutros?

6 DR. STELLA KOUTROS: I wanted to say that

7 it's my understanding that it's relatively new for the Epi

8 studies to be included in this kind of discussion, so I really

9 appreciate that we have the opportunity to be here and do that.

10 And I really commend the agency on their review of the

11 epidemiologist studies.

12 DR. JEAN HARRY: First I'd like to say, just

13 to reiterate what we've all said, is, the report is a very

14 excellent piece of work. It's very nice to see. 

15 I'll reiterate a couple of people's comments,

16 and one is, it's very nice to see the Epi data be brought in. 

17 And I think that takes the sting somewhat out of the very

18 detailed review that Dr. Vorhees did of the behavioral data in

19 the animals. But it's looking at the two together, and it's

20 the whole body of it that's very nice to see that the agency's

21 trying to look at all of that for, and to consider the in vitro

22 for biological plausibility of whether there's anything showing
Page 770

1 up there, not just to dismiss it, even though we are very

2 hesitant about how you would put it in, but if it does start to

3 support anything that you're looking for. Again, I think it's

4 just trying to pull everything together, and you just wish that

5 those behavioral studies had been a little stronger when they

6 were initially designed.

7 DR. KENNETH PORTIER: Dr. Gold?

8 DR. ELLEN GOLD: I'm not sure I have much to

9 add except I would like to again congratulate the agency on a

10 very careful review and for incorporating the epidemiologic

11 data using the framework that we developed a couple of years

12 ago. And I think making policy is the root. Virtually, what

13 you're doing is trying to make your best decisions in the

14 absence of perfect knowledge. And given how carefully you

15 undertook this effort, you know, I have some confidence that

16 you'll reach some conclusions.

17 There is lots of uncertainty but that's sort

18 of the nature of the best. So I also wish you lunch.

19 DR. KENNETH PORTIER: Dr. Collier?

20 DR. ABBY COLLIER: I'd like to reiterate what

21 the rest of the panelists have said regarding the really high

22 quality of the report that we got, because we can't make
Page 771

1 comments unless we get something really good to start with.

2 I would like to make a small suggestion that

3 if agency is going to focus on pregnancy and pediatrics they

4 include maybe a few more people that specialize in toxicology

5 and pharmacology in pregnant women and pediatrics. I know

6 there's not a huge number of us out there, but I think it would

7 eliminate the discussion somewhat. But other than that, it's

8 been very large undertaking, but I see where agency is trying

9 to come from and I think it's really fantastic, so thank you.

10 DR. RUSSELL CARR: I just want to

11 congratulate the agency on a well written document. There was

12 a lot of useful information and I learned a lot, like I do

13 every time I come to one of these.

14 I also agree with Dr. Terry in that I think a

15 developmental neurobiologist would have assisted us in

16 understanding what some of the scores in the epidemiology study

17 meant, and how we could understand that better. Because I

18 think those are impact things that we need to understand, is

19 that the human effects are real. And I think we definitely can

20 all agree that there are some serious issues that we have to

21 address there. But we also have to recognize also that it's

22 not a lab study. And there are so many things out there and so
Page 772

1 many other chemicals and so many opportunities for one chemical

2 to alter the physiology of another chemical and to just change

3 its projection. And so we just have to make sure that we just

4 kind of understand that.

5 DR. KENNETH PORTIER: Dr. Buckley?

6 DR. BRIAN BUCKLEY: I want to start by

7 applauding the EPA for taking on a more comprehensive risk

8 model than they've taken on in the past, including the Epi

9 data, but also hopefully exposure reconstruction or dose

10 reconstruction data as well.

11 What was enlightening to me, and it's

12 something that we need to think about from a study perspective

13 is that we don't generally study things that have already been

14 done before. In other words, we don't apply to validate a

15 study and repeat a study, and we don't fund those studies

16 either. And I think that represents a serious deficit in our

17 approach because we get to a situation like this and we can't

18 even compare two studies because they'd been tweaked or done

19 something different. So from a scientific perspective, it's

20 something that we need to keep in mind and maybe reconsider the

21 way we've done these things or will do them in the future.

22 At the same time, I think that the Columbia
Page 773

1 study is very important. And if you make guidelines based on

2 it, I think it will probably wind up being your most protective

3 that you can be. 

4 I think you should consider it very, very

5 carefully in your risk assessment just because it is a human

6 population. Even though the animal data doesn't provide a

7 mechanism of action, per se, at least it probably provides more

8 data than some of the other things that we've considered.

9 I also echo the notion that it was a very

10 well written report.

11 DR. KENNETH PORTIER: Dr. Aschner.

12 DR. MICHAEL ASCHNER: I'm the last one maybe.

13 I also want to echo the good job. As I said yesterday, I

14 think that the document was very logic, very easy to follow. 

15 And I think it framed the problem in a very logic constructive

16 way.

17 Leaving this place I've learned actually

18 quite a bit of terminology that I wasn't aware of before. I'm

19 not sure I understand it all, but one thing that comes across

20 is that this is one of the few compounds that we have where we

21 have epidemiological data, three sets from different studies

22 where there's some uncertainties, but certainly there's quite a
Page 774

1 bit of data. And one thing that I heard today or yesterday was

2 about the possibility of pulling some of this data and pulling

3 them together, which I think is going to be very useful. 

4 In some ways, the data here appear to be much

5 more consistent than some of the other literature. I think Dr.

6 Meek brought in lead and mercury and some others. I would say

7 that when mercury -- for example -- I don't think you could do

8 a lot of the things that came up today in the discussions

9 because, actually, the data are very contradictory, which

10 doesn't seem to be the case with chlorpyrifos.

11 And for those of us that do animal studies, I

12 think what this does is, it's sort of putting in perspective

13 that dose is important. So if you heighten the dose, you

14 always get some kind of an effect. But I think, when you get

15 the effect, what you need to do is put in perspective and think

16 whether it really mimics and reflects physiological levels of

17 exposure. But those of us that are doing animal studies with

18 whatever it is, I think it's a good lesson that, when we do

19 those studies, we need to keep in mind physiological levels of

20 exposure.

21 And I think, in terms of the human data,

22 there have been a lot of suggestions about implementing some
Page 775

1 new methodologies or refining methodologies. And I think this

2 is going to be useful not just for the chlorpyrifos. I think

3 when you are done with this model, overall, the new type of

4 risk assessment that you may come up with might be very

5 beneficial and looking at many other compounds. So again, I

6 would encourage you to do that because I think it's going to

7 transcend to many other compounds.

8 DR. KENNETH PORTIER: I'm trying to capture

9 all these comments so we can kind of write something up at the

10 end. 

11 One thing that I heard that I would like to

12 point out to EPA, I wasn't looking forward to this panel

13 because I did chair the 2008 panel. And it's one of those

14 panels that I remember very vividly because the epidemiologist

15 weren't communicating with the toxicologists. I think we had

16 some AG types in there, as well, and it was a big panel.

17 And I think the big difference this time is

18 that EPA has employed a professional epidemiologist, Dr.

19 Christensen, to help them kind of get the interpretation and

20 the integration correct. So I want it on record that I think

21 that's a good move on EPA's part. It's helped us a lot in this

22 panel. It's meant that we've been able to focus on the value
Page 776

1 of the information on the epidemiology and not on the things

2 that weren't helpful the last time around.

3 And then finally, I'd like to say this is my

4 last panel as a permanent panel member and as Chair. And I

5 really have appreciated EPA's giving me the opportunity to do

6 this. 

7 By my count, this is either the 61st or the

8 62nd panel that I've sat on, starting in 1999. So you can see

9 I've enjoyed this a lot and I'm going to miss it a whole lot. 

10 I encourage those of you who are new -- there

11 are I think four newbies on the panel here, but I think they

12 have proved their worth at this meeting. I'm sure you'll be

13 invited back. And I can tell you, you learn more each time you

14 come here, and you actually are of more use to EPA each time

15 you come and participate. So when invited, I would encourage

16 you to take up the opportunity, as I have.

17 With that, I'm going to turn the meeting over

18 to Fred Jenkins, the DFO, for some final comments. Dr. Lowit

19 and Dr. Whitby?

20 DR. KAREN WHITBY: 60 meetings? Wow, that's

21 quite impressive. I'd like to thank the SAP staff for all that

22 they've done to organize us for this meeting, particularly the
Page 777

1 efforts of Fred Jenkins, Joe Bailey and Laura Bailey and others

2 that have been working behind the scenes. 

3 I'd like to thank the team, the chlorpyrifos

4 team, and I'd also particularly like to thank ORD for their

5 contributions and support. We will continue to rely on them

6 for their expertise as we move forward with the chlorpyrifos

7 assessment.

8 And I'd very much like to thank the SAP panel

9 for being very helpful and productive with this meeting. We

10 know how hard you've worked. There's a lot of material here to

11 go through and it took a lot of time to prepare and to be here.

12 I particularly recognize a level of effort

13 with emails through midnight last night. Very thoughtful

14 recommendations and feedback and I appreciate your talent.

15 And again, finally, I'd like to thank Ken. 

16 And it's been great; 60 meetings, wow. Thank you for running

17 such an efficient meeting here, my first as acting Division

18 Director. You've really kept us on task and focused and I

19 appreciate what great job you've done here. Thank you.

20 DR. FRED JENKINS: Before I give my closing

21 statements, I want to turn the floor over to Laura Bailey, the

22 executive secretary to the FIFRA SAP.
Page 778

1 LAURA BAILEY: Good afternoon. I am Laura

2 Bailey and I am the FIFRA SAP Executive Secretary. And first I

3 would like to say thank you to the panel. I'd also like to say

4 thank you to the agency, Karen Whitby, Anna Lowit and the

5 entire team and OPP, and to Fred Jenkins who served as the

6 designated Federal official for this meeting, and to the entire

7 SAP staff and all the participants who contributed to make this

8 an outstanding meeting.

9 And I personally think that the

10 recommendations that had been provided at this meeting will

11 play a significant role in the direction that the agency goes

12 forward from this point. 

13 Well, I am before you today to recognize a

14 very special individual, one who has served with tremendous

15 dedication, competence and often perseverance. Undoubtedly a

16 great deal of the SAP success can be attributed to his

17 expertise as a statistician and his unique leadership skills. 

18 And I am referring to none other than Dr. Kenneth Portier. 

19 I mentioned to Dr. Portier on yesterday, I

20 said, "Well this is a bittersweet experience for me and the SAP

21 because this marks the last meeting that he will serve as Chair

22 of the FIFRA SAP, as his tenure as a permanent panel member
Page 779

1 expires this month.

2 Now although he has promised that he will

3 return as an ad-hoc member -- and we're going to hold you to

4 that promise -- but Dr. Portier has provided exceptional

5 service to the SAP. And he has served as a permanent panel

6 member for eight years and he's already told you his remarkable

7 record of the number of SAP meetings that he has participated

8 in. And not only has he participated as a member but he also

9 served as the SAP Chair for the past year.

10 And on behalf of the agency, Dr. Portier, I

11 want to publicly thank you for your tremendous service to the

12 SAP and let you know that we are grateful for the contributions

13 that you have made to advance in science here at the agency. 

14 And I want you to know that you have set the bar high for

15 participation on the SAP. And it has been a joy and a pleasure

16 having you on the panel and we wish you the best as you

17 continue your illustrious career.

18 And at this time we have a beautiful plaque

19 of appreciation. And I'm just going to read to you what the

20 plaque reads: "It is an appreciate award presented to Kenneth

21 M. Portier, PhD for outstanding contributions and leadership of

22 the FIFRA Scientific Advisory Panel 2004 to 2012" and it is
Page 780

1 signed by James J. Jones, who is an acting Assistant

2 Administrator for the Office of Chemical Safety and Pollution

3 Prevention. Please join me in congratulating Dr. Kenneth

4 Portier.

5 DR. KENNETH PORTIER: Thank you very much. 

6 Charlene is going to take a picture here of us. One more,

7 because Fred's got to finish the meeting.

8 LAURA BAILEY: Okay. At this time, I'll turn

9 the meeting back over to our designated Federal official, Fred

10 Jenkins.

11 DR. FRED JENKINS: I just have a few closing

12 remarks. First I want to thank this entire panel for agreeing

13 to participate in this meeting. I think you all did a great

14 job. As I've told you in the beginning, on behalf of the

15 agency, we appreciate your time, your talent and your expertise

16 and willingness to participate.

17 A special thanks to Dr. Portier. It's been

18 truly an honor and a pleasure to work with you, and as we've

19 shared these meetings and as serving as DFO along side of you. 

20 And I wish you the very best in all of your future endeavors. 

21 I hope to see you again soon.

22 Thank you public for attending this meeting. 
Page 781

1 Special thanks to all those who are listening out on the World

2 Wide Web via our webcast. Thank you to our oral public

3 commenters, including DOW and its affiliates. Thank you to

4 NRDC for their public comments and to Dr. Dale Hattis.

5 I want to give a special thanks to the Office

6 of Pesticide Programs team. Dr. Anna Lowit, thank you so much.

7 It's been a lot of hard work working with you in preparing for

8 this meeting. Thank you to the entire team. Dr. Whitby, thank

9 you for your leadership in helping prepare for this meeting.

10 Also, I have to say a special thanks to the

11 EPA FIFRA SAP staff, my colleagues, who are tremendous support

12 to me and other DFOs when we serve in these meetings.

13 The meeting report will be available no later

14 than 90 days after the close of this meeting. This meeting is

15 official closed now. I just want to say to the panel we want

16 to meet very briefly in the breakout room after the meeting. 

17 Thank you everyone, again.

18 (Whereupon, the meeting was adjourned.)

19 * * * * *

20

21

22
Page 1

A

Aaron 20:2 41:3

172:7,8 188:12

188:15,22 189:6

190:1,14 191:9

196:18 197:9

198:18 199:14,21

200:13 441:12

445:22 621:19

655:19 666:7

690:12 720:5

abbreviate 397:3

Abby 6:15,15

153:3,10,21

193:18 195:8

307:15 317:6,6

334:8,8 335:1

341:13,13,19

342:4,6,8 364:22

365:4,8,16 366:6

366:9 367:7,11

367:15,18 368:7

368:17 369:3,9

369:21 370:1,11

370:17 371:5,18

372:9,19 373:2,7

373:13,18,21

374:16 375:2,8

375:20 376:3,9

376:12,17 377:13

377:20 378:11

379:16 380:1,12

410:2,2,4 438:17

444:15 456:7

603:12 605:18

630:20 634:3

664:3 676:5

695:21 705:5

706:7 711:6

714:6 717:6

727:7 741:20

770:20

abducting 457:16

aberrations

240:14,16

ability 11:8 90:12

121:20 126:9

140:20 142:2

167:9 459:4

545:9 547:22

580:2,15,19

618:10 624:7,13

624:21 663:13,21

682:9 693:21

741:14 759:9

able 63:7 66:4 85:8

86:15,22 90:1

94:4 103:22

106:20 107:3

139:4 166:8,19

182:14 342:14

351:16 362:12

387:3 388:8

427:19 443:6,21

448:2 449:12

451:14 514:1

526:14 552:8

559:20 586:7

654:17 674:19

690:7 693:14

694:18 714:22

726:6 756:12

763:2 775:22

abnormal 134:19

134:22 135:1

145:19 150:12

169:9,12 210:21

360:2 577:8

Abou-Donia 474:1

absence 44:12

57:20 58:1,9,13

183:22 243:12

287:16 290:13

301:4 327:19

328:4,6 340:7

355:10 491:13

589:14 613:18

770:14

absent 329:14

absolute 18:17

400:1 594:10

absolutely 152:5

152:22 158:4

310:18 479:7

537:1 603:16

637:15 649:9

761:5

absorbed 265:9

411:20 416:12

678:15

absorption 191:6

323:19 347:20,22

411:5,5,13,17

414:19

abundant 644:17

abuse 747:4

academia 36:3

93:9

academic 92:14,16

93:2 94:15

249:22 347:13

512:7

Academies 391:5

accelerated 747:9

accept 290:6

303:18 371:3

408:18 523:18

533:15

acceptable 390:2

394:15 395:4

493:22 691:15

acceptance 85:1,6

accepted 73:14

78:1 82:8 99:4

251:7 441:6

445:7

accepting 371:9

access 226:1 247:9

308:17,17 474:5

679:8

accessible 514:12

accidental 323:3

accommodate

3:12

accommodated

236:15

accommodates

323:19

accommodating

388:8

accompanied

438:4

accomplish 195:21

230:2 709:13

accomplished

708:8

accomplishment

713:8,12

account 165:8

174:6 190:6

238:18 288:21

289:1,6 290:4,6,8

290:10 304:9

338:1 385:9

399:6 401:10,18

411:13 498:4

555:13 643:3

687:5 735:21

739:8 753:12

accounted 163:12

554:22 582:16

663:1

accounting 157:20

242:3 569:22

663:3 691:10

accounts 237:11

237:15 238:14,14

239:19 245:16

288:22 323:18

accrued 326:20

accumulate

457:18 625:14

628:16

accumulated

515:6

accumulation

629:12

accuracy 127:9

418:15 434:19

accurate 165:21

170:9 284:10,13

435:4,9 541:21

553:21 635:11

747:19

accurately 120:22

125:21 309:4

354:19 435:6

695:16

acetyl 120:2

682:15

acetylcholine

90:20

acetylcholineste...

43:16 44:13 53:4

54:6,10,15,16

55:5,13,14,17

56:2,3 57:16,17

57:20 58:2,6,10

58:14 59:9 64:11

64:14 66:11 97:9

172:14 179:12,13

179:15,16,19

180:4,12,18,22

181:22 182:3,10

182:19 184:3

196:22 197:18

205:16 208:22

211:16 214:8

243:9 251:10

267:2 268:15

269:5 286:7,10

287:10,13,16

288:7,12 289:10

290:14 299:10,14

300:12 301:5,9

301:19 303:10

304:4 307:4,8

351:1 382:14

384:13,17 385:18

386:4,16 438:4

438:11 439:9

440:2 457:16

461:6,22 462:9

462:12 471:13

496:14 518:3,9

518:11 525:9,15

525:18 526:4,8

526:11 528:9

529:22 544:1,5

551:6 552:1,4

565:18,20 570:10

572:20 595:8,22

596:13,15,21

597:2,9,16,19

598:11,12 599:5

599:9,11 600:6

600:10,15,21

601:6,8,9 603:14

604:10 605:1,21

605:22 613:3,18

614:20 615:1,11

615:17 621:4,8

623:5 624:8

625:2,11 641:4,6

641:11 666:10,17

668:12 669:21

670:20 671:14

672:13 675:10

676:12 677:9,12

677:16,18 678:2

678:7,19 679:2

680:8,10,14

681:21 682:21

683:15 685:1,11

685:14,21 686:2

688:8,11,16

689:7,9 701:1

720:12,20 721:5

721:11 724:4

725:17 726:19

727:12,14,19

728:9 729:19

752:9,20 754:7

754:10 757:22

759:18,22 760:6

766:16

acetylcholineste...

322:14 432:9

ACh 438:5 670:6
Page 2

AChE 269:13

432:10 436:21,22

437:2,4,8,11,15

446:8,20 467:16

471:17

acid 48:7,14,15,18

48:19,19 49:6,15

49:22 50:10,17

51:3,14,18,19

52:7,9 56:16

322:16 334:20

445:11 451:10

465:3

acknowledge

19:22 163:19

165:5,9 209:15

520:6 698:5

acknowledges

541:8

acknowledging

607:14

acoustic 537:9

acquire 391:22

acquisition 87:13

87:21 88:1 93:1

374:22 375:17

508:14

act 8:5 54:11 67:8

751:18

acting 19:9,14

687:12 777:17

780:1

action 12:9 20:21

21:2 23:13,16

27:2 28:7 32:15

33:11,12 38:6

39:7 44:22 45:2

46:21,22 95:13

113:3 146:10

202:21 205:11

206:12,21 225:14

226:11 229:19

231:6,8 232:2

233:7 234:12

241:1,11,12,12

241:19,22 242:17

243:2 245:14

246:10 298:5

305:14 306:11,12

306:13 307:3

343:15 344:22

350:14 351:17

352:16 368:19

381:10 390:8

391:20 431:20

432:1 437:6

441:18 443:16

544:22 551:18

581:5 593:22

594:7 611:1

615:18 641:10

679:2 691:19

750:21 752:2

753:14 757:16,19

757:21 773:7

actions 528:17

action/adverse

436:21 437:1

442:7 446:1

activate 51:3

605:3

activated 192:15

321:11

activating 48:21

56:10 604:9

activation 45:21

49:1 61:12

604:11 605:2

active 39:6 205:4

209:17 323:8

327:22 446:4

447:12 455:13

457:1,13 485:22

603:17 627:3

630:5 655:1

683:21

activities 19:3 96:9

97:2 192:5

265:14 266:7

442:15 564:19

670:1

activity 57:17

58:14 66:21 74:5

74:7,8 78:12,13

81:7,13,17,20

82:1,2,8 88:10

89:7,8,10,11

100:10 103:7

180:4,22 181:22

196:22 197:4,18

265:16 320:22

324:21 330:6

341:11 356:13,21

357:6 361:4,5,6

438:5 457:10

517:21 562:18

563:15 570:10,13

570:16,20,22

590:3 598:12

600:11 615:17

677:1 684:2

688:14 692:11

701:8 728:2

actor 562:1

actors 729:11

acts 48:19 629:11

actual 70:22 77:7

100:2 120:10

121:1,7 127:5

181:18 200:15

214:2 284:19

285:15 360:21

393:11 399:5

439:22 546:22

553:18 601:14

631:15 661:3

670:1 720:16

732:11

acute 92:3 95:13

96:13,22 213:1

251:6 253:18

254:7 330:11

382:13,21 383:15

384:2,11,13

385:1 386:11

393:16,19,22

440:18 614:3,8

684:9 728:2,5

745:20 746:2

750:3 752:1,5

753:5 757:15

759:18 760:9,12

760:22 768:1,3

ad 245:2 290:5,7

491:1

adaptation 216:9

adapted 34:13

262:14 401:9

adaption 710:14

adaptive 47:16,20

51:8,10 67:20,22

add 109:9 129:17

160:13 231:6

273:20 295:22

376:7 384:22

385:15 402:17

428:13 438:18

439:19 440:6,8

444:16 462:21

464:13 465:14,16

472:6 499:15

503:7 521:17

524:4 528:5,21

531:6 538:4

552:11 553:5

559:20 581:19,21

582:11 583:19

584:2,5 592:17

602:2 612:1

630:22 631:4

632:2,14 633:21

636:20 664:4,7

664:10 665:3

674:17 681:10

682:3 684:7

712:8 718:6

728:15 730:8

756:17 759:6

763:13 764:16

766:14 770:9

added 260:12

266:17 463:7

513:1 546:18

551:20 552:14

553:20 572:22

619:15 644:4

719:21

adding 70:7

238:11 312:9

389:21 460:18

665:7 705:10

744:19

addition 55:7

113:4,20 114:8

130:5 171:10,12

171:20 173:5,20

175:12 176:9,20

177:5 227:22

249:13 259:13

324:21 326:1

331:20 385:4

408:18 441:3

475:7 542:5

545:12 547:17

551:16 576:12

578:3 584:8

598:21 602:17

605:15,18 612:2

624:13 679:5

690:17 692:20

719:22

additional 21:18

43:1 67:5 90:3

116:8 140:16

173:21 175:15

183:19 184:4

220:21 222:19

249:17 254:5

259:8 262:10

267:13 278:10

324:10 334:4

350:14 380:10

385:5,11 387:8

389:21 391:22

413:7 418:5,16

420:6,8 423:4

428:1 429:14

430:5 434:8,12

440:11 441:5

445:18 446:15

456:8 467:9

470:7 472:6

483:7 495:13

512:16 513:8

515:1 536:9

540:17 545:17

551:19 552:12

553:19 566:3

581:11 585:6

587:1 592:17

597:13,15 598:15

598:22 603:10

605:8 606:12

615:3 619:5,8

620:8,9 621:13

644:3 645:9

655:5 656:12,15

665:5,22 681:8

690:5 693:3

710:3 718:16

719:8 720:2

728:19 736:2

761:16,19 762:5

additionally

140:15 612:14

629:13 691:10

additions 542:22

554:1

additive 725:12,17

730:4,7,14,21

761:10

additives 729:22

address 22:15

91:21 93:15

123:14 230:4

254:17 306:22

319:8 351:8,18

385:6 387:19

452:7 502:15

525:7 531:15
Page 3

608:21 611:6

612:17 646:8

647:18 649:5

670:12 702:18

725:18 761:21

771:21

addressed 16:1

307:17 376:19

385:12 569:18

574:8 725:14

761:4

addresses 353:21

590:20 619:5

addressing 194:4

225:3 387:16

423:16

adds 175:3 245:3

763:22

adduct 340:13

adducting 340:15

625:11 627:17

adducts 456:9

557:12 625:21,21

629:20

adequacy 391:2

656:10 762:13

763:7

adequate 131:21

143:11 481:13

493:18 509:10

519:5 663:7

adequately 264:5

297:22 385:12

390:20 493:16

542:19 547:16

550:19 554:18

618:3 708:3

ADHD 137:8,13

152:7,11,14

161:2,14,14

164:14,16 575:21

578:3

ADHD-like 137:9

152:14 210:22

ADIs 405:8

adjacent 123:22

adjourned 381:19

540:11 781:18

adjudicate 240:18

adjunct 282:10

adjust 389:19

411:22 466:16

481:6 672:22

adjusted 163:18

557:2 575:7

674:7

adjusting 556:12

adjustment 172:2

679:18 740:7

adjustments

297:18

administer 160:20

161:5,13

administered

120:4 134:13

136:3 161:10

166:6 185:1

326:10 518:1

522:14 598:11

600:13

administration

71:11 336:5

347:10,14 402:13

438:6 503:9

505:7 506:1

administrative 2:7

administrator

8:13 780:2

admittedly 599:19

adolescences

503:2

adolescent 531:19

adolescents 496:17

adopt 409:12

adopts 508:13

adult 31:7 58:12

85:13 178:5

185:2 187:6

193:21 194:8

195:2 214:16

251:6 322:9

324:2 343:8

395:7 401:14

502:16 531:20

534:18 535:6,18

538:8,9,18 598:9

598:10 675:17,21

682:8 683:9,13

696:15,21 705:8

adulthood 323:20

503:2 504:10

adults 36:20 84:11

178:7 189:10

194:5 195:1

210:5 281:1

325:12,16 352:4

393:22 452:4

496:17 596:22

728:10 741:3

advance 779:13

advanced 713:4

advancements

381:5

advances 12:5

Advancing 391:6

advantage 225:7

346:16 456:2

701:22

adverse 9:19 10:14

10:19 11:16 12:6

16:9,21 17:2

18:1 20:21 22:18

23:4,7 24:1

25:15 26:6,15,21

33:13,17 34:11

34:22 35:6 37:18

38:6,7 39:7

40:11 43:19 44:3

44:7,15,15,18

45:6,7,8 46:6,7

46:11,22 47:2,4

48:1,4,6,8,13

50:2,7,21 53:2

54:1,5,8 55:20

58:21 60:16,19

61:17,21 64:21

67:20 97:6,7,12

97:19 112:17

116:22 120:13

136:17 202:20,21

203:4,6 204:11

205:11 208:9,20

209:12,14 211:8

211:22 217:13

241:13 243:17

245:15 262:5

284:17,19 285:15

286:5 297:15

306:7 344:14

351:7,10 353:2,5

353:9 354:22

357:9,13 358:12

359:13 362:1,6,7

363:16 370:13

381:8,18 390:8

390:16 392:2,6

431:20 432:17

441:18 445:8

446:12 447:5,6

472:12,20,21

493:11 502:3

551:4 573:7

581:13 593:22

595:21 611:10

752:11,15 754:19

755:13,14

adversely 118:5,16

578:6

adversity 441:17

advice 2:15,17 8:7

8:21 9:6 12:4

15:10 17:21 18:3

18:6 21:17 63:16

219:22

advised 594:21

Advisor 736:17

Advisory 1:1 2:3

2:10,14 5:10 8:2

8:4,6,14,18 315:2

343:19 539:20

779:22

advocates 249:6

ad-hoc 779:3

affect 60:5 74:2

89:21 102:14

118:5,16 165:6

208:2 209:18

285:19 293:1

299:5 300:1

305:17 446:4

457:1,1 464:22

466:3,4,5,6

504:20 528:10

548:2,12,16

567:20,21 580:18

698:6 725:15

affective 454:1

affectively 629:7

affiliates 781:3

affinities 457:8

affinity 456:11

affording 661:2

afraid 643:14

651:22 730:17

African 189:13

745:1,9

African-Americ...

114:21 115:10

afternoon 72:7

107:14 112:3

172:7 201:21

220:18 224:3

255:17 425:17

436:16 437:20

470:22 716:12

778:1

afterward 670:15

Ag 170:14 260:2

263:8,15,18,21

775:16

age 88:8 89:11,12

118:13,14 133:14

133:17,19 137:10

138:22 150:9

167:11 176:10

177:12 194:16

305:7 324:13,22

335:3,8,13

341:15,20 352:3

395:7 412:2,7,10

412:16,22,22

413:3,22 488:10

503:4 504:12

524:11,17,18,20

524:20 531:12

532:1,8,12,20

534:3,4,7,10,14

534:16 543:7

547:7 562:5,6

574:22 575:12

578:3 615:9

664:19 671:18

746:16,20

aged 36:20

agencies 92:6

391:8,10,16

476:4 517:6

agency 1:12 2:8,16

2:19,19 8:7,14

12:16 15:14 16:4

18:13,16 22:1,6

27:1,22 60:11

71:20 91:15,19

91:20 107:19

126:4 153:4

172:17 177:4,7

180:2,3 183:10

187:21 192:3

206:17 232:15

248:15 250:15

253:16 299:14

305:16,18 333:21

364:3 368:18

370:2 391:1

433:5 434:22

435:11 437:7,11

442:11,12 443:17

444:4,10 451:4

452:11 465:17

473:15 495:20
Page 4

496:22 525:22

540:2 541:7,22

543:18 571:20

572:19 584:6,8

595:5,8 603:12

608:10 609:2

612:21 619:11,22

622:5 627:5

628:21 637:22

639:9 658:9,13

666:8 690:19

692:1,13 695:22

696:6,13 697:18

699:14 700:4,14

700:17 703:17

704:19 705:14

706:18 707:6,13

713:8 726:14

750:9 751:9

752:1 753:4

756:5 760:20

766:21 769:10

770:9 771:3,8,11

778:4,11 779:10

779:13 780:15

agency's 12:16

13:11 17:19

20:13 21:2,21

32:15 33:11

206:21 220:1

251:22 346:11

350:4 437:14,21

442:3 541:1,13

542:19 545:13

549:14 561:22

571:17 572:5,14

584:3 637:7

656:10,13 690:22

769:20

agenda 3:17 7:12

314:10

agent 109:10,10

501:10 505:21

agents 659:1

725:21

ages 84:10 88:10

92:4 178:7

296:19 346:19

352:22 401:12

502:20 534:8

543:16

age-dependent

686:9

aggregate 408:10

409:9

aggression 88:22

aggressive 80:9

349:18

aging 532:13

535:15,15,18,19

ago 23:2 32:9 70:3

113:15 138:1

194:13 204:19,19

219:3 250:13

251:18 271:17

277:2 384:4

388:14 463:4

506:7 565:1,11

607:3 644:15

672:16 676:15

700:16,16 722:16

745:10 770:12

agonistic 80:8

agree 67:4 99:2

236:13 240:2

250:22 299:18

308:9 311:12

313:4 333:21

336:1,17 343:22

346:15 350:8

353:15 359:17

364:3 368:1

371:20 378:7

387:18 437:21

438:17 439:2,17

440:2 442:11,11

444:4,4,10,15,22

445:15 454:20

456:7 459:17

462:19 463:14,20

469:20,21 498:6

499:20 502:8

504:8 520:14

521:8 525:11

528:4 529:13

530:5 538:14

542:22 543:18

553:20 565:13

582:6 591:17

602:3 606:14

626:12 630:10

631:6 635:16

637:15 652:20

674:16 676:5

682:7 685:2

689:17 690:2

695:21 704:22

713:1 718:17

727:7 732:3

756:1 766:13

771:14,20

agreed 224:2

247:17 472:5

760:19

agreeing 2:12

780:12

agreement 27:22

agrees 572:19

agricultural 28:2

116:10,12,13

123:12 154:15

155:18 263:11

430:19 583:9

650:16 657:9

658:16 660:6

733:6

agriculturally

656:21

agriculture 388:20

392:20 395:11

428:18 731:21

AgroScience

342:20

AgroSciences

224:12 247:18

248:1 254:15

Ah 255:19

ahead 68:7 123:16

220:17 223:6

244:16 281:22

417:13 436:11

440:7 470:8,14

471:2 484:22

523:6 571:18

586:17 593:12,16

653:18 655:17

767:11

aid 126:8 140:1

227:20

aim 396:15

aiming 648:9

air 123:18,21

124:2,6 125:8,10

165:17 190:7

194:17 196:7

265:8 393:16,19

397:13 399:1,5

415:6,14,19,21

416:1,2 417:2,16

476:13 489:8

558:15 579:12

601:15 652:7,11

694:10 701:12

707:10,21 708:15

715:22 732:9,9

732:17,20 733:1

733:17,18,20,21

734:3,13 735:6

755:4

airborne 415:4

al 48:10 123:18

130:12,22 177:8

178:4 180:19

181:3 182:7,12

182:22 185:1

186:3,17,21

252:15,15,16

263:3 264:16

332:12 333:10

348:8 354:16

365:10 402:13

423:1 426:10

427:9 435:2

440:12,19 460:15

471:7 517:22

578:7 596:18

597:17 623:21,21

623:21 624:1

727:16

albuterol 322:14

alcohol 118:8

413:16 527:7,19

547:8

Aldridge 486:12

alert 648:2,8

Alex 6:3 316:21

386:7 593:6

661:14 718:4

Alexander 263:19

265:13

Alexander's

263:12

algal 48:14

algorithm 405:18

aligned 27:11

alkyl 428:4

allele 577:20

allometric 696:16

705:8

allow 149:13 254:6

337:5 438:8

474:13 697:17

allowed 366:2

allowing 250:4

342:17

allows 46:15 64:22

252:21 253:1

alluded 115:4

252:8 474:19

480:16 492:16

Alpha 495:3

alright 445:19

484:18

alter 52:6 54:17

55:9 61:12

440:18 517:20

571:8 772:2

alteration 54:15

354:15 438:13

453:12 454:3

alterations 82:5

85:18 98:9

355:10 433:19

532:2

altered 45:21 46:2

46:3 56:6 69:14

72:2,19 77:12

78:3 80:4 394:13

altering 52:5

54:14 56:3

336:11 432:12

alternate 22:1,1,16

191:5 271:19

390:8 392:2

477:16 509:4

alternation 73:10

366:17 477:10

490:5,16 537:16

538:6

alternative 21:18

24:7 27:6,7

38:18 63:19

73:16 98:3

187:20 234:5

239:19 242:4,10

274:12,22 276:15

286:8,18,19

288:22 289:15

290:16 300:22

352:16 385:22

398:14 432:1

686:4 690:18

705:1 716:5

Alternatively

696:19 699:3

alternatives 14:17

23:9 98:5 431:20

704:11

alters 55:6

altogether 287:18
Page 5

Alvin 5:11,11

316:9,9 335:18

374:11,17 375:5

375:10 376:1,6

376:11 444:21

462:18 507:13,13

508:8,12,21

509:19 588:16

682:18 683:17

765:4

Alzheimer's

571:12

ambient 393:16

ambiguities 227:7

ambitious 697:20

ameliorate 138:14

amenable 346:18

347:3 517:15

America 28:8

American 4:8

189:13 248:2

315:12

amino 48:15,19

322:16

amnion 714:17

amniotic 627:8

628:13 634:17

698:4 711:11

712:13 714:18

715:8

amount 19:20

36:22 49:6,16

53:6 111:9

169:21 170:2,6

185:16 189:11

215:4 377:2

397:11 403:19

410:18 418:10

431:9 446:11

455:22 517:19

570:21 586:13

623:18 643:22

645:12,13,22

646:5

amounts 604:2

631:15 643:8

647:6,14 752:7

AMP 56:12

amphetamine

629:5

ample 112:10

amplitude 537:10

537:11,22

analysed 427:22

analyses 84:21,22

86:11 89:14

94:13 113:8,9

114:4 118:18

129:21 130:4

131:16 132:2

142:10 147:1

259:5,14,17,18

279:5 284:3,22

285:1,3 292:10

295:9 308:11

312:19 379:3

393:14 394:5

458:2 487:2

494:14 511:21

522:22 545:18

576:9 584:16

585:6 608:15

619:6 669:15

690:18 721:1,8

analysis 20:20

21:4,6,9,10 27:2

27:15 29:18 30:8

38:7,10 39:8

92:22 97:15

114:10 130:13

139:8 142:6

165:7 184:13

202:3 204:7,10

207:3 208:5

210:15 220:10

259:13,22 263:10

271:1,12 273:14

274:1 275:6

276:9,10,17

282:12 283:4

285:5 291:5

296:11 299:7

300:20 301:2,11

311:11 313:8

348:9 356:6,20

378:14 379:5,6,9

379:11,18 381:16

386:2 397:6,17

397:18 400:9

401:9 402:9

426:8 435:11,11

457:19 475:2

483:2,7,12,14,19

483:21 485:5

487:5 513:1,15

552:12 560:5

573:20 575:6,15

579:2 585:17,17

587:2 592:9,22

607:7 608:14

609:3,14 611:6,7

612:9,11 618:1,2

618:14 620:9

625:16 644:3

648:17 649:10

656:20 663:17

680:4 703:4

704:15 705:21,22

706:8 708:9,12

710:17 737:5

747:17 750:14

752:16 767:22

analysts 559:16

574:7

analytes 311:15

analytic 116:16

126:11 424:5

603:1

analytical 7:1

115:3 175:11

198:15,19 222:7

273:10 278:12

279:9 308:6

426:7,16 626:16

626:17 648:11,16

649:6

analyze 187:15

276:7 493:22

519:8 540:14

643:20 648:10

672:20 699:20

702:11 717:14

analyzed 82:4

228:11 357:4

373:16 414:13

491:13 546:3

559:18 562:10

643:11

analyzing 690:15

702:9,12 703:20

anchored 381:9

Anderson 705:12

705:12

and/or 58:20

78:21 116:17

117:3 129:14

437:6 446:3

447:4,11 472:11

472:19 572:22

604:4 608:15

610:14 627:9

721:1

Angerer 189:3

angle 344:2

anilities 624:14

animal 39:14

41:19 49:14,15

49:17 50:18

68:20 69:2 72:4

72:6 74:21 96:1

98:13 99:22

154:5,7 212:17

213:11 218:13

224:15 225:1

226:10,14 227:4

228:14 229:19

231:7,12,20,20

232:6,11 233:8

233:13 235:8,12

236:20 240:22

241:2,5,7,17,20

247:20 252:22

268:16 283:18

285:5 287:7,22

288:4,20 289:20

290:3 300:8

301:15 302:5,20

306:7 307:16

322:1 325:22

328:7 329:5

332:19 341:1

352:3 353:22

359:15 380:22

381:13 396:20

408:8,9 409:18

413:14 453:14

461:18 463:15

466:9 468:3

474:3,21 481:9

485:11,12 486:2

490:11 492:2

498:8 501:4

507:9,11 508:13

510:12,14,16

511:9,17 515:6

518:7 527:17

529:16 531:20

535:12 536:6,13

538:18,21 562:21

581:6 597:3

600:4 602:17,21

603:3,9 605:11

607:15 610:21

611:19 619:20

621:11 678:10

685:2 700:22

701:17 742:1,20

746:18,19 748:16

752:4 757:7

766:4,14,18

767:4 773:6

774:11,17

animals 29:1 37:2

48:16 66:21

70:13,14 72:1

75:1,17,21 76:4,8

76:15,17 77:2

79:11 88:8 89:9

93:14 94:20,22

102:13,14 103:13

103:16 106:22

232:4,8,10

242:16 244:1

245:9 289:3

299:15 322:9,10

333:22 364:4

369:19 372:6,17

374:20 377:17

392:4 437:13

462:1 463:3

468:16 474:10,21

476:8,22 482:22

487:3,12 498:18

502:13 509:10

511:1,13 514:3

519:15 530:3

531:22 532:18

533:18 534:11

535:2 538:8,10

595:9 597:20

598:16 683:1,2

701:4 757:9,22

766:18 769:19

animal-specific

236:21

animated 676:17

animation 34:19

anionic 55:8

Anna 20:1 22:10

25:1,1 42:2

60:15 96:4

107:12 155:1,6

169:17 170:7,13

191:18 194:12

201:13 221:3

422:9,9 425:15

434:18 436:3,7

436:17 441:6,8

471:3,18 512:4

524:5 532:22
Page 6

606:15 618:4

620:10 666:3

690:8 704:3

705:16 710:5,8

719:21 735:1

736:3,7,11,14,21

737:21 747:16

778:4 781:6

announced 395:13

annually 388:20

anolyte 663:7

anomalies 287:6

anomaly 623:18

ANOVA 483:22

484:5,7 494:1,15

494:15 523:8

ANOVAs 489:19

494:18

answer 14:8 18:9

60:14 61:20 65:1

87:4 92:2 98:16

98:22 109:22

110:12 148:4

150:17 166:22

167:2,13 219:13

233:4 269:7

275:17 283:8

289:17 305:10

307:10,12 308:10

374:9 389:16

395:16 415:11

422:12 441:6

442:11 467:17

473:5 477:6

502:20 518:22

525:13 538:1

606:11 613:14,20

613:22 639:10

666:4 685:15

710:1 712:6

716:22 719:9,10

719:12 729:13

answered 100:14

351:11 420:4

618:3 645:4

701:13

answering 450:21

512:15 529:4

605:10

answers 195:7

219:14 447:14

501:17 521:3

645:9

antagonist 52:8

antianxiety 501:8

501:10

antibiotics 629:3

725:20

anticipate 596:15

anticipatory 23:6

antipuradine

629:4

antipurine 629:10

anti-logs 400:7

anxiety 77:9,10

78:9,10,11,19

79:1,3,8,18 85:22

88:9 98:10 99:6

99:17,18 103:7

106:7,13,16

210:12 285:7

366:12 477:20,21

478:7,8 491:22

492:2,4,6,7,21

501:6,9

anybody 102:8

105:5 147:20

421:21 465:13

468:20 513:19

540:8 586:3

592:4 654:15

687:1 739:15

746:5,8

anymore 92:7

411:14 497:13

539:8 588:8

674:21

anyway 420:11

523:6 642:1

644:5 706:19

728:17 739:13

AOP 33:3 39:4

43:22 44:16,20

96:15 105:15

212:4 437:2

AOPs 22:16 40:3

AOP/MOA 609:5

Apad 394:20

apart 518:19

apartments 217:5

397:14

apologize 131:1

260:14 539:10

apoptosis 440:13

440:15

apparatus 88:12

123:19

apparent 234:19

289:10 300:22

491:20 662:17,21

696:5

apparently 95:21

appear 29:2

237:16 240:8

267:20 288:19

374:7 377:3,4

459:14 473:22

483:12,20 503:2

549:19 571:1

753:20 757:20

774:4

appearance

239:14

appeared 87:12

139:19 413:10

424:15 549:16

appears 95:9,12

98:13 383:16

657:14

appendices 275:11

293:22

appendix 113:8

114:7 129:19

130:5 147:1

165:14 171:3

275:11,17 422:16

422:20 424:12,19

473:9,20 553:11

553:17 575:15

735:11

appendixes 112:9

540:22 541:14

applauding 772:7

apples 176:15,15

215:12 438:20,21

applicability

285:20

applicable 9:9

439:14 501:4

531:21 667:16

675:18 724:13

application 101:6

121:3 181:5

196:16 217:5,7

393:19 394:19

647:19 653:1,2,5

660:19 669:7

679:10,12 684:19

701:13 703:9

720:16 722:9

732:15 733:2

applications 217:8

217:10 691:10

722:10 751:8

applicator 263:17

266:16,18

applicators 197:7

263:20 264:1

265:11,13,18

266:12 269:18

668:1,2,15

applied 85:1 197:4

379:16 418:15

583:10 696:21

722:12,20 723:1

applies 235:9

265:21

apply 226:17

236:2 265:15

272:4 283:7

290:19,21 351:19

352:18 368:12

408:15,17 409:11

603:3 772:14

applying 176:22

178:13 263:14

416:2 590:12

668:2

apportionment

627:2

appreciably 128:9

appreciate 19:21

20:9,12 25:13

32:11 107:12

224:16 251:22,22

312:9 314:5

318:16 500:14

515:20 566:17

568:13 719:21,22

764:4,11 769:9

777:14,19 779:20

780:15

appreciated

513:11 776:5

appreciation 4:3

19:17 491:21

779:19

appreciative

248:14,21

approach 16:11

27:16 36:6,10

37:6 183:15

184:7,9,15,19,20

184:22 185:4,7,8

185:17,18,20,21

186:11,12,14

187:17 194:9

203:11 225:15

226:10,22 229:1

244:6 251:9

282:21 291:8

329:12 331:5

333:13 337:2

354:12,20 379:4

389:22 390:19

454:6 465:18

468:1 483:18

635:7,11 683:3

683:10 690:14

700:20 705:8,9

705:13 706:15

725:9 730:18

738:1 748:1

752:3 772:17

approached

197:16 756:21

approaches 14:9

14:10,20 15:14

22:1 27:7 29:4

38:18 41:4 82:6

184:6,8,10 185:5

185:6 187:15,20

187:21 193:19

194:22 218:16

219:12 250:9

252:10 300:15

379:11 457:22

608:15 618:1

661:4 690:13,17

690:18 691:4,17

702:14,16,21

703:17 704:7

705:2 710:22

721:1,9 726:13

738:1,4,6,8,13,20

739:11

approaching

683:8

appropriate 2:19

2:20 30:16 54:2

88:21 131:19

139:19 141:8,10

142:12 250:8

259:3 285:11

299:15 300:12

435:4 438:6

479:22 481:4

483:20 489:13

492:22 493:22

494:13 495:7,9
Page 7

495:11,11,11,12

495:13 519:7

532:19 545:14,19

595:9 599:12

623:4 625:10

675:16,22 690:17

694:2 719:11

appropriately

130:19 278:15

494:15 495:10

545:2,12 550:14

622:6

approximate

525:20 705:11

722:17

approximately

216:16 402:6

404:3 525:20

526:1 576:5

694:13 723:20

approximations

693:15

APRIL 1:17

APS 423:11

Arabia 671:2

arbitrarily 52:19

ardent 249:5

area 21:10 38:3

81:5 124:13

136:22 154:17

164:13 168:15

192:22 212:8

218:5,10 219:11

263:6 266:21

320:7 343:3

352:2 414:9

431:4,9 433:11

454:1 462:21

476:2 481:12

488:1 507:1

512:7,20 514:14

520:21 537:8

636:13 643:10,11

672:19,19 698:16

698:19 740:1

areas 8:19 20:20

21:6 39:15 69:5

123:12 136:11

146:8 161:21

191:19 204:11

231:11 394:14

430:22 454:1

512:13 553:17

576:17 607:4

660:6

arena 511:11

argue 60:3 300:2

386:15 465:4

579:9 741:12,13

arguing 303:14

argument 335:20

508:12 510:5

537:21 582:22

698:22 732:22

arguments 306:12

arisen 129:22

arises 236:2

474:16

ARLINGTON

1:16

arm 72:16,18

73:17,21 76:1,3,6

99:9 104:18

109:13 349:5

358:1 361:18

374:15 376:4

378:20 477:14,16

477:17 478:14,18

479:3,6,8,9,17

487:10 490:16

495:8 507:21

509:21 510:3,3

510:14,15,16,17

519:22 520:5

532:7

arms 76:4,5,5 79:7

79:17 375:6

479:3 510:4

511:17

aromatic 158:18

555:6

arrangement

387:16

arrangements 3:9

3:14

array 233:10

732:17 749:12

arraying 748:2

arrived 283:6

717:18

arrow 75:8 216:4

216:4

arrows 75:19

242:13

art 36:5

arteries 627:21

artery 411:8 628:3

article 569:20

590:22 668:13

articles 342:3

articulate 235:6

237:8 245:6

articulated 13:13

13:15,20 238:15

472:22

articulates 16:10

articulating 14:14

14:21

artifactual 141:5

331:3

artificial 485:1

ascertain 542:11

ascertainment

144:17 612:10

Aschner 7:5,5

59:15,18,18 61:3

61:6,10 167:18

167:19 168:14

317:17,17 365:2

365:3,3,11 366:3

366:8 433:16

467:10,11 497:15

497:16,16 499:19

502:11 504:6,7,7

520:13,14 528:3

528:4 533:7

535:9 586:4,5,12

773:11,12

aside 250:10 302:5

304:8 474:15

556:10

asked 19:21 92:16

105:1 168:7

275:11 289:15

306:9 342:20

421:2 437:19

439:3 447:15

456:19 471:6,19

530:9 584:8

592:7 620:7

637:7 654:11

682:14 699:14

700:4 710:1

740:4 742:19

744:10

asking 17:21 18:3

21:12,13 92:18

97:22 106:18

114:12 152:20

166:5 207:10

220:5 300:14

349:13 355:6

367:16 373:11,11

389:7 432:20

447:19,22 448:1

451:17 452:17

468:21 473:7

513:10 525:8,10

588:19 607:17

700:14 742:19

752:13 764:12

asks 291:1

aspect 13:22 17:4

166:9 305:1,15

367:22 453:3,4

453:22 495:15

496:9 582:13

745:2

aspects 85:13 89:1

92:8 131:4 246:9

306:20 418:8

444:7 450:8

608:11 609:11

610:3

assay 91:6,11

311:14 330:15,17

330:19 332:1

368:13 484:15

676:13,14,17,18

676:19,21

assays 90:15 91:1

321:6 327:11,21

assembling 207:7

assertion 239:8,9

239:10,10 497:22

assess 95:3,7

131:21 134:3

135:20 136:2

160:21 191:5

197:17 233:15

323:11 354:21

357:18 394:3

545:9 550:19

assessed 57:17

70:15 89:11

119:15 134:13

137:9 284:14

525:15,17 552:21

597:9 694:7

assessing 93:5

156:2 191:8

250:16 544:11

548:18 573:15

607:16 741:15

748:6

assessment 4:18

5:22 12:10,12,18

12:19 13:1,10

14:1 15:17 17:3

18:4 20:15 22:2

23:10 27:10,15

28:5 29:8,16,19

29:21 30:1,1,7,7

30:17 32:19

35:10 36:12 40:8

42:20 44:19 45:8

54:9 71:21 96:5

107:3 113:11

114:11,14 118:19

118:21 120:7,7

127:10 130:22

132:13 134:2

135:4 140:1,6

143:14,18 144:19

145:3 172:18

174:17 175:5

183:14 189:22

192:2,4 193:6

202:5 203:22

204:4,9 206:9

207:5 211:13

248:20 249:8

250:19 252:6

254:1 256:5

282:19 283:9

284:8,13 290:19

290:22 298:22

299:1 300:2,3,16

315:21 316:19

323:22 334:2

342:19 343:2,9

343:12 347:17

349:14,15 354:21

355:7 356:8

359:20 362:19

363:14 364:19

381:7 383:14

385:3 388:13

389:6 390:4

391:15 394:11,13

395:2 447:7

455:15,17 467:6

470:2 472:22

501:2 505:8

518:15 541:4,10

542:1,12 543:8

548:13 557:9

582:13 594:17

595:1,3,5,7

599:15 601:12,18
Page 8

602:7 605:14

606:1 608:12,17

609:11 618:18,19

637:9 638:13

642:13 653:15

692:4,16 693:1

693:16,17,17

703:2,14,21

704:20 720:9

724:20 727:4

735:13,15 747:20

748:1 753:12

754:4 757:11,11

763:9 773:5

775:4 777:7

assessments 6:2

10:15,22 13:1

15:17 23:21

32:11 33:1 62:5

94:17 192:6

203:17 206:7

300:1 391:6,11

394:9 545:9

548:21 703:18

assessor 768:6

assessors 206:7

assign 174:21

308:13 469:18

assigned 256:9

400:2

assist 22:6 625:9

assistance 249:2

Assistant 780:1

assisted 771:15

associate 4:21 5:21

6:4,16,18 160:6

316:18,22 317:6

317:9 442:16

562:17

associated 1:5

18:12 22:5 37:17

43:15 49:9 129:7

129:8 137:4

140:9 156:19

158:16 165:16

169:3 185:9

187:15 192:20

204:15 205:7

239:14 275:14

315:3 398:15

407:13 408:13

434:15 462:16

502:7 518:15

539:21 551:8

567:17 572:10

577:8,12,17

578:2,3,6,15,18

596:16 631:18

632:7 662:18

685:6 691:4

692:18 693:8

720:15 721:8

749:2

associates 574:3

association 130:7

133:2,9 136:19

136:21 145:10

146:16,19 149:18

159:20 166:16

169:6 180:4,17

181:21 182:2,9

182:18,20 183:1

183:5 260:1,3,16

261:21 263:4

290:12 291:19,21

292:8 295:2,15

295:21 296:2,20

297:3 298:11

301:3 360:3

383:10 386:15

424:15,16 425:7

425:9 476:15

561:18 565:2,5

567:1 574:12

576:15 577:13

578:19 579:16,17

584:12,13 613:16

666:9,17 686:17

686:22

associations 129:9

134:20 136:14

137:12,19 138:18

140:14 199:6

232:2 239:22

287:6 289:10,12

290:15 291:18

297:1,4,13

300:22 385:16,21

386:5 543:22

547:14 552:19

561:16 562:7

568:7 573:11

574:19 575:13

582:17,21 594:12

606:6 702:13

assume 89:18

239:3 328:16

337:13 684:20

assumed 338:11

assuming 238:10

330:9 337:16

399:14 677:5

724:6

assumption 13:13

551:7 615:5

616:19,20 732:22

assumptions 13:14

13:15,17,22

14:10 17:1 186:9

227:7 239:15

245:2,3 290:5,7

390:18 391:9,17

391:18 398:12

545:18 692:11

729:16 738:14,16

740:6,10 763:17

assurance 143:11

487:12

assure 684:21

assures 146:4

astrocytes 469:14

asymptotic 374:20

375:11,14

ate 264:12

Atlanta 4:8 5:17

279:9

atrazine 10:10

203:19

atrocities 743:16

743:18 744:6

attachment 422:20

attempt 55:18

89:13 441:7

651:17 665:9

attempted 712:20

attempting 629:2

645:19

attempts 458:5

573:18

attend 553:2

attending 2:13

780:22

attention 137:6,17

158:20 159:1,2,3

159:11,17,17

160:22 161:4,10

167:7 248:21

251:22 252:11

253:18,20 291:16

307:15 350:11

364:14 473:16

476:15 480:17

501:22 540:12

543:20 544:8

575:19,20 577:22

578:6 697:4

attentional 137:21

attenuate 94:4

attenuated 136:21

attenuation 75:14

attitude 232:18

attributable

287:13 289:12

424:10 566:10,15

567:16 583:3

attribute 612:13

attributed 37:19

208:21 290:16

555:5 595:21

645:6 778:16

audience 220:17

540:4

auditory 82:14

augmented 747:10

author 343:10

authority 2:18

123:2,6,7 251:1

389:7 392:11

authors 76:17

99:11 114:19

122:3 123:2

124:16 125:13

126:2 127:11

128:16 131:22

132:15,16 133:1

133:7,8,22 136:1

138:1 139:4

146:14,22 150:18

153:16 157:5,9

276:6 311:4

331:21 332:15

336:10 352:11

354:18 480:7

496:6 509:14

512:9 520:8

558:10 582:20

583:5 585:22

590:22

author's 212:20

549:15 582:14

autism 101:14

492:17 501:2,4

526:22 527:2

639:1

automatically

505:11,14

autonomic 363:22

availability 329:16

622:19 691:13

available 3:18,19

8:16 10:2 28:14

28:17,22 30:5

38:15,17 112:15

122:4 126:3

144:8 168:5

194:14,22 204:2

226:7 244:2

310:16 319:9,21

325:2 329:17

357:17 420:14

421:8 426:11

430:21 437:8

439:13 440:3

450:7 451:13

461:2 472:6

541:1 571:8

572:2 574:5

586:1 606:1

610:11 611:16

628:21 647:2

656:17 658:19

691:18 695:22

696:6,12 703:8

704:19 715:12

726:12,15 748:14

758:3 781:13

average 118:12,13

121:8 138:22

142:20 162:20,22

163:2 221:16

222:6,21 361:19

362:13 399:7

401:17 408:5

415:19,20 416:1

416:3,4 417:2

418:1,6 580:10

580:13 698:16

averaged 374:19

averages 417:8

456:13

Averse 426:2

avoid 70:18

avoidance 73:12

485:21,22,22

avoided 462:9

avoids 347:19

awake 255:18

award 779:20

awarded 252:5

aware 273:8
Page 9

281:19 604:14

632:21 651:3

697:11,11,15

756:4 773:18

awareness 249:6

awhile 224:17

axis 326:12 361:21

axon 55:15 56:8

482:22 625:11

axonal 57:12 58:9

332:7,12,16

446:9 454:1,3

464:21 465:8

466:4 468:4

axons 54:17

B

B 33:22 34:2 359:5

359:6 525:1,4,14

530:12 619:3,5

babies 399:2,5

727:17

baby 386:17

back 23:15 25:20

32:12 34:5,16

38:22 41:6 47:17

51:16 59:17,20

68:15 69:1,9

96:14 97:13

105:1,15 108:9

109:4 111:13

145:6 148:21

161:15 165:14

167:1,17 186:22

195:17 201:14,18

201:22 202:8,9

204:18 208:14

213:6 214:17

217:20 223:18

235:16 247:20

249:4 251:1

260:8 279:13

292:10 304:10

305:21,22 310:12

322:12 328:15

331:5,7 338:5

340:20 345:1

365:12 367:8

369:7,16 374:12

377:15 380:18

387:10 415:9

416:17 417:12

418:22 419:6,10

419:12,19 420:11

422:5 447:16,22

452:6,13,19

458:17 465:2

466:1 475:10

477:14 479:5

486:10 499:2

512:19 522:5,6

524:22 527:6,13

530:3,11 531:11

533:11 539:7

553:14 555:3

556:7,11,19

559:15 588:16

589:13,16 590:19

593:13 597:4

601:11 604:8

627:22 638:14

643:5,17 646:15

651:21 652:22

653:10,20 661:21

674:13 683:18

700:12 702:18

703:22 707:3

709:1 715:18

716:13 722:14

723:1,16 729:14

734:10 745:18

746:10,10 759:3

762:21 765:6

767:21 776:13

780:9

backed 232:3

background 3:17

6:1 156:10

196:20 320:5

396:11 471:15

570:11 654:6

668:9,21 669:1

670:5 671:10

753:13 755:2,10

759:9

backpack 123:19

125:8

backward 128:7

backwards 559:14

bad 196:1 223:8

244:15 474:6,14

485:21 492:3

501:15 659:3

669:11 677:1

705:13

badly 378:11

baffled 274:5

Bailey 412:19

777:1,1,21 778:1

778:2 780:8

baited 76:4,5

balance 233:20

239:6 241:3

242:4 245:16

373:22 620:18,21

729:4 747:12

balanced 292:16

balancing 239:16

ballpark 188:9

band 325:12

bands 325:6,14,15

329:6

bang 665:6

bank 714:13 715:8

bar 327:4 779:14

barefoot 265:21

736:1

barefooted 668:15

barely 43:5 493:20

679:20

Barnes 511:7,8,10

Barr 189:3 263:2

277:12,13,17,21

308:16 426:10,13

427:9,11 695:13

barred 402:15

barrier 629:7

Barry 5:14,15

161:19 162:5,12

162:18,21 163:5

163:8,21 164:3

164:10 310:20

311:5,16,19

312:1 316:12,12

632:1,2 636:20

642:22 646:8

661:17 662:3

664:10 676:16

681:7 702:3

728:13 764:14

bars 269:13

Bartells 696:10

Bartels 255:3

271:7,7 273:9,11

273:22 278:4,5,5

279:21 280:15

287:14 308:9

309:9,19 310:4,8

311:2,13,17,20

457:6

Bartles 319:8

320:2,3,5 334:15

335:10 336:1

337:15 338:5,22

339:8,16,20

340:6,16 341:17

341:22

basal 57:7

base 93:3 176:15

320:16 364:19

368:16 409:16

based 28:20 30:2,3

32:18 48:17

52:19 54:5,9

95:13 104:2,5,5

106:8,9 128:4,11

136:2 141:2

177:18 180:5

182:5 186:13,20

187:6 190:7

198:8 204:1

211:16 212:17

256:11 260:11

287:22 288:10

324:14,18 325:1

327:6,19 353:17

357:8 364:11

365:20 381:4

390:15 397:19

407:4 412:5

414:20 416:7

420:5 435:19

438:2 441:22

461:11 463:12

497:3 512:1

513:18 518:11

529:6,16 533:4

564:13 594:7

596:10,21 600:9

600:10 603:13

611:1,22 621:3

628:6 637:22

655:22 656:22

659:12 663:21

676:21 689:5

692:10 693:2

694:14,15 695:11

698:21 700:2

716:3 722:19

723:2 731:13

735:6 748:21

753:21 759:8

768:19 773:1

baseline 51:16,21

196:22 492:10

670:2 671:16

686:8

baselines 689:20

bases 15:20,20

443:11

basic 15:14 122:13

129:3 428:7

440:2 478:6

635:6,6 706:12

711:13 717:21

basically 17:10

66:6 81:11 86:10

94:14 104:7

109:22 172:19

197:7 198:7

251:18 254:10

303:8 335:4

357:5 399:4,8,14

402:22 403:9

405:17,22 410:12

411:6,6 414:2

417:7 427:10

440:12,19 454:20

455:1 478:1

490:2 514:19

555:20 570:1,18

571:6 602:3

605:9 616:14

644:14 686:4

687:4,20 700:10

739:14

basing 683:9

basis 45:1 50:9

65:6,22 66:1,3,12

104:12 113:1

164:15 193:10

206:22 227:16

234:9 235:7

236:17 343:5

355:17 366:2

370:7 396:20

401:15 407:22

412:20 418:17

444:4,9 465:20

512:9 573:16

609:16 611:18

647:14 657:1

688:4 748:15,16

749:1 750:5

751:2,4

basket 421:16

724:19 734:14

bated 479:3 510:4

510:4,16

bathroom 233:18
Page 10

batteries 543:15

battery 452:4

677:3

Bayes 379:13

398:5

Bayesian 379:4,4,9

379:9 397:17,18

398:3

Bayley 130:9

135:10,11,13,22

145:20 166:6

167:2,5,17 535:3

535:4 575:4

577:12

Bayleys 534:16,17

bear 225:20 235:3

685:13

beast 377:15

beautiful 366:17

779:18

becoming 11:9

136:20 370:12

bed 123:22

beg 524:19 704:21

began 527:16

beginning 9:17

26:7 27:17 77:4

106:10 124:18

141:15 161:21

247:3,16 477:15

670:19 673:14

702:18 756:21

780:14

begins 26:12 29:9

550:6

behalf 25:5 779:10

780:14

behave 382:20

386:21 708:22

behaves 506:6

behavior 31:16

38:9 57:7 69:5

80:12 81:3,10

82:10 86:1 93:4

98:10 102:22

104:13 106:12

136:1 165:6

197:21 198:10

210:22 262:8

285:7 355:22

357:19 379:18

380:9 382:16

384:18 440:18

473:17 474:8

484:17 492:20

501:1,9 504:9,19

504:20 524:21

525:19 533:14

545:8 576:13

578:4 660:22

behavioral 50:12

50:17 69:6,12

82:21 85:18

86:11 89:1 90:16

92:22 98:9

102:13 105:13

134:2,6 135:3

136:2 284:5

344:6,10 349:2

353:7,16 356:4

361:3,10 382:9

385:19 423:11,14

448:2,11 484:16

487:2 507:15

514:1 519:7

526:1,7,9 527:1

533:11 545:16

742:9 767:4

769:18 770:5

behaviorally

498:17

behaviorists

376:13

behaviors 77:12

80:1,5,10,17,22

89:21,22 94:21

106:8,16 118:15

265:12,19 379:19

380:2 383:9

504:11 524:13,16

531:12

beholder 622:14

beings 492:4

belabor 482:10

belabored 482:10

belief 728:8

751:22 753:1

believe 112:9

117:18 131:21

134:22 138:12

141:4 144:14

145:9 147:5,10

149:21 168:11

171:15 187:2

256:7 287:14

292:1 343:12

347:9 353:12,18

357:8 362:18

364:12 401:14

409:14 426:17

456:9 463:3

478:4 492:19

544:2 561:17

581:15 583:14

592:6 608:10

621:10 626:2,14

654:20 656:18

657:20 665:19

701:6 707:9

721:20 728:3

747:2

believed 551:5

believes 392:4

437:11 541:20

bells 193:7

belongs 697:14

belt 319:15

benchmark 36:6

37:6 209:1 398:9

398:10,14 406:7

406:14,21 407:1

408:14,19 409:17

benchmarks

665:11

bend 356:14 357:2

bends 359:4

beneficial 688:21

775:5

benefit 132:11

175:6 230:20

291:9 292:16

293:13,18 443:21

467:8 487:9

534:9 542:18

benzene 309:11

Berkeley 151:8

152:12 168:15

258:19 259:17

262:22 283:16

288:1 294:14

296:15 576:16,20

577:7,16,22

578:9 580:10

Berkley 116:5

Berman 268:9

309:21

BEs 693:1

best 8:16,17 10:1

10:12 25:21

27:12 31:3

108:15 131:6

134:4 166:22

167:2 189:4

199:8,9 200:3

214:14 252:5

257:20 297:7

313:18 334:1

370:19 371:16

402:21 440:3

467:8 492:5

554:6 600:5

605:22 607:1

616:2 656:17

660:14 669:10

717:12 719:7

723:11,22 758:3

758:8 770:13,18

779:16 780:20

bet 346:1 526:21

beta 425:6

betas 568:18

Bette 5:21,21

110:8 316:18,18

608:22 690:21

702:20 704:6

719:3 739:18

748:10 762:8

763:4

better 26:17,17

27:6 62:9 66:3

91:20 120:18

146:21 189:18

191:9 201:9

230:7 276:5

298:21 302:22

353:4 360:21

361:5 373:19

374:8 415:9

459:19 497:19

504:2 519:4

527:12,12 529:2

532:11 544:17

563:15 591:17

613:5 619:17

628:17 631:9

636:7 641:15

654:18 661:3

662:8 693:17,18

695:15 715:19

716:3 720:18

724:6,10 753:6

760:14 771:17

beyond 195:14

254:6 324:9

332:6 334:4

385:8 398:11

406:22 429:4

609:12 627:15

653:3 667:15

674:9

bias 85:9 127:7

129:2 138:9,14

140:3,12 143:19

144:4,5,14,14

146:2 156:4,13

157:3 257:2,17

272:6 284:21

289:13 292:19,21

509:10 544:11

548:9,15 549:2

569:3 573:15,17

biased 548:20

biases 156:2 542:6

548:11

bible 652:15

bidirectional

497:22

big 18:20 34:9

35:4 97:9 165:12

191:19 192:11

205:10 295:14

305:20 335:21

336:3 358:18

360:7 371:22

401:2 475:8

481:7 497:20

642:4 704:21

711:4 730:10

745:20 746:3

751:11 767:19

775:16,17

bigger 191:8

271:16 418:9

489:5 560:17

592:17 631:17

682:15

biggest 39:12

455:16 631:12

667:14

Bill 20:7,9 35:7

40:16 43:6 59:19

62:7 426:1

433:17

Billauer 366:10

Billauer-Haimo...

480:8

billion 271:1,9

279:2

bimodal 670:11

bind 55:4 456:14
Page 11

binding 45:18

48:20 53:7 54:14

56:3,10,14 57:10

61:11 446:8

461:3 628:11

bindings 45:17

binds 62:16,16

bioavailability

347:21,22

bioavailable

730:12

biochemical 103:1

biochemistry

605:16

biologic 65:14

173:6,7 174:11

581:3 754:14

biological 16:15

16:16,17 24:16

34:7 43:21 44:10

44:18 45:13

46:14,16 48:12

50:3,3,9,15 51:1

51:9 52:5,14,18

53:10,13 55:3,19

55:22 59:3,6,12

62:7 63:2,4,20

65:19 67:19

123:17 124:14

128:4 146:7

205:17 222:19

231:21,22 232:7

285:14 321:3

322:22 327:9

350:5 351:20

360:2,5,19 364:5

441:16 447:3,5

453:6 455:14

456:20 460:22

469:22 472:18,20

496:1 538:1

556:2,10 561:11

586:1 592:9,16

632:22 661:4

739:10,10 769:22

biologically 43:11

46:9 52:22 55:20

58:19 113:3

146:9 209:17

297:17 352:14

381:10 390:7

441:21 442:4

446:4 447:11

551:3 555:17,21

557:4 560:10

561:5

biologist 767:6

biology 60:20

451:2 739:19

747:1

biomarker 39:16

115:15 116:17

118:9 119:20,21

133:6 134:16

142:17 153:20

154:8,11 179:20

182:6 183:20

185:9 186:10

190:6 192:9

197:19 257:20

258:6 260:11

296:1 321:2,14

334:1 341:11

386:4 457:19

543:10 544:17,21

585:17 622:20,22

623:3 624:12,19

624:19 625:9,16

627:6 635:17

638:7 639:18

659:20 662:5

663:20 678:3

686:4 691:4,6

692:18

biomarkers 32:1

115:13 119:14

121:13 122:8

124:14,22 125:2

125:6 133:20

142:14 154:19

172:11,21 173:2

173:15,22 175:6

175:18 179:14

199:20 200:1,20

312:10 381:17

396:16 399:3

545:4 594:17

610:14 621:21

622:5,7 624:9

625:19 626:6,8

631:8 635:18

636:14 637:8,14

638:19 639:7

653:14 661:18

662:15 664:15

727:3

biomonitoring

26:11 27:3 38:15

38:20 41:4 172:6

172:12,16,17

173:1,11 175:20

184:5,10,20

185:7,15,19

187:15 202:19

204:11 208:10

262:3 263:9,18

265:11 320:8,9

320:12,13 326:4

326:9 334:18

393:10,13 609:20

610:10 621:15,21

622:8 625:10

654:2 655:20

656:12 690:15

691:1,9,13 692:2

692:5,8,15,17,21

696:7 700:6

702:10 703:5,13

703:15,18,20

719:18 720:14

758:19

biorepository

714:18 715:10

biostatistician 4:6

315:11 485:9

550:1 559:15

Biostatistics 5:9

biowater 628:13

birds 63:6

birth 36:21 116:21

117:3,8 118:7,13

127:21 131:18

132:17,17,20

133:2,2,3,4 139:2

144:6,16 146:17

146:17 257:12,12

258:14,14,18,18

259:8,15 260:4,4

263:3,22 268:16

274:15,15 275:6

275:7,12,15,15

280:10 335:7

363:19 399:2,5

412:15 415:19

457:11 459:17

476:11,18 544:8

575:2,2 599:20

604:5,6 615:8

718:8,14 720:15

722:15

births 221:20

Bisphenol 410:8

bit 13:3 27:19 29:9

30:12 34:12 40:4

43:18 44:1,11

55:21 74:22 77:7

102:5 103:4,5

115:14 143:5

155:3 161:20

167:15 178:16

194:19 195:13

207:8 230:13

234:15 254:7

262:2,16 263:5

277:10 311:5

351:8 365:5

374:13 404:3

406:6 435:1

436:1 439:4

467:13 471:16

492:8,8 502:12

519:6 521:10

523:4 524:9

538:7 547:3

551:1 558:1

567:6 585:13

606:16 607:11,12

632:6 675:20

694:5 695:5

703:10 705:1

716:14,22 719:22

728:17 732:12

739:12 740:14

744:16 763:10

773:18 774:1

bittersweet 778:20

Blacks 577:13

blah 94:9,9,9

blinded 144:20

block 52:9,11

358:5,6 494:3

blocked 630:7

blocking 494:2

blocks 357:6 358:2

494:4

blood 65:16 66:2

125:18 129:10

136:13 153:11,17

157:13 163:16

165:17 173:6

175:6,7,9,14,17

179:14,16,19

181:21 182:3,9

182:18 213:22

214:7 215:3,8,18

216:21 217:6,6

218:1 221:17,18

221:21 222:9

223:2 256:14,17

256:19 257:10,22

259:21 260:10

261:6,7,16 262:3

264:20 265:3,5

267:13,14,16

269:4,5 270:9,11

271:4,13 273:4

274:1 278:16,19

279:20,21 280:1

280:3,8,8,16,18

280:22 281:1,5

281:10,10,12,17

309:3 311:21

320:11 321:16

322:6 324:15,17

324:22,22 325:7

325:11,16 326:9

326:11,13,14

328:16,17 329:18

330:1 332:11

335:3 339:17,18

339:20 340:8

396:17 397:5,11

398:2,20 399:10

401:4,7 403:2,3,7

404:6,22 405:3,6

405:6,19 412:14

415:20,20 417:18

417:18,19 418:1

418:1,2,3,11,13

418:14,15 426:8

427:4,17,19

438:19 440:1

459:8 543:11,13

543:13,14 559:16

574:15 580:13

584:13 586:21

589:5 594:8

596:7,20,20

599:20,20 600:10

601:5,15 602:13

613:2,3,6,10

614:10,16 616:9

616:10,11 623:1

625:20,21 626:2

626:7,8,9 627:18

627:21,22 629:16

631:20 632:19

633:8,14 634:8

635:17 637:16

638:6 659:18
Page 12

669:13,14 671:16

671:20 672:2,4,9

672:19 686:8,13

687:3 698:11

699:22 701:10

707:21 708:6

713:7,10 714:4,7

714:8 716:4,4,8

717:14 718:7,12

718:14 719:1,2

720:10 722:15,18

732:14,16,19,20

733:16,21 759:1

760:4 768:11

bloods 672:6,6

711:21,22

blow 286:6

blown 652:1

blue 205:10

266:22 325:11,16

blunt 705:11

706:21

bluntly 478:17

BMDL 346:6

471:13 601:5

BMDL10 344:15

351:3 363:14

364:3 518:4

525:9

board 5:10 31:14

214:19 282:10

295:4 351:20

358:7 516:14

body 15:21,21

23:12 209:16

324:13 336:3

348:6 386:17

410:15 446:2

456:5 465:16

512:13 637:3

640:16 643:4

661:5,22 675:3

678:15 695:12

723:7 769:20

bolas 382:22

bolsters 552:20

bolus 723:20,21

book 391:7 751:17

bore 473:11

born 267:17,17

271:22 272:1

276:2,3

bother 498:3

bothered 477:10

bottom 156:21

325:15 327:11

329:7 330:16

363:14 427:2

428:6 490:18

515:5 563:18

684:18

Bouchard 551:7

554:13

bought 170:10

bound 570:3

611:18 612:6

678:16 697:5

748:15

boundant 678:16

bounded 749:13

bounding 397:18

398:21 606:7

607:6,18 616:1,5

618:11 710:22

738:9 739:20,21

748:20

bounds 400:3

599:16 600:3

607:7 740:11

box 60:5 61:13

62:3 77:11 78:20

79:4 100:1,2

156:21 295:17

387:1 428:11

451:15 455:19

711:5

boxed 296:22

boxes 386:11,13

BPA 196:4

bracket 107:6

brackets 221:9

Bradbury 7:16,17

22:12 23:1 26:16

27:8 62:1 101:20

143:2 248:16

249:5

Bradbury's

380:19

Bradford 145:6

206:20 220:10

229:20 230:14,15

231:5,11 232:20

234:11,12 238:17

542:4 550:20

551:2

Bradford-Hill

21:2 23:16 32:16

33:3,9

brain 41:1 48:21

57:5 58:6 65:16

68:21 69:16 83:5

83:14 84:16

106:21 121:22

159:6 208:10

209:18 288:11,12

324:17,18 327:1

344:9 345:6,14

345:21 348:5,10

348:11 352:2

358:20 441:19

446:5 450:5,10

450:20 466:11

482:19 503:16

517:19,21 524:17

535:16 551:12,15

555:15,15 571:2

597:22 684:10

687:21 707:5

742:22 743:2,2

743:18,21,22

744:5,13,13,20

744:21 745:3

761:7

Branch 6:7

brand 36:15

768:16

Braquenier 491:5

517:22 522:3

Brazelton 134:1,7

135:4,4 169:1

535:3

breach 386:13

bread 482:13

break 40:21 68:7

68:12 111:12,20

201:12 220:17

223:7 224:4

260:13 314:6

355:18 381:2

386:8 387:5

417:14 467:12

470:9,11,15

471:4 476:14,19

484:22 485:3

518:19 593:3,13

593:14 621:16

653:18,19,22

655:11 716:13,17

717:3 719:9

736:10,14

breakdown 265:15

645:18

breaking 736:22

breakout 781:16

breakpoints

687:22

breakpoint/infle...

686:17

breaks 363:5

477:4 510:4

breast-feeding

547:8

breath 147:19

694:16

breathe 196:6

breathing 416:3

654:6

breed 504:20

Brenda 429:8

brevity 30:13

Brian 6:21,21

147:22 148:22

188:3,3,14,20

189:1,17 190:3

191:4 195:19

270:18 276:21

277:6,9,13,16,20

277:22 317:12,12

338:1,18 339:1

339:11,18 340:1

427:8 530:13

622:12 635:14

637:13 639:15

640:11 641:13

642:15 649:5,20

650:17,21 651:3

651:10 652:19

654:10 656:16

665:8 674:16

693:11 709:3

716:7 721:16

729:20 734:9

735:5 740:22

745:17 761:2

772:6

bridge 396:15

brief 380:16

387:12 644:5

briefly 48:5

114:15 115:4

202:4 248:22

250:5 270:5

290:20 470:10

781:16

bring 14:1 21:3

26:8,10,11 29:12

30:15 32:20 33:5

35:5,9 38:22

39:13 41:6 42:12

42:16 195:17

201:18,22 202:6

221:1 225:20

227:4 229:8

231:8 234:14

249:6 289:14,18

291:16 299:19

306:15 379:14

385:11 387:3

393:12 473:16

478:13 514:16

540:12 590:8

591:5 603:5

734:10 759:3

760:18

bringing 8:16

11:12 30:19

42:18 70:3

149:12 220:4,8

235:3 287:18

420:11 432:6

473:14 663:11

738:19 756:8

brings 39:2,9

287:17 593:18

603:7

broad 8:19 9:4

85:19 130:15

174:21 176:4,14

210:3 231:17

430:12 459:21

509:14 631:7

667:12 750:7

756:17

broader 23:19

102:3 238:3

273:15 658:8

703:14 749:17

753:10

broadleaf 169:20

broadly 9:9 170:4

209:6 238:2

249:4 428:17

733:7

broke 116:1 424:5

brought 10:16

29:6 32:9 101:20

194:9 202:16

207:21 248:9

278:8 284:15

285:10 377:1
Page 13

423:7,12 449:20

464:15 474:22

497:9 652:17

664:11 704:4

728:14 769:16

774:6

buck 665:6

buckets 756:22

758:4

Buckley 6:21,21

147:20,22 148:22

188:2,3,3,14,20

189:1,17 190:3

191:4 193:19

195:18,19 206:5

218:17 270:18

276:21 277:6,9

277:13,16,20,22

317:12,12 337:22

338:1,18 339:1

339:11,18 340:1

427:7,8 530:12

530:13 622:10,12

630:21 631:3

635:13,14 636:21

637:13 640:11

641:13 642:15

649:4,5,19,20

650:17,21 651:3

651:10 652:18,19

654:7,10 656:14

656:16 664:4

665:4,8 674:15

674:16 676:6

681:9 693:10,11

702:5 709:2,3,21

716:6,7 721:14

721:16 727:7

728:16 729:19,20

734:8,9,19 735:5

736:5 740:21,22

745:16,17 761:1

761:2 772:5,6

Buckley's 341:14

689:18 732:14

Buffalo 712:15

buffer 51:17 52:10

687:13

buffering 51:22

bugging 512:15

build 704:17 707:2

building 1:14

63:16 711:14

717:10

builds 204:8 747:8

built 206:22 410:6

412:2 717:19

bulk 97:3,4 110:15

242:11 638:4

bullet 365:14

447:19 726:18

bullets 297:10

448:19

bunch 198:6

497:11 591:13

burden 175:12

238:11 245:3

542:16 585:4

Burns 253:4

254:13 255:12,16

266:19 269:15,19

269:22 270:3,5

270:15,21 271:10

271:21 272:16,19

272:22 273:5,9

275:5 277:3,8,11

277:15,18,21

278:2 279:16

280:1,5,12 281:4

281:19 289:15

293:3 310:15

319:2 326:11

business 317:22

673:20

butoxide 729:6

butyl 677:13 678:6

678:15,16 680:17

680:22

butyrylcholinest...

120:2 179:15,18

182:6,20 183:6

215:6 624:8

625:2 666:10,18

669:20 670:14,19

671:13 673:7,9

673:15,18 674:2

676:12 677:10,19

677:22 679:3

681:21 683:15

685:18,20 686:7

686:10 688:4,5,7

688:10 689:4,10

689:12 701:1

buy 537:21

buying 643:19

bypass 724:15

bypassing 192:16

bystanders 388:22

393:13,15

C

C 34:1,2

Cadillac 691:20

cage 361:4

calcium 49:4,7,8

51:6,13,14,17,20

51:21 52:9,10

61:9

calculate 390:2

700:12 722:14

calculated 393:21

399:20 401:17

416:2 692:17

calculation 398:22

418:7 623:4

calculations 409:3

409:6 457:20

555:7

calibrate 624:15

calibrated 186:15

404:11

calibrating 39:10

402:11

calibration 39:15

206:11 625:1

California 4:18

116:5 151:8

155:7,11,12

261:9 384:10

431:3 550:5

California-Davis

6:14

call 33:13 36:1,16

47:10 203:13

244:6 249:9

294:8 370:3,13

370:13,15 387:9

411:7 421:11

434:5 436:5

522:17 556:16

567:17 649:21

664:22 668:3

671:3

called 9:15,19

226:2 230:16

432:2 475:2

477:10 479:13

644:14 662:17

calling 226:9

230:6

calls 300:9 723:7

Cal/EPA 315:21

Cambridge 224:10

282:8

cAMP-related

285:18

cancel 28:8 389:4

392:10

cancelation 579:2

canceling 388:15

389:9

cancellation 119:4

122:21 123:9

124:10 127:1

146:15,19 213:20

214:1 579:11,13

579:15,17 656:8

cancellations

222:15

cancer 4:8 6:7,8

45:10 315:12

391:1

candidate 439:22

492:18 526:22

582:15

candidates 438:10

439:19

capabilities 51:22

capability 332:19

713:22

capable 93:5

622:21

capacity 167:8

458:11,12 488:10

505:4 532:10

533:9

capitalize 78:20

capture 24:18

506:19 632:1

636:5 775:8

captured 100:14

captures 24:18

carbamate 91:10

carbaryl 644:15

carbufuran 218:4

carcinogenicity

228:4,17

cardiac 698:13

care 117:17 134:4

217:4 255:22

420:19 520:22

career 779:17

careers 12:14

careful 64:19

99:19 216:1

528:19 770:10

carefully 69:19

187:18 356:5

380:8,8 520:22

522:16 556:8,9

603:8 680:6

691:22 770:14

773:5

caregivers 134:4

cares 478:4

Carey 4:10,10

101:18 102:21

169:14,18 170:11

170:17 201:5

315:13,14 340:12

413:9,13 414:8

423:6 424:20

439:2 444:18

460:10 506:12

536:11,21 537:3

538:3 565:13,13

645:10,16 646:1

647:3 684:7

731:12 767:2

Carlo 323:21

Carol 20:2 41:1

112:3,4 148:3,14

148:20 149:2,12

149:21 150:5,14

150:17 151:5,10

151:14,16,18,21

152:5,8,13,18,22

153:8,15 154:6

154:21 156:5,8

157:18 158:2,4,8

158:11 159:8,19

160:1,9 161:11

162:4,11,17,19

163:1,7,17 164:1

164:8 165:20

166:21 168:2,17

169:5 170:19,22

171:2,6,14,18

201:14 221:14,19

222:3,12 253:4

254:13 255:16

269:15,19,22

270:3,5,15,21

271:21 272:16,19

272:22 273:5,9

275:5 277:3,8,11

277:15,18,21

278:2 279:16

280:1,5,12 281:4

281:19 319:2
Page 14

423:18,18 425:3

429:17 430:3

540:21,21 592:5

593:21 608:9

617:3,7,11,20

712:12

Carole 342:13,21

343:1 347:9

350:12 363:19

Carolina 43:9

68:19

Carr 6:18,18

66:13 158:13,14

159:16,21 161:1

161:17 164:18

165:1 317:9,9

348:4 376:19,22

377:8,12 378:18

378:18 435:18,19

437:20,20,21

438:17 440:6,7

444:11,12,16

454:19,19,20

456:8 463:3

487:14 499:17,18

504:22 506:4,5

516:5,6 519:2

521:6,7 524:3

528:20,21 531:6

533:12 554:10,11

557:7,7 558:9,19

559:2,13 560:6

560:13,18,21

561:4,10 684:12

684:13 686:6

711:3,7 712:7,8

728:20,21 771:10

carried 498:9

carriers 577:14

carry 10:8 627:20

carrying 627:22

Carr's 435:2

cartoon 324:1

338:6

case 13:21 25:22

29:22 48:7,9

50:1,10 51:2

58:14 67:14 76:2

77:4 85:6 89:20

121:8 131:4

136:17 137:20

142:19 156:20

157:4,20 164:22

174:3 201:15

224:21 226:17

228:8 250:18

292:20 294:19

328:3 334:20

349:4 352:4,9

360:6,8 362:20

378:8,9 386:12

386:20 387:21

400:21 459:8

463:22 503:6

568:6 583:5

586:22 611:15

627:13 652:5,8

652:12 673:20

678:4 712:4

715:22 731:2

748:4 749:21

751:13,22 752:17

755:22 762:18

774:10

cases 55:12 64:10

65:11 75:19

76:18 80:21

84:14 85:2 91:2

93:21 94:6,20

102:5 109:17,18

139:18 144:21

221:11 237:16

238:7 240:6

250:19 286:20

394:4 434:16

464:4 483:20

517:16 547:10

599:6 671:12

742:8

catch 41:21 414:2

414:2 706:17

723:10

catching 746:20

categories 145:14

174:21 428:7

493:17 557:15

categorization

121:1 125:22

142:9

categorized

327:18 678:9

category 519:1

557:16,18

Caucasian 115:11

189:13 386:22

causal 12:2 24:9

24:10 202:22

205:12 225:15

233:14,22 234:3

238:20 283:11

290:12 298:20

301:3 350:9

381:17 441:16

561:17

causality 33:22

230:22 233:17

249:11

causally 129:6

156:15 462:11

causation 285:4

286:17 289:11

292:4 293:11

300:21 385:21

386:6 566:17

causative 245:17

245:20

cause 11:21 49:6

50:22 51:2 54:21

62:15 63:2

158:20 233:5

286:18 287:5,12

299:9 301:8

304:3 321:7

411:22 413:17

414:4 455:14

457:19 459:3

625:16 754:19

760:5,11

caused 239:12

333:16 551:11

555:11 556:4

560:8 614:5

657:22

causes 44:1 49:2

61:2 225:10

230:20 231:22

245:13 303:10

350:22 382:11

641:4

causing 105:15

106:12 236:19

243:7 288:19

359:2 378:5

381:11 560:9

585:5 590:9

caution 304:11,18

305:13 446:11

460:17 466:1

500:21 501:19

565:4 603:12

675:4,15

cautioned 305:16

348:20 594:15

cautioning 589:12

cautions 532:20

730:18

cautious 467:21

cautiously 470:4

caveat 211:8 398:4

495:10

CB1 56:15

CCA 253:15

CDC 122:8 150:20

181:9 256:16

267:21 269:3

273:3 277:1,10

309:18,21 320:13

662:19

CDCs 177:18

CDC-published

271:11

CD1 522:14

Cecelia 20:10

cell 45:15,15,18,22

47:14,16,17,21

47:22,22 49:3,9

49:12,17 50:18

51:17,18,22,22

52:11,11 54:11

65:8,8 68:1

75:15 94:3

149:16 179:14,16

179:19 181:22

182:3,9,19 215:3

216:21 256:19

269:5 285:18

337:5,21 432:10

432:11,12 448:14

450:2,2,7,16,17

450:18,18 461:5

466:11 468:2,18

551:13 598:6

686:8,13

cells 49:20 52:1

68:1 323:2

327:13 337:11

433:1 450:6,9,10

466:7 468:3

469:11 481:10

600:10 601:5

613:3 743:18

744:4

cellular 45:20 46:1

47:8,13,17 48:3

49:5 53:18 54:8

54:18,19 55:10

56:6,11,19 57:3

57:11 327:12

348:15 442:16

456:14 460:12

598:20 628:19

Center 1:13 4:7,12

5:22 7:7 315:12

316:19 317:19

541:3

centered 403:6

675:12

centers 117:10

centimeters

507:20

central 37:8,8 56:8

57:5 398:16

402:22 403:4

404:2,3,17,22

445:15 455:5

508:1,11 678:19

century 9:17 10:18

12:7 26:6 47:8

202:11 284:17

certain 8:19 92:19

93:16 131:7,8

207:22 297:21

382:10 412:10

430:1,22 431:11

461:8 513:3

514:19 531:21

532:12 534:16

564:10 600:13

603:19 620:17

699:22 749:20

752:7,16 754:20

certainly 15:13

16:7 27:7 34:9

34:18 35:3 38:3

39:16,22 40:11

42:4 96:13 127:3

132:19 167:3

170:7,15 173:10

190:14,15,20

194:20 207:13

208:11 209:22

210:11 212:6,18

212:18 214:4

217:6 218:8

303:2 322:7

328:5 329:3

421:17 463:9,18

495:21 513:1,3,6

524:8 587:18

588:1,11 602:16
Page 15

620:11 638:19

665:8 668:16

681:12 716:9

722:2 730:9

761:20 773:22

certainty 18:17,18

391:3 469:17,18

545:1 642:17

certification

282:10

cessation 146:13

146:20

cetera 132:5,17

201:1 397:6

427:19 547:8

588:14 625:22

653:16 659:3,3

660:21 694:10,17

716:1 722:20,20

758:12 759:10,12

chain 510:4

674:12

chaining 479:13

479:13 487:11,11

487:13 507:21

508:2,14 510:21

511:10 514:8

chair 3:22 4:1 6:13

19:8 315:4 317:4

539:22,22 775:13

776:4 778:21

779:9

Chairman 315:5

chairs 201:14

challenge 11:11,12

12:13 21:11

26:18 42:12 61:1

109:10 130:19

210:9 486:14

535:14 602:21

633:5 680:12

696:9 740:13

challenged 535:12

challenges 9:10

11:4 15:19 16:1

23:20 31:21 35:4

39:12 42:5 57:8

62:2 93:14

183:16,19 192:11

205:8 535:13

542:20

challenging 8:8

31:13 127:20

213:9 217:13

248:19 249:3,21

307:13 319:14

697:20 751:15

CHAMACOS

38:12 116:6,18

117:17 119:8,17

120:1 121:8

128:18 133:8,17

134:15 136:18

137:14 142:20

155:10 169:10

178:22 261:8

267:6 425:5

429:9,17 431:4

541:4 656:1,4

695:2 712:13

chambers 78:22

81:11

chance 150:1

236:16 341:21

400:1 420:10,10

420:12 549:10

608:20 648:11

717:2 763:13,20

change 51:2 55:6

56:7 57:11 62:11

62:15 63:8 64:14

64:15 71:14

76:20 77:18 78:7

79:7,21 82:1,14

82:15 85:22 86:2

99:7 103:7 105:8

105:20 113:11

157:10 166:20

179:21 203:4

262:18 339:3

351:20 352:1

353:1 354:9

359:12 367:22

408:13 409:12

410:20,21 416:21

416:21 446:11

447:21 448:4,10

448:10,13,14,15

451:16 476:21

492:21 532:10

534:22 537:21,22

542:13 560:3

630:2 657:6,15

669:22 670:3

671:13 673:7,22

677:17 682:19

687:15 688:11,16

713:9,11 736:15

747:12 772:2

changed 92:5

128:9 277:1

349:22 492:11

679:20

changes 45:22

46:1,1,3 50:12,17

57:3,4,7 58:12

63:5 69:12 74:8

75:11,22 79:20

80:12,22 81:12

82:21 85:12,21

90:9,13 93:16

99:12 100:10

101:8 102:1,1,4

102:14 103:6

105:13 106:12

107:2 166:18,19

187:7 210:4

323:5 337:21

348:20 350:10

370:11 423:11

453:1 459:8,9,10

460:10 461:16

462:10 472:4,9

501:7 533:11

535:18 569:11

584:12 597:22

598:1 602:20

603:8 624:22

625:5 662:17

672:21 673:1

699:4

changing 166:13

281:7 352:22

chapter 41:3

characteristic

130:15

characteristics

117:15 118:18

120:5 123:4

127:8 130:14

131:20 132:14,20

146:17 548:7

580:3,21 628:6

630:1,2

characterization

12:17,18,20 13:4

13:11,12,16,19

13:20 14:15

17:19 20:13

21:21 23:2,8,18

24:6,17 39:4

113:2 114:1

120:19 135:7

190:12 202:10

204:21 220:1

301:6 447:6

472:21 608:12,13

608:13 609:13,13

609:14,19 610:17

611:6 641:22

720:5 753:10

754:5 755:17

756:9,15

characterizations

747:19

characterize 126:9

185:18 191:12

196:3 207:3

259:6 413:20

607:8 737:19

738:12,17 748:18

749:10

characterized

184:8 413:20,22

characterizing

154:15 751:2

characters 578:19

charge 9:2 11:3,17

12:3 13:6 17:20

32:4 41:7 63:15

107:14 202:1

204:14 209:10

212:2,9 218:15

219:15 248:10

249:18 254:17

256:3,8 290:22

291:1 298:14

302:17 320:21

342:18 343:16

346:10 515:5

597:6

Charlene 780:6

Charles 5:1,1 64:1

91:14 95:8

100:16 271:15

272:13,18,20

302:2,9 303:3,6

303:12,18 307:21

309:5,17 316:3,3

376:15,15,18

377:6,10,14

378:9 473:3

485:13 502:18

505:2,17 506:21

508:3,3,10,18,22

510:8,8 515:9

518:18 520:2

522:6,13 525:5

530:20 532:9

533:20 536:19

537:1,6 538:14

540:11 765:20

chart 151:8 326:12

Chartered 5:9 8:4

chasing 422:10

471:5,6

ChE 269:13

437:22 442:15

449:19 456:12,17

465:18 687:21

cheap 195:12

check 148:15

165:4 514:7

540:8 556:8,19

584:19 661:6

712:14

checked 557:3

checking 426:8

checklist 136:1

232:13 578:4

chelate 52:10

chemical 5:22 8:22

14:7 18:8 23:14

44:1 45:14 46:20

46:20 47:1,10,12

60:1 62:14,18

63:2 67:21 71:14

85:15 106:11

159:5 181:1

201:7 210:6

234:3 238:21

254:14 255:20

271:8 273:20

278:6 316:19

320:6 333:7

338:15 340:17

378:6 383:8

391:11 454:2

455:2 461:5,12

461:14 499:20

623:6 652:3

718:12 746:21

767:8 769:4

772:1,2 780:2

chemically 322:16

330:21 339:5

chemicals 18:8

46:19 60:3,5,10

67:7 106:5

110:10,11 111:6
Page 16

111:10 112:20

129:1 218:6,12

273:15 324:5

331:19 382:20

386:13,21 441:4

558:15 559:17

583:15 620:11

750:19 755:11

760:21 761:6

772:1

chemist 7:1 273:10

426:15

chemistry 321:8

chemists 307:21

CHENSHENG

6:3 171:9,16

198:13 273:2,7

273:19 314:1

316:21 382:3

384:1 386:9

411:1,11,16

412:1,6,20 517:1

553:1 583:21

586:18 601:21

608:2 612:19

619:10 631:2

643:14 645:14,21

646:3 647:22

651:16 655:14

664:7 676:9

681:2 683:14

684:15 688:3

689:1 699:17

707:5 713:1

718:5 727:9

731:1,19 733:10

733:22 734:4

751:11 759:14

768:9

Chester 20:2 41:3

194:6,6,12

197:14 198:5

Chiang 662:4

child 136:1 159:2

160:19,22 161:2

161:4 165:5

177:16 194:16

284:5 296:18

405:6 418:1,6

504:19 541:3

547:6 576:13

578:4,5,12

581:14 611:11

625:18 639:1

665:2

childhood 120:13

132:8 187:8

574:21

children 28:14,19

112:15 113:14,19

115:7 116:6

117:1,5 138:3,22

146:6 158:21

161:9 162:14

167:3,11 267:17

267:17 276:1,3,4

283:16 325:17

388:16 389:9

392:8,17 393:4

394:1,17 395:11

534:16 541:4

542:9 543:15

546:2 558:21

564:17 574:8

577:14,19 588:9

614:17,22 622:2

661:15,16 662:7

667:16 675:22

682:12 696:18,22

697:7,13 724:14

729:8

children's 5:4 20:6

25:7 38:11 112:6

113:16 114:17

117:9 121:6

176:2,5 178:9,10

178:18 205:22

254:14 316:5

534:21 596:3

638:17 655:21

656:11 661:13

664:11,13,22

768:17,20

child's 167:6

Chimariko 392:19

Chinese 70:8 88:3

chloride 61:7,8,10

chlorphyrophos

169:18

chlorpyr 647:17

chlorpyrifos 1:5

2:4 8:22 9:5,7

10:8 20:1 21:5

22:19 23:20 25:4

27:14,18 28:3

29:14 30:2 31:2

31:20 32:7 34:17

36:17 37:12

39:12,19 43:12

44:6,9 52:15,17

55:1,2,4,4,7,11

55:11,15 58:4,17

59:8 60:1,3,9

68:20 70:1 75:14

75:17,18 91:8

94:1,4 96:10

100:19 101:13

106:6,10 108:3

108:13 110:15

111:3 112:17,19

115:2,13,16,16

115:16 116:4,17

117:2 118:1

119:2,16 120:12

121:13 123:8

125:18 126:1,15

129:14 131:11

132:22 133:3

135:8 136:12,16

138:16 140:8

142:14 144:3

145:1 146:15

147:7 148:7

150:11 153:13

154:17 156:6,20

157:20 158:16,22

159:10,11,20

160:10,12 163:15

165:17 168:11

170:2,5 171:22

172:11,21,22

173:5 174:4,5

175:7,8,14,16

179:17,22 180:21

181:5 183:22

184:1,11,17

185:2,22 186:20

187:10,16 190:17

192:15 196:20

197:17 199:10

200:4 201:6,8

203:8,14,20

204:22 205:2

206:1,4 208:17

209:17 210:16

211:7 213:20,22

214:15 217:11

221:17,21 222:9

223:2 224:18,21

226:4,12,17

227:22 228:7

230:3 239:13,14

239:18 240:1

243:2,6 248:5

249:13 251:11

254:22 255:4

256:6,11,14,17

257:1,9,20,22

258:7 259:3,21

260:3,9,17 261:1

261:2,7,12 262:2

263:14,16 264:18

264:22 265:4,8

269:2 270:8

271:9 272:2,3,4

272:17 273:4

274:1 275:4

276:19 282:14

283:9 284:8

286:5,9 287:5

289:16 290:12,19

291:17 294:15

298:17 301:3

315:3 320:10,10

320:20,22 321:2

321:9,15,22

322:6,7,8 323:1,8

323:9 324:3

325:7,8,13,14,15

326:18 327:16

328:1 329:7,14

330:20 333:5

338:8 340:22

341:6 346:14

353:8 358:19

375:18 378:20

381:7,18 388:14

388:16,18,19

389:1,4,9 392:6

392:10,18 393:7

394:8,21 396:13

396:17 397:11

399:10,16 403:2

405:19 411:19

413:10 416:11

421:15 424:6,7

428:16 429:12,16

429:20 430:8,8

430:12,15 431:9

431:13 434:2,11

434:16 436:22

437:16 438:1,3,7

439:22 442:13

443:22 444:9

446:3 447:11

453:18 458:10,12

460:13,13,18

461:8,11 462:11

462:14,22 472:10

478:15 536:4

539:21 541:11

544:9 548:13

551:4,9 555:8,18

557:1,9,10,18

558:11,13 559:5

559:6,12,19,21

560:3,18,19

561:1,19 562:1

571:17,21 572:6

572:15 573:1,2

574:5,14 575:16

575:22 576:7,10

577:2 579:11

581:13 583:4,6

584:4,9,13,17

585:11 586:14

587:2,7,11 589:4

594:3,19 596:4,6

596:8,16,19

597:1 598:12

599:1,9,13,16,19

601:15,16 604:9

608:11,17 609:11

610:7,15 611:4

611:10 612:14

613:1,8,10,17

614:5,8,15 615:8

615:18 620:10

621:21 622:5,8

623:1,3,9,11,20

626:4,5 627:1,6,8

627:10 628:7,15

629:2 630:1

631:20 632:5,21

635:17 636:1,8

637:17 638:6

640:14 641:18

642:3,6 643:4,22

644:16,16 645:17

645:18 646:2,12

646:14 647:6,12

647:15,16,17

648:10 651:1

652:7,9,12 653:2

654:12 656:5,7

656:11 657:3,8

657:10,17 658:3

658:5,18 659:3

661:5 662:19

663:10 668:1,3,8
Page 17

671:6,10 674:22

678:14,14,17

679:16 684:9

687:9 690:15

692:3,8 697:5,10

698:20 699:20,22

700:11 701:10

707:10 708:5

711:16 713:7,9

714:3,20 715:22

719:15 720:6,16

721:9,10 722:6

723:7 724:9,18

725:6,7,7 729:18

730:11,12 731:4

731:9,15,18,21

732:5,10 733:5

733:12 742:9

748:4 751:13,22

752:11,14 753:21

754:16 755:3

760:4 763:16

768:11 769:3

774:10 775:2

777:3,6

chlorpyrifos-me...

623:10

chlorpyrifos-oxon

184:1 251:11

chlorpyrophos

155:4,8 169:17

chocolate 77:16,19

choice 104:4 406:5

477:18 479:8,9

489:15 512:12

622:22 631:18

choices 14:14

398:13 406:6

cholera 231:18

cholinergic 40:12

53:5 56:9 96:15

96:17,20,21

97:11,17 109:10

205:17 243:3,7

334:1 341:9

363:13 437:3

563:12 564:14

591:8,12 683:3

683:11 684:11

cholinesterase

28:18 30:2,4,16

30:18 31:17 32:7

35:15 36:11,16

37:20,20 38:3

40:4 58:13 63:6

64:5,7,20 66:15

66:18,21 69:7,16

70:17,19 82:19

82:22 83:5,8

84:16 85:16

86:14,17,19 87:1

90:6,11 91:18

92:3 95:10,11,19

96:13,16,19,21

97:8,16 106:21

107:5,6 204:1

205:4 212:5

214:16 215:3

216:21 217:17

218:4 243:13

250:14 251:5

253:1,7,15

256:19 259:7,14

266:2,9,11 267:1

267:4,5,7,10,11

268:2,13,14

269:5 288:5

303:15,20 305:13

305:22 306:6

321:1,7,20,21

323:4 326:19,20

327:1,4,6,10

328:9,13 329:4,6

331:17 332:5,16

332:20 333:3,17

333:22 336:7

341:10 343:13

344:5,9,16,17

345:1,2,6,9,13,14

345:17,21 346:6

346:12,16 348:5

348:10 363:9

364:1,17 381:11

381:14 382:9

383:15,20 384:2

384:3,5 389:12

389:15 390:16

431:21 438:19,21

439:5 463:17

464:5,17 491:2

496:18 502:6

506:9 517:19,21

518:5 528:18

529:6,14 551:11

554:17,18 589:8

602:19,21 603:8

606:22 607:9

614:3 655:8

668:22 669:16,18

669:22 670:1

672:11,21 673:8

677:1 679:6,20

679:21 680:21,22

682:15 683:3,7

683:11 684:10

686:8,14 688:14

732:20,21 739:1

759:1 760:21

cholinesterases

432:19

cholinesterase-i...

217:16,22

choose 104:5

373:8 477:14,16

486:16

chorion 714:17

chose 307:2

332:11 547:3

565:6 672:3

724:1 732:18

741:5

chosen 488:5

Christensen 20:2

41:1 111:22

112:3,4 148:3,14

148:20 149:2,12

149:21 150:5,14

150:17 151:5,10

151:14,16,18,21

152:5,8,13,18,22

153:8,15 154:6

154:21 156:8

157:18 158:2,4,8

158:11 159:8,19

160:1,9 161:11

162:4,11,17,19

163:1,7,17 164:1

164:8 165:20

166:21 168:2,17

169:5 170:22

171:2,6,14,18

201:14 210:19

221:14,19 222:3

222:12 257:1

258:12 423:6,18

423:19 425:3

429:17 430:3

540:20,21,21

592:5 593:21

606:10 608:9

617:2,3,7,11,20

618:1 712:11,12

775:19

chronic 37:4 40:9

213:2 254:8

382:15,21 383:15

384:14,22 386:12

396:18 397:4,12

567:15 614:5,8

728:2,5 750:3

752:1,5 753:5

757:16 760:9,12

760:22 768:1,2

Chuck 497:17

498:7 499:15

504:8 516:11

528:6,15,22

Cincinnati 5:3

316:5

circle 201:19

205:10 524:11

circled 268:4

circles 34:14 362:4

circuitry 49:18,19

circular 485:3

circulate 388:7

circulated 388:12

436:8

circulating 321:16

circulation 400:21

401:3

circumference

133:13,16,19

149:9,11,19

165:16,18 283:22

284:1

circumstances

187:12 273:3,6

cite 90:18 360:1

cited 186:3 228:19

228:22 352:15

473:9

city 114:19 115:8

116:10 117:19

123:2,6,7 214:2

263:5 276:14

279:9 656:22

claim 66:17

554:15 556:3

557:16

claiming 360:18

CLANEY 766:20

clarification 87:8

92:18 105:2

149:8 153:4

164:11 165:13,21

166:4 198:22

201:15 221:8

271:2 319:3

334:9 335:19

341:14 367:2

371:13 411:1

426:2 432:21

433:13 524:6

540:16 588:19

593:6 606:12

620:7

clarified 420:1

471:18 616:21

clarify 92:15

106:19 113:9

148:20 149:13

150:1 151:11

156:5 162:5

423:15 431:18

432:3 510:3

552:5 553:20

554:1 620:6

639:15 655:7

682:19 683:4,6

683:14 684:15

733:10 749:1

clarifying 13:6

41:9 112:11

114:2 220:13

221:2 318:20

319:22 419:4,7

420:9 424:10

436:5 540:18

606:18

clarity 15:6 17:22

19:4 20:14,16

24:2 25:15 32:21

120:16 121:19

126:16 205:5

211:11 217:13

291:2 380:20

425:19 426:9

428:14 434:19

524:14 541:12

580:7 581:4

592:6,17 733:4

737:6 738:19

Clark 396:5,9

class 282:12

classes 273:14

311:2,18

classic 324:2

classical 73:11

634:14
Page 18

classification

272:6 530:17

569:5

classifications

569:6

classify 331:11

clause 153:9

clean 671:3,3

clean-cut 451:11

clear 12:19,21

13:9 15:9,12

16:8 22:21 26:1

41:19 79:5 85:11

86:6 100:8 113:9

127:12 156:2

158:9 181:8

202:7 207:19

213:7 221:12

223:4 242:13

259:1 297:2

343:22 364:21

388:5 415:1

431:22 441:8

445:20 470:7

488:4 491:2

515:10,14 516:9

522:11 525:12

541:21 552:8

573:6 585:13

618:5 620:8

666:3 689:5

690:6 711:21

738:13

clearance 341:20

402:4 410:10,15

458:21,22 725:15

clearcut 516:13

clearly 8:10 13:13

13:15,19 14:13

14:21 30:7 63:22

84:19 92:10

117:7 126:19

196:11 253:22

306:4 363:16

379:14 388:3

405:16 424:11

425:10 456:17

495:20 499:13

515:8 536:6

553:21 610:17

612:4,12 632:5

658:20 681:15

686:2 723:11

cleavage 334:21

Clemson 4:14

315:17

click 525:2

clinic 215:13

clinical 135:5

139:13 143:10

215:16 284:15

498:14 589:14

673:3

clinically 498:17

clock 68:8 387:11

419:11 593:13

653:19

close 24:9 31:5

88:10 196:2

370:4 379:10

416:22 419:5,9

422:1,4 436:6,11

499:14 515:18

524:6,7 527:2,18

533:5 674:19

677:7 696:22

708:5 733:15

781:14

closed 362:3

781:15

closely 36:21

103:10 112:8

379:2

closer 76:22

167:12 616:17

669:4 745:3

closes 511:4

closing 318:4

380:14 593:4

660:14 777:20

780:11

cluster 433:8

clustering 476:22

477:4

clusters 476:14

cm 481:3,5

CNS 53:18 58:13

597:18 598:9,10

cocaine 527:9,16

527:19 629:7,9

629:12

coefficient 405:19

425:6

coefficients 135:18

163:18 398:2

403:7

coffee 716:15,18

cognition 38:9

54:22 72:14,15

72:20,20 85:20

98:10 99:5 167:9

210:12

cognitive 74:4

78:11 98:18

109:14 284:5

285:8 493:2

505:3 532:6

533:20 534:21

coherency 622:13

coherent 203:9

298:12 350:6

442:7 445:3,7

622:11

cohort 31:21 41:2

112:6,15 113:14

113:17 114:4,6

114:18,22 115:1

115:4,8,10,11,12

116:6,9,15,18

117:4,8,13,18,19

119:8,16 120:1

121:16 122:11,15

122:21 123:1,3,5

127:4,21,22

128:18 130:21

132:21,22 133:5

133:8,16,17

134:15,16 136:12

136:18,21 137:11

137:14 138:1,17

138:20 139:2,17

139:22 144:6

151:8 157:1,6

158:15,21 159:9

168:12 171:15

175:3 179:1

205:22 216:17

217:19 222:6,18

222:18,21 266:16

268:21 274:8

275:1 326:4,13

372:7 406:3

420:15 425:5

429:11,18 541:8

541:9 554:22

555:4,10 574:13

574:13,18 575:20

576:19,22 577:11

577:16,21,22

578:9,13 579:2

580:10 585:22

588:7,8,9,14

589:4 594:16

599:18,18 601:11

614:16 651:17

656:6 708:13

720:7,12 721:3

721:12 729:8

754:21,22 768:14

768:16

cohorts 38:11

114:15 116:21

117:12,14 118:11

118:13,21 119:5

119:13 120:4

121:6 126:22

129:8,15 130:17

131:18 132:7

133:20 134:1,19

135:3,9,16 136:7

136:9 137:7

138:15 139:22

140:22 141:2

143:9 144:22

145:17 153:19

157:11,11 167:20

172:1 176:2,5

178:9,10,19,22

179:6 205:22

210:22,22 211:1

222:17 259:9

266:16 283:15,17

288:1 291:4

294:14 296:12

313:7 360:3

372:8 421:18

572:16 574:16

576:17,21 577:3

577:7 580:20

581:6 596:3

610:2 615:7,15

655:21 656:2,4

656:12 729:1

coke 629:7

collaborate 507:5

collaborated

320:11

collaborative

339:5

collaborator

218:22

collaborators

396:10

collapsing 749:8

colleague 115:15

253:4 342:13

396:12 713:17

colleagues 20:11

38:4 299:6

781:11

collect 175:14

253:10 262:8

270:15 468:3

479:18 634:17

644:22 669:10

672:19 712:10,17

713:14,15,19

715:5

collected 46:15

123:16 125:6

128:21 164:4

179:7 196:22

197:5,22 262:9

264:20 265:3,7

266:5,19 267:4,6

281:6 415:18

427:3 545:13

660:16 664:14,18

664:18 672:2,6

712:18 723:21

757:1

collecting 638:19

collection 116:8

121:3 122:14

127:15 177:13

279:8,8 429:10

542:10

collective 461:16

collectively 202:13

465:6,9 519:10

collects 124:17

713:18

College 5:7 6:19

316:7 317:10

Collier 6:15,15

153:2,3,10,21

193:17,18 195:8

317:6,6 334:7,8,8

335:1 341:13,13

341:19 410:1,2,2

410:4 438:16,17

444:14,15 456:6

456:7 603:11,12

605:18 626:12

630:19,20 633:15

634:2,3 664:2,3

676:4,5 695:20

695:21 702:6

705:4,5 706:7

709:21 711:3,6,6
Page 19

712:22 714:5,6

717:4,5,6 722:3

727:6,7 741:18

741:20 770:19,20

colonies 745:7

colony 745:9

Columbia 31:21

31:22 38:11

114:4,6,17 115:1

115:12 119:15

122:11,21 123:4

125:21 127:22

130:20 132:21,22

133:18 136:12

137:11 139:17

140:17 149:9

150:11 152:10

157:6 158:15

159:9 166:15

168:12 171:21

175:3 179:1,5

205:22 210:18

213:8,9,13 215:9

216:13,17 218:11

219:1 256:14,17

257:10,11 258:16

259:11 261:16,16

262:22 267:14,15

268:7,20 269:2,4

274:6 276:16

280:19 283:16

288:1 309:20

310:3 319:7

326:14 397:15,20

398:7,19,22

405:6,10 406:3

406:17 409:14

415:5,7 420:15

425:20 426:22

430:3 541:2,9

543:9 550:3

554:21 574:13,18

575:19 576:19,22

577:6 578:10

579:2 584:10

587:19 589:3

594:15 596:5,13

599:18 601:11

611:12 612:20

613:7 614:1,6,16

642:9 656:6

658:3 695:1

701:9 707:8

708:14,22 709:10

720:6 721:3

731:3 752:10

755:4 759:19,21

760:3 765:22

772:22

Columbia's

261:22 415:14

Columbus 727:12

column 221:9

259:11 294:7

296:19

columns 294:7,8

295:18 327:16

428:11

combination

135:22 137:7

199:1 398:3

404:14 475:15

559:20 571:3

572:22 594:20

611:3

combinations 32:6

199:19

combine 251:19

combined 132:7

167:9 292:5

403:19 526:3

739:7

combines 55:8

combining 107:9

226:10 404:5

732:13

come 9:4,8 11:19

18:10 28:5 30:11

34:7,15 51:16

63:13 69:18 70:4

70:6 80:18 88:16

98:5 104:22

107:22 166:8

167:12 201:14,18

206:10 220:22

226:1 231:12

232:11 233:19

242:21 246:1,15

255:8 260:10

279:13 290:4

298:9 305:11,19

305:22 313:19

342:10 344:13

350:20 357:10

363:7 369:7,16

378:15 399:14

400:19 402:20

408:10 417:12

418:22 419:6

429:5 443:22

447:22 452:6

454:7 466:1

467:19 480:2,4,4

514:13 516:8

520:19 524:22

527:16 529:20

530:11 531:11

535:15 538:11

539:13 544:3

556:10 566:11

568:16 601:18

602:4 605:2

620:12 651:19

653:20 697:4

699:11 702:18

714:9 716:13

717:4 729:12

746:17 751:14

753:4 754:2

755:18 771:9,13

775:4 776:14,15

comes 18:13 37:8

231:10,21 237:2

237:11 363:18

374:6 408:14

409:2 452:19

463:11 480:14

487:8 496:22

499:2 506:6

507:15 533:11

554:13 596:1

597:3 598:17

706:11 742:13

773:19

comfort 195:14

comfortable

426:16 569:11,18

coming 13:18 17:9

23:21 25:20

32:12 34:5 39:7

93:9 169:15

184:11 265:12

272:9 305:20

365:12 382:5

448:18 458:16

470:2 500:18

501:1,13 503:11

514:2 591:4

602:15 619:19

637:2 717:15

742:6 767:21

commend 468:12

541:22 553:7

554:4 595:18

769:10

comment 3:7,8,9

91:3 95:9 164:15

194:3 253:17

304:15 312:5,8

318:6 334:11

341:14 346:11

350:3,15 353:6

353:21 359:14

363:8 365:5

386:7 387:14

389:12 414:18

419:2,5,21

421:22 422:2,4

433:16 437:14

440:5 442:3

446:17 472:8

480:14 503:7

505:2 513:19

518:6,8,19

520:15 524:6,12

526:5 529:19

535:8 540:10

541:12 552:12

557:5 567:4,5

568:19 572:5

582:12 584:2

588:18 590:18

616:7 618:7

622:4 624:17

639:16 645:10

649:6 654:8

656:9,15 666:13

666:15 680:19

685:18 690:16

718:4 732:13

733:11 742:18

commented

257:14

commenter 341:22

387:15 396:5

commenters

220:19 223:14

387:6,9 419:4

420:9 421:21

781:3

commenting

515:21 533:13

637:7 647:9

comments 3:15

19:11 20:10

167:22 194:13

204:9 221:1

225:21 226:6,13

229:6 246:17

247:9,13 249:1

255:7,7 318:3,4

319:12,17 342:13

342:14,15,20,21

350:12 365:4

386:10 388:7,11

394:13 395:19

420:4 422:5

441:5 445:18

446:21 447:1,17

454:16 456:8

462:18 463:9

467:9 469:2

470:7 472:14,16

502:9 515:1

518:14 521:18

535:21 536:9

543:21 546:20

549:6 553:5,8

565:15 566:3,11

566:21 572:9

581:16,21 582:7

588:20 595:12

602:10 603:10

605:8 608:4

618:3 619:8

621:13 622:15,16

622:17 630:21

631:7 635:15

645:11 650:9

655:5 666:1,22

674:18 690:6

702:4,4 710:3

720:3 723:2,4

724:15 725:4

728:20 732:8,14

735:19 736:2

737:13 739:16

761:16 763:5

769:15 771:1

775:9 776:18

781:4

commercial

169:22

commercially

519:15

commitment

19:22 20:16

542:15

committed 220:4

422:10
Page 20

committee 2:10,14

391:13 702:7

commodity 430:20

common 60:18

118:17 122:2

173:2 175:9

212:12,13,14

229:12 231:8

232:8 233:14

235:11,19 236:7

237:20 238:20

241:19 250:15

292:18 485:12

552:15

commonality

228:7,16 237:8

245:13

commonly 186:3

676:21 731:20,22

communicate

455:7

communicating

749:22 775:15

communication

168:6 585:21

communications

426:21

community 758:17

comorbidities

118:4,15 547:20

companies 36:3

companion 601:3

company 248:6

254:15 255:21

569:21 697:15

comparability

86:4 139:21

140:2 141:16

142:15 750:9

comparable 130:7

139:6 144:10,11

263:22 271:9,10

330:8 359:6

409:8 418:4

550:22 581:9

comparably 57:18

comparative 36:16

184:9,19,22

250:14 253:15

691:2

compare 15:22

36:19 71:8 86:5

152:3 175:21

267:14 281:2

300:20 366:2

368:16 376:16

408:7 427:5

468:2 490:15

525:8 625:3

639:3 644:20

694:20 751:4

772:18

compared 37:22

38:14 75:21

120:10 170:2,5

184:20 185:15

186:1 212:15

264:14 271:3

324:20 336:13

344:10 352:9

358:22 361:9

403:3 525:15

576:16 604:1,3

624:21 647:14

661:3 667:8

670:3 725:20

732:5 751:7

compares 655:20

comparing 189:13

198:15 215:9,12

226:15 260:9

262:2 276:20

280:21 292:20

361:3 362:11

414:9 438:20

518:2 525:8

551:1 575:5,12

575:21 578:11

663:18 695:15

754:21

comparison 36:14

65:21 127:13

140:19 141:5

144:8 172:12

175:20 176:3,7

176:15,15 178:5

178:19,19,21

180:21 181:9

189:10 212:13

214:5 215:10,11

216:2 253:1

297:18 298:20

301:15 326:1,4

335:15 348:4

349:17 402:18

586:19 621:21

625:4 655:22

656:10,13 657:15

738:5 750:12

comparisons 82:7

110:18 111:10

184:16 185:4

199:16 259:6

285:2 291:11

333:18 354:11

357:2 370:22

446:10 460:17

484:14 549:5,7

549:11,11 738:9

743:5 763:16

comparison-wide

497:14

compartment

324:7,13,15

629:15,17 678:18

698:4 699:1,1,5,6

710:20 711:9,17

712:4 713:3

715:18 721:22

compartmentali...

193:6 194:15

compartments

324:3,12

compatible 23:6

24:19

compelling 133:14

234:18 242:14

289:2 294:12,13

298:19 582:14

compete 726:7

755:12

competence 136:2

778:15

competing 22:20

238:14 245:16

competition

725:16

competitive

725:11 726:4

compilation 402:3

complete 53:12,21

291:9 297:10

298:22 322:18

334:21 338:16

354:10 541:22

700:15

completed 21:6

29:19 114:5

661:11 703:19

completely 100:8

297:21 370:18,18

468:6 476:10

477:7 494:2

517:7 635:16

completes 436:4

complex 486:19

646:18

complexity 50:4

225:11

complicated 25:22

34:16 40:9 41:14

64:2 192:18

195:21 207:18

276:17 469:15

473:4,4 482:16

569:7 618:18

701:16 731:2

complications

628:4

complimentary

433:10 639:18

component 8:3,15

12:18 19:18

117:7 118:19

145:20 155:19

305:2 379:18

432:15,17 449:9

449:10 453:13

456:14 481:2

501:21 656:20

695:18 708:4

725:10 730:19

741:12 743:15

components 39:3

138:5 214:22

456:15 461:13

534:21 640:19

708:1 741:10

composite 578:6

composition

138:12 469:11

577:1

compound 61:1

104:6,8 115:2

133:1 200:14,16

204:1 205:2

274:8,20 306:2

311:7 434:12

445:13 451:9

455:13 463:22

464:4,12 466:3,4

466:5,6,21

474:11 489:14

496:3 497:3

503:10,13,16,21

527:3,18 541:11

543:10 596:7

599:19 624:6

639:19,21 640:6

640:7 641:3,8,14

642:12 644:12

646:20 648:12,17

648:21 649:8

658:6,18 659:4

737:6

compounds 15:22

36:11 39:5 60:12

158:18 173:18,21

174:10 200:18

305:17 311:1,11

311:16 423:20

428:9 430:4

466:2,18 499:7

556:3 572:20,22

626:19 649:21

663:8,9 699:10

750:9 758:11

773:20 775:5,7

comprehend 308:8

comprehensive

36:1 177:8

308:10 437:7

772:7

comprised 115:10

116:10 628:12

compromised

331:14,22 359:20

427:12

computer 318:7

computers 216:4

706:9

concentrate

361:22 658:9

concentrated

702:15 710:2

concentration

51:8 57:18 65:16

66:2 90:19,20,20

115:18,22 124:2

127:3 134:20

143:6 169:7

221:17,21 222:9

288:13 309:16

327:15 328:15,22

332:14 333:14

338:20,22 339:2

339:14 340:19

393:20 449:22

461:12 463:21

589:9 599:17
Page 21

600:3 606:6

622:22 629:15

640:19 660:13

663:13,14 694:13

694:20 700:2

707:17,22 708:15

722:15,18 724:18

730:8 732:10

733:12 734:1,17

concentrations

51:3,4,5 175:22

181:10,15 188:7

190:22 191:7,13

200:6,8 255:4

288:11 320:18

325:8 326:2,3,6

328:7,16,17

329:11 330:7,10

332:4,13 339:21

340:4 393:16

415:5,6,15 418:3

457:1 578:5

599:4 626:8

629:16,21 651:4

657:12 659:10

662:6,19 663:15

675:5 692:12

694:21 709:18

732:9,11,18

734:11

concentration-d...

341:1

concept 9:18 11:4

11:18,18 15:5

17:18 24:5 33:3

39:5 41:12 43:19

44:21 45:2 47:5

47:7 60:22 62:4

63:17 64:22

67:12,17 96:15

195:17 367:20

477:12 760:19

concepts 9:8,12

10:7,10,17 12:5,8

13:4,11 16:7,8,15

18:10 20:13

21:20 29:10

32:14 41:17

44:21 203:7

conceptual 44:16

97:14 202:13

conceptually

415:13 511:13

concern 45:8 54:9

69:5 71:13,13

284:17 285:2

306:17 307:9

313:5,19 370:12

382:8,19 389:18

390:4,11 393:17

393:20,22 394:5

449:1 519:16

545:21 549:12

562:15 566:18

580:9 590:21

746:13

concerned 305:10

352:7 377:2

467:6 564:7

566:6,16 705:14

706:18 717:7

concerning 44:17

71:21 127:16

129:18 167:17

424:1 519:12

610:22 680:5

concerns 73:5

278:8 372:3

382:12 402:14

440:10 448:21

450:22 473:15

485:11 487:15

503:20 519:14

520:4 533:22

575:9,13 669:15

concise 308:11

concisely 681:15

conclude 22:8

86:16 112:18

247:20 360:22

362:6 382:17

398:14 462:13

586:7 764:5

concluded 69:2

76:20 112:16

264:21 266:12

287:4,8 381:13

391:13 571:20

581:10 595:5

611:8

concludes 633:22

concluding 318:18

344:3 364:10

conclusion 15:4

112:10 147:6,7

209:13 211:3,6

241:9 260:5

261:4,5 272:9

290:10,11 298:10

298:17,19 299:12

299:18 301:2

344:13,16 346:12

346:15 350:4,20

353:8 357:11

360:16 363:7

378:16 382:6,7

382:10 392:5

395:9 437:15,18

437:22 442:4

445:1 472:5,10

472:13 496:22

498:2 499:9

561:22 572:4,6

572:11,14 581:8

581:12 584:4,6

584:17,20 586:15

595:13 597:7

606:3 608:4

611:9 749:2

conclusions 13:18

15:7,11 71:22

98:5 145:5

177:22 180:8,11

202:17 227:17

241:9 246:2

260:11 261:18

283:7 350:8

381:5 434:21

480:4 512:21

571:20 572:17

576:18 594:6

770:16

conclusive 382:18

concordance

33:20

concur 444:12

497:9 499:18

516:6 521:7

552:10 554:3

582:2 583:18

584:3 601:22

602:10,15 615:20

620:18

concurred 434:21

concurrence

213:10 572:15

concurrent 70:17

296:18 352:21

concurrently

345:4 350:20

692:15

concurring 439:12

condition 104:14

conditioning 73:11

conditions 88:19

89:16 211:9

333:15 334:22

346:2 351:14

368:21 427:12

461:4 462:5

477:20 499:8

567:15 645:19

692:10 767:10

conduct 147:9

204:5 237:17

250:7 343:4

542:8 621:6

692:16 752:15

768:14

conducted 70:5

80:19 81:11 89:8

92:2 94:11

104:19 176:18

214:20 250:13

253:6 282:15

295:9 322:11

323:6 327:21

333:16 345:3,3

360:6 368:21

394:10 462:8

483:14 520:20

543:11 544:3

545:18 597:14

conducting 343:7

CONFERENCE

1:13

confidence 16:16

257:7 294:19,20

296:21 361:11

373:17 381:6

398:15,17,18

400:3 401:18

402:6 404:20

483:15 499:13

516:16 546:10,22

550:12 577:4

584:5 601:2

606:8 607:9

644:18 648:20

701:19 751:2

752:16 756:13

770:15

confidence-bound

222:4

confident 35:21

354:11 426:22

600:20 601:7

751:7

configuration

448:14

confine 508:4

511:4 767:7

confinement 508:5

510:21

confirm 170:8,15

170:19 610:22

752:16

confirmed 118:9

256:7 264:16

265:18 335:21

confirming 261:22

301:10 615:5

Conflict 3:1

conformational

55:6

confound 70:16

74:6 78:12

483:11 538:10

confounded

330:20 481:20

confounder

113:22 129:4,4

129:15,16 131:14

136:8 156:7,11

156:18 157:8

158:7 337:14

348:14 638:10

639:17,19 640:9

confounders 129:5

130:10 132:5

148:18 329:11

331:4 405:13

542:12 555:1

587:9 638:8

confounding 127:7

127:11 128:2,10

128:13 129:2,5

129:10 130:2

139:18 140:7,12

140:15 143:19,21

148:12 156:2,4

156:13,14 157:3

157:12,14 160:2

170:21 240:16,17

257:2,17 272:5

284:21 289:13

297:21 438:7

495:12 544:12

545:14,15 547:21

573:13 574:9
Page 22

576:2 582:13

583:14 612:10

740:2

confounds 347:19

confused 199:22

435:20 645:11

700:5

confusing 151:18

151:22 411:12

750:3

confusion 260:14

703:10 716:22

750:2

congratulate

460:6 770:9

771:11

congratulating

780:3

congressed 736:14

conjecture 151:1

495:22

conjugate 338:17

conjugated 338:11

339:15,22 457:17

625:12

conjugates 321:13

334:21 410:17

conjugation

334:12 338:12,18

339:10,12 340:2

340:8 341:17

457:4 458:10,15

conjugative

341:15

conjunction

252:22 380:22

407:10 551:2

conjunctive 625:8

connect 456:4

connected 455:6

connection 53:1

95:18 191:14

386:18 710:11

connections 38:20

50:3,9 54:20

739:15

connectivity 16:2

54:19,21 105:17

connects 16:19

consecutive 357:6

consensus 154:10

459:21 717:19

consent 402:14

consequence

656:17

consequences

237:19 393:5

conservative 85:5

254:1 326:22

465:17 729:16

749:21

consider 19:21

22:1,4 46:5

120:6,9 127:8

131:3 140:5

263:10 284:3

289:20 298:13

343:17 352:19

353:2 373:3

393:6 394:7,20

396:19 399:22

407:16 408:1,14

439:20 457:5

514:6,17 517:6

528:11 583:12

612:4 615:10

625:1 626:7

630:3 658:14

692:14 694:4

718:11 729:2

766:14 769:21

773:4

considerably

404:16 629:17

consideration

103:5 129:17

146:13,21 175:15

183:19 204:14

209:11 210:3

249:7 306:14,19

329:12 518:14

590:15 605:4

612:8 623:4

625:8 629:22

658:8 691:3

697:6 762:19

considerations

21:19 145:7

173:10,19,21

184:4 186:7,9

193:15 258:21

398:9 407:7

450:22 486:11

627:4 657:11

693:2,3

considered 73:6

113:1 119:19

125:6 127:18,19

135:19 161:9

172:1 173:15,17

176:7,17 179:16

179:20 187:18,21

199:8 237:4

238:5 263:16

297:20 355:14,22

358:12 359:2

361:11 371:16

381:16 390:4

408:4 428:8

432:1 435:5

437:5 442:1

446:11 481:4

500:20 514:20

520:20 528:8

531:18 541:7

551:13 586:2

591:1 592:3

623:6 628:14

629:1 643:15

659:16 675:14

677:3 686:3

698:22 725:8

726:21,21 759:10

773:8

considering 88:17

120:11 130:13

131:9 145:7

149:5 285:9

287:3 368:16

459:10 615:12

622:19 675:8,19

675:19

considers 402:15

consistencies 84:6

consistency 15:13

15:16 17:22 19:4

20:14,17 25:15

32:21 33:4 34:8

60:11 85:19 89:2

101:21 102:16,18

102:20 103:2

136:11 145:17

183:4 210:9,11

210:20 227:13

231:1 232:5,9

244:18,19 285:12

294:11 295:3

313:21 355:10

380:20 529:8

545:10 573:9

576:15 577:4

consistent 12:19

12:22 13:1,10

15:14 16:12 22:3

22:21 23:6 24:5

80:7 81:20 85:20

88:18 98:9 99:1

101:20 102:1,4,5

122:22 134:17

135:2 137:19

138:17 191:2

219:22 220:1

234:11,20 238:1

250:15 253:10,11

262:13 264:4

266:21 268:1

272:12,12 291:3

291:14 294:22

298:6,12 313:9

313:11 322:1

323:8 325:20

326:9 333:7

353:15 354:22

357:16 370:21

372:3 397:14

437:10 541:6

576:18 606:20

631:2 692:18

719:15 774:5

consistently 78:2

80:15 85:1 313:7

547:9 566:8

577:6

consists 627:19

conspecific 89:22

constant 198:8

386:19 487:2

constantly 620:13

constitute 245:7

constitutes 235:11

238:4

constrained 526:6

construct 403:10

609:5,7 691:8

constructed

238:10 406:10

445:8

constructing

723:12

constructive

725:13 773:15

consultation 3:3

693:1

consulting 224:10

282:8

consumers 389:2

consuming 413:14

consumption

167:21 168:3,8

168:13 413:17

contact 512:9

contacts 501:15,16

contain 640:13,14

contained 541:13

671:10

container 170:11

contains 491:8

627:20

contaminant

46:21

contaminated

701:12

contamination

279:7

contemplate

107:17

contention 234:22

context 10:12 13:8

15:17 17:6 29:12

43:21 46:16

63:15 101:2,21

198:4 398:10

407:12 445:2

464:15 492:21,22

523:20,21 542:3

553:22 609:4,8

611:7 653:13,15

692:2,21 702:22

703:2,14 704:8

723:10,14 738:8

739:10 740:5

749:17 750:10

757:5,12,18

758:8

contexts 101:9

489:16

continually 252:19

continuation

654:8,10 748:11

continue 50:5

147:13 150:7

213:11 250:20

252:2,8,9 255:11

310:16 315:6

339:14 381:1

387:14 562:12

571:16 574:21

610:19 654:1

658:15 716:13

727:9 730:15
Page 23

744:2 777:5

779:17

continued 35:15

315:5 348:21

351:4 392:11,13

393:7 394:8,21

574:3 593:17

623:20 624:1

continues 30:4

47:21 389:1

581:11

continuing 30:8

88:6 318:13

419:21 588:7

670:17 764:11,11

765:1

continuous 291:17

continuum 535:19

contradictions

234:19 239:2

contradictory

433:10 774:9

contrast 115:12

116:9 143:13

179:6 181:12

182:22 629:7

656:5

contribute 78:5

355:4 425:21

429:11 571:2

609:20 610:19

615:16 658:15

707:10

contributed 20:4

428:12 755:9

778:7

contributes 428:17

610:17

contributing

427:14 572:21

691:11 763:21

contribution 25:13

207:1 217:15

391:21 410:7

428:20 609:2,3

610:8 612:15

627:16 668:10

contributions 20:6

542:20 653:7

734:14 777:5

779:12,21

contributor 40:18

40:19 112:19

573:3

contributors

431:16

control 49:14

54:11 74:3 75:21

76:8 84:13 91:5

92:14 127:11

143:11,20 215:22

225:14 274:11

327:7 335:20

337:16,20 352:2

352:8,21 353:3

356:5,15 357:17

357:19 358:7,9

359:3 361:13,22

362:14 368:2,9

368:16,20 369:18

369:19 370:8

374:3 375:18

413:14,15 432:10

474:12 482:6

486:22 487:10

488:18 489:19

493:18 494:8

495:3 497:14

509:8 545:15

593:7,10 652:9

669:14 672:15

715:21 731:22

743:6

controlled 184:21

215:16 297:22

374:19 432:19

480:3 507:10

519:5 573:13

575:14,17 691:3

767:16

controlling 127:13

281:5 405:12

430:21 598:5

controls 75:3,10

76:15 79:20

92:19 278:21

285:11 335:22

336:2,17 337:18

359:5,8 362:3

367:17 368:1,3

370:9 372:13

375:11,13 474:13

480:1 495:11

671:7 672:11,18

convenience

473:20 671:8

conveniently

313:15 365:17

convention 235:14

410:12

convergence

527:17

conversation

29:10 435:6

512:6 524:20

563:8 593:18

607:12 618:8

716:19

conversations

426:15

conversely 60:18

263:15

convert 66:2

185:13 405:5

707:19

convinced 363:13

convincing 353:19

526:2

convincingly

520:11

convoluted 655:2

coordinating

247:14

coordination

167:3

copied 294:3 428:4

655:12

copies 260:13

342:2 396:1

428:14

copy 223:17 429:1

copyright 223:19

cord 125:18

136:13 153:17

163:16 165:17

213:22 217:6

221:17,18,21

222:9 223:2

259:22 261:6

268:6 279:21

280:1,3,8,13,16

280:18,21 281:10

281:12,17 426:7

427:4 543:11,12

543:14 557:10

559:16 574:14

584:13 586:21

589:4 594:8

596:7 599:20

613:2,6,10

614:10,16 616:9

616:10,16,22

626:8 627:17,19

628:3,8 632:19

633:13 659:18

699:22 701:10

707:21 708:6

711:21 713:7,10

714:3,7,7 716:4,8

717:14 718:7,12

718:14 719:1

720:10 722:15,18

760:4 768:11

cords 557:13

core 405:6 417:19

418:3,10,15

corner 445:10

608:20

correct 67:3

149:10 150:14

155:5 157:18

158:2,5,8 162:11

170:22 171:6

189:2 280:11

302:3 310:4

338:21 339:4,7

339:13 340:5

384:8 395:20

500:16 504:11

540:13 557:22

570:15,19 589:3

607:14 625:9

775:20

corrected 151:15

189:19 411:19

695:8

correction 94:13

329:21 418:5,16

695:9

correctly 99:3

148:6 154:21

366:12 374:15

375:3 425:5

502:2 546:4

correlate 140:8

255:4 274:21

280:9 323:13

correlated 156:7

156:22 164:5

169:8 264:22

560:14,16,22

707:21

correlates 329:1

correlating 156:3

correlation 125:14

125:17 129:13

153:17 157:1,2,3

157:7,8 163:8,18

168:10 417:21

423:13,16 429:19

560:22 616:13

677:20

correlations 125:2

157:5,16 163:20

163:20 424:22

429:16 535:5

543:12 580:12

583:11 669:21

correspond 331:7

correspondently

329:2

corresponding

321:12 325:12

331:15 629:17

corresponds

406:12

corroborate

241:17

cost 510:13,15,18

costs 510:16,19

cotinine 629:8,16

629:19

cotton 181:4 668:2

668:16

could've 331:1

671:20

Counsel 3:4 28:7

count 7:13 36:9

528:18 652:22

776:7

counted 236:14

counter 478:5

countervailing

239:9

country 17:11

128:19,20 618:20

647:16 660:4

727:21 745:7

counts 88:11 89:10

153:12

county 123:11

429:6 431:4

couple 2:7 7:13

25:19 26:3 32:9

68:14 72:11

77:15 78:18 88:7

108:1 166:14

173:18 180:15

188:5 193:11

196:13 210:21
Page 24

242:21 247:19,20

250:13 269:12

277:14,16 318:20

319:2 335:8

365:4 367:2

380:18 385:2

398:1 419:3

422:6,10 449:5

451:11 599:5

607:2 634:22

642:7 643:19

647:1 676:15

680:7,16 705:10

708:19 732:7

737:2,8 749:9

765:11 769:15

770:11

course 7:22,22

9:11 15:6 29:17

50:20 51:15 66:3

69:15 73:17

74:12 85:9 86:22

91:9 98:9 102:21

104:22 113:4

117:1,12 118:19

119:8 122:4

124:22 125:19

126:11 128:13

129:4,10 135:8

144:11 146:7

147:12 162:3

176:17 205:2,7

215:17,19 225:7

244:13 295:1,3

296:18 298:11

319:12 375:13

407:19 436:19

495:10 503:16,18

531:4 585:16

609:6 630:16

632:18,20 634:10

687:10 697:4

716:3 722:11

725:3 744:18

754:12,17

covalent 464:16,19

465:7

covalently 62:15

cover 69:21 93:13

254:18 256:3

257:3 425:18

582:8 702:16

726:17 766:22

767:20

covered 30:13

462:22 582:3,3

608:18 724:15

755:20 767:15

covering 390:20

462:20 638:4

covers 431:5

486:10

co-author 342:13

co-authors 226:5

co-discussants

612:17

co-exposure

256:12

co-exposures

582:15 761:11

CPF 251:7 493:12

494:12 519:13

525:16

CPFoxon 251:7

CPPAES 660:7

CPS 660:7

crammed 296:4

create 23:11

159:16 238:14

603:17 644:17

675:17 682:16

700:11 701:18

created 15:3 18:11

261:14 429:9

649:12

creates 337:11

401:2,5

creating 694:10

727:4

creatinine 181:11

182:21 183:3,8

189:2,7,9,12,16

189:19 266:12

399:12 674:7

679:17 695:8,9

695:10,18 727:14

creatinine-adjus...

181:9 186:5

creative 524:15

CREB 330:17

331:21 340:13,15

credibility 175:3

591:6

credible 442:1

credited 565:11

criteria 21:3 23:16

32:16 33:3,9

44:2 50:6 85:1,6

117:13 118:14

127:12 140:18

141:14 146:12,21

156:12,17 206:21

220:10 229:20

230:16 231:6,11

231:15 232:17

233:1,15 234:8

234:12,12 244:13

244:16 353:17

361:8 365:12

407:4 445:11

542:4 550:20

551:3 553:22

558:21 665:12

762:13

critical 40:7 42:21

45:3 52:2 69:8

69:14,16 86:7

87:1 91:6 94:17

98:17,20 120:15

120:17 121:20

143:1 204:10

217:9,11,14

226:5 355:2

381:16 383:21

462:1 466:11

503:18 513:14

514:9 571:9

594:13 610:19

613:14 632:9

633:20 637:11

656:20 720:17

746:21 754:18

criticality 456:15

656:19

critically 278:10

378:17

criticism 507:16

criticisms 374:13

criticize 276:16

critique 278:6

279:12 765:9

critiques 512:17

512:19 765:7

crop 430:13

crops 28:3 430:12

cross 95:7 355:10

386:13 453:6

466:5 520:17

661:4 663:18

crossed 88:12

361:9,12 486:20

crosses 104:8

crossing 361:7

cross-cutting 9:4

cross-reacted

331:1

cross-reactivity

332:1

cross-sectional

723:9

cross-validation

320:12

crowds 733:7

crude 448:7

578:22 579:9,19

587:18

CRYSTAL 1:15

crystalized 505:4

532:8 534:22

cubical 104:7

cuff 723:2

cultural 138:9

culture 327:19

328:5 329:14,21

335:19 336:4,15

337:4 460:19

cultures 57:22

58:8,9 464:11

cumulative 580:16

curbed 469:7

curiosity 135:13

curious 63:1,18

150:16 744:19

current 21:9 29:2

69:11,22 147:5

256:15 267:8,20

268:8 351:11

381:7 390:12

392:21 441:22

450:16 611:8

657:6 660:12

664:22 758:11

currently 29:13

44:9 121:13

172:16 184:4

186:19 623:12

643:10 692:7

726:12 749:4

curve 75:2 356:15

358:16,16 374:22

377:7,8 500:3

539:1

curves 75:18 327:6

363:1,2 496:2

curvilinear 495:19

496:2

cut 224:3 247:18

304:7 707:6

cutoff 537:15

cutoffs 536:22

cutpoint 182:21

183:2,7

cutting 431:1

cut-off 714:21

cycle 536:6 633:8

634:8

cyclic 56:12

cyclical 647:3

cycling 62:17

CYP 335:16 604:6

CYPs 335:13

603:17 604:5

698:19

cytochrome 605:2

cytochromes

603:16 604:21

cytotoxicity 323:4

D

D 34:2

daily 186:6 215:15

399:13

Daland 247:15

248:1 307:10,11

310:18 318:15

380:15 383:13

384:21

Dale 218:22 396:5

396:6,7 409:22

410:2,3,11 411:4

411:15,18 412:4

412:14 413:1,12

413:18 414:12,21

415:6,8,10,17

416:14,19 417:6

417:11,20 418:14

418:20,22 420:7

506:17 781:4

Dallarco 736:16

736:21

dam 83:7,7 476:21

476:21 486:10

493:6 518:1

damage 640:16,20

640:20

damaged 68:1

407:18

dams 344:10

476:12 489:10

513:11,14 517:21
Page 25

600:22 601:4

763:1

Dana 188:21

Danelle 20:8

DAP 115:21

154:19 155:16

171:10,13 198:22

199:4,5,10 200:4

200:5,12 261:2

261:11 423:21,21

424:3,10,14,17

425:5 428:9

577:11,18 578:1

578:3,12,16,18

587:17 631:20

DAPs 116:1,16

119:18 134:16,20

135:7 150:10,12

151:9,12,13

169:7 174:9

258:9 259:3

260:9,18,18

261:6 294:14

295:21,22 425:21

425:22 429:18

434:17 544:17

577:9 623:2,12

624:10 632:4

661:18

dark 74:22 78:21

100:1,6 523:1,20

531:17,18

data 14:2,21 17:11

17:17 20:20

28:18,22 29:8

30:4,4,5,9,9,10

30:16,16,18,19

30:21,22,22 31:4

31:5,12,13,22

35:15 36:4,7

37:6 38:5,15,16

38:20 39:13,14

39:14,16 43:10

43:20 44:2,5,9

46:15 49:21 50:4

50:7 52:17,18,20

53:10,13,16,18

53:22 54:2 55:1

55:10,14,16,19

55:22 56:5,7,14

56:18,21 57:2,14

58:16,18 59:2,4

59:11 63:14

64:18 69:7 70:11

72:11 75:16

76:14,22 85:10

86:13,15,22

88:10,15,18 91:7

92:17 93:9 94:2

94:18 95:1,2,2,3

99:2,13 104:15

104:22 105:4,10

107:3,5 109:19

110:4,9 111:3,9

111:14 114:13

116:8 122:13,15

122:20 124:3

127:15 129:17

130:21 131:20

132:4,9 140:5

141:10,18 142:11

148:1,2,8 149:6

154:9 162:6,7

167:20 171:11,12

171:12 172:17

179:7 181:20

184:5,10,20

185:15 187:15

188:18 190:9,16

190:20 196:15,15

196:15,16 197:21

204:2,10 205:9

207:3,7,8 218:11

221:8,12 224:15

226:11,14 227:11

227:17 228:14

229:8,9,9,16

230:19,21 231:7

231:12,17 232:6

232:11,21,22

234:19 235:2,2,3

235:6 236:3,5,10

236:18 237:22

238:10,14 239:9

240:21,22 241:2

241:5,5,7,17

242:5,13,18

245:4,7,11 246:2

246:13 249:7

250:10,19 251:12

251:19,19 252:2

252:20,22 253:10

255:10 257:11

258:13 259:11

260:15 261:9

262:10,17,17

263:1,9 264:1

265:7 266:5,14

266:17,18,19,22

267:7,14,15

268:7 271:20

272:15 275:5

277:19 278:10

279:22 280:5

282:12,18 283:8

284:8 285:16,22

286:12,15 287:3

287:7,21,22

289:1,7,19,21

290:3,8,11 292:4

297:8 298:2,13

298:22 299:14,15

300:3,4,5,15,16

300:17,20 301:7

302:6,11,20

303:1 305:6

306:7,10 308:4

309:2,7 310:3,21

313:1 322:10

325:11 326:4,11

326:18 327:9,14

327:20 328:3

331:7,11,15

333:11 335:15

336:13 343:13,21

343:21 344:2,5,8

344:12,19,20

345:17 346:3,6,7

346:12,13,18

347:1 348:8

350:6,20 351:11

351:22,22 353:4

353:16 354:13

356:7,8,10,12,16

356:17 357:4,12

357:17,21 358:6

360:13,16,20,21

362:10,10,13,21

363:12 366:17

367:17 368:5,9

368:21 369:18

370:2,5,6,8,20

371:1,3 373:1,8

376:20,21,22

378:6,16 380:4

380:22 381:5,8

381:13 384:15

386:2 389:13,16

389:17 390:7

391:10,20 392:1

392:4,5,22 393:7

393:11,13 394:2

394:8,20 397:20

398:22 402:15

414:20 415:5

420:14 421:3,16

421:19 422:1

427:3 429:10

431:2,13 433:9

434:6 435:13

436:2 437:4,5,8

437:11,12,15,15

437:22,22 439:5

439:7 441:15

442:1,6,13

443:14 444:1,6,8

445:3 449:13

450:7 451:5,7,9

451:13 452:10

453:2,10 454:8

458:21,22 459:18

459:20 461:16

462:13 463:13

465:16,20 467:2

467:7 469:9,16

470:4 472:2

480:12,13 483:2

483:19 484:5

486:3 487:7

488:2,13,15

491:11 492:12

493:9,9,22 497:4

500:15 502:4,19

506:14 508:6

512:13 517:6,13

519:8,9,12 521:3

521:12,13,14

525:22 526:20

527:5,16,18,21

529:18 530:21

532:3,4,21

534:17 539:9

540:15 541:9

542:10,11 545:13

545:19 546:2

549:20 550:1,2

554:12 562:20,21

568:17 569:13

570:1,2,5 582:14

582:15 584:7,9

584:20 586:2

587:19 588:4

590:12 592:3,15

592:15 594:16,21

595:6,9,10,19

596:10,12 599:12

600:4,7,7 602:3

603:9,13 604:18

605:11,11,12,13

605:22,22 606:7

606:21,22,22

607:9,15,20

608:10,16 609:10

609:15,20 610:11

610:17 611:14,16

611:19 612:20,20

614:1,10 615:11

615:22 616:2,4

619:12,13,17,18

620:3,6,8,20

622:8 625:10

628:21 630:14

631:14 639:6,8

643:10,11 644:17

646:4,19,21

647:4,7,8 649:1,4

657:5,18,21

658:11,19,21

659:1,2,3,6,6,8

660:3,8 661:10

662:16 663:12

676:12 677:6

678:4,12 679:3

679:13 680:3

683:19 684:16

689:16 690:16

691:1,8,9,13,18

692:2,6,8,21

693:13,18 694:9

695:4,22 696:6,7

696:22 698:6

699:20 700:6,12

700:22,22 701:4

701:17 702:10,11

702:12 703:5,7

703:13,15,18,20

705:7 709:5,7

716:9 717:17,18

718:19 719:18

720:8,10,14

723:8,13 724:6

724:18,19 726:2

726:11,12,15

727:16 732:2

738:12 740:6,11

742:1,9,10 747:5

748:14,16 749:5

749:13,21 750:16

751:4 752:4

753:22 754:10
Page 26

756:7 760:3

763:6 765:8,8,9

766:4,14,18,19

769:16,18 770:11

772:9,10 773:6,8

773:21 774:1,2,4

774:9,21

database 31:8

69:20,21 112:15

113:5,12 147:6

359:17 435:15,17

439:13 609:21

610:18 611:9

621:10 749:8,18

dataset 36:2

619:19 647:16

751:3,7 767:17

datasets 36:14

37:5 38:21

280:21 607:6

750:12 751:8

data-dependent

693:13

date 108:1 399:2

494:22 601:5

David 120:9

Davis 317:5

676:16

Dawley 485:13,14

485:16,19,20

520:2

Dawley's 519:16

Dawson 20:3

422:14,14 423:21

425:13,18 426:5

426:6 428:3

429:7 430:6

649:3 733:3,4

735:9,10

day 25:14 29:13

40:9 73:3,3,8

75:5 77:14 83:10

86:12 88:2 107:1

107:1,21 174:18

186:1 212:12

248:14 253:22

254:3 265:3

270:10,12,12

288:2,6,8 314:12

315:2,8 344:15

345:7,12,20

347:5 349:7,9,16

350:2 353:19

379:14 399:9,19

399:21,21 400:13

403:1 405:2

459:17 468:3

488:4 489:9

490:22 499:12

517:18 518:1,2,3

518:4 520:12

521:9,11 528:5

532:18 539:20

543:21 583:1

600:20 601:4

603:1,2 658:20

694:15 723:1

744:16 746:19

750:11 765:5

days 7:13 22:10

25:19 36:15

83:10 96:10

134:14 198:3

248:20 278:22

374:5 476:19

488:5,6 494:5

505:4 532:1,8,11

533:15,15,17

534:2,3,10,11

607:2 632:22

644:1,2 658:21

669:6,13 670:14

672:4 685:1,4

696:6 697:5

737:2 742:1

753:16 767:3

781:14

day-to-day 8:14

174:8 672:21

de 66:22

deadline 310:11

389:6

deal 38:4 206:5

239:17 515:22

665:15 708:8

726:11 742:19

766:7 778:16

dealing 18:18

206:17 327:12

368:18 569:15

730:18 748:3

761:2 764:4

deals 619:4 675:3

dealt 149:1,3

549:8

dearth 350:8

dearylation 338:9

death 48:17 49:9

49:12 51:19,22

52:11 413:17,19

414:4 539:9

680:11

debating 24:1

decade 388:14

decades 73:18

413:5

decades-long

107:17

decay 538:16

539:1

decays 478:22

decent 114:21

154:11

decide 106:1 237:6

300:5 361:9

374:1 574:17

741:10 761:7,12

decided 399:22

545:3 719:6

724:9

decides 665:16

deciding 246:10

decision 2:18 8:8

15:2 18:17,18

19:2 26:22 28:4

37:21 107:19

131:7 206:8

474:6 704:14

757:11

decisions 8:12,17

18:14,15 19:19

21:22 27:5

131:19 193:10

202:11 208:6

371:17 391:6

463:11,12 470:5

620:19 747:17,21

753:11 758:8

770:13

decision-making

209:1 254:2

391:11

declarative 480:20

480:20 481:1

decline 213:20,22

267:20 399:6

658:3

declined 214:3

656:7

declining 124:11

decrease 55:12,15

56:12 57:22

76:12 332:12

339:14 352:5,13

357:6 410:19

492:2 578:14

657:22 658:2

decreased 56:6

78:10,11 79:18

80:15,17 81:16

348:6 554:21

555:4,10,11

556:4 579:8

decreases 75:3

99:18 100:11,12

576:4 577:17

578:18

decreasing 36:8

415:15 684:2

decrements 575:7

decrying 658:21

dedication 778:15

deemed 696:6

deep 147:19

248:21 249:6

deeper 38:7,10

93:4 452:7

deeply 491:7

default 390:18

391:9 532:2

740:5,10

defaults 391:14,16

defeat 479:14

defect 198:3 731:6

defects 118:7

628:4

defend 374:13

485:10 538:15

defended 523:18

defense 28:7 474:9

defer 314:3

deficiencies 91:16

490:20 520:18

deficiency 768:13

deficient 475:5

625:13

deficit 72:20

158:20 159:17

161:4,10 305:2

492:19 501:21,22

502:1,2 509:12

509:13,14 536:2

578:10 772:16

deficits 167:14,15

490:11 498:14

502:15 533:16

544:8 551:8

575:2,4 577:12

615:9 729:8

define 41:13,14,15

42:20 238:2

244:16 365:6

433:4 447:22

580:15

defined 103:16

143:4 275:1

309:13,13 386:19

398:22 433:15

defines 200:5

defining 398:9

406:7 432:7

definitely 68:8

89:21 190:21

584:19 622:13

672:5 726:21

733:5 771:19

definition 43:19

44:14 238:4

294:16 406:21

433:4 448:8

462:4

definitions 398:15

definitive 91:21

92:11,20,20

180:10 530:16

750:21 755:8

761:8

definitively 86:16

deformed 324:4

degradate 623:11

623:15 626:5

627:1,2 638:9

645:13,20 646:20

648:4 651:5

655:2

degradated 646:5

degradates 173:22

623:22 650:2,3

654:12,16,19

662:11,14

degradation 571:4

643:20 644:3,7,9

645:12 646:13

647:18 648:14

649:8,11,16

650:6 651:21

652:1,11 654:11

654:15,19 660:21

662:12

degradator 637:3
Page 27

degrade 648:11,16

648:21 650:8

651:13 652:3,7

degraded 640:21

644:11 652:16

653:3

degration 677:21

degredate 115:19

degree 47:15,18

63:9 66:5 68:3

126:20 214:6

215:6 216:20

217:11 219:2

245:1 350:16

353:6 363:9

446:18 467:14

472:8 518:8

565:20 594:9

609:22 610:12,22

622:4 645:5

679:21 691:15

701:19 720:19

721:2 748:18

749:22 752:16

degrees 141:9

delay 188:16 479:8

479:9 508:6,11

575:11,11

delayed 366:17

490:5,16 537:13

537:14,16 538:6

747:13

delays 490:7

536:17 537:12,17

538:17

delegate 700:16

delegating 436:18

deletions 91:11

deliberate 214:16

deliberation

246:17 701:16

deliberations 17:5

22:4,9 108:3

120:8 126:5

251:13 539:15

553:10

delineate 221:16

delineated 121:16

559:9

deliver 117:21

634:18

delivered 162:14

724:4

delivery 119:21

124:19 125:11

134:14 147:16

153:18 543:6

720:13

Dell 696:10

delve 730:17

delved 729:20

demand 452:20,21

481:10

demanding 514:19

demo 735:22

demographic

128:14 189:9

demonstrate 31:15

209:16 265:7

392:17 440:17

446:3 447:11

503:1 509:10

598:19

demonstrated

69:4 264:13

439:10 443:21

462:5 520:12

658:2

demonstrates

511:17

demonstrating

360:1

dendrites 54:18

dendritic 460:11

denied 234:3

denoted 114:15

DEP 173:4,14

199:1,2,2,5,11

200:12,19 201:6

259:13 261:1,5

261:12 294:15,15

423:8 424:5,10

424:16 425:6,7,9

428:13 578:5

623:2,2 631:18

631:18 648:12

649:17 651:2,9

department 5:8,15

6:13 255:22

317:4 339:16

635:1 638:22

departure 30:6,17

31:13,17,22

35:12 36:12 37:1

96:18 113:2

204:3 211:16

212:16 213:9

250:16 253:16,21

299:17 301:11

326:21 329:4

346:13 347:6,11

385:1 389:13

390:15 409:8

435:13 437:12,16

438:1,9 439:6,15

449:12 518:11

529:6 594:16

595:7,11 599:13

600:8 602:5

603:13 605:17

609:17 615:11,12

616:2 737:15,20

748:16 749:9,16

750:1,4 753:3,5

755:18 756:7

758:16 759:1

departures 550:16

depend 64:16

66:19 469:11

dependant 663:16

691:17

depended 686:12

dependent 337:4,9

337:13 522:19

depending 36:9

47:14,15 88:11

546:2 691:12

702:22 703:3

755:17 760:16

depends 93:12

160:13 339:8

345:9 352:1

374:8 376:7

478:22 550:7

703:7 706:18

depicted 424:2

depiction 162:2

depo 629:11

depolarization

49:2

depose 513:20

depot 348:11

depravation

299:16

depressed 267:10

depression 268:2

deprivation 378:3

depriving 378:1

DEPs 258:6 259:2

295:19 296:17,18

423:13 428:10,17

429:11 648:4

651:18

depth 38:2 448:15

Deputy 3:3

derivate 694:19

derivation 595:10

599:13 692:15

derivative 629:5

derive 35:11 66:7

347:5 612:21

755:18 756:6

derived 43:20

321:5 356:6

400:17 403:7

408:7 415:2

468:21 675:21

deriving 30:5,17

31:13,22 253:16

346:13 347:10,15

348:1 389:13

437:12,16 438:1

439:5 594:16,22

595:6

dermal 30:22

119:10 187:11

191:6 194:18

215:1 269:21

270:16 506:16

describe 60:15

193:2 344:12

350:13 353:17

361:8 392:1

509:2 708:3

713:8

described 21:21

193:1 340:19

346:17 357:5

386:10 484:12

488:11 547:9

670:4 726:13

741:1,2

describes 20:13,19

21:1 714:9

717:15

describing 282:20

350:7 593:7

595:20 637:14

702:12 725:19

description 335:14

471:15,16 487:12

524:2 529:12

676:21 682:4

690:10

descriptive 443:10

452:11 668:14

desert 671:2

deserve 677:7

design 84:7 91:16

118:18 127:14

139:21,22 140:2

140:19 141:1

144:15 147:9

293:3,8 324:1

349:11 355:9

360:8 363:6

372:20 373:4,11

373:18,19 374:3

374:7 453:13

471:16 473:11,18

483:13 484:12

486:11,19 487:15

489:2,13 494:3

514:5 520:19

523:15 536:16

543:3 577:1

612:5 661:2

763:2 768:22

designated 2:5

318:2 778:6

780:9

designed 36:19

116:6,22 136:1

147:3 275:17

276:7 350:9

372:14 373:3,15

397:6 426:18

453:16 491:13,21

504:3 520:22

579:18 614:7

635:12 644:19

662:11 673:3

770:6

designs 229:12

336:18 493:14

543:2 573:6

desirable 612:4

659:20

desire 426:12

699:14

despair 77:20

despite 108:7

345:14 456:11

457:15 458:14

625:11

destructive 725:13

detachment

537:13

detail 30:12 55:21

90:22 115:6
Page 28

127:2 132:11

141:9 146:9

173:8 177:10

256:21 426:12

452:12 455:22

547:2 551:19

582:10 630:13

693:6

detailed 21:6

38:15 43:18

44:22 126:9,13

147:10 201:20

293:21 294:1

299:1 434:20

553:11 554:8

684:17 705:2

765:6 769:18

details 64:9,12

94:16 103:18

151:6 219:17

308:18 325:1

512:16 513:4,6,9

524:8 573:21

592:10

detect 90:12 94:8

166:9 464:5

546:6 547:11

550:16 552:14

579:22 588:5

602:20

detectability

731:13

detectable 265:4,4

408:3 576:10

589:6

detected 171:22

217:18 218:1

296:16 588:21

589:8 597:17

731:4 733:15

detecting 166:19

detection 148:8

149:6 198:17

216:18 222:22

270:19,22 273:15

274:2 293:6

308:3,5,12,22

309:6,13,18

310:7,22 311:6

311:10 345:9

425:20 427:6

662:18 731:6,9

731:13 732:4

733:12,13

detects 663:22

deteriorate 532:13

determination

348:15 396:20

405:8

determine 91:18

156:7 292:11

298:20 301:13

337:4 370:10

438:9 474:13

545:18 613:1

688:4 708:11

determined

159:11 301:7

333:11 438:13

548:7

determining

439:14 465:19

494:11 630:12

detoxification

183:21

detoxifying 461:2

DETP 173:4,14

199:2 200:19

423:8 457:3

623:1 626:21

628:18 631:9

635:22 636:3

650:18 651:3

654:12,17 697:9

DETP/DEP 650:3

detrimental 492:4

devaluating 343:8

develop 10:3 167:3

167:8 211:15

451:8 606:6

634:20 648:19

742:22

developed 36:2,5

41:3 43:22

100:21 167:1

194:8,10 249:22

342:21 405:11

417:17 419:8

488:10 534:3

611:18 662:19

696:9 698:3

699:2 701:21

748:15 770:11

developers 138:8

developing 41:1

54:12,20 65:12

66:21 68:21

124:18 208:10

209:18 351:10

353:11 397:16

441:2,19 446:5

453:14 462:1

472:12 535:1

555:15 697:3,7

697:19 768:3

development

20:17 25:7 43:7

45:10 46:3 54:20

72:2 106:11

115:7 121:22

122:7 130:8

135:12,12 136:15

137:2,3 143:14

145:20 150:6,19

176:19 187:8

193:20 276:14

284:5 305:1,18

305:22 320:8

382:16 432:10

442:18 446:10

450:16 482:19

488:8 524:17

527:8 528:11

541:3 544:10

551:13 563:12,14

571:2,9 575:5

577:10 589:18

636:13 682:11

692:14 696:8

742:4 745:3

747:18

developmental

43:12 71:21 72:1

72:3 81:3,6

85:14 86:17

91:16 96:2

110:13 137:13

166:9 210:5

260:16 304:22

305:8 306:20

343:2,5,8 348:19

352:6 356:2

359:22 360:17

363:20 364:4

438:3 439:13

440:13 443:12

445:7 453:12,20

454:13 455:20

459:13,18,22

461:17 472:3

473:17 474:21

475:8 489:8

493:11 494:12

497:3 527:10

534:5,20 535:2

537:12,17 581:7

610:21 615:18

745:21 746:1,6

747:1 753:16

771:15

developmentally

532:4 750:18

developments

611:21

develops 564:14

569:8 747:12

deviate 721:17

deviates 648:1

deviation 222:1,2

403:5 406:11

576:6

devote 252:1

DFO 2:8 776:18

780:19

DFOs 781:12

diabetes 118:6

diagnostic 673:3

diagram 45:5

diagrams 655:12

dialed 654:3

Dialkyl 115:20

dialkylphosphate

173:3 423:8

428:8

dialkylphosphates

173:17

diameter 481:3

diazinon 32:8

110:20 130:1,6

171:20,21 217:17

428:16 429:12,20

575:14,18 584:15

593:8 721:12

725:7,8 728:22

731:5,8,13,14,17

731:20 732:9

733:13

dichlorpyrifos

593:8

dictates 377:16

died 401:12,20

414:7

diet 264:13,14,15

430:18 715:22

734:15 735:22

dietary 168:3

187:10 190:16,17

191:6 256:12

346:21 422:19

423:2 632:8

661:15 755:5

diethyl 116:2,3

201:9 321:12

423:16 424:7

429:18

diethylphosphates

258:4

diethylphosphor...

330:22

differ 74:2 411:21

626:8 697:7

differed 141:11

difference 77:6

79:5 86:9 121:17

127:5 235:2

274:15 277:4

281:11 297:15

338:2 358:22

359:7 369:4,16

375:18 400:17

401:2,6 402:1,6

410:19 413:3

414:20 417:22,22

418:9,10 427:9

427:11,15,20,21

480:12,13 616:15

616:15 617:17,18

635:17,18 654:21

662:21 739:2,9

740:17 742:20

745:21 767:19

775:17

differences 71:3

74:1,14 78:6,6,14

80:11 84:7,9,9,10

84:11,13,14,16

84:20 86:1,2,3

88:7 91:10 99:6

99:17,20,21

100:4 102:13

103:22 105:16

109:14 111:5

133:11 142:17

167:12,22 176:6

176:8,10 189:8

189:11,11,15

191:3 334:17

340:10 403:16

418:17 420:14

456:22 458:17
Page 29

488:14 580:1,5

587:20 588:5

663:4 739:5

743:3,5 754:14

754:14

different 9:12 12:1

13:17 14:9,10,14

15:3 17:2 18:3,5

18:9,10 23:13

24:4,16 29:18

31:7 32:18 33:5

34:15 35:1 38:1

40:1,11 42:8,8,9

42:10,11 43:21

44:10 46:16,18

52:12 55:19,22

57:5 60:12 63:13

63:20 70:4,21

71:1,5,6,7,10,11

72:9 73:19,22

74:1 75:4,8 77:5

77:8 78:4,4

79:13,21 82:2,5

86:1 92:5 94:5

94:10 97:5,6,11

97:12 100:21

102:7 103:1,3,6

104:17 109:14,17

110:5 115:17,21

118:22 119:1

123:17 125:2,3

131:12,17 132:3

132:3,4 133:10

133:20 139:14,14

141:12 142:10,11

142:14 149:3

152:1,1,2 164:1,2

166:7 167:20

173:18 177:12,13

183:11 191:15

192:5,13,14

194:2,10 197:16

199:18 200:1

201:2 205:15

206:6 207:13

208:16 219:13,13

220:11 221:12

227:4 228:11,11

233:12,12 237:1

237:5,6,8,12

250:6 260:10

261:4 262:16

272:1,7,8,8 275:2

278:21 280:22

281:2,3,13 284:2

284:3 285:3

291:13,15 294:7

294:9 296:7,13

296:19 298:2

304:14,19 305:7

307:6 309:10,10

311:7,10,11,17

312:18 322:16,17

334:18 337:9,11

339:22 340:1,21

343:18 344:2

346:19 347:14,20

349:19 351:17

354:6 355:6

357:10,21 358:2

362:10 363:7

377:21 378:16

380:4 382:13

383:8,12,12

384:19,20 386:14

402:3 422:16

423:21 428:4,11

428:21 429:22

441:15,16 451:16

455:4,6 458:21

467:3 468:6

469:8 471:19

490:7 491:18

492:9 499:1,7

500:10 501:3,20

502:20 506:10

508:16,17 528:9

529:16 535:10,20

535:20 551:14

552:3 555:12,20

558:4,5 562:8

569:5,6 573:10

601:16 602:16

603:22 604:3,12

606:16 607:20

608:16 616:20

617:13 618:19

626:9 633:19

639:1 646:10

651:6 667:9

678:22 681:12,13

683:12 691:10

717:16 728:3,6

732:5 737:9,22

738:6,13 739:4

742:17 743:4,5,6

743:14 744:12

746:12 748:18

749:3,13 753:1

768:12 772:19

773:21

differential 143:17

144:14 145:3

548:6,14

differentiate 264:8

433:2 450:19

differentiated

558:20

differentiating

433:2 636:7

differentiation

258:22 285:17

358:20 432:11

450:18 468:3,18

differently 88:20

161:13 238:8

382:21 386:22

473:7 607:11

703:11 743:11

differing 118:22

629:8

differs 695:11

difficult 21:11

32:2 70:9 75:7,7

82:7 86:5 88:4

101:12,15 131:5

174:16 180:10

195:7 206:4

210:1 220:5

225:9 262:9

308:7 370:10

377:19 445:10,14

450:14 451:18

455:16 460:8

461:15 481:8

486:4 487:6

491:10,16 495:21

497:1 501:2

510:11,12 515:22

516:3,13 526:18

542:8,14 551:1

563:2 567:12

607:15 620:19

624:15 706:14

715:17 726:2

729:12 757:13

difficulties 486:8

536:15

difficulty 65:11

497:21 625:1

diffusion 627:9

630:6

diffusive 629:6

digging 38:5

155:13 589:11

dilemma 515:10

526:17

dilution 695:16

dimension 593:19

dimensions 403:10

dimethyl 116:2

429:19

dimethylphosph...

200:7,7,8 201:7

dimethylphosph...

258:6

dinner 553:3

dioxin 228:4

direct 44:17 65:21

67:8 137:8

142:15 174:6

175:7,8 184:17

191:14 200:14

213:9 253:1

315:17 338:8

402:13 590:21

591:3 596:6

623:10,15 625:20

631:16 637:12

658:16 662:11

directed 421:13

direction 78:6

79:21 85:22 86:2

99:6 109:14

130:6 131:12

193:14 207:14

240:3 245:21

260:21 261:3

292:22 293:9

295:5 364:13

456:1 460:8

498:1 567:1,4

568:22 627:10

704:2 706:1,4

710:20 740:18

744:18 778:11

directional 103:1

directions 103:3,6

207:22 250:6

569:1 620:17

directly 36:19

39:18 53:4 83:13

86:5 115:2

119:16 133:1

173:6 185:14

232:8 382:5

401:3 411:8

428:5 460:18

467:4 517:20

522:16 529:4

541:11 569:16

574:14 582:22

606:2 622:17

636:8 640:14

641:20 659:16

668:4 694:7

696:13 705:7

director 4:7,14

5:22 6:22 19:10

19:15 315:11

316:19 317:13

390:22 714:18

736:17 777:18

dirt 196:5 309:11

694:16

disagree 264:6

299:11 313:10

383:9,11 384:14

489:12 543:20

disappear 534:11

752:12

disassociation

687:6

disclosure 3:2

disconnect 95:9

466:14 672:5

discordant 299:2

discount 496:20

504:4 761:5

discounted 456:17

491:14 494:10

496:6 544:2

discounting 648:7

discourage 348:21

discovered 568:4

discrepancies

245:19 497:20

581:10

discrepancy

616:11,18 617:15

648:13 675:9

discrimination

626:22

discuss 7:14

172:12 530:10

622:6 648:2

670:2 695:5

716:13 717:5

721:7 754:4

756:9,10
Page 30

discussant 541:18

discussants 470:11

581:16 601:20

637:18

discussed 141:8

146:10 172:15

173:1,9 175:2

227:18 287:4

295:11 301:12

320:19 336:10

424:6 426:4

439:11,20 441:13

472:1 491:18

495:1 508:5

517:13 551:18

597:11,12 598:16

601:13 603:4

619:13 620:4

630:13 638:8

641:21 656:16

659:16 660:11

664:4 666:11

686:2,5,5 693:5

694:1,5,15

695:12 724:14,17

730:10 753:15,18

754:1,13 755:11

755:16 756:15

767:9

discusses 226:8

291:4 292:13

387:4 537:19

discussing 123:3

321:4 325:5

593:22 655:8

682:10 738:7

discussion 42:4

61:19 111:14,21

113:4 140:21

172:5 179:4

191:10 195:17

225:5 242:21

243:18 267:16

291:9,10,12

292:17 293:16,18

293:20 299:4

315:6 336:13

397:1,16 426:7

430:7 435:1,3,16

436:13 437:17,20

440:9 446:21

447:2 459:16

469:2 470:20

472:15,17 496:5

496:6 517:4

531:13 536:1,10

540:1 562:12

563:19 569:8

571:17 572:9

585:1 589:21

590:5,10 591:4

591:21 593:17

595:16 597:5,6

619:16 621:14,15

621:18 637:8

638:2 653:13

654:2,9,11

666:14 675:12

690:11 705:2

719:4 721:18

726:16 736:6

737:1 748:11

750:2 756:17

762:16 768:11,19

769:8 771:7

discussions 69:1

257:17 278:7

310:16 326:19

419:10 420:2

445:5 570:12

582:2 611:13

626:12 699:9

720:22 721:9

723:3 748:5

762:11 765:5

774:8

disease 117:10

413:17 745:12

754:15

diseased-based

571:12

dish 453:13 467:4

469:16

dismiss 370:3

505:14 770:1

dismissed 451:3

537:15

dismissing 304:2

disorder 137:13

161:4,10 260:16

492:17,18 527:1

575:21

disorders 101:7

750:20

disparage 534:14

displayed 702:5

disposal 319:9

disprove 242:9

dispute 303:12

disqualify 158:7

disregulation

598:5

disrupt 26:20

33:16 455:7

538:19,20 746:20

747:8

disrupted 33:16

461:7

disrupting 53:1,3

disruption 26:14

41:16,22 49:3,19

551:13

disruptions 203:5

462:14

Disruptor 228:21

disrupts 358:19

dissolve 695:17

dissymmetry

625:10,17

distillation 622:15

distinct 137:13

570:14

distinction 565:19

689:6

distinguish 623:9

distinguished

243:1

distribute 165:22

342:10 395:22

distributed 223:18

324:19 342:12

420:20

distribution 149:5

178:20 324:4,14

330:10 398:1

400:4,6 403:11

403:12 404:16

406:20 417:17

450:18 459:9

732:11 745:4

distributions

178:20 325:1

335:3 404:5

725:15

disturbances 74:6

disulfoton 429:12

429:20

ditto 454:19

766:11

diverse 442:17

divided 149:4

292:4 404:11

division 5:4 6:7

19:9,10,15 25:3,6

172:9 422:15

426:6 777:17

divisions 335:5

DMP 199:1,5,10

200:6,12 201:6

260:22 261:11

423:8 424:5,6,11

424:15,22 425:9

623:2

DMPs 261:1

425:22

DMS 440:15

506:10

DMSO 71:12

285:10 330:2

331:12,13,16

335:19,21 336:3

336:8,10,14

337:3,4,9,13,14

345:8 347:9

348:6,14,19

349:20 363:4

367:6 438:8

440:9,13,17

441:1 503:22

504:5 598:12

730:10,11

DNA 45:18 61:11

714:13,15

docket 1:7,9 3:16

3:16,18,20 29:21

192:7 223:16,20

226:1,7 247:6

249:2 279:12,15

279:18 283:2

325:2 335:12

395:20

doctorate 646:22

doctorial 646:9

document 9:15

10:8 15:12 19:6

23:5 26:22 32:12

40:20 64:9 69:17

70:12 293:13,18

384:6 432:14

447:19 451:1

468:10,12 471:4

489:13 553:18

595:18 596:17

597:13 598:3,19

598:21 600:16

735:12 750:17

771:11 773:14

documentation

762:10

documented

490:21

documents 3:18

9:3 12:15 15:9

471:15 679:19

doing 27:1,15

29:18 36:7 42:8

42:11,18 88:9,9

90:16 91:1 94:1

99:19 106:4

111:7 153:6

184:22 193:2

218:21 219:4

224:22 240:20

241:14 242:8

246:5 247:16

254:1 294:3

295:1 329:9

351:8 355:6

378:4 383:14

433:13 439:8

458:22 474:4

478:2,18 484:3

489:19 497:11,13

502:19 505:8,9

511:3,5 514:18

520:21 527:12

534:5 565:10,11

588:8 599:21

602:6 611:21

616:5 620:15

624:20 639:3

643:11 673:21

697:21 703:4

710:22 711:15

719:18 737:9

770:13 774:17

domain 53:1,15

77:9,10 79:22

81:7 82:12

164:14

domains 52:19,21

53:3,11,22 69:12

72:10 83:3 85:19

98:12 134:7

135:4 210:10

212:14,15 237:3

431:22,22 434:6

434:11 472:4,9

498:10 533:12

domestic 610:7
Page 31

dominant 176:11

603:19

dominate 191:16

dominated 256:12

657:7

Dominican 114:20

domoic 48:7,14,18

48:19 49:6,15,22

50:10,17 51:3,14

51:18,19 52:7,8

445:11 451:10

465:3

don 556:15

dopamine 352:5,8

352:16

dopaminergic

498:12,15 533:8

533:18

dorms 653:1

dosage 415:4

455:11,13 456:20

500:3

dosages 506:9

521:10,13 554:14

554:16

dose 11:20 30:5

31:12 33:20 34:3

34:3,5 35:12,13

35:17,18,18 36:6

37:6 39:8 47:15

47:19 50:20 51:8

52:1 57:18 59:4

59:5 64:19 65:7

65:16 66:1,2,16

69:4,6 71:14,15

74:10,12 75:9

76:11 78:14 79:6

79:9,16,18,19,20

83:2,6,7,15 84:9

86:13,21 93:7

94:21 97:16,18

104:21 105:8

106:20 121:18

175:8 182:5,15

185:1,10,13

190:4,22 191:21

191:22 192:8

195:20 196:1,4,9

197:20 204:2

212:11,12 214:12

215:1,1,2,4,14,21

218:16 219:19

228:11 233:12

241:12,21 243:16

244:3 246:9,15

251:10 265:9

266:9 288:14,15

291:22 292:3

307:14 325:7,8

325:20 326:2,10

330:8 340:5

341:2,4 344:4

345:20 346:17,18

347:2,2 349:8,10

351:1 353:13

354:14 355:11,19

355:21 356:20,22

357:15,16 358:7

358:14,14,16

360:7,8,13,21

361:22 362:12,16

362:17 363:1

364:14 365:13,22

385:1 397:4,19

397:19 398:10

399:16 404:6

406:7 407:1

408:11,13 409:9

409:18 411:2,3,8

411:12,22 412:4

416:20,21 422:16

422:19 437:11

439:5 440:14

447:2 449:2,11

449:17 451:20

452:2,5,13,14,21

460:4 466:21

469:4,5,6,10

472:17 486:9

488:2,19,19

491:7 495:17,19

499:22 500:1,4,4

500:6 515:7,11

515:12,13,18

516:7,10,17,20

517:13,15,16,17

517:18,18 522:19

523:13 524:9

526:10,15 527:22

529:16 530:1,2

535:11 537:13

550:9,19 573:14

573:19 579:9,19

580:8 587:18

594:6,13 595:10

598:13 599:12,15

600:5,12,13,14

601:8 602:14

605:14 606:1,7

606:21 607:15

609:16 611:7,16

612:6,9 621:7

645:6 653:7

658:6,7,12

659:11,12,17

660:10,15,20

662:2 663:12

665:16 684:9

687:13,16 692:5

692:16 693:2,15

694:11,11,18

700:13 722:7,12

722:14,19,21

723:15,16,19,20

724:3,21,22

732:21 734:3,12

734:16 738:5

741:7 747:10

748:14,20 749:5

749:8,11,12,16

751:5 754:9,22

755:6 756:6

757:6 758:18

759:12 766:15

772:9 774:13,13

dosed 86:19

597:20 601:4

doses 31:15 35:17

37:19 50:22 51:2

57:15 64:5,6

70:18 71:17

74:11 75:5,6

78:16 83:22

86:12 107:5,7

197:17 208:21

242:16 243:15,18

243:19,22 244:1

246:12 286:6,10

287:8 289:4,9

303:10 322:2,7

350:22 351:2

360:14 362:15

364:6 365:22

372:17 383:20

396:18 397:12,13

398:18 405:7

416:19 449:5,6

488:1 490:21

491:1,4 495:18

496:11 497:21

498:22 499:1,11

500:5,9,11

504:14 506:18

517:16 518:1

519:4,11 520:18

525:14 526:1,17

595:21 597:8,15

597:21 611:18

668:11 682:8,16

721:4 748:7,15

757:20

doses/concentra...

446:16

dose-related 347:3

dose-response

207:5 211:17

285:13 299:16

301:10,11

dosimeter 691:6

dosimetry 11:19

11:19 16:16

39:11 185:13,21

186:4 191:12,21

192:14 206:12,14

214:12 218:16

341:6 396:13

397:2,4,16,19

398:17 399:8

400:9 404:8

405:5 457:20

692:5 700:8,11

707:20 708:2

dosing 42:8,15

70:13 71:5,6,7,9

83:9,15 214:16

216:13 345:6,10

345:11 346:2

471:17 506:15,16

518:3 521:1

600:9,18,19

631:16

dot 523:3

double 148:14

540:8 584:19

double-check

148:10 161:15

223:19 314:9

doubt 276:22

278:1 652:11

680:1

doubts 289:8

519:18 598:13

624:22

Dow 27:21 36:3,5

176:21 178:11

193:4 196:15,15

196:16 216:8

219:1 224:2,11

247:13,17,18

248:1 252:17

254:14,15 255:20

255:21 265:2

271:2,7 278:5

282:16 305:19

315:7 318:14,18

320:6 342:20

382:7 413:21

421:22 457:6

613:15,21 781:3

download 247:10

downside 668:20

downstream 56:16

58:20 60:18

434:3 437:3

455:3 465:1

494:14

Dow's 318:6

Dr 2:1 3:22 4:2,8

4:10,13,16,20 5:1

5:5,11,14,18,21

6:3,5,9,12,15,18

6:21 7:5,8,16,17

7:17 19:12 22:11

22:11,12,13 23:1

23:17 24:21,21

25:1 26:16 27:8

41:8,11,12 42:2

43:1,2,5 59:14,15

59:18 60:14 61:3

61:4,6,8,10,15

62:1 64:1,8 65:4

65:10 66:6,9,13

67:4,5,11 68:6,12

68:16,17 87:5,6,7

87:15,17,19,20

87:22 88:6 89:6

90:3,3,5,14 91:12

91:12,14 92:1,12

93:12 95:8 96:4

97:5,5 98:6,6,7

98:15 100:16

101:17,17,18,19

101:20 102:3,21

103:4 104:1,1,2

105:11 107:11,11

107:12 108:17,20

108:22 109:6

110:7,7,8,12

111:11,22 112:3

120:16 143:2
Page 32

147:18,20,21,22

148:3,9,14,16,20

148:22 149:2,7

149:12,17,21

150:2,5,8,14,15

150:17 151:3,5,7

151:10,13,14,15

151:16,17,18,19

151:21,22 152:5

152:6,8,9,13,16

152:18,19,22

153:1,1,3,8,10,15

153:21,22,22

154:2,6,13,21

155:1,6,15 156:8

157:15,18,19

158:2,3,4,6,8,9

158:11,12,12,13

158:14 159:8,16

159:19,21 160:1

160:3,9 161:1,11

161:17,18,18,19

162:4,5,11,12,17

162:18,19,21

163:1,5,7,8,17,21

164:1,3,8,10,12

164:12,13,18,21

165:1,3,3,4,11,12

165:13,20 166:1

166:2,2,3,21

167:18,18,19

168:2,14,17,19

168:19,20 169:5

169:13,13,14,17

169:18 170:7,11

170:13,17,18,18

170:19,22 171:1

171:2,4,6,7,8,8,9

171:14,16,18

172:4 188:1,2,3

188:14,20 189:1

189:17 190:3

191:4,17,17,18

193:17,17,18,19

194:12 195:8,16

195:18,19 196:11

196:12,13 197:6

197:10 198:1,11

198:12,13,20,20

198:21 199:17

200:2,21 201:4,4

201:5,5,10,11,13

206:5 209:8,22

210:19 212:7,11

212:14 218:17,21

220:13,20 221:3

221:5,5,7,14,18

221:19 222:2,3

222:11,12 223:3

223:6,12,21

224:6,8 226:18

246:19,21,22

247:2,4,5,8,12,14

247:15,16,17

248:13,16 249:5

250:14 253:4

254:13,19,21

255:2,6,12,14,16

257:1,14 258:12

264:12 269:9,10

269:11,15,16,19

269:20,22 270:1

270:3,4,5,14,15

270:17,18,21

271:7,14,14,15

271:21 272:13,16

272:18,19,20,22

273:1,1,2,5,7,9,9

273:11,19,22

274:3,3,4,5 275:5

276:21,22 277:3

277:6,8,9,11,13

277:15,16,18,20

277:21,22 278:2

278:4,4,5 279:14

279:16,17,18,19

279:21 280:1,3,5

280:7,12,15,20

281:4,15,19,20

281:21 282:1,1,5

285:10 287:14

289:15 293:2

302:1,1,2,8,9,11

302:21 303:3,5,6

303:7,12,17,18

303:21 304:5,5,6

305:3,5 307:1,11

307:15,20,20,21

308:9 309:5,9,17

309:19 310:2,4,6

310:8,10,10,14

310:14,15,15,18

310:19,19,20

311:2,5,13,16,17

311:19,20 312:1

312:2,2,4 313:4

313:22,22 314:1

314:5,9 315:1,13

315:16,19,22

316:3,6,9,12,15

316:18,21 317:1

317:3,6,9,12,17

317:20,21 318:1

318:9,12,14,15

318:15 319:1,5,8

320:1,3,4 334:6,7

334:8,15 335:1

335:10,18 336:1

337:1,1,2,15,22

337:22 338:1,5

338:18,22 339:1

339:8,11,16,18

339:20 340:1,6

340:12,16 341:13

341:14,17,19,21

341:22 342:1,2,4

342:5,6,7,8,13,21

343:1,20 350:7

354:9 364:20,22

365:1,2,3,8,16

366:6,6,8,9,21,21

366:22 367:7,8

367:11,14,15,16

367:18,20 368:7

368:11,17 369:1

369:3,7,9,11,21

369:22 370:1,7

370:11,15,17

371:2,5,12,18

372:1,9,10,19,22

373:2,6,7,10,13

373:14,18,20,21

374:10,10,11,16

374:17 375:2,5,8

375:10,20 376:1

376:3,6,9,11,12

376:15,17,18,19

376:20,22 377:6

377:8,10,12,13

377:14,20 378:9

378:11,18,18,21

379:1,2,16,21

380:1,2,10,11,12

380:13,13,15,19

381:21,21 382:1

382:2,3 383:13

384:1,16,21

385:13,13,15

386:7,9 387:2,13

388:1,2,4,6

390:22 395:17,18

395:19,21,22

396:2,3,4,5,7

409:22 410:1,2,3

410:4,11,22,22

411:1,4,11,15,16

411:18 412:1,4,6

412:14,20 413:1

413:7,8,9,12,13

413:18 414:8,12

414:17,17,18,21

414:22 415:6,8

415:10,12,17

416:13,14,18,19

417:3,6,10,10,14

417:15,20 418:12

418:14,19,22

419:1,17,22

420:2,3,6,7,12,13

420:17,21,22

421:5,5,7,8,10,12

421:20 422:8,9

422:12,14,15,21

422:21,22 423:4

423:5,6,6,7,18

424:20 425:1,3

425:13,13,13,15

426:5,9,21 427:7

427:7,8 428:1,3

428:22 429:1,3,7

429:13,15,17,22

430:3,5,6 431:18

432:4,5,6,9,13,16

432:20 433:14,16

433:21 434:8,9

434:10,14,18,20

435:11,18,18,19

436:4,7,7,10,15

436:17 437:19,19

437:20,21 438:15

438:15,17,17

439:1,1,2,16,16

439:17 440:4,6,7

441:5,6,8,10

442:9,9,10

444:11,11,12,12

444:13,14,15,15

444:16,17,17,18

444:19,20,21,22

445:17 447:8,12

447:13 452:16

454:18,19,19,20

454:20 455:18

456:6,6,7,7,8

457:6 458:5,20

460:9,9,10

462:17,17,18,19

463:3,14 465:13

465:14,15 467:9

467:10,11 469:21

470:6,16 471:3,3

471:6,12,18,21

471:22 472:1

473:1,2,3 474:22

475:18 480:16

484:19 495:13

497:6,14,16

499:3,17,17,18

499:19 500:12,12

500:13 502:10,11

502:12,18 504:6

504:6,7,17,22

505:2,5,5,6,17

506:4,4,5,11,11

506:12,21 507:8

507:8,12,13,13

507:14 508:3,8

508:10,12,18,21

508:22 509:19

510:8 512:2,2,4

513:10,17,18

514:21 515:2,3,9

516:5,5,6,15,22

517:1,9,12

518:16,16,18

520:13,13,14

521:6,6,7,16,16

521:17 522:1,1,6

522:8,13 524:3,3

524:4,5,22 525:4

525:5,7 528:3,3,4

528:20,20,21

529:3,3,4,10,11

529:12 530:6,6,8

530:11,12,13,20

531:6,10,11,12

531:14 532:9,15

532:17,22 533:7

533:12,14,20

535:7,7,8,9,21

536:10,11,12,19

536:21 537:1,3,6

538:3,4,14 539:6

539:19 540:9,10

540:11,12,17,20

540:21 541:17,18

541:19 552:7,9

552:10,11,22,22

553:1,6,6,7,7

554:2,2,3,10,10
Page 33

554:11 556:6,6,7

557:7,7 558:2,9

558:16,19 559:1

559:2,13,13

560:6,11,13,15

560:18,19,21

561:2,4,6,10,13

561:13,14 562:11

562:13,14 563:21

563:21 564:1,5,8

564:14,16 565:12

565:12,13,14,15

566:1,1,2 567:5

568:2,9,15 569:4

570:7,7,8,9,18

571:15,19 572:12

572:12,13 581:18

581:18,20,20,22

581:22 582:1,5,5

582:6,7 583:17

583:17,18,20,20

583:21,21 584:1

584:21,21,22,22

586:4,4,5,10,12

586:16,18 587:4

587:4,5,13,13,14

588:6,11,15,15

588:16,18,19

589:11 590:17,17

590:18 591:3,7

591:13,15,18,19

591:20 592:1,1,2

592:5,11,13,19

593:4,5,12,15,21

595:15,15,17

597:12 599:1,22

601:21,21 602:8

602:8,9 603:10

603:11,12 605:6

605:7,18 606:10

606:10,11,15,16

606:17,19 607:5

607:13 608:1,1,2

608:2,7,9,19,19

608:22 611:17,21

612:18,18,19

614:13,13,14

615:3,19,19,20

616:3,6,7,8,9

617:1,1,3,6,7,9

617:11,16,20,22

617:22,22 618:4

618:16 619:2,3,3

619:9,10,11

620:2,9,22,22

621:1,12 622:9,9

622:12 624:16

626:12 630:18,18

630:20,20 631:1

631:1,2,3,5,5,6

631:22 632:1,2,3

633:14 634:2,2,3

634:3 635:13,13

635:14,15,15

636:19,19,20,21

637:6,13 638:12

638:13,14 639:13

639:13,15 640:11

641:2,13 642:2

642:15,16,16,19

642:19,20,21,22

643:9,13,13,14

645:8,9,10,10,14

645:16,21 646:1

646:3,7,7,8 647:3

647:21,21,22

649:2,3,4,5,5,14

649:14,15,18,19

649:20 650:9,10

650:17,19,21

651:2,3,7,7,8,10

651:15,15,16

652:18,18,19,19

653:9,10,12,17

654:1,7,10 655:4

655:5,7,10,11,12

655:14,16 656:14

656:14,16 661:14

661:17 662:3,4

664:1,1,3,4,6,6,7

664:9,9,10,11

665:3,4,8,14,14

665:19,19,22

666:1,3,5,20,20

666:21 669:9

670:10 674:15,15

674:16,17 676:4

676:4,5,5,6,8,8,9

676:16 680:20,20

681:2,3,7,9,14,20

682:1,1,1,3,17,17

682:18 683:14,17

683:18 684:6,6,7

684:12,12,13,13

684:14,14,15

685:16,16,17

686:6,15 687:2,3

688:2,2,3,18,18

688:20 689:1,14

689:18 690:5,6,8

690:9,20,20,21

693:10,10,11

695:13,20,20,21

695:21 696:10

697:21 698:7

699:16,16,17

701:16 702:2,2,3

702:5,6,8,8,20

703:19,22 704:3

705:4,4,5,16

706:7 707:4,4,5

709:2,2,3,4,15,20

709:20,21,21,22

710:5,7,8 711:2,2

711:3,3,6,6,7

712:2,7,7,8,8,9

712:11,11,12,14

712:21,21,22

713:1 714:5,5,6,7

715:14,14,15

716:6,6,7,11,18

716:20,20 717:1

717:3,4,5,6,7,7,9

718:5,20 719:2,3

719:5,5,21 720:2

721:14,14,16

722:3 724:21

727:6,6,7,7,8,8,9

728:12,12,13,15

728:19,20,21

729:15,19,20

730:22,22 731:1

731:11,11,12,12

731:19 732:6,6,7

732:14,14,17

733:3,3,4,9,9,10

733:10,14,16,18

733:20,22 734:2

734:4,5,7,8,9,10

734:19,19 735:1

735:2,5,9,10,16

735:16,17,18

736:1,2,3,5,5,7,9

736:11,12,14,16

736:21 739:17,17

739:18 740:20,21

740:22 741:17,18

741:20 742:16,16

742:17 744:14,14

744:15,22 745:16

745:16,17,18,22

746:8,9 747:14

747:16 748:9,9

748:10 751:10,10

751:11 753:8,8,9

755:21,21 756:4

756:16,19,20

758:14,15 759:4

759:6,13,13,14

761:1,1,2,15,16

762:6,8,21 763:4

763:11,11,12

764:13,13,14

765:3,3,4,5,14,14

765:15,19,19,20

766:9,9,10,11,12

766:12,13,20

767:1,1,2 768:1,7

768:7,9,18 769:5

769:5,6,12,18

770:7,7,8,19,19

770:20 771:10,14

772:5,5,6 773:11

773:11,12 774:5

775:8,18 776:18

776:19,20 777:20

778:18,19 779:4

779:10 780:3,5

780:11,17 781:4

781:6,8

draft 20:17 29:7

32:10 48:10

52:22 64:9 112:8

112:9 114:7,10

132:12 139:9

146:8 203:16

206:19 223:17

229:4 230:7

257:6 292:13

293:13 294:4

302:17 541:14,21

543:9 547:17

551:19 570:11

595:18 596:17

597:13 598:3,19

598:21 600:16

621:20 631:3

655:20 659:15

666:8

dramatically

732:5

drastically 630:2

697:7

draw 180:10

252:11 253:20

526:18 677:14

692:22 750:15

751:11

drawing 525:21

drawn 525:22

dream 194:19

195:10

drift 393:21 394:3

drink 694:17

drinking 394:9,11

394:22

drive 1:15 273:20

564:17 570:21

driven 92:15

424:15

drivers 727:4

drives 513:16

driving 204:2

271:16 424:17,22

532:15 586:17

drop 254:7 262:19

322:9 339:21

550:6 660:4

dropout 139:3

dropped 162:13

486:6 684:10

drops 345:8

drop-off 687:17

drug 57:8 118:9

458:13 505:21

629:1 742:4

747:4

drugs 101:8 402:3

501:9 503:11

571:12

dry 761:16

DSMO 474:15

DSVM 137:8

Dubois 696:20,20

due 63:6 88:21

112:19 113:2

133:11 141:5

142:17 145:11

174:10 180:12

265:9 272:1,17

348:10 385:22

502:6 544:10

549:10 550:14

555:7,8 559:6,10

559:11 560:8,18

563:5 574:18

580:5 581:4

583:14 589:17

590:2 594:17,19

594:22 595:2
Page 34

625:14,17 630:6

656:7 661:4

662:17,20 754:5

755:6

Duke 109:7,15

110:1,2,4,20,21

Dunnetts 523:3,6

523:8

duration 11:22

40:8 174:18

262:4 351:6

380:5 383:19,22

447:4 460:5

472:19

durations 31:7

40:11 42:21 96:7

96:12 383:15

594:14

dust 174:1 190:8

661:20 734:15

dwarfed 431:8

dwelling 656:22

dwellings 123:6

dying 386:8 711:7

dynamic 555:16

570:22 739:3

740:6,11 743:20

744:5

dynamics 304:22

443:11 444:2

535:16 557:22

714:3 744:1

dysfunction 47:22

285:18

dysregulation

51:13

E

earlier 26:18,18

109:7 142:14

143:3 153:6,11

169:15 193:9

210:12 257:19

284:15 285:10

310:4 311:14,15

325:21 329:13

336:14 338:7

345:16 346:1,17

349:20 352:20

363:3 368:12

412:18 426:4

464:15 498:11

499:3 535:6,21

543:17 575:10

617:14 664:11

669:16 697:18

698:2 699:8

724:22 730:10

earliest 89:10

early 31:10,15

37:14 59:16 73:1

83:12 135:20

179:16 201:20

217:20 272:15

345:2 356:3

392:5 413:9,19

415:13 416:16

432:10 462:10

472:11 482:18

498:18 504:8

527:10 531:19

536:2,5,7 538:7

543:16 551:9

574:20,21 623:17

634:18 643:17

683:19 686:3

early-life 209:5

easier 11:10

167:15 195:20

296:22 479:20

482:11 668:7

712:2

easiest 98:16

613:22

easily 75:2 376:16

481:10 523:18

613:4 683:2

696:21 740:8

easy 34:18 66:14

413:14 444:14

445:19 584:18

585:9 721:15

725:9 731:7

773:14

eat 196:5 654:14

694:16

eating 640:13

Eaton 177:8

252:15 319:13

Eaton's 280:6

287:22

echo 709:3 765:20

766:20 773:9,13

ecological 8:20

ecosystem 45:11

ecotoxicologists

248:8

edge 109:4

editorial 590:16

editors 514:16

educate 463:16

education 127:16

effect 1:5 45:1

46:4 50:22 51:9

51:11 52:5,6

54:18 59:12

60:22 61:2 65:12

67:13,20 68:4

73:6,10,12 74:13

76:18 77:9,18,21

78:16 79:4,13

81:17,18 85:2,8

94:3 95:13,15,16

95:21 100:11,12

101:1 102:9,10

102:15 118:1

129:10 130:1

132:1 133:7

136:17 138:14

142:3,3 157:10

157:21,22 190:4

233:5 236:21

237:3 238:20

239:7,13 240:1

240:13 241:20,20

245:17,20 262:6

274:19,21 275:16

303:15 305:1

306:5 327:15

328:2,6,7,15

329:15 331:13,14

331:22 332:7,10

332:17 333:6

335:21,22 336:14

337:4 340:3

344:14 347:4

349:7,9,15 350:1

354:22 356:17,20

356:21 357:7,8

358:8,12 359:10

359:13 360:17,21

360:22 361:14,14

361:17 362:2,6,7

362:19 363:16

366:13,19 369:15

370:13 371:20,22

372:11 378:5,13

378:14,22 382:9

385:7,8 400:19

409:1 416:20

435:21 436:1

440:17 441:4

443:12 446:12

452:8,8,9,15

464:20 469:13

476:20,22 481:18

481:22 483:3,6

485:3,4 490:10

490:15 491:8

494:18 498:1,2

500:9 508:2

509:1,2,3,8

510:10 516:2,3

519:22 520:8

523:14 526:14

530:18 534:10,15

536:20 537:9,18

544:6 546:11

547:1,11 550:3

550:10 551:16

552:12 555:19

557:1,3,4 559:18

559:21,22 560:16

560:20,21 566:20

566:21 567:3

569:5 573:14,18

573:19 578:9

580:2,9 583:3

585:5 597:19

598:6,10,10,21

599:10 600:17

613:17 622:7

633:19 681:12

725:6 726:10,19

730:14 746:22

747:10 748:7

749:13 753:18

767:8 774:14,15

effecting 337:3

352:16

effective 189:20

393:1 543:3

663:21 698:8

724:11,12,20

effectively 393:2

effectiveness 45:19

579:6

effects 2:4 19:10

19:15 25:2 29:1

31:10,15 32:6

37:14,18 38:8

40:22 44:10

45:20 50:14,15

52:4 59:3,7

60:19 64:3 68:21

69:4,6,10,13 72:1

72:3 73:2 75:5

75:14 77:13 83:3

84:14 85:4,14,20

86:7,11,14 94:4,8

95:5 96:1,2,13

97:7 98:9,18

99:2,12,20

101:20 102:7,17

103:1,8 105:16

106:5 109:12,15

111:5 131:22

137:1,14 138:20

146:4 172:9

208:9,18,21

209:4 226:4

228:12,14 231:2

232:10 233:7,7

234:20 237:4

238:3 241:2

242:3,15 243:3,7

243:12,14 244:20

251:19 253:1,2

255:1 268:20

276:8 282:15

283:16 284:17,19

286:6,9 287:8

288:20 289:6

290:13 292:14,16

298:7 299:9,20

300:9 301:4

303:9,13,13,20

304:3,14,16

306:8 315:3

320:20 321:3,6

322:22 323:10,11

327:9 330:11

331:17,18 332:9

332:18,22 333:5

333:19 334:1

336:3,11,19

337:9 341:9

344:6,6,11,14,18

347:2,21,21

348:15 349:20

350:2,21 351:2,7

351:10 353:2,9

353:10,17,19

355:16 360:15

362:16,17 363:4

363:5,16,20,22

364:4,4,6 365:21

366:18 368:20

380:3 381:15

383:19 385:19
Page 35

386:3,5 389:20

392:6 403:8

409:18 422:15

426:6 434:3

437:3 438:2

441:17,19 446:7

446:14 447:5

453:16 455:4,8

456:16 457:2

460:5 461:13,15

463:2,5,6 464:8

465:4 467:20

469:14 472:3,12

472:20 474:7

476:13 485:6

488:15 489:22

490:20,21 491:6

493:12,12,19

494:4,8,12

495:22 496:9,12

496:13,19 497:12

497:22 498:7,17

498:20 499:10,10

499:13,21,22

500:1,4,5,9

502:12,14 503:1

503:4 504:3

515:19 516:11,12

517:17,18 519:5

519:9 520:11

521:13 522:18,19

522:20,21 523:12

523:13 524:1

525:16,17 526:3

526:10 531:2,7,9

533:9,20 535:11

539:4,5,21

545:16 549:3,16

551:22 552:2

556:12,18 560:8

561:3,11 562:4

562:17 563:5,6

563:13 564:10

572:22 573:19

574:20,22 580:5

581:7,13 583:12

585:10 588:5,21

589:7,22 591:9

591:12 594:7,19

595:21 596:18

597:15 598:9,20

598:22 599:1,3

605:20 607:22

611:4,11 612:13

612:22 615:6,8

621:4,6 641:7,11

675:10,13 681:13

682:11,16 716:2

725:12,17,19

730:8,21 738:22

749:17 750:4,6

753:16,19,22

754:8,20 761:6,9

761:10 762:12

764:6 766:2,17

771:19

efficacy 725:1

efficient 403:3

777:17

efficiently 692:22

effort 7:20 20:12

450:15 512:22

542:16 592:16

626:13 768:14

770:15 777:12

efforts 7:21 8:15

19:17 20:1 92:5

193:8 252:12

337:8 458:3

521:19 582:14

592:6,8 710:15

777:1

Egeghy 178:3

Egypt 113:16

181:4 666:12

667:10 680:2

Egyptian 265:17

266:12,18 675:5

685:7

eight 72:18 77:11

81:12 94:20,21

125:11 126:19

163:7 259:14

264:8 270:7

288:2 319:15

328:10,11 358:7

358:10 361:5,18

376:4 380:4

417:22 630:4,5

698:12 714:14

779:6

eighties 214:17

eight-arm 375:6

eight-week 162:17

162:18,20 163:2

either 14:4 38:2

59:7 71:16 75:15

77:12 80:5 81:13

82:19 83:1,6

85:3,5 94:9 99:5

112:11 116:11

137:4 144:16

157:5 174:21

187:8,11 192:8

196:16 214:2

216:6 247:1

294:17 300:7

327:21 335:7

336:9 338:14,16

340:8,22 383:19

384:6,10 389:20

397:4 405:6,13

408:4 409:3

425:17 449:9

464:3 496:5

533:16 534:12

535:13 546:15

592:16 596:13

597:21 613:2,4

626:13 627:10

648:10,12 651:18

652:4 664:8

674:1 678:8

683:17 684:4

700:13,16 755:14

759:8 772:16

776:7

elaboration

650:11

electronically

388:12

element 225:15

234:3

elements 228:6

242:17

elephant 713:2

elephant-in-the-...

710:19

elevated 77:10

78:19 79:6,15

477:22 478:2

501:7 523:4,19

531:17,20

elicit 37:19 208:22

214:8 218:7

323:10 446:12

536:1 565:17

629:1 671:13

elicited 720:20

eliciting 341:8

565:16,21

eligibility 28:4

117:13,15 206:8

eliminate 28:1

339:22 668:9

771:7

eliminated 158:17

240:17

elimination 324:6

338:13

ELISA 330:19

ELISA-based

330:16

Ellen 6:12,12

317:3,3 541:19

541:19 556:7

558:2,16 559:1

564:1 567:5

568:15 581:20

590:18 638:14

770:8

Ellman 90:18

eloquently 702:5

else's 502:8

elucidated 594:1

email 152:20

583:22 601:21

emails 170:14

512:15 553:3

777:13

emanate 143:10

embryos 440:20

440:22

emerged 98:17

Emeritus 5:19

316:16

Emory 5:16

316:13 661:17

662:4

emotion 77:9

285:7

emphasis 139:20

187:10 292:15

342:18 474:20

604:22 623:14

624:4 626:10

632:14 637:16

638:7 652:20

emphasize 177:12

503:8 509:17

548:2 637:18

648:2 727:22

emphasized 503:8

737:21

emphasizing

156:21

empirical 14:4,14

17:11 36:7 379:4

379:9 517:15

718:21

employed 14:9

116:12,13 775:18

employees 256:1

265:2,2,22

267:10 671:17

emulation 611:5

enable 750:15

enclosed 78:21

encompasses

23:19

encounter 146:2

445:12

encourage 193:13

194:21 219:9

255:9 308:16

319:9,11,16,19

353:3 599:21

601:17 602:17

620:17 621:5

639:9 692:13

700:17 709:14

734:13 775:6

776:10,15

encouraged

229:17 252:1

294:4 611:20

703:16

encourages 229:15

229:18

encouraging

368:17 588:12

670:22 704:22

endeavors 780:20

ended 92:10 188:5

210:7 238:7

374:2 669:8

endocannabinoid

53:5 56:13

endocrine 99:12

99:12 228:21

348:19

endorse 229:17

endothelia 628:1

endpoint 213:1

215:7 237:17

238:4 283:20,21

285:6,6 294:6,6,7

297:8 301:19

437:2 438:22

452:17 518:6
Page 36

529:7,7,18 600:6

607:21 689:10

728:5,5 752:1,3

endpoints 233:12

237:2,5,6,8 238:2

239:21 241:4,17

258:20 261:21

272:9 291:5

296:7 297:6

298:6 299:3

304:15 305:7

423:14 446:18

448:7 459:5

461:17 467:3

518:12,12 529:9

529:15 641:21

697:10

endpoint-by-end...

291:5 293:20

ends 339:9 346:8

482:15 487:19

energy 706:11

enforcing 536:22

engage 255:9

engaged 539:8

engagement

310:12

engineers 668:3

England 697:15

English 29:15

30:18 138:7

enhance 141:11

331:17 545:8

726:9

enhanced 547:15

577:14

enhancement

336:7 556:16

enhances 141:15

142:2 337:19

enjoyed 776:9

enlightening

772:11

enormous 454:22

487:3

enrolled 114:22

115:8 117:4,18

enrollment 114:20

118:3 122:14

127:12

ensure 2:20 3:4,18

8:15 10:1 13:9

16:7,11 21:9

98:1 435:3,7,15

533:2 649:7

ensuring 15:10

entered 27:22

entering 103:17

enterprise 42:5

entertain 419:2

entire 2:11 19:22

25:5 27:15 164:6

164:7 178:20

221:3 344:8

346:3 431:6

435:17 633:7,17

633:18 754:20

778:5,6 780:12

781:8

entirely 30:1 259:1

496:15 505:18

633:19

entitled 251:5,10

254:21

envelop 707:3

environment 14:7

93:9 119:3

123:10 124:1

128:21 136:6,7

140:22 146:16

258:3 392:12

393:2 477:21

545:8 557:19

568:10 631:15

637:3 638:9

640:12,22 644:10

646:5 650:13,15

650:15,15,16,22

652:16 654:13,19

661:22 662:13

734:5 743:11

758:10 763:18

environmental

1:11 4:14,17 5:8

5:15 6:6,11,22

7:2 46:21 114:17

115:18 116:22

117:9 119:14

123:17 126:7

128:14,17 129:1

143:22 145:1

173:22 174:1

184:2 196:21

222:19 224:10

248:4,15 256:13

282:8 315:18,20

316:13 317:13,15

356:5 545:17

555:2 557:12

558:15 569:10,16

582:15,17 588:1

609:19 610:10

623:15 643:20

645:5 648:3

650:22 651:4,22

655:11 659:1,9

661:16,20 664:15

709:11 714:22

734:11 741:9

748:2 769:4

envision 60:2,5

665:4

envy 463:10

enzymatic 340:9

570:13,14,16,20

652:5 726:8

enzyme 56:16

461:9,9 564:19

564:22 565:10

570:10 677:1

678:20 679:3,20

679:21 680:8

687:7 688:14

enzymes 334:12,19

368:14 403:17

414:16 459:2

726:6 755:12

enzyme-based

332:1 338:15

EPA 2:17 3:19

4:18 5:10 7:14

7:15 62:3 68:15

112:14 117:11

194:7 223:13

224:20 226:14

229:5 231:5

241:14 244:12

250:3 284:15

299:12 308:11

320:12 324:1

326:8 336:21

343:3,5,19

348:18 353:3,15

363:7 367:9,12

376:13 378:13

388:8,14 389:3,5

389:5,7,19 390:1

390:10,14 391:16

391:19,22 392:9

392:11,15,22

393:6,21 394:7,9

394:16,20 395:10

395:14 396:9,11

396:13,18 402:14

402:14,16 405:7

419:6,8 421:14

422:5 423:5,19

436:4,14,15

471:7 473:21

493:16 498:2

523:11 541:8

553:11 554:4

595:18 598:19

599:14,21 600:16

601:1,17 605:10

607:3 611:8

613:4,5 615:9

621:5 626:7

647:10 656:18

665:5,16 686:19

699:11 716:21

731:15 752:15

760:6 761:21

762:3 764:10,17

766:14 768:6,21

772:7 775:12,18

776:14 781:11

EPA's 176:18

228:20 234:13

250:19 298:19

346:5 357:11

392:21 393:14,17

394:5,12,12,15

395:2 409:8

416:9 775:21

776:5

EPA-HQ-OPP-2...

1:7

epi 95:22 106:9,10

199:15,17 206:20

225:6,7 233:6

237:4 239:17,17

240:21 244:19

245:14 257:1

262:7 268:22

271:19 281:17

284:20 290:1,16

293:17 297:18

300:1,15,16

312:13,17 423:1

423:11,17 424:1

424:5 470:20

561:15 564:12

569:21 574:1,5

579:5 585:2

593:17 605:13

606:4 607:17

612:20 615:22

635:4 658:6

659:2 667:8

738:12 757:19

766:5,19 767:22

769:7,16 772:8

epidemic 231:18

epidemiologic

229:5 236:10

391:3 392:7,16

398:19 542:1,7

571:22 572:3,4

595:4,6 608:10

770:10

epidemiological

10:1,6,12 11:6

14:3,6 15:20

16:20 17:7 144:5

381:16 396:16,17

409:15 527:15

596:2 606:5

609:10,15,20

610:17 611:9,14

612:3 739:22

740:15 748:14

751:3 765:22

773:21

epidemiologist 5:8

6:6 112:4 240:21

255:20 316:1

539:10 740:14

766:1 767:12

769:11 775:14,18

epidemiologists

230:17 360:1

epidemiology 4:21

5:9 6:7,7 20:18

20:22 26:11

28:15,22 29:7

30:9 31:19 32:10

32:19 38:10

39:14,15 41:2

111:14,22 112:6

112:22 114:11

118:20 121:5

122:3 126:7

127:8,9,21

130:18 132:12

146:3 156:10

174:20 175:5

202:19 203:17

204:11 208:10

209:9 210:13
Page 37

212:1 213:5,10

224:22 228:9

232:1,1 247:19

249:14,22 252:14

254:13 256:4

264:7 282:9,18

283:8,13,14

284:7 287:3

288:18 289:2,11

289:19 293:4,14

298:18 299:5

300:4 312:7

316:7 385:16

386:5 392:15

396:19 405:7

512:8 539:16

540:19 541:1,13

541:20 542:2,3

545:2 546:4

551:20 564:12

567:7 572:7,18

594:22 599:15

608:16 635:3

720:10 729:7

737:18 738:22

753:17 756:1

757:4,12,15

758:3 759:7

765:7,10 771:16

776:1

epigenetic 476:20

episode 747:3

episodic 198:9

epithelia 628:13

epitomized 244:9

equal 15:3 18:11

261:20 371:9

402:7 616:18

643:8

equally 233:5

359:19 457:7

equals 696:4

equate 468:5

499:4

equation 280:19

307:16 714:9

717:15,19 722:3

749:4

equations 705:10

equilibration

711:20 712:1

equilibrium

627:11 629:20

equipment 74:1

84:8 395:5

equivalence 185:7

equivalent 326:5

329:22 376:10

452:3 531:19

692:15,17

equivalents 729:18

equivocal 371:20

ERDEM 644:14

error 76:6 121:15

121:17 122:5

143:17,18 145:3

145:11 149:15,22

216:14 236:16

279:1 289:13

374:6 510:15

540:13 625:16,16

errors 76:9,12

77:3 122:4

149:14 151:12

358:1,14 361:18

361:19,21 362:8

362:22 374:19,21

375:4,12,15,22

457:20 475:18

494:10 496:21

509:22 510:6,7

510:11,22 631:17

escape 511:12

Eskenazi 429:8

especially 65:12

85:6,21 88:8

89:21 92:3 106:7

140:5 190:11

236:9 279:5

321:10 346:22

356:2 357:15

360:9 368:19

369:5 372:12

389:8 456:3

458:5 535:11

555:15 584:10

612:20 615:9

624:22 633:19

657:15 660:9

679:14 694:3

696:7 698:7

722:5 729:18

745:21

essays 308:4

essence 307:4

444:22 452:2

584:18 607:13

essential 479:7

480:22 486:11

530:1

essentially 17:12

176:4 284:6

287:21,22 290:3

290:11 300:14,19

301:9 311:9

391:21 400:19

401:5 404:7,12

405:8,11,17

406:18,19 413:19

467:13 636:4

677:17 688:11

establish 109:4

441:16 605:16

established 87:11

128:4 209:14

391:14 436:20

441:20 446:2,14

455:2 527:9

544:22 750:19,21

establishes 492:12

establishing 543:3

594:11 659:17

esterases 321:1

324:8

esteratic 55:5

estimate 120:14

184:11 185:8,21

185:22 186:5

199:9,9 296:20

320:17 326:18

329:10 333:13

402:21,22 403:4

404:2,3,17,22

426:19 455:11

456:4 643:6

645:6 694:18

700:12 707:16

709:19 722:4

723:15 724:3

734:14 751:5

754:2

estimated 288:1

288:12 364:7

407:13 416:20

644:21 645:1

estimates 128:9

131:11 142:17

147:11 157:10

163:19 176:17

192:4 201:3

321:5,14 325:6,9

332:3 333:10

393:17,20,21

394:12 398:16

400:16 421:15

422:17,19 546:22

547:1 691:11

692:9 703:6

711:14 725:19

739:21 748:18

755:1,8

estimating 65:16

594:10 691:4,5

700:2

estimation 268:12

323:15 324:8

341:8 548:12,16

609:21 723:19

estimations 625:17

625:18

estimator 379:10

et 48:10 123:18

130:12,22 132:5

132:17 177:8

178:4 180:19

181:3 182:7,12

182:22 185:1

186:3,17,21

201:1 252:15,15

252:16 263:2

264:16 332:12

333:10 348:8

354:16 365:10

397:6 402:13

423:1 426:10

427:9,19 435:2

440:12,19 460:15

471:7 517:22

547:8 578:7

588:14 596:18

597:17 623:21,21

623:21 624:1

625:22 653:15

659:3,3 660:20

694:10,17 716:1

722:20,20 727:16

758:12 759:10,12

ethanol 445:5

505:19 750:22

ethenol 505:19

ethically 214:19

ethics 2:20 3:3,5

ethnic 580:20

ethnicity 138:13

141:17 547:19

548:3 580:2

ethyl-containing

201:7

etiologic 113:15

121:11

euro 615:7

evaluate 44:2

46:17 50:6 70:9

92:17 100:19

103:22 124:22

132:3,14 133:22

142:10 180:3

184:10 240:10

244:11 245:9,12

259:3 286:15

287:1 292:10

300:17 302:12

313:21 344:12

352:12 353:4

354:20 355:8

373:7 449:21

497:18 518:10

609:4 662:11,12

662:18 663:12,13

725:1 750:14

763:7

evaluated 90:11

129:2,15 134:12

147:13 154:9

157:12 172:16

177:7 183:10

186:8,12,15

187:2 207:8,20

231:8 261:20

283:15,22 301:7

332:17 356:6,7

358:17 360:13

369:6 442:5

446:16 461:22

475:13 597:8

625:5 626:15

691:22

evaluates 661:17

evaluating 22:6

27:6 127:9

132:12 142:5

173:10 174:3

179:21 180:17

181:21 235:5

286:8 288:22

344:21 350:17

364:15 439:13

446:19 467:15

594:11 623:6

626:6 656:11,18
Page 38

763:9

evaluation 4:7

20:20 38:15

111:8 113:21

132:1 138:2

139:9 147:3

150:18 163:3

172:13 179:10

204:5,6 222:7

226:3 244:14

246:5 254:22

282:14 290:20

293:21 294:3

297:11,12 315:12

321:4 325:5

333:21 337:21

341:8 353:20

355:2 356:8

361:16 364:11

380:22 383:17

473:8

evaluations 90:2

117:2 144:9

146:14 159:10

168:4 214:13,21

233:15 291:13

657:12

evaluator 105:21

event 44:18 45:7

45:16 46:10

47:11 48:3,12,20

53:16 54:7,14

56:3,10,14,18

57:2,10 60:18,19

61:18 62:13

64:19,20 96:10

96:14,16,17,18

96:22 121:4

231:7 433:20,22

437:1 453:7

614:9 679:10

746:2

events 23:14 26:8

33:14,15,15,20

33:21,22 35:2

40:9,9 45:3,13

48:12 54:7 58:17

58:19,20 60:17

61:16 62:11 69:8

97:19,19 202:21

202:22 205:12

348:17 350:6

351:9 434:10,15

442:7 445:3

451:16 478:22

480:21 674:13

eventually 26:21

155:17 273:20

447:20 448:1

486:5 719:11

everybody 17:11

222:17 470:22

499:21 502:8

530:19 540:4,4

643:16 654:3

everybody's

604:10 712:2

everyday 18:15

evidence 20:19

21:4,7,11,13,15

25:17 26:9,13

29:11 37:11,12

37:16,18 59:7

86:6 98:11,13

99:11 108:10,10

110:9 122:19

125:20 128:5

134:17 136:13

137:19 138:17

140:13 145:13

159:19 164:17

202:5,18 204:5

204:16 206:15

207:3,13,18

208:5,16 209:4,7

211:21 214:10

220:9 224:14

225:1,1 226:3,10

226:21 227:3,5,8

227:9,16 228:20

228:21 229:1,5

229:19,19 232:19

233:18,20 234:6

234:10,16,17,17

234:22 235:2,5

235:11,12,13,22

235:22 236:18

238:12 242:8,11

243:12 245:5,7

246:11 249:12

252:14 254:22

275:3 282:14

283:4,12 286:2,3

287:2,18 289:2,3

291:4 292:3

294:5 296:10

297:18 299:7,8

300:8,11,20

301:2 303:8

344:8 352:15

353:10,16 364:16

366:20 375:17

377:22 382:18

383:17 386:2

390:13,14 391:3

392:20 393:3,7

394:8,21 395:1

446:6 456:17

472:13 493:10

494:11 496:3,4

496:12 515:6

520:10 526:2

542:1 546:14

549:17 574:1,6

574:22 576:12

577:19 579:18

581:12 595:12,14

595:20 596:1

597:3 598:17

599:8 605:19

609:4,6,7 619:5

651:20 666:16

737:4 750:10,14

750:15 753:4

757:7 758:4

762:11 764:6,8

765:22

evolution 10:20

evolved 719:4

evolving 609:9

610:18 620:13

exact 99:20 102:9

102:10,22

exactly 84:15

102:11,12 120:17

156:2 230:1

351:13,16 360:14

368:13,13 398:5

432:7,22 447:18

448:8 451:6

483:5 490:15

516:19 537:7

539:2 581:9

586:16 637:4

672:3 703:22

709:4,17 716:21

731:19 766:11

exam 161:10

examination 144:1

549:21 677:7

679:11

examinations

546:16

examine 116:22

548:5 614:7

examined 547:16

548:4,8 662:5

example 32:1

43:22 44:8 45:9

45:18,20 48:6

50:5 53:14 57:21

57:21 58:3,7,11

66:10 72:21

74:16 99:22

101:1 102:5

103:7 105:12

109:7 110:14

134:8 142:10

156:15 157:6

176:12 178:3

183:21 184:14

186:4 191:11

196:22 211:18

218:1 226:12

228:22 253:14

257:11 259:7

260:10 283:14

289:13 292:18

293:5 294:13

295:19 296:14

312:18,19 328:9

328:13 329:22

332:7,11 347:18

349:6 350:13

351:14 355:13

356:11,12 357:20

359:21 361:2,16

369:8,8 382:21

386:20 391:19

412:7 415:17

428:20 430:14

432:18 450:6

460:18 462:5

468:4 476:6

482:7,22 483:17

491:22 498:11,15

504:19 506:14

510:13 511:7

519:21 522:22

528:13 536:4

537:8 545:19

546:13 548:12

550:2 584:11,14

586:6,20 607:1

611:20 627:17

629:4,9 638:17

657:16 658:13

692:3 698:9

703:5,12 704:13

705:16,18 707:8

713:13 721:11

731:14 740:4,16

744:17 749:9

750:17,22 755:4

759:16 763:6

767:9 774:7

examples 9:5

50:16 52:7 57:16

57:18 61:15

62:20,21 64:12

65:15 66:8 331:6

331:10 343:17

348:21 352:18

354:6

exampling 323:3

exams 160:20

exceed 394:15

exceeded 393:17

394:11,19 395:4

exceeding 393:22

exceeds 394:4

excellent 66:5

220:6 293:3

343:20 350:7

442:12,20 451:1

609:5,7 637:4

664:18 705:16

710:10 737:2

764:17 769:14

excepted 50:8

exception 73:15

110:18 121:12

122:6 388:1

exceptional 779:4

exceptionally

596:11

exceptions 188:5

excess 406:8,14

407:2

exchange 107:13

107:13 117:22

218:17 524:10

532:22

exchanged 553:3

698:12,18

exchanges 108:11

excitable 134:10

excitatory 48:15

48:19 50:11

exciting 201:19
Page 39

697:22 715:16

excitotoxicity

50:12

exclude 32:5

594:18

excluded 139:5

144:9 558:11,13

exclusive 626:4

exclusively 681:10

excrete 189:12

excreted 324:6

338:11 643:2

695:11

excretion 189:8

399:11,13 709:8

excuse 327:10

executive 6:21

317:12 777:22

778:2

exercise 299:8

708:15,17 714:4

760:18 768:21

exercised 14:17

exert 499:21

exhausted 172:5

exhibit 216:21

exhibited 215:2

exist 23:5 38:18

192:6 250:1

276:13,14 413:2

450:5 512:18

715:13 724:9,19

726:4 762:9

existed 210:7

existence 686:17

existing 44:16

69:20 70:1 108:5

192:20 326:4

462:13 638:3,16

639:5 692:22

710:13

exists 34:17 44:5,9

52:17 55:22

142:3 195:14

438:20 697:14

expand 113:13

409:21 439:3

502:12 609:12

expanded 443:10

expanding 444:8

expands 700:10

expansion 319:6

460:11

expect 14:7 102:11

102:22 166:12

191:15 267:4

275:8 298:6

428:19 466:8

498:16 564:10

600:3 622:11

670:21 746:6

expectation 379:6

379:7

expectations

400:18 448:8

610:22 670:20

expected 132:20

256:18 269:4

377:3,9 399:7

401:18 418:7

625:20 628:8,10

669:5 716:18

750:5

expecting 9:7

447:20 448:6

646:1

expeditions 549:9

expelled 124:18

expensive 89:4

697:16

experience 40:5

94:8 97:1 203:18

206:16 343:7,9

373:21 486:21

583:8 600:2

720:12 740:1

752:8 778:20

experienced 7:9

656:3 666:18

720:6 721:3

experiences 8:11

experiencing

614:19,22

experiment 58:15

65:1 167:10

231:15,19 276:11

354:9 368:10

477:11 579:3

600:22

experimental 9:21

10:5,13 11:5

14:2 15:20 16:19

17:7,10 20:22

29:1 30:9 33:19

46:12 48:16

59:10 84:7

209:19 212:1,10

231:20 274:10

333:15 335:15

336:18 350:5

351:14 356:1

360:8 363:11

437:13 442:6

472:2 474:6

485:15,17 514:5

517:14 518:13

536:13

experimentally

52:4,11 59:11

experimenters

413:18 487:22

experiments 65:12

65:20,21 91:3

107:16 231:20

326:14 329:21

336:9 337:17

349:2 396:20

397:8 467:3

479:22 481:18

495:18 519:14

520:19 525:17

527:12 629:6

704:17,17

expert 343:1

442:21

expertise 95:12

320:5 389:14

777:6 778:17

780:15

experts 7:9 8:7

422:1

expires 779:1

explain 99:17

100:7 140:13

199:12 206:17

227:15 233:4

234:1 236:18,21

238:7 245:4,18

245:18,19,20

248:7 271:19

290:2,2 308:4

458:20 497:19

498:5 516:1

567:19 582:21

617:17 685:19

740:17

explained 141:13

236:9 239:12

241:18

explaining 234:6

366:7

explains 566:9

explanation 179:8

233:22 234:3

286:19 298:21

300:22 385:22

433:5

explanations

233:8,14 238:9

238:16 240:9

241:11 244:22

290:17 496:1

explicit 13:14

48:11 57:2 101:7

391:18 632:4

763:10

explicitly 45:6,12

46:9,22 50:2

54:1 58:21 204:4

544:20 548:17

exploration 77:18

exploratory 81:9

explore 161:19

167:9 252:10

explored 583:7

exploring 167:7

477:18

Exponent 342:1

342:22 350:12

exponential 36:8

expose 393:8

440:20 489:6,9

exposed 39:22

40:1 74:19 79:2

79:15 83:13 96:9

96:9 98:18,19

112:20 119:2,9

128:15 181:12

188:8,8 190:13

215:14 262:11

264:9 288:11

289:4 383:3

388:17 392:8,17

393:5 407:17

430:17 474:13

504:19 506:16

557:19 558:14,22

559:7 562:3

615:2 636:3

667:10 668:4

671:12 672:20,22

673:16,22 679:15

683:9 694:14

721:12 722:1,4

728:10 729:1,3

734:18 758:9

763:21

exposer 422:15,18

423:2 438:8

exposure 11:22

13:21 14:4,6

31:2,10 37:4,5,13

37:14 41:15,20

47:16,20 58:3,11

58:15 59:3 69:3

69:9,15 71:1,4

76:10 82:20 84:1

84:1,10,11 85:14

85:15 86:7,8

87:3 96:8,11

97:1 98:12,17,20

101:13 106:21,22

113:5,18 114:1,3

115:3,14,19

116:4,17,20

117:3 118:2,19

118:22 119:10,11

119:11,14,15,20

119:22 120:3,7,7

120:12,14,19,20

120:21 121:1,17

121:20 123:13,14

123:16,18 124:6

125:1,3,7,20,21

126:1,5,12,15,17

126:22 127:6

129:7,13,14

133:3,7,12,21

134:17,18 136:16

137:20 138:2,19

139:15 140:5,8

141:20,22 142:13

142:15,20,21,22

143:1 144:20

145:2,15 146:3

146:13 148:7,8

148:13,17 153:5

153:14,20 154:11

156:6,20 157:8

157:22 158:17

161:20 168:7

172:11,22 173:2

174:3,5,6,11,17

174:18,21,22

175:5,17 176:11

177:19 179:19,22

182:12 184:12,16

185:10,22 186:6

186:13 187:10,10

187:12,16 190:4
Page 40

190:5,7,16,22

191:5,15 192:3

193:6 196:20,21

198:8 201:1,3

206:3,6,7 209:5

210:6,7 211:7

215:19 217:11

218:6 222:19

247:21 249:12,13

251:19 253:2

256:4,14 257:10

257:19,20 260:9

262:1,4,10 264:5

264:17 267:19

268:22 269:2,21

270:10 273:15

274:8,13,20

275:4,9 276:11

279:6 281:8

284:2,8,9,10,11

284:14,21 288:2

288:9,16 289:13

291:13 294:9,12

297:13,19 299:22

301:4,18 303:14

318:21 319:7

321:2,14 323:14

323:22 328:17

333:20 345:18

346:21 347:17

348:1,5,14

349:16 352:6

353:8 355:4,15

364:8 381:17

383:15 384:3,6

384:11 390:3

392:6 393:11,12

394:4 395:3,3

400:13,14,18

401:7 406:2,3

411:4 424:4,6

428:19 434:11

438:3 440:13,18

453:6 454:2

460:13 461:17

462:6,11,15

468:5 472:4,10

488:3 489:7,9,10

493:12 494:12

496:16,17 498:19

505:15 542:11

543:4,5,8,12,14

543:19 544:7,11

544:14,15,20

545:4,6 546:7,12

547:7 548:19

549:1,17 550:9

551:9,10,22

555:5 558:3,9,12

558:13 559:4,6

559:10,12 573:7

573:10,12,15

574:4,10,15,18

575:6,12,22

576:7 577:2

578:22 579:7,8

580:4,10,14,19

581:5 583:4,6

585:9 587:7,10

587:17 594:3,10

594:11 595:1,3

596:6 600:1

601:12,19 602:6

606:6 608:12

609:13,18,21

610:6,14 612:9

614:4,5 615:8

618:19 621:15

622:6 623:10

624:6,9,12 625:5

625:18 626:4,22

627:3,3,6 630:9

630:12,16 633:16

636:1,2,7 638:7

639:17,18,20

640:3,10 641:1,7

641:19,22 642:11

642:13 646:4

654:2 655:1

656:11,21,22

657:12,22 658:7

658:11,15,16

660:2,8,13,15,16

660:18,20 661:3

661:7,13,15,16

662:9 663:12

666:18 667:4

669:3 670:16

673:14 675:2,2,8

678:4,14 681:16

682:11 683:2,13

686:13 689:2,3

691:5,5,11 692:9

692:18 694:9

698:7 699:20,21

700:3,13 701:9

701:19 703:6

707:15 708:21

709:5,18 714:8

715:20 716:1

718:10 720:6,9

720:17 721:2

723:15,20,21

724:3,20 725:2

734:21 735:6,21

740:16 745:20

746:4,13 747:6

747:11 750:20

752:7 754:3,19

755:10 756:3

757:5,8,16 769:1

772:9 774:17,20

exposures 41:13

73:2 77:13 80:6

81:14,18,19

106:11 116:22

122:1 124:10,15

128:14 143:22

144:2,15 145:1

146:4 157:17

174:5 181:8

201:9 225:10

251:7 252:13

253:6,18 255:5

256:11 265:18,19

266:6 287:12

288:18,19 293:5

293:6 297:21

299:9 300:10

301:13,14,15

307:7 320:17

321:5 323:13

327:18 329:10

331:7 332:3

333:10,14 346:21

348:19 356:3

381:11 384:14

385:17 386:3

390:21 392:13

393:9,14,15

394:1,15 395:2,6

414:1,16 499:4,5

499:6 506:17

507:3 512:8

545:17 546:17

548:13 550:17

552:17,20 558:4

572:19 573:16

575:14,18 580:15

580:16,22 582:16

582:21 585:5

593:8 594:14

614:8 615:14

623:9 633:11

655:22 656:3,22

657:10,17 661:18

662:1 667:15,16

668:7,17 669:19

670:17 671:12

674:14 707:10

716:4 720:19

723:10 735:8

753:13 755:2,3

755:11 758:11

761:5

exposure's 661:19

exposure-respon...

145:13

express 69:5

expressed 49:11

expresses 351:22

362:9

expression 45:21

56:22 440:21

598:1

extend 2:11,12

3:21 4:3 20:7

231:4

extended 87:13

174:18 229:20

231:5 234:12

542:10

extending 230:15

extensive 28:17,18

31:10 122:9

127:15 139:16

141:15 172:22

192:3 397:2

439:12 487:2

542:10 545:13

extensively 41:2

135:5 146:10

154:9 693:5

extensiveness

92:22

extent 105:12

124:11 126:10

134:10 144:10

207:3 208:20

251:12 397:13

459:15 473:14

504:5 524:9

531:1 547:1

610:1 611:2

613:12 620:16

653:3 691:18

748:17 751:4

external 120:14

121:17 123:1

397:19 406:2

411:2,12,14

580:19 691:11

692:9 703:6

extra 632:14

633:21

extraction 458:13

extrapolate 14:3,6

65:18 211:19

309:6 467:22

469:8 603:14

613:7 718:13

734:12 742:1

extrapolating

35:21 195:1

463:15 630:9

extrapolation

13:19 30:20

65:17 211:18

235:17 308:19

332:6 446:13

461:14 604:17

605:5 742:7

extrapolations

13:22 617:19

extreme 24:11

501:19 767:6

extremely 218:4

276:20 347:17

486:19 492:8,9

575:20 588:10

679:16 697:20

698:13,14 762:10

eye 49:1 622:13

679:10

e.g 446:8

E14 522:15

F

F 484:1

face 16:4 18:15

454:21 458:13

500:19

faced 16:3

face-to-face

387:20

facilitate 530:2

677:15 762:14

facility 180:20

680:2 763:20

fact 23:13 51:21
Page 41

71:4 73:4 112:19

131:8 144:15

149:14 153:18

165:18 168:10

174:10 230:2

234:2 237:21

271:11 291:10

323:9 330:20

331:11 340:16

349:9 356:19

359:1,4,9 362:9

369:15 373:22

386:1 392:12

415:14 425:3

442:17 450:1

475:22 478:11

483:11 484:5

500:18 503:13

508:1 516:6

527:5 534:6,11

534:14 537:10

540:15 552:18

554:16 556:16

562:22 570:1

575:17 612:12

634:22 653:3

670:10 672:14

694:1 703:16

733:1 743:21

757:19 763:4

factor 394:17,17

404:8 408:16,17

411:6 461:20

463:1,6 476:21

476:21 483:12

484:22 494:2

567:16,18 599:2

615:13 663:3

727:17 767:15

factored 507:19

factorial 494:15

factors 47:15

100:9 118:4

128:13 129:13

130:19 140:6,7,8

141:17 143:21

144:10,11 145:12

240:16 254:5,5

284:7 285:12,19

287:4,4 292:13

297:11,20 298:3

377:21 385:5,11

389:21 392:1

409:11 427:14

463:5 481:20

487:2 494:1,4

495:12 545:16,17

548:3,7 588:14

611:3 612:5

676:14 691:10

740:7 753:13

756:14 762:19

763:8

factory 311:10

facts 99:3 233:4,6

234:2,6 238:17

faculty 7:2 282:11

317:15 550:1

failed 123:3 494:8

failure 63:6

failures 237:15

fair 62:3 80:22

185:16 189:21

370:18,18 410:18

589:6

fairly 36:15 53:12

85:3 172:22

174:8,11,13,14

176:3 177:8

180:16 185:4

211:13 213:7,20

217:18 223:7

224:14 242:13

324:2 331:21

358:5 415:11

422:13 434:20

463:21 541:22

615:2 669:21

676:18 686:20

687:8,17,17

688:1 709:14

fall 262:14 268:16

493:17 557:15,17

569:7

fallen 98:19 559:3

falling 79:19 434:7

500:18

falls 52:20 452:10

557:16

false 85:4,5 245:22

489:21

familiar 33:8 90:7

228:8 230:15,17

241:4 252:4

families 388:22

395:13 622:3

660:1,5,6

family 458:18

fantastic 458:3,8

635:4 771:9

far 64:6 71:17

89:16 93:17 97:3

98:21 110:14,14

111:7 169:19,21

170:5 176:7

188:6 201:5

204:10 234:13

242:7 243:9

279:4 290:7

403:22 413:16

414:19 439:6

456:21 457:10,22

498:9 500:1

506:9 526:5,13

531:8 556:2

602:3 622:18

637:1 670:15

708:9 710:2

714:2 742:8,8,12

Farahat 181:3

182:7,12,22

197:12 266:18

267:2 667:2,10

667:22 674:3

681:6 682:20

686:19

Farahat's 677:2

farm 116:11

388:21 395:7,12

622:3 659:22

660:5 675:21

farmed 334:15

farmer 265:21

660:1

farmers 384:5

farther 230:13

234:15

fashion 323:21

324:7 339:22

570:22 635:20

745:7 761:13

fast 66:22 300:15

302:3 577:15

faster 83:18

705:14

fat 324:13 418:2

418:10 627:11

628:7,7,15 629:8

629:14

fatal 500:19

fate 248:8

fatty 56:16

fault 489:10

faulting 509:15

favor 414:6

favorable 228:22

FDA 62:4

feasible 634:21

feature 484:20

489:5

features 349:11

480:9 486:11

493:16 514:9

523:15

February 9:14

251:16 735:18

fecal 124:17

feces 411:21

fecundity 63:8

federal 2:5,9,14

3:1 8:5 318:2

395:13 778:6

780:9

feedback 17:5

19:6,17 21:8

22:6 191:20

192:12 202:2

207:10 209:21

212:3,10 214:12

218:8 220:6,7

724:10 777:14

feeding 411:9

feel 64:6 143:16

255:8 286:11

295:14 366:4

373:4 427:17

507:17 516:18

524:10 528:1

530:18 549:6,11

719:17 722:13

725:11 766:18

feeling 424:21

516:16 522:10

702:15 719:10

feelings 530:14

feels 8:13

fell 562:9

fellow 250:22

601:20

felt 73:5 297:20

299:8 426:16,22

445:9 549:14

710:2

female 80:16

195:2 435:22

485:1 682:5,6

females 63:8 76:13

76:16 77:2 79:3

88:20 93:6 99:10

215:13 237:13

349:5 358:4,9,22

359:1,3,6 363:1

488:18 519:21

female/female

80:3

fetal 83:5,8 84:18

119:21 122:7

132:7,9,14,20

143:13 150:5,18

365:15 517:21

527:7 574:20

575:1 576:21

604:7,16 627:6

627:18,22 628:4

629:15,16,21

630:9,12 634:17

698:4 699:1,5

710:19 711:9,17

713:3 714:8

715:18 717:14

722:14 741:12

fetus 40:1 82:19

119:20 121:22

124:18 345:18

346:7 457:13

601:10 604:5

625:18 627:21

629:12 633:21

698:4 711:20

715:18 716:2

717:21 722:14

724:7 734:17

754:11 756:12

fetuses 457:10

603:15 604:4

fewer 110:14

175:13 200:17

290:7

FGF 463:3

field 96:9 181:4

279:6 361:6

433:11 475:20

501:18 619:13

647:5 696:9

747:1 763:16

fields 81:12 389:1

395:8,11 668:2

668:15

FIFRA 1:1,8 2:2

2:14 3:22 112:16
Page 42

315:2 539:20,22

699:14 777:22

778:2,22 779:22

781:11

fifth 406:22

fight 89:5

figure 34:11,21

39:9 162:2

177:14 181:16

182:1,3,7 204:22

205:2,21 216:9

262:14 290:1,2

361:5 369:18

415:1 479:10

497:4,7 500:7

621:2 687:19

689:17 715:17

741:8

figured 382:4

figures 181:18

279:19

figure-8 81:11

filed 3:2

filling 108:7

filter 447:16

final 186:11 221:2

244:5 363:8

390:4 418:12

505:8 761:20

776:18

finalize 395:10

finalized 395:14

583:22

finally 46:19 52:3

57:9 59:10

172:15 253:4

293:19 298:11

299:13 300:13,14

301:12 319:11,21

330:14 344:3

381:12,15 459:13

511:14 653:9

666:22 776:3

777:15

financial 3:2

find 14:19 74:21

103:10 110:21

149:20 155:3

168:22 197:17

225:20 228:12

244:14 246:17

279:16 294:19

309:6 337:3

430:20 449:5,5

451:11,20 452:3

452:6,7 455:4

456:5 466:12

468:10 471:6,10

471:20 481:9,18

483:3 485:19

490:9,15 496:11

505:12 511:11

519:9,20 521:8

522:18 523:22

525:18 527:11

529:15 531:1

536:20 537:7

562:2 564:18

583:9 608:8

654:15 689:2

706:5 711:16,19

724:11 732:19

745:13 747:1

766:4

finding 92:15

109:17 135:2

308:7 357:9,12

364:13 478:10

483:22 520:6,6,7

522:4 523:5

552:20 598:14

findings 31:20

99:1 103:20

108:2 133:15

179:2 180:2

181:17 182:16

238:22 239:11

240:12 295:4

303:19 353:5,14

354:22 355:11

359:18,19 405:5

405:11 406:17

409:15 423:17

443:19 469:9

473:12 493:14

505:20 506:3

514:2 519:10,19

519:21 521:4

523:19 524:2

537:4 544:2

545:10 549:10,12

552:15 573:9

655:20 666:9,14

738:21 757:20

fine 251:2 369:4

564:5 673:4

714:10 719:3

766:21

finer 66:4

finish 41:5 57:13

246:16 426:3

473:12 608:3

780:7

finished 223:13

646:9,22 653:19

653:21 721:18

firm 224:10 282:8

firmly 209:14

first 7:19 27:20

28:13 37:9,18

45:14 46:8 48:3

57:21 60:6 66:1

77:5 79:1 88:15

96:7 98:21 107:4

109:11 118:13

162:8 165:15

177:14 180:19

184:13 185:6

188:4 201:22

208:14,20 213:16

224:6 226:9

228:1,17 229:7

231:14 243:19

244:11 247:22

252:19 256:22

267:15 286:13

287:1,2 289:1

290:4 291:5

324:9 328:10,11

328:13 331:6

334:10 343:11

347:20,21 349:19

352:7 358:5,6

359:4 360:6

361:18,20 365:5

367:4 372:10

373:8 374:1,14

375:13 382:7

397:1 398:21

400:19 406:8,11

421:21 423:19

425:18,19 436:18

452:5 462:19

467:13 469:6

479:8 490:19

513:16 543:21

546:19 554:12

569:10 605:3

609:18 622:22

632:10 642:22

663:1 675:1,4

678:15 687:10

693:21 702:5

703:20 717:21

718:22 750:11

765:17 768:14

769:12 777:17

778:2 780:12

firstly 609:1

fish 167:21 168:2,7

168:13 332:18

333:2 440:17,20

Fisher 484:16

Fisher's 494:22

497:10

fishing 549:9

fit 178:11 268:5,12

465:3 486:4

670:8 676:2

691:21 693:6

fits 100:17 439:6

676:7

five 60:7 73:9

78:14 80:9,12

88:11 137:9,15

245:13 250:1

279:1 287:17

328:14,18 329:2

356:15,17 357:7

363:20 401:14,17

407:2 419:12

464:10 473:1

475:3 546:1

560:4,8,8 600:12

600:14 616:17

629:15 644:1

669:1 717:20

741:4

five-minute

356:14 357:6

361:7

five-year 555:5

fixed 494:3

Flasco 333:10

flavor 735:12

flawed 491:8

526:16 597:10

flaws 491:9 498:5

500:19

flexibility 33:5

flexible 692:2

697:16

flip 59:17

floaters 745:18

floating 419:20

floor 777:21

flow 60:16 459:9

627:18 628:19

711:18,18

flowcharts 639:22

fluid 627:9 628:13

634:17 711:11

712:13 714:18

715:8

fluxes 61:9

focus 30:1,6 42:16

43:14 47:1 69:22

75:16 92:4 93:18

247:19 302:16

342:18 351:5

358:4 361:22

371:10 521:10,19

536:18 597:6

618:22 638:9

658:11,12 659:6

729:10 753:5,9

771:3 775:22

focused 28:11

70:12,20 94:2

141:19 180:20

181:17 203:22

282:17 302:5

304:3 363:19

365:18 432:18

496:7 543:20

574:13 602:6

638:2 747:17

777:18

focuses 32:14

172:10 229:7

661:14

focusing 8:19

117:2 175:22

208:13 624:2

703:21 707:15

fold 64:10 65:2,2

66:7,9 418:10

706:3

foliage 668:17,18

folks 133:18

599:21 624:20

642:7 668:12

675:8 758:9

follow 107:8

148:22 154:3

160:3 172:20

195:9,19 270:18

276:21 339:1

422:22 425:16

451:13 486:16
Page 43

552:8 664:19

711:13 714:6

735:17 747:5

773:14

followed 41:1

117:5 157:15

207:12 244:8

254:19 422:11

468:9 484:6

542:9 623:1

following 15:14

42:2 48:18 85:14

87:13 210:5

230:8 323:1

325:7 326:12

438:6 440:13,18

462:10,14 472:3

514:17 610:6

616:9 627:8

638:18 744:15

follow-through

443:18

follow-up 341:14

494:16,16 522:22

621:1 652:19

744:20

font 131:1,11

food 76:5,7 96:8

174:1 192:4

194:16,17 258:3

265:8 378:1

389:2 640:13

646:6,13,20

647:18 652:21

661:20 662:13

694:10 701:11

708:19,20 735:1

735:1,2,3 751:17

751:19

foods 421:16

623:16,22 646:10

foot 736:19

forage 111:13

foraging 72:17

73:15 93:21

forced 77:20

forces 46:9

forcing 46:13

forefront 163:10

Forest 745:10

forestry 430:22

forever 496:1

forget 310:14

429:15 719:14

forgot 300:13

forgotten 479:5

672:17

form 24:11 95:3

98:1 119:6

140:15 155:4

184:1 223:18

339:13 454:14

480:19 508:16

603:5

formal 128:6

223:13 257:14

formaldehyde

228:6

format 563:7

formation 56:19

338:7 339:7

480:2 520:9

551:14

formatting 149:13

151:11

formed 139:9

183:21 321:18

322:13 338:10,13

343:5 532:10

640:15 650:12

former 390:22

forming 122:13

forms 339:3

447:19

formula 398:4

404:5

formulate 12:2

23:12 245:15

formulation 27:12

27:14 28:13

203:12

forth 10:8 11:12

225:2 240:18

243:21 248:8

257:17 267:18

307:16 428:5

713:16 769:2

fortification

308:20

fortifications

279:6

fortified 278:17

643:22 644:6

fortify 278:13

fortunate 250:2

forum 620:16

forward 7:14 8:16

9:4,5 14:1,11

15:11 17:20 18:6

19:7 22:9 128:7

185:13 194:3

203:20 224:7

436:13 443:8

458:7 540:6

592:22 609:8

619:6 717:8,13

740:17 766:22

775:12 777:6

778:12

forwarded 622:16

foster 480:10

fostered 480:9

fostering 480:11

480:11

found 94:8 128:3

128:10 129:16

131:18 135:6

141:7,10 142:6,9

142:12 143:11

144:9 158:20

199:6 214:19

259:22 260:3

261:19,21 263:3

265:14 274:13

291:19 296:5

313:8 332:15

348:4,9,18

349:20 350:1

354:15 362:14

372:11 402:2

427:9 440:19

443:1 451:1

454:4 482:7

484:8 491:6,8

504:3 509:15

515:21 522:20

526:6 531:8

537:9,11 554:21

555:4 560:2

561:3 576:12,17

577:11 581:10

582:13 583:10

623:19 644:5

669:12 673:8

698:21

foundation 5:6

8:11 13:9 16:11

19:18 26:3 33:4

202:14

founded 206:20

four 57:16 60:7

75:4 78:15 80:12

93:7 138:5

245:12 250:1

252:22 260:6

261:17 262:21

268:4 319:14

345:8 361:21

375:3 381:2

416:7 425:16

464:2 469:22

600:12,14 663:3

669:13 732:9

739:2 741:4,21

767:18 776:11

Fourteen 81:9

fourth 162:12

362:5 379:8

479:9

four-dimensional

60:21

four-fold 402:7

FQPA 408:17

615:13

fraction 566:10

661:7 697:11

frame 176:4

304:16

framed 699:18

773:15

framework 9:15

10:7,8 18:2

20:18,19 21:1,2

24:4 29:7 32:10

32:14,16,17 33:2

33:11 34:13

39:19 44:16 54:2

59:21 60:9,12

64:22 97:14

114:10 132:12

139:10 203:16,18

206:19,21 225:4

226:14 229:4,21

234:13 237:7

257:6 283:5

284:16 290:20

304:12 444:8

448:16 505:14

609:3 750:13

770:11

frameworks 286:3

291:4

framing 692:1

frank 385:7

Frankly 64:17

Fred 2:1,4 318:1,2

387:10 395:21

420:18 776:18

777:1,20 778:5

780:9,11

Fred's 780:7

free 226:2 247:10

255:8 338:10

frequency 11:22

380:5 447:4

451:18 460:4

472:19 594:9

731:5,8,12 732:3

733:11,13

frequently 171:22

495:20 659:20

731:4

fresh 470:20

660:16 668:8

763:17

Friday 220:14

Fridays 240:6

friends 540:5

front 23:10 234:2

290:11 387:10

668:18 679:7

762:14 763:10

frustrating 513:13

full 7:12 69:21

107:8 114:7

138:5 171:3

201:19 204:5

337:20 390:20

395:4 405:14,20

483:13 491:20

524:11 532:10

576:5 578:10,14

625:8 679:9

746:3 757:3

fully 186:12,15

193:5 194:15

308:10 372:15

438:12 488:10

532:1,10 534:3,7

594:1

full-blown 704:19

full-scale 409:7

550:4

fun 92:1 537:3

540:20 709:22

function 35:2,3

45:22 46:3 47:14

67:16 69:13

72:19 85:13,20

210:5 285:8,8
Page 44

397:11 448:11

516:21 535:1,18

538:16 539:1

556:2 559:18

570:15 598:6

628:9

functional 72:10

105:9 350:10

447:21 448:3,10

452:4 466:13

533:9,16 552:16

functioning

134:12 403:17

532:6

functions 407:8

409:20 570:14

fund 621:6 772:15

fundamental

97:20 233:3

464:22 495:15

538:19 619:21

funded 117:11

396:13

funders 396:9

funding 92:6,7

282:16

Fungicide 8:5

further 21:1 93:4

93:10 112:22

114:2 115:14

116:1 123:14

172:17 225:5

227:6 228:6

237:10 246:17

248:12 253:5

254:20 297:9

307:19 336:22

381:20 413:20

434:3 449:8

452:12 472:6

494:17 517:6

530:4 549:20

569:8 594:8

598:7 618:21

630:22 657:2

664:3 675:20

677:7 680:4

693:16 700:7

701:21 705:17

724:21 725:19

752:15

Furthermore

544:6 698:16

future 16:5 452:16

521:9,18 587:16

587:19 617:19

625:19 636:12

657:11 659:21

661:12 701:8

772:21 780:20

G

gage 758:6

gaining 203:18

238:3

game 16:11 195:21

470:16 635:16

730:5

gap 396:15 630:14

660:15 701:3

gaps 14:22 15:2,2

15:3 91:21

Garabrant 180:19

181:20 182:17

197:11 266:4,17

271:2 667:3

671:5 674:4

676:10 682:4

687:19 763:14,18

Garabrant's

182:13

garnered 252:5

gastrointestinal

347:20

gates 511:4

gavage 597:20

GAYLIA 6:9

166:3 274:4

304:6 305:5

GD 518:1

GD20 83:7,10

GD9 74:19

geared 571:11

gears 262:1

gender 74:1 86:3

109:14 413:22

536:5

genders 497:22

gene 45:21 61:12

440:21 492:18,20

552:16 597:22

genealogy 302:18

general 3:4 61:19

117:14 127:4

128:13 130:18

131:15 142:1

143:6 154:10,12

166:12 175:9

176:1,17 177:22

178:16 181:10

184:7,15 185:4

185:11 188:10,18

224:14 261:2

262:13,20 313:6

346:20 349:17

367:20 372:22

414:14 419:9

428:20 461:16

462:15 484:13

493:17 494:14

519:6 545:7,21

548:5 596:10

602:10 610:6

614:17,18 615:16

622:1 632:22

647:10 656:2,9

658:17 660:4

689:11 692:19

700:9 727:20

generalizability

17:6,14 141:18

143:2

generalizable

17:17 610:5

682:8

generalization

235:19 236:1,17

240:5

generalizations

245:9

generalized 81:10

105:14 167:22

412:7 466:15

547:21 580:19

682:9

generally 31:14

125:7 126:6,22

127:19 128:3,8

133:14 142:8

146:8 173:17

192:15 210:6

321:6,17,18

322:8 327:12

328:1,4,6 333:6

336:2 389:2

421:13 467:18

519:4 553:21

579:6 730:2

772:13

generate 10:13

generated 54:3

197:13 261:15

459:20 640:12

689:22

generates 308:6

generating 724:20

generation 610:20

generic 45:5

generous 524:2

genes 53:19 56:22

58:5 526:22

597:18 598:2

genesis 468:19

genetic 101:8

444:1 449:9

476:13,20 528:7

573:18 580:3

759:9

genetically 414:15

genetics 6:8

754:15

genomic 323:4

genotype 133:18

546:15

genotypes 611:2

genuinely 25:12

geometric 400:4,4

400:5 404:19

406:18

George 4:21 316:1

Georgia 5:7,12

316:7,10

gestation 69:3

74:19 79:2 86:8

472:11 590:1

600:19 601:4

634:18,19

gestational 71:4

73:1 77:12 80:5

81:13,19,22

84:18 98:14

125:5 133:14,16

133:19 150:9

345:18 356:3

547:7 704:9,18

gestationally 83:4

98:18 517:22

getting 9:6 18:2

26:16 64:21

97:21 170:14

188:16 191:22

200:22 215:14,19

227:17 260:8

298:5 307:9

308:2 345:14

363:22 372:4

411:10 419:20

447:14 449:19

454:9 457:16

465:2 489:14

498:6 505:1

512:17 513:8

516:11,12 532:12

563:15 567:6

576:11 588:9

591:13 620:14

636:22 646:15

661:21 668:4,6

686:3 687:11

705:14 745:17

766:14

GI 347:22

giant 477:5 711:12

gigantic 713:2

Ginger 20:7,8 31:9

40:19,21 68:17

68:18 87:7,15,19

87:22 89:6 90:14

92:1 93:12 98:15

101:18 102:3

103:4 104:2

105:11 108:20

109:6 110:8,12

209:8 343:20

472:1 517:12

521:11 538:4

Ginger's 42:6

girl 553:4

gist 164:9

give 8:7 16:10,16

16:20 18:3 19:11

27:18 34:14

40:17 43:19,22

44:4,7 64:6,8

72:10,12 83:15

90:17 94:16 95:2

103:5 105:7,10

107:10 114:21

148:4 207:10

212:3 251:1,2

257:11 278:6

279:1 301:13,17

311:8 318:5

321:11 329:9

349:8 356:11

361:10 368:20

372:14 397:7

398:4 402:18

418:19 425:15

431:15 447:16
Page 45

449:2 450:16

492:7 503:12

505:19 506:2

512:11 522:15

606:12 633:7,19

634:8,9 642:11

643:12 661:6

663:19 689:3

696:17 698:9

704:13 707:1

711:1 717:20

722:13 732:21

734:16 761:8

777:20 781:5

given 57:16 82:22

83:4,14,16 84:8

90:15,22 91:5

108:6,14 109:19

118:14 128:11

140:18 144:14

147:9 155:10

175:16 208:20

217:12 219:14

226:21 242:16

243:22 244:1

249:1 252:15

274:22 282:1

312:6 319:19

331:2 336:21

340:4,5 363:6

371:20 435:14

437:4 446:16

459:20 482:18

512:21 517:20,21

520:17 552:20

564:18 580:20

594:8 604:16

611:15 612:2

615:6 684:9

723:15 748:7

757:9 770:14

gives 55:21 234:6

350:12 361:12

399:8,13 419:11

502:7 577:3

591:5 640:4

701:18

giving 17:16 40:22

43:18 64:12

101:7,8,8 224:16

248:13 282:3,4

303:1 320:4

354:6 388:13

400:14 449:15

489:10 503:11

662:8 776:5

glad 228:19

471:18 506:22

768:5

glance 488:17

glancing 635:20

glasses 43:3

glee 743:11,16

744:2

glossed 761:3

gloves 265:14

glucanotransfer...

457:4

glucuronidase

334:20

glucuronidation

334:13 338:2

457:11,17 625:12

glucuronide

334:16 626:13

glucuronides

457:7 625:22

626:11

glucuronyl 698:19

glutamate 48:21

go 2:7 13:5 14:11

15:10 17:20 19:7

22:11 24:13

27:13 29:8 31:3

33:9 34:20 35:8

35:17 44:4 52:22

59:20 60:4 61:13

68:7,20 71:17

72:6,8 76:9

92:22 93:3,4,4,13

96:6,14 97:13

105:5,5 108:9

109:4 110:15

112:1 114:1

118:17 128:22

132:6 139:8

161:3,15 165:14

166:22 167:17

173:8 177:10

187:5 189:4

190:12,22 192:9

192:22 193:14

195:13 196:2,12

201:13 204:18

206:18 208:11

217:20 219:10

220:16 223:6

230:5,12,17

232:15 234:14

247:10 248:12

253:22 255:18

279:12,14 281:22

282:22 283:1

286:16 288:4,10

293:15 295:11

311:6 315:9

321:8 328:15

331:4 338:5

345:1 360:19

367:8 369:8,12

374:11 380:18,19

388:22 406:1

409:9,11 411:9

415:7,21 416:17

417:11,13 424:6

424:7 428:11

431:1 436:11

440:7 445:20

451:14,21 452:2

452:8 454:17

469:14 470:3,8

470:13 471:2

473:10 476:16

477:13 479:3,4

479:16,18 482:11

483:3 484:22

485:14,17 486:10

497:5 504:2

509:3 510:14,20

511:13,14 512:6

522:5,6 523:2

525:13 527:9

534:12 536:13

540:3,19 555:3

556:7,19 567:1,3

571:16,18 575:13

582:9 586:17

590:19 593:12,16

595:2 604:8

632:5 633:3

638:14 640:2

653:18 655:17

674:22 675:1

677:4 678:18

690:9 693:5,16

701:7 706:5

710:3 716:14

721:20 723:3,16

736:13 741:14

744:1 745:4

746:10 747:15

762:4 764:7

767:10 768:7

777:11

goal 93:15 134:2

175:21 176:14

184:10,15 185:8

321:4 476:12,19

477:21 481:6

511:15

goals 704:21

goal's 174:20

goes 51:14 65:13

76:6 105:14

162:2 202:9

226:13,16 267:9

358:10 405:4

457:10 475:18

493:6 518:22

535:2 579:7

588:16 640:1

778:11

going 7:14 14:3

15:12 16:18

17:21 22:21

25:16,17,20

26:13 29:16

30:13,14 31:3

34:19 37:7 40:17

41:1,13 43:9,14

43:17 44:4 59:16

60:13 61:13

62:20 63:10,11

65:6,12,22 67:15

68:7,8,15,19

69:21 70:10

71:19 72:6 87:8

87:9 88:22 89:5

91:14 96:6

100:13 102:9

103:2 105:5,5

106:2 107:22

109:20 111:12

145:6 159:2,4

161:1,6 167:14

168:4 172:19

175:19 195:22

196:2,14 198:9

200:2 201:14,17

204:13 209:20

210:2,2 212:21

213:14 216:8

219:8 220:16,21

223:9 224:12

225:3,21 226:18

229:5 231:3

235:16 236:21

239:21 242:15

247:13,14,22

249:17 250:4

251:18 254:18

257:3,11 258:1

271:18 282:13,17

282:20 286:13

287:14 292:22

294:21 295:6,13

296:7,22 299:8

302:21 304:10,21

306:15 315:6,7

320:15 327:5,20

329:20 339:3,6

342:18 344:19

347:16 348:16

354:14 358:4

367:4 369:12

370:13 371:7,21

372:1 374:20,21

375:14 376:13

380:6,14 381:2

381:19 382:4,22

383:4 385:10

387:4,7,14,16

393:11 398:21

407:3 417:13

418:19,20 419:10

422:4 429:3

430:17 433:1,2,5

436:11,12,16

438:21 441:10

443:22 445:18

448:9,15 449:7

449:16,17,18

450:2,5,11,14

451:1,6,6,13,14

451:15,18,20,21

453:1,15 455:10

456:14,18 461:10

462:7 465:18,22

466:3,4,5,6 469:8

469:18 470:8,8

470:17,21 473:10

475:20 476:22

480:22 481:9

482:3 483:3

485:3,4,6 487:3

489:18 490:18

496:21 500:14,17

501:12 503:14,15

503:17,18 506:15

510:16,22 511:3
Page 46

511:17,18 515:19

518:20 524:3

525:2 526:13

527:11 529:10,19

529:22 530:3

532:2,9,11

539:14 540:2,5

542:21 553:13,14

558:17 562:11,16

563:2 564:16

566:22 570:21

571:3 587:16

589:13 591:21

595:2 601:11

603:15 604:3

605:16 606:8

617:12,18 619:16

619:18 622:12,17

630:12 634:8,22

642:15 649:18

650:5,9 651:21

653:18 654:18

659:2,11 660:19

662:13 663:15,19

664:14 665:10,20

667:2 669:2

670:11 672:17

674:17 679:1

681:4 685:17

687:16 689:2,21

690:11 693:12,17

693:22 694:4,8

695:5 696:18

697:8 704:1

705:21 706:15,17

710:17 711:7,9

711:11,17 712:3

713:19 714:16

716:20 717:3

719:11,12 721:17

722:12,21,22

723:3,11 724:15

725:4 726:10

727:11 730:4

736:17 737:6,12

743:10 744:6

747:1 748:17

753:9 754:4,7

756:5,6,10,11

759:2 760:16,18

761:8,13,21

763:14 766:10

767:17 768:15

771:3 774:3

775:2,6 776:9,17

779:3,19 780:6

gold 6:12,12 317:3

317:3 327:4

541:18,19,19

552:11 553:8

556:6,7 558:2,16

559:1 563:21

564:1 565:15

567:5 568:15

581:18,20 588:19

590:17,18 638:13

638:14 664:11

705:22 770:7,8

gong 566:11

good 2:1 4:2 5:5

5:14 6:3 7:9

24:11 25:19,19

26:1 65:11 68:17

91:8 105:21

106:4 110:1

112:3 126:6,14

135:6,19 155:20

172:7 196:6,7,8

201:8 208:5

214:11 215:4

221:5 223:8

228:20,22 230:11

244:7,14 255:15

257:3 270:14

276:17 278:18,22

291:8 293:13

305:3 308:21

315:13,16 316:21

318:1 320:3

333:9 335:14

341:11 344:8

346:1,18 356:17

361:10 364:22

366:7 371:11

378:3 387:2,18

420:8 435:1

440:1 444:6

454:12 459:18

465:19,20 466:19

480:9 481:15,22

488:9,16 493:4

497:8 501:15

502:7 506:21

515:4 516:21

521:21 522:11

529:12 533:4

539:19 540:4

541:19 546:20

552:9,22 587:19

593:2 604:17

606:20 608:7

621:13 627:7

628:9 633:2

637:14 647:6

653:13 656:18

661:7 666:5

667:13 669:21

673:21 677:1,6

679:13 686:12

688:13,17,21,22

689:1 693:22

697:16 701:6,17

710:14 713:6

714:11 716:19

736:3,6 739:6

740:9 754:2

763:19 764:19

765:1 771:1

773:13 774:18

775:21 778:1

Goodman 226:18

254:19 282:1,2,5

282:7 302:8,11

303:5,7,17,21

305:3 307:1

310:15 313:4

385:13,15

gotten 252:7

274:11 285:21

305:2 399:17

419:22 512:14,15

633:3 716:19

724:10 744:8

Government 16:4

go-around-the-r...

761:18

grabbed 297:10

grabbing 339:2

Gradient 224:9

226:5 254:20

282:7

gradually 527:15

graduate 7:1,3

282:12 317:15

377:1 549:22

grain 430:16

Grains 155:6

gram 181:11

182:21 183:2,8

213:18 214:7

216:12,16,22

217:3 221:22

222:10 223:2

266:11 326:3,5,6

399:9 403:1,19

404:22 596:19,20

674:7 679:17

727:14

grand 295:16

Grant 396:13

graph 41:14 49:8

51:13 322:5

325:6 326:14,16

326:17 327:3

328:15 329:2

374:13,15,18

667:20 679:9

688:6 727:11

graphic 75:6

graphically 351:22

graphs 95:2

549:18 550:2,5

550:10 677:8

684:18

grapple 226:22

227:2

grasp 65:5 639:21

grateful 779:12

gratified 697:20

gratitude 20:7

Graub 683:19

gravity 695:15

great 38:4 85:7

90:22 193:9

206:5 274:10

355:6 485:22

500:17 542:15

590:5 612:21

621:5 630:13

633:1 644:18

648:6,8,20 651:4

652:10,22 665:15

705:18 708:8

766:7 777:16,19

778:16 780:13

greater 31:15

47:18 69:4

123:10 126:6

127:2 132:11

133:3 134:20,21

136:16 137:20,21

138:18 143:17

144:14 175:12

296:16 321:17

329:15 337:5

344:9 348:4

361:13,15 372:16

425:8,8 545:5

623:19 626:22

629:22 642:17

greatly 621:10

637:18 695:11

green 267:1

325:12,17 745:1

745:9

grew 265:20

Gross 407:10

grossly 708:21

ground 394:1

692:8 703:5

766:22

grounded 108:14

211:13 704:13

grounding 35:9

group 6:10 75:9,17

89:5 94:21 131:3

131:7,8 140:20

155:10 158:19

181:1 218:21

219:1 223:7

248:3 258:9

275:15 293:7

297:14 332:17

357:1 359:3

362:9 463:2,6

464:17 475:4,16

483:17 486:18,21

487:19 488:18

489:3,6 493:18

494:1 518:17

537:13 559:3

575:7 579:16,17

584:3 613:15

622:10 623:17

647:12 657:16

661:13 669:14

670:6 672:20,22

673:16 712:17

728:16 765:17

grouping 142:6,8

groups 75:17 93:7

127:13 140:2,19

141:12,16 186:19

189:9 218:20

220:18 221:11

292:20,21 325:9

412:7 423:21

475:4 486:20,20

495:2,2 508:16

575:6,12,22
Page 47

594:11 618:20

639:2 682:6

group's 169:21

group-housed

88:20

group-level 130:13

131:9

Grow 300:15

growing 66:22

154:10 209:16

446:2

growth 54:11,18

115:7 121:22

122:7 132:8,9,14

132:20 143:13

150:5,19 332:7

332:12 432:11,11

463:1,5,6 468:4

574:20 575:1

576:22 599:2

guards 495:12

guess 65:5 88:7

103:22 104:15

111:22 148:3

149:8 154:15

155:15,16,22

161:20,22 189:6

196:14 197:6

199:3,6,21

200:13 216:10

220:14 221:7

230:7 242:12

247:13 280:20

289:18 304:10

305:9 314:1

369:12 384:21

386:9 414:13

416:13 417:4

445:2 454:7

465:15,22 471:14

484:2 486:7,22

494:21 498:4

506:12 509:16

522:10 524:19

525:6,10 563:18

569:9 586:18

588:12 606:15

610:12 641:9

642:4 644:22

645:8 650:20

668:16 680:13

682:13,14 686:16

700:5,14 714:12

746:10 755:7

758:15 763:13

767:2,5

guessed 488:12

guessing 489:4

guidance 114:12

228:22 230:8

231:5 692:19

707:1 748:6

758:5 766:7

guideline 92:14

94:10,12 104:3

185:14 308:12

372:6 448:5

471:8,9,10 489:4

489:12

guidelines 15:14

71:20,22 185:10

234:13 241:14

343:6 374:2

691:5 773:1

guides 230:19

232:20

guiding 380:21

gut 411:5,17

guy 712:16

guys 190:8 306:21

367:2 632:10

635:22 642:21

736:10

H

H 685:11,12

habit 479:20,20

480:2 508:18,20

509:4 520:9

habituation 82:1,4

356:15

hair 634:17

half 71:15,17,18

74:11 170:1

184:14 224:3,3

247:18 268:11

273:16 345:8

379:14 393:19

399:8,19 587:15

658:21 663:2

668:12,14 714:14

721:22 722:15

half-an-error

369:16

half-life 174:11

175:16 456:11

632:22 660:16

722:6 723:6

half-lives 661:5

725:15

half-milligram

325:20 326:10

hampered 351:13

hand 193:2 225:8

225:13 233:21

234:4 239:5,20

240:11 283:11

528:19 566:16

643:7 668:1

677:8 686:1

handbook 12:17

13:4,12 17:19

20:13 21:21

22:15,19 23:2,8

23:18 24:6,17

202:10 220:2

handed 436:10

731:15

handful 513:6

handing 649:2

handle 22:16 93:7

231:17 563:1

607:7 618:12

706:2 710:20

711:1 715:19

729:13

handled 88:20

372:15 484:11

handling 668:5

handout 670:10

686:19

hands 351:15

591:13 669:18

694:17

hand-to-mouth

735:22 746:13

hang 286:22

Hanslick 440:12

happen 25:12 60:8

103:8 105:17

234:21 238:6

240:4,6 243:8,15

244:21 345:15

353:12 359:22

453:1,7,14 454:5

466:16 469:19

477:15 487:13

501:8 535:16

571:7 633:1

727:19 764:9

happened 63:4,8

94:9 216:3

274:20 365:19

487:11 524:14

582:2 589:18

678:6,10 708:3

718:8

happening 16:14

244:3 246:12

275:3 304:22

385:19 443:12

465:22 466:8

470:1 502:5

516:14 521:20

590:15 690:2

711:10 747:8

happens 34:1

107:1 216:1

240:21 295:19

372:12 412:9

465:11 469:17

496:3 556:13

563:2 568:6

585:3 690:3

happenstance

236:17

happy 148:14

230:11 301:22

336:21 395:16

409:20

hard 51:12 62:8

91:6 92:12

101:11 111:9

242:6 296:5

312:13 354:10

396:1 469:16

488:22 496:20

499:4 522:21

531:4 555:17

604:15 606:5

622:10 684:16

777:10 781:7

harder 95:7

105:20,22 110:17

479:12

hard-working

553:2

Harlan 485:13

520:3

harm 382:11

Harry 6:9,9 41:11

41:12 87:6,7,17

87:20 88:6 92:12

104:1,2 166:2,3

274:3,4 304:5,6

305:5 317:1,1

337:1,2,2 366:21

366:22,22 367:8

367:14,16,20

368:11 369:1,7

369:11,22 370:7

370:15 371:2,12

372:1,10,22

373:6,10,14,20

420:12,13,21,22

432:5,6,13,20

442:9,10 444:12

444:15 447:12,13

454:21 456:8

462:19 465:14,15

480:16 495:13

500:12,13,13

505:5,6,6 513:17

513:18,18 521:16

521:17 529:3,4

530:6,8 531:12

531:14 532:17

535:7,8 562:13

562:14,14 564:5

564:15,16 565:14

570:9,18 571:19

581:22 582:1

588:18 589:11

591:3 742:16,17

744:22 746:8,9

769:12

Harry's 444:22

Harvard 6:4

282:11 316:22

407:11 661:14

harvested 413:19

hashed 686:11

hat 241:8 286:22

hate 649:21

Hattis 218:22

396:5,7 410:3,11

411:4,15,18

412:4,14 413:1

413:12,18 414:12

414:21 415:6,10

415:17 416:14,19

417:6,20 418:14

418:22 420:2,7

458:5,20 506:17

599:22 611:21

616:3 697:21

698:7 710:14

712:3 781:4

Haviland 484:8

Hawaii 6:16 317:7
Page 48

635:1 714:13

Haynes 188:13

hazard 4:17 246:6

246:7 249:12

256:5 301:6

315:20 364:19

381:16 608:12

609:12 610:16,18

hazards 301:8

head 5:8 6:9

133:13,16,19

148:5 149:9,10

149:18 159:5

161:16 165:16,18

280:20 281:14

283:21 284:1

311:3 365:14

386:16 414:6

424:18 537:7

588:18 642:14

737:1 742:21

765:6

headed 41:7

193:13 204:6

heading 219:9

heads 354:13

591:18 642:20

711:4

heals 589:11

health 1:5 2:4 4:12

4:17,22,22 5:6,7

5:12,16,16 6:4,11

6:13 7:1,2 8:20

19:10,15 20:6

25:2,7 29:8

30:20 112:6,15

113:14,16 114:17

116:22 117:6,10

118:4,15 121:6

172:9 176:2,5

178:9,10,18

184:17 205:15

248:2,4 254:6,14

256:7 260:2

263:8,15,18,21

282:11 315:3,20

316:2,2,8,10,13

316:13,22 317:4

317:14,16 323:12

342:22 385:4

388:15 389:6,8

390:17 391:4,9

391:12 392:12

394:10 422:15

426:6 447:7

453:7 467:5

472:22 539:21

541:3 542:12

543:4 567:22

602:6 624:11

655:11,21 656:12

658:10 659:8

661:14 671:9

672:7 673:12

675:9,13 692:19

692:21 726:20

748:2 754:15

755:18 758:17

759:3,15,17,22

769:4

healthy 681:11

health-related

128:14

hear 21:17 25:17

26:14 31:8,12

33:21 36:15 41:4

63:18 115:14

127:1 143:5

156:10 192:12

307:13 374:16

379:13 388:3,4

415:9 522:3

560:4 654:5

683:5 687:1

710:10 711:8

765:8,12,13

heard 25:14 27:7

27:10 29:6 47:6

88:16 97:5

120:15 164:1

193:19,21 201:19

207:15 209:3,8,9

209:22 210:11,19

212:11 224:19

243:17 271:17

312:3 381:4

383:18 430:7

434:19 497:19

498:11 507:14

515:8 524:7

538:7 564:9

591:7 596:2

599:22 606:16,17

606:18 607:5,10

607:11 611:12

612:12 618:1

637:8,10 704:8

710:10 715:16

736:15,18 737:13

737:15,17 738:4

739:14 746:5

774:1 775:11

hearing 272:13

313:6 503:19

530:14 606:13

758:21

hearings 400:12

Heather 4:20,20

165:4 170:19

171:1,4,7 315:22

315:22 422:22

554:3 561:14

572:13 615:20

766:10

heavily 32:15

193:5 404:17

435:17 491:14

496:7

heavy 233:19

383:1

HED 248:17 252:8

heighten 774:13

held 395:14

Hello 158:14 388:2

help 12:4 25:7

38:4,19 42:19,20

62:5,10 96:2

97:22 109:4

113:9 166:17

202:2 214:13

223:4 225:5

230:12 242:3

246:2 248:22

251:12,12,13

255:10 294:3

307:22 320:17

344:2 379:12

453:17,18,19

512:18 554:1

607:8,12 618:13

619:20 620:6

662:1 705:19

719:16 739:12

759:2 763:2

766:21 775:19

helped 737:3

775:21

helpful 13:5 15:4

15:15 18:7,13,19

21:17 63:14

200:21 202:2

293:22 296:3

553:13 615:5

619:1 653:16

690:2 692:8

705:3 706:4,6

738:20 739:6

749:10,12,22

751:2 762:11

766:4,5 776:2

777:9

helping 15:16

744:10 781:9

helps 24:3 47:1

294:10 497:19

738:9

herbicide 170:10

430:10,20

hesitant 770:2

heterogeneity

142:16 144:2

275:9

hets 482:14

he'll 670:12

hi 5:1 6:5 59:18

278:3 334:8

410:2,3 552:10

hidden 74:21

hierarchical

113:22 140:17

257:21

hierarchy 200:22

high 19:22 35:18

42:3 79:6,18,20

123:13 125:16

126:19,22 129:9

135:17 141:20

144:7 163:20

168:13 174:8,14

177:2 178:15

198:16 217:18

219:16 242:16

265:18 279:5

287:9 297:13,16

321:22 358:7,14

362:15,17 388:17

393:14 394:21

403:5,6 426:14

456:11 458:11,12

458:13,13 463:21

478:8 488:2,19

492:4,8 499:12

519:1 537:13

542:21 575:5,12

575:21 596:11

643:6 660:6

668:6,11,17,21

669:1,19 670:14

671:11,12 672:12

677:16 679:16

680:14 687:8

696:6 770:21

779:14

higher 63:2 75:6,9

77:3 141:22

161:7 168:15

178:2,16,22

181:14 182:13

212:12 213:19

216:19 243:20

257:6 263:5,13

264:9 265:10

268:17 272:15

273:16,21 274:2

275:12 289:4

292:1 322:8

325:20 327:5

332:10,16,21

333:1,19 341:1,2

351:3 364:7

394:18 409:19

447:5 461:17

462:5 472:20

477:22 489:20

500:5,11 517:17

645:2 656:2

657:17 675:5,6

732:12 743:17

745:4 756:3

higher-level 210:3

highest 291:22

292:2,5 362:16

578:11 658:15

732:18,19,19,20

733:1

highlight 42:7

205:20 630:15

677:11

highlighted 178:8

252:5,6 394:14

463:18 473:15

highlighting

250:12

highlights 32:13

346:5 768:13

highly 302:17

321:10 329:15

339:21 587:7

603:20 629:14

697:5,5 760:7
Page 49

high-risk 639:2

Hill 145:6 206:20

220:10 229:20

230:14,15 231:5

234:11,12 542:4

550:20 551:3

Hills 238:17

Hill's 231:11

232:20

hinder 142:15

Hines 604:19

hinges 544:21

hints 349:1 468:7

516:18

hippocampal

49:12,18,19

460:18 481:10

hippocampally-...

480:19

hippocampus

49:11,13 50:18

52:1 352:5

Hispanic 115:10

Hispanics 577:13

histopatholgical

448:4

historic 155:6

529:14

historical 353:3

357:17 359:8

367:17,17 368:1

368:3,9,16

372:13 631:13

historically 437:4

history 27:18,21

118:6

hit 32:13 42:4

191:19 380:6

542:21 633:1,11

633:17

HIV 118:7

hoc 245:2 290:5,7

hold 195:17

452:14 713:6

779:3

hole 511:12 767:19

holes 108:7 511:11

767:14,18

holistically 287:19

Holsen 475:10,18

home 119:3

123:10,21,22

136:5,5 139:15

146:15 170:12

272:4 289:19

361:4 545:8

650:15 658:1

667:16

homeostasis 26:14

26:20 33:16 35:5

41:16,22 67:10

203:5 285:15

746:21

homeostatic

284:18 747:12

homeowners

214:2

homes 215:15

home-use 430:21

homogenates

323:2

homogeneous

138:12

homologous

480:19

homologs 603:22

hone 248:9

honest 377:1

636:11

honor 780:18

hope 11:1 19:5,6

89:5,18 91:15

105:8 224:17

225:4 246:16

283:2 287:20

343:17 348:20

363:12 420:7

452:6 456:2

486:15 590:20

649:6 761:3

764:7 768:17,21

780:21

hopeful 749:19

hopefully 3:20

74:22 91:10

223:21 255:18

380:16 448:19

453:8 454:18

456:1 529:1

592:22 631:22

636:15 724:11

756:12 766:21

772:9

hopes 29:19

hoping 188:16

220:6 224:4

354:17 379:17

388:7 470:11,13

521:21 619:18

751:12

hormone 228:4

hospital 5:4

134:14 316:5

hospitals 117:21

hot 636:13

hour 83:18 107:1

147:16 223:10

282:1 361:5

514:22,22 621:17

653:19

hours 83:6,7,14,16

83:18 204:19

215:18 345:6,8

345:11,20 598:13

600:12,14,17

687:10

house 89:5,19

382:13,15 661:20

736:2

housed 88:21

household 388:18

houses 397:14

housing 88:18

89:16 94:18

123:2,5,6,6,7

127:17 276:14

hovering 358:11

ho-hum 477:6

HPA 474:5

HR 729:4

http 1:8

hub 508:1,4,4

huddled 736:22

huge 50:10 61:5

284:8 297:5

352:6 368:9

400:19 401:5

418:2 439:4

585:4 635:18,18

675:8 701:3

713:7,12 747:9

749:8 771:6

hugely 749:3

huh 188:21

Hull 705:12

human 8:20 29:7,8

30:20 31:6 35:21

36:21 39:10 72:7

101:6 154:4

172:14 179:12

180:5 184:21

185:2 187:6

194:8 195:1

206:12 214:18

218:14 224:15

226:10,14 227:5

229:7,9,16,20

231:7,16 233:11

234:13 235:8,18

238:22 241:5,9

241:20 242:5,13

242:18 246:14

248:2 254:6

271:19 287:21

301:14,15 302:5

323:12 326:10,13

329:5 333:22

338:20,21 340:3

364:8 380:22

381:13 388:15

389:8 390:21

392:12 403:17

413:16 414:11

438:19 447:7

453:7 455:15,17

457:9 458:22

459:3,14,20

463:16 467:5

472:22 492:4

563:2 601:10,11

602:11 603:2,15

604:3,4,13,16

612:7 616:4

659:7 674:14,20

691:3 700:22

701:4 738:11

739:8 742:5,12

742:20 743:2,17

743:22 744:4,13

748:21 752:4,6

757:6 766:5

771:19 773:5

774:21

humans 48:15

72:3,5 211:19,20

225:2,17 228:14

232:9,10 235:18

235:21 236:19

239:7 242:3,15

244:2 245:10

285:20 290:14

301:5 338:4

348:1 381:10,18

413:11 414:14

458:10,19 459:21

480:20 552:2

603:20 604:1

610:18 613:19

686:9 739:3

750:20 751:5

754:11 757:4

human-related

603:6

hundred 546:1,1

644:8

Hurl 92:7

Hurl's 92:4

hurt 281:14

hydrocarbons

158:18 555:6

hydrolase 56:16

hydrolysis 324:21

334:21 338:9,15

338:15,16 339:9

340:7,10 341:3

652:5

hydrolyzed 321:19

hydrophilic

628:14

Hygiene 5:19

316:16

hygienist 172:8

hyperactivity-re...

137:17

hypertension

118:7

hypotheses 10:4

10:13 12:9 18:4

18:5 21:18 22:1

23:13 38:6 40:6

42:10 93:16

97:10 99:8 141:3

141:6 205:18

235:1,5 245:12

261:15 271:18

286:1,15 287:1

350:5 390:7

441:15,21 442:5

hypothesis 10:3

12:2 24:12,13,15

46:14 99:13,15

99:16 142:8

207:4 238:11

271:19 272:21

276:15 286:4,9

286:12,17,18

298:21 300:21

302:14 307:2,4

350:9 359:1

452:1 454:4
Page 50

610:20 614:11

678:11

hypothesis-based

17:2 226:3,9,20

227:15 234:10

244:6 254:21

282:14 286:2

299:7 301:2

hypothesis-driven

549:8

hypothesize

241:19 344:22

hypothesized 64:3

132:15 140:11

237:20

H&E 448:5

I

Icengole 476:6

idea 27:10,12

31:19 32:5,20,21

33:3,22 34:1

35:1 39:5,7,10

62:17 67:12,18

96:20 131:4

147:22 185:8

188:9 192:12

205:10 217:16

231:1 236:14

237:2 238:1

242:18 282:22

286:14 290:18

294:10 295:2,18

296:20 298:4

301:17 313:5

377:11 415:1

423:7 431:15

463:20 477:19

479:3 480:18

482:18 538:11

627:7 633:22

635:9 688:13

704:22 717:21

722:1,5,17

734:17 738:1

740:2,9 767:22

768:1

ideal 120:10,14,18

121:6 189:14

194:13 200:13

517:2 614:11

707:16

idealized 445:12

ideally 121:2

189:6 191:1

630:8

ideas 9:8,17 16:9

18:9 33:18 34:7

37:8 202:9,14

467:19 524:14,15

533:5,5 653:21

identical 58:15

368:21

identification

144:16 246:6

594:6

identified 61:18

136:15 137:12

138:9,20 141:21

145:8 146:19

266:10 324:20

392:16 443:5

452:12 525:19

622:5 623:18

678:8 690:18

identifies 685:9

746:16

identify 128:2

132:16 142:3

167:15 224:7

241:22 246:2

286:1 318:11

354:14 443:14

491:3 558:10

643:17

identifying 137:16

383:10 541:15

idle 461:10

ignorant 307:22

ignore 355:15

506:13

ignoring 75:16

391:21

II 458:11

illegal 118:9

illicit 595:22

596:21 668:11

illustrate 250:5

735:14

illustrative 130:20

illustrious 779:17

ill-defined 459:15

imagine 276:18

345:15 668:19

744:17

imaging 468:17

immature 37:3

251:6

immeasurable

131:6

immediate 25:2

219:7 328:16

718:18

immediately

721:18

impact 18:21,22

86:4 88:22 91:11

103:18 144:4

299:1 348:3

610:6 611:3,4

612:5,9,10 740:2

746:7 748:21

771:18

impacting 762:15

impacts 13:15

70:16 352:22

impair 383:4

409:19

impaired 49:21

54:21 72:20

493:3,4

impairing 57:12

impairment 54:22

impairments

392:17

impetus 633:21

implantation

489:9

implement 542:15

implementation

2:18

implemented

125:11 144:19

implementing

774:22

implication 299:20

688:9 689:12

759:22

implications 15:1

224:20 227:6

398:12,14 412:16

520:1 536:5

759:16,18

implicit 13:14

391:17

implicitly 410:16

implied 155:19

286:12 306:3

400:12

implying 153:7

453:11

importance 12:17

340:18 361:2

423:9 566:9

741:6

important 2:16

8:13,15 13:22

15:6 17:18,18

19:18 21:9 27:1

31:1 34:4 35:12

35:13,13,14

36:13 39:22 40:7

49:18 50:21 51:7

74:4 75:11 82:18

98:13 108:12

127:8 139:18

144:10 152:2

173:13 189:2

192:21 203:7

207:16,19 211:8

213:12 216:2,11

219:5 231:1

233:2 237:7

243:3 244:12,17

246:4,10 250:11

254:4 257:5,18

258:5,8 259:4

260:19 262:7

286:11 313:3

323:12 334:12

335:9 340:14

341:7 343:13

347:7,17 348:16

352:21 354:5

356:12 357:14,16

368:22 388:13,14

393:12 395:6

402:11 407:8

435:10,14 455:12

456:10 457:5

458:14 461:20

463:18 475:1

485:4 487:21

492:20 503:9,15

505:8 506:18

529:14 537:20

538:12 544:10

546:9 547:13

549:6,12 561:15

584:17 602:13,14

610:8 611:16

613:13 615:22

628:22 639:20

657:11,21 659:13

659:19,22 660:8

661:8,9 693:21

697:8,9 698:10

698:13,14 699:5

700:19 707:1

708:17 710:9

720:8 723:6

737:7,22 738:15

748:13 749:7

754:17 773:1

774:13

importantly 22:4

125:3,13,15

137:3 254:4

395:5 440:18

649:12 695:14

impossible 71:8

652:6

impressed 380:9

455:21,22 765:21

766:2

impression 148:11

impressions

658:17

impressive 776:21

improperly 650:8

improve 276:2

619:16 621:10

725:19

improved 27:1

77:2 99:10

252:22 349:4

378:20

improvement

72:21 76:13

363:1 378:19

379:8 579:14

improvements

258:21

improves 586:8

improving 19:18

697:1 726:13

747:17,20

imputation 141:9

141:11 148:11,18

148:19 149:5

impute 122:4

imputed 148:1

280:13,17 576:11

inability 623:8

inadequacy

625:15

inadequate 390:8

390:14 392:1

531:3 547:10

548:5 550:16
Page 51

inborn 388:15

incidence 406:14

incident 29:8

incidents 440:15

include 60:21 67:7

67:17 70:10 71:3

73:1 84:7 121:2

121:7 123:12

139:16 194:10,16

196:9 229:19

311:1 410:6,16

437:17 446:21

447:1 469:2

472:14,16 489:5

494:13 514:5

518:14 558:11,12

572:9 592:20

600:7 605:7

625:21 658:9

666:14 681:4

721:8 771:4

included 21:19

113:15 118:6

134:7,8 139:5

144:8 157:9,13

176:18,20 177:5

177:13 178:4

181:18 423:2

461:3 508:11

543:19 625:4

630:9 656:13

659:14 726:17

769:8

includes 38:12

81:10 176:2

178:6 266:1

324:7 603:17

618:19 625:15

649:4 658:6

including 27:2

53:3 112:21

127:12,16 143:21

173:6 193:3

249:11 291:10

356:4 388:21

391:17 392:18

551:17 572:3

608:12,17 609:21

622:2 625:9,20

627:17 662:13

692:2 698:4

712:2 735:21

772:8 781:3

inclusion 127:18

128:10 312:9

495:9 541:22

Inclusions 142:11

inclusive 62:20

68:5 222:16

income 127:17

141:17 407:9,14

407:18 408:3

incomplete 710:15

inconsistencies

227:6 236:8,9,12

290:1 491:17

517:5 527:6

657:5,5 757:14

inconsistency

136:22

inconsistent 69:6

84:6 103:21

132:19 265:19

289:3 498:6

563:7,19 589:20

590:9 657:14

incorporate

172:17 185:12

187:7 190:13

300:15 323:21

379:12 469:15

609:8

incorporated

183:13 300:16

302:18 340:11

402:8 422:17

468:15 564:11

581:17,21 745:11

incorporates

44:21 186:5

273:14

incorporating

29:7 32:10

132:12 203:17

206:19 224:15,22

229:5 379:6

385:6 630:21

693:3 709:22

770:10

incorporation

114:10

increase 47:19,19

49:6,8 51:5,20

52:9 53:17 56:21

56:22 58:4

260:18 325:18

358:13 440:14

477:21 478:8

492:2 510:9

564:10 576:6

586:13,21 597:18

646:14 677:21

increased 45:21

56:19 76:12 78:9

79:3,8 80:8,8,17

81:16,20 99:17

134:21 261:1

349:18 381:6

434:2 575:10,16

577:8,17 578:2

578:17

increases 49:1

51:17 100:11,12

260:17 330:17

546:12 586:15

591:9 686:9,22

increasing 45:13

48:12 49:6,9

50:3 53:6 59:3,5

67:22 136:13

167:4 169:6

260:17,18 261:1

550:8,9 691:8

699:9

increasingly 11:9

609:8

incredibly 36:10

456:11

incrementally

97:21

incubations 323:2

328:5 330:3

incur 417:5

indentified 322:19

independent 2:15

8:6 30:4 69:2

158:1 159:12

160:4,10 249:9

250:10 252:12

342:20 344:22

381:12 473:7

556:12,17 557:1

557:2,4 561:1,2

562:4 583:13

718:12

independently

159:21 160:1,7

160:17 282:16

493:15 561:18

index 130:9 134:7

135:4,12,12

137:2,3 145:20

165:7 405:14,15

405:18 406:9,9

406:20 407:1,3

407:12 408:14

409:2,12 416:22

482:1 575:5

Indiana 254:16

Indianapolis

254:16

indicate 72:2

127:5 238:22

239:6 262:5

289:11 295:5

301:8 303:8

550:12 596:4

599:11 620:5

657:17

indicated 75:19

117:20 138:11

215:5 250:15

299:8 398:18

426:10 475:22

611:17 614:14

620:6 629:13

696:11 748:12

752:6

indicates 79:18

124:5 301:3

386:4 393:16

456:13 477:17

633:15

indicating 122:15

125:5 131:13

249:5 327:4

623:21

indication 72:12

154:18 161:2

409:16 573:6

614:18 661:7

672:8 689:3

696:17

indicative 79:8

136:17 240:1

385:21 386:6

498:1 500:4

624:11 657:6

672:13 673:19

indicator 431:5

438:12

indices 398:19

587:17

indirect 225:16

544:17 658:16

indirectly 188:8

324:14

individual 21:14

45:9 46:5 116:13

120:20 124:7

126:17,20 130:13

140:20 225:10

279:5 283:11

334:17 379:19

399:2 407:6

435:13 453:2

467:12 575:8

580:19 622:16

637:1 684:21

686:7 695:11,11

762:17 778:14

individually

497:18 507:22

561:8

individuals 36:2

170:10 176:22

178:12 180:9

250:10 270:7

325:10 524:13

533:1

individual's 661:7

individual-based

634:11

individual-level

127:17 131:10

indoor 657:3,7

701:12 707:9,21

708:15

induce 286:5

287:9 414:16

induced 58:17

induces 286:9

440:21

indulge 224:17

industrial 5:19

46:20 172:8

252:18 316:16

430:14

industry 169:22

inert 330:5

inevitably 487:4

infant 132:8 134:5

135:9,13,14,14

146:5 284:4

575:3 577:10,18

722:13

infants 28:14 40:1

134:9,10 135:20

389:9 542:9

612:22
Page 52

infer 225:16

inference 113:11

127:9 144:5

225:17 526:12

750:15 768:21

inferences 17:9

391:15 526:19

inferential 227:16

inferred 403:18

inferring 235:7

inflation 688:3

inflection 182:4

183:1,7 266:11

266:22 267:1

670:12 674:6

688:9,12,17

689:8,16,19,21

inflexion 674:3

influence 13:17

14:15 15:4 19:2

85:10 100:2

120:21 176:11

284:7

influences 356:5

404:17

influx 49:2

inform 142:21

265:12 289:21

293:14,17 301:6

307:22 456:22

599:15 605:13,14

609:17 620:20

721:2

informal 70:10

88:4 168:12

information 9:22

10:1,2,3,5,5,6,12

10:13,21,21 11:5

11:6 12:11,21,22

14:3,15 15:1,18

15:21,22 16:18

16:19,20 17:7,8,9

17:15 18:12 21:3

23:9,12 24:5,7,9

24:11,12,14,20

26:7,9 27:4 33:6

33:6 34:15,17

35:16,20 42:16

62:6 63:20 69:19

83:8 84:8 89:17

89:20 90:1,15,17

94:12,18 103:22

104:5,6 107:8,20

108:5 117:22

122:2 123:1,12

126:3 127:15,16

128:20 129:17

134:3 141:3

147:12,14,15

166:12 168:3,13

168:18,21 185:12

186:8 191:10

202:6,16,17

203:8 205:13,21

206:1,6 207:20

207:21 209:3

211:3,4 214:15

218:10 220:9

263:17 286:20

295:6 302:7

360:9 366:4

382:5 390:2

391:8 401:11,13

401:19 423:20

424:11 429:21

454:2 471:11

476:1 512:10,11

512:18 513:5

514:11,15,20

541:7 542:6

552:16 572:2

601:14,18 609:9

609:15 610:3

611:2 637:2

647:9 660:17

663:20 664:12

665:15,20 684:17

689:8 694:12

695:8 722:8

723:12,16,18

734:14 738:11

747:19 749:7

755:8 756:5,22

759:7 764:2

771:12 776:1

informative 470:1

547:4 647:8

682:13,14 688:21

689:15 749:15

informed 114:9

390:19 402:14

691:8

informing 541:10

608:11 609:11

informs 301:9

ingest 382:22

ingested 400:20

640:22

ingesting 674:21

ingestion 121:4

191:6 264:10

397:5 400:13

402:22 403:4

405:1 406:2

408:12 409:2,2,5

410:19,21 411:22

416:17 421:15

640:12,17 653:8

ingestion-absor...

411:8

inhalation 31:1

119:10 187:11

191:6 322:11

393:14 394:1

396:18 397:5

398:16 400:14,18

401:2,6 403:2

404:6 405:1

406:2,4 408:12

408:13,20 410:20

410:20 411:3,4

415:4 416:19,20

416:21 506:17

507:2,3,3,10

700:13 701:12

707:15 708:1,3

735:7,22

inhalational 698:8

inhalation-no

708:10

inhalation-yes

708:10

inhaled 404:21

640:22

inherent 539:4

654:21

inherently 62:8,11

inhibit 55:13,14

57:15 58:5 107:6

286:10 287:9

289:9 307:8

320:22 383:20

491:1 613:2,11

613:12 689:6

727:18 760:21

inhibited 179:18

678:7,17,20

685:11,20 686:3

689:4,13

inhibiting 30:2

32:7 36:11 56:15

64:10 204:1

554:16 572:20

623:5 721:11

inhibition 37:20

37:20 38:3 40:4

43:15 44:12

55:16 56:15

57:20 58:1,9,10

58:13 59:9 63:7

64:5,7,20 66:15

66:18 67:2 69:7

69:16 70:17,19

82:19,22 83:5,6,8

83:10,14,16,17

83:21,22 84:2,3

84:17,19 85:16

86:14,17,20 87:2

91:9,19 92:3

95:10,11,19

96:13,19,21 97:8

97:10,17 106:21

179:12 180:10,12

180:18 205:4,16

208:22 210:7

211:16 212:5

214:8,16 215:3

217:17 218:7

242:2 243:2,9,13

251:5,10 253:7

256:19 267:2

269:6 286:7

287:13,16 288:5

288:7,12 290:14

299:10,14 300:12

301:5,9,20

303:11,15,20

304:4 305:13

306:1,6 321:1,7

321:21 323:4

324:8 326:19,21

326:22 327:4,5,7

327:10 328:10,14

329:6 332:5,16

332:20 333:3,17

333:22 336:8

341:10 343:13

344:5,9,16,17

345:1,2,6,9,13,14

345:18,21 346:6

346:16 348:5,10

351:1 363:9

364:1,17 381:11

381:14 382:10

383:16,20 384:2

384:5 385:18

386:4,16 389:15

390:16 409:17

431:21 436:21,22

437:2,4 438:4

439:9 446:8,20

449:19 456:18

462:4,6,12,16

464:5 465:18

466:8 467:16

471:13 496:14,18

502:6 506:9

517:19 518:4,5,9

518:12 525:9,15

525:18,20 526:3

526:11 528:18

529:6 544:1,5

551:5,11 552:1,4

554:17,18 562:18

563:5 565:18,20

589:8 595:9,22

596:13,15,21

597:2,9,16,19

598:11,15 599:5

599:7,10,11

600:6,16,21

601:3,6,8,9

603:14 604:11

605:1,21,22

613:18 614:19

615:1,1,11 621:8

624:9 625:2

641:4,6,12

666:11,18 670:6

673:8,15 675:10

677:13,14,22

679:3,20 680:8

680:10,14 681:21

681:22 682:15,22

683:8,11,16,22

684:3,11 685:2,4

686:21,22 687:9

688:9 689:2

720:13,20 721:5

724:5 727:12

728:9 729:19

739:1 752:9,20

752:22 754:7,10

757:22 759:18,22

760:6,7,10

766:16

inhibitions 382:14

725:11

inhibitor 218:4

inhibitory 726:19
Page 53

initial 28:11 60:18

87:20 203:12

505:9 571:19

618:2 687:9

717:22

initially 274:7

327:20 434:6

740:7 770:6

initiated 57:15

748:11

initiating 23:14

33:15 44:17 45:7

45:16 46:10

47:11 48:2,12,20

53:15 54:7,13

56:2,10,14,18

57:2,10 58:19

60:17,19 61:16

61:18 62:13

96:16,18 97:18

433:20,22 437:1

initiative 251:17

injection 348:11

411:13,15,16

427:16 700:13

701:11 708:19,20

injects 663:2

injury 47:22 414:6

inline 618:5

innate 134:11

innumerable

61:16

input 319:19

342:15 697:17

700:12 707:22

762:15

insect 652:9

insecticide 8:5

430:9,12,16

657:4

insecticides 582:19

insects 657:1

Inside 205:10

insight 18:20

319:19 383:12,12

397:7 610:4,13

619:19,20 711:3

insights 8:17 16:20

719:8

inspired 397:17

instance 93:21

99:8 156:6

157:17 281:16

415:4 425:9

440:1

instances 128:6

132:2 139:3

145:10 148:5

424:14

instantaneously

383:2 685:3

Institute 4:14 6:8

6:10 7:1 315:18

317:14

instrument 308:6

308:22 576:14

instrumental 8:3

instruments

548:19

insufficient 624:4

insult 768:3

insuring 2:9

intake 174:18

185:22 398:16

399:1 408:12

416:2 623:15,20

632:8 653:11,11

661:15 662:11

intakes 692:11

integrate 9:22

10:3,4 24:4

29:11 147:19

282:18 283:8

381:13 500:22

integrated 21:7

25:16 69:20

123:21 125:10

147:13 164:6

256:4 390:1

448:20 458:1

518:10 532:1

601:19 718:10

integrates 163:22

integrating 20:18

21:11 26:12

193:19 252:19

553:8

integration 21:8

34:18 207:13

230:2,5 249:12

312:7 404:13

691:20 719:18

775:20

integrative 119:20

609:3 750:14

integrity 538:19

intelligence 104:7

113:18 137:22

138:3,4,19

160:21 425:1,8

425:12 534:18,21

545:8

intelligent 576:4

578:8

intended 100:21

176:3 177:2

234:8 373:5

691:15

intensive 717:11

intent 117:20

691:14

interact 61:2

62:14,21 441:2

474:11 678:15,20

interacting 53:4

63:11 167:8

461:2 767:15

interaction 45:14

45:16,17 62:19

76:20 101:12

116:20 132:1

144:2 356:22

461:4 487:5

546:14 556:16

559:22 561:9,11

interactions 61:12

80:3,4,8 142:5

485:6 493:6

503:21 546:8,16

547:6 548:6

552:14 555:8,20

interacts 62:18

135:14

intercepted 743:15

intercity 119:1

interest 3:1 120:12

143:3 146:8

167:7,7 349:18

410:5 552:17

566:8 571:11,12

583:7 650:12

661:12 691:19

699:9,11 718:9

718:13,14 764:11

765:1

interested 47:3

64:2 92:6 96:7

96:12 104:20

161:22 201:8

358:14 378:7

404:4 457:9

477:18 503:17

639:7,11

interesting 42:9

262:19 266:13

370:20 443:1

480:8 482:12,18

483:18 484:20

486:5 487:20,22

489:18,20 492:15

497:4 507:6

513:5 563:10

576:8 670:7

671:1,4 707:7

interestingly 182:8

265:17 669:22

interests 224:13

interfere 378:4

457:19 478:9

interferences

725:13

interfering 459:4

755:12

intermediate

321:11

intermingled

256:6

intermitted 683:13

intermittent

174:13

intermittently

683:2,9 684:8

intermixed 672:20

internal 121:18

141:16 150:21

175:8 190:22

397:19,20 577:3

758:18

internally 91:21

92:2

international

206:22 391:1

interpret 11:13

38:19 101:13

103:9 203:5

293:17 296:5

300:4 326:17

353:1 398:7

399:12 492:21

501:14 502:3

534:19 690:3

757:3

interpretation

21:14 72:1

113:10 172:16

175:15 183:9,12

183:15 184:6

187:14,18 210:1

229:12 274:18

281:8,9 285:19

293:14 299:5

313:2 401:7

495:6 506:2

509:4 515:16

523:18 594:5

612:6 622:8

625:9 682:19

692:20 775:19

interpretational

491:16 493:5

511:22

interpretations

27:6 84:20 85:10

242:10 312:17

744:9

interpreted 14:11

217:4 275:16

700:5 703:11

interpreting 14:21

173:11 205:9

218:11,11,12,13

433:9,12 691:1

interpretive 41:4

interprets 284:7

interrater 135:18

interspecies 338:2

338:6 340:10

742:6

interval 294:19,20

296:21 404:8,10

404:11 503:4

508:11

intervals 494:5

538:17 542:11

546:10,22

intervention

135:21 231:16

inter-individual

124:5 610:1

inter-species 605:4

intestinal 628:13

intestines 124:17

intracellular 49:3

49:7,8 51:6,17,20

52:9,10,10

intrauterine

476:13,20

intra-individual

124:4

intricate 555:16
Page 54

intricative 633:17

introduce 3:21 4:5

19:9 24:22

240:22 254:11

315:9 425:14

540:3 718:6,16

introduced 198:14

690:14 702:20

704:6

introduces 446:14

introduction 7:16

40:15 47:10,12

68:22 554:12

introductory 22:8

intruder 80:15,16

invalid 370:15

737:14

inverse 133:1,15

136:14,19 138:17

146:16 166:14,16

274:16 294:17

295:20 312:14,15

425:6,8 519:21

677:20

inverted 358:16

363:2 496:2

investigate 573:18

610:9 646:6

648:5

investigated 598:7

616:12,21

investigation

633:4,22 661:9

664:21 673:13

investigations

116:1,16 249:7

424:4 541:8

572:3 595:4

610:20 632:15

662:10 723:9,11

investigator 369:5

634:5

investigators

114:6 155:1

168:6 179:3

272:8 367:9

429:9 443:10

453:9 454:10

501:12 511:21

512:6 513:8

521:22 543:10

investigator's

648:7

invite 612:17

invited 776:13,15

invoking 231:22

738:14,16

involve 249:8

269:18 551:5

involved 70:20

180:21 181:5

464:21 465:8

473:6 668:1

involvement

486:14

involves 207:5

397:16

involving 181:4

551:13 622:1

666:11 676:16

721:9

in-between 240:22

in-depth 204:10

303:1

in-factory 763:19

in-home 119:4

214:2

in-house 271:1,12

in-utero 146:3

IQ 138:5 145:21

148:7 159:2

161:10 164:14,16

164:19 211:1

261:7 383:3,5

398:8 405:14,20

406:9 407:6,7,20

408:21 409:1,4,7

412:2,10,22

416:20 544:10

546:13 550:4,6

554:21 555:4,10

555:11 556:4

557:15 559:3,4,6

559:7,10,11

560:9 576:5

578:11,14 586:8

586:15 615:9

746:7

IQs 158:16

IQ's 159:2 161:6

IQ-point 408:19

IRB 680:11

IRIS 390:2

irrelevant 88:11

302:10,12

irreversible 95:16

95:21

irritable 134:9

isoform 603:18

isolate 118:1

225:10

isolated 460:22

isolating 230:20

isolation 450:2,4

450:11,17 461:10

743:10

Israel 268:10

310:1

issue 7:13 16:3

22:15 29:2 32:4

39:18 48:11 51:7

66:19 95:1 97:14

172:10,20 177:15

178:1 181:19

182:17 183:10

184:7 196:3

220:5 285:2

292:13 293:2

294:4,16 295:1,8

295:14 302:17

312:20 343:19

348:13 351:5

369:18 370:8

385:16 386:19

391:2 397:2

423:19 425:21

426:3 434:18

464:13 476:5

480:14 494:19

496:5 503:8

506:1,6 507:9,17

507:21 511:22

515:22 526:6

530:22 532:13

541:14,21 549:5

549:11 552:5

557:5 563:4

565:8,9 566:7

590:6 602:11,22

603:4,5 620:7

621:20 646:9

650:6 651:22

655:20 666:8

682:21 704:4

711:18,18 713:3

713:4,20,21

729:17 750:8,11

757:15 762:22

764:7

issued 71:20

issues 1:5 2:16 8:8

8:19,20 9:6

11:19 13:2,10,20

16:3 23:7 65:18

73:4 91:19 95:10

109:2 137:17,21

139:2,8 140:4

141:1 160:22

165:6 167:10

173:12 183:17

193:12 203:14

211:13 218:9,9

223:19 229:12

272:5 284:9,10

284:12 285:9

293:4 315:3

343:12,17 350:14

364:14 436:13

449:4 468:15

469:8,15 473:14

473:16,18 482:9

493:5 497:9

499:2 515:14

520:17,22 521:1

521:1 528:7,11

529:16 539:16,21

540:8 553:20

554:1 562:22

574:9,10 593:16

645:9 674:10

676:17 682:10,15

693:8 715:6

718:18 753:10

759:4 760:16

761:19 762:1,5

769:3,4 771:20

Italian 110:2,3,5

483:17 486:18,19

493:9

items 2:7 546:18

It'll 339:22

it-must-be-some...

582:22

i.e 134:20 138:18

139:21 167:10

425:8

J

J 604:20 780:1

James 780:1

jargon 30:17

33:17

Javelin 540:14

Jean 6:9,9 41:12

87:7,17,20 88:6

92:12 104:2

166:3 274:4

304:6 305:5

317:1,1 337:2,2

366:22,22 367:8

367:14,16,20

368:11 369:1,7

369:11,22 370:7

370:14,15 371:2

371:12 372:1,10

372:22 373:6,10

373:14,20 420:13

420:22 432:6,13

432:20 442:10

447:13 465:15

500:13,13 505:6

505:6 513:18,18

521:17 529:4

530:8 531:14

532:17 535:8

562:14,14 564:5

564:16 570:18

571:19 582:1

589:11 591:3

742:17 744:22

746:9 769:12

Jean's 763:5

Jeff 20:3 422:14

422:14 423:21

425:18 426:5,5

428:3 429:7

430:6 733:4

735:9,10

Jenkins 2:1,4

247:18 317:21

318:1,2 381:21

387:10 395:21

776:18 777:1,20

778:5 780:10,11

Jennifer 387:15

388:2,6 396:3

Jersey 259:21

263:2 266:16

268:5 277:15,17

281:12,15 283:17

659:17 716:8

Jeter 336:12

Jett 487:20 491:5

491:7,11

jibber-jabber

201:17

job 27:6 180:16

257:3 266:6

293:13 343:20

350:7 355:6
Page 55

366:7 442:12

444:6 529:2

554:7 633:2

636:7 637:9,14

638:4 656:18

739:6 757:13

764:17,19 766:21

773:13 777:19

780:14

Joe 777:1

Joel 20:2 407:10

John 5:5,5 98:7

149:7,17 150:2,8

150:15 151:3,7

151:13,15,17,19

151:22 152:6,9

152:16 164:13,21

165:13 166:1

198:21 199:17

200:2,21 279:19

280:3,7,20 310:2

310:6 316:6,6

417:15 418:12

434:10 515:3

553:7 560:11,15

560:19 561:2,6

583:18 591:15

593:5 639:15

641:2 642:2

653:12 682:3

712:9,14 756:20

759:6 765:15

Johnson 426:3

435:2,8 487:9

join 201:15 385:14

780:3

Joint 7:3

Jones 780:1

journal 497:13

513:21 514:6,12

514:14

journals 513:21

514:16,17,18

journey 249:20

255:11

journeys 250:20

joy 779:15

Juberg 247:14,15

248:1 257:14

307:11 310:15,18

318:13,14,15

380:14,15 383:13

384:21

judge 286:21

313:9,10 676:22

764:6

judging 234:18

762:13

judgment 227:19

313:13 370:12

judgments 227:12

357:12

juice 539:7 643:21

643:22 644:1,2,6

645:17,20,22

646:17 648:18

juices 643:19

Julie 226:18

254:19 269:7

282:1,5,7 302:8

302:11 303:5,7

303:17,21 305:3

307:1 313:4

385:15

July 29:21 30:7

35:9 192:2

203:21 569:18

jump 66:14 162:7

504:22 512:3

642:21,22 649:18

742:11

juncture 438:12

June 27:21 389:6

junk 696:3,4,5

justice 287:20

justification 488:4

667:13 760:12

justifications

106:1

justify 484:3

529:19 760:13

juvenile 36:20,20

37:1,2 502:16

538:18

juveniles 496:16

K

kainate 48:22,22

49:1,10 51:4

52:8

Karen 19:9,13,14

27:8 776:20

778:4

keep 25:20 30:14

39:20 71:20 72:5

109:3 151:19

162:6 205:3

208:13 216:16

256:9 305:5

314:2 354:5

431:1 439:12

458:12,16 487:2

564:21 618:7

700:21 710:8

729:14 758:19

761:9,12 772:20

774:19

keeping 97:15

192:14 261:14

keeps 602:15

767:21

Ken 4:6 108:17

315:4,10 379:1

502:10 504:17

516:15 539:22

777:15

KENETTH 188:1

Kenneth 3:22 4:2

7:8 22:11 24:21

41:8 43:1 59:14

67:5 68:6,12

87:5 90:3 91:12

98:6 101:17

104:1 107:11

108:17,22 110:7

111:11,21 147:18

152:19 153:1,22

158:12 160:3

161:18 164:12

165:3,11 166:2

167:18 168:19

169:13 170:18

171:8 172:4

191:17 193:17

195:16 196:11

198:11,20 201:4

201:10 220:13

221:5 223:6,12

246:19,22 247:5

247:12 255:14

269:9 271:14

273:1 274:3

278:4 279:14,17

281:20 302:1

304:5 307:20

310:10,19 312:2

313:22 314:5,9

315:1 317:20

318:9 334:6

337:22 341:21

342:5,7 364:20

365:1 366:21

374:10 379:1,21

380:10,13 382:1

385:13 386:7

387:2,13 388:4

395:17,22 396:4

409:22 410:22

413:7 414:17

415:8 417:10

418:19 419:1,17

420:6,17 421:5

421:10,20 422:21

423:4 425:1,13

427:7 428:1,22

429:13 430:5

432:4 434:8

435:18 436:4,10

437:19 438:15

439:1,16 440:4

441:5,10 442:9

444:11,13,17,19

445:17 447:8

454:18 456:6

460:9 462:17

465:13 467:9

470:6,16 471:22

473:1 497:6

499:17 500:12

502:10 504:6,17

505:5 506:4,11

507:8 512:2

513:10 514:21

516:5,15 517:9

518:16 520:13

521:6,16 522:1,8

524:3,22 528:3

528:20 529:3,10

530:6,11 531:10

535:7,21 539:6

539:19 540:17

541:17 552:7,22

553:6 554:2,10

556:6 559:13

561:13 562:11

563:21 564:8

565:12 566:1

570:7 571:15

572:12 581:18,22

582:5 583:17,20

584:21 586:4

587:4,13 588:6

588:15 590:17

591:7,18 592:1

592:11,19 593:12

593:15 595:15

602:8 603:10

605:6 606:10

608:1,7,19

612:18 614:13

615:19 616:6

617:1,22 619:2

620:2,22 621:12

622:9 630:18

631:1,5,22 634:2

635:13 636:19

637:6 638:12

639:13 642:19

643:13 645:8

646:7 647:21

649:2,14,18

651:2,7,15

652:18 653:9,17

654:1 655:4,10

655:16 656:14

664:1,6,9 665:3

665:22 666:5,20

674:15 676:4,8

680:20 681:3

682:1,17 684:6

684:12,14 685:16

686:15 688:2,18

690:5,9,20

693:10 695:20

699:16 702:2,8

703:19 705:4

707:4 709:2,20

710:7 711:2

712:7,11,21

714:5 715:14

716:6,11,18

718:20 719:5

720:2 721:14

727:6,8 728:12

728:19 729:15

730:22 731:11

732:6 733:3,9,16

733:20 734:7,19

735:2,9,16 736:1

736:5,9,12

739:17 740:20

741:17 742:16

744:14 745:16,22

747:14 748:9

751:10 753:8

755:21 756:16

758:14 759:13

761:1,15 762:21

763:11 764:13

765:3,14,19
Page 56

766:9,12 767:1

768:7 769:5

770:7,19 772:5

773:11 775:8

778:18 779:20

780:3,5

kept 565:22

702:10 777:18

ketanserin 486:13

486:14

key 20:14 26:7

28:3 30:14 33:14

33:20,21 38:3

39:15 45:3,13

48:12 54:7 96:14

96:17 97:19

118:19 120:8

128:15 136:8

143:3 146:8

156:18 176:6

183:16 187:4

202:21,22 205:12

208:12 218:10

231:7 244:4

302:12 343:12

350:6 351:9

398:1 442:7

445:3 493:16

550:19 608:11

609:11 612:16

621:6 624:18

630:11 642:6

660:19 662:13

695:6 720:21

kg 554:15,16

kid 383:2,3 412:9

506:16 557:14

741:13

kids 106:12,13

161:13 189:10

194:4 196:5

264:12,14 271:22

272:1 275:12,13

275:15 276:5,9

335:8 382:22,22

412:13,21,22

555:4,10 557:9

557:10,11 558:3

558:10,11,11,13

558:14,22 562:3

562:8 567:11

644:22 664:19

694:16 701:9

728:10 741:2

kill 652:12

kilogram 212:12

212:19 253:22

254:3 288:2,8

322:3 325:20

327:1 329:1,3

330:9,13 331:8,9

332:8 344:15

345:7,12,19

347:5 349:7,9,16

349:21 350:1,2

350:22 353:13,19

356:16,18 357:7

358:10,13 359:7

359:10 360:7

361:1 362:4,20

363:5,21 364:9

366:14 380:3

399:9,19,21

400:13 403:1

405:2 440:14

kilograms 288:6

kilogram/day

404:21 408:11,15

408:17,20 409:4

416:4,15

Kimmel 342:13,21

343:1

kin 692:14

kind 10:21 12:8

14:6 19:6 22:6

22:18 23:6,22

24:14,19 27:4

60:12 70:2,8

71:20 72:5 74:5

79:19 82:7 90:5

90:7 92:7 93:12

94:10,16 97:2

99:2,14 100:12

100:17 106:1,17

109:2 110:17,18

120:9 124:22

125:4 128:3,8

129:3 130:14

131:3,3,7 135:13

145:6 148:4

149:4,5 154:10

156:1 162:2

167:6 195:9

199:9,12 201:1

211:14 219:5

224:3 227:19

228:5 229:2

232:3 234:2

236:11 239:6

241:17 244:2

246:11 247:14

286:12 295:6

296:4,5 300:17

300:19 302:5,12

310:17 329:9,12

340:14 351:6

356:16 368:19

371:20 374:4

378:12 379:6,17

379:21 382:18

383:9 405:15

410:17 413:14,15

414:10 417:11

423:13,15 424:18

428:19 429:3

431:13 439:11

441:1 460:19

461:6 462:3

468:1 469:10

470:19 473:18,19

480:7 482:12,15

489:4,20 495:9

500:2,8 504:20

508:21 517:5,10

522:8 527:17

531:3 534:15

535:22 536:1,17

536:22 539:8

540:12 553:14

559:2 561:21

565:22 569:9

571:16 581:1

586:9 592:21

603:7 605:8

618:11 619:3,5,6

619:6 632:1,19

633:9 634:11

638:12 642:20

643:15 644:17

645:19 647:3

648:1 650:10

672:17 677:14,14

678:8,12 679:17

679:19 680:2

681:22 685:9

686:10,11,18,20

686:21,22 689:3

697:19 700:16

701:6,9,18

702:12 706:4,5

710:1 711:4

713:22 716:12

718:20 719:12,18

729:17 731:2

733:7 738:19

740:19 741:18

748:19 751:14

756:17,18 758:6

760:8,17 761:18

762:4 766:7

767:7,12 768:9

768:12 769:8

772:4 774:14

775:9,19

kinds 15:18,19,22

16:1 24:4 34:15

100:8 134:11

202:1 237:3

240:17 242:16

244:3 252:2

380:5 383:8

414:3,5 460:17

461:3 463:11

468:20 513:12

528:14 533:12

664:14 670:20

740:5 759:4

767:9,14

kinesin 464:20

kinetic 252:20,22

320:16 323:16

333:12 340:2

341:5 740:6,10

kinetics 325:19

624:2 646:15

662:12

Kisicki 402:13,15

kit 673:10 677:3

680:15

Klaine 4:13,13

315:16,17

knew 274:19

548:19 586:3

knocked 11:14

492:20

knocking 492:18

knockout 482:13

knockouts 482:14

know 3:10,12 7:11

8:10 12:13 25:11

39:21 40:4 46:19

47:6 51:12 55:3

60:20 61:20

66:21 67:9 90:7

92:4 94:7,18

101:4,13,15

103:7 105:2,6,12

105:14,18,19,20

105:22 107:6

108:8 110:20,20

121:5 125:1

126:6 129:8,12

134:4,9,9 137:16

143:4 149:5

150:8,18 151:1

155:9,18 156:10

156:16 159:14

160:12,16,20

161:1 164:21

166:6 167:3,6,11

169:20 170:3

188:9,16 189:8

190:11 194:7,8

197:7,19 198:5,7

198:19 199:1,8

199:11 200:4

214:18 216:8

217:7,9,15,17

218:3 220:22

236:10 244:8

248:13,17 249:21

250:5,19 257:15

258:5 262:5,12

265:20 267:21

269:22 270:4

273:5 275:1

276:6,16,18,19

277:5 278:3

279:4 280:14

282:21 287:19

288:10 289:15

292:9,22 293:5

293:10 296:12,14

296:16 300:13

301:18 304:8

306:8,21 308:5

309:10 312:5,19

313:1,5,16 319:6

319:14 331:15,16

334:14 335:6

348:2 350:22

361:10 365:9,14

368:2 369:15

370:19 371:19

372:11 375:11

377:4,20 378:6

379:17 380:1,2

380:16 382:8,17

383:7,10,14

385:9 387:3
Page 57

388:9 389:16,16

396:11 399:18,18

400:7 401:1,12

403:8 407:7

409:3 413:3,5

414:2 415:2

419:17 420:22

421:8,22 422:5

424:13 432:7

433:1,11,17

443:15 450:9,14

454:11,15 455:5

455:19 457:8

463:2 464:3,17

464:21 467:18

474:11,22 475:19

476:4,13 477:3,9

477:11,11 478:18

480:6 481:19

482:1,18 485:10

486:7 488:19,22

488:22 489:11,18

491:10 492:4,11

492:12 493:7

496:4 497:21

499:6 501:11,17

502:6 503:6

505:15,17,18

506:2 507:11,22

510:1,2,11,13,17

511:1 512:1

513:19 514:5

515:8 521:5,19

523:19,20 526:13

527:1,17,18

528:13 531:8

534:2 535:9

537:11 540:5,9

552:17 554:16

555:13,17 558:19

560:7,11 565:8

570:17 585:12,15

585:19 586:3

589:8,18 592:4,7

592:10,15 604:7

612:13 616:18,19

619:16 632:3,5

634:22 636:12

637:10 639:22

641:16 644:16

646:21 650:8,12

652:21 653:6,6

655:1 660:21,22

668:14,17 672:16

673:3 679:18

680:6,9,18 681:4

685:18 688:12,13

689:15 690:3,3

690:11 694:13

701:3,8 702:8

705:18,20 709:13

711:12 712:17

713:13,13 716:7

716:20,21 718:8

718:17 728:7

729:9,12 730:3,4

730:7,10 731:19

732:15 739:15

740:9,11 741:1

743:13 744:5,7

744:11 746:1,5,8

746:9,11,15

747:7 751:20

753:16 754:18

756:5 757:18

759:20 760:17

762:22 763:5

764:2 768:13,15

770:15 771:5

777:10 779:12,14

knowing 13:13

14:9 163:12

412:10 633:3

660:18,18 679:14

707:9 718:15

752:3

knowledge 15:20

15:20 17:9 44:16

154:10 434:17

455:11 458:10

528:15 660:15

700:7 712:12

747:18 770:14

knowledgeable

532:4

knowledge-base

564:22

knowledge-build

719:19

known 83:19

118:7 120:15

129:11 140:12

156:17 161:14

196:19 212:3,3

236:11 274:19

320:22 321:7,9

322:13 334:17

391:7 401:21

443:16 457:22

474:7 476:1

603:2 628:22

643:22 646:4

649:16 720:18

730:11 750:18

knows 102:8

470:22 559:16

746:8

Koutros 6:5,5

148:9,16 312:2,4

429:15,22 552:9

552:10 582:5,6

584:21,22 586:10

586:16 587:4,5

592:1,2,13 769:5

769:6

kudos 583:21

L

L 344:13 363:21

364:12

lab 89:15 100:4

102:18 109:15

110:1,2,2,3,4,5

110:20,22 252:18

279:9 309:21

349:19 358:22

372:7 378:14

413:13 426:16

435:2 595:9

604:19,20 771:22

label 392:21,22

laboratories 6:22

43:8,8 70:4,7,22

73:22 82:6 85:12

122:9 150:20

317:13 334:18

512:18 520:20,21

613:4 622:21

625:6

laboratory 12:10

28:15 73:19

74:17 76:2 78:3

80:19,21 89:18

91:4 106:4

107:18 109:17

122:4 150:21

273:13,18 278:9

320:6 336:12

351:15 358:18

381:8 392:4

437:13 472:2

476:10,17 477:22

520:17 554:13

613:4,14 624:14

624:15,21 625:6

643:18 644:3

648:17,21 649:6

672:10 695:9

laboratory-based

676:19

labs 42:8 62:5

68:19 94:15

108:21 210:4

252:18 344:20

488:9 507:4

521:2 637:19

649:9 711:15

lack 69:6 84:9

113:3 120:16

121:18,20 126:16

140:13 144:1

146:21 205:5

211:11 217:12

241:18 292:3

351:13 360:20

458:10 513:4

520:17 580:7,17

581:4 641:15

724:9

lacking 513:5

lacks 146:9 186:8

lactation 489:14

601:4

lactational 518:1

lactationally 79:2

ladies 682:12

lag 399:4 412:8

laid 46:22 47:7

218:18 228:18

291:3 293:8

313:12,15 424:11

424:12 443:17

444:5 449:18

595:19

land 285:22

430:22

landmark 537:17

landmarks 537:12

languages 139:15

Lanson 753:11

lanugo 628:13

large 19:20 31:8

49:16 51:20

74:21 86:9

126:10 139:4

145:8 180:13

223:7 400:5,17

418:18 439:18

481:8,11 486:21

487:4 490:17

495:7 496:19

539:3,5 542:8,15

544:6 545:22

546:12 575:20,22

580:4 594:9

610:2 618:18

633:17 657:10

663:2 667:6

670:18 698:18

723:9 744:4

771:8

largely 32:14 39:4

180:5,12 191:21

193:2,8 204:8

208:14 213:10

216:18 434:21

490:4 539:3

606:19,20 618:5

618:11,12

larger 224:19,21

225:17 399:13

402:8 527:21

547:14 656:20

663:6

Larry 281:4

lasting 392:17

393:5

lastly 137:22 258:8

260:2

late 201:20 255:17

581:15 582:8

623:17 643:1

latency 74:20 75:2

481:19,19 507:18

537:9,11,22

lateness 282:1

latent 379:22

latest 273:17

launched 705:6

laundry 528:13

Laura 777:1,21

778:1,1 780:8

lavatory 299:15

Laviola 482:12

law 8:12 456:13

Lawrence 671:2

Laws 3:1

lay 46:13 50:2

227:16 239:1

285:22 313:11
Page 58

736:18

layer 49:13,20

718:6

laying 24:6 737:10

lays 45:6,12

473:20

lead 25:4 26:21

45:3,20 46:1,4

51:10 52:14 68:1

129:10,15 143:22

156:14,16,19

157:4,9,11,12,13

157:17 158:1

206:13 351:7

379:22 382:22

383:4 386:20

407:13 437:20

441:17 443:3

445:5 447:4

455:18 466:1

472:19 517:3

527:6,19 541:18

554:17 582:17

661:13 680:10

699:21 700:7

713:11 750:22

751:6,18 753:6

774:6

leader 248:2

310:15

leadership 778:17

779:21 781:9

leading 26:15

33:17 47:22

60:17 96:15

97:11,17 202:21

205:16 351:10

441:19 520:21

561:12,21 729:7

leads 49:20 56:5,7

56:12,21 57:11

60:19 63:8 67:20

211:12 517:5

583:14 753:1

leaky 630:2

lean 695:12

leaning 242:7

leap 227:10,19

learn 76:16,17

479:12,20 508:15

534:8 539:11

564:19 565:1

570:10 571:6,7

571:13 719:2

765:21 776:13

learned 75:1 76:9

475:21 765:17

771:12 773:17

learning 49:19

72:16 73:10 75:2

75:18,20 76:15

76:16 77:3 85:21

88:1 104:4,17,18

359:12 366:16

369:19 370:9

371:22 376:20

377:2,7,8 474:14

479:20 480:19

482:4 485:12,15

485:16,18,18

488:7,8,9 493:4,4

495:6 501:20,21

502:1,14,20

508:16,18,20,21

509:13 511:14

532:5 534:2,7

571:1,10

learns 479:13

leave 95:5 382:1,2

417:12 419:1

483:14 488:11

530:17 581:16

644:6 693:9

699:6 764:3

leaves 196:10

Leaving 773:17

led 253:6 290:9,11

292:13 746:11,14

746:15 751:17

Lee 319:13 354:16

435:12

Leeder's 604:20

Lee's 302:21

left 49:14 60:6

70:21 112:10

156:21 177:17

178:6 182:1

188:21 205:1

238:21 239:20

247:3 287:21

327:16 347:4

370:5 402:21

416:4 497:7

644:1 678:17

704:4 710:18

leftover 540:8

legal 238:12

legitimate 347:14

752:2

lend 301:18

654:21

lending 125:20

length 56:8 132:17

133:2,4 146:17

257:13 258:14,18

260:4 274:15

386:17 482:1,1

488:13 507:19

537:19 547:7

575:3 742:22

lengths 507:18

lengthy 434:20

542:17

lesson 475:21

774:18

letting 93:10

255:16 719:22

let's 97:1 150:10

155:17 157:22

209:3 256:22

262:1,13 266:14

267:12,15 307:18

307:18 365:1,2

372:10 387:13

397:1 407:16

416:17 419:12

445:20 491:2

497:6 513:4

524:18 531:11

593:12 608:8

654:1,7 707:7

716:15 727:16

leukemogen 228:7

level 1:13 16:14

37:21 42:3 44:18

45:9,10,19,20

46:1,2,4 49:3,5,7

49:11 50:14,22

51:2,9,10,11,14

51:18,19 52:5,6

53:10,13,18,18

54:8,8,18,19 55:9

56:6,8,11,20,20

57:3,6,11 59:12

60:22 61:1,2

62:15,19 63:2,4,5

64:20 65:7,13

66:1 67:19,21

68:2,4 73:6 74:5

93:11 97:1 99:17

102:3 103:15

131:3 140:20

181:8 183:12

185:9 190:6,11

200:7 219:16,18

241:10 243:16

245:15 246:14

264:8 268:10,14

268:22 273:4

278:13,14 279:2

303:14,15,19

306:5 308:20

326:3,15 331:22

341:1 344:14

347:2,5 349:8

353:13 354:14

357:15 358:8

360:8 362:19

363:16 370:11

374:20 375:11

382:19 383:3

390:2 393:17

394:5 395:4

397:5 401:8

403:3 404:6

412:11 415:21

417:2 435:21

459:21 460:14

461:8 477:22

478:8,9 499:12

517:17 523:8,14

537:16 543:22

559:16 571:4,8

575:8 578:12

581:5 588:10

596:19 597:1

598:14 605:21

607:19 613:1,8

613:10 633:14,14

633:18 644:21

645:20 658:15

660:5 661:8

669:2 679:16,19

683:16 685:2,4

688:8 689:2,2,4

692:18 703:3

707:20 708:5

713:5,7 717:9

718:13 727:13

728:9 736:19

753:22 758:19

759:1 766:8

768:22 777:12

levels 24:16 43:21

44:10 45:13,21

46:14,16,18

48:12 49:4,6

50:3 52:12,17

55:3,12,13,19,22

57:15 58:5 59:3

59:5 63:20 64:7

64:13 65:18 74:9

78:13 81:13 82:3

106:13 108:4

125:19 163:15

165:17 169:15

172:13 174:9

177:15,21 178:3

178:16,21 179:8

179:11,19 182:11

182:12,13 184:2

186:5 190:17,19

193:3 196:21

213:13 214:7

215:8 217:6

218:3 220:11

242:2 243:8,9,20

254:2,7 256:17

262:10 264:3

265:9,10,16

267:13,14,16,19

267:22 268:1,7

268:18 269:4

272:2,3,15 276:9

276:19 280:17

281:7 288:3

307:8,15 321:7

321:18 322:13

323:14 325:7,11

325:13,15 326:9

326:11,13,14

328:2,6,8,16,20

329:16 330:8

331:13,14 332:4

332:8,10,10

333:6,16,16,20

336:14 341:2,4

346:17 347:2

348:1 349:10

351:1 352:5,8,16

355:3,19 360:13

375:14 382:11

385:7,8 388:17

393:22 397:8,13

398:20 399:5

404:20 405:19

406:2 409:18

415:19,20,22

416:1,16 417:1

426:18 434:1
Page 59

438:19 440:20

441:16 447:5

457:18 462:6,15

463:1 464:3,4,5,9

466:7 472:20

529:21 548:20

549:1 551:10,22

576:10 579:13

589:15 596:8,9

596:11,16,20,22

598:20 599:4,9

600:1,2 601:15

601:15,16 610:10

613:7,11 614:5

614:15 615:2,16

621:22 622:22

629:18 633:16

656:1,6,8,11

658:1 661:20

666:10,18 668:8

668:17,19,21

670:16 681:16,17

684:2 691:4,6

699:22 701:10

713:9 714:3

720:9 721:3

731:14 753:19,20

753:20 757:8

758:9 760:3,5,10

760:11 774:16,19

Levin 484:19

486:9 508:8

519:2,14

Li 252:15 307:15

342:1,2,4,6,8

364:22 365:8,16

366:6,9 367:7,11

367:15,18 368:7

368:17 369:3,9

369:21 370:1,11

370:17 371:5,18

372:9,19 373:2,7

373:13,18,21

374:16 375:2,8

375:20 376:3,9

376:12,17,20

377:13,20 378:11

378:21 379:2,16

380:1,11,12

384:16 434:20

liaison 2:8

licks 493:6

lie 42:20 191:3

lies 242:11

Lieutenant 41:2,5

115:15 193:1

215:5 262:15

441:12 445:22

621:19 655:19

666:7 690:12

life 31:3,10 35:7

35:13,19,20

37:14 194:10

215:13,20 323:19

324:11 333:11

433:6 437:8

438:19 442:18

498:21 504:13

533:10 536:6

712:2 721:5

lifestyle 545:16

lifetime 407:9,14

407:18 408:2

413:4 665:2

life-based 721:22

life-stage 187:7

696:11 754:17

ligand 45:17 61:11

light 100:1,5

103:14 107:19

255:3,10 290:3

349:21 392:10

523:1,20 531:17

531:18 546:20

606:15 629:1

657:15 720:22

748:4

light/dark 77:11

78:20 79:4

liked 671:2

likelihood 217:1

447:6 472:21

548:10 573:15

591:10 756:3

likewise 627:18

limit 3:15 148:8

149:6 189:18

198:16 216:18

222:22 256:15

267:20 268:8,11

269:3 270:19,22

271:3 273:15

274:2 277:3

293:6 308:3,5,12

308:13,22 309:6

309:11,13,18

310:6 311:6,10

394:15 399:1,10

399:15,18,19

407:19 425:19,20

426:19 427:5,6

579:22

limitation 135:8

138:9 142:4

546:5 548:2

587:8 628:21

limitations 17:2

18:5,12 24:8

63:22 72:13

108:6,8 140:4,22

143:13 187:4,17

187:20 194:22

195:6 208:1,2,16

219:6,14,21

258:2 287:14

291:7 299:1,22

312:12 491:17

495:1 541:15

544:18 546:9

553:15 595:1,3

595:14 600:7

620:4 622:6,7

637:15 666:15,16

690:16 701:22

704:8,10 723:13

737:3 738:7

limited 31:12 59:4

59:11 82:12 92:8

92:9,21 137:18

140:21 141:5

147:2 214:10

310:22 428:18

430:15 434:16

444:8 546:8

550:18 552:13

581:6 686:10

limiting 339:9,12

457:17 564:22

625:12

limits 395:4

398:15,17,18

399:22 400:1,3

401:18 402:7

570:3 580:1

593:18 662:17

line 39:4,13 42:9

194:13 267:1,9

283:12 287:2

294:5 312:5

331:6 361:7,9

363:14 447:9

490:19 515:5

563:18 654:5

675:10 677:15

689:21,22 761:7

linear 59:20 60:4

60:16,20 309:7

357:5,8 397:11

449:11 451:10

469:7 495:18

515:18,18 549:17

580:8 677:21

linearity 278:18

308:21 325:19

550:16

lined 628:1

lines 20:18 21:7,11

21:15 25:17 26:9

26:13 29:11

34:18 37:10,12

37:16 108:10

147:13 202:18

204:15 207:13,18

209:7 211:21

218:10 220:8

225:1 227:4

229:18 287:18

327:10,11 565:16

595:20 599:8

605:19 609:4

737:4 750:14

link 11:22 26:8,20

62:11 63:19

132:15 184:17

190:4,6 191:10

192:7 247:9,10

383:6 384:15,17

384:19 428:5

521:20 641:20

701:19 719:1

linkage 16:18

44:17 65:5

117:22 132:16

734:21

linkages 16:19

24:16 46:10

202:22 205:12

441:16 530:3

linked 159:7

186:13 251:17

383:10 447:20

570:22

linking 33:22

60:22 206:14

466:5 606:2

links 52:12

lipid 281:7 551:18

lipids 281:5

lipophilicity 626:9

list 155:2,2,10

172:3 217:22

322:18 428:17

429:5,10 513:21

514:4 527:9

528:13

listed 3:16 48:11

152:12 228:1

273:22 294:13,14

425:6 431:21

432:2 659:15

729:6

listened 570:11

listening 512:5,16

515:3 701:16

767:13 781:1

listens 489:16

listing 414:4

literature 28:11,17

36:5 44:3 50:4

50:11 52:7 54:10

55:20 70:1 73:14

78:1 89:3 91:18

91:22 92:14

100:20 105:4

110:15,19 111:7

130:18 137:18

140:11 141:4

168:5 172:22

174:8 175:10,22

177:6 180:3,7

186:4 199:19

209:16 248:18

249:21 250:21

324:20 335:15

336:3 340:18

345:2 371:7

396:16 410:9

433:22 441:14

443:3,4,8 445:15

474:16 475:9

478:15,16 485:15

485:17 486:5

494:20 496:1

501:13 505:3,19

521:14 526:18

527:7,7 542:2

554:8,8 581:11

629:1 638:5

641:17 648:13

654:14 676:11
Page 60

679:18 685:13

686:5 707:14

746:11,14 747:5

774:5

litter 79:10,11

403:20 475:1,2

475:12,14,17

476:16 477:4

482:14 483:10

484:11,21,21,22

485:1,3,4 487:17

487:17 493:19,19

493:20,21 494:8

514:7 519:5

540:15

litters 360:11,14

475:3,16 476:14

480:10,10 484:9

484:9,10 487:15

488:20,22 494:2

494:9 522:17

little 13:3 18:21

29:9 30:12 43:18

44:1,11 55:21

64:6 74:10,22

77:7 96:19 102:5

103:4,5 115:14

134:9 143:5

155:3,13 156:21

161:20 167:15

194:19 195:13

199:22 201:13

221:8 230:13

234:15 251:2

262:2,16 263:5

274:12 296:5,6

302:4 305:9

309:1 320:5

321:8 326:11,22

332:19 334:16

345:17 351:8

365:5 374:13

393:11 406:6

408:22 415:9

429:4 430:11

434:18 435:20

436:1 439:3

443:4,5 467:13

471:16 485:2

487:19 488:22

499:6 502:12

509:14 510:17

514:22 515:15

519:6 521:10

524:19 532:19

542:17 547:3

551:1 558:1

562:15 566:13,13

566:16 567:6

569:7 585:13

591:6 606:16

607:11,12 632:6

637:12 668:13

675:2,20 694:5

695:5 698:12

703:10 705:1

716:14,22 719:22

728:17 733:4

739:12 740:14

747:2 763:1

767:2 770:5

live 388:22 450:9

672:18

liver 192:16

324:17,18 340:8

356:2 400:20

401:4,21 402:2

403:17 410:13,18

411:9,9 413:11

413:19 414:16

457:12,14 699:6

living 215:20

368:5 557:18

loaded 387:7

loaders 197:8

loading 422:11

LOAEL 288:6,11

LOBBY 1:13

Lobdell 20:8

local 170:10

localized 348:11

located 114:19

115:8 116:9

123:22

location 176:10

Lockridge 464:17

locomotion 81:10

locomotor 88:9

483:2 523:22

LOD 149:4 426:13

576:12

LODs 662:22

663:4

log 268:4 325:1

366:2 400:4,7

404:8 405:17

logged 324:19

550:16

logic 16:8 245:6

485:2 631:10

773:14,15

logical 16:12 34:3

592:21

logistically 542:14

long 27:21 41:20

70:16 85:15

91:17 95:15

201:17 210:6,8

219:7 224:13

248:14 351:4

436:19 444:3

453:19 454:15

501:22 503:4,17

593:19 620:18

644:6 648:4

650:4 671:17,18

671:19 693:18

702:17 706:7

717:10 718:3

720:4 723:21

longer 119:21

125:19 146:19

274:16 447:13,14

454:16 504:14

538:17

longest 661:8

longitude 173:12

longitudinal 117:7

117:8 139:2

174:7 181:4

183:18 543:2

573:5 596:3

615:7 630:10,11

632:13,15 633:4

633:22 635:7

638:15 661:2,9

662:9 664:21

723:11

longitudinally

542:9

longstanding

252:17

long-term 29:1

31:10 37:13

95:21 96:1,2

97:1 107:2

208:17 209:4

353:9,10 392:6

407:9 472:12

474:7 490:21

493:12 496:12

499:21 502:11,13

502:14,15 504:3

519:9 531:11

610:14 719:17

745:20 746:22

769:1

look 8:22 9:16,18

11:7 15:21 17:6

17:7 22:9 23:9

23:12 24:6 42:14

51:3,12 56:1

69:10,12,14,15

69:18 70:15 74:7

75:13 78:5,13

82:18 83:12,17

84:1 85:19 87:12

89:12 91:17

98:22 101:11

103:10,12 106:17

107:4,7 109:20

150:15 155:9,17

157:16 158:6

165:10,14,18

166:5 170:21

181:7,12 188:10

190:21 191:1,12

191:15 198:18

200:3 210:13

213:12,21 216:7

220:10 225:14

226:7 233:20

236:5 244:18

245:13,20 250:10

257:16 258:17

259:10,21 262:13

264:2 265:10

266:14 267:13,15

272:6 274:5

276:1,3 283:20

284:20 294:18

295:19 296:19

297:11 305:14,21

306:10,19 308:16

309:3 312:13

313:15,16 319:19

327:8 330:17

332:7 337:12

344:7 346:3

352:8,17 356:12

357:17 358:6

359:4,9 367:21

367:22 368:1

369:17 370:4

372:12 375:8

376:18,19 377:21

377:22 378:5

380:3,5,7,8

384:12,13 386:2

386:14 396:1

412:8 415:7,10

415:21 420:21

421:1 422:19

426:11 427:4

431:2 441:6

443:3,11 445:4

445:11 448:16

449:7,8 450:15

450:17 451:6,9

451:12,15 453:8

453:19 459:5

463:21 464:1

465:6 466:21

468:4,15,18

470:4 475:1,1

483:4 485:5,6,16

485:18 486:14

488:7 489:1

490:1 497:2

515:13 521:2

527:6,20 539:1,2

550:1,2 556:13

556:14,14 562:22

567:16,22 574:12

580:2 584:6,11

585:17,18 587:1

588:4 589:13

590:19 591:10

592:18 601:19

605:1,12 613:22

615:4 617:11

619:17,22 621:8

626:13 633:10

639:9 644:20

646:10,10 651:20

656:21 660:12

672:11 673:20

676:11 677:2,12

677:16,19 679:1

679:19 683:18

684:17 689:7,16

689:17 699:15,17

700:17 718:9

722:2 727:3

732:2 735:15

737:18,22 738:6

738:21 739:1

740:4 743:9,11

744:3 745:3,6

746:10,14,18
Page 61

747:4 759:14

760:17,21 767:8

769:21

looked 30:10

70:14,17 71:1,16

72:15,16 73:9,11

75:4,18 77:7,15

77:17,20 79:1,3

79:14 80:2,10

81:8,9,21 82:13

82:22 83:5,11,22

86:18 88:1 89:7

90:10,17 106:17

110:10,20 111:2

111:4 145:17

150:12 152:10

155:17 165:7

188:5 199:4

200:11 215:18

263:3 274:7

281:17 283:15,21

284:1,2,6 285:6

287:3 291:17,20

296:12,14,15,17

306:1 311:11

327:9 335:5

337:8 340:8

345:22 356:4,16

357:11 366:11

379:2 417:16,17

425:11 431:12

461:7 475:11

480:11 483:6

490:17 498:10

504:16 507:2

522:9 528:16

533:13 536:17

543:12 544:16

549:18 556:8

559:17,21 574:7

576:10,13 579:1

579:12 603:8

626:17 641:18

647:6 652:20

717:18 734:21

735:19

looking 9:5,21

11:5 14:19 21:8

22:18 24:7 28:13

29:3 38:5 39:21

41:14 42:13 58:7

65:15 67:6 68:20

74:18 78:19 80:1

87:12 89:9,22

92:7,13,17 94:3

95:7,14 102:19

105:9,12 106:16

108:4,6 110:13

117:2,12,14

131:16 133:5

139:12 141:1

143:11 148:6

154:11 156:3

163:14 164:4

166:7 167:1,6,20

168:21 178:1

204:13 210:9

214:11 218:8,19

222:22 240:21

242:5,6,7,22

246:6,7 248:11

257:4,12,16

258:14 259:8

260:2 261:5

264:12 275:19,21

276:10 283:20

284:18,19 285:6

285:12,14,15,17

285:19 287:18

288:11,14 289:19

290:21 291:18

294:6 298:1

304:19 306:11

340:10 347:15

360:16 361:13

367:3 368:15

370:5,8 371:7,14

375:12 376:6

378:16 379:21

380:4 384:5

414:11 415:14

421:14,18 425:4

440:12 443:22

444:1,5 445:2,14

448:21 449:2

450:1,4,6,11,22

451:4,8 452:3,18

455:1,5,19 459:2

460:1 462:2

464:3 465:16,18

467:3 471:15

473:5 482:4

492:19 501:7

527:12,22 528:12

531:16,16 532:21

550:8 552:21

559:14 561:16

563:8 569:11,13

575:19 576:3,15

579:15 585:14

588:1 590:2

598:22 601:1

604:21 616:9,10

635:21 637:4,16

646:19,20 654:20

665:12,13 669:20

679:14 687:4

691:2 697:1

699:11 702:13

703:17 711:2

717:17 726:1

727:10 732:8

743:1,14,15

745:13 752:3

754:21 755:3

760:20 762:16

766:15,17 769:1

769:19 770:3

775:5,12

looks 58:3 76:21

84:17 104:16,18

200:10 239:13

251:1 271:22

274:13 346:6

400:6 417:16

473:1 488:17

511:9 515:20

526:9 540:18

601:2 647:15,16

676:21

loop 196:2 661:22

LOQ 273:3,21

Lorenz 224:8,9

246:21 247:4,8

Lorenzo 390:22

lose 163:9 533:9

744:4 749:7

losing 310:12

486:22

loss 45:11 48:17

49:17,20,21

50:18 52:1

274:20,21 407:13

411:20

lost 274:19

lot 25:10,11 26:4

34:10 37:7 38:2

40:5 41:16 64:1

64:9 66:19,22

68:13 70:19 71:2

73:5 74:12 81:5

84:4 85:2 90:8

90:15,17 91:4,4

93:19,20 94:12

94:15,21 95:3,11

100:18 102:14

104:15 105:20

106:15 107:15

147:15 151:5

161:5 163:12

173:8,8 190:8,15

190:16,17 193:20

193:21 195:20

201:19 203:19

204:10 205:8

206:16 207:15

208:8 220:15

228:18 229:2

238:9,19 242:20

257:2 267:15

286:2,11 302:16

302:20,21 305:6

326:19 330:4

337:16 343:9

346:7 351:17

356:16 379:5

380:3,17 383:18

410:9 411:20

417:4,7 430:12

435:8,16 439:20

442:21,21 443:4

443:5 444:1

447:8,15,19

450:15 454:2,8

454:22 461:11

464:18 465:1,9

465:21 466:10,17

467:3 468:14

474:14 478:17

483:15 486:22

488:11 489:19

493:8,9 498:12

500:18 501:3

503:14 507:14

508:15 511:11

512:10,11 513:16

513:16 514:2,11

521:12 524:8

528:7 529:8

536:15 539:11,12

547:2 554:8,18

563:13 564:6

565:11 567:8,19

567:21 569:14

586:6 590:10,11

591:18 592:10

602:16 604:17

606:2 620:3,8,20

621:2 625:3

631:13,17 635:4

637:10 639:8

649:22 657:11

665:18,20 667:1

667:20 669:3

673:11 675:7,9

686:6 693:7

695:4 706:13

712:2 716:19

719:17 724:14

729:11 735:5

740:1 743:7

744:22 745:1,11

745:13 746:12

755:16 761:22

764:2,3,3 765:18

765:21 766:22

768:14 771:12,12

774:8,22 775:21

776:9,9 777:10

777:11 781:7

lots 18:9 30:21

33:5 39:16 42:6

42:7,7,8,8 52:7

130:18 231:13

255:17 293:6

297:6 305:7

365:21 371:2

433:10 448:19

567:20,21 663:14

663:15 694:21

745:15 768:5

770:17

Lovasi 130:12,22

love 360:1 710:9

lovely 194:1

195:12,12

LOVIT 96:4

low 35:17 79:6

97:1 118:8,14

129:14 157:6,7

168:9,11,16,17

177:2 217:1

256:15 264:14

269:3 276:19,20

276:20,20 279:2

297:15 300:10

322:13 328:7

331:21 352:10,11

352:13 358:7,14

359:3 361:22
Page 62

362:14 363:21

364:12 375:3

383:2,3 403:5,6

406:15 426:18

440:14,20 460:14

464:3,4,10,11

477:20 488:19

492:5,9 500:4,4,6

500:9 527:22

535:11 557:14

559:3,4,6,7,10,11

560:9 575:5,12

575:22 581:5

583:10 588:10

589:5 599:4

614:5 615:2

633:16,18 668:8

680:17 684:20

720:11 747:10

753:22

lower 31:17 37:19

49:8 50:14,22

51:5,13 63:4

70:18 83:22

89:10 124:4

136:16,17 138:19

158:16,16 159:3

164:16,19,20

179:1,9,18

208:21 212:17,18

213:3 263:5

264:9 268:2,10

275:15 288:19

322:7 326:6

328:1,6 331:10

331:13 332:8,10

333:7 336:14

341:4 344:13

385:1,17 398:15

398:17 399:1,18

409:12,13 413:11

427:18 491:7

499:11 504:14

517:16,22 526:15

589:15 595:21

615:16 629:17

656:8 731:6

lowest 73:6 212:21

291:21 292:1

308:19 326:15

351:3 354:14

435:21 471:13

578:12

Lowit 20:2 22:10

22:12 24:22 25:1

25:1 42:2 107:11

107:12 155:1,6

169:17 170:7,13

191:17,18 194:12

201:11,13 220:20

221:3 247:16

422:8,9,9 425:14

425:15 426:9

434:18 436:7,15

436:17 441:6,8

471:3,18 512:2,4

524:4,5 532:15

532:22 606:11,15

617:22 618:4

619:11 620:9,10

666:1,3 690:6,8

703:22 704:3

705:16 710:5,8

711:2 716:20

717:1,7 719:5,21

735:1 736:2,3,7

736:11,14 747:16

761:17 776:18

778:4 781:6

Lowit's 531:11

low-does 745:20

low-dose 289:5

766:16

low-level 41:20

384:14

low-risk 547:19

LSD 484:16

494:22

LT 720:5

Lu 6:3,3 171:8,9

171:16 198:12,13

218:21 250:14

273:1,2,7,19

313:22 314:1

316:21,21 382:2

382:3 384:1

386:9 410:22

411:1,11,16

412:1,6,20 423:1

499:3 516:22

517:1 552:22

553:1 583:20,21

584:22 586:18

593:6 601:21

608:1,2 612:18

612:19 615:3

619:9,10 623:21

631:1,2 642:16

643:9,13,14

645:10,14,21

646:3 647:21,22

651:15,16 652:19

655:11,12,14

660:7 661:14

664:6,7 665:14

665:19 676:8,9

680:21 681:2,14

681:20 683:14

684:14,15 688:2

688:3 689:1

699:16,17 707:4

707:5 709:4,15

709:21 712:21

713:1 714:7

717:4,7,9 718:5

724:22 727:8,9

730:22 731:1,12

731:19 732:17

733:9,10,22

734:4 745:18

751:10,11 759:13

759:14 768:8,9

lucky 90:18

lunch 111:12

209:9 417:11,12

417:13 418:20,21

419:2,18 420:8

436:11 553:3

653:18,22 654:14

654:14 655:11

770:18

luncheon 419:14

lunch)DR 111:20

Lu's 264:12

276:22 616:9

649:5 669:9

670:10 710:14

732:14 768:1

Lyon's 460:15

M

M 779:21

macromolecules

45:17 461:1

Mage 186:3

magic 214:4

351:19 455:19

magnitude 11:21

86:2 130:6

131:12 181:14,14

213:2 319:1

326:6,15 328:1

351:6,19 361:17

364:7 369:14

385:2,17 425:8

447:3 449:17

451:17 456:16

460:4 472:18

515:8 568:3,7

618:12 646:6

651:22 652:15

706:3 739:2,9

740:17 741:22

742:6 745:18

746:4 766:2

767:19

Mai 662:4

main 131:22

156:20 175:21

179:14 180:8

181:17 182:16

232:10 485:6

596:19

mainstream 520:2

maintained 391:14

466:11 650:1

maintaining 89:3

major 11:20 38:11

39:3 40:18

201:18 294:7

475:5 487:6

493:17 541:15

582:9 587:9

655:21 733:6

734:20 754:6,9

majority 123:4

128:18 347:8

351:21 642:5

707:10

makeup 759:9

making 2:18 8:8

14:16 15:2 17:8

18:14 19:2 37:21

42:1 94:17 117:6

335:20 368:3

387:22 463:11

470:4 502:17

535:9 565:14

608:3 622:21

719:1 736:22

758:8 763:16

770:12

maladaptive 47:21

malathion 171:13

171:15,19 584:15

585:14 586:7,8

586:13,15,20

593:11

male 80:16 349:18

358:22 435:22

485:1 596:22

males 76:12,14

77:3 80:9 88:21

89:5 93:6 99:11

214:17 215:12,13

237:12 358:3,8

359:5 362:22

488:18 519:21

male/male 80:3

mammal 457:13

mammalian

241:19 354:2

mammals 321:1

manage 223:20

manageable 431:1

managed 123:5

management 19:3

398:9 406:5,6

407:6 408:1

692:4 747:21

749:10

managers 18:14

27:11

managing 4:7

315:11

manifest 305:2

449:2 498:17

504:14 533:10

manifestation

448:3,12 492:3

manifestations

237:9,16 498:14

504:9

manipulation

708:10

manipulations

729:5

manner 16:13

376:1 455:4

658:2 690:22

717:8 718:2

762:19

manpower 377:16

manually 511:9

manufacturers

266:20 269:19,20

671:6,7 673:17

manufacturing

180:20 181:1

253:6 265:22
Page 63

266:10 281:6

666:12 667:11

669:2

manuscript

514:20

map 538:18

mapping 480:18

481:14 482:2

495:16

MAPs 53:8

March 29:15

margin 288:9,16

739:12

marginal 350:2

405:21 524:1

marginally 628:17

margins 301:18

marker 95:19

120:3 153:13

179:16 260:22

261:12 307:5

383:17,21 502:7

534:20 629:6

686:12 727:1

markers 142:20

544:14 661:19

662:6

market 274:9

421:16 658:1

724:19 733:6

734:13

marketplace 119:7

marks 578:7

778:21

marmoset 745:2

Marsha 20:10

Marson 471:7,8,11

Martha 4:16,16

315:19,19 471:12

471:21 570:8

591:20 595:17

607:13 614:14

621:1 649:15

651:8 715:15

735:17 753:9

756:4 766:13

mass 695:12

Massachusetts

224:10

massive 675:2

master's 282:9

match 32:2 36:21

197:21 216:12

245:10 436:2

670:20 752:5

matched 217:6

672:3,4,10

matches 687:22

material 19:20

112:12 113:8

114:7,9 119:5

132:10 138:11

146:22 147:10

223:15 327:15,18

333:8 336:17

337:18,19,20

339:21 387:7

401:1 540:10

585:21 592:12

596:7 616:22

626:9 761:22

777:10

materials 3:17

167:1 319:20

396:12 421:19

424:12

maternal 80:1,10

80:11 124:16

133:6 134:21

137:7 162:1

163:13,16 165:17

259:22 268:6

279:20 280:8,13

280:18 281:10,12

285:7 296:15,17

363:21 365:6,9

365:15 404:22

405:2,6 417:18

417:18,21 418:1

418:2,6,11,13,14

457:12 504:19

577:9,14 578:12

578:15,18,19

579:12 580:13

599:20 616:11,16

627:12,14,18

629:16,17,21

632:19 707:18

711:22 715:19

716:1,4 717:14

719:2

mates 79:10,11

math 705:10

mathematical

17:13,15 555:7

mathematically

555:1,12 556:1

556:11 557:8,21

558:5

matrices 124:15

646:17 659:9

matrix 278:14,20

309:10 311:19

628:15 646:18

734:15

matter 16:5 65:13

124:17 211:4

390:13 414:14

498:1 590:13

644:5 677:16

679:12 727:11

728:10 767:17

matters 227:18

734:7,8

maturation 533:1

mature 534:7

Maurice 188:13

Maurice's 188:14

max 734:12,17

maximally 611:16

703:18 748:13

maximize 479:15

691:9

maximum 162:19

163:1 479:18

723:19 724:3

maze 72:16,17,18

73:8,10,17,17,21

74:16 75:1 76:2

76:3 78:19 79:7

79:15 87:11,14

94:2 99:9 102:6

104:14,16,16,18

109:13 237:13

349:5 358:1

361:5,18 374:15

374:22 375:6

376:4 377:18

378:20 477:13

478:1,2,2,14,18

479:2,18 480:15

481:3,5,7,9,12,19

482:9 487:10

488:6,13 490:12

490:13 495:8,8

501:7 507:16,21

508:11 509:6,21

510:3 511:7,7,8

511:10 519:22

523:5,20 531:17

531:20 532:7,7

534:1

mazes 77:10

481:14 495:11

510:19 520:5

McLaughlin 5:22

316:19

MDA 171:13

MDI 137:1,4 211:1

291:18 295:17,19

296:4,11,13,14

297:12 298:7

299:3 577:12,17

577:19 579:15

MDI/PDI 275:20

mean 16:22 22:17

60:11 87:16

92:16 101:14,22

102:16 129:13

137:20 152:1

159:17 160:4,6,7

160:15 161:8

164:16 165:1

195:12 196:4

198:3 202:5,7

203:3,4 206:15

206:18 213:22

221:17,21 222:3

222:8 232:21

236:20 272:3

274:13,14 289:17

289:18 293:2,3

293:10 297:13

298:4,8 300:18

304:2 307:1,18

309:8 313:10

337:10 339:5

340:13 355:20

356:21 365:15

368:7 371:20

372:11 375:20

376:3 383:2

400:4,5 404:19

406:10,16 407:3

408:6 412:7

421:1,17 432:22

492:1,3 500:16

501:14,17 502:13

503:10 506:16

517:2 520:7

523:11 528:13,22

530:15 534:13

552:1 556:1,4,8

558:14,21 559:8

561:6,14 562:2

562:21 564:12

567:10 568:9

569:1 571:3

585:14 586:5,7,8

590:1,5 592:15

616:16 632:7

637:10 647:7

654:20 659:8

666:1 668:12

678:1,21 680:1

680:17 684:16

687:3,19 688:21

688:22 689:16,18

689:20 702:10,14

702:20 714:10

715:8 729:3

732:10 740:17

743:3 744:20

752:13 764:1,9

767:6,9,13,19

768:2

meaning 235:1

284:2 459:17

538:1 644:9

688:15

meaningful 352:14

547:11 579:22

726:15 752:19

means 29:15,17

30:18 36:7,18

76:12 101:14,15

160:4 263:4

309:14 310:12

344:14 345:13

353:1 406:18,18

457:15 485:21

523:20,21 537:11

538:9 547:14

566:14 668:8

669:3 689:1

698:11 704:14

757:19

meant 234:7 382:3

415:16 548:2

579:3 591:16

617:4 771:17

775:22

measurable 218:3

752:8

measure 11:8,10

74:7 75:11

105:21 115:2,3

119:16,17 120:10

120:10,14 121:6

121:18,20 125:18
Page 64

125:21 126:12,14

127:20 131:5

133:3,12,18

134:18 135:6,10

136:12 137:3

138:5,16,18,19

140:5 152:3

153:5,18 166:19

166:20 175:8,14

200:3 203:2

225:9 278:13,14

280:22 298:2

309:3 338:14

363:9 384:2

395:12 397:12

412:17,21 424:4

427:4 455:15

478:7,7,8,10,20

478:21 480:18

494:4,6 518:8,9

529:14,21 537:10

541:10 543:14,19

544:17 554:18

565:6,6 567:9

573:12 577:1

578:6 579:6,8

596:6 599:6

603:1 613:12

624:6,8,13 626:2

630:16 633:13

637:20 638:6

639:20 640:2,5,9

642:10,11 651:14

651:17 668:7

673:4 695:3

707:18 712:13

718:7,8,13

768:11

measureable

438:14

measured 32:1

58:14 65:7 67:3

72:10 74:20

77:10 78:2 81:1

86:19 115:13

116:1,16,18

119:19 120:2

122:8 124:15,16

124:20 127:5

128:16 132:22

133:6 134:16

135:15 136:8,19

137:5,6 138:3

139:4,19 140:13

144:15,22 145:14

146:4 149:15

152:14,15 157:11

169:7,9,12

171:15,21 173:6

196:7 237:17

280:17 284:14

326:13 329:4

330:19 336:2

340:7 349:19

363:10 398:20

399:1 402:4

404:8 412:18

415:5 429:18,18

430:4 438:5

481:21 482:5

518:13 520:5

543:6,10 548:15

558:6 577:6

585:9,15 596:8,9

598:13 613:2,7

614:15 621:3,22

623:19 632:22

644:21 645:2,21

646:13 649:12

653:4 656:8

661:20 663:15

677:17 708:16

720:10 753:21

measurement

78:12 79:4

105:10 114:3

121:15,17 122:5

122:6 136:6

142:13,18 143:18

144:3,16 145:3

145:11,21,21

146:3 150:20,22

175:7,9 200:14

200:16 289:13

384:8 399:4

547:7 574:10,15

574:18 600:10

601:14 602:20

612:10 622:21

625:20 632:18

637:20 639:17

653:3 659:9

677:4,12 679:6

679:14 680:15

688:4 689:7

measurements

14:4,5,13 74:5

78:12 90:8 124:6

174:22 189:3

197:1,5 279:3

308:3 399:11

401:21 402:10

403:18 409:7

410:14 412:14

413:2 417:4

418:16 584:7

585:12 588:14

589:4 624:18

637:17,22 640:3

642:7,8 659:18

662:7 679:22

688:6 708:19

757:5,6

measures 72:15

82:16 101:3

115:20 117:6

119:13,14 121:7

121:10,11,12

122:9 123:17

124:2 125:1

126:5 128:16

135:11 137:22

139:12,15 143:8

143:20 152:1

169:2,3 172:2

199:18 200:3

201:2 206:2

222:19 228:12

232:19 233:12

240:18 356:19

383:7 425:2,7,12

430:1 542:11

543:5,13 544:6

545:7 546:11

548:16 569:16

573:11,13 574:14

575:17 576:4

577:3,18 578:2,8

580:19 599:18

610:14 638:20

639:3,4 668:22

753:17 755:5,5

757:10

measuring 26:17

66:10 138:4,16

174:4 281:1

308:2 412:15

471:17,17 480:1

501:22 508:19,20

525:9 594:17

596:6 599:20

600:15 602:6

626:20 636:6

639:6 640:18

645:12,13,18,18

653:1

meat 214:13

mechanics 685:19

mechanism 44:22

46:22 52:20 64:3

252:6 287:15

289:5 299:21

307:6 323:10

389:17 439:9

442:14 443:13,16

446:17 452:1,8

452:12,13,14

496:15 527:2

544:4 564:3

581:4 665:12

678:8 741:4

773:7

mechanisms 47:2

232:2 287:12

289:9 306:19

391:20 392:2

444:7 462:21

463:17 468:7

528:17 551:12,17

551:20 552:2

564:10 591:11

635:6 687:5,14

687:15,18 688:1

730:13

mechanistic 38:5

93:3 209:7,11

211:22 232:3

283:13,18 285:16

287:11 289:20

290:3 298:12

299:4 300:8

347:8,13 350:5

355:5 390:7

391:2 441:14

442:6 446:2,6

455:17 467:20

552:5 562:20

563:9 590:8

598:18 718:22

725:18

meconium 119:19

119:19 121:12

124:16,20 125:6

125:19 153:5,12

153:14,17,19

154:5,6,11

163:14,15,21

173:7 543:13

580:12 601:17

627:5 628:12

630:16 633:14

media 173:6,7

174:1 190:12

191:7 192:7

206:6 327:19

329:14 330:3,8

332:4 336:4

618:21 660:9,13

692:12 694:9,14

709:5,7,11,18

724:18 734:7,8

734:11 741:9

median 127:2

178:2,21 179:8

262:19 296:16

616:14 656:1,5

mediate 456:15

mediated 57:7

mediation 576:12

mediator 28:21

media-specific

692:4

medical 7:7

117:22 132:16

317:19 634:15

medicated 161:14

Medicine 6:17,19

317:5,8,11

medium 329:21

331:14

mediums 460:19

Meek 5:21,21

23:17 110:7,8

316:18,18 608:19

608:22 619:3

690:20,21 695:22

702:8,20 709:22

716:20 719:2,3

739:17,18 748:9

748:10 758:15

762:6,8 763:4

774:6

meet 223:8 387:10

387:10 445:11

553:3 718:1,2,2

765:16 781:16

meeting 1:2 2:3,3

2:5,12,21 3:6,16

4:1,4 7:21 35:10

223:14 314:12
Page 65

315:2 318:2,10

436:6,12 539:20

627:15 761:17

762:10 776:12,17

776:22 777:9,17

778:6,8,10,21

780:7,9,13,22

781:8,9,13,14,14

781:16,18

meetings 7:19

25:11 569:19

776:20 777:16

779:7 780:19

781:12

member 4:15,18

5:9 7:2 317:14

553:8 776:4

778:22 779:3,6,8

members 2:22

7:18 187:2

318:16 515:21

762:15

membrane 331:18

337:5,19

memorized 537:8

memory 48:16

49:19,20,21

72:16 76:6 85:21

104:4,17,18

138:21 145:22

158:16 159:4

165:6 359:13

366:16 370:9

371:22 374:19,21

398:8 405:13,15

405:18 406:9

408:22 409:1

419:20 435:21

478:8,20,21,21

479:12,19,21

480:2,20,20,20

481:1 482:4

485:13,15,18

490:8,8,9,10,11

490:15 495:7

501:20 502:1,14

508:7,20 509:2

509:12,13,22

510:6,7,10,13,15

510:22 520:8,8

532:5 534:2,7

536:18 537:16

539:3 550:4

555:4 576:6,13

578:15

men 215:2,2

216:20 266:10

675:17 681:9,11

681:11 682:8

mental 130:8

135:11 136:15

137:2 145:19

167:8 544:9

575:11 610:21

mention 94:6

108:19 161:12

169:14 227:21

420:13 422:7

462:19 484:9,10

484:11,14,15,15

530:9 536:16

553:10 715:6

mentioned 7:18

26:16 44:20

46:13 47:6 58:20

85:2 98:8,10

101:12 104:9

112:13 143:3

144:21 150:2

161:3 173:14

187:1 222:14

254:21 299:22

322:12 329:13

331:20 334:2

338:7 383:8

403:21 411:2

420:15 421:3

455:18 464:7

495:13 499:3,3

503:18 509:21

514:9 520:3

533:8 550:17

566:4 598:22

619:11 624:4

632:8 655:5

669:16 670:18

674:11 682:21

697:18 698:2,2

735:11 743:8

744:17 778:19

mentions 547:18

548:9

mercury 143:22

156:15,19 443:3

445:5 466:2

582:17 746:15

750:22 751:6

774:6,7

merely 163:18

281:16

merge 225:20

merging 8:20

225:1 226:14

241:9,10 488:8

merit 289:10

mess 527:10

message 719:13

messaging 719:16

messed 466:9

met 2:10 3:5

156:13 717:4

metabolic 28:19

37:3 61:5 173:20

332:19 334:11

410:6 414:10

459:10 604:8

605:2 698:14

699:4 725:16

726:5,8 742:5,10

754:16 755:12

metabolism

192:13,17,17

248:8 320:7

321:22 322:4

323:19,22 324:4

324:17 325:10,21

335:16 340:21

341:3,15 348:12

398:1 400:16,22

401:11 402:2

403:6,16 410:13

410:18 413:10

458:13 459:7,11

604:2 625:8,15

627:15 639:21

640:15,21 699:7

711:18 712:5

726:6 742:12

754:17

metabolisms

401:10

metabolite 58:18

171:12,13 173:18

200:6,7 205:3,4

264:11 320:10,11

321:12,13,15,18

321:19 322:14,20

325:16 333:7

334:19 340:20

428:13 601:16

638:1 640:7

641:9 643:21

651:5 660:5

metabolites 39:5,6

43:12 44:10

52:16,17 59:8

173:3,4,16 199:1

199:7 200:5,9,18

308:2 321:16

428:7 434:12,14

439:20 457:18

544:14 583:6

623:13 625:14

626:11,14,15,20

626:21 628:8,18

642:12 651:11,13

652:5 659:10

661:5 694:20

721:10 723:7

734:1

metabolize 258:5

759:9

metabolized

321:10 552:18

640:4,8 698:20

metabolizers

577:15

metal 383:1

metals 517:3

meters 698:17

meth 445:5

methamphetami...

527:19

methamphetami...

527:8

methanol 228:4

method 84:22

90:18 118:18

186:4 191:12

198:15,19 226:10

226:15 228:5

256:16 267:21

271:10,11,11

273:6,11,12,13

273:17,20 274:1

274:2 277:1

278:6,10,11,16

278:20 308:15

309:3,14,20

310:21 311:4,13

311:15 320:8

426:10,18,22

476:10 477:7

648:9,11,16,19

648:20 663:1,2,6

672:13 673:6,19

696:12,20

methodical 203:11

methodologic

364:14 674:10

methodological

8:19 113:20

128:1 156:22

491:9,17 515:14

516:8 521:1

methodologies

123:15 427:10,20

602:22 775:1,1

methodology

114:3 161:8

185:11 226:11

228:6,18 249:11

278:8,12 308:10

390:12 426:7,17

427:16

methods 90:10

122:3 127:11

128:2,4 131:14

139:7 141:11

142:7 143:20

147:9 149:3

161:3,12 175:11

185:13 198:15

268:10 270:19

271:8 272:8

277:7,9 291:15

291:16 320:9,12

320:13 338:13

355:3,9 356:4

357:18 361:3

449:1 494:16,16

495:8,13 496:7

500:22 513:13

519:7,8,17

545:15 569:19

622:20 655:8

662:18,20,22

663:5,16 672:10

method's 572:4

methyl 111:2,4

115:16 155:5,8

169:15,17,18

430:8,15 750:22

methylmercury

129:10 156:16

157:4 168:7,10

527:19

metric 174:17

185:14 268:22

281:2 284:9,10
Page 66

metrics 257:10

284:2 288:10

291:13 294:9,12

319:8

Mexican-Ameri...

116:10

Mexico 128:20

mg 79:6,9,19

496:11 523:12

528:2 554:15,16

mg/kg 31:15 69:4

69:10 73:2,3,7

75:5,9,18 76:10

77:13 79:5 82:22

83:3,21 86:12,19

435:22 489:6

490:22 491:4,15

499:11,22 516:3

517:18 518:2,3,4

519:11,13 520:12

521:9 522:14

526:1,2,10 528:5

597:8,22

mic 318:12 342:5

415:8

mice 71:18 74:18

79:2,14,15 80:19

81:21 83:21 84:3

86:10 88:16,17

88:19 89:22 91:5

480:7 481:4,4

482:13 522:14

523:7 527:1

531:16,18 603:21

Michael 7:5,5

59:18,18 61:3,6

61:10 167:19

168:14 255:2

271:7 273:11,22

278:5 279:21

280:15 308:9

309:9,19 310:4,8

311:2,13,17,20

317:17,17 320:2

320:3,5 334:15

335:10 336:1

337:15 338:5,22

339:8,16,20

340:6,16 341:17

365:3,3,11 366:3

366:8 433:16

467:11 497:16,16

504:7,7 520:14

528:4 533:7

586:5,12 773:12

Michigan 254:15

266:9

microglia 469:14

microgram 679:17

micrograms

181:11 182:21

183:2,7 266:11

288:5 674:6

727:13,18,19

micromolar

288:13,15 325:8

326:2,5 328:7,14

328:19,22 464:4

464:11

microphone 40:16

41:11

microsomal

403:19

microsome 401:21

403:18

microtubule-ass...

53:8 57:9

mid 362:15,17

492:5 716:12

midafternoon

470:9

middle 291:22

503:4 540:1

631:8 632:10

730:1 745:14,15

Midland 254:15

266:9

midnight 777:13

midpoint 117:17

midway 117:4

might've 337:14

399:14 669:5

722:1 754:3

migrations 468:18

Mike 270:21 271:5

271:7 278:3,5

340:12

mil 589:5,9 597:1

milk 543:11

milligram 212:11

212:18 253:21

288:7 322:3,9

327:5 329:1,3

331:8 332:8

344:15 345:7,12

347:5 349:7,9,16

349:21 350:1,2

350:22 353:18

356:15,18 358:10

358:13 359:6,9

362:4,20 363:5

366:14 380:3

milligrams 254:3

327:1 330:9,13

331:8 345:19

353:12 357:7

360:6 361:1

363:20 364:8

million 388:20

millions 733:8

mills 440:14

mils 698:11

mimic 17:16

mimicking 674:20

mimics 774:16

mind 21:12 22:14

62:2 71:20 72:5

107:17 192:14

205:3 208:14

216:16 261:14

305:5 331:4

354:5 448:9

512:20 564:21

565:19 622:17

657:20 685:13

700:21 704:1

729:14 744:11

761:9,12 772:20

774:19

minds 212:8

307:18

mindset 94:10

mine 224:13

minimal 148:12

355:20

minimization

573:14

minimum 288:14

692:7 717:8

749:21

minor 91:10 294:8

321:16 681:7

695:7 702:3

minus 559:19

minuses 631:9

minus/plus 559:19

minute 68:7 72:22

73:20 96:6

194:13 202:4

206:17 294:2

415:20 483:2

509:20 698:11

minutes 2:7 3:15

27:13 29:9 31:16

35:7 40:16 43:10

51:16 68:10

204:18 271:17

301:21 312:6

401:4 419:11,12

470:9 483:4,7,8

605:7 698:13

miscellaneous

154:14

misclassification

201:1 257:2

284:11 292:19

544:11,20 545:6

548:9,10,21

566:3,18,19

567:2,12 568:5

568:20,21 569:2

573:16 580:18

misconception

292:18

misinterpret 589:9

misinterpretation

375:16

misinterpreted

374:18

missed 153:8

162:21,22 196:14

291:12 302:2

592:12 714:20

755:22

missing 87:1 122:2

139:3 141:10

391:8 468:13

545:19 570:1,6

706:16 742:11

749:3

mission 255:21

Mississippi 6:20

mistaken 302:4

mistakes 487:22

527:13

misunderstandi...

662:20

mitigation 28:2,3

393:1 704:14

mitigations 392:21

393:4

mitochondrial

464:8,12 465:7

599:2

mix 296:7 562:3

728:6

mixed 81:15

132:18 133:19

136:10,11 197:7

228:9 435:20

581:6 728:4

mixture 726:2,2

728:22 729:17,21

731:1,7

mixtures 334:19

725:6,12,20

726:11 730:18

ml/l 440:21

Mm-hm 272:22

Mm-hmm 272:19

372:9 373:20

mode 12:8 21:2

23:13,16 27:2

28:5 32:15 33:11

33:12 45:2 113:3

146:10 205:11

206:21 225:13

226:11 229:19

231:6,8 233:7

234:12 241:1,11

241:11,12,19,22

242:16 243:2

245:14 246:6,10

298:4 305:14

306:11,12 307:3

352:16 381:10

431:19 436:21

437:1 441:18

442:7 443:16

446:1 528:17

544:22 551:18

593:22 594:7

611:1 641:10

679:2 691:19

750:21 752:2

753:14 757:16,19

757:21

model 17:11,12

36:8 87:11

101:14 128:8,11

139:20 140:20

142:12 157:9

158:17 160:11,14

172:2 186:13,15

186:17,21 187:5

187:6,9 190:13

194:1,2,7,9,15

195:22 196:1

197:12,15,16

198:4,8 228:20
Page 67

255:3 320:16

323:16,18 324:1

324:3,6,7,9,11

325:4 326:21

332:18 333:2,12

335:2 338:1,7

340:11 341:5

379:4 387:1

400:10,10,18

401:9 402:12

403:21 404:11,15

404:15 410:6,17

411:7 412:2,6,8

412:12 451:10,11

456:3 458:6

459:1 488:12

492:17 517:2

529:22 555:13,14

561:3 585:10

616:14,19 623:5

644:14,17 658:5

658:18 659:4,12

670:21 674:4,5,8

674:22 675:17,21

677:15 685:18

687:20 691:6,21

693:4,6,22 694:1

694:2,6,12,19

695:5 697:13,16

697:19 698:3,3

699:1,7,13,19

700:7,10,20

701:5,6,7,15,18

701:22 702:11,11

703:12 704:17,18

705:7,8 706:12

706:21 707:2,16

707:19 708:4,7

708:17,18,21

709:6,9,16,19

711:14 712:3

716:2 718:11,20

718:22,22 719:11

719:20 721:22

723:12,14,22

724:2,6,6,12

725:2 735:19

737:14,16 738:2

738:11 741:1,5,7

741:11,13,15

772:8 775:3

modeled 139:19

698:5

modeling 36:7

127:18 130:14,19

131:18 132:3

141:7 186:20

193:6 197:12

251:9,11,17,19

251:20 255:8

299:16 346:19

347:3 381:6

397:6 410:12

413:21 417:17

418:9 458:4,21

492:17 517:15

595:10 599:12,22

605:14 611:22

615:4 658:7

660:20 690:14

696:2,11,15

704:9 721:20

732:15 734:21

754:2

modelling 420:5

models 14:13,14

17:10,13,15,15

17:16,16 38:18

101:6 131:12

132:4 186:14

190:5 195:11

196:5 198:6

199:5 200:11

253:2,12 338:3

412:9 451:8,12

461:18 494:1,1

494:15,17 501:4

517:7 613:6

616:5 660:11,14

667:14 675:16

676:3,7 683:10

693:12 696:3,5,8

697:4,6,8 700:20

700:21 702:15

705:6 707:14

710:13 717:10,19

718:19 724:9,10

725:10 726:4

735:6 744:9

745:12

moderate 125:14

125:16,17 145:10

153:16 157:2,2

163:19 492:6

546:12 580:4,12

583:10

moderately 129:9

144:7 545:22

modes 20:21 38:6

39:7 202:21

206:11 232:2

306:13 343:14

344:21 350:14

351:17 368:18

390:8 432:1

437:6

modest 297:15

403:8 487:16

546:5,7 550:15

550:18 579:21

580:6

modification

132:2 340:17

464:16,19 465:7

547:11 548:3

551:16 552:12

556:16 557:3

573:19 580:2

696:19 764:15

modifications

324:10 722:3

modified 21:2

32:16 33:11

542:3 551:2

552:19 746:18

modifier 67:10

modify 455:3

726:6 729:4

modifying 340:15

451:16 461:4

726:3

modulates 506:1

molar 65:6,7,16,22

66:3,12 200:5,6,8

molecular 44:17

45:1,7,15,19

46:10 47:11 48:2

48:11,20 52:20

53:15 54:7,13

56:2,9,18 57:2,10

61:2 64:19 66:14

455:1,2 598:20

molecule 249:16

309:10

molecules 45:15

320:14 628:18

654:20

mom 40:2 136:3

711:20 722:1

724:4

moment 143:5

195:10,11 384:1

386:20 412:2

459:22 460:3

556:10 638:16

moms 169:7

Monday 270:8

monitor 651:11

monitored 178:12

215:17

monitoring 30:9

123:19,21 125:10

179:13 266:2

311:7 557:12

558:15 609:19

629:2 671:9

monitors 125:8

monkey 745:2,9

monotonic 495:19

578:20

Monte 323:21

Monterey 429:5

431:4

month 113:15

648:19 718:9,14

718:15,15 779:1

monthly 122:17

266:1

months 135:17

136:19,20 137:12

137:15,17 152:11

152:15 166:8,9

166:15,16,18,20

295:20 304:20,20

304:20 496:17

498:19 504:12

532:14 533:19

543:6 577:12,17

613:9 621:9

633:1 647:1

713:10 718:10

722:16

Morgan 20:10

264:16 265:6

623:21

morning 2:1 4:3

5:5,14,18 6:3

27:10 68:17

112:13 120:16

143:3 152:21

201:18,20 202:9

207:16 210:1

220:21 224:4

243:18 252:9

253:17 310:16

314:2,4,7,10

315:6,13,16

316:21 318:1,3,4

320:3 382:5

425:17 435:20

470:18,20 539:13

539:16,19 541:19

585:1 611:13

612:12 669:9

672:1 731:16

739:20 740:19

morning's 224:20

morphogen 54:11

433:15

morphogenic 53:3

54:6,14 55:6,9

56:1,4 432:8,12

morphological

54:16

morphology

432:12 464:8,12

465:7 599:2

morris 72:17

73:17 74:16 75:1

87:10,14 94:2

102:6 104:13,16

104:16 480:15

481:19 488:6,13

495:8 509:6

532:6 534:1

701:16

Morrison 365:10

366:15 489:3

491:6 519:3

536:18,19 538:5

Moser 20:7 31:9

40:19,21 68:16

68:17,18 87:15

87:19,22 89:6

90:14 92:1 93:12

97:5 98:15 102:3

103:4 105:11

108:20 109:6

110:12 120:16

209:22 212:11,14

285:10 343:20

354:9 366:6

380:2 452:17

472:1 474:22

517:12 538:4

540:12

mosquito 394:1

mosquitos 652:12

mother 522:16

701:10
Page 68

mothers 116:6

389:8 398:20

399:17 541:2,4

542:9 546:2

614:22 662:6

720:12

mother's 543:11

543:13 698:11

mother-infant

114:22

mother/child

714:14

motivated 142:7

motor 74:5 81:7

82:8 89:7,8,10,11

100:10 167:3

284:5 285:7

356:12 361:4,6

464:21 502:1

533:17

Mount 541:2

550:11 576:16,21

577:7,10,20

578:13 656:2,4

695:2

mouse 78:8,8

360:20 501:15,16

522:13 532:4

603:13 604:7,12

mouth 676:7

move 7:14 12:1

18:6 43:2 52:13

111:21 120:6

172:5 179:10

201:11 202:3

203:20 205:1

213:4 224:7

247:13 253:13

281:22 346:10

348:16 393:11

419:9 436:12

441:10 486:1

493:1 510:18

515:2 517:10

525:2 540:5

592:22 593:3,16

608:8 609:8

621:14 653:21

655:17 666:6

709:9,10 717:8

717:13 720:3

728:18 775:21

777:6

moved 216:4

movement 460:7

moves 504:20

moving 70:2 97:15

127:7 145:5

175:6 178:5

183:9 194:3

207:14 220:8

222:13 223:13

298:14 368:5

385:8 455:17

456:1 458:7

619:6 620:17

706:8 736:7

MSU 317:10

Mt 38:12 115:7

116:15,18 119:17

122:8 133:5,16

134:15 136:21

150:3,12 169:11

171:15 178:22

258:17 259:12

262:22 267:6

283:16 294:14

mucus 628:13

multi 159:14

multiagency

768:20

multicompartm...

698:3

multidimensional

42:14,18

multifactorial

567:17

multifunctional

311:4

multigen 504:18

multimedia

190:15,18,21

191:13

multiple 20:20

23:14 25:6,17

26:9,13 34:18

35:17,19,19

38:16 40:3,6

42:13 43:11

58:17,19 60:17

60:19 63:10

74:18 85:12

94:14 96:6 108:4

108:10 117:6

119:13 141:4

143:20,20 145:17

160:5 170:4,5

173:16 199:1

202:18 210:4,4

215:20 218:6,10

225:1 229:18

257:5,8 278:22

285:2 291:11

295:9 297:18

324:3 346:17

350:4 353:14

389:22 390:4,6

390:19 437:8

441:20 442:4

464:16 475:4

519:4 538:21,22

549:5,10,11

569:6 573:13

595:20 605:19

609:4 618:19

626:20 660:8

662:7 710:22

741:8 750:14

761:5,6 769:1

multiplicative

725:17

multitudes 439:10

multi-compound

311:14

multi-contamin...

663:6

multi-dimensional

219:18

multi-ethnic

115:10 118:12

multi-exposure

644:20

multi-factorial

218:9

multi-level 130:12

130:15

multi-variable

545:15

multi-verse 474:21

Mundi's 597:12

Mundy 20:8 40:17

43:2,5,6 60:14

61:4,8,15 64:8

65:10 66:9 67:4

67:11 97:5 209:8

212:7 350:7

426:1 431:18

432:9,16 433:14

433:21 434:14

469:21

muscarinic 56:11

446:9

musical 201:13

mute 511:19

519:12 654:4,4,5

Myers 20:3

M2 56:10

M3 696:12

N

N 93:6

naEve 538:10,16

naked 679:10

Nale 348:4

name 2:4 5:14

19:14 25:1 41:10

43:6 112:4

212:20 226:21

315:13 320:4

named 52:19

names 480:7

name's 172:7

nan 372:2,5,7

nanogram 352:10

399:9,19,20

400:12 403:1

404:21 405:2

408:15,16,20

409:4 416:4,15

nanograms 288:2

326:3,4 408:11

nanomolar 329:22

330:1 332:17,21

333:1,2 464:3,10

naphthalene

228:17

narrow 178:9

375:21 729:10

739:12

narrower 175:17

404:16

Narrowing 102:4

178:18

narrowly 141:19

143:4

NAS 391:5,13,15

natal 577:10

nation 647:14

712:18

national 6:8,10

28:7 391:5

405:17 638:17

664:11,13,22

768:17,20

nationwide 647:19

715:10

native 331:2

natural 276:11

579:3 628:7

naturally 338:20

nature 18:20 19:1

136:7 144:6

174:11 176:4

375:1 377:15

435:15 538:20

539:4,11 667:5,8

671:6 691:8,13

749:11 751:4

770:18

nauseam 491:1

NCR's 343:10

near 389:1 462:15

660:6 661:12

nearly 28:1 111:9

333:15

neat 107:15

Nebraska 265:20

necessarily 18:11

57:15 203:3

273:17 297:17

313:6 374:8

383:2 384:14

421:17 433:18

435:14 452:13

467:4 500:16

510:5 535:18

583:6 585:13

606:13 609:16

624:19 628:4

691:17 692:10

714:22 720:15

726:22

necessary 242:17

243:18,20 244:1

605:16 649:7

neck 477:5 713:18

need 8:21 18:10

65:1 67:18 86:21

104:21 106:20

107:4,5 108:2,7

135:20 187:17

195:13 207:11

220:22 223:19

227:7 232:16

242:16 243:1

250:9 252:9

253:18,22 273:3

290:1,2 294:22

297:9 298:9

304:12 306:19
Page 69

342:16 351:6

352:7,18 354:5

355:20 361:20

367:8,21,22

368:1 385:20

391:14 394:2

396:18 406:5,16

407:19 427:17

447:22 452:17

453:8 454:10

459:9 481:14

498:10 500:19

509:8 511:19

521:10 522:11

530:3 531:22

565:22 587:1

591:10 605:12

621:7 626:7

629:22 630:3

632:15 633:9

635:6 637:11

639:8 661:21

665:18 675:13

692:4 701:20

702:17 704:15

709:6 711:9,10

711:13,17 712:4

716:2 718:2,2,11

719:12,12 722:7

722:8,21 726:17

726:18 729:14

735:20 738:7

741:10 754:4

759:3 760:21

761:12 763:10

766:18 771:18

772:12,20 774:15

774:19

needed 24:14

409:19 427:13

580:5 639:6

764:22

Needham 281:5

needing 88:15

needs 14:10 27:11

27:11 50:8 87:2

217:4 234:2

356:2 443:18

448:8 501:18

514:14 552:5

590:6 598:7

603:7 612:7

616:21 620:4,6

620:21 626:10

676:1 691:21,21

704:13 720:1

740:18 753:4

754:2 758:16

763:7

negate 574:17

negative 85:9 95:1

95:2 233:7 336:2

336:18 337:18

438:2 509:17

519:12 521:12,13

521:14 526:16

536:14 567:3

597:10 673:6

726:20

negatives 85:5

236:6 238:6

neglected 495:16

neither 215:1

neonatal 58:3

124:21 133:22

134:1 146:5

193:21 463:3

577:5 604:16

neonate 463:5

604:3

neonates 463:4

577:8 603:15

604:4

neonatologist

134:13

nephron 325:10

nerve 463:5,6

678:19

nervous 54:12,20

56:8 57:5 351:10

353:11 446:9

455:6 472:12

net 628:19 725:16

726:10

Network 28:8

neural 54:21

104:12 105:13

343:5 344:5,10

382:16 384:18

385:19 445:7

450:6,15 461:16

466:6,14 551:13

551:14

neural-develop...

462:14

neurite 44:8 54:17

54:18 55:12 56:6

58:1 66:10

432:18 446:8

453:12 460:10

464:1 465:6

466:3,20

neuro 303:8 743:9

neurobehavior

284:4 614:4,7

619:14,21 753:6

759:19 760:11

neurobehavioral

69:13 72:2 85:13

91:17 93:18

107:7 109:3

210:5 271:19

303:13 344:14

355:12 364:12,17

381:15 383:22

462:8,10 472:3

493:11 494:12

507:2,5 515:19

517:3,7 519:9

520:10 525:16,16

526:3,14 529:9

536:14 588:21

597:5,8 713:11

729:7 738:22

752:11,19 765:7

neurobehaviors

701:20 752:15

neurobiological

50:11 54:10

neurobiologist

771:15

neurochemical

348:20

Neurochemistry

513:22

neurodegenerati...

745:11

neurodevelopme...

117:4 118:5,16

130:3 132:8

133:22 135:9

143:14 144:19

150:13 249:14

252:13 412:16

567:15,20 570:13

572:7 581:14

739:4 754:8

766:17

neurodevelopme...

37:13 38:8 40:6

40:13 44:7 52:15

53:2 95:14 97:7

97:20 112:18

116:19 118:2

120:13 122:2

140:9 145:18

147:8 205:7,18

208:18 209:15

210:17 211:8

213:16 226:4

254:22 282:15

283:15 286:6,9

289:6 290:13

298:18 301:4

303:9 342:19

344:6,11 346:22

350:17,21 351:2

353:22 354:3

359:16 363:11,17

365:21 374:2

381:15 392:3

405:11 409:19

412:11 413:5

434:4 446:19

467:15 517:14

518:7,10,13

536:10 539:9

543:15 551:5,8

561:19 562:4

571:22 572:16,21

573:7 574:4,8,22

575:3 577:5

594:2,7 596:5

597:4,14 599:10

607:21 611:11

612:22 613:17

621:4 633:20

638:20 639:4

neurogenic 432:18

neurological 69:13

85:19 134:12

398:19 407:8

409:20 472:4,9

750:20

neurology 5:4

316:4

neuromotor 82:11

neuromuscular

82:16

neuron 450:4

neuronal 49:2

57:22 58:8

105:17 285:17

327:13 328:5

332:22 337:10

432:11 440:12,15

743:15

neuronal-centric

744:3

neurons 460:19,21

469:12 498:16

743:6,16,19

744:5

neuropsych 765:9

neuropsychologist

765:9

neuroscience 99:4

100:20 101:6

104:11 478:16

491:20 492:16

513:20 514:18

neurosciences

87:11

neuroscientist 5:3

307:22 473:17

474:5

neurotoxic 71:22

81:6 438:14

440:17 453:21

492:13 750:19

neurotoxicants

129:11 156:17

neurotoxicity

43:13 44:1 48:6

71:21 81:3 237:3

343:8 439:13,14

neurotoxicologist

43:7 68:18 317:1

neurotoxicology

6:10 492:1

neurotoxin 503:5

neurotoxins 101:7

502:22 527:10

neurotransmitter

93:16 498:13

504:15

neurotransmitters

468:6

never 76:7 99:16

274:11 378:19

420:10 449:13

463:10 465:3

478:1 531:4

554:14 648:15

650:8 752:8

Nevertheless

418:8

new 11:9 12:7,7

24:19 28:11,13

32:18 33:17
Page 70

36:15 68:13

69:19 70:7 86:10

93:22 108:18,21

109:1 113:15,20

114:19 115:8

116:10 117:19

123:2,5,7,11

126:3 168:15

204:10 250:7

253:16 259:20,20

263:2 266:16

268:5 271:10

273:20 276:14

277:15,16 279:9

281:12,15 283:17

310:21 311:4

379:11 381:5

382:9 387:22

392:16 500:22

529:1 572:3

585:16,17 588:9

592:16 619:12,13

619:19,19 620:20

636:14 659:17

661:11 676:19,22

716:8 718:19,19

764:15 768:16

769:7 775:1,3

776:10

newbies 776:11

newborn 31:5,6

134:3 283:21

284:1 541:2

662:7 727:17

newer 38:12 73:7

109:6,18 268:10

271:11 273:11

274:1 663:1,6

newest 36:4

273:13 437:9

news 223:8,9

ng 597:1

NHANEs 176:17

178:7,19 179:9

181:15 198:15

262:17 263:7,13

266:15,22 271:4

428:5 581:1

596:10,14 614:17

656:1,9 657:16

657:17,21 660:3

665:10 731:3

732:2 753:21

758:10

NHEXAS 658:14

709:10

NI 509:19

nice 33:6 34:15

39:1 79:16

103:21 107:13

180:16 189:6

195:10 198:6

332:18 356:15

362:11,12 363:1

366:16,17,22

370:6,22 371:19

374:3 421:4

469:5 470:19

484:17 489:5

490:6 621:7

622:11 727:1

746:7 758:22

765:12 769:14,16

769:20

nicely 443:21

449:18 553:9

664:5

niche 450:9

nicotine 75:15

629:14

NIEHS 117:11

390:17

night 419:8 422:11

431:2,12 553:3

581:15 582:8

601:22 631:3

777:13

NIH 62:3

NIHS 317:2

367:10,14

Niman 20:2 41:3,5

115:15 172:6,7,8

188:12,15,22

189:6 190:1,14

191:9 193:1

196:18 197:9

198:18 199:14,21

200:13 215:5

262:15 441:12

445:22 621:19

655:19 666:7

680:22 690:12

720:5

nine 108:20,22

259:18 430:13

472:6 543:6

713:10 714:19

718:10

nM 288:3

NOAEL 364:12

366:14 378:8,15

NOEL 518:2,2

525:8

NOELs 212:22

noise 371:9,10

Nolan 185:1

215:21 216:14,20

326:8 402:13

414:20

Noland 596:22

nondetext 569:14

569:16 570:3

nonlinear 62:8,8

62:12 63:11

nonparametric

569:12,17

nonspecific 115:13

115:15,20 424:4

544:13 574:18

577:2

non-changed

679:11

non-cholinergic

243:14 320:20

321:6 333:5,19

334:1 341:9

343:14 344:7,21

347:1 350:14

351:21 368:18

381:9 389:20

non-cholinester...

382:16 554:15

563:5 588:21

589:7

non-comparative

184:9,15 691:2

non-correlation

157:21

non-covalently

62:16

non-detects 149:1

149:3 663:14

non-differential

143:17 144:13

145:11 292:19

548:11,22 569:2

non-enzyme

562:18

non-human 745:5

non-inhibited

689:6

non-monotonic

359:12

non-occupation...

188:7

non-orthologous

458:19

non-pregnant

195:1 402:1,5

410:16

non-significant

133:8 136:19

259:1 294:21

546:21

non-smokers

547:19

non-specific

121:13 134:16

138:16 173:3,15

non-targeted

688:13

non-U.S 128:19

non-valid 677:4

non-verbal 545:7

noon 111:11

621:17 653:18

normal 11:15 35:1

35:3 47:8,13,17

49:3 54:19,20

67:13,15 68:3,3

297:14 359:7

400:4 442:15

670:4

normalized 399:12

normally 44:21

315:8 324:19

540:2 740:6

norms 138:7

657:16

North 28:8 43:9

68:19 248:2

Northern 662:5

notable 167:13

176:6 182:11

210:21 212:16,22

215:1 349:17

579:1

notably 115:1

138:20 577:13

604:6

notated 425:10

note 2:16 45:8

70:7 112:7 113:9

115:22 117:9

123:3 124:8

138:8 140:10

141:8 142:3

146:14,22,22

156:9 178:2

181:6 182:22

244:15 252:3

361:20 367:4

402:11 549:6

551:8,12 553:16

576:8 579:21

580:11 617:10,12

617:20 695:7

704:5

noted 80:18 81:2

112:22 117:14

119:4,15 124:11

126:21 137:18,22

141:14 142:13

144:5 145:9,16

146:16 179:2,3,5

180:10 182:4

183:4,16 187:5

212:6,7 221:21

424:1,15 545:12

551:21 552:13

573:2 574:2

576:20 580:22

596:17 598:3

608:17 624:16

656:3 676:2

724:8

notes 131:15 545:3

546:4 550:14

553:22 620:5

709:21 736:6

763:15

notice 83:2

noticed 504:18

noting 547:5

notion 661:21

773:9

notoriety 252:7

notwithstanding

633:14

novel 73:15 77:17

477:20

novelty 77:16,17

novo 66:22

NRC 9:16,20

10:16 11:7 16:10

18:1 23:3,11,21

24:3 26:3 29:10

32:14 34:13,21

39:2 47:7 62:9

62:18 63:3
Page 71

202:10 204:19

207:1 219:22

472:22 747:16,22

751:11,16

NRC's 21:22

NRDC 29:5

387:16 389:3

390:10 392:4

781:4

nuances 90:8

nuclei 743:6

null 149:10,11,15

165:19 240:12

258:18,19 259:1

292:20 294:17

295:14 296:1,8

297:4 312:14,14

312:14 457:10

490:4 531:4

546:21 548:15

549:2 569:3

573:17

nulls 240:3 295:4

number 1:7 3:16

29:21 46:8 50:16

52:17 76:9 77:3

80:22 104:9,13

104:14 131:1

134:18,21 139:3

145:19 148:4

161:22 162:22

176:13 178:3

180:5 207:5

211:12 214:4

218:20 221:14

250:3 251:18

252:22 259:6

274:14 308:6,14

309:15 311:1

320:8 322:1,16

349:11 351:19

354:4 355:19

358:15 360:10,11

360:14 361:7

367:6 369:8,10

371:17 384:9,11

396:12 412:10

430:13 439:4

446:4 449:17

451:16 461:18

466:2 469:1

472:4,9 474:18

475:13,16,16

479:1,15,18

480:15 481:20

484:10 487:3,15

489:22 495:17

496:19 504:4

513:20 514:10

519:17 528:16

535:9 563:10

565:1 567:13,14

574:19 618:1

620:5 622:20

631:20,20 637:20

638:18 646:12

661:6 667:6

679:17 681:2

691:7 695:9

728:1 743:3,3

750:18 752:21

755:19 756:10

761:8 764:14

771:6 779:7

numbering 259:18

numbers 36:13

163:11 169:2,5

170:8,15 308:6

408:7 409:8

410:21 415:2

449:14 487:4

542:8 643:9,12

684:20 760:5

numerous 321:1

322:15,22 333:4

333:8 441:13

529:20

nutrient 61:4

62:21

nutrients 627:21

nutritional 167:20

nutshell 239:19

N-O-A-E-L

344:13

O

OBGYN 635:1

objective 364:18

573:12 692:3

objectives 176:14

718:1

obligation 389:8

392:12

obscure 15:11,11

obscured 15:8

observable 552:19

713:11

observation 136:5

137:8 438:2

452:5 537:21

616:8 624:5

observational

113:10 176:18,22

178:4 196:18

225:8,11 230:19

452:4 655:21

667:5,7 671:6

738:21

observations

141:5 145:8

238:16 253:8

397:8 414:12

610:5 612:3

614:3 674:18

693:11 695:19

observe 182:8,14

182:17,19 232:8

327:20

observed 57:5

140:14 145:10

147:8 174:16

178:15,16 180:13

182:4,12,13

183:6 262:12

287:5,8 290:15

299:20 363:15

399:1 416:16

418:4 500:1

531:9 543:22

544:7 551:16,22

572:7 573:3

582:16,21 583:3

599:3,17 611:4

611:10 612:13

614:17 615:7

621:6

observing 232:7

500:10 606:4

obtain 663:7

obtained 543:5

obvious 15:9 53:12

363:22 384:18

528:22 536:7

752:4

obviously 14:22

60:16 61:16

163:9 220:4

289:17 379:2

382:22 383:4

411:14 439:7

461:3 469:5

481:20 504:13

580:15 651:1

665:10 678:4

683:12 689:22

752:2 759:20

768:2

occasion 451:14

Occasionally

628:2

occasions 510:1

occupation 174:12

occupational 6:6

6:22 119:9 120:5

141:22 177:1,2

178:13 181:8

197:3,8 317:13

384:4 395:1,6

666:19 667:4

671:9 672:7

673:12 681:16

occupationally

188:8

occur 35:2 45:13

50:14,15,16 51:4

156:13 167:13

183:22 190:6

211:9 299:21

303:10 336:19

340:19 393:9

459:14 498:14

499:11,14 500:3

504:13 524:17

605:20 630:6

660:14 725:13

occurred 142:22

214:1 217:10,12

287:8 440:11

512:5 543:22

548:22 558:9

579:4 615:15

656:6 660:19

occurring 64:4

167:14 290:5

351:2 383:19

385:17 468:5

564:13

occurs 64:5 243:10

303:16,20 324:5

338:8 434:12

456:5 462:15

498:1 552:1

599:10 687:7

odd 435:17 483:1

oddball 486:2

odds 134:21 145:8

169:8 567:8,11

568:2,12,16

575:11,16,20

578:2 593:9

753:17

odd-looking

732:11

offer 19:5,5 230:12

247:22 452:16

520:15 612:21

649:20 650:9

742:15

offered 541:7

725:4

offers 126:13

offhand 198:19

office 3:4,10,11

4:17 19:15 20:5

25:2,3,6,8 29:13

43:7 111:13

112:5 172:8

176:19 248:15

315:20 736:17

780:2 781:5

Officer 3:3

offices 25:5

official 2:5 318:2

540:13 668:22

778:6 780:9

781:15

officials 2:20

offline 336:22

offspring 475:13

475:17 493:19

496:16

oftentimes 250:8,9

oh 91:12 154:6

155:22 156:11

171:1,14 198:12

198:13 231:15

232:16 235:12

241:7 255:19

269:20 273:7

303:7 340:16

342:6 364:22

365:8,16 367:11

367:15 369:3

372:19 396:22

415:10,19 417:6

632:13 645:21

735:2 762:3

oil 349:22 504:1

506:8,8

okay 19:8,12 24:21
Page 72

34:19 41:8 43:2

43:5 68:17

109:19 148:20

150:2,8,9,11

151:3,22 152:6

152:16 153:1

154:13 158:11

159:8 161:3,17

162:4,12 163:5

164:10 171:1,4,5

172:4 188:1

190:3 197:6,10

197:14 201:14

221:5 223:6

247:5,12 255:15

269:17 270:17

281:22 303:18

304:12,16 312:1

327:8 341:20

342:16 356:14

358:3 365:8

366:8 367:15

369:3,22 370:17

371:6 373:21

374:17 376:17

377:10 378:11

386:9 387:13

388:6 396:22

405:9 406:13

407:7 414:21

421:12 425:13

445:18 471:18,19

483:9 488:17

508:8 514:21

517:9 518:18

522:13 523:16

524:4 525:13

539:17 540:18

558:5 562:5

617:11,20 619:8

621:13 627:4

645:8 653:17

655:17 668:16

673:2 674:15

683:5 688:7

690:3,9 697:14

711:4 722:16

724:7 734:2,4

757:2 780:8

Oklahoma 4:11

315:14

old 12:8 273:3

322:10 323:20

412:13 443:3

533:19 557:15

683:19 746:7

older 89:12 485:15

485:17 534:12

556:9,20 574:8

663:2 672:15

olds 555:5

once 83:1 122:16

214:1 232:15

285:21 338:10,13

452:12 469:14,15

473:22,22 590:17

650:12 661:21

678:14 723:16,22

725:3 726:16

758:15

ones 106:8 115:2

154:18,18 172:1

228:10 230:22

240:2,12,12

254:8 275:2

294:13 391:18

428:10 429:11

434:12 508:1

519:4 535:6,6

551:8 576:11

660:1 735:14

one's 657:19

one-compartment

724:2

one-hour 417:13

one-size 676:7

one-time 121:7

125:18,20 126:14

142:19

ongoing 592:6,7

638:21 671:8

672:7 673:12

onset 524:13,21

ontogenetically

625:13

ontogeny 28:19

604:21

on-board 746:21

oomph 24:15

OP 27:20 91:9

134:17 154:22

171:10 173:16

179:22 362:12

370:22 423:2

424:4 582:18

open 1:2 41:9

59:14 79:7,17

81:12 87:5

147:19 225:22

247:9 269:9

306:17,18 308:17

334:6 337:5

361:6 417:12

419:1,2,7 422:5

440:5 444:5

445:17 487:13

490:10 511:11

523:7

opened 29:14

305:20

opening 19:11

248:1 317:21

318:13 529:17

711:4

opens 730:12

operated 548:11

operating 242:1

OPI 661:19

opinion 24:18

53:21 68:5 98:11

278:11 299:6

310:8 371:13

552:21 614:2,11

OPP 1:9 20:1

778:5

opportunities

68:14 175:14

190:21 587:20

588:3,5 671:21

730:9 772:1

opportunity 3:6

151:11 224:11

282:6 314:2

318:3,16 320:4

462:3,4 606:13

610:9 612:21

620:3 671:8

693:19 756:19

769:3,9 776:5,16

opposed 91:10

135:8 153:6,9,11

283:19 296:5

340:15 594:11

626:4 638:11

641:18 647:5

655:1 762:16

opposes 530:18

opposite 239:10

240:2 261:3

436:1

OPP's 20:17

OPQ 28:5

OPs 110:14,16

112:21 115:21,21

155:11 171:21

258:5 424:7

425:21 436:22

437:5,6 632:21

729:10

optimal 488:7

600:15

optimally 494:2

optimistic 768:19

option 479:17

options 22:4 132:3

172:16 183:9,12

187:14 208:4,4

690:15,22 691:8

748:3

oral 30:22 37:5

112:11 119:11

187:9 214:22

215:4,9,21

216:13 270:16

321:10 325:7

342:21 346:19

348:2,7,10

349:16 355:15,17

360:14 363:4

364:11 396:18

398:16 400:18

401:6 411:16

414:19 499:6

505:13 506:8,15

506:18 698:7

701:11 781:2

orally 349:8

orange 646:17

oranges 215:12

ORD 20:7,11 38:4

68:18 178:3,4

190:18 777:4

order 156:13

163:6 181:14

213:2 216:15,17

216:22 218:2

227:8 231:8

245:2 257:21

290:4 326:5

382:18 398:11

399:19 406:16

415:11 417:1

473:10,19 487:17

566:13 567:10

583:3 584:5

587:5 590:7

633:10 663:7

704:14 706:3

722:4 736:19

745:5 746:4

ordering 474:10

632:4,9

orders 181:14

326:15 328:1

364:7 385:2

739:2,9 741:21

742:6 767:18

ordinarily 358:21

ordinary 752:7

organ 46:2,4 49:11

54:8 56:20 57:6

organic 264:13,13

organism 54:22

305:1 442:19

535:12

organization

16:15,17 24:16

43:21 44:11,19

45:14 46:14,16

46:18 48:13 51:1

51:9 52:5,12,18

53:10,13 55:3,19

56:1 59:4,6,13

63:3,5,20 65:14

65:19 67:19

243:21 248:3

389:3 441:17

447:5 469:22

472:20

organizations

50:15

organize 26:7

63:12,17 235:4

776:22

organized 419:20

organizing 25:20

32:22 39:19

63:13 202:8

241:14

organochlorine

582:18

organochlorines

128:17

organophosphate

116:17 117:3

118:2 120:3,12

129:14,18 130:2

144:3 145:2

430:9 648:10

652:1,16 678:3
Page 73

organophosphates

258:11 335:16

392:18 729:5

oriented 262:16

569:10

origin 128:20

428:4 616:12

original 277:1

324:2 356:10

400:22 653:10

700:13

originally 186:22

400:11

ornamentals

430:14

orthologs 604:1

OSHA 266:1

Osmotic 61:6

other's 725:15

Ottawa 6:1 316:20

ought 232:8

237:20 244:21

549:4

outbreak 431:11

outcome 9:19

10:14,19 11:16

12:6 16:9,21

17:3 18:1 20:21

22:18 23:4,7

24:1 25:16 26:6

26:21 33:13,14

33:17 34:11,22

38:6 39:7 40:11

40:13 43:19 44:3

44:15,15,18 45:3

45:6,8,9 46:8,11

47:1,2,5 48:1,4,6

48:8,13 50:2,7,21

53:2 54:1,5,8

55:20 58:21

60:17 61:17,21

64:15,21 97:12

100:3 101:3

103:19 105:9

129:6 132:17

135:6 139:12

140:1 143:8

144:17 145:4

152:3 156:16

157:17 159:12,13

159:20 160:10,12

160:17 184:18

202:20 203:4,6

204:11 205:11

208:2,9 209:12

209:14 211:22

213:17 217:13

241:13 243:17

245:15 284:12

355:9 370:12

382:14,15 386:14

390:9 392:2

412:22 426:2

431:20 432:17

436:21 437:1

441:18 442:7

445:8 446:1

453:6 454:13

466:13 483:19

492:13 493:8

530:1 545:9

550:7 559:15

561:7,8,12

566:10 573:11

579:7 587:11

593:22 602:6

612:10 614:7

638:20 639:4

658:10 729:19

742:12 749:6

752:10 753:7

755:13,14 759:19

759:21 760:11

769:4

outcomes 20:21

26:15 35:6 38:8

40:6 44:7 72:11

84:5 86:17 93:18

97:6,7,19 100:9

107:7 110:13,21

112:18 116:19

117:1,3,6 118:2

120:13 122:2

140:9 143:14,18

144:17 145:18

147:8 169:1

202:21 205:7,16

205:19 209:15

210:17,20 211:8

233:11 256:7

258:15 260:6

280:10 284:5

285:13 287:5

298:18 350:17

354:4 363:19

364:13,17 381:9

381:18 382:13

383:22 384:19,20

446:19 447:6

467:15 472:21

484:16 507:3

518:10 528:17

542:12 543:4,6

544:16 545:10

546:7 548:14,19

548:21 549:8

550:17 551:5

552:21 561:19

562:4 566:14

567:15,22 571:22

572:7,16,21

573:3,8,13,15,16

574:4,8 594:2

596:5 659:7

695:3 698:6

726:20 727:3

752:11,19 757:4

757:9

outgassing 416:10

416:11

outgrow 590:7

outgrowth 44:8

54:17,17 55:12

55:15 56:6 58:1

66:10 323:3

432:18 446:8

453:12 460:11

464:2 465:7

466:3,20

outliers 263:14

outline 40:15

43:17 97:14

172:20

outlined 47:9

58:21 183:10

184:7 185:5

186:11 187:14

428:10 493:13

665:13

outlining 337:7

outlook 768:18

output 325:4

326:21 645:1

698:14 709:12

746:3

outset 762:11

outside 68:3 129:8

130:17 153:18

156:22 171:14

174:12 252:11

330:10 400:2

410:18 444:8

449:8 670:17

704:9 753:20

outstanding 778:8

779:21

overall 8:3,15 15:2

15:4 58:16 76:18

77:6 82:2,8

93:19 102:16

111:7 113:11

114:12 118:11

120:5 122:5

128:10 131:16

133:19 135:4

137:1 138:5,15

139:11 142:7

143:16 144:1

168:3,8 180:11

219:20 222:6

280:17 303:8

319:7 322:20

344:16 402:9

403:8,10,11

406:20 408:6

499:9 502:4

519:1 530:4

572:13 596:8

602:9 603:7

609:21 619:15

630:12 671:1

674:11 681:11,12

682:19 693:17

701:21 722:5

730:2 748:7

764:2 775:3

overcome 47:20

567:12 568:4,7

568:19

overcomes 51:10

67:20

overcompensate

68:1

overestimate

174:5

overestimation

292:14

overextended

380:16

overlaid 34:14

overlapped 656:4

overlarge 625:17

overlay 204:22

overly 685:11

oversample

475:16

oversampled

493:20

oversamples

475:13

oversampling

475:12

overtime 683:2,13

overview 43:18

44:5 52:16 72:10

179:13 295:16

426:14 509:21

621:20

overviewed 507:16

overwhelmed

51:21 687:16

767:3

over-estimated

147:12

oxidation 45:18

61:11

oxidative 56:22

58:4 285:17

551:17 597:18

598:1,4

oxon 55:2,4,7,11

55:15 192:15,17

205:3,6 209:17

320:10 321:15,18

322:12,13,20

323:1,7,7,9 324:4

325:15 327:17,21

327:22 328:20,22

329:7 332:14

333:6,7 338:9,20

338:22 339:3,9

339:13 340:4,7

340:20,22 341:3

341:7,8,10,11

434:2,16 446:3

447:11 456:9,10

456:16 457:16

460:13,18,20

461:8,11 463:1

464:1,3,9,10

564:20 603:17

604:9,10,11

605:3 625:20

626:1 627:18

628:11 640:15,20

641:3

oxon-based 332:20

oxygen 53:6,14,17

53:19 56:17,19

56:21 434:1
Page 74

oxygenated 627:21

627:22

o'clock 111:14

539:16 553:4

P

p 261:19 340:12

485:7 489:20

package 442:21

453:2

PAD 254:7,8

Padilla 20:8 40:17

Paediatric 195:11

page 33:10 165:15

196:14 258:14

266:3 394:14

445:4 471:14

598:3 600:16

655:11 681:6

pages 257:15

287:19,20 301:21

PAH 128:16

159:10,10,11,20

160:11,12 557:11

557:12,16 558:12

559:3,19 560:3,3

560:20

PAHs 158:20

555:1,8,18

556:20 557:2,11

557:17 558:12

559:4,11,22

561:1,18 729:3

paid 638:10

pain 477:5 710:9

713:18

painted 445:10

pairs 114:22

pair-wise 484:14

Pam 460:15

pan 239:5

panel 1:1 2:3,8,12

2:17,22 4:3,5,15

4:19 7:9,14,18

8:2,4,6,6,15

19:16 21:12

22:12 28:10

32:10,11 36:9

41:9 59:15,21

60:15 61:21 62:9

63:3 87:6 112:22

113:7 122:10

126:4 132:10

152:20 165:12

166:12 187:2,3

216:11 220:14,17

220:22 223:15

224:16 246:20

248:13 255:15

310:11,16 314:2

315:2,8 318:11

318:16 334:7

343:19 365:1

370:6 381:22

383:12 387:4,17

387:19,20 388:3

395:18 396:5

409:21 410:5

418:20 419:3,7,9

419:17 420:2,7

422:6 436:13,14

440:5 445:18

461:21 512:21

515:21 518:19

539:7,11,15,20

540:1,3,18

541:20 553:2

554:4 572:13,18

581:10 592:12,20

603:11 608:21

616:7 623:8

624:7 630:22

634:4 648:3

678:2 679:7

681:1 699:9

719:10,15 720:22

734:20 735:19,20

746:12 760:20

761:16,22 762:15

766:21 775:12,13

775:16,22 776:4

776:4,8,11 777:8

778:3,22 779:5

779:16,22 780:12

781:15

panelists 381:20

497:7 723:4

724:8 741:4

770:21

panels 8:18 206:16

250:3 256:2

379:13 439:11

554:6 775:14

panel's 114:12

196:11 593:18

606:11 637:9

pans 245:16

PAN1 324:21

335:3,4,13

paper 11:1 21:19

22:17 26:4 29:2

39:18 48:11 70:3

70:10 71:2 73:7

73:16 85:6 88:3

97:4,14 112:8,9

124:11 125:13

140:17 146:8

159:11 161:9

165:5,8 172:10

172:20 175:20

177:15 178:1

179:6 181:16,19

182:17 183:10

184:7 185:5

186:11 187:14

189:3 204:8

211:1 216:9,10

220:5 222:13

223:17,21 225:22

226:8,19 227:22

230:7,9 233:2

244:9 247:9,10

252:5 254:20

263:22 265:6

275:19,19 276:4

280:9 283:1

287:22 292:13

294:4 296:9

302:17 308:16

309:3,22 313:16

325:2 335:11

336:9,11 342:4,8

343:19 352:14

354:16,19 355:1

363:18 365:10,17

365:19 366:15

378:13 414:4

422:16 423:1

425:10 426:3,11

426:15 427:11

435:2,2,3,4,8,21

436:2 445:1

455:21 462:20

463:3,18 464:8

477:3 481:17

506:7 514:4,6

523:12 531:15

538:5,12 541:14

541:21 543:9

547:17 550:6

551:19 553:14

554:13,21,22

556:8,20 559:4,5

566:4,7 570:11

580:11 590:5

596:18 603:5

617:4,4,15

621:20 637:7,14

643:1,15 645:7

648:14 651:19

652:14 655:12,20

659:16 666:8

677:6 679:8

680:6,11 681:6

682:4 684:22

686:19 689:18

695:13 700:9

708:20 761:20

papers 69:18,19

69:22 70:4,6,7,14

70:18,21,22 71:1

71:16 74:17,18

75:13 87:9 88:18

100:3 103:18

108:18 109:16

110:3,6 113:15

113:21 213:17

217:21 228:1,3

252:16 261:6

280:19 319:14

336:10 352:15

354:3 355:5

357:1 365:22

423:3 440:11

445:6 449:21

496:5,10 500:15

505:16 507:22

508:9 509:7

525:21 526:1

549:19 551:7

554:19 555:21

558:20 568:17

572:4 623:20

638:3,5 647:1

648:7 764:21

paradigm 438:8

465:3 490:14

658:6 742:4

paradigms 71:4

paragraph 165:15

589:19 590:4,14

590:16,19

parallel 409:15

paralleled 674:5

parallels 667:20

parameter 354:15

403:6 405:16

708:17

parameters 187:5

324:7 355:18

380:5 398:1

403:13 641:8

707:22

paramount 660:10

665:10 741:15

paraoxonase

28:21 546:15

paraphrasing

591:16

parathion 111:2,2

111:4 169:16

parathyroid

661:19

parathyroid's

657:7

parent 115:2

133:1 173:18

200:14,16,17

205:2,6 327:22

423:20 430:4

434:11 463:22

464:4,11 541:11

543:10 599:19

624:6 639:19,21

640:6,7 641:3,8

641:14,19 642:5

642:12 644:12

646:20 648:12,16

648:21

parentheses 256:9

parents 134:3

Park 43:9

parsimony 128:11

139:20

part 4:4,18 15:4

17:18 22:17 29:5

29:20 32:13 48:8

54:16 65:11

85:15 90:16,19

98:21 100:1

102:17 105:19

109:11 112:19

113:2 117:11

121:10 131:7,8

144:18 154:9

160:18 175:10

182:14 183:21

187:3 199:2,18

223:16 226:17

234:11 245:1
Page 75

246:10 250:11

253:16 263:6

278:7 280:20

297:6 302:3

309:1,20 351:12

356:7 389:4,13

397:2,22 429:10

436:6,12 470:13

470:18 517:11

525:7 528:14

530:12 531:14

542:16 590:21

594:17 595:2

605:11,12 618:5

630:9 640:9

641:22 672:6

682:7 698:14

699:6 701:20

728:21 735:3

749:3 750:13

758:21 759:10

764:9 775:21

partial 534:18

partially 705:9

participant 3:2

participants

112:20 114:18

115:8 116:7,11

117:20 118:5

119:9 122:15

123:4,20 125:22

126:14 128:15,19

137:8 144:7

175:13 263:6,9

263:18 542:17

548:20 596:14,14

667:7 671:18

682:5 721:3,12

729:1 778:7

participate 250:2

250:4 342:17

633:6 776:15

780:13,16

participated 779:7

779:8

participating

117:21 343:4

participation

665:6 779:15

particular 13:21

58:14 67:19

90:10 101:6

154:19 169:3

180:9 183:6

184:17 191:14

213:13 231:13

251:5 311:7

349:4 352:4,9

354:4 359:3

361:7 362:20

365:19 368:10

375:6 378:2,8,9

396:17 405:18

417:8 462:7

474:16 476:9

496:3 513:3

522:3,4 534:6

543:19 556:8

566:6,7 567:18

585:12 606:14

610:4 616:20,22

632:21 647:7

650:20 668:20

683:20 757:16

762:12 763:14

765:22 766:3

particularly 20:1

26:5 31:20 32:8

38:9,16 42:6

114:3 127:1

128:18,20 145:18

145:21 156:9

161:22 173:12

174:12,20 175:4

175:16 178:15

184:2 201:8

207:4 210:17,20

242:14 248:16

318:6 392:19

393:8,22 424:20

458:18 461:22

485:21 503:11,16

507:17 522:10

529:13 530:2

537:20 569:13

581:5 582:13

602:20 609:2,22

611:7 650:13

720:7,17 740:13

768:3 776:22

777:4,12

particulate 190:7

partition 398:2

403:7

partly 416:9

parts 16:4 96:6

97:15 271:1,9,12

278:12,17,20

279:2 308:20

309:22 397:1

470:12 515:2

593:4 601:13

608:16

pass 226:2 400:19

429:1 431:17

443:10 701:12

709:21

passage 751:17

passed 389:7

400:20 680:6

passing 40:16

passive 73:12

485:22

pasteurized 383:7

patency 537:12

path 33:14 59:20

203:6 457:17

482:1,1 488:13

507:18,19 766:22

pathology 466:12

pathway 11:5,8,9

11:16 12:1 16:9

16:10 18:1 24:1

34:12 35:1 43:19

44:3,8,15,15 45:6

46:8 47:1,5,5,7,8

47:9,13,17 48:2,4

48:6,18 50:2,7,21

53:2,15 54:3,5,9

54:13 55:21 60:5

61:5 64:22 67:12

67:14,14,15

96:14 97:11

191:8 205:14

209:14 211:22

217:13 243:17

245:15 339:2

340:2 385:20

394:3,4 410:20

432:17 436:22

437:2 441:19

442:1,8 443:6,14

445:8,12 446:1

456:10 465:1

552:18 561:17

594:1 603:19

604:9 605:2

628:19 640:10,21

644:20 707:16

726:5 735:22

755:13

pathways 9:19,19

10:4,14,19 11:15

12:6 16:21 17:3

20:21 22:18,21

23:4,4,8 25:16

26:6 33:13,17

38:6 39:7 43:11

43:14 44:5,6,12

48:8 52:14 53:11

53:14 54:1 57:14

58:21 60:17

61:17,22 63:10

68:3 118:22

191:5 202:20

204:12 205:11

208:9 209:12

215:20 241:13

334:11 389:22

390:9 392:2

426:2 431:20

432:3 434:6

598:5 618:19

681:17 725:18

726:4 755:14

pattern 77:1 82:5

354:21 360:20

361:16 364:14

370:21 371:8,19

380:8 511:2

670:10,19 674:11

708:22

patterns 121:19

142:22 230:21

231:22 232:1

262:18 355:8,11

371:11 657:6

692:11 731:20

Paul 255:6 319:5

paused 763:15

paved 760:8

paws 361:12

pay 468:20 476:15

480:17 620:9

PBBK 529:20,22

PBK 455:10 456:3

PBPK 186:14,15

186:20 187:6

193:6 194:7,15

195:22 196:1

197:12 216:10

251:17 253:2

338:7 397:5

400:9 402:10

403:21 410:12

420:5 602:15

613:6 616:5,19

644:13,17 645:1

658:7 659:3

660:11,20,22

667:13 670:9,21

674:4,4,8,12,22

675:16,17,21

676:3 677:15

685:18 691:6

693:4,6 694:1,2,6

694:12,19 695:5

696:2,15,21

697:13,19 700:1

700:6,10 701:5,7

701:13 702:11

703:12 704:9

705:6,8 706:9

707:13,16,19

708:17,18 709:6

709:19 710:2

718:11 719:11

721:20,22 724:5

724:6,12 725:9

726:3,6 734:21

741:1,5,7,11,13

741:15

PBPK-based

397:22

PBPK/PD 255:3

690:14 691:20

696:12 699:19

700:20 701:22

702:15 735:19

PB/PK 458:3

PB/PK/PD 381:6

458:4

PCBs 582:17

PD 456:21,22

701:7,15,18

PDD 260:16,17

575:21 578:2

PDI 137:4 276:1

291:18 295:18

296:4 297:12

298:7 579:15

peak 215:8 596:22

600:17

peak-o-grams

596:19,20

peanut 349:22

Pearce 475:10

pediatric 5:4

194:2 195:11

316:4,4 604:16
Page 76

697:2

pediatrics 5:2 7:6

276:4 317:18

457:9 460:1

634:5 696:14

705:13 706:20

771:3,5

peer 2:15 15:15

32:13 168:5

500:15 644:14

678:12

peers 714:15

peer-reviewed

177:6

Pellston 48:9

people 3:7 90:16

95:11 96:8 103:9

190:12 193:14

202:16 215:17,20

231:14 256:1

266:15 270:6

286:11 293:7

327:9 336:7

387:19 393:2

394:4 396:14

399:16 401:11,12

401:13,17,20

402:1,1,5,5 408:1

408:5 409:20

410:9 414:1

415:9 416:3

433:10 449:8

474:20 475:10

477:9 478:17

479:10 480:17,21

481:2 492:5

493:3 503:1

512:11,15 514:1

517:2 519:20

525:3 527:11,13

535:14 569:17

616:20 620:14,17

635:9 661:6

669:17,19 670:15

674:21 679:15

680:18 683:20

684:4 689:19

703:11 706:17

711:14 715:4

722:19 726:11

728:9 730:2,16

730:17 740:1

745:1 747:1

752:7 759:3,20

767:13 771:4

people's 769:15

perceived 146:20

percent 79:17

115:11 148:2

154:16 157:6,21

208:22 211:16

267:11 269:14,15

278:22 279:1

280:14,16 321:17

326:22 327:7

329:15,16,17

330:7 345:6,9,13

345:20 351:20

352:1,5,13 353:1

367:22 378:3

390:15 400:2,3

401:1 404:20,20

406:8,14,15,15

407:2,2,2,14

409:17 414:20

417:22 440:21

469:17 477:15

546:22 566:14

576:5,5 586:21

595:22 616:15,17

616:17 629:10

643:2,5 644:7,8,8

644:9,10 645:2

647:13 648:14

652:22 653:8

663:21 670:6

673:5,15,16,17

673:18,22 677:13

677:13 680:9,10

682:5,6,21 683:8

683:10,15,21,22

684:2,10 685:2

689:20 721:4

726:19 727:1,12

728:8 731:9,10

percentages

697:12

percentile 177:20

223:1 328:21

406:9,11,22

727:20

percentiles 177:20

perceptual 578:14

578:18

perfect 14:19

101:1 418:15

769:2 770:14

perform 21:8,13

492:5 663:16

743:10

performance

49:21 74:6 82:16

99:10,10 349:4

375:11 378:20

492:5 502:1

532:13

performed 35:22

115:5 117:11

122:14 123:16

124:9 127:16,22

132:3,16 139:16

140:16 176:21

197:3 206:7

437:7 595:3

617:14,14 627:9

performer 485:20

perfused 324:15

perinatal 58:11

71:7 81:19 98:14

period 3:8 41:20

71:9 79:16 84:1

84:2 86:7 98:12

119:3,6 121:9,22

122:18,20 123:9

123:20 124:10,13

125:5,7,9,20

139:1 146:5,5

155:12 162:16,18

163:3 175:17

196:21 197:2

224:5 356:13

369:2 383:4

399:7 419:2,5

472:11 542:10,17

584:12 586:20

613:9 633:19

646:16 661:8

684:1,20 687:7

708:2 713:10

718:10 720:7

754:3,19,20,21

periodicity 120:19

126:17

periods 41:13

69:15,16 71:2,5,6

71:7 84:10 86:9

87:3 177:13

432:10 459:13,19

460:1 489:8

498:21 543:16

peripheral 55:8

permanent 4:15

4:19 95:15 776:4

778:22 779:5

permeability

331:18 337:6,19

permission 342:9

permit 573:6

permits 692:1

permuting 403:12

peroxidation

551:18

perseverance

778:15

persist 499:10

504:9

persisted 527:14

575:7 576:9

Persistence 496:9

persistent 74:15

498:7 499:14

person 174:9

368:13 388:10

407:17 460:16

463:10 568:10

569:10 634:16

635:10 642:13

670:5 673:13

680:16 689:7

742:2 744:2

personal 123:18

125:8 165:17

168:6 395:5

396:8 512:11

579:12 614:11

624:17

personally 170:12

604:14 778:9

persons 680:16

684:22 685:1

person-to-person

401:16

perspective 116:21

144:6 249:13

307:14 343:18

354:7 357:21

402:18 430:11

527:5,14,21

573:5 626:16,17

667:9,12 673:20

689:11 712:16

739:7 749:4

767:7 772:12,19

774:12,15

perspectives

249:18 255:10

319:18 655:12

persuasive 528:2

pertaining 655:14

perturb 47:13

54:19

perturbation

11:14,21 35:5

47:15,18 56:13

67:19 228:5

455:14 459:14

746:6

perturbations

11:7,8,10,13

33:16 203:5

205:12 350:10

351:6,9 368:19

381:9 446:13

447:3,4 456:20

472:18,19 515:7

746:17

perturbed 47:9,14

48:3 68:3

perturbing 53:5,6

67:13,15

pervasive 137:12

260:16

pest 431:11 731:22

pesticide 2:16

12:16 19:10,16

25:3,6 28:7

29:13 112:5

116:20 119:2,4

120:5 121:3

122:16,17,22

123:10 126:21

169:22 170:2,4

171:11,12 172:9

174:17 197:3,4

248:15 343:9

392:14 427:21

428:13 431:3

583:12 584:7,9

585:9 652:1

660:19,22 661:13

661:15,18,19

662:5,11 678:3

732:4 733:7

781:6

pesticides 29:4

32:7 110:15

112:21 115:17

129:18 130:2

154:17 170:21

173:16 176:22
Page 77

178:13 179:22

217:16,22 273:14

427:12,13 428:12

428:21 429:5

430:1 574:10

582:18 583:9,11

594:20 612:15

642:6 661:17

699:10 716:8

721:11 722:20

726:7 731:5,7,8

740:16

petition 28:6 29:5

petitioned 389:3

pg/g 426:14,20

427:5,6 589:5,9

720:11

pharmaceutical

46:20

pharmacodyna...

323:16 333:13

719:20

pharmacodyna...

457:2

pharmacokinetic

38:18 185:12

186:13 348:9

456:22 599:22

611:22 615:4

634:14 719:20

739:5 754:1

pharmacokineti...

713:9

pharmacokinetics

27:3 28:20 66:3

71:14 193:22

320:7 348:3

634:15 714:3

715:17

pharmacologic

348:15

pharmacological

93:14 109:9

330:6 444:1

449:10 501:10

535:13

pharmacology

5:12 6:16 7:6

82:9 316:10

317:7,18 742:4

771:5

phase 28:1 416:10

458:11 495:15

640:8 657:3

phased 416:9

phase-out 176:9

179:7 584:12

586:19,20 656:5

707:11 708:2,16

731:21 752:10

PhD 624:2 779:21

phenol 338:10

phenomena 245:8

phenomenon

235:7 478:22

479:13 487:10

phenotype 133:18

546:15

phenotypic 573:19

phone 512:14

phonetic 433:18

460:15 569:14

597:12 619:14

623:18 629:4,11

630:6 647:8

662:3,4,22 671:7

680:12 683:19

695:13 696:11,19

697:14 705:12

715:8 730:10

746:11 753:11

762:12

phosphate 116:2,3

423:16 424:8

phosphates 115:20

321:12 428:4

phosphorylased

330:18

phosphorylate

322:13

phosphorylation

321:20 330:17

331:2,20 340:13

340:15 687:7

Photo 650:2

photomicrograp...

49:13

phrase 42:3 303:9

607:19

PHs 582:18

physical 17:12,16

537:17

physically 254:15

644:21 645:2

physiologic 690:1

physiological

179:21 320:16

626:16 696:21

774:16,19

physiologically

159:1 186:13

611:22

physiologically-...

38:17

physiologically-...

705:9 719:19

physiological-ba...

323:16 333:12

341:5

physiology 323:21

324:12 325:10

335:13 739:8

772:2

Ph.D 282:9

pick 75:8 85:8

344:1 346:7

373:8 489:7

500:8 504:12

532:6 547:2

632:6 762:6

picked 344:20

491:22 558:15

559:3,5

picking 360:18

409:18

picogram 213:18

214:7 216:12,16

216:22 217:3

221:22 222:9

223:2 326:6

399:9,21 403:1

404:22 405:2

picomolar 332:13

332:14 460:14

pictorially 262:15

picture 34:16

132:19 133:20

136:11,11 208:5

271:16 366:1

662:9 757:3

760:14 780:6

pictures 49:16

255:18 751:12

piece 40:19 56:7

63:9 96:7 258:13

296:10 533:6

556:10 706:7

769:14

pieces 98:1 204:20

209:2 443:4,5

618:14 710:15

756:22

pilot 91:3

pin 497:1

pinna 537:13

pioneer 343:4

pipe 194:19

195:10

piperonyl 729:6

pivot 466:18

pivotal 166:18

PK 194:2 195:11

456:21,22 459:6

605:14 696:15

697:19 705:7

706:8,12 709:16

726:13 738:2

PK/PD 723:14,22

724:9 725:2

726:13

place 25:21 26:1

27:12 32:20

39:11 43:20

46:15 55:18

75:20 89:13

100:11 103:6

104:14 138:22

212:14 221:20

257:6 268:9

279:8 411:17

412:3 467:20

468:8 469:6

481:10 492:15,16

505:21 531:17,18

574:1 649:10

652:20 680:1

773:17

placed 80:16

103:16 623:14

624:4 626:10

637:16

placenta 410:7,10

457:12 627:10,16

628:1 629:11,20

630:4,6 634:17

698:5,10,12,17

698:21 699:4,6

711:10 712:10,10

712:17,19,20

713:13,14,15,15

713:18 714:8,10

714:13,15,16

placental 628:20

629:5,9 630:2,3

placentas 714:21

places 70:5 72:11

175:12 214:14,15

231:13 232:10

238:6 480:21

620:5,8 647:20

767:14

plagued 466:17

plan 16:11 29:15

194:4 204:5

470:16 768:16

plane 650:4

planned 278:7

plant 266:10 270:6

270:7,8 671:17

plaque 367:12

779:18,20

plasma 179:15,17

181:22 182:6,20

215:4,5 253:7

256:18 259:13

266:22 267:5,9

268:14 269:5

288:5 311:22

418:11,11 678:16

680:22 687:12,21

697:6 720:11

plasticity 528:10

plate 456:5 469:12

platform 74:21

481:8,12

platforms 649:22

plausibility 34:7

50:8 58:18 146:7

205:18 231:21

232:16 241:2

300:9 360:2,5,19

364:5 581:3

739:10 769:22

plausible 14:16,20

23:9 38:5 43:11

46:9 50:2 53:1

55:20 58:17,19

97:10 113:3

146:9,10 219:18

228:14 236:13

244:2 268:2

290:14 299:10

301:5 381:10

382:10 390:7

441:21 442:5

551:4 613:18

739:13 748:6

757:4

plausibly 242:1,2

play 16:17 122:1
Page 78

191:7 364:15

435:8 503:14

506:6 516:8

551:15 584:4,17

587:3,6,10

590:20 596:4

729:12 752:14

778:11

played 112:17

158:22 210:17

298:17 571:21

572:6

playing 458:15

591:2 706:9

plays 31:20 147:7

284:8 561:19

562:1 572:15

581:13 611:10

please 3:10,15

24:22 224:7

255:8 307:17

318:6 319:3

334:9 342:5

346:11 350:3,15

353:6,21 359:14

363:8 369:10

387:6,10 437:14

437:17 442:3

446:17,21 447:1

469:1 472:8,14

472:16 518:6,8

518:13 541:12

572:5,9 595:12

608:15 622:4

654:5 656:9

666:13,14,15

690:16,17 721:1

721:7,8 748:5

780:3

pleasure 765:16

779:15 780:18

plot 39:2 216:7,8

687:20

plotted 400:7

plus 77:10 78:19

79:6,15 424:7

478:2 523:4,19

547:21 559:19

668:6 670:18

721:10

pluses 631:9 669:4

plus/minus 559:21

559:22

PND 76:10 83:13

83:15,17,17 84:2

345:12 440:13

PND1 345:5

PND11 31:5 36:20

POD 347:15

594:22 612:22

760:9,12,17,21

point 3:21 4:5

25:10 26:7,22

30:19 32:1,2

35:21 36:11,14

37:1 40:8 41:8

53:20 59:14 66:5

68:8,15 82:7

87:7 92:15,18

96:18 100:17

105:6,7 106:11

113:2 136:14

153:3 156:8

178:11 182:4

183:2,7 185:16

194:11 196:3,9

201:10 202:6

210:14 211:2,13

211:18 212:13

213:2 214:5,11

216:6 222:22

223:12 230:18

241:21 243:4

252:21 253:9,20

264:7 266:11,22

267:1 282:4

289:18 301:11

305:3 307:13

310:13 314:6

318:12 320:1

327:16 330:2

332:1,15 335:18

339:18 340:12

341:14 344:18

345:15 347:7

350:11 351:9

352:19,19 358:18

358:21 363:15

364:16,18 365:20

366:15 367:5

369:13 370:1

371:6,12 372:15

380:13 384:12

385:1,18,20

387:4,8,14

389:18 390:12,15

394:2 402:18

407:12,20 409:2

409:4 412:20

419:8 422:4

424:9 433:12

435:13 436:11

438:6,9 440:4

443:2,9 447:9

449:12 452:7

454:9 466:18

474:2,17 477:8

478:10 486:15

492:10,11 499:20

504:10 506:22

508:19 509:7

511:20 516:17,18

516:20 523:5

528:12 529:5,18

530:10,16 531:10

537:9 538:5,12

539:8,14 540:4

546:22 547:13

549:20 552:22

558:17 561:4

566:7 567:12,14

568:11,12 569:9

578:21 580:14

592:14 593:5

594:18 599:13,19

600:11,14,15

602:5,18 607:1

607:10 609:17

612:19 615:10,12

616:2,2,21 617:3

620:14 622:11

624:16 632:12,17

633:20 634:14

635:3,20 636:13

638:15 640:2,7

640:15,17 643:12

643:16 646:22

647:2 653:18

654:5 655:2

656:19 658:22

670:12 674:3,6

681:8,10 683:6

686:18,21 687:12

688:3,10,12,17

689:5,9,17,18,19

693:20 694:19,22

697:3 701:1

716:12 717:22

718:3 726:18

728:15 730:14

737:15 749:16

750:1,4 753:3

755:18 756:6

758:16,22 761:16

762:4,20 767:22

775:12 778:12

pointed 206:5

259:2 310:21

325:21 354:10

522:9 525:7

528:22 618:17

681:9,14 747:22

pointer 357:22

pointers 282:3,4

pointing 245:21

538:15 562:5

633:15 737:3

739:6

points 30:6,17

31:13,16,22

35:11 36:12

42:13 69:9 75:9

76:22 78:2,5

80:20 86:20 98:3

99:7 101:22

102:19 110:17

149:7 161:7

197:5 204:2

211:15 212:5,16

213:9 230:10

242:22 244:14,15

250:16 253:16

255:19 256:20

295:11 299:16

318:20 319:18,22

323:3,17 326:1

326:21 327:12

329:4 330:14

333:13 334:9

336:2 341:11

344:7 345:16

346:9,13 347:5

347:10 348:17

350:13,16 352:20

363:10,10,13

365:18 369:6

381:2 390:4,6,11

390:17 406:20

407:3 409:8

435:12 437:12,16

438:1 439:5,14

459:6 467:12

471:17 518:11

542:21 543:9

552:11 554:11

558:5 562:8,10

565:14 573:21

578:10 582:7,9

594:16 595:6,11

600:8 603:13

605:16 628:22

630:15 632:18

681:8 728:13,16

737:9,12,20

739:14 748:16

749:9 753:4

758:5

poising 383:1

poke 767:14

polar 663:8,9

polarities 663:7

polation 615:15

poles 242:10

policy 395:10,13

619:1 770:12

Pollution 780:2

polycyclic 158:18

555:6

polymatrix 627:19

polymerization

464:21

polymorphic

603:20

polymorphisms

603:21 754:16

PON1 116:18

133:18 259:7,13

458:1 552:13,19

577:20 611:1

PON1QR 577:15

pooled 480:13

pooling 592:3,8,15

592:16,21

poor 159:2,3

268:22 489:15

522:11 577:19

628:15 680:14,15

poorer 524:8

poorest 535:6

POO2 536:22

pop 188:18

Pope 4:9,10,10

101:17,18 102:21

169:13,14,18

170:11,17 201:4

201:5 315:13,14

340:12 413:8,9

413:13 414:8

423:5,6 424:20

439:1,2 444:17
Page 79

444:18 460:9,10

463:14 506:11,12

536:10,11,21

537:3 538:3

565:12,13,13

645:9,10,16

646:1 647:3

684:6,7,13

731:11,12 767:1

767:2

Popendorf 5:18,19

65:4 66:6 90:4,5

153:22 154:2,13

155:15 157:15,19

158:3,6,9 168:19

168:20 196:12,13

197:6,10 198:1

221:6,7,18 222:2

222:11 223:3

269:10,11,16,20

270:1,4,14,17

281:15,21 316:15

316:15 414:17,18

414:22 415:12

416:13,18 417:3

417:10 419:22

420:3 421:5,7,12

422:16 429:2,3

439:16,17 529:11

529:12 566:1,2

568:9 569:4

587:13,14 588:11

602:8,9 616:7,8

617:6,9,16

618:16 624:16

631:5,6 632:3

642:20 650:9,10

650:19 655:5,7

666:20,21 674:17

676:5 683:18

685:16,17 687:2

687:3 688:19,20

689:14 732:6,7

733:14,18 734:2

734:5,10 744:14

744:15 763:11,12

Popendorf's

422:12 635:15

733:11

Pope's 223:22

popped 742:21

744:11

pops 18:9

population 45:10

46:5 63:5 118:8

121:1 127:4

142:1 143:7

154:14 163:3

176:1,17 178:17

181:10 189:18

235:9 236:3

245:8 262:13,20

264:4,10 267:5

268:1,22 275:1,2

324:14 325:9,14

328:20 346:20

399:7 406:10,16

406:18,18 407:19

408:4,10 409:9

428:20 596:10

609:22 610:6

614:17,19 617:13

622:1 634:20,21

635:5 656:3,9

660:4 672:9

682:5 691:16,19

692:19 727:20

738:2 753:21

756:2,12,14

758:20 764:6

773:6

populations 38:16

39:21 116:11

118:12 119:1

138:7,13 141:19

141:21 143:4

154:4 172:14

174:12 176:1,10

177:13,16 178:6

179:12 180:5

181:13 188:6

189:13,20 192:4

206:3 235:18

323:22 388:21

392:20 393:8

394:19 412:18

450:7 573:10

615:14 618:20

622:1,2 657:12

660:2 666:19

675:18,21 681:12

682:8 694:3,4

697:2 706:16,19

724:13 753:13

population-based

634:16 635:11

populous 188:10

Portier 3:22 4:2,6

7:8,17 19:12

22:11 24:21 41:8

43:1 59:14 67:5

68:6,12 87:5

90:3 91:12 98:6

101:17 104:1

107:11 108:17,17

108:22 110:7

111:11,21 147:18

152:19 153:1,22

158:12 160:3

161:18 164:12

165:3,11 166:2

167:18 168:19

169:13 170:18

171:8 172:4

188:1 191:17

193:17 195:16

196:11 198:11,20

201:4,10 220:13

221:5 223:6,12

246:19,22 247:5

247:12,17 248:13

255:14 269:9

271:14 273:1

274:3 278:4

279:14,17 281:20

302:1 304:5

307:20 310:10,19

312:2 313:22

314:5,9 315:1,4

315:10 317:20

318:9,15 334:6

337:1,22 341:21

342:5,7 364:20

365:1 366:21

374:10 379:1,1

379:21 380:10,13

381:21 382:1

385:13 386:7

387:2,13 388:4

395:17,22 396:4

409:22 410:22

413:7 414:17

415:8 417:10

418:19 419:1,17

420:6,17 421:5

421:10,20 422:21

423:4 425:1,13

427:7 428:1,22

429:13 430:5

432:4 434:8

435:18 436:4,7

436:10 437:19

438:15 439:1,16

440:4 441:5,10

442:9 444:11,13

444:17,19 445:17

447:8 454:18

456:6 460:9

462:17 465:13

467:9 470:6,16

471:3,22 473:1

497:6 499:17

500:12 502:10,10

504:6,17,17

505:5 506:4,11

507:8 512:2

513:10 514:21

516:5,15,15

517:9 518:16

520:13 521:6,16

522:1,8 524:3,22

528:3,20 529:3

529:10 530:6,11

531:10 535:7,21

539:6,19,22

540:17 541:17

552:7,22 553:6

554:2,10 556:6

559:13 561:13

562:11 563:21

564:8 565:12

566:1 570:7

571:15 572:12

581:18,22 582:5

583:17,20 584:21

586:4 587:4,13

588:6,15 590:17

591:7,18 592:1

592:11,19 593:12

593:15 595:15

602:8 603:10

605:6 606:10

608:1,3,7,19

612:18 614:13

615:19 616:6

617:1,22 619:2

620:2,22 621:12

622:9 630:18

631:1,5,22 634:2

635:13 636:19

637:6 638:12

639:13 642:19

643:13 645:8

646:7 647:21

649:2,14,18

651:2,7,15

652:18 653:9,17

654:1 655:4,10

655:16 656:14

664:1,6,9 665:3

665:22 666:5,20

674:15 676:4,8

680:20 681:3

682:1,17 684:6

684:12,14 685:16

686:15 688:2,18

690:5,9,20

693:10 695:20

699:16 702:2,8

703:19 705:4

707:4 709:2,20

710:7 711:2

712:7,11,21

714:5 715:14

716:6,11,18

718:20 719:5

720:2 721:14

727:6,8 728:12

728:19 729:15

730:22 731:11

732:6 733:3,9,16

733:20 734:7,19

735:2,9,16,18

736:1,5,9,12

739:17 740:20

741:17 742:16

744:14 745:16,22

747:14 748:9

751:10 753:8

755:21 756:16

758:14 759:13

761:1,15 762:21

763:11 764:13

765:3,14,19

766:9,12 767:1

768:7 769:5

770:7,19 772:5

773:11 775:8

778:18,19 779:4

779:10,21 780:4

780:5,17

portion 551:20

568:12 643:21

673:21 713:15

portions 713:14

portrays 44:16

position 193:10

497:18

positive 88:5 133:9

134:19 137:11,14
Page 80

137:19,20 140:13

143:21 169:6

228:10,10,12

233:7 236:6,20

238:3,5 292:12

294:18,21 295:20

357:17 405:16,20

460:7 526:16

536:14,15 546:21

567:3 597:10

667:5

positively 169:3,8

positives 85:4

169:1 239:22

240:13 489:21

possibilities 193:1

243:5 563:9

639:9

possibility 32:6

37:12 233:16

238:19 243:6

406:7 564:3

588:20 590:8

594:19 648:7,9

774:2

possible 100:19

118:21 119:10

138:9 263:21

286:15 289:1

292:7 296:6

306:17 340:13

350:5 397:18

399:15 406:5,6

406:21 407:5

444:7 446:7

505:10,15 524:10

534:10 546:7

547:1 555:1

594:8 599:16

626:2 627:2

634:7 650:2,3

659:6 684:4

700:8 720:19

748:17

possibly 115:17

122:1 131:6,22

164:19 197:7

378:21 410:13

442:18 500:3

506:16 546:9

557:17 575:13

650:15 761:19

post 89:8 318:21

579:10,13,15,17

584:12 586:14,19

653:4

posterior 404:5

posteriori 494:21

523:3

postnatal 69:3

71:5 73:2 77:13

79:16 80:5 81:13

81:18 83:12 86:9

98:14 128:21

140:21 392:5

459:17 486:20

488:3 489:9,10

498:19 551:9

578:1 587:10

746:19

postnatally 81:22

84:19 88:19

98:19 517:20

522:15

postulated 33:12

627:12

postulates 230:14

230:16,16 231:11

232:13

postweaning

531:19

post-application

393:13 684:19

post-natal 356:3

472:11

post-tox 357:1

potency 398:18

461:14

potent 218:4

potential 28:19,21

30:2 44:5,6

52:14 54:5 72:2

75:14 83:22 84:4

85:5 113:21

116:3 119:12

129:2,9,15 130:2

131:14 138:14

140:3,12,16,21

141:4 143:16

144:13 145:3

146:2 157:3,12

179:17,21 190:7

191:8 194:9

204:1 208:17

225:10 233:8

347:19 348:14

389:20 391:21

401:22 423:20

432:1,16 437:6

439:22 441:2

442:14 446:19

462:21,22 463:17

464:15 467:15

473:15 483:11

503:20 542:5,12

544:19 545:14

547:14,20 548:8

551:20 557:14

572:22 574:4

579:7 580:8,18

587:9 589:22

594:1 610:18

612:2,4,9,15

624:11 625:15

631:16 635:21

636:5 641:15

654:12,22 690:15

691:9 694:11

723:14 724:21

727:3,3 730:6

735:8 757:8,10

761:6

potentially 140:9

143:21 195:22

305:20 330:19

331:22 339:13

340:4,14 369:19

372:14 402:16

405:7 420:1

426:11 431:16

438:7 456:10

460:20 461:4

462:11 478:9

542:15 547:15

570:21 588:12

608:16 610:22

622:2 624:18

640:16,20 641:7

641:12 660:22

695:4 697:22

703:6 711:11

712:5 716:9

726:18 729:12

746:22

potentiate 441:3

potentiated 746:3

POTOMAC 1:14

pounds 388:20

431:8,10 647:7

647:11 733:8

power 92:16 94:13

95:1 141:11

142:2,4 147:2

274:7 275:18

546:6 547:11

550:15,18 552:13

579:22 628:19

696:17 706:13

745:13,20

powered 390:20

677:3

powerful 196:10

ppb 188:9

practical 487:1

practice 32:19

244:7 539:2,4,5

practices 211:15

265:20

pre 318:21 579:12

579:15 584:11

586:19

preamble 299:19

prearranged

387:21

preceded 146:4

precedence 387:22

precedent 336:16

387:18 514:19

precious 380:17

precise 284:10,13

301:13

precisely 120:22

precursor 348:17

predicative 410:10

predicator 413:4

predict 159:12

190:19 341:6

454:12 465:11

466:22 600:1

655:1 685:20

716:3 725:2

predictable 687:22

predicted 266:8

267:9 268:14,15

325:11,15 328:15

599:5 658:2

749:5

predicting 253:12

700:2

prediction 197:20

328:20 484:4

534:18 697:2

predictive 160:11

160:17 265:16

453:17,20 454:11

467:18 534:17

535:5 601:9

predictors 159:12

160:10 535:6

predicts 454:5

predominantly

155:5 647:15

predominately

429:11 430:16

701:11

preface 396:7

preferable 200:17

520:1 580:16

preference 77:16

77:16,19 78:21

104:14 523:2

531:17,18

preferred 186:14

194:13 200:14

preformed 256:13

pregnancies 117:5

460:1 712:16

pregnancy 117:5

117:17,19 121:9

122:16,18 125:8

125:12 126:19

153:6,11,14

164:5,6,7 187:8

193:20 195:9

410:6 457:9

458:6 459:7,11

547:19 613:9

627:7 628:5

630:10,11,17

632:16,18 633:7

633:17 634:5,10

638:15 639:3

662:8 664:19

697:7,19 698:3

699:12 704:9

706:20 711:13

713:10 722:11

725:3 771:3

pregnant 28:14

31:4 37:5 194:15

195:2 215:13

395:8 401:21

402:1,5 410:15

416:3 474:3,10

476:8 519:15

600:9,22 601:9

638:18,22 662:6

676:1 682:11

724:13 754:10,11

756:3 759:2
Page 81

771:5

preliminarily

571:20

preliminary

203:13,22 209:13

211:3,6 214:9

298:16 346:11

350:4 394:10

434:21 437:14,18

442:3 472:5

572:5,11 584:3,6

584:16 594:5

666:9

premise 440:2

445:16 614:21

premises 478:6

prenatal 113:18

117:17 120:12

126:1,15 129:13

129:15 138:2,18

144:3 148:7

157:11,12 162:8

162:9,9,13 392:5

393:9 474:4,7

486:20 527:9

551:9 577:11,18

578:1,3,15,17

587:7 594:2

754:3,19

prenatally 392:8

392:18 496:16

543:5 558:10,14

Prendergast

336:13

prep 65:13

preparation 649:8

649:13

preparations

323:14 340:9

prepare 8:1 223:9

777:11 781:9

prepared 9:15

255:7 303:22

388:6

preparing 7:21

781:7

preps 461:19

preputial 537:13

presence 44:12

52:8 55:16

133:17 160:11,12

262:5 278:19

327:19 328:8,14

341:9 355:11

469:13 623:21

648:3

present 29:13

108:19 112:5

113:5 123:15

174:1 181:19

189:7,14,19

230:22 256:2

258:3 274:16

308:18 322:21

326:2 335:7

337:20 340:20

357:20 377:9

407:4 423:20

520:9 555:18,18

560:9 582:14

604:5,7 623:16

628:3 667:9

696:8 749:12

presentation 40:17

40:22 42:6 59:16

59:19 68:16

112:7,10,11

114:8 127:2

141:15 149:8

150:3 172:9,19

173:2,9 177:11

184:14 212:7

223:10 224:3,20

247:18 271:17

304:7 305:6

315:7 318:7,14

318:18,19 328:12

367:1 413:9

423:10 424:2,21

434:20 436:5,6

469:21 500:17

597:13 613:16

627:5 651:19

680:22 691:7

702:17

presentations 3:19

7:15 13:7 201:20

208:8 209:3

223:13 247:17,19

248:22 305:11,19

306:16 318:6

326:20 436:12

589:2,3 686:19

presented 73:16

99:13 112:14

113:8 129:19

130:5 132:9,10

172:12 175:20

177:14 181:16

184:13 186:17

193:4 199:15

219:2 223:15

276:8 305:18

309:2 312:10,13

320:16 324:1,11

326:8,16 327:14

343:19 377:4

380:2 394:10,12

400:11 421:19

458:4 512:1

525:21 546:17

549:19 569:19

573:22 627:14

647:4 649:3

677:6 690:22

698:6 717:17

763:7 764:2

779:20

presenter 224:6

696:10

presenters 253:14

254:11 255:9

423:12

presenting 258:13

304:11 329:20

423:22 748:6

presents 393:7

394:6,8 621:20

626:3 698:13

749:16

preserve 651:12

press 342:5 363:18

pressing 593:16

presumably 270:9

presume 22:19

413:4 415:15

presumed 256:11

pretty 7:12 36:1

68:5 75:2 79:5

80:7 81:15 86:7

102:7 135:19

163:11 168:16,17

181:7 256:15

266:13 275:2

297:2 300:15

302:3 344:8

347:4 365:14

407:20 424:11

426:16 428:17

430:18 441:8

442:10 445:20

465:12 469:20

470:7 481:11,12

483:20 502:8

503:4 504:3

511:9 519:7

522:16 529:5

530:22 532:7

538:18 553:4

605:17 650:21

651:11 674:5

676:22 740:9

742:14 753:18

prevalence 118:15

123:13 126:21

141:20

prevalent 334:16

430:18 457:7

prevent 96:20,21

prevented 508:2

preventing 629:12

629:20

prevention 117:10

579:6 780:3

previous 7:10 8:10

59:17 63:16

86:11 109:1

173:1 180:2

193:20 277:7

387:16 439:18

521:8 601:13

614:15 629:13

685:13 693:5

718:9 724:8

727:10

previously 177:6

199:15 343:3

362:22 461:21

573:21 725:8

751:16

pre-cancellation

579:16 720:7

pre-exposure

124:12 668:21

671:16,20,21

pre-weaning 89:9

pre/post 579:1

Price 255:6 319:5

primarily 49:11

258:2 282:17

321:11 346:21

675:11 715:11

735:6

primary 57:22

58:8 69:15

139:15 249:15

321:13 436:21

464:11 622:5

626:14 635:3

724:19

primate 517:4

744:20 745:5

primates 517:3

744:17 745:6

primer 229:13

primitive 134:6,11

principal 114:5

168:6 224:9

241:14 248:5

282:7 379:18

658:11,12 659:19

principals 9:7

17:22

principle 427:20

475:3 482:1

493:14 537:10

621:20 637:14

638:1

principles 25:19

25:20 26:1 32:22

201:22 202:8

249:11 380:21

printed 223:3

260:13 420:18

prior 3:9,14 50:15

117:17 119:3

122:20 123:9

127:1 144:16

146:14 262:10

273:12 274:2

397:17 400:15

404:2,10 414:1

514:20 544:8

630:5 638:22

666:22 669:3

priori 132:15

141:3,6 373:5

484:3 762:12

priorities 631:8

priority 665:5

privacy 541:9

private 715:9

probability 404:6

404:7,9,10

510:15

probably 23:2

25:21 49:7 59:16

63:7 84:17 92:4

99:5 100:9 147:2

161:6 162:13
Page 82

168:16 175:10

182:11 184:14,21

200:17 215:14

247:6 261:16

273:22 293:12

308:17 312:16

322:17 335:6

360:4 374:15

375:12 403:15,20

407:8 416:11

419:11 421:13

428:19 432:21

441:3 451:11

455:16 470:21

498:9 504:9

508:19,20 509:17

510:19 514:2

521:9 526:21

533:15 553:2

557:5 560:2,3

562:12 568:21

569:5 587:12

612:7 621:9

625:5 631:12

639:7 646:17

650:9 667:14

668:8 669:11

670:1 673:21

675:16,22 676:2

694:2 704:18

714:12 719:14

720:11 725:22

726:20 730:1

746:17 747:5

748:10 756:1

758:16 765:13

773:2,7

probe 87:16 88:2,3

93:1 482:3,5

509:9

probed 522:20

probes 93:14

problem 14:18

27:12,14 28:13

159:17 203:12

264:3 330:5

337:12 351:12

364:6 397:9

450:12 454:22

474:15,18 475:5

475:8,11 476:7

476:21 477:7,8

478:14,16,19

479:11 482:16

484:2 493:8

521:11 524:19

586:8 640:11

643:8,17 664:13

714:21 719:6

743:1 773:15

problematic

227:12 463:16

484:8 488:21

516:1 632:4

problems 61:17

136:2 137:6

159:18 276:13

378:5 439:21

459:4 476:6

483:13 487:1,6

490:13 495:6

516:8 523:10

575:19 578:1,4

632:7 685:7

748:2 767:3,4

procedural 84:8

procedure 369:20

409:16 477:4

482:6 510:21

procedures 74:1

78:4 86:4 109:9

128:7 192:20

355:9 402:14

653:15

proceed 19:11

process 10:22 22:2

22:16 26:18

29:14,20 114:11

114:14 159:6

170:13 172:18

207:17 234:18

237:20 246:12

283:6 297:7

305:8 380:21

403:12 442:22

443:16 535:15,16

624:2 640:13

648:17 649:13

650:1 651:6

655:2 662:12

668:14 692:22

722:5 739:3

processes 159:7

209:17 229:2

284:18 323:18

391:12 414:10

434:4 442:14

446:4 464:22

466:10 726:8

processing 741:18

produce 48:16

107:2 130:21

416:20 476:22

486:3 512:19

517:17 526:13

636:3 732:20,21

746:3

produced 83:3

129:19,20 130:12

348:19

produces 481:12

517:19

producing 403:3

486:21 646:2

product 170:16

255:22 404:7,9,9

627:1 648:14

649:11 651:5

production 56:12

61:12 180:21

productive 230:10

419:18 777:9

productivity 407:9

products 119:4,6

123:10 170:9

256:2 404:12

430:21 647:19

652:21

professional

775:18

professionally

604:15

professor 4:11,21

5:2,6,11,15,19

6:4,13,16,19 7:6

315:14,17 316:4

316:6,9,12,16,22

317:3,7,10,18

604:19,20

profile 173:20

353:16 621:15

634:20 654:2

698:15

profiles 118:22

629:8 661:3

profused 628:10

630:5 698:10,18

profusion 629:5

progenitor 450:6

598:5

program 7:3 8:3

12:16 19:11 25:6

112:5 228:21

390:2 673:12

Programs 19:16

25:3 172:9

248:16 781:6

progress 107:14

455:9 529:1

633:21 658:1

693:14

progression

405:12

progressively

538:17

project 396:10

709:12 734:12

projected 399:2

403:2

projecting 722:15

projection 404:15

404:16 772:3

projections 402:21

prolapsus 386:12

proliferating

433:1,3

proliferation

67:22 68:2 450:8

prolongs 348:9

prominent 364:15

promise 193:9

298:15 513:5

518:20 636:8

779:4

promised 422:6

514:21 779:2

promising 489:1

promote 747:19

749:20

promoter 67:18

promoters 67:7,8

promoting 517:4

promulgated

550:20

prone 494:9

pronounce 212:21

452:22 480:7

proof 101:4 242:6

251:2

properly 96:1

323:11 488:11

491:13 747:12

properties 53:3

54:6,14 55:7,9

56:1 214:3 232:7

proportion 122:17

222:17 642:2,3

652:2,17 743:18

744:4

proportional

653:11

proportionately

481:6

proposal 213:11

234:10 236:7

proposals 193:4

propose 46:9

48:18 54:5

514:15

proposed 53:11,15

54:1,7 55:5,9

56:18 131:22

185:17 228:20

235:7 238:16

254:9 289:4

299:14 305:13

432:17 433:22

435:12 535:14

600:8 615:3

755:13

proposes 595:8

proposing 50:9

94:1 194:7

313:14 710:12

745:1 756:7

propoxur 32:8

130:1,6 171:20

171:22 217:17

575:14,18 593:8

721:12 725:7,7

728:22

proprietary

673:10

prospect 543:2

prospective

131:18 139:21

146:1

protect 253:18

254:6 286:6

389:8 390:11

391:9 392:12

394:17 395:10

489:21 754:7

758:9 759:3

764:10

protected 266:1

381:14 393:4

protecting 96:17

388:15 391:3

393:2 754:6
Page 83

756:13

protection 1:12

20:6 25:7 248:15

381:13 385:4

395:12 749:22

751:18,19 766:8

protective 340:3

344:18 364:3

383:21 390:16,18

391:12 395:5

437:3,5 518:5

606:3 607:17,21

658:13 692:19

729:22 730:9

750:5 755:19

756:10 761:7,13

773:2

protein 45:18,21

53:4 54:11,15,16

55:7,8 61:11,12

97:9 323:10

329:13 330:18,18

330:21,22 331:2

331:21 403:19

456:9 570:12,13

570:20,21 571:4

571:4,4,8 697:5

proteins 53:8

57:10,11 322:17

322:19,21 323:2

333:8 340:17,21

461:3 464:16,21

465:8 466:6

625:22 697:6

protein-bound

329:15

protocol 58:15

384:9,10 487:4

488:3 768:10

protocols 87:14

664:22

prove 242:9 584:5

proved 394:11

776:12

proven 531:4

provide 21:14

23:22 24:3 26:3

36:13 134:3

166:11 176:4,14

177:2 179:8

184:15 202:13

219:12 249:13

262:4 343:18

353:16 354:6,16

363:10 395:11

412:21 420:17

421:3 441:15

446:6 449:13

451:5 453:9

518:12 541:1

579:18 607:9

609:15 610:2,4,8

610:13 611:2

626:22 631:7

655:9 658:11

659:7 660:8,14

660:17 665:15,20

667:13 668:17

684:17 692:20

723:15 724:6,17

739:7 748:5

766:7 773:6

provided 2:15,22

9:3 114:9 147:1

155:2 172:10

250:18 270:8,11

429:8 546:15

553:11 598:19

750:1 758:5

778:10 779:4

provides 2:17 3:6

30:19 33:4 37:1

37:5 205:22

235:21 241:8

392:20 553:21

574:19 773:7

providing 93:9

302:7 360:5

393:1 512:16

662:1 664:12

Proving 531:3

provisions 2:9 3:1

Pruiett 252:16

Pruitt 319:14

psychologist 136:4

160:19

psychologists

535:2

psychology 82:9

485:15,17

psychometricians

144:18

psychomotor

135:12 136:15

137:2 167:10

544:9 575:4,11

public 2:13 3:7,8,9

3:16 4:22 5:6,7

5:16 6:4,13 7:2

123:6 192:7

204:9 220:18

221:1 223:14,14

223:16,20 224:6

247:8,13 282:11

305:12 306:16

315:6 316:1,7,13

316:22 317:4,15

318:3,4,5,10,13

341:22 385:4

387:6,8,14,15

391:3 396:4

419:4,5,21 420:9

421:21 422:2,4

436:11 549:6

589:2 661:14

692:21 748:1

758:17 759:2,15

759:17,21 780:22

781:2,4

publication 85:9

125:16 148:6

152:14 158:19

247:2,3,6 250:21

280:6 328:10

543:16 550:11

647:10 763:6

publications 86:10

141:9 144:22

179:3 180:16

181:7 221:12,16

251:4,12,13

252:13 319:13

320:19 322:15

328:4 543:17

548:18 617:10

620:12

publicly 779:11

publish 250:20

521:12 651:18

published 48:10

113:17 114:7

122:9,11 131:17

138:2 141:3

167:1 168:4,5

170:20 171:5

186:22 188:16

228:2 252:14

273:13 277:6

278:10,16 310:6

320:9 338:6

342:9 351:22

352:14 363:19

392:16 400:11

401:13 407:11

426:10 445:6

463:3 477:2

508:6 574:2

613:5 624:3

645:7 646:22

648:8,14 661:12

680:11 700:9

704:19 708:18

728:7 751:17

753:1

publisher 342:9

pull 89:13 96:3

104:7 105:22

209:2 250:22

306:10 339:6

461:9 522:2

694:18 770:4

pulled 104:10

105:3 223:18

673:16

pulling 219:16

339:6 367:1

442:13 444:7

501:13 586:2

774:2,2

pulls 107:20

pull-it-all-togeth...

39:2

pump 231:17

pup 82:19 83:13

83:14 84:17

pups 80:17 83:19

87:2 288:7,11

344:10 346:7

348:6 360:10

476:11 484:21

489:14 493:6

499:7 517:20

522:16 597:21

pure 629:6

purely 66:12 94:12

purified 323:2

purpose 75:13

117:21 124:21

147:4 154:9

249:4 355:7

467:7 595:1,4

648:20 691:15,21

693:7

purposely 344:20

purposes 34:4

94:5 100:21

117:1 169:22

170:5 174:20

175:1,4 179:21

214:5 329:19

334:2 354:21

355:3 362:11,19

541:9 656:10

749:10 763:9

pursued 441:21

pursuing 218:20

219:1,2 513:8

push 251:15 252:2

293:9 704:3

710:5 719:22

pushing 109:4

248:20 249:10

250:8 739:20

748:19

put 7:20 11:15

12:9,10 18:6,22

24:15 25:11

29:15,20 34:6

35:1 39:18 43:3

48:7 60:11 62:2

62:5 63:21 87:10

99:22 100:1

104:8 158:21

160:5,6,6 193:4

203:16,21 214:13

223:20 227:1,5

233:18 240:8

241:8 245:1

254:5 262:15

294:17,17 295:18

304:18 307:14

309:15 350:12

354:17 366:1

410:11,12,18

426:9 436:13

439:8 441:1

442:20 443:8

477:12,14 478:17

479:2 491:10,22

505:14 515:14

531:1 535:17

537:18 565:4

585:10 599:16

600:2 606:15

631:10,18 652:6

653:13 678:2,12

694:17 723:4,10

727:16 735:13

738:8 739:9

757:11 761:21
Page 84

762:2 764:16

770:2 774:15

puts 204:20

357:21 372:5

388:21

putting 7:20 10:14

12:20 16:8,12,21

17:8 62:10 203:8

205:14 225:4

237:21 304:14,14

365:14 389:2

453:1 514:10

542:2 618:14

624:5 645:17

696:4 757:2,18

758:7 764:17

774:12

puzzles 558:1

puzzling 418:8

519:22

pyramidal 49:13

49:20

p-value 296:21

405:19,21

p.m 314:13 583:22

601:22 631:3

P14 522:15

p450 335:5 603:16

604:21 605:2

729:4

P450s 335:6 458:1

458:14

Q

QA/QC 122:9

QQ 578:19

qualified 557:5

qualifiers 332:2

qualify 156:4,4

331:11

qualitative 37:9

114:13 125:4

183:11 184:8

195:4 204:17

206:13 208:15

209:4,11,20

210:14 211:2

246:7,11 298:17

353:8 472:10

572:5,17 594:5

618:11 690:13

691:1 693:14,16

qualitatively 37:10

168:8 191:11

446:2 571:21

quality 19:22

35:12 75:6 120:6

127:17 143:11

207:9 210:15

244:12 278:21

291:2 300:18

353:22 359:15,20

518:6,22 519:1

524:8 541:13

545:9 662:16

666:13 676:22

677:5 679:6

680:14,15 691:12

694:6 695:22

696:6 751:17,19

757:5 770:22

quandary 306:9

500:8 555:9

556:5

quantification

426:19 427:5

quantified 663:21

quantify 174:16

264:5,17 618:10

quantitation

256:16 267:21

268:8,11 269:3

271:3 277:3

308:13 309:12

425:20

quantitative 24:10

30:6 37:15 66:4

97:18 114:13

183:12 184:11

186:12,14 195:4

204:17 206:13

208:19 211:12

246:5,9,14

346:18 347:3

595:4,7 606:1

608:13 609:13

611:5 686:22

693:15,16,22

706:2 720:9

737:18

quantitatively

65:18 389:21

391:21 415:13

631:17 674:11

quart 580:13

quarter 170:1

218:2 393:16

401:3 696:16

706:13

quartile 291:21

292:5

quartiles 291:20

292:1 550:21

562:9

qued 482:6

question 32:5

37:15,18 59:19

61:20 64:2 65:1

65:11 90:5 91:15

92:13 95:17 96:5

97:20 98:2,7,12

98:15 100:14

108:18 110:8

120:11 128:12

129:22 131:19

152:6 160:18

166:3,22 167:2

167:19 168:7,21

169:19 171:9

189:1 190:3

192:21 193:18

198:12,13 200:1

206:11 208:20

209:10,20 210:14

210:15 211:12,14

212:2,9 214:6

218:19 219:15

224:14 225:4

227:10 229:1

230:2,3,5,12

231:3,6,10,12

233:3 234:16

235:3 236:1

242:15 243:11,22

246:7,13 256:8

269:17 275:10,12

275:13 276:22

281:16 282:17

283:11 289:14

291:1 298:14,16

299:12,13 300:9

300:14 314:3

335:1 343:14

345:22 349:13

350:3,15 351:4,5

351:18 353:6,21

355:6 356:9

359:12,14 362:6

364:5 365:11

366:3 382:4,5

389:12 390:6

391:19 392:3,15

393:10,12 406:13

410:4 412:1

414:19 415:11

417:15 419:22

420:20 421:1,2

421:13,21,22

422:7,13 436:20

439:2 440:6

441:11,12 445:20

445:22 447:13,18

450:21 451:22

467:13 469:1,4

469:22 470:10,12

470:17,18,19,21

470:21 471:1,2

472:1 473:4,5

487:13 490:10

493:10 501:5,6

502:11,21 509:20

512:4 513:15

515:1,2,4,5 517:6

517:10,12 520:7

522:21 529:5

530:9,12,13

531:11,15 536:9

538:1 539:16

540:7,9,19,22

570:8 571:18

581:1 585:2

586:11 588:17

591:21 592:7

593:17,21,22

597:6 601:13

602:2,11,19

603:6,7 605:10

606:12 608:5,9

608:18 612:17,20

613:15,20 614:15

616:7 617:17

618:2 619:9

620:3 621:14

624:5 631:20

641:2,9 642:4,13

642:16 645:4,9

649:15 650:11

651:8 653:10,20

654:2 655:15,18

662:17 666:6

678:1 679:13,21

682:14 686:16

690:12 691:14

694:5 699:17,18

700:5 702:9

704:1 710:1,2,6

712:6,9 716:21

717:1 719:8,9

720:3,21 723:8

727:10 728:1,1

728:14 736:2,8

736:16,19 739:19

745:19 747:15

748:5,13 750:7

752:18,20 756:17

758:13 759:15,16

759:17 760:2

761:4 762:7

questionable

537:22

questioned 696:13

questioning 423:8

questionnaire

120:4 122:14,20

136:3

questions 7:13 9:3

11:3,17 12:3

13:6,6 17:20

22:12 35:10 37:9

41:7,9,10 43:2

59:15 63:15 67:6

68:6,13,14 87:4,6

87:9 89:7 98:8

105:1 107:14,15

108:14 112:11

147:17,20 154:3

154:14 177:11

187:22 188:1,4

195:7 196:12

201:16 202:1

204:14 206:13,14

208:12 218:15,21

220:6,13,15,16

220:22 221:2

224:15,19,22

227:1 228:13

230:10 240:17

241:16 246:19

248:10,19 249:3

249:18 254:17

255:13,14 256:4

256:10 257:19

269:7,10 283:8

290:22 301:22

302:17 318:21

319:17 320:21

334:5,7 342:18

343:16 346:11

351:17 365:2,4

367:2 380:11
Page 85

381:20 382:2

389:11 395:16,18

396:2 409:21

410:1 413:8

419:3,4,6,7,9

420:4,7,9 421:6,7

421:9 422:3

423:4 427:6

428:2,22 429:13

429:14 430:5

432:4 434:8

436:5,16 445:19

447:15 448:19

468:21 471:2

473:2 507:14

512:9,16 532:17

540:2 581:3

590:11 592:18

593:3 602:1

618:22 623:13

637:6 639:11

648:15 656:15

662:20 685:6,15

686:16 701:13

719:8,12 720:22

721:15,17 725:14

728:15 737:10

748:5 752:13

761:21 764:12

question's 196:15

197:11 228:16

quick 167:19

171:9 179:13

229:6 426:14

590:18

quickly 33:9 53:12

76:17 83:19

192:18 302:5

422:13 639:21

645:6 678:16

705:19 709:14

quiet 376:14

608:20

quintile 550:7

578:12

quintiles 550:9,22

quite 23:17 27:19

34:12 38:1 40:4

65:5 72:14 79:22

81:8 170:6

178:16 207:7

217:1 271:8

277:10 323:9

330:4 363:22

417:7,21 421:13

478:12 485:2

494:17 515:17

523:4 524:9

550:13 563:16

565:9 568:15,18

572:18 632:11

637:8 653:4

664:14 715:15

732:12 745:10

773:18,22 776:21

quotation 233:2

quote 390:17

391:1,14 590:4

590:21 591:3,16

quotes 566:8

Quotient 113:18

Q&A 269:8

R

R 528:9 566:8

race 127:16

138:13 141:17

547:19 548:3

racial 580:20

radial 72:16,17

73:17,21 76:1,3

99:9 104:18

109:13 349:5

358:1 361:18

374:15 376:4

377:18 378:20

478:14,18 479:2

487:10 490:12,13

490:16 495:8

507:21 508:11

509:21 519:22

520:5 532:7

rain 288:3

raise 304:11

307:18 370:8

465:22 728:13

750:8

raised 306:17

319:17 372:3

382:8 449:4

461:21 500:21

549:5 562:21

563:11 565:9

613:15,21 699:8

750:11 762:1

raises 105:6

351:16 359:11

469:7 624:21

raising 406:14

468:16

Ramirez 715:8

ran 197:16 418:7

482:6 483:19

490:14 511:21

random 103:8

403:11 489:8

522:18 690:1

randomize 476:11

476:12,18,18

randomized

484:20 494:3

randomizing

484:21

range 65:2 90:21

127:3 134:8

143:6 144:8

164:3 181:10,13

193:1 262:12

263:7 268:15

269:14,15 281:13

297:14 301:14

308:12 309:7

324:19 326:9,12

327:4 330:7,11

330:12 331:9

332:8 361:20

375:21 390:21

397:18 398:13

416:16 417:4

430:22 431:13

460:14 467:5

490:22 492:5

493:22 580:22

596:9 606:9

613:8,10 614:16

621:22 657:2

663:6 670:5

677:18 707:17

720:9,19 748:2,6

748:8 749:17

753:20

ranges 21:20 22:7

42:21 177:18,19

178:1 208:4,4

219:12 220:10

267:17 280:6

427:4 607:4,4,8

759:12

ranging 402:7

545:22 646:17

690:13 691:1

rank 120:22

126:14 174:21

ranking 125:22

653:13

rapid 121:22

175:16 213:20,22

338:19 456:11

rapidly 124:11

214:4 321:18

339:2,4,13

352:22 479:1

620:14 656:7

658:4 698:18

rapid-fire 147:16

rare 451:14

601:14

rarely 392:22

rat 31:5 49:22 58:4

58:12 76:6 78:10

81:15 88:17

211:18 288:7,11

338:3,19,21

340:2 413:14

458:21 477:12

479:2,13,16

503:3 532:3

597:21,22 598:8

600:8 601:9

603:13 604:8,12

605:21 606:7,22

607:20 615:11

737:14,20,20

738:11 739:8

742:12 745:14

756:7

rate 144:7 338:19

339:9,12 398:2

399:11,13 400:16

401:11 402:2

403:16 416:3

580:2 625:12

629:10 660:21

686:21 687:8

rates 217:18

324:17 338:12

340:7,9 341:2,3

341:17 347:20

394:19 400:22

414:10 597:15

654:11,19

ratio 322:6,8 397:4

397:4,7,19

398:17 399:1,8

400:9,12,14,22

403:1 405:1

418:6 481:13

568:12,16 636:4

rationale 485:9

ratios 145:9

280:18 410:15

481:14 567:8,11

568:2 575:20

593:9 626:21

635:21 654:17,22

753:17

rats 31:4,5,7 36:20

36:20 37:1,5

71:18 76:3 81:19

83:20 84:3 86:10

88:20 251:6

338:3 413:12,13

435:22,22 485:20

510:18 600:9

603:21 604:1

739:3 754:11

rat-derived 400:22

Rauh 148:6

152:14 158:15

165:8 213:15

260:15 276:3

291:17 551:7

554:13,20 596:18

Rauh's 593:7

Ray 597:17

RBC 253:7 326:22

344:9 680:21

687:4,14,21

RBCs 687:11

RCM 279:1

reach 190:10

374:20 375:14

460:20 537:14

747:11 752:7

770:16

reached 480:5

600:21 601:3

680:8 724:5

reaches 400:21

react 322:16,21

330:21 340:20

740:21 758:17

reactions 80:15

reactivate 685:14

reactive 53:6,14

53:17,19 56:17

56:19,21 323:9

434:1

reactivity 333:8

reacts 135:14
Page 86

639:22

read 164:2 223:22

226:2 233:1

246:22 247:11

254:17 279:14

282:21 283:3

319:16 381:2

419:6 436:18

471:2 513:13

525:11 540:22

562:15,16 570:11

571:18 585:21

593:19 622:17

655:18 680:6

684:16 736:16

758:14 763:15

779:19

readable 613:22

readers 348:20

reading 11:2 43:4

65:4 160:20

388:11 406:12

432:14 436:16

443:1 445:1,9

522:11 523:11

702:9

readmission 653:2

reads 447:10

779:20

ready 111:21,22

196:12 505:1

524:7 525:13

540:19 661:1

736:12 747:14

read-across

328:19 329:8,9

329:19 331:5

real 66:14 206:10

215:13,22 225:11

228:13 240:5

242:14 243:22

244:20 303:20

305:10 359:13

392:20 393:3

400:1 445:11

449:16 450:21

452:19 465:11

503:5,9 552:2

589:4 620:20

654:13 673:7,22

695:12 753:19

771:19

realistic 724:7,9

realistically 633:9

reality 17:16 53:22

279:1 730:3

realization 23:11

realize 23:22

161:6 168:15

246:4 254:5

259:4 298:2

372:5 547:1

616:1 723:6

realizing 63:10

235:16 563:13

really 8:2 13:9

18:13 24:13 26:3

29:9 32:17,18

39:11,12 42:12

60:22 64:17

65:10 66:16 70:2

71:8 73:5 75:7

85:8 86:4,21,22

90:22 92:10 93:3

93:7 94:17 95:3

95:14 99:16,18

100:10 101:3

106:20 109:3

110:3 134:2

147:3 159:7

162:15 164:4

166:6 191:19

194:21 195:7

196:15 199:7

203:12 204:14,20

205:20 206:20

208:13 218:18

219:15 224:16,18

225:18,19 226:22

227:3 228:18

229:7,15,16

230:1,20,22

231:10,12,16,19

231:20 232:12,16

233:2 234:9

235:19 236:4

237:2 238:13

239:22 240:13,14

241:16 242:18

245:13,17 246:5

246:6,10 262:9

264:8,13 277:4

281:2 283:3

284:17 286:1,14

286:21,21 287:18

291:9 292:8,8,15

292:22 293:10,18

294:10,22 295:3

295:10,15 296:3

296:6,9 297:7,10

300:13 303:22

304:2 305:18

312:5,11,13,20

312:22 313:1,9,9

313:12,20 346:18

347:7 350:1

352:10 354:10

355:5 356:17

360:7 361:9

362:11,12 366:7

366:17 368:5,22

369:4 370:6

374:8 375:21

377:18 381:2

411:2 422:1

426:18 428:9

442:17,20 443:5

443:14,18,20

444:5,6,13

447:17 449:11

451:4 455:4

456:8,15 457:16

458:2,4,4,11,12

458:14 459:3,18

460:6,7 465:20

466:9,19 467:2

467:17 474:6

478:16,18 480:1

481:2 487:6

497:10,13,17

499:15 500:9,15

501:11,17 502:6

502:15,21 505:19

505:22 506:3,8

509:10 510:16,18

513:11 515:20

516:13 521:5,17

521:20,21 525:6

525:7 527:16

528:21 530:16

531:5,5,22

538:20 544:22

548:7 550:19

552:15 553:9,12

553:13,14 554:1

562:19 563:1,1

566:12 567:1

575:22 582:9

585:7,14 590:2

592:14 601:17

603:4 606:5

612:16 616:12

620:14 632:17

633:2,9 647:6

648:5 653:13

657:5 664:20,22

670:2 671:21

672:1 673:18,20

673:22 674:8

676:19 679:6

680:5 682:9

685:12,15 686:1

690:21 691:14

696:4,6 701:17

705:10,11,20

706:18,18 709:3

710:9,9 711:8

713:21 714:11

715:20 717:7

722:5,7,8 728:15

729:12,13 732:15

735:7 737:3,6

742:11 744:8

748:13 755:14

758:18 759:3

769:8,10 770:21

771:1,9 774:16

776:5 777:18

real-time 193:10

real-world 215:19

225:8

reanalyze 726:14

reared 477:1

rearing 81:10

reason 53:21

103:11 105:22

162:13 174:13

182:14 190:10

235:15,16 236:11

264:9 272:10

276:22 278:1,2

286:11 300:2

307:12 366:9

378:3 384:4

410:8 432:20

451:21 493:3

497:9 519:18

603:3 626:14

637:19 663:11

680:13 682:14

706:8 749:12

reasonable 12:20

13:2,10 14:20,20

17:18 22:16,20

23:9 34:2 190:2

239:3,15 375:12

397:10 398:13

399:22 404:13

410:13 413:3

462:12 489:7

551:6 561:17

562:2 638:4

687:1 723:19

724:3 739:13

745:6 763:17

reasonableness

17:4 18:1 19:5

20:14 21:20

25:15 380:20

reasonably 16:6

628:14 635:11

650:5

reasoning 237:21

240:9,15 245:1

578:14,19

reasonings 239:1

reasons 208:3

214:11 233:17

236:13 239:7,11

240:4,8 347:14

347:18 384:22

503:14 627:8

690:1 695:10

698:1,2

reassess 736:10

Rebecca 20:5

rebound 276:8,10

recalculations

625:17

recalibrated

402:10

recall 148:6,18

154:16,21 161:11

161:15 198:16

424:18 430:2

435:21 556:21

585:7,11 654:13

recalling 425:5

recapitulate

450:13

recapitulating

450:19

receive 604:2

received 171:11

204:9 278:11

receptor 45:17

48:22 49:1 51:4

52:8 56:11,15

61:11 463:1

486:14 729:4
Page 87

receptors 48:21

49:10 53:5 56:9

446:9 463:7

744:7

recess 68:11

223:11 387:12

419:14

recessed 314:12

recognition 621:16

638:11 657:14

recognize 20:5

352:20 476:5

609:2 762:18

771:21 777:12

778:13

recognizes 623:8

624:7

recollection

149:14 408:10

425:10

recommend

257:16 389:19

391:22 392:9

393:6 394:7,20

395:10 417:11

591:15 615:12

665:17 694:8

725:18

recommendation

368:3 592:20

730:20 737:17

751:14

recommendations

2:17 30:3,14

31:18 104:4

187:19 372:6

380:19 437:10

541:6 553:19

570:4 696:1

751:12 777:14

778:10

recommended

69:8 81:4 113:7

391:7,16 495:3

542:4 630:8

recommending

738:1 753:11

recommends

21:22 27:5

reconcile 236:6

364:6

reconsider 772:20

reconstruct

722:18 734:16

reconstruction

191:21,22 192:9

195:20 196:1,4,9

214:12 218:17

658:7 659:12

660:10,10,20

662:2 663:12

665:17 692:5

694:11,11 722:7

722:12,21 723:17

724:21 725:1

738:5 741:8

755:6 772:9,10

reconvene 314:10

387:13

record 90:22

117:22 132:16

281:20 422:14

440:8 540:14

680:20 775:20

779:7

recorded 318:11

records 428:15

recovered 67:2

85:16 107:1

recoveries 663:7

recovers 66:22

recovery 83:18,19

278:18

recreate 699:20

recruit 250:9

588:7

recruited 114:18

116:7 117:16

recruiting 638:17

recruitment

155:12,21

rectify 189:4

recurrent 768:1

recurring 611:13

red 75:8,19 179:14

179:15,19 181:21

182:3,9,18 215:2

216:4,4,21

256:18 269:5

297:1 428:11

600:10 601:5

613:3 686:8,13

redox 62:17

reduce 140:20

141:18 145:2

547:20,21 693:7

reduced 398:7

523:7 544:8,9,10

reduces 140:2

reducing 114:2

reduction 407:12

416:8,10 579:11

reductionist 468:1

redundancies

504:15

redundancy

498:13

redundant 467:13

reeler 482:13

reelers 482:17,18

Reeses 745:14

REEVALUATI...

1:4

refer 130:15

335:10 389:14

475:10 597:4

604:18,19

reference 76:6

176:5 177:10,17

181:1 185:10,13

328:11 332:7,15

336:14 459:2

482:4 509:11,13

510:7,10,13,14

510:22 520:8

534:2 569:22

634:14 635:3

676:20 681:5,6

693:2 749:11

referenced 48:10

247:3 385:1

423:3 569:21

611:18 672:9

682:6 692:16

748:15 749:16

references 167:17

328:10,11 334:3

334:4 335:11

566:7 655:9

referred 114:5

415:13

referring 148:19

154:7 169:6

411:2 429:19

582:8 650:11

680:21 709:4,17

778:18

refined 186:21

refining 775:1

reflect 9:11 13:5

124:12 125:3,19

125:22 177:19

356:9 384:2,11

657:22 670:17

672:22 689:18

reflected 11:2

20:17 131:6

295:10 354:19

435:6 546:10

665:21

reflecting 397:12

549:9 752:20

reflective 120:22

125:7 636:11

659:7

reflects 549:15

569:9 670:2

695:16 774:16

reflex 134:7

reflexes 134:19,22

135:1 145:19

150:13 169:9,12

210:21 359:22

360:2,17 577:8

refrain 702:6

refresh 169:2

refrigerator 644:1

644:7

refutations 236:14

refuting 245:3

regard 62:13

124:14 133:13

135:9 143:8

175:3 179:5

183:5 287:7

302:6 355:20

573:4 577:22

578:8 579:20

631:8 729:16

757:7 758:2,8

regarded 355:13

519:10

regarding 114:13

120:4 142:13

187:19 254:14

285:22 287:11

289:5 299:13

350:9 389:17

392:3,15 393:10

424:9 543:8

545:10 549:12

580:7 636:21

681:20 770:21

regardless 183:15

332:2 363:15

364:16 383:22

751:16

regeneration

324:8

regimens 42:8,15

region 352:11

508:11 675:12

regional 743:4

regions 57:6

352:10 551:15

Register 395:13

registered 27:20

258:5,10 387:9

430:13

registrant 27:22

249:15

registrant-subm...

180:6

registration 28:4

29:14 206:8

registrations 28:9

regression 113:22

140:17 568:10,11

570:1 616:14

718:22

regressions 160:5

568:14,16

regular 512:9

542:11 657:1

regularly 244:21

regulate 47:3

306:1

regulates 450:10

regulating 465:19

regulations 2:20

regulatory 2:18

6:1 19:19 27:18

29:12 37:21

95:12 108:2

185:9,14 186:1

193:10 209:1

232:15 254:1

305:15 383:14

385:3 437:2

463:10,12 476:4

489:11 520:1

691:5 693:1

701:8 728:2

748:1

reinforce 728:8

reinforcement

477:13

reinforcements

479:19

reinitiated 588:13
Page 88

reintroduce 315:8

reiterate 19:16

380:18 528:15

615:21 632:14

705:5 769:13,15

770:20

reiterated 468:11

573:22 724:22

reiterates 572:14

reiterating 726:18

766:15

rejected 384:9

relate 44:12 82:20

95:20 247:20

251:4 323:12

429:4,6 451:18

530:1 688:14

703:13

related 3:17 40:11

47:2 48:13 97:7

97:19 129:6

134:11 156:16

173:12 176:8

183:17,17 186:10

187:7 188:6

237:1 266:6

272:3 299:3

354:4 365:13

405:17 407:8

421:9,17 422:18

426:7 434:11

459:11 462:11

463:6 528:18

533:17,17 549:8

552:21 561:7,8

563:14 566:3

568:11 583:4

587:6,10,11

588:22 589:7

628:4 641:7,8,11

641:12 642:3,4,5

661:15 688:10

753:10 754:16

757:9,21 769:4

relates 97:18

335:1 452:1

588:19 750:8

relating 141:17

438:19 543:4,5

relation 110:9

113:18 116:19

117:3 120:13,21

122:7 130:3

133:15 135:7

136:8 235:1

398:20 544:7

609:10,18 610:16

693:4 749:5

relations 546:7

relationship 34:3

46:17 50:12,13

52:4 59:1,2

63:12 64:16,19

66:4 91:18

163:13 166:14

172:13 179:11

180:11 182:15

184:3 197:18

272:14,17 280:18

357:16 382:18

405:13 412:5

415:3 453:5

460:4 500:2

515:7 524:19

543:14 544:15,16

549:17 551:4

573:7 578:22

579:10,19 580:8

610:9 612:6

636:1 670:11

686:18 719:1

748:21

relationships 12:2

24:10,10 33:21

57:19 59:5

350:10 405:12

406:1 447:3

456:20 472:18

495:19 515:11

594:10 616:10

679:11 702:13

750:19

relative 21:15 22:5

36:19 37:11

86:14,16 175:17

176:9 180:22

186:8 199:22

200:16 212:4

284:14 318:22

329:10,17 343:14

431:16 457:8

461:14 573:9

594:11 604:2

607:8 609:15

622:21 647:5,11

647:14,17 650:22

653:7 731:15

749:14,22 751:6

757:12,19

relatively 91:9

122:22 126:14

141:19 162:16

163:11 344:4

397:14 411:19

416:11 448:7

456:11 461:17

487:16 579:21

580:4 628:7

633:16 646:16

658:19,22 661:6

665:1 667:6

705:19 769:7

relaying 153:15

release 348:11

389:5 436:15

released 687:10

relegated 236:16

relevance 180:1

183:20 207:9

229:21 231:7

234:13 323:12

relevant 29:4

44:19 185:12

187:12 215:7

225:7 232:6

283:11 297:17

340:9 346:20

347:10,22 349:14

350:16 368:9

381:10,17 410:5

446:19 506:18

608:5 675:7

682:9 691:18

740:10 754:11

reliability 90:12

135:17,18 352:1

357:18 381:6

602:19 603:7

606:22

reliable 302:7

521:3 529:7,15

607:10 707:16

718:11

reliably 264:17

520:11

reliance 459:1

641:3

relied 491:12

766:6

relies 32:15 501:6

relooked 704:12

rely 12:15 236:10

615:10 708:18

777:5

relying 479:11,21

545:4 706:12

749:20

remain 126:11

213:10 232:16

319:21 346:12

381:20 437:11,15

585:11 623:13

644:11 660:6

670:14

remainder 150:6

remaining 327:3

385:6,9 387:6

389:4,9 392:10

491:15 660:2

remains 2:19

30:16 212:8

214:6 437:22

487:13 490:10

595:9 720:8,21

727:14

remarkable 266:8

779:6

remarkably

460:14

remarks 22:8

248:1 317:22

318:13,18 344:3

364:11 380:14

396:8 540:18

593:4 634:1

737:21 780:12

remember 34:3

108:14 148:10

163:22 232:12

258:9 259:12

260:19 262:7

366:12 375:3,5

384:16 395:6

414:4 477:17

479:1,4 524:12

525:3 531:16

532:3 533:1

537:6 561:15

583:2 607:2

618:16 652:3

676:15 683:22

685:10 732:3

760:2 765:5

775:14

remind 41:10

107:18 203:10

318:10 457:8

654:3 709:20

reminded 506:22

reminding 223:14

reminiscent 241:6

remised 463:12

removal 586:14

658:1

remove 579:7

removed 64:7

119:6 590:22

rental 123:5 214:3

repeat 120:18

124:16 125:9

126:18 200:1

326:20 351:16

586:10 600:9

737:8 772:15

repeatability

603:1

repeated 37:4

96:10,11 125:15

251:7 253:18

349:3 356:19

494:4,5 518:3

525:8

repeatedly 83:1

635:7,10

repeating 511:18

rephrase 759:16

replace 68:1

391:18 438:11

740:10

replacement

687:11

replacing 546:21

687:14 740:5

replant 431:1

replicability

516:17

replicate 102:11

109:1,3,8,12,16

109:20 110:4

372:4,14,19,20

373:1,2,3,15,18

373:19 374:3,7

514:2,10 576:21

763:2

replicated 102:18

109:15 131:2

171:2 257:7

260:6 268:21

279:4 516:19

676:20
Page 89

replicates 372:15

replication 79:12

257:5,12 272:7

351:13 372:8

491:12

replications 73:18

78:18 87:3

replied 696:13

report 3:2 8:1 9:16

9:20 10:16 11:7

16:10 18:2 21:22

23:3,21 24:3

34:13 47:7 52:22

62:18 75:11

103:14,15 109:18

116:11 118:4

122:16 137:7

149:18 177:19

210:4 261:10

325:2 331:21

333:5 335:12

343:10 352:12

357:1,11 391:5

391:13 428:5

429:15 434:19

435:5,7,8 443:20

445:4 460:14

480:12 482:1,2

484:20 488:13,13

488:15 497:12

510:7,9 517:18

563:11 566:5

568:2 573:22

591:5 593:1,7

619:7 622:11,13

661:1 665:21

669:22 681:5

700:18 726:17

747:16,22 753:11

761:20 762:3,22

764:17 769:13

770:22 773:10

781:13

reported 72:19

73:7 74:2 75:5

76:11 77:12

78:14 79:3 80:4

80:15 81:12,18

82:5,15 85:12

103:14 118:6,9

123:2,8 125:14

128:19 133:1,7,8

134:19 137:14

138:17 153:16

157:5 161:14

168:3 175:21

177:18 178:21

181:9 182:2

210:18 213:14,15

223:1 260:3,21

263:16 268:20

287:5 323:1

330:12 331:1

332:9,12 333:20

336:6 346:4

350:21 353:11

354:19 357:3

363:17 381:18

386:5 426:13

438:2 460:12

507:18 518:2

523:12 525:17

547:16 554:12

567:8 568:8

571:22 584:1

592:11 596:18

597:15,18 598:9

611:12 612:9

648:13,15 649:1

658:9 670:1,7

749:17

reporting 94:15

122:17 125:2

366:1 381:8

431:3 507:18

510:6

reports 3:4 26:3

202:10 207:1

395:2 545:4

562:16

repositories

715:11

repository 715:13

represent 41:21

85:3 118:11

237:9 258:10

269:14 305:7

325:11 329:16

641:14 658:16

733:1

representation

427:3

representative

72:11 322:18

358:5 396:9

459:3 580:14

735:14

represented

221:13 263:11

403:15 423:9

535:13

representing

425:6 636:1

represents 162:9

175:17 305:8

325:13 399:15

428:9 500:15

626:3 630:14

658:14 686:20

695:17 772:16

reproduced 84:15

257:7

reproducibility

514:12

reproducible

364:18

reproductive 63:6

682:11

reprotoxicology

343:2

request 22:3 319:4

353:4 471:4

705:1

requested 319:6

requesting 319:2

require 35:5

175:10 391:15

407:3 491:12

542:8,15

required 26:20

332:5 446:12

461:17 544:1

603:17 723:18

requirement

117:20

requirements 3:5

448:17

requires 326:18

355:2,8 356:8

406:19 549:20

658:8

reread 199:4

rereading 702:9

research 12:10

25:6 43:7,8 62:5

68:19 81:5 92:5

92:8 114:11

117:10 120:11

131:19 135:5

139:13 143:10

176:14,19 193:8

218:20 223:17

282:12 347:13

355:5 391:1

441:14 442:5

476:1 512:7

517:4 613:21

621:16 624:20

654:3 666:16

715:9,12 764:22

researchers 64:18

113:16 115:1,4

118:3 120:2

122:11 127:22

130:20 139:17

140:17 144:18

441:22 541:10

669:12 715:10

researches 115:13

715:7

resembles 404:15

reservoir 153:5,13

628:9,15

reside 116:13

123:5

residency 117:19

residential 28:1

119:2,7 123:8

141:22 170:9,15

176:9 177:1,3

178:13 179:7

197:3 393:13,15

416:10 656:5

657:18 731:22

733:6

residents 214:3

residual 140:15

240:16 576:1

678:18 763:20

residue 190:17

308:11 321:20

389:2 584:13

699:22

residues 557:10,11

588:1 760:4

resolutions 239:4

resolve 512:1

resolved 239:2

resources 28:7

92:8,9 512:21

768:14

respect 37:4 126:1

126:15 127:19

130:8,11 132:7

134:18 135:3

138:13 142:4

144:17 145:18,19

145:21 146:12

160:19 168:11

209:19 218:5

245:10 286:15

312:17 338:19

355:9 541:15

548:14 549:1

594:13 605:4

606:21 628:22

681:18 699:13

717:15 722:9

728:14

respective 113:16

424:3

respond 67:21

343:16 549:4

592:5 635:15

727:10

respondents 667:1

responding 562:17

response 30:5

31:12 33:20 34:3

34:4,5 35:12,13

35:18 37:21 39:8

47:9,17,22 48:3

49:11 50:21 51:8

51:10 52:1 53:19

55:10 56:22 57:8

57:19,19 58:5

59:4,5 64:19

66:16 67:20,22

69:6 74:10 76:11

78:15 79:17

82:15 84:9 86:13

86:22 97:16,18

101:19 104:22

105:8 106:20

120:21 180:13

182:5,15 197:21

204:2 235:20,20

241:12,21 243:16

244:3 246:9,15

251:11 266:9

284:21 285:14

291:22 292:3

297:19 298:15

323:4 336:20

344:5 346:18

349:18 355:11,21

357:16 358:16

360:21 362:13

363:1 364:14

384:2,11,13
Page 90

398:10,15 406:21

412:4 433:19

434:2 437:12

438:14 439:5

445:20 447:2

449:2,6,11,18

451:20 452:3,14

452:15,21,22

456:20 460:4

469:4,5,10

472:17 486:9

495:17,19 500:2

507:12 515:7,11

515:12,13,19

516:7,10,17,20

517:13,15 536:17

537:9 550:19

573:14,19 578:22

579:10,19 580:8

587:16,18 594:6

594:10,13 595:10

599:12,15 600:5

601:8 605:14

606:1,6,7,21

607:15,16 609:16

611:7,17 612:6,9

614:9 621:8

634:3 673:18

748:12,14,20

749:5,8,13 751:5

754:10 755:1

756:6 757:6

766:15 768:3

responses 47:16

47:20 51:8 80:9

134:11 219:19

285:15 389:11

442:16 460:12

466:15 469:6

599:17 600:3

668:12 748:7

767:19

responsible 2:9

255:22 321:19

434:3

responsive 320:21

501:8,9

rest 111:13 358:5

400:21 445:18

470:22 554:3

644:11 670:6

713:12 723:4

770:21

restate 26:2

restating 716:21

restricted 114:19

118:3

restriction 118:14

127:12 140:18

547:18

restrictions 392:21

393:1 548:4

result 37:13 43:12

44:6 55:6 57:6

115:17,21 116:3

129:20 187:7,11

208:17 211:7

236:19 294:17

333:20 353:8

388:16 392:6

404:17 405:9

406:17 416:9

438:13 472:11

545:5 548:12

549:10 566:20

594:2 598:4

617:15 701:12

721:4

resultant 253:7

resulted 393:17,20

548:15 549:2

573:17

resulting 53:2

321:20

results 49:3

102:22 110:10

113:9,17 124:12

130:12 131:16

132:7,19 133:11

133:14 134:17

138:17 139:8

142:16 173:11

175:21 178:7,8

181:7,9,20

185:18 197:12

238:18 239:6

257:7 260:6

261:10 266:19

267:16 285:3,20

285:20 289:21

291:15 292:21

293:1,10,16

295:18 296:1

299:2,4,5 312:8

312:14,17,18

313:7,8 331:3

335:15 349:3

351:16 354:3

355:3 398:7

400:3,10,11

402:12,20 403:8

403:9,21 405:16

407:4 409:14

424:3,10 459:19

484:4,12 496:8

498:6 543:20

544:1 547:22

549:19 574:16

576:9,16,21

577:4,20 597:1

613:14 614:3

669:9,19 670:8

674:5 687:20

716:1 751:3

retaining 615:13

retention 144:7

return 47:17 51:20

68:9 542:18

716:15 779:3

reversal 87:17

495:15,15

reverse 185:21

186:4 191:11,20

214:12 218:16

516:13 691:6

692:5 700:8

708:2

reversion 687:6

review 2:15 9:8

15:16 21:14

28:11,16,17,18

28:20,22 29:14

29:18 30:4,8

31:11 32:13 69:8

69:11,17 70:12

91:6 98:1 112:6

112:14 113:14

122:10 168:5

172:11 177:8

203:14 206:22

214:18 229:15

244:11,13 283:10

291:2 330:12

333:9 392:8

437:8,9 471:7,7

471:10 496:10

500:15 507:22

541:1,13 542:2,3

542:19 545:2,13

546:4,17,19

547:17 548:1,8

549:14 550:14

551:3,21 553:11

553:14,16,20,22

554:5,6 566:4

572:18 692:22

765:21 769:10,18

770:10

reviewed 3:4 36:8

69:2 139:7 177:7

214:18 216:11

306:8 323:17

495:18 508:1

525:17 542:6

601:2 644:15

678:13 704:18

reviewer 495:21

reviewers 509:17

reviewing 278:10

343:20 473:6

542:5 764:19

reviews 10:10 98:2

98:3 226:5

229:11 249:9

250:10 257:15

283:19 381:12

513:12

revise 273:3

revised 256:15

259:10 269:3

436:8

revoke 28:8 392:9

rewards 479:16

reword 306:22

re-emphasize

635:19 734:10

745:19 751:21

re-read 152:16

725:4

RfC 398:8

RfCs 396:21 405:8

RfD 398:8

RfDs 396:21 405:8

RfDs/RfCs 408:7

Rhomberg 224:7,8

224:9 246:21

247:2,4,8 254:21

Ricceri 486:17

519:3

rice 430:20

Richardson

569:21,21

rid 371:10 576:11

right 3:11,11 14:8

18:8 22:17 23:17

43:3 49:14 60:6

65:6,22 67:10

75:8,10 91:15

124:9,19 149:17

156:8 157:16

159:9 160:16

163:14,16,17

164:8 165:19

166:1,21 169:18

170:7,14,20

178:8 182:4

193:3 198:5

204:6,19 205:21

220:15 221:9

222:12,15 223:18

231:15 232:18

247:4 250:21

269:10 272:14

274:13 277:7,10

280:7,10,12

282:3 289:5,18

293:22 298:14

301:19 302:6

303:22 305:4,19

310:8 327:6,17

337:15 339:6,11

340:6 342:3

346:10 349:14

359:10 362:4

369:4 374:2

376:5,21 377:21

378:1 379:20

383:1 411:11

412:6 415:17

419:2,5 420:21

443:13 445:14

447:10 454:19

456:1 457:17

459:22 462:2

470:8 471:6

479:17,17 491:2

497:5 516:9

521:4 527:12

531:8 556:5

559:17 565:3

583:19 588:7

589:16 604:8

617:9,16 634:22

641:13 649:10

665:4 676:6,13

677:8 678:5

680:11 681:3

704:2 707:11

713:20 714:1

718:21,22 719:18
Page 91

722:20 726:5

729:10 734:22

735:3 736:6

739:16 744:18

746:2 756:7

759:4 760:8

right-hand 221:22

244:9

ringer 529:11

rings 211:4,4

rise 399:8 401:20

604:6

risk 5:22 6:1 10:15

10:22 12:9,11,17

12:18,18,19,20

13:1,1,4,10,11,12

13:16,18,20 14:1

14:15 15:17,17

17:3,19 18:4,5,14

19:3 20:13,15

21:20,21 22:2,7

23:1,8,10,18,20

24:5 27:10,11

29:8,18,20 30:17

30:20 32:19

35:21 39:4 40:8

42:20 44:19 45:8

54:9 62:4 71:21

96:5 113:10

114:11,14 118:4

118:21 127:10

128:9 132:13

136:13 137:21

140:6 142:17

145:16 147:11

172:18 174:17

183:13 186:12

190:12 192:2

202:5,10 203:17

203:22,22 204:8

204:20 206:8

211:13 220:1,10

238:22 248:20

249:7,10 250:9

250:16,19 252:6

254:1 282:19

283:9 290:19,21

300:1,3,16

316:19 334:2

342:19 343:2,9

343:12 347:16

349:13,15 354:21

355:6 359:20

362:19 363:14

364:19 381:7

383:14 385:3

389:1,2,6 390:1

391:6,15,22

393:8,17,20,21

394:6,9,10,12

398:9 406:5,6,8

406:14 407:2,6

408:1 447:7

455:15,17 465:19

467:5 470:2

472:22 475:18

505:8 541:10

542:1 548:3,7

594:16 595:1,5,7

607:4,8 608:11

608:17 609:11

618:18 623:4,6

624:11 626:3

630:21 641:22

653:15 656:20

657:13 658:3,6

659:17 692:21

693:17 703:1,14

703:18,21 704:13

704:20 720:9

722:14,22 727:4

735:13,15 747:19

747:20,20 748:1

749:9,11 753:10

753:12 754:4,5

755:16 756:9,15

757:10,10,11

763:9 768:6

772:7 773:5

775:4

risks 389:20,21

675:7,13,19

risky 118:15

risk-based 172:15

Ritten 20:3

River 485:13

520:2

road 432:21 730:1

760:8

roadmap 737:10

737:11

Roberto 715:8

robust 35:16 36:10

86:21 88:15

104:21 105:7,10

206:1 257:10

261:17 300:5

346:12 359:17,18

389:13,16 412:12

437:11,15,22

439:7 452:17,22

519:10 574:15

robustly 412:17

robustness 300:18

353:22 359:15,20

452:16 518:7

Rodan 619:14

rodent 31:9 70:20

392:4 414:11

459:1,15 481:1

517:6 554:12

604:2 683:10

743:2,2,16

744:12,21 765:7

Rodenticide 8:5

rodents 38:8 85:14

85:17 330:11

458:19 472:3

479:10 492:6

505:3,4 517:1

532:12 551:10

683:12,13 739:1

rodent's 78:20

Rodriguez 20:2

41:3 194:6,6

197:14 198:5

role 31:20 97:9

112:17 122:1

128:1 147:7

158:22 191:8

210:16 225:16,18

256:6,22 281:9

284:8 298:17

364:15 381:18

432:12 442:17

458:15 503:15

551:4 561:20

562:1 571:21

572:16 573:1

581:13 584:4,18

587:3,6 590:20

591:2 596:4

611:10 752:14

761:5 764:10

778:11

roles 25:3 550:13

551:15 564:18

744:5

Rollins 5:16

316:13

rolls 714:17

Ron 604:19

room 103:15

470:11 540:3

713:2 717:3

719:6 762:5

781:16

root 30:22 149:4

735:7 770:12

ROS 336:11

rough 721:22

722:17

roughly 23:3

170:1 181:11

647:13 653:8

669:6 674:6

round 660:3

route 31:2 35:13

35:20 84:10

119:12 176:11

191:14 219:19

321:10 347:10,17

348:2,5,13

349:16 355:15

401:6 503:9,14

505:7,9,11,13,15

505:22 506:7,14

735:6 755:8

routes 31:2 35:19

71:10 119:1

184:17 191:15

192:14 215:22

336:5 347:14

506:20 555:20

735:21

route-specific

186:8 192:13

routinely 651:11

683:1,10 735:15

748:1

row 294:6 296:6

rows 213:21

221:11

RRR 577:15

RSD 278:22

rubric 121:14

rug 303:2

rugged 709:9

rule 8:12 480:1

505:11 509:3

563:16 564:2

573:1 590:14

ruled 572:20

run 92:20 107:17

278:21 372:7,8

377:18 453:19

476:7 477:10,19

477:20 478:5,17

483:21 490:13

521:11 539:6

603:1,1 616:14

634:7 693:18

704:16,17

running 198:7

310:11 368:13

373:12 436:17

478:2 711:16

714:19 715:2

777:16

runs 76:4 477:22

run-through

681:18

run-to-run 669:15

672:21

rural 388:21

392:20

rush 419:19

Russ 433:17

Russell 6:18,18

66:13 158:14

159:16,21 161:1

161:17 164:18

165:1 317:9,9

378:18 435:2,19

437:21 440:7

444:12 454:20

499:18 506:5

516:6 521:7

528:21 531:6

554:11 557:7

558:9,19 559:2

560:6,13,18,21

561:4,10 684:13

686:6 728:21

771:10

Rutgers 7:3

317:14

Ryan 5:14,15

158:13 161:18,19

162:5,12,18,21

163:5,8,21 164:3

164:10 310:10,14

310:19,20 311:5

311:16,19 312:1

316:12,12 623:17

624:1 632:1,2

634:3 636:19,20

642:16,19,21,22

646:7,8 661:17

662:3 664:9,10
Page 92

665:14,19 681:7

702:2,3 728:12

728:13 764:13,14

768:18

Ryan's 635:15

R&D 248:3

R-squared 566:9,9

566:12,19 567:2

R2 426:14

S

S 528:9

sacrifice 336:20

345:10 663:9

sadly 392:7

safe 348:1 355:3

465:18 495:12

safeguard 649:9

649:10

safely 582:1

safety 95:20 108:2

246:14 254:5

389:21 727:17

780:2

sake 533:5 592:6

salient 209:2

Salinas 116:7

431:4,6 541:4

saliva 270:9,11

sample 66:18,20

85:8 94:7 121:2

128:11 131:21

142:7 222:6

263:18 273:16

308:14 309:16

355:21 360:10,15

403:11 475:6

487:16,18 488:17

489:4 519:5

545:21 546:5,8

547:10 548:4,5

550:5,15,18

579:21 580:6,10

586:22 592:17

602:13 613:2

635:10 643:20

644:2,6 645:2

649:8,13 650:14

651:18 652:11

659:19 663:2

669:10 672:4

716:4 723:21

763:1

sampled 635:7

samples 120:18

121:8 124:16

125:10 126:18

153:11 174:2

175:4 190:17,18

279:5,6,7,10,11

280:9,16,22

281:1 309:20

335:20 413:11

414:11 415:18

416:7 547:18

579:12 584:14

586:1 592:3,9,16

613:10 633:7,8

634:15 638:19

648:15 649:17

650:1,5 651:12

664:15 669:8,13

671:20 672:1,2,2

672:3,3,9 700:1

714:14 722:10

731:4 760:4

769:1

sampling 493:19

594:8 656:6

669:14

sand 589:12

Sandy 4:16,16

315:19,19 471:6

471:12,21 570:7

570:8 591:19,20

595:15,17 605:7

606:16,17 607:5

607:13 611:17

614:13,14 619:3

620:22 621:1

649:14,15 651:7

651:8 715:14,15

735:16,17 753:8

753:9 755:21

756:4 766:12,13

Sandy's 601:21

606:19

SAP 1:1,8 2:14

3:10 4:1 7:10,19

8:2,11 9:14

10:10,17,18

28:12 29:3 30:3

31:18 69:1 71:13

73:4 112:16

129:21 147:6

177:7 180:3,8,15

186:18 187:2,5

187:19 193:5

204:7 208:13

213:7 219:3

250:3 251:16,21

275:10 315:4

318:8 320:17

323:17 335:12

342:17 376:13

388:12 389:19

391:22 392:9

393:6 394:7,20

395:9 400:11

437:10 439:11

440:11 472:5

539:22 541:6

542:4 569:18

573:22 581:11

585:20 594:15,21

676:16 693:5

700:15 762:6

765:17 768:12

776:21 777:8,22

778:2,7,16,20,22

779:5,7,9,12,15

781:11

SAPs 9:12 63:16

644:15

SAP's 389:14

572:14

saran 270:7 671:7

673:17

Sass 387:15 388:1

388:2,6 395:18

396:2,3 421:8

Sass's 395:19

sat 776:8

satisfactory

518:21

satisfied 2:21

156:17 708:7,11

714:4

saturable 323:18

saturated 681:18

saturates 341:1

saturation 322:3

325:21 397:8

save 87:10 711:7

712:10

saves 432:6

Savitz 120:9

saw 69:17 79:7

212:14 266:15

282:22 349:21

352:5 354:18

365:8 436:1

523:1,6 557:4

575:10 576:4

577:6 578:9

597:22 598:1

642:19

saying 91:3 94:22

100:18 153:4,10

156:5 164:18,19

221:10 232:6

233:10,19 238:1

238:3 242:18

247:1 269:1

272:14,16 289:16

293:9,14 306:4

339:12 362:1

368:8 370:5

371:8 380:7

387:1 396:8

397:3 411:6

421:2 444:3

450:15 459:18

465:10 487:11

506:17 508:22

509:5 513:11

523:6,11,13

532:5 533:16

534:15 536:12

556:13 557:1

558:6 559:8

560:4,12 561:16

562:5,6,7 563:4

564:2,4,12

565:21 585:15

587:1 591:1,8

605:9,10,19

639:16 652:10,14

678:20 681:20

685:15 689:15

696:3 702:10

703:20 737:8

756:14 757:2

758:22

says 68:9 90:19

104:4 162:8

166:15 233:2

234:1 271:17

296:20 312:14

353:7 362:16

469:4 529:5

533:14 590:10,19

592:20 600:16

704:6 712:8

713:18 729:1

757:4,15 759:8

SC 345:7 347:18

348:2,5,6,9 363:4

364:11

scale 107:19 130:9

135:22 136:15

138:4,5,9 145:20

167:2 233:18

268:4 405:14,20

409:10 534:19

576:5 578:11,14

696:13

scaled 137:8

324:12,16,22

scales 135:10,11

135:13,15 167:17

412:19

scaling 341:15,19

696:16

scattering 325:18

scenario 276:18

281:3 614:4

699:21 700:3

701:9 729:21

scenarios 14:7

177:1,3 178:14

198:7 394:18

395:3,3 422:18

735:13

scenes 471:5 777:2

schedule 29:16

scheduled 220:14

220:18 318:5

389:5 470:13,18

schematic 125:5

186:16 197:15

school 4:22 5:16

6:4,17 7:2

282:11 316:1,13

316:22 317:5,7

317:15 388:22

550:1 661:14

school-age 113:19

138:3,22 146:6

Schwartz 407:10

science 5:10 8:2,3

8:11,12,14,16,18

8:20 11:12 19:18

19:22 21:22

26:16,22 27:5

28:13 37:8 98:2

108:13,15,16

193:13 202:11

203:1 219:7,9,9

248:20,21 249:6
Page 93

249:10 250:5,8

253:10 317:15

343:19 385:11

391:6 441:22

524:15,16 533:4

589:14 618:9,22

620:13 635:6,6

648:22 704:15,22

713:4,21 747:16

779:13

Sciences 4:12 5:12

6:11,13 7:1,3

316:10 317:4,14

342:22

scientific 1:1,5 2:2

2:15 8:6,7,8

20:19 21:6,9,18

173:10 208:12

227:18 235:15,16

249:3,10,11

315:2,3 353:21

359:15,19 384:20

441:14 447:2

456:19 460:3

472:17 477:2

518:6 539:20,21

542:20 595:12

619:5 666:13

713:4 720:21

747:18 772:19

779:22

scientifically 20:15

214:19

scientifically-ba...

391:9

scientist 25:2,4

248:4 252:9

320:6 343:3

367:13 422:15

715:2

scientists 348:18

383:18 390:17

442:5

score 136:16,17

165:6 297:13

scores 164:19

166:7 276:1,2,3,4

276:5 296:11,13

296:14 298:3

319:1 406:15

413:5 543:14

555:5 577:19

579:15 771:16

scoring 511:8

scratching 588:17

screen 39:18 43:6

100:22 444:20

screening 93:11

135:20 185:6,18

186:1 228:21

script 721:17

se 471:11 641:16

773:7

sea 297:3

search 110:19

115:3 481:12

searching 497:11

season 669:1 675:1

seated 3:11

second 37:15 58:3

59:20 98:12,15

160:18 162:8

181:3 182:7

184:19 208:19

251:9,15 286:12

286:13 290:6

315:1,8 330:2

334:10 354:20

365:11 372:11

397:22 407:4

427:10 447:9

477:16 479:9

507:20 550:7

554:20 568:10

569:9 598:8

631:19 663:5

718:15 747:7

secondary 128:17

427:13 443:15

636:2,14 678:18

secondly 381:8

547:5 603:22

681:14

seconds 211:5

510:19

secretary 777:22

778:2

section 40:19

112:8 145:16

161:12 172:10,20

177:14 179:4,4

193:20,21 221:1

252:6 291:2

293:19 387:15

419:21 422:2,5

441:13 472:2

509:20 540:22

541:14,20 568:3

621:19 655:19

656:17 666:7

676:17 702:4

sectional 661:4

sectioned 252:7

sections 496:6

sectoral 358:20

secular 399:3

416:8

SEC/fluid 698:4

see 7:9 9:2,7 11:1

11:17,19 12:3

17:21 24:13

25:16 31:14,16

43:6 45:6 49:5,7

49:12,16 50:17

50:18 51:9,12,13

51:18,19 53:9

63:7 67:9 74:22

75:2,7,9 77:1

82:20 83:13,16

83:17,20 84:5

99:9 100:12

102:22 106:6

109:1 125:16

126:6 130:5

131:10 132:18

134:17 136:10,13

136:18 138:16

150:10 155:2,16

155:17 157:21

160:16 165:9

166:4,13,16

174:14 182:1

198:11 202:16

213:11,19 215:3

228:19 230:11,21

231:22 232:1

234:10 236:4,4

237:21 239:22

240:16 242:3,7

254:18 256:8

258:17 259:11

260:15 262:19

263:4,13 265:8

266:21 267:3,4

267:19 268:5,6

268:17 271:21

276:10 281:7,11

282:3 283:6

284:21 286:16

288:16,22 292:6

292:9 294:5

295:3,16,20,22

296:21 297:5

303:14 304:3,22

305:14 308:22

309:12,14 313:7

314:11 318:6

325:18 327:14

328:6,18 331:7

331:12 332:3,9

337:8,11,12

344:2 345:5,11

346:1 348:7

349:6 356:15,17

357:22 358:4

360:19 363:20

365:1 369:11

371:8 372:20,20

373:7 374:21

379:7,8,17 381:1

383:6 386:18

387:20 397:1

403:5 404:1

408:12 413:15

421:15,18 427:4

428:12,16 429:10

431:3,7 448:5

449:11 450:14

451:4,12 452:5

452:14 453:16

458:7,7 461:10

462:4,7 463:7

466:8,10,22

469:5 470:10

473:7 474:18

480:11 496:4,5

498:17 499:8,22

503:15,17,18

504:21 505:10

508:14 511:13,16

511:19 514:13

515:19 516:17

521:9 522:5

533:9,14,15,18

534:9,11 535:10

535:11 536:4

538:17 546:13

550:21 556:12,15

557:3 558:6

559:9 561:4

564:10 567:18

568:14 574:20,21

576:22 579:8,14

579:16 580:5

584:12,14 585:10

587:20 591:18

592:14 596:22

599:7 604:13

608:8 613:7,11

616:15 621:12

635:2,22 636:13

638:12 639:17,18

639:19 640:9

646:2 654:7

656:14 657:10

660:2,4 664:19

665:6 669:2

670:11 672:20

677:21 679:16

683:3 685:3,12

686:18 687:14,16

688:8,16 696:22

697:20 703:22

707:11,21 708:2

708:4,9 711:10

716:14 727:12

728:8 730:21

738:22 746:6

747:10 751:13

752:9,21 759:4

760:5,10 762:5

769:14,16,20

771:8 776:8

780:21

seeing 76:10 89:2

93:8 220:16

246:20 272:7,11

285:3 288:5

294:11 296:2

310:11 347:16

380:11 382:9

383:18 386:3

466:7 532:15

556:17 562:18

568:3 607:22

632:17 652:11

672:12 677:20

683:11 689:4

757:21

seek 353:3

seeking 117:16

seemingly 133:9

seen 23:20 29:22

32:12 48:3 72:4

72:5 73:2 74:10

77:13 78:15 79:5

80:9 81:20 85:15

86:7,12 102:10

106:5 137:1

154:8,10 230:9
Page 94

235:8 237:20

243:12 259:12

266:4 277:12

333:19,22 336:19

340:11 364:6

379:12 381:1

388:10 439:6

460:7 464:9

475:21 478:1

490:20 495:20

504:21 507:9

528:11 531:7

535:14 536:3

549:16 551:10

554:6 568:7

574:22 580:22

581:5 594:19

596:18 597:19

598:14 615:8

618:17 753:17,19

759:19 761:10

762:9 765:6

segregation 400:5

seizures 48:17

seldom 489:13

select 67:3 484:22

516:7 707:14

selected 176:16

488:21 557:9,11

623:12 750:1

selecting 558:21

750:4

selection 128:7,11

139:20 141:14

144:5 327:8

390:12 522:18

542:5

selections 71:15

selective 430:9

484:3 623:2,11

625:19

selectively 483:22

Selena 647:8,20

self 545:4

self-identified

114:20 115:11

self-physiology

448:11

self-proliferation

432:15

self-promoting

643:15

self-report 120:4

122:19

selves 328:18

semantics 508:13

590:13

semi-quantitative

183:11 185:6,20

612:5 691:3

695:7 737:19

739:21 740:18

748:20

semi-quantitativ...

612:8

semi-volatile 29:4

send 111:12 271:5

411:8

senior 25:2,4

343:3 736:17

sense 12:12 17:17

26:12,19 27:18

34:8,14 35:18

37:2 39:13 42:1

155:18 184:5

196:2 203:9

213:14 219:5,8

231:19 232:5,9

241:22 245:5,22

301:14,17 355:21

361:13 369:4

425:16 427:16

502:17,18 512:17

515:4,7 516:4

518:19 524:21

531:3 538:15

570:19 571:1

600:1 602:12

607:18 631:7

642:10 658:18

700:7 706:4

756:2

sensing 524:5

sensitive 28:20

30:5,19 37:2,6

59:8,8 175:11

179:20 182:6

203:3 204:2

211:19 321:2

328:21 330:15

344:17 346:5,8

350:18 363:10

364:18 383:16,21

426:17 438:11

446:7,7,20

448:22 449:14

461:9,11 467:16

477:9 503:11

505:22 518:5,12

525:10 529:7

534:6 576:9

642:11 676:14

678:3 686:13

721:5 746:16

sensitivity 28:21

36:19,22 86:16

132:2 137:16

142:10 212:4

278:19 343:14

344:9 427:18

437:4 449:9

461:7,8 545:18

663:9 708:9,12

713:20

sent 152:19 170:14

644:2

sentence 488:21

525:6 703:20

Sentinel 319:13

separable 243:11

separate 58:15

97:16 159:1

246:13 336:20

410:14 481:18

483:7 555:22

556:4 557:4,8

560:7 561:11

565:22 614:2

627:2 698:5

699:1 712:4

726:19 728:1

752:1

separated 254:16

619:4,8 760:17

separately 160:17

161:13 283:12,12

429:19 518:17

757:1

separates 588:3

separating 555:18

separation 537:14

587:21 760:9

September 112:14

sequence 60:8

543:3 631:10

sequences 451:15

sequentially

511:18

sera 717:14

serial 639:3

series 10:10 33:14

228:17 405:12

483:16 521:2

646:21 683:20

serious 322:2

474:15 478:14

491:9 503:20

638:10 771:20

772:16

seriously 475:5

526:16 615:12

692:14

serotonergic 53:7

57:1 58:12

285:18 350:13

446:9 598:9

serotonergically

57:7

serotonin 57:3,4,4

109:10

serum 259:22,22

268:6,6 311:22

327:19 328:4,8

328:14 337:10,11

627:12,14 629:18

632:5 662:8,19

711:22

serums 328:6

serum-free 329:21

329:22

serve 2:8,12 467:7

693:7 778:21

781:12

served 32:11 778:5

778:14 779:5,9

service 388:13

779:5,11

Services 4:22

316:2

serving 19:14

247:16 780:19

SES 128:1 129:2

130:19 140:16

144:1 297:21

session 315:5

540:1 562:12

sessions 376:7,8,20

set 46:9 53:12,21

97:5,6 102:14

109:19 132:19

133:14 233:4,6

234:1 238:15,17

246:14 275:2

304:12 326:9

328:3 344:8

346:4 350:6

360:11 368:5

373:11,14 374:20

377:17 387:19

433:4 439:7

442:7,17 445:3

452:20,21 475:17

476:5 485:5

489:19 494:7

502:19 519:10

526:20 527:5,21

552:16 570:3

590:6 645:11

657:21 721:15

766:7,21 779:14

sets 8:5 110:9

336:9 373:13

399:21 532:21

738:10 761:21

773:21

setting 88:21

95:20 101:11

119:10 123:8

134:14 139:13

143:10 176:11

215:16 228:6

355:3 384:4

387:22 390:15

461:15 486:3

492:1 493:2

556:10 583:9

731:22

settings 135:5

583:13 657:7,9

657:18

settled 476:2

settlement 389:5

setup 736:19

seven 77:11 139:1

144:6 244:6

288:2 402:3

403:3 417:22

543:16 560:1,9

672:4 722:16

seventh 169:21

severe 684:11

severely 475:12

severity 452:22

615:6

sex 80:2 90:1

359:7 483:12

484:11 487:17,18

493:20,21 494:1

494:3 522:18

547:6
Page 95

sexes 77:6 435:20

shaded 263:6

266:21

shadows 237:13

shaking 591:19

642:20 711:4

shape 9:17 454:13

shaped 469:7

shapes 371:21

share 168:18

420:1 622:15

725:18

shared 397:21

780:19

sharing 513:21

shed 255:3,10

Sheffield 697:15

she'll 302:22 647:1

shielded 146:1

shift 241:3 293:6

406:16,19 407:3

407:19 409:5

509:22

shifted 319:1

shifts 407:6

shipped 474:3

476:8

shock 61:6 485:21

shocked 486:1

shoes 463:11

short 106:22 148:3

162:16 174:11

445:20 452:10

462:3 524:10

584:22 610:14

661:4 687:7

690:10,11 722:6

723:6

shortcomings

470:3

shorter 83:9

298:15 386:17

471:9 518:20

743:1

shorthand 397:3

shortly 196:16

476:11,18

short-lived 626:1

short-term 95:13

398:7 478:20,21

478:21 479:11,21

480:2 719:16

shots 665:1

should've 638:10

669:16

show 35:7 39:17

59:2 61:1 67:18

72:20,21 73:20

74:8 77:3 80:12

82:14 83:21

84:18 85:18 95:2

95:4 96:1,2

126:19 131:2

139:4 212:21

215:10 278:14

279:7 283:5,7

309:3 312:18

321:5 333:14

335:22 338:16

343:13,21 344:19

358:16,19 360:19

361:3 362:12,21

369:19 370:9

371:19 384:17

398:12 441:17

452:20,21 461:8

466:22 472:3,9

475:19 488:14

492:2 493:11,12

496:19,19 498:20

502:15 503:1,5,5

504:13 509:12

515:12 516:7

526:2,14 536:1,2

536:6 537:4

539:3,5 559:20

564:10 574:3

575:15 578:20

579:10 583:5

585:4 586:21

666:17 670:3

showed 59:6 73:10

73:12 77:18 78:9

78:9,11 79:12

83:6 84:2,3 88:1

94:2 102:6 229:2

270:13 278:18

289:7 299:3

308:20 332:19

334:11 345:19

351:15 359:5

362:21 363:3,12

376:3 384:16

508:6 562:7

575:16,20 577:16

577:18,21 578:1

578:9,13,17

582:20 586:6

593:9 616:13

670:3 679:7

731:16

showing 34:12

52:16 53:16,18

54:10 55:10,15

56:5,7,18,21 57:3

57:14,22 58:4,12

59:5,11 64:13

81:16,16,17

161:4 221:15

242:19 274:15

276:8 278:22

296:8,11 376:20

423:11 463:4

480:12 506:14

509:11 531:2

581:6 616:3

629:8 673:22

769:22

shown 48:16 49:1

51:15 55:16 57:6

58:8 72:21 76:3

82:21 99:2

179:18 287:15

321:9 322:1,2,15

323:7 324:2

327:22 329:5

330:21 331:18

333:18 334:21

336:7 340:21

343:21 379:10

390:11 434:1

454:1 463:2,6

490:22 509:13

681:16 699:3

shows 36:22 49:21

50:1 78:18 83:18

98:20 181:20

186:16 325:6

326:22 362:21

363:3 378:12,13

505:3 509:10

616:16 678:4

679:9 684:18,19

687:21

shrunk 379:9

Shu 460:15,19

shut 681:8 708:1

shy 487:19

sic 708:22

side 49:14,15 90:6

90:6 91:2 100:2

100:5,6 103:12

103:17 205:1,21

221:22 244:9

249:10 307:16

375:3 523:1

560:3,19 667:6

668:6 673:9

677:9 685:18,21

710:3,3 780:19

sidetracked

632:13

side-by-side 348:3

349:2 354:8

signaling 45:22

57:4,4 61:13

285:18 598:5

signed 780:1

significance

163:10 275:21

284:15 285:14

292:6 351:20

357:3 497:11

537:5 538:1

612:2

significant 12:13

20:4 50:18,22

57:19,20 58:1

64:14,15 69:4

76:18,19,21

80:22 95:4,5

99:1 103:9

122:17 125:14,17

136:20 148:5

153:17 157:3,22

160:5 163:9

182:2,5,9,15,18

182:19 183:1

243:8 258:15,19

259:16,19 260:20

261:8,11,12,13

261:15,21 268:20

275:7,9 290:15

294:18 297:3,13

297:16 345:14

356:21 379:11

394:3 436:1

462:16 478:11

483:8,22 484:5,6

490:1 495:5

497:12 510:10

517:17 523:8

526:3 543:1

546:7,13 547:12

549:10,16 551:22

552:14 560:22

566:12 575:2,4

575:16 576:4

577:16 578:10,14

579:11 580:1,5

580:12 593:9

626:13 630:14

643:8 644:13

660:2 674:10

751:19 778:11

significantly 51:14

77:5 88:22

165:16 297:1

491:1 492:9

494:10 523:7

577:11 578:2

604:11

signs 208:16

363:22 683:4,11

684:11,11

silico 726:13

silos 287:2

Silver 391:7

Simcyp 195:11

similar 13:2 15:18

15:22 110:10,21

115:9 116:15

131:13 134:18

137:13 140:1

145:17 154:19

178:2,20 193:18

212:10 258:12

260:18 261:6

263:11 284:12

285:3,5,9 298:4

355:16 390:1

408:22 409:6

486:17 500:5

506:9 511:8,12

545:7 568:15

571:19 573:10

577:1,20 578:9

601:5 616:3

658:2 662:6

670:9 674:3

678:11 693:2

726:7 743:7

744:20,22 749:3

751:8

similarities 111:5

139:22 581:9

588:13

similarity 140:19

similarly 21:22

59:7 115:9 118:7
Page 96

135:2 136:10,18

143:9 173:16

222:5 285:16

319:5 406:10

446:7 461:6

548:17

simple 110:12

167:13 308:10

328:19 387:1

479:14 494:18

646:16 705:7,13

710:3 721:21

724:2

simpler 386:10

simplest 479:11

simplified 60:16

460:22

simplify 729:17

simplifying 699:7

simplistic 697:1

simply 89:17

174:21 230:4

241:4 351:11

357:3 483:21

565:5 696:15

Simsit 697:14

simulate 187:11

198:6

simulated 325:9

475:11

simulates 187:9

simulation 701:14

simulations

475:18

simultaneous

646:14

simultaneously

113:17 138:2

555:19 626:20

Sinai 38:12 115:7

116:15,18 119:17

122:8 133:5,16

134:15 136:21

150:3,12 169:11

171:15 178:22

258:17 259:12

262:22 267:6

283:16 294:14

541:3 550:11

576:16,21 577:7

577:11,21 578:13

656:2,4 695:2

sincere 20:7

single 40:8 83:15

121:18 185:1

215:14 216:13

265:21 311:11

325:7 440:14

477:2 528:12

580:9,14 600:11

602:14 635:10

684:9 694:1

695:18 702:22

721:21 725:20

726:5 749:8

767:22

single-day 96:10

single-dose 213:1

singly 88:21

singularly 391:2

sit 539:10

site 1:8 46:21 55:5

55:8 56:4 348:11

393:19 455:11,12

455:14 457:1

530:2

sites 170:5 322:16

322:19 330:20

430:14

site-specific

192:17

sitting 310:12

539:7,12 588:17

620:13 707:2

741:17 746:12

situation 89:4

194:14 200:13

204:22 207:18

454:21 459:3

509:9 557:22

699:21 772:17

situations 64:11

383:16 424:16

six 60:8 74:6 78:8

78:13 80:4 82:3

82:4 93:7 125:15

136:19 163:6

200:5 279:2

288:21 314:3

319:14 350:21

360:22 363:17

403:2 428:7,9

432:2 543:6

621:9 698:12

717:20 722:15

761:21

size 74:13 78:21

85:3,8 128:12

131:21 142:7

222:7 242:12

273:16 324:18

355:21 360:10,15

386:16 475:6

481:4,6 487:18

495:11 519:5

522:17 592:18

663:2 676:2

763:1

sizes 78:16 94:7

489:4 493:18

545:22 546:5,9

547:10 548:5

550:15,18 579:21

580:6

skew 501:3 506:3

skills 778:17

skip 61:11 742:4

skipping 60:6

slice 494:17

618:17

slide 3:19 34:12

35:11 38:22

40:14 45:5 54:4

55:21 59:7,13,17

59:20 67:9 97:13

98:8 101:19

129:3 149:8

150:10 152:10

155:22 156:9,15

158:14 161:22

164:9 169:1

170:20 178:10

204:18,19,21

212:7 220:12

221:8,14 244:5

250:12 253:13

266:4 267:20

269:12 274:14

275:22 282:21

295:12 321:9

328:11 334:3,10

340:20 363:3,8

364:10 365:7

367:5,5,18 369:9

369:17 374:12

396:22,22 416:17

419:12 420:14

424:21 425:1

428:3,5 429:7,8

522:5 525:3

677:8 681:2,3

684:18

slides 76:9 118:17

128:22 132:6

153:4 161:21

162:7 260:12

274:14 319:2

342:2,10 365:12

380:18 384:16

387:8 418:12

422:11 649:3

680:21 704:12

slight 133:9

325:19 377:8

427:15 578:13

slightly 178:22

179:1 263:13

336:19 411:22

487:16 546:1

617:12 629:8

656:2,8 696:20

703:11 717:16

slimmest 731:16

slipped 35:11

slope 357:5

Slotkin 463:2

484:19 486:9

519:2,14

slow 416:10,11

684:3

slower 75:20 76:16

348:10

slowing 524:5

slowly 324:15

smack-dab 359:11

small 36:21 49:16

74:13 78:16 85:3

90:9,12 131:1,11

163:11,11 194:4

361:20 377:2

414:20 425:16

426:3 475:15

478:12 481:5,7,9

481:12 527:5

529:1 566:12

602:20 603:8

624:22 661:6

675:22 712:17

724:14 745:5

771:2

smaller 26:17,17

203:3 243:19

386:16 427:16,17

427:19 662:10

743:22

smarter 620:15,15

Smith 325:3

335:11 348:8

401:14

smoke 128:17,17

555:2 582:18

smoking 118:8

snap 665:1

snapshot 640:3

social 80:1,2,7

81:2 86:1 89:21

98:10 101:5,11

106:7,12,16

136:2 285:7

379:18 380:2,9

492:15,18,19,20

493:6 501:1

Society 4:8 252:3

315:12

socioeconomic

113:21 127:19

130:11,16 131:5

131:13 545:16

574:9 575:8

580:20 588:14

sodium 61:7,8,10

soil 190:8 265:8

661:20 694:10

sole 112:19 562:1

573:2

solely 612:13

735:7

soliciting 187:19

solid 25:22

solids 695:17

soluble 627:11

628:7,8,15 629:8

629:14

solution 14:19

718:18 744:19

solve 479:11 719:6

solved 715:6

solvent 278:18,18

308:21 330:4,6

330:11 331:13

337:9 383:7

440:10

solvents 278:19

308:22 331:16

somebody 164:16

232:15 459:16

506:14 514:9

516:19

somebody's

365:14
Page 97

someone's 643:4

someplace 643:3

somewhat 71:14

84:6 92:14 94:5

118:13 119:21

136:10 138:12,14

141:5 142:15

143:4,15 157:2

178:2,20 274:7

409:15 413:11

451:18 473:4

501:5 609:12

648:1 673:5

707:17 730:8,16

732:3 746:15

769:17 771:7

soon 3:19 780:21

sooner 456:13

sophisticated

38:19 193:5

203:2 219:4

426:17 690:14

697:13 699:19

705:1 706:10

717:10 724:5

sophistication

183:13 193:3

703:4 706:10

sorry 43:3 148:9

150:6 153:8

162:6,21 199:17

199:21 222:3,4

260:12 365:16

367:11 374:16

415:10 421:1

592:2 683:4

710:8

sort 15:7 18:2

24:15 39:15

40:12 46:8 52:19

57:13 59:22

90:11 92:16

93:15 105:2

119:5 121:14

123:19 124:12

135:13 146:12,20

153:6,12 167:6

176:14 179:8

188:5 189:4,18

189:21 195:13

197:3 227:12

229:8,13 230:3

233:21 235:12

237:10 238:11,15

239:18 241:8

242:17,22 245:14

256:5 257:21

271:16 285:21

292:5 293:13

298:4 301:14,14

302:10 304:8,16

306:3,22 312:22

313:1,14 328:20

336:8 337:13

365:9 369:13

372:5 374:12

386:15 397:3

400:6 403:20

421:14 424:2

432:14 442:17

443:2,13 445:5,9

448:18 466:12,17

468:1 476:7

482:17 483:4

484:13,13 487:6

489:18 490:12,18

494:22 511:4

512:19,22 519:22

523:17 526:6

527:21 530:14,17

537:17,21 569:11

571:5 576:1

582:21 584:19

588:12,16,17,20

590:5,10 616:1

635:22 649:21,22

650:3 658:17

668:3 670:4

671:8 678:11

687:13,15 696:17

700:5 701:20

704:13 709:15

710:18 711:8

714:9 717:9

726:20 728:8

732:13 736:17,18

738:5 741:22

742:19 743:16

744:11 747:13

752:8 760:6

763:15 765:6

770:17 774:12

sorted 228:15

372:16

sorting 487:7

sorts 218:18

709:10

SOT 514:16

sound 98:2,2,3

228:8 519:7

sounded 585:20

sounds 270:14

273:7 340:14

607:11 653:7

source 35:16 122:5

262:4 346:13

389:13,16,17

430:18 437:15,22

627:2 723:12

755:10

sources 30:21

42:16 84:4

121:15,16 140:3

144:4 146:2

399:15 423:2

430:7 639:6

755:5

SOUTH 1:14,15

Southern 155:7

so-called 643:20

760:9

space 400:7 404:8

spaced 665:1

spacial 72:15

85:21 480:17,18

481:2,2,13 482:2

482:4 488:7,7

490:5,16 495:16

509:10,12 511:14

534:2 537:16

538:6,8

span 119:5 657:2

Spanish 138:7

spans 25:5 663:6

665:1

spatial 121:19

142:22 366:16

speak 3:13 42:3

154:7 254:13

282:6 292:8

303:22 320:4,15

329:17 499:20

506:13 530:21

639:14 742:10

speaker 22:10

314:8

speakers 318:11

319:21 439:18

607:3

speaking 3:8 20:9

125:7 128:8

138:7 170:1

209:6 278:9

510:1 634:4,4

639:12 658:19,22

speaks 15:7 500:2

502:22 582:22

658:17

special 778:14

780:17 781:1,5

781:10

specialize 771:4

specially 36:18

252:7

species 45:11 53:6

53:14,17,19

56:17,19,21

71:17 84:9 86:9

219:19 338:19

434:1 458:17

459:15 474:21

604:3 626:3

specific 8:21 9:5,6

10:18 13:21

15:17 16:2 21:6

30:22 31:3,4

35:20 44:7 47:2

72:3 78:1 93:14

100:13 103:18

117:13 121:12

129:1 146:9

156:12 167:16

173:17 176:13

200:20 206:1

235:1,5 239:21

240:12 241:17

254:20 257:22

258:9 259:2

260:11 261:2

294:15 296:1

303:22 330:15

338:12 352:2

353:16 451:22

454:16 459:8,11

459:13 542:18

543:19 563:1,14

564:18 577:2

602:1 605:10

624:10 656:21

661:19 688:15

689:11 691:13

695:14 696:1,11

699:12 702:1

703:8,9 758:5

specifically 48:21

122:14 171:19

248:9 273:12

290:22 330:15

335:11 423:16

510:2 528:17

533:11,13 543:4

573:5 574:14

596:17 597:20

611:11 613:6

634:9 648:19

651:9

specificities

466:16

specificity 34:8

86:3 173:11,19

183:17 186:10

231:1 237:16

241:18,18 244:18

260:8 284:9

291:14 364:13

466:12 580:17

622:20

specifics 283:1,3

313:2 704:10

706:5

specified 763:5

specify 237:10

specs 337:13

spectrum 526:22

speculate 262:9

speculating 390:14

speculation 270:16

377:13 390:13

533:3 678:21

speculative 443:13

565:4 591:4

698:22

speed 75:11,22

391:11 481:21,21

488:15 507:19

509:8 716:15

speeds 507:19

spend 43:9 302:15

302:18 539:12

spending 344:21

621:2

spent 79:17 205:8

648:18 658:20

717:1

spike 431:11

spiked 646:12

spikes 649:7

spine 460:11

split 743:16

spoke 312:7 427:8
Page 98

spoken 139:15

sponsored 224:11

spontaneous 81:9

477:10

spontaneously

652:4

spot 661:6 723:8

758:1

spot-urine 174:22

175:4

spouses 263:11,12

264:1

Sprague 485:13,14

485:16,19,20

519:16 520:2

spray 393:21

spread 377:17

spreading 296:2

374:4

spreads 404:4

spring 10:18

668:22

spurious 505:20

square 149:4

squared 698:17

squeezed 761:15

squirrelly 523:16

stab 96:5

stability 124:6

128:8 279:10,11

649:17,22 650:4

651:9,17

stable 91:9 650:14

650:22 651:1,10

651:13 653:4,5

686:8

staff 3:10,11

144:19 220:20

318:8 420:18

542:1 776:21

778:7 781:11

staffers 436:15

stage 21:5 22:19

31:3 35:7,14,20

195:21 324:11

402:12 438:19

443:10 488:8

498:21 527:8

592:7 634:14

635:16 711:19

712:5 730:4

746:6

stages 29:18 35:19

194:10 323:19

437:8 442:18

511:13 721:6

755:13

stage-based

333:12

stagnant 368:4

stain 448:5

stamp 583:22

stand 411:18

708:14

standard 32:18

89:4 95:20 143:9

149:4 222:1,2

403:5 406:11

407:21 408:19

409:13,17 453:15

481:4 522:17

543:15 545:7

548:19 576:6

622:19 705:22

standardized

80:20 138:6

244:13 573:10,12

standards 278:18

308:21 398:8

stands 445:14

496:10

Stanebar 662:3

STAR 396:13

staring 711:6

start 4:8 7:15,19

9:21 10:2,20,21

12:9,10 23:15

24:15 25:21

27:12,17 43:18

61:8,17 63:9

68:22 100:5,5,13

159:8 196:19

202:8 209:3

230:11 257:11,18

282:20 314:6

321:8 339:6

351:7 365:2

432:22 433:6

436:18 443:11

444:2 455:10

456:2 459:4,10

470:20 486:22

500:3 501:12

511:15 513:10

531:13,14 532:12

532:15 535:17

539:15 540:7

565:10 571:8

595:16,19 614:21

616:3 635:5,21

656:4 663:12

667:2 677:20

689:2,4 711:14

730:2,20 743:9

770:2 771:1

772:6

started 2:6 88:14

103:13 202:9

282:5 317:21

378:2 458:22

512:15 528:6

715:1

starting 12:2

24:20 25:19

30:19 35:21

203:11 207:16

211:17 327:5

410:9 443:3,9

448:16 500:22

527:7 565:1

607:1,10 638:6

656:19 674:22

717:22 718:3

725:11 745:13

762:20 776:8

startle 82:15 537:9

537:18,22

starts 51:16 62:17

370:12 400:10

532:13

state 4:11 5:19

6:20 36:5 41:10

76:18 123:11

134:8 197:17

198:1,8 315:14

316:16 317:10

384:10 441:22

459:8 556:5

600:22 601:3

602:12 669:5,11

671:17 708:20

stated 306:4 310:6

338:14 347:19

465:5 509:7

544:20 548:17

553:4 573:2

589:14 594:6

750:18 758:17

762:14

statement 445:2

522:2 529:13

530:16 562:15

563:19 566:17

572:15 587:14

589:6 606:14

702:19

statements 41:17

88:5 94:7 521:8

526:20 528:1

683:7 777:21

states 1:11 30:7

204:4 272:8

413:15 551:3

655:22

stating 50:2

stationary 123:20

stations 125:10

statistic 404:18

statistical 82:6

84:21 94:13

127:18 128:4

131:15,18 132:4

139:20 141:7,11

142:6 147:2

160:11 182:2

259:22 275:18,20

275:21 284:3,22

285:1 287:6

291:15,16 292:6

292:10 295:9

312:19 356:6

357:3 379:11

401:18 482:19

483:2,18 488:12

494:1,14 495:1

497:8 519:8,17

537:5 545:15

546:6 547:11

550:15,18 552:13

579:22 612:11

statistically 64:15

125:17 129:6,7

136:20 156:19

163:9 182:5,8,15

182:18 183:1

258:15 259:16,18

260:20 261:7,13

261:15,21 268:19

275:7,8,14

290:15 297:1,3

297:12,16 356:21

362:14,15 478:11

484:12 489:17

523:17 546:6,13

547:12 549:16

556:2 575:2,3

580:1

statistician 778:17

statistics 4:7

177:18 296:9

315:11 373:4

380:8 435:5,6

436:2 488:11

status 113:21

127:19 130:11,16

131:5,13 144:20

574:9 754:15

statute 8:4

stay 255:18 443:6

565:2 591:22

634:8

staying 453:5

steady 197:17

198:1,8 600:21

601:3 602:12

669:4,11

steady-state

723:20

Stella 6:5,5 148:9

148:16 312:4

429:15,22 552:10

582:6 584:22

586:10,16 587:5

592:2,13 769:6

stem 75:15 94:3

450:6,16,18

466:7 496:13

601:22

step 16:17 34:16

92:20 207:12

213:6 244:11

245:12,13 282:22

283:10,19,20

287:17 288:21

292:10 297:9

339:9,12 388:14

443:18 455:16

468:13 473:13

494:17 530:4

592:21 705:17

715:18 744:18

746:10

Stephanie 20:8

40:17 64:11

Stephen 4:13

315:16 604:20

steps 12:1 20:19

45:7 48:4 202:3

207:5 220:8

231:7 241:11
Page 99

243:19,20 244:6

290:18 391:15

618:14

step-by-step-by-...

709:16

step-wise 27:16

128:7 203:11

stereology 528:12

steroid 459:11

Steve 4:13 19:13

19:13 46:12

61:20 315:16

750:11 766:20

STEVEN 7:17

23:1 62:1

Steve's 19:16

stick 295:13

419:20

stimulating 136:7

450:8

sting 769:17

stock 520:2

stocks 485:20

stop 42:22 93:1

147:16 439:15

497:5 552:6

714:1

stopped 486:6

stopping 93:11

231:18

stops 499:21

508:15 588:8

storage 279:10

430:17

store 170:10

713:19 715:5

stories 272:12

story 12:11,12

63:21 77:6 191:2

203:9 233:22

234:1 272:11

281:10

stpA 394:16,17

straight 34:21

39:20 61:13

151:4,20 374:9

511:15

straightforward

142:9

strains 84:10

strange 362:5

482:19,21 483:5

stranger 501:15

501:16

strategy 479:14

508:14,15 510:12

511:9,16

stratified 403:11

493:20

stratify 522:18

Street 231:17

strength 18:11

22:5 37:11 42:18

145:7 175:7

189:21 295:2,15

296:20 481:17

543:2 562:15

563:4,19 568:14

573:11 574:12

587:18 607:4

631:12 706:8

strengthen 584:16

strengthened

545:3 574:5

strengthens 147:6

253:2 572:4

581:12 611:9

strengths 17:2

18:5 21:15 24:8

34:8 37:17 42:14

42:20 63:22

72:12 98:4 108:5

108:6,8 139:11

187:20 192:19

195:5,6 199:22

204:15 207:22

208:15 219:6,14

219:21 229:11

291:6 437:17

439:3,4 446:22

457:21 469:2

472:15 473:8

490:5 493:13

507:15 518:14

541:15 545:20

553:15 572:10

573:4 595:14

601:12 618:8

620:4 622:6,7

666:15,16 667:19

667:21 690:16

704:8,10 721:7

737:3

stress 53:19 56:22

58:4 285:17

474:5,7 507:12

535:17 551:17

597:18 598:2,4

741:6

stresses 13:12

stressful 507:11

stressor 47:10

474:11

stretching 296:3

strict 489:21

strictly 243:7

strikingly 145:8

stringent 407:20

408:2,22

stroke 509:14

strong 21:10 35:18

35:20 130:9

161:2 206:22

208:6 275:3

276:17 355:17

377:9 417:20

444:9 490:14,17

509:16 515:20

523:15 526:20

528:1 530:14

567:1 568:18

574:19 575:4,10

590:4 751:21

753:18

stronger 349:10

349:11 355:13,14

355:14,22 360:13

373:1 486:12

529:2 547:14

555:14 560:2,4

564:6 567:9

575:17 766:17

770:5

strongest 98:11

366:13 489:2

strongly 159:7

300:21 394:2

489:12 610:18

struck 227:14

274:6

structural 447:21

448:13,13 743:5

structure 45:22

47:14 48:15,18

48:22,22 379:22

struggle 741:21

struggled 373:22

struggling 693:9

stuck 225:11 500:8

635:9

stud 371:16

student 377:1

624:2 646:9

studied 58:22 81:4

101:22 118:12

138:13 149:9

601:4 641:16

654:15 671:7

studies 17:15

20:22,22 26:10

26:11,11 28:15

30:10 31:9,19

35:17 36:5 38:10

38:13 39:16

40:22 41:19 42:6

42:7 59:2 64:4

65:3,8 66:19

68:20 69:2 70:5

70:13,20,22 71:3

71:5,6,7,9,15,18

71:22 72:4,6,7,9

72:9,13,15,18,19

73:1,9,11,19 74:7

74:8,11,12,18

75:20,21 76:1,11

77:7,11,15,17

78:3,4,8,9,10,13

78:13,15 79:21

80:1,2,4,10,12,13

80:14,18,20 81:4

81:8,9,12,15,21

82:3,13,14,17,20

82:21 83:4,6,12

83:20,21 84:15

84:18,22 85:9,12

86:5,18 87:12,22

88:16,17 89:19

90:11,12,16,17

91:17 92:2,11

93:2,8,13,19,20

94:5,13,15 95:7

95:14,22 96:2

98:16 102:2,5,7

102:10 106:9,10

106:14,15 107:9

109:1,6,7,18

110:11,13,16

111:1,3 112:6,15

113:10,14,17

117:8,11 118:20

122:3,12 126:5,7

126:11 127:4,8

127:10,21 128:16

129:12 131:17

132:18 133:10

138:22 139:12

141:2 143:12

144:6 145:9,14

146:1 147:3

148:1 149:2

152:4,17 153:6

153:18 154:4,5,7

157:1,13 166:5

166:14 171:11,17

171:19 176:6,13

176:16,18,21,22

177:4,5,9,12

178:1,2,3,4,11,12

180:6,6,9,14

181:6,13 183:5

184:16,21 185:19

188:4 190:15

196:7,19 198:14

198:22 199:15,17

202:19,19 205:13

205:13 209:7,8

209:11,20 210:2

210:2,4,10,13

211:17,22 212:1

212:4,11,13,15

212:17,22 213:5

214:18 225:14

227:6,7 228:9

233:6,8,11,11,13

237:4 239:2,18

240:14 244:15,15

244:20 249:8,14

249:16,22 250:1

256:1,1 257:1,5,8

259:5,8,20 260:7

261:17,20 262:8

262:13,21 264:7

264:10,19 265:11

265:13 266:17

267:3,5,13

268:13,16,21,22

271:8 272:7

277:17 281:17

283:11,13,14,18

283:22 284:1,22

285:6,13 287:11

288:4,15,18,20

289:11 290:16

291:11 293:4,15

293:17,17 294:8

294:11 297:18

298:18 299:2,4

299:21 300:1,8

301:13,16 302:5

303:19,22 304:2
Page 100

307:16 311:21

322:1,10 323:6

325:22 329:5,5

333:5,18 335:19

336:6 337:7,17

342:19 343:5,8

344:4 345:3,4

346:4,17,19,22

347:1,8,13,15,16

348:22 349:3,19

349:22 350:9,16

350:19 351:13

352:12 353:7,14

353:20 354:1,8

354:20 355:10,12

355:13,16 356:2

357:15 358:15,17

358:19 359:11,16

359:18,22 360:1

360:4,5,12,21

361:10 363:6,9

363:11 364:12

366:13 371:11,16

372:4 379:2

383:14 384:17

385:17 386:6

390:20 392:7,16

396:17 398:16

402:13 408:8,9

409:18 414:3,5

423:11 424:1,5

435:12,17 437:9

437:13 438:5

439:10 440:16

441:14 443:9

446:3,6,17,18

449:4,12 452:16

452:20 454:22

455:17 460:15

461:21 462:8

463:15,21 465:10

466:19 467:14,18

468:22 469:12

472:6,9 473:6,8

473:14,16,21

474:19,21 475:8

476:4 483:16

484:19 485:11

486:4,6,9,13,18

486:19 489:11,12

490:11,20 491:3

491:5,11,15

493:13,17,17

494:7,13,15

495:9,16 496:11

496:15,20 497:18

497:20 498:5,8

498:18 499:1,4

500:19 502:2,14

503:22 504:1,18

504:22 506:13

507:10,15 508:5

509:16,21 510:2

510:7,9,10

511:19 512:7,8,8

513:3,3 514:1

515:6,10,11,12

515:20,22 516:7

516:21,21 517:14

517:15,17 518:7

518:8,13,22

519:2,3,9,13,17

520:1,11,18,22

521:2,10 525:18

526:7,15,16,22

527:1,3,15,22

528:8,14,16,19

529:8 530:15,21

530:22 531:2,3,5

535:10 536:3,12

536:14 541:2

542:6,7,14,21

543:1,5 544:13

544:14 545:3,6

545:11,13,22

546:10,16 547:9

548:9 549:7,13

549:15 553:12,13

554:14 563:9

572:1,7 573:5

574:2,17 575:10

581:6,8 583:7

587:8 590:8

594:5,22 596:2,3

597:4,5,8,10,14

597:15 598:18,22

599:15 601:1

602:4,5,17 603:2

603:3 604:13,15

606:4,5 609:20

610:10,21 611:17

612:3,7 614:7

621:3,6,11,22

624:20 627:8

628:20 629:10,13

630:3,8,11

631:13 632:14

634:22 635:3,4

636:10,12,15,18

638:15,16,21

642:17 646:15

651:17 654:13

655:22 656:12,16

656:19 657:16

658:8,9,10,11

659:7,8,13,15,18

659:20,22 660:7

660:12,15 661:1

661:11 662:16

663:14,19 665:5

665:9,13,14,18

665:19 666:11,14

667:5,8,13,20

672:15 673:2

674:18 675:16

683:1 685:7

691:3 694:21

695:3 709:6,11

711:15 712:10,18

715:3 717:13,16

717:20 720:10

724:17 725:19

726:2,15 729:7

732:4 739:22

740:15 741:9

742:5 746:18

748:15,21 751:3

753:17 757:7

762:13,16,17

764:15 766:5,16

767:4,4,5,13

768:10 769:8,11

770:5 772:15,18

773:21 774:11,17

774:19

study 13:21 14:6

36:16,19,22 48:7

48:9 50:1,20

73:4,5,8 75:4

77:1,19,20 78:18

79:1,12,14 81:16

82:15 84:2,3

85:7 92:15,21

94:11 102:8

103:1 105:6

109:19 113:1

114:18,19 115:5

117:21 118:18

119:18 121:1,5

121:16 122:8

123:1,16 124:9

124:16,21 125:21

126:2,13 127:21

131:16,22 132:16

139:2,5,7,21

140:2 141:1,12

141:19 142:2,4,8

144:15,18 146:3

147:9 148:2,2

149:20,22 150:4

150:18 152:12

153:16 157:5

161:20 162:3

163:3 166:15

172:12 175:13,20

176:1,10 177:12

177:16 178:5,6

180:20 181:3,4

181:21 182:8,22

184:21 185:1

196:19 197:22

199:15 211:19

212:20 213:8,10

213:11,13,14,16

214:16,18,22

215:2,8,9,21

216:21 222:16

229:12 234:20

235:8,8,18 236:2

236:11,20 237:12

237:12,17 240:7

240:7 244:12

249:7 250:7,14

250:15,18 251:5

253:6,15,19

256:15,18 258:16

258:18,19 260:2

261:19 262:22

263:1,2,8,8,12,15

263:17,18,19,21

264:12,16,20,21

265:1,17 266:4

266:12,19 267:2

267:8,22 268:6,9

269:2,4 270:20

271:2,10,22

274:6 276:7,16

276:17 277:11,12

277:13,15 281:7

281:12,16,17

282:15 283:20,20

283:21 287:13

291:17 293:3,8

294:1,1 295:10

296:13,15 309:20

309:21 311:14

321:14 322:11

326:13 330:5

335:21 341:2

345:19 349:4,10

349:11,20 355:9

355:14,14,17,17

355:22 359:5,6

359:21 360:18,18

361:8,17 365:6

366:10,11,18

367:6 370:3,16

371:16,21 372:2

372:8,14,16

373:1,2,7,15

374:5 376:4

378:2 392:19

393:3 397:15,20

398:7,19 405:7

407:11,11 408:9

415:7,14 417:1

425:20 430:1,3

435:13,16 448:5

453:14,15,15

460:8 461:10

462:7 466:20

471:8,9,10,12,16

471:16 473:11,18

474:1,4,12,18

475:3,5,10,12,22

476:7 477:5

478:11 480:3,6,8

480:9 482:8,16

482:16 483:1,12

483:13 484:8,9

486:10,12,17,21

487:1,9,9,14,20

487:20,21 489:2

489:3,4 490:4,14

490:17 491:5,6,6

491:7,8,12,13,14

493:11,14 500:16

505:9 507:2,4,6

507:16,21 509:19

512:10 513:14,17

516:12,12,19

517:22 520:17

521:21 522:2,4

522:11,11,12,14

522:17 523:10,14

523:15 526:8

528:12 536:17,18

536:19,21 537:4

540:14 541:2,3,5

541:16 542:16
Page 101

543:9,12,19

544:3,7 547:18

548:4 550:3,11

550:22 561:15

563:2 576:3

577:6 578:7

579:9,10 584:10

585:7,12 588:4

588:13 594:9

596:5,9,13,22

597:17,17,20

598:6,8,8,11,15

600:9,19 602:11

612:5 613:7

614:6 619:14

621:22 625:3,3,6

625:7 627:14

630:10 634:16

635:7 638:17

642:9 643:18

644:19,22 648:18

658:3,14 660:7

661:13 663:13,13

663:19 664:11,13

664:15,19,22

667:2,3,7,10,11

667:22 668:20

670:17 671:3,5

671:15,22 672:17

674:3,4,20,20

675:5,6 676:9,10

676:18 677:2,5

678:10 679:4,5

679:15 680:3

682:5,5,20 685:7

685:8 687:19

688:5,7 689:5

695:1,2,15,15

707:8 708:13,22

709:1,7,10 711:9

712:13,15 715:1

716:8 727:13

728:7 730:3,15

730:19 731:3,3,3

743:9 751:13

752:4,6,10,22

754:21 755:4

759:19,21 760:3

760:5 763:3,8,14

763:16,18,22

765:22 767:22

768:14,16,18,20

768:21,22,22

771:16,22 772:12

772:13,15,15

773:1

studying 299:1

459:22 634:21

711:16 714:10

726:11

stuff 94:16 101:16

151:4 152:4

163:12 170:20

196:6,8 225:8

246:14 379:19

400:19 405:4

411:7 413:15,21

441:7 444:20

465:9 562:3

593:9,17 651:4

724:14 726:1

757:12

style 484:13

590:16

subcholinesterase

242:2

subclass 423:21

subcomponent

138:21

subcutaneous

345:8 347:8

355:16 499:5

505:11,12 506:7

506:8,13,19

subcutaneously

503:12 505:20

684:9

subgroup 189:19

subgroups 547:15

548:6 611:1

759:8

subject 251:15,21

494:6 619:17

648:1 652:15

700:17 702:1

731:2

subjective 143:15

400:2

subjects 14:5

414:5 483:10

submission 457:6

submit 514:4

647:2

submitted 177:4

226:6 247:7,8

249:1 319:12

subpopulation

189:20

subpopulations

138:8 139:14

328:21

subsequent 52:1

127:2 146:18

243:20 349:22

569:14 724:20

subsequently

659:17 660:11

subset 176:20

177:1 222:18

524:8 546:2

691:19 712:20

subsets 617:13

substance 77:17

715:21

substances 751:5

substantial 170:3

170:6 322:3

402:2

substantially

323:7 327:22

332:20 333:1,6

623:19

substitute 440:1

substrate 90:19,20

104:12 105:13

255:4 320:18

323:13 332:4,13

333:14

substrates 601:17

subtle 85:3 99:21

348:17 350:2

368:19 453:1

subtleties 589:20

706:22

subtly 565:2

subtype 56:11

sub-class 116:2

sub-studies 460:12

success 778:16

such-and-so

236:19

such-and-such

156:11

sudden 636:6

730:13

suffer 407:17

suffering 393:5

Suffice 409:7

sufficiency 291:2

541:12

sufficient 86:13

90:21 94:8

441:15 442:1

523:2 546:6

547:10 595:6

602:4

sufficiently 141:12

289:2 548:1

699:4,5

sugar 61:3,4,5

suggest 37:12

106:11 157:1

208:16 234:14

244:7 350:16

390:10 395:9

409:14 446:18

489:6 496:13

506:15 513:7

528:14 542:22

544:4 550:3,4,6

569:15 584:6

589:6 599:17

607:5 608:2,15

615:9 646:4

669:9 690:17

698:22 721:1

751:22

suggested 120:9

126:2 129:20

133:11 142:16

174:15,19 407:12

423:13 434:1

461:16 598:4

627:5,7 629:11

629:19 637:10

643:1 698:1

732:17

suggesting 56:14

58:19 130:8

166:14 189:3

241:1 570:15

585:2,7 605:13

605:20 606:4

739:11

suggestion 126:13

133:15 573:14

606:19 608:7

616:9 637:5

742:15 760:2

771:2

suggestions 409:21

618:2 764:15

774:22

suggestive 550:10

598:6

suggests 43:10

44:6 57:14

140:18 174:8

235:21 306:7

441:1 502:4

518:4 525:10

551:17 583:11

595:20 599:8

suitability 610:13

suitable 153:5

174:22 184:22

suite 427:12,13,21

sulfatase 334:20

sulfate 338:3

626:14

sulfates 626:11

sulfation 338:2

sulfonates 457:7

sulfonation 334:13

625:12

sulfotransferase

458:11

sulfotransferases

457:4 458:17,19

698:20

sum 200:5,6,8

404:12 636:22

735:7

summaries 152:17

summarization

201:11

summarize 72:9

85:11 143:19

287:19 343:12

441:7 493:16

553:4 575:1

summarized

182:16 199:19

211:4 445:6

493:15 546:17

553:9 595:19

598:18,21 664:4

666:8 728:16

summarizes

542:19

summarizing

43:10 139:8

554:7

summary 58:16

98:8 104:21

165:14 175:21

177:9,15,18

187:13 199:15

258:22 268:19

279:11 323:8
Page 102

324:10 333:4

605:17

summation 147:5

222:13

summed 222:8

summer 10:18

super 325:19

superior 637:1

supermarket

643:19

superseding

273:12,17

supplemental

114:4 147:1

514:11

supplementary

113:7 165:7

supplemented

36:4 437:9

supplied 474:3

519:15

supplier 474:2,3

support 20:12

33:19 44:3 50:4

86:11 99:13

146:20 207:4

211:5 237:7

255:22 261:17

286:13,13,17,18

290:8 300:21

301:18 302:13,14

350:6 353:7

381:17 390:8

392:5 442:6

444:2 445:3

447:6 453:3

472:7,10,13,21

493:10 500:21

581:12 595:13

629:22 632:3

636:21 674:8

770:3 777:5

781:11

supportable 20:15

21:10

supported 31:19

298:21 392:7

497:13

supporting 153:19

237:4

supportive 576:18

637:11 747:5

supports 50:7

300:11 674:12

681:22

suppose 587:15

supposed 59:21

60:7 230:8 490:9

648:2

supposedly 669:20

671:16

sure 3:12 14:16,19

33:10 60:9 65:10

68:13 86:22 90:6

100:14 101:15

106:18 110:21

149:19 151:16,21

155:19 164:15,22

165:21 197:11

199:3 214:17

215:10 217:21

221:14 281:2

305:10 308:17

313:20 339:1

369:5 374:18

387:3,7 421:13

423:18 425:19

431:5 433:14

434:19 436:7,9

438:20 449:16

450:21 451:19

452:19 462:2,6

468:2 471:4

498:7 501:1

505:16 515:3

523:17 525:12

532:15 540:8

564:2,16 585:22

586:16 593:11

595:17 600:13

605:6,8 606:18

607:16 619:3,7

620:11 632:11

637:8 649:11

653:20 664:14

667:8 675:6

676:13 702:18

704:1 710:7

726:14 727:16

729:9 736:22

737:7,11 738:10

752:21 758:12

770:8 772:3

773:19 776:12

surface 698:16

surfaces 265:8

416:12 650:13

surprise 311:9

418:2 657:19

surprised 712:19

surprising 174:13

311:6 527:11

670:16 685:22

surprisingly 64:18

133:7 670:13

surrounding 146:9

survey 273:15

421:16 672:7

734:14

surveys 722:19

survive 680:18

685:1

survived 680:9

susceptibility 32:3

40:10 41:21,22

42:17 120:15,17

217:10 547:15

594:14 720:18

754:14

susceptible 41:15

611:1 738:2

756:2 759:8

suspect 160:15

585:16 646:4

sustain 685:3

sweep 303:2

sweeping 484:13

swept 302:4

swim 75:11,22

77:20 481:20,21

507:18,19 509:8

switch 262:1

switching 536:5

symbols 75:8

symmetrical 400:8

symmetry 658:7

659:12

symptoms 137:9

152:14 161:4

383:1 551:14

670:7 683:8

684:4 685:12

synapse 448:14

synaptic 528:10

551:14

syndrome 158:20

159:17

synergistic 336:7

synergistically

331:17

synonymous

328:17

synthesis 67:1

139:9 282:12

571:3

synthesize 313:1

synthesizing 145:6

system 11:14 14:5

17:10 37:3 53:5

53:7 54:12,21

56:8,14 57:1,5,17

62:12,22 64:13

64:16 66:11,13

66:15 93:16

331:16 332:11

336:11 351:10

353:11 441:2,4

446:10 455:5

461:1,1 468:6,8

472:12 498:15

511:4 532:1

533:8,18 563:12

563:16 564:14

571:1,6,8 599:7

615:17,18 621:7

678:19 747:11

768:4

systematic 21:4

27:16 87:2

203:11 225:15

229:15 244:11,13

401:22 524:19

612:7 648:5

systematically

283:10 473:21

systemic 255:5

287:9 400:21

401:3 551:11

565:16,21

systems 11:10

16:16 62:7

336:10,15 446:12

448:2 449:8

450:1 451:2

455:6,6 468:17

498:12,12,13,15

504:15 529:17

565:21,21 629:21

629:22 725:10

T

t 73:10 477:13,22

478:2 501:7

531:17,20 535:22

table 40:18 97:10

125:15,15 128:12

129:19,20,22

131:1,2 149:10

149:14,22 150:10

151:8,9,12,15

152:9,11,20

162:6,8 165:19

165:22 201:15

202:15 213:19,21

215:11 225:4

227:1,11 228:16

230:3 237:22

238:18 240:9

244:9 245:1

247:2 258:13

259:10 261:14

287:20 289:7

294:1,4,11 295:7

295:10,13,20

296:10 297:5,6

304:13,14 312:14

313:20 330:16

331:5,6 334:4

346:5 354:18

378:13,14 394:12

419:6 420:18,19

421:9 422:20

424:12 425:4

426:13 427:2

434:13 436:8

473:22 493:16

513:19 546:19,20

559:18 589:21

591:9,12 639:12

639:14 731:15

750:3

tables 150:3 199:2

293:21 312:10,12

313:1 354:17

377:21 431:21

471:14 694:17

table's 165:18

tabular 95:3

tabularly 424:2

tack 636:16

tackle 14:18

470:14 718:18

769:3

tackled 16:2

tackling 13:2

tagging 462:9

tails 354:13

take 2:6 9:18

11:21 17:6,7

24:6,14 26:18
Page 103

28:13 34:22 35:3

66:18 68:7 91:15

93:10 96:5 99:14

103:6 114:21

147:18 155:3,13

176:7 187:22

198:2 199:13

201:12 202:4,17

206:17 208:11

211:3 213:6

220:17 223:7

231:3 250:5

292:9 294:2

297:9 301:22

302:9 304:9

306:13,19 327:8

328:9,13 332:6

344:7 346:3

352:8,17 354:12

357:16 359:4

370:4 387:4

397:10 406:21

411:7,13 417:13

420:19 433:6

450:6 451:9

453:9 454:3

468:8 469:16

470:8,22 473:19

475:15 486:2

498:4 504:14

512:22 518:17

531:22 568:12

571:17 589:16

590:7 591:8

593:3,12 607:6

618:13 621:9

634:15 635:10

643:19 644:2

665:16 679:4

705:7,17 710:13

710:16 716:12

717:2,10,13

730:7 735:20

736:9 743:9

753:12 756:22

766:6 768:22

776:16 780:6

taken 52:18 68:11

101:2 104:15

111:20 121:8,10

124:3 190:5,18

223:11 226:22

237:9 280:16

296:10 329:12

337:3 352:19,20

362:18 387:12

412:3 419:14

454:10 457:22

484:10 671:20

672:4 674:7

708:4 720:14

722:10 762:19

772:8

takes 25:11 62:2

401:10 742:22

768:13 769:17

take-home 119:11

288:17 341:7

talent 25:11

777:14 780:15

talents 20:12

talk 23:7 28:12

33:15,21 34:10

37:7 38:1 39:1

41:2,6 43:14,17

43:20 44:1,8,11

47:4 48:5 50:6

52:13 61:21 62:8

65:17 68:19

88:14 101:19

110:1 162:1

192:18 202:1,4

208:3 224:18

227:9 253:5,21

254:20 255:17

256:20,22 257:4

257:19 262:1,3

269:8 286:16

290:21 294:2

296:22 302:20

303:1 304:1

307:19 311:21

312:16 320:21

336:21 344:3

351:21 376:14

385:7 398:6,21

406:6 420:5

423:1 428:8

458:6 509:15

522:6 528:10

540:19 587:16

620:3 635:5

636:17,18 643:16

647:13 670:11

675:19 708:13

738:20 758:22

767:13

talked 64:2,4

87:10 97:3 111:3

193:9 224:2

231:2 257:1

291:6 295:8

454:15 475:9

486:8 519:20

524:9 566:4

607:3 619:11

631:14 639:22

648:4 702:5

716:20 717:9

754:15 757:1

764:3

talking 9:10 22:15

26:14 34:1 41:16

61:17 65:9

102:20 105:16

106:19 155:7

165:5 192:8

199:14 202:14

203:14 205:8

207:6,17 216:17

224:12 226:18,19

229:9 231:16

256:6 267:12

269:13 280:7

282:13 287:14

302:15,18,21

309:9 310:2,22

310:22 312:6

339:17 362:7

367:18,21 369:14

369:14 372:13

376:13 379:5

382:13 384:19

386:11 397:2

416:14,15 425:11

453:5 455:2

458:12 501:7

513:8 525:3

528:6 533:2,22

561:15 566:13

570:9 571:5

604:10 641:5,6

645:12,16 650:17

667:9,17 677:7

678:22 681:9

687:1 688:13

698:18 702:14

703:12 706:3,19

715:4 717:2,7

718:21 722:3

727:17 728:3,6

733:11,18,20,21

733:21,22 734:2

739:2 741:21

763:14

talks 11:7 12:17

22:20 23:8

284:16 302:22

428:3 647:11

728:22 759:1

Tan 20:10

tank 74:21

Tapanawan 662:4

tape 511:1

tapping 639:5

target 66:2,14 70:2

235:9,18 236:3

245:8 249:17

368:5 409:9

455:1,3,11,12,13

466:19 481:7

530:2 628:11

724:13

targeted 69:9,17

112:14 113:13

176:1 454:5

610:20

targets 322:19

task 34:18 49:22

72:17 73:15,16

76:15 104:5

376:2 480:18

538:10,16 709:22

757:2 777:18

tasked 248:10

tasks 485:21 538:8

TCBy 320:10

638:9

TCCR 12:20

TCE 390:3

TCP 155:4 265:9

271:8 322:8

334:18 338:10

339:9,12 340:22

399:11,14 431:16

434:16 587:17

647:5

TCPy 119:17,18

122:7 124:20

125:19 126:20

127:3 133:6

137:3 143:6

150:4,9,15,19,20

151:9 155:16

163:13,15 169:15

171:10 172:12,13

173:3,16 174:4,4

174:6,10 175:19

175:22 177:15,21

179:7,8,11 180:4

180:11,17,22

181:11,21 182:3

182:9,18,20,21

183:2,5,7,20

184:3 188:18

197:19 200:17

205:8 206:2

256:12,13 258:1

259:13 260:9,19

261:5,10 262:2

262:20 263:1

264:3,6,10,14,17

264:21 265:4,7

265:11 267:22

268:21 270:22

296:15 322:6

338:14 339:2,3

339:13,14 340:2

341:3,3,18 430:7

434:13 457:3

544:15,21 580:13

584:9 601:16

621:22 623:1,9

623:11,15,18

624:5 626:5,21

628:18 631:9,12

631:15,16,19

632:4,7 635:22

636:2,3,7 637:2

637:21,22 639:16

640:4,12,14,16

640:19 641:12,13

641:14,18,20,20

642:2,10 643:2

644:10,21,21

645:1,5,19,22

646:2,5,14 648:4

648:12,21 650:17

650:20,21 654:18

656:1,5 666:9,17

668:9,21 669:1

669:20 670:13

671:11 674:6

677:16,18,20

679:14 697:9

700:11 727:13,18

727:19 763:18,20

763:21

teach 282:11

team 20:1,7 25:5,5
Page 104

221:3 248:7,17

310:15 382:7

553:9 613:21

777:3,4 778:5

781:6,8

tease 206:3 731:7

technical 25:4

27:2,22 201:19

238:15 320:5

747:17

technicians 668:4

techniques 113:22

468:14 494:18,21

technologies 456:3

609:9

technology 10:20

11:9 12:7 203:1

387:20

techy 398:11

tedious 297:7

telephone 1:9

387:17 479:1

tell 12:11 88:18

191:2 223:4

241:3 273:2

277:4 285:4

295:15 360:14

367:5 369:9

377:12 474:6

482:7 483:18

484:5,7 492:11

509:18 511:20

513:13 535:3

603:15 622:14

633:5 635:4

642:17 665:10

711:7 714:2,12

722:22 758:10

776:13

telling 12:12 245:8

671:19

tells 281:10 322:20

513:16 622:10

677:14 757:7

temporal 33:20

50:13 59:1,2

121:19 142:21

162:15 543:3

573:6 610:5

672:5 687:5

ten 259:15 311:10

360:22 380:4

tend 37:2 76:17

109:7 110:9

174:5 254:2

332:2 338:3

460:16 463:21

477:15 497:9

534:16 569:3

634:10 708:14

750:10

tended 79:19

89:15 91:4 110:5

tends 174:12 322:9

499:21

tenets 380:21

tentative 240:9

tenuous 215:11

tenure 778:22

ten-gallon 170:11

term 70:16 95:16

119:21 153:12

219:7,7 238:12

238:15 432:22

433:6 630:2

634:20 641:15

698:10,17 762:15

terminology

411:12 431:19

432:3 773:18

terms 9:16,18

11:13,20,21 12:8

14:11 15:16,18

15:19 16:15

18:13 19:2 23:15

24:14 46:7 47:5

50:21 58:18 59:1

59:4,10,22 65:6

69:12,19 72:9

74:2 77:2 100:7

102:4 156:1,3

173:10 174:7

176:16 177:12

180:2 184:6

185:22 186:14

190:11 196:4

198:15 199:19

200:21,22,22

204:17,17 241:11

250:8 284:9,20

290:18 291:18

293:3,10 296:4

297:11 326:3

327:15 328:17

360:5 365:13

366:16,19 382:8

382:10,21 398:8

399:12 400:14

401:7,7 405:5

406:17 416:15

418:14 424:3

431:19 439:19

467:19,21 468:7

468:13 469:1,3,8

469:18,22 470:1

470:2 487:5

498:14,22 499:4

504:9 515:5

528:15 529:13

533:8 536:3

542:16 566:18

579:5 602:1,2

605:9 613:16

619:14 630:11

637:21 638:7

643:18 645:1,5

648:13,22 650:6

652:15 660:5

663:20 664:15

665:11 671:13

673:8 674:22

676:2,17,20

679:1,5 685:10

689:3 696:1

699:19 700:1

701:4,7,15,22

707:20 708:8,10

709:5,17 713:2,9

713:12 726:9

730:2 731:6

732:10 733:12

734:3 739:4

741:6 752:5,19

753:3 754:13

758:12 759:11

760:3,14,20

763:6 768:13

774:21

terrible 485:22

531:5

terribly 527:11

Terry 5:11,11

316:9,9 335:18

374:10,11,17

375:5,10 376:1,6

376:11 444:20,21

455:18 462:17,18

507:12,13,13

508:8,12,21

509:19 588:15,16

599:1 682:2,17

682:18 683:17

765:3,4 771:14

tertile 213:15,18

tertiles 550:12,22

562:9

test 24:13 54:3

73:19 75:12

77:21 78:11 81:2

89:15 91:21 93:1

93:22 94:12

105:9,21 106:1

164:19 260:4

304:20,21 327:15

327:18 331:16

336:17 337:18,19

337:20 350:9

366:17 392:22

412:2 454:4

477:10,12,19,19

477:22 478:6

479:7,14 481:13

481:15 482:22,22

483:22 484:1

488:6 490:5

491:22 492:7

493:2 494:22

496:16 497:10

500:7,22 505:10

505:22 523:1,21

523:22 532:2,11

537:10,16 538:20

543:15 564:11,17

579:3,18 614:11

677:3 680:15

tested 79:11 84:11

85:17 88:8 160:2

239:9 349:8

412:9 477:3

482:13,15 488:1

490:6 494:8

503:3 532:18

533:17 534:1

538:10

testing 9:17 12:6

26:5 47:7 89:12

94:14 103:13

109:8,11 128:8

202:11 237:22

252:20 275:20

284:16 286:3

374:4 484:3

485:1 490:7

517:2,3,7 523:3

524:11,14,18,20

531:12 532:18,19

532:20 533:2

534:5,14 538:8

538:21 564:13

613:14 619:20

685:2

tests 72:19 73:13

74:4 77:22 78:2

78:20 79:13

81:11 89:8 90:8

99:3,3,18 100:18

101:1,5,12 104:9

106:3,5,6,15

139:3 154:20

161:13 361:6

454:12 485:13

491:18,18 492:15

495:7,9 502:20

523:6,21 530:4

531:21 534:1

538:21 569:17

570:5 615:5

text 149:18 156:1

168:22 293:21

355:1 362:16

547:5 553:18

582:11

textbooks 495:1

Thailand 662:5

thank 4:1,2 7:8,17

19:13 24:21 25:8

111:14 149:12

151:10 153:21

154:2 158:10

164:11 171:7

194:12 223:5

224:8 246:18,20

246:21 247:11,12

248:13,14,16

250:3 255:15,16

255:19,19 282:6

317:20 318:8,9

318:15,17 320:3

341:20,21 342:7

342:16 364:19,20

367:11 378:12

380:11,12 381:21

388:2,8,12

395:16,17 396:1

396:3 409:22

419:12 420:3,8

426:5 428:2

429:6 438:15

439:1 460:8,9

462:17 465:13
Page 105

470:6,14 471:21

471:22 499:17

515:1 541:17

552:7 581:18

605:5 614:12

622:9 630:18,20

631:22 635:12

654:6 655:4

664:1 666:4

676:7 690:8

699:15 720:1

736:21 753:7

765:18 766:8

771:9 776:21

777:3,4,8,15,16

777:19 778:3,4

779:11 780:5,12

780:22 781:2,3,6

781:8,8,17

thanking 7:19

thanks 2:11,13

3:22 59:19 150:1

152:17 165:13,20

195:15 301:22

365:3 367:1

433:14 540:16

608:22 664:8

690:21 736:4

780:17 781:1,5

781:10

theme 11:20 27:9

227:13 611:13

themes 8:13 9:4

11:1 13:5 25:14

25:16,18 27:7

29:2 31:11 41:6

theorem 398:5

theoretical 50:8

694:19

theorize 424:17

theory 420:11

710:12

Thermal 650:3

they'd 671:18

772:18

thin 526:18

thing 42:19 53:11

69:15 82:18 91:8

93:2,15 104:21

109:21 152:11

219:5 228:15

237:10 243:4

244:2 281:4,6

292:12 293:12

304:21 307:1

312:11 342:12

352:7,17 357:14

365:9 368:14

374:4 378:12

401:15 404:13

438:18 439:18

445:14 447:9

449:20 457:5

465:3,15 468:10

471:6 474:17

476:9 478:13

482:12 489:17,18

493:4 497:1

506:2,15,19

507:20 511:10

532:7 533:21

535:2 538:4,12

552:15 554:20

557:20 560:2

562:5,6 564:8

576:8 584:2

618:13 619:1

634:11 644:14

645:15 682:18

699:8 700:19

706:14,21 713:19

713:22 718:5

726:9 728:4

730:11 735:10

742:20 744:4,10

745:17 758:9

759:6,14 761:3

763:13 765:4

767:21 773:19

774:1 775:11

things 22:14 26:17

26:19 30:11 31:4

34:6 37:22 42:9

42:11 46:8 47:19

60:2 67:7 88:16

93:10 95:5,6

100:16 102:14

103:12,14,15

109:13 110:5

134:8 152:2

164:2 166:7,19

167:12 189:7

191:7 192:18

194:21 196:14

201:18 203:3

206:10,12,20

207:21 209:6

216:17 218:18

225:9,14 226:8

229:14 234:21

235:10 236:13

238:5,6,12 239:3

240:2 243:1,7,14

244:3 245:3,18

245:20 246:1

247:6 274:5,19

291:10 304:19

305:8 306:16,20

306:22 310:17

311:7 337:12

344:1 371:3,21

382:17 383:9

403:20 412:17,18

412:19 414:6

422:6,10 425:16

433:8 447:20

449:17 453:8,20

455:3,18 464:7

464:20 465:6,21

474:2,14 476:5

480:15,16,21

482:20,21 486:18

490:6 493:15

495:14 500:18

503:18 507:1

514:7,7,8 522:20

523:16,17 527:13

528:6 534:8

535:10,11,20

536:1 553:19

555:12,13 557:19

563:11 565:3,22

567:7,20,21

569:3,7 570:9,16

582:10 589:18

590:7,11,15,20

591:1 618:11

619:6 620:15

635:2 636:6,16

637:1,10 646:13

646:21 651:12

664:14,17 667:1

675:4 693:19,20

694:4,15 695:12

699:14 706:11

707:6 727:2

728:3,6 730:16

736:15 741:20

742:5,10,18

743:4,7,7,14,14

744:3 745:1,12

745:15 746:12

747:7 753:2

755:19,22 758:7

759:9 763:1

764:3,21 767:10

771:18,22 772:13

772:21 773:8

774:8 776:1

think 7:10 9:2,11

10:2,17,20 11:1,4

11:11 12:3 13:4

13:20 15:7,15,16

16:9 17:4 18:17

23:13,19,19,21

24:1,3,9 26:6,7

26:19 27:1,9

28:12 31:6 32:20

33:13,14,19 34:6

35:4 36:1,14

37:9,16 39:3,11

39:22 40:7,14,21

42:4,6,21,22 43:2

44:21,22 45:2

46:12 49:15 50:7

51:7 52:3 53:9

53:11 59:13,15

59:22 60:1,8

61:19,21 62:1,3,4

62:9,10,14,17

63:1,3 64:21,22

67:6,11,17 68:4

75:10 89:6,10

92:6 95:22 96:4

96:15 97:9 98:15

98:21 100:17

104:6,22 105:11

106:3 107:13,19

108:3,4,6,7,10,11

110:9,19 111:4

111:11 120:9

121:14 123:3,22

141:20 148:17

150:6,21 152:19

153:22 155:4

162:20 163:2,22

164:14 166:21

167:13 169:10

171:20 172:4

188:16 189:2,14

189:17 190:1,14

190:20 191:1,4

191:18 192:16,22

193:12,12 194:20

194:21 195:16

201:2,10 203:7

204:16 205:11

208:4 210:11,14

211:5 212:14

213:4,5,13 214:5

217:14 218:11,18

219:12,20 220:3

220:12 221:16

227:14,17 229:13

230:10,11 232:19

234:7 235:10

242:13 243:1

245:7 246:4

252:8,15 254:2

257:5,18 258:4,8

258:12 259:4

261:8 263:19

266:3,8,13 270:7

272:5 274:4,14

275:11 281:8,11

281:22 286:1,11

291:8 292:16

293:12,17,20

296:2 297:7

298:7,9 300:16

300:19 302:12,15

304:1 305:12

306:4,6 307:14

309:22 310:20

312:10,12,16,22

313:2 314:6

319:18 335:18

341:7,18 342:2

342:10 343:20

347:7,9 352:18

354:18 357:14

360:2 362:1

364:9 365:7

366:5,9,11 368:7

368:8,22 370:3

370:17,18,19,20

374:7,9 375:2,8

375:16,20 376:12

380:18,20 384:18

384:22 385:21

386:20,21 404:18

409:10 410:5,21

411:11,18 413:2

413:6 414:3

418:20 419:3

420:4 422:3,4,12

422:18 423:2,7

423:12,19 431:11

433:13 434:20

435:10 438:18
Page 106

439:2,4,12,18,19

439:21 440:19

442:10,12,20

443:7,17,19

444:3,9 445:19

447:9,14,16,21

448:7,21 449:7

449:20 451:5,11

451:20 452:10

453:4,8,22 454:8

454:8,15,21

455:16 456:1,4

456:15,16 457:3

457:21 458:2,3,9

458:14,20 459:5

459:7,21 460:2

460:16 462:12

463:4,16 464:14

464:19 465:4,9

465:11,17,19

466:18 467:17,18

468:9,13,19,20

469:3,21 470:2,7

470:16,19 471:1

473:15,17 474:6

474:15,19 475:4

475:8 476:2,12

476:19 477:7

478:14,17,20

486:4,13 487:9

487:14,21 489:2

489:13,15 490:10

491:9,11,14,15

492:6,14,16,21

493:2,3,10,21

494:7,18,20,22

495:5,7 496:12

496:21,21 497:15

497:18 498:9,22

499:3,12,13

500:15,16 501:12

501:17 502:4,8

503:7,9,19 504:8

505:3,7,13,21

506:5,17 507:5

511:21 512:2

513:2,7 516:8,9

517:9 518:18

520:1,6 521:4,9

522:21 523:3,10

523:11,14 524:1

524:7,17 525:1

525:22 527:2,4,4

528:7,16 529:4

529:17,20,22

530:20,21 531:10

532:17 533:12,21

535:3,8 537:20

538:12,14,19

539:6,8,14

546:11 547:3

552:4,14 553:9

554:9 556:11

558:2 559:13

560:4 561:14,21

562:11,19 563:6

563:6,10,13,16

563:18 564:2,4

564:17 565:3,4

565:14 566:21

567:6,14 568:1,2

568:6,18 570:4

571:5,11,15

582:2,8,10 583:1

583:2,14,15,22

584:3,18,19

585:1,6,14 587:7

587:8,12,18,19

588:3 589:2,13

590:6,9 591:11

591:13 592:19

593:2,10,18

600:20 601:12

602:9,15 604:22

606:17,18 607:11

607:13 608:4,5

609:5,6,7,17,19

610:7,8 611:12

612:3,16 613:3

613:13,22 614:10

615:22 616:20

618:2,4,5 619:21

619:22 620:2,14

625:4 631:10,14

631:19 632:9

634:10,13 635:20

636:8,10,22

637:4,13,16,17

638:4 639:10

642:1,9 643:5

648:22 649:3

650:4,9,11,21

651:16 652:15

653:9,12,17

659:5,13 664:18

664:21 665:9,9

665:13 667:1,5

667:12,14,20

668:7 670:10,21

673:9,19,20

674:9,12,19

675:9,11,16,17

676:1,10 678:7

680:3,16 681:14

681:15 685:7,21

686:20 687:8,17

687:22 688:5,12

689:14,16,21,22

690:2,6 691:7,14

692:1,7 693:4,15

695:6,15 696:4

699:8 700:15,17

700:19 701:15,20

702:11,21 703:1

703:10 706:14

707:5,6,7,15

708:8,22 709:15

710:10 711:19

712:21 713:2,3,7

713:11,20,21

714:7,20 715:1

716:7,11,22

717:12,22 718:17

719:3,8,16 726:4

727:4 728:17

729:13,22 731:1

731:2,3,20 732:2

733:14 734:20,22

735:3,11 736:3

736:12,15,18

739:18 740:8,14

741:18 742:3

743:13 745:12

746:8,22 747:14

748:13 749:7,12

749:21 750:1,10

753:6,18 755:22

756:11,13 757:13

758:1,4,21 760:1

760:6,12,19

761:15 762:15

763:9 764:16,21

764:22 765:11

767:7,11,14,16

768:5 769:2,17

770:3,12 771:6,9

771:14,18,19

772:12,16,22

773:2,4,14,15

774:3,5,7,12,14

774:15,18,21

775:1,2,6,15,17

775:20 776:11,11

778:9 780:13

thinking 10:14

15:9,12 24:19

26:4,14 27:3,13

38:8,17 41:19

46:7 47:18 63:13

97:16 98:3

102:20 104:3

191:20 192:19

198:2 200:22

201:6 202:20

203:1,19 207:4

210:16 218:6,16

219:16,16,18

229:3,19 230:19

232:21,21 233:17

234:7 261:16

370:4 374:2

375:10 379:12

415:12 442:15

445:1 466:1

497:8 506:14

524:16 536:2

559:15 570:19

579:5 589:1

607:3 618:6

641:10 647:4

688:20 700:8

703:13 715:16

740:18 741:18,22

742:14 745:22

746:19 758:3

759:11 762:3

764:1 767:3

thinks 310:17

419:3 738:9

third 58:7 60:5

61:13 120:3

121:11,21 122:1

142:21 162:9

169:7 218:2

226:17 361:16

381:12 469:4

479:9 631:19

643:7 687:9

718:15

Thirdly 604:4

Thomas 263:17

265:13

thorough 38:10

542:2 553:21

thoroughly 704:18

thoroughness

554:5

thought 10:22

35:22 63:3

126:22 135:17

136:7 144:1

181:17,19 207:22

208:1 214:9

251:16 257:3

283:6 304:8

312:20 367:12,12

370:21 442:21

444:6 463:8

482:17 486:12

504:22 509:19

512:5 522:9

527:9 533:21

544:19 546:19

549:4 553:11,12

564:1,9,12

578:22 579:8

589:19 591:12

595:18 606:19

621:14 645:4,14

666:1 697:21

699:18 740:8

741:2

thoughtful 22:9

777:13

thoughtfully 458:5

thoughts 15:8

16:12 18:2 23:5

62:10 63:12,17

105:15 736:16

761:19

thousand 431:10

711:13 714:14

thousands 445:6

thousandth 736:19

736:19

thread 233:14

three 60:7 73:18

75:17,19 78:9,9

81:21 82:4,13

109:15 112:15

113:14,16 114:15

115:17 116:21

118:11,12,21

121:16 129:8

131:18 132:7

133:10 134:1

135:9 136:6,9

137:7,9 138:1,15

141:2,2 157:11

157:11 167:22
Page 107

169:10,11 171:11

171:16,19 176:2

176:5 178:9,18

181:14 194:20

200:6,8 209:7

211:1,21 213:21

223:1 259:9

260:12 264:8

266:15 267:22

281:17 288:14

292:5 313:7

326:15 327:10

349:10,21 350:2

353:19 355:19

360:13 362:8

365:12 375:11

376:9,11 416:16

434:14 464:2

470:12 476:19

541:8 543:1

544:14 545:3,6

545:13,22 547:9

549:7,13,15

572:16 573:4

575:5 581:8

592:9 596:3

597:6,7 602:4

615:7 621:5

639:1 644:1

651:16 655:21

656:11 663:1

672:2 696:16

706:13 708:13

741:4 746:16

768:10,16 773:21

three-dimensional

60:21

three-hundred

114:22

three-year 709:12

three-year-old

325:13

threshold 353:18

549:18 550:3,4

550:10,12 551:10

580:9 724:5

727:2 747:11

throw 529:10

534:13 716:5

throwing 533:4,5

thyroid 228:4

484:15

tie 355:18 363:6

tied 726:22

tier 452:5

tighter 294:20

tightly 450:9

till 716:16

Timchalk 186:17

186:21 400:10,10

400:16,18 674:4

687:20 710:14

time 3:13 18:19

19:17 20:12

25:11 28:2,5,18

28:22 30:11 32:2

32:2 41:20 47:16

47:19 51:15

52:21 60:21

66:11,17,18,20

67:2,3 69:3,7,8

70:13 75:1 76:19

79:7,17 82:2

84:1 85:17 86:20

86:22 87:1,10

88:15 89:15

91:17 98:12

104:22 106:22

107:19,22 112:5

112:7,10,16,16

113:6,12 114:12

117:6 119:6

120:20 122:10,20

124:7,19 126:4

126:18 127:1

132:11 136:13,14

136:21 139:1

146:5,5 147:7,14

155:3 162:3,16

167:4,6,14

174:18 176:8

178:7 196:3,10

197:5 203:13

205:8 208:11

214:1 215:17,19

217:5,7 222:15

222:22 224:5,13

224:17 225:15

227:9 235:13

244:17 248:13

252:1 262:8

264:20 267:16

275:22 283:2

302:15,18 318:17

319:2,6 339:19

340:4 342:14

344:21 345:10,15

345:16 351:5

352:19,20 356:14

356:22 359:4

369:2 372:15

373:12 374:3

380:17 383:4

399:4 412:8,8,15

413:2 415:15

418:8 438:6

441:20 457:11

461:22 462:2

471:17 477:15

479:1 481:18

488:7 489:7

507:9 508:14

519:15 523:7

527:13 530:16

532:20 534:3

535:17 539:1,12

542:13,16,17

543:16 555:17

558:5 562:8,10

564:13 565:6

580:15 583:10

585:3,10 586:13

587:21 588:3

593:2 594:18

596:10 599:19

600:5,11,14,15

600:17 614:18

620:20 621:2,7

630:17 633:18,18

636:13,22 640:7

640:15,17 642:9

646:11 648:4

655:3 658:22

664:4 665:1

669:10 673:16

675:1 676:11

684:1 687:8

694:19,22 702:6

706:11 716:3

717:2,10 720:13

720:15 723:1,15

724:3 725:22

730:14,16,19

733:5 735:20

742:22 746:2,5

746:13,21 747:6

747:9 765:17

767:11 768:15

771:13 772:22

775:17 776:2,13

776:14 777:11

779:18 780:8,15

timed 474:3,10

476:7

timeframe 115:9

162:20 163:2

431:10 588:2

683:22 745:21

timeframes 137:5

timeline 106:17

timely 717:8

times 36:9 83:9

92:21 95:4

109:15 110:4

161:5 227:14

265:9 268:17

301:1 336:20

356:22 394:16,18

403:3 404:11

445:12 449:19

498:6 512:6

529:20 535:20

538:21,22 563:1

571:9 629:15

645:17 646:2

657:2 669:2

674:12 699:18

722:10 732:9

timing 121:2 262:4

462:1 532:3

565:8 590:1

602:11,13 705:15

739:4 742:21

743:21 746:20

tinier 527:22

tiny 244:8 481:3

526:20 527:21

tip 242:4,17 246:2

tirade 705:6

tiresome 388:10

tissue 46:2,2,3,4

62:15 66:2

153:14 192:14

324:4,12,18

341:6,12 398:2

403:7 461:7,10

461:13 604:16

627:16,17,19

628:10

tissues 320:11

328:18 341:10

413:16 414:11

503:15

title 248:2

titled 391:6

tobacco 128:17

413:16 555:2

582:18

today 3:9,20 20:9

25:18 28:12

30:15 31:9,12

33:13 34:10 37:8

40:15 69:21 97:4

108:8 112:12

113:13 117:1

147:10 172:10,19

188:10 194:14

205:8 208:9

210:12 226:18

247:19 248:9

249:4,17 251:16

253:14 254:11

255:17 256:3

257:4 278:7

282:6,13 284:15

285:10 289:18

302:16 313:6

319:8,22 320:4

320:15 321:5

325:5 343:11

384:6 388:9,16

421:3 445:6

470:12,14,17

473:12 498:11

526:13 539:15

588:9 609:1

619:12 664:11

737:13,15 774:1

774:8 778:13

today's 108:15,15

114:8

told 209:8 232:15

779:6 780:14

tolerance 28:8

tolerances 392:10

396:21

Tom 20:3

Tomatis 390:22

tomorrow 152:21

155:14 165:22

191:10 220:21

221:2,4 224:4

247:20 254:11

255:2 279:13

287:15 302:21

307:13,17 308:18

314:2,3,7,10

470:13,18,20

539:13,15

tone 58:12 588:20
Page 108

598:9

Tookie 483:21

484:6,6

tool 134:2,12,13

135:5,16,20

136:5 138:6,10

138:21 160:19,21

196:9,10 424:5

530:1 665:17

673:3

toolbox 196:10

659:2

tools 139:13 140:1

143:9,9 144:19

659:2 724:19

top 148:5 161:16

213:21 321:9

325:6,14 327:10

328:19 362:5

377:17 378:5

385:2 424:18

537:7 582:15

604:8

topic 2:3 113:4

278:7 320:19

333:9 681:8

704:6 717:5,7

734:20

topics 28:11 38:2

torn 515:15

toss 505:16,18

total 70:3 82:2

169:2,5 199:4,10

200:4 222:17

330:8 359:17

401:13 578:1

623:2 640:10,18

645:21 692:9

695:17 698:14

totality 108:12

208:6 219:22

750:16 767:16

totally 307:6

500:10,13 506:10

678:22 742:17

touch 13:3 16:6

82:11 377:15

602:11 704:4

touched 469:3

tough 725:10

730:19 758:1

tougher 371:5

toughness 371:6

tox 212:1 225:6

244:19 245:14

252:18,20 330:11

343:10 348:16

350:5 365:21

507:4 512:7

514:13,17

toxic 45:1,3 67:13

184:1 389:20,22

438:7 492:3

493:7

toxicant 641:15

toxicants 67:8

445:7 455:20

466:14

toxicities 437:6

toxicity 9:19 10:4

11:5,8,16 12:1

16:9 23:4 26:5

40:12 46:7 47:5

47:6,7,8 48:2

50:11 52:15

67:12,14 70:19

96:14,15,17,20

96:22 97:11,17

179:17 202:11

205:17 287:9

341:2 363:21

365:6,10,13

386:11 389:18

392:2 439:9

517:14 518:7

551:11 565:16,22

610:21 706:22

toxicokinetic

293:15,16

toxicokinetics

251:6 691:18

699:13

toxicological

100:22 101:3

180:1 183:20

348:22 381:5

492:10 493:2

641:21 688:15

689:9,12 752:3

toxicologically

215:7

toxicologist 4:17

46:12 248:4,5

315:20

toxicologists 64:21

248:8 307:22

775:15

toxicology 4:11,14

5:12 6:19 7:3

9:16,22 10:5,19

12:6 14:2 20:22

29:1 30:9 32:19

39:14 50:10,20

82:9 212:10

225:13 226:5

229:9 252:4

282:10 283:13

287:7 298:12

299:4 315:14,18

316:10 317:10,16

320:6 348:3

437:13 442:6

472:2 501:1,18

700:10 771:4

toxin 48:14 461:2

toxins 100:19,19

TPCy 115:14,18

116:16

trace 321:17

track 90:22 112:8

256:9 670:15

tracking 40:3

205:15

tracks 216:15

362:4

tractor 265:15

trade 234:19

traditional 128:3

264:15 408:16

traditionally

252:17 739:22

trained 134:13

136:4

training 5:3 76:8

76:21,22 77:4

248:3 342:16

510:12

trajectory 11:15

678:22

transcend 775:7

transfer 629:12

682:15 717:21

transferase 698:20

transferred 499:7

629:9,14

transform 726:15

transformations

132:4 142:11

transformed 356:8

550:17

transgenic 514:3

transient 74:14

transition 175:19

translate 101:10

406:1 448:2,4

453:10,16 468:8

501:18

translated 448:9

translating 467:4

translation 70:10

88:4 449:22

450:12 459:19

transmitter

551:14 563:15

transmitters 743:6

744:7

transparency 8:12

13:8 15:5 17:22

19:4 20:14,16

25:15 32:21 33:4

207:15,16,16

220:4 227:13,20

380:19

transparent 12:19

12:21 26:1 33:7

36:10 202:7,15

207:19

transplacental

630:1

transplantation

75:15 94:3

transplanted

101:4,16 491:19

491:20

transport 57:12

58:9 279:8 446:9

454:2,3 460:11

464:22 465:8

466:4 627:9

629:6 730:13

transported 630:5

transporting

464:9

traveled 507:20

treat 485:4

treated 49:15,17

49:22 75:21

76:15,17 77:2

359:19 375:19

475:3,4 657:1

treating 650:8

treatment 76:19

76:19 84:14

394:1 487:18

494:3 598:2

treatments 75:14

tremendous

498:13 586:13

778:14 779:11

781:11

tremors 365:9

trend 357:5,8

416:6,8 523:1,5

578:20 584:14

trends 184:16

399:3 610:5

691:2

trial 77:5 88:3

358:2,5,6 477:16

482:3,5 490:7,7

509:9 722:4

trials 87:16 88:2,2

358:2 375:3

488:14 494:5

495:12 508:5

634:7

Triangle 43:9

68:19

trichlopyr 623:10

trichloroethylene

390:3

trichloropyridinol

55:2,11

trichlorpyridinol

321:11,17 324:5

338:8

tricky 90:9

triclopyr 115:17

155:9 169:16,19

170:9 430:9,19

431:7 647:11,14

647:19

tried 10:8 110:3

190:19 227:1

284:3 287:19

354:16,17 355:1

358:19 377:21,22

377:22 486:22

502:19 599:6

719:9

triggered 533:21

trillion 271:13

278:12,17,20

308:20 309:1,20

309:22

trimester 121:11

121:21 122:1

142:21 162:1

169:7 630:4,4

747:7,7
Page 109

trimesters 125:8

trimmed 304:8

trivial 408:4 465:4

trivialized 465:10

trouble 95:17

274:12 514:1

604:17 619:12

636:17

troubled 495:21

troublemaker

444:22

troublesome 632:6

troubling 495:20

true 101:5 120:22

198:17 236:17

239:22 240:12

261:6 290:4

303:20 369:16

378:21 397:18

492:6 510:11

533:3 537:2

566:22 568:13

583:13 623:3

626:3 636:1

638:7 649:1

658:5 678:21,22

685:10 694:3

708:21 722:22

747:2 752:14

truly 20:12 337:13

353:5 459:3

607:16 780:18

trump 225:19

trust 761:8

truth 286:22 292:7

703:5

truthing 692:9

try 14:18 17:1 19:5

63:12,19,21 66:1

69:14,21 72:12

82:20 87:3 99:19

190:4,11,22

191:12 212:21

213:14 226:22

234:9 249:17

308:7 323:12

372:17 373:8

414:5 419:10,12

450:12 451:18

455:11 456:4

470:17 476:12,19

489:7,19,21

502:20 504:20

505:13 509:6

515:22 521:19

525:5 565:6

585:4 619:7

621:16 644:20

651:11 674:10

688:14 696:22

720:1 735:14

738:12 739:9

746:18

trying 10:11 11:6

11:18 12:3 17:16

62:2,4,10,14

63:16 67:6 91:17

93:15,17 95:6

131:2 151:3,19

159:4 160:8

166:4 168:22

184:5 189:9

191:5 199:3

223:4 227:15

230:12 233:15

234:14 242:7,9,9

245:4 271:15

303:16 306:1

344:1 354:6

365:20 366:1

368:10 371:18

374:1 378:5

385:3 414:22

417:16 435:21

447:16 449:21

450:13 451:22

455:10 476:14

478:6,7,7,9,20

479:1 481:16

487:1 492:17

501:17 504:10

505:10 508:19

509:1 527:20

530:18 535:1,22

535:22 536:8

562:17 571:13

602:20 621:2

625:3 639:20

642:14 673:4

685:5 707:2

710:1 722:14

738:17 740:19

742:11 758:13

763:15 769:21

770:4,13 771:8

775:8

tubulin 53:7 57:9

464:20,20

Tuesday 270:10

584:8

Tuesdays 240:6

tumor 68:2

tuned 591:22

Turgeman 482:8

turn 68:15 255:12

318:12 320:1

405:10 422:8

426:1 479:17,17

539:12,12 559:4

601:19 604:18

642:15 776:17

777:21 780:8

Turner 440:19

turning 716:14

turns 654:3

tweaked 772:18

twice 445:18 663:3

731:17

two 8:21 60:6,7

73:11,18,19

74:11,17 77:17

78:10,15 79:13

81:16,16 82:14

83:4 89:7 98:7

108:21 110:2

111:1,5 116:9

121:8 125:8

129:11 130:1

134:1,19,22

135:3,11,15

137:6 142:20

145:14 149:4

155:4 157:16,21

159:7 167:11

169:1,8,11,11,12

194:20 198:14

205:15 208:12

214:22 220:18

231:13 239:16,17

242:4,22 245:16

246:1 250:21

251:4 254:11

259:8,20 260:5

264:19 267:13

269:13 270:12,22

271:3,9 275:6

277:2 280:19

283:19,20 288:1

289:1 292:20,21

297:1 304:15

306:16 325:6,14

325:15 326:15

327:16 334:9

336:8,9 342:3

345:6,19 347:1

348:18 349:19

355:19,20 359:8

359:22 360:4,10

361:12,21 368:12

372:8,15 382:12

382:13,17 383:7

383:8,9 384:19

384:20 386:11,13

397:1 403:10

404:5 407:13

416:16 421:5,7

425:18 427:10,20

449:6 460:19

469:1 491:4

544:13 546:18

554:11 555:20

556:3 560:7

565:5 566:2

567:7,14 577:19

582:3 588:18

593:3 597:9,14

614:2,6 617:9,18

620:21 621:4

627:21 628:2

631:8 632:10

634:14 637:1

638:5 640:19

646:16 654:12,21

656:15 658:21

662:18,22 666:11

667:13,19 669:6

675:4 677:8

681:7 684:17

685:7 687:4,14

687:15 693:20

700:16 702:3,4

704:16,20 707:7

726:7 728:3,6

730:8 741:2,10

753:1,4 769:19

772:18

two-and-a-half

223:10

two-by-two 559:18

two-day 633:8

634:8

two-fold 402:6

740:3

two-pan 233:20

two-thirds 164:7

two-time 126:12

two-to-three-fold

126:2

two-way 569:4

two-week 123:21

two-year 369:1

726:1

type 69:13 77:10

79:12 92:21 93:1

93:11 102:13

119:9 127:20

130:13 145:7

184:22 187:18

258:22 265:15,16

332:11,18 335:16

367:22 368:14

372:17 374:5

398:8 407:20

421:16,16,19

449:11,13,14,22

451:4 465:3

466:18 469:13

475:18 494:9

496:20 514:19

517:2 528:9,10

529:22 532:7

534:6 568:10

588:4,13 592:8

604:17 629:2

633:22 643:9

659:19 664:20

667:7 681:17

683:12 706:15

722:12 744:3

747:10 775:3

types 72:3 87:18

92:19 126:7

129:1 137:21

285:9 305:17

319:1 330:5

449:12 461:20

468:17,21 469:12

482:15 514:8

535:10 590:7

609:9 610:11

646:10,19 656:21

665:14 674:13

715:11 749:13

750:5 762:12

775:16

typesetting 763:19

typical 174:17

187:6 194:8

211:15 509:22

567:8
Page 110

typically 65:7

94:16 141:22

209:1 600:17

719:7 761:17

T-test 379:19

U

U 469:7

ubiquitous 657:3

UC 258:18 262:22

317:5 676:16

uco-polysacchar...

627:20

UGA 5:6

UGT 457:10,13

UGTs 458:11,18

Uh-huh 646:1

ultimate 45:3 98:1

405:9 408:11

409:9 719:10

ultimately 46:4

47:22 48:17 49:2

250:16 254:8

umbilical 125:18

163:16 557:10,13

627:14,16,19

628:3,8,10

711:21

unacceptable

393:8 394:6,9

unacceptably

394:21

unanswered 685:6

unattainable

121:7

unavoidable

387:21

unaware 217:5

unbaited 510:17

510:18

unbiased 444:5

unbound 697:11

uncertain 211:10

402:9 403:15

uncertainties

16:22 18:12

21:16 34:9 37:11

37:17 42:15,19

113:5 114:2

120:7 123:15

126:10 185:3

192:12,19 193:15

195:3,5 204:15

208:2 213:8

217:2 385:10

401:10 402:19

403:10,14 437:18

438:18 439:3

446:15,22 454:21

469:3 472:15

518:15 521:5

533:3 572:10

579:20 618:9,10

619:4 631:17

705:20,22 706:1

721:8 737:4

738:7,15 740:15

748:22 749:2

754:12 758:7

761:13,14 773:22

uncertainty 14:11

18:16,19,20,20

18:21,21,22 19:1

19:2 21:20 22:5

22:7 42:17

107:20 121:15

124:13 126:9

144:12 185:17

191:19 200:15

211:11 212:8

218:5 219:20

220:11 385:6,10

392:1 397:22

401:9,15,16

402:8 403:21

408:16 409:11

533:4 580:17,21

581:4 594:9

606:2 607:4,5,7

607:20 608:13

609:14 611:5

615:14 618:22

619:15 625:4

630:15 691:15

693:8 705:21

706:14 710:19,21

713:3 717:9

718:7,16 720:8

737:15,19 738:10

738:17,18 740:5

748:8,19 749:14

754:5,6,9 755:7

755:15 758:6

760:16 761:3

763:22 764:4

766:8 770:17

unclear 180:1

184:4 211:9

215:7 496:15

515:17 534:15

580:14 767:18

uncommon 567:18

uncommonly

167:12

uncontrolled

225:9 544:12

uncorrected 251:2

uncoupled 456:21

undeniable 439:12

underestimate

566:22 708:21

745:19

underestimated

292:15

underestimates

568:22

underestimation

292:14 549:2

573:17

undergoing 29:13

underlie 8:8

underlies 235:20

underlines 241:20

underlying 232:2

237:10 238:20

241:10 245:14

379:22 451:2

452:9

undermine 594:4

underpowered

85:7 132:1 494:9

understand 10:11

11:6 17:1 19:1

19:20 42:19 48:8

63:21 91:19

95:10,19 106:21

124:3 152:2

161:6 185:18

189:15 191:2

202:16 219:13

227:20 241:12

271:16 284:18

297:8 298:5

302:6 312:21

347:12 369:5

385:20 470:21

485:2,8,9 488:3

495:22 497:19

526:2 553:15

555:12 557:7,21

558:14 559:8

560:6 561:5

600:4 606:11

612:14 637:18

651:5 662:14

671:2 703:9

705:19 711:17

714:2 720:1

737:7,11,22

738:15 739:3,5

763:2 768:20

771:17,18 772:4

773:19

understandably

12:12

understanding

11:20 15:1,3

16:22 17:14

18:11 45:1 95:22

118:19 165:2

167:5 168:8

273:19 308:1

374:14,18 447:2

456:19,21 460:3

472:17 493:8

516:9 525:12

563:11,15 564:9

564:14 565:10

605:15 609:5

620:19 654:18

661:3 663:16

665:11 718:3

720:18 721:2

723:13 738:13

769:7 771:16

understood 91:16

309:17 364:21

536:11 564:1

570:12 605:9

619:10 687:18

688:1

undertake 10:22

undertaken 91:20

undertaking 7:22

771:8

undertook 251:17

770:15

underway 592:8

661:2 662:10

undetermined

628:20

undoubtedly

568:5,5 778:15

unexposed 181:1

265:2 669:14

672:18 673:1

unfortunate 538:9

unfortunately

73:22 357:21

388:9,19 488:2

664:21

uniform 414:15,15

unifying 520:19

uninhibited

727:15

Unintentional

119:11

unique 176:8,9

579:3,9 580:20

610:9 737:6

778:17

Uniquely 120:1

unit 356:1 475:2

698:5

United 1:11

655:22

units 123:5,21

216:11,12 656:22

universally 137:4

240:8

University 4:11,15

4:22 5:2,7,13,16

5:20 6:1,14,16,20

7:4,7 114:4

116:5 130:21

151:7 315:15,17

316:1,4,7,11,14

316:17,20 317:7

317:10,14,19

396:6,9 611:12

661:17 662:4,5

714:13

unknown 140:7

144:12 236:12

272:18,21 446:13

530:17 587:12,12

594:15 601:10

627:11,16,18

unlogged 400:7

unmask 535:17,19

unmasked 498:20

unmeasured 140:7

144:11

unnamed 232:16

unnecessary

392:13

unorthodox 519:6

523:17

unpack 227:19

unprotected
Page 111

497:10

unproven 476:10

477:7

unpublished

129:20 130:4

171:3 575:15

unrealistic 460:20

528:14

unreasonable

583:12 592:20

unreliable 391:8

673:5

unreliably 352:11

352:13

unsafe 388:17

392:11,13 395:2

unsaid 497:7

unspecified 488:12

unstated 360:11

391:17

unsure 435:5

unusual 273:8

unusually 359:3

un-toxic 457:18

update 113:13

152:20 391:16

updated 342:12,15

389:5 401:11

404:19

updating 425:4

upfront 373:3

uploaded 318:7

upper 213:15,18

398:18 399:10,15

399:19 402:21

570:3

upper-tertile

216:20 217:3

uptake 192:13

uptick 362:5

upward 411:22

urban 262:18

urge 599:14

urgent 198:3

urinary 38:16

39:16 115:18

122:7 143:6

169:6 172:13

173:3 175:18,22

177:15,20 179:6

179:11 180:11,17

181:10,15 182:2

182:9,12 183:5

184:3 186:5

188:18 190:19

192:9 197:5

256:12 262:20

263:1,17 264:3

264:14,17 265:16

268:21 296:15,17

296:18 321:14

324:6 577:18

578:16,18 621:22

623:8,13 624:5

626:11 631:8

635:18 638:1

659:10 660:13

666:9 700:2

709:12

urinate 578:1

urine 115:22 121:8

124:16,21 125:10

126:18,20 127:3

133:6 134:21

162:1 163:13

189:12 258:2

260:9,20 261:5

261:11 262:2

264:20 265:3,11

267:12 270:9,11

270:22 339:17

580:13 594:8

601:17 623:1,2

623:19 625:21

632:20 633:7

634:9,9 637:22

642:3 643:2

644:22 645:2

651:9,14,18

660:16 662:7

669:8 672:1,2,3,6

674:7 677:17

679:15 694:20

695:8,17 707:17

707:18 709:7

727:18 734:1

741:9

urines 262:9

use 10:12 13:19

14:13 15:15 21:1

23:15 27:3,21

35:19 37:21

41:10 44:2 50:5

50:6 52:7 60:8

62:4 63:17 64:21

65:15 73:8 87:11

104:7 105:22

108:15 109:8

113:10 114:13

118:8,9,21 119:2

119:4 120:5

121:18 122:16,17

122:22 123:8

127:10 140:1,6

142:9,14,19

150:19,20 153:12

155:11,12,16

169:22 170:4,12

170:14 177:1,3

178:13 185:7

186:15 191:11

194:7 195:6,14

195:22 198:22

199:9,18,20

200:2,3,5,10

201:2 212:14

213:11 214:2,20

216:8 235:12

238:12 244:8

255:3 256:1

257:9 262:18

280:9 299:15

300:1,6,6 301:10

307:7 308:5

318:12 320:15

327:3 331:12

337:3,9,10,17

341:5 347:16

348:21 349:15

366:16 370:19

387:7 389:7

391:8 393:7

394:8,21 396:19

405:4,7 407:1

415:8 428:19

430:12,16 431:3

438:7,21 454:9

455:15 467:7

479:19 480:22

481:3,5,7,11,19

482:9 483:12,17

484:16,20 485:5

485:11,12,14

486:3,13,13

494:15 495:12

497:10 499:5,5

504:1 517:2,16

519:4,15,16

520:1 522:14

523:16 531:21

545:6 547:8

569:16 573:11

580:9 586:20

595:10 599:11,14

601:18 606:5

607:6,17 613:5,6

615:3 616:19,20

617:18 625:9

634:16 635:6

638:5 644:13

652:8,12 654:5

654:17,22 656:7

657:3,6,6 659:2,6

670:9 672:18

674:10 679:10

680:3 689:9

691:9,14 692:2

692:10 693:22

696:3,16 700:6

700:11 701:18

703:4,6,14 705:8

707:7,13,19

731:20 733:5,7

734:13,13,15

737:14 740:6

741:7,11 745:8

747:18 751:13

755:17 756:6,7

760:4 763:1

766:18 776:14

useful 120:8

141:10 181:17,19

246:17 302:7

312:11 330:3

341:7 366:5

402:16 438:9

507:6 570:5

574:17 592:14

608:11 618:13

624:6 660:9,17

664:12 697:22

699:13 762:20

771:12 774:3

775:2

usefulness 229:8

uses 28:1 198:22

226:12 261:16

388:15,18 389:4

389:10 392:10,11

416:10 430:13

511:10 610:7

616:21 657:4

663:1 742:7

usual 409:16

usually 14:17 18:8

18:9 198:2

231:12 337:3

398:11 402:6

404:9 466:12

532:14 550:6

Utah 5:19 316:16

utero 600:2 614:8

utility 249:14

354:20 624:5

661:18 699:12

747:20 757:10

utility-consistent

390:20

utilize 438:8

455:10 611:16

694:9 695:14

699:19 700:21

701:5 726:5

741:7

utilized 135:16

136:1 143:9,20

335:19 700:1

741:16 761:13

utilizing 326:17

329:12 330:16

333:13 682:20

694:12 695:8

709:17 748:13

U-shaped 358:16

363:2 496:2

500:3

U.S 38:17 138:6

143:7 176:1

178:17 264:4

265:20 268:1,7

596:10 666:12

675:6 753:21

V

vacuum 289:20

298:2

Vaginal 537:12

valid 125:1 153:19

278:20 308:4

375:21 507:17

510:5 614:12

649:1 688:6

709:16 741:11

validate 109:5

694:12 695:4

701:5 709:18

723:22 741:5,15

772:14

validated 73:14

77:22 139:14
Page 112

310:9 323:15

333:11 338:16

438:13 545:7

573:12 660:3

700:22 709:5

validating 660:11

validation 101:16

114:1 115:5

119:18 121:12

122:12 123:16

124:8,12,15,21

126:8,12 139:16

153:16 154:8

161:20 162:3

163:3 175:2

195:22 222:13,16

264:21 267:22

309:2 543:12

574:11 617:13

665:17

validations 663:18

validity 118:20

135:18 141:16

453:17,20 454:11

577:3

Valley 116:7 431:5

431:6 647:8,20

valuable 264:7

507:1 692:20

708:14 718:1

value 186:2 261:19

400:1 404:10

407:6 489:20

513:1 529:19

544:21 552:15

602:15 631:13

667:14 686:2

692:14 702:22

719:17 731:16

732:18,19,20,20

733:1 750:13

775:22

valued 670:3

values 74:3 77:7

84:13 91:5

221:10 277:1

278:1 324:20

352:2,21 353:3

368:2,20 402:4

404:13 409:13

530:14 566:8,12

568:17 569:11

570:6 576:11

616:14,16 671:11

671:16 672:22

692:19 732:10

741:9

Vanderbilt 7:6

317:18

variability 71:3

84:5 120:19

124:4,5 126:17

126:20 127:13

162:15 173:12

174:7,14,16

180:13 183:18

219:21 335:2

352:2 357:18

414:8,10 417:4,7

609:22 610:1

655:9 673:19

686:7,10 691:11

742:13 748:8

754:13,18,22

755:6 758:6

759:11 760:15

variable 127:20

128:10 129:5,5

140:18 145:15

156:14 157:12,14

291:18 414:14

476:16 686:12,14

variables 127:17

128:3 130:2,7,9

131:3,9,10 139:5

139:18 142:11

148:12 157:22

354:5 545:14

546:8 573:14

575:8 612:11

740:3 767:8

variance 356:20

476:13 483:19,21

487:5 566:10,14

567:19,19 672:11

677:11

variant 696:16

variants 485:5

528:9 552:17

variation 120:20

174:9 237:11

401:16 408:5

673:11 689:10

760:18

variations 323:21

335:16 672:12

varied 89:14 149:6

324:13,18,22

335:2,13 424:17

609:9

varies 94:14 286:2

519:1 677:13

variety 22:4 192:1

206:2 321:13

322:15,17,21

330:21 333:11

336:5 598:20

704:7 733:7

various 16:21

17:15 64:3 85:13

92:4 144:9 145:1

178:1 187:13

193:3 271:8

312:10 319:1

322:19,19 323:14

327:11 401:12

425:7 503:15

535:5 610:13

645:17 646:10

692:9,12

vary 88:8 127:17

183:12 335:4,7

494:16 624:14

varying 141:9

354:3 398:1

580:15

vehicle 285:11

330:3 336:4,8

367:6 438:7

474:14 503:19,21

506:6

vehicles 71:11,12

331:12 660:9

vein 627:20 628:2

Vena 5:5,5 98:6,7

101:19 147:21

149:7,17 150:2,8

150:15 151:3,7

151:13,15,17,19

151:22 152:6,9

152:16 164:12,13

164:21 165:12,13

166:1 198:20,21

199:17 200:2,21

201:5 279:18,19

280:3,7,20 310:2

310:6 316:6,6

417:14,15 418:12

423:7 434:9,10

515:2,3 525:7

553:6,7 560:11

560:15,19 561:2

561:6 583:17,18

591:14,15 593:4

593:5 639:13,15

641:2 642:2

653:12 682:1,3

712:8,9,14

756:19,20 759:5

759:6 765:14,15

Vena's 653:10

vendor 102:15

Venerosi 483:17

519:3

ventures 398:11

verification 59:11

59:12

verified 693:7

verify 279:11

verifying 52:4,11

versa 192:9

verse 700:11

707:20 708:7

verses 708:10

version 33:11

324:9,11 330:18

436:8 471:8,9

481:1 482:6

509:22 584:1

743:2,22

versions 223:3

versus 88:17 92:14

93:1,17 99:17

103:17 201:6

215:13,19 237:12

276:20,20 281:10

281:12 292:5

301:15 328:7

332:21 333:2

338:19 348:2,10

361:22 362:8

382:21 386:16

390:13 400:18

410:16 414:11

418:11 424:10,11

427:22 524:20

531:12 632:4

633:18 636:2,7

637:20 642:6

646:20 663:15

669:20 681:21

689:6 701:1

722:18 730:20

734:5 745:14,20

760:22 761:14

768:1

vessels 628:1,2

Veterinary 4:12

6:19 317:11

vexing 248:19

viable 439:21

vice 192:9

Vicky 736:16,21

739:19

victims 414:6

video 511:1

videotaping 511:5

view 40:8 42:13

98:4 202:6

210:14 211:2,14

241:22 242:12,20

243:4 303:3,7

363:15 364:16

371:22 372:7,18

373:1,11 433:17

476:3 612:19

615:22 702:21

728:16 758:12

viewing 368:2

views 396:8

549:15

villi 630:6

VIRGINIA 1:16

virtually 338:11

502:19 530:1

770:12

virtue 131:8

vision 96:16

619:15

visit 162:13 479:5

visited 479:4

visits 361:18

vital 20:11 156:18

207:17

vitro 14:5 26:10

39:10,13 53:16

55:2,10,12 56:20

57:21 58:7 64:10

64:17 65:3,8,13

65:14,20,22

66:13 202:18

205:13 206:11

218:12 249:14

255:4 287:12

288:15 301:16

307:14 320:17,18

321:6 323:1,6,11

323:13 326:18

327:9,11,12,16

329:1,10 330:3,5
Page 113

331:1 332:4,5,10

332:13,21,22

333:4,11,14,15

333:20 336:4,10

336:16 403:18

446:12,13,17

448:1 449:1,22

450:1,13 451:2,9

451:19,21 453:2

453:10 454:4,12

454:22 455:5,9

460:13,19 461:4

461:15,18 463:13

463:15,21 464:10

465:10,22 466:14

466:21 467:6,14

467:22 469:5,17

530:4 590:11

598:18 599:4,6

624:20 674:13

679:1 742:4

743:8 744:9

765:8 769:21

vividly 775:14

vivo 20:21 31:9

40:22 53:16,19

55:3,14 56:20

64:11,17 65:14

65:15,21,22

66:15 202:19

205:13 209:8,19

212:1,10 218:13

218:13 255:5

287:16 321:5

323:14 329:1,10

330:8 331:1,7,17

332:3,5,11,11,18

332:19,21 333:1

333:10,14,17,19

336:4,5,17,19,20

341:4 342:19

354:2 402:4

437:12 438:3

446:12,14,17

449:22 450:3,14

451:19 453:7,10

454:5 455:9

460:16 461:15,18

461:20 466:8,15

466:16,22 467:5

467:6,14,22

468:8,16 469:19

517:13 530:4

597:14 598:16

621:11 624:20

674:13 742:2

vocabulary 23:4

23:22 24:18

void 648:22

voids 669:9

volatilization

393:15 394:3

volume 324:15,15

324:22 330:8,9

335:3,3 401:4

427:16,17,19

459:9 528:12

volume-adjusted

189:8

voluminous

248:18

voluntarily 674:21

voluntary 119:3

124:10 146:15,18

214:1 222:15

318:22 544:8

656:7 657:22

volunteer 122:21

volunteers 185:2

270:6 326:11

Vorhees 5:1,2 64:1

91:13,14 95:8

100:16 271:14,15

272:13,18,20

274:5 302:1,2,9

303:3,6,12,18

307:20,21 309:5

309:17 316:3,3

376:14,15,15,18

377:6,10,14

378:9 473:2,3

502:12,18 505:2

505:17 506:21

507:8,14 508:3,3

508:10,18,22

510:8,8 515:9

518:16,18 522:2

522:6,13 525:4,5

530:20 532:9

533:14,20 536:12

536:19 537:1,6

538:14 540:9,10

540:11 765:5,19

765:20 766:11

769:18

vulnerability

497:3 615:17

753:12,13

vulnerable 615:14

622:2 675:20

694:3 706:16,19

756:11,14

W

Wade 20:3

WAIS 534:18

535:5

waist 534:17

wait 302:22

415:20 512:20,22

608:3 642:21

waiting 188:13

342:10 391:10

539:12 645:17

718:19

Wake 745:10

walk 25:10 667:18

walking 668:2,15

668:18

Wang's 696:19

want 2:2,11,12

3:21 4:3 7:19

15:13 16:7 42:4

52:3 60:11,15

61:21 68:22

70:16 71:19 93:3

103:20 104:3

106:6,19 107:13

108:13 147:20

148:10 155:13

162:5 176:7

190:10 195:16

207:2 215:10

219:11 225:17,19

230:18 235:6,6

238:13 250:5

265:10 276:16

278:13 283:5

284:17 289:14,18

290:20 294:2,8

295:3 298:5

299:19 303:21

304:18 306:18,21

306:22 307:10,11

313:20 334:15

343:22 361:21

366:14 370:19

371:17 374:17

378:4 381:1

384:6 385:13

387:19 406:13,20

407:1,17,18

408:2,17,18

419:8,18 422:1

422:19 425:14

433:4,13 435:3

440:7 447:17

449:11 454:16

465:13 468:11

471:4 478:5

481:5,7 483:4

485:10 495:14

497:11 500:21

504:22 506:13

511:16 514:5

515:14 524:14

530:9,17 531:1

531:12 540:13,15

554:4 556:5

563:21 578:21

581:19 582:9,12

585:16 605:6

615:21 624:16

632:14 634:13

635:14 639:13

648:2 654:3

655:13 664:17,20

664:20 665:3

677:11 679:6

681:4,18 684:15

693:20 705:10

709:3,20 711:8

712:14 715:20

718:4 727:22

734:9 735:15

736:9 737:7,8,10

738:10 740:20

741:7,11,14

745:3,19 746:9

751:21 758:18,19

765:13,15 766:20

771:10 772:6

773:13 775:20

777:21 779:11,14

780:12 781:5,15

781:15

wanted 2:6 59:15

69:14,18 70:15

70:18 72:8 75:16

164:11 178:11

197:11 205:20

227:21 230:4

244:8 302:18

307:12 310:14

312:4,8,11

362:20 380:17

385:15 407:16

419:22 420:1,21

421:21 422:7,22

432:7 487:18

533:2,5 540:7,9

552:11 588:18

592:13 605:8

609:1 611:6

612:1 632:12

633:2,21 654:8

683:4,6 703:21

704:1 719:7

728:13 750:8

756:21 766:11

769:6

wanting 319:10

521:12

wants 61:20

396:19 440:5,6

477:4 479:16

716:21 739:15

warm 762:6

wash 710:21

washing 417:8

Washington 4:21

316:1 384:10

wasn't 149:19

156:2 158:9

181:16 221:12

222:17 278:6

297:17 306:15

309:2 359:2

377:9 388:8

555:7 559:9

560:22 561:22

579:18 585:20

589:11 763:18

773:18 775:12

waste 627:22

wasting 769:2

Watanabe 48:10

watch 511:2

water 72:17 73:8

73:17 74:16,21

75:1 87:11,14

94:2 102:6

104:13,16,16

192:4 194:17

196:7 374:22

394:9,11,22

480:15 488:6

495:8 507:16

532:7 534:1

646:17 694:10
Page 114

716:1

waving 193:2

way 10:14,22 12:8

14:18 21:4 24:4

24:19 26:12 31:3

31:6 33:6,7

37:10,16 38:2

40:1 43:4 59:16

59:22 60:2,4

62:14,21 63:12

63:13,19 68:22

89:19 90:2 91:6

97:18 99:14

101:2,4 103:9

107:9 126:14

134:4 148:10

151:2 167:8

177:17 178:6,8

189:4 191:10

192:10 196:1

197:15 198:10

216:1,6 217:7

218:14 225:20

227:14 228:22

229:8,13,21

230:10 233:3

237:17,21 238:8

238:8 239:18

242:7 245:19

246:3 251:3

276:7 287:1

289:6,8 290:10

291:3 292:9,9

293:15 294:21

295:1,16 297:7

300:17 304:16

313:9,10,12,12

313:15,18,19

319:7 354:17

356:7 362:10

372:12,14 373:15

373:16 377:4

378:1 383:10

384:20 391:10

394:13 397:7

399:20 407:5

414:13 417:8

433:4 442:15

443:20 444:3

448:6 450:2,5

454:13 463:15

466:20 475:18

478:5 479:11

482:22 483:1,13

489:20 490:13

492:22 496:13

504:2 509:11,15

510:18 511:20

520:16 521:15

526:19 534:12

535:12 538:9

562:8 571:5

584:15 590:13

591:7 592:16

606:17,18 607:10

632:5 633:3

634:12,18,19,19

637:3 643:17

648:5 676:22

689:22 697:1

699:19 700:4,6

701:7 702:12

705:14 706:2

709:1 716:2

717:12 721:20

723:22 725:9

737:18,19 739:22

742:1 744:3

745:2 750:12

764:15 772:21

773:16

ways 12:7 14:20

20:4 24:7 62:9

98:22 114:2

189:7,14 192:1

219:4 228:11

229:13 231:13

234:5,14 242:4

242:22 258:12

284:1 285:3

289:1 296:14

404:4 453:13

501:3 519:6

569:15 602:16

649:8 663:1

767:14 774:4

weak 208:7 360:9

weaken 584:16

weakens 245:5

weakest 360:4,18

weakly 530:18

weakness 458:16

weaknesses 98:4

108:5 229:11

458:9 460:2

473:8 493:13

507:14 587:15

618:8 667:19,21

671:15

weaning 70:14,15

89:8 538:6,7

wearing 265:14

Web 1:8 781:2

webcast 781:2

Wechsler 138:4,8

138:21 158:15

160:19,21 161:5

166:6 534:18

weeds 169:20

219:17

week 7:21,22 9:1

9:11 19:7 88:2

113:4 120:8

124:18 125:9,11

162:10 163:22

188:11 251:8

306:16 361:19,19

361:19,19 362:5

374:19,21 375:13

376:10 377:6

378:19 379:8

620:12 633:10

weekend 270:10

weeks 70:3 89:11

117:18 119:20

125:11 126:19

162:20 163:4,6

319:15 323:20

375:13 496:17

630:3,4,5 646:16

653:2,5 669:6

weigh 33:6 100:9

227:3 234:17,17

234:18 271:18

weighed 108:8

207:21

weighing 234:5

246:1

weight 21:4,13

108:9 110:9

132:17 133:2,4

146:17 202:5

204:5 206:15

207:2 208:5

220:9 224:14,22

226:3,9,20 227:8

227:9,16 228:20

228:21 229:1

233:18,20 234:6

234:10 235:5

241:8,8 242:8

245:5 249:12

252:13 254:21

257:12 258:14,18

259:8,15 260:4

263:3,22 268:16

274:16 275:6,7

275:12,15,16

280:10 282:14

283:3 286:2,3

291:4 294:5,6

296:9 299:7

300:20 301:2

324:13 361:15

364:15 365:15,15

366:20 372:16

383:17 386:1

390:13,13 456:17

491:10 494:11

515:15 520:10

531:2 537:19

544:8 575:2

609:6,7 638:3

750:10,15 762:11

763:8

weighted 435:16

weights 348:6

welcome 2:2 7:18

19:16 313:16

315:1,5 380:12

380:16

well-accounted

131:14

well-aware 249:21

well-conducted

536:17

well-designed

544:3

well-discussed

142:12 323:17

340:18

well-documented

676:10

well-established

66:16

well-founded

141:6

well-justified

572:18

well-preserved

650:5

well-recognized

742:7

well-understood

485:12

went 26:4 87:8

93:17 100:3

109:21 123:15

151:2 156:1

162:14 302:3

413:21 417:1

424:18 429:9

432:14 434:13

442:21,22 455:22

477:17 479:5

500:14 523:6

527:13,13 562:20

562:21 573:20

591:13 643:4

646:6 696:9

weren't 234:20

275:17 291:14,15

297:14,17,21

345:3 401:21

550:22 610:2

637:19 658:12

672:3,14 775:15

776:2

wet 668:18

we'll 119:18

128:22 132:6

139:8 143:5

147:18 201:11,12

206:17 212:2,9

220:7 223:6,8,20

223:21 230:6

242:20 247:18,19

251:2 255:2,18

255:18 269:9

281:22 285:14,15

304:9 314:11

315:9 387:10

395:22 398:13

405:10 417:12,12

419:1,2,6,9,11

593:13 617:20

619:6 634:8,9

636:17 647:2

653:19,20 655:16

664:19 665:8,21

677:4 716:13,15

730:15 737:5

762:4

we're 155:6

163:14 164:4

166:4 168:4

170:13 187:19

188:16 193:2,9

194:7 203:6,14

203:17,19 204:6
Page 115

204:13 205:15

207:6,7,9,14,17

208:13 209:20

213:14 214:11

215:11 216:17

217:4 218:8,18

220:4,4,5,16

223:9,13 224:4

225:11 226:9

227:15 230:6

237:1 239:21

241:9,10 242:6,6

242:7,8,9,9

247:13 248:14,20

248:22 249:5,16

249:21 250:4

251:18 252:17

254:16 255:9

266:3 272:11

280:7,22 284:16

286:3,8 295:13

297:14 301:10,19

307:8,12 310:10

310:12 315:6,7

319:2 325:4

337:15 344:1

345:13 355:6

362:7 380:7

382:12 383:18

384:5,18 385:3,5

386:8 387:4,14

387:22,22 389:7

397:7 412:15

416:14,15 417:13

419:5,10 698:17

703:12 706:19

715:7,21 727:17

733:11 736:7

738:14 739:2

741:18 742:10,11

747:14 752:13

758:21 761:8

766:16 768:15

769:2 779:3

we've 147:10

170:14 172:4

178:8 185:17

187:14 188:4,12

188:12 193:21

201:19 202:14

203:10,18 204:7

207:7,8,10,15,21

207:22 208:1,8

223:12,18 224:2

226:21,22 232:13

232:14 233:12

235:8 236:19

238:17 239:3,17

239:17 249:8,16

249:20 250:2

252:12,18 259:12

266:4 267:12

285:21 296:12

301:7,7 310:21

310:22 312:5

319:12 334:20

336:21 340:6,7,8

340:21 379:12

582:3,3 596:2

598:15 611:12

620:12,19 626:18

633:3 639:22

641:21 653:20

667:16 682:10

686:1 702:14

715:6 716:19

719:9 724:15

725:4 727:2

729:2,11 736:18

748:11 757:1

759:19 761:15

764:3,21 765:6

767:9 769:13

772:21 773:8

775:22 780:18

whatsoever 383:11

477:13 479:19

689:8

wheat 155:5

169:16

wheels 716:14,15

whichever 741:5

whirlwind 304:7

whistles 193:7

Whitby 19:9,13,14

22:13 27:8

776:19,20 778:4

781:8

white 21:19 22:17

26:4 97:4 112:8

112:9 204:8

230:7,9 445:1

462:20 464:8

637:13

Whyatt 122:12

123:18 125:16

146:13 149:18

165:15 217:21

221:15 222:13

264:21 426:22

580:11 593:7

616:13,16 617:4

617:14 637:7

732:8

wide 330:21

428:19 546:10

550:13 677:18

748:2 781:2

widely 71:12 73:14

81:3 82:8,9

330:4 388:20

494:17 624:14

665:1

wider 225:3

wild 482:14

wildlife 48:15

WILLIAM 5:18

43:5 60:14 61:4

61:8,15 64:8

65:4,10 66:6,9

67:4,11 90:5

154:2,13 155:15

157:15,19 158:3

158:6,9 168:20

196:13 197:6,10

198:1 221:7,18

222:2,11 223:3

269:11,16,20

270:1,4,14,17

281:15 316:15

414:18,22 415:12

416:13,18 417:3

420:3 421:7,12

429:3 431:18

432:9,16 433:14

433:21 434:14

439:17 529:12

566:2 568:9

569:4 587:14

588:11 602:9

616:8 617:6,9,16

631:6 650:10,19

655:7 666:21

683:18 685:17

687:3 688:20

689:14 732:7

733:14,18 734:2

734:5 744:15

763:12

willing 468:20

509:3

willingness 252:1

780:16

Wilson 623:21

676:16

wind 509:22 773:2

window 42:17 67:1

98:17,20 120:15

120:17 121:21

143:1 462:3

580:18 726:1

754:18

windows 32:3

40:10 41:15,21

125:3 217:9,11

217:12,14 594:14

720:17

wipe 498:16

WISC 535:4,5

WISC-IV 298:3,8

wise 510:16

wish 3:9 267:14

279:13 744:1

770:4,18 779:16

780:20

wishes 7:14

withdrawal

318:22 610:7

withdrawals 544:9

withdrawn 673:14

Wolf 20:3

woman 194:15

759:2

women 28:14

114:20 117:4,16

117:18 126:18

162:14 215:14

216:19 217:7,18

218:1 260:3

262:11 263:10,14

263:15,22 266:10

395:8 399:2,11

401:14 410:15,16

415:18 547:20

633:5,6 634:4,7

634:18,19 638:17

638:22 639:2,2

639:12,14 676:1

711:13 720:6

724:13 756:3

771:5

wonder 100:13

103:2 502:12

585:19 658:10

742:13 746:2

768:9

wondered 90:9

155:22 379:15

686:20

wonderful 376:12

468:17 554:7

wonderfully

468:12

wondering 88:12

88:14 101:21

157:19 166:11

168:14 194:3

302:22 379:3

742:8

wonders 716:19

word 15:7 101:21

103:2 146:21

379:13 523:16

702:9

worded 304:16

443:20

wording 307:2

564:5,6 565:19

589:12,19,20

590:14

words 23:15 34:9

96:18 232:20

243:3 565:15

676:6 693:13

694:13 738:16

772:14

work 3:18 8:14

25:9,10 43:8

60:4,10,13 62:5

87:8 92:7 96:9

98:5 101:8

107:17 114:1

116:12,14 122:12

122:13 126:8

128:1 129:8

139:16 140:16

153:16 154:8

156:22 169:21

175:2 190:18

203:19 218:22

219:1 222:14

223:21 227:5

231:20 232:3

234:9 243:4

245:2 248:18

252:4 269:17

281:9 310:5

357:22 372:18

380:17 388:22

395:8 417:11
Page 116

421:16 442:21

460:15 464:18

501:2 552:5

585:8 599:14,21

604:19,20 606:5

611:20 615:3

617:13 621:10

623:20 624:1,3

637:21 639:10

643:9,11 646:10

647:12 652:22

659:4 669:15

672:19 693:19

694:21 695:3

716:10 744:6

745:15 765:1,16

769:14 780:18

781:7

worked 2:19 320:7

320:13 474:4,20

728:17 777:10

worker 97:2

116:11 192:4

270:19 407:9

659:22 660:5

workers 28:3

111:13 116:6

180:20 181:2,4

266:1,5,9 267:8

388:21 395:3,7,7

395:10,11,12

622:2 666:12

669:3 671:11

675:21 680:7,9

working 9:12 10:9

11:3 44:14 46:14

138:21 145:22

158:16 159:4

165:6 186:19

266:10 343:3

374:19,20 392:22

396:12 405:13,15

405:18 406:9

408:21 409:1

419:19 490:8,8,9

490:10,11,14

508:7,20 509:2

509:22 510:6

520:8 536:18

537:16 539:3

550:4 555:4,14

576:5,13 578:15

622:10 669:6

671:19 735:3

768:6 777:2

781:7

workplace 650:15

works 60:9 330:4

624:3 634:5

673:4 716:19

719:7

Workshop 48:9

workweek 384:7

world 12:11

135:14,15 167:7

167:8,10 216:1

225:12 364:9

392:20 393:3

465:11 503:11

744:8 781:1

worms 305:20

306:18 529:17

worn 123:19 125:9

worried 235:21

539:9 591:11

715:21

worry 760:15

worrying 351:7

worse 359:1

501:16 550:8

761:10

worsened 99:10

worst 485:19

worth 215:9

401:13 538:15

584:19 589:5

606:18 682:20

776:12

worthwhile 235:16

wouldn't 201:7

275:8 289:17

311:9 335:21

379:7 399:22

407:17 408:3

585:16 642:1

668:16 671:3

682:19 714:22

758:19 760:15

764:7

would've 377:3,9

379:10 394:18

694:14

wow 478:4 776:20

777:16

WPPSI 535:4,4

wrap 159:5 470:19

525:5

wrapped 471:1

write 62:8 448:20

647:1 690:7

775:9

write-up 593:6

632:1

writing 244:8

246:22

written 20:9 23:2

112:12 114:9

119:5 132:10

138:11 146:22

147:10 257:15

281:5 308:15

395:19 444:20

462:20 468:12

489:16 541:21

553:21 554:22

563:7 564:6

666:22 771:11

773:10

wrong 347:12

411:21 504:11

506:3 510:14

564:13 589:3

767:10

wrote 270:21

617:10 645:6

www.epa.gov/sci...

1:8

X

X 278:13,14

469:16

xenobiotic 62:21

xenobiotics 628:16

x-axis 216:7,12

356:14 358:1

Y

Yan 277:21

Yang 332:12

YARD 1:14

yeah 100:15

109:17 148:16

150:8 151:5,10

151:14 152:5,8

152:18,22 154:7

158:3 164:2

168:20 169:14

170:3,17 188:15

188:22 190:1,14

191:18 194:9

195:9,19 197:9

197:15 201:5

221:3 232:18

235:12 275:5

277:18,21 279:17

280:1,5 289:18

302:15 303:21

304:1,6 307:18

314:8 339:19,20

365:8,16 366:11

367:7 369:3

371:18 385:7

387:2 411:16

415:17 416:18

417:6,6,20

420:16 503:4

561:2 586:12

592:13 593:5

620:10 632:13

635:14 642:19

650:20 673:10

684:7 690:2

733:19 734:9,19

735:17 736:21

739:18

year 10:11 28:6

29:21 113:15

138:1 188:17

203:21 219:3

221:10,11,19

251:16 252:5

253:5 267:20

277:2 324:11

333:10 335:12

392:16 417:8

431:11,12 503:3

661:8 671:7

700:16 707:6

733:8 779:9

years 9:13 18:16

23:2 27:14 29:17

32:9 92:5 96:11

108:1 118:14

137:9,15 139:1

144:6 193:11

194:20 206:22

207:8 221:11

222:14 223:1

249:15 250:1,13

251:18 255:21

262:18 268:1,3,5

275:6,7,21

276:10,12 277:2

319:12 320:8

335:8 343:7

368:12 384:4

396:13 412:13

415:16 416:9,16

427:3 431:7

439:10 463:4

466:18 474:5,22

475:9,21 506:7

527:16 532:14

543:6,16 549:13

557:15 565:1,11

575:5 577:19

578:4 633:1

644:15 671:20

672:16 676:15

700:16 704:16,20

714:20 744:12,19

746:7,11 765:12

770:11 779:6

year'ish 704:15

year's 320:17

325:2 673:15

yesterday 188:10

216:3 318:20

319:17 324:2

326:8 382:4

421:2 422:6

423:7 424:2,7,15

425:17 426:6,9

430:7,8 431:19

474:22 491:19

495:14 512:5

540:12 563:20

584:8 589:2,21

590:10 597:5,11

598:16 599:1

606:21 613:15

616:4 618:17

619:14 647:4

649:3 701:17

735:11 737:14

744:16 773:13

774:1 778:19

yesterday's 563:8

York 114:19 115:8

116:10 117:19

123:2,5,7,11

168:15 276:14

279:9

young 4:20,20

28:19 83:19

118:13 165:3,4

167:11 170:18,19

171:1,4,7 315:22

315:22 322:9

325:16 335:8
Page 117

352:22 395:12

422:21,22 531:21

532:19 535:18

554:2,3 561:13

561:14 568:2

572:12,13 581:20

582:7 583:22

584:1 615:19,20

766:9,10

Y-axis 322:5 327:6

328:19 358:1

Z

zebra 332:18

333:2 440:16,20

zero 327:7 680:17

680:18

Zivic-Miller

485:14,16,19

486:7 519:16

zones 195:14

Zubo 20:5

$

$100 407:17 408:2

$14,500 407:14

#

#10 424:12

0

0.02 489:20 537:4

537:15

0.03 254:3 489:22

518:4 525:9

537:9,14

0.05 261:20 485:5

489:22 537:4,14

0.08 157:7 523:2

0.1 216:5,14

597:21 677:19

0.10 523:9

0.2 73:7 212:22

213:1 491:5

518:2,2 522:14

523:13 525:8

0.3 73:3 488:1

489:5 491:4,8

0.33 288:3

0.36 254:2

0.4 271:4

0.5 288:13

0.51 597:21

0.8 90:21

0.85 135:19

0025 440:20

01 326:5 332:22

405:20

01-point 409:12

416:21

02 366:14 427:21

427:22 715:1

03 344:16 346:6,8

363:14 366:5

715:2

05 405:21

1

1 31:15 69:4,10

70:5 71:15 73:2

75:5 76:10,10

77:13 79:5,6,9,19

82:22 83:3,13,14

83:15,17,18,21

86:12,19 111:13

188:9 216:5,5,15

288:7 320:21

327:1,5 330:7,10

332:14,22 334:3

342:18 343:16

345:12 361:19

364:13 366:5

376:7,10 377:6

389:12 409:4

416:6 422:20,20

426:13 427:6

435:22 481:13,13

481:14,15,15

489:5 490:7,22

491:4,4,7,15

493:19 494:10

496:11,20 499:11

499:22 502:5

516:2,3,12

517:16,18,22

519:11,13 520:12

521:8,19 522:14

523:12 524:12

526:1,2,10,16

528:2,5 530:14

536:13,14,14

546:15 554:14,16

566:13,19 567:2

567:9 597:8

627:8 632:17

735:11 748:5

1A2 604:6

1mL 90:21

1,000 219:17

288:16 329:22

330:9 333:1

1,000-fold 288:19

1.0 436:20

1.1 406:15

1.2 352:10 416:6

1.3 352:10

1.4 576:4

1.5 181:10 400:14

1.8 408:16 409:5

1:00 419:10,12

10 23:2 65:2 71:4

75:6 80:4 93:6

94:7 149:10,22

151:9,12 152:20

154:16 157:6,20

161:7 165:19

181:11 188:9

208:22 211:16

216:6,18 223:8

258:5,13 265:9

269:14,15 288:9

294:4 295:13,20

326:22 329:1,3

329:22 331:8

332:14,17,21

333:1,2 355:22

358:11 372:7,8

373:13,15 388:20

390:15 394:16,18

406:8,14 407:2

409:17 416:7

425:4 436:8

459:17 487:18

488:18,18 489:9

510:19 523:5

546:19 565:11

595:22 597:1

601:5,5 616:17

643:5 670:14

673:4 716:16

721:4 726:19

727:1,12,18,19

728:8 731:9

736:19 740:12

746:19

10TH 1:17

10% 37:19,20

96:20 218:7

268:14 485:7

518:11 525:14,19

529:21

10,000 216:16,22

10,000-foot 219:17

10-fold 332:16

417:2 615:13

740:3

10:04 583:22

10:15 593:2

10:30 593:13

10:37 68:9

10:54 601:22

100 64:10 65:2

66:7,9 268:17

288:5 322:8

327:7 329:16

332:17 406:10

431:10 677:21

712:15 727:13,17

740:12

100th 407:20

408:19

100% 18:18

100,000 288:10

100-fold 329:20

1000 526:22

677:19 740:12

11 70:4 84:2

161:21 374:15

412:13 488:4

553:3 575:20

698:17

11-year-olds

177:21

11:00 387:11

110 182:21 183:7

266:11 415:18

674:6

114 183:7

12 161:21,22

304:19 364:2

409:4 510:3

577:12

12TH 1:17

12-arm 375:6,9

377:18

12:05 417:13

12:15 621:17

120 211:5

122 481:5

13 71:5 119:20

124:18 153:4

161:21 163:22

164:2 358:11

359:10 376:5

415:2 673:17

14 81:12 84:2

124:18 153:4

161:21 163:22

164:2 415:2

518:1 668:1

685:1,4

14-day 684:19,20

140 677:13

15 68:7,10 277:4

278:12,17,20

279:1 308:20

310:9 312:6

406:11 470:9

474:4 488:4

518:1

15,000 714:13

15-minute 223:7

387:5 593:13

716:12

16 7:13 265:2

510:3 677:8

681:2,3

16-arm 375:7

376:16

16-day 362:13

17 255:21 280:14

600:16 647:13

669:6 681:2,3

684:18

175 405:2

18 74:19 134:6,22

169:12 304:20

408:15,18 532:14

533:19

180 532:11

182384 603:17

188 402:22

19 155:22 471:14

1940's 439:8

1965 27:20 233:2

1980s 348:18

1984 596:22

674:20

1990s 623:17

643:1

1998 114:18 115:9

222:5 416:6

431:7 709:1

1999 116:7 221:20

222:7,11 276:5

415:18,21 416:1

416:3 776:8

1999-2000 657:16

2
Page 118

2 7:10 37:8,8 48:3

57:11 69:5 70:3

70:5,7 71:6,16

83:14,16 96:6

131:1 211:12

341:3 361:19

390:6 396:22

487:17 488:1,22

490:7 491:6

493:17,21 495:18

498:19 502:20

504:12 508:16

515:2 517:16

521:20 524:12

526:16 530:22

531:2,3,5 532:14

536:14 578:3

597:22 627:9

632:17

2B 458:18 603:18

2C19 603:17,19

2C9 603:18,19

2-TN 440:19

2-year-olds 407:14

2.l 441:12

2.0 593:22

2.1 209:10 441:11

2.2 212:2 320:22

351:4 416:5

445:21,22 471:1

2.4 575:11

2.43 400:5

2.5 223:1 404:20

2.63 416:5

2.7 362:7

20 43:9 51:15

70:21,22 115:11

170:20 178:7

267:11,20 271:4

271:12 278:22

279:1 280:15

309:18,21 311:7

311:11,15 312:6

345:13,20 372:2

372:5,16 373:12

377:17 404:4

435:17 471:14

600:20 673:14,17

20s 356:1

20,000 330:13

20-fold 273:16

274:2 662:21

20.5 404:21

20/10 309:22

20/30 672:16

200 481:14

2000 27:21 28:14

116:7 192:3

222:5 266:6

267:17 271:22

272:1 274:17

276:2 365:10

416:5 471:7

643:17

2001 115:9 124:9

222:5,14 272:1

276:2,3,4 416:5

487:14 569:14

2001-2002 656:1

2002 114:18 116:8

186:17,21,22

222:6,8,11

308:16 311:15

324:9 338:6

400:11 407:11,15

416:5 426:10

431:8 588:2

2003 192:3 217:21

221:15,16 416:6

569:22 588:2

2004 124:9 146:14

149:18 165:15

186:3 188:5

217:21 221:15

222:14 588:2

616:13 617:4,7

779:22

2005 146:14

616:16 617:5,6,8

2006 28:4 152:14

213:15 248:5

260:15 475:22

596:18

2007 9:16 10:16

11:7 16:10 18:2

23:11 24:18

29:10 32:14

34:22 122:12

123:18 204:19

206:8 207:1

276:4 389:4

431:12 472:22

732:8

2008 24:18 28:10

28:12 30:3,10,12

30:14 31:11,18

35:22 37:22

38:14 69:1,18

70:7,8 71:13

73:4 108:19,20

112:14 129:21

132:10 147:6

177:7,8 180:3,15

203:11 204:8

213:7 275:10,22

279:12 305:12

332:12 400:12

437:7,10 440:11

472:5,6 541:6

571:20 572:2,14

573:22 574:3

575:9,13 581:11

594:15,21 611:9

666:8 686:11

719:13 775:13

2009 24:3 29:3,11

29:15 32:15

122:12 180:19

198:14 207:1

222:13 264:21

391:5 395:14

431:13 440:12

555:3 580:11

617:4,5,14 671:5

676:10,18 677:5

679:4 685:8

688:7 689:5

705:12 747:16

752:6

2010 9:14 29:6

114:9 130:12

131:1 203:16

256:16 267:21

269:3 273:4

310:21,22 427:11

427:21,22 483:17

517:22 522:2

542:4 569:18

597:17 627:15

645:7 705:12

708:18 734:22

2011 10:18 148:6

158:15 182:8

186:18 192:2

193:5 198:14

203:21 216:10,10

323:17 325:3

389:6 394:10

543:16 554:20

647:10,12 677:2

679:5 688:5

719:14 734:20

735:18

2012 1:17 271:10

326:11 700:10

779:22

2013 107:22

2014 107:22

2015 29:19 704:14

21 221:8,15 343:10

348:16 473:8

491:4 515:22

527:3 664:20

21st 9:17 10:18

12:6 26:6 47:8

202:11 284:16

210 668:13

22 682:4

22202 1:16

24 83:7 136:20

137:14 166:9,13

166:15,18,20

295:20 304:20

345:11,20 488:6

534:1,11 577:17

598:13 668:3

687:10

24-hour 345:15

25 70:22 71:3

93:19 118:14

301:21 360:6

440:21 483:4,7,8

527:15

26 70:4 108:18

117:18 149:8

404:18,20 408:20

682:6

26-fold 400:13

27 258:10 404:3

2777 1:15

28 134:6 150:10

151:8 169:1

403:4 488:6

534:1,11

3

3 8:13 25:5 36:9

38:11 41:17

71:16 75:5,9,18

277:4 342:18

343:17 353:12

361:19 369:8

376:7 392:3

426:19 427:5

440:14 470:10,12

470:17,18,20,21

473:9,20 481:17

486:20,20 491:3

495:2,2,18

498:19 501:15

504:12 516:3

521:2 526:15,15

527:22 536:9,12

539:20 540:9

567:10 575:12

576:5 627:15

687:19 746:20

3D 48:22

3,100 183:2

3-dimensional

42:14

3-Hydroxycarb...

218:3

3-year 579:15

3.1 209:21 353:6

471:2 472:1

493:10

3.1B 525:2

3.2 212:9 497:5

515:2 517:10,12

525:7

3.2.1 441:13

3.2.2 472:2

3.3 288:3

3.4 416:1,4

3.5 326:4,6

3:00 716:16

30 18:16 51:16

263:19 270:7

332:21 333:2

401:13 597:1

598:3 644:7,9,10

669:13

30-year-old

325:12

300 408:11 409:10

481:15

3000 431:8

305-5805 1:9

31 416:21 417:1

314 222:8

32 125:9,11 162:10

33.5 404:21

34 158:14 424:21

35 644:7,9,10

645:1 652:22

653:8

350 395:3

36 135:17 137:12

137:17 152:11,15
Page 119

166:8,13,16,18

166:20 445:4

630:3

36-month 136:14

304:21

38 667:10

4

4 36:9 71:10,11

76:10 83:6,13,14

83:16,17,17,18

89:11 102:6

112:8 113:8

114:7 129:19

130:5 147:1

165:15 171:3

275:11 282:18

293:19 332:7

345:12 346:5

361:19 378:19

392:15 416:22,22

426:13 470:21

521:2 539:16

540:7,22 541:14

567:11 575:15

601:14 617:10

619:9 627:16

631:21

4,000 330:12

4-6 112:9

4.0 222:9 291:2

4.1 210:14 291:1

540:22 588:17

620:3 621:13

4.2 210:15 298:15

299:12 561:22

567:6 571:16,18

593:16

4.3 299:13 300:13

593:3,17,19,21

602:2 608:5

721:1

4.3(a) 591:21

608:6

4.3(b) 608:3,4,8,9

4.6 288:13

4:00 223:8

40 216:9 345:8

400:12 404:4

40s 683:19

40% 67:2 148:7

40-some 369:12

400 481:15

4000 431:8

43 673:16,21

44 647:13

45 483:2

46 369:12

48-hour 123:20

125:9

5

5 3:15 22:20 71:6

71:16 74:8 75:6

172:10,20 177:14

179:4 196:14

262:14 274:14

296:10 304:14

327:2 330:7

353:12 393:10

394:14 400:15

421:9 424:12,19

489:6 490:22

516:2,3,11

522:14 540:22

541:14 557:15

562:6 567:11

575:11 578:4

621:19 628:12

654:2 655:19

689:17 748:5

752:21

5th 177:19,20

5,000 325:9 330:9

714:16

5-HT2 486:14

5-years-olds

555:10

5-year-old 559:10

5-year-olds 559:17

5.0 221:22 666:7

5.1 621:14,18

698:2 724:17

5.1a 654:8 655:5

5.1B 655:17

5.1(b) 636:18

5.2 653:21 655:15

666:6

5.3 218:15 690:10

690:12 720:3

721:1 725:19

726:14

5.4 218:15 575:21

608:18 675:20

694:5,9 695:6

716:14 720:3

5.5 400:5 404:2

407:2

5:30 224:5

5:45 224:5

50 270:6 352:5,13

400:15 431:10

524:18 532:1,18

586:21 616:15

50s 683:20

50% 67:2

50/50 743:17

5000 679:17

53 667:10 671:6

54 399:20 406:20

55 399:20 419:11

59 178:8 258:14

6

6 74:8 89:11

135:17 165:14

177:21 216:19

217:1,3 501:16

510:19 540:22

541:14 553:11

567:11 578:16

600:20 601:4

628:20 654:5

728:1 736:8,16

736:20 738:10

747:15

6.0 219:15

6.07 596:19

6.17 213:18 214:4

6.25 162:20 163:4

6.5 575:21

6.91 415:19,21

6:00 223:9 314:13

60 144:8 354:2

505:4 524:18

532:1,8 533:14

533:17 534:3,9

671:6 776:20

777:16

60% 498:16

60-minute 356:13

600 698:11

61st 776:7

62nd 776:8

65 406:12 644:11

67 477:15

7

7 71:5 407:3 409:4

412:2,10,16,22

413:3 416:21

440:13 488:2,4

559:11 562:6

574:22 578:10

615:9 746:7

7-day-old 597:21

7-point 408:13

409:5

7-year-olds 554:21

555:11 560:16

576:6 578:11

70 414:19 677:13

70's 476:2

70/80 731:10

703 1:9

74 481:13,13

75.3 406:22

8

8 277:4 334:3

355:22 488:19

517:11 670:14

8-fold 416:22

80 345:5 629:10

689:19

80% 144:8 265:3

498:16 533:9

804 681:6

82 301:21

82-page 283:1

85% 122:15

87 221:20 481:3

9

9 70:6,21 72:18

73:1 412:22

486:21 488:18,19

539:16 613:9

617:10 669:6

9-month 716:3

9-year-olds 578:16

9:00 314:7,10

9:45 631:3

90 378:3 505:4

533:15 566:14

643:1 670:5

680:9,10 682:21

683:3,8,10,15

684:10 685:2

781:14

90s 653:1

90% 533:9

95 404:20 546:22

648:14

95th 177:19,20

223:1 328:21

727:20

95% 222:4

99 266:6 267:17

271:22 276:2

321:17 329:15

400:3 401:1

683:21 684:2

99-item 136:3