FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

PRIORITIZING THE UNIVERSE OF ENDOCRINE DISRUPTOR SCREENING 

PROGRAM CHEMICALS USING COMPUTATIONAL TOXICOLOGY TOOLS

DOCKET NUMBER: EPA-HQ-OPP-2012-0818

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

JANUARY 29TH - JANUARY 31, 2013
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1 DR. SHARLENE MATTEN: Good morning. My name

2 is Sharlene Matten, and now I forgot anything else -- no just

3 kidding. I'm the designated federal official for this

4 Scientific Advisory Panel Meeting. This is the first day of a

5 four-day meeting. 

6 The SAP will consider and review scientific

7 issues associated with prioritizing the Universe of Endocrine

8 Disruptor Screening Program chemicals using computational

9 toxicology tools. 

10 As a designated federal official for this

11 meeting, I serve as a liaison between the Panel and the Agency,

12 and am responsible for ensuring the provisions of the Federal

13 Advisory Committee Act. 

14 I want to thank Dr. Dan Schlenk to my left for

15 serving as Chair of the Permanent Panel of the FIFRA Scientific

16 Advisory Panel. I also want to thank the members of the Panel

17 for coming and appreciate their time waiting for the Washington

18 area airports to open so they could fly in for this meeting,

19 members of the public for coming to this meeting and providing

20 written comments and oral comments, and all of the staff and

21 members of the EPA who contributed to the materials provided for

22 this meeting, and finally, but not least, are all my colleagues

23 in the Scientific Advisory Panel Staff for their commitment and

24 assistance with the Scientific Advisory Panel Meetings. 

25 The FIFRA Scientific Advisory Panel is a

26 Federal Advisory Committee that provides independent scientific
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1 peer review and advice to the Agency on pesticides and

2 pesticide-related issues regarding the impact of proposed

3 regulatory actions on human health and the environment. 

4 The FIFRA Scientific Advisory Panel only

5 provides advice and recommendations to the Agency. It does not

6 make comments on regulatory decision making, regulatory policy

7 and implementation of any of the recommendations that the Panel

8 might have. The authority for all of those resides within the

9 Agency. All of your Scientific Advisory, advice and comments

10 from this meeting will be included in a report. 

11 As a designated federal official for this

12 meeting, a critical responsibility is to work with appropriate

13 Agency officials to ensure that all appropriate ethics

14 regulations are satisfied. In that capacity, Panel members are

15 briefed on the provisions of the Federal Conflict of Interest

16 laws. 

17 In addition, each participant has filed a

18 standard government financial disclosure form. I, along with

19 our Deputy Ethics Officer for the Office of Science Coordination

20 and Policy and in coordination and consultation with the Office

21 of General Counsel, have reviewed these reports to ensure all

22 ethics requirements are met. 

23 For members of the public requesting time to

24 make a comment, please limit your comments to five minutes

25 unless you previously arranged for additional time with me. For

26 those who have not preregistered and wish to provide public
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1 comments, please contact me or another member of the FIFRA

2 Advisory Panel staff. The public comment period will be

3 tomorrow morning starting at approximately 9:15. 

4 All of the background materials, agencies

5 charged to the Panel, any other documents related to this

6 meeting are available in the public e-docket, which is

7 accessible by going to the website http://www.regulations.gov

8 and entering the docket number EPA-HQ-OPP-2012-0818. All of

9 that information is provided on the agenda. 

10 All presentations made by the Agency, all

11 public comments that have provided written and/or oral comments

12 and any other documents provided during the discussion by the

13 Panel will be added to the e-docket a few days after this

14 meeting. They will become available sometime next week. The

15 agenda prepared for this meeting lists all the contact

16 information for the docket. If there are any questions, please

17 don't hesitate to ask me.

18 After this meeting is concluded, the SAP must

19 prepare a meeting report, meeting minutes. The report consists

20 of a detailed summary of the responses to the charge questions

21 considering all of the background materials, presentations and

22 public comments. The report serves as the meeting minutes and

23 will be completed within about 90 days after the close of this

24 meeting, no later than about May 1st. 

25 Again, I wish to thank the Panel for their

26 participation. I'm looking forward to this meeting and all of
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1 the challenging interesting discussions that will occur over the

2 next three to four days. I also want to mention to those in the

3 public on the webcast or teleconference line to please mute your

4 phones so the Panel can hear the presentations. If there are

5 any questions about any of the technical information, we have

6 people that can help you with that. 

7 With that, I am going to introduce the Chair

8 of the FIFRA Scientific Advisory Panel, Dr. Dan Schlenk. Thank

9 you. 

10 DR. DANIEL SCHLENK: Thanks Sharlene. Good

11 morning everyone and welcome to our SAP that is focused on

12 Prioritizing the Universe of Endocrine Disrupting Screening

13 Program Chemicals using Computational Toxicology Tools. At this

14 point in time, I'd like to go around the room and have the Panel

15 members introduce themselves, their affiliation and a brief

16 description of their area of expertise. 

17 My name is Dan Schlenk. I am a professor in

18 the Department of Environmental Sciences at the University of

19 California Riverside. My expertise is primarily mechanisms,

20 modes of actions of chemicals in aquatic organisms and

21 particularly endocrine disrupting compounds. 

22 DR. STEPHEN KLAINE: Good morning, I'm Steve

23 Klaine. I am a professor at Clemson University, and I direct

24 the Institute of Environmental Toxicology. I'm an aquatic

25 ecotoxicologist and I conduct toxicology research and aquatic

26 ecosystem risk assessment. Oh, and I'm a member of the
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1 Permanent Panel. 

2 DR. MARTHA SANDY: Good morning, I'm Martha

3 Sandy. I'm a toxicologist with the Office of Environmental

4 Health Hazard Assessment, which is part of the California

5 Environmental Protection Agency. I am chief of the Cancer

6 Toxicology and Epidemiology Section. Carcinogenesis is my

7 specialty, and I'm a member of the Permanent Panel. 

8 DR. JAMES MCMANAMAN: Good morning, I'm Jim

9 McManaman. I'm a professor at the University of Colorado. I'm

10 a reproductive biologist and molecular biologist with interest

11 in endocrine disrupting compounds, and I'm a member of the

12 Permanent Panel. 

13 DR. PRAKASH NAGARKATTI: Good morning, I'm

14 Prakash Nagarkatti, Vice President for research at the

15 University of South Carolina. My research interests are in the

16 area of immunotoxicology. That is how environmental chemicals

17 as well as drugs can affect the immune system, and I'm a

18 permanent member of this Panel as well. 

19 DR. MARION EHRICH: I'm Marion Ehrich from

20 Virginia Tech, and I'm a toxicologist, but also pharmacologist

21 who teaches at their medical school and at their college of

22 veterinary medicine, and I'm a member of the Permanent Panel. 

23 DR. KATRINA WATERS: I'm Katrina Waters. I'm

24 Associate Division Director for Computational Sciences and

25 Mathematics at the Pacific Northwest National Laboratory. My

26 expertise is in computational toxicology. 
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1 DR. MARK CRONIN: Mark Cronin, Professor of

2 Predictive Toxicology at the School of Pharmacy at John Moores

3 University in England. My expertise is in computational

4 toxicology for environmental and human health effects. 

5 DR. TERRY SCHULTZ: Good morning. I'm Terry

6 Schultz, Professor Emeritus at the University of Tennessee. 

7 I've just completed a five-year requirement at OECD where I was

8 responsible for the development of phase 2 of the OECD (Q)SAR

9 Toolbox and the implementation of the Adverse Outcome Pathway

10 Program within OECD. 

11 DR. JANICE CHAMBERS: I'm Jan Chambers. I'm a

12 profession at the College of Veterinary Medicine at Mississippi

13 State University, pesticide toxicologist with special interest

14 in neurotoxicology and metabolism. 

15 DR. BETTY MEEK: I'm Betty Meek. I'm

16 Associate Director for Chemical Risk Assessment at the

17 McLaughlin Centre at the University of Ottawa. I've spent most

18 of my career in regulatory risk assessment with Health Canada

19 and have a background in toxicology. 

20 DR. EDWARD PERKINS: My name is Ed Perkins. 

21 I'm from the U.S. Army Corps of Engineers Environment Lab and

22 I'm senior scientist. My area is molecular biology, toxicology

23 and endocrine disruption.

24 DR. THOMAS BURRIS: Tom Burris, I'm a

25 professor at the Scripps Research Institute. My area of

26 expertise is pharmacology of nuclear receptors. 
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1 DR. MICHAEL KEISER: Michael Keiser, Chief

2 Operating Officer of SeaChange Pharmaceuticals. My area is

3 network pharmacology in computer science and other predictive

4 tools for drug off-target effects.

5 DR. STEPHEN SAFE: My name is Steve Safe from

6 Texas A&M University. My area of research is cancer research

7 and drug development and endocrine disruption. 

8 DR. NANCY DENSLOW: Good morning, my name is

9 Nancy Denslow. I'm a professor at the University of Florida,

10 and my area of research is endocrine disruption, especially with

11 environmental media.

12 DR. MIRIAM JACOBS: Good morning, my name is

13 Miriam Jacobs. I'm a toxicologist and nutritionist from the

14 European Food Safety Authority and my background is on

15 computational toxicology, molecular toxicology, endocrine

16 disruptors and reproductive toxicology. Thanks. 

17 DR. DANIEL SCHLENK: Thank you, everyone. At

18 this point in time, we would like to have some introductory

19 comments from Louise Wise, who is the Deputy Assistant

20 Administrator from the Office of Chemical Safety and Pollution

21 Prevention. 

22 MS. LOUISE WISE: Good morning, everyone. I'm

23 just going to briefly say welcome to everyone because I'm going

24 to be turning it over to Steve Bradbury, who will do the opening

25 remarks, but I want to thank everyone for being here. 

26 This is such an important topic for us. It
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1 has a lot of precedent for how we move forward on a lot of

2 fronts. So this is very, very important. I'd like to thank

3 actually Steve and Mary Manibusan for the work that they've done

4 to get us where we are as well as ORD. We've been working very

5 closely with them. And I thank the SAP, OSCP folks who have

6 organized this. It is quite an endeavor. What you're doing in

7 the next three to four days is very important for our Agency,

8 not only in the endocrine disruption science, but also as a

9 precedent for other things that we'll be doing with chemicals in

10 the future. So thank you very much for being here. I'm turning

11 it over to Steve Bradbury. 

12 DR. DANIEL SCHLENK: Thank you very much. 

13 Some opening remarks from Dr. Steve Bradbury, who is the

14 Director of the Office of Pesticide Programs. Dr. Bradbury? 

15 DR. STEVEN BRADBURY: Thanks and I want to

16 welcome you all to Washington for the next three or four days. 

17 We are really looking forward to the time to spend with you

18 talking about the computational toxicology efforts that we are

19 undertaking in terms of prioritizing chemicals for that program.

20 I want to thank Sharlene and all the staff

21 that help make these meetings go. I'm sure you all appreciate

22 the amount of work that goes on behind the scenes in terms of

23 everything from travel to getting the charge questions right. I

24 want to thank Sharlene and all the staff there. Many people

25 don't see all the work that goes on behind a meeting to make it

26 happen, so we greatly appreciate their efforts. 
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1 Also of course, I want to thank all of you in

2 advance for the time and effort that you've put in already to

3 prepare for the SAP as well as during the meeting and afterwards

4 as you prepare the report. It is a lot of work, and I know you

5 have a day job too, so we really appreciate the time and effort

6 that you spend. 

7 I also want to thank the public for the time

8 they'll be spending listening and also participating in the

9 meeting and providing their insights as well as we move forward.

10

11 I think I can't state it enough how important

12 the Scientific Advisory Panel is to the Pesticide Program, and

13 to our broader efforts within the Office of Chemical Safety and

14 Pollution Prevention. The administrators made it very clear

15 that science is the backbone, the foundation of the

16 decision-making process we make in the Agency and the Scientific

17 Advisory Panel for the work we do is a key foundation to

18 achieving that objective of ensuring that the science that we're

19 employing as we move forward is based on the best available

20 science and the best available input from an external

21 peer-review process. 

22 It's not just an external peer-review process,

23 but it's also a public external peer-review process so that the

24 steps that we're taking as the science goes forward is drawing

25 upon public input and the rationale and the ideas as we move

26 forward are transparent so everybody can see what's happening,
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1 how the science is being developed, what ideas are being offered

2 through the peer review process and ultimately how we fold those

3 comments into the steps as we go forward. 

4 Very good science without transparency isn't

5 very good. So it's not only getting the peer review, but having

6 this kind of a process so that everybody can see what's going on

7 and provide some advice to us as we move forward.

8 As we've done over the years with lots of

9 topics, we like to come to the SAP periodically as we move

10 forward in whatever endeavor we're undertaking. In the context

11 of approaching computational toxicology tools and how we might

12 start to use them in our program, this is one of several that

13 we've had in the past. I'll touch upon a few of them, and this

14 won't be the last time we come to visit with you about using

15 computational toxicology techniques as we move forward.

16 As we've talked about in some of our previous

17 Science Advisory Panels, the concept of using integrator

18 approaches to testing and assessment is a principle or

19 foundational theme that we're trying to use increasingly within

20 the program. We've talked about at previous SAPs the NRC Report

21 of 2007, the NRC report of 2009, Computational Toxicology in the

22 21st Century of Science and Decisions 2009 Report, and numerous

23 SAP discussions we've had as we try to move forward with those

24 concepts in technology. 

25 A couple of concepts that I think are

26 important in all that paper and all those discussions, and one
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1 of them is not really 21st century per se, but it's more about

2 being clear about what's your question. What's the question

3 you're trying to answer and how good is good enough in coming up

4 with an answer to the question. That'll come up over the course

5 of this week, but it's realizing these challenges that we have

6 in using computational toxicology, 21st century toxicology,

7 being really clear about what are the questions we're trying to

8 answer because there are lots of different questions that are

9 embedded within this emerging technology and approaches. There

10 isn't a one-size-fits-all solution because different questions

11 have different approaches to come up with the answers. So we'll

12 try to bring that out as we go forward, and we've talked about

13 that in different SAPs looking at different parts of what we're

14 undertaking.

15 Certainly as we see moving forward with 21st

16 century toxicology, computational toxicology, yes it's about how

17 to come up with better answers, more effective approaches to our

18 risk assessment challenges, and also hopefully do that in a more

19 timely and efficient manner so that we can focus our resources

20 appropriately as we go forward.

21 In that context, we have different kinds of

22 questions. Sometimes it's about how to prioritize what we need

23 to tackle. Sometimes it could be about screening and sometimes

24 it could be actually changing the risk assessment foundation or

25 the approaches to risk assessment and moving into a whole

26 different knowledge system for doing our risk assessments. 
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1 But today, what we're focusing on -- this week

2 what we'll be focusing on is the prioritization question how to

3 take a look at thousands of chemicals and try to figure out

4 where to get started. Not to be too simplistic, but as you know

5 from our White Paper, we can do better than cast number or

6 alphabetical order to start going through chemicals. Having

7 said that, how do we employ some of these new techniques and

8 technologies to figure out where to get started first? That's

9 the focus for this coming week, one of many questions that these

10 kinds of technologies could help us solve.

11 I think another important theme that comes out

12 of this week and in other discussions we've had is the concept

13 of the Adverse Outcome Pathway or the toxicity pathway, which

14 the idea is starting in the NRC 2007 report and actually even

15 before that. If you go back into the literature, mode of

16 action, but thinking about that Adverse Outcome Pathway, we feel

17 is a very important underlying principle as you go forward. It

18 connects to what's the question you're trying to answer because

19 as you start to formulate what the question is you're trying to

20 answer, thinking about the Adverse Outcome Pathway can help get

21 your thoughts organized and get your ideas organized. At least,

22 that's the way we've been approaching it. It helps figure out

23 what are -- how to adapt or how to advance in different

24 technologies for the question you're trying to answer. You use

25 that Adverse Outcome Pathway as a way to help focus your

26 thoughts as you go forward. 
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1 So our concepts in going forward are that

2 let's figure out what the question is, let's figure out what the

3 decision criteria are for that question, and then start taking a

4 look at different tools that can help us answer those questions

5 realizing we're going to adapt the technologies and the tools

6 for the questions we have. We're not going to have technologies

7 and tools and then figure out what the questions we want to

8 answer. Questions come first, solutions follow, and hopefully

9 that'll come across as we go through some of the presentations

10 today. 

11 The 2009 SAP that looked at the first cut at

12 this question and then the 2011 SAP that looked at the broader

13 computational toxicology efforts, we've used a lot as we've

14 prepared for this SAP. There was a lot of advice you gave us

15 back in 2009, very specific to this specific topic, but also the

16 2011 SAP gave us also good food for thought. Hopefully as you

17 see the presentations and discussions, you'll give us some

18 advice where we might have got it right and if we didn't get it

19 right in some spots, I'm sure you'll let us know and we'll

20 continue to try to move forward. Building from the past is

21 really important. 

22 So some of the specifics as we go forward,

23 you'll see how the team has worked on expanding the chemical

24 domain in terms of moving forward. Most importantly, getting

25 back to what the question is, you'll see some continued efforts

26 to really focus on the underlying in vitro information that's
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1 the knowledge base for the Expert System, and knowing as you

2 recall back in 2009, looking at this universe of chemicals

3 realizing these chemicals aren't very potent. In fact, most of

4 them don't seem to be able to bind to the estrogen receptor and

5 the ones that do generally have very low potency. 

6 So it's understanding how to interpret the

7 empirical information that underlies the Expert System is really

8 important. We're looking at different platforms by which that

9 in vitro information may be generated and getting some feedback

10 on understanding that empirical information as it forms our

11 category approach will be really important. Especially in the

12 context of a group of chemicals for which many of them don't

13 seem to have activity. 

14 So getting back to decision criteria, having

15 some insights as to when you're predicting or looking at

16 empirical data and can have what degree of confidence to say

17 this chemical, in lay terms, probably doesn't have a lot of

18 potential to start the pathway. So when can you start taking a

19 look at chemicals and go, you know, this one probably can't

20 start the pathway versus the chemicals that might be able to

21 start the pathway. So we focus where we need to focus and how

22 much confidence can we have in finding those ones that might

23 start the pathway versus the ones that may not start the

24 pathway. 

25 So in closing, I just want to thank you again

26 for all your effort in the next several days as we go through
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1 the presentations and into the charge questions. Also as Louise

2 indicated, we want to acknowledge the team that's taking on this

3 effort. It's folks from across the Office of Chemical Safety

4 and Pollution Prevention. It's not just the Pesticide Program,

5 lots of colleagues from across RAAship as well as colleagues in

6 the Office of Research and Development in EPA. So it's a

7 cross-agency effort. As we go through the presentations today,

8 you'll get a flavor of the diversity of individuals and

9 expertise that have come around the table to help get us to

10 where we are today. 

11 So if it's okay with the Chair, I may turn it

12 over to Mary Manibusan who can provide a little more background

13 and context.

14 DR. DANIEL SCHLENK: Thanks, Dr. Bradbury. 

15 Yes, let me just introduce Mary as the Director of Exposure

16 Assessment Coordination and Policy Division in the Office of

17 Science Coordination Policy. 

18 MS. MARY MANIBUSAN: Good morning everybody,

19 and I as well would like to extend my warm appreciations to the

20 SAP Panel for all the work that you've done to date and over the

21 course of the next few days. Thank you also in advance for the

22 year 2013, where we'll be coming back to the SAP with a series

23 of endocrine disruption chemical issues for your advice and

24 recommendations. Thank you, Dr. Steve Bradbury and Louise Wise

25 for the opening and keynote address. 

26 As Dr. Bradbury had indicated, the work that
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1 we'll be bringing to you today and you'll be hearing about is

2 really a cross-agency effort. We have expert scientists from

3 across Office of Pollution Prevention and Toxics, our pesticide

4 programs as well as Office of Water, and of course our Office of

5 Research and Development colleagues from our National

6 Computational Tox Center as well as our National Health Effects

7 and Research laboratory, so quite a wide range of expertise that

8 will be speaking over the course of today.

9 Today as Dr. Bradbury had indicated is a

10 discussion of the future of the endocrine program. So we're

11 starting with the future work, and then we'll move forward. 

12 In speaking about the future, I think it's

13 really important to make sure that we're anchored with what

14 we've done to date, what has been the past for our program,

15 where we've been, to decide how we plan to move forward. So in

16 my overview, I'd like to just touch upon the history of the

17 program, walk you through our current activities and then

18 project out into the future with respect to how we plan to use

19 computational toxicology to move us faster, quicker and more

20 efficiently towards our mission. 

21 Overlaid on top of this evolutionary timeline

22 will be a discussion of mission vision as well as implementation

23 of the program, but speaking more to today, the process for

24 execution. How do we go about making this happen? Next slide

25 please. 

26 So the EDSP Mission is very focused. Our
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1 mission is to protect public health and wildlife by screening

2 and testing chemicals for endocrine activity, very specific

3 clear endpoint, very specific pathway. We know what we're

4 looking for, and our research is to help us endeavor to complete

5 this mission. So the research that you'll hear about today is

6 really focusing on application for regulatory objectives. 

7 We accomplish this mission under two

8 legislative mandates. Under the 1996 Federal Food, Drug and

9 Cosmetic Act, specifically directed the Agency to develop this

10 screening program using appropriate validated test methods and

11 test systems in order to screen and test chemicals for endocrine

12 activity and highlighting estrogenic effects for the human

13 population. 

14 The 1996 Safe Drinking Water Act Amendments,

15 section 1457, went on to include chemical substances found in

16 drinking water where there is substantial human populations of

17 exposure. These are the two legislative mandates that we work

18 under. 

19 Shortly after the 1996 statutes, we had formed

20 EDSTAC, the Endocrine Disruptor Screening and Testing Advisory

21 Committee. In 1998, they put forward their recommendations. 

22 These recommendations really formed where the program is to

23 date. They went on to provide some key recommendations. The

24 first one is to expand beyond human health to include wildlife. 

25 This issue, of course, is based on published literature that

26 indicates that the endocrine system is conserved across species.
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1

2 It also made the recommendation to expand

3 beyond the estrogenic effects to be inclusive of androgen and

4 thyroid pathways. You will see that as we look at the Tier 1

5 battery and as we look forward to the Tier 2 battery. 

6 We also had the recommendation that is

7 probably the most impactful, and that is to develop a two-tiered

8 screening and testing program. Tier 1 screening is to answer

9 the question of whether a chemical has even the potential to

10 interact with the endocrine system, the estrogen, androgen and

11 thyroid systems. While the Tier 2 test is really to determine

12 whether that interaction occurs to have evidence as an apical

13 adverse endpoint for risk assessment, as well as quantifying

14 that dose-response relationship. 

15 Here's what the current Tier 1 screening

16 battery looks like. It is inclusive of five in vitro assays as

17 well as six in vivo assays that make up the full 11 screening

18 battery. Here's another look at the screening assays. Here,

19 the point to take away is that we were focusing on pathways at

20 the very beginning. So in terms of a screening battery, it was

21 intended to be complimentary and that's why it's put forward as

22 a battery. We look at different pathways. We're covering the

23 estrogen, agonist, antagonist, androgen as well as the thyroid

24 pathway, and then covering the hypothalamic, pituitary, gonadal

25 as well as thyroid axes as well. 

26 For those chemicals where weight of evidence
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1 determination has been made based on the Tier 1 battery of assay

2 results as well as the 158 data for pesticide active ingredients

3 and public literature, we make a determination that a chemical

4 warrants advancing forward for Tier 2 testing, here are the

5 proposed Tier 2 test methods that we're currently working on. 

6 Again, the stress here is that this is not a battery as the Tier

7 1 is. This is a more targeted testing focused approach, so only

8 dependent on what we see in a Tier 1 data would determine which

9 of the Tier 2 will be necessary. 

10 The Mammalian Two-Generation Reproduction and

11 Extended One-Generation Reproduction Studies -- these are

12 already validated and OECD validated as well. They are

13 available. The four that we're working on currently as a

14 program are the ecological assays. So the Avian, Fish and

15 Invertebrate Multi-Generation Reproduction Studies as well as

16 the Larval Amphibian Growth and Development Study in the frog. 

17 These four will be undergoing inter-laboratory validation and we

18 plan to bring that before the SAP in 2013. 

19 Here's a quick timeline snapshot of where the

20 program has been. So just the recount, the 1998 and 1999 was a

21 critical year where we formed the EDSP as well as receiving

22 EDSTAC's critical recommendation to move us forward. The first

23 part of the decade, we had focused on validating the Tier 1 test

24 methods. In 2008, we brought that battery to the SAP for

25 review, which led to the issuance of our initial test orders for

26 List 1 chemicals as comprised of 67 chemicals, primarily active
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1 ingredients and a few high production volume inert ingredients. 

2 In 2010, we also put forward our proposed List 2 chemicals,

3 which is a combination of active ingredients as well as drinking

4 water contaminants.

5 Shortly after, we refocused our program to

6 laying out the foundation for how we plan to move forward with

7 the Tox21 effort as well as continuing our efforts to validate

8 the Tier 2 test. In 2011, it's important to note that we put

9 forward our EDSP21 Work Plan Summary. It really starts our plan

10 in moving forward with these computational tools, as well as our

11 clerical document, the Weight of Evidence Document, that

12 articulates how the Agency plans to melt together all of this

13 very critical information in terms of the Tier 1 data, the 158

14 and OSRI information to make a decision of whether chemicals

15 should and need to advance forward to Tier 2. 

16 Highlighted in red, which is not quite visible

17 on screen, is the work that we have just completed in 2012. 

18 That's completion of the comprehensive management plan, which

19 I'd like to spend just a few minutes just talking you through. 

20 This comprehensive management plan was issued

21 in June of 2012. It really provides that strategic

22 implementation plan or guidance document for internal use, for

23 staff and managers. It really reflects across a time year

24 horizon of about five years out. The primary focus is not only

25 on how do we advance forward with utilization of new

26 technologies, but how do we operationally, as a program, move
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1 forward with our data review processing as well as making some

2 of these critical decisions.

3 As a supplement to the comprehensive

4 management plan, we issued a document that describes our EDSP

5 Universe of Chemicals as well as how we plan to use some general

6 validation principles to help guide us with how to validate some

7 of these advanced technology tools. That was issued in November

8 of 2012, and I'll talk a little bit more about that. 

9 Important to note that this management plan is

10 a living document. It will be updated on a yearly basis and our

11 next update will be in October of 2012. At that time, I hope

12 that we'll be able to update the comprehensive management plan

13 with a completion of several significant SAPs as we have planned

14 for 2013. 

15 So between the time of spring and summer of

16 2013, we plan to bring before the FIFRA SAP our Tier 1 data

17 reviews on a subset of the critical initial list of chemicals to

18 really review the assay as well as the battery performance, that

19 is did it function the way we had projected to function back in

20 2008. We have a wider variety of chemicals and structures that

21 will help inform us of that. This is also a recommendation and

22 followup to the 1999 SAB/SAP review where they had asked the

23 agency that after you've reviewed a subset of chemicals to come

24 back to the SAP for how we might be able to look at updating the

25 battery as well as some possibilities for optimization. 

26 We also plan to bring before the SAP our
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1 Weight of Evidence analysis on merging all of the Tier 1 and

2 OSRI as well as the 158 data together. We will be bringing a

3 subset of that to the SAP. Then just to complete the Tier 2

4 validation effort between spring and summer of 2013, we do plan

5 to bring that before the SAP and all four assays will be subject

6 to review. 

7 But today, our focus is on the FIFRA SAP on

8 how to take that EDSP universe of chemicals and prioritize it

9 with computational tox tools. The work that we're going to be

10 speaking about today really was laid out in the EDSP Work Plan

11 that was issued back in September of 2011 where we lay down the

12 foundation for how we visually plan to move forward in

13 implementing this work plan. 

14 The work plan objectives are several. The

15 first one is just to maximize what we have in terms of data, the

16 current information as well as new information in emerging

17 technologies, but our focus is really to target our testing. So

18 maybe we don't have to ask for all of the assays, but how do we

19 get to the point where we're smarter, we're quicker about

20 targeting which in vivo studies we want to select among the Tier

21 1 battery. 

22 We're proposing a multi-tier testing and

23 assessment framework utilizing multi-disciplinary tools that

24 we'll be talking about in a few minutes. But we plan to do this

25 in a very systematic and incremental fashion so that we're

26 building confidence as we go. So the term of learning by doing
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1 and taking significant steps is really critical. When I talk

2 about confidence, it's not just confidence that model developers

3 have, but confidence there are risk assessors, there are

4 transdisciplinary, the confidence that our risk managers make

5 and understanding the uncertainty and the bounds of uncertainty

6 around each of those assays, as well as our public and our

7 stakeholders at large just making sure that we're doing this in

8 an open and transparent fashion, very important to us. 

9 But anchoring all of these particular

10 decisions and utilization of tools is a firm understanding of

11 the biological mechanism. Making sure that we understand what

12 we're seeing on the front end on the in vitro side really

13 correlates to downstream effects that we're seeing that leads us

14 toward that apical endpoint. 

15 Here's a road map. This is housed in the

16 EDSP21 Work Plan. But again, it's a multi-phased implementation

17 approach. The first one, the first phase is where we are today,

18 which is utilizing high through put and in silico technologies

19 to help us prioritize the universe of chemicals, determining

20 which chemicals to move forward first for screening,

21 understanding that we have limited resources.

22 The second phase is taking the high through

23 put and in silico technologies and think about using it to help

24 better target the in vivo studies that we are going to ask for

25 in a Tier 1 battery. So this is the in vitro to in vitro

26 replacement phase. 
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1 And then the final phase is to explore the

2 opportunity of doing a wholesale replacement of the Tier 1

3 battery with some of the in vitro high through put in silico

4 technology. Again, the stress here is on building confidence as

5 we go. So while we might have articulated some general

6 timeframes. The timeframes are only dependent on the confidence

7 that we exhibit and demonstrate as we go forward.

8 Moving form vision mission to implementation,

9 there are a couple of key considerations that the Agency makes

10 and the first one being clarity of programmatic goal. I liken

11 this to an ecotoxicology risk assessment. We start with the

12 problem formulation. So starting with the end in mind, what is

13 the question as Dr. Bradbury had indicated and how you plan to

14 use these tools and what is the level of uncertainty that is

15 acceptable for where you are in that risk management

16 determination phase.

17 For us, the clarity of programmatic goal is

18 determining in what order we screen chemicals for endocrine

19 activity. We know we have a clear defined endpoint. We know

20 what we're looking for. I think it's critical for our program

21 as we move forward to ensure that, for example, a negative is a

22 negative. So minimizing false negatives as well as false

23 positives as we move forward is very, very important. 

24 Having a defined domain of application and

25 regulatory decision making contexts -- here it's critical to

26 again understand what is our universe of chemicals, what's
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1 encompassing that domain of applicability for regulatory

2 purposes. We understand that in our chemical universe, there is

3 a majority of weak binders. So how do we better target those

4 chemistries with the tools that we have today in terms of

5 optimization. 

6 But overall, building transparency in the

7 strategy and ensuring scientific soundness is really critical. 

8 So we look to the OECD (Q)SAR Validation Principles. We know

9 that they're built for (Q)SAR, but we believe that they're

10 flexible and general enough to apply for all computational tox

11 tools, and we're not prescriptive in how we apply these

12 validation principles but using them as a guidepost for what it

13 significant and measurable and what is appropriate for use in

14 regulatory decision making. 

15 The last bullet, to ensure scientific

16 soundness and rigor and what we do in providing an open

17 transparent process for public outreach, and that is what we're

18 here today doing with the SAP. 

19 So the EDSP Universe of Chemicals -- the

20 Universe of Chemicals as well as the General Validation

21 Principles was housed in the document that we had issued in

22 November. The Universe of Chemicals as was defined in the

23 document is drawn upon the scope of several authorities, the

24 FFDCA as well as the Safe Drinking Water Act. 

25 This is the inventory of the Universe of

26 Chemicals. It amounts to about 10,341 unique chemicals. About
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1 1500 are the active ingredients and 5000 are the inert

2 ingredients, both nonfood and food use as well as fragrances

3 used in inert ingredients. Then there are about 3600 Drinking

4 Water Act Chemicals. These are both the unregulated chemical

5 lists as well as the regulated drinking water lists that

6 combines to form that 3000. 

7 So with 10,000, I think it's important to

8 recognize what is our current pace of operation. So far, just

9 to recap, in October of 2009 and February of 2010, we had issued

10 test order for 67 chemicals, 58 active ingredients and 9

11 high-production volume inert ingredients. 

12 Since then, about 15 have decided to leave the

13 pesticide market. So we're looking at 52 chemicals. We allowed

14 registrants approximately two years to generate that data. So

15 data has been streaming into the agency between 2012 and 2013. 

16 There has been some extensions due to laboratory capacity

17 issues. We have about a year for the Agency to review all that

18 data. 

19 From the time of test order issuance to the

20 decision making of whether that chemical advances to Tier 2 is

21 almost about five years. 

22 So based on the current pace and trajectory,

23 it would be very difficult to complete all of the screening for

24 10,000 chemicals. It would take decades to complete. But we

25 understand and why we're here today is that there is

26 computational toxicology and clear advancements in that area. 
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1 It's really heralding and important evolutionary trending point.

2 So recalibration of our studies state show that we're

3 accelerating the testing pace and movement of the endocrine

4 program.

5 So we're here today with a proposal to

6 prioritize our chemical universe using a number of different

7 tools, both current and emerging technologies. But this

8 approach of prioritizing chemicals is not new to the endocrine

9 program. As we look back in history, back in 1998 and 1999,

10 both the EDSTAC as well as the SAP and SAB were clairvoyant as

11 they forecasted where we would be here today. At that time,

12 they agreed that while high through put and in silico technology

13 was not ready for regulatory prioritization efforts, they

14 understood that that future would be coming. 

15 Here's a quote that EDSTAC made back in 1990,

16 "that existing (Q)SAR models be derived and supplemented with

17 data from high through put assays and thereby expanding the

18 predictive ability of these models." That's exactly the

19 recommendation that we've taken to heart. 

20 In 2007, NAS put forward their critical vision

21 and strategy for Tox21. There, they had advocated integrating

22 all of the existing knowledge and employing both in silico

23 technology as well as high through put to accelerate that pace

24 of testing. As Dr. Bradbury had summarized, in 2009 and 2011,

25 this became really critical for our program as the SAP reviewed

26 the ER Expert System, and confirmed that that was going to be an
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1 important tool to how we move forward as a program for ER

2 binding mechanism.

3 SAP in 2011 advocated for the use of the

4 Adverse Outcome Pathway. As that umbrella organizing principle

5 for us to demonstrate the utilization of these computational tox

6 tools for regulatory decision making as well as looking to OECD

7 validation principles to ensure that there is general acceptance

8 and utilization of these tools in a transparent manner. 

9 So we've taken all of that good advice through

10 many, many peer reviews and brought that to bear in our proposed

11 prioritization method. We've approached this method by merging

12 and building upon current and emerging technologies, looking to

13 physico-chemical properties, exposure information, together with

14 the in silico Expert Systems, read across concepts as well as

15 effects-based chemical categorization approaches using both low

16 and high through put in vitro assays. 

17 The prioritization criteria that are

18 established are meant to be very inclusive. So as we move

19 through the process, we're trying to make sure that the sieves

20 of the filter are wider so that we advance chemicals where we

21 believe might be straddling the line. 

22 Today, we are demonstrating a prioritization

23 approach that is focused on ER mediated Adverse Outcome Pathway

24 as an organizing framework, but this is merely a demonstration

25 of a pathway. We intend to draw upon the SAP recommendations to

26 move us forward with addressing the androgen and thyroid
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1 pathways, understanding that the thyroid pathways would be quite

2 different than the ER and the androgen pathway. This is a

3 generic approach.

4 What emphasized that the prioritization

5 process is not fully completed while we've indicated some

6 numbers of chemicals that flow through the filter. It's only

7 intended to give you some potential impact of each filter. A

8 lot of the work still remains to be done. There is preliminary

9 assessments that are being described only to demonstrate the

10 methodology and not to demonstrate the actual list that will be

11 prioritized and moved forward for Tier 1 screening. 

12 Of course, I want to highlight that the OECD

13 Validation Principles was our guiding principle for how we look

14 across these tools to ensure that we have a defined endpoint, we

15 have clear biological mechanism as we move forward,

16 understanding what we're seeing in terms of the molecular

17 initiating event, leading to a higher Tier downstream event like

18 gene activation that we'll talk about in a few minutes, to make

19 sure that what we're seeing at the early time point really has

20 correlation with what we're seeing downstream on the apical

21 endpoint. So really, that's anchored by biological mechanism. 

22 Here is our proposed chemical prioritization

23 process. Starting from the top as I indicated the Universe of

24 Chemicals totals about 10,341. We split that list to active

25 ingredients on the left-hand side, acknowledging that we already

26 have a FIFRA Registration Review Schedule for these compounds. 
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1 So every 15 years, we will revisit each pesticide and ask the

2 question of whether it needs to be updated with the state of the

3 science, requesting additional data in order to do the risk

4 assessment. But that workflow process is already establishes

5 and can indeed accommodate for the endocrine data call in for

6 Tier 1 as well as subsequent Tier 2 tests.

7 On the right is where we'll be focusing on

8 today. You'll see this figure multiple times as we move forward

9 throughout the day. But on the right, we move the Safe Drinking

10 Water Act chemicals as well as the inert ingredients where

11 they're not captured in the registration review process, and we

12 move that forward. 

13 So as we think about this flow diagram, I want

14 to highlight that we're moving from a least resource intensive

15 approach, so no testing, no animal testing, no cost in the

16 laboratory, all the way down to the high through put where we're

17 increasing in resources, but again, reducing the number of

18 animals for consideration in the screening approach. 

19 So the first filter is on physico-chemical

20 properties. This filter is asking the question whether the

21 chemical is even testable under our current assay approach, and

22 whether it can have systemic effects. So if a chemical is

23 causing portal of entry where you're seeing effects already at

24 the tissue level, then those chemicals would not be subject to

25 screening through the EDSP. 

26 So physico-chemical properties is not a new
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1 concept. This concept was raised in the EDSTAC report as

2 indication that if a chemical is too acidic or too basic or if

3 the polymer is too large, greater than 1000 daltons, it does not

4 need to be included for screening and testing under the

5 endocrine program. 

6 Subsequent to that as we prepare to issue our

7 List 1 initial chemicals for screening, we have put forward a

8 federal register notice where we laid out exclusion criteria. 

9 Among those criteria, we had articulated already that we were

10 going to remove and exclude chemicals that were too acidic, too

11 basic, too corrosive or too reactive in terms or having portal

12 of entry issues before we even see systemic effects.

13 So all of those had been articulated

14 previously. What we're doing today is laying down some firmer

15 boundaries on how we define each of those physical chemical

16 properties, how acidic is acidic, how basic does it need to be

17 to be excluded. What is the corrosivity criteria and how to we

18 think through that? Again, we're being as inclusive as

19 possible, not removing chemicals that need to be screened, but

20 removing chemicals where we really don't need to invest

21 resources in declaring whether it has anticipation of being an

22 endocrine-active chemical. 

23 As those chemical move forward, they move into

24 the exposure potential criteria box. That is only applicable

25 for SDWA chemicals as consistent with the statute. Here we draw

26 upon the candidate contaminate list occurrence information. We
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1 draw upon the public water systems monitoring data, the ambient

2 groundwater and surface water concentrations as well as

3 environmental release and public release and production

4 information. If the chemical resides on any of those lists, it

5 moves forward through our prioritization. If however, it does

6 not appear on any list, we put that to the left as a lower

7 priority for endocrine screening.

8 Chemicals that would advance forward moves

9 into the ER Expert System where there is a prediction on whether

10 that chemical has the ability to bind to the ER receptor. This

11 particular tool had been brought to the SAP back in 2009. It's

12 chemical coverage at that time included food use inert

13 ingredients as well as antimicrobials, and it drew upon two

14 particular in vitro assays that were highly optimize to identify

15 weak bindings, again because that domain of coverage really

16 demonstrated the need to focus in that way for the regulatory

17 purpose. 

18 So the two in vitro assays that were built

19 were one, based on rainbow trout, competitive binding assay for

20 the binding activity as well as following up with the rainbow

21 trout gene activation assay. Both again were highly optimized.

22 When we move the chemicals from the exposure

23 potential pool into the ER Expert System, it had demonstrated

24 that it provided 71% coverage of the universe of chemicals,

25 identifying about 5% that had relative binding affinity that

26 could be moved forward to prioritizing it for EDSP Tier 1
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1 screening. 

2 What remained was about 1700 chemicals. Here

3 the double arrow is to indicate that we're looking as an agency

4 to utilize the high through put technology to do our jobs

5 faster, quicker and more efficient, and helping us build that

6 chemical coverage from the ER Expert System to expand the ER

7 Expert System to fully cover our EDSP universe of chemicals. 

8 In doing so, the White Paper had provided two

9 critical analyses. The high through put analysis against the

10 reference chemicals, so 37 reference chemicals were identified

11 to demonstrate that. We selected eight high through put assays

12 as compliments to the two in vitro assays that the ER Expert

13 System was built on to do that. 

14 The second analysis that you'll hear more

15 about from Dr. Mike Hornung is the analysis where we're

16 comparing the high through put assay results to the in vitro

17 training set that the ER Expert System was built on. This again

18 is to help the Agency better understand how to interpret the

19 high through put data, what are things that we should consider,

20 can we blend the two systems together in terms of mixing the low

21 through put with the high through put. Again, our interest is

22 in expanding the chemical domain so we cover our entire universe

23 of chemicals. 

24 When there are chemicals that the ER Expert

25 System does not have full coverage and we do not have high

26 through put data on, we've developed a strategic testing
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1 approach. You'll hear more about that from Dr. Pat Schmieder

2 and Rick Kolanczyk on how we develop a strategic approach to

3 address the remaining thousand chemicals where we don't have to

4 test all thousand. Maybe we can just test 15 percent of those

5 chemicals, and we do it in a smart way.

6 So that's the totality of the process itself. 

7 I want to make a clarification as well here that once chemicals

8 have moved through this prioritization process, before it

9 advances to a prioritized list for EDSP Tier 1 screening, the

10 regulatory programs need to confirm that the chemical meets the

11 Safe Drinking Water Act Amendment Requirement in terms of having

12 a substantial exposure to human population. 

13 We also need to ensure that that chemical is

14 still being produced and used in the United States, and that's

15 where we receive our authority to issue test orders.

16 What we learned on the right-hand side in

17 terms of process we will certainly apply to the left-hand side

18 in terms of considering read across and formation of

19 effects-based chemical categories. So maybe we don't have to

20 test all pyrethroids, for example. Maybe we can just test one

21 or two to provide that coverage for the entire class. We'll be

22 looking for opportunities to do so. 

23 That completes my presentation of the overview

24 of the prioritization process, which resides in section two of

25 document, and it speaks to charge 1 in your list of charges. 

26 Just want to note here the order of subsequent USEPA
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1 Presentations throughout the day. Following me will be Dr. Ray

2 Kent, who will speak more on the details of the physical

3 chemical properties issue and where we've established some

4 proposed cutoffs, and we'd like your recommendations on those. 

5 Following Dr. Kent will be Rick Kolanczyk and

6 Dr. Schmieder who will emphasize the Adverse Outcome Pathway

7 approach, and speak to you about the expansion of the ER Expert

8 System beyond the food use inerts and antimicrobials to also

9 capture the nonfood use inert ingredients. 

10 Then Dr. Richard Judson from our NCCT Group

11 will provide an overview of the high through put technologies

12 that we're bringing to SAP today in terms of the ER binding and

13 gene activation assays, as well as the analysis that we had done

14 using reference chemicals. 

15 Dr. Mike Hornung will talk about the

16 preliminary results in again, that second analysis, which is

17 just comparing the high through put results with the ER Expert

18 training set in vitro to in vitro.

19 Then finally, we'll hear from Rick Kolanczyk

20 and Dr. Schmieder again on talking about how to move us forward

21 to ensure that we're covering all of the universal chemicals, so

22 strategic testing approach to get at the unknown binding

23 potential chemicals.

24 Wrapping us up, Dr. Vicki Dellarco, who is my

25 coach here on the EDSP21 work group, will provide a synthesis of

26 today's presentation and a good look forward on the next few
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1 days in terms of some of the recommendations that we'll be

2 asking from the Panel. With that, thank you for your attention.

3

4 DR. DANIEL SCHLENK: Thank you for the

5 presentation. At this point, any questions or clarification on

6 the general overview? Dr. Chambers? 

7 DR. JANICE CHAMBERS: Jan Chambers. I don't

8 see one of the other presenters really answering this question,

9 so let me ask you this. The only exposure potential that you're

10 really looking at is the Safe Drinking Water Act -- how come not

11 the others? 

12 MS. MARY MANIBUSAN: We look to our statutory

13 authority under FFDCA for pesticide active ingredients that

14 includes the inert ingredients. We were required to screen and

15 test all of them where under the Safe Drinking Water Act, there

16 is a distinction that it must meet the exposure criteria before

17 it moves into EDSP screening. 

18 DR. JANICE CHAMBERS: So is what you're saying

19 that with the ones that are not under the Safe Drinking Water

20 Act, you will be looking at exposure then or not? 

21 MS. MARY MANIBUSAN: We will not be

22 considering exposure for the prioritization of inerts. We will

23 move those forward for prioritization. 

24 DR. DANIEL SCHLENK: Dr. Schultz? 

25 DR. TERRY SCHULTZ: Yes, Terry Schultz. Mary,

26 can you give me an Agency definition of what lower priority
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1 means? 

2 MS. MARY MANIBUSAN: So maybe I can start and

3 then I can pass it to Dr. Bradbury. The focus of this

4 particular SAP is to help identify those chemicals that should

5 be pushed up higher on our priority list for screening purposes.

6 Lower priority means that we're putting those chemicals on a

7 later time scale for screening. So not never, but a lower

8 priority. So not the first ones that bubble up for

9 prioritization, but in a subsequent list perhaps.

10 DR. STEVEN BRADBURY: Another example going

11 back to the EDSTAC report or the SAB/SAP with regard to

12 mixtures, it recommended first take a look at individual

13 discrete chemicals. It could be components of mixtures and get

14 information on the individual compounds before trying to figure

15 out how to look at mixtures. So that would be -- and you can

16 see it on the chart where we mention mixtures. It is sort of a

17 lower priority with the idea of following up on that

18 recommendation, being discrete individual chemicals first. 

19 DR. DANIEL SCHLENK: That was Dr. Bradbury. 

20 Any other questions from the Panel? Okay. Since we're a little

21 bit ahead of schedule, if Dr. Kent doesn't mind, I think we'll

22 go ahead and try to get his in before we take our first break in

23 the morning. 

24 DR. RAY KENT: Good morning.

25 DR. DANIEL SCHLENK: Excuse me just a minute

26 Dr. Kent. Can I just remind those of you that are on the phone
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1 to please mute your line? We're getting some static through on

2 that. So if you're listening in on the phone, please mute your

3 line. Thank you.

4 DR. RAY KENT: You'll recognize this as the

5 same slide that Mary showed just a few seconds ago. What I'll

6 be talking about for those who can see it is the first diamond

7 that says physico-chemical properties approximately N equals

8 5000.

9 So I think it's long been recognized that

10 given the universe of EDSP chemicals that we have that many of

11 them are I think apparent, they're really not appropriate to

12 test. So, what we've been trying to do is to come up with

13 objective criteria to determine which ones should be tested and

14 which ones that do not need testing. 

15 I am sort of covering kind of the low tech end

16 of the spectrum today. I'll be -- even though it's low tech,

17 it's definitely as scientifically robust as necessary. I'll be

18 looking at some physico-chem properties of chemicals and how

19 they're being used to base decisions on whether a chemical

20 should be tested or not. Application of these filters actually

21 -- the whole collection of filters enabled to whittle down the

22 chemicals from roughly 10,000 to about half that size. 

23 Mary covered this to some extent is the

24 history of prioritization that the EDSTAC and the SAB/SAP

25 recommended that measures be done to avoid testing all of the

26 chemicals. In the Federal Register Notice of September of 2005,
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1 there actually were a list of chemicals that were actually

2 specified, including polymers with number average molecular

3 weight exceeding 1000, strong mineral acids and bases, highly

4 reactive and corrosive chemicals among them, defer mixtures and

5 then some others as well. 

6 What we'll be talking about today are the

7 exclusion principles, primarily the second two bullets,

8 exclusions based on ionization and electronic charge, and then

9 on the third bullet exclusions based on chemical reactivity. 

10 The ionization and electronic charge would include strong acids

11 and bases as well as charge species at pH 7, and the chemical

12 reactivity where chemicals that are corrosive as well as

13 chemicals that hydrolyze in the environment. 

14 For the first set, for the exclusions based on

15 ionization and electronic charge, the criteria are based

16 primarily on the acid dissociation constant in the form of the

17 pKa. For acids themselves, the estimated pKa is the parameter

18 that we're interested in. For bases, it's actually the pKa of

19 the conjugate acid. I'll do a little bit of acid-based

20 chemistry. I promise not to do very much.

21 So for our purposes, an acid is a proton

22 donor. We're using an acetic acid here as an example. The

23 important point on this slide is this is an equilibrium process.

24 For the acids and the acid dissociation constant can

25 characterize that equilibrium. What we typically use for the

26 acid dissociation constant is not the constant itself, but the
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1 negative log of the constant, which we call the pKa. 

2 The base is a proton acceptor. Ammonia is our

3 example here. It also has a base dissociation constant

4 comparable to the acid dissociation constant, but what we're

5 looking at is the Ka, the acid dissociation constant of the

6 conjugate acid. In other words, if we were looking at ammonium

7 ion, that's the conjugate acid of ammonia. What we're looking

8 there at is the dissociation of ammonium ion back to ammonia and

9 a proton. So that is characterized by its own Ka or pKa. 

10 For strong acids and bases, the concern is for

11 the testability of the chemical. In doing in vivo and in vitro

12 tests, you expect to have tissue destruction with strong acids

13 and bases. With a lot of the testing, you're never quite sure

14 whether you're actually measuring nothing more than at a pH

15 effect or if you're actually looking at a property of the

16 molecule itself.

17 The criteria for acids and bases that we're

18 using are a chemical is excluded if the lowest pKa is less than

19 2 or the highest pKa of conjugate acids is greater than 11.5. 

20 When I say the lowest and highest, a lot of these chemicals that

21 we're looking at have multiple dissociation constants. So

22 again, if it's the lowest pKa is less than 2 for strong acids or

23 the highest pKa of the conjugate acid is greater than 11.5. 

24 We have a couple of examples. Phosphoric acid

25 is a well-known example. It actually has three pKas, 1.8, 6.95

26 and 12.9. The lower pKa of these is 2. So, we would exclude
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1 this chemical. On the other hand, guanidine is a strong base. 

2 The guanidinium ion is a weak conjugate acid. In other words,

3 once you form the guanidinium ion, it's reluctant to give up

4 that proton. So it's a very weak acid. The pKa is 12.5. So we

5 would exclude that. 

6 For charged species at pH 7, the basis for

7 this is a little bit different, whereas for strong acids and

8 bases, the concern was for tissue destruction. For charged

9 species at pH 7, we're actually looking at the molecular

10 initiating event of that Adverse Outcome Pathway. The natural

11 substrate for the estrogen receptor and many of the inhibitors

12 are nonpolar compounds so that it's sort of reasonable that a

13 chemical that actually has a charge at pH 7 is much less likely

14 to interact with the estrogen receptor. 

15 So our criterion for exclusion is if less 1

16 percent of uncharged species at pH 7. So what we're using to

17 characterize that or identify such chemicals are again, the pKa.

18 A chemical is excludable is the lowest pKa is less than 5 for

19 acids or the highest pKa of the conjugate acid is greater than 9

20 for bases. 

21 Again, an example is barbituric acid has 3

22 pKas. The lowest of these is 3.95. It is less than 5 so it is

23 excludable. I forgot to actually mention that what we're using

24 for the pKas are predicted pKas. What where using is our source

25 of information is ChemAxon. ChemAxon actually enables you to

26 look directly at the percent charged or uncharged species at any
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1 given pH. Direct calculation from the ChemAxon website

2 indicates for barbituric acid, 0.085 percent is uncharged at pH

3 7. So this would be excludable. 

4 On the other hand, CI Basic violet 14 is a

5 basic dye again with 3 pKas. The highest of these is 8.85. Our

6 criterion is 9, so it does not actually meet the criterion. 

7 Again, if you look at the direct calculation from the ChemAxon

8 website, the calculation shows 1.65 percent uncharged species at

9 pH 7, so it does in fact exceed our 1 percent criterion. 

10 We have criteria then based on chemical

11 reactivity, chemicals that are corrosive or reactive. Again,

12 the focus is on chemicals that cause tissue destruction in vivo

13 or in vitro. We sort of extended that somewhat and we're

14 looking for chemicals that are particularly unstable in a

15 hydrolyzing environment. The focus is on that they have a

16 relatively short half-life in the environment. 

17 For corrosive chemicals, what we've used are a

18 couple of sources. One is the Department of Transportation's

19 List 8, which we are calling the DOT8 List. Chemicals that are

20 listed as corrosive on Material Safety Data Sheets or MSDSs. So

21 some examples again included phosphoric acid and ammonia. We've

22 already seen these as examples of chemicals that were excludable

23 as strong acids or bases, and not surprisingly, they're also

24 corrosive chemicals and they're on the DOT8 List. 

25 The dodecyltrimethylammonium chloride is a

26 very strong cationic detergent. It does not have a function of
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1 what you can consider like a reactive functional group as many

2 of the chemicals have, but it nevertheless is a very, very

3 corrosive chemical. Potassium permanganate is a well-know

4 strong oxidizing agent. 

5 For hydrolyzable chemicals, we came up with an

6 arbitrary cutoff of 40 days, which we recognize is a bit long,

7 but lowering the half-life duration to one day actually made

8 very little difference. Some examples of estimated hydrolysis

9 half-lives, isobutyric anhydride 0.02 days, peroxyoctanoic acid,

10 methyl 2-chloropropinate, all of these actually would qualify as

11 excludable under our current criterion. Aspirin with a

12 half-life of 61 days would not. 

13 This is a very important slide in the

14 presentation that we don't just turn on autopilot for these

15 criteria, but for every single chemical that is excluded, we ask

16 the question, is this really scientifically supportable and

17 biologically plausible. 

18 So this is sort of a summary then of the

19 chemicals that were excluded applying each of these filters. 

20 All together, we're talking about 1700 chemical that were

21 excluded. For too acidic or basic, approximately 700, and then

22 for charged at pH 7, approximately 700. We recognize there's a

23 complete overlap here that for any chemical that's too acidic or

24 basic automatically is excludable as charged at pH 7 given those

25 criteria of less than 2 or greater than 11.5 versus less than 5

26 or greater than 9. 
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1 We're keeping them separate at this point

2 because of the different basis for their inclusion in the set of

3 criteria. One is the testability or the tissue reactivity

4 versus the other is unlikely to be involved in a molecular

5 initiating event. Thank you very much. 

6 DR. DANIEL SCHLENK: Okay. Any questions or

7 clarification for Dr. Kent? Yes, Dr. Cronin? 

8 DR. MARK CRONIN: Mark Cronin. Just two very

9 quick clarifications with regard to charge species, you use the

10 term nonpolar. You said compounds were being excluded on being

11 nonpolar. Could you just clarify your definition of nonpolar? 

12 DR. RAY KENT: I may have misspoken. The

13 natural substrate for the estrogen receptor is nonpolar. So

14 chemicals that would be charged at pH 7 are much, much less

15 likely to interact with the estrogen receptor than the natural

16 substrates or many of the known inhibitors of the estrogen

17 receptor. 

18 DR. MARK CRONIN: Mark Cronin again. Just to

19 -- so in terms of nonpolar, you mean a hydrophobic substance? 

20 DR. RAY KENT: Yes.

21 DR. MARK CRONIN: Okay. Thank you. And just

22 for clarification again, you talked about exclusions and needing

23 expert input into that. I wonder if you could just tell us a

24 little more about what that expert's opinion might be with

25 regard to interpreting the physico-chemical cutoffs. 

26 DR. RAY KENT: I think the intent is that
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1 we're going to have a group of expert chemists and perhaps

2 toxicologists look at each of these predictions and determine

3 whether they make chemical and biological sense. I mean, I

4 think a lot of these things -- we're thinking that they're going

5 to work well, but we need to see what the output of these

6 algorithms are before we actually accept them as valid.

7 DR. DANIEL SCHLENK: Yes, Dr. McManaman? 

8 DR. JAMES MCMANAMAN: Yes, Jim McManaman. 

9 Could I get a little more clarification on the ionic and

10 polymeric reasons for exclusion? I assume at the time when you

11 made these exclusion criteria, you were thinking only about

12 direct estrogen activators. But now, it's known that peptide

13 hormones can also activate the estrogen pathway. So would they

14 be excluded because they would be both polymers and polar. 

15 DR. RAY KENT: You're speaking about polymers

16 in particular.

17 DR. JAMES MCMANAMAN: I was thinking that a

18 peptide hormone, a string of amino-acids is going to be a

19 polymer. So would it be excluded under your criteria? Like for

20 instance, the epidermal growth factor has -- it would be

21 excluded under your criteria, but it can't activate the estrogen

22 pathway.

23 DR. RAY KENT: I think -- and I'll invite

24 others to chime in on this. The polymers that we're talking

25 about as far as excluding if their number average molecular

26 weight are typically those that you might think of as industrial
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1 polymers where they're made up of subunits and you have a

2 species of particular numbers of subunits. If the number of

3 average molecular weight from of those subunits is an excess of

4 1000, then they would be excludable. We don't expect that these

5 particular kinds of polymers would have any particular

6 biological activity such as a peptide hormone might have.

7 DR. DANIEL SCHLENK: Ms. Manibusan, you want

8 to respond to that too? 

9 MS. MARY MANIBUSAN: Just to add onto Dr. Kent

10 -- this is Mary Manibusan. When we're speaking about these

11 physico-chemical properties, we're really talking about these

12 properties being intercorrelated. So it's not just one

13 particular criterion that's applied per chemical. A chemical

14 could meet multiple of these criteria. That would make sense

15 and that's what Dr. Kent has laid out in terms of that

16 rationale. And then bringing back behind that, the expert

17 consultation, so not making that minimal at all, that expert

18 chemistry group will be drawn from the whole Agency. So folks

19 from our toxics as well as ORD and OPP will be engaged in that,

20 bring in toxicologists. We really think about this in a weight

21 of evidence approach. We making sure as we're discussing today

22 that we're anchored with the Adverse Outcome Pathway. So

23 identifying which chemicals would have the ability to interact

24 at the molecular initiating event, making sure we secure that,

25 but also understanding the downstream pathways. 

26 So today, we're focusing on the AOP for the ER
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1 Expert System recognizing that these physical chemical

2 properties might have application across multiple pathways. 

3 DR. DANIEL SCHLENK: Dr. McManaman? 

4 DR. JAMES MCMANAMAN: So the reason I bring

5 this up is because there is this so-called alternate pathway of

6 activating the estrogen system. Many of the compounds that you

7 would normally exclude as being direct activators of the

8 estrogen receptor can activate the estrogen system indirectly. 

9 So there would also be some difficulty in defining what the

10 molecular initiator is because at the molecular initiating event

11 is direct interaction with estrogen receptor, then these other

12 compounds would be excluded. So I just need some clarification

13 on what the agency has in mind about how to deal with this

14 alternate pathway or if you have something in mind. 

15 DR. STEVEN BRADBURY: It's a very good point

16 and it will be important to get the advice from the Panel, but

17 we're trying to approach this in being clear about the

18 initiating event. So in this case, compound binding to the

19 estrogen binding, ligand binding domain, realizing there could

20 be other pathways that could initiate perturbations in the

21 estrogen signaling pathway. So we're using that as a framework.

22 This conversation was just getting that if there was a

23 different initiating event we were looking at, there may be

24 different exclusion parameters that would make sense or not make

25 sense in the context of different initiating pathways. 

26 Getting feedback from the panel will be
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1 important in terms of to what extent is something that is highly

2 reactive and just destroys tissue, as an example, kind of

3 broadly usable whereas there could be other components of these

4 exclusion parameters that may or may not. We will get your

5 advice that are appropriate for this specific initiating event,

6 but they may not be appropriate for a different initiating

7 event. So your point's well taken. That's kind of how we're

8 trying to hope to get some advice from you.

9 DR. DANIEL SCHLENK: Dr. Schultz and then Dr.

10 Safe. 

11 DR. TERRY SCHULTZ: Thank you. Terry Schultz.

12 Following up on what you said, Mary, that you would be using an

13 Adverse Outcome Pathway specific series of criteria for your

14 exclusion factors. Have you thought about having additional

15 exclusion factors that maybe other molecular properties, not

16 just physico-chem. I mention that because you go from 9000 to

17 5000 with some exclusion principles. If you expanded that for

18 other exclusion principles for reactivity, maybe you could get

19 that down to 4000 or 2500. 

20 DR. RAY KENT: Yes, we're hopeful that the

21 panel actually will make that kind of a recommendation. We know

22 we've identified a few, but we're confident there are more out

23 there that would be helpful. 

24 DR. DANIEL SCHLENK: That was Dr. Kent. Dr.

25 Safe? 

26 DR. STEPHEN SAFE: Yes, Steve Safe. I was a
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1 little bit -- not worried, but interested in the one category or

2 criterion charged at page 7. What about heavy metal ions that,

3 you know, there is quite a literature on heavy metal ions

4 activating the estrogen receptor and maybe even some other

5 receptors.

6 DR. RAY KENT: That's a very good point. 

7 That's one reason why we're saying that this expert Panel at the

8 end of the process is very critical. I think that we know that

9 there's a lot of literature on heavy metal ions. We would

10 anticipate that anytime that we would run into that situation,

11 we would say we would recognize that and not exclude on that

12 basis. 

13 DR. DANIEL SCHLENK: That was Dr. Kent. Dr.

14 Chambers? 

15 DR. JANICE CHAMBERS: Jan Chambers. Real

16 quick, you mentioned with the hydrolysis that the cutoff was 40

17 days was arbitrary for the acidic and the charged. Were those

18 cutoffs like pK less than 2? Was that somewhat arbitrary too,

19 or is there some biological reason for those particular cutoffs?

20

21 DR. RAY KENT: Ray Kent. I think for the

22 acids and bases, I think those are sort of recognized as pH 2

23 and pH 11.5 to 12 are recognized as sort of universal cutoffs

24 for strong acids and bases. I mean -- below the pH 2 and above

25 pH 12, I mean the pH itself is sort of a corrosive environment. 

26 For the charge that pH 7, again that was more -- that was
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1 perhaps not as robust in terms of definition, but we felt that 1

2 percent of uncharged species was a reasonable cutoff to use. So

3 the pKs that were chosen, 5 and 9, then match that 1 percent

4 exclusion.

5 DR. DANIEL SCHLENK: Okay. Something to add,

6 Ms. Manibusan? 

7 MS. MARY MANIBUSAN: Just to add as we're

8 considering the kind of holistic process of applying the

9 physico-chem properties and a glimpse into the next discussion

10 where Dr. Schmieder and Rick Kolanczyk will speak to you more

11 about the ER Expert System. They actually took the list of

12 chemicals that were excluded based on physico-chem properties,

13 ran it through the ER Expert System, was able to confirm that 92

14 percent were not known bindings, had no binding potential at all

15 with the ER Expert System. Eight percent remained, and those

16 are still up for question in terms of whether or not they would

17 bind to the ER receptor. Those would be the focus of the

18 chemistry expert panel that Dr. Kent was speaking about, that

19 they would look at that list more closely and make the

20 determination whether based on weight of evidence, whether it

21 was biologically plausible that we exclude them or not. So

22 that's really critical, and I think that's good information, to

23 also confirm that as we laid out the prioritization process. 

24 None of the filters are independently applied. Everything moves

25 forward and it speaks to one another. So while we're talking

26 about physico-chem properties at the onset, we'll be hearing
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1 more about that in the ER Expert System and how each one will

2 inform each other on how do we move forward. But again as Dr.

3 Kent and some of the charge questions really focus on, we really

4 invite the Panel's recommendations and suggestions and how we

5 move forward in a smart way, because this filter requires no

6 testing at all, the least amount of resources. When we can

7 begin to do this kind of filtering here, we can then focus our

8 resources on the remaining chemicals that really require a lot

9 more thinking, testing and predictivity in terms of how do we

10 focus on those chemicals with higher priority that we should

11 move into the Tier 1 screen. 

12 DR. DANIEL SCHLENK: Thank you. Any other

13 questions or clarification? Okay. Let's go ahead and take a

14 break here. I'm going to go on this clock, which is probably

15 not accurate, but it's the closest one that I can see. So let's

16 come back at 20 till 11. 

17 (WHEREUPON a recess was taken)

18 DR. DANIEL SCHLENK: Okay. Our next

19 presentation is going to be presented by Dr. Schmieder and Rick

20 Kolanczyk. 

21 DR. PAT SCHMIEDER: Thank you. Rick and I are

22 going to tag team this one, going back and forth a little bit. 

23 So we're going to talk about both Adverse Outcome Pathway, the

24 approach and the Expert System and the expansion of the system.

25 So again, we go back to try to ground you in

26 the pathway and where we are. As you see, we're down here
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1 talking about the Expert System. The topics I am going to cover

2 in this presentation is first, we're going to go back and give a

3 little bit of history of the model development of Expert System

4 version one, which was basically what we took to the 2009 SAP

5 for review. I'm going to talk about some background and

6 principles from that review because they are very important to

7 understanding the Expert System approach and to our expansion of

8 the Expert System. 

9 Then we'll talk about after we expanded it to

10 version two, what was included in that, assessing the coverage

11 then of this new universe we were given to look at, and then

12 also as Mary had mentioned earlier, the effect of some of the

13 physico-chem filtering. 

14 So under the history of the development of the

15 Expert System, again reviewed in 2009, we'll talk about model

16 domains as well as regulatory domains. Mary mentioned that the

17 regulatory domain was built specifically to cover food use inert

18 ingredients and antimicrobials.

19 Also in the review, we had talked about in

20 2009, some initial steps to automate the system we'll bring

21 around and tell you where we are with that, and then again,

22 expanding the domain of the system, further evaluations of

23 chemical groups. Rick will go through how we do that, and again

24 building of version two. 

25 So the whole approach is really moving

26 incrementally. You're building from initial steps and
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1 addressing new challenges. The ultimate challenge we want to

2 talk about here is addressing the EDSP Universe, which it not

3 only has food use inerts, but also nonfood use, fragrances and

4 the Safe Drinking Water Act chemicals. 

5 So as we went into this, we were actually

6 addressing five different goals. The first one was from again,

7 Expert System version one and I don't want to get confusion here

8 on the different versions. So we took to 2009 SAP basically was

9 what we're calling version one of the Expert System. So then we

10 were asked, now we have a new universe. So how did that -- how

11 does that system cover that universe? 

12 We also have been working on expanding to

13 Expert System version two. We're also going to talk about how

14 that covers this new universe. Then, we're going to identify

15 the remaining chemicals of unknown binding potential that are

16 outside the model domain. Then, this is also we tried to

17 determine where the high through put data could possibly cover

18 those unknown binding potential chemicals, and then also the

19 question of determining if and then how we would use the high

20 through put data to expand training sets to cover unknown

21 binding potential chemicals. As you see, the different sections

22 of the White Paper are addressed here. It is kind of going to

23 be our road map to lead you through where we're going through

24 all the talks today. 

25 So the mechanistic basis of the Expert System,

26 again you've heard about the Adverse Outcome Pathway context. 
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1 I'm going to talk more about that, the (Q)SAR principles, and

2 also in vitro assays designed for a purpose, this specific

3 purpose here. So just for those that might not be familiar with

4 Adverse Outcome Pathways, it's a conceptual approach. 

5 To look at chemical effects across levels of

6 biological organization where -- and the levels of organization

7 are where you can make measures, but what it is, is to really

8 try to look at that linkage so that when you measure something

9 that is the initiation, the chemical interacting at the receptor

10 in this case. That's the initiation of a pathway. It doesn't

11 mean everything will always go all the way down that pathway,

12 but it's a way of starting to build linkages and understand so

13 that when we understand more about chemicals that can start the

14 initiating event, we start to then try to be able to get to

15 where we can, in this case, prioritize chemicals that might

16 eventually -- and there's plausible linkage. 

17 There's a plausibility that a chemical that

18 acts at the receptor could be an induce vitellogenin, for

19 instance, in fish. Then at the tissue level, it will alter the

20 proteins and the hormones. At another tissue, in the gonad, you

21 might get ovotestes. Then even in the individual, it might

22 initiate sex reversal or altering of behavior because of

23 changing in secondary sex characteristics, decreasing in

24 reproduction. There can be population consequences of that. 

25 So the pathway -- it helps you understand

26 where you are and where you have information, and where you
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1 don't have as much information or where there is more

2 uncertainty, but it gives you a place, as you can see, the

3 greater toxicological understanding is usually to the left side

4 of the pathway where we can really look more at mechanisms. But

5 the risk assessment relevance on the right side, what's

6 happening to the individual and to the population. 

7 So using the ER binding affinity as an

8 indicator of potential reproductive effects, we're not

9 predicting in vivo, but we're looking at -- we try to use this

10 to have mechanistic linkages. We know that they exist between

11 the endpoint of ER mediated impairment and the hazard endpoint

12 of just binding to the receptor. 

13 The foundation of the assays used to build the

14 Expert System is the AOP, the molecular initiating event we're

15 looking at in the pathway is identified. It is ER binding. The

16 Expert System identifies which chemical structures can initiate

17 that pathway. The pathway context provides again the conceptual

18 model used to generate testable hypotheses. It also provides

19 decision making rationale for the regulatory community again,

20 understanding how much you know at different points along the

21 pathway.

22 This particular pathway also is an area focus

23 consistent with the legislative directive. Estrogenicity was

24 the initial legislative directive behind endocrine disruptors. 

25 Chemical binding to the ER is known to have potential to cause

26 adverse effects. Evidence existed that very many diverse
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1 chemical structures could bind to the ER. So it seemed like a

2 good logical place to start. 

3 It's important again in earlier discussion

4 that was had here already is to understand that yes indeed, it's

5 the ER binding -- it's this pathway we're talking about and it's

6 not other ways of causing disrupting the estrogen pathway that

7 what might work through a different molecular initiating event. 

8 As was discussed, that's important because the chemical

9 structural drivers, that chemical biological interaction, will

10 be different, although the downstream effects may be very

11 similar if you're initiating event is different than the

12 chemical drivers getting you to that initiating event will be

13 different. 

14 So the two levels that we're looking at, if we

15 talk about the in vitro assays or our training set, our focus

16 area is we're looking at receptor binding. We're also verifying

17 within these chemical structural groups that we're looking at. 

18 We're verifying that that binding can translate to a

19 tissue-level effect. In this case, it's altered vitellogenin

20 protein. But the focus area of our Expert System or the (Q)SAR

21 part of it is on that receptor binding itself. 

22 So we're working at two different levels along

23 the pathway, but it's really the initiating event that we're

24 trying to predict, what chemicals can potentially start that --

25 well can start the pathway, but potentially lead to other

26 effects. 
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1 Okay. So in applying the OECD Principles, the

2 desired outcomes are really transparency and usefulness. I will

3 walk through these and what this means in the context of how

4 we're using it. 

5 So first, can the (Q)SAR estimates be

6 explained mechanistically. The (Q)SAR, the OECD principles for

7 (Q)SAR validation include a defined endpoint. Well, we're

8 talking about in this context, a well-defined endpoint or what

9 we called in the last review we called the bioassay domain, so

10 that you start to understand the endpoint both from the context

11 of the biology but also the chemistry and the interaction. So

12 it informs an important risk assessment endpoint, the AOP ending

13 in impaired reproduction as we talked about, plausible linkage

14 of that initiating event to higher level, adversity,

15 interpreting the measurements, understanding what it is in those

16 assays that you measure and confounding factors to those assays.

17

18 On the chemistry side, using in vitro assays

19 that allow testing of the types of chemicals found on that

20 regulatory inventories, the range of the chemical properties

21 then that are imparted by those chemicals. Again as we'll talk

22 about, these are really the very low affinity chemicals. 

23 Understanding the chemical form and the concentration in the

24 assays, we also had talked about and had additional review on

25 how important that was. 

26 Mechanistic interpretation, being able to
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1 explain the predictions mechanistically again with both respect

2 to the chemistry and the biology, the relationship of the

3 predicted parameters to a regulatory question, the linkage

4 through the AOP, the relationship of chemical parameters to

5 biological activity, are they reasonable, do they make sense to

6 similar chemicals.

7 The model applicability domain -- so that is

8 the training set measurements that are used to then build the

9 logic-based rule Expert System is the model. So how well does

10 that cover the regulatory domain, which is the EPA question, the

11 chemicals on the list of the regulatory domain -- so

12 understanding that. That Expert System again is decision tree,

13 it's logic, rules. The local in vitro training sets are what

14 these rules are based upon. The Expert System model domain

15 should adequately cover the regulatory domain. Again at that

16 time, it was the food use inerts and antimicrobials. We

17 completely covered that domain when we built the first system. 

18 Appropriate measures of goodness of fit,

19 robustness, ability to predict -- those measures that are

20 appropriate for regression models are not appropriate when

21 you're looking at the Expert System. When you're looking at

22 logic rules, when you're looking at these -- in fact the

23 chemical categories and then the sub-models -- and as was talked

24 about in the 2009 review, the Expert System is actually a series

25 of different smaller sub-models.

26 And an unambiguous algorithm -- there again,
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1 the system is logic tree, it's rules, it's chemical queries and

2 supporting information behind that. 

3 So another one of the outcomes -- how

4 reasonable a question you can ask, how reasonable is the

5 estimate compared with data for similar chemicals? So let's

6 talk a little bit about chemical similarity when we're doing

7 chemical similarity in the context of effects-based chemical

8 categories. So building effects-based categories -- structural

9 similarity is in the context of the biological activity and in

10 this case, that molecular initiating event, so chemical binding

11 to the estrogen receptor. 

12 But actually it turns out that there are

13 multiple ER-interaction types. You can interact at different

14 points within the estrogen receptor and chemicals interacting at

15 different points have different properties. The chemicals that

16 initiate the molecular initiating event at the same point within

17 the receptor are similar. That's what our similarity is. It's

18 getting the same type of effect. Then you group those chemicals

19 and then you start trying to understand what the properties are.

20 Chemicals sharing similar structural features are grouped if

21 they impart the same biological activity.

22 So the figure here is the inside of the

23 estrogen receptor with estradiol located in there, which

24 interacts at two points within the receptor. But the chemicals

25 that we're talking about and that we built the original Expert

26 System for do not interact. They are not the steroid type
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1 chemicals. They interact -- if they interact at all -- they

2 interact at one point within the receptor. 

3 So for instance, here we have alkylphenols

4 that interact at this point and the charge on the oxygen is

5 similar to how estradiol interacts at that point. You can also

6 look at our lead series as we built this system, which was the

7 alkylanilines with a different charge. So you have hydrogen

8 bond donating and accepting type behavior at these different

9 points, but it imparts different types of activity. So that was

10 the basis of identifying our chemical similarity.

11 We're also -- okay. Let's talk about a little

12 bit of the desired outcome is usefulness of your predictions. 

13 Are they applicable to all the chemicals of regulatory concern? 

14 So in the initial system, prioritizing chemicals with the food

15 use and antimicrobial inventories again were covered. There

16 were no steroidal type chemicals on it. So the interactions

17 that we saw were very low-affinity interactions. 

18 So to look at those kind of chemicals, we had

19 to take the in vitro assays that we used to build the training

20 set and optimize them to detect any indication of binding and

21 then verified with gene activation in a metabolically competent

22 tissue slice to increase the confidence in the non-binders. So

23 to do this, we test the chemical solubility within that assay or

24 if it's a cell-based assay, you look at cytotoxicity if that

25 happens first. Otherwise, you keep testing the chemical to the

26 solubility within the assay. A lot additional information is
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1 used in the data interpretation and that will be covered more in

2 section seven also. 

3 So this is just to go through what the assays

4 are, the estrogen receptor binding displacement assay. 

5 Estradiol was run with every chemical. Negative chemicals look

6 like this and again, they stop at different points because they

7 have different solubilities. They have different properties. 

8 Again, the positive control and what test chemicals look like. 

9 You can get full displacement curves for a very soluble chemical

10 up to very high concentrations. Then again, you go into the

11 tissue slice assay and you verify that that actually can

12 translate to gene activation. In this case, it is vitellogenin

13 induction using a QPCR technique, again the positive control,

14 estradiol is always used, example of some negative chemicals on

15 the right side, estradiol and some positive chemicals that are

16 giving induction. 

17 I also talked about the importance of

18 understanding something about the chemistry. We look at the

19 chemical identity and concentration within and try to understand

20 different types of chemical within the binding assay. These are

21 zero and 20 hours at the beginning and end of the incubation. 

22 These are stock solutions. These chemicals across the dose

23 response range are what we would call well behaved. In the

24 series B, you have an initial loss. You've got binding to the

25 glassware and stuff, but you still have a well behaved. The

26 loss happens right away and then it continues throughout the
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1 assay.

2 When we see these kind of relationships where

3 you get a drop from the initial concentration, we've actually

4 gone back and now we know that this or the one on the right is

5 even more exaggerated. It's an indiction that there's

6 metabolism occurring, that there's a transformation of that

7 initial parent compound because these here are showing the

8 parent compound. So again, having some idea of what's happening

9 across the broad range of chemicals with different chemical

10 properties so that you can help to interpret what you see in the

11 assays. 

12 Another important thing is the assay total of

13 protein affects chemical bioavailability. So an ER binding

14 assay is not an ER binding assay. You have to look at the

15 components within the assay and how the assay is run. But the

16 total chemical concentration, for instance this was some work at

17 Utrecht University by a student of Joop Hermens, and they looked

18 at different increasing chemical -- or total protein

19 concentrations in the assay. They also looked at what was the

20 free fraction. In each case, they're showing you to get the

21 response, you have to increase the applied concentration because

22 you've got so much nonspecific binding, but in each case, where

23 you're getting the effect, the free fraction is the same.

24 So that will be discussed more as we talk

25 about different assays and comparing across assays and comparing

26 total protein concentrations. This is just another example of
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1 that. Again, the binding assay we've used mostly to build the

2 Expert System is a cytosolic preparation of trout receptors with

3 a total protein concentration of 4 mg/mL. We also have a

4 recombinant rtER alpha and in that system, you only have 0.01

5 total protein. 

6 So this curve right here, it's the same

7 chemical and that's for the recombinant receptor. Everything is

8 shifted to the left because the free fraction is the same in

9 both of these, but you need to apply a lot more. When you've

10 got the total protein concentration, you've got a lot of

11 nonspecific binding. So you increase the chemical concentration

12 and the free fraction is the same on both of these. So when

13 comparing assays you have to have some idea of what you're

14 comparing. What's within the assay so that you're comparisons

15 are apples to apples if you will.

16 Another thing that we talked about in the

17 other review was using an effects based chemical category and

18 the read across approach allows you to extrapolate from a

19 smaller number of training set compounds to a broader inventory.

20 What we had determined for the chemicals that we were testing

21 is that for the ones that were binding, we found a relationship

22 with LogKow, relative binding affinity LogKow. Again, we talk

23 about those two different interaction sites and we got a little

24 bit different -- and this line is just drawn in there. It's not

25 a fitted line. But what we see and across all of these is that

26 we get increased to a certain level and then it levels off, but
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1 there is a relationship with LogP. 

2 So if you test at the higher end and you test

3 maybe one or two chemicals in the middle and at the lower end,

4 you can extrapolate to the ones in between. That's what having

5 a category-based approach allows you do that. We also found

6 that there were a lot of chemicals. We didn't find anything to

7 bind below this LogP cutoff. That's also a part that was

8 incorporated into the Expert System rules. 

9 Okay. And then finally, does the model and

10 the Expert System answer your regulatory question? Well we did

11 cover -- with that approach we covered all of the inventory of

12 the food use and antimicrobials and about five percent of those

13 chemical were prioritized to be looked at first.

14 We also talked about that we looked at some

15 interspecies comparisons. This is a plot of rat uterine cytosol

16 binding and trout cytosol binding for 55 very diverse chemicals.

17 You can see that there's a good correlation. The reason we

18 looked at those two together was their similar chemical

19 bioavailability in both of those systems. Again, just reminding

20 you here with recombinant receptors versus cytosolic receptors

21 just within the trout, I'm going to show you here what we also

22 looked at for a series of alkylphenols, we looked at recombinant

23 trout and recombinant human. This top line is their basically

24 on top of each other, the human and trout. So the species

25 wasn't making the difference. It was the cytosolic protein

26 versus the recombinant receptor and the amount of protein there.
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1 So again, the species correlation seemed very

2 strong with the alkylphenols. We did the same with our other

3 lead series, which was the alkylanilines. Again, the

4 recombinant receptor systems are the top line and the cytosolic

5 are the bottom line. 

6 So we also talked in that review, we talked

7 about how can we expand to additional inventories, other data

8 types and additional endpoints. We also talked some about early

9 attempts to think about automating that Expert System. So I'm

10 going to turn it over now to Rick, and he is going to talk about

11 automating the Expert System and moving on to then building

12 Expert System version two.

13 MR. RICK KOLANCZYK: Okay. Thank you. I

14 thought we'd start out with the conceptual slide. This is the

15 decision tree as we left off from the 2009 SAP review. Roman

16 numerals one through seven, I believe, on here the idea of seven

17 models within a model. Maybe just want to point out that we

18 were looking at the issue of charge. Ray went into some

19 description in his presentation a bit earlier. Also the A type

20 and the B type groups that Pat had mentioned, this idea of the

21 LogKow cutoff, and then also a special rules group. We'll be

22 touching on that a little bit later in some of the

23 presentations. 

24 So moving from a drawn-up conceptual diagram

25 to an actual piece of automated stand-alone software. We

26 achieved this by a collaborative effort between EPA, OECD and
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1 LMC, the Laboratory of Mathematical Chemistry, headed up by

2 Ovanes Mekenyan. I just want to maybe point out that this

3 Expert System version one is available to the public through the

4 most recent version of the OECD Toolbox. So you can get at the

5 model there yourselves. 

6 I just want to maybe mention that this -- and

7 Pat had led into this, that it's a rules-based logic decision

8 tree. So you're moving through a series of nodes starting at

9 this point and going through a series of yes/no questions to be

10 asked at each of the nodes. I just want to point out that the

11 goal of this system was to be transparent to the users. What

12 you see is what you get, and in addition to that to be a fully

13 expandible system. So when you encounter new groups or new

14 nodes, you can easily accommodate those and put those into the

15 various branches of the model.

16 What I've got is another screen shot of how

17 you're actually going to use the system. This happens to be a

18 means of loading up a single chemical. You would profile this

19 starting out at our start point where the boxes turn green is an

20 affirmative answer or a yes answer to that particular question

21 being asked. Red boxes denote no. You will continue through

22 the tree until you reach a decision. 

23 Listed over here below the target structure is

24 the decision for that. This happens to be unknown binding

25 potential. So it resides outside of the domain of the model. 

26 You might equally see greater than or a less than an RBA cutoff.
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1 We chose that since this is a prioritization model to simply

2 state that we did see binding higher than the lowest potential. 

3 We ran through -- in the training set, we've got a lower

4 threshold, the lowest threshold for a binding affinity. 

5 This slide represents the ability to

6 incorporate an inventory list so you can load up an entire

7 inventory, run those through in a batch mode, if you will. This

8 happens to be a single structure that you are highlighting at

9 the time and the predicted potential for that. If we were in

10 the view for single mode as you went through these, you would

11 see -- as we saw on the previous slide with the green and the

12 red boxes, taking you through the tree as to where it would

13 parse out and eventually settle in. This happens to be the show

14 for the entire inventory. This entire inventory contained 211,

15 I believe somewhere in that neighborhood, structures and you'll

16 notice that there are numbers in between each of the purple

17 nodes, and that is kind of to give you a rough idea of which

18 branch and which direction your chemicals might be parsed into

19 as far as coverage. 

20 This is the meat and potatoes behind this. 

21 This is the boundaries as described. So search criteria in the

22 form of structural fragment, in this case for an alkylaniline

23 might also include exclusionaries in here, masks to exclude

24 particular fragments. In here, you can also include things such

25 as LogKow cutoff and just want to maybe state here that this

26 really ties in the structural component for each of the groups
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1 that we're talking about as well as the binding results from the

2 rainbow trout assay.

3 Also within each of the nodes, and this one

4 happens to be for a group of salicylates, target structure here,

5 LogKow, CAS number, the training set that went behind the logic

6 that was introduced here, and just with the structures. So you

7 can see where potentially your chemical might fall into line

8 with what has been tested. 

9 Also, this is another view of that training

10 set. It happens to be for a different node. These anilines in

11 a list fashion rather than a grid still contains all of the

12 information. What I really want to point out down below is that

13 we've included notes on interpretation here. This set happened

14 to be so that aniline itself was a negative for binding, but the

15 methylaniline and subsequent structures higher up on that group

16 were positive. What if you happen to have another structure

17 that was maybe beyond this, but we hadn't tested up to that

18 particular LogKow range. You would still have the basis of

19 information here so that you could reach your own assumption

20 based on these notes on interpretation. 

21 So that took us through actually Expert System

22 version one, an automated stand alone piece of software. Next,

23 I had mentioned about the transparency and the expandability. 

24 We moved into building an Expert System version two, taking on a

25 new inventory of nonfood use inerts. 

26 So Expert System one as it was stated before
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1 based on food use inerts, antimicrobials, moved into a set of

2 nonfood use, 1500 or so, actually discrete structures that when

3 into this. Maybe run through the steps that were used to

4 develop Expert System version one were then just expanded to

5 version two. So we acquired the specific inventory, the nonfood

6 use inerts. Next in line was to characterize those structures. 

7 Now I mentioned about using discrete structures. So we went

8 through the SOP that was included in the 2009 SAP. We stayed

9 away from mixtures at this point, polymers and nondiscrete

10 substances. We checked on verification of the structures as

11 well as CAS numbers to make sure we were all set to go at that

12 point. 

13 Next stage here was to actually apply the 1500

14 or chemicals to the Expert System version one. What we wanted

15 to do was to focus on those chemicals for which we had an

16 unknown binding potential. Those which resided outside of the

17 domain of the model since we were interested in expanding said

18 model. Now obviously we're going to encounter chemicals that

19 were found in known classes, but maybe they were just outside of

20 the LogKow range. So that was one angle that we took on. 

21 Secondary to this, I think I want to maybe

22 take a step back here and talk a little bit about the model. As

23 you filter through and very easy to come up with nice discrete

24 classes of chemicals, but what happens when you run into a

25 structure that has multi-functionalities associated with it. So

26 in other words if you have a phenol, but it also has a halogen
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1 or a carboxylic acid or a sulfonic acid, it gets very

2 complicated which discrete group does it fall into. Until we

3 can formulate a new group to capture those chemicals, you'll

4 fall into two categories that we're called mixed phenols and

5 mixed organics. We've separated these out out of importance for

6 the phenol functionality being the three position of the

7 estradiol, the endogenous ligand itself. So we've found a

8 number and the majority of those unknown binding potential

9 chemicals within those mixed groups.

10 Next was to strategically test those potential

11 new groups or new categories that may arise out of the mixed

12 phenols and the mixed organics, and then eventually expand upon

13 the model. 

14 So again, collect structures with multiple

15 functionalities, we went through that, may encounter specific

16 functionalities that will render a specific structure positive

17 or negative. Really, this might fall into play as if you have

18 one of those mixed groups that I was talking about and you might

19 happen to have -- and I'll throw in an example out there such as

20 sulfonic acid. The sulfonic acid at pH 7 or in this case at pH

21 76 of the assay pH would end up as a charged species. It

22 actually impairs binding for us. I just want to point out here

23 too that it's a series of generating questions, coming up with a

24 hypothesis and testing those hypotheses. 

25 I think the best way to illustrate this is to

26 take you through an example. So delved into a group of mixed
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1 phenols, I grabbed five said structures out of those mixed

2 phenols, and this whole idea of grouping chemicals with respect

3 to structural similarity. Now I wanted to look for the largest

4 commons structural fragment. In this case, common to all the

5 structures was this 2-hydroxybenzophenone. I think it would be

6 a mistake to look at it as a phenol or just a ketone or an

7 alkoxyphenol, but we're looking that largest fragment. 

8 Next was to go through some strategic testing.

9 This is the idea of formulating that question. So of interest

10 is the root structure. We've just applied an acronym here, HBO

11 for this. It actually was a non-inventory chemical, but does it

12 bind to the receptor? Pink here would illustrate an

13 affirmative, a positive.

14 Next question, what about additional hydroxyl

15 groups on the A-ring? That too was positive. I'll keep rifling

16 through these looking at about multiple hydroxylation on both

17 rings, and again a positive effect there. What if capped by a

18 4-methoxy group in place of the second phenol functionality,

19 that too, positive. 

20 Where it gets interesting is what about the

21 effect of a longer chain alkyloxy? We had negative result on

22 this. About all I can formulate is that when you run into a

23 longer chain like this, you get the back folding after about

24 four carbons or so. I think you might run into some steric

25 hindrance on the hydroxyl group may be responsible for binding. 

26 There is a feature of attenuation involved here. I will take
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1 you through just one more. What about the effect of a sulfonate

2 acid group. That too was negative for binding to the receptor. 

3

4 What you can do is come up with a conclusion

5 here that the 2-hydroxybenzophenones seem to be a positive

6 group. There are some exceptions here due to attenuation. In

7 the first case, there is the idea of the steric hindrance. 

8 Second was the idea of the charged species. 

9 Another example looking at phenones within the

10 rainbow trout training set. I've sorted these according to

11 LogKow, increasing left to right. When we apply the results of

12 the rainbow trout assay upon these, again green are negatives

13 and pink were positives for binding. The LogKow relationship

14 didn't seem to hold, but what you do notice here is maybe best

15 not to look at phenones as a single group. In other words split

16 these into those that are branched and those that have a

17 straight n-alkyl chain. 

18 So in expanding from version one to version

19 two posed some new groups. In the interest of time, we'll just

20 move through. Another example -- I gave you the one previous,

21 the 2-hydroxybenzophenones series, benzotriazole phenols and

22 down below, just to point out the inventory chemicals within the

23 series, these in the training set, we actually did test two of

24 these out of the group, one at the lowest Kow for the series,

25 this one up at 7.63, so near the top. We tried to get coverage

26 across these and a negative group for us. 
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1 Phenolsulfonphthaleins, again just to point

2 out maybe a limited representation within the inventory, but

3 what I want to maybe point out -- and you'll notice we end up

4 with some mixed results positive for this, negative. The

5 negative may be due to some attenuating factor. These large

6 bromine groups on either side of the hydroxyl groups imparting

7 some form of steric hindrance there as well. 

8 Also expanded, moving out of the mixed

9 organics from Expert System version one to two, again an example

10 here, amino anthracenediones. In this instance, we grabbed

11 three adequate LogKow coverage of the series, three out to

12 eight. So we want to make sure we test everything out to cover

13 the inventory. 

14 Phosphoric acid esters -- it's very interest

15 set in that it did not meet the criteria that Pat laid out about

16 the A-type binders and the B-type binders for the atomic charge

17 on what we perceived were what could be the responsible atom or

18 site for binding here. The charges on the oxygen and the

19 phosphorus just do not meet that. 

20 What I want to point out is I rolled in a

21 couple of new colors for you here. This tan, we could not find

22 this chemical. In other words, I could not procure that for

23 testing. So we invoke the use of a surrogate, this phos here. 

24 If you'll notice the other two members within the training set

25 that we tested were positive. This was a negative, and I think

26 what we're running into is that idea of a charged relationship. 
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1 So in other words, the PKa for this OH group unesterified is

2 another attenuating factor, which would render that negative

3 within the series.

4 We're a little short on LogKow coverage, so

5 the additional color I brought in here, yellow, is for future

6 testing to go after LogKow 8. If you'll notice, we've kind of

7 truncated the LogKow calculation using KOWWIN out to 8. I know

8 the program calculates things much higher. A LogKow of 17 for

9 some structure, in particular some of the pHs and the like. For

10 purpose, we've kind of cut off at 8 for realistic view of

11 things. 

12 Back to the decision tree and what I've added

13 in here are where those perspective new groups that I've

14 incorporated, where they might reside within the model. Any

15 additions to known groups that we have that we would just simply

16 extend the LogKow coverage to account for those. 

17 Now, what goes into forming a new group? And

18 I threw up a series of slides that when you keep trying to pull

19 out new group after group and what constitutes a group, and it's

20 really a play on words and something that I've assigned. I call

21 them analogs. It's kind of a precursor to that. Eventually,

22 you're going to run into that bottom of the pit, those mixed

23 organics and mixed phenols. In a perfect world you're not --

24 well it may be in a perfect world, but not in this world. You

25 can't eventually define every single group there is. So, you're

26 eventually going to run into the bottom dwellers, if you will,
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1 and the way that we've been handling the mixed phenols and the

2 mixed organics as a group within the tree is by exact match. In

3 other words, gut it out and test them.

4 But in the interim, what you try and do is the

5 best you can is to pull these analogs out. It could be the

6 precursors for eventual groups. So I guess I've furthered those

7 inventory chemicals from the 2009 nonfood inerts and attempted

8 to parse out maybe the next step here. 

9 This was a very important slide for me to look

10 at the aldehydes and ketones. In other words, these structures

11 are going to come back to us later only. Primarily, and I hate

12 to jump too far ahead because it will be the subject of another

13 presentation, but aldehydes and ketones are going to be found in

14 your fragrances for the EDSP Inventory of Chemicals. 

15 So you can see that I've got a good start here

16 on starting to pull things out for new groups. Eventually, you

17 run into something such as this slide where you've only got

18 really two representatives and pair of chemicals. So, now

19 you're down to is it really a true group, or are you just using

20 a surrogate here? It's a work in progress, an integrative

21 process, and hopefully continue to work on this. 

22 I think I'd like to maybe turn this back to

23 Pat. Were you going to start with this slide or the next? 

24 DR. PAT SCHMIEDER: I'm using this then to

25 wrap up what Rick was talking about again where the position of

26 those new groups that we know something about. 
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1 So now, okay. So this is Expert System

2 version, what we're calling version two. So let's look at the

3 coverage of that, of the EDSP Universe with that version of the

4 Expert System. In the White Paper, we went through both version

5 one and that coverage, but I'm just going to talk about version

6 two here. Again, as we think about this, what Rick talked about

7 in that inventory -- let me describe this newer inventory and

8 what we had worked on because that's important to understand

9 that there are some differences there. So I'm going to try to

10 talk to -- we talked about determining the coverage of version

11 two and then identifying the remaining unknowns. 

12 So the composition of this new universe is

13 it's not exactly the food use inerts that we built the system

14 on. This is a new list in 2012. The nonfood use that Rick just

15 talked about to expand it to Expert System version two, this is

16 a new list in 2012. So there is definitely a lot of overlap,

17 but there are some additional structures on it. 

18 What we hadn't tested at all or hadn't

19 specifically looked and tried to cover a list of fragrances used

20 in inert ingredients, and then the Safe Drinking Water Act

21 chemicals. There's the overlap with these lists up here, and so

22 we probably would cover those pretty well, but then there were

23 these new chemicals on the SDWA list that we did not

24 specifically try to build the model domain to cover.

25 So, let's look at what's in some of these

26 inventories. So we're talking about again not -- these are the
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1 discrete chemicals, so they're not the mixtures, they're not

2 undefined. They're not polymers. If you look at, if you want

3 to take from the right side, here is the more pure class, if you

4 will. So a chemical that is only used a food use inert and it's

5 not also on the SDWA list, it's not nonfood, it's not on the

6 fragrance list, would that be these 239 here. Four more here

7 are Food use and also fragrances. This was just a way to build,

8 to parse out the inventory so you get some idea of that there's

9 a lot of overlaps in these groups.

10 So overall, the inerts were about 2600. The

11 chemicals that were not also inerts that were on the SDWA list

12 but not also inerts are down here just to give you an idea of

13 what the makeup is. So 43 percent of this inventory are those

14 inerts from those classes, including the SDWA overlaps, and 57

15 percent were only on that Safe Drinking Water Act list. 

16 If we look at the predictions then, as I think

17 Mary said earlier, 71 percent coverage from Expert System two of

18 this list of around 6000 chemicals. The first version was 68

19 percent coverage. 

20 Similar coverage, whether it was inerts or the

21 Safe Drinking Water Act chemicals actually, 71 percent, 72

22 percent here, again 72 for the total group, all the inerts group

23 together. So very similar. If we parse those out by use

24 classes, 97 percent of the food use inert alone, those kind of

25 chemicals, were covered, which is not surprising because it

26 tells you, I would guess, a couple things that we built the
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1 initial system to cover those and that the list probably didn't

2 change that much from when we built the initial system to 2012.

3 We got 86 percent coverage of the nonfood use

4 inerts with expanding to Expert System version two. We didn't

5 cover the fragrances as well. Again, we did not have this

6 inventory to specifically target, but overall, we've got 72

7 percent coverage of all inerts, 71 percent coverage of the

8 Drinking Water Act chemicals that are not also inerts. So that

9 was pretty good, even though we didn't specifically necessarily

10 target those. So overall, 71 percent coverage. 

11 The largest increase from version one to

12 version two were for nonfood use inerts as expected because

13 that's what we were building version two to cover. There're

14 modest increases in coverage for the other groups, but the

15 initial coverage was pretty good. If you just look at then --

16 and that was the total coverage, but if you look at which

17 chemicals are in the model domain and where you can make

18 predictions for whether they bind or don't bind, overall they're

19 four percent predicted to be binders. 

20 But if you look within the groups, one percent

21 of the food use inerts, four percent of the nonfood, and this is

22 interesting, the 10 percent that we saw in the fragrances. 

23 Again, Rick talked about some of the groups of concern there,

24 the aldehydes and ketones that need more looking at because

25 we're getting a higher percentage of predicted binders. Within

26 the Drinking Water Act chemicals, only three to four percent.
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1 Again, the new groups that Rick talked about,

2 building on from version one to two, I just highlighted some of

3 these that were actually containing some of the binders here,

4 but these were the overall groups that we had added. 

5 So now let's talk about the ones that are not

6 in the model domain, the unknown binding potential chemicals. 

7 There are 1704 within the Expert System version two groups, and

8 they're distributed across the system, as you can see here,

9 although number wise, the vast majority are mixed organics and

10 mixed phenols, not surprisingly. We'll come back to that and

11 talk more about those specifics of the unknown binding potential

12 chemicals in section eight.

13 So I'm going to move on to talking then, okay

14 so if you apply that inventory and that assessment was before

15 any application of any physico-chem filters, so how does that

16 affect the system. Well one of the things that we did was we

17 applied the Expert System to predict the chemicals that were

18 filtered out. As Ray emphasized, the physico-chem filters are

19 subject to further examination. The expert chemistry group is

20 going to look at this, but we thought this was a good exercise

21 to get some idea of what types of chemicals were being filtered

22 out. I think as Mary talked about before, 92 percent of those

23 filtered out chemicals were predicted to not have any binding

24 affinity. 

25 There's another group of those that were

26 unknown binding potential. To me, this helps you target where
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1 to look. Especially if you look at ones that we did predict to

2 have some binding potential and another group of unknowns, they

3 were the perfluorinated chemicals. As a chemist, they look at

4 these and say, oh yeah, perflourinated chemicals have very

5 unique chemistry. It makes sense to not just rotely filter them

6 out, but to take a lot harder look at those kind of chemicals,

7 but there is a reason to because they have such unique

8 chemistries.

9 Actually this alkylphenol here -- it's

10 interesting that it's cresol and -- there's ortho-, meta- and

11 para-cresol individually, and then a mixture of all of them. 

12 Two of them had been filtered out and two hadn't. It had to do,

13 I believe with, having an automated filter that looked at

14 corrosivity being indentified on an MSDS sheet, and for some

15 reason, two of them were filtered out and two others weren't. 

16 We tested them. So the fact is, they are testable in these kind

17 of systems and we did -- or we at least, I don't know which

18 form. One of the forms we did test.

19 So again, using this as a way to raise

20 hypotheses to say these are the ones that we need to look at

21 more closely, and these are the ones down below here that we're

22 pretty confident that we don't have to look at, perhaps, really

23 closely. Again, or at least we're pointing you where to look. 

24 This is a list of those chemical groups where those nonbinders

25 were predicted. It's a wide range of chemical categories, and

26 you'll note that many chemicals in the same class were not
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1 excluded by the filters. 

2 So if you look at then what happened to the

3 inventory -- it went from 5975 to about 5000. The inerts

4 decreased to 2080. The SDWA only decreased here and you can see

5 the whole breakout there obviously. 

6 So since about 950 chemicals with predictions

7 were removed by the filter, the apparent coverage decreases, the

8 number of binders remain the same because you're filtering out

9 all those nonbinders and a few of the unknowns. So the

10 percentages change and it looks like you're not covering as

11 much. It's just a matter of what you've left in and what you've

12 taken out, if you will.

13 The predictions then, again now it's

14 percentage wise is two percent. The same number -- the same

15 chemicals are predicted to bind here as they were before you

16 filtered the inventory, which tells you you're not filtering out

17 a lot of binders. Overall results using the inventory, there

18 were 165 chemicals overall that were prioritized.

19 So in summary, the ER Expert System

20 development is an effects-based chemical categories approach. 

21 It's designed around the ER-mediated Adverse Outcome Pathway. 

22 It incorporates the (Q)SAR principles. It can be expanded to

23 cover additional inventories, as we've demonstrated here. The

24 results are before the P-chem filtering. The version two

25 covered 71 percent of this larger list, 165 predicted to bind,

26 1704 unknown, and then after filtering. The only thing that
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1 changes is the number of the percentage coverage because you've

2 taken out a lot of nonbinders and a the number of unknowns, but

3 again, it's the same number of chemicals predicted to bind.

4 This is just the road map again to tell you

5 where we're going. It's actually the next talks after lunch. 

6 Hopefully, we'll address the study to talk about the high

7 through put data and how you could use this to expand, and then

8 we'll actually kind of come back to number four -- well in

9 section eight, we'll talk about where the high through put

10 covers the unknowns and also how you could use that data

11 strategically. 

12 DR. DANIEL SCHLENK: Thank you. Any questions

13 or clarification from the Panel? Dr. Schultz? 

14 DR. TERRY SCHULTZ: I want to thank you two

15 for an excellent presentation having been involved in this in

16 the 2009. I've seen where you've gone. I have a number of

17 questions I would like to ask. First of all, in 2009 you said

18 one of the first rules you're using for exclusion was whether it

19 was a cyclic or a noncyclic chemical. After having two more

20 years of experience with this, do you still feel comfortable

21 with that as your first triangle square, whatever it is in your

22 Expert System? 

23 MR. RICK KOLANCZYK: Kind of stealing a little

24 bit of the thunder on this afternoon's presentation, but

25 following Panel recommendations from 2009, we were directed to

26 look at P-phos and P-fluor, again those perfluorinated
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1 compounds. So in fact, we did those so quickly that we

2 incorporated those into Expert System version one. I still

3 believe it. At that time, I should say that the Panel was in

4 agreement for the inventories of concern. Those speaking of the

5 food use inerts and the antimicrobials, our coverage was

6 adequate. Our model was correct in saying that the acyclics

7 could be ignored so to speak. I think later this afternoon, I'm

8 going to maybe illustrate a couple more examples of some more

9 cautionary steps that I'd want to look at further. 

10 As far as where it belongs in the actual tree

11 itself, I think we could leave it right up front as long as we

12 include nodes to account for binders. So in other words, I'm

13 not using it to say that all acyclics are negative in that

14 portion of the tree. There obviously is the box, as we

15 accounted for as I mentioned before for P-phos and P-fluor. It

16 will give you a positive. So if you ran it through the system,

17 you went off on your own, pulled up that version of the tree in

18 the OECD toolbox, plugged that chemical in, ran it, you would

19 get an affirmative answer for binding. Anything else you want

20 to expand on, Pat? 

21 DR. DANIEL SCHLENK: That was Rick Kolanczyk. 

22

23 DR. TERRY SCHULTZ: Another question. You had

24 mixed phenols, you had mixed organics, the logical question is

25 why not mixed anilins? 

26 MR. RICK KOLANCZYK: Just lack of grouping. I
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1 guess I put higher precedents on the mixed organics themselves

2 -- I'm sorry the mixed phenols themselves because of the

3 3-hydroxyl group on the estradiol itself. There appears to be a

4 little higher affinity, I believe, on one of Pat's charts that

5 showed that there's a little bit higher affinity for the phenols

6 over the anilin series. As far as B-type receptors, you're

7 going to see other functionalities other than just anilin

8 itself. I'm think of a couple -- phthalates would fall under

9 that area. Cyclohexanol, there're just more examples, and then

10 you run into the issue well now you've got mixed phthalates or

11 mixed cyclohexanol groups. I guess just for a simpler way of

12 grouping, we kept it that way.

13 DR. DANIEL SCHLENK: Again, that was Rick

14 Kolanczyk. 

15 DR. TERRY SCHULTZ: Lastly, could you

16 comment on the testing of the Expert System? As you know when

17 you start to take extended fragment rules and start to program

18 them up into a software, sometimes there is a translational

19 problem between the toxicological test and the delta squiggle

20 squiggle that is put into the code. I noticed in here that this

21 is a pretty sophisticated program software.

22 MR. RICK KOLANCZYK: Again, this is Rick

23 Kolanczyk. I get the thumbs up. Just to elaborate on that, the

24 initial coding of this, we relied quite heavily on the

25 programmers, our collaborators in Burgas, Bulgaria LMC. I felt

26 confident enough that in going from Expert System version one to
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1 two, I wrote those changes myself into the program. So you can

2 learn with time, I guess. But you're right, talking about

3 introducing masks, it can get a little more complicated the more

4 you learn, but by the same token, it's all there. All of the

5 boundaries are defined within the system. Do you want to add

6 anything? 

7 DR. PAT SCHMIEDER: This is Pat Schmieder. We

8 also did an exercise that relates to this, where we took Expert

9 System version one and we took additional data that we developed

10 that we did the additional test results that we're going to use

11 to build version two and ran it through version one to see how

12 it did. Now you can -- that kind of analysis approach is often

13 used and you can get numbers on it, but I think the most

14 important -- there are a couple of things. Not to sidetrack,

15 because I will get back to your question. 

16 One of the things we found is that most of the

17 chemicals were unknown binding potential, which is why we were

18 testing them. Just to do that analysis rotely, I don't know

19 that that's the best to evaluate this type of model, this

20 decision tree model. But one thing, the only chemical that was

21 not correctly predicted out of, I think the 46, around 50 that

22 were in the domain of the model, was one in which the mask --

23 and Rick can describe this better. The chemist should be

24 talking about this. It had two hydroxyl groups on it. One of

25 the hydroxyl groups was 26 substituted around it, but the second

26 one, there was just a hydrogen. It was substeal on one side and
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1 not the other side, and that showed us that we had to write that

2 specific query, and then it was captured well. 

3 DR. DANIEL SCHLENK: This is Dan Schlenk. I

4 have a question just for a followup on that. Your model seems

5 to be all in two dimensions. I'm curious, is there any interest

6 in terms of developing any stereoisomer components in this in

7 terms of three dimensions? Because we have some ____ centers on

8 a lot of the molecules, and I wonder if that could actually, is

9 that part of the next step, I guess, or has that even been

10 considered? 

11 MR. RICK KOLANCZYK: That's a very good

12 question. In fact, we have looked at some isomers in our

13 training set. I think -- I'm trying to think of the situation,

14 stereoisomer. Still beam, trans and cis -- you get completely

15 different activity, but we have accounted for that. It's

16 currently a group, and again, kind of stealing a little bit of

17 thunder from this afternoon's presentation, I've got a series of

18 slides. Could I defer that until later and then we could talk

19 through that? 

20 DR. DANIEL SCHLENK: Yes, definitely.

21 MR. RICK KOLANCZYK: Okay. I think as far as

22 looking R versus S type stereochemistry differences, while we've

23 looked at the fipronil isomers and we haven't seen any

24 differences between -- of course they were both negative. I

25 just want to state that maybe the software has the capability of

26 incorporating that into very sophisticated smiles so that we are
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1 prepared to handle that situation when it does arise. I think

2 it's going to fall into play later on when we get into those

3 multi-cyclic hydrocarbons. We'll get to it later.

4 DR. DANIEL SCHLENK: Great. Thanks. Any

5 other questions? Yes, Dr. Keiser? 

6 DR. MICHAEL KEISER: This is Michael Keiser. 

7 So I have three quick, just sort of brief technical questions. 

8 One of them is going back a little bit. What would you say was

9 the key rationale motivating a decision tree based approach

10 compared to some of the other (Q)SAR or three-dimensional

11 approaches out there? 

12 DR. PAT SCHMIEDER: This is Pat Schmieder. We

13 wanted the system to actually be as transparent as we could get

14 it. So while there are a lot of other approaches that gather in

15 large numbers of chemicals as we're going and testing. As we

16 encounter the inventory that we had to address, these are not

17 the typical kind of chemicals that weren't really potent

18 chemicals. So as we're going, we determine we had actually,

19 early on, been using some broader-based, more typical (Q)SAR

20 approaches, but determine that we were seeing activities within

21 these chemical categories and the chemical groups within them

22 that seemed to make logical sense. By using the decision tree,

23 we could incorporate a lot of the other information around the

24 assays. Does that make sense? 

25 DR. MICHAEL KEISER: It does thank you. My

26 second question was in terms of molecular similarity that has a
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1 couple of different definitions out there. It seems to me that

2 the way you guys were approaching it is in terms at looking at

3 common substructures again as a way that would make sense from a

4 minicell chemistry perspective and for transparency, I'm

5 imagining, but I was curious too if that is indeed the key way,

6 you're grouping the different chemicals into the decision tree

7 nodes or if you've also looked at things such as molecular

8 descriptors, fingerprints, ways like that.

9 DR. PAT SCHMIEDER: Yes, at this point, I

10 think it is a better way of describing it that we're basically

11 using substructures, but we're also letting the biology tell us

12 if that's right. Our initial hypothesis is around the classical

13 chemical classes and the substructures at letting the biology

14 help us. The molecular descriptor that we found to be most

15 important here was within the smaller groups, there was a

16 relationship with LogKow. So we did not try to look for a lot

17 of additional descriptors because for what we were doing, we

18 could answer our question in a way that we thought was pretty

19 transparent without trying to pull in additional descriptors. 

20 DR. MICHAEL KEISER: Thank you. That answers

21 that question. And then the third one and final question that I

22 had was looking in terms of as you begin to acquire more

23 information for any particular decision tree nodes, some of the

24 examples you've shown where you have things that deem for the

25 relative binding affinity above or below the cutoff. Have you

26 looking into ways to assigning confidence or other predictive
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1 numerical scores to the particular nodes that you result in the

2 decision tree? I'm just curious what your path forward is to

3 take the empirical data as you acquire it and consider weighting

4 particular nodes in the tree once you've reached them? 

5 DR. PAT SCHMIEDER: This is Pat Schmieder. I

6 guess I would ask for a little clarification or advice from the

7 panel however it's formulated to say to advise on what we would

8 gain by weighting specific nodes. At this point, a chemical

9 comes in and it's handled in a series of yes/no questions within

10 the tree and we get a prediction on whether the chemical is

11 positive or negative. I'm not certain how weighting a node

12 would help that frankly.

13 DR. MICHAEL KEISER: So I get the impression

14 that perhaps this might be for later discussion in detail to

15 clarify my question only. I was asking whether assigned any

16 different confidences to a yes/no prediction dependent on which

17 node that prediction comes from. 

18 DR. PAT SCHMIEDER: No we don't. The chemical

19 goes within the chemical class and the group we look at the

20 training set of chemicals within that group and you look at the

21 chemical target. We don't assign any weight to that other than

22 trying to say is it in this group or is it outside, and is it

23 within the LogKow range or outside for the activity, and if it

24 does not fit within this group, it is given an unknown and

25 predicted as being unknown. But we are not weighting any of the

26 particular nodes within the tree.
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1 DR. MICHAEL KEISER: Thank you. 

2 DR. DANIEL SCHLENK: Other questions? Okay. 

3 Let's go ahead and break for lunch. We're right on time, so

4 let's be back at 1 p.m. Thank you. 

5 (WHEREUPON lunch was taken)

6 DR. DANIEL SCHLENK: Okay. Our next

7 presentation is by Dr. Judson. He's from the National Center

8 for Computational Toxicology and ORD. 

9 DR. RICHARD JUDSON: Okay. Great. Thank you

10 very much. So, you've seen this slide before. What I'm going

11 to be speaking to is the high through put assay data. What our

12 group was asked to do was not to tell you everything we know,

13 but to really focus on the beginning steps of using the high

14 through put screening data, which is to say, okay, how do we

15 gather the data, how do we analyze it, how do we think about

16 doing quality control on it, and very little on what we're

17 actually going to do with the data. We're going to take baby

18 steps here with this group. 

19 However, given that, there is some context

20 that I'll have two or three slides of context. So why did we

21 generate the big data set that we're presenting to you here? So

22 in my time here, I'm going to talk a little bit about, like in

23 the background, for this assay battery we're developing, I'll

24 very briefly go through the eight assays that we're presenting

25 to you, which is a subset of the total set of assays, talk about

26 how we selected the chemicals we've tested, how we process
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1 those, a reasonable amount about our data analysis and

2 interpretation methodology. None of this data is complete. We

3 got another 100 chemicals worth of data yesterday. 

4 Okay. So the data processing is still

5 preliminary and we're actually looking for feedback on how we're

6 doing that. This is a good time to actually be in front of this

7 group. And then finally, talk a little bit about the kinds of

8 analyses on assay performance we're using, developing. 

9 Okay. So the goal of the high through put ER

10 data, estrogen receptor data, we're using and this is really --

11 the estrogen receptor work we're going to talk about here is

12 kind of a model of how we're looking at pathways in general. So

13 we have equivalent data for the androgen receptor for

14 steroidogenesis for the P-par pathways and so on. So with ER,

15 so much is known about that pathway that this is good starting

16 point for understanding high through put screening and data in

17 this context -- high through put data in this context, I'm not

18 allowed to say screening in this context, so sorry. 

19 Okay. ORD is evaluating a very large battery

20 of endocrine-related assays. So we have 21 estrogen receptor

21 assays. We have five or 10 androgen receptor, we have a number

22 of in the steroidogenesis and so on. 

23 The assay data is coming out of two programs

24 that many of you've hear about, but some of you may have not,

25 called the ToxCast and the Tox21 program. So ToxCast is an

26 EPA-only program. It's been going for about six years. The
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1 goal was to bring in high through put assays and think about how

2 they could be used in toxicity testing for prioritization, one

3 day maybe for replacement, but it's a big research program where

4 we are looking of hundreds of assays. We're up to about 900

5 assays total and we're up to about 1800 chemicals that we've

6 tested in at least some of those assays. The ER data we'll be

7 talking about today is a very small but very important piece of

8 the ToxCast program.

9 Tox21 is a cross-Agency program, which has the

10 same goals as ToxCast, but we have collaborators at the National

11 Toxicology Program, the NIH Chemical Genomic Center and the FDA.

12 Some of the assays I'll be talking about today are coming out

13 of the NIH NCGC Lab. 

14 Okay. So if we focus down on the estrogen

15 receptor assays, we're going to talk specifically about just a

16 subset of eight. We're not going to talk about the whole

17 battery. I'll mention what those other assays are, but these

18 are again being used to illustrate the process for gathering the

19 data, analyzing it, doing quality control and so on. These

20 assays were all run at either contract labs, and I'll mention

21 the names of the labs as we go through, or the NIH Chemical

22 Genomic Center up in Rockville. They're a subset of the 900

23 ToxCast assays. This little graphic is just a good way to help

24 understand what the chemical space. It is even confusing for us

25 sometimes. 

26 So ToxCast went in two phases, phase one and
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1 phase two. Phase two, we're just finishing up and there are

2 some publications coming out of that. That will bring us to

3 about 1000 chemicals across the whole set of assays. We ran

4 another 800 assays in the endocrine-related subset, and in the

5 8200 chemical library, which is what's being run at the NCGC is

6 covering about 15 to 20 assays per year. We've done estrogen,

7 androgen, some steroidogenesis to get started. 

8 Okay. So the high through put assay overview,

9 I'm going to again talk about just this eight assays. All of

10 the details of the assay protocols, at least at a summary level

11 are in Appendix F. So maybe tomorrow if you want questions,

12 we'll go back and forth referring back to some of the details in

13 Appendix F. I'm going to put very little detail in today's

14 presentation. 

15 We were asked to simply say okay, what is high

16 through put mean? It's not all clear what that actually means. 

17 It's just as a working definition. It's assays that can get

18 through somewhere between 1000 and 100,000 chemicals in a

19 reasonable period of time. That's a real wishy-washy -- it

20 could be anything you want. All of the assays we're talking

21 about, the labs can run thousands of chemicals a year in these

22 assays, at least 10,000 chemicals a year if they devoted all

23 their time to doing that. They're run in microtiter plates,

24 they're typically run using robots, and they can either be

25 single-plex or they can be multiplex readouts. We have some of

26 both. This is just an example applied. I'll talk about this in
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1 more detail later. It's the results from one of the NCGC

2 estrogen receptor assays where they ran 30,000 samples in a week

3 for that one assay. 

4 Okay. So as we plan this project over the

5 last two or three years, we decided to use estrogen receptor as

6 a test case for how you really think about pathways. So for

7 those of you, if there's anybody on the Panel that actually

8 doesn't know all the details of the estrogen receptor, this

9 cartoon is there. So a ligand, which may be an activated ligand

10 what gets dosed to the cells or the animal, may not be the

11 active ligand that gets metabolized. So the active ligand binds

12 to the estrogen receptor, estrogen receptor dimerizes,

13 translocates to the nucleus, cofactors are recruited, the mature

14 transcription factor binds to DNA, then you have RNA produced,

15 protein produces and with the estrogen receptor, at least one

16 downstream consequence, you can have cell proliferation. 

17 So we actually have assays from top to bottom

18 here. We have binding assays, dimerization assays, DNA binding

19 assays, RNA and protein expression assays as well as

20 self-proliferation. We're only going to focus on the boxes that

21 are in yellow here. So Novascreen ran the DNA binding assays,

22 and then we have transcription factor assays from Attagene, NCGC

23 and Odyssey Thera we'll talk about. But all the others are

24 there.

25 A question was raised earlier in the day. 

26 Okay, what happens if you have chemicals which perturb the
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1 pathway? They make something happen to the pathway, but they're

2 not hitting, not actually binding to the standard binding site. 

3 Well in principle by looking at the Weight of Evidence

4 downstream from the binding assays, if everything else lights

5 up, that's at least some evidence. So we're thinking about how

6 you actually make predictions about off pathway estrogen

7 receptor non-ligand mediated activity. 

8 What is really important and a lot of what

9 I'll talk about later on are these side pathways, which cause

10 the assays to light up, but they have nothing to do with

11 estrogen receptor activity. So these are assay interference

12 pathways. You're causing false positives. So a big issue that

13 we have to deal with in the quality control and all the data

14 filtering is how you deal with those false positives. We'll

15 talk about the preliminary approaches we're developing to do

16 that. 

17 Okay. So starting off at the top of the

18 pathway, we have three binding assays. These are cell-free

19 assays, radioligand displacement assays, very standard that have

20 been around for forever. What's special about the way we run

21 these is they're quite expensive to run, so we can't afford to

22 run all chemicals in full concentration response from the get

23 go. If we did, we couldn't run other assays. So fall the

24 self-rebinding assays and we have 230 or so in our whole panel,

25 we run everything at 25 micromolar to begin with. If you see

26 some level of activity and there's some statistical metric that
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1 we use to say what that is, then we follow up in concentration

2 response. 

3 So as a consequence and later in the next

4 talk, Dr. Hornung was going to talk about comparing this data to

5 their really well-run binding assays. We miss chemicals that

6 they see largely because they're looking up at 100 or 200

7 micromolar, and those we don't even see any activity. 

8 One of the rationales for doing this is with

9 the whole ToxCast program, sort of a base principle is that what

10 we're really worried about are chemicals that people are being

11 exposed to at relatively low doses. So you have to have

12 relatively potent chemicals to see any activity. It's a design

13 choice that may or may not be right in certain circumstances. 

14 That's what leads to at least one thing that leads to the 25

15 micromolar prescreen. 

16 Anyway, so then we follow up and go up to 50

17 micromolar. Again, not terribly high, but if you're assuming

18 you have to have pretty potent chemicals to see an effect,

19 that's a reasonable choice. 

20 For each of the assays, I'm going to show one

21 of these little concentration response plots. Later on, I will

22 show one blown up so that you can actually see what all the

23 symbols are, and we can talk about that more tomorrow. But the

24 basic thing is the black curve is the test chemical we're

25 looking at and in all of these, I'm using bisphenol A for all of

26 the assays. The red is 17 beta-estradiol. So just kind of
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1 orient your eyes as you're going through all these. 

2 Okay. So the rest of the assays are

3 transcription factor assays. The first is a beta-lactamase

4 assay being run by NCGC. They run from six nanomolar up to 92

5 micromolars, so a little bit higher in concentration, run a

6 15-point concentration response series. Here they're measuring

7 two channels, a blue and a green channel and then what you

8 actually call the estrogen receptor activity is the ratio. One

9 of the issues that's kind of special about these, again we talk

10 about that more later, is that if you have cytotoxicity, quite

11 often cytotoxicity can mimic activity. Because if you goof up

12 the channel in the denominator of the ratio, it actually looks

13 like something is active. So inactive in this assay, you need

14 to check against some other assay. That's a kind of assay

15 interference pathway we'd worry about. 

16 Also run at the NCGC is the luciferase BG-1

17 assay. Again, all of their assays are run at the 6 nanomolar to

18 the 92 micromolar. What's interesting specifically about this

19 assay is there is BG1 assay that was just validated by OECD and

20 ECCVAM for use -- I guess it could be used in the endocrine

21 program or other programs. That validation process took seven

22 years to get that assay validated. So it was kind of a heroic

23 effort. The number that people worked on validating that low

24 through put method has been looking at this data set. So this

25 data set is available to all of our collaborators, and they're

26 quite impressed. You're getting all of the reference chemicals
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1 right that you got with the low through put method. This is

2 something they were able to run 10,000 chemicals in a week on,

3 so very high through put. It doesn't have quite the dynamic

4 range of the lower through put assay, but it's doing a pretty

5 good job despite that. 

6 Okay. An important thing that will come up in

7 some of the potential technical discussions about the NCGC

8 assays is they're run in a little bit different way than most

9 high through put assays. Typically, you'd have a 96 or 384-well

10 plate, and you concentration response series would be down a row

11 or down a column. Instead what they do is they actually start

12 off with 384-well plates and then they merge those. They

13 interlead those to create the 1536 well-plates. So all of their

14 assays are run in 1536. I'll point out later why it's important

15 that they actually run three replicates where each of these four

16 quadrants are shuffled. So each chemical tends to have

17 different neighbors from each run, because there tends to be

18 some slop over or there can be signal slop. That's a technical

19 term. I'll talk a little bit more about that later and how we

20 try to deal with that.

21 So they take a master plate, the 384, they

22 have three different interlead versions of that for the 1536 and

23 then what they do is the concentration response is done by

24 taking the master plate at the top and diluting down. So

25 there're actually 15. Of these 15, 36-well plates that they

26 dilute down. Those are all put on a robot and then away you go.
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1 All of the details will be important as I come back to talk

2 about some of the assay interference issues. 

3 Okay. The next assay is another

4 Transactivation assay, very much like the BG1, but it's run by a

5 company called Odyssey Thera. It's run up to 100 micromolar. 

6 It's a luciferase readout, nothing particularly special about

7 that. 

8 Okay. The next and I think this is a final

9 set of assays, these are a set of transcription factor assays

10 run by a company called Attagene. What's particularly

11 interesting about these is they're very, very high multiplex. 

12 They actually run just two assays or two wells per chemical

13 concentration. In one well, they get 52 readouts. In the

14 other, they get 29 readouts. They run up to 200 micromolar. So

15 this is the assay in this panel anyway where we run to the

16 highest concentration and start looking for potentially the

17 weakest compounds. 

18 They way they do the multiplexing is that

19 readout is actually a sequencing-based readout rather than a

20 fluorescence-based readout. So they have many transcription

21 factors and the cells are engineered to have many of these

22 reporters. The reporter, RNA construct, all have a single tag

23 that has different cut sites. 

24 So what you do is you take this big mess of

25 RNA that comes off of the cells. You digest them with enzymes. 

26 The report from each transcription factor has a different
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1 length. You run that out on a capillary electrophoresis

2 machine, so therefore you can read quantitatively how many

3 transcripts you are getting from the 52 or the 29. The

4 difference between the cis and the trans is these are run in

5 HepG2 cells and many of the transcription factors are

6 endogenously expressed. All of those you can run, and that's

7 the Cis assay. 

8 The trans assay, you actually transfect,

9 transiently transfect the transcription factors that are not

10 native there in the cells. For a number of cases, they have the

11 same assay, and particularly for estrogen receptor, they have it

12 in both the trans and the cis assay. So again, you'd want both

13 of those assays to light up to feel like you have true activity.

14 So that's a very very quick, high-level

15 overview of what the assays are and we can talk about that more

16 later. 

17 Okay -- so the chemicals. We start off with

18 this very large set of chemicals that were nominated for the

19 ToxCast program. Many stakeholders within the Agency, outside

20 we had academic groups, folks from the OECD, folks from the

21 industry who wanted chemicals tested. For example, everyone of

22 the chemicals that's in the 10,000 chemical library that we

23 spoke about earlier was put in as a nomination. The total list

24 was about 20,000 chemical nominated, 20,000 unique cast numbers

25 by the time you looked at physical chemical property. So we

26 can't test volatiles. We can't test compounds, which are
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1 insoluble for instance. We don't even try those. We chose not

2 to test mixtures initially. 

3 We came to about 10,000 chemicals that we went

4 to our chemical vendor. There's a vendor compound focused

5 incorporated, who actually did all of this. We said okay, go to

6 Sigma and go to one other company. I don't remember what

7 company two was. Because those are trusted vendors and get

8 everything you can. They were only able to get about one-third

9 of those 10,000 compounds. 

10 The National Toxicology Program had their

11 10,000 compounds that they nominated. There was some overlap

12 with ours. They had about the same experience. They went to a

13 couple of other vendors, but this is actually a very practical

14 issue that we don't quite know how to deal with yet, is that

15 they're a lot of chemicals that are out in the environment. You

16 could probably buy a carload, but you can't buy 100 microgram at

17 enough purity to do this sort of testing. We haven't figured

18 out what to do with that. 

19 Our requirements were that you had to have 90

20 percent purity and 80 percent of the samples we got, at least on

21 the certificate of analysis were at 95 percent. We're not

22 requiring 99.99 percent purity just because you can't get that

23 for lots of these chemicals. Most everything we got reported to

24 be 95 percent purity. 

25 They were solubilized in 20 micromolar DMSO,

26 put onto 96-well plates and frozen and then shipped to vendors
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1 as requested. So again, you'll hear a little bit more later

2 about the Duluth Protocol where they carefully test every

3 chemical that they've tested up to the limit of solubility. 

4 From a practical standpoint, it's really really hard to do. 

5 It's no longer high through put if you have to figure out what

6 the limit of solubility is for every one of 10,000 compounds. 

7 Okay, so that's why we have this very standard protocol. We'll

8 solubilize up to 20 micromolar. If it's not soluble there, we

9 would try a 10 micromolar. If it's not soluble at 10

10 micromolar, it's out. We don't worry about it. 

11 Because you can't always trust the certificate

12 of analysis, the entire 10,000 chemical library that's going to

13 the NCGC is going through further analytical QC. So we take the

14 master plates, the 20 millimolar master plates, and they're

15 going out. Right now, we only have about 30 percent of QC

16 results for that. That data is slowly rolling in. 

17 Okay. We have chemicals, we send them off to

18 the various labs who are going to run them, and they send us

19 back data. For the entire ToxCast program, there're about 15

20 labs that generate the data over the last three years or so, and

21 every lab sends this data in a different format. This is a huge

22 informatics issue. Because of this, we have many many, hundreds

23 of assays, lots of different formats, lots of ways that people

24 would like to analyze the data. We had to put together a robust

25 and highly automated informatics system to deal with all of

26 that. We've tried to make most of the assays fit a little bit
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1 of a one-size-fits-all. 

2 An important point -- we didn't make up a

3 bunch of statistical methods. All the statistical approaches

4 and curve-fitting approaches are things that are really standard

5 in the high through put field. The total data set, which is

6 almost complete, is about 28 million data points and it's about

7 1.5 million chemical assay pairs, 1.5 million curves, like the

8 curves we'll talk about. We've developed this eight-level

9 process and I'll go into more detail on the next slide. 

10 The final output of this is all the data

11 finally flows into a database, and the database is in part of

12 what we call the Actor System. So if you go to actor.epa.gov,

13 that's where all of our data is. Once the data is ready -- it's

14 clean and we have a process for actually doing the data release,

15 which is going to happen during this calendar year, all the data

16 goes up on the website and everybody can see all the data. We

17 make the data not only available to browse through the website,

18 but we provide all of the data files including if people want

19 all the back to the raw data. If somebody wanted to start from

20 scratch and really wanted to deal with everything there to the

21 end, we would provide all that data to them.

22 Okay. So the pipeline -- there're these eight

23 steps. In a very cartoonish way, I'll walk through this and

24 will be happy to talk about it more. We get raw data files,

25 which are typically at a plate level. So here's the number of

26 counts, photon counts per well on each of the plates. We take
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1 that and we do some mapping. The vendors only see blinded

2 chemical data. They have no idea what chemicals are on the

3 plates. 

4 So at step two, we actually map the blinded

5 chemical tracking IDs back to what was there, and we move things

6 into a standard format instead of dozens of different formats

7 here. Standard format, then we do data normalization. So we

8 typically normalized for estrogen receptor to 100 percent of

9 estradiol on the plates, then we turn that into

10 concentration-response files. 

11 We do outlier detection. Throughout outliers,

12 then in step six, we do the curve fitting and hit-calling. We

13 say okay. If you look at just that curve for just that

14 chemical, just that assay, it looks like there is activity. In

15 level seven, which I'll spend a fair amount of time here talking

16 about, we do systematic filtering on there. Looking at data

17 than just that chemical, just that assay, we don't believe that

18 it's a hit anymore. 

19 And then finally, we have a level eight, which

20 we haven't done any of here where if it seems appropriate, we'll

21 actually have manual intervention where some set of experts will

22 sit there and they'll look at every curve and say yes, I believe

23 that or I don't. But we're really striving for level one

24 through seven to be very objective and there's no bias. Okay I

25 know that chemical is supposed to be positive, so you have to

26 make it positive. There's enough noise in the data that I think
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1 that would be a bad thing to do. The data is what the data is. 

2

3 Okay. So the curve fitting steps -- this is

4 level six -- so we just use a four parameter Hill function. 

5 We're using R, which is an open source code. So again, we can

6 hand anybody in the world all of our software. They could start

7 from the beginning. We ask, do you see any significant

8 activity? I can talk about thresholds there. If it does, then

9 we calculate an AC50, what's the concentration where you get to

10 50 percent of the maximum you get in that assay. Then we

11 calculate various confidence intervals around that. Then there

12 is an Emax and the T is the top of the Hill fit parameter.

13 An important parameter is what we call the

14 response cutoff. Every assay has noise around the baseline. 

15 Some of them are very noisy. Some of them are really, really

16 tight. In order to call a chemical active in an assay, the mean

17 response at some concentration has to be above what we call the

18 response cutoff, which is 10 median absolute deviation. It's

19 kind of like standard deviation around the baseline noise. So

20 it's been conservative, conservative meaning that we would

21 rather call something inactive if it's kind of in the noise

22 rather than calling it active prematurely, because we seem to be

23 having in general, too many false positives in some of these

24 assays. But they merge all the assays together ultimately and

25 deal with those issues. 

26 As we go on, we may see more and more of
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1 these, so let's just go through one in a little bit of detail,

2 one of these concentration response plots. This is for the NCGC

3 betalactamase assay and it's bisphenol A. 

4 So the black curve is the test chemical and

5 the whiskers are one standard deviation. In this case, there

6 were three technical replicates. That's the number up there. 

7 The AC50 is the concentration at which you got 50 percent up the

8 curve to wherever it got to. There is actually also a relative

9 AC50, and we had a bunch of discussion, is this the relative or

10 the absolute, and I don't remember, which is what concentration

11 did it take to get to 50 percent of estradiol. So that's

12 another parameter.

13 The estradiol curve is the red and again with

14 the standard deviation. For assays where we had concurrent

15 cytotoxicity, that's given in gold, again with the whiskers for

16 one standard deviation. This dotted-dashed line is the response

17 cutoff, the 10 MAD. Notice that this assay, the variation

18 around the curve is very, very tight. This is probably the

19 cleanest assay in terms of just technical variability. This

20 little green tick is what we call the LEC, the lowest effective

21 concentration. It's the first testing concentration where you

22 got above the response cutoff. For every chemical, we have a

23 picture like this and we have the table, and it gives the Hill

24 fit parameters, various other fit parameters and confidence

25 intervals. Okay. So that's how to read one of those plots. 

26 Okay. So we do this very standard, hands-off
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1 process to get to an initial call. We think in this assay, this

2 chemical is either inactive or it is active. Then we go into

3 level seven, where we start bringing in other information. We

4 bring in cytotoxicity data or autofluorescence data where that

5 is appropriate. We can turn active calls into believe them

6 active. We may say no, I believe that's inactive. We may say

7 set aside, and set aside means that the computer algorithm just

8 doesn't know what to do with that data. So a human needs to

9 look at that. We admit that the algorithms are smart enough to

10 know that they're not as smart as they could be. So that's what

11 set aside is.

12 Finally, we may say discard. The only time we

13 discard data is when we subsequently get the chemical QC data

14 and it turns out what was in the well was not what was reported

15 to be in the well. So I'll go through a little bit about this

16 level seven filtering. 

17 A next step which is planned, but we've not

18 actually implemented, is to now take what we bring out of level

19 seven I'll talk about and start bringing in the chemical

20 information, which is something the Duluth group has done quite

21 a bit of. That will help us again decided finally, okay

22 something is called active, does it really look like an active

23 chemical or can I make a case that physically there's no way it

24 would actually bind to the estrogen receptor. That's work in

25 progress.

26 Okay. So here's an example. I'll show you
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1 several examples of each rule. This comes from this Attagene

2 assay. Again, this is the highly, multiplexed assay. We have

3 lots of readouts going on all of the time. One of the things we

4 see in this assay is that quite often as you get toward

5 cytotoxicity, chemicals that are going to be cytotoxic, you see

6 this big burst of activity, lots of the endpoints are turning on

7 at relatively high concentrations, and none of those are

8 probably receptor mediated. It's false activity. This is an

9 are-you-awake test. 

10 So normally you go these plots from low

11 concentration to high. Here we did it the other way around. So

12 this is high concentration to low, and just look at the black

13 spiky curve there. It's a histogram of the number of readouts

14 as a function of concentration. So you see at relatively high

15 concentration, and this is about 100 micromolar, lots and lots

16 of endpoints are turning on. So it's sort of nonspecific

17 activity.

18 Included in this assay are three readouts for

19 cell stress, oxidative stress assays. So when those turn on,

20 you know the cells are being very unhappy. Instead of just

21 having a little rule that says if at concentrations where you

22 have those cell stress assays are turning on and you have this

23 big burst of nonspecific activity, I'm just going to ignore any

24 specific readouts. I think, and this is bisphenol A, that

25 estrogen receptor assays are hitting these two little green

26 symbols here. So they are at concentrations much below where
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1 this nonspecific stuff is going on. So we would believe that. 

2 Here's an example where you see some activity

3 in the estrogen receptor assay, the black curve, but the gold

4 bar is where we have that threshold. So any activity above

5 where that gold bar is, I'm not going to believe. So this would

6 be a case of level seven filter out. Level six said active,

7 level seven says inactive. I have to say, this whole process of

8 dealing with this, we're not done with that. We're still

9 defining this process of filtering out these high concentration

10 bursts. 

11 Okay. There are a number of assays, and I

12 mentioned the betalactamase assay earlier, where cytotoxicity

13 can give you a false readout. So again, the betalactamase

14 assay, you have two channels and you're taking the ratio

15 cytotoxic compound could actually cause a decrease in the bottom

16 channel, which looks like an increase in the ratio. So here's

17 an example of a compound where you start to see cytotoxicity,

18 the gold at that same time you're seeing the activity in the

19 true channel. So we would say this we don't believe is truly

20 active. We believe this is an assay artifact caused by

21 cytotoxicity. 

22 Again, the betalactamase assay is a

23 fluorescence-based assay. So if you have a fluorescent

24 compound, you could get fooled there. So the NCGC has run red,

25 blue and green assays, both with and without cells. So we have

26 the AC50 for everyone of the 10,000 chemicals in all of those,
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1 and we use that as a guide. 

2 If this is a chemical that's going to glow

3 blue, or glow green or red, do we really believe that that's

4 active based on the potential for this assay interference. 

5 Because you can't absolutely say that oh, this has being active

6 because of that autofluorescence. We typically call those a

7 SETASIDE. So a human has to go in and look and make some

8 decision about whether that's really active or not. 

9 Here's an ongoing problem with the NCGC assay,

10 something that is very specific to this technology. We call

11 that flare. So the assays are running in 1536-well plates and

12 especially in the BG1 assay. An active compound would be very,

13 very bright. It will cast this flare across the neighboring

14 wells on the plate. So if you actually have a plate that has a

15 bunch of active compounds on it, and there're reasons why that's

16 actually true, and if they're all in the same region on the

17 plate, we see these bright, flarey regions. This is the plate

18 and a green circle. You can't really see it here in the room,

19 but a green circle is a positive activity. You see these big,

20 like a big lamppost, which is spreading light all over.

21 So essentially what we do is we detect these

22 flares and any chemical, which is in the shadow of that flare,

23 we just delete the data and say you can't trust the data. We

24 take that out. That does a pretty good job of filtering that

25 out. There are a handful of compounds that are still in the

26 data set, which are potentially flare-related activity. So we
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1 have some filters to try to say, okay we think that that

2 activity is coming from a neighboring well. 

3 Again going back, I talked about the way the

4 plates, the three to four well plates are shuffled in the three

5 different replicates. It's because in each one of those cases,

6 some of the neighbors of the well are different. So you're not

7 always sitting -- each of those three replicates are not always

8 sitting next door to something that is really bright. Using

9 that triplicate, we're actually able to solve, we believe, most

10 of the -- correctly call a chemical being active is either in

11 the flare or not in the flare. This is an example of where

12 there is some activity at high dose in this particular compound,

13 and turns out this point on the plate is sitting next to some

14 really bright compound. So we believe this little activity is

15 caused by flare.

16 Okay just in summary, there're actually a lot

17 of reasons you can have assay interference, false positives,

18 false negatives, and none of these reasons are really

19 potentially -- except for that flare issue -- have anything

20 specific to high through put. These are things that really have

21 to do with in vitro screening. We just list these because

22 they're ones that we're having to, through our level seven to

23 level eight, having to deal with. 

24 Cytotoxic or cytostatic compounds, they can

25 cause various kinds of assay interference. Surfactants -- so in

26 the cell free binding assays, you can get beautiful binding
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1 curves, whether you're in high through put or low through put if

2 you put a surfactant in there. So we need to have filters that

3 are based on chemical structure. This chemical lights up all

4 those assays and you look and it's a surfactant, so I don't

5 believe that. Flare we talked about. Chemical contamination --

6 we're going through and getting all of that data to deal with

7 those.

8 In these assays, we see a number of compounds

9 and the bisphenols are a good example. Across many assays, you

10 get this partial agonist behavior. They come up to 50 percent

11 of E2 or 25 percent or 40 percent of E2. It's very

12 reproducible. It happens across multiple assay technology. So

13 there is something real about that. But if you were in an assay

14 where the noise level is at sort of 20, 22 percent and you top

15 out at 25 percent, do I call that active or not. Then you

16 really want confirmation from other assays to decide what to do

17 with that. 

18 I'll talk a little bit about chemical batch

19 discrepancy. It's part of the Tox21 Program. We have maybe

20 about 1000 compounds where we independently procured the

21 samples. The NTP bought it, we bought it, the NCGC bought it,

22 and so we can actually say here's supposedly the same compound. 

23 It's the same chemical structure, but it two different batches,

24 two different vendors, and you get different answers. Okay, how

25 do you deal with that? We haven't solved that problem yet. 

26 Activity of weak compounds -- I've said
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1 something about that. If chemicals are not soluble in DMSO, we

2 simply can't deal with them. We've talked about building water

3 plates, being able to run assays in water. That's a

4 possibility. We can't deal with volatile compounds. 

5 Then metabolism -- we'll talk more later this

6 afternoon about how you deal with that. Some of the assays

7 we're investigating, not the ones we're talking about today, can

8 be run plus or minus S9. So, we can actually see if there is a

9 metabolic activation, something going on there. Those assays

10 are not ready for primetime, but it's something being

11 investigated. 

12 Okay, so we now have lots of data, now we'll

13 start seeing how well these assays run. So I'll get a little

14 more specific there. 

15 We selected a set of 37 reference compounds,

16 all agonists, although we have some antagonist assays also. 

17 Then we're going to talk about overall accuracy and something

18 about reproducibility. Then because we have all of these

19 assays, all of these chemicals, in all cases we've run at least

20 1800 chemicals and in some cases we've run 10,000 chemicals

21 through these assays. We actually have a lot of statistics of

22 assays performance that we can start looking at. 

23 Okay this is an eye test. This is the list of

24 37 reference chemicals. These came -- they largely came from

25 the OECD, ICCVAM and ECCVAM validation processes on estrogen

26 receptor in vitro assays. Susan Laws did a very careful look to
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1 try to make sure these were ones that not only -- is there a lot

2 of in vitro data, but is there corresponding in vivo data. 

3 Again, the details -- you're not expected to read all of this,

4 but what we're going to look at is there are four colors here. 

5 White says that the assay -- and these are the

6 eight assays -- the assay agreed with the expected call. Okay

7 so the NCGC BG1 assay agreed on all 37 chemicals. A red cell

8 says the assay was inactive where it was supposed to have been

9 active. So the betalactamase assay missed a couple of the

10 actives. A blue says it was active where it should have been

11 inactive, and a yellow says there's some ambiguity about the

12 call. I'll talk about that a little bit. 

13 Okay by just glancing at this, we see that the

14 BG1 assay got everything corresponding. The betalactamase assay

15 missed a couple of the actives. The Attagene assay has got all

16 of the actives. It had a couple, two or three, inactives that

17 showed up activity, and I'll talk about those. This Odyssey

18 Thera transactivation assay -- that's just a bad assay. So it

19 missed a lot of the active and it has a very high noise level. 

20 So that is an example of an assay, once we've gone through this

21 process, we just won't use that anymore probable. 

22 Then finally the binding assays, the last

23 three columns, there's lots of red. So they're missing a lot of

24 the weak -- I know again you can't ready this -- but going from

25 the top, these are the strong positives, moderate strong,

26 moderate weak, very weak, negative. So what it's doing is it's
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1 missing a lot of these very weak compounds, and again that's

2 primarily because of the assay protocol. We didn't run these

3 chemicals up. We ran all of these in concentration response,

4 but only up to 50 micromolar. A lot of these compounds you only

5 see activity at 100 micromolar or more. So that's a quick

6 summary. 

7 Let's look at a little bit of detail. These

8 are the betalactamase -- the chemicals in the betalactamase

9 assay, which should have been positive; that was the nominal

10 call, and were either negative or questionable. Here on the

11 left-hand side, this is methoxychlor and bisphenol B. Here we

12 see, these are examples of these sort of partial agonist

13 activity. They're very reproducible. You get a nice

14 concentration response profile, but the top is right around

15 where our statistical threshold is. So the algorithm says you

16 didn't meet the threshold or you're right at the threshold. So

17 if we go back and look at this, you'd say there really is

18 activity, but our noise level is such that it's a little hard to

19 see. 

20 This was a chemical -- this is really bad, so

21 even I can't read these. This is a chemical which was called

22 initially level seven fail. We think it's inactive because it

23 hits the blue channel. Okay so you'd say if it's hitting the

24 blue channel about the same time you see the real activity, but

25 again, this is something people know, this chemical really is

26 positive, so we're going to call that positive. 
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1 Finally, this was another one of those yellow

2 ones were for ethylparaben, there were two samples independent

3 procured, one of which met the criteria. It was very weak, but

4 it did meet the threshold for activity and one of the sampled

5 didn't. This point out this issue of we have a lot of data for

6 two different samples. We can get different results from the

7 two differently procured samples. So a work in progress to deal

8 with these sort of issues. 

9 Okay. The Attagene assays, all of the

10 positive reference chemicals, they got all of those, they got

11 the call, but they had a number of blue things, so they were

12 positive where chemicals were expected to be negative and those

13 were corticosterone, progesterone and haloperidol. 

14 Corticosterone and progesterone were active in both of those

15 assays. Haloperidol was only active in the CIS assay. This

16 one, I've no idea what's going on there. That's a question

17 we're digging into to decide whether that's really a believable

18 result. 

19 I'm going to dig a little bit more into the

20 progesterone. This is a chemical where you can come up with a

21 story where you go through a metabolic transformation and you

22 have something estrogenic. Okay I see some nodding head or some

23 shaking heads there. These HepG2 cells were selected to be --

24 they're not terribly metabolically competent, but they have some

25 metabolic capacity there. So, that's one possibility. 

26 Now if you dig into the rest of the assays
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1 though, it's not just the Attagene assays that are lighting up

2 progesterone. The Attagene assays are both on the left. 

3 They're positive. Two of the binding assays are positive. The

4 BG1 shows activity, and it shows activity not in just one

5 sample, but in three independently procured samples. So there

6 is something about progesterone, which is at relatively high

7 concentrations. These are high concentrations where you're seen

8 activity is doing something in multiple cell backgrounds to the

9 estrogen receptor. So the only point about this is that picking

10 true negative reference compounds can be tricky sometimes. 

11 Again, a work in progress to understand what all this means.

12 Okay. If we just summarize that table with

13 all the reds, the blues and the yellows, and just ask what is

14 the concordance, and that's the second line. The concordance

15 for all of these assays with the reference compounds, the Tox21

16 BG1 got them all right, 94 percent for the betalactamase, the

17 Attagene were both 90 some odd. This Odyssey Thera luciferase

18 was at 80 percent and the binding assays were middling to bad. 

19 Again, that's because we're missing a lot of those weak

20 positives. A table -- we can talk about this more, but these

21 are simply some summary statistics about the standard deviation

22 around from the technical replicates for some of the reference

23 compounds. They really mimic what we see across the whole

24 compound library. So there is nothing specifically noisy or

25 unnoisy about the reference compounds. 

26 So that we actually now have lots and lots of
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1 data on these assays so we can actually do a little bit more

2 looking at quality reproducibility. What I'm showing you here

3 is again for the Tox21 assays and it's the betalactamase on the

4 left and the BG1 on the right, we have a 1000 or 1400 compounds

5 which were procured independently. We have sample one and

6 sample two. We can say how to those agree with one another? 

7 Ideally to read the plot, only plot against these is the AC50. 

8 So what concentration got you half way up the

9 curve and the X axis is one replicate, the Y axis is the other

10 replicate. It's one point per pair of chemicals. If everything

11 was right, everything would be on the diagonal. So compounds

12 which are really potent are down here at the bottom left. This

13 cluster stuff at the top right are all the inactives. Those are

14 inactive in both assays. Just for plotting purposes, there's a

15 little jitter put on it so all the points are not sitting on top

16 of one another. The colors have to do with the strength of the

17 reference compounds. The reference compounds are colored by

18 their proportion strength. 

19 So things on the diagonal were all happy. 

20 These that are off the diagonal, this group up here -- so along

21 the top and on the right are ones that were active in one

22 replicate, but not in the other. So that's where you start to

23 worry about something. Okay what we see is most of the density

24 there are things that are really weak. They're one micromolar,

25 10 micromolar, 100 micromolar, AC50 in one replicate and they

26 miss in the other. That says they're really borderline in one
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1 replicate and they miss the other one. So we're not too

2 concerned about that.

3 There are a handful of compounds where they

4 are close to nanomolar activity in one replicate and inactive in

5 the other. Those are ones where we're going to do the followup.

6 We haven't done that yet. The data, again, going through final

7 clean up and we'll do that. This gives you some idea of how

8 well you can trust. The other thing we haven't done is filter

9 out all of the chemicals which are simply not what they're

10 supposed to be. We don't have all of the analytical QC data. 

11 This is tool. Once we have all that in, we can quickly focus in

12 on the chemicals that something really funny is going on. 

13 Okay then we can ask the same sort of question

14 about comparing two different assays from the same lab or two

15 different assays slightly different technologies. Let's focus

16 on the left. These are the Attagene system trans. Again, you

17 would like to have, if it's active in one, it would be active in

18 the other at about the same AC50, so you would like everything

19 to be on the diagonal. Once again we see a big group of

20 chemicals that are weakly active on one, but inactive in the

21 other. So they are kind of on the border one way or the other,

22 but a handful of chemicals which are really active in one, but

23 inactive in the other. Once we do all the rest of our

24 filtering, we'll go back and look at those again. 

25 You see more of these real heavily discordant

26 compounds looking at the betalactamases versus the BG1s, so
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1 things that are sub-nanomolar in one and inactive in the other. 

2 We're digging into those. 

3 Okay in standard assay validation, you always

4 think about transferability. We were asked, okay at least think

5 about what would transferability be here. We're not asking some

6 other lab to rerun these assays, but you can at least ask

7 somebody else to take the data and analyze it. Would you get

8 the same answer with your assay analysis protocol as we did? So

9 we're actually running an experiment with our Tox21 partners. 

10 The folks at the NCGC are taking the raw data. The folks at the

11 NTP are taking the raw data. The folks at the FDA are taking

12 the raw data. We're all analyzing and seeing what do we agree

13 and disagree on the activity calls, the AC50s and so on. There

14 is a manuscript that is being prepared on that. 

15 Okay finally in summary, we're showing you

16 some of the real baby steps in dealing with the high through put

17 data for using for prioritization around the endocrine program. 

18 We looked at eight of 21 assays. Already there were orthogonal

19 -- we have other orthogonal assays. We believe it's really

20 important -- there's no new here. You have to use this as a

21 battery. There is no perfect assay. 

22 One of the analyses we're going to be able to

23 do once we get all the assays together is to say what is the

24 minimal set of assays to get, to meet some criteria? You

25 certainly don't need 21 assays. Do you need four assays? Do

26 you need eight assays? Do you need two assays? So we can ask
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1 questions like that. Again, we're not asking is this chemical

2 an endocrine disruptor, we're simply asking does this chemical

3 interact with the estrogen receptor in vitro. That's what we're

4 trying to say. 

5 We've shown that you can analyze generate data

6 on thousands of compounds. You can analyze all the data in a

7 very systematic objective way. It's not perfect. We're looking

8 for better ways and advice from this group is very welcome. The

9 next challenge once all the data is together is how you actually

10 combined it into this final prediction. Again, something we're

11 just starting on. Okay. That's what I had. I'm happy to take

12 any questions. 

13 DR. DANIEL SCHLENK: Thank you. Any questions

14 for Dr. Judson? Dr. Safe? 

15 DR. STEVEN SAFE: The betalactamase assay is

16 with the GAL4 and the GAL4 response element promoter? 

17 DR. RICHARD JUDSON: I'm going to throw out a

18 lifeline and ask Keith Houck, who's our assay expert, to come up

19 and sit next to me. He can address some of the technical

20 questions about the assays, if that's okay.

21 DR. STEVEN SAFE: Well what is the assay, is

22 that what it is? 

23 DR. KEITH HOUCK: It is. It's the GAL4

24 mammalian 1 hybrid system.

25 DR. DANIEL SCHLENK: Can you just state your

26 name please again? 
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1 DR. KEITH HOUCK: Keith Houck.

2 DR. DANIEL SCHLENK: Thanks.

3 DR. STEVEN SAFE: All estrogenic compounds

4 basically of these xenoestrogens are selective estrogen receptor

5 modulators. Basically what you've done is you're using two

6 assays, a GAL4 ERS assay, where you're activating a yeast

7 reporter and an ERE luciferase assay, right? I mean, those are

8 the only two assays you're using. So it's not eight assays,

9 it's really only two, but you've got different cell contexts, et

10 cetera. Is that correct? 

11 DR. KEITH HOUCK: Well there're also the

12 binding assays.

13 DR. STEVEN SAFE: Binding is separate, that's

14 fine. That's straight forward. 

15 DR. KEITH HOUCK: Two different formats of the

16 transactivation assay.

17 DR. STEVEN SAFE: That's really all -- it's

18 not eight different assays, it's really two assays. Two assays

19 in a different context. All I'm saying is that this shouldn't

20 be the same. There's no way. If there's selective estrogen

21 receptor modulators, cell context and whatever is around it,

22 dictates differences because the compounds are different because

23 they're selective, estrogen receptor modulators. All of them

24 are. The fact that -- don't waste any time by looking at why

25 they're different. They should be different. 

26 DR. DANIEL SCHLENK: Dr. Safe? I think those
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1 are things we would save for the comment period after the fact. 

2 So we just want questions at this point. 

3 DR. STEVEN SAFE: Okay. I won't make a

4 speech. 

5 DR. DANIEL SCHLENK: Dr. Burris? 

6 DR. THOMAS BURRIS: Tom Burris. I have a

7 question concerning of prioritization using (Q)SAR. Given the

8 capabilities of these assays and dealing with a 5000-compound

9 library, it seems like the prioritization could be based in

10 empirical data, not just modeling. I'm not sure why that

11 strategy has been taken to even consider the (Q)SAR. 

12 DR. DANIEL SCHLENK: Does anyone want to

13 answer that? Yes, Ms. Manibusan? 

14 MS. MARY MANIBUSAN: So as the endocrine

15 program moves forward, what we're stressing is that we're taking

16 incremental, small steps in integrating some of these

17 computational tox tools into the regulatory decision making,

18 starting first off the block with chemical prioritization. What

19 we're bringing forward to the Panel is that demonstration, but

20 the question is how does the Agency go forward in comparing and

21 understanding how to use high through put along side with the

22 benchtop assays. 

23 The empirical data for sure, but what we're

24 demonstrating again is how do we begin to evaluate this new set

25 of data. What are the uncertainties that we should just be

26 aware of? As Richard said, that assays are imperfect. There
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1 are going to be strengths and limitations. The agency needs to

2 understand what those limitations and strengths are before we

3 move forward to fully adopting high through put in whatever

4 fashion the Panel is suggesting that we can at this point. 

5 So there're no limitations, but what we're

6 bringing forward again is just an incremental step-wise

7 progression demonstrating how can we evaluate this new set of

8 data alongside with our low through put. 

9 DR. RICHARD JUDSON: Can I just make one --

10 another problem. This issue of the 30 percent of chemicals

11 we're able to procure. There're some real practical issues. 

12 There're chemicals that you want to test, but you can't get MSDS

13 or you can't even buy. So there's a point of diminishing

14 returns trying to go buy every chemical and run every chemical. 

15 So you need something to extrapolate where it's really hard to

16 get the data. 

17 DR. DANIEL SCHLENK: That was Dr. Judson. 

18 Yes, Dr. Burris? 

19 DR. THOMAS BURRIS: This is Tom Burris again. 

20 That was actually my follow-up question. Given that if only 30

21 percent of these compounds are accessible, is there a pathway

22 for synthesis that the Agency has, or is this something -- in

23 that case a prioritization would be very valuable. Which ones

24 would you synthesize first? But I don't know. This is the

25 first I've heard that these compounds are not available. 

26 DR. RICHARD JUDSON: Just to be a little more
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1 specific on numbers, which doesn't change the basic issue. 

2 Between all of the Tox21 partners, we're able to buy -- it's

3 about 45 or 50 percent of the 5000 compounds we're worrying

4 about. Still, there's 50 percent that we can't. The NCGC has

5 synthesized compounds, but that's really in a research context. 

6 I don't know that we've exhausted from a research standpoint. 

7 We weren't tasked to go find every last chemical. So it may be

8 that if we tried harder, we could get up to 75 percent if

9 somebody was tasked with that. It's an issue that has to be

10 dealt with at some point though.

11 DR. DANIEL SCHLENK: That was Dr. Judson. 

12 Again, just to remind you, if you could just state your name

13 before you respond, that would be great, thanks. Dr. Waters? 

14 DR. KATRINA WATERS: High, it's Katrina

15 Waters. I have a couple of questions. One thing you mentioned

16 was using the estradiol as the 100 percent normalization

17 standard on each plate. Did you have a threshold that that had

18 to cross in order for it to be a positive plate with the

19 positive control? 

20 DR. RICHARD JUDSON: Yes, there are various

21 steps for throwing out plates. Unfortunately, our colleague who

22 knows all those details is home sick and wasn't able to make it

23 here. If a plate is bad because of the positive control didn't

24 work well -- and typically I think there were be sort of a Z

25 score between the positive controls and the negative controls,

26 we would just throw the plate out.
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1 DR. KATRINA WATERS: Right. I mean you should

2 have enough replicates of the positive to be able to says that a

3 certain plate is below the standard.

4 DR. RICHARD JUDSON: Yes, all the assays have

5 that on them. 

6 DR. KATRINA WATERS: Okay and my second

7 question is you mentioned using the interference measurement

8 with the cell stress and oxidative burst and all of that as a

9 way to minimize false positives, but yet I thought earlier we

10 described a goal of being minimizing false negatives. Could you

11 comment on how that affects your false negative? 

12 DR. RICHARD JUDSON: So we're not done yet. 

13 So let me just talk -- this again Dr. Richard Judson. Let me

14 talk about the way we're thinking about doing that. If we can

15 definitely say, okay I have a good scientific reason to believe

16 that is a false positive, I think that's a good thing to do, and

17 we'll throw those things out. There's some parameters you tweak

18 that can let you say, okay I'm going to err on the side of

19 having more false positives than false negatives. Definitely,

20 almost certainly, you can say that chemical is a false positive.

21 I understand that. I want to throw it out, but your point is

22 really important that I would be easy to say none of these

23 assays are perfect so we're not going to use any of the data and

24 we'll have nothing but false negatives, right. So it's a little

25 bit of a judgement call that we're learning about how to do. 

26 DR. DANIEL SCHLENK: Okay. Dr. Nagarkatti? 
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1 DR. PRAKASH NAGARKATTI: I had a question

2 about the transcription factor assay. So in those assays, are

3 you looking at just the ERE, detection of ERE binding or are you

4 looking at other transcription factors as well? The reason I'm

5 asking is that because it is known that ligand activation of

6 estrogen receptor can also directly activate other transcription

7 factors like AP-1, NF kappa B. So it is not clear from your

8 mechanistic studies whether you do take into consideration other

9 transcription factors are all you're looking at is only the

10 Attagene. 

11 DR. KEITH HOUCK: This is Keith Houck. So we

12 do look at a panel of transcription factors in both the cis and

13 the trans. It is a multiplex system, so all of those results

14 come along with the ER results. One clarification, so in the

15 cis system, we're using an ERE and there's greater likelihood of

16 crosstalk with some of the other transcription factors. The

17 trans system is the mammalian one hybrid system, the GAL4

18 system, which is a little less sensitive to that type of

19 crosstalk. 

20 In terms of the analysis for this specific

21 project, I don't believe we're really using the activity for

22 those other transcription factors other than for that

23 cytotoxicity filter that we heard about.

24 DR. PRAKASH NAGARKATTI: Right. When you take

25 into consideration the wide area of these chemicals, do you know

26 like what proportion of them would trigger the classical ERE
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1 pathway versus the nonclassical activation of the transcription

2 factors? The reason I'm asking is just to see whether -- are we

3 missing out on some chemicals that might bypass or go through

4 the alternative pathway, is that a significant number that we

5 need to be concerned about or is it just not something that

6 needs to be taken into consideration? 

7 DR. KEITH HOUCK: This is Keith Houck. That's

8 not a trivial question to answer. We have a lot of data that

9 will, I think, help us get a handle on that, but we're not

10 really at that level of analysis of the data at this point. 

11 DR. PRAKASH NAGARKATTI: Thank you. And I had

12 one more question. When you're comparing the assays, correct me

13 if I'm wrong, but you said that in order the minimize the

14 concentration levels, you first take into consideration maybe

15 like 25 micromole. If that turns out positive, then you do a

16 dose response, maximizing up to 50 micromoles. If when you do

17 that, what I'm wondering is then at 50 micromole it comes out

18 positive, where as the first screen at 25 micromole turns out to

19 be negative, wouldn't that impact your correlation between the

20 two assays? So my question was why in the first place that

21 single dose was not used at 50 micromole, if I understood right.

22 DR. RICHARD JUDSON: This goes back to -- this

23 was a design decision for the program, three or four years ago. 

24 The main focus, again the estrogen receptor endocrine focus is a

25 tiny part of this. A lot of what we're looking at are pathways

26 that are hit a relatively potently in targets we don't really
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1 know much about. So we're screening lots and lots of targets

2 that are not their traditional ones that everybody knows about. 

3

4 Again, these assays are quite expensive. If

5 we were to run that at 100 micromolar, say that was our first

6 screen and you get lots of false positives there, then we spend

7 a lot of money following up. So if you say what we really cared

8 about, at least for that assay set or the ones that are pretty

9 potent, doing the 25 micromolar first screen seemed reasonable. 

10

11 In this context, I think we're not there yet

12 to be able to definitely say this, but if we look at what is the

13 minimum set of assays we would run all the chemicals on. So if

14 we were going to run 5000 or 10,000 chemicals, we wouldn't run

15 the binding assays first. We would run these transcription

16 factor assays or the protein complementation assays first

17 because they're relative to the binding assays are inexpensive. 

18 You get a lot of data out of them, and then take consistent

19 positives there and go follow up in the binding assays. 

20 DR. DANIEL SCHLENK: That was Dr. Judson. Dr.

21 Houck, do you have something to add?

22 DR. KEITH HOUCK: Yes, Keith Houck. Just to

23 clarify, it was only the binding assay where we did the single

24 concentration and initial screen. All the others were run in a

25 full concentration response the first time through. 

26 DR. DANIEL SCHLENK: Okay. Dr. Jacobs? 
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1 DR. MIRIAM JACOBS: Miriam Jacobs. Related

2 really to some of the questions that were coming earlier, I just

3 wondered what the rationale was originally for this election of

4 the 21 assays, and then the eight assays that are being

5 presented here? 

6 DR. RICHARD JUDSON: I'll start. Richard

7 Judson. So we don't -- for this program, with the exception of

8 the NCGC, none of these assays are custom. These are

9 off-the-shelf assays you can go out and buy. So the ToxCast

10 Program, the way that's run is we have several rounds where

11 we've put out requests for proposals. Companies would say --

12 for these class of assays -- and companies would come in, okay

13 we have assays that will do this, that or the other with

14 particular technologies. So the assays, the total battery, the

15 21 assays are coming from companies who bid on these proposals

16 that met the kinds of assay batteries we were interested in. 

17 So that's where the 21 comes from. In

18 particular, estrogen receptor pathway is an interesting testing

19 ground. I mentioned this earlier. For understanding how you

20 can use lots of assays across a pathway to figure out what's

21 going on. So we wanted to have at least one pathway where we

22 had every assay we could imagine run. Then you can start asking

23 these questions, what is the minimal set. So that was the

24 rationale for having lots of different assays. 

25 Now why do we choose eight of the 21 to bring

26 to this Committee? It was partly because they were the same
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1 technologies as the Duluth Lab has used to train the Expert

2 System. So binding assays, transactivation assays, and partly

3 the workgroup, the ORD Workgroup who went through this for the

4 last six months, there was just a limited bandwidth to sort of

5 dig in and learn all the details about all the assays, and you

6 felt like we could do a reasonable job having the group look at

7 the eight assays that were kind of familiar to everyone and not

8 worry about the others for this meeting, anyway. 

9 DR. MIRIAM JACOBS: A couple of more points

10 just of clarification from the slides earlier run. Sorry,

11 Miriam Jacobs. On slide 143, you said 20 micromolar, but on the

12 slide it was 20 millimolar. So I just wondered which one was

13 correct. 

14 DR. RICHARD JUDSON: What's on the slide is

15 correct. This is correct, yes. So 20 millimolar is correct.

16 DR. MIRIAM JACOBS: Thank you. 

17 DR. DANIEL SCHLENK: Okay, Dr. Denslow? 

18 DR. NANCY DENSLOW: Nancy Denslow. Most of

19 your assays appear to be activators of the estrogen receptor. 

20 Have you looked ever at antagonists of the estrogen receptor,

21 and are they included in any of the chemicals? 

22 DR. RICHARD JUDSON: So in the 21, there are

23 actually four antagonist assays. We didn't talk about them, but

24 they're part of the panel. The NCGC, each of their assays they

25 ran in both agonist and antagonist mode. Then what were the

26 other two? Odyssey Thera ran the other two. There are two more
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1 antagonist assays. I'm drawing a blank. 

2 Okay, but then the other set of assays, which

3 are interesting is the protein complementation assays. Again,

4 we didn't talk about that so I just mention this as a sort of an

5 interesting scientific point. Antagonist in those. So these

6 are assays when the two dimers come together. All of the

7 classic antagonists, instead of coming up to 100 percent of E2,

8 they go to 200 or 300 percent of E2. There is a specific

9 antagonist signal there. So we are screening in our model for

10 antagonist as well as agonist. The antagonist assays have a

11 problem though. They are very much more sensitive to

12 cytotoxicity compounds. So trying to sort out cytotoxicity from

13 antagonism is tough. We're not done with that. 

14 DR. DANIEL SCHLENK: That was Dr. Judson.

15 DR. NANCY DENSLOW: And I have one more

16 question. Nancy Denslow again. So like the Attagene assay, it

17 sounds wonderful, but there's only one company that's doing this

18 assay and it's all proprietary. So how do you validate that the

19 assay would work in the same way if somebody else was doing the

20 assay? Isn't that something that needs to be done for the

21 validation of assays? 

22 DR. RICHARD JUDSON: Richard Judson. So that

23 -- I could talk for an hour about that, but we don't want to do

24 that. There's been a lot of discussion, and I'm just going to

25 point to a paper which was just published this week that came

26 out of a cross agency worldwide work group looking at exactly
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1 that issue. 

2 It came out in Alltech. Judson is the first

3 author. Looking at how do you think about validating these

4 Tox21 assays. There were representatives from EPA, ORD, from

5 the regulatory offices from NTP, from all the Tox21 partners,

6 people who had been in ICCVAM, ECCVAM and OECD industry

7 academia. There were some ideas that we've put out on the table

8 of how you deal with that that I'd be happy to talk about, but

9 it's outside of the scope of what we're discussing today. 

10 DR. DANIEL SCHLENK: Thanks. Okay. Dr.

11 Waters and then Dr. .

12 DR. KATRINA WATERS: Katrina Waters. So I

13 have a question about your set of reference chemicals that you

14 chose. There're only 37 and only eight that are negative based

15 on your expert classification of them as being positive or

16 negative. I'm surprised you don't have more negative chemicals

17 available to you out of the hundreds that you have screened that

18 would have some animal data available. 

19 DR. RICHARD JUDSON: Susan Laws, Dr. Laws is

20 here. She worked on that, and I think she would be a good

21 person to address those issues.

22 DR. DANIEL SCHLENK: Will you please state

23 your name for the record? 

24 DR. SUSAN LAWS: Hi, I'm Susan Laws. Yes, you

25 are correct. What we did when we started this, our strategy was

26 to first look at chemicals that were historically recognized as
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1 being ER agonist. This was just a starting point for his whole

2 performance exercise. Of those, we used chemicals that we not

3 only had both confirmation in vitro, in vivo and other assays,

4 they were also harmonized with some of the assays that we had

5 validated during the early work within the Agency for the Tier 1

6 screening battery and they were also harmonized with the OECD's

7 test guidelines, specifically the updated test guideline 455,

8 which is an ERTA assay. 

9 Now the next phase would be -- there are

10 certainly many negatives. The question with some of those

11 negatives, we don't have as much strong documentation that they

12 really were supposed to be negative. Even with the progesterone

13 example, a negative may not be a negative all the way across the

14 battery of assays. 

15 The next stage and partly what will be

16 discussed in the next presentation is looking at all of the

17 negatives that were produced here, and then we'll be able to

18 look -- and looking at their consistency with being negative

19 across the battery. So I think ultimately, there will be more

20 negatives and there will be more to base a performance. 

21 DR. DANIEL SCHLENK: Okay. Dr. Burris? 

22 DR. THOMAS BURRIS: I have a question

23 concerning the criteria used to call something as active, this

24 threshold is 10 fold over median absolute deviation. In

25 particular, based on what you had mentioned about some of the

26 partial agonists potentially having enough noise that they may
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1 be at the level of at least statistical activity, at least as

2 it's defined here. I'm just curious if there has been any type

3 of evaluation of a variety of statistical tools to make sure

4 that compounds such as those weak partial agonists that still

5 have toxicological relevance don't get missed.

6 DR. RICHARD JUDSON: Richard Judson. So we

7 did a lot of sensitivity analysis over choosing that threshold,

8 worrying about that and looking at this enormous dataset that we

9 can then look at to say okay, if you change it from 10 MAD to 12

10 MAD, or 10 MAD to 8 MAD. What changes across 1000 compounds or

11 so, and so I guess the first answer is that we're getting ready

12 to go back through one final iteration on setting these

13 parameters. So 10 MAD may turn into 8 MAD. 

14 The more sophisticated, at least what I think

15 is the more sophisticated thing we're doing is we're coming up

16 with a consensus call. We're not going to say, we're not just

17 going to count how many assays -- was it positive to say okay

18 that's the consensus I believe this chemical is estrogenic. 

19 We're doing a variety of ways to do that. One way is to forget

20 the call. Let's just take all of the concentration response

21 profiles for all 10 assays or 12 assays or 15 assays, whatever

22 we're going to use and say are they all consistently going up. 

23 If they're all consistently going up, but none

24 of them do anything more than just tickle that threshold, we say

25 yes, we really believe something is going on and it's really

26 just a weak partial agonist. That's kind of a research effort
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1 to deal with that issue, but we recognize this is an issue. So

2 those would be real false negatives. They really are doing

3 something, but we set that threshold and lost it.

4 DR. DANIEL SCHLENK: Dr. Houck, would you like

5 to add something? 

6 DR. KEITH HOUCK: Keith Houck. Yes, and we

7 also have the antagonist data as well as the receptor

8 dimerization data, which we haven't discussed today. But those

9 partial agonists, which may be potent, raloxifene is a good

10 example -- raloxifene has minimal activity in this agonist

11 assay, but it's clearly a potent bioactive estrogen compound,

12 and it shows up very clearly in the antagonist assays and the

13 receptor dimerization assay. So again, once we've got the whole

14 battery together, we'll be able to pick those things out

15 readily, I believe. 

16 DR. DANIEL SCHLENK: Okay. Dr. Keiser, I saw

17 your hand up earlier. Do you still? 

18 DR. MICHAEL KEISER: Michael Keiser. So two

19 quick questions. One is just a very quick clarification or

20 confirmation. I heard you say earlier that the only case of the

21 single dose was for the binding assay, and that's the NVS, and

22 that the remainder ones are done all in full dose response in

23 the very first pass, is that correct? 

24 DR. RICHARD JUDSON: That is correct.

25 DR. MICHAEL KEISER: And then the second

26 question because of this issue of antagonist has come up is I
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1 was curious what your approach has been to dealing with

2 promiscuous aggregation from small molecule, promiscuous

3 inhibition -- excuse me -- from small molecule aggregation in

4 terms of detergents, looking at hill slope coefficients,

5 anything like that.

6 DR. RICHARD JUDSON: We haven't solved that

7 problem yet. That's on the queue. I can tell you how -- what's

8 written down is the plan to deal with those. So we know that

9 there are certain issues like that where we know you can have

10 technology-specific interference. So these self-rebinding

11 assays, detergent sort of things that everyone knows, those will

12 fall apart and give you a signal. If we break the assays into

13 these technology groups, the self-rebinding assays that Attagene

14 is kind of its own group. The antagonists are their own group. 

15 You see strong activity, but only in one of those groups.

16 Then you take that sort of chemicals and you

17 say, okay what structural classes are they part of, and if you

18 find out -- my guess is the ones that are positive only in those

19 binding assays. They're going to tend the detergents and we see

20 a series of chelators that are positive there. You throw in a

21 couple of different kinds of data, which we haven't shown you

22 today how we're actually doing. 

23 DR. MICHAEL KEISER: Just one followup on that

24 is then in terms of things such as dyes or other cases where you

25 might in fact want to consider a detergent or to look at the

26 slope of the curve, are there any ideas for that that are
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1 already on the docket? 

2 DR. RICHARD JUDSON: One of the problems with

3 looking at the slope of the curve -- so we get a slope is that

4 for most of the assays, we don't have enough tightly enough

5 spaced points to really resolve that. The slope has pretty big

6 confidence intervals. One of the guys in the group has spent a

7 lot of time trying to convince us that there's a lot of

8 information there. I think the confidence intervals are so wide

9 that there isn't enough information to say something specific,

10 but it may be there're chemical classes that would let us prove

11 yes or no that that is an issue or there's information there to

12 be able to say this is a false positive versus a true positive.

13 DR. DANIEL SCHLENK: That was Dr. Judson. Dr.

14 Jacobs? 

15 DR. MIRIAM JACOBS: Miriam Jacobs. Just

16 coming back to the antagonist assays again. I wondered if it

17 was perhaps the BG1 assay that you've been running in that

18 antagonist mode as well because that's a test guideline TG457. 

19 Is that one of the assays? 

20 DR. RICHARD JUDSON: We do run both -- the

21 NCGC runs both the BG1 and the betalactamase in the antagonist

22 mode. So, yes they do have that. I believe it's Odyssey Thera

23 that has the other two antagonist mode assays. 

24 DR. DANIEL SCHLENK: That was Dr. Judson. Dr.

25 Safe? 

26 DR. STEVEN SAFE: Just a comment on some of
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1 your comments. With the exception of the estrogen receptor

2 binding, the transactivation assays in a different cell context

3 would not necessarily -- a false negative in one and a positive

4 in the other, they don't have to be consistent because cell

5 context dictates for these selector receptor modulators. 

6 DR. RICHARD JUDSON: This is Richard Judson

7 again. This is a great dataset because we have so many

8 chemicals that we can start actually addressing some of those

9 issues. Are these false positives or is it some really

10 interesting biology that we didn't have a chance to test before?

11

12 DR. STEVEN SAFE: I agree. 

13 DR. DANIEL SCHLENK: That was Dr. Safe. Any

14 other questions or clarification here? Dr. Judson, you will be

15 available tomorrow, is that correct? 

16 DR. RICHARD JUDSON: I'm here the next two

17 days.

18 DR. DANIEL SCHLENK: Great. So again, just to

19 remind the Panel, we will allow you to stew on this overnight if

20 other questions come up in your deliberations. We will bring

21 the Agency before the Panel again. So just to let you know

22 that. 

23 I have a quick question. In looking at the

24 metabolic profile that was in some of the documentation that

25 Attagene actually has a certain P450 profile that's been, I

26 guess, inserted into the cell are enriched, it sounds like. I
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1 was curious. Is there any rationale for the levels of the

2 specific isoforms that are present there? Because I noticed

3 they are very different than what you'd expect in say human

4 liver microsomal sample, for example, three As like one of the

5 lower content where as 2E1 is very high. I'm just curious if

6 you have any information on why that profile is the way it is,

7 if they provided any of that information.

8 DR. KEITH HOUCK: Keith Houck. They selected

9 for that, an enrichment in the set activity. I don't remember

10 the details of how they did that, but they didn't transfect in

11 cDNAs, so there wasn't any specific rationale for what

12 proportion of level they would get in the cell line. 

13 DR. DANIEL SCHLENK: Thanks. Okay. Dr.

14 Denslow, one more? 

15 DR. NANCY DENSLOW: Nancy Denslow. My

16 question is with the Attagene assay. Some of the chemicals will

17 bind to multiple different kinds of receptors, so they're not

18 specific for a particular receptor. So I was wondering in the

19 Attagene assay when they're multiplexing 52 different receptors

20 and you're dealing with very low concentrations of the chemical,

21 are there any interferences from the compound binding to a

22 different receptor that you might miss then because it's sort of

23 used up or something like that. 

24 DR. KEITH HOUCK: Keith Houck. The chemical

25 wouldn't be used up. I mean the chemical is going to be in

26 excess. There is, with some very potent full agonist for some
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1 of the nuclear receptors, there's some evidence that you see a

2 squelching type of effect on other transcription factors, but

3 it's very secondary to the predominant activity that you see at

4 the primary target of that chemical. There's some evidence that

5 that's going on, but it doesn't seem to be a problem in

6 preventing you from seeing activity on other transcription

7 factors. 

8 DR. DANIEL SCHLENK: Okay. I've been informed

9 that coffee is available for the panel. At this point, unless

10 there's any other burning questions, we'll take a 15 minute

11 break. We'll be back at 20 till, how about that? 

12 (WHEREUPON a break was taken)

13 DR. DANIEL SCHLENK: Okay. Our next

14 presentation is by Michael Hornung, who is from Mid-Continent

15 Ecology Division, NHEERL, ORD. 

16 DR. MICHAEL HORNUNG: Thank you, Dr. Schlenk. 

17 What I'm going to present to you is some of the preliminary

18 findings of analysis of the high through put in ER Expert System

19 Training Set Data. Just to put you back in touch with where we

20 are on the road map, we're down in the lower right corner

21 looking at two in vitro assay type systems. 

22 In the context of the five goals for the SAP,

23 we're in number five determining if and how the high through put

24 data could be used to expand the Expert System Training Set. 

25 Essentially, we are looking at if we have a group of similar

26 chemicals, how do they respond in the training set assays that
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1 we have and the high through put assays that we're beginning to

2 look at.

3 So just a quick outline -- I'm going to talk

4 quickly about the chemicals used in the analysis and then bring

5 some of you up to speed in the in vitro assays used as the

6 Training Set. Dr. Judson talked about the high through put

7 assays and how some of that data is processed. I'm going to do

8 the same for the ER Expert System Training Set assays. And then

9 just look at the results for these common chemicals in the

10 binding and transactivation assays, and discuss some of the

11 factors that can contribute to discordance in these assays.

12 So we looked at 296 chemicals that we had data

13 for in the Training Set assays and the high through put assays. 

14 They represent a broad range of chemical classes and selected to

15 cover a LogKow range within that class. These were selected to

16 cover the EPA inventories of food use inerts specifically and

17 antimicrobials. There are other chemicals in there from the

18 nonfood use inventory. These include active and inactive

19 chemicals and classes. So we weren't just looking at one type

20 or another. 

21 This bottom panel just shows you -- this is a

22 2-hydroxy benzophenone example. I think Rick showed some of

23 these chemicals. These go from LogKow of 2.2 on the lower left

24 to I think 7.3 up on the upper right. Again, the purplish-pink

25 color are those chemicals that were active in the training set

26 and the chemical on the right in the green was an inactive
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1 chemical in the training set assays. 

2 So I think the next two slides are just going

3 to bring a quick review of the assays we looked at. So at the

4 time we started the analysis, we had data available for five

5 high through put assays. These are the three Novascreen assays.

6 In looking at comparing results, we wanted to look at some of

7 the assay parameters. The things we're looking at and thinking

8 about in comparing the results from these assays are the types

9 of receptors. In the mouse ER-alpha is a recombinant ligand

10 binding domain of the mouse ER-alpha receptor. 

11 As Pat talked about earlier, we also think

12 about these things in the context of the total protein in the

13 assays. What you can see across these three assays, the total

14 protein amounts can be quite different. The human and bovine

15 assays have full assays that are endogenous to the cells and the

16 MCF7 cells and the bovine uterine, of course, and the proteins

17 are 0.28 mg/mL and 5.6. So all three of these assays use

18 treated E2 displacement, so that's similar across the assays. 

19 We talked about the concentration suggested earlier. 

20 The two transaction activation assays have

21 been discussed. If we just look at the receptors involved

22 there, the ER-alpha ligand binding domain is in the BLA, and

23 again, it's the endogenous ER-alpha and beta in the BG1 assays. 

24 One is fluorescence and one is a luciferase readout. The

25 highest concentration there tested was just under 100

26 micromolar. 
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1 In comparison of the trout competitive binding

2 assay, this was an assay very similar to the Novascreen assays

3 in that the treated E2 displacement was used as the endpoint. 

4 These were cytosolic receptors prepared from liver cytosol, so

5 whatever endogenous estrogen receptors were expressed in those

6 livers. The highest concentration tested, Pat discussed this a

7 bit earlier. We tested these up to chemical solubility in the

8 assay media. In this assay and in the other binding assays,

9 we're essentially looking at that molecular initiating event of

10 the chemical binding to the estrogen receptor to initiate that

11 ER mediated Adverse Outcome Pathway. 

12 The rainbow trout liver slice vitellogenin

13 gene activation assay utilizes rainbow trout liver slices,

14 precision cut slices that respond to E2 by expressing

15 vitellogenin mRNA. That's measured by real time QPCR. The

16 highest concentration tested in these assays is to solubility or

17 to cytotoxicity if noted. These liver slices also have

18 endogenous metabolic capability. We have conducted multiple

19 experiments to look at metabolism in these assays. You can have

20 activation by parent chemical or determine if the activation in

21 that assay is by an active metabolite. 

22 So the next couple of slides will take you

23 through the decision process involved in the training set assays

24 with the trout systems. What I want to show here is the first

25 step is running the binding assays. These give five examples of

26 kind of the gradations and the types of responses you can see. 
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1 From on the left, a full displacement curve to all the way on

2 the right to a flat line displacement curve. Depending on what

3 those curves look like, assuming you get reproducible binding

4 curves, those will go on to be processed slightly differently. 

5 The first example I want to show are those

6 examples where those curves that give greater than 50 percent to

7 full 100 percent displacement of estrogen from the receptor. 

8 Now these chemicals that do this, the first thing we make sure

9 that there's a good shape to that binding curve that it looks

10 like -- and these are all done in parallel with an E2 curve. So

11 the first question we ask is it a known active training set

12 class. 

13 If it's in a known active class and in the

14 next question, is it within the LogKow range that we know for

15 that class, we'll assign it a binder status. So in the context

16 of knowing something about that chemical class, we can move that

17 chemical fairly quickly along. 

18 If there are some questions about the binding

19 curve, if it's very steep, you maybe have one point that tips

20 over and it looks like you just move the concentration up a

21 little bit and you've got full displacement. That raises some

22 questions. If it's known to be in a class that produces false

23 positive results, we'll move that chemical to a non-binder

24 category. If it's not in some known chemical class that gives

25 false binding activity, then the chemical will get moved on to

26 being tested in the liver slice assay. 
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1 So the liver slice assay isn't always done in

2 coordination with a binding assay. There are these criteria

3 that it meets to go on to that, although many of them do. 

4 In addition, a chemical will go into a binding

5 slice assay if we have uncertainty about where it would fit

6 within a known chemical class. If it's not in a known training

7 set class or if it's in a training set class, but it's outside

8 of the LogKow bounds where we have activity information, we'll

9 test that chemical in the slice assay to try to help further

10 define that chemical class activity boundary. 

11 If the chemical gives us an agonist or

12 antagonist response, it will then be called a binder. So that's

13 the confirmation. If it doesn't give agonist or antagonist, we

14 can do other assays like we can perform a Ki binding assay to

15 determine if it's a truly competitive binder. If it is not a

16 true competitive binder in that type of assay, we'll just call

17 it a non-binder. This other arrow that shows going from the Ki

18 category to the binder, we don't have any chemicals that would

19 go that pathway. We haven't seen any. I don't know -- well,

20 I'll just leave it at that. 

21 If a chemical gives us, so this is less than

22 50 percent displacement, but we do see some displacement of E2

23 from the receptor, these chemicals unlike the ones that give us

24 full displacement, these chemical won't be called a binder

25 without going through a vitellogenin assay. So we need to have

26 confirmation for these weak displacement chemicals that there's
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1 activity there. 

2 So if there is a poor shape to the binding

3 curve or it looks like a questionable binding curve, potentially

4 a false positive. We can go through those same questions as we

5 did earlier. If it's in a chemical group that we know can

6 produce false negatives, we can call that a non-binder. If it's

7 not in that chemical group or there's a lot of uncertainty about

8 it, it will then go on to slice assays. 

9 One of the other things we can do with the

10 slice assay or in addition to the slice assay is if we have a

11 weak binder and there is some agonism or antagonism response, we

12 do have a recombinant trout receptor that we can use to test to

13 see if we are getting displacement with that chemical. So that

14 would be a confirmation that we have a binder if in the

15 recombinant trout assay we get activity. 

16 This goes back to the slide that Pat showed

17 about the differences and total protein in those assays. The

18 recombinant binding assay has a lot less protein in it than what

19 our cytosolic assay does. The cytosolic trout assay here at 4

20 mg/mL is similar to the range that the Novascreen bovine uterine

21 assay is. We can use this assay, and I think this is just the

22 reminder that by utilizing an assay with a lower amount of

23 protein in it, you'd have an equivalent of free fraction of

24 available chemical to that assay at lower nominal

25 concentrations. 

26 The last example here is for a flat line
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1 binder or non-binder. If this chemical belongs to a known

2 inactive class, then we'll call that a non-binder. If it's not

3 in a known inactive class, but there's evidence in the

4 literature, strong evidence that it's a non-binder, then it will

5 go to a non-binder class, but I think we have to have strong

6 evidence for that. If there is evidence in the scientific

7 literature to suggest that there's potential for binding, then

8 we can go on to a liver slice assay. 

9 In that liver slice assay -- so the main thing

10 there is if we get activity on the liver slice assay, but

11 remember back we had no flat line binding assay, that might

12 suggest that you're getting metabolism from that non-binder in

13 the slice assay that in the liver slices, it's getting

14 metabolized to a metabolite that can actually bind to the

15 receptor in those slices, activate it and produce a

16 vitellogenin. So if you're seeing that activity, that's

17 suggestive that there's metabolism, and we can also go back to

18 that recombinant assay and just to verify that a parent is

19 inactive and we don't have the sensitivity with the cytosolic

20 receptor for activity. 

21 So in these cases, if we run the metabolism

22 assays and we can identify a metabolite as positive, we would

23 call that a binder and call the parent chemical then a

24 non-binder. 

25 So I'm going to start off talking about the

26 estrogen receptor binding assays, the four binding assays, the
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1 three Novascreens in the trout. I'm going to give some examples

2 of some of the types of responses we saw with some of these

3 similar chemicals, and then give a little summation of a couple

4 tables at the end. 

5 So one of the example chemical sets again we

6 had here was the 2-hydroxy benzophenones. We had four active

7 chemicals in that class in the training set. What I've done on

8 these curves -- these curves show four rainbow trout binding

9 assay results, and I've superimposed on it the 50 micromolar

10 cutoff line that the high end of the dose response curve for the

11 Novascreen binding assays. Assuming that -- remember these

12 assays have the same tritiated E2 displacement response as the

13 endpoint, assuming that these assays are all similarly active

14 and have similar activity, what you might expect from the top

15 two chemicals is that you can see that the displacement occurs

16 to the left or at the lower concentrations below that 50

17 micromolar cutoff. So those two chemicals on top you'd expect

18 everything else being equal, would be positive across all four

19 assays. Those two curves on the bottom, there is just a little

20 short animation here, shows if we would have stopped the trout

21 receptor binding assays at 50 micromolar, we would not have seen

22 any displacement. 

23 As we increase the concentration for this

24 chemical, you begin to see displacement of E2 from those curves.

25 So you'd expect that in the binding assays with that cutoff

26 that the two top chemicals would be positive and the two bottom
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1 chemicals would likely be negative. 

2 These next couple of examples takes us through

3 those four assays comparing the trout and the Novascreen results

4 for those. As we expected with the trout assay as shown on the

5 left panel, and in this slide and in the subsequent slides after

6 this, if we have three panels in a column, the Novascreen mouse

7 ER-alpha assay is on top and the human in the middle and the

8 bovine on the bottom. Those are oriented in relationship in

9 going from lowest total protein in the mouse assay down to the

10 assay, the Novascreen assay with the highest total protein 5.6. 

11 So, just to put them in some type of relationship there. But

12 again, the 50 micromolar cutoff is there, and you can see that

13 for the Novascreen assay, all three of those binding curves fall

14 to the left of that curve and were all active in this assay. 

15 For that benzophenone that was in the upper

16 right corner in the first slide, you can see that that 50

17 micromolar cutoff is -- this chemical had a little lower

18 affinity than the first one, but most of that competition

19 binding curve is still at the lower concentrations below that 50

20 micromolar cutoff. So what we see in the Novascreen assay is

21 was yes, that these did produce active calls in these assays. 

22 The one on top, I just wanted to note, when I first looked at

23 this, this looked active. I just assumed all three were active,

24 but looking at this one is one that gets called as inactive. 

25 You can see that the top displacement points just reach that

26 threshold level. 
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1 So that's something where Richard talked a

2 little bit about manually looking at some of the curves and also

3 the replication is an N of 1 in this. The replication for the

4 binding assays and the reference chemicals, I think, were an N

5 of 2 or 3, but some of these test chemicals, it's just an N of

6 1, which gives you -- when there are questions about the data,

7 it can lead to a little bit of uncertainty. 

8 So this slide shows the two chemicals that

9 were the two lower ones on that first slide of the four trout

10 bindings assays. What we see here is that where these binding

11 curves were to the right in the trout assays of that 50

12 micromolar cutoff, these two chemicals were inactive in the 25

13 micromolar prescreen for all three of those Novascreen assays. 

14 So as expected for this lower affinity chemical, it was inactive

15 in the Novascreen assays due to that cutoff. 

16 The next couple of slides here talk about a

17 couple of other things that we saw with these binding assays. 

18 This was a potent chemical, hyphenated chemical in the trout

19 assays. When I talked about earlier about replication and some

20 questions about it, this assay showed -- what I wanted to just

21 bring out here, I think, for the first time is that in the

22 training set binding assays, there're two technical replicates

23 per concentration per run and at least two runs per chemical. 

24 For some of the test chemicals in the Novascreen assays due to

25 some of the concerns Richard brought out earlier, those are

26 limited to one technical replicate and one run per chemical. 
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1 What we see here on the right might be

2 something you might expect if that total protein and fee

3 available chemical is playing into the response of these assays

4 where the mouse ER-alpha assay has the least amount of total

5 protein, we are seeing a binding curve there, but for the human

6 and the bovine, you're not seeing activity. If you look at

7 where that 50 micromolar cutoff is, that is right at the bottom

8 of that shoulder of that binding curve. So you might expect to

9 see that type of response at the mouse ER-alpha assay, maybe

10 more sensitive because of more free fraction chemical available

11 due to the lower protein concentration. 

12 The replication question comes up especially

13 for something like there where we're comparing the assays or

14 looking at the responses in these assays where the activity, the

15 binding affinity and the chart assay falls right on that 50

16 micromolar curve. In this curve, this also looks like, on the

17 right hand side in the Novascreen assays that we are going from

18 a mouse assay that looks potentially more sensitive to a human

19 assay where I think there's one point that just touches the

20 significance threshold there, and then the bovine not showing

21 activity. 

22 So it looks like for some of these chemicals,

23 this type of relationship with protein may hold out, but for

24 some of them, it doesn't. The chemical on the left is

25 chlorhexidine diacetate. This one chose a response that you

26 might expect for that protein free chemical relationship. 
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1 However, on the left is a chemical, pentyl-cyclohexanone, for

2 which there is no activity in the mouse, but there is activity,

3 almost full displacement curves in the bovine or human. 

4 So some of these things raise questions about,

5 we try to apply these principals of trying to understand how the

6 binding assays -- what the binding assays are telling us and why

7 they are positive or negative in one assay versus another. In

8 some of those, they make some sense and others, maybe it's

9 question about the ligand binding domain versus the full

10 receptor. The human and the bovine in this one are full

11 receptor where the mouse is just the ligand binding domain. So

12 maybe that plays a role in this binding assay or in the response

13 to this chemical sort.

14 So I have two slides with tables on here that

15 kind of summarize the activity of the training set chemicals

16 among all the binding assays. What we see is in the upper table

17 are those out of the 296, there were 172 chemicals in the

18 training set that were inactive. So we give them an RBA equal

19 to zero. About 90 percent of those inactives were also inactive

20 in the Novascreen assays. That's fairly good concordance across

21 those assays, but the other thing to keep in the back or your

22 mind is because of that cutoff, you may be -- because of the 50

23 micromolar cutoff or the 25 micromolar cutoff, you may see

24 having more negatives by default showing up in that because

25 you're not testing as high as what was tested in the trout

26 assays. 
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1 The bottom table shows out of that 296

2 chemicals tested, 124 were positive in the training set. Out of

3 these are about 30 percent that were active in the high through

4 put assays. So there's less agreement here. This is where I

5 think the next table kind of discusses where some of that 70

6 percent of discordance was. What we see is if we took the

7 training set active chemicals and bend them into RBA groups,

8 each is one 10 fold RBA group, with the highest affinity

9 chemicals on top and the lowest ones on the bottom, what we see

10 is that for the high affinity chemicals, there's really good

11 concordance across the binding assays. As you go to the lower

12 and lower affinity chemicals, the binding affinity, the

13 agreement, the concordance between the training set call and

14 those Novascreen binding assays tends to drop off. Most of that

15 we can explain. We can explain for a lot of these by that

16 cutoff.

17 One little note here, when we looked at these

18 assays, it looked like the human ER assay was actually picking

19 up more chemicals than the bovine and the mouse. It's a limited

20 data set of these 296, but that seemed to pick up or identify

21 slightly more chemicals as positive. 

22 So I'm going to switch gears right after the

23 binding assay summary. So the concentration cutoff seemed to

24 play a big role where the discordance was between the training

25 set binding assays and the Novascreen binding assays. Some of

26 that where that didn't explain the discordance, then questions
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1 about whether the ligand binding domain receptor of the full

2 receptor played a role in there or chemical bioavailability

3 based on those proteins, but there didn't seem to be any real

4 clear answer there. Really digging into that deeper isn't

5 something that we've done. One of the other things that jumped

6 out at us in looking at this was when you do have limited

7 replication, that can lead to some uncertainty in looking at

8 making these calls across these assays. 

9 Now I'm going to switch gears and include the

10 gene activation assays. This again is just a slide to just as a

11 reminder of the transactivation assays, the BLA assay. So

12 instead of the full set of eight chemicals -- remember this is

13 five chemicals -- we had the three binding assays and the two

14 Tox21 gene activation assays that we compared to the trout

15 assay. So we have the BLA assay again with the ligand binding

16 domain, a fluorescence readout, the BG1 with a luminescence

17 readout and the training set liver slice assay with the

18 vitellogenin readout by QPCR.

19 So I'm going to do the same thing with these

20 assays, give you some examples of some of the things we looked

21 at, and then some summary tables at the end. But just up front,

22 some of the factors in interpreting these assays Dr. Judson

23 touched on some of these things in his presentation. The

24 concentration test can affect these assays. Chemical

25 interferences, cytotoxicity, chemical solubility and media pH

26 are all things to think about, and they get back to some of the
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1 things that Dr. Kent talked about with some of the chemical

2 P-chem parameters. We'll also discuss a little bit about

3 metabolism and how that plays into interpreting these assays.

4 So the concentration cutoff that we saw in the

5 binding assay with the 25 and 50 micromolar prescreen may also

6 affect interpretation of discordance between different assays in

7 the gene activation assays. If assays are run to different

8 toxicity limits -- not toxicity limits sorry -- concentration

9 limits, some assays may not be at a high enough concentration

10 assuming everything else is equal in those assays as well to

11 pick up active chemicals. 

12 What we see here on the left is the trout

13 liver slice vitellogenin mRNA response for pentylaniline. You

14 can see at the -4 Log molar, so 100 micromolar is where we first

15 get the first significant induction by pentylaniline and

16 vitellogenin RNA in the liver slices. That's a very significant

17 response near to that of E2. In the BLA and the BG1 assays, it

18 went up to 92 micromolar, so very similar concentration. If

19 everything else was similar in those assays, you'd expect that

20 we'd start to see some induction with those chemicals at those

21 concentrations. It might be that we're just right at the tip of

22 seeing some activity there. 

23 So I have two examples here to look at

24 interpreting activity within chemical groups. This first

25 example is alkylbenzenethiol. These are going to be where we

26 have two chemicals, structurally similar chemicals that we've
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1 classified in the same chemical group. In one set of assays,

2 they're inactive across the assays that we had data for. For

3 the other similar chemical, there was some active and some

4 inactive activity. 

5 So for this example, we had p-toluene thiol or

6 methylbenzenethiol. That chemical gave us a ski slope-shaped

7 binding curve. So when we push at the high concentrations,

8 there is something that goes on with that assay. We call that a

9 negative binding. It doesn't look like what Pat showed earlier

10 with what a binding curve should look like, what you've seen in

11 the previous ones. Butylbenzenethiol gives the same type of

12 response. In the butylbenzenethiol, that was associated -- that

13 uptake and that curve was associated with precipitation forming.

14 Sometimes that curve is associated with precipitation, but no

15 displacement of E2 until we got to that displacement. 

16 For the toluene thiol, that was inactive in

17 the 25 micromolar prescreen, but in the three Novascreen assays,

18 that showed a strong indication that of displacement of E2 from

19 that receptor. 

20 In the BLA assays for this chemical, there was

21 really no indication that there was any activity in either the

22 methyl or butylbenzenethiol. In the BG1 assay, which is the

23 luciferase based assay, you see that for the methylbenzenethiol,

24 there's no activity. You can see here that this assay may be a

25 little noisier than the BLA assay. There is some activity in

26 the butylbenzenethiol, but you can see those red asterixes there
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1 are just notations that there're some flare effects in that

2 plate or associated with that. That just raises a little yellow

3 flag that when you see those things that you need to look at

4 that a little more closely. 

5 So interpretation of these benzenethiol

6 results where we're looking at a chemical class, but we're

7 getting discordant activity within those classes, one chemical

8 is inactive across all assays. The higher LogKow

9 butylbenzenethiol was inactive in the binding assay, the trout

10 binding assay, but it was active in the Novascreens and inactive

11 in the BLA assay. In the BG1, it was active, but there was some

12 indication of flare effect, so it raises a little question

13 there. 

14 So if there is truly activity with the

15 butylbenzenethiol, how do you go about trying to discern that? 

16 One of things to do, the second bullet down here is to expand

17 the chemical in that class to establish greater confidence in

18 the structure activity relationships. You can bound these two

19 chemicals by adding a straight chain butylthiol or increasing

20 the LogKow, go one chemical to either side of it, or go ahead

21 and test chemicals in other assays. We could include the trout

22 liver slice assay. We didn't test this in the trout liver slice

23 assay when we started doing these because these were not

24 inventory chemicals. The only chemical in this group if

25 benzenethiol. So depending on where you want to put your

26 efforts in what you're looking for, I think, can determine which
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1 chemicals you're testing. So if this were a group with a lot of

2 inventory chemicals, it would make more sense to pursue those

3 things further.

4 On the next example are the benzotriazole

5 phenols. Again, two structurally similar chemicals and really

6 it's just the extent of alkylation on the phenol group on there

7 that's different. In this one, you can see that in this case,

8 the higher LogKow chemical is inactive across all the assays and

9 the lower LogKow is active in two assays. Again, these are the

10 trout receptor binding assay for those two chemicals showing a

11 flat line binding curve, no indication of toxicity up to

12 solubility. 

13 In the Novascreen assays, there was no

14 activity for either of these chemicals except for the mouse

15 ER-alpha assay. I think this was an N1. It is hard for me

16 looking at that if that curve looks like that's a little flat

17 for a binding curve, but it's hard to tell based on those two E2

18 points. 

19 The benzotriazoles in the BLA assay, all three

20 of those were clearly negative. As you remember Dr. Judson

21 talked about, these came from three replicate sources. So that

22 would a strong indication that that was a negative chemical, but

23 in the BG1 assay, again these are three replicate chemical

24 sources and there's strong activity for the methylphenyl

25 chemical here and no activity in the octylphenyl, but you can

26 see in some of those curves, there's a little bit of flare
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1 effect there. 

2 In two of those curves for the methylphenyl,

3 there's a little red asterisk at the top concentration for the

4 metylphenyl at the top of that curve, so showing that there's

5 some flare activity in the plates associated with those curves. 

6

7 So based on chemical categories, it would be

8 great if your chemical categories or your assays respond

9 similarly for similar chemicals that are in the same category. 

10 As you can see, that's not always the case. When we get

11 equivocal responses among the assays, from this one, the parent

12 chemical based on the binding assays that are not binding to a

13 receptor, there's a flare effect in the BG1. We might

14 hypothesize because we had three chemical sources for the BG1

15 assay and they gave strong responses, even with that flare

16 effect signal there, you might have hypothesize that there may

17 be metabolic activation, but I think you'd want to have

18 verification of that.

19 So a couple other artifact types of signals

20 that we saw or that kind of came up in our analysis of some of

21 this data. Charge species are chemicals that we'll see in these

22 inventories. In 2009 SAP, we talked about octylbenzene sulfonic

23 acid, and this dataset that we looked at, we had the

24 dodecylbenzwnesulfonic acid that produced similar false binding

25 curves we can see across the four binding assays. They all gave

26 very strong evidence of displacement of E2 from the receptor. 
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1 So that would like a positive chemical if we didn't know

2 anything else, but looking at the trout vitellogenin slice assay

3 on the left, we can see that in the agonist mode, this did not

4 produce any vitellogenin signal above background. 

5 In the vitellogenin slice assays, we run these

6 in antagonist mode. You can see it on the far right in that

7 middle panel, there is a red line there that says LDH. The

8 antagonism dropped off, but that's at the same concentration

9 that we saw activity or cytotoxicity as measured by the leakage

10 of lactate dehydrogenase into the culture media with that liver

11 slice. So that would be evidence that that decrease in E2

12 signal in that antagonism figure in the middle is a false

13 antagonist effect. 

14 The other thing to bring up is that for this

15 charge species, if we put this through the PKa filter that Ray

16 talked about earlier, this has a PKa of -1.84. So this chemical

17 would get flagged if this were an inventory chemical to test. 

18 Another thing that we know can affect our

19 assays as we have run these and looked at some of the different

20 parameter is pH. Hydroxyquinoline sulfate is a chemical that in

21 the human ER binding assay and the trout liver cytosol binding

22 assay gave displacement of E2 from the receptor. One of the

23 things that we measured in the liver cytosol prep is pH. As we

24 push these concentrations, the pH dropped well below 7, which

25 isn't compatible with getting good binding data. So that can

26 produce false negative data. 
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1 We found pH can also effect, which may not be

2 surprising, can also effect the gene response in the slices. 

3 This quinoline sulfate does produce a decrease in pH as shown in

4 the panel on the right. Again on the left is the vitellogenin

5 agonism. There is no activity there. In the middle panel,

6 that's our antagonist assay. One of the things we started doing

7 is running antagonist assays with two different concentrations

8 of E2. So if you have a true antagonist, those antagonism

9 curves should be separated by an equivalent amount of -- so if

10 you have 10 to the minus 6 E2 and 10 to the minus 8, you'd

11 expect your antagonism curves to be sifted by two orders of

12 magnitude by two log units. So we don't see that. 

13 We also found that the vitellogenin induction

14 in these slices is actually more sensitive to pH change than

15 what the binding assays were. That does go down a little bit as

16 seen on the slide on the right. Another thing to note is in the

17 filtering, this chemical would be highlighted, would be flagged

18 by the PKa filter. That is a very low PKa. There is only

19 ionized forms at the assay pH. 

20 So the last example or thing to look at for

21 understanding the activity of the parent chemicals in these

22 assays is metabolism where you get no binding, but potential

23 gene activation. Here we had methyltestosterone as an example. 

24 You can see in the Novascreen mouse ER-alpha assay and the

25 bovine ER-alpha, the bovine ER assay, there is no displacement

26 and in human and the trout on the bottom, there is just a low
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1 amount, let's say partial displacement or limited displacement. 

2 But when these chemicals are tested in the BG1

3 assay and the BLA assay, they give very strong responses. So

4 the no activity in the binding but strong activity in the cell

5 based assay suggests that there's metabolism going on. 

6 In the trout assays, we've determined that

7 17-methyltestosterone gets metabolized to 17-methylestradiol,

8 and in running binding assays with 17-metylestradiol, this

9 chemical has very high affinity for the fish ER. So we would

10 call 17-methyltestosterone a non-binder and 17-methylestradiol a

11 binder. So where we had apparent discordance between a call in

12 the training set, 17-methyltestoserone was called inactive, the

13 parent chemical would be considered inactive, but it's the

14 metabolite that would be or a non-binder and the metabolite

15 would be called a binder. So that discordance would actually be

16 agreement if the metabolic conversion is to that bioactive

17 compound. In thinking about developing a chemical category and

18 structure-based activity, you want to be assigning the correct

19 structure to the activity. 

20 So I think there's just a last table or two to

21 kind of summarize some of these things. One of the things we

22 wanted to look at is if we had discordant calls, was there a

23 pattern to it. With the things we knew about LogKow and binding

24 relationships, did it match up with LogKow, were there specific

25 classes that were effected more than others, and generally what

26 we saw was similar to what we saw at with the binding assays
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1 that the discordant calls were mostly training set chemicals

2 with low affinity. You could see a lot of these RBAs from the

3 training set assays have three or four zeros in them. 

4 I just want to give an example. I just have

5 some overlays with alkylphenols was a group where there were a

6 lot of chemicals for these five assays. These clear boxes or

7 the white boxes are those chemicals that were training set

8 actives but high through put assay inactives. These were going

9 from low RBA from the left to higher RBA on the right,

10 continuing on that second row on the bottom. What you see is

11 these low affinity alkylphenols were HTP inactives. There were

12 two of them in here that were active in the BLA assay, so they

13 gave a significant response. 

14 In the alkylphenols, if we continue that set

15 up to the higher LogKow and higher affinity LogKow chemicals,

16 you can see that these pink boxes again or purple show the high

17 through put transactivation or the Novascreen assays where they

18 were positive again going from low LogKow to high LogKow. For

19 the most part, these were detected as active with just missing

20 or having some discordance with the training assay on that ortho

21 para t-butylphenol. 

22 So I think this is last table slide here. In

23 these assays, we're looking at two levels along the adverse

24 outcome pathway, at binding and at the transactivation level in

25 a pathway that Dr. Schmieder showed earlier. 

26 So in looking at the assays, the concordance
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1 between the training set assays and the high through put assays,

2 if we look at the first column, in the training set assays,

3 there were 172 chemicals inactive, and 134 of these were

4 inactive in all five of those high through put assays. There

5 was pretty good concordance between those inactive groups. 

6 Thirty percent of the high through put inactive calls were

7 training set active in that box below the 134. That row shows

8 -- the middle row in the table -- we have 124 active training

9 set chemicals, 57 of those were inactive in the high through put

10 assay, but 34 were active and 33 were active in at least one or

11 two of the binding and gene activation assays in the high

12 through put mode. 

13 So I think the bottom line here was that

14 probably for the 43 chemicals active and at least one binding

15 and one gene activation assay, we had good agreement with the

16 high through put actives. Eighty percent of those actives agree

17 with the training set. I think 62 chemicals were active in the

18 binding or a transactivation assays. So that was kind of a less

19 constrictive categorization or they weren't active in both. 

20 Fifty-three percent of those high through put actives agreed

21 with the training set actives. So it was about a 50 percent

22 inactive versus active agreement when we reduce that criteria. 

23 So the preliminary assessment of our analysis

24 of these data in looking at the training set assay and the five

25 high through put assays, the five high through put assays

26 detected the moderate to high affinity chemicals well, but not
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1 the low affinity chemicals based on what we saw with the

2 training set. That is likely largely contributed to the

3 chemical concentration cutoffs. pH and charge effects can play

4 a role in producing false positive and false negative effects,

5 but I think we have ways of looking at the data and looking at

6 across assays to try to determine where those types of responses

7 and looking at the P-chem data where those types of responses

8 fall out. 

9 We can look at metabolism across assays and

10 verification of metabolism to try to understand what is the

11 parent chemical or what is the chemical that's producing the

12 estrogenic activity. So on further analysis like we talked

13 about earlier, this is a preliminary analysis of a small set of

14 these high through put assays. With further analysis of

15 chemicals where we have discordant responses in these assays

16 will help to verify the activity of the chemicals and improve

17 our interpretation of the high through put in vitro and training

18 set in vitro assay results. 

19 DR. DANIEL SCHLENK: Okay. Thank you. Any

20 questions or clarification? Dr. Schultz? 

21 DR. TERRY SCHULTZ: A lot of information. I'm

22 attributing it to the lateness in the afternoon why I didn't

23 capture it all has nothing to do with my age, but it sounds to

24 me like we have two things here. One, you're using the rainbow

25 trout cytosolic binding assay and then the liver slice to find

26 your gold standard that you're going to compare the high through
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1 put assays too. Is that correct? 

2 DR. MICHAEL HORNUNG: This is Mike Hornung. 

3 What we're doing initially here is looking at where we have the

4 data for the trout assays where we've optimized it for these low

5 affinity chemicals by pushing the concentrations up and doing

6 these other things to look at how the high through put assays

7 that we're talking about using for prioritization. Are we

8 getting the same types of responses for these assays? 

9 DR. TERRY SCHULTZ: Correct, but you're saying

10 that whatever my rainbow trout assay, that is what I'm saying. 

11 If it's positive in that, that's a positive. If it's negative,

12 it's negative and I'm comparing the other one to it, correct? 

13 DR. MICHAEL HORNUNG: I think -- this is Mike

14 Hornung again. I think some of the discussion we had earlier

15 about the different assays and what we see in that, you may get

16 discordance in assays where, for one reason or another, one

17 assay doesn't give you a correct -- not a correct response. 

18 There is no correct response. Where a chemical may be positive

19 in one assay and positive in another. I don't think you should

20 look at it as assay versus assay and one being right or one

21 being wrong. I think you have to look at how they're performed

22 for answering the question that you're asking. Do we have them

23 optimized? Answer the question for looking for assays that act

24 via this mechanism or do this. Do we have our assays optimized

25 for the questions we're asking? We're going to get discordance

26 across assays. If we look across any assays, we're going to get
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1 -- some of those topics came up today. So I don't know -- I

2 tend to trail off. I don't know if I answered your question

3 quite right. 

4 DR. TERRY SCHULTZ: I agree with you. I get

5 very concerned when we start to basically be dealing with

6 statistics in order to make these mechanistically relevant

7 comparisons. So I do agree with you on that. The second

8 question I have, if I may, it sounds to me like the number one

9 discrepancy that you have is because of the concentrations that

10 are used within the different assays. The high through put

11 being defined at 50 or 25, and then going down as opposed to the

12 Duluth Protocol where basically you go up to side (inaudible)

13 limits. 

14 DR. MICHAEL HORNUNG: This is Mike Hornung

15 again. I would agree. I think that is our major finding that

16 the concentration differences in the assays can play a big role

17 in whether you're detecting low affinity binders or not. There

18 are other chemicals that are discordant across the assays where

19 we may have other questions that we can deal with in the future,

20 but for a lot of these chemicals that we saw, and I think that

21 was one of those numbers with 30 percent concordance and 70

22 percent discordance, I think a lot that value is from the

23 different concentrations tested in the assays. So to be able to

24 compare those assays, and this might have been mentioned earlier

25 also, if you're not comparing the same concentrations and kind

26 of getting back to what Dr. Schmieder talked about, the free
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1 fractions, those types of things, if you're not comparing

2 chemical at site of action in equivalent amounts across the

3 assays, that raises some questions about the comparisons that

4 you're doing and how you need to take that into consideration in

5 interpreting the results from those assays. 

6 DR. TERRY SCHULTZ: Thank you.

7 DR. DANIEL SCHLENK: Dr. Denslow? 

8 DR. NANCY DENSLOW? I'm Nancy Denslow. So one

9 of my questions is like with the rainbow trout estrogen receptor

10 binding assay and the transactivation assay for vitellogenin,

11 the liver probably has more than just estrogen receptor-alpha

12 being expressed. It has beta -- well in rainbow trout, there

13 are four estrogen receptors and then there's a membrane receptor

14 as well. So the other assay is really estrogen receptor-alpha

15 specific. So I was just wondering if some of the differences

16 could also be ascribed to these other receptors being in some of

17 the assays? 

18 DR. MICHAEL HORNUNG: Mike Hornung. There may

19 be some of that in there. The rainbow trout liver slice assay

20 has all the endogenous receptors. In that liver cytosolic prep,

21 we also have a mix of receptors. So comparing the activity

22 between the cytosolic prep and the liver prep, you have a fairly

23 uniform comparison of receptors across those assays. So the

24 slice assay works very well as a verification of the activity --

25 yes, the slice assay looking at vitellogenin induction is a very

26 good verification based on the receptor population that's in the
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1 cytosol activity. 

2 In the other transactivation assays, right. 

3 Some of those may have human estrogen receptor alpha expressed. 

4 I think the BG1 cells -- that has whatever endogenous alpha,

5 beta, gamma, whatever receptors those cell lines express. So

6 there may be more similarity there. Some of the data that I

7 think Dr. Schmieder showed earlier about differences between rat

8 cytosolic receptor binding and a trout receptor binding that a

9 lot of the differences in the activity was more related to the

10 assay type and the amount of protein that may be in some of

11 these factors, although receptor abundance in numbers may be

12 different, but I guess as a first blush looking at an estrogen

13 receptor assuming alpha mediated responses, I think we need to

14 start somewhere at that molecular initiating event and think

15 about the pathway that we're looking at in induction of that

16 receptor type. 

17 DR. DANIEL SCHLENK: Dr. Nagarkatti? 

18 DR. PRAKASH NAGARKATTI: Nagarkatti. I just

19 wanted to follow up on the previous question about the

20 discordance within the various assays. So my question was

21 again, I'm a little bit confused. You attribute that to the

22 different concentrations that were used in the training set

23 assay versus the other three HTP assays. But then why would it

24 impact only those chemicals that are found to be active by the

25 training set assays, but you got only like 35 percent agreement

26 whereas when you use inactive compounds, you find that there is
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1 88 percent match. So if you can go back to that 7.2.1 table --

2 you talk about 88 percent versus 35 percent? 

3 DR. MICHAEL HORNUNG: Back in the binding

4 assay table. 

5 DR. PRAKASH NAGARKATTI: So my question was if

6 it was due to concentrations, why would it impact only the

7 active compounds? In fact, the only thing that it would be the

8 other way around that it should affect those compounds which

9 were found to be inactive at 25 micromolar, when you raise it to

10 50 micromolar, they may find to be active. So there should be

11 more discordance with the top line there compared to the bottom

12 line. 

13 DR. MICHAEL HORNUNG: This is Mike Hornung

14 again. I believe you would expect to see -- so in the top line,

15 you would see more agreement, more concordance between these

16 assays. If you are pushing one assay up to -- so assay A goes

17 up to a higher --

18 DR. PRAKASH NAGARKATTI: So the first one is

19 assay A there, 172 chemicals, by the training set were found to

20 be inactive based on 25 micromolar concentrations.

21 DR. MICHAEL HORNUNG: So the training set

22 assay are taken up to solubility. So if you've taken them up to

23 solubility and you don't see any activity, you've gone as high

24 as you can test those. So if you are comparing that to an assay

25 where you haven't gone as far, if they have low affinity and

26 you're going to see that activity at higher concentrations, but
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1 default, you're going to call those inactive because you haven't

2 reached the active range yet. You should get more agreement

3 concordance in the inactive set. It would make sense that then

4 where you get the discordance is in looking at the active

5 training set where you've taken those out to that high

6 concentration. So the low affinity chemicals will get detected

7 out there. So you're going to have a higher number of those

8 that are called active. So then if you use a lower

9 concentration cutoff, you're not achieving that same

10 concentration, so you're going to have -- so you will miss some

11 of them.

12 Dr. PRAKASH NAGARKATTI: So there's no way

13 that you can compare the data. Like say based on the highest

14 level of solubility to those HTP data, and say that okay, this

15 discordance was -- yes it was due to the differential

16 concentrations that were used. 

17 DR. MICHAEL HORNUNG: I think -- 

18 DR. PRAKASH NAGARKATTI: Because you should

19 have that data that you could compare right? 

20 DR. MICHAEL HORNUNG: I think -- let me go

21 back to the benzophenone table. I don't know if this might

22 help. So this is a chemical class, the 2-hydroxybenzophenones. 

23 So this is the rainbow trout data that was taken up to

24 solubility. So those are the curves for the rainbow trout data.

25 But what I've superimposed on there is the 50 micromolar

26 cutoff, which is the high concentration for the dose response
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1 for the Novascreen assays. So if those assays were all equal in

2 sensitivity and everything else, we would expect that those two

3 chemicals on the bottom if just tested up to that solubility

4 line, the red line, would not -- so if we just stop the

5 concentration there, the trout assays would also call those

6 inactive or whatever assay would call those inactive. As you

7 increase that concentration, then those become -- you've gone up

8 into that active range or the concentration where you have the

9 affinity then to displace the E2. So for those bottom

10 chemicals, if you -- for chemicals that are tested to a high

11 concentration, you're hedging your bets for detecting those

12 affinity chemicals. If like in the bottom example there, if you

13 cut that concentration off at 50 micromolar, those would called

14 negative. 

15 DR. PRAKASH NAGARKATTI: Nagarkatti again. So

16 my question was can you normalize it to the highest level of

17 solubility and then compare the data and say that okay, if you

18 do that, now you're at almost 88 percent or 90 percent

19 concordance. I mean, can you do that? 

20 DR. MICHAEL HORNUNG: This is Mike Hornung. 

21 So I think what you're saying is if we just looked at everything

22 at that 50 micromolar cutoff level or we picked that

23 concentration that was similar across all the assays and said at

24 this concentration, do you give 10 percent or 20 percent or zero

25 percent displacement? I don't know if that would change what we

26 saw with the data. 
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1 DR. PRAKASH NAGARKATTI: So you attribute the

2 entire discordance due to the fact in one assay used only

3 specific concentration of the highest solubility or whatever,

4 and you used a range in the other one, and you attribute the

5 entire discordance due to that factor. So I was just wondering

6 whether you could revisit the data to address that issue or it's

7 not possible. So that's all I was trying to find out.

8 DR. DANIEL SCHLENK: Dr. Judson? 

9 DR. RICHARD JUDSON: Richard Judson. I want

10 to just make a couple of clarifications about this. So it's

11 really important to stress this is a very preliminary analysis. 

12 Let's take one of the specific points that you raised, which was

13 why would the HTP assays be positive when the low through put

14 assays are negative? You can't explain that with this

15 concentration cutoff. 

16 I'm going to ask a question of Mike. The

17 calls of positive or negative are kind of a weight of evidence

18 looking at both the trout assays, both the binding and the

19 transactivation. So for instance, one of these surfactants --

20 we've talked about detergents. They will be called positive in

21 the HTP assays because we haven't done that weight of evidence

22 filtering out yet. So some of those HTP positive -- they're

23 actually positive at very low concentrations because that's what

24 the surfactants do whereas in the trout side, they would have

25 been positive in the trout binding assay just taken as it is,

26 but looking across both the assays and the chemical information,
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1 you say they're negative. 

2 So getting sort of down in the weeds of these

3 table comparisons where they finish the weight of evidence on

4 their side and we're looking at this unfiltered data on the HTP

5 side is you're seeing some interesting trends, this

6 concentration trend holds. If you go and add other assays like

7 the Attagene assays, which are taking up to 200 micromolar, we

8 see activity in some even weaker ones. So it lends more weight

9 to this theory. Most of the HTP negative trout positive ones

10 are really just because the trout has run out to these higher

11 and higher concentrations. 

12 DR. DANIEL SCHLENK: Dr. Safe? 

13 DR. STEVEN SAFE: Steven Safe. I just wanted

14 to ask you one question. When you use the thiols, you had some

15 discordance, did you use (inaudible) to keep everything reduced?

16 Because thiols can interact and inactivate proteins. 

17 DR. MICHAEL HORNUNG: This is Mike Hornung. 

18 We don't use that in the binding assay or the slice assay, so

19 that is something that --

20 DR. STEVEN SAFE: Yes, that could be a problem

21 with the thiols. 

22 DR. DANIEL SCHLENK: Any other questions? Dr.

23 Keiser? 

24 DR. MICHAEL KEISER: Michael Keiser. So two

25 questions about sort of the decision process. Both of them are

26 from earlier on in the slides near the beginning. The first one
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1 is about slide 174, looking at type A and B curves. I was

2 curious and this actually also a general question across it. 

3 When you talk about cases of chemical classes or categories that

4 are known to create false positives, obviously that has some

5 consequence here in this case because you might take a

6 relatively strong curve, move it all the way to a non-binding

7 decision. So I was curious how those classes are defined and

8 whether it is more of a binary decision as to class membership

9 or if there is a gradient, maybe computationally or some other

10 approach.

11 DR. MICHAEL HORNUNG: This is Mike Hornung. 

12 That's a question about the potential false or known false

13 positives and moving them. As I was given that talk, I was

14 thinking about the benzine and sulfonic acids that we had DBS,

15 so the dodecyl that was positive. We know the octyl was. So we

16 have a group there that is a known -- we already have good data.

17 We have Ki data for that chemical class that is negative. 

18 You're right that there would be questions

19 about structural similarity. I think getting back to part of

20 what Rick talked about with defining the chemical classes and

21 looking at the chemical class based on activity. If you start

22 getting away from these long alkyl chain sulfonic acids, where

23 would you make that cutoff to say okay, we're still going to

24 call this in this group and move it -- we'll just call that a

25 class that will produce false positives and throw it in that

26 group. 
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1 There is kinda of a binary decision. Yes,

2 it's in the group, throw it in there, and no it doesn't fit that

3 classification, leave it out. But part of it is in having

4 confidence and where you define the end of that chemical class. 

5 So if you have sulfonic acid that's on an alkyl chain or there's

6 other substructures on that, does that really classify as that

7 type of chemical. Some of those, I think you'd want to go back

8 and take a look at them and verify them. You might have data

9 from P-chem data, PKa data, other things that tell you it's

10 likely not to have activity, but as you get further away from

11 where you have the comfort of what you know is active and you

12 have the tested data, and I think part of that is there's a

13 judgement on having the experience and the expertise in running

14 the assays and seeing what the results are from the different

15 assays and what the activity is when making those types of

16 decisions. 

17 When you're confident, you can turn it into a

18 binary decision, but when you starting getting in that realm of

19 questioning yourself whether you really feel confident in

20 including that, then I think you need to do an additional --

21 move it onto a slice assay or something to verify.

22 DR. MICHAEL KEISER: So then just to repeat

23 back to make sure I understand the process, you would say that

24 you would first start with something like a defined substructure

25 match, and then maybe depending on the size of the molecule,

26 other things you have in mind, you might also subject that to
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1 some human decision making process or supervision afterwards, is

2 that correct? 

3 DR. MICHAEL HORNUNG: Yes, I think so. I

4 don't know if Rich or Pat would have any clarification on that. 

5

6 DR. PAT SCHMIEDER: This is Pat Schmieder. If

7 I understand your question right, what Mike is talking about

8 here is when we're developing the classes within the training

9 set and the confidence -- when we build the Expert System, we're

10 pretty confident in the calls within that class. So I don't

11 know if we're mixing up, you know, using it afterwards versus

12 building it and trying to make sure that we go through all the

13 steps so that we're really confident when we say this is a class

14 and these are the activities we get within that class. 

15 DR. MICHAEL KEISER: Okay. Yes, thank you for

16 the answer. I have a second question, which is a little more

17 specific. It should be quicker, which is namely on 177 when

18 we're looking at flat line curves in this case, I was curious

19 just in terms or rough numbers how many cases have been actually

20 resurrected through this decision pathway, and by resurrected I

21 simply mean in this case, how many have been indeed resurrected

22 as metabolites? I was curious how often we end up and the very

23 right most decision point where it's classified as a binder for

24 input to this sort of decision making algorithm. 

25 DR. MICHAEL HORNUNG: I'm not sure I can give

26 you a number like out of the 296 how many of those would fall
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1 into that category. I think it would be a very small

2 percentage. We'd have to look into that and then I could give

3 you a more concrete answer.

4 DR. PAT SCHMIEDER: Pat Schmieder. If I could

5 just add to that. One of the ways when it does occur, and I

6 agree with Mike, it's not that often, but when we do end up

7 pursuing that, it's because within again the chemical category,

8 we look across that category and we get non-binders, for

9 instance. And then all of the sudden, we get a chemical that's

10 doing something else. When I went through pretty quickly

11 earlier on where we look at the chemistry within the section

12 three presentation earlier, we look at the chemistry in the

13 assay, in the binding assay. If we see the zero hour and the 20

14 hour a lot different across that, we get some indication there's

15 something else going on and there's possible metabolism. 

16 Now that's just the cytosol, but we can get it

17 into a binding assay. If we don't get consistency across a

18 chemicals class, we really start asking a lot of questions

19 because it's either that we don't have the class specified very

20 well or there's a metabolism or something else going on. Like

21 he's got that whole box. We try to keep pursuing it. Overall,

22 that percentage of when that would happen is pretty low.

23 DR. MICHAEL KEISER: Thank you, that does

24 answer my question.

25 DR. DANIEL SCHLENK: Any other questions? Dr.

26 McManaman? 
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1 DR. JAMES MCMANAMAN: So this is a pretty

2 complicated problem. So I can see that there could be several

3 areas where you could have different classes of compounds

4 interacting in various steps along the process. For instance,

5 there's the hormone binding domain of the receptor, but there is

6 also the DNA binding domain component of that. 

7 So there are interactions that would affect

8 dimerization and there's also interactions that would affect

9 cooperativity with the DNA elements. So any one of those things

10 could be influenced by a chemical and in fact, some chemicals

11 are known to influence the cooperativity more than the

12 dimerization. 

13 I think without a full understanding of what

14 chemicals are doing and how they're affecting the process

15 because each of your assays is going to be -- I mean your

16 binding assay is just going to be binding to the hormone binding

17 domain, but the other assays are going to involve binding to the

18 hormone binding domain and binding to the DNA. So when you set

19 up an assay in which you've got multiple ERE strung together, I

20 think that you're going to may be select for those things in

21 which you're looking at more cooperativity with DNA binding than

22 you are with dimerization. I think that that could explain why

23 you're getting some variability in your assays, but it comes

24 back to you really have to know what the compounds are doing. I

25 just don't see that you really have a handle on that yet.

26 DR. MICHAEL HORNUNG: This is Mike Hornung
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1 again. I think yes, it can get very complex looking at all of

2 those different things, but I think what we're trying to do is

3 look at if a chemical can bind to the receptor and we can put it

4 in the context of that molecular initiating event leading to

5 some adverse outcome downstream that that initial binding tells

6 us yes, this may start that process. The multiple EREs that

7 could put in cells in vitro assays, I believe a lot of times are

8 one just to try to increase your signal for the reasons you talk

9 about. 

10 So they are artificial systems that look at a

11 lot of different things, but I think if we go back and look at

12 it and say if we have binding in this chemical and this type of

13 structure can bind, then it produces a potential that it can

14 lead to these downstream activation and the downstream signaling

15 and momentum that gets you through some adverse pathway. What

16 all those steps are in the way and what the different assays,

17 how those different assays respond, maybe, I don't want to say

18 isn't as important, but the critical point is understanding that

19 initial binding and activation part of that pathway that does

20 the chemical have the potential to at least initiate that

21 process that leads to some endpoint. 

22 DR. JAMES MCMANAMAN: Jim McManaman again. So

23 let me give you an example of where you might get some

24 discordance here is that let's say that you have a compound that

25 has weak affinity to the receptor itself, but in the context of

26 an ERE and the cooperativity that you generate from there, you
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1 do get a response because that weak affinity is now compensated

2 for increased cooperativity. So you would get a positive

3 response that would be a positive binder in one assay, but not

4 in the other assay.

5 DR. MICHAEL HORNUNG: This is Mike Hornung. I

6 think that gets to one of the points I think we tried to discuss

7 here is trying to understand our assays, how they're responding

8 and what are the components of things in each assay that will

9 lead to a positive or negative response in that assay. I think

10 taking all of that in context helps you decide whether that

11 chemical has the potential to initiate that pathway. What the

12 level of induction is, I don't think for this question is as

13 important as the initial question of can it turn that pathway

14 on. 

15 DR. DANIEL SCHLENK: Dr. Judson? 

16 DR. RICHARD JUDSON: Richard Judson. So I

17 mean, addressing questions like you're asking is one reason for

18 having this big battery of assays all up and down the pathway

19 that we have, the high through put assays where we have across a

20 very large set of chemicals. I don't claim to -- I can't answer

21 that question for that today, but we have a lot of data that

22 lets us go address those sort of issues. 

23 In just one example, if you look at a series

24 of chemicals that interact with that are positive in most of the

25 assays. They're positive in the binding assays, the

26 dimerization assays, the DNA binding assays, the transactivation
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1 assays, the proliferation assays. Each assay, for instance, the

2 binding assays tend to be left shifted. So you would have an

3 AC50 of 10 nanomolar and in the transactivation assays, it would

4 be 1 micromolar. So they tend to be that way.

5 There are -- for all the phenols, you see a

6 particular trend across assays. But then there're certain

7 chemicals that suddenly you reverse. Some of the

8 transactivation assays suddenly have AC50s which are much more

9 potent in the binding assay. So there is either some assay

10 interference going on, which doesn't have anything to do with

11 the real biology or there is something interesting there. 

12 By looking across all these chemicals, we can

13 pull out anomalous chemicals that are worth looking at. But

14 reiterating something a point Mike has made is, are there two

15 kinds of questions we worry about in research. So there are

16 some really important research questions. There are the

17 questions the program offices need. I want to prioritize a

18 bunch of chemicals. What chemicals do I run first? What's

19 really important is there good evidence that this chemical is

20 hitting the estrogen receptor, and we can make everybody believe

21 that. So we would want to go do something with those chemicals

22 first. But you need to understand what's happening in the

23 assays obviously to have confidence in making those decisions. 

24 DR. JAMES MCMANAMAN: Yes, Jim McManaman

25 again. I bring it up because I think it raises a question about

26 how you strategize your plans for evaluating your compounds
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1 because if you were to use the ER Expert Assay where you're

2 looking only at binding, then you might miss compounds that

3 really are -- especially that you were concerned about the low

4 affinity ones. So you might miss those low affinity compounds

5 and say that they're really negative when in fact they're

6 positive if they were put into a biologic context. 

7 So it might be worthwhile to rethink about

8 which assays you could as your first screen in terms of

9 evaluating your compounds. I know that's kind of in general,

10 the (inaudible) of what you're trying to do, but it wasn't clear

11 to me that considering the two components, something that really

12 bound to the ER and something that bound to the ER in the

13 context of DNA were logically separated and put into a logical

14 evaluation.

15 DR. DANIEL SCHLENK: Let me just point out,

16 again let's hold the comments until we get to the comment

17 period. This is for questions. Yes, go ahead.

18 DR. PAT SCHMIEDER: Okay. So I don't know if

19 I should respond or not, but what I was going to say is what we

20 started here was with one pathway. The ER mediated pathway. 

21 Your points are correct. We know there are many other pathways

22 even within the ER system, if you will. But we also knew when

23 we got into this and I think that it's consistent with the

24 legislative mandate to look at estrogenicity. But we also know

25 that there was a lot of evidence that many different wide

26 variety of very diverse structural compounds can bind to the
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1 estrogen receptor. 

2 So we started with the ER mediated pathway. 

3 By using the AOP concept, the approaches to go to the other

4 pathways and to start understanding which are the assays that

5 give you an idea of something happening through another pathway,

6 and kind of which ones you go to next and how you prioritize

7 that, I think would be in the -- hopefully Mary or Steve would

8 respond also to this -- would be where you think the biggest

9 problem might be, you know, the most chemicals. So do you go to

10 the AR receptor next because you think it could do that more

11 often with the kind of chemicals we're trying to make the

12 decisions on here, or would you go to try to look at the DNA

13 reactivity and do you think there's a big universe of compounds

14 that would do that or is there evidence for a lot doing it that

15 way. 

16 So what you're saying is absolutely correct,

17 but we're also trying to prioritize the kind of pathway that we

18 do the work on by, I guess, a guess and it would be good to get

19 the Panel's input on where to go next where you think the

20 biggest problem would be for these inventories of chemicals that

21 are not steroids, that are not designed to do this. 

22 DR. DANIEL SCHLENK: Okay. Thanks Dr.

23 Schmieder. Any other questions or clarification? Dr. Keiser? 

24 This hopefully will be our last one because we're running

25 behind.

26 DR. MICHAEL KEISER: Michael Keiser. I
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1 understand. If we could just go to slide 197 please. So for

2 this example and for the one on 198 also, I wanted to ask a

3 clarifying question basically with respect to the earlier

4 discussion about chemical classes. So in these cases, we have

5 examples of chemicals that look quite similar and yet there is

6 some discordance in the results between them. Do you feel that

7 for this example and the one on the next slide, this is more

8 aptly a demonstration of limitations currently in the screening

9 or the approaches that we have, or on the other hand, is it a

10 case where grouping these into the same chemical category might

11 incorrectly bring them together? 

12 DR. MICHAEL HORNUNG: This is Mike Hornung. I

13 think one of the things here that gets at that is we're looking

14 at chemical categories that are based on two chemicals. For

15 looking at within a chemical class where we know that LogKow

16 plays an important role, if we look at these two chemicals where

17 we have the methylphenol and the octylphenol, there may be

18 something there where as Rick showed for one of the large two

19 hydroxybenzophenones, adding that long alkyl chain pushed it out

20 beyond that activity ring. So maybe something like that is

21 going on here. 

22 So where you just start putting a chemical

23 class together and you have two chemicals, that may play a role

24 in some of that discordance. Are you on that edge between where

25 you are structurally in having activity and not having activity?

26 And the structure in all these things can affect free fraction
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1 in the chemical too, so what the solubility is in water and the

2 availability at the site of action. I think there is something

3 to that in looking at the chemical category description, and

4 that, I guess, developing those categories, it's a continuing

5 process. So it's not fun.

6 DR. MICHAEL KEISER: Thanks for clarifying

7 that the classes were so small definitely helps. Thank you. 

8 DR. DANIEL SCHLENK: Okay. Let's go ahead and

9 move on. Again, we'll have another opportunity to ask questions

10 tomorrow morning, so it's not the end of the world if you don't

11 get a question in right now. I think you just need to forward

12 the slides forward to the next presentation. The next

13 presentation is by Dr. Schmieder and Rick Kolanczyk again. 

14 DR. PAT SCHMIEDER: Thank you. Now we're

15 going to talk a little bit more about the category-based

16 approach and how you can strategically pick chemicals to cover

17 these unknown binding potential areas as you're moving to a new

18 list that we're trying to cover here. Again, we're in the lower

19 right box here, kind of both considering the Expert System and

20 high through put, and we're also trying to address this,

21 determining where the high through put data covers the unknown

22 chemicals and how you could use that to expand the training set.

23

24 So first I'm going to talk a little bit --

25 this is kind of an outline assessing the distribution of the

26 unknown chemical, putting them in the categories from the Expert
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1 System version two, determining the number of the unknowns

2 within the different groups and how much high through put data

3 is available and the type of data, is there data on the eight

4 assays where you have two levels of the AOP. So you have

5 binding the initiating event and something at a higher level of

6 organization or where you just have the two assays, which is at

7 the higher level and you don't have the binding information. 

8 Then we're going to talk some about assessing

9 where the high through put is across the untested ranges within

10 chemical categories and selecting chemicals for testing to cover

11 the ranges within the categories. There are two different parts

12 to this. One is within already defined groups if you're just

13 outside of the LogP range of some examples there and then also

14 going within the larger groups of mixed phenols and mixed

15 organics. 

16 So again, we're talking about Expert System

17 version two with these added-in classes. So we assess the

18 distribution of the unknowns based on those classes. We're

19 going to kind of start from the bottom up here. 

20 The largest number of the unknowns were in

21 this site B or roman numeral 7 of our Expert System that

22 contains the site B type interaction. There were 1400 chemicals

23 in this or 82 percent of all these unknowns of the 1700

24 unknowns. The high through put data is available for 611 of

25 these chemicals or 44 percent. Thirty percent of that high

26 through put data is for the eight assays again at two levels of
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1 the AOP and the remaining 70 percent is for the two assays. 

2 Again, I'm talking about the eight assays and the two ToxCast

3 assays or the NCGC assays, the BLA and the BG1 that Richard

4 spoke about. 

5 So that leaves of course, the remainder. You

6 do math, 789 chemicals that don't have any data. Of that, if

7 you break out that site B category, we have unknowns largely in

8 the mixed organic group here, 98 percent of all of these, 80

9 percent of the 1704, but 98 percent within this class. So

10 everything that I say about the mixed organics goes for the

11 whole group obviously. But we will talk a little bit about some

12 of the other categories within that. 

13 So the largest majority here is the mixed

14 organics. Rick will talk more about some examples within that. 

15 Forty-three percent of those, as I said, it's driving the

16 category. So 43 percent have high through put or 589 chemicals.

17 Then we have the number listed 175 have high through put from

18 eight assays and 414 or 70 percent of high through put from the

19 two assays, leaving 57 percent with no high through put data. 

20 As we walk up, we go to the site A the part of the decision

21 tree, which is the second largest number. Now there is eight

22 percent of the unknowns are within this category. Again, the

23 coverage is about 40 some percent of the coverage with high

24 through put data, and the eight assays, you have about 36

25 percent and the two assays you have the large majority of what

26 the high through put data covers, which is no surprise as you
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1 realize there's a lot more of the Tox21 data for a lot more

2 chemicals. Then, 60 percent with no high through put. So

3 again, the mixed phenols are the largest in this category and

4 they're really driving the group. So the percentages are pretty

5 much the same. 

6 The third largest number within this are in

7 this special rules category, which is basically they don't

8 interact with the same atomic charge characteristics as the site

9 A or B, but we know they interact with the receptor. So we have

10 88 chemicals in this category. They have a higher

11 representation of high through put data. Of these 58 chemicals,

12 66 percent of them have high through put data, 41 percent of

13 those data for the eight assays and about 60 percent for the two

14 assays, leaving 30 chemicals with no high through put. 

15 The groups within this are a large group of

16 multi-cyclic hydrocarbons. While we did have a group of these

17 before based on our earlier inventories, now there's a lot more

18 of these chemicals. So, it's a lot larger group and that will

19 come into play when Rick describes an approach of how to cover

20 these. But they are well covered with high through put data to

21 the extent that the data and the uncertainties in this data can

22 be used to build an Expert System, there is good coverage within

23 that class. 

24 The thiophosphate esters, 10 of 14 have high

25 through put data. If you look in the remaining in that if

26 you're looking through the categories for the unknowns, the
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1 remaining are 66 chemicals that belong to these known inactive

2 classes. I will show you a breakout of that in a second, and

3 then the rest are the acyclics. 

4 So these are known inactive classes. The

5 unknowns are spread across a lot of these well studied groups,

6 but they're in LogP ranges outside of the well-studied area. 

7 The number of the high through put within these -- it varies

8 depending on which group you are. The benzoates have quite a

9 high percentage of, but there are only six compounds we're

10 talking about there. The 27 alcohols, about 11 percent

11 coverage, the mono-cyclic hydrocarbons 31. You can do this

12 breakdown and understand where the data is potentially

13 available. 

14 The acyclic perfluoro compounds -- there're

15 these 16 chemical within this group and we have very little

16 information on those, high through put or otherwise. 

17 So in summary, 44 percent of the high through

18 put coverage, about 44 percent overall, and it varies between 66

19 percent for one class and one percent for another depending on

20 the chemical class, chemical group, but the larger groups have

21 40 to 60 percent coverage with high through put. There are 509

22 chemicals or 70 percent is for the two assays, the remainder for

23 eight high through put assays, leaving 960 chemical that we have

24 no data for.

25 So let's then talk about, so how can we use

26 this information to select chemicals for testing? First, let's
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1 talk about within defined groups. Again, we're using this

2 example of these classes that we have a lot of information on,

3 but -- so take the alcohols up here. What we've said is we have

4 information on them, but not if the LogKow is above 3.4. I

5 looked at it and all of the chemicals are in that higher range. 

6 So we need to cover that higher range of this class for the

7 benzoates for instance. I've broken them out down here and

8 there're two. 

9 Our testing is between 3.0 and 4.3, but where

10 we don't know is below that and above it. So we have three

11 chemicals that fall below that of these six unknowns and we have

12 three above. So our testing would be to start covering and

13 expanding through those categories. Everything we've found for

14 these chemical classes has been a non-binder within a class. So

15 you might want to jump right down and get the lowest end and get

16 the highest end. If those are non-binders, the question is, is

17 that enough confidence. 

18 But the point is you won't have to test every

19 single thing in here to make a good decision and to expand that

20 class. The same kind of approach is basically used throughout

21 the classes. So we try to test a number of chemicals to get

22 confidence, but not test everything within the classes. 

23 This is an example of the 2-alkylanilines. So

24 it's not necessarily a defined group. It's maybe a potential

25 new group. The reason I say it that way -- so we have the

26 training set, which is for the 4-alkylanilines. We have a good
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1 coverage here mostly N and we have a tertiary branch structure

2 and we get a good correlation with LogKow. So the question

3 would be several questions. The two substituted anilines versus

4 the four, two alkylaniline compared to the four. Do ortho

5 substitute alkylanilines behave the same as para-substituted? 

6 Another question because of the group, what

7 kind of chemicals are in the unknowns, do 2,6-substituted

8 alkylanilines behave like the two? So depending on what the

9 results are, as you select chemicals and test them, you get the

10 answers back. Then you determine whether you can combine those.

11 They're all one group, or you need a separate group for the

12 2-alkyl versus the 4-aklylanilines. 

13 So this just shows how you would pull those

14 out. Our hypothesis would be that the two are maybe closer to

15 the four. So the 4-methylaniline was a binder is 2-methyl, and

16 we can test right there first. If we got a positive there, we

17 might test the higher end of this, but these are the only ones

18 in the inventory, so we'd stop there. We wouldn't have to test

19 all of them. 

20 Then take one of the lower end of the 2,6 and

21 see if you get activity, or are you starting because you've got

22 substitution on both sides of that aniline group. Have you

23 gotten rid of the activity? So again, you don't have to test

24 all four, test the low end and the high end. 

25 So if you look at where the high through put

26 data is, using that same strategy -- so let's look at the
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1 2-methylanilines. So we have some data here, but we don't at

2 the high end. So you could just pick up one more test point and

3 not have to look through all of it, and the same thing here. 

4 But you'd have to look again at -- part of it is like what was

5 presented just before this as look at that type of data. Are

6 the assays that you're looking at catching these chemicals to

7 begin with? But this is a strategy if you're confident in the

8 certainty around those assays, then this is a strategy of how

9 you could test. 

10 Those were just a few chemicals, but now we

11 have this larger group. We have 67 unknowns in here. This is

12 actually redefining what had been a very small chemical group,

13 now we have a lot more chemicals in it. So we're going to talk

14 about redefining a large chemical group. I'll turn it over to

15 Rick.

16 MR. RICK KOLANCZYK: What I have is the

17 multi-cyclic hydrocarbons. That pretty much followed suit. The

18 way we were handling those was an exact match theory. I just

19 want to point out that it was a relatively small representation

20 across the three inventories of concern at the time. I'm going

21 to go ahead and advance this and show you the activity that we

22 observed within the rainbow trout training set. 

23 Green again were represented negatives, pink

24 positives within the series. They're ordered right now with

25 respect to LogKow. That may be better to start to rearrange

26 these to something that might make better sense in terms of a
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1 bridged versus a fused ring structure. Again, apply that same

2 activity here. You get the feel and the flavor. We'll start

3 with the fused ring where they impart no activity. We just

4 wanted to cover the inventory, the unknowns, at this point. I

5 would feel fairly confident that this serves as a good surrogate

6 for these other methyl-substituted forms of naphthaline, a good

7 representative here for the coverage of these other three

8 analogs. Lastly, this structure over here was a close analog

9 for this.

10 So the task at hand was really to cover this

11 structure space. We could adequately do that through an exact

12 match. Then we went through some other expansions of looking at

13 this. 3PE was of great interest to us. We looked at what was

14 done in the Seri, the Japanese. They showed binding here and

15 we're just out to look at, mentioned before about the different

16 modes of binding to the receptor. Not just merely the A type

17 and the B type, but perhaps all I could think of is some type of

18 a pie ring substitution or pie ring activation within the site

19 for hydrogen bonding.

20 I guess I owed somebody an example here

21 looking at these still beans with trans versus the cis and

22 negative versus the positive. So again here, maybe some of that

23 stereochemistry we talked about in this morning's session, and I

24 kind of pushed off. I think there could be something to explain

25 that, why this particular structure is a negative. Again,

26 moving from what was an exact match boundary or criteria for
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1 this set to maybe developing some more concrete rules here

2 involving some stereochemistry aspects. 

3 The other negative within this set was 4PE. I

4 think what we ran into here was a solubility issue where we're

5 pushing the envelope of the LogKow and I just don't think we

6 could get enough into solution for the assay to perform to get

7 any type of binding activity. 

8 So, in what respect does this apply to the

9 EDSP Inventory? What I've done here is I've pulled some

10 representative structures. Pat mentioned there were 67. This

11 is a subset of those. I guess I'm trying to give you a feel and

12 a flavor of where I would go to expand this set. We only had

13 tested one representative within the inventory. But what I

14 would propose to do is to grab these others, highlight it in

15 yellow to perhaps expand that training set to give greater

16 coverage and greater credence to a hypothesis that perhaps fused

17 ring, multi-cyclic hydrocarbons were going to pose as negative

18 with respect to binding versus some activity that we could find

19 in the bridged. 

20 So just to help sort that out, I would propose

21 that we pick these structures out. As far as the structure

22 space, I've added those. Again, yellow proposed testing I feel

23 would give adequate coverage of that set. Beyond that, do the

24 same with the bridged ring again to better resolve that. So

25 again, I could see using the EDSP universe to expand that node

26 into potential future nodes.
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1 So it gives you an idea, I guess down here at

2 the bottom of the slide is where we're taking off. In summary,

3 there are 203 of those unknown binding potential chemicals that

4 are found within defined groups. Seventy-seven of those

5 chemicals have high through put. If we could potentially use

6 some of that data, probably test 10 or 20 percent more of the

7 chemicals to cover that remaining structure space, many of those

8 taking into account LogKow ranges.

9 Okay, so part two, within the mixed functional

10 group classes, again looking at mixed phenols and mixed

11 organics, again another group that we've relied on as far as the

12 exact match, I proposed an expansion of moving from Expert

13 System version two to version three. I've got 16 proposed

14 groups, not limited to that with the EDSP Inventory, but just to

15 give you a flavor and an idea of how the system could be

16 expanded to capture. 

17 I've listed those in this table. It will give

18 you some idea as to the representation within the EDSP universe

19 as far as number of chemicals. These first two columns

20 represent some knowledge that we may have already. So in other

21 words, what we've done is I've run these roughly 500 chemicals

22 through the Expert System and came up with a predicted value,

23 either negative or positive. Many o these may have been tested

24 as an exact match, working back from the old systems. So in

25 other words, they were found in one of the other inventories. 

26 We ran them through, did an exact match, analyzed and we may
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1 have had positive binding for them or negative binding. The

2 other is that we may not have tested them at all. There may be

3 a LogKow cutoff that was influenced there. That certainly would

4 fall into the negative categories. 

5 I guess where we want to really focus where

6 we're getting these positive hits. Those were probably already

7 tested. Aldehydes and ketones, we mentioned those before. 

8 Cyclohexanones, those would be on the top of my radar,

9 sulfonamides, I guess we'll work through some of these. The

10 aldehydes, focus in. I'm a big structure guy obviously. That's

11 kind of turned into my life here lately as far as Expert System

12 one, two and proposed three. 

13 I guess what I would do is I'd start throwing

14 some of these structures up on a screen. Then you start

15 grouping them. The aldehyde group is branched off of an

16 aromatic ring, a benzine ring. Now here, you've got multiple

17 substitution on that ring. Here in this case ortho, meta and

18 para where you have multiple substitution. Some of the flavor

19 for this is do I need to test all of these structures on this

20 screen. I would say absolutely not. You could grab one or two

21 within a given box here and represent that. I guess what I've

22 attempted to do here is where we had a larger category or class

23 of aldehydes and I've broken them down a little bit only to

24 perhaps build them back up depending on the activity that we see

25 within the assays. 

26 The next slide is just an extension, more of
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1 those aldehydes. In this case, cyclohexane ring as the base

2 here rather than a benzene ring. Again same strategy would be

3 applied. What happens when you have a multi-cyclic group with

4 an aldehyde? And then I've got this little orphan here on the

5 right. 

6 I'm just going to cycle through some

7 structures. Here are ketones. This could be very important,

8 especially in light of the fragrances, which we really haven't

9 covered in Expert System version one or two. I just went

10 through that same grouping exercise pertaining to the ketones

11 again.

12 Cyclohexanones, just trying to give you a

13 flavor of what's in the inventory and the attempt that I've made

14 to start grouping these things. I think that's a good first

15 cut.

16 Pyridines and quinolines -- now in this case,

17 I've kind of mixed potentially two groups into one really. 

18 We've got the pyridines over here and the quinolines. Within

19 this grab paraquat, diaquat, bipyridines, again do I need to

20 test everything on this slide? I've got to keep stressing a

21 strategic approach of grabbing some good representative

22 structures, one or two out of each of these boxes and then

23 proceed through. 

24 Anthraquinones -- I haven't grouped them

25 necessarily, but I've got the hydroxylated across the top, more

26 or less started forming them up and again, things looking very,
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1 very similar. Benzothiazoles -- a more limited set. 

2 Chlorinated cyclic hydrocarbons -- I show a

3 bit of concern with this group. Again, we haven't seen many of

4 these or any of these at all in the food, nonfood or

5 antimicrobials. Halo benzenes -- again. Halo phenols --

6 interesting.

7 The one that I do know we've tested within the

8 trout training set is pentachlorophenol, negative. I think the

9 interesting fact here is that when you start looking at

10 chlorinated phenols, the more chlorine rings that you hang on,

11 the further it shifts that PKa value in. The potential of

12 having a charged species at the physiological pH of the assay,

13 fluorophenols, bromophenols, iodaphenols.

14 Isocyanates -- reactive group, in fact. I

15 suspect it would go down to the amean in solution. 

16 Diisocyanates, monoisocyanate. Nitro aromatics, again, you pull

17 these things out, start developing them as a class, not

18 necessarily having to test everything. Do we need to test all

19 500 of these? Absolutely not. 

20 Nitro phenols, again looking at the influence

21 of the nitro groups. The more nitro groups you hang on here,

22 it's going to influence that PKa value as well, may draw in some

23 idea of a charged relationship here. And lastly, the

24 sulfonamides. Again, a little more diversity here. This might

25 be a little tougher class to actually work through. 

26 Bottom line -- really this is a coverage
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1 slide. I put this up here just to give you some flavor of where

2 the high through put coverage is and what we have proposed to

3 add to that, to use the high through put data, possibly another

4 10 to 20 chemicals to look at, at testing. Obviously, you

5 substitute in for the high through put some lower through put

6 testing as well, but just to give you an idea of the coverage. 

7 Now I promised somebody something about

8 acyclics this morning too. Expert System version one,

9 consideration of the acyclics as negative, it seemed to be

10 appropriate at that time. We talked a little bit about that

11 this morning during the question period. 

12 For expansion into other inventories and most

13 certainly the chemical universe, you require further testing. 

14 So I guess the third point there, caution in making that rule

15 that all acyclics are negative. We were steered by the Panel to

16 look at PFOS and PFOA. Immediately, we went after that,

17 probably within a week or two of our departure from DC.

18 As suggested, PFOA and PFOS were both positive

19 in our binding and activation assays. What I would say to look

20 at other structures with respect to the EDSP Inventory. Looking

21 beyond these perfluoro compounds, I would also use caution maybe

22 in looking at some halogenated alkanes. In addition to

23 fluorene, looking at chlorinated and brominated,

24 multi-chlorinated, multi-brominated, again they're a bit foreign

25 to Expert System version two in that we just haven't seen that

26 coverage. They are more of a SDWA type chemical. 
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1 The aldehydes and ketones, mentioned those

2 this morning, but they're largely going to be found in the

3 fragrances. We found a couple of representatives here and

4 there. I threw this slide up just to give you a flavor of what

5 some of the perfluoro structures that we're looking at and

6 expanding to version three. 

7 In EDSP Inventory, there's this substituted

8 amino perfluoro compound. Some of these charge groups, I don't

9 know how they're really going to play out and behave. But what

10 we do know is that the PFOA and the PFOS were positive in here. 

11 Certainly some work ahead here on tackling that. 

12 Outside of those preliminary groups for the

13 mixed organics and the mixed phenols, there is high through put

14 data out there with respect to the eight assay up above, 125 of

15 those chemicals. Where there are two assays only, 294, and 576

16 where there is no high through put. There could potentially be

17 some work ahead for those. 

18 So in summary, same approach that was used to

19 build the initial Expert System to assess the degree to which --

20 I hate to just read slides. You can read slides, but what we'd

21 want to do is assess the degree to which the existing Expert

22 System covers the new list. That's looking across the LogKow

23 ranges, the categories that we have before us, and

24 systematically select chemicals for in vitro testing. Again, I

25 just want to reiterate that it's not testing everything. We

26 don't need to test all 5000 chemicals of the EDSP Inventory. 
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1 You work through a strategy, good

2 representation, and to do that to extend those areas that are

3 not completely covered with respect to those current classes

4 that we have, namely looking at larger LogKow ranges, we'd

5 expect to maybe test 10 percent or less more. Then to tackle

6 the mixed organics and the mixed phenols, I would estimate maybe

7 another 10 to 25 percent of those to get that coverage down. 

8 So the take home summary here is continue the

9 iterative approach. Assess what cannot be adequately predicted,

10 and that's looking at the unknown binding potential result. 

11 Develop groups, test within those groups, and then reevaluate

12 the activity within the tested ranged for those chemical groups.

13 So again, that iterative approach, select chemicals to test at

14 bound ranges of unknowns, et cetera. Continue through until you

15 arrive at Expert System version three. And that's it, thank

16 you.

17 DR. DANIEL SCHLENK: Thank you. Any questions

18 or clarification? Dr. Schultz? 

19 DR. TERRY SCHULTZ: Terry Schultz. Have you

20 put any thought into developing your scheme for what you're

21 going to be selecting your chemicals? When you flashed up, for

22 example, pyridines and quinolines, I guess what I'm getting at

23 is how do you every get around saying, well we need to do one

24 more test. Have you got a logical program with which you're

25 going to select your chemicals? 

26 MR. RICK KOLANSZYK: We start out with
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1 basically LogKow. We want to cover that range. Maybe start

2 ordering the chemicals lowest to highest, you want to pick

3 something on the low end, something on the high end, obviously

4 maybe a character in between. I guess I don't go into it saying

5 I'm going to pick these 12 compounds, these six compounds, these

6 three compounds and be done with it. Much of it's the feedback

7 that you get from the result of the biological assays. It's

8 structure activity relationship that we're working through. 

9 I guess the hope is that eventually, just as

10 an example, I tried to break them down into the little red

11 boxes. Eventually, hopefully, you can form them back up. Pat

12 even eluded to the fact when she gave her aniline example,

13 looking at the 2-alkylanilines versus the 4-alkylanilines. Is

14 there hope that we could eventually put them together, just deal

15 with alkylanilines? I don't know. I'm rambling a bit.

16 DR. TERRY SCHULTZ: We can discuss and we'll

17 have time for comments on this later. I just think you need to

18 come up with a strategy so that it's very clear to everyone how

19 it came about selecting those 10 or 25 chemicals you're going to

20 test.

21 DR. DANIEL SCHLENK: Dr. Safe? 

22 DR. STEVEN SAFE: Steve Safe. Could you go to

23 slide 274? I'm just sort of interested in why you selected the

24 compounds because there are chemicals, polycyclic aromatic

25 hydrocarbons on that slide that are known to be estrogenic

26 published. Methylcholanthrene binds to the estrogen receptor
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1 and induces many different estrogen responses. If you want a

2 positive control for that slide, then you should have

3 methylcholanthrene benzopyrene. These are published papers on

4 polycyclics as estrogens, and they include binding data,

5 transactivation data and even in vivo data. You might want to

6 just check with the polynuclear. 

7 DR. DANIEL SCHLENK: You'll have ample

8 opportunity to talk about that. Thanks. Yes, just don't

9 forget. 

10 MR. RICK KOLANCZYK: Can I respond? 

11 DR. DANIEL SCHLENK: Sure.

12 MR. RICK KOLANCZYK: Rick Kolanczyk here. 

13 Those examples you just laid out are not part of the inventory

14 here or are they? No.

15 DR. STEVEN SAFE: 3-methylcholanthrene is

16 there I think if I'm not mistaken on CAS 56-49-5. You should

17 put a yellow thing around that please.

18 MR. RICK KOLANCZYK: I value the suggestion,

19 thank you.

20 DR. PAT SCHMIEDER: This is Pat Schmieder, if

21 I could also add a comment, and it gets to the other question

22 asked by Dr. Schultz is our strategy overall is because we don't

23 have like everyone else, we don't have unlimited resources. So

24 where we try to do our testing is really targeted at these

25 inventories. And yes, we would look and we haven't done this

26 group and we appreciate any comments you could have on positive
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1 controls to use is really good, but we would also look in the

2 literature. We try to look those up, but we haven't. This is

3 pretty new that we're looking at the multi-cyclic hydrocarbons.

4 Overall, the approach is to really -- and the

5 approach that we took to build the first expert systems were

6 that we are targeting those inventories. So yes, there were a

7 lot of interesting things that we found that we couldn't pursue

8 because what we had to do is -- our question was can you develop

9 these assays, can you do this and build these systems to predict

10 within these targeted inventories. Another reason to do it and

11 to do this approach is you don't know what you're next list is

12 going to be, you don't know for sure whether you're going to be

13 able to procure the chemicals. 

14 Another aspect of this is metabolism is a huge

15 question of course. I know one of the charge questions was

16 around that, but we are trying to assemble metabolic maps. So

17 if you have metabolic maps, you might know something about a

18 parent compound or not, but if you have a whole list of

19 metabolites and you're asking the question, is there potential

20 for any of these metabolites to do this, then a (Q)SAR based

21 system will help you see where you have knowledge and where you

22 don't have knowledge without doing any testing because I don't

23 think we're going to be able to test all the metabolites that

24 are potentially we're going to be encountering. 

25 The reason to try to build these systems --

26 and if you remember some from this morning, we looked at the
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1 Safe Drinking Water Act list, and there were over 2000 chemicals

2 on that list that weren't overlapping with inerts. We covered

3 71 percent of them without doing any testing. So there is a

4 reason, I think, from the resource perspective to build some

5 systems so that I can maybe helps us tell where to test, but

6 also give us hopefully some answers in the absence of being able

7 to test. 

8 DR. DANIEL SCHLENK: Dr. Jacobs? 

9 DR. MIRIAM JACOBS: Miriam Jacobs. I have a

10 practical question that's perhaps more relevant for the high

11 through put screening too. Some of these compounds that you're

12 proposing to test are quite volatile. Some of the volatility

13 was an exclusion factor that was mentioned earlier on. But the

14 ketones particularly and some of the brominated compounds and

15 chlorinated compounds, how do you propose to deal with those

16 especially with respect to the flares that we saw earlier with

17 the high through put system? 

18 DR. PAT SCHMIEDER: Well, I can't answer for

19 the high through put systems, but for the systems that we're

20 using on the lower through put side, we have been able to handle

21 chemicals that are more volatile. I know this because we

22 propose some for them to test that they said that they couldn't

23 test because of volatility issue or solubility in DMSO. 

24 Another way we can approach this and see if

25 we're able to test them is we do the chemical concentration and

26 we can look. So we might get some loss, but if we have steady
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1 concentration of a more volatile chemical through the time over

2 the period of our assay because we're doing the analytical, then

3 we could use that data. So that might be one way at least to

4 approach it. 

5 DR. MIRIAM JACOBS: Miriam Jacobs. I can

6 appreciate that very much for the low through put, but what sort

7 of suggestions would you have for dealing with cross

8 contamination with a high through put? 

9 DR. DANIEL SCHLENK: Dr. Judson, do you want

10 to take a crack at that one? 

11 DR. RICHARD JUDSON: Richard Judson. So the

12 volatility issue, we haven't made any promises at all. There

13 are some folks in research and development who are looking at

14 doing medium through put systems so essentially, you have --

15 it's like a mini hood that you put over your maybe 24 well

16 plate. I don't think people have gone beyond 24 well plates. 

17 So we can test a few chemicals in a few assays potentially. 

18 Then there're some other kind of crazy ideas of testing against

19 fruit flies or other kind of whole animals where you just dose

20 them with the volatile chemicals. That's very much a research

21 effort today for even medium through put they're doing, but the

22 medium through put kind of robotic is -- people are working on

23 it.

24 DR. DANIEL SCHLENK: Thank you. Dr.

25 McManaman? 

26 DR. JAMES MCMANAMAN: So have you considered
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1 using modeling for training? Because the ligand binding domain

2 has been crystalized. Have you thought about trying that

3 approach? 

4 DR. PAT SCHMIEDER: Yes, in fact we have in

5 our past looked at modeling and building 3D, incorporating

6 conformational flexibility and building models from that. What

7 we realized when we started working with these inventories that

8 the standard approaches at that time and maybe there're better

9 ones now -- because of how they interact only at one part of the

10 receptor, they didn't fit into the standard template models. So

11 we do have a past in trying that, but with these kind of

12 chemicals and the fact that they aren't designed to do this, it

13 may be more appropriate as these domains are expanding to try to

14 go back to some of that. At the time, we found that this

15 approach actually worked better for these types of chemicals

16 that were on the inventories.

17 DR. DANIEL SCHLENK: 

18 DR. RICHARD JUDSON: We're setting up a

19 collaboration with a couple of academic groups who claim to be

20 the best dockers in the world. So we'll find out. We're going

21 to give them -- there's a much larger inventory of chemicals

22 that we're interested in that include these and we'll see what

23 happens. It's a research effort and we may learn something. 

24 DR. DANIEL SCHLENK: Any other questions? 

25 Okay. Thank you. We have one more final -- Mary, do you have

26 something to add? 
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1 MS. MARY MANIBUSAN: I just want to offer an

2 option to the chair in acknowledgment of the long day and the

3 dense presentations that we could move the summary and recap to

4 the next morning if you'd like, but it's entirely your option.

5 DR. DANIEL SCHLENK: Is your morning thing

6 exactly the same sort of presentation, a wrap up essentially? 

7 Okay. I'm for that. We can do that for sure. I don't hear

8 anybody complaining. Except for Dr. Schultz, he can stay here

9 while all of us leave of course. With that, I'll turn it over

10 the Sharlene.

11 DR. SHARLENE MATTEN: So some updates on the

12 docket. Those who are looking for the presentations today,

13 there's also a presentation of the charge questions, the final

14 roster, final agenda. I put those into the front-end system,

15 the hidden away site where you put things from our end to be

16 approved by those that do the approving in the Office of

17 Environmental Information and I hope I wrote a note to the two

18 people who make those posting things happen. Hopefully, they'll

19 be posted in the next day or two. 

20 I wanted to alert any public commenters that

21 are still hanging on and haven't yet made a decision to comment,

22 we're still welcoming comments. You can come see me or anyone

23 in the FIFRA SAP staff. If you've already talked to me, I'm

24 expecting your presentations by 8 a.m. no later than tomorrow

25 morning, or you can email them to me tonight. I will take care

26 of them in the morning. I'm expecting 13 presentations. So I
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1 know the four people that are going to make that happen. So I

2 expect everyone will follow through. We've done so well so far.

3 So I really appreciate that. I also wanted to have the Panel

4 indulge me in about five minutes or so in the room over there

5 and then you're free. 

6 With that, we will start up again at 9 a.m. 

7 Thank you very much. Bye to those on the phone if you're still

8 there.

9 (WHEREUPON the meeting was adjourned)

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1 DR. SHARLENE MATTEN: Okay. Good morning. My

2 name is Sharlene Matten. I'm the designated federal official

3 for the Scientific Advisory Panel Meeting. Today is the second

4 day of the Scientific Advisory Panel Meeting on scientific

5 issues associated with prioritizing the Universe of Endocrine

6 Disrupter Screening Program, EDSP chemicals using computational

7 toxicology tools. All of the information that was presented

8 yesterday is now posted to the docket. The docket information

9 is on the top of the agenda and if you need help in finding that

10 information please see me or a member of the EPA staff.

11 We're going to after the EPA's last summary

12 presentation go into the public comment period. If anyone has

13 public comments that hasn't -- if anyone has public comments and

14 they haven't spoken to me please come forward and talk to

15 members of the EPA staff. I'll turn the meeting over to our

16 chair, Dr. Dan Schlenk.

17 DR. DANIEL SCHLENK: Thanks. Let's hope all

18 the other questions can be solved that easily huh? Good morning

19 everyone. I'd like to just briefly go around the panel just if

20 you wouldn't mind to state your name and just affiliations

21 before we get going so everybody puts with a face together. My

22 name is Dan Schlenk. I'm from the University of California,

23 Riverside.

24 DR. Stephen Klaine: Steve Klaine with Clemson

25 University.

26 Dr. Martha Sandy: Martha Sandy with the
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1 Office of Environmental Health Hazard Assessment.

2 Dr. James McManaman: Jim McManaman University

3 of Colorado.

4 DR. Prakash NagArkatti: Prakash Nagarkatti

5 from University of South Carolina.

6 DR. Marion Erich: Marion Erich, Virginia

7 Tech.

8 DR. KATRINA WATERS: Katrina Waters, Pacific

9 Northwest National Laboratory.

10 DR. Mark Cronin: Mark Cronin, Liverpool John

11 Moores University.

12 DR. Terry Schultz: Terry Schultz, the

13 University of Tennessee.

14 DR. JANICE CHAMBERS: Jan Chambers,

15 Mississippi State University.

16 DR. Bette Meek: Bette Meek, University of

17 Ottawa.

18 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

19 Corps of Engineers.

20 DR. THOMAS Burris: Tom Burris, Scripts

21 Research Institute.

22 DR. Michael Keiser: Michael Keiser, Sea

23 Change Pharma.

24 DR. Steve Safe: Steve Safe, Texas A&M.

25 DR. Nancy Denslow: Nancy Denslow, University

26 of Florida.
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1 DR. Miriam Jacobs: Miriam Jacobs, European

2 Food Safety Authority.

3 DR. DANIEL SCHLENK: All right, thank you

4 everyone. So as we left off yesterday, I believe our next

5 presentation will be from Vicki Dellarco who will give us a

6 summary overview and then what I'd like to do is possibly have

7 the presenters from yesterday be available for the panel if

8 there are any questions at that particular point. But let's

9 first go with a summary. Vicki?

10 DR. VICKI DELLARCO: There were a number of

11 presentations that you heard yesterday so I'm going to do a

12 recap of those and I want to begin with the legislative drivers

13 that were mentioned early in the morning yesterday and that is

14 the requirements under the Safe Drinking Water Act and FFDCA as

15 amended by the Food Quality Protection Act to evaluate chemicals

16 for potential endocrine activity.

17 And so given the number of chemicals that are

18 regulated under those statutes has certainly presented a risk

19 management challenge. The inventory was defined around 10,000

20 chemicals or so and so it's that legislative framework and those

21 risk management challenges that help drive the scientific

22 attributes of the scientific framework that we presented. 

23 And so what I want to do is go through some of

24 the important attributes of that scientific framework and relate

25 it back to the programmatic context and your advice on the

26 charge questions is going to help us move forward and fine tune
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1 this approach. So the first point that was made yesterday is

2 what is the question? Begin with the end in mind. And

3 beginning with the end mind will ensure clarity over the

4 questions that need to be addressed. So the questions matter

5 and in this case, what chemicals should we move earlier in the

6 program as we look at future lists.

7 It's not only what the question is that

8 matters but how certain the answer needs to be and if you look

9 at the charge questions, there is a number of them that are

10 getting at the issue of uncertainty and increasing the

11 confidence around the approach and when we think of 21st century

12 methods people discuss how the first operational use of those

13 are in a prioritization context.

14 But it's important to point out not all

15 priority setting activities in this Agency are the same nor do

16 they tolerate the same degree of uncertainty. So if I was in a

17 program and I had thousands of compounds and I had to make a

18 decision which ones I was going do risk assessments on first, my

19 focus might be on the more potent ones. But in this case the

20 focus is a little bit different. We want confidence in the

21 negatives that we're seeing with our predictive system because

22 we know from experience that the inventory that we're dealing

23 with, many of those compounds will not be active and those that

24 may be active they will be of low potency not necessarily the

25 high binding affinity like the steroid compounds.

26 So an important aspect and you've seen this in
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1 the charge questions -- several charge questions is having that

2 reasonable certainty -- you can't have absolute certainty but

3 high confidence in identifying low potency compounds, minimizing

4 the negatives with the affects based approach that we're using. 

5 Another area that we want high confidence in is around the

6 P-chem filters too. It's a large inventory and pragmatically it

7 would important to see if we can reduce that to a more

8 manageable size. Not every chemical needs to be focused on or

9 evaluated and so we have questions about the P-chem filters in

10 terms of how we might improve them, how do you double check

11 that, how do you use them with care. There are some filters

12 that may be generic in the sense that it doesn't matter that the

13 affect that you're looking at, it is just their inherence

14 chemical properties they're unlikely to pose the systemic risk

15 because they're just simply too corrosive and they're just too

16 reactive.

17 Some of the criteria actually relate to the

18 molecular initiating event that the effect space prioritization

19 systems focused on for ER receptor. And so again your input

20 into how we can increase that confidence and how we can employ

21 those factors in a scientifically sound and transparent way,

22 which takes me to the next point about ensuring transparency. 

23 We emphasize this in every review that we do with you no matter

24 what the topic is because the basis of the science methods that

25 we're going to use in our work and that we'll make decisions on,

26 they need to be understood and they need to be transparent in a
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1 manner that maintains that public confidence.

2 And so our methods flow from peer review, like

3 what's happening right now with this expert peer review as well

4 as public participation. So the public comments that you'll

5 hear after our presentation are important and again

6 transparency. So a number of the questions get at that

7 transparency is the rationale around our thinking, around the

8 P-chem filters, clear have we been clear about how we've built

9 the expert system, how we've selected the training sets. Have

10 we been clear about how the in vitro assay systems work? How

11 we're making decisions about what is inactive and what is not

12 inactive. 

13 The next point is and Dr. Bradbury mentioned

14 this yesterday, we want to leverage what we've already had and

15 build from that and this relates back to the meeting that we had

16 with you May 2011 when we were talking about our 21st century

17 vision. Because there are existing frameworks and concepts that

18 are internationally accepted that will help promote this

19 transparency and then ensure the utility of the approach to

20 answer the regulatory questions. 

21 And this prioritization scheme is actually a

22 real example of those concepts that we discussed a year ago and

23 the first one that I want to come back to that was discussed by

24 a Dr. Schmieder is the OECD (Q)SAR validation principles. There

25 is a paper that we wrote that is a supplemental material in how

26 we look at these validation materials in the matter what the
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1 empirical information is, whether it is low through -- low

2 through put in vitro or high through put in vitro, how broadly

3 applicable they are when you're looking at the utility, the

4 scientific soundness of our approach.

5 And the -- I'm not going to go through all the

6 principles, you heard them yesterday but an important one is

7 this domain of applicability. I mean this gets -- again this is

8 science for a purpose in answering the questions. So the first

9 thing that you want to ensure is your predictive method is

10 representing that chemical space. And so it is important to

11 understand -- that is why it was important for us to define that

12 inventory to be able to move this approach forward to look at

13 the types of chemicals and the range of their properties found

14 in the regulatory inventories.

15 We want in our training sets that we're

16 building the models with to represent that chemical similarity

17 and then what is being measured with the in vitro assays and so

18 the selection of the training sets and the confidence around

19 them, again to answer that question, to have confidence and

20 active means inactive is every important. Another aspect of the

21 principles that I want to highlight is the issue of the defined

22 end point and the mechanist interpretation.

23 Again, the goal that whether a chemical having

24 confidence, a chemical just doesn't have the ability to initiate

25 that molecular event. In this case again we're focusing on the

26 ER pathway and the training sets that were used to build the
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1 expert system were actually developed for that purpose and they

2 were optimized for that purpose and that's why the chemicals

3 were run up to limits of solubility and side of toxicity. But

4 now we're looking at the high through put, which was data --

5 which was generated for a general research approach, which Dr.

6 Judson discussed and how we might be able to employ that to

7 expand the coverage of the expert system for a chemical

8 inventory, where our rules won't cover those structures. And so

9 there were analyses that we've done to sort of get at the

10 performance of those assays and the confidence that we would

11 have that it would be able to discern that relationship of the

12 chemical structures interacting with the molecular target.

13 We looked at the assays with respect to know

14 reference compounds that had a range of potencies for estrogen

15 agonism, but we also took the rainbow trout training set data

16 where we had a couple of hundred compounds and we wanted to find

17 out how would that match with the comparable ER binding and

18 transactivation assays for the high through put. It's not that

19 we expected everything to match perfectly, we actually expected

20 differences and so an important point is understanding empirical

21 data. So looking at that comparative analysis to understand

22 those differences so we understand how the assays, what the

23 assays are telling us, what the strengths and limitations of the

24 high through put assays are so we can move forward and decide

25 how we can employ that data within this approach.

26 We made it clear yesterday that we're not
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1 finished, there is other assays we haven't done that analysis

2 with and so your advice is timely right now and how we might

3 proceed in doing that analysis and how we might do that

4 differently. 

5 The other thing that was mentioned yesterday

6 is integrative approaches to testing and assessment and this is

7 actually a fundamental theme that we took to you in May. It's

8 simply an approach where you promote the use of all existing

9 knowledge in a very integrative structure way and its moving

10 away from sort of a black box empirical approach to more of a

11 hypothesis based approach. The prioritization scheme really is

12 a reflection of this because IATA approaches they have different

13 components and we're employing all of that looking at inherent

14 chemical properties. Trying to incorporate in vitro assay

15 systems to help look at these hypothesis and answer the

16 regulatory question using category-based read-across approaches.

17

18 What you told us in May is that the adverse

19 outcome concept is actually a way to strengthen integrative

20 testing strategies and as was mentioned yesterday it's the

21 conceptual framework on how you look at measuring information or

22 integrating information at different biological scales and

23 understanding those linkages and how they relate to the next

24 event that could be happening. 

25 Now I want to make it clear that we're focused

26 more on the mechanistic understanding not at the apical
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1 endpoint. We're not trying to predict an in vivo endpoint. 

2 We're just trying to get that mechanistic understanding of how

3 chemical structures can initiate a pathway. Again, it's a

4 defined end point, the ER pathway. And so using this concept

5 allows you to employ lower level testing, information from in

6 vitro assays and places assays and the context of an AOP and

7 provides that confidence in terms of those linkages. So the

8 linkage that we're looking at is the initiation and then after

9 the initiation what may happen at the cellular level or tissue

10 level, the activation of the pathway. 

11 And again, having confidence and the training

12 sets that it can discern that relationship and that mechanistic

13 understanding because what that will allow us to do is reliably

14 enable chemical extrapolation approaches. As pointed out

15 yesterday with respect to the inventory and the Expert System

16 there is 1700 chemicals that aren't covered. They're outside

17 that domain. And the point was made, we don't have to test all

18 of those. If we're very strategic and understanding those

19 relationships, selecting the right training set data we may only

20 have to test about 15 percent of that 1700 to finish out our

21 logic based rules. But again we have to have confidence and the

22 assay systems to do that. 

23 So an important aspect of the ER Expert

24 System, it is a logic based decision tree that's a chemical

25 category approach too. And again it allows us to really focus

26 government resources and channel them in a smart way. 
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1 This is my last slide. I'm trying to give you

2 a break because you saw over 300 slides yesterday. So this is

3 like looking towards the future. This effort focused on one

4 molecular initiating event, the ER pathway, the binding affinity

5 to that. And it was a reasonable starting place for the first

6 AOP. We know the environmental chemicals can interact with that

7 receptor. So it wasn't just that's in accordance with the

8 language and the laws FQPA and so and it's just the means that

9 we're using to take that huge inventory and just how to

10 logically filter it and make priority setting decisions. Again

11 this is a priority setting.

12 So your advice on the questions is important

13 on how to improve this approach. We've expanded the Expert

14 System to version two. We want your input on how we've done

15 that. We've been using the same concepts that were used with

16 version one. Are there any deficiencies in that, do we need to

17 tweak it in some way to improve the confidence around it? We

18 really would like to finish the ER Expert System for the

19 coverage of our inventory very quickly. We want to do that in

20 the short term. A lot of work, a lot of years have been spent

21 on that. And we realize there are other intricate signaling

22 pathways and we want your advice how we take some of the

23 concepts and principles that we've used in building this system

24 and those that may apply in building other predictive systems. 

25 A logical next one to look at is the AR

26 receptor. In looking thyroid, although we realize there'll be
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1 some challenges around the thyroid system given that people are

2 still trying to develop the in vitro assays for that not very

3 many if any environmental chemicals act at the ER receptor, I

4 mean the thyroid receptor. And again, not trying to capture

5 every AOP but taking logical ones to again to sort of continue

6 to sort and funnel that inventory and order things up in a

7 logical way. Another issue in terms of looking at the future

8 that we threw at your is the metabolism issue and that came up

9 yesterday. If understanding the interaction the chemical

10 structure bioactivity relationship is important. 

11 It's important to understand whether it's the

12 parent compound or the metabolite. I mean that showed up in the

13 high through put data when we were looking at methyl

14 testosterone. It gave a positive but it's likely what happened

15 there was some inherent ability of the cell system being used to

16 metabolize it to methylestradiol. What's important to

17 understand that -- important to understand the inherent ability

18 of metabolism with the ER expert system they were using the

19 liver slices but we're looking again for your thoughts about how

20 we can advance and improve and get at this issue. 

21 And so I'll end with saying that it takes time

22 to build approaches and to it takes some time to build the

23 approach around this first molecular initiating event for this

24 AOP but the lessons learned will help us accelerate the work for

25 the next AOP and the next AOP. So this is why it's important to

26 learn by doing. And so we want to hear from you about how we
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1 can move onto these other pathways in a much more efficient way.

2 And I think -- let me ask Mary, my co-chair, on this effort if

3 she wants to add anything or is there anything you want to

4 stress that --

5 MS. MARY MANIBUSAN: That was perfect.

6 DR. VICKI DELLARCO: Okay. So we're not done

7 yet.

8 DR. DANIEL SCHLENK: Okay. Thanks Dr.

9 Dellarco. Okay at this point in time what I'd like to do is

10 allow the panel to express any other questions that they may

11 have. This concludes the presentations by the agency. So we

12 will have some public comments and then we'll go into the

13 questions. But at this point this is your opportunity to ask

14 any questions that you've been stewing on over the night. I

15 know many of you have met last night to get your ducks in a row

16 so to speak. So if you have any questions that came out of

17 those deliberations now is the time to bring those forward. So

18 anybody have any questions? Dr. Burris.

19 Dr. Thomas Burris: There was a figure, or a

20 series of figures shown in a few of the presentations indicating

21 an A-mode binder and a B-mode binder and I'm curious how that

22 was derived. Is that based on modeling or some other scientific

23 process?

24 DR. PATRICIA SCHMIEDER: This is Pat Schmieder

25 the - it was derived first from the theory of the -- the fit of

26 estradiol into the pocket. And some conversations with -- on
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1 the slide it was actually personal communications with John

2 Katzenellenbogen who has done a lot of research on the pocket. 

3 And then it was understanding that there're ranges of atomic

4 charges with the fit of estradiol in the pocket in the main

5 sites that estradiol binds. So there was the range. The A type

6 has a range of atomic charges around the oxygen group. I think

7 it's -- I can't remember -- it's a pretty tight range. 

8 But we then calculated for the chemicals that

9 we put in that series that they fit it within that range so the

10 alkylphenols, the alkoxyphenols, salicylates and parabens all

11 met within that range. The same thing on the other side, the B

12 side where the anilines, the alkylanilines were binding, our B

13 type lead group if you will. We calculated the range of atomic

14 charges there, which is different on that side. It's

15 electronic, it's hydrogen bond donor versus accepter generally

16 in those two. 

17 So we use that calculation to tell us whether

18 those groups of chemicals are within that charge range and would

19 fit within the A type or the B type. And then there is several

20 chemical classes that don't fit either one of those and that's

21 where we have -- we talked some of the multi-cyclic

22 hydrocarbons, which don't fit either one of those and so we have

23 -- we called them at the time special rules until we understand

24 exactly what the interaction is that is driving it.

25 So it was a combination of that calculation to

26 tell us that it fit the criteria of either an A type or a B type
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1 but then the empirical data within those groups that like the

2 alkylphenols the whole group and looking at the relationship of

3 RBA to LogKow that we found within that that allows us to the

4 extrapolation within the category. So I hope that answers the

5 question.

6 DR. STEVEN BRADBURY: Steve Bradbury. I'll

7 just expand a little bit from what Pat said. The work with the

8 research group at University of Illinois in Katzenellenbogen,

9 there are papers that are related to the logic of the A site and

10 the B site and some of the questions yesterday about three

11 dimensional modeling and how to look at that. In fact a lot of

12 that research is behind the logic behind the A site and the B

13 site and the distances and the charges associated with that. It

14 was through that work with three dimensional modeling that gave

15 us the hypotheses that we're pursuing and then sort of trading

16 off the CPU time in terms of chugging thousands of chemicals to

17 three dimensional docking versus being able to use categories,

18 but founded on the understanding of the three dimensional

19 aspects of the receptor binding the steric and the electronic

20 characteristics of that three dimensional space. So if you go

21 back to the 2009 SAP there is some literature cited that lays

22 down the foundation for what Pat was just summarizing at this

23 SAP.

24 DR. DANIEL SCHLENK: Okay, thank you. Any

25 other questions? Speak now.

26 DR. EdWARD Perkins: This is Ed Perkins. Can
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1 I get a clarification on the reasons why certain compounds

2 weren't tested or obtainable for empirical testing? Because we

3 only had what 30 percent or so?

4 Dr. Richard Judson: Hi this is Richard

5 Judson. So going back to summarizing our protocol. So we had a

6 large 10,000 compounds that were nominated that seemed to

7 Physico-Chemical properties that would allow them to be tested

8 in the high through put so they looked like they would be

9 soluble in DMSO, they wouldn't be volatile. 

10 So we went and ordered them in our ordering

11 protocol, to go buy things is we said we have to get compounds

12 from Sigma or Vendor B. And I don't know what Vendor B is who

13 have a reputation for being able to give you compounds of pretty

14 high purity and we told those vendors you need to have -- give

15 us things purity of 90 percent or better. And that's where,

16 it's 30 or 40 percent of compounds that we ask for from reliable

17 vendors we can get. Okay, NTP did the same thing with a

18 different set of vendors and again we're getting the 30 to 40

19 percent. So it really is it's a lot of these compounds are

20 industrial compounds which the standard chemical vendors don't

21 -- they can't give you in those high purities.

22 DR. PATRICIA SCHMIEDER: This is Pat

23 Schmieder. With regard to the lower through put assays we did

24 not find this -- we were able to obtain a much higher percentage

25 of the chemicals. There are some chemicals we could not obtain

26 and if so then we looked for surrogates within a chemical class
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1 that would address that. But I can only imagine that the

2 structure space that Richard is talking about is much -- is

3 somewhat different because we were able to obtain a higher

4 percentage.

5 DR. DANIEL SCHLENK: Okay any other questions?

6 Oh sorry, Dr. Sandy.

7 DR. MARTHA SANDY: This is Martha Sandy. So

8 the high through put assays, the eight that were presented to

9 us, were they done in the same contract laboratories?

10 DR. RICHARD JUDSON: No they were done -- so

11 Attagene is one contract lab. They did two of them Odessy Thera

12 another contract lab did one of them, NovaScreen Caliper did

13 three of them and the last two were them were run at the NIH

14 Chemical Genomic Center. And we have a couple of other vendors

15 within the bigger set of assays.

16 DR. DANIEL SCHLENK: That was Dr. Judson. Dr.

17 Meek.

18 DR. BETTE MEEK: A couple of questions. So

19 Pat the proportion of chemicals that you were able to access

20 then was about you said higher than 30 percent was about but you

21 didn't specify?

22 DR. PATRICIA SCHMIEDER: We haven't run the

23 numbers but it's -- oh this is Pat Schmieder - it's definitely

24 over 50 percent --

25 DR. BETTE MEEK: Okay.

26 DR. PATRICIA SCHMIEDER: I think it's -- my
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1 guess would be it's over 80 percent. I mean -- and it might

2 even be higher.

3 DR. BETTE MEEK: Okay, it's Bette Meek. One

4 additional question, maybe Mary to you, I guess I was trying to

5 determine the chemicals that are not produced and used in the

6 U.S. you said you will not issue testing rules but I wasn't

7 entirely clear where that fit in the -- in the process.

8 MS. MARY MANIBUSAN: So under our statutory

9 authority we can only issue test orders to manufacturers or

10 pesticide registrants that are currently manufacturing that

11 chemical in the U.S. So that would come into play when I talked

12 through flow diagram at the end right before it enters the Tier

13 1 for screening officially, we make that determination whether

14 it continues to be manufactured in the United States or not. 

15 And if not, then it's removed from the list before we put it

16 forward for Tier 1 screening. Just to add we rely on production

17 information through our Office of Pesticide Pollution Prevention

18 and Toxics. 

19 DR. DANIEL SCHLENK: Just to add that was Mary

20 Manibusan. Dr. Denslow.

21 DR. Nancy Denslow: Nancy Denslow. Do you

22 have access or contract labs of organic chemists that could take

23 those impure compounds and purify them to higher purity and has

24 that ever been tried? 

25 DR. RICHARD JUDSON: The EPA/ORD does have

26 lots of chemists who do have the ability to do those sorts of
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1 things. We've never approached them to ask whether they can do

2 that. And it just thinking back on the history of ordering

3 those chemicals, another criteria that I'd forgotten to mention

4 is cost so there is another set I don't -- ten, twenty percent

5 of the compounds we could get at very high cost and is just --

6 we had only so many slots to run high through put screening

7 assays so we took the less expensive compounds first. There are

8 ways if you're willing to spend more money to get more of the

9 compounds.

10 DR. DANIEL SCHLENK: That was Dr. Judson. Dr.

11 Sandy.

12 DR. MARTHA SANDY: Martha Sandy. So to follow

13 off with Bette Meeks question, Mary you said that you would look

14 and see if the chemical was produced in the U.S. What about

15 chemicals that are used in the U.S. but imported?

16 MS. MARY MANIBUSAN: So sorry, just to

17 clarify. We can issue test orders to manufacturers, pesticide

18 registrants that are producing and using that chemical in the

19 United States so it includes importers as well.

20 DR. DANIEL SCHLENK: That was Mary Manibusan. 

21 Dr. Chambers.

22 Dr. JANICE Chambers. Now I'm a little bit

23 confused. There are 30 percent of the chemicals on one list and

24 80 percent -- are you using different lists then and if you have

25 more, Pat, can you not share with them with the high through

26 put?
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1 DR. RICHARD JUDSON: So very very different

2 lists, so I'll put Pat -- this is Richard Judson -- put words in

3 Dr. Schmieder's mouth that they are specifically looking at the

4 chemicals on the EDSP regulatory inventories. We are looking at

5 a much broader set of chemicals that many other stakeholders

6 requested us to look at. So it's very different. And there is

7 this idea of heroic efforts. So if we were told that you -- if

8 we told our person in our group who manages all the chemicals,

9 you have to get all those chemicals, you know scour the earth,

10 she probably could have gotten 60, 70, 80 percent but it's --

11 you know it's again a process, a research process that led to

12 the 30 percent number.

13 Dr. Vicki Dellarco: This is Vicki. In fact

14 the 290 some chemicals that were used in the comparative

15 analysis with the rainbow trout data and the high through put

16 data the samples for that to run it through high through put was

17 provide it for the most part from Duluth. 

18 DR. PATRICIA SCHMIEDER: Yes this is Pat, we

19 did provide them chemicals that they didn't have.

20 DR. DANIEL SCHLENK: Dr. Perkins?

21 Dr. EDWARD Perkins: Was there a comparison

22 made between the Expert System rule-based classification and the

23 high through put results?

24 DR. RICHARD JUDSON: This is Richard Judson. 

25 We have started that. So mechanically we have started that, but

26 again the appropriate comparison will be with the final
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1 consensus calls from all of the high through put data which is

2 not there yet. So that is something that needs to happen but it

3 just hasn't happened yet.

4 DR. DANIEL SCHLENK: Okay, no further

5 questions? I guess this concludes the presentation by the

6 Agency. Thank you very much. Okay, we're now about to move

7 into our public commenting period. I have a list of public

8 commenter's that are going to be coming forward. Our first

9 presenter is Scott Slaughter he is from the Center for

10 Regulatory Effectiveness. 

11 MR. SCOTT SLAUGHTER: Good morning. My name

12 is Scott Slaughter and I'm presenting comments today on behalf

13 of the Center for Regulatory Effectiveness. I want to point out

14 at the beginning that we agree with Dr. Bradbury who in his

15 remarks yesterday commented on the importance of this SAP and

16 all SAPs to EPA's actions and processes. And we thank you for

17 the opportunity to comment here today. 

18 We also agree with EPA that they're not done

19 yet. The proposed Comp Tox methods that EPA is talking about

20 may be a promising alternative to the current approach to doing

21 EDSP tests. However, these methods must be validated before

22 they can be used for any regulatory purpose. 

23 For example, the Information Quality Act and

24 the Paperwork Reduction Act both require that EPA demonstrate

25 that its decision tree generates accurate, reliable and

26 reproducible predictions. As detailed in our written comment,
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1 which I hope you all have by now, we do not believe that the

2 record before this SAP contains this required demonstration for

3 either the decision tree or for the proposed use of HTP data. 

4 So we asked this SAP to advise EPA on how to validate its Comp

5 Tox methods so that they can be justifiably be assumed to

6 generate accurate, reliable and reproducible predictions about

7 endocrine effects. 

8 In this regard, we'd like to draw your

9 attention to the recent paper by Patlewicz, and I hope I

10 pronounced his or her name correctly, which provides some useful

11 discussion on this validation issue. There is a link to it in

12 footnote one of our written comments I believe. And there is

13 another important point we want to make, in addition to the

14 validation for the Comp Tox methods themselves, they ultimately

15 will be based on the results of experimental tests using more

16 traditional assays. All the underlying assays and experimental

17 data should be derived from properly validated tests, which have

18 shown to be accurate, reliable and reproducible. 

19 And we also do not believe that the EPA should

20 use any proprietary algorithms in the EDSP. The Agency has

21 laudably expressed its concern and interest in ensuring

22 transparency. The use of any proprietary algorithms would work

23 against that goal. 

24 Finally we request that the SAPs either -- not

25 this one but some other SAP in the future, review every EPA

26 conclusion which states that a test is validated for use in the
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1 EDSP. We believe that this review is necessary because EPA

2 subjected its current EDSP Tier 1 test to external peer review. 

3 As discussed in Appendix A to our written comments that you

4 have, we believe that those pier reviews often produce negative

5 reports on the current EDSP Tier 1 test. Yet EPA, on the basis

6 of what we believe to be very negative peer review reports,

7 concluded that those EDSP Tier 1 tests are properly validated

8 and proceeded to use them. And once again this issue is

9 discussed in extreme detail in Appendix A to our written

10 comments. 

11 We believe that this Tier 1 test validation

12 issue should be included in the Tier 1 test SAP review that EPA

13 has announced will be scheduled for later this year. We also

14 believe that an SAP should review EPA's validation conclusions

15 for any Tier 2 test and of course, we also believe that another

16 SAP should review any EPA decision that Comp Tox methods have

17 been properly validated for use. Thank you and I'll try to

18 answer any questions you might have.

19 DR. DANIEL SCHLENK: Thank you Mr. Slaughter. 

20 Any questions from the panel? Okay. 

21 MR. SCOTT SLAUGHTER: Thank you.

22 DR. DANIEL SCHLENK: Thank you. Our next

23 presenter will be Kristie Sullivan from the Physicians Committee

24 for Responsible Medicine. 

25 MS. KRISTIE SULLIVAN: Hi there, Kristie

26 Sullivan. I'm from the Physicians Committee for Responsible
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1 Medicine. We're a group of physicians and scientists who

2 promote ethics in medicine and science, including for my

3 purposes, regulatory testing. Sorry about that. So I just

4 wanted to remind everybody that the EDSP program is really a

5 huge program in terms of the amount of effort that is being put

6 into it, the numbers of really thousands or hundreds of

7 thousands of animals that could be used and the resources that

8 are being spent and so we think this prioritization effort by

9 EPA is -- should be given kudos. We really appreciate their

10 efforts to speed up the process. 

11 Just some initial thoughts. We think

12 prioritization based on exposure effect and physical chemical

13 properties is a reasonable scheme. The tools the EPA is

14 proposing are robust and clear and useful for that purpose and

15 we also think that an effects-based chemical category is a key

16 feature of the plan. So we support and encourage the SAP to

17 support this work. 

18 Excuse me, please bear with me I'm getting

19 over a cough. So the rest of my comments are fairly general but

20 I wanted to start with on what EPA cites as one of its

21 motivations. Obviously it stated this program before 2012 but

22 the U.S. House of Representatives put appropriations language

23 into a bill that directed EPA to accelerate ToxCast and other

24 screening methods and to adjust individual test or use those to

25 adjust individual test orders. I wanted to make it clear that

26 the -- if you read the entire language, the intention of the
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1 language is to use the new tools to in -- where possible, to

2 replace the current Tier 1 assays.

3 However, according to the documents that we've

4 seen, it would appear that EPA has created a screen for a

5 screen. Of course it's extremely useful to get from 10,000

6 chemicals to a couple of hundred chemicals. And it's useful for

7 that purpose but we're still seeing this and it's being

8 presented, it seems to us as a new layer in front of the current

9 Tier 1 battery. And then you still have a couple of hundred, or

10 a couple of thousand discreet chemicals still heading into Tier

11 1, which will still take a while to get through with some of the

12 same mechanistic end points that are already addressed in the

13 screening strategy. So I used this -- you have this in front of

14 you and I hope it looks better in front of you than it does up

15 on the screen but I tried to locate the rest of our suggestions

16 on the screening map here.

17 First of all we think that it would be useful

18 for pesticide active ingredients to be folded into the screening

19 process now if they haven't already gone through registration

20 review before being screened in the current Tier 1 battery. We

21 think there is no reason that inerts couldn't also be

22 deprioritized for screening based on exposure considerations. 

23 And this has come from the panel from Dr. Keiser is it,

24 yesterday actually it seems kind of like a good idea that you

25 could consider weighting some of the assays -- assay results to

26 rank the chemicals heading into Tier 1 screening. We also
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1 believe that the Expert System and HTPS should have the power to

2 both exclude inactive chemicals from further work and allow very

3 active chemicals to skip further screening. Obviously these

4 comments would pertain to the estrogen pathway at this point.

5 We think it's important to integrate the

6 results from the screening activity into the existing Tier 1 by

7 replacing individual in vitro or in vivo assays on a case by

8 case basis. And that replacing most or all of the Tier 1 assays

9 with higher through put methods should remain a short term goal.

10 In fact the cytosolic binding assays that are currently in the

11 battery consume and inordinate number of animals just to do --

12 even though they are in vitro assays.

13 Finally, after the high through put or expert

14 system screening but before Tier 1 screening, other available

15 information should be considered. Sometimes this is called

16 other scientifically relevant information, other studies,

17 literature, et cetera. And you could use the AOP framework to

18 organize this available other information. Finally, as many

19 people in this room already know of course the OCD published in

20 2008 a detailed review paper on metabolizing systems for in

21 vitro endocrine disrupters. This is being -- in the process of

22 being up dated for peer review publication and it includes

23 actions that member countries of the OECD like the U.S. could

24 take to increase the metabolic capacity of in vitro assays.

25 From the presentations yesterday, it looks

26 like EPA is taking some of those recommendations but we just
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1 wanted to support this idea and bring it to the Panel's

2 attention. We finally also support the translation of this

3 approach to other pathways. We think it makes sense to do that.

4 And we recommend overall using the information from this

5 screening strategy to save further resources by cutting out

6 current Tier 1 assays for clear positives and negatives. So I

7 can be reached at the email address on this screen and you

8 should have a copy of our written comments for more detailed

9 information.

10 DR. DANIEL SCHLENK: Thank you. Any questions

11 for Ms. Sullivan? Okay. Thank you very much. Our next

12 presenter is Catherine Willett from the Human Society of the

13 United States.

14 DR. CATHERINE WILLETT: Hello that's actually

15 Humane Society, but so we -- not to be confused with human. So

16 I just want to reiterate what Kristie said is we submitted some

17 joint written comments and so my comments will overlap a little

18 bit with Kristi's, but there will be some extra stuff in there

19 that I didn't think about before we submitted our written

20 comments and as with Kristie I'll start off in a positive vein

21 and I hope a positive vein with some other stuff in the middle. 

22 Okay. Is this, where is the projector, wait what do I point at?

23 Oh never mind. Okay.

24 So first of all I'd like commend the EPA on

25 these developments to develop tools that evaluate molecular

26 mechanisms of chemical activity as everyone pretty much
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1 appreciates by now, mechanism based approaches are appropriate

2 in the short term for things like prioritization, also to inform

3 chemical categories and structure activity relationships for

4 read across and also to increase the certainty of interpretation

5 of both existing data and new incoming information. And also as

6 also been alluded to in a lot of these discussions, this

7 information can be used to inform testing strategies. 

8 In terms of this SAP, the information that

9 we're provided includes a lot of steps forward in the endocrine

10 prioritization and screening process including a rational

11 information-based approach to prioritization so it incorporates

12 the potential hazard along with exposure considerations and also

13 legislative mandates for the prioritization which is a definite

14 step forward.

15 The idea of this iterative classification to

16 cover all chemicals in the applicable inventory, I mean I know

17 EPA has been doing this for some time. This sort of formalizes

18 the process into an increasingly larger chemical space and I

19 think this is a very good way to be able to provide educated

20 guesses at least with respect to covering and identifying the

21 proverbial needles in the environmental chemical haystack. 

22 The other thing that is good about this

23 process is that it begins with a prototype, which is the

24 estrogen receptor. You work out the details using this

25 prototype and sort of focus your energies on a prototype. And

26 also this is a nice discussion about a validation that is fit
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1 for purpose. In other words, when is it appropriate to do a

2 formal validation or when is it more appropriate to do something

3 like what is included in the OECD (Q)SAR validation principles

4 and also there is some discussion about the quality control

5 metrics for the HTP assays, which I'll discuss a little bit more

6 in a minute.

7 However, there are some major concerns about

8 this process and from our perspective -- our perspective is we

9 want to streamline this and try to get away from the animal

10 tests and stuff. So the whole discussion is focusing on how do

11 you put things into the lineup to get into the Tier 1? So

12 they're being prioritized for, as Kristie mentioned, additional

13 screening in a Tier 1 that includes the same exact assays. And

14 it's also prioritization to determine the order of testing and

15 to bin chemicals, I mean maybe practically it'll bin chemicals

16 but the discussion is all about how to order them and not to

17 exclude and I think the focus might be -- could be a little

18 stronger on an exclusion part. 

19 Also as Kristie mentioned to find that on page

20 five the intention of the bill language is sort of misconstrued

21 and the intention was clearly to adjust Tier 1 -- to adjust the

22 test orders. So it's to use this information for Tier 1

23 information, not to prioritize chemicals for Tier 1 testing at

24 any rate. In this discussion, there hasn't been any mention

25 about how this approach is moving toward the eventual

26 replacement of the Tier 1 battery. As Kristie mention from our
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1 perspective, it is essentially adding another layer to a program

2 without describing how the whole thing is going to be eventually

3 streamlined.

4 Now specific concerns is that the HTP as we've

5 heard many times, I mean the HTPC data is still preliminary. 

6 The data that we are trying to use for comparison has not been

7 -- or it wasn't produced for the current purpose so it's kind of

8 difficult to evaluate the comparisons. The assays are still not

9 that well characterized. I'll get to that in a little bit. And

10 the data interpretation filters are still in process so we don't

11 really know what they are and they're not really -- they're not

12 completely transparent, at least not in my reading of this. I

13 mean, they've changed since the last publically available

14 discussions and I'm not sure. So but that's still in process I

15 understand. So it's hard to evaluate that's what I'm trying to

16 say.

17 The other major concerns are that Expert

18 Systems don't currently exist for other endocrine pathways so

19 androgen and thyroid hormone systems primarily -- and to develop

20 a similar expert system approach to each of these would take a

21 very long time. So as Vicki mentioned thankfully that the

22 lessons learned from this evaluation could be used to move

23 forward more quickly and I'll talk a little bit about that in a

24 second hopefully. 

25 Okay so some observations from reading this, I

26 thought actually personally that the reference chemicals in
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1 table Susan were actually pretty good and that's just because we

2 have a lot of experience in discussing the old ECCVAM lost and

3 which chemicals have the most supporting information and what a

4 good range of chemicals in terms of activity and chemical space

5 are and I actually thought that was a really good binning of

6 that listing. So and there was fairly good concordance except

7 for that one assay which I was happy to hear yesterday, it was

8 probably going to be jettisoned. The other thing is that a

9 larger set of assays is not necessarily superior to a smaller

10 set of well characterized assays. 

11 Having more information is not necessarily

12 better if you don't really -- can't fully appreciate what the

13 information means. And the reference chemicals are a very good

14 way to standardize the methods and the data interpretation

15 procedures. Okay so some more observations are you need to

16 compare similar concentration ranges of the same chemical

17 preparations in order to really evaluate how well the

18 performance is between the tests.

19 And the importance of the replicates

20 especially in the high through put screening and the importance

21 of establishing appropriate data and interpretation data and

22 procedures. So in terms of the appropriateness of the HTPC

23 Assays for expanding the chemical domain, the purpose here as we

24 heard a lot of times is to identify relatively weak binders out

25 of a large population of non binders. The HTP assays may be

26 less sensitive than the ES assays but we don't really know
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1 because the correct comparison hasn't really been made. 

2 The current level of discordance is a little

3 bit concerning. I think it's just concerning because we don't

4 fully understand the assays and so there is a lot of explaining

5 that is not quite -- doesn't feel quite confident yet and as

6 we've heard, it's a process. 

7 To balance the minimal faults negatives

8 against an over abundance of false positives, it seems to me at

9 some point along the line it would be really useful to know what

10 a biologically relevant signal is and I'm not talking about use

11 of this in regulation but at some point you know is an RBA of

12 0.000001 whatever biologically relevant and are the HTP assays

13 good enough for the purpose? That may not be a question that is

14 really answerable at this point, but there might be a lot of

15 data from the expert system that could give you a handle on that

16 and you probably already know that. But the thing is, now your

17 screen is based on your technology -- your available technology.

18 So you're calling a positive is just based on the sensitivity

19 of your most sensitive assay and is that biologically relevant. 

20 Don't know, but it's worth a discussion.

21 Okay, in terms of -- this is way forward

22 really quick for the ER Pathway. It would -- I mean you guys

23 know about this and it would be interesting to share experiences

24 with the ECVAM approach where they're taking the MELN assay and

25 they're using -- they have some standard -- it's in the

26 validation process and they're using high through put screening
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1 to expand the validation of that. And it's similar to what you

2 guys did with the BG1-Luc, and it also incorporates some of the

3 performance-based test guideline principles from the OECD. So

4 the OECD has this whole process where they're developing

5 performance-based test guidelines. You can take some of those

6 principles and apply them in this case. It should be very

7 appropriate.

8 Test appropriate chemical range and

9 concentration in the HTP assays for direct comparison with the

10 expert system which you said you're in the process of doing, I

11 think it would be interesting to characterize, not interesting

12 but important to characterize the metabolic capacity of each

13 test system so you understand when you get discordant results

14 you can understand what the metabolic probabilities are. 

15 Incorporate metabolic systems now -- as Kristie mentioned, there

16 has been a paper that reviews the available metabolic systems

17 that was published in 2008. There is an update in process that

18 discusses how one might go about applying those specifically in

19 the realm of endocrine disruption.

20 Okay in terms of the ER pathway, it should

21 soon be possible to compare these data that you are generating

22 with the Tier 1 data for select phase one chemicals that would

23 be appropriate for comparison. And it would be important to

24 review the data interpretations after you've compared all this

25 stuff. Is it appropriate, for example, to base the TA screening

26 on binding first? I mean I don't want to talk about it because
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1 I don't have time. 

2 Okay and then review the performance of all

3 the assays to see which ones could be removed from the Tier 1. 

4 I'm almost done, sorry. And then in terms of the way forwards

5 for the androgen and thyroid pathways, the development of AOPs

6 and the relevant assays is it's being discussed and is in

7 process in several groups. I just wanted to point there is an

8 OECD thyroid scoping effort, there is an OECD validation

9 management group non animal and the advisory group on molecular

10 screening. So these guys are developing AOPs. They're talking

11 about assays and in terms of a streamline fit for purpose

12 validation that should be applied to these assays that would

13 streamline this whole process, there is the applicable OECD

14 performance based test guideline principles. Then there is a

15 paper from Richard which discusses all of this. Ways that you

16 could streamline this and the important elements of validation

17 and those things that could be jettisons that aren't appropriate

18 for high through put and testing of the reference chemicals as I

19 think is critical for this kind of assessment and the OECD

20 validation studies for many of these they have lists of where

21 you could start. 

22 Okay that's it I think it's a -- EPA is

23 walking in the right direction and I think they have a very good

24 handle on what it is they need to do but I think that the

25 information that we're given for this AP is just not there yet.

26 DR. DANIEL SCHLENK: Thank you. Any questions
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1 would like to stay for questions or -- if there are any? Any

2 questions for Dr. Willett. Thank you for trying to keep on

3 time. Let's go ahead, we need to take a break at this point. 

4 Let's be back at 10:15 please. 

5 (WHEREUPON a break was taken)

6 DR. DANIEL SCHLENK: Take your seats please. 

7 Our next speaker is Richard Becker from the American Chemical

8 Council. Is Dr. Becker around? Oh, Okay. Okay lets we can go

9 ahead and go with Christopher Bogart? No? All right Dr.

10 Becker. Is Dr. Becker here. Okay, thank you.

11 DR. RICHARD BECKER: Thank you. I'm sorry to

12 keep everybody waiting. I'll be brief today I think and

13 probably talking at a 10,000 foot level. Other folks will

14 provide much more granularity, I guess. 

15 First I want to thank EPA and the Science

16 Advisory Panel for convening the meeting. This is a very

17 important topic obviously. And we submitted public comments,

18 which everybody should have and I urge you please to take a look

19 at those. In our public comments, which are joint comments

20 between ACC and CropLife America, we've actually gone through

21 and tried to address each of the charge questions, provide some

22 analysis and then some recommendations. So I would encourage

23 the Science Advisory Panel members to please look at those.

24 Overall, I think everybody is in support of

25 improving prioritization and the EDSP itself. Obviously it's

26 important for EPA to conduct the research, bring that
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1 information forward and to develop improved scientific tools to

2 improve all aspects of the EDSP. Today we are talking about

3 primarily prioritization but future SAPs and future work we'll

4 be focusing on EDSP itself and Tier 1 and testing as well. 

5 I think the other thing that is clear is there

6 has been a lot of progress made and that is a lot of hard work

7 by some dedicated scientists and very challenging. So I think

8 we all have to recognize how important that is. To date on the

9 Expert System and (Q)SAR and HTP really been focusing on the ER

10 but there is an opportunity to expand that going forward. 

11 Finally though of course, the goal is to have

12 risk-based prioritization. So obviously there is a whole

13 component of exposure that needs to move forward kind of in a

14 similar vein. So thinking ahead it's important to also consider

15 how the exposure components of prioritization will be developed

16 and to plan accordingly. 

17 One of the things that is also clear is the ER

18 Expert System and ToxCast HTP assays are separate research

19 programs. We've seen today, I think and yesterday as well and

20 the documents we're looking at now an attempt to marry these up.

21 But they're clearly not adequately integrated yet. I think one

22 of the problems we've had in looking at the charge questions was

23 they are quite general. And so by having such general charge

24 questions, you're going to end up perhaps providing very general

25 answers or direction and I think what's needed is more

26 specificity here. So I would encourage you to think about how
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1 that integration could occur in a more logical manner than it's

2 occurred today. 

3 Obviously to establish scientific confidence

4 in the assays and prediction models is a goal. Everybody has to

5 have that scientific confidence, the Agency to use them, the

6 stakeholders who are going to rely on them or use them for their

7 products and the public who are going to rely on those decisions

8 for the protection of health and the environment. And with that

9 of course, you have to understand for the assays reliability,

10 sensitivity and specificity. 

11 One of the problems we've seen is that not all

12 the assays even the ER assays have been run on the same sets of

13 chemicals. So it's kind of hard to mush these things together

14 and make direct side by side comparisons. So one of our

15 recommendations is to go back, look at the recommended list of

16 chemicals that ICCVAM put forward and have a single set, adopt a

17 single set of reference chemicals, if you would, for which all

18 of these assays could be evaluated. That will allow you to make

19 those side by side comparisons, A priori and not post facto I

20 guess I would add. 

21 Scott Slaughter cited our paper here which I

22 show and so it's always good to know that at least one person is

23 reading your paper. I would just say that we just published

24 this so it's in press, it's open access and we talk about

25 methods and approaches to conceptually get around some of the

26 challenges with addressing validation of high throughput models.
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1 So I encourage you to look at that. We discuss it somewhat in

2 our comments. 

3 In that paper or course we talk about adapting

4 to OECD (Q)SAR principles to HTP assays. I think that's the

5 goal of the Agency but we've also explored that in a little bit

6 more detail how you would do that. Not just do it conceptually

7 but how one could go about doing that. We also point out that

8 it's possible to use the IOM approach, the framework for

9 qualifying biomarkers can be adapted. And that is another

10 complementary approach that might be useful to consider. And

11 then in that paper we discuss how these things can fit within

12 adverse outcome pathways. So I think we made a contribution in

13 that area and would encourage the Agency to look at those and if

14 there are questions about those, call us and we'd be glad to

15 talk about that. 

16 In establishing scientific confidence in the

17 prediction models, I think everybody used the word transparency.

18 I don't think anybody who presented from the Agency who

19 summarized from the agency or any of the -- all of the

20 commenters used the same word transparency. I think it's

21 important to have transparency to model algorithms, all the

22 steps, the filters the decision points. Clearly the ER expert

23 system is a good example of working towards that transparency. 

24 I don't know if everybody has done it but you can go on the OECD

25 (Q)SAR toolbox, you can download the ER Expert System version

26 one, you can run it. It's very transparent. They've got a user
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1 guide or guidelines you can follow, very helpful. I think that

2 same level of transparency could be mirrored with high through

3 put assays.

4 These high through put models shouldn't be

5 black boxes. All of the data the model construct parameters,

6 model runs to demonstrate performance should be available. I

7 don't know how you can peer review such things unless you have

8 that kind of data. And of course you shouldn't have to reverse

9 engineer those models in order to evaluate performance or

10 conduct sensitivity analysis. 

11 And so in looking ahead what could be very

12 helpful for this SAP and to help EPA do is to develop a road map

13 to guide the Agency and a roadmap that they can follow to

14 conduct those specific studies and the analysis, to develop that

15 scientific confidence in the assays and then also in the

16 prediction models for the intended uses. And we've heard that

17 over and over, fit for purpose or intended use. There may be

18 different degrees of this kind of validation or model

19 sensitivity, specificity, applicability for different purposes. 

20 It's important to keep that in mind. We're not saying that you

21 have to have one size fits all here. 

22 I think for prediction model transparency, I

23 coined the term borrow from the open source folks with respect

24 to model development, applications analyses data, user guides

25 and those kinds of things. I think that would be very important

26 to do in order to improve everybody's understanding and
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1 confidence in these models, and I think we'll allow also outside

2 folks to bring data and information forward to upgrade those

3 models. And obviously of course it will help peer reviewers as

4 well. 

5 So and the last slide I just want to talk a

6 little bit about -- this is a picture out of our paper about

7 AOPs and our conceptual framework, which we must remember that

8 of course what we're looking at on these molecular initiating

9 events, and we prefer the term initial molecular events because

10 they really don't initiate anything per se. It's the first

11 event, so it's the initial event. It's not like turning the

12 light switch on and then all the sudden you get an adverse

13 effect. There are a number of steps between that. So typically

14 the initial molecular advance at protein binding or receptor

15 binding, those kinds of things but in order to get effects at

16 organ system or the organism, there are key events that have to

17 occur after those initial events. I think everybody has this

18 kind of concept in mind. We're just trying to suggest that we

19 need to be a little more explicit in our thinking about this and

20 how we talk and deal with the model development and validation

21 of AOPs because they're also not just key events but then dose

22 responses from each of those steps that are important to

23 consider in those key events. 

24 So I'll end there and just say that I hope

25 I've stimulated your thinking somewhat and again, I would

26 recommend that you consider helping EPA develop that roadmap. 
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1 Thank you.

2 DR. DANIEL SCHLENK: Any questions for Dr.

3 Becker? Okay thank you. Our next presenter is Christopher

4 Borgert from Applied Pharmacology and Toxicology.

5 DR. CHRISTOPHER BORGERT: Am I on here? Okay

6 thanks. First of all I'd like to thank you for the opportunity

7 to make some comments this morning and I want to start off also

8 by saying that I think that the overall idea here is a good one

9 and I think there is a lot of good thinking that has gone into

10 this. I think we would all support the need to prioritize

11 chemicals and I think the general ideas of how to go about doing

12 that are in place. But I do have some concerns and some things

13 that I hope will be thought about in a little more detail. So

14 what I'm going to do is present some over arching ideas and pull

15 out a few of the details to I hope illustrate my point. 

16 So if we could go to the - do I have an

17 advancer here. So I want to step back to, again, more of a

18 10,000 foot level and think about what's going on with this

19 prioritization process. On the one hand it is, and we all

20 understand this, just being used as a screening tool, but on the

21 other hand we're appealing to a confidence and a logic that

22 comes through use of the adverse outcome pathway. So we're kind

23 of dancing between saying well we don't necessarily have to lay

24 out the entire pathway of course because this is for a screening

25 for prioritization, but at the same time we're saying we gain

26 confidence from the logic of the AOP. This is right out of the
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1 EPA document, this is a quote. 

2 So the focus is on this molecular initiating

3 event for prioritizing chemicals and then you're going to move

4 up through higher levels of biological organization in the

5 Adverse Outcome Pathway. So that really does imply that we've

6 got a demonstrated pathway and I'm going to now talk about that

7 now and then pick out some just specific details. 

8 Okay, so as Rick Becker mentioned, these

9 molecular events are initial but not necessarily initiating and

10 the difference there is that the latter is obligate but the

11 former is not. Okay, so that's an important concept I think we

12 all get it. But I think it's important to think about the words

13 we use as well because it influences the way we do data

14 comparisons and how we think about the data that we're

15 evaluating and how we interpret it. So if we're putting it into

16 this mind set of an adverse outcome pathway it colors the way we

17 think about things a little differently than if we're saying

18 it's all correlational and there is nothing more to it than

19 that.

20 And so I think an important point is here that

21 key events are key to this concept. We have some well worked

22 out frameworks for using key events and a mode of action. But

23 at this point in the EDSP, there is no point at which a causal

24 connection is sought between these logical or assumed or

25 proposed key events. It's not in the proposed prioritization

26 approach. It is nowhere in the EDSP Tier 1 because again that
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1 is just screening, but it's not in EDSP Tier 2 either. So at

2 some point I think it's important to either define that we're

3 not doing that and we're not gaining confidence in an Adverse

4 Outcome Pathway because we've demonstrated it or we do a little

5 more work to try to demonstrate that and there are some good

6 models to follow such as the human relevance framework. 

7 So just to put a finer point on that what

8 we're focusing on what I illustrate here as initial molecular

9 events, but these may not be more than an initial event, they

10 may actually not be key. There are very few pathways that I

11 think we can envision where you have a molecular initiating

12 event and just go straight through to an adverse affect without

13 some intervening key nodes where there is a lot of intersection

14 with other pathways and of course this whole diagram is very

15 simplistic. We know that the biology works by complex causal

16 networks. We all know that but again I'm just encouraging

17 everyone to think about what we're actually doing here and what

18 data we need in the context of that thought process and then

19 again, how we're -- this has been mentioned by other speakers

20 but how do we integrate all of what we're doing, not just one

21 piece at time but how does the prioritization then in this logic

22 integrate with Tier 1 screening and Tier 2 testing. I think we

23 need to think about how that all fits together because it is

24 going to impact things like cutoffs and the logic we use to set

25 those.

26 So as I say let me get back to a positive
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1 point I think the overall logic there, the overall idea is good,

2 but we really need to drill down and think carefully about some

3 of these finer points. 

4 So one of those detailed concerns is around

5 potency and we hear a lot about low dose hypotheses and all

6 sorts of things these days but potency is a fundamental

7 pharmacological tenant. It's not been disproved. It's one of

8 the most useful characteristics of endocrine-active substances

9 along with concepts like selective estrogen response,

10 modification, et cetera. I think it might be better exploited

11 in this process if we think a little bit more about what

12 information these assays -- different assays are giving us on

13 potency and how that might play into an integration between

14 prioritization and screening. 

15 So for instance this 1.0E-07 cutoff, I was not

16 able to completely follow the logic on that. Of course I don't

17 have the raw data to go through and look at that but let's just

18 look at kind of a crude analysis on that. So here is a graph

19 where we're just looking at some crude estimates of relative

20 potency for various test chemicals that are in the validation

21 set so to speak. 

22 Now this is paper by Chen et al. is actually

23 Katzenellenbogen's group. So they have looked at the potency of

24 various steroids to the native human estrogen receptor and for

25 instance testosterone has a relative potency. I want to point

26 out those data I just pulled off graphs because I didn't have
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1 the raw data there either. So it's a little different than

2 these where I was actually was able to calculate a mean EC 50

3 quotient relative to 17 beta estradiol. But none the less it

4 tells you that testosterone can be in that range. These are

5 actually data from the ICCVAM BG-1 validation peer review panel

6 report where they reported all the mean EC 50 values. So I

7 understand this is not a true pharmacological demonstration of

8 relative potency but it gives us a ball park.

9 So we have these androgens and there has been

10 a lot of attention paid to methyltestosterone, which is an

11 aromatizable androgen and the explanation for its activity in

12 these assays is it aromatized to an estradiol compound. So it's

13 aromatization that is supposedly responsible for its activity. 

14 That might be true also for testosterone but what about 19

15 nortestosterone. That's otherwise known as nandrolone and the

16 long acting nandrolone decanoate ester is one of the favorite

17 anabolic steroids of body builders because it's long acting, it

18 doesn't aromatize and cause estrogenic effects and it has a

19 relative potency in the same ball park.

20 So I'm not completely sure that we're getting

21 all the information that we can out of this potency information.

22 We can look at these various comparators and see the true

23 estrogens even physiological -- physiologically very weak

24 estrogens have a much higher potency, and there are the things

25 in between but I think we need to reconsider some of these cut

26 offs or at least think more fully about what information they
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1 can give us. 

2 And as a final note then to pick up on this

3 idea of the integration between priority setting and Tier 1

4 screening. They both have the goal of minimizing false

5 positives and other words relaxing the stringency. So we're

6 doing that step twice, that's kind of a wide open sieve. 

7 We use substantially similar assays, binding,

8 transactivation or gene activation assays. So if we're going to

9 use a relaxed stringency in sort of a wide open mode twice, it's

10 really not serving much of purpose. I think it needs to be

11 thought through where we narrow down on some of these aspects

12 like potency and what information the various different assays

13 are giving us rather than you know focus on trying to seek

14 concordance between assays that, as Dr. Safe mentioned, really

15 would be expected to give us different results and see if we can

16 do something like either increase the stringency over here in

17 these in vitro assays or simply eliminate them, think very

18 carefully about our stringency here and whether then we can

19 simply use a very specific in vivo assay like the Uterotropic or

20 the Hershberger then to get a more definitive call.

21 So that's really all I have to say and I

22 appreciate your attention and if there is a question and we have

23 time I'll be happy to entertain it.

24 DR. DANIEL SCHLENK: Any questions from the

25 panel? Okay. Oh got one. Dr. Shultz.

26 Dr. TERRY Shultz: So you believe that the cut
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1 off down to the minus seventh is too conservative or not

2 conservative enough?

3 DR. RICHARD BECKER: I'm not making a clear

4 call on where the level would be, but it seems to me that from

5 the data that I've looked at that it might be too conservative. 

6 You may be -- you may actually and then go back to the slide

7 here, the argument that well that the cut off would include

8 methyltestosterone, which doesn't seem like it ought to be a

9 potential estrogen, but the rationale for that is but we can

10 explain its activity because it's metabolized to an estrogen. 

11 That's fine as a logic if you really know the metabolic capacity

12 of that cell line and you can actually demonstrate then that

13 enough should be aromatized to give you an estrogenic response

14 but I don't know how you explain then that a non-aromatizable

15 androgen has -- those are really indistinguishable relative

16 potencies given the statistical power of these assays. So

17 you've got a non aromatizable androgen that isn't exactly the

18 same class. So that logic isn't stacking up for me. I think

19 another interpretation might be that the biological relevance of

20 this potency -- the biological relevance is falling off earlier

21 than ten to the minus seventh at a higher concentration.

22 So that's what I'm suggesting and that's the

23 reasons for it. But I'm not trying to say exactly where the cut

24 off should be because I don't have the raw data to look at

25 myself. 

26 DR. DANIEL SCHLENK: Okay, any other
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1 questions? All right thank you. Our next presenter is Lisa

2 Ortego from Bayer CropScience.

3 DR. LISA ORTEGO: Good morning, my name is

4 Lisa Ortego as Dan said and I'm making comments this morning on

5 behalf of CropLife America. So I don't know how the panel feels

6 but it was quite a challenge for us to get through all of these

7 documents, some of them very heavy with technical information. 

8 What is clear is that EPA has worked very hard to document

9 research in this very important area and we really do appreciate

10 that. 

11 However, the timing of the document release

12 was such that it left insufficient time for a thorough peer

13 review. And in fact some information that is needed to complete

14 the peer review is still not available and you'll hear other

15 commenters give more specifics on that information later. 

16 So what this results in among other things is

17 the inability of the stakeholders to provide their information

18 in time for adequate panel review. And we think that is a bit

19 unfortunate because some of the presentations that you will see

20 today do have technical information that we think would be

21 valuable for the evaluation of the program. 

22 You've also heard the EPA say they have two

23 more -- at least two more endocrine SAPs planned for 2013 and we

24 would expect those SAPs to be equally data review intensive if

25 not more. So we have some recommendations going forward that we

26 thing would improve the timing and the availability of time for
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1 peer review. 

2 What we would like to recommend is that all

3 the documents should be released for a 60 day public review and

4 comment period. And that the written comment period close about

5 20 days prior to the start of the SAP and that the stakeholder

6 comments be transmitted to the panel members no later than 15

7 days in advance.

8 We think that gives the panel adequate time

9 for their review and it also provides the stakeholders time to

10 get their comments in for the panel to consider them and would

11 give the panel a better opportunity for asking questions during

12 the oral commenting period. 

13 Something else that we'd like to recommend not

14 on this slide is also to have all the presentations available on

15 the public docket 48 hours prior to the start of the meeting. 

16 My eyes are not as young as they used to be, it's kind of hard

17 to read all of the fine print on some of the slides. 

18 So moving forward then, the other topic I'd

19 like to comment on is one of communicating the priority setting

20 results. It's very important moving forward that EPA

21 communicate the results of the priority setting activities in a

22 way that the public understands the potential hazards and risks

23 of the information. And if we don't do that well then we can --

24 things can happen for example unintended de-selection in the

25 market place, application of the data in other state governments

26 or other countries or within other parts of EPA for which the
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1 data were not intended to be used. And we also can

2 unintentionally promote the use of alternative products

3 presuming a risk of the listed ones, where the alternatives may

4 actually pose a greater risk than the ones that were listed for

5 a priority setting.

6 So those are very important things related to

7 communication and we have some recommendations for that as well.

8 That EPA should consider establishing best practice guidelines.

9 That those would be applicable to all agency programs that

10 would apply to the generation dissemination and characterization

11 of the results, of course generated for the priority setting

12 activities but also by all parts of the endocrine disrupters

13 screening program. We think this is very important. EPA should

14 consider issuing data use guidance for these data both inside

15 and outside of EPA. Now this was a recommendation that the

16 Scientific Advisory Board made for Comp Tox and we also think

17 this is applicable here. So it is very clear what these data

18 are intended to be used for and what they're not intended to be

19 used for. 

20 And that is a communication that ensures

21 consistency across the Agency but also enables the public and

22 other governments to understand the activities that EPA is

23 undertaking and what the implications for hazard and risk are.

24 I'd like to finish by commending EPA for their

25 efforts in communicating on list one and draft list two

26 activities and making the public aware that these are not lists
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1 of endocrine disruptors. That is very important to us and we

2 appreciate those efforts. We would like to see that carried

3 forth for the priority setting activities. We would like to see

4 EPA once again confirm that they're not generating lists of

5 endocrine disrupting chemicals. We would like to see EPA

6 communicate that appearance on a list does not -- should not be

7 interpreted to mean the EPA has a significant concern for a

8 significant ecological human health risk at an environmentally

9 relevant concentration. 

10 And for substances that have existing data we

11 would like to see EPA communicate to the public clearly that

12 they do have safety data for the compounds that they do, and to

13 use those also in a priority setting process. Thank you. 

14 DR. DANIEL SCHLENK: Okay Dr. Matten has a few

15 comments to make.

16 DR. SHARLENE MATTEN: I just wanted to remind

17 the Panel that their focus is on the charge that the Agency has

18 provided on scientific issues and issues of regulatory policy,

19 regulatory communication, regulatory management are those of the

20 Agency and as I understand the Agency Steve Bradbury, the Office

21 of Pesticide Program Director and two other office directors

22 have written a formal written response. Although it's not in

23 this docket, it certainly could be provided to this Panel so you

24 can see the official response from the Agency about these exact

25 issues. Of course we appreciate the public comment. I just

26 wanted to make sure that you understood that your role is not to
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1 address that sort of areas that were related to regulatory

2 policy, regulatory management decision making. 

3 DR. DANIEL SCHLENK: Okay, thank you and with

4 that any questions for Dr. Ortego from the Panel? All right

5 thanks Lisa. Okay your next presenter is Ted Simon with Ted

6 Simon, LLC. Can you push the button there please.

7 DR. TED SIMON: Okay, I want to thank the EPA

8 for this opportunity for me to present comments to the Panel. 

9 I'm here as an individual, as former EPA staffer and as a

10 consultant in toxicology who has provided assistance to both

11 government agencies and to private sector entities. I'm going

12 to discuss today charge questions two and three. 

13 Charge question two, which I'm sure you can't

14 read, deals with physical chemical properties and can we do an

15 initial screening with physical chemical properties? There were

16 four items mentioned and I'm going to discuss each of these. 

17 The first one was the presence of acids, bases and charged

18 species. Is there some way to eliminate these from the get go. 

19 There was a discussion in the prioritization document about the

20 use of pKa and perhaps some pKa screening levels. 

21 We would think -- we would say that a pKa

22 value of say five on the low end and nine on the upper end would

23 be appropriate. This would mean that less than one percent of

24 the chemical be present as an uncharged species and thus

25 available to bind to the estrogen receptor. Now that said,

26 there was the discussion in the prioritization document
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1 neglected to include the presence of the ER as something that

2 might possibly change the equilibrium. There was an equilibrium

3 discussion without the estrogen receptor as something in that

4 equilibrium process binding the uncharged species.

5 As far as reactive and unstable chemicals, a

6 value of 40 days for the half life was given and I was left

7 scratching my head about this, was this based on eco

8 considerations, was it based on human health considerations. I

9 mean is this the length of time it would take for biod in the

10 wild to be affected. Is this the length of time perhaps for

11 some developmental critical period? Now those -- those are just

12 two ideas I had but in any case, what is the rationale and that

13 rationale should be put in the document for full transparency.

14 Corrosive chemicals were another category that

15 was mentioned. And there needs to be additional modification of

16 the corrosively filter to take into account certain phenolic

17 chemicals. Creosol is one that comes to mind. It has

18 estrogenic properties. Nonetheless it is corrosive so how do

19 those two match up. So some additional thought is required

20 there. 

21 There were some other properties that were

22 mentioned, LogKow that of course has been used in the Expert

23 System. Volatility, physical form, whatever properties are

24 chosen and certainly the Duluth Laboratory at EPA has manifold

25 experience with physical chemical properties and relating those

26 to biological proprieties. Whatever properties you use specific
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1 measures of the property should be provided. 

2 Now there was a question in this part of

3 charge question two as to whether weight of evidence for

4 estimates of these chemical properties should be used, and my

5 response would be not at this point and time. Which properties

6 are going to be used as this initial screen should be detailed. 

7 These properties should be selected and the sources of the

8 estimates should be selected and at that point it may be you can

9 do a weight of evidence. 

10 Now if you do weight of evidence, it should

11 not necessarily be saying software A software B. You need to

12 think about the algorithms involved. If there are some

13 different algorithms that provide different estimates, then

14 those are the -- it's those algorithms that really should

15 provide the differences rather than just say two software

16 packages that implement the same algorithm.

17 The weight of evidence framework that is used

18 needs to be described in a transparent fashion so that the

19 reverse engineering that we've heard about in some of the other

20 comments doesn't have to occur and that users of -- and the

21 public can go back and recreate what was done easily. And so

22 sort of the take home from all of these is to provide a

23 rationale for all of these physical chemical screening methods,

24 the data sources and the cut offs. 

25 And I'm going to move now to charge question

26 three, which has to do with the expert system that is based on
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1 the rainbow trout's ER binding assay, the rainbow trout

2 vitellogenin assay and the use of the structural alerts for the

3 chemical categories.

4 Now it's -- the developers of the expert

5 system I think had a really difficult job. The estrogen

6 receptor is eclectic. It binds to many substances, albeit with

7 very low affinity. I think we saw that in some of the data that

8 Dr. Borger presented. So, as I read through the document, it

9 seemed to me that the training set and the test set seemed

10 somewhat ad hoc. They were based on regulatory needs rather

11 than trying to find the training set that included high affinity

12 chemicals, low affinity chemicals and then chemicals somewhere

13 in the middle so that there would be some idea of the

14 performance of the system. So we need a standard test set and

15 the data from this standard test set should be made public. I'm

16 going to come back to this point. 

17 How well does this Expert System, based on the

18 rainbow trout estrogen receptor, predict the results -- the

19 activity we see in human-based assays? So in terms of the test

20 system, the ICCVAM 2000 list of validation substances should be

21 used. This is one that is commonly available. I can go find

22 them and we can use that to test the performance of the expert

23 system and indeed the HTP assays. 

24 Now following this that data need to be made

25 public and this has been done and I, again, the documents as I

26 got them from the docket did not have this in there and this is
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1 why I'm making this last comment on this slide is that there

2 needs to be a comparison between some of the human based assays

3 and the rainbow trout assays. This could certainly be expand

4 and I think if the ICCVAN 2000 list is adopted then there will

5 need to be additional work in this area.

6 So you could do a comparison between the

7 Novascreen assay results and the rainbow trout ER binding, you

8 could do for transactivation assays that measure messenger RNA

9 synthesis, you could do a comparison between the BG1 assay and

10 the vitellogenin assay. 

11 Now in terms of the cut off levels, these

12 seemed and this is specifically related to the Expert System

13 where it looked like they were sort of categories developed

14 based on order of magnitude or two order of magnitude

15 differences in the relative binding affinity. There needs to be

16 some science-based aspect of the cut off levels for designating

17 a substance positive, negative or equivocal. 

18 By Dr. Willet in her comments mentioned

19 biological relevance. Dr. Borger provided a really good example

20 of where -- what is a potency level that we should care about in

21 terms of endocrine disruption. And so the thinking about

22 potency that's been expressed today should be incorporated in

23 the determination of these relative binding affinity cut off

24 levels so that we can say that there is true biological

25 relevance here, lack of biological relevance over here and maybe

26 the middle is a bit more equivocal. 
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1 Throughout the documentation on the expert

2 system the policy generalization of the expert system being

3 health protected based on some comparisons between the human

4 binding -- human ER binding affinity and rainbow trout ER

5 binding affinity. These were throughout the document and this

6 is really a policy generalization and I would, as far as the

7 document goes, I think the science and the policy should be

8 separated so that you may want to put that somewhere else in the

9 document. Okay and I have a summary of my recommendation for

10 charge question three.

11 First as a validate using the ICCVAM list,

12 provide that comparison with a human based assays from that same

13 list, don't mix policy generalizations with science and as

14 others have mentioned be transparent. Make the data and the

15 analyses available to the public. That's all I have. Thank you

16 so much for this opportunity.

17 DR. DANIEL SCHLENK: Thank you. Any questions

18 for Dr. Simon? Okay, thank you. Our next presenter is Sue

19 Marty from Dow Chemical Company. Can you press the button

20 there. Thanks.

21 dr. Sue Marty: My name is Sue Marty I'm with

22 the Dow Chemical Company. I just wanted to share a few

23 observations on the program that is being developed here at the

24 Agency. 

25 As we learned yesterday, the HTP assays that

26 are relevant for estrogen and signaling pathways are still being
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1 evaluated. At last count there were 21 ER related assays. And

2 I'm a little curious as to the rationale for ER assay selection.

3 So are the assays being selected to evaluate different aspects

4 of ER signaling pathway as we mentioned yesterday, different

5 levels of the AOP. It seems to me that it would be really

6 valuable to have some criteria for assay selection. Otherwise

7 the landscape that we're working will be continually changing

8 and what you can envision from a regulatory context is that

9 compounds will move up and down the prioritization list or in

10 subsequent stages may move on or off of prioritization list just

11 based upon the changing last scope of assays that are included. 

12

13 There are multiple assays and it is unclear

14 how data from multiple assays are going to be managed. Are

15 compounds going to be prioritized based upon the number of

16 positive ER assays? Is it based upon the lowest concentration

17 in which a compound had a positive assay result? So it's a

18 little bit unclear yet how these different decisions are going

19 to be made. 

20 I think it's important even though it's not

21 critical for prioritization, I think it's important to keep

22 looking at the long term goal. How predictive our in vitro

23 assays for in vivo outcomes. If this is the first tip in an

24 AOP, what's the adverse outcome? How predictive is it of the

25 adverse outcome. So I think it's important that we keep that in

26 mind as we move forward. 
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1 So the SAP that the agency is having right now

2 is a great first step. We'd like to encourage that the panel

3 encourage the agency to continue to provide additional

4 information on how the results will be used and how the assays

5 are interrelated. 

6 One of the things that make it difficult to be

7 prepared to present on this particular topic is that it's really

8 an evolving field. So in October of 2012 there was an

9 e-publication that came out from the agency from Rotroff using

10 HTP assays to identify endocrine disruptors, so using ToxCast

11 data. Rotroff used nine ER assays in order to evaluate

12 endocrine active compounds. Three months later, the White Paper

13 came out for the SAP. Three of those ER assays have dropped off

14 the plate, and we don't know why the dropped off the plate. Two

15 additional assays were added. The data from those assays, as

16 near as I can tell not publically available so it makes it

17 really difficult to be able to evaluate the process. 

18 From a public perspective the procedures are

19 evolving and it's difficult to determine which approaches will

20 ultimately be adopted. So as it's been said already the process

21 is incomplete there are a number of things that are still being

22 developed. But some of these are really critical for

23 understanding the program performance. Things like the final

24 set of filters that are going to be used to combine data across

25 assays. They mentioned a level eight option where you can

26 designate a manual hit call for something that wasn't chosen in
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1 previous steps, and the adoption of performance metrics. So all

2 of these things are really critical. So we would really

3 encourage the agency to continue to host public data summits and

4 review their results and methodologies with the public and allow

5 comment. 

6 Several experimental factors can affect assay

7 performance results and interpretation and the Agency has

8 recognized some of these things like cytotoxicity data. But

9 it's not always clear which cytotoxicity assays are being used

10 with which HTP assays and then when they've used the

11 cytotoxicity data to filter HTP results. It's not always clear.

12 And there are a number of other factors that can confound assay

13 results, things like compounds that are not stable in DMSO or

14 for compound inhibits luciferase or causes membrane

15 perturbations or protein denaturation. 

16 It's not clear how many of these factors the

17 agency is looking at. They are looking at some and I think

18 that's really -- really commendable and they're developing

19 algorithms for a number of those and so we would really

20 appreciate it those algorithms and the results could be

21 publically available and shared. 

22 And I think that is the intent. It's just not

23 always as easy to recreate the results as what the agency has

24 intended. So I'll give you an example. This is in the White

25 Paper. This is using the three stress assays to filter the

26 Attagene hits. So it starts out that you -- they give you a
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1 very nice straight forward four-step process. It starts out by

2 taking the number of assay hits and creating histogram, which is

3 that black solid line against the concentrations at which

4 positive results were seen. Well okay you can see this is an

5 example for bisphenol A. They have 14 hits. Okay, the problem

6 is the White Paper is only talking about eight ER assays and

7 three stress assays. So I don't know where those 14 hits came

8 from. Because of that and what we know now are two of the

9 assays the data are not publically available for. So it's

10 difficult for me to be able to recreate how they did this

11 filtering based upon the information that is available.

12 The data in ToxCast -- I'm curious to know

13 whether or not the data an ToxCast will be annotated to indicate

14 when the EPA has employed filters. Our company downloaded

15 ToxCast data in 2009, and looking at those data in 2013, a

16 number of results have changed for compounds used in the same

17 assays. And there might be a number of reasons for that. I

18 mean it could be QA, it could be data filtering, but it's not

19 clear how the results got from one to the next over you know

20 that span of time. So again we would encourage the agency to

21 make the data available and allow independent peer review.

22 Lastly, the ER expert system and the HTP

23 assays are focused on chemical prioritization so we want to

24 minimize false negatives and that's true but some specificity

25 would still be valuable for efficient and effective

26 prioritization. So we would encourage the Agency to be able to
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1 look at the predictiveness of the ER expert system and the HTP

2 results against the list one chemicals that were screened in the

3 EDSP program. 

4 So how often to the HTP data agree with the

5 validated low through put ER binding and ER transactivation

6 assays that we're using Tier 1, and how often do the high

7 throughput assays accurately predict in vivo uterotropic

8 outcomes. In the Rotroff paper, there was balanced accuracy

9 prediction of 89 percent. That 89 percent of the time the in

10 vitro ER results predicted in vivo uterotropic outcomes. Our

11 initial assessment is that it's not going to be as predictive as

12 all that. So it would be nice to see this kind of an analysis

13 done and be made publically available. Thank you.

14 DR. DANIEL SCHLENK: Thank you. Any questions

15 from the panel. 

16 DR. SHARLENE MATTEN: Just wanted to -- Sue

17 Marty yes, your oral presentation doesn't match up with your

18 written handout, if you could resend your presentation. You

19 have more slides than are in the photocopy.

20 DR. Sue Marty: You should have seven. I'm

21 sorry I'll check and see what you have and I'll look at it.

22 DR. SHARLENE MATTEN: They have five. 

23 DR. Sue Marty: They must be missing a page

24 then. Let me check in to that for you.

25 DR. SHARLENE MATTEN: Okay. Thanks.

26 DR. DANIEL SCHLENK: Okay. Thanks. Our next
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1 presenter is Katy Goyak from ExxonMobil Biomedical Sciences.

2 DR. KATIE GOYAK: All right good morning. My

3 name is Katie Goyak. I'm from Exxon Mobil Biomedical Sciences. 

4 I'm a toxicologist with that group and I'm happy to have the

5 opportunity to talk with you this morning. 

6 So I'm going to be talking today about charge

7 question five, specifically looking at the performance

8 evaluation of the in vitro high through put assays against a set

9 of reference chemicals. So I've placed the full text of the

10 charge questions here for your reference but basically I'm going

11 to be speaking about the choice of references, the individual

12 assay performance and comparative assay performance. And also

13 I'd like to just mention that I'm really just going to be

14 hitting the highlights, if you would like more detail please

15 refer to the written comments that were submitted by ACC and

16 CropLife America. 

17 So just as a general comment and some of the

18 other presenters have noted we really agree with the approach

19 for using the reference chemical list to assess assay

20 performance that is laid out in section six -- yes section six

21 of the prioritization White Paper. I think this is really a

22 critical part, a really critical evaluation in order to

23 understand how well the assays perform and start to look at the

24 strength and limitations of the individual assays and this kind

25 of analysis is really consistent with the OECD validation

26 principles. Specifically, it starts to define the biological
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1 endpoint because you're using reference chemicals that are

2 related to estrogen receptor activity and then it also provides

3 a measure of predictiveness of the individual assays. So really

4 what I'm hoping to do today is to provide a few recommendations

5 for how to build on this approach.

6 So one of the first recommendations that we

7 wanted to note is that we think that the classification of each

8 reference chemical as an active or an inactive should really be

9 carefully considered. So just in this evaluation, the reference

10 chemicals are really acting as the gold standard against which

11 in each in vitro assay is going to be assessed. So I just put a

12 few examples here to show the point that I'm trying to make. 

13 So when you go back to the ICCVAM background

14 review document and you look to see what data there are to

15 support each of the reference chemical classifications, in a few

16 cases there are classifications made either based on only one or

17 two binding assays or transcriptional activation assays. In

18 some cases there is not any experimental data on either the

19 binding assay or the transcriptional activation assay. So I

20 just think that it should be a point that should be really

21 carefully considered. 

22 Actually moving into the actual assessment of

23 the individual assays -- so as I mentioned, this is a providing

24 these kinds of performance statistics. So looking at overall

25 accuracy, sensitivity and specificity of each assay is really

26 helpful to understand the strengths and limitations of each
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1 assay. 

2 So our recommendation is really that to take

3 this analysis one step further. So start with these performance

4 statistics and then make an overall interpretation on whether

5 each assay is useful to the overall prioritization scheme or as

6 has been mentioned earlier perhaps to start to rank the assay

7 results. So as an example of what I mean for over all

8 interpretation can be -- I have shown here is that if we look at

9 the results of the three Novascreen binding assays, so there is

10 one assay for human, one for mouse and one for bovine. You can

11 see that the human assay results really have a higher overall

12 accuracy and a higher sensitivity than the mouse and the bovine

13 binding assays. 

14 When you breakout which chemicals the mouse

15 and the bovine assays are getting incorrect, you can see that

16 the, which I don't have shown here but the mouse and the bovine

17 assays are really missing the weak and the very weak reference

18 chemicals. So one question to consider might be is considering

19 that there is lower performance of the mouse and bovine assays

20 and that they also missing the weak potency agonist is it really

21 useful to continue to use the mouse and the bovine binding

22 assays or are they bringing in additional benefit to the overall

23 prioritization benefit?

24 And then a brief note on how to address

25 comparative assay performance. So there seems to be two

26 different approaches used in the prioritization White Paper. So
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1 in section seven and also in the Rotroff et al. article that was

2 mentioned earlier, this is an article looking at how to use the

3 in vitro high through put assays to identify potential endocrine

4 disrupting chemicals. There's a use of a specific cut off

5 criteria. So if a chemical is a positive in two out of the

6 eight or two out of the nine assays, then that chemical is

7 called a positive. 

8 An alternative approach is as I talked about

9 on the previous slide using summary statistics like specificity

10 and sensitivity to assess the performance of each assay. So our

11 recommendation is that the use of the performance statistics is

12 really more consistent with the validation frameworks. This

13 starts to address analytical performance of each individual

14 assay and also you know measuring how well each assay predicts. 

15 And also if you're looking at the reference chemicals, this

16 helps define the biological endpoint for each assay. 

17 The cut off criteria approach, while this does

18 recognize that each individual assay measures a specific, has a

19 slightly distinct cell context. You wouldn't expect to have

20 exactly the same kinds of results. But you start lose -- you

21 start to lose a measure of assay performance and also you start

22 to lose what that assay is actually -- the biological signal

23 that it is actually measuring.

24 So overall we recommend the summary

25 statistics. So just in conclusion again we really think that

26 his approach of using the reference chemicals to assess assay
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1 performance is consistent with OECD validation principles and I

2 think this is a great start and you hope we'll consider some of

3 our recommendations to expand on this approach. And also these

4 comments were developed in collaboration with ACC and CropLife

5 America and more detail can be found in our written comments. 

6 Thank you.

7 DR. DANIEL SCHLENK: Thank you. Any questions

8 from the Panel? Okay thank you. Our next presenter is David is

9 it Getter?

10 DR. DAVID GETER: Geter.

11 DR. DANIEL SCHLENK: Ah, I had a 50/50 chance.

12 Sorry. From Bayer Crop Science. Sorry. Apologies.

13 DR. DAVID GETER: Good morning ladies and

14 gentlemen of the board. My name is Dave Geter and I'm from

15 Bayer CropScience and I will be addressing charge question six,

16 which is comments on the analysis of the ER expert training set

17 and the high through put in vitro assay data. 

18 Yesterday there was discussion several times

19 on concentrations used within the assays and within the ToxCast

20 cell free assays the top concentration is 50 micromolar. And

21 then when you add cells to some of those high through put assays

22 it goes up to about 200 micromolar. Within the BioReliance,

23 which is a contractor that the Comp Tox within the EPA are

24 running some of their high through put assays. They also use

25 200 micromolar. Within the EPA EDSPR binding guideline, they

26 use a top concentration of 1,000 micromolar. Within the rainbow
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1 trout ER binding, it's about 10,000 micromolar. So just putting

2 these into perspective but you can understand where they chose

3 that 10,000 micromolar because that is the limit dose that is

4 used within guideline in vitro assays, albeit those are

5 predominantly gene tox assays. 

6 This is a figure from a paper by Aylward and

7 Hays. The title of the paper is Consideration of Dose in

8 Evaluation of ToxCast Data. Within this figure, you have the

9 box plots which show the plasma and serum concentrations that

10 are relative to the LOAEL and NOAEL for these five compounds and

11 the figures that are used, the symbols on the figure are ToxCast

12 results. At least for these five compounds it shows that within

13 ToxCast that there is a very nice correlation between where

14 you're seeing results within ToxCast and the physiological

15 concentrations that were identified within the study. So when

16 you add in the concentrations that you're looking at within the

17 EDSP, it's starting to climb up a little higher and within the

18 rainbow trout ER binding, it gets even more so. And for some

19 chemicals this could start to enter the range of a

20 supraphysiological concentration. 

21 Also yesterday there was some talk about

22 correlation between the high through put assays and rainbow

23 trout ER binding. Within the White Paper, this figure was given

24 as an example of how concentration was very important within

25 their evaluations and that when you looked at the highest

26 concentration that was within ToxCast if you stop there, that
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1 number of compounds that was within the rainbow trout assay

2 would be called negative. It gave within the high through put

3 assays that there was a very small degree of correlation, only

4 about 35 percent. 

5 However, if you just use the concentration

6 that would be within the EDSP ER binding assay that you now

7 start to see where you had seven positives, you would have three

8 and this would start to drive that correlation between the

9 positives that you see would make a much stronger correlation

10 between the rainbow trout and the high through put assays. So

11 it's just a suggestion.

12 Now making comparisons between the high

13 through put and rainbow trout ER assays, within ToxCast there

14 are nine ER assays, six reporter gene and three binding. Only

15 two of the six reporter gene assays were used within the

16 comparison and the author cited there was just lack of time to

17 include those other four assays. Also very soon, phase two of

18 ToxCast will release data using 50 endocrine-related assays and

19 there is going to be a thousand plus compounds within there. So

20 EPA has a stable of very well trained top line scientists and I

21 would love to see that they be given enough time, give them

22 enough resources and that they can compare, contrast and adapt

23 this ER Expert System using these data.

24 Now also uncertainties within building up this

25 next version, it would be nice if they could establish a

26 physiologically relevant, limit dose for the prioritization
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1 efforts. This could help compare it to a lot of the other

2 assays. Also if they could include dosimetry as it was given in

3 Wetmore et al. 2012. They also included metabolism within that

4 manuscript. And in addition to comparing our Expert System to

5 the phase two ToxCast, if they could also look at ToxRef data

6 base and also the validated Tier 1 EDSP results. And that's all

7 I have. Thank you for your time.

8 DR. DANIEL SCHLENK: Thank you. Any questions

9 from the panel for Dr. Geter? Okay thank you.

10 Dr. david Geter: Thanks.

11 DR. DANIEL SCHLENK: Our next presenter is

12 Ellen Mihaich from Environmental and Regulatory Resources. 

13 DR. ELLEN MIHAICH: Okay, good morning. 

14 Thanks for letting me speak to you today. I'm going to be

15 talking about a comparison that's been done with the high

16 through put data and actual EDSP Tier 1 data. I'm speaking on

17 behalf of the Endocrine Policy Forum. And I just wanted to

18 briefly tell you who we are. 

19 The Endocrine Policy Forum is a consortium of

20 list one test order recipients so the EDSP Tier 1, list one test

21 order recipients and some other interested and involved parties.

22 We represent more than 95 percent of those registrants that

23 receive test orders. So we're the ones that have the experience

24 of running all eleven EDSP Tier 1 studies. It is a self funded

25 group that is administered through CropLife America. And we do

26 have some additional stakeholders there, the American Chemistry
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1 Counselm who will have a very much vested interest in when list

2 two comes out, American Cleaning Institute Chemical Specialty

3 Products Association and some consulting companies. Our

4 objective really is to address regulatory and policy issues as

5 well as technical guidance. We've had very good communications

6 with EPA in trying to help this program go forward, help us

7 understand what we have to do, help us all communicate some of

8 the challenges that we face with doing these eleven screens.

9 So again we have a huge amount of data. And

10 so using that huge amount of data what we've done is we have

11 collected the information from the ER binding, the ER

12 transactivation and the uterotropic studies from our members for

13 40 of the list one chemicals that have been run through this

14 program. There were actually 54 chemicals that were tested and

15 we were able to get information from 40 of them. It was again,

16 unfortunately if we had a little more time we probably could

17 have gotten them all but we didn't. 

18 And these are -- this is data from the 890

19 guideline series, which are the required guidelines for doing

20 the Tier 1 studies and in a couple of cases it's from

21 functionally equivalent data which the EPA deemed that it was

22 sufficient to be able to be submitted to satisfy the

23 requirement. 

24 Out of this, six chemicals did not have

25 ToxCast data. Six of those 40 did not have ToxCast data. We're

26 assuming that's because they are in a later phase of ToxCast and
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1 so the data wasn't available. So when I'm talking about the

2 information that we're going to present it's really on a total

3 of 34 chemicals, not the 40 because we needed to be able to

4 compare. 

5 Now I need to clarify what we're -- qualify

6 what we're doing here. We found it a little difficult to follow

7 some of the process that went on with using say, for example,

8 the cell filter assays. It was a little hard to follow, so we

9 have to make some assumptions. So right up front I want to say

10 that if we did it wrong then I apologize but we did the best we

11 could reading through the White Paper. 

12 So we did look at the data using stress

13 filters -- with and without stress filters and we assumed that

14 if two or three of the stress filters were positive, then we

15 removed those two Attagene studies, which is sort of what we

16 were able to read and I think it's Section 7 of the White Paper.

17 We need to also clarify that two of those HTP

18 screens were not publicly available that we could find the data

19 so we were not able to compare our studies to the BG1 or the

20 Odyssey. So we have to limit it to the other three that are

21 available. And we also looked at model predictions from the

22 rainbow trout ER and the Oasis (Q)SAR models and compared them

23 back to our data. So just to remind everybody all of this

24 information is in our written comments that I think you got a

25 couple of days ago at least, so if you want more detail it's in

26 there. 
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1 Sue Marty talked about the Rotroff study

2 earlier and so we actually did two different ways of looking at

3 this. One was using the Rotroff criteria, which was that two

4 out of the nine had to be positive in order to call something a

5 positive. And these are the assays that are used in the Rotroff

6 analysis. You have three binding assays and six reporter gene

7 assays. And again -- oh and in that case the cell stress

8 filtering wasn't specifically brought out in their text. There

9 was some discussion of it in the supplemental materials. But it

10 was, again, not really clear, but we went ahead and performed

11 this using the stress filters anyway.

12 And then on the other one is the SAP criteria

13 where we have the eight HTP assays and I just highlighted the

14 three, because there is more discussion on the stress cell

15 filters in the SAP document and just noting again that the two

16 BG1 an the Odyssey were not available for us to use. 

17 Okay so the methods that we went through, just

18 as a reminder, is that we had 34 chemicals and we were focusing

19 on the ER binding, the ER transactivation assay and the

20 uterotropic assay. So we have two in vitros and one in vivo. 

21 And we again compared that to the hits that came out of the HTP,

22 the rainbow trout ER and the Oasis (Q)SAR models, because there

23 is the two different models that can be looked at. 

24 And in the case of using it for the Rotroff

25 methods, two positive hits in any of the nine HTP assays is

26 considered a positive. And what we did just to be a little bit
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1 more conservative is we had some findings within the Tier 1 data

2 that were equivocal and I went ahead and grouped the positives

3 and the equivocals together just to make sure that we didn't

4 miss anything. And in this slide, there is no stress filtering

5 that has been done. And what we found was that of the 34

6 chemicals, there were nine positive HTP hits. Of those nine

7 positive HTP hits when we looked at our data, the data that was

8 turned in to satisfy the Tier 1 requirements, five of those were

9 positive but four of them did not hit any of the three Tier 1

10 screens.

11 Of those, there -- if you then look at how the

12 models performed for the five that were positive, there were

13 three times that the models, either the Oasis model and it would

14 be the Oasis model with S9, the Oasis without the S9 did not hit

15 anything. Three were positive, two were negative. But in all

16 cases the models predicted accurately for those that were

17 negative. 

18 Okay so essentially looking at that, we had a

19 44 percent false positive rate, if you look at that from the

20 HTP, and I think I want to point out too that if you ever are

21 going to get to the point where we're going to look at HTP for

22 predictions for in vivo results, which -- and so this contrary

23 to actually what Rotroff found which was a very high correlation

24 or concordance, that it's really only about if you think about

25 it an 11 percent concordance here because we only had one

26 uterotropic positive in our studies but you might think with
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1 that nine HTP actually should that also be predicting an in vivo

2 response and it didn't.

3 If you go to the next with stress filtering,

4 same information there, two or three positives and grouping the

5 equivocals. Here we have a few less positives than the HTP

6 because some of them have been gotten rid of and so we only have

7 six but now it's about a 50 percent false positive rate. 

8 However the models, using the Rotroff methods did very well. 

9 Where they should have predicted they did. Where they shouldn't

10 have predicted they didn't. However, with the stress filters,

11 we've now lost the uterotropic positive. The stress filters

12 took out that the response from that chemical and so you would

13 have missed the in vivo completely. 

14 And I do want to add actually, I have an error

15 in there because you might ask the question about false

16 negatives because I think that is also important. In using the

17 Rotroff methods without stress filtering, there were two false

18 negatives so there was twice that the chemical was missed, and

19 when we used the stress filtering it's an error in our written

20 comments it's actually four false negatives so it missed it what

21 four times where we actually had a response and it didn't get

22 picked up. 

23 Here we have using the SAP methods so the same

24 kind of set up with no stress filters here. Because it's only

25 one positive where as the Rotroff was two positives, here you

26 only had one positive and it was that way because even though I
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1 think someone just brought up the fact that in section 7 there

2 were two different ways you could look at the criteria and that

3 is either it's one hit on a transactivation and one hit on a

4 binding or just the hit on the transactivation. We actually did

5 not have any hits on the binding studies in HTP, so they were

6 not predicting very well for our compounds. 

7 And so using one positive hit we had 16

8 positive HTP hits but again, when you look at what came out with

9 the actual data, there were seven positives but nine negatives

10 so a false positive rate of 56 percent in that data. And here,

11 unfortunately, the models did not do very well at all. They did

12 not -- they did not match up with what the information was. I

13 will qualify though if I have forgotten to say it the rainbow

14 trout ER only hit once in all of those 34 chemicals but it was

15 -- it predicted correctly all the way through. So we never lost

16 it and it carried through with the compound you would expect it

17 to.

18 But here just like in the -- with no filtering

19 with the Rotroff methods, we did pick up the uterotropic

20 positive but again, you would wonder if there it shouldn't have

21 found more if HTP is going to be predicting 16 positives there. 

22 So there is certainly a watch out from the HTP to a prediction

23 of an in vivo result. 

24 And then last with the stress filtering on the

25 same data, a few less HTP hits, so 12 but an even worse response

26 with the EDSP data. So here it is now a 58 percent false
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1 positive rate and once again we lost that uterotropic positive. 

2 So the stress filters I think are a watch out, but again I could

3 be using the wrong, but it was -- that was unfortunately one of

4 the areas that I think much more transparency needs to be shown.

5 So in conclusion for us, the unavailable data

6 and the poor transparency specifically with the HTP data really

7 kind of probably limited the ability to fully assess the

8 reliability, sensitivity and specificity of the HTP assays and

9 the prediction models. Over all in your charge questions,

10 you're being asked to look at this. So I think it's something

11 to keep in mind. 

12 However, just what we were able to do with

13 actual data, we came up with a 44 to 58 percent false positive

14 rate for HTP. And overall, the majority of the in vitro HTP

15 data failed to predict an in vivo response. While it may not be

16 such a big deal for prioritization, I think we have to keep that

17 in mind where these things can go to. 

18 But the endocrine policy forum is conceptually

19 supportive of advanced molecular screening and profiling

20 methods. I mean it -- we think it's a great idea. However,

21 we're not sure that the EPA's current approach will achieve the

22 stated goals for affective priority setting at this time. I

23 think we've heard from a lot of people that just said it is

24 still a work in progress. It says that in the document, but

25 still you're being asked to sort of pass some judgment here and

26 I think what Rick Becker said earlier about setting up a road
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1 map would be very helpful in what needs to happen. So things

2 like setting the criteria a priori -- make that decision up

3 front. Don't manipulate it in the end because things can easily

4 get changed.

5 Methodologies, assays and data must be

6 available, transparent and validated and even though you say

7 it's not the charge, I think adequate time needs to be allowed

8 for appropriate peer stakeholder review, as well as appropriate

9 review for the SAP. Thanks.

10 DR. DANIEL SCHLENK: Any questions for Dr.

11 Mihaich? Dr. Burris?

12 Dr. Thomas Burris: Did you do an analysis to

13 look for the potential for HTP to miss actives in the EDSP so

14 basically the reverse?

15 DR. ELLEN MIHAICH: Yes, that was -- and I

16 mentioned in the -- so we had two -- and I should have done the

17 math really quickly but I didn't. So two out of the 34 were

18 missed in the Rotroff type analysis when there was no filtering,

19 four were missed when there was filtering, and in the SAP type

20 of analysis none were missed without filtering and two were

21 missed with filtering.

22 Dr. Thomas Burris: Okay.

23 DR. ELLEN MIHAICH: So there were some false

24 negatives.

25 Dr. Thomas Burris: In the general scheme when

26 you look at the HTP positives, did you do any type of rank order
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1 to look at if the very weak actives were the ones being missed

2 or the ones that were negative in the EDSP?

3 DR. ELLEN MIHAICH: You know it's a little

4 hard to do that because really what ended up happening most of

5 the time from -- so again looking at the ToxCast data most of

6 the time the hits were in that two Attagene assays. And the

7 reason that -- in fact almost always were the two Attagene

8 assays there was really very little hit in anything else. Then

9 what kept in or didn't keep it in from the stress perspective is

10 whether or not there was two or three or one stress assay that

11 got hit. 

12 So I'm not sure that I'm answering your

13 question right but I don't -- do you want to try that one again?

14 Dr. Thomas Burris: Well I was just concerned

15 that since the HTP assays can many times be much more sensitive

16 than assays that are further downstream, that basically the

17 negatives that are coming out from the EDSP maybe the weak

18 actives that were found HTP, which is what you would expect. 

19 DR. ELLEN MIHAICH: Oh, I mean I can't -- I

20 can't -- then that would take me having to look at how --

21 Dr. Thomas Burris: Because my expectation

22 would be that the HTP would detect a number of false positive

23 based on the sensitivity of some of these assays.

24 DR. ELLEN MIHAICH: Oh, well definitely. Yeah

25 I mean that's the 44 to 58 percent false positive. So of those

26 they -- I mean those are ones that we ran through the EDSP
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1 studies and they did not -- they were not active.

2 Dr. Thomas Burris: Yes, my concern is that

3 there is no analysis to say that -- if you did an HTP and you

4 have a very active compound and that didn't show up in the EDSP

5 that would more worrisome than if you found a weekly active

6 compound in the HTP and you know it looks like you're not

7 understanding my question.

8 DR. ELLEN MIHAICH: Well I guess because I'm

9 trying to figure out how do you know that it's very active in

10 HTP? What's the criteria that says it's very active in HTP?

11 Dr. Thomas Burris: Well you have efficacy and

12 potency.

13 DR. ELLEN MIHAICH: Oh.

14 Dr. Thomas Burris: So it's not just an on

15 off, it's how much on.

16 DR. ELLEN MIHAICH: Yeah, I mean really were

17 only able to look at it as was it -- would it have been

18 considered a positive. So no we did not go to that -- and I

19 will, again, and I think it's a fair question and there is

20 absolutely no time to do that. The other problem that we ended

21 up having was and it was something that Sue Marty brought up,

22 which was I did the entire assessment based on a download form

23 ToxCast from 2009, 2010 and a significant number of things that

24 were positive became negative and negative became positive in

25 the 2012 download of it.

26 And again the transparency of why that
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1 occurred, I don't know. So I kind of looked at it was it

2 positive or negative and left it go at that. So --

3 interestingly though, when you look at the data since I had to

4 do it twice, when I look at the output from what I would have

5 said the first time with the 2009 ToxCast data even though some

6 compounds completely switched around, things that were positive,

7 again were not, it came up with almost the same statistics,

8 which to me is a bit worrisome if we're getting better at curve

9 fitting or criteria setting, whatever to what is a positive and

10 a negative, but I can still come up with about a 50 percent

11 false positive rate. 

12 But again so much work has been done and I

13 think it's -- I mean I really commend all of the work that EPA

14 has done. I think maybe part of it could be that if we had a

15 little more transparency about what's going on within HTP, it

16 might help us try to figure out where the, you know, the issues

17 are. Sorry.

18 DR. DANIEL SCHLENK: Okay, any other

19 questions? Yes, Dr. Keiser?

20 DR. MICHAEL KEISER: Michael Keiser. So I

21 could be getting it a little bit wrong because we only have time

22 to go over it right now but it seems as though what we're

23 basically looking at is positive predictive value. In this case

24 if you think of sensitivity, specificity and negative and

25 positive predictive value and so -- and why I'm motivated by

26 that is we're coming at it from the positive predictions of the
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1 HTP. Although there was some discussion a moment ago about ones

2 HTP might have not predicted positively. 

3 I was curious though, more generally, in

4 sensitivity and also in concordance when you begin to take into

5 account true negatives that have come out of that too because of

6 course if you imagine the hay stack that has been referred to

7 earlier, much of the haystack is empty presumably. And so I

8 realize that we don't have the full matrix -- full negative data

9 to compare against the HTP results, but I was curious how some

10 of these rates might change taking into account the true

11 negative predictions that are present in this too.

12 DR. ELLEN MIHAICH: Okay so the true negative

13 predictions from the EDSP data or the true negative predictions

14 from the HTP?

15 DR. MICHAEL KEISER: It would be from the HTP

16 in this case because that is the starting point that you're

17 using.

18 DR. ELLEN MIHAICH: Okay so you're asking if

19 they were truly negative in HTP, what does that predict for --

20 DR. MICHAEL KEISER: How does that change the

21 concordance rates overall?

22 DR. ELLEN MIHAICH: Well, I'm trying to figure

23 out how to answer that question. I mean essentially what -- if

24 you take that data and if you figure there were say 16 positives

25 then there is 34 minus 16 negatives in HTP and I had talked --

26 DR. MICHAEL KEISER: I'm sorry I was speaking
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1 more generally beyond that set.

2 DR. ELLEN MIHAICH: Oh.

3 DR. MICHAEL KEISER: Because as we saw earlier

4 from some of the screening data and I see that you're

5 restricting this case --

6 DR. ELLEN MIHAICH: Yes, I mean I can't speak

7 any more than to what the 34 chemicals that we've got data for.

8 DR. MICHAEL KEISER: Understood.

9 DR. ELLEN MIHAICH: Yes, and I don't know that

10 I want to go out there. I mean it could be better. I don't

11 know. All I know is that if you were to look at, I mean these

12 are chemicals that had to be run through the first, you know,

13 Tier 1 of EDSP and so we have that data. We have the data from

14 validated studies and we're trying to sort of back look at if we

15 had put our chemicals through HTP first, what would we have been

16 asked to do. 

17 DR. MICHAEL KEISER: I understand, the

18 motivation from my question had only been the larger set and the

19 larger space, but I see that you can't address that.

20 DR. ELLEN MIHAICH: I can't really -- I can't

21 address that.

22 DR. MICHAEL KEISER: So thank you.

23 DR. ELLEN MIHAICH: Thanks.

24 DR. DANIEL SCHLENK: Okay. Any other

25 questions? Great, thanks Ellen. Our final presenter, according

26 to my list is Terry Quill from Quill Law Group.
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1 MR. TERRY QUILL: Good morning, thank you for

2 this opportunity to comment. Let's see if I can figure out --

3 we're good, okay. I'm Terry Quill I'm commenting on behalf of

4 American Chemistry Council. And my comments are going to be a

5 little shift of focus here. I'd like to comment about the

6 universe of chemicals. And that applies more to your charge

7 question one. Since enactment of the Safe Drinking Water Act

8 amendments and the FFDCA section that we're dealing with here,

9 I've dealt as much with legal regulatory, statutory issues,

10 policy and procedure, as technical issues and I just want to

11 focus on the former and talk about one regulatory issue and a

12 policy issue. 

13 Why don't I start out with my conclusion, and

14 maybe that will just make it easier for all to see where I'm

15 going with this. My conclusion is that EPA should determine the

16 scope of its authority prior to prioritization as opposed to

17 during or after prioritization and what do I mean by that. When

18 I say authority looking over my presentation, I don't mean it in

19 a pejorative sense. I'm not challenging EPA's authority. What

20 I'm really talking about are what chemicals are correctly within

21 the scope legally of the EDSP. That's what I'm referring to.

22 And I'm saying they should determine that

23 before they get started instead of after they get started --

24 after they finish with prioritization and my presentation really

25 deals with, although that sounds like a very simply thing and

26 all it would require is if you recall that chart EPA put up
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1 yesterday where you start out with the universe and there is a

2 few filters, you go through prioritization and at the end EPA

3 determines whether they have the authority to address those

4 chemicals. All I'm saying is just move that up to the beginning

5 -- and there are good reasons to do it. 

6 And what I'm going to say here isn't any

7 different than what EPA said yesterday, but I'm going to dig a

8 little bit deeper in that EPA correctly pointed out what its

9 authority was and I assume the reason why they do that is so

10 that they can, in the context of prioritization, they can

11 demonstrate what authority they're acting under and correctly

12 they say the FFDCA, which really is the Food Quality Protection

13 Act and Drinking Water Act amendments. 

14 So for pesticide chemicals, the authority is

15 pretty clear. The Act explicitly says the EPA should undertake

16 endocrine testing of pesticide chemicals but for non-pesticide

17 chemicals, there is some language in there that makes a

18 difference. Congress had something in mind and it says that EPA

19 could also -- may also require screening and testing of

20 substances that may be found in sources of drinking water if a

21 substantial population may be exposed to the substance. 

22 Well those words have meaning. And those

23 terms or phrases need to be interpreted by the Agency, may be

24 found in sources of drinking water. What's the source of

25 drinking water? Finish water, is it an aquifer under a super

26 fund site? Substantial population, two people, 10,000 people
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1 may be exposed. Should there be a demonstration that it was

2 actually found in drinking water. Those are questions that need

3 to be answered. 

4 Unfortunately EPA has not defined those

5 limiting terms of the Safe Drinking Water Act. And keep in mind

6 this is important because EPA has turned to these acts for its

7 authority to address prioritization of substances for the EDSP. 

8 And what did Congress mean by this? Well I don't think Congress

9 had I mind a research program. I mean Congress made it very

10 clear the end game was important and EPA said that this morning.

11 At the end of the day EPA wants to determine which substances

12 pose a risk to human health in the environment and they want to

13 regulate them. 

14 But what -- maybe the way to look at this is

15 Congress helped out in putting a prioritization filter in here. 

16 We're going to ask EPA to look at those things that pose the

17 greatest potential threat because there is a lot to do here. 

18 And therefore EPA you figure out which substances those are and

19 address those and at least address those first.

20 Okay, so let's talk about EPA's universe, the

21 current universe. Well it clearly includes substances outside

22 of EPA's authority. And it's a little difficult to tell because

23 EPA has not clearly defined its authority in this regard instead

24 what it did is it relied on some existing lists of chemicals. 

25 Again, EPA didn't exactly say how those lists fit within the

26 terms of the statute. 
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1 And I'll give you an example. The

2 contaminants in the CCL3 list, well those -- that list was

3 developed with other things in mind. And some of those

4 substances are not in sources of drinking water and it never

5 really determined whether a substantial population is exposed. 

6 And for example, in developing the CCL3 list, the EPA looked at

7 things such as TRI releases, production volume, adverse health

8 effects. Those don't really go to the statutory requirements. 

9 So what really is EPA's current universe of chemicals? I think

10 it is safe to say that it isn't a list of chemicals that EPA may

11 include in the EDSP because EPA doesn't have authority over some

12 of those chemicals.

13 And it's not clear that EPA disagrees with

14 that. I can't really tell what EPA's definite position is. On

15 one hand it says it has authority over the entire universe

16 including the CCL3 list. On the other hand and I just point out

17 a few things in the White Paper and this was repeated by EPA

18 yesterday morning, it acknowledges that there may not be

19 exposure potential for some of the universe chemicals. But that

20 is a requirement of the Safe Drinking Water Act. 

21 For example, EPA says it will determine a

22 universe chemical has the potential for human exposures during

23 prioritization. And that's like paper and there's the page. 

24 And EPA will use an exposure filter, again implying that

25 prioritization will be used to determine exposure and therefore

26 authority. And prior to officially advancing chemicals for EDSP
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1 Tier 1 screening, the Agency must make an exposure finding

2 stated in the 1996 Safe Drinking Water Act amendments. 

3 And again EPA repeated that yesterday morning

4 and it clearly states that EPA doesn't believe it has the

5 authority for all the chemicals going through prioritization. 

6 Well it may sound like a minor point but -- did I miss a slide? 

7 No. It is significant for a couple of reasons and let's put

8 aside the first questions to whether EPA has the legal authority

9 to prioritize substances for the EDSP that are not within its

10 EDSP authority. Let's just put that aside for this moment and

11 let's look at why EPA should not, for good policy reasons,

12 prioritize substances outside of its EDSP authority. 

13 Well first of all it's just a misallocation of

14 resources. If EPA at some point determines that it doesn't have

15 authority and it can't do anything with those chemicals wouldn't

16 we be better off using those resources to prioritize chemicals

17 that may in fact pose a threat to human health in the

18 environment through exposure? 

19 And then secondly and maybe perhaps more

20 importantly as you all know, prioritization -- the assays are

21 designed to minimize false negatives. And what that generally

22 means is there is likely to be a significant number of false

23 positives. Well picture a substance that is identified and it

24 turns out to be a false positive and that EPA determines then it

25 does not have the authority to send to Tier 1 screening or Tier

26 2 testing. 
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1 Well generally people believe that well okay,

2 false positives it's a good thing to have because at least we

3 won't lose any chemicals. But they're relying on Tier 1 and

4 Tier 2 to determine whether that is a false positive or not. 

5 There won't be that opportunity for those chemicals. So in

6 other words, chemicals will languish on this list of positives

7 and they may very well be false positives. And that will lead

8 to what we call product deselection. And the only things I can

9 think that would solve that problem would be to ensure that you

10 really minimize false positives or do a real good job in

11 communications and try to determine exactly how many false

12 positives are expected in the screening assays.

13 You've got to let people know that these

14 results may not be true and there is not going to be an

15 opportunity to check that. Or, and getting back to my

16 conclusion, actually let me go here first. How should the

17 universe of EDSP chemicals be used? The way it should be used

18 is perhaps as a starting point. 

19 Okay, we have the universe the next step

20 should be that EPA would look at those chemicals and determine

21 which fall within its authority. The next step would be then to

22 prioritize the chemicals and move on to Tier 1 screening. That

23 would be the appropriate way of doing it. 

24 And so my conclusion again is without reading

25 all these points, is basically why don't we take that last step

26 where EPA determines the scope of the EDSP and its authority,
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1 move it up to the beginning so that we're not misallocating

2 resources for chemicals that EPA has no intention of doing

3 anything with and secondly so that we're not causing product

4 deselection, in other words having chemicals languish on a list

5 of positives when in fact they truly may be false positives. 

6 Thank you.

7 DR. DANIEL SCHLENK: Thank you. Any questions

8 from the Panel? Okay, thank you very much.

9 DR. TERRY QUILL: Thank you.

10 DR. DANIEL SCHLENK: Okay any other public

11 commenters that we have? Okay this concludes our public

12 commenting period. At this point in time, I think it's probably

13 a good idea to maybe go for an early lunch to beat the crowds

14 and let's be back at 1:00 and we'll start the charge questions

15 then. 

16 (WHEREUPON lunch was taken)

17 DR. DANIEL SCHLENK: Can we take out seats

18 please? Okay let's go ahead and get started. I need to read

19 the charge questions into the record. I believe you've got the

20 remote there yeah. You can -- if you wouldn't mind go ahead and

21 reading question one in. Just state your name Mary that's

22 great.

23 MS. Mary Manibusan: Mary Manibusan. question

24 number one -- please comment on the appropriateness of the

25 overall conceptual approach for prioritizing the EDSP chemical

26 inventory noting subsequent charge questions address issues
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1 associated with specific components of the prioritization

2 approach. In your comments, please address the extent to which

3 EPA's description of the process transparently captures and

4 describes the key technical steps of the prioritization scheme

5 and whether there are other scientific considerations the agency

6 should incorporate into its EDSP prioritization scheme; the

7 robustness of the scientific support for the overall approach

8 and, in particular, the logical sequence of filters, and whether

9 changes to the approach could set priorities more efficiently

10 and effectively.

11 DR. DANIEL SCHLENK: Thank you. The lead

12 discussant for question one is Dr. Chambers.

13 DR. JANICE CHAMBERS: Thank you. Thank you

14 for the opportunity to start off the discussion here. I think I

15 can probably speak for the whole panel by saying that the EPA

16 should be commended for its efforts to develop a logical

17 prioritization scheme for screening a massive number of

18 chemicals. Clearly it's been a lot of work that's gone into it

19 up to this point and we appreciate that. 

20 EPA has incorporated the advice from previous

21 SAPs and this SAP recognizes that and appreciates that as well. 

22 This question addresses an overview of the process so we will

23 not be addressing as your sub -- as your parenthetical says we

24 will not be addressing the components of the approach in detail.

25 The SAP appreciates that this effort is still a work in

26 progress and probably the number of questions that arose for
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1 clarification and everything indicate that we do appreciate that

2 much is not really totally finalized yet.

3 The SAP also appreciates that EPA will need to

4 consider both the legislative mandates to test certain chemicals

5 that drive what you do and also the level of effort resources

6 that would be required do a completely through job of screening.

7 And EPA will need to balance its efforts guided by multiple

8 factors, some of which are not scientific factors and therefore

9 are outside the purview of the SAP to recommend.

10 To a certain extent, it appears that EPA is

11 trying to compare and match results from diverse methods that

12 were run independently and more transparency is needed in

13 approaches, methodology and rationale, and again I think that

14 was reflected by the number of questions that we had about

15 different things especially rationale. 

16 The SAP appreciates being convened early in

17 this process for comment and while this is still a work in

18 progress there is an opportunity to provide suggestions for

19 changes and approach that perhaps you'll be able to utilize. 

20 The SAP understands that the point of this activity is to make

21 an enormous process manageable guided by the best scientific

22 principles. 

23 Overall, I think we find the approaches

24 logical and that filters can remove from the possibility of

25 testing those compounds that are unlikely to be systemically

26 active early in the process as well as employing methods early
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1 that are less labor intensive and do not require intact animals.

2 For example, the fiscal chemical factor such

3 as corrosivity would make the compound destructive at the site

4 contract and would preclude systemic distribution and therefore

5 the filter would remove from testing compounds that would not be

6 systemically active. However, the rationale for the cut off

7 levels for some of these filters should be provided in more

8 detail than it is in the present White Paper for better clarity

9 and transparency. 

10 The adverse outcome pathway is a logical

11 organizing principle because it relates potential overall toxic

12 response and biological plausibility to a meaningful molecular

13 initiating event relevant to a particular toxicological

14 mechanism and will allow the assessment of internally consistent

15 data within the AOP, when data are accumulated at subsequent

16 levels of screening. You might just say that the terminology

17 introduced by one of the public commenter's initial molecular

18 event does potentially have some merit and might be worth

19 considering. 

20 This categorization and organizing principle

21 AOP should assist in the predictions that might be required for

22 different chemicals and different endocrine pathways in the

23 future. And one concern that at least I and at least one other

24 of us here has is the bioavailability and exposure. Those are

25 not well described and it think it's critical that a potential

26 natural exposure levels be assessed, not necessarily measured
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1 just assessed in order to determine whether the compound is

2 likely to reach target organisms and if so then it's molecular

3 targets. 

4 The White Paper would benefit from greater

5 clarity and transparency regarding how exposure is being taken

6 into consideration and the prioritization process. I do

7 understand that some of that is driven by the regulations, but I

8 think just better explanation would help those of us in the

9 ivory tower academic institutions to understand what is going

10 on.

11 To a certain extent the water solubility --

12 excuse me the water stability and the other physico-chemicals

13 filters do address this in terms of environmental stability, but

14 consideration of which of these chemicals are measureable or

15 likely to be measureable in an environmental matrices or food

16 source does seem to make some sense and might give some thought

17 to that. If information about production or use of certain

18 listed chemicals provides no evidence that these chemicals are

19 entering the environment or have the potential to enter the food

20 chain, then the theoretical estrogenicity seems to be a moot

21 point from a practical stand point. I would think those would

22 end up being low priority or off the list for testing. 

23 If production or other processes change in the

24 future such that the excluded chemicals are likely to enter the

25 environment, then the screening can be conducted at that point. 

26 Again the SAP does understand that regulations could override
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1 exposure considerations. 

2 Most of the initial screening techniques can

3 be conducted relatively quickly and efficiently, some

4 computationally and therefore it will not invest animal's lives

5 unnecessarily. Such information will give insights into whether

6 the estrogen receptor activation pathway is likely to be

7 activated and if so whether the concentrations at which a

8 compound is active are of concern.

9 However, a couple of the panelists do have

10 additional concern regarding the sequence of steps presented

11 here for determining the prioritization. That suggestion is

12 going to be made for an alternative as well as suggestions

13 regarding obtaining more chemicals to perform laboratory tests

14 and Drs. Burris and Perkins will address that in just a minute. 

15

16 So in conclusion of my remarks here while the

17 methodology presented at this SAP focused on the ER binding AOP,

18 the overall concept and logic behind it makes sense and it

19 should provide a generic approach that would be adapted --

20 should be adapted to other ED pathways in the future.

21 DR. DANIEL SCHLENK: Thank you. Our first

22 associate discussant is Dr. Burris.

23 DR. THOMAS BURRIS: So commenting on setting

24 the priorities for the compounds, I think that there is a

25 significant issue using computational models that predict --

26 that are made to predict receptor binding when the capabilities
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1 are present to measure the actual data. My suggestion is that

2 these HDP assays be utilized as a priority setting compound for

3 progression to the later Tier 1 assays and of course there has

4 been some discussion about replacing the earlier Tier 1 assays

5 with these HTP assays. 

6 My suggestion would be screening the entire

7 compound library that is amendable to testing. The risk I

8 believe in using the computational prediction is they're based

9 on clearly defined high affinity binding models and the risk

10 there is that they're not likely to be predictive of compounds

11 that display unexpected modes of binding. So they're basically

12 designed to predict what we already know and we'll clearly miss

13 potential modes of binding that have never been described before

14 and I think that creates a clear risk. 

15 The other risk is that low affinity binding

16 molecules may not fit into the models that have been designed

17 for high affinity binding molecules. So that's a risk. So here

18 again is pointing to the issue of with the ability to be able to

19 run those 5000 compounds and generate empirical data the powers

20 is much more impressive. 

21 I would suggest that an effective HTP approach

22 would employ both biochemical and a limited number of cell based

23 assays. There have been some presentations combining those

24 assays to make a reasonable prediction of what should go

25 forward. One of the public commenters had a nice model looking

26 at various methods to evaluate whether a computer should go
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1 forward looking at the concordance with EDSP data. 

2 I think this is something that the FDA should

3 look at, this type of analysis of positives coming through the

4 HTP assay and the concordance with EDSP. I think that it's

5 critical that the assay be designed or the selection procedure

6 be designed so as to minimize the number of false negatives that

7 is clearly a risk. 

8 When I did some statistical analysis on the

9 data that was presented today, I thought the filter was quite

10 good, at least with the one model that was used where no false

11 negatives were detected. Approximately 44 percent of the

12 compounds that were detected in the HTP assay were positive in

13 the EDSP. I think it's unreasonable to expect that there be

14 complete concordance between the HTP assays and especially the

15 in vivo EDSP assays. I think that clearly the tools are there

16 and it can be used to set the priorities.

17 One issue that I'm not absolutely clear on is

18 the availability of the 5000 compounds within the compound

19 universe. I think that the computational models that are

20 available could be used to prioritize what compound should be

21 accessed first if there is limited availability of resources to

22 obtain those compounds. For example if some of these compounds

23 have to be synthesized that, of course, will take time and the

24 computational models could be used to set priorities on what

25 compounds need to be synthesized and then tested first. 

26 And I believe that is the end of my comments. 
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1 Oh one more comment. Within the series of data that will be

2 generated from the HTP there will clearly be various levels of

3 activity, and so I believe in setting the priority in the active

4 compounds from the HTP assays. It's critical to define the

5 potency and the efficacy of those compounds as a priority to go

6 into the in vivo EDSP assays. 

7 The other point that should be considered is

8 potential exposure. When comparing two compounds especially at

9 low affinity, one that has potential high exposures is what I

10 would consider higher priority than one that has low exposure

11 and low potential to ever reach those concentrations in the

12 environment.

13 DR. DANIEL SCHLENK: Thank you. Dr. Meeks.

14 DR. BETTE MEEKS: Well first of all just to

15 echo a few of Jan's remarks. I think you've put an awful lot of

16 effort into this and it is really appreciated and we recognize

17 that it is a moving target. I really think that the Expert

18 System has been thoughtfully developed and well communicated in

19 the context of the organizing construct of the Adverse Outcome

20 Pathway as a basis to anchor prediction or observation of early

21 key events to adverse outcomes traditionally considered in

22 regulatory risk assessment and we certainly need that in the

23 early stages. 

24 In essence, depending upon the proximity of

25 these key events to the adverse outcome of interest, it also

26 provides a basis for assessing confidence or uncertainty in the
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1 outcome of various approaches to prioritize chemicals for

2 testing and I think that's probably a recurring theme that

3 you'll hear from me that I think that we can weight different

4 input to setting up a prioritization scheme, but we need to be

5 very clear about that. And it's often -- I think we can set

6 those criteria a priori in terms of what we're waiting most.

7 Currently unspecified in the conceptual

8 approach are target capture rates for the initial phases of the

9 prioritization. So what's at least a preliminary goal? What

10 would be considered a reasonable proportion initially for

11 testing? I think that's kind of a rather critical aspect to

12 determine the required degree of discrimination for the purpose

13 at hand. So again, it comes down to really understanding what's

14 our endgame here? Where are we trying to get to?

15 So in my view as well, the overall conceptual

16 approach does not yet fully address the stated programmatic goal

17 which is to determine the order in which to screen chemicals for

18 endocrine activity. And I heard that and I wrote it down and I

19 said okay, that's what we're working for. This would require

20 relative ranking of the candidate chemicals for screening. So

21 in my view this would be facilitated through consideration of

22 incorporation of a broader range of potentially more

23 discriminating surrogates of, for example, potential for

24 exposure. And as Jan has mentioned, to a small degree some

25 parameters related to potential for exposure have been addressed

26 in the existing filters, though they're not explicitly described
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1 as such.

2 And again we keep hearing reference to

3 exposures so to contextualize this from my perspective in terms

4 of what are the most important roads to pursue, I kind of need

5 to understand what this nebulous taking account of exposure

6 actually consists of. So, in addition, I think that because of

7 the way the system has been developed, they've also not

8 necessarily been introduced in a logical sequence because

9 they've been variously included in filters for, for example

10 physical chemical properties, predicated principally on

11 testability. So for example, exclusions based on chemical

12 reactivity, for example chemicals that hydrolyze in the

13 environment.

14 And then of course, one of the simpler filters

15 from an exposure perspective not produced or used in the U.S. is

16 only considered prior to the introduction of testing rules. So

17 again, I think it would be helpful to kind of overlay this with

18 what are the really important considerations from the exposure

19 perspective.

20 I think that more systematic consideration of

21 potential for exposure through introduction of information on

22 parameters expected to be most discriminating at earliest stage

23 from an exposure perspective is really advised. These

24 parameters in my experience provide much greater potential for

25 relative ranking of the candidate chemicals. For example, the

26 fact that a significant proportion of the relevant substances
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1 with unknown binding potential fall into one use category class

2 indicates that this avenue could net considerable benefit. So

3 again using the use category information to bound the estimates

4 of exposure, so that's it.

5 DR. DANIEL SCHLENK: Thank you. Dr. Perkins.

6 DR. EDWARD PERKINS: Well I would like to echo

7 the sentiment of the others, I think it's a great start. It's a

8 very complicated process. In general, I think it's fairly

9 clearly outlined. 

10 There are a number of improvements that people

11 have mentioned that I would reiterate, perhaps moving HTP

12 testing results up earlier to compensate for some of the

13 failings or drawbacks of just computational models. It's clear

14 from some of the evidence that you're getting capturing more

15 false positives than false negatives, is more important.

16 Along the same lines, I think the program

17 needs to put more effort into ensuring people that this is a

18 prioritization and ranking, it's not saying that these are

19 endocrine disrupting chemicals. It's just a priority for

20 further testing. And in that context, I think expectations do

21 need to be somewhat managed of what sort of false positive rate

22 would be acceptable. It's going to be very difficult to develop

23 absolutely predictive models and absolutely predictive assays. 

24 But there's got to be some acceptable reduction in total numbers

25 as long as you're not excluding potentially positive ones.

26 So, I think in those veins, the Expert System
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1 analysis may be more valuable in prioritizing chemicals that you

2 need to go further get for testing that may have specific

3 effects. I'm a little concerned that in the broader space, that

4 you're only able to get 30 to 40 percent currently. I know

5 that's because of sort of programmatic limitations at the

6 moment. But if the current program scheme is to be in effect

7 then it would really, I think, benefit to put more effort into

8 actually acquiring these things. Or synthesizing things that

9 the Expert System analysis says may be problematic and putting

10 those as a priority.

11 Along with the physical chemical and exposure

12 filtering, I think chemicals such as fragrances, are going to be

13 causing a problem. One the HTP assays can't really deal with

14 that at the moment. There's also going to be respiratory type

15 inhalation exposures for that or just through dermal exposures. 

16 So the route of exposure is going to be different perhaps from

17 some other things. So I think that's going to be a problem and

18 the physical chemical logic rules may be challenged with that. 

19 And again to reemphasis this idea of ranking

20 chemicals, I think we have a lot of the data, a lot of the

21 potential for ranking things either based on potency or activity

22 or exposure, a combination of exposure and physical chemical

23 properties and effects based. So I think another priority would

24 be how you can use these categories, aggregate them, to get a

25 better ranking of potent estrogen chemicals that are going to be

26 high exposure levels.
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1 I really commend the program for trying to

2 integrate new approaches in there, but also I think that it's a

3 little narrow in that a lot of other areas of information are

4 being excluded for providing background information on some of

5 the chemicals that are being used or attached. So some

6 component of, I think, background literature for chemicals

7 should be also included, at least in the Expert System analysis

8 to support some of the conclusions and perhaps acquired data

9 that hasn't been through EPA programs, as part of the weighting.

10

11 And I guess finally, coming to this sort of

12 ultimately, you're going to need some sort of a weight of

13 evidence or aggregate analysis to make sense of this. I think

14 there needs to be a very clear, transparent way that you do

15 this, with the pluses and the minuses to that as a final segment

16 for prioritizing or passing things on.

17 I have one last comment. On the AOP thing, I

18 think it's a great organizing principle. One caution I have in

19 the document, it's only put down for fish species. There are

20 detailed differences between human and fish, in between one can

21 be indicative of the other but some of the details are

22 different. So, I think it also would be good for you to present

23 both human and fish side by side to show what the steps are,

24 what's common between them and how you can extrapolate between

25 the different model systems more directly because as people have

26 said, and they will continue to say, the context in which you
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1 are doing the assay is very important. 

2 So a tumor cell line versus a fish ovary or

3 fish liver is going to be very different. So by framing the

4 question very narrowly and providing the sort of information

5 space you're dealing with, and explicitly saying we know there's

6 these differences and here's how we're handling them. I think

7 it will help circumvent a lot of just basic confusion about the

8 program.

9 DR. DANIEL SCHLENK: Okay. Thank you. Dr.

10 Shultz.

11 DR. TERRY SCHULTZ: I concur with the comments

12 that were said by my colleagues on the panel before me. I would

13 like to address one point which will be discussed in detail

14 later on and that is the ability to put together a second

15 generation of filters that would assist you in reducing the

16 inventory you have. You list Phys-Chem properties but I believe

17 that there are other molecular properties or even biological

18 properties or chemical properties that would certainly help you

19 to do those types of things and I encourage you to look at that.

20 Thank you.

21 DR. DANIEL SCHLENK: Thank you. Other Panel

22 members? Dr. Keiser.

23 DR. MICHAEL KEISER: First I agree with

24 everything that has been brought forward already. I wanted to

25 add one extra point because one concern that has come up is the

26 idea that with some of the predictive computational models, we
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1 might be missing out on chemotypes or examples of chemicals that

2 would be non-intuitive or don't match ones we've seen so far. 

3 And to that point, we'll go into more detail later, but I will

4 point out there there's some additional techniques that could be

5 added in for cases where we have unknown categorizations to help

6 address that.

7 And then the final point I wanted to make

8 right now is again specifically at green, with the idea of

9 assigning confidences, weightings or other numerical values, to

10 some of the predictions and to the different approaches that we

11 have in hand, and that in fact in most cases data sets do

12 already exist to do this. It's simply a question of putting

13 together a transparent, clear and open process for combining it.

14 And the advantage of course of this, is then these different

15 data sources, either computational or HTP, may be brought

16 together in a way that the biases or limitations of them are not

17 necessarily aligned and so you have orthogonal approaches that

18 can reinforce each other. Thank you.

19 DR. DANIEL SCHLENK: Thank you. Any other

20 comments from the Panel? Dr. McManaman.

21 DR. JAMES McMANAMAN: I also agree with

22 everything that's been said so far. I would like to urge the

23 EPA to think about clarifying the language regarding physical

24 properties used for discrimination -- discriminating compounds. 

25

26 In particular, as I indicated in my question
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1 in the beginning of this session was that by not defining

2 polymers and ionic substances well, I think that you would

3 eliminate many of the compounds that would activate the

4 alternative ER pathway such as proteins and peptides, because

5 they are both polymeric and ionic at neutral pH. So if we could

6 just get a little more clarity on that, it would be great.

7 DR. DANIEL SCHLENK: Okay. Thank you. Any

8 other comments from the Panel on Question 1? Okay, let me go

9 back to the Agency, any questions or clarification that you have

10 for any of the responses that you received.

11 MS. MARY MANIBUSAN: No we appreciate the

12 Panel's consideration and comments, and we'll look forward to

13 the written comments. Thank you.

14 DR. DANIEL SCHLENK: Okay. All right, let's

15 move on to Question 2. If we can get that up on screen and if

16 you wouldn't mind again reading from the italics would be great

17 at the number.

18 MS. MARY MANIBUSAN: Question Number 2. 

19 Whether there are additional chemical characteristics that

20 should be considered (e.g. volatility), whether there are

21 additional data sources for each parameter identified (e.g.

22 chemaxon for pKa values, EpiSuite for half-life values, et

23 cetera), and how to consider multiple data sources in a weight

24 of evidence approach.

25 DR. DANIEL SCHLENK: Do you want to keep

26 going, there's actually -- next slide in.
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1 MS. MARY MANIBUSAN: Sorry. Whether the

2 exclusion criteria cut offs and the confidence that that

3 application of the exclusion criteria will reasonably identify

4 those substances that are untestable and/or unable to trigger

5 the molecular initiating event of the ER pathway. Also, please

6 discuss options for evaluating cases where uncertainty in a

7 chemical's specific parameter range overlaps a p-chem filter cut

8 off. Please consider in your comments the following exclusion

9 criteria: pKa values less than 2 (<2) or greater than 11 point

10 5 (>11.5); hydrolysis half-life of less than 40 (<40) days and

11 charged species of 99 percent or greater at pH7.

12 DR. DANIEL SCHLENK: Okay, thanks. Our lead

13 discussant on this is Mark Cronin.

14 DR. MARK CRONIN: Thank you. Mark Cronin. As

15 a note of order, the question that describes this is Section

16 2.2.2 and I think that should be 2.2 as a note of order. I've

17 compiled together comments from the associate respondents and

18 also some of the discussions and submitted material. And whilst

19 the question is all labeled 2.1, I will deal with it

20 sequentially. There're a number of sub-questions there so,

21 perhaps if we could back onto the first slide. Thank you. 

22 I'm going to make some general statements

23 which really relate to the first paragraph, not yet the bullet

24 points. And we agree that as the Agency expands its universal

25 structures considered for prescreening, it's important to remove

26 those from consideration on the reasons for phys-chem properties
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1 falling outside of the main of testing. So therefore, exclusion

2 criteria looks on as a very positive addition to the EDSP

3 screening and testing scheme. Its agreement that those

4 chemicals that are too acidic, too basic, too reactive or

5 unstable, charge species or too corrosive need not be

6 considered. I'll come back to those and put some more detail on

7 that.

8 To support this hypothesis however, it will be

9 necessary to demonstrate this, in other words, the use of

10 exclusion criteria by in vitro testing of selected model

11 substances to justify their exclusion.

12 The study described in Section 2.2 of the

13 documents and the Agency's presentation yesterday is important,

14 well informed, we agree it demonstrates that exclusion criteria

15 could be very powerful to reduce rationally the number of

16 compounds needed for testing. And as far as I'm concerned, the

17 first time that such comprehensive approach has been formulated

18 and applied to screening and entering, it could be much widely

19 applied and I would be interested to see that, for instance in

20 the European context.

21 As stated by the Agency yesterday, the

22 principles depending on the approach are well founded in the

23 EDSTAC approach. Therefore, it's a logical understanding that

24 compounds with the appropriate physico-chemical properties were

25 not reactive or may not be suitable for in vitro testing. It

26 may however, be helpful to note that at least three distinct
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1 reasons, yes we're lumping a lot of things together here, to say

2 that something can be excluded. And I'll just give our point of

3 view on those.

4 So for instance some compounds are inactive

5 systemically in whole organisms. This has been known in the

6 pharmaceutical industry for a long time, particularly following

7 oral dosing. For instance, compounds that are not susceptible

8 to passive transports, in other words, cross concentration

9 gradients due to molecular dimensions, those that are too

10 isolable or show poor with regard to pharmaceutical substances,

11 this is ADME properties are likely to be unactive due to low

12 bioavailability. Some of these properties, at least, are

13 captured by the exclusion criteria, in other words, looking at

14 molecular size and ionization. 

15 Second, some compounds will be degraded

16 rapidly in the environment. This means that they will not

17 themselves be bioavailable and we see the inclusion criteria of

18 the half-life. With regard to HTP, each in vitro assay will

19 have cutoffs, in other words, solubility and may have its own

20 applicability domain. This criterion must however, be treated

21 with caution whilst it may not be possible for in vitro testing

22 of the compound, it may still capture or elicit an in vivo

23 response. 

24 This general group of concepts is well

25 established, although precise cut offs may be lacking. So with

26 regard to pharmaceuticals, there are some very well established
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1 rules of thumb. Lipinski rules of five is the most well

2 established, that's been around for about 15 years. And that

3 identifies compound with poor solubility. There are other rules

4 of thumb for other rotate parameters and perhaps those could be

5 developed as well. And I'll include more text in the report. 

6 They hypothesis governing the exclusion, the

7 use of exclusion criteria in the context of the AOP philosophy

8 is that the MIE will not be triggered unless a concentration

9 representing what is sometimes called point of departure, is

10 reached. It may be assumed that cutoffs indicate that none of

11 the substance or worse a tiny proportion is likely to reach the

12 site of action and it's a good assumption to apply these

13 criteria, or place at least, these chemicals at lower priority.

14 With regard to Section 2.2.2, exclusion of

15 polymers, mixtures and undefined substances, the panel agrees

16 that exclusion of polymers from the EDSP is justified. Such

17 compounds are typically high molecular weights and effectively

18 inert. The assumption that they will not interact with the ER

19 is justified due to their size. There is also little evidence

20 they will end some systemic circulation and sufficient

21 concentration to promote an effect. 

22 And I take one point, I was going to repeat

23 the comments made previously by Dr. McManaman about to avoid

24 confusion with regard to definition of a polymer, this should be

25 clearly stated, so in other words, manmade substances repeating

26 subunits. And they're probably a lot better definitions that I
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1 can come up with off of the top of my head. 

2 With regard to mixtures, if they are

3 constituted of defined chemical structures then these, in other

4 words, the individual chemical structures should be placed back

5 into the screening process. The panel agrees that doing so

6 after single substances is being considered is a pragmatic

7 solution. This will enable the single, hence probably simpler

8 to deal with at the time, compounds to be dealt with most

9 efficiently and rapidly. It should be recognized however, that

10 there may be compounds of concern within a mixture, and the

11 single substances should not be discounted.

12 With regard to undefined substances, the panel

13 agrees it's not possible to assess these substances using the

14 exclusion criteria proposed or indeed in the ES or in the in

15 vitro assays potentially.

16 Generally speaking, better and clearer

17 definitions are required to many terms used in the report. For

18 instances, polymers we've already discussed, charge species, and

19 any reference to specific chemical domains. We'll supply

20 appropriate references for the material above. 

21 I'll now move on, if I may, to the first

22 bullet point, whether there are additional chemical

23 characteristics that could be considered. We agree volatility

24 is a logical extension to the above mentioned exclusion rules. 

25 In vitro testing of highly volatile substances often gives

26 results that are less active and hypothesized because
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1 concentrations needed to elicit the response must compensate for

2 loss. Possible descriptors for that or cutoffs could be the

3 Henry's law, constant or calculated vapor pressure. 

4 Some of the characteristics that may be

5 considered, for instance, with regard to poor in vivo

6 bioavailability, I've mentioned the Lipinski rules or

7 conservative adaption thereof, they're not perfect the Lipinski

8 rules, it must be said. However, we do appreciate that some of

9 the properties considered within the Lipinski rules have already

10 been included in the current set of criteria. Also issues, or

11 the possibility of including indicators of rapid metabolism or

12 clearance in vivo. Other parameters solubility with particular

13 reference to the applicability of the HTP and in vitro assays,

14 and we've also had mention of LogP cutoffs which covers that to

15 some extent.

16 Potentially other measures of stability or the

17 fact that compounds may be too unstable to test, for instance

18 oxidation in the air, if that's the significant route to

19 exposure. And in future, with regard to in vitro testing, there

20 may be interest in using some of the information that's now

21 coming from the expanding science of what's sometimes called

22 quantitative in vitro in vivo extrapolation. And there's some

23 very nice work that we can cite for instance from the University

24 of Utrecht in the Netherlands, and other places Hamner and so

25 forth. 

26 The models themselves, we're not suggesting
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1 they give cut off criteria, but the way they look at other

2 factors may be relevant. For instance, some of these approaches

3 are attempting to identify and model the properties of chemicals

4 that infect in vitro concentrations, volatility, absorption to

5 cells, and so forth. So these may provide models and further

6 input into development of P-chem filters.

7 Other characteristics the Agency may consider

8 in excluding chemicals, I think Dr. Schultz has already

9 mentioned by analogy, include electrophilic and nucleophilic

10 reactivity, especially soft electrophilic reactivity as related

11 to covalent protein binding. Similarly, chemicals acting as

12 redox cyclers may be excluded from screening and indeed there

13 are a number of ways in which those could be screened out, using

14 structured approaches. And we can provide references for all of

15 the above material.

16 I'll now move on to the second bullet point on

17 the screen, whether there are additional data sources. There

18 are indeed, as widely known, many possible sources of predicted

19 values for the descriptors. However, there should be some

20 rationale to the selection of predictor models. And I think we

21 may have already had a comment along this line already. 

22 For instance, in such a scheme it should

23 really be preferable to have freely available, open access

24 models both that are transparent, well documented, with

25 definable and defined applicability domains, as well as being

26 demonstrably robust. The SAP agrees methods chosen so far are
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1 robust, within reason, freely available and good examples of the

2 state of the art thereof. Therefore, they are appropriate when

3 used carefully and are appropriate to calculate cutoffs. 

4 There are a number of other methods to

5 calculate pKa, degradation, et cetera. And what I would propose

6 here, with the Chair's acceptance, I would add later on a list

7 of those rather than -- I didn't have it to hand at the moment

8 and there is a European Chemical Agency's document that may be

9 of use. I'm sure you are familiar with it. 

10 Should a number of predictive algorithms be

11 utilized, some issues must be recognized. A strategy needs to

12 be adopted to use such data, simply calculating more and more

13 values isn't necessarily helpful and doesn't necessarily

14 increase accuracy or the quality of the output. Multiple data

15 sources are often algorithms based on the same experimental

16 data. Since the model's often fitting exercises, you're not

17 actually gaining anything from it. You're just getting a

18 different model from the same data.

19 Using the worse-case scenario to set the

20 cutoffs reduced the probability of excluding an active chemical.

21 However, at this time it will increase the probability of

22 evaluating a chemical that could be included.

23 The panel agrees that should multiple data

24 with which predictions for compound be evaluated, a weight of

25 evidence approach may be appropriate. This may be based on the

26 relative performances of the model. It may, for instance, be
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1 dictated by the sensitivity and specificity and productivity of

2 the particular models.

3 And if we go to the next slide, I'll deal in

4 turn with each of three phys-chem properties noted there. So

5 I'll start with the consideration of pKa values of less than 2

6 or greater than 11.5. 

7 SAP agrees that it is likely that compounds

8 with pKa values of less than 2 or greater than 11.5 are likely

9 to trigger the MIE of the pathway. There is no evidence that

10 they associated with the effect. And not only do we have the

11 stated criteria, of it not being able to bind, but also they're

12 unlikely to be bioavailable due to ionization. And again,

13 highly acidic and basic compounds are usually intrinsically

14 corrosive and unsuitable for testing. 

15 Perhaps it's possible to use, as a suggestion,

16 possibly use the physico-chemical exclusion criteria twice. 

17 Firstly, I think this is intended to mean to eliminate the

18 obvious untestable substances and a second stage within a

19 chemical category. So for instance all chemicals outside of

20 those pKa limits, could be eliminated and then within a category

21 for instance, within a phenolic category, a refined set of pKa

22 based criteria could be used to further prioritize for testing.

23 Hydrolysis half-life of less than 40 days is a

24 cutoff. The Panel agrees that highly degraded compounds are

25 unlikely to cause in vivo effect following exposure through

26 environmental waters. This is due to a rapid decrease in
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1 concentration or elimination. However, the recommendation of

2 less than 40 days seems rather long and needs to be justified if

3 it's to be used. Indeed the presentation yesterday suggested

4 that there was little difference between less than one day and

5 less than 40 day half-life, but public perception will be much

6 greater about the difference in terms of their understanding of

7 it. Therefore, one recommendation could be to restrict it to

8 less than one day, particularly for multiple exposures relating

9 to lipophilic type compounds. So I think there we need much

10 greater clarity and explanation of where this figure has come

11 from in terms of the less than 40 days.

12 Moving on to charge species, 99 percent of

13 greater pH 7. We agree that compounds are likely to be charged

14 are greater than 99 percent are unlikely to be bioavailable in

15 vivo. It would be good to be, again, to have some clarity over

16 the precise pH there and why that was considered. Is that

17 relating to pH in environmental waters or the in vitro test

18 systems, and consideration that, for instance, strong acids are

19 ionized in the stomach and will be naturally absorbed. However,

20 it is agreed that the value of 99 percent ionization of the

21 compound in test system is appropriate. The Panel or the Agency

22 may wish to consider the possibility of combining the criteria

23 for pKa in charge as there seems to be considerable overlap as

24 shown in Table 2.2.1. 

25 The definition of a charge species is another

26 example where greater clarity in the definition is required. 
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1 And yesterday we had a comment regarding methylanilines and it

2 may be that certain species need to be removed from that

3 definition. 

4 And lastly with regard to the comment in the

5 main paragraph of this question, also please discuss options for

6 evaluating cases where uncertainty of the chemical's specific

7 parameter range overlaps the P-chem filter. And I'm sorry I

8 should have asked before now for clarification on that. 

9 What I assume that meant was that if a

10 compound had a range of parameters, for instance from different

11 methods or there was uncertainly or 95 percent confidence

12 limits, if that range straddles a cut off. Yes, that's what I

13 assumed. Okay. In such a case, the precautionary approach

14 would be to assume or could be to assume that it does not fully

15 meet the criteria for exclusion although this is a good example

16 of an area where more work needs to be placed into it.

17 There was some other general comments which I

18 think address all of Question 2, and again, I've tried to

19 capture some of the discussion as we've gone through. As with

20 all models, the model cutoffs are only as good as the date from

21 which they're modeled. The criteria being applied in the

22 context we are binding analyzed and I think I'm summarizing a

23 comment from Dr. McManaman yesterday and he's welcome to correct

24 me if I'm wrong. So the criteria and the data should be clearly

25 defined and acknowledged that other alternative pathways of ER

26 binding exist. Some compounds could be excluded that could
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1 activate further, and when I say they could be excluded by the

2 current cutoffs, it could activate estrogen receptor. The need

3 here is to define differently with regard to different MIEs and

4 domains. 

5 Second general comment, we also agree that it

6 is also relevant to consider the criteria for the corrosivity. 

7 And the list of structure that is given in Section, I think it's

8 2.2.3.4, is comprehensive and of course may overlap with

9 ionization. 

10 In terms of defining the alerts, it may be

11 better to define them in more detail. They're just alerts which

12 could have mitigating factors so that's an issue that those

13 alerts could be defined or if they are just going to be used as

14 they are. That could be done on paper, electronically in terms

15 of smart strings, other technologies such for instance as the

16 definition of the alerts and the OECD (Q)SAR toolbox.

17 With regard to expert opinion used to apply

18 the criteria, I think it's great, the Panel believes it's great

19 to use expert opinion. Clearly there may be time constraints

20 and again I wonder whether there's a possibility to prioritize

21 the prioritization so obviously, excludable compounds require

22 less attention of the experts as opposed to those that may be on

23 or near because of criteria. Of course this could in part at

24 least be performed with weight of evidence based approaches to

25 refine further confidence. So if for instance, three out of

26 three exclusion criteria pass then there would be confidence
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1 than if it was only a single criterion.

2 Other comments, it would be helpful to have

3 statistics on the relative performance of the cutoffs. I think

4 there was some allusion to it yesterday about some performance

5 statistics helping defining the domain of applicability of

6 approach and also where it may be failing so areas where a

7 better definition may be required. 

8 And lastly in terms of cutoffs, which I should

9 have probably put further up, is the issue of developing further

10 cutoffs for exposure. And I think that was probably dealt with

11 in Question 1.

12 So in conclusion, the cutoffs are logical. 

13 They're developed, those proposed are on the whole appropriate

14 but need better definition and there should be consideration of

15 other properties and the limitations at times on the data which

16 they have developed. And as with the other questions, full

17 references will be provided, as required, for the text.

18 DR. DANIEL SCHLENK: Thank you Dr. Cronin. 

19 Dr. Schultz, anything to add?

20 DR. TERRY SCHULTZ: I agree with what was

21 stated. I have nothing else to add.

22 DR. DANIEL SCHLENK: And Dr. Ehrich?

23 DR. MARION EHRICH: This is Marion Ehrich and

24 my comments were included in the statement made by Dr. Cronin.

25 DR. DANIEL SCHLENK: Thank you. Other Panel

26 members? Dr. Perkins.
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1 DR. EDWARD PERKINS: I have one comment on the

2 hydrolysis half-life cutoff. That this should be revisited in

3 the context of -- that may be appropriate for a single point

4 source where there is only a single omission of that. However,

5 when you have a chronic continuous source then you're still

6 going to be exposed to a chemical even if it's degrading rather

7 rapidly, the closer you are to the source point. And this can

8 be proved especially critical for sensitive life stages which

9 may only last for a few hours to days within an organism as it's

10 spawning or developing within these critical systems. So, I

11 would suggest to revisit that and the logic of it because it may

12 not be protective of what is really happening under chronic

13 exposure conditions.

14 DR. DANIEL SCHLENK: Any other panel members?

15 Yes, Dr. Jacobs.

16 DR. MIRIAM JACOBS: Miriam Jacobs. I would

17 just like to endorse the last comment.

18 DR. DANIEL SCHLENK: Okay. Anyone else? 

19 Question 2. Okay, let me at this point go to the Agency. Do

20 you have any questions or clarification? 

21 DR. RAY KENT: No, we don't have any

22 additional comments or clarifications necessary. Thank you very

23 much for your comments.

24 DR. DANIEL SCHLENK: Great. Thanks. Okay

25 since the coffee's not here yet, we should go ahead and move

26 forward to Question 3 if that's okay. And again, maybe we can
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1 just do 3.1 for now and then take a break for 3.2, go that

2 route.

3 MS. MARY MANIBUSAN: Great. So Question 3, ER

4 Expert System Development 3.1. Please comment on the approach

5 used to develop and build ER Expert System Version 2 (ESv2)

6 through chemical testing and effect-based category and read

7 across methods to provide a scientifically defensible approach

8 to predict ER binding potential for a larger number of chemical

9 groups, particularly with regard to defining new groups from

10 within the Mixed Organics and Mixed Phenols. Please indicate

11 any considerations unique to ESv2 that indicate the approach

12 used to develop ER Expert System Version 1 (ESv1) in the SAP,

13 2009 reference needs to be modified.

14 DR. DANIEL SCHLENK: Oops, excuse me. Dr.

15 Madden has a statement to make. 

16 DR. SHARLENE MATTEN: No, not a statement. I

17 just got an email from the beyond and the Panel members should

18 be very close to their mike and speak loudly like Mary

19 Manibusan. 

20 MS. MARY MANIBUSAN: Am I too harsh?

21 DR. SHARLENE MATTEN: No the person is very

22 appreciative of your vocal quality and loudness. So, be like

23 Mary instead of being like Mike.

24 DR. DANIEL SCHLENK: Okay, Dr. Jacobs. You're

25 the lead discussant for this one.

26 DR. MIRIAM JACOBS: Miriam Jacobs. Can you
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1 hear me loud and clear? Thank you very much. I'm going to

2 present the comments from this working group. And before

3 addressing Question 3.1, we agreed together to give a brief

4 overview and lead in so I'm going to start with reading that.

5 The ER Expert System Version 1, the ESv1, is

6 in effect-based Expert System which predicts relative ER binding

7 affinity for food-using inert ingredients and antimicrobial

8 pesticides. The ESv1 was built using data from rainbow trout ER

9 binding and liver slice gene activation assays, as was the

10 subsequent ESv2. It's a transparent and flexible and expandable

11 logical rule-based decision tree that encodes mechanistic

12 understanding with respect to both the chemical and biological

13 aspects of the ER binding and ER agonists stimulation of

14 vitellogenin production in the liver slices. 

15 A positive hit or active classification is any

16 statistically significant signal above background at any

17 concentration up to solubility of cytotoxicity, whichever is

18 reached first. Based on the training sets of representative

19 chemicals, an ER binder is defined as a chemical having an RBA

20 compared with the endogenous ER-alpha ligand 17beta-estradiol or

21 greater than 0.00001 percent, and as a non-binder as having an

22 RBA of less than 0.000001 percent. It's therefore very

23 sensitive to chemicals with low potencies, those which some of

24 the HTP ER assays, for example, could not detect, and provides

25 mechanistic information that cannot always be deduced from the

26 higher to in vivo tests.
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1 The applicable chemical domain was expanded to

2 include nonfood use and inert substances that remained

3 unclassifiable by ES Version 1 which contained 223 discrete

4 substances, SDWA chemicals and fragrances, primarily mixed

5 organics and mixed phenols. Selected chemicals from these

6 groups were then tested and the results modified the ESv1 rule

7 set to extend the chemical coverage, i.e. ESv2. 

8 This is a robust approach and the Agency have

9 made a very good start at coming up with the yes-no nodes on a

10 decision tree. Indeed the Agency should be congratulated in

11 putting together a well thought out and consistent approach. 

12 There is a strong philosophy behind the approach, i.e.

13 attempting to expand the current ESv1 through rational analysis

14 of chemical groups and directed testing. 

15 And here we're going to start having a little

16 bit of repetition from some of the earlier points and I think

17 it's just going to happen again and again with all the

18 questions. However, the output of an Expert System is only as

19 good as the inputs. Future considerations may include making

20 some yes-no decisions more important than others and this would

21 recommend waiting.

22 The platform in which the software is

23 presented is developed from a proprietary system. And while

24 this is a robust and proven system, the Agency could consider

25 other platforms, e.g., KNIME Work Flows, which are open access

26 and would offer greater flexibility. And possibility of
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1 developing complex structural alerts, for example, for grouping,

2 as well as links through a wide variety of (inaudible) tools and

3 databases. 

4 The coverage of each ESv1 is given in terms of

5 percentages. This number is useful but it may be illustrative

6 to show the coverage graphically. For example, the

7 distributions of chemicals and coverage in terms of two

8 dimensional or three dimensional plots of descriptors. This

9 could have the advantage of: (A) being simpler to comprehend;

10 and (B) illustrating where there may be large areas of the

11 chemical universe that have not been covered in this approach. 

12 And other tools that could be used for example would include

13 tools like Spotfire or commercial tools.

14 Regarding Question 3.1. The level of

15 confidence in a prediction based on an alternative method, for

16 example in vitro or in silico, is often centered on the summary

17 experience with the method and the robustness of the data which

18 it has provided. Increasing confidence in a prediction is

19 related to having a transparent mechanistic probable rationale

20 for the prediction and increasing the weight of evidence

21 supporting the prediction. There is growing agreement that the

22 mechanistic basis for prediction is best provided by an adverse

23 outcome pathway, which is done in the case of ER binding leading

24 to potential reproductive toxicity. The weight of evidence is a

25 combination of the number of events that comprise a particular

26 AOP, the relevance of each event to the final in vivo endpoint
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1 in question, the number of methods used to assess each event,

2 the relevance of each method, the particular event and the

3 reliability of the databases supporting each method.

4 In the case of ER binding, potentially leading

5 to reproductive toxicity, the rainbow trout ER binding and liver

6 slice GA assays are highly relevant. Moreover, using them in

7 tandem increases confidence and predictions. As the ER ES

8 training set is expanded, confidence will increase. 

9 A second aspect of confidence in a prediction

10 is that in the perceived consequence of the prediction being

11 incorrect, being wrong, the net result is that there is a

12 greater confidence in accepting a positive or toxic prediction

13 than accepting a negative or non-toxic prediction. One can take

14 this argument one step further. For example, if both one

15 butanol and one octanol were tested and shown to be negative in

16 both the rainbow trout ER binding and liver slice GA assays,

17 there should be a greater confidence than on hexenol would also

18 be a negative chemical. However, extending that prediction to

19 112 dihydroxy dodecane, another aliphatic alcohol, but one with

20 two polar groups which can be configured 10.2 angstroms apart,

21 should be made with lesser confidence. 

22 The weak point in the approach is how

23 similarity is used to develop the chemical groups. Confidence

24 may vary with the rationale and structural boundaries of the

25 chemical group on which the prediction is based. Relevant,

26 current and emerging approaches that should be considered in the
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1 building of ES Version 2 and also ES Version 3, include naive

2 basion classifiers, statistical similarity methods, such as the

3 similarity ensemble approach, and with that we can get

4 references -- the Kaiser references.

5 The second two approaches in particular are

6 less susceptible to the (Q)SAR limitations described on Page 24

7 of Appendix B and may defensibly be applied across the diverse

8 structures with widely varying physio-chemical properties. This

9 is referring to Appendix B, Page 27. They characterize the

10 inventories of interest. By contrast, the current ESv Versions

11 1 to 3 approaches must rely on an exact match for chemicals,

12 such as MO and MP that lack general rules and this is referring

13 to Appendix B, Page 34. And this alone may not scale well. 

14 The 14 new groups added during expansion of

15 ESv1 and ESv2 did not completely cover all of the 2009 mixed

16 phenols and mixed organics. The additional groups or sub-groups

17 within those currently proposed are likely to be found as the

18 additive process continues and this will mean further

19 modification and this is stated in the White Paper.

20 Furthermore, however, as the new data becomes

21 available it will become important to ask what performance

22 statistics can be assessed per node. When used as an augmenting

23 or second pass analysis, additional chemical informatics and

24 systems pharmacology computational methods may increase the

25 recovery rates of compounds that would otherwise remain unknown

26 binding potential or false negatives. 
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1 More generally, it would be sensible to

2 subject ES Version 2 to quantitative performance testing. 

3 Approaches include, for example, analysis of blind out group

4 chemicals not explicitly included within the model but within

5 the model domain; and (2) cross fold analysis for sensitivity,

6 specificity, and positive and negative predictive value by

7 automated data subdivision and model rebuilding; and (3)

8 parallel analysis and testing of the new chemicals. Such

9 analysis would be consistent with the earlier SAP

10 recommendations given in Appendix B and quantify the model's

11 dependence on known chemistry. And that's the answer to

12 Question 3.1.

13 DR. DANIEL SCHLENK: Thank you. Dr. Denslow.

14 DR. NANCY DENSLOW: All of my comments were

15 incorporated and I have no further comments.

16 DR. DANIEL SCHLENK: Oh, Dr. Jacobs has

17 something else to add.

18 DR. MIRIAM JACOBS: Sorry, I thought were just

19 doing 3.1. I haven't gone on to 3.2 yet.

20 DR. DANIEL SCHLENK: That's correct. We're

21 only doing 3.1, yes. Okay? Dr. Shultz.

22 DR. TERRY SCHULTZ: I'll hold my comments

23 until after we finish the entire question.

24 DR. DANIEL SCHLENK: Okay. Dr. Keiser?

25 DR. MICHAEL KEISER: I agree with everything

26 that has been stated so far. I'll pull the microphone a little
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1 closer here. A few additional points just to add. One,

2 reiterate that the current ES as a decision system is both

3 defensible and robust. And my comments mostly focus on ways in

4 which it may be extended not simply in terms of coverage but

5 also in terms of predictive ability into chemical classes that

6 may be encountered in the future or those that are currently in

7 the unknown binding potential category.

8 And so currently there are 576 of those that

9 have come at least through the current proposal that remain in

10 that category. Given the motivation to minimize the false

11 negatives and indeed that more new compounds are expected per

12 year as mentioned in Appendix B, then some of the second pass

13 chemical informatics approaches that have been mentioned before

14 would fit. 

15 I wanted to add one additional one that has

16 come up from some discussion today and yesterday, which is the

17 idea of using data from screens and this would borrow from the

18 high content screening approaches. There's some published

19 information that we can put in the written form of these

20 comments that might be used as an additional augmentation to the

21 Expert System. 

22 Additionally, it might be worth revisiting

23 some virtual screening and docking approaches and I was glad to

24 hear by Dr. Judson that there's some discussion there about

25 collaborations going forward. 

26 So then of course, the idea is not to replace
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1 human decision making or human judgment but rather to augment it

2 and that speaks to the transparency concern. Many of these

3 approaches that have been brought up and are suggested here are

4 not traditional (Q)SAR approaches and as a result some of them

5 go beyond it and have the ability with the algorithms and other

6 information in the literature and open source approaches, to be

7 queried in terms of transparency.

8 And then finally the one additional point I

9 would like to make is first to commend the EPA on providing open

10 access to the Expert System and given the system such as it is,

11 it might make sense, if possible, to make the data sets or the

12 code underlying it as available as possible to analyses such as

13 those already mentioned but one additional example would be

14 random deletion of rule sets, of known correct rule sets from

15 the Expert System and based on that random deletion, application

16 of that now restricted model to the data that you have and

17 asking what the stability of predictions are in the face of

18 random deletion as one way to model uncertainty going backwards

19 in that case instead of going forward, if additional data are

20 hard to come by at this point. Thank you.

21 DR. DANIEL SCHLENK: Thank you Dr. Keiser. 

22 And Dr. Cronin.

23 DR. MARK CRONIN: No further comment. Mark

24 Cronin.

25 DR. DANIEL SCHLENK: Thanks. And Dr. Safe?

26 DR. STEPHEN SAFE: I think I'll wait until the
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1 end.

2 DR. DANIEL SCHLENK: Okay. Everybody wants

3 coffee I guess. Dr. Burris?

4 DR. TOM BURRIS: Nothing to add.

5 DR. DANIEL SCHLENK: And Dr. Ehrich?

6 DR. MARION EHRICH: My comments have already

7 been spoken by Dr. Jacobs.

8 DR. DANIEL SCHLENK: All right. Other panel

9 members which I think we covered everybody there. Just wanted

10 to make sure I didn't miss anybody. Any other panel members

11 want to comment on 3.1? Okay. We go to the Agency. Do you

12 have what you need for that particular question? 

13 MS. MARY MANIBUSAN: Yes. We got that

14 covered.

15 DR. DANIEL SCHLENK: All right, well let's go

16 ahead and take a break. We'll do 3.2 when we get back. Let's

17 be back at 2:25. 

18 (WHEREUPON a break was taken)

19 DR. DANIEL SCHLENK: Okay. Let's go ahead and

20 start with 3.2. If you can read that into the record that would

21 be great.

22 MS. MARY MANIBUSAN: Question 3.2. Building

23 from the Expert System Version 1 training set, please comment on

24 the level of scientific confidence that a chemical is unlikely

25 to initiate the ER AOP, if the in vitro assay Training Set data

26 shows no activity. Please comment on the extent to which the
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1 level of confidence may vary by chemical category.

2 DR. DANIEL SCHLENK: Thank you. Dr. Jacobs. 

3 DR. MIRIAM JACOBS: Well I'm going to start --

4 Miriam Jacobs, sorry. I'm going to start by just flagging up a

5 discussion we've had during the break. It's also going to be a

6 discussion that will come up again and again through the various

7 questions regarding the estrogen receptor AOP or an estrogenic

8 Adverse Outcome Pathway because binding with the ER is one of

9 the molecular initiating events that contributes to downstream

10 protein signaling and is certainly a major MIE for an estrogenic

11 AOP that could have reproductive and/or carcinogenic

12 consequences. 

13 However, all of the tests that were being seen

14 here describe or deal with ER binding or with the HTP assays

15 also, but there are other relevant MIEs that contribute to the

16 downstream protein signaling including membrane ERs, enzymes,

17 sulfur transfer raises, other estrogen receptors, ER related

18 receptors, NFkB, we've just had this discussion in the break,

19 map kinases. 

20 And this is an area that we really feel needs

21 more discussion and exploration and it will come up again and

22 again. And perhaps this question can be refined to address

23 estrogenic pathways not only those mediated first by the

24 estrogen receptor. It doesn't follow that a chemical is

25 unlikely to initiate the ER AOP if the in vitro assay training

26 set data shows no activity.
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1 This would also affect the extent to which the

2 level of confidence may vary by chemical category. One example

3 might be the phthalates, which they not only fit into an ER AOP

4 but also to an estrogenic or steroidal AOP to which the ER MIE

5 belongs. 

6 Another point is that the group felt that at

7 the moment it is not possible to answer a question relating to

8 unknowns. We need to have measures of confidence and these may

9 vary a lot particularly according to the size of the categories

10 and there is therefore a need for better definition of

11 categories. Categories need to be correctly defined and be

12 defensible. And there needs to be a clear rule set. Could a

13 protocol be defined so that the prediction confidence can be

14 quantified per category? 

15 And then also in -- oh this is more for

16 Question 4, so I'm going to leave this point. And that's it. 

17 Thank you.

18 DR. DANIEL SCHLENK: Dr. Denslow.

19 DR. NANCY DENSLOW: My comments are included

20 in the comments just said.

21 DR. DANIEL SCHLENK: Okay. Thanks. Dr.

22 Schultz.

23 DR. TERRY SCHULTZ: The panel realizes that

24 the reason for using the Adverse Outcome Pathway was so you

25 could identify a singular molecular initiating event or

26 initiating event leading to a particular outcome. I think the
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1 question it's raising is, are there other initiating events that

2 would be of importance leading to the same outcome? That is are

3 there other issues involved, molecular initiating events that

4 relate to endocrine disruption by way of estrogenic activity. 

5 That's sort of the situation.

6 Obviously the importance of the AOP as you

7 have used it is that it allows you to form the chemical category

8 when you mix your molecular initiating events and then you have

9 difficulty in forming your categories. So if you're going to

10 build a category approach to your testing an assessment, and you

11 have to base it on a single initiating event, but if you want to

12 address the issue of am I making the chemicals safe from ER

13 related endocrine disruption, then you have to be able to

14 address other possible initiating events. And so we realize

15 that this is a good start because you've identified the most

16 prominent initiating event, but you should not ignore that there

17 are other possible initiating events that could follow part of

18 the pathway, at least at the higher levels of organization.

19 To go back to the category approach that you

20 used in defining mixed phenols, and in that scenario, the

21 question that becomes on that is why not mixed anilins or more

22 specifically the fact that you identified chemicals that have

23 the probability to bind to the A site, why was there not equal

24 emphasis put on chemicals that bind to the B site? That's all I

25 have.

26 DR. DANIEL SCHLENK: Okay. Dr. Keiser.
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1 DR. MICHAEL KEISER: First just to say I agree

2 with what's been stated before. One thing in the current

3 decision path, literature evidence is taken occasionally into

4 consideration and one nice subset or single source that might be

5 considered to be added for this literature evidence will be the

6 Chembl database, C-H-E-M-B-L, an open source, freely available

7 database that reflects current scientific literature. That

8 might also catch cases that would be otherwise be hard to find

9 by a manual review of literature for known estrogenic binders.

10 Secondly, regarding the issue of the AOP,

11 whereas some of these other pathways or other potential

12 initiating events might be less likely and indeed the current

13 one does encapsulate the primary one as has just been mentioned,

14 there are additional sources that might specifically be used for

15 this approach, in addition to expert manual curation. One such

16 source would be work done at Stanford recently by Atul Butte,

17 looking at network analyses to find potentially unexpected or

18 less appreciated initiating events in these outcome pathways. 

19 And a second one, also coming from that is

20 work now going on at Mt. Sinai and we'll be happy to provide

21 further information in the written form. 

22 And then thirdly, with respect to specific

23 chemical categories and confidence, as mentioned before, right

24 now it's a bit hard for the panel to assign confidences to

25 specific ones of the categories without having seen some

26 numerical retrospective work on the predictive ability of the
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1 model at those categories. So if you imagine that this decision

2 tree, if you see it as a tree and you start at the root of the

3 tree with your question and progress down the tree to the

4 leaves, it would be assigning as we were talking about

5 weightings or confidences, as best we can, even if they're only

6 preliminary to the particular leaves of those trees. Thank you.

7 DR. DANIEL SCHLENK: Okay. Dr. Cronin.

8 DR. MARK CRONIN: Mark Cronin. No further

9 comment 

10 DR. DANIEL SCHLENK: Dr. Safe.

11 DR. STEPHEN SAFE: I don't talk in terms of

12 leaves and trees and I'm trying to -- I want to say some things

13 but I want to get them right. And I look at what you're doing

14 from an endocrine perspective and the fact that I view all of

15 the compounds you're looking at, even the groups, although less

16 so for the groups, as selective receptor modulators. And so

17 these selective receptor modulators depend on some context and

18 all sorts of different things. They may act through the

19 estrogen receptor binding DNA or not binding DNA. And there's

20 lots of evidence for that. 

21 If you knock out the DNA binding domain of the

22 estrogen receptor, and knock that in, have an estrogen receptor

23 that can't bind DNA in a mouse, it's functional in a lot, but

24 not all estrogenic responses are ongoing. So what you're trying

25 to do I think from an endocrine point of view is to have some

26 confidence that what you're doing will predict. And then when I
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1 look at the assays you're doing, you're really, except for the

2 mammalian one hybrid assay, you're really only doing two assays.

3

4 Estrogen receptor binding which I think is

5 essential but will depending on how you do it, and the context

6 and you've shown this yourself, you're going to miss some low

7 binders. Or you're going to have a lot of inconsistency, and

8 that's problem. You only can get over that problem by using a

9 number of estrogen receptor binding assays. And I think you've

10 done that to some extent and I think that's good. 

11 The other problem is the estrogen

12 transactivation assays. Now every time you do an ERE assay, and

13 we discussed this in the group, what you're looking at is an

14 artificial system where you have a frog estrogen responsive

15 element inserted into a Luciferase or whatever reporter gene

16 system. And that tells you how well you're going to active a

17 frog gene, in a different context. 

18 And so I was initially and still am a little

19 bit, worried about repeating this same type of assay but again

20 if you do it in a different cell context then maybe it's telling

21 you something because you're going to get some variability and

22 you should get variability if all of the compounds you're

23 looking at are selective receptor modulators.

24 So I guess what I would try to emphasize or

25 recommend, number one if you're so stuck on these assays and I

26 know why you are because they're sensitive. And that makes a
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1 lot of sense. You want a sensitive assay to pick up a signal. 

2 But the more of those assays you do, then you can use the

3 differences, compound dependent differences between the assays

4 as maybe a prognostic sort of indicator. Not a mechanistic

5 indicator because it's not, it's a prognostic indicator. And we

6 discussed this before, it's really the same thing as when you do

7 the gene array on a woman's tumor and that array has certain

8 peaks that dictate the success of treatment.

9 So in a sense if you're going to do the same

10 assay all the time, in a different context, try to see if you

11 can get some prognostic features about it that will say this

12 fingerprint of assay positives or negatives is typical of this

13 type of weak binder. Because those are your problematic ones,

14 the strong binders are going to come through all the time.

15 The other alternative to the prognostic

16 approach that I see is to -- there are many assays out there

17 that will detect not at a high efficacy, but that will detect

18 sub-nanomolar concentrations of weak ER agonists. A lot of them

19 are membrane ER signaling pathways. But look at them -- that's

20 an alternative if you don't want to be stuck in the ERE

21 activation mode, you might want to consider another relevant

22 assay and just see how it goes. And I guess I was surprise that

23 you hadn't done it but I sort of understand why. Because you

24 want sensitivity. 

25 DR. DANIEL SCHLENK: Okay. Dr. Burris.

26 DR. THOMAS BURRIS: I have just one comment to
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1 add. That if the EPA decides to go after more nonclassical

2 estrogen pathways that aren't mediated by the classical binding

3 of the compounds to ER, that the computational models have to be

4 revisited because they are based on a lot of the physical

5 chemical properties of compounds that bind to ER and in

6 particular the one that I've been worried about is the one with

7 the charge at pH 7. Clearly if you're not binding to the ligand

8 binding domain of ER, charge isn't going to be something that

9 you can eliminate compounds with. That's all.

10 DR. DANIEL SCHLENK: Okay. Dr. Ehrich.

11 DR. MARION EHRICH: No further comment. 

12 DR. DANIEL SCHLENK: Okay. Other Panel

13 members? Dr. Nagarkatti.

14 DR. PRAKASH NAGARKATTI: So I wanted to bring

15 this up in response to Question 5 but now that a lot of people

16 have raised similar issues about the comparison of the tests, I

17 thought I would bring this up right now. 

18 And as Dr. Safe said, that you are primarily

19 relying on ER binding and activation of EREs and you also

20 mentioned that the results from both of these assays if they

21 were to be positive in both of these, would be used as a

22 criteria for classifying this compound as active or inactive. 

23 And I think the drawback there is that we know mechanistically

24 that there are a lot of compounds which might activate ER, but

25 not activate ERE pathway, but then go through the nonclassical

26 pathway of activation of AP1 or SP1 or NFkB pathways or they
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1 might directly activate kinases phosphatases and so on. 

2 So I think if you don't take that into

3 consideration therefore you -- if the ultimate goal is to look

4 at modelation of some physiologic functions or toxicity, that

5 you might find these alternative pathways also to contribute

6 towards those modelations of physiologic functions or toxicity

7 that you may not be able to catch that. 

8 So I don't know whether to welcome that or

9 whether we should call what you are looking at is only the

10 classical pathway and that you're not right now looking

11 alternative pathways or that you need to let us know whether

12 you're also looking at that and -- but in this -- what you

13 presented, you are only trying to focus only on the classical

14 pathway because that might really help us focus on saying that

15 okay, we can do everything but we are trying to narrow down to

16 the classical pathway and that's what we are trying to focus on

17 right now. 

18 But otherwise it causes so much confusion

19 about what are the typically -- how do we define going about the

20 endocrine disrupting chemicals. 

21 DR. DANIEL SCHLENK: Okay. Any other

22 comments? Yes, Dr. Waters.

23 DR. KATRINA WATERS: Hi this is Katrina

24 Waters. I wanted to expand on something that Dr. Keiser

25 mentioned earlier in terms of uncertainty and model predictions.

26 And the very last sentence of commenting on the extent to which
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1 the level of confidence can vary by chemical category. 

2 If the categories are going to be used to

3 assign some level of guilt by association for the unknown

4 binding compounds, then the size of the category is going to be

5 a big factor in the consideration of the confidence you have in

6 your predictions. So, very, very small categories don't have

7 enough sampling in order to be able to truly estimate false

8 discovery rates or errors in the confidence in that. 

9 And likewise, very large categories that have

10 a very broad range of binding affinity predictions are going to

11 be really unreliable in terms of your confidence of predicting

12 things that are going to be positive or negative for the unknown

13 binders. So you might consider some unsupervised informatics

14 types approaches to cluster the large categories to try to get

15 them down into smaller sizes that have enough sample to have

16 some confidence but yet aren't too big to incorporate the wide

17 range of properties that they contain that really add to the

18 uncertainty.

19 DR. DANIEL SCHLENK: Any other comments? 

20 Yes, Dr. McManaman.

21 DR. JAMES McMANAMAN: Okay one last comment

22 about alternatives. 3.2 says please comment on the level of

23 scientific confidence that a chemical is unlikely to initiate

24 the ER AOP if the in vitro training set data shows no activity. 

25

26 From what I understood from the presentation
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1 and what I just finished reading, the ER training set looks like

2 it's mainly built on binding to the ER receptor -- the rainbow

3 trout receptor. Binding is only one component of that

4 activation process. The other component is binding to the DNA. 

5 So even if we're talking about the classical ER pathway, you're

6 only looking at one aspect of that. So therefore, you could get

7 a compound that does not affect binding of estradiol to the

8 receptor, that's not an antagonist. But it still could affect

9 the interactions at the level of the ER binding to the DNA

10 elements. So that's something that seems to be missing in the

11 training set.

12 DR. DANIEL SCHLENK: Okay. Anyone else that

13 would like to throw in on that? Okay it seems like a lot of

14 Panel discussion regarding alternative pathways in terms of

15 what's also out there besides just the ER and I guess you guys

16 are focused on ER so, I think those written comments will

17 actually have a lot of material about alternative pathways that

18 are present that could probably be used in future studies. But

19 I guess I would also again like the Panel to know that they are

20 really focusing this one sort of very specific entity here and

21 they really need your comment on that one specific entity. 

22 I think they recognize that there are other

23 entities out there and other pathways that are there, but at

24 this particular point in time, they want your help on this one

25 entity at this point. So, if you could focus your comments on

26 that, I think they would be very appreciative in that particular
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1 regard. Just my own little thoughts there. Any other comments

2 from the Panel for that? Yes, Dr. Ehrich.

3 DR. MARION EHRICH: Marion Ehrich. Relating

4 to that comment, I think that just saying it in your document

5 that you are dealing with the primary pathway I think would be

6 helpful and that would answer a lot of the concerns that were

7 here at the table.

8 DR. DANIEL SCHLENK: Okay with that, we'll go

9 back to the Agency. Any questions or clarification? There's a

10 lot thrown at you there so anything that you have specific

11 questions about?

12 DR. STEPHEN BRADBURY: Thanks and a couple of

13 the Panel matters brought it up and Dr. Schlenk reinforced it

14 and perhaps the document could have been written clearer, but

15 building from the 2009 SAP to this one, we do acknowledge that

16 there are multiple ways that the estrogen signaling pathway

17 could be perturbed sufficiently to cause sex change in fish or

18 whatever adverse outcome you want to describe. 

19 The specific effort right now is to look at

20 and people use different words, but whether or not a chemical

21 has the potential to bind to the estrogen receptor, I think Dr.

22 Burris called it the classical pathway or somebody called it the

23 primary pathway. But, binding to that nuclear receptor doing a

24 binding assay and the charge question was about the training set

25 data, so binding to the rainbow trout liver site is solid prep. 

26 And then based on different decision criteria,
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1 whether or not to use a liver slice to see if in fact you're

2 getting the gene turning on, you're getting expression -- you're

3 getting a result of that binding, it's actually playing out. So

4 those assays aren't designed to pick up different mechanisms. 

5 It never was intended and never meant it to be intended.

6 What the question is getting at is for that

7 specific molecular initiating event if you look at binding up

8 the solubility, and you've looked at gene expression up to the

9 point the cells are getting sick and nothing happens, how

10 confident can we be or not be that that chemical's -- where is

11 it? And if you can't have, and it doesn't mean that we know

12 nothing about what it might do in another pathway because we

13 didn't look at, but for that specific interaction that question

14 was getting at -- I'm going to be overly simple, but if you got

15 no response, is that as same -- do we have the same thoughts

16 about that structure as the structure for which we did see

17 something happen. 

18 If everything is the same, it sort of begs the

19 point, why do it? So that's what that question is trying to get

20 at. Otherwise, what is it that you would want beyond nothing

21 happening to get a sense of how do you relate that to a chemical

22 for which something did happen, in the context of this very

23 specific -- and the I reason I'm kind of bringing this up is I

24 think you've touched on a very important point that the feedback

25 will be important because yes there are many different

26 initiating events and depending upon where you are in that
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1 pathway, you could be assimilating a lot of different ways to

2 get to a certain place on the pathway.

3 So I think it is important to get clarity as

4 where is your physical model of providing empirical data? Where

5 is it on that pathway? And we weren't trying to talk about the

6 relationship of these structures to a membrane receptor. We

7 weren't testing that. We never intended -- we may have

8 inadvertently picked that up and that would important to know. 

9 But yes, by definition these chemicals don't tell us anything

10 about those initiating events because the assays weren't

11 designed to pick up whether or not they could initiate that way.

12 So I realize that the verbal comments are

13 supposed to try to capture what's going to be in writing and at

14 least me, I don't know for the whole team, I'm a little confused

15 right now as to where we are at.

16 DR. DANIEL SCHLENK: Let's go to Dr. Jacobs

17 since you're the lead discussant you get the job of educating

18 Dr. Bradbury with it.

19 DR. MIRIAM JACOBS: We, the Panel really felt

20 and we've stated therefore that we consider that it is a

21 particularly sensitive ER in vitro test and particularly that

22 using the rainbow trout, we are binding a liver slice, gene

23 expression assays in tandem increases the confidence in

24 predictions. And we've stated that, and that's clear. What our

25 discussion and our questions have grown around how the next ES

26 version 2 is being built and also version 3 and the information
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1 that we've been hearing on the high through put assays and the

2 knowledge -- the understanding that we have of the different

3 estrogenic mechanisms going on in those assays as well. So

4 that's where a lot of our discussion has really started to come

5 from.

6 DR. DANIEL SCHLENK: Does anyone else want to

7 chime in on that? I think that again, the question is are you

8 confident? Are you confident if you get no response? I guess

9 that's the bottom line. So is the Panel confident that if

10 there's no response that -- that's there on its own so I see a

11 lot of shaking heads that they're not confident. Dr. Sandy.

12 DR. MARTHA SANDY: So I have a question to

13 other Panel members. Is the human ER the same as the rainbow

14 trout ER?

15 DR. DANIEL SCHLENK: I think Nancy would like

16 to answer that.

17 DR. NANCY DENSLOW: Nancy Denslow. They're

18 not the same. They're 60 percent similar and while they're 95

19 percent similar in the DNA binding domain, in the ligand binding

20 domain they're not, they're about 60 percent and in other parts

21 they're only 19 percent depending on what part of the estrogen

22 receptor you're looking at. So and there's the alpha estrogen

23 receptor and then there's the beta which is again, even in same

24 organism, a very similar to DNA binding domain but then very

25 different in the ligand binding domains, again around 60, 70

26 percent similarity. So they're not identical either. So
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1 estrogen receptor alpha doesn't necessarily report on what

2 another soluble estrogen receptor beta would do in the same

3 organism. 

4 DR. DANIEL SCHLENK: Okay. Yes, Dr.

5 McManaman.

6 DR. JAMES McMANAMAN: Yes, I intended my

7 comments to be related to the question and I think that restate

8 them, I think that the training set assays that you have set up

9 will capture those compounds that are bona fide estrogen

10 receptor binders and lead to transactivation. But so you would

11 get those strong ones. I'm not convinced that you'll get the

12 weaker binders and the weaker transactivators because of the

13 complexity of that we just talked about in terms of the

14 particular assay that you're using -- the binding assay that

15 you're using and secondly the transactivation assay that you're

16 using, you may lose response because of interactions with the

17 tissue that are non-predictable. So I still have concern that

18 you won't be picking up the weaker ones and that still may be

19 important. I think that you'll get the robust ones but not

20 necessarily the weaker ones.

21 DR. DANIEL SCHLENK: Okay, so there's an

22 estimate of confidence there, or not, lack of I guess. Yes, Dr.

23 Schultz.

24 DR. TERRY SCHULTZ: I believe that the way you

25 have set up your series of assays, you're asking the question is

26 the chemical likely to bind to either the A site or the B site,
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1 or both sites in a receptor? And I'm using the rainbow trout

2 receptor as my model receptor because studies that have been

3 done with model chemicals have shown that the binding potential,

4 strong, moderate, weak for classic groups of chemicals,

5 steroids, phenols, parabens, anilins is the relative ranking of

6 the chemicals is the same within those categories. So you're

7 basically saying that I've come up with a biological model and

8 then I've come up with binding constraints within that and my

9 justification for that is this is the most likely way that

10 you're going to get ER related endocrine disruption. It doesn't

11 mean that you've not had other ways of doing that. 

12 And from my perspective it doesn't mean that

13 you don't have other ways of binding to that receptor. I have

14 never heard anything from the Agency on the C region of the

15 receptor. And it's kind of ignored as a regular binding site. 

16 Clearly the occupitation of that C site has something to do with

17 potency. So as long as the Agency continues to emphasize that

18 and realizes the limitations imposed by that, I don't see any

19 way around -- or other way to move forward without doing those

20 types of situations. If you wait to try to say I have a master

21 battery of assays that account for all possible ways that I can

22 interfere with ER-led endocrine disruption, we're not going to

23 get very far in any reasonable period of time.

24 So as long as you understand those caveats and

25 recognize those things, I don't see a problem. But on the

26 flipside of that, I say this is a good thing because this means
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1 that the Agency has done a good job as they defined it and now

2 the committee is asking to move forward looking into these other

3 scenarios, these other possibilities on that. I think if they

4 hadn't done a good job on the A-B side, we wouldn't be talking

5 about all these other things yet because you hadn't even been

6 able to come close to solving the biggest problems, so let's not

7 worry about the secondary or tertiary problems on that. 

8 I think what the Panel was saying, at least

9 from my perspective as a Panel member, I'm saying you've done a

10 good job with this, but let's not forget that there's other

11 issues that you're going to have to address if you want to

12 capture all of this, even within the ER related scenarios. And

13 that's probably because the questions eight and nine, we get

14 into asking about moving into other pathways. Thank you.

15 DR. DANIEL SCHLENK: Thank you Dr. Shultz and

16 Dr. Jacobs. Let me just again, if you wouldn't mind making sure

17 that the comments are incorporated into the final written record

18 that would be awesome. Anyone else? Yes, Dr. Safe.

19 DR. STEPHEN SAFE: And if we look at the

20 question, I think what we have to understand is that you wanted

21 to find out if -- please comment on the extent to which the

22 level of confidence may vary by chemical category, I don't know

23 that we can do that. All we can say is that if a compound in

24 the assay doesn't bind or activate vitellogenin gene expression

25 in the trout assay, if it doesn't do that, then all we can say

26 is it doesn't do that. And that's what you're asking us and
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1 that's what we can say. If it doesn't do that, it doesn't do

2 that. 

3 Now that is sort of an adverse response in the

4 trout context but that's all we can say. However, as more

5 chemicals get fed into this system, it may then become in the

6 future a very useful prognostic assay, even though it may not

7 directly refer to the many adverse responses. So it could

8 develop into a great prognostic assay. But you just need more

9 data.

10 DR. DANIEL SCHLENK: Okay. Anyone else? Dr.

11 Keiser.

12 DR. MICHAEL KEISER: So coming at this from

13 the perspective of someone a little more on the computational

14 side, who does not necessarily have the same background in some

15 the estrogen receptors specific nuances, I'll simply say that

16 what I'm observing on the Panel is some questions about the

17 remet and the extent of what we mean by AOP and how far out

18 we're going to go with that definition. If we begin to extend

19 to human, clearly there're other questions. If we extend to

20 other sites, there're other questions. But if the definition of

21 ER AOP for this question is indeed focused on these particular

22 in vitro and in vivo outcomes, then it seems that it's much

23 easier for us to say that the unlikelihood of missing something

24 is lower. So, just my two cents.

25 DR. DANIEL SCHLENK: Dr. Perkins.

26 DR. EDWARD PERKINS: Yes, and I want to
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1 reiterate that also that's why I emphasized the definition of

2 the AOP and its relation to the human AOP the critical events in

3 there and the actual outcomes. It's a common reproductive

4 toxicity but the manifestations are different. One can be used

5 to infer effects on another but I think for transparency and to

6 avoid sort of misinterpretations of goals like this, you have to

7 be careful to very, very clearly define what your system is and

8 what the expectations are. Thank you very much.

9 DR. DANIEL SCHLENK: Okay. Any other

10 comments? Good discussion. Let me go back Dr. Bradbury to

11 Mary. Do you have any other questions or clarification? Okay. 

12 All right. Let's move on. Let me just remind the folks that

13 actually did again verbally provide comments that you need to

14 get those into a written form to Dr. Jacobs here so she can have

15 the fun job of assimilating all of that together in some

16 coherent fashion. Okay. So please do that before the end of

17 the week. Oh, well, the end of the week. Okay so let's move on

18 to Question 4 and if you could start Mary at 4.1, that would be

19 great.

20 MS. MARY MANIBUSAN: Great. Question 4. High

21 through put ER binding and ER transactivation Data. 4.1 Given

22 the importance of a well-defined endpoint to determining if or

23 how endpoint data are appropriately used, the panel is asked to

24 comment on: Whether the assays are sufficiently described so

25 that others can reconstruct the assay conditions and data

26 analysis and to what extent additional information would be
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1 useful? Whether EPA has described sufficiently the important

2 experimental conditions that affect the assay measurements, and

3 whether EPA has sufficiently discussed their potential impact on

4 assay results and interpretation of the results? 

5 DR. DANIEL SCHLENK: Okay. Our lead

6 discussant for that is Dr. Denslow.

7 DR. NANCY DENSLOW: Nancy Denslow. This is as

8 far as it goes, okay. So in response to this question I do have

9 this broader statement that I wanted to get into the record so

10 even though we've just discussed this, I'm just going to read

11 it. This is still that we think that you've done an excellent

12 job on the assays that you are using for the specific purpose

13 that you're using it. But I still want to read this one little

14 piece that is going to relate to the discussion that we've just

15 had. 

16 So there was considerable discussion among the

17 Panel members concerning the ER AOP that the ER Expert System is

18 hoping to model. If the ER Expert System is supposed to

19 validate chemicals that are estrogen, agonists and antagonists

20 in the broadest sense, then the HTP assays evaluated may not

21 capture the entire set as the assays are specific to only one

22 aspect of estrogen action and that's through the estrogen

23 receptor Alpha and through canonical estrogen response elements

24 in the DNA. Not included are affects through estrogen receptor

25 Beta or the membrane receptor and nongenomic estrogen action. 

26 Even if the ER Alpha is the only focus, not all aspects of ER
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1 Alpha transactivation were tested. 

2 For example, specific steps in the process

3 that occur after binding of the ligand to the receptor were not

4 necessarily captured by the assays. There are HTP assays

5 available in the Tox21 repertoire that capture these other steps

6 as was indicated in the presentations but these assays were not

7 used in this context. For example receptor dimerization,

8 receptor binding to other response elements, cofactor binding,

9 et cetera. And so the Expert System could be broadened to

10 include these endpoints by using additional HTP assays already

11 available in Tox Cast. And we'll provide references for all

12 those statements.

13 But we are sure that the ER AOP that was

14 defined was much narrower interpretation of estrogen affect and

15 that it was estrogen binding to an ER Alpha which binds to a

16 canonical estrogen response element that turns on vitellogenin

17 synthesis in fish hepatocytes, rainbow trout and the HTP assays

18 that were used cover this AOP. So the ER Expert System as

19 envisioned for the ESv3 has several advantages that expert

20 systems for other endocrine disruption mechanisms currently

21 lack.

22 First the Expert System is based on a very

23 well documented AOP where the downstream effects are at the

24 different levels of biological organization are hardwired and

25 easily scaled. The net result of having a hardwired AOP is that

26 very few events, for example, the rainbow trout estrogen
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1 receptor binding and the liver slice gene activation assay need

2 to be assessed with large numbers of chemicals.

3 Second, the ER binding is a very well

4 established molecular initiating event with two decades of

5 studies supporting its application. To a lesser extent, this

6 gene activation assay is an established key event being examined

7 in a variety of protocols such as yeast mammalian cells, et

8 cetera. More specifically the liver slice assay dating back

9 from 2004 has withstood the scrutiny of time, thus the

10 mechanistic interpretation and the real liability and

11 consistency of data derived from these assays are acceptable for

12 this narrower ER AOP. 

13 And so now I want to answer the question. 

14 That was just a comment that we want in the record. So the

15 first question was whether the assays are sufficiently described

16 so that others can reconstruct the assay conditions and data

17 analysis and to what extent the additional information would be

18 useful. And so we thought the assays were very well described,

19 but we did have some discussion about these. So and they really

20 do work, the detection system for each was very well

21 established, the endpoint tested. How the assays deal with

22 cytotoxicity was described, the acceptance criteria and known

23 issues that affect the performance of the assays, all were very

24 good.

25 However, there were some criticisms leveled at

26 the description of the assays. Several panel members noted that
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1 the original references given for the assays were difficult to

2 find and in one case, I think a reference was wrong. The

3 primary reference for the BG1 assay was a four page abstract,

4 although a link was also provided to a full SOP. The primary

5 description of the assays needs to be better referenced.

6 There was also considerable discussion about

7 the use of proprietary assays that only one vendor can perform

8 and for which the performance of the assay cannot be tested by a

9 third party. Assay performance and real liability can therefore

10 not be assessed independently of the vendor. 

11 Okay, so for the second part, whether EPA has

12 described sufficiently the important experimental conditions

13 that affect the assays measurements and whether EPA has

14 sufficiently discussed their potential impact on assay results

15 and interpretation of the results. There was a lot of

16 discussion. We thought that the descriptions were excellent. 

17 However, there was still some question about how or it was not

18 clear how the eight assays were picked from among the 21 assays

19 available from the Tox21 and the criteria for picking the eight

20 assays should be more transparent. 

21 There was a worry that the eight assays picked

22 were selected to test basically the same steps in the process

23 and therefore would give the same type of data rather than

24 picking assays that would test different steps in the process

25 and which could then provide additional criteria for deciding

26 that a chemical is not an estrogen.
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1 It might have been better to pick assays that

2 would not necessarily test the exact same step to broaden the

3 information used for grouping chemicals into whether they are or

4 are not an estrogen mimic. For example, a panel of different

5 gene activation assays that recognize different promoter

6 elements may have added more information for the ER Expert

7 System. 

8 Also there was discussion that more

9 information could have been provided about the cellular

10 background for each of the transactivation assays. 

11 Interpretation of results should be made in terms of the

12 cellular background as estrogen receptors require cofactors for

13 function and these cofactors vary by cell type. 

14 The Panel thought that there was insufficient

15 discussion about the potential metabolism of chemicals in the

16 different cellular backgrounds and how this might compare to an

17 in vivo situation. Were all chemicals that can normally be

18 metabolized into estrogen active derivatives metabolized

19 correctly in the cells? 

20 And the Panel appreciated the efforts that

21 were put into understanding the effect of the protein

22 concentration on the binding assays. This is very well done. 

23 There was no mention of plans however, how to improve these

24 assays, no experimental design for improvement was proposed.

25 And then assays that rely on cell cultures may

26 be subject to bioavailability constraints that media
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1 constituents may impart. These constraints may result in false

2 negatives since protein constituents may bind and reduce

3 bioavailability. Alternatively, protein binding may enhance

4 material uptake, depending on the mechanism of transport from

5 the medium into the cell.

6 Klassen, et al., in 1999 reported that binding

7 the retinoids to B assay, significantly reduce their uptake by

8 keratinocytes. The permeability of mannitol was significantly

9 different in caco-2 cells cultured in fetal bovine serum versus

10 iron supplemented calf serum. And that's a reference by Lentz

11 et al., 2000. Note it seems like some of the effects might be

12 predicted from the physico-chemical characteristics of the test

13 substance and hence could further be scrutinized when and if

14 that compound is tested. And there're some references.

15 The next question, which was to see whether

16 the chemical library purity and analysis, chemical exposure,

17 solvents used, chemical dilutions and plates and plate layouts

18 provides information necessary for data interpretation. In

19 general we were very happy with the results and how this was

20 presented. But there's always considerable discussion and on

21 this point too. While the Panel appreciates that it's difficult

22 to obtain all chemicals at high enough purity, they caution

23 against using chemicals that are not fully characterized.

24 An example of a problem in this regard was

25 provided by a Panel member that described a test of PCBs in

26 Europe. This was the EU Project SysTax where one company
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1 synthesized the test chemicals at 98 percent purity but the

2 impurity was dioxin. That actually had a profound effect on the

3 assays. So depending on what the impurities are, they might or

4 might not change interpretation of the assay and there will be a

5 reference for that.

6 And then finally (d), the adequacy of test

7 concentrations maximum and minimum concentrations tested and

8 cutoffs and whether the concentration cut offs affected some

9 assay results more than others, especially in the context of

10 false negatives. We thought in general these were all adequate.

11 And more discussion on this point too.

12 The Panel appreciated the care that was taken

13 for calculations and rejection of outliers. The very

14 conservative threshold calculation for the lowest effective

15 concentration LEC, could lead to identifying a chemical as

16 nonactive when it is actually active, i.e. increase false

17 negatives. This could explain some of the discrepancies

18 observed among the assays. The use of one concentration at 25

19 micromolars in the binding studies was considered insufficient

20 and that it missed some chemicals. It would be better to have

21 full dose response curves, even for estradiol, as a positive

22 control which in some cases this one had only had two points. 

23 And that's it.

24 DR. DANIEL SCHLENK: Thank you. Our first

25 associate discussant is Dr. Perkins.

26 DR. EDWARD PERKINS: I don't have anything
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1 more to add.

2 DR. DANIEL SCHLENK: Dr. Burris.

3 DR. THOMAS BURRIS: Nothing more to add.

4 DR. DANIEL SCHLENK: Dr. Safe.

5 DR. STEPHEN SAFE: Nothing to add.

6 DR. DANIEL SCHLENK: Dr. Schultz.

7 DR. TERRY SCHULTZ: Basically I felt that the

8 high through put screening data that you were using, that the

9 incomplete development of the filtering protocols which were to

10 address false positives and the issue of the limit to test

11 concentrations to address the false negatives, made the Panel's

12 decision here to be too early. I would like to see both of

13 those issues better addressed so that we could more competently

14 comment on the high through put screening as used to help

15 supplement your categories. I'm getting tired, I don't if I

16 made sense there or not.

17 DR. DANIEL SCHLENK: You could say it again if

18 you want. Okay. And Dr. Jacobs.

19 DR. MIRIAM JACOBS: I agree with what's been

20 said and my comments were included.

21 DR. DANIEL SCHLENK: Okay. Other Panel

22 members? It must be 3:30 I think. Okay. Okay, let me go to

23 the Agency. Any questions or clarification?

24 DR. RICHARD JUDSON: Richard Judson. I would

25 just like to make one clarification or comment on the preamble. 

26 It has to do with the eight assays versus why did we bring the
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1 eight assays here rather than the full complement. And the

2 eight assays were brought forward not because that's what we

3 planned to use to help train the Expert System, it was a subset

4 that we felt like our internal review panel could get through

5 and present an approach. The intention is to bring the complete

6 set of all the assays, merge those together into a consensus

7 model from the HTP. And there was some discussion earlier about

8 Alpha versus Beta. We actually had ER Beta assays so there will

9 be information on both Alpha and Beta and these other events

10 that are not transcriptional or binding captured in these other

11 assays.

12 DR. DANIEL SCHLENK: So is that a question or

13 a comment?

14 DR. RICHARD JUDSON: It was just a comment.

15 DR. DANIEL SCHLENK: Yeah so this is a period

16 of questions, you can't add any more.

17 DR. RICHARD JUDSON: Then I retract what I

18 just said.

19 DR. DANIEL SCHLENK: Yeah, just to let you

20 know. You're not supposed to do that, so, okay. All right. 

21 Let's go ahead and move to 4.2.

22 MS. MARY MANIBUSAN: Question 4.2. With

23 respect to data interpretation, please comment on the approach

24 for defining an active chemical, i.e., to initiate the ER AOP,

25 and an inactive chemical, i.e., unlikely to initiate the ER AOP

26 compound and whether the method of data interpretation is
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1 adequately described and if the rationale for the approach is

2 sufficiently presented. Please include in your comments: The

3 adequacy of the approach to generate the data plots; how and

4 what parameters are calculated, et cetera; How the background

5 data were used to establish the control level; Whether the

6 process for identifying assay interference is adequately

7 described. Adequacy of data normalization, outlier

8 identification, curve fitting, background subtractions, and all

9 other data processing and calculation techniques, and

10 appropriateness of the statistical analyses. 

11 DR. DANIEL SCHLENK: Okay. Dr. Denslow.

12 DR. NANCY DENSLOW: Nancy Denslow. A preamble

13 here too. The method of data interpretation defined in Figure

14 3.1.2, the Expert System decision tree base on the rainbow trout

15 ER binding and the liver slice GA knowledge base, which is a

16 2009 SAP and EPA HQ-OPP-2009-0322-0002 and expanded in Figure

17 3.2a.1 is adequate and sufficient. It is consistent with the

18 current thinking by others, e.g. OECD. It has withstood the

19 test of time be it short. Moreover, as demonstrated in screen

20 shots Figures 3.1.3a and 3.1.3b, it is amiable to automation. 

21 The supporting information presented from Line 642 to Line 862

22 is sufficient to support the expansion of the Expert System

23 decision tree.

24 Okay, so now let me get to the specific. The

25 adequacy of the approach to generate the data plots, how and

26 what parameters are calculated, et cetera, the approach is clear
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1 and as concise as required detail permits. And the Panel

2 unanimously agreed that the approach was very clear.

3 All right. How the background data was used

4 to establish the control level. The background information

5 permits the unambiguous establishment of control criteria. 

6 However, there was considerable discussion on whether DMSO which

7 is weakly estrogenic should be called a negative control. So

8 because it does have a weak estrogenic activity, it's not really

9 negative.

10 Whether the process for identifying assay

11 interference is adequately described. Assay and data

12 interference which is based on the AOP is adequately described,

13 but there was still some question about whether the method

14 described fully how the EPA will deal with assay interferences. 

15 Right now the plan appears to be simply to exclude data that

16 comes from the interference and there was no discussed plan for

17 how they were going to fix this. And there was no explanation

18 about how the assay interference could affect false negatives.

19 Okay, (d) was adequacy of data normalization,

20 outlier identification, curve fitting, background subtractions

21 and all other data processing and calculation techniques, and

22 the appropriateness of the statistical analyses. Data analyses,

23 et cetera, meets the currently accepted standards, especially as

24 stated by OECD, and there was one question that was brought up

25 and that was there was no mention of when human intervention is

26 necessary. If this is done in a blind manner, i.e. without the
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1 knowledge of other results for the chemical so that you're not

2 already putting subjective information into there. That's it.

3 DR. DANIEL SCHLENK: Thank you Dr. Denslow. 

4 Dr. Perkins.

5 DR. EDWARD PERKINS: No other comments.

6 DR. DANIEL SCHLENK: Dr. Burris.

7 DR. THOMAS BURRIS: No comments.

8 DR. DANIEL SCHLENK: Dr. Safe.

9 DR. STEPHEN SAFE: No comments.

10 DR. DANIEL SCHLENK: Dr. Schultz.

11 DR. TERRY SCHULTZ: No additional comments.

12 DR. DANIEL SCHLENK: And Dr. Jacobs.

13 DR. MIRIAM JACOBS: Just one really small

14 comment. I agree with everything that was said, but normally

15 one would term the DMSO control as a vehicle control not a

16 negative control.

17 DR. DANIEL SCHLENK: Okay. Thanks. Let me

18 just also reiterate this subgroup met together so that's why

19 they're saying no comment so that Dr. Denslow actually read the

20 comments. They've been working very hard together to get that. 

21 So, just to let you guys know that. Any other Panel members? 

22 Okay. Let me go to the Agency to seek any questions or

23 clarification.

24 DR. RICHARD JUDSON: We just have one

25 question. When you write up your report could you include the

26 reference for the DMSO in its estrogenic activity please?
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1 DR. DANIEL SCHLENK: Yes. Okay. All right. 

2 Let's move on then to Question 5. Oh sorry, I didn't see 4.3 on

3 the bottom there. They snuck it in.

4 MS. MARY MANIBUSAN: So Question 4.3. Would

5 you recommend other considerations or approaches to analyzing

6 the high through put data?

7 DR. DANIEL SCHLENK: Dr. Denslow.

8 DR. NANCY DENSLOW: While data analysis will

9 always be subject to other techniques, the approaches used are

10 transparent, mechanistically probable, allow for a clear

11 statement of the weight of evidence and allow for hypothesis

12 based targeted testing. There may be additional software

13 packages that could help, for example Spotfire, Hierarchal

14 Analyses to aggregate the results by chemical class.

15 DR. DANIEL SCHLENK: Okay. Dr. Perkins.

16 DR. EDWARD PERKINS: I guess, one of the

17 comments that I'd like to make is that there's significant

18 progress I think in using agglomerative techniques to pull

19 together multiple types of analyses and build relationships from

20 them that might be utilized here. Especially if you want to

21 start to actually rank and prioritize, somehow there will have

22 to be a weighting scheme applied to the HTP assays.

23 DR. DANIEL SCHLENK: Thank you. Dr. Burris.

24 DR. THOMAS BURRIS: I think my comment's very

25 similar just stating a different way that although each of the

26 individual assays that I've looked at I think are well
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1 controlled and validated, the next step of how to deal with

2 eight or 21 assays and how to integrate all of that date into a

3 decision point, is going to be critical and potentially quite

4 difficult to deal with. But certainly, I would hope that there

5 is some type of validation set that could be used to bring about

6 some level of comfort that the processes would be valid.

7 DR. DANIEL SCHLENK: Thank you. Dr. Safe.

8 DR. STEPHEN SAFE: No comment.

9 DR. DANIEL SCHLENK: Dr. Schultz.

10 DR. TERRY SCHULTZ: No comment.

11 DR. DANIEL SCHLENK: And Dr. Jacobs.

12 DR. MIRIAM JACOBS: I have a comment to add

13 for more explanation with respect to hierarchal analysis and

14 cluster analysis of assays. An example that I would like to

15 give, but it also fits in Question 5 so I can give it there

16 instead, rather than repeating.

17 DR. DANIEL SCHLENK: Okay we'll hold off until

18 that then. Any other input from the panel? Yes, Dr. Waters.

19 DR. KATRINA WATERS: Hi. I just had one

20 comment to make. I think it was pretty clear to the Panel when

21 we were reviewing the documents that there was a lot of

22 transparency in how you presented the data and how you did the

23 filtering and stuff. But I think that it was clear from the

24 public comments this morning that a lot of that is not portrayed

25 when the data is publicly released. And so I think developing

26 some standard protocols and really clear descriptions with those
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1 releases for how this data is changing from one release to

2 another, how the filtering is done so that the replication of

3 this can happen in the larger community, would be really useful.

4 DR. DANIEL SCHLENK: Okay. Any other comments

5 for 4.3? Okay now let's move to Question 5.

6 MS. MARY MANIBUSAN: Question 5.1. Please

7 comment on the selection of reference chemicals and whether they

8 are sufficient to assess the performance of each high through

9 put assay for ER agonists. How well do the reference chemicals

10 represent the range of potencies needed to establish the

11 reliability and relevance of these assays for use in the EDSP

12 chemical prioritization for the inert ingredients, fragrances,

13 and Safe Drinking Water Act chemicals? 

14 DR. DANIEL SCHLENK: Thank you. Dr. Jacobs

15 you are lead on this one.

16 DR. MIRIAM JACOBS: Okay. Miriam Jacobs. 

17 With this question we have been working on it early this morning

18 and though it may not be as perhaps as well integrated as some

19 of the earlier questions, so I apologize for that and we will do

20 our best but there may be a lot more comments at the end.

21 Reference chemicals for test development are

22 as derived from the OCED performance based test guidelines for

23 TG 455 and 457. These are based on validated data. They took a

24 lot of exhaustive discussion and development. There is good

25 representation of a range of substances, negatives, eight

26 inactives, very weak, weak, moderate and strong chemicals. So
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1 the panel supported very much the use of those reference

2 chemicals.

3 The list is very necessary and a strong basis.

4 However, how well they represent the range of potencies,

5 particularly negatives and inactives needed to establish the

6 reliability and relevance of these assays and models,

7 particularly the models that are built or are going to be built

8 from these assays for the use in the EDSP chemical

9 prioritization for the inert ingredients, fragrances and SDWA

10 chemicals is less certain.

11 The panel felt that it should be augmented

12 with data from the literature and particularly to include more

13 inactives, at least to 50 percent, to have an adequately

14 balanced statistical model to calculate sensitivity and

15 specificity to further elucidate. There are two types of

16 evaluation which appear to be confused in the documents and

17 presentations that we've seen so far.

18 Assay validation against reference chemicals

19 and classification performance of the assays to protect activity

20 of the AOP. Here the reference chemicals represent a broad

21 range of estrogen receptor activity, classified from inactive to

22 weakly active to strongly active. However, assay performance

23 was evaluated only on a binary scale of active or inactive which

24 is statistical but not relative to potency. It would be useful

25 to evaluate assay performance and reproducibility to the goal

26 standards set before in estradiol because this is used to
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1 normalize all the raw data in each plate.

2 Assays that do not reproducibly produce high

3 quality data for the goal standard should be removed rather than

4 simply normalized and compared to other assays. Assay

5 sensitivity should then be evaluated separately for each assay

6 using weakly active to moderately active standards to establish

7 the dynamic range of the assay.

8 Statistical evaluation of the performance of

9 the assay requires a different set of reference chemicals

10 because the model is evaluating active versus inactive classes. 

11 In this case, size and composition of the truth sets is

12 insufficient because only eight inactive chemicals are included.

13

14 Additionally, many tested chemicals have

15 substantial literature to support classification as active or

16 inactive, which could be used to increase the size of the truth

17 set for the statistical evaluation of the assays. An equal

18 balance of classes is necessary for accurate estimates of

19 sensitivity and specificity so that false negatives can be

20 minimized and capture weakly active chemicals.

21 It's therefore important to conduct a robust

22 literature search on chemicals and chemical families to identify

23 lead compounds. For example, the PAH chemicals discussion that

24 we had yesterday that Dr. Safe was identifying. And it's

25 assumed that the antagonist assay is going to be utilized but

26 there was no specific information provided.
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1 Additional comments with respect to the

2 ToxCast databases that the reference set could really be

3 expanded over 100 including more diverse chemical classed that

4 are known to be ER active in the literature so that's really

5 what I've been saying.

6 So the next question would be 5.2. So perhaps

7 there are.

8 DR. DANIEL SCHLENK: Dr. Waters you're our

9 next associate for 5.1

10 DR. KATRINA WATERS: My comments are included

11 in that statement.

12 DR. DANIEL SCHLENK: Okay. Dr. Safe.

13 DR. STEPHEN SAFE: Mine as well.

14 DR. DANIEL SCHLENK: Dr. Burris.

15 DR. THOMAS BURRIS: No comments.

16 DR. DANIEL SCHLENK: Dr. Perkins.

17 DR. EDWARD PERKINS: My comments are included

18 in the previous statement.

19 DR. DANIEL SCHLENK: Dr. Nagarkatti

20 DR. PRAKASH NAGARKATTI: My comments are

21 included as well.

22 DR. DANIEL SCHLENK: And Dr. Sandy.

23 DR. MARTHA SANDY: And mine are included.

24 DR. DANIEL SCHLENK: Great. Any other

25 comments from other Panel members? Okay. Let's go to the

26 Agency. Any questions or clarification on the comments?
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1 DR. RICHARD JUDSON: Richard Judson. It's

2 just that, could the committee be quite clear on at least a

3 general scheme for increasing negatives that we believe are

4 negatives, to the extent that you can say here are some rules

5 that you would have us follow. That would be appreciated. 

6 That's it.

7 DR. DANIEL SCHLENK: Is that clear to you

8 guys? Do you know how to answer that? Can you answer that I

9 guess in the written comments.

10 DR. MIRIAM JACOBS: From our discussions we

11 felt that there should be a lot of information coming from the

12 high through put on inactives. And that would be able to be

13 used and we've said already also that there should be a lot of

14 information also from the literature and this is really

15 specifically dealing with the model building aspects. Dr.

16 Waters you might have something more to add.

17 DR. KATRINA WATERS: Sure. Katrina Waters. 

18 So I think there was fairly good and we'll get to this in the

19 later questions, comparison of the inactive compounds between

20 the ES Version 1 predictions and what came through the high

21 through put screening. So I think for statistical evaluation

22 you don't necessarily need the truth set to have the very weak

23 to the moderate to the strong activity when you're really

24 capturing that binary kind of classification. So if you can

25 increase your number of inactive compounds, you can get more

26 confidence in that prediction.
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1 DR. RICHARD JUDSON: May I make a statement

2 and see whether this captures what you're -- so what I think I'm

3 hearing is that if we go to the literature and we find a 100 or

4 200 chemicals that have been tested two or three times in in

5 vitro assays in different labs and they're always negative,

6 those could be a reasonable set of negatives to use for

7 comparison. 

8 DR. MIRIAM JACOBS: Yes but also your own

9 data. 

10 DR. DANIEL SCHLENK: Pretty clear? Any other

11 questions. Okay, let's move on to 5.2 and read that into the

12 record Mary. Thanks.

13 MS. MARY MANIBUSAN: Question 5.2. Please

14 comment on whether the Agency's evaluation of the performance of

15 the HTP ER binding and activation assays has considered and

16 accurately assessed all relevant aspects of the assays. For

17 example, have signal-to-noise ratios, background subtraction and

18 interferences been adequately characterized when describing the

19 strengths and limitations of the assay? 

20 DR. DANIEL SCHLENK: Okay. Dr. Jacobs.

21 DR. MIRIAM JACOBS: Miriam Jacobs. The

22 results and explanations for poor data, we considered that they

23 were not always adequate. For example, there was no

24 documentation provided regarding the algorithm for dealing with

25 the flares and we were sure that you must have one and that

26 would be standard practice but we couldn't see that. The
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1 standard protocol for monitoring assay validation at the

2 contractor sites would be useful and it was unclear if this was

3 going on in practice.

4 We also were initially unclear as to whether

5 the HTP assays were run in-house or outside but this was

6 clarified in an earlier question today that it was by the

7 contractors. 

8 The evaluation of performance against

9 reference of the eight assays wasn't evaluated based on data

10 processing thresholds. Sometimes poor performance for single

11 chemicals was blamed on individual artifacts such as flares. 

12 The limitations of all assays should be evaluated against

13 reproducible measurement of response to the positive control and

14 limit of detection. Interference from bursts of cell stress or

15 toxicity is used to reduce false positives but will coordinately

16 cause more false positive also.

17 There were too many questions for

18 clarification and they were answered as tweaking of the data

19 using expert knowledge. Unbiased system performance requires

20 more objective evaluation, for example, standard procedures in

21 relation to the data in order to identify new data requirements

22 for assay developments. It's critical to have standard

23 procedure for dealing with manual procedures, specifically that

24 the data be handled in a blinded structure or class. 

25 DR. DANIEL SCHLENK: Dr. Waters.

26 DR. KATRINA WATERS: My comments are included.
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1 DR. DANIEL SCHLENK: Dr. Safe.

2 DR. STEPHEN SAFE: My comments are included.

3 DR. DANIEL SCHLENK: Dr. Burris.

4 DR. THOMAS BURRIS: My comments are included. 

5 DR. DANIEL SCHLENK: Dr. Perkins.

6 DR. EDWARD PERKINS: My comments are included. 

7 DR. DANIEL SCHLENK: Dr. Nagarkatti.

8 DR. PRAKASH NAGARKATTI: My comments are

9 included as well.

10 DR. DANIEL SCHLENK: And Dr. Sandy.

11 DR. MARTHA SANDY: My comments are included.

12 DR. DANIEL SCHLENK: Other panel members? 

13 Anything to add? Okay. The Agency, clear on those comments?

14 MS. MARY MANIBUSAN: No additional comments. 

15 Thank you.

16 DR. DANIEL SCHLENK: Okay. Let's read in 5.3

17 please.

18 MS. MARY MANIBUSAN: 5.3. Based on analyses

19 in sections 5 and 6, please comment on the comparative

20 performance of the eight high through put assays for detecting

21 ER reference agonists, e.g., do some assays perform differently,

22 and if so to what extent? Was sufficiently detailed information

23 provided explaining the likely reasons for assay differences,

24 when they are observed, e.g., cutoffs, assay interferences,

25 background differences? 

26 DR. DANIEL SCHLENK: Dr. Jacobs.
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1 DR. MIRIAM JACOBS: The Panel felt that the

2 U.S. EPA had provided extremely useful information but that it

3 was far from complete, and it was often preliminary and stated

4 as such. There was evaluation of differences between the eight

5 high through put assays was based on the assumption that they

6 provided redundant measures of the same biological activity when

7 in fact they represent complimentary but distinct components of

8 the mechanism of initiation mode of action for the ER AOP. 

9 This appears to be mostly a statistical

10 approach which did not sufficiently describe the effects of

11 thresholds, limit of detection or sensitivity of each assay to

12 detect the range of estrogen receptor activity for the

13 referenced chemicals. The eight assays chosen represent the

14 distinct and yet limited survey potential MIEs for ER adverse

15 outcome pathways. 

16 Was repetition really there? They are

17 compared statistically as though they are expected to represent

18 the same biological activity. It's possible that these eight

19 assays and even some of the remaining 13 assays not used,

20 represent additional distinct modes of activation which when

21 combined with the eight, would improve predictions of active and

22 inactive compounds and integration of the asset performance

23 should be considered for future predicted models. 

24 It would be relevant to conduct a systematic

25 integrative aggregate assay result to assess the overall utility

26 of the assays. For example, withhold an assay and see how the
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1 data performs and then after analysis of those, to stop working

2 with the poor performance, so to get rid of the redundant

3 assays. 

4 This sort of approach is used extensively in

5 the pharmaceutical industry and there are some small case

6 studies that I referred to in the earlier question that might

7 provide useful case study models. 

8 For example, there's an endocrine related

9 example in the literature from one of the EU Fifth Framework

10 Programmes, a project called Dysregulation of Endogenous Steroid

11 Metabolism potentially alters neuronal and reproductive system

12 development, effects of environmental plasticizers. So this is

13 not by the ER AOP although some aspects could be considered, and

14 it was for a very small chemical class, but it's an interesting

15 model. This EndoMet as the acronym is called, it provides a

16 case study for the use of a battery approach. The selected

17 chemicals represented different classes, i.e. phthalates,

18 alkylphenols and adipate classes. Their effects were measured

19 on parameters from five different tests selected from 23 in

20 vitro tests using cluster analysis to identify bottleneck

21 potential AOP with steroid metabolism and function. The

22 analyses identified four different pathways for which the in

23 vitro test panel of the five selected assays could be

24 potentially used as an indicator for the classification as a

25 potential human endocrine disruptor.

26 For this example, critical pathways were shown
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1 to include estrogen synthesis and sulphonation, synthesis of

2 sulphate/PAP and that's 3-phosphoadenosine, 5-phosphosulfate and

3 thyroid hormone regulation. 

4 The five tests identified were sulfide

5 oxidase source activities, sulfotransferases, sults 101 activity

6 (phonetic), Pap synthese, Pap S activity, aromatase, and the T

7 screen assay.

8 These five tests utilized human or rat cell

9 lines and are considered to be relevant for humans. The initial

10 23 tests included the thyroid as a target, neuronal cell lines,

11 and also included tests from steroid synthesis and metabolism as

12 well as the classical nuclear receptors. And while for this

13 group of tests, there still needs to be further optimization and

14 more substance testing, they have only been tested on very

15 limited class of chemicals.

16 The project does demonstrate how multi-variant

17 analysis can be used for designing specific tests batteries for

18 different chemical classes and such assays could be considered

19 to be more prognostic and methods would be a methodology that

20 would be appropriate for the one under discussion here. And the

21 references for that are Wearing (phonetic) et al 2012 and Song

22 (phonetic) et al 2012.

23 Then there are some additional methods that

24 were touched upon earlier by Dr. Keiser. They're in the Mt.

25 Sinai, there's a group that has been doing analysis of MCF7,

26 gene array pathways and those sorts of references would be
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1 appropriate to include here as well as analytical approaches.

2 DR. DANIEL SCHLENK: Thank you. Dr. Waters.

3 DR. KATRINA WATERS: My comments are included.

4 DR. DANIEL SCHLENK: Dr. Safe.

5 DR. STEPHEN SAFE: I just wanted to point out

6 a couple of things. Just based on the question and the

7 comparative performance, again I would emphasize that if an ERE

8 assay is done in a different cell context you would not expect

9 to get concordance. If you did get concordance with every

10 compound inducing relatively the same way in two different cell

11 line, then you would have a redundancy. But you would expect

12 differences for selective estrogen receptor modulators and so

13 the fact that there's not a coordinate response I think is

14 promising and the more compounds you do that the different assay

15 systems or combination of assay systems may turn out in the

16 future to be prognostic.

17 DR. DANIEL SCHLENK: Dr. Burris.

18 DR. THOMAS BURRIS: I have nothing to add. 

19 DR. DANIEL SCHLENK: Dr. Perkins.

20 DR. EDWARD PERKINS: Nothing more to add. 

21 DR. DANIEL SCHLENK: Dr. Nagarkatti.

22 DR. PRAKASH NAGARKATTI: My comments are

23 included.

24 DR. DANIEL SCHLENK: And Dr. Sandy.

25 DR. MARTHA SANDY: I have nothing else to add.

26 DR. DANIEL SCHLENK: Other Panel members? 
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1 Okay. Let me go back to the Agency. Any questions or

2 clarification?

3 MS. MARY MANIBUSAN: Just to echo a general

4 request and I've heard this mentioned already, if you're citing

5 a reference, please provide that in your written write up

6 please.

7 DR. DANIEL SCHLENK: Okay. I think I have

8 that. All right. Well I think we are at a stopping point at

9 this particular point in time. So I thank the panel for the

10 work they've done so far. We will adjourn today and get back

11 tomorrow morning. Do you need to state anything?

12 DR. SHARLENE MATTEN: So tomorrow we'll start

13 at 9 o'clock in the morning. If Group 5 still needs to clarify

14 anything, I'm sure you'll add it in the morning. There's

15 opportunities so don't feel that you have to stop. We always

16 encourage more work. So for those who are on the phone or Adobe

17 Connect, we're going to end and we'll resume at 9 o'clock in the

18 morning.

19 DR. DANIEL SCHLENK: And just if the panel

20 could meet in the back room in about five minutes that would be

21 great. I have a couple of things to go over. 

22 (WHEREUPON the meeting was adjourned)

23

24

25

26
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1 DR. SHARLENE MATTEN: Everyone, please take

2 their seats. Or if you're not taking your seat, kibitz out in

3 the hall. This is the third day of the Scientific Advisory

4 Panel Meeting, concerning Prioritization of the EDP -- EDSP

5 Universe of Chemicals with the Purpose of Looking at

6 Methodologies Related to Prioritization. 

7 My name is -- oh, I forgot to say who I was. 

8 My name is Sharlene Matten. I am the FIFRA Scientific Advisory

9 Panel designated federal official for this meeting. Thank you

10 very much for coming to this meeting; it's been a very enjoyable

11 meeting, fruitful discussion. And with that, I'm going to turn

12 the microphone over to Dr. Daniel Schlenk, who is the Chair of

13 the FIFRA SAP.

14 DR. DANIEL SCHLENK: Thanks, Sharlene. As

15 is customary, let's just briefly go around the room and for the

16 record, state your name and your affiliation. As Sharlene

17 mentioned, my name is Dan Schlenk, I'm a professor in the

18 Department of Environmental Sciences at the University of

19 California, Riverside.

20 DR. STEPHEN KLAINE: Good morning. I'm Steve

21 Klaine. I'm a professor at Clemson University, Environmental

22 Toxicology. 

23 DR. MARTHA SANDY: I'm Martha Sandy. I'm a

24 toxicologist with the Office of Environmental Health Hazard

25 Assessment of Cal EPA.

26 DR. JAMES MCMANAMAN: Good morning. Jim
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1 McManaman, University of Colorado, professor of reproductive

2 sciences.

3 DR. PRAKASH NAGARKATTI: Good morning. I'm

4 Prakash Nagarkatti, vice president for research at the

5 University of South Carolina. I'm sorry; my throat is really

6 bad.

7 DR. KATRINA WATERS: I'm Katrina Waters from

8 the Pacific Northwest National Laboratory.

9 DR. MARK CRONIN: Mark Cronin from Liverpool

10 John Moores University. 

11 DR. TERRY SCHULTZ: Terry Schultz, the

12 University of Tennessee.

13 DR. JANICE CHAMBERS: Jan Chambers,

14 Mississippi State University.

15 DR. BETTE MEEK: And I'm Bette Meek,

16 University of Ottawa. 

17 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

18 Corps of Engineers. 

19 DR. THOMAS BURRIS: Tom Burris, Scripps

20 Research Institute. 

21 DR. MICHAEL KEISER: Michael Keiser, SeaChange

22 Pharmaceuticals. 

23 DR. STEPHEN SAFE: Steve Safe, Texas A & M

24 University. 

25 DR. NANCY DENSLOW: Nancy Denslow, University

26 of Florida. 
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1 DR. MIRIAM JACOBS: Miriam Jacobs, European

2 Food Safety Authority.

3 DR. DANIEL SCHLENK: Thank you. To get us

4 started this morning, I believe Mary has a few comments that she

5 would just like to get before you in terms of summing up where

6 things are going and where things have been, and some

7 recommendations in terms of some of the questions that we've

8 been dealing with over the last couple of days. Mary.

9 MS. MARY MANIBUSAN: Thank you, Dr. Schlenk. 

10 I asked for an opportunity just to provide some clarification to

11 the Panel from yesterday's discussion of multiple MIEs and

12 different modes of action. 

13 For clarification, the Agency has specifically

14 brought before this Panel a specific mode of action, with a

15 specific MIE that starts with the ER receptor binding

16 interaction, and then following up in higher organization

17 levels, the gene activation in rainbow trout for the purposes of

18 prioritization. 

19 So we would appreciate your clarity and your

20 crystallization, your comments centered on just that, as our

21 primary mode of action. And we recognize, again, that there are

22 other ways to trigger estrogenic effects from endocrine active

23 chemicals, but this is the particular MIE that the Agency would

24 appreciate comment on.

25 Just again, to just reconfirm that we're here

26 for prioritization of chemicals and our task before us is, as
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1 I've discussed, 10,000 plus chemicals of broad chemistries. How

2 do we move forward in terms of smarter, strategic testing and

3 chemical categorization? Those comments from the Panel would be

4 greatly appreciative. 

5 And just understand that from the Agency's

6 perspective, it's very important to make sure that while we're

7 moving forward and using computational toxicology, that we're

8 not starting from scratch; we're really starting from where the

9 SAP had provided guidance to the Agency back in 2009, with

10 respect to the ER Expert System, and moving from there and

11 specifically bring before the Panel the newer technologies, the

12 high through put assays and the five that we've demonstrated are

13 comparison between the training set that form the basis for the

14 ER Expert System, and then your thoughts about expanding that

15 chemical domain coverage with the high through put, recognizing

16 again, with the Agency's need to minimize any false negatives

17 and any false positives as we move forward. 

18 So again, Chair, thank you for that

19 opportunity to provide that clarification. I want to make sure

20 that we're all centered on the same focus.

21 DR. DANIEL SCHLENK: Okay. Thanks. At

22 this point, let's go ahead and read in our next question, which

23 is Question 6.1, I believe. 

24 MS. MARY MANIBUSAN: Question 6.1: "Please

25 Comment on the Agency's assessment of how high through put in

26 vitro assays performed with respect to expectations of
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1 performance for chemicals within chemical categories when

2 compared with the in vitro training set data used to build the

3 Expert System Versions 1 and 2."

4 DR. DANIEL SCHLENK: Our lead discussant

5 for Question 6 is Dr. Perkins.

6 DR. EDWARD PERKINS: Thank you. We had a few

7 comments. We think the Agency's assessment of high through put

8 assay performance was clear and able to provide explanations for

9 most instances where assay results were different from the

10 training set.

11 In general, the high through put assays

12 appeared biased towards identifying high-potency, high affinity

13 ligands, while the rainbow trout assays detected low-potency

14 chemicals. Much of this appeared due to use of a high single

15 concentration pre-screen, such as in the receptor binding assays

16 or because a lower concentration was used in the HTP assays than

17 in the low through put rainbow trout assays.

18 As a result, this drove appearance of a high

19 degree of false negatives in the HTP assays. The Panel felt

20 that adjusting dose or concentration exposures would help

21 minimize this. The Panel also felt that Agency expectations is

22 that categories of chemicals should be behave similarly, may be

23 unreasonable, as categories were not really created on

24 effects-based data, but were constructed using more expert

25 opinion. And this is reflected in some of the results that you

26 had. So that's the sum for that question. 
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1 DR. DANIEL SCHLENK: Okay. Anything to

2 add, Dr. Waters?

3 DR. KATRINA WATERS: No. My comments are in

4 that.

5 DR. DANIEL SCHLENK: And Dr. Burris?

6 DR. THOMAS BURRIS: Nothing to add.

7 DR. DANIEL SCHLENK: Okay. Dr. Safe?

8 DR. STEPHEN SAFE: Nothing to add.

9 DR. DANIEL SCHLENK: Dr. Nagarkatti?

10 DR. PRAKASH NAGARKATTI: Nothing to add. 

11 DR. DANIEL SCHLENK: And Dr. Sandy?

12 DR. MARTHA SANDY: Nothing to add.

13 DR. DANIEL SCHLENK: Other Panel members

14 for 6.1?

15 (No response.)

16 Okay. Let me go to the Agency. Any questions

17 or clarification that you need for that first part of the

18 question?

19 DR. MICHAEL HORNUNG: No questions. 

20 DR. DANIEL SCHLENK: Great. Okay. Let's

21 go to 6.2

22 MS. MARY MANIBUSAN: 6.2: Please comment on

23 EPA's approach to comparing the Expert System Training Set data

24 with the high through put data and the interpretations regarding

25 discordant results between the two assay approaches. Please

26 recommend other considerations or approaches to conducting this
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1 comparative analysis.

2 DR. DANIEL SCHLENK: Okay. Dr. Perkins?

3 DR. EDWARD PERKINS: I think overall, we

4 appreciated the approach the EPA used, going from a fairly

5 simple and clear approach and then trying to bring in more

6 complex ones. As was presented by some of the public comments,

7 other types of analyses can come up with other types of answers,

8 such as pulling into more positives, but covering all the

9 negatives. So it's mostly biased towards false positives. 

10 The Panel believes that the assays should be

11 put into context, such as isolated receptor may be give you a

12 slightly different result that a transfectant expression assay

13 versus native receptor MRNA behavior, just because of the

14 cellular context and the MiLu that they're behaving in. This is

15 especially apparent because of the concentration ranges they

16 would be using. Also, the protein concentration that may absorb

17 some of the chemicals, but also the behavior of MRNA initiation,

18 given the different situations. 

19 So you shouldn't really expect complete

20 agreement between receptors, transcriptional assays because of

21 that. So you should look for more support between them rather

22 than complete concordance. 

23 There appears to be a relatively good

24 concordance for the two methods to predict inactive chemicals,

25 however, more comparison between Tier 1 results and HTP results

26 would be useful since the ESv1 is trained based on these data. 
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1 It would be useful to note if these chemicals

2 that were inactive by ESv1 and active by HTP assays were

3 predominately the result of assay interference, such as

4 fluorescents, cytotoxicity, or flash sites. 

5 The concordance of positive results appears to

6 be extremely poor in the analysis, and it appears to be the

7 result of two inconsistencies between the method. First, the

8 use of single concentration screening approaches in the high

9 through put assays, such as in the receptor-based assays. It

10 seems to clear even less concentrations necessary to measure

11 responsive nuclear-active chemicals as seen in Tier 1 assays. 

12 Also with the high through put assays, where

13 it wasn't done with a single dose screening, the whole

14 concentration range was shifted lower than in the in vitro

15 rainbow trout assays. So there is a natural mismatch there. So

16 as you would expect, a priority to miss some weak binders and

17 things like that. 

18 Second, the -- let's see, one, two, three,

19 four, five six -- 10 to the minus 7th, 10 to the minus 6th, E2

20 activity threshold considered as positive by the ESv1 framework,

21 needs to be confirmed as measurable by both Tier 1 assays and

22 HTP assays if you expect them to be concordant. 

23 If this activity level is not measurable, then

24 the group should also consider adjusting threshold accordingly. 

25 This was also brought up in the public comment that need to

26 reexamine, if this is an overly conservative binding affinity
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1 value or not, compared to other published values, I think.

2 Identification of protein interference on

3 binding was a very good explanation for some of the discordant

4 results and lack of metabolism, I believe, too. The comparative

5 analysis may be more efficiently performed using the larger

6 assay data set, for instance, all 21 of the endocrine -- all 21

7 of the estrogen receptor AOP-related assays that were mentioned

8 but not brought into the analysis. 

9 In comparison with the training set, this may

10 enable further assays or combination of assays that might better

11 capture the training results, using a -- take one out and look

12 at different combinations. 

13 The Agency should consider analyzing results

14 of combined or separate assays with or without filters and a

15 combination of different filters. Obviously, the minimal amount

16 of manipulation would be ideal. That's very clear that way. 

17 With the goal of trying to minimize false negatives, we don't

18 really feel the false positives is a critical issue since this

19 is a prioritization effort and we're expecting that potency or

20 some other approach would fall into play with actually ranking

21 prioritizing efforts.

22 Let's see. Interpretations regarding

23 discordant results highlighted differences in the approaches,

24 but did not address a path forward strategy to fix the problem

25 or validate the HTP assays against the results of the Tier 1

26 assays.
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1 The Agency needs to establish criteria for how

2 accurately the in vitro assay results cross Tier 1 and HTP

3 predict in vivo AOP activity. If it's an AOP, they should

4 either be causally or highly statistically linked to the adverse

5 outcomes. So we need to have that. And one that also falls

6 into earlier comments about very clearly defining molecular

7 events and cellular events, graphically, so we can understand

8 exactly how the assays link between all the different events,

9 closer to the figure that was presented, showing where the 21

10 assays fell within the cellular context.

11 More assays covering other key events in the

12 estrogen receptor, AOP, given if the AOP is very strictly

13 defined in a mechanistic manner, would likely increase

14 confidence that low-affinity binders were not excluded, since

15 higher-level assays of higher levels of organization are likely

16 to be able to detect more complex subtle effects. 

17 The inclusion of other assays with increased

18 concentration ranges and/or increasing the concentration range

19 of existing assays would probably help address low-affinity

20 binding chemicals. Additionally, assay-specific weighting of

21 data could also be used to inform prioritization, address

22 potency problems and also address issues of false positives or

23 false negatives.

24 And I think that's all we have for this

25 question. 

26 DR. DANIEL SCHLENK: Anything to add, Dr.
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1 Waters?

2 DR. KATRINA WATERS: I have nothing to add.

3 DR. DANIEL SCHLENK: Dr. Burris?

4 DR. THOMAS BURRIS: Nothing to add.

5 DR. DANIEL SCHLENK: Dr. Safe?

6 DR. STEPHEN SAFE: Yes. I just wanted to

7 emphasize the points that Ed made in the sense that since the

8 assays being used are fairly similar, but a little bit different

9 in terms of self-context, one wouldn't expect a complete

10 concordance, but there would be modest concordance. And the

11 lack of concordance shouldn't be a worry because of the nature

12 of the compounds. 

13 I would just reiterate what Ed said in terms

14 of the need for, perhaps, adding one or two additional assays

15 that are different than the classical transactivation assay that

16 may be useful for low-affinity binders.

17 DR. DANIEL SCHLENK: Okay. Dr.

18 Nagarkatti?

19 DR. PRAKASH NAGARKATTI: Nothing to add.

20 DR. DANIEL SCHLENK: And Dr. Sandy?

21 DR. MARTHA SANDY: Nothing to add.

22 DR. DANIEL SCHLENK: Other Panel members?

23 (No response.)

24 Okay. We can go back to the Agency. Any

25 questions or clarification regarding 6.2?

26 DR. STEVEN BRADBURY: A couple of thoughts to
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1 maybe get some additional clarity in the Panel's response, and

2 some of it tracks back to some of the discussion yesterday. I

3 can't remember which Panel member talked about comparing apples

4 to apples and oranges to oranges or oranges to apples. It was,

5 in part, around the concentration range used in the various

6 assays in sort of the confounding situation wherein where the

7 rainbow trout assays are running up to solubility or running up

8 to cell intoxication. And as was mentioned by Dr. Perkins, high

9 through put assays may have different concentration ranges. 

10 In fact, some of those responses may have been

11 positive if they had been running up to higher concentrations,

12 which then makes it difficult, it seems, and then realizing

13 we're in a world of low-potency compounds. 

14 We already know from 2005, 2006, 2009, there

15 aren't high-affinity compounds in this structure space. We're

16 dealing with low-potency compounds. We're dealing with maybe 95

17 percent of the compounds that don't even bind to the receptor. 

18 So that's where the action is. The action is being able to be

19 confident nothing is happening and then finding those few

20 needles in the haystack.

21 So what I chased and sort of alluded to but

22 the strategy to understanding how these different assays work

23 and understanding how much variability may be in the context of

24 different cell lines or trout versus mouse or cow or whatever,

25 it seems it's pretty hard to get to the bottom of that without

26 being able to compare apples to apples.
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1 So yes, it will probably be some noise across

2 cell lines or across species, but our biggest variability we're

3 dealing with is variability across chemicals, it seems, at least

4 based on the 2009 SAP, where we were looking at human trout. 

5 Yeah, there's noise, but the noise isn't that big compared to

6 what we're trying to figure out across chemical structure space.

7 So I guess my first area of clarification from

8 the Panel would be are we going to learn a lot by comparing

9 assays that are using different concentration ranges or is it

10 important to be first comparing the different context of

11 different cell lines, at least making sure we're looking at same

12 concentration response curve so we can figure out how much noise

13 there may or may not be across these different assay methods in

14 the context of the structure space, which is our big variability

15 question that we got right now. 

16 So is getting some advice on what you think

17 the first step should be and then the second step. Because

18 right now it seems like there's lots of things do to, but there

19 isn't clarity on knocking off the biggest areas of uncertainties

20 in a systematic manner.

21 DR. DANIEL SCHLENK: Okay. Dr. Perkins,

22 do you want to take a crack at that?

23 DR. EDWARD PERKINS: Sure. I mean, that's

24 somewhat addressed in answers to 6.3; however, I think

25 essentially what we're saying is that if you can understand the

26 limitations or the behavior of the assays, they may not be
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1 exactly the same result, but they're likely be at least

2 complimentary. That's probably actually a better strategy to

3 have complimentary assays that are measuring the same thing a

4 few times in a slightly different way than to have several

5 identical measures of the exact same thing.

6 I personally feel that this is more likely to

7 capture diverse effects, and that was the comment about trying

8 to have a few more assays that would cover different aspects of

9 the ER post-MIE, the early key events in there and pair them

10 together. So that would help capture it, thus, to allude to the

11 broader problem of low-affinity binders and the diversity of

12 chemicals versus classes of chemicals.

13 From listening to a lot of the presentations

14 and reading the problems and looking At how some of the results

15 and seeing some of the other high through put focused efforts

16 outside of these presentations, we think that it may be time to

17 really consider, truly pursuing an effects-based categorization,

18 using the HTP assays to classify -- think about classifying the

19 chemicals on effect-based, using the high through put assays as

20 a variable in, along with physico-chemical properties and

21 classification. This may help add a biological aspect to the

22 categorization that doesn't currently appear to be there. 

23 Categorization currently appears to be based

24 on expert opinion and on structural characteristics. And if we

25 let biology help decide what the categories are, we may rise and

26 be able to address some of the issues that you're seeing now. I
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1 guess that's about it. Does anyone else have anything to add?

2 DR. DANIEL SCHLENK: Yes. Dr. Burris?

3 DR. THOMAS BURRIS: I think just to address

4 that question and to reinforce what Ed said, I think diversity

5 in the assays in the assay panel is critical. I think we've

6 seen data to suggest that even a positive result from a single

7 assay can lead to positive results in the Tier 1 in vivo assays.

8

9 In terms of a decision tree, picking up

10 activity in a single assay may be enough to lead to a reason to

11 test those assays further downstream. But in term of

12 prioritization, with a diverse set of assays, clearly compounds

13 that are active in multiple assays would be higher priority than

14 in fewer assays, but I think the idea of being able to cast a

15 net across assays that detect diverse types of activity,

16 targeting ER is absolutely critical.

17 DR. DANIEL SCHLENK: Yes. Dr. Waters.

18 DR. KATRINA WATERS: So I'll just add to sort

19 of address your comment. You're correct. You're kind of in an

20 odd situation. I don't know that the ToxCast program was really

21 focused, initially, on the identification of weakly active

22 compounds. So because of that, they're screening for the

23 receptor binding is done at the lower level, and that's where

24 you essentially missing data for those that would need the

25 higher concentration to show activity.

26 However, the public commenters yesterday
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1 morning demonstrated, I think, pretty clearly that in the

2 absence of the extra filtering to get rid of the false

3 positives, many of those compounds still do show up positive. 

4 And if your goal is to prioritize but not lose anything, I think

5 you could consider, you know, reducing some of the stringency on

6 the filtering of the data and you're still going to identify all

7 of the positive compounds. And then, as the other Panel members

8 said, you can use the results of that data to help rank and

9 prioritize, based on their activity in the multiple assays. 

10 DR. DANIEL SCHLENK: Okay. Anyone else? 

11 Dr. Perkins? Dr. McManaman. I'm sorry.

12 DR. JAMES MCMANAMAN: I appreciate your

13 question about the diversity of the chemicals and it seems to me

14 that the high through put assay that you guys have used has the

15 best chance of ranking those chemicals according to whether they

16 are going to be active or inactive. 

17 Because it's an amplified assay, you've got

18 multiple EREs, so you would pick up anything that I think would

19 likely to be active, whereas some of the other assays that you

20 would use may not be as sensitive as this one. So I think that

21 this would be -- I would say that if it were me, I would be

22 wanting to use something like this as my first screening to rank

23 my chemicals to find out whether they're active or inactive and

24 then go on further to validate on another assay.

25 I think that there is another control

26 experiment that you might use too, so aligned that you can
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1 eliminate the nuclear localization signal on the receptors and

2 thereby get a specific effect of the nuclear effects of a

3 particular compound by comparing those two.

4 DR. DANIEL SCHLENK: Dr. Perkins?

5 DR. EDWARD PERKINS: I had one final thought

6 -- well, maybe not final -- one thought. That it would be nice

7 to -- one problem is trying to address all chemicals equally

8 across the universe. I think I do feel that in the initial

9 stages, during the filtering, screening of the physical chemical

10 effects where you have exposure that you potentially factor in

11 potential for high exposure to chemicals to identify ones,

12 perhaps, as a class of ones that if they are weak binders, they

13 would cause effects because of the potential high exposure rates

14 and that might help reduce the universe of potential things that

15 you have to examine with very sensitive assays. 

16 So exposure potential or production volume

17 might come in to categorizing things that you need to screen

18 with more sensitive assays.

19 DR. DANIEL SCHLENK: Dr. Schultz.

20 DR. TERRY SCHULTZ: In response to the

21 question raised by the Agency, it noted that the action was at

22 the low end of the binding affinity and they want to use the

23 high through put assays to expand their tested chemical universe

24 as the inventories increased. If that's the case, they must

25 adapt the high through put assays to a concentration response so

26 that they could assure that they are focusing on the
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1 low-affinity binders because what came out of the 2009 SAP was

2 the case of doing the full concentration range and the argument

3 for the 0.00001 cutoff. 

4 So in order to be consistent and use the high

5 through put data to further define the chemical space as the

6 inventories increase in diversity, they must maintain that

7 continuity. 

8 DR. DANIEL SCHLENK: Dr. Sandy.

9 DR. MARTHA SANDY: Just to follow up on what

10 Ed was saying, I believe when he's talking about high exposure

11 levels, you're also including in that chemicals that you may be

12 exposed to every day, maybe not at a high level, but they're

13 ubiquitous exposures. 

14 DR. DANIEL SCHLENK: Dr. Keiser. 

15 DR. MICHAEL KEISER: So picking up a moment on

16 Dr. Schultz's point and responding to the Agency, one thing that

17 seems to be coming out is a pattern -- and others can comment if

18 they agree -- is that specifically because we're dealing with a

19 case of low potency compounds and diverse compounds and we

20 expect that space to grow, the idea that has come out is not

21 simply to attempt to choose one or two of the high through put

22 assays as simply indicative, but rather to have several shots on

23 goal as we go through this, and look at several different ones

24 of these assays from either the eight that we've looked at or

25 even potentially up into the 21, but then there should also

26 specifically be a way to prioritize. 
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1 There is some discussion of a per assay

2 weighting or ideas might also be borrowed from the analysis of

3 high content screens, where one screen, of course, brings

4 multiple different sorts of data together. This is also picking

5 up some of the ideas from Dr. Safe yesterday, where a pattern or

6 a fingerprint or a combination of differently weighted high

7 through put assays could be collapsed into a single ranking or

8 initial filtering score. And in this case, you might not

9 necessarily have a fully mechanistic rationale for why one assay

10 is rated more highly than the other, but rather as a prognostic

11 approach.

12 So in that case, you would, however, be able

13 to select which assays should not be part of the initial high

14 through put panel, simply by those which are not prognostic of

15 the outcome that you were focused on through this single MIE

16 that the different assays all have different viewpoints onto. 

17 DR. DANIEL SCHLENK: Any other response

18 from the Panel on this 6.2? Yeah, Dr. Denslow.

19 DR. NANCY DENSLOW: It seems to me that you

20 could adopt a strategy where the first assay is one that you're

21 doing, which is great because if it's a positive in that assay,

22 then you know that those chemicals need to be prioritized. If

23 it's a negative, you don't know. So then you do other assays

24 for the ones that are negative that could be a different

25 activity, different type of thing, where you don't have to run

26 all chemicals through all assays. 
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1 So depending on -- some of the questions you

2 asked us was how sure were we that your negatives were really

3 negative, and I think that's where we're coming down hard is

4 that we're not sure. We're sure the positives are probably

5 positive and so those are good ranks. Those are good chemicals

6 to then prioritize, but we're not sure that the negatives are

7 really negative, and that's where I think all this other comment

8 is coming from.

9 DR. STEVEN BRADBURY: Thank you. And that's

10 where I'm confused. We have one assay that's running up to

11 solubility or running up to cell toxicity. And as the 2009 SAP

12 said and if nothing happens, I think Dr. Safe said, "No

13 activities. No activity." I mean, if you can't get any more in

14 the buffer or if the cell is dying, probably other stuff is

15 happening of that cell before it can screw up that estrogen

16 signaling pathway.

17 So positives with the one assay, the trout

18 assay and no activity with the others, it could still be

19 activity there. Maybe there's a way to jam more into the cell

20 before it gets sick. I don't know. But it's the flip side

21 that's the issue, is that we know from the analysis done

22 already, probably largely due to the cutoffs, it's the negatives

23 from the high through put that we don't have confidence that

24 they're negative. 

25 It's not so much the negatives out of the

26 trout that -- I'm not saying there's 100 percent confidence that
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1 nothing is nothing, but there is probably a higher degree of

2 confidence that nothing is nothing with the trout assay just

3 because of the way it was run. 

4 The negatives with the high through put in

5 fact may be negatives, but we don't know. And as I think the

6 Panel is indicating, the first sort of thing to come to grips

7 with is that concentration curve that's been used. So that's

8 the part I'm struggling with. And somebody else mentioned, you

9 want to make sure you sweep up the positives. 

10 Well, if we use the high through put right now

11 there would be a lot of things that we know are positive in

12 trout we wouldn't be sweeping up because the high through put

13 would be saying negative, but we've got data with the trout that

14 saying positive. And it be solely be -- there's going to be

15 noise across species, I get that, but I bet you a big chunk of

16 that difference is just due to concentration curves, the

17 exposures that are being used. 

18 So that's the part of the advice, so far, from

19 the Panel that is confusing. Not to say you're wrong. It's

20 just if you could help explain it so we can understand how to go

21 forward.

22 DR. DANIEL SCHLENK: That was Dr. Bradbury. 

23 Yeah, I think you might have to wait until the report comes out

24 to get that put together.

25 DR. STEVEN BRADBURY: To get where we're

26 trying to get the clarity.
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1 DR. DANIEL SCHLENK: I think the Panel has

2 understanding there. Yes? I see a lot of everyone shaking

3 their heads. Dr. Safe?

4 DR. STEPHEN SAFE: You know, I think we

5 understand that you're worried about the negatives and that

6 you're not sure they're negative or not. And I think one of the

7 reasons for that is although you're trying to maximize your

8 sensitivity, there are classes of compounds that don't work

9 exactly in the classical way and that's what you're measuring. 

10 So the alternative to that, if you want to

11 look at these negatives that you're worried about, would be to

12 think about another assay, and there are a number of them out

13 there that will get nominal or concentrations of weak agonists

14 in a pathway, for example, that activates the membrane estrogen

15 receptor. 

16 So I'm not saying do this assay or that assay,

17 but there are alternatives that you might want to consider for

18 the negatives you're worried about. 

19 DR. STEVEN BRADBURY: And then I'll be quiet. 

20 And those are great suggestions, but if you could also help us

21 by being clear on which MIE you're discussing when you do that

22 because again, I think Mary pointed out -- I apologize for being

23 a few minutes late -- we're focused right now on one very

24 specific MIE that nuclear receptor binding domain and not the

25 membrane. But that doesn't mean that we aren't going to move

26 into other MIEs. If you just make sure your comments are crisp,
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1 in terms of this MIE versus a different MIE that may perturb the

2 estrogen-signaling pathway. Thanks. I'll be quiet.

3 DR. DANIEL SCHLENK: Thanks. Okay. Anything

4 else on 6.2?

5 (No response.)

6 Okay. Want to read 6.3 into the record?

7 DR. MARY MANIBUSAN: 6.3: "Given that there

8 may be varying occurrences of false negatives and false

9 positives based on different sources of training set data,

10 please include in your comments options for addressing these

11 uncertainties for different categories in the context of

12 building Expert System Version 3."

13 DR. DANIEL SCHLENK: Dr. Perkins?

14 DR. EDWARD PERKINS: Okay. And you'll have to

15 clarify us. We may not have answered this one exactly the way

16 you'd like either -- or sufficiently, to clear it up.

17 The Panel suggests that the Agency examine

18 effects of over-fitting and overtraining on the data. The

19 potential of weighting data, depending on the source, if the

20 different sources that you're mentioning come from other people

21 or other data sets or if we're just talking about adding to a

22 new one and adding new assays, adding, weighting the data,

23 depending on reliability, how sure you are of the data, whether

24 it's been QC, QA and measures like that will help.

25 Also, one thing that will help is if somehow

26 you can find at least a few core assays that you can at least
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1 compare directly between them to look at a performance-based

2 measure as a control, at least, that's performed exactly the

3 same way, even if the methods change, at least you'll have one

4 thing you can compare across them. And perhaps that would be

5 like the fish liver assay or one of the other assays. So you'll

6 always have some constant comparison between the two spaces.

7 I can see a lot of benefit for pulling in

8 other data sets, but you do have to be careful about how they

9 did them, their proper controls. So the reliability of the

10 assays is a big issue when you look at that, I think, but you

11 can address that by incorporating specific types of controls in

12 there, I think.

13 Also, the Agency should look at the number of

14 assays that are being used in the analysis. Just as we

15 mentioned before, just comparing on a one-to-one basis. It's

16 unlikely that you'll find the perfect homerun assay and that

17 different combinations of assays may be better. And as you grow

18 a dataset, you're going to put in more different chemicals,

19 potentially more high through put assays as the technology

20 evolves. 

21 So things will change and you may not have a

22 comparable data set to the others; however, the expansion of

23 high through put assay data with the chemical set, I think would

24 take a priority. However, if you do have some coherent assays

25 and keep standards through them, that might help a lot for some

26 cross comparison. 
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1 The other area we think it may be very

2 sensitive to between training set data are different chemical

3 categorization strategies, just by looking at how you're

4 comparing assay results versus chemical classes, that could also

5 alter how you're doing it between data sets. 

6 As you add more chemicals in, you're going to

7 probably be expanding data classes or splitting them or fusing

8 them, so that could also cause confusion on the latter analysis.

9 I guess in relation to that, I would personally recommend a

10 coherent framework for making your data transparent and

11 independent of platform, such as big data approaches, where you

12 can use meta-analysis triplets. Things like that. 

13 So you can basically do ontology searches

14 throughout your data, no matter if it's chemistry, biology or

15 annotations so that it's independent of whatever software

16 package you're using or whatever analysis package you're using. 

17 That will help keep your data available and also be useable for

18 other people to link their databases too, so that they can also

19 provide more information.

20 Some of that has been talked about with

21 bringing in high through put data about putting it all into a

22 coherent format, but I don't know about all the other types of

23 data either. And it will be very important when you continue to

24 evolve the data sets not to change, to come up with a simple

25 standard that you keep coherent across time.

26 We think ESv3 should continue, if that's
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1 continued throughout routes if you continue those cycle of

2 prediction validation at filling the gaps for how well the

3 predictions validate with the Tier 1 assays; however, it wasn't

4 clear how the class definitions for guilt by association are

5 actually being used given that several classes contain chemicals

6 with both inactive and active binding potentials.

7 Specifically focusing on false negatives

8 predicted by high through put positive activity, it could be

9 determined if there are artifacts of assay interference, but it

10 should be positive and it comes out negative. Otherwise,

11 identify how does positive activity is missed by the ESv3 using

12 additional chemical properties or weighting of nodes within

13 decision trees.

14 The Panel suggests that Agency efforts should

15 be focused on using additional HTP assays targeting different

16 effects specifically related to the ER AOP key events in order

17 to add more context and more connectivity to the AOP. Closer

18 concordance between the training set and HTP data sets, now and

19 in the future, should evolve and incorporate more knowledge of

20 the biology that's happening, especially if you use more of an

21 effects-driven comparison, e.g., categorized compounds based on

22 HTP activity and physico-chemical properties to form categories

23 of chemicals. Using that, you'll evolve the categories based on

24 what data you're putting in, but since you're letting the

25 biology, different aspects of biology shape your responses and

26 categories, this may be a more closer effect than what you might
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1 see in the more complex liver slice systems.

2 Finally, we have a suggestion or thought. 

3 Since high through put screening has become much more

4 sophisticated over the last several years, it appears that

5 screening of 5,000 chemicals, such as the 5,000 inerts and Safe

6 Drinking Water Act chemicals is relatively inexpensive and

7 rapid, given a few select assays. 

8 We feel the concept of high through put assays

9 and effects-based screening can be rapidly extended further by

10 focusing on acquiring the HTP assay results on all chemicals in

11 the space being tested, trying to encompass the 5,000 inerts and

12 SWDA chemicals.

13 If select ER AOP-specific HTP assays are

14 validated on a small scale on what you're comfortable with what

15 they're saying to you, then these could provide critical effects

16 data necessary for prioritization in conjunction with the

17 development of ESv3.

18 So the last comment was a little bit farther,

19 probably, than the question, but we think that right now that

20 ESv2 could take significant advantage in evolving it by

21 incorporating the effects data as part of the decision rules. 

22 So that's all I have.

23 DR. DANIEL SCHLENK: Dr. Waters?

24 DR. KATRINA WATERS: I have no additional

25 comments.

26 DR. DANIEL SCHLENK: Dr. Burris?
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1 DR. THOMAS BURRIS: No additional comments.

2 DR. DANIEL SCHLENK: Dr. Safe?

3 DR. STEPHEN SAFE: No comments.

4 DR. DANIEL SCHLENK: Dr. Nagarkatti?

5 DR. PRAKASH NAGARKATTI: No additional

6 comments. 

7 DR. DANIEL SCHLENK: And Dr. Sandy?

8 DR. MARTHA SANDY: Just to emphasize the last

9 point there of the suggestion that you test all these chemicals

10 and then you get a much better handle on how to categorize them

11 by structure, it would be much closer to a true structure

12 activity relationship that you could derive. And then that

13 information can be used for the larger universes of chemicals

14 that are out there for other programs. We think it's a

15 worthwhile thing to do.

16 DR. DANIEL SCHLENK: Any other Panel

17 members? Dr. Chambers?

18 DR. JANICE CHAMBERS: Let me just raise a

19 question with that last suggestion of doing all the compounds. 

20 I thought we heard that many of them are not available, at least

21 at the purity that you would need to have some confidence in the

22 assay results. So if that's the case, then should there be some

23 modification to that answer that ideally, all of them should be

24 run? But if they're not available, what do you do?

25 DR. DANIEL SCHLENK: Yes. Dr. Sandy.

26 DR. MARTHA SANDY: That's a very practical
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1 addition, yes. But we would encourage the EPA to look further

2 than just the two contract chemical suppliers they had

3 experience with the HTP assays. But to do a little more digging

4 and look, you could probably find 70 percent of those chemicals

5 at least and test them if possible.

6 DR. DANIEL SCHLENK: Dr. Perkins.

7 DR. EDWARD PERKINS: It does come down to

8 practicality, cost and urgency of screening; however, there is

9 the caveat that if you can't get the chemical, then is it really

10 a problem, I suppose. 

11 From earlier ones, at least myself, I try to

12 emphasize that more effort should be put into obtaining

13 chemicals for the testing. We understand the time constraints

14 and budgetary constraints and programmatic constraints, but

15 there are still 30 percent of 10,000, I believe, for ToxCast. 

16 So that's still 3,000 different chemicals. There's still a

17 large -- a lot more than 1,700 chemicals out available that you

18 already have. So that space alone is much larger. So it should

19 still be able to get a lot more data by incorporating the

20 effects-based data.

21 DR. DANIEL SCHLENK: Any other comments? Let

22 me just remind the Panel that if you made comments in addition

23 to what you already provided to Dr. Perkins, that you provide

24 those in a written format so that he can incorporate this

25 together so that they all go into the final minutes.

26 At this point, let me go to the Agency. Any
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1 questions or clarification regarding the comments?

2 DR. STEVEN BRADBURY: I'm baffled. I'm

3 confused, but I think Question 8 will probably be helpful. 

4 Maybe then I can synthesize some of the latter discussion

5 because I think some of the latter discussion gets into what

6 we're getting at in the charge questions for Number 8. So I

7 think I'll just defer 'til now and then maybe I'll -- at least

8 me -- I'll get it because I don't get it right now.

9 DR. DANIEL SCHLENK: Okay. 

10 DR. STEVEN BRADBURY: There are a couple of

11 things that may be helpful. There is a discussion about are

12 these effects-based categories in the Expert System. As a point

13 of clarification, they are. They're based on low through put

14 empirical data as opposed to high through put empirical data,

15 but the categories are based on empirical data. 

16 So that's one area that I'm just confused as

17 to the recommendation. Then I think Charge Question 8 will get

18 at some of the things we just discussed and that might help sort

19 things out. But if not, then we'll let you know. 

20 DR. DANIEL SCHLENK: Can someone respond in

21 terms of what effects-based data you're talking about, outside

22 what has already been presented?

23 DR. EDWARD PERKINS: I can attempt to. Part

24 of the -- I think for a number of us, a number of the questions

25 were sort of overlapped - felt kind of overlapping, so we

26 probable did talk about things and other things, but I think for
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1 us, the fundamental driver, I believe, was the observation that

2 high through put assays have a lot of -- you can generate a lot

3 of information fairly quickly for a large number of chemicals. 

4 I think the general feeling is that this data

5 is not being used in conjunction or with structure to try to

6 understand relationships as much. We appreciate that the

7 initial categories were done on the training set that is a

8 fairly small selection of chemicals. 

9 We did note that a lot of the discordant

10 results between categories, which is actually another one,

11 however, we talked about expectations of performance for

12 chemicals within categories as part of this too, so it kind of

13 overlapped.

14 So I think what we're trying to communicate is

15 that we feel that you're missing an opportunity to integrate the

16 data as part of the categorization effort. We're not saying to

17 replace something. We're saying that this is -- in the concept

18 of AOP is that these are different steps within the same

19 continuum, ideally, of the ER AOP, and that measurements along

20 this line should be indicative of what's happening. 

21 So if you look at that receptor binders, an

22 early effects stage, along with transcriptional activation and

23 just the feeling that you should be able to incorporate that

24 into the Expert System. Either I missed it or it wasn't quite

25 there. 

26 There's really no plan for integration of the
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1 new data that's coming in as part of a logic for categorizing

2 and classes. So that's pretty much where it came from, if that

3 clarifies it.

4 DR. DANIEL SCHLENK: Dr. Keiser. 

5 DR. MICHAEL KEISER: I'd like to just put out

6 sort of clarifying questions to the Panel by stating, you know,

7 coming from the perspective of hearing it today and thinking

8 about of "staying back" one way I would phrase it and seeing if

9 people agree.

10 So I have in front of me on my computer,

11 opened up, the schematic diagram of the Expert System. 

12 Following the decision rules, each sort of diamond or process in

13 it, many of the rules, as would make a lot of sense, are defined

14 by structural features or occasionally by things such as LogKow,

15 physical property in that case, but in terms of an effect-based

16 decision or logic, one example, perhaps, of a new category of

17 rule that might be added from this now available and

18 increasingly available high through put data, would be the

19 combination or the particulate results that came from a high

20 through put as a node in the decision tree. 

21 So to try to be very specifically clear about

22 that, instead of contains a charge or in addition to contains a

23 charge, as one example, or Site 8 contains phenol fragment. 

24 Additional one could be Screens 1, 2, and 3 out of HTP Panel

25 show positive above preset threshold as a single decision rule. 

26
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1 And I see it looks like it might be some

2 question for clarification on that.

3 MS. MARY MANIBUSAN: This is Mary Manibusan. 

4 I'm equally confused because I think what you're talking about

5 is empirical data and the Expert System was indeed built in

6 response to empirical data, but what it's based on is a (Q)SAR

7 approach. So all those nodes are based on empirical data. So

8 I'm not quite getting the suggestion of incorporating high

9 through put data into nodes. 

10 DR. MICHAEL KEISER: The suggestion, as I

11 understand it -- because again, this is only my attempt to

12 clarify from what I understand from the discussion here -- is

13 the steps along the way before we end up at a categorization of

14 chemicals into a single group could contain empirically derived

15 rules along the decision pathway. Maybe someone else might also

16 want to phrase that or clarify.

17 DR. DANIEL SCHLENK: Dr. Burris.

18 DR. THOMAS BURRIS: I think the issues, at

19 least from my perspective is that those rules are based on --

20 these (Q)SAR rules are based on what we currently understand

21 about how compounds bind to the receptor, but there are issues

22 that there are compounds that may bind to it that we completely

23 would be unable to predict that bind in the mechanism that is

24 unclear or have a low-affinity binding that does not fit the

25 current (Q)SAR rules. So by generating the actual biological

26 data through an HTP, you would be able to capture those
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1 compounds.

2 DR. DANIEL SCHLENK: Would it help the Panel

3 to actually put the scheme up on the screen so that you can

4 direct that or do you need that? Yes?

5 Can we get Version 2? Do you want the Version

6 2 paradigm?

7 In the meantime, any question? Yes. Dr.

8 Hornung?

9 DR. MICHAEL HORNUNG: I was wondering if I

10 could try to provide some clarification on the Expert System.

11 DR. DANIEL SCHLENK: In terms of?

12 DR. MICHAEL HORNUNG: In terms of the

13 categories and the effects.

14 DR. DANIEL SCHLENK: Okay.

15 DR. MICHAEL HORNUNG: The effects-based

16 categories. 

17 DR. DANIEL SCHLENK: Okay.

18 DR. MICHAEL HORNUNG: Those structure rules

19 are based on the effects that are seen in the binding and the

20 gene activation assay. So a chemical, initially, before any

21 testing is done, is put into a category, but then the testing

22 that gives you the activity or inactivity result, active or

23 inactive result, is then what helps define that category. 

24 So if you see within that category, based on

25 the activity of your category that you initially may have set up

26 with 15 chemicals, if it looks like that doesn't appear to be a
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1 category based on the activity, then you go back and try to do

2 refinements. 

3 From what I was hearing, I'm not sure if that

4 concept had gotten relayed, I guess. I don't know if that helps

5 clarify.

6 DR. DANIEL SCHLENK: Dr. Keiser.

7 DR. MICHAEL KEISER: Yes. The Panel does

8 appreciate the fact that the existing rule sets and categories

9 are based specifically on the effects in the attempt, indeed, to

10 reflect that. I think maybe a point of confusion is that the

11 actual proposal that I heard at the end of the comments earlier

12 was a) wherever possible, or its pragmatic, to actually do the

13 initial high through put screening, and then -- and this could

14 simply be an augmentation or variant of the current Expert

15 System -- molecules going through the augmented or Expert System

16 that incorporates effect-based rule sets would need to be

17 prescreened. Because as you, I think, rightly, would point out,

18 otherwise, you would not have this screening data as the input,

19 along the decision chain. And it was literally using empirical

20 data that would then be present for a prescreened compound as a

21 decision point within this system. 

22 So it's describing a system that is not solely

23 computational and does not solely derive from the chemical

24 structure and thus, you would need a physical instantiation and

25 you need to source it, test it, and put it through the high

26 through put screen and then get the empirical data for that
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1 compound to even put it through the augmented system.

2 So obviously that is a limitation of the

3 system that would be proposed here because it would not have the

4 same breadth of being able to apply it to any chemical

5 structure. You would need to test the chemical.

6 However, where such data are available, if

7 indeed the Agency were to pick up this idea of putting as many

8 compounds as possible through the HTP, in parallel or even ahead

9 of parts of the augmented Expert System, we would actually be

10 interested to see better results, perhaps, coming from it by

11 incorporating that empirical data as an input and not simply as

12 the means to create the categories that are structurally

13 organized in the decision tree. Excuse me. That are

14 structurally tied to the effects that had been observed.

15 DR. DANIEL SCHLENK: Dr. Perkins, did you have

16 something that you wanted to add?

17 DR. EDWARD PERKINS: The other way that --

18 excuse me. I'm getting all choked up about it. The other way

19 that this can be beneficial is that the (Q)SAR approach is

20 limited by the data that you put into initially and by trying to

21 add as much high through put data across a large a space as

22 possible. And this goes back to a future question, I think, is

23 that that can help evolve the (Q)SAR. 

24 Essentially what we're saying is you can

25 either use this to evolve the (Q)SAR to make it more refined

26 because clearly, it doesn't predicate accurately everything
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1 right now. So by adding more features -- well, if it predicated

2 everything correctly now, we wouldn't have to do all this. 

3 To enhance the predictive capability of the

4 ESv3, incorporation of data results, more structure activity

5 relationships and development of the (Q)SAR that forms the basis

6 of that would be a great, great advantage; to allow, as was

7 mentioned, about adding it potentially an empirical testing as a

8 node there that could potentially facilitate a fluid evolving of

9 the rules or adapting to, as you get more and more data, rather

10 than trying to recomputed everything. 

11 So it provides a couple of different

12 approaches that are not necessarily incompatible with what

13 you're already doing. So that's why we're suggesting it. I

14 think in our full report we'll try to get more clearer on it,

15 but we understand that this was built on (Q)SAR with structure

16 activity relationships, but somehow we would like to capture it

17 right, but one of the problems is we don't have enough structure

18 activity relationships across the chemical space that we're

19 talking about. That's why you want to expand the list, isn't

20 it?

21 DR. DANIEL SCHLENK: Go ahead, Dr. Bradbury.

22 DR. STEVEN BRADBURY: Yeah. And I think

23 that's where Question may help sort of --

24 DR. EDWARD PERKINS: As I said, it falls into

25 question, but again, and that's why I said it wasn't directly

26 addressed in the question, but we were talking about
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1 uncertainties in different categories for building ESv3. So

2 it's somewhat related to the question. The question overlaps,

3 so you have to forgive us.

4 Just to avoid the confusion, the real

5 statement is just that we think that an opportunity is there to

6 either incorporate high through put assay data for a larger

7 space of chemicals already -- and I'm sure we'll talk about it

8 more in Question 8 -- either as part of development or

9 refinement of the (Q)SAR approach or as some other type of

10 feature. And we'll try to give more specific instances of that

11 in a fuller report.

12 DR. DANIEL SCHLENK: Okay. Let's go back to

13 the Agency. Are we good at this point? Sorry, I got a comment

14 over here from Dr. Schultz.

15 DR. TERRY SCHULTZ: The Panel was somewhat

16 divided in the fact that some of the Panel members looked at

17 this very narrowly and said can high through put data be used to

18 expand the chemical categories that define chemicals that have

19 the potential to bind to the A or the B site in the estrogen

20 receptor, which is the basis for the diagram that you had there.

21

22 But in the presentation by the Agency, it kind

23 of opened up Pandora's box and there are other members of the

24 Panel that say, well, wait a minute; if we have these high

25 through put capabilities and we can test thousands of chemicals

26 relatively quickly and inexpensively -- relative is a term --
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1 then perhaps we need to think of it as a paradigm shift where we

2 can do the high through put screening first and then come up

3 with our algorithm based on the fingerprint of the experiential

4 evidence that comes off of the eight or the 21 estrogen

5 binding-related high through put assays. 

6 So those are two different things that are

7 being looked at in the Panel and I think that what has come out

8 is that in Question 6, the answer from the Panel has said

9 Agency, you may want to look at this way of doing it and when we

10 get to Question 7, you'll hear the other side of the Panel

11 saying this. And the reason for that is the word "category" is

12 used both in Question 6 and Question 7. 

13 So the different groups who were addressing

14 these looked at them in different situations on that. Perhaps

15 after we get through the answers to Question 7, it may be a

16 little bit clearer to the Agency. 

17 DR. DANIEL SCHLENK: Dr. Burris.

18 DR. THOMAS BURRIS: This point may seem a

19 little blunt, but the (Q)SAR is designed to predict ER binding. 

20 You have three high through put assays that don't predict it,

21 they show it. And you could run, and they are capable of

22 running the number of compounds that we're considering. So why

23 predict it when you can determine it directly?

24 DR. STEVEN BRADBURY: I understand, and some

25 of them I question -- gets back to the conversation we had

26 before on the concentration ranges. So in other words, we have
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1 empirical data that shows 30 percent -- quite a range of what

2 would be predicted as active through the trout assay versus the

3 high through put assay. 

4 And, in fact, we would conclude with the high

5 through put assay there are a lot more chemicals that are

6 negative than what the rainbow trout data suggests.

7 I think that previous question was getting at

8 -- and we'll see what your recommendations are -- to use high

9 through put data at full dose response curves or use high

10 through put data at cutoffs because we'll get different answers,

11 the White Papers shows it. You get different answers for the

12 same chemical with the two assays and it may just be because

13 they were run at different concentrations. 

14 If our goal is to be confident that "no

15 activity" means no activity for this specific MIE, it will just

16 be helpful to hear from the Panel how to interpret whatever in

17 vitro platform you want to talk about, in terms of what's the

18 concentration range you're testing at; what are the solubility

19 limits, or not? What's the cell toxicity, or not, that should

20 be used?

21 I think the Agency will absorb all the

22 comments and we will use the OECD principles for (Q)SAR

23 validation where the very first principle is a well-defined

24 endpoint. Know exactly what your endpoint is. Know what your

25 bioassay methods are because in my opinion, an in vitro platform

26 or a (Q)SAR system are just models. One is a physical model and
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1 one is a "mathematical model." They're all models trying to

2 recreate reality, which we'll never find.

3 So it's really important to find what your

4 physical model is or your mathematical model is because if you

5 don't do that, you don't know how to interpret the information. 

6 So one would be very helpful to hear about not high through put,

7 low through put, but what concentration range should we be

8 looking at. What should be the cutoffs we're using because

9 we're not looking for new drugs, we're looking at chemicals that

10 we know don't do much. 

11 The vast majority don't do anything, but we

12 want to find those needles in the haystack. So assuming a

13 chemical isn't one of the needles, just because we may not have

14 tested it high enough, it's something that we just get your

15 advice on how to sort our way through that. 

16 DR. DANIEL SCHLENK: Okay. Dr. Meek.

17 DR. BETTE MEEK: Just to make the point again

18 that some of those aspects do come up in response to Question 7.

19 DR. DANIEL SCHLENK: Great. Dr. Jacobs.

20 DR. MIRIAM JACOBS: And also to make the point

21 that some of those questions have been addressed in previous

22 questions with respect to the dose response curve and the 25

23 micromolar not being sufficient. 

24 DR. DANIEL SCHLENK: Okay. Any other

25 comments? Dr. Schmieder, you have something?

26 DR. PATRICIA SCHMIEDER: Yes. I was just
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1 going to ask for a clarification to see if I understood Dr.

2 Schultz's comment. And it's addressed to all the Panel too. 

3 Does the Panel think that we only use empirical data for the

4 Site A and B chemical groups and not realize that we use for

5 every one of those little groups up there that that is all based

6 on empirical data that is binding in gene activation? And that

7 the whole system is not just based on Site A and B and that,

8 like for instance, the special rules class. 

9 We're not saying that we understand where

10 those chemicals interact within the receptor, but that they do

11 interact. Does the Panel understand it different than that?

12 DR. DANIEL SCHLENK: Dr. Schultz.

13 DR. TERRY SCHULTZ: No. The problem you have

14 with the special rules is you cannot explain, from the

15 mechanistic standpoint of the rationale for that. The A and the

16 B site, you have an explanation for that. 

17 The special rules are, we think it fits in the

18 receptor because we get activity, but we don't know how that is

19 and that becomes much more difficult to deal with if you're

20 going to be doing a chemical structure-based approached as

21 opposed to the approach suggested by just doing the high through

22 put screening and getting the information and working back from

23 that.

24 DR. DANIEL SCHLENK: Okay. Dr. Jacobs.

25 DR. MIRIAM JACOBS: Which is where some of the

26 suggestions yesterday might come in helpful, doing 3D docking
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1 studies, which we understood that were going to be conducted on

2 some of the high through put chemical data. And then you would

3 have more information on what's going on within the receptor.

4 DR. PATRICIA SCHMIEDER: Another --

5 DR. DANIEL SCHLENK: Hold on a second. Dr.

6 Keiser had something -- no? Okay. Dr. Schmieder.

7 DR. PATRICIA SCHMIEDER: Just a point of

8 clarification, are you saying that the docking studies can only

9 be done with high through put data and not the same empirical

10 low through put data? 

11 And that the high through put data gives you

12 different mechanistic understanding of where it binds in the

13 pocket, versus low through put empirical data that's also

14 measuring binding and transactivation?

15 DR. MIRIAM JACOBS: No. The docking studies

16 can be used across the board. It's just that we heard yesterday

17 from Dr. Judson that there were plans to be using those sorts of

18 tools, particularly with the chemicals from the high through put

19 data or perhaps I misunderstood.

20 DR. DANIEL SCHLENK: Okay. Any other

21 comments? I think we've beat this one to death a little bit. 

22 So I've been informed that we need to actually

23 implement a fairly longer break than normal to deal with flight

24 changes and check-outs, et cetera. So let's be back at 10:45 if

25 possible. That will give us a nice down time for Question 7 to

26 come up, I hope. So let's adjourn until 10:45, please.
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1 (Whereupon, a brief recess was taken.)

2 DR. DANIEL SCHLENK: Let's go ahead and move

3 on to Question 7. Again, let me just reiterate a couple of

4 things that I've heard during the break. If the Panel could

5 please consider their responses in terms of what the Agency can

6 do now with the data that is available and the information that

7 is available now, and prioritize that as top priority. And then

8 in terms of research ideas, you can do that as a separate sort

9 of subsequent component of your comments. That would be greatly

10 appreciated. 

11 With that, let's go ahead and read into the

12 minutes Question 7.1, please.

13 DR. MARY MANIBUSAN: Question 7.1: "Please

14 comment on the adequacy and efficiency of the category-based

15 approach to select chemicals for testing to expand the training

16 set in terms of covering the approximately 1,700 chemicals not

17 covered in the domain of the Expert System Version 2."

18 DR. DANIEL SCHLENK: Thanks. Our lead

19 discussant for that is Dr. Meek.

20 DR. BETTE MEEK: Thanks very much. I'm going

21 to just hit the high points here, recognizing, of course, that

22 some of the discussion has evolved in relation to the previous

23 question, but I'm still going to go through what we have because

24 I think it provides, I hope, helpful advice to the Agency,

25 whatever road they consider to take. 

26 First of all, there's a bit of background
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1 information in here, understanding the challenge posed by the

2 task. We appreciate what a significant challenge it is. And

3 also, we have some background text on how EPA has proposed to do

4 that and it's really to illustrate, I think, our understanding

5 of what you're proposing to do. And I'm not going to go through

6 that here, but it is a kind of summary of what you're proposing

7 to do.

8 Just to get into the responses, essentially,

9 in theory, examining new chemical categories is an adequate and

10 efficient approach to expand the training sets, and the Panel

11 recognizes and appreciates the significant and thoughtful effort

12 invested, to date, to increase the chemical space.

13 However, the difficultly lies in defining the

14 categories and selecting derivatives to assess them. In the

15 extreme, every chemical is its own category, however, this

16 conservative definition of a chemical category does not allow

17 for reduced testing, even in the screening scenario. 

18 Similarly, broadly characterized chemical

19 categories are those defined by simple structural alerts are of

20 little value because of the likelihood of a large number of

21 false positives. So the answer lies then in assessing the

22 tested or screen domain in relationship to the possible

23 structural space of the chemical.

24 So we have provided an example here, which I'm

25 not going to go through, but will include in the written report

26 with a particular class of chemicals, but it's essentially to
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1 encourage the Agency to think of different ways of selecting and

2 prioritizing derivatives for assessing new categories,

3 especially as molecular complexity increases.

4 In terms of where clearly defined new chemical

5 classes can be determined as with those proposed for the Expert

6 System Version 3, the category-based approach to select

7 chemicals for testing is efficient and the proposed Expert

8 System Version 3 expansion does seem sensible; however, for

9 compounds remaining outside of clearly defined groups or the

10 high through put data, the 576 ungrouped compounds,

11 category-based strategies may be inadequate.

12 So Appendix B notes that inventories of

13 concern contain diverse structures with widely varying physical

14 chemical properties and this may confound straightforward

15 categorization. 

16 For example, Expert System Version 1, it was

17 observed that there is not sufficient data at this time to

18 further categorize these compounds into subgroups which bind ER

19 and those which do not, nor to hypothesize structural parameters

20 associated with the activity of those compounds. Within such

21 categories, the Expert System version 2 saw only modest

22 increases, such as three percent increase for fragrances. 

23 Expert System version 3 may be expected to

24 increase coverage for many of the chemicals with unknown binding

25 potential, but 576 still remain. So one way to address this may

26 be the incorporation of supporting or second pass chemical
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1 informatics and system pharmacology computational approaches,

2 particularly for recalcitrant and diverse unknown binding

3 potential compounds.

4 This would be consistent with earlier

5 recommendations to consider SMILES, SMARTS, or structural

6 similarity approaches in Appendix C. Relevant tools include

7 naive Bayesian classifiers, statistical similarity methods, such

8 as the similarity ensemble approach, and stack neural network

9 classifiers. The second two approaches, in particular are less

10 susceptible to the (Q)SAR limitations described on page 24 of

11 Appendix B.

12 So the recommendation really is to use

13 unsupervised grouping, especially on the remaining 576 chemicals

14 with unknown binding potential, then apply predictive methods on

15 the groups. So this is an ideal test case for these new

16 predictive methods.

17 So noted as well, the uncertainty associated

18 with classifying the universe of chemicals into smaller groups

19 as a basis to select representative chemicals for a training set

20 was stressed by one Panel member. For example, selection of

21 chemicals for opposite ends of the spectrum, for example, in

22 relation to LogKow in a class may result in mixed responses or

23 missing the effects of intermediate chemicals. 

24 It was noted that the classification approach

25 clearly introduces biases since the categorization is based on a

26 rather seductive structure and LogKow approach that may be
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1 unrelated to the endocrine -- to the ER effects.

2 It was suggested then that the category's

3 training data set approach be pursued only if constraints such

4 as time and resources prevent high through put screening of the

5 entire chemical space, prior to the Expert System analysis. In

6 this scenario, only chemicals that cannot be obtained would need

7 expert analysis and hence, would not be available for a training

8 set. 

9 Immediate expansion to high through put

10 screening using select high through put assays may provide a

11 better solution or classifier for further use in more detailed

12 analysis in the training set. Another comment in relation to --

13 while the effort is focusing on filling out the unknown binders,

14 it was unclear to what extent the predicted binders were

15 validated so confidence can be derived as more than just active

16 or inactive using measurable thresholds. Specifically, the in

17 vitro assays should be verified to have sufficient sensitivity

18 to measure the cut point for 17B-estradiol, the activity

19 threshold. 

20 This is really a reference back to some of the

21 information that was provided in Question 2, the chemical

22 categories in the Expert System should be well defined. This is

23 in terms of the structural characteristics of the category, as

24 well as the relevant physical chemical properties. The

25 properties will include LogKow, but also other relevant

26 descriptors as appropriate; for example, PKa for phenols,
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1 hydrogen bonding and size and shape descriptors for other

2 categories. So that's it for 7.1.

3 DR. DANIEL SCHLENK: Okay. Thank you. Dr.

4 Cronin, anything to add?

5 DR. MARK CRONIN: No. No further comments.

6 DR. DANIEL SCHLENK: Dr. Waters?

7 DR. KATRINA WATERS: I have nothing to add.

8 DR. DANIEL SCHLENK: Dr. Schultz?

9 DR. TERRY SCHULTZ: I think the Panel is

10 basically asking EPA to come up with a clear and transparent way

11 of which they are selecting not only their categories, but also

12 the derivatives within those categories they're going to test so

13 that it is apparent to everyone of how you define your

14 categories, the chemical you are using to cover that category to

15 lead up to why not this chemical or add an additional chemical. 

16 I think what came out about methylcholanthrene yesterday was an

17 example of that.

18 DR. DANIEL SCHLENK: Dr. Keiser?

19 DR. MICHAEL KEISER: Nothing to add. Thank

20 you.

21 DR. DANIEL SCHLENK: Dr. Perkins?

22 DR. EDWARD PERKINS: Nothing to add.

23 DR. DANIEL SCHLENK: Other Panel members for

24 7.1?

25 (No response.)

26 Okay. We can go back to the Agency. Any
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1 questions or clarification regarding comments?

2 MS. MARY MANIBUSAN: No additional comments. 

3 Thank you.

4 DR. DANIEL SCHLENK: Okay. Let's move onto

5 7.2, please.

6 MS. MARY MANIBUSAN: 7.2: "Based on the high

7 through put assay performance (analysis of reference chemicals

8 and comparative analysis with the Expert System Training Set),

9 please comment on the adequacy of the high through put data for

10 advancing the ER Expert System's rules to cover the additional

11 groups of chemicals in Expert System version 3."

12 DR. DANIEL SCHLENK: Dr. Meek.

13 DR. BETTE MEEK: Right. Clearly, one of the

14 strengths of integrated assessment and testing strategies is the

15 breadth of information that can be incorporated, often from in

16 vitro and high through put methods, which are typically designed

17 to provide information about a single biological property or

18 event that reflects the in vivo toxicology to test chemical or

19 chemical category, which can be interpreted in the construct of

20 the Adverse Outcome Pathway. 

21 There are reasons for the variability and

22 results from the in vitro methods as compared to outcomes from

23 in vivo methods and the outcomes of in vivo tests. Vitro tests

24 are largely associated with a single factor or a limited set of

25 factors, while outcomes from in vivo tests are influenced by

26 many factors. 
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1 For that reason, the statistical-based

2 acceptance of an in vitro method is less important than the

3 mechanistic relevance of each in vitro method to a given key

4 event and the relevance of the key event to the final adverse

5 effect in question. So all of the high through put methods used

6 in the Agency's assessments are standardized, well-established

7 assays that typically quantify ER binding and gene activation

8 with a reporter system; therefore, all are relevant to RE (sic)

9 binding and gene activation which were used in assessment of the

10 Expert System version 1 and Expert System version 2. And the

11 reason for the variability and results from different in vitro

12 methods is largely a factor of protocol. 

13 So a number of the aspects that we've heard

14 about here, including abiotic factors like solubility and

15 volatility, which would largely be controlled, but also

16 biological factors such as cytotoxicity metabolic capability,

17 which are more difficult to standardize.

18 So the importance of weight of evidence is

19 more directed towards accepting the results from any

20 mechanistically transparent integrated approach. The weight of

21 evidence is typically assessed by examining several factors,

22 including the number of key events that comprise the scheme; the

23 relevance of each key event of the final adverse effecting

24 question; the number of methods used to assess each key event;

25 the relevance of each method to a given key event, and the

26 reliability of the data measured or otherwise determined for
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1 each method.

2 So the HTP assay data may be useful to cover

3 additional groups of chemical in Expert System version 3, when

4 used in full dose response format. Single concentration data

5 appear to be inadequate, as in the NVS HTP assays. The White

6 Papers states,

7 "The number of prescreened negatives that

8 would have been binders if run in full dose response mode would

9 argue that the prescreening procedure be reassessed for

10 improvements or that running in a full dose response mode would

11 be more appropriate for prioritization approach."

12 The HTP data can be incorporated, but should

13 be weighted by the observations with a rationale that where HTP

14 data are included as a training set, statistical power grows,

15 but each observation becomes less certain. This is appropriate,

16 but to address weakness, quantified sensitivity should be

17 reflected in leave for category weightings. 

18 Second major concern is the false positive

19 increase, but this is acceptable with respect to the

20 programmatic roles. The HTP assay performance against

21 sensitivity to detect weakly active reference chemicals has not

22 been sufficiently evaluated and hence, would not advance the ER

23 Expert System predications for weakly active chemicals. 

24 However, incorporation of missed active chemicals, the false

25 negatives from Expert System version 2 should be evaluated for

26 missing chemical properties within the Expert System are the
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1 common points of departure in the rule-based system that are

2 breaking down for these missed chemicals.

3 It was also noted, as we've heard, with

4 additional evolution, it was considered that the high through

5 put screening was significantly enhanced or even replaced the ER

6 Expert System. That's it.

7 DR. DANIEL SCHLENK: Thank you. Dr. Cronin?

8 DR. MARK CRONIN: No further comment.

9 DR. DANIEL SCHLENK: Dr. Waters?

10 DR. KATRINA WATERS: I have no additional

11 comments.

12 DR. DANIEL SCHLENK: Dr. Schultz?

13 DR. TERRY SCHULTZ: No additional comments. 

14 DR. DANIEL SCHLENK: Dr. Keiser?

15 DR. MICHAEL KEISER: I'm the first to say I

16 agree with what's been stated. One clarification I would add is

17 that there has been mention of potentially replacing the system

18 entirely. I, however, would like to state that I think there is

19 value to working in parallel or putting the two systems

20 together, the Expert System with the high through put screening

21 data. I think the data can be combined, and there is some

22 discussion I think we might be about to have about that. 

23 But additionally, regardless of combining or

24 integrating the two approaches, the fact that each one has its

25 own source of error, and these errors are not correlated, speaks

26 to the value of including both because you decorrelate error and
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1 have a better chance of compensating for errors in each

2 different approach. 

3 Turning away from what has been strictly

4 defined in the question, and what we've just discussed, and to

5 some of the comments from earlier, I first want to preface it by

6 saying this is responding to things brought up in Question 6

7 that pertain to this question and is not meant to supplant or

8 change any of our answers to Question 7 as defined and

9 responded.

10 So with respect to the additional idea of

11 integrating some of the high through put data as input into the

12 decision rule set for the Expert System, the idea behind this,

13 first off, as I think has been brought up, if resources do not

14 allow, then it would probably be within the Agency's remit to

15 decide that it is not an appropriate path, given lack of

16 resources to do the high through put screen at that point as an

17 input.

18 However, if such resources are available, if

19 such screens may be run, I'd like to clarify a few points. One

20 is none of the discussion or points, as I understand it, pertain

21 to the ES TrSet assays. So this was simply with respect to the

22 high through put assays. No changes are proposed in that

23 discussion to the concentration levels, in the RtER assay or to

24 the Vitellogenin assay. Rather, the discussion is simply about

25 the high through put ones.

26 Under that focus with the high through put
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1 assays being run in parallel or even ahead of, depending on

2 timing, changes in the Expert System from version 2 to version

3 3, and additional suggestion that the Agency might want to take

4 under consideration is the ability to incorporate preliminary

5 results from first screen with the high through put data on

6 compounds that are easily available to purchase, as described as

7 almost properties of the molecule. 

8 Could we pull up that diagram really quick

9 that we had up earlier in Question 6?

10 I just want to illustrate it visually. Again,

11 this is my attempt just to clarify the question as much as

12 possible. So we have this up here. The idea behind this as an

13 additional suggestion to be taken under consideration is that

14 where you see diamonds in this diagram, where there are specific

15 rules to finding the progression from one decision point along

16 the tree to the next, that an augmentation of this system might

17 incorporate specific diamonds where instead of based on a

18 physical characteristic of the molecule, such as its typology,

19 its chemical structure, the presence of a particular ring

20 system, any of the above, that there may additionally diamonds

21 that could be added to this decision schematic where the diamond

22 would be defined as a fingerprint or other combination of a

23 small number of HTP assay results. 

24 So an example of a diamond that I'm just going

25 to make up to be illustrative is a compound, having been through

26 the high through put screen, where the high through put
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1 screening question might be the BG1 assay and the screen

2 might've been done at three concentration points across a range

3 to be written down or defined by the Agency; that a compound

4 that is positive in that screen above a preset threshold that

5 two out of the three points then progresses along a certain

6 branch compared to the other one. 

7 So you see in this case, it is not a question

8 of redefining the effects categories, but rather, using the HTP

9 data within the decision-making steps in the diamonds,

10 specifically, to move to categories as a result that downstream

11 of it are not based on chemical structure in a way that a

12 medicinal chemist might interpret, but rather based on the

13 direct groupings coming from the assays, where the end result

14 group -- I'll stop there and open up further comments. Thank

15 you.

16 DR. DANIEL SCHLENK: Okay. Dr. Perkins?

17 DR. EDWARD PERKINS: No further comments.

18 DR. DANIEL SCHLENK: Okay. Any other comments

19 from the Panel on 7.2? Yes, Dr. Jacobs?

20 DR. MIRIAM JACOBS: I would just like to

21 specifically endorse what Dr. Keiser was saying, especially in

22 relation to thoughts on replacement of the ESv1. I would really

23 like to see that it's not replaced, but the work goes on in

24 tandem. So I was a bit concerned with the item that was

25 highlighted by the group from Dr. Meek's presentation. 

26 DR. DANIEL SCHLENK: Okay. Sounds like
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1 there're some suggestions there for some augmentation of the

2 system that's been presented. Any other comments?

3 Yes, Dr. Meek?

4 DR. BETTE MEEK: Maybe just to clarify that it

5 may have been the brevity of the presentation in terms of -- but

6 I think I captured the concept of the parallel approaches are

7 always informative.

8 DR. DANIEL SCHLENK: Okay. Any other input on

9 7.2?

10 (No response.)

11 Okay. Let me go to the Agency. Any questions

12 or clarification regarding the comments?

13 MS. MARY MANIBUSAN: I have just simple

14 question of clarification for Dr. Keiser. Currently, the ER

15 Expert System is built to predict for relative binding affinity

16 to the ER receptor. What you're suggesting, it sound like is to

17 add additional diamonds to capture, perhaps, the downstream key

18 events along the AOP. 

19 So the BG1-Luc is gene activation. So

20 exploring the expansion to cover those key events based on high

21 through put information. Is that accurate?

22 DR. MICHAEL KEISER: First, I will say, yes,

23 that was the intent that I had had, additionally responding to

24 an implication of that question is that were the desire to keep

25 the Expert System and to be completely focused on binding and

26 not to cover the realm of things such as the Vitellogenin --
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1 well, excuse me -- such as the BG1 HTP. I would then, under

2 that remit, modify my response to say that to focus completely

3 on the binding-specific assays from HTP, such as the Novascreen

4 ones. I want to make sure that this is a clear point. 

5 In that case, that if the Expert System only

6 is focused on binding, I still feel that it could be valuable to

7 bring in the HTP binding-focused assays as decision points in

8 the diamonds, as we've described because as I think Dr. Bradbury

9 pointed out, these are all models. Some are physical models,

10 some are computational models. And the advantage of this

11 computational approach is that even incorporating the direct

12 physical model of the HTP into the computational system should

13 still give us additional power compared to using that alone.

14 So I will ask, does that answer your question

15 for clarification?

16 DR. DANIEL SCHLENK: Dr. Schmieder.

17 DR. PATRICIA SCHMIEDER: Given that the Expert

18 System is based on binding verified by Vitellogenin activation

19 in each group currently, then I'm not sure I understand how you

20 would recommend combining -- would you say that you could

21 combine just BG1, which is just transactivation without knowing

22 the binding part? Or you could do one or the other, but you

23 don't need both, given that it's already built on both, the

24 binding verified by transactivation.

25 DR. MICHAEL KEISER: And again, this whole

26 realm of discussion, I do want to just clarify and keep on
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1 record is an additional suggestion as opposed to a

2 recommendation to change the underlying system entirely. 

3 So to respond to that, my recommendation on

4 this, if I understand your question correctly, is I am treating

5 the RtER, as confirmed by Vitellogenin, as a gold standard

6 against which the Expert System model is attempting to

7 recapitulate computationally. 

8 As we know, it's not perfect by any means, and

9 the full Tier 1 is indeed, but this all about prioritizing

10 forward; it's not meant, at this stage, to replace that. But in

11 so much as the Expert System is basically an attempt at a

12 computational first approximation of that TrSet or RtER in

13 Vitellogenin, then I believe its power could be improved -- and

14 this taking Mary Manibusan's point into consideration too --

15 that by specifically, where available, incorporating HTP binding

16 data. And we can put BG1 aside for this and not cloud matters,

17 and maybe others can weigh in on specific details of that, but

18 based on my understanding right now, I would say the binding HTP

19 data, where available, could increase the predictive power if

20 integrated as a property, as a diamond, as opposed to an

21 eventual category into the Expert System.

22 DR. DANIEL SCHLENK: Okay. Dr. Schultz.

23 DR. TERRY SCHULTZ: My interpretation on the

24 question that was asked can high through put assays be used in

25 place of the rainbow trout cytosolic binder and liver slice

26 assay. So the knowledge-based Expert System says I have to have
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1 binding and I have to verify binding with gene activation. 

2 Therefore, if you're going to use that analogy that you have to

3 have a high through put screen for binding, followed by a high

4 through put screen for gene activation. 

5 We're not asking the question: Are these

6 really key events? That's accepted. Those two key events are

7 relevant to the question that you're asking. What we're asking

8 is, we have different methods for assessing those key events. 

9 The methods you're accepting, relevant to the particular key

10 event, a particular high through put relevant, just like the

11 rainbow trout cytosolic is. 

12 And then the next question: What is the

13 reliability of the data measured by those events? We think that

14 they're high through put assays can meet those criteria to

15 expand your knowledge-based Expert System, which is basically

16 chemical category formation. But the reliability of the data,

17 because of the incompleteness of the dose response analysis and

18 the incompletion of addressing the questions of the false

19 positives, flash, et cetera, et cetera, means that that's not

20 there yet. Thank you.

21 DR. DANIEL SCHLENK: Dr. Perkins?

22 DR. EDWARD PERKINS: Yes. I'd like to support

23 what Dr. Schultz just said. The main problem with adequacy of

24 the HTP data now, especially in the case of the receptor

25 binding, which is a critical factor, you're using there as this

26 initial prescreen which kicks out a number of low dose effects,
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1 which we also know is stated by Dr. Bradbury. That's a

2 principal area of major concern is low-affinity binders.

3 So immediately there, it's going to kick out a

4 lot of potential assays that would throw out a lot of chemicals

5 that could potentially have useful information and it's probably

6 a source of a lot of the false negatives in the HTP screening

7 assays versus the liver slice assays.

8 As we've talked about in several of the other

9 questions, that really has to be addressed before this is going

10 to be useable and to also reiterate in the previous discussion

11 when we were talking about increasing dose range, we were only

12 referring to the HTP assays, where full dose range was not done.

13

14 We realize you did a great job with the fish

15 liver assays and the full range was done and that's why all the

16 low-affinity binders were found. We would've like to have seen

17 that replicated in the high through put assays, but I think I

18 might get some agreement that unless you can do the full dose

19 testing, the receptor binding assays may not be useful. 

20 However, given the caveat of limited funding, the expense of

21 receptor binding assays, you may become much more reliant on

22 transactivation assays and things like that.

23 So again, you're going to have to either add a

24 weighted scheme for using these types of things, given some of

25 either the practical constraints for using them. That might

26 dictate how you're treating some of the assays. You have to be
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1 careful about exactly what the model will predict. So I think

2 that's going to have to factor in to what you're doing. 

3 DR. DANIEL SCHLENK: Thanks. Dr. Keiser.

4 DR. MICHAEL KEISER: I just wanted to thank

5 both the Panel members and I endorse both of those points. 

6 Thank you.

7 DR. DANIEL SCHLENK: Okay. Anyone else on

8 7.2?

9 Any further questions from you guys, from the

10 Agency?

11 MS. MARY MANIBUSAN: No additional comments. 

12 Thank you. 

13 DR. DANIEL SCHLENK: Okay. Let's go for

14 7.3, please.

15 MS. MARY MANIBUSAN: 7.3: "To the extent there

16 are differences between the Expert system TrSet and high through

17 put data in detecting the ability of chemicals to initiate the

18 ER AOP, especially in the context of minimizing "false

19 negatives" for low potency compounds, please comment on the

20 strengths and limitations of combining data from the different

21 assays to generate training set data for building the structure

22 based rules within the ESv3. 

23 What are the strengths and limitations of

24 using training set data from assays that have the same or

25 similar degree of sensitivity?"

26 DR. DANIEL SCHLENK: Dr. Meek.
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1 DR. BETTE MEEK: Thanks. Again, the

2 discussion has evolved a bit, additionally, I'm going to address

3 what we had previously. So when using alternative methods to

4 assess chemicals for toxicity, the issue of false negatives is

5 always of concern because of the consequences of being wrong. 

6 An examination of the data in Table 6.21

7 reveals false negatives are more likely to be an issue for

8 chemicals that are weak or extremely weak binders. Using the

9 relative binding affinity of 0.000001 percent is the cutoff

10 value, appears to minimize the false negatives to an acceptable

11 level. 

12 It is appropriate to combine data from

13 different assays with the same or similar degree of sensitivity

14 to generate the training data set for Expert System version 3. 

15 One approach is to prioritize a compound for testing via the

16 full ES Training Set, the rainbow trout ER and the Vitellogenin

17 gene expressed liver slice assay as a gold standard if a

18 compound was positive in any two of the HTP assays to which it

19 was subjected.

20 A weakness is that this may increase the

21 false positive rate for the initial prioritization step, but

22 given the key programmatic goal of minimizing false negatives,

23 it seems acceptable. Again, depending on testing resources and

24 encountered false positive rates, the weakness might also be

25 mitigated by adopting, instead, a consensus-based method -- and

26 this has come up previously -- wherein a weighted average across
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1 assay positives contributes to a single aggregate score by which

2 further testing is prioritized. 

3 Considering the careful work that has been

4 done to build Expert System versions 1 and 2, it would seem

5 sensible that Expert System version 3 compounds be subject to

6 confirmatory RtER and JA liver slice assay for all positives or

7 borderline cases.

8 The HTP data appeared to have a high false

9 positive rate, but also a very low false negative rate when

10 interference thresholds are ignored. In order to capture the

11 chemicals with low potency, the filtering thresholds should be

12 reevaluated and possibly eliminated if minimizing false

13 negatives is the goal. A combination of assays should be

14 explored, as opposed to individual assay performance. 

15 In addition, strategies to augment the HTP ER

16 binding data with at least a single higher concentration for all

17 negative chemicals should be explored in order to synchronize

18 with the higher concentrations used for the ES Training Set. 

19 More assays covering other key events in the ER AOP would likely

20 increase confidence that low affinity binders were not excluded.

21 The inclusion of other assays are increased dose range of

22 existing assays would help address the low-affinity binding

23 chemicals. 

24 Additionally, assay specific weighting of data

25 could also be used to inform prioritization and address potency

26 problems. That's it.
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1 DR. DANIEL SCHLENK: Dr. Cronin?

2 DR. MARK CRONIN: No further comment.

3 DR. DANIEL SCHLENK: Dr. Waters?

4 DR. KATRINA WATERS: I have no additional

5 comments.

6 DR. DANIEL SCHLENK: Dr. Schultz?

7 DR. TERRY SCHULTZ: One issue that comes up

8 with this is if you look at the Expert System as a series of

9 (Q)SARs and then you start mixing your assays and therefore

10 mixing your endpoints, you end up violating one of the

11 principles of (Q)SAR, of having a well-defined endpoint. 

12 If you look at your Expert System as a

13 knowledge-based system and you say I'm gathering knowledge on ER

14 binding and ERG in expression, you can then probably do that

15 under the caveat that I'm building a category that has these

16 types of components. So that becomes a very fine line that

17 you're walking on and I only mention that because during the

18 Agency presentations, a number of times you have said that

19 you've been following the OECD (Q)SAR principles. Then in this

20 question you open up the issue that you're violating one of

21 those. Thank you.

22 DR. DANIEL SCHLENK: Dr. Keiser.

23 DR. MICHAEL KEISER: So responding to the

24 interesting point just brought up about OECD principles and not

25 violated the well-defined endpoint, one point that might

26 partially mitigate that is the endpoint are the RtER and the
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1 Vitellogenin liver slice assay. And hence, in the

2 recommendation from Dr. Meek, the suggestion that HTP data, when

3 incorporated into the structure-based rules training set be

4 confirmed in the positive case by the TrSet assays;

5 specifically, RtER and Vitellogenin, would, at least in the

6 positive case, if not the negative case, keep us focused on the

7 single well-defined endpoint. 

8 I'd leave it to the Agency or the rest of the

9 Panel to decide whether the fact that negatives in that example

10 are not progressed to the TrSet assays, does or does not

11 potentially violate the well-defined endpoint. But for

12 positives, if that recommendation is followed, you will remain

13 focused on that endpoint. 

14 DR. DANIEL SCHLENK: Dr. Perkins?

15 DR. EDWARD PERKINS: No further comments.

16 DR. DANIEL SCHLENK: Other Panel members?

17 (No response.)

18 Okay. Go to the Agency. Any questions or

19 clarification for 7.3? 

20 MS. MARY MANIBUSAN: No additional comments. 

21 Thank you.

22 DR. DANIEL SCHLENK: Great. Let's go

23 ahead and move onto Question 8.

24 MS. MARY MANIBUSAN: Question 8.1: "Please

25 provide any initial thoughts on how varying empirical and/or

26 computational techniques could be employed to account for
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1 metabolites that may trigger the ER molecular initiating event."

2 DR. DANIEL SCHLENK: Our lead discussant

3 is Dr. Schultz.

4 DR. TERRY SCHULTZ: Thank you. This was

5 basically a question for the future. It wasn't in response to

6 anything that had been proposed by the Agency and that really

7 opened it up for a lot of diverse thoughts by the Panel.

8 I believe in my written comments I have

9 captured those. My presentation will not be in any order of

10 importance of relevance; it's more of a first come, first serve

11 type of a situation.

12 The Panel acknowledges that the toxicity of

13 chemicals which directly elicit a mode of action such as our ER

14 binders, are more easily explained than those that are mediated

15 by metabolism. So we understand that there's a challenge here

16 that has a higher degree of uncertainty that you have to deal

17 with.

18 If the Agency was to increase the confidence

19 that has identified the complete universe of chemicals,

20 including the daughter products, will require prioritization for

21 further testing and valuation. It will need to improve its

22 capability to do encyclical metabolic simulations in addition to

23 using high through put assays which incorporate metabolism. 

24 The Panel noted that new capabilities in the

25 next generation of metabolic simulators that would assist the

26 Agency in its endocrine description screening program,
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1 especially in regard to ER binding Expert System may include the

2 ability to identify the most likely or probable metabolites

3 based on the structure of the parent compound and not just a

4 list of possible metabolites that are generated from the current

5 simulator, such as the OECD toolbox. 

6 As a sidebar on that, dealing with the OECD

7 toolbox, because you're doing metabolites of metabolites of

8 metabolites, it is likely that you're finally going to find

9 something that has a ring and an OH group to it, which isn't

10 going to help you very much on that sort of situation. We

11 realize that's the limitation of the capabilities that are

12 currently available in the public domain.

13 In regards to computational techniques for

14 predicting metabolites, the Panel notes that further

15 improvements in metabolic simulation that may assist the Agency

16 within its program would include designing simulators that would

17 predict the percent, such as 90, 75, 25, 10 of the parent

18 compound that has transformed into the most likely metabolites. 

19 Whether that's the top three or the top five, we really don't

20 care. The idea here is to get away from a shopping list and

21 focus on the things that are the most likely ones that are going

22 to be formed. 

23 The Panel also notes that improving the

24 metabolites may assist the Agency, especially in relation to the

25 exposure filter. If you look at your Figure 2.21, where you

26 have your scenario, it says one of your filters is exposure. 
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1 So you can design simulators that might

2 predict the rate of metabolism that would assist you in that

3 sort of a situation, so things that would be rapidly converted,

4 seconds to minutes versus hours versus days, may assist you in

5 refining that exposure filter.

6 I'm looking at metabolism here and its

7 broadest context so that could be metabolism in relation to

8 microbial degradation as opposed to liver or skin or whatever

9 other simulator that you wanted to look at. 

10 Additionally, the Panel noted that

11 predications of reactivities such as half-life in water and the

12 exposure filter may also serve as an early indication that

13 metabolism is likely to be of concern. In other words, use all

14 the information that you have to suggest where you might want to

15 put resources early into the issue of metabolism.

16 Predictions of metabolism had been

17 demonstrated to be better if several methods are combined. For

18 instance, there are several studies that demonstrate when you

19 combine predictions that are for rule-based system with those

20 that predict the coverage based on known metabolites of combing

21 those things you tend to do a better job. So you may want to

22 look at that.

23 The Panel suggests the strategies for using

24 predicted stable metabolites may also be investigated. There

25 are at least two possible strategies for this. The first one

26 includes the prediction of metabolites within the categories. 
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1 The second is that cyclic procedures where metabolites would be

2 predicted and then run through the Expert System. Each

3 strategy, of course, has its advantages and its disadvantages,

4 but it seems more than likely that the second strategy would be

5 more practical.

6 Again, balancing the fact that you don't want

7 to have a shopping list of metabolites because it's likely

8 you're going to come up with something in there that is going to

9 be important, but the fact that you need to look at more than

10 just the initial metabolite, but you don't have to look at every

11 single possible metabolite.

12 Changing the view a little bit, one Panel

13 member noted that when ER binding takes into consideration the

14 dynamic and plastic characteristic of the ER receptor and the

15 subpocket theory and you can define those by the inergetics and

16 cysteric constraints on that sort of a situation. 

17 If you look at the empirical data for that,

18 you find that the vast majority of ER binders, maybe with the

19 exception of the perfluorinated compounds are aliphatic

20 chemicals. 

21 Looking again at this A site and the B site,

22 you know that these are basically polar in character, so there

23 is a limited number of hydrogen binding groups, for example and

24 OH groups and amino groups that seem to interact with these

25 subpockets.

26 So it's possible that you could use this
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1 information to design a particular metabolic simulation where

2 you were looking at, for example, oxidation of cyclic compounds

3 as a means of identifying your pro ER binders for the A site and

4 B site. That doesn't assist you with your special cases, but we

5 have difficulty understanding the mechanistic understanding of

6 the special cases. So you really don't have a solid ground of

7 which to anchor that in.

8 For example, you could restrict simulations,

9 cyclic compounds to only those that have uncovered hydrogen

10 substitution carbon atoms in a five or six membered ring system.

11 And then restrict the reactions to only those that lead to

12 oxidation, et cetera, et cetera. 

13 So this may provide you a simplified means of

14 getting at your primary metabolism derivatives -- metabolites in

15 relation to your Expert System. The Panel further notes that

16 bench level in vitro assays such as the rainbow trout liver

17 slice are employed in the AOP. That confirms that you have ER

18 binders. The importance of that, of course, is the fact that

19 the trout liver slice is considered to be metabolically

20 competent system. 

21 So that you could use chemicals that are

22 negative for ER binding, but positive in the liver slice assay. 

23 So a negative, positive response might provide you a means of

24 looking at when metabolism would be important and capturing that

25 type of information that provides you with a second generation

26 of simulators that you could use.
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1 Obviously, taking that logic one step further,

2 the same sort of scenario could be said for the high through put

3 cell-based systems. Some of them incorporate metabolisms so

4 they could be paired with similar endpoints to look for the

5 potential of metabolic activation. Obviously other points in

6 that regard as noted by the Panel members was the potential of

7 any co-culturing factors that may assist in metabolism

8 peroxidases has been used in some of the approaches for protein

9 binding through activation.

10 It must be noted, however, that we're

11 concerned about that the rainbow trout AOP-based prediction of

12 metabolic similarities may not be amenable to mammals, where

13 metabolite stability and viability may be much more important. 

14 It was noted that the cytochrome P450 activity varies between

15 species. 

16 So the rainbow trout may not be applicable to

17 other species, and the difference, of course, different species

18 have different capacities to inactivate or activate metabolites

19 through Phase II metabolism, so the question of stability and

20 viability becomes important especially when the site of

21 activation would be far away from the site of action.

22 In the case of the rainbow trout assay, you

23 basically were thinking of the site of metabolism was in the

24 hepatocytes and the gene expression of Vitellogenin was also in

25 the hepatocytes. You don't have that comparable system in

26 mammals and that may influence how metabolism can actually be
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1 used in your expert system.

2 The Panel did want to alert the Agency that

3 several reviews on metabolic simulators have recently been done

4 and those specific citations will be added to the document. And

5 that basically captures the main points that were presented to

6 me by the other Panel members. Thank you.

7 DR. DANIEL SCHLENK: Dr. Perkins?

8 DR. EDWARD PERKINS: I second what Terry said

9 and I wanted to add a few more things and a couple of

10 clarifications. 

11 I think there's some potential for new work in

12 genome constrain metabolic modeling, tissue-specific modeling

13 that allows you to combine tissue-specific expression of P450s

14 between different species that might help resolve some problem. 

15 It can also help understand the type of metabolic enzymes Phase

16 II, I guess, metabolic enzymes that might be in Phase I that

17 might be present and available to act that might help direct

18 some of the other metabolic simulators based on a species

19 context that could address some of the issue of aquatic versus

20 mammalian questions.

21 Also, on point of positive and negative

22 controls, it seems like for ones that you might have a priority,

23 you think there might be a metabolism or potential for

24 metabolism based on a computational approach. If you could

25 develop cell lines that were enriched for metabolism, such as

26 the ATG cell lines, have that cell line, but also try the same
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1 assay in the background of a cell line that does not have

2 metabolism. So that would be a good plus/minus characterization

3 type issue there.

4 If there could be further enrichment of cell

5 cultures that can either have a specified profile of metabolic

6 enzymes or enriched such that you can characterize it and you

7 have kind of a full range of different metabolic capabilities,

8 that would be great. I think there's been a lot of evolution of

9 coculture of different cell types and tissue types that may be

10 amenable to these new developments in microfluidics and I guess

11 three-dimensional matrices for cells that might let you look at

12 effects in the context of metabolism and then look it at without

13 coculture so you can look out without metabolism in certain

14 cases.

15 Another potential area is high through put

16 PBPK modeling, such that aquatic organisms are often goes in

17 through the gills and it's getting into the blood directly. So

18 it goes straight to target points without passing through the

19 liver. So you bypass metabolism. 

20 In certain cases metabolism is an issue. In

21 certain cases it may not be an issue, but having that to augment

22 exposure and target sites of concern may help direct when you

23 expand out to new MIEs and potential metabolic areas. 

24 Let's see what else was there. I think that

25 was about it for me. Thank you.

26 DR. DANIEL SCHLENK: Dr. Chambers.
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1 DR. JANICE CHAMBERS: Terry pretty much

2 captured my points, but I really wanted to make that you kind of

3 keep in mind is kinetics, and it kind of touches on what Ed just

4 brought up too, is that many of these metabolic enzymes are not

5 particularly efficient and if you have a system where you're

6 putting in an extraordinarily high dose of the parent chemical,

7 that may not reflect what an intact organism would experience

8 and how much of that active metabolite gets formed. Is it ever

9 a fast enough and high enough level to be effective in the

10 target system? 

11 Also again, how quickly does it detoxify and

12 then picking up the PBPK approach of the intact animal in a

13 mammalian system anyway, if it's bioactivated the liver, it's

14 got to get into the blood stream. It could bind into the

15 albumin or it could be detoxified other places and so forth. 

16 It's got to go a fair distance to get to a target organ.

17 So if this is all an exercise to prioritize

18 the chemicals to put into the screening, do you really need to

19 have a lot of detailed information about metabolites and all or

20 do you really need to think in terms of whether or not a

21 metabolite would ever reach a critical concentration to trigger

22 a pathway?

23 If that's not the case, then you should

24 prioritize the chemicals which are more active in the parent

25 form or do produce a lot of metabolite in a hurry.

26 DR. DANIEL SCHLENK: Dr. Jacobs.
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1 DR. MIRIAM JACOBS: I would like to follow up,

2 especially with Dr. Perkins' comments and to reiterate with

3 respect to the relatively recent publications on incorporating

4 metabolizing systems in vitro tests for endocrine activity. So

5 there was a detailed review paper, Number 97, that was published

6 from the OECD in 2008 in the general publication, which pretty

7 exhaustively characterizes the available cell lines, including

8 many of those that were included in the HTP assays, where the

9 data is available on the characterization, and give suggestions

10 on methods for incorporating metabolism.

11 In addition, that paper has been updated

12 recently. It's currently in review and hopefully it will be

13 published during the time of this report. So that could be an

14 additional citation that will be able to be used. But some of

15 the information that comes from that paper is also in relation

16 to discussion we again had yesterday on looking at how to

17 interpret combined computation and empirical testing that needs

18 to be developed, so using principal component multivariates and

19 class analyses, for example -- they gave some examples yesterday

20 -- but also one of the groups at Mount Sinai New York have been

21 doing metro analyses of gene expression of microarray data, such

22 as that used for PBDE 17-estradiol on MCF-7 cells, and this all

23 published, combined with the characterization of the metabolic

24 capacity of MCF-7 cells. And there is freely available

25 reference collection of gene expression profiles from cultured

26 human MCF-7 cells, for example, in the Connectivity Map, which
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1 has data for over 15,000 substances, some of which may be

2 metabolites. It would be a useful resource to explore and it

3 included pattern-matching software for data mining.

4 There are a number of other databases that

5 would be useful to search. There is one under construction at

6 the moment at the JRC on endocrine active substances. Not

7 specifically in relation to metabolism, but there are

8 metabolites included. Thank you. 

9 DR. DANIEL SCHLENK: Thanks. Any other

10 Panel members? Any questions or clarification from the Agency?

11 MS. MARY MANIBUSAN: No additional comments. 

12 Thank you. 

13 DR. DANIEL SCHLENK: Okay. We are at a

14 quarter to 12 right now. Let's go ahead and break for lunch and

15 we're come back and do Question 9. You should beat the crowds

16 in terms of getting out to lunch lines right now. Let's be back

17 at 12:50. Let's do that. 

18 (Whereupon, at 11:45 a.m., a 

19 luncheon recess was taken.) 

20 * * * * *

21 DR. DANIEL SCHLENK: Let's go ahead and get

22 started. Okay. Let's read 9.1 into the record. 

23 MS. MARY MANIBUSAN: Question 9.1: "Please

24 comment on whether the principles and concepts used to develop

25 the ER Expert System are generally applicable for any

26 category-based prioritization system for other molecular
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1 initiating events, e.g., other AOPs for perturbing estrogen,

2 androgen receptor, and thyroid hormone systems."

3 DR. DANIEL SCHLENK: Lead discussant on

4 that is Dr. Schultz.

5 DR. TERRY SCHULTZ: Thank you. The comments

6 from the Panel are prefaced by a boiler plate that you have

7 heard a number of times, basically demonstrating that the Panel

8 understands the context that you have presented this in the ER

9 Expert System based on rainbow trout cytosolic binder and then

10 gene activation in the liver slice on that.

11 Basically, we feel that this is a scientific

12 and sound and transparent means of doing targeted testing, which

13 subsequently will provide data that increases the weight of

14 evidence that, in turn, increases confidence in the chemical

15 categories and predications to which they're based upon. So we

16 do think that this is a way of identifying your more likely

17 candidates for further evaluation. 

18 This work, if taken in total, as clearly moved

19 from the principle's concept discussed in 2009 into a proof of

20 concept stage. However, with that said, there is clearly some

21 things that have been introduced during this SAP that will need

22 to be reexamined, and I don't think we need to discuss that

23 anymore since we spent an amount of time on Questions 6 and 7 to

24 kind of address that. 

25 However, since the Expert System is only used

26 in prioritization, we don't feel it really needs to predict
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1 higher-level effects. That we're happy with the fact you

2 developed an Expert System on lower-level, mechanistically

3 relevant endpoints because its prioritization and other things

4 can be captured later.

5 So in that regard, the ER-mediated fish

6 reproduction of permanent AOP, as described in the SAP can be

7 used as a road map for other molecular initiating events related

8 to other scenarios involved in endocrine disruption; however,

9 the Agency needs to be more explicit and include more details on

10 key events that can be used as assay points, especially in

11 providing more detail in an AOP on the species of concern and

12 the model assays where those assays on based on human, rat,

13 mouse, frog, fish, whatever the scenario is, so that it is

14 transparent and the relationships between each one of the assays

15 -- the endpoints of those assays within the context of the AOP

16 is well-documented, scientifically feasible and defensible. 

17 In other words, you're basically applying the

18 principles of the Adverse Outcome Pathway and the principles of

19 (Q)SAR within OECD context to make those. The better those

20 cases made, the less likely that we're going to have issues in

21 accepting the initial things that are put forward.

22 With that said, there are several issues,

23 especially weighting to expanding the test chemical space to

24 cover more structurally diverse chemical inventories that was

25 presented in the context of the ER binding AOP pathway that has

26 been used during this SAP that will need to be resolved, and the
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1 Panel strongly encourages the Agency to move forward quickly to

2 resolve these issues and ER pathways so that protocols and

3 schemes that you devise can be readily transferred to other MIE,

4 AOP-based Expert Systems. 

5 The solution proposed by the Agency to expand

6 test chemical spaces and using high through put new screening

7 test data ToxCast and Tox21 to inform our chemical spaces,

8 followed by a strategic experimental testing protocol, is still

9 preliminary. 

10 However, this has significant promise and if

11 placed before or in combination with the Expert System analysis,

12 can also be used as part of development of new MIE AOPs

13 associated with endocrine disruption.

14 Again, emphasize that issues identified by the

15 Panel relating to chemical selection strategies needs to be

16 developed. So a protocol for selecting chemicals with that

17 inventory testing is very transparent and has a rationale that

18 can be justified so we don't end up again saying, well, we need

19 to test one more chemical or we need one more assay.

20 The Panel noted that one challenge for moving

21 forward to other molecular initiating event AOP-based Expert

22 System is the choice of the molecular initiating event. This

23 appears to be a very critical issue that the Agency should

24 emphasize why the ones that they're selecting as their first

25 cuts are the most likely to get the biggest bang for the buck

26 and where the limitations are so we don't end having to go over
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1 this territory again, but there many ways of interfering with AR

2 endocrine receptor or thyroid. In the case of thyroid, it's

3 going to be much more important because they seem to be a lot

4 less consistence on what's more important. There seems to be a

5 lot more on that. 

6 As further noted by some of the Panel members,

7 a second challenge is identifying the truly positive chemicals

8 that you could use for the development of the AOP initial

9 development of the Expert System and to validate the assays and

10 components associated with that. 

11 In that context, what will be in the Paper was

12 specific examples of talking about in the chem, EMBL-14

13 database, which demonstrates that there's a lot more information

14 related to ER-related chemicals and pathways than there is for

15 AR. And in the case of thyroids disruption, it's smaller and

16 it's subdivided into a number of different pathways or

17 mechanisms. So that's going to be a real challenge to the

18 Agency.

19 The Panel agrees that the principles and

20 concept used in developing the ER binding Expert System are

21 generally applicable to performing category-based prioritization

22 for other MIEs leading to endocrine disruption-related final

23 endpoints. That's basically the summary of that first part.

24 The Panel also believes that there are growing

25 agreement on what issues can reduce the uncertainty that will be

26 presented as these new concepts are brought forward, and these
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1 include, obviously, a transparent mechanistic plausible or

2 probable scenario which is best met by a high-quality Adverse

3 Outcome Pathway that is used to define the system, the

4 weight-of-evidence that could be applied to that and the ability

5 to do hypothesis-based testing, especially with rapid,

6 inexpensive assays. 

7 Note: We said rapid, inexpensive assays. It

8 doesn't mean they have to be high through put, but we certainly

9 would encourage you to start making the effort into that

10 paradigm shift.

11 As far as the AOP, remember, it's a framework

12 that comprises events at different levels of biological

13 organization and other key dimensions such as gender, life

14 stage, which may be much more critical in the way that you deal

15 with thyroid disruptions because of the different endpoints that

16 thyroid disruption effects. And we need to identify the causal

17 relationships to the final adverse effect that's under

18 consideration. So we're back to following the best principles

19 for developing to the Adverse Outcome Pathway.

20 Issues we see that will be critical to the

21 Agency as they put forth new Adverse Outcome Pathways with which

22 to base their prioritization schemes on is the ability to

23 separate events that control toxic potency from events that

24 report symptoms. I think that you've done an excellent job in

25 your ER example because you identified binding and then gene

26 activation, and those things happen early on and they can be
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1 identified. 

2 While the Adverse Outcome Pathway will need to

3 be linked to events at high levels of biological organization,

4 they will not necessarily have to be measured or evaluated

5 because you're using it in a prioritization scheme.

6 You will need to attain acceptance on the key

7 events included in the Adverse Outcome Pathways, especially

8 those at lower levels of biological organization because those

9 are the ones that you're actually going to be generating data

10 for. You're going to have to attain agreement on the methods

11 used for investing those key events and you're going to have to

12 get agreement on the in vitro data that's used a standard for

13 verifying the method/data or the key events, or in that case,

14 the whole Adverse Outcome Pathway. And I think that one of the

15 successes that you've had in your ER binding example is you were

16 able to demonstrate that very, very clearly and concisely in the

17 2009 SAP. And that is basically the answers from the Panel for

18 9.1. 

19 DR. DANIEL SCHLENK: Thank you. Dr. Meek,

20 anything to add?

21 DR. BETTE MEEK: I have nothing to add. 

22 Thanks.

23 DR. DANIEL SCHLENK: Dr. Burris?

24 DR. THOMAS BURRIS: Nothing to add.

25 DR. DANIEL SCHLENK: And Dr. Perkins?

26 DR. EDWARD PERKINS: I agree with everything
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1 that was said and I just had one comment to add and it may apply

2 to 9.2 or 9.3 is that in discussion with people from the Panel

3 it seems clear that it's going to be hard to avoid -- get a

4 perfect system to include all potential chemicals that may cause

5 an effect on any one pathway. So I think it's very necessary

6 that EPA clarify what rate of false negatives they'll be able to

7 tolerate in reducing the space of chemicals that they're looking

8 at. Associated with each filter, there's a certain uncertainty

9 that you may be filtering out, something may cause effects down

10 the line. 

11 As we come to learn more about mechanisms such

12 as methylation status and epigenetics causing subsequent

13 generations to have effects, these types of information may need

14 to be built in on how you may capture false negatives in other

15 AOP strategies or do you need a garbage bin that just searches

16 for general toxicity. So that was one comment I had. 

17 DR. DANIEL SCHLENK: Thanks. Dr. Jacobs.

18 DR. MIRIAM JACOBS: In addition to the

19 comments that have been presented, I would just like to

20 reiterate again how important it is to go back to the literature

21 and see what's there as well. And with respect to the androgen

22 receptor, there are a couple of useful models and there is one

23 particularly that has been developed by a Swiss group, based on

24 three-dimensional automated docking techniques that includes the

25 androgen receptor and some thyroid receptor endpoints, and many

26 other endocrine-related receptor endpoints as well. But
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1 particularly, I think the androgen receptor one will be worth

2 exploring as you start developing your pathways. And then the

3 Danish group and Danish EPA have also published large,

4 reasonably large keys over the androgen receptor.

5 DR. DANIEL SCHLENK: Dr. Keiser.

6 DR. MICHAEL KEISER: General comment on the

7 question itself. First, I'll say I agree with everything that

8 has been stated. Picking up on Dr. Jacobs point, there will be,

9 especially as you move forward to additional systems, situations

10 where a category-based approach, as we've discussed today and

11 yesterday, is going to run into limitations. 

12 So one example of a way to address that would

13 be some of these other models or systems that you could apply to

14 molecules that by the category-based approach still remain in an

15 unknown binding potential regime, in addition to the chemical

16 informatics techniques talked about yesterday and some of the

17 ideas that came up around the time of Questions 6 and 7, all of

18 those would be appropriate approaches that you might evaluate

19 for cases where the category-based approach does not cover the

20 chemical domains that you're working in.

21 DR. DANIEL SCHLENK: Thanks. Dr. Safe.

22 DR. STEPHEN SAFE: I've got just a few

23 additional comments. It always sort of worries me when you have

24 definitions that you seem to be married to. MIE; I would ask

25 Nancy, "What's MIE?" And AOP and all these things. Really, all

26 you're doing is looking at binding and nothing to do with an
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1 adverse anything and rather than continue on that for the

2 androgen receptor, you know, why don't you just call it an

3 androgen ligand assay system, which would be ALAS, or something

4 that like that, that really reflects what you're doing. 

5 Because, you know, there's no Adverse Outcome Pathway by --

6 well, Vitellogenin, maybe, but you're not really doing that. 

7 That's just a minor little thing. 

8 The other thing, of course, with the androgen

9 receptor is that from an environmental and an exposure point of

10 view, you're going to be looking for anti-androgens. So the

11 assay systems you're going to be looking for, I mean, you can

12 think of a number of them, but remember, it's going to be an

13 anti-androgen assay and it would be a nice way to look at it or

14 have a system to look at it where you don't have to add

15 something where it's already activated and you can look for

16 deactivation in one assay system, rather than inducing with

17 androgen and then inhibiting with an anti-androgen because

18 you're going to be looking, primarily for anti-androgens.

19 DR. DANIEL SCHLENK: Thanks. Any other

20 Panel members? Dr. McManaman. 

21 DR. JAMES MCMANAMAN: I agree with everything

22 that has been said. I want to make a last pitch, in terms of

23 reading it into the minutes, is that in some of these other

24 systems you may not be dealing with receptor binding. You may

25 be dealing with activation of systems that involve the receptors

26 that are activated by other kinds of hormones, meaning, peptide
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1 hormones, in which case the structure function activity that you

2 would use to define the ER or the AR or the thyroid may not be

3 applicable and you might have to go try additional approaches to

4 define their activities.

5 DR. DANIEL SCHLENK: Okay. Dr. Keiser.

6 DR. MICHAEL KEISER: I just wanted to add one

7 thing, following up on that comment. Going forward especially,

8 as you move into systems like thyroid and beyond and as the

9 field advances, you will begin to encounter cases where a single

10 binding event is not, in a sense, the atomic initiating event --

11 and I think we're touching on that today -- it is not

12 necessarily something that needs to be immediately addressed and

13 changed, but it should be in the minutes and in the mind for the

14 future to think about how this approach can be extended to

15 multi-target or multi-system initiation events where you have to

16 consider that to be the event that might be comprised of

17 individual units that are needed in concert to follow the

18 pathways in question.

19 DR. DANIEL SCHLENK: Okay. Any other

20 comments on 9.1?

21 (No response.)

22 Okay. Let me go to the Agency. Any questions

23 or clarification for the responses?

24 MS. MARY MANIBUSAN: No additional comments. 

25 Thank you. 

26 DR. DANIEL SCHLENK: Okay. Let's go ahead
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1 read in 9.2 then.

2 MS. MARY MANIBUSAN: 9.2: "What lessons have

3 been learned from the development of the ER-focused

4 prioritization model that will facilitate more efficient and

5 effective development of an effect-based prioritization model

6 for the androgen pathway?"

7 DR. DANIEL SCHLENK: Dr. Schultz.

8 DR. TERRY SCHULTZ: As was stated during this

9 Panel meeting, it appears that the primary AR path we're leading

10 into conception will be similar to the ER pathway and that the

11 seminal event would be receptor binding, though it be agonist

12 versus antagonist. In the downstream events, especially at

13 higher levels of biological origin will largely be a cascade of

14 events that do little to mediate the final outcome.

15 If this assumption is true, the scheme

16 developed for the ER pathway is directly applicable to the AR

17 pathway, as long as we consider the fact that we're not looking

18 at antagonists, we're looking at agonists.

19 What we've learned from the work on the ER

20 pathway and the associated chemical groups defined by the

21 knowledge-based Expert System includes the importance of having

22 a well-developed AOP, and an example that has been presented by

23 the Panel is that AR-binding alluding to fish growth inhibition.

24 So you're having a different apical endpoint that you're

25 looking at in this case.

26 It's also demonstrated the importance of being
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1 able to demonstrate the typical responses that observed at the

2 levels of biological organization. So in the context of the AR

3 scheme, that would be AR binding, altering the growth, the most

4 susceptible cell types, leading to malformation and dysfunction

5 in the most likely organs or organ systems in following the

6 Adverse Outcome Pathways. 

7 So feeling at least part of the Panel members

8 is the importance of having the Adverse Outcome Pathway provides

9 you with the opportunity of linking the high through put or in

10 vitro tests that you're going to use to the regulatory outcome

11 that you are actually being charged with protecting.

12 Thirdly, the importance of having developed

13 and standardized assays for in vitro and/or in vivo for which

14 data can be gathered in a systematic manner. Many of the

15 problems you have in using Adverse Outcome Pathways is you run

16 into identifying a key event and there's no assay that's

17 developed that actually addresses that event as needed within

18 the confines of the adverse outcome.

19 And fourthly, of course, the importance of

20 being able to apply the mechanistic information and data to

21 define chemical categories that can be developed into

22 structure-based Expert System, which in turn can be validated by

23 hypothesis-based testing. So again, back to the principles, you

24 bind the best principles for an AOP and the best principles for

25 (Q)SAR.

26 DR. DANIEL SCHLENK: Dr. Meek?
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1 DR. BETTE MEEK: I have nothing further. 

2 Thanks.

3 DR. DANIEL SCHLENK: Dr. Burris.

4 DR. THOMAS BURRIS: I just want to mention one

5 technical aspect of the androgen receptor that is important as

6 these assays are being developed, especially in comparison to

7 the ER assay Panel.

8 The GAL4 assay that's typically used in the

9 field as a GAL4 DNA binding domain fused to the ligand-binding

10 domain of the nuclear receptor, I just want to point out that

11 the androgen receptor does not work in that assay. I assume

12 that through ToxCast there may already be an AR assay that's

13 using that's a modified version of that. 

14 So one is restricted either using the intact

15 androgen receptor or you can completely replace the androgen

16 receptor DNA binding domain with the Gal-4 DNA binding domain so

17 that the chimeric receptor has the amino terminus followed by

18 the yeast transcription factor DNA binding domain, then the

19 androgen LBD. That will work. This is just from a significant

20 amount of experience working on this receptor. You wouldn't be

21 able to predict that a priori, but it's been seen by a number of

22 groups that this is needed. But a GAL assay can be used with

23 some modifications.

24 DR. DANIEL SCHLENK: Okay. Dr. Jacobs. 

25 Sorry, I skipped Dr. Perkins down there.

26 DR. EDWARD PERKINS: No further comments.
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1 DR. DANIEL SCHLENK: Dr. Cronin?

2 DR. MARK CRONIN: No further comment.

3 DR. DANIEL SCHLENK: Now Dr. Jacobs.

4 DR. MIRIAM JACOBS: There are a couple of

5 androgen receptor transactivation assays in validation; one is

6 the Japanese ones that they currently have to test a few more

7 substances, so they're reasonably close to being developed into

8 a test guideline, hopefully. And then there's another one being

9 developed for validation in the European Commission, the POLM

10 assay. So I think looking at these assays a bit further down

11 the line will be very helpful. 

12 DR. DANIEL SCHLENK: Dr. Keiser.

13 DR. MICHAEL KEISER: So speaking to what

14 lessons have been learned, a general comment, I think one thing

15 that has come up has been -- especially in the computational

16 side -- but additionally on the HTP side. 

17 As you move forward through this process over

18 the course of years, whatever the current techniques are will

19 remain a moving target. I recognize the need of the Agency to

20 have a core at the center of that to direct the efforts, such as

21 the Expert System, for instance, with the category-based

22 approach.

23 So speaking generally, going onto these other

24 systems, it will be in years afterwards, it should always be

25 important that in addition to the core, for whatever parts of

26 your inventory you are unable to cover, you have in place
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1 protocol system or approach to fill in whatever other

2 technologies might be still rather cutting edge, but will be a

3 way to get some initial grip on parts of your inventory that you

4 don't have coverage from you core. But of course, you should

5 keep the core as the basis and guiding effort and then when

6 things are not covered by it, turn to some of these other

7 current approaches, whatever they may be at that point in time.

8 DR. DANIEL SCHLENK: Dr. Safe.

9 DR. STEPHEN SAFE: Nothing further. 

10 DR. DANIEL SCHLENK: Any other comments

11 from the Panel. Yes, Dr. Denslow.

12 DR. NANCY DENSLOW: The in vitro assay for

13 androgen receptor that's based on the Blah System, works in my

14 hands. So I think it should work. 

15 DR. DANIEL SCHLENK: Okay. Thanks. One

16 comment I had, I hadn't heard anybody mention the AR Spigen

17 (phonetic) relationship, where that is. I know OECD has

18 actually been developing that and that would seem to be a

19 comparable AOP, if you will, in terms of if we use the

20 Vitellogenin for ER and then the Spigen model for the AR. I

21 don't know. Dr. Jacobs, you probably have some insight there.

22 DR. MIRIAM JACOBS: It was never developed

23 further as a test guideline, but there is a guidance document

24 available for it.

25 DR. DANIEL SCHLENK: So that may be

26 something, as a parallel system. Any other comment on 9.2?
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1 Okay. Let's go to the Agency. Any questions

2 or clarification for 9.2?

3 MS. MARY MANIBUSAN: No additional comments.

4 DR. DANIEL SCHLENK: Okay. Can we go ahead

5 and read 9.3 in?

6 MS. MARY MANIBUSAN: 9.3: "What will be the

7 challenges in developing a prioritization using in vitro methods

8 and computer-based simulations for the thyroid hormone system?"

9 DR. DANIEL SCHLENK: Dr. Schultz.

10 DR. TERRY SCHULTZ: The Panel feels that

11 developing any computational Expert System to prioritize testing

12 for a thyroid hormone system mediators may need to consider a

13 variety of potential molecular initiating events which generally

14 have less data to model than just ER or even AR.

15 The Panel feels that this increase complexity

16 and data limitation may lead, at least initially, to weaker

17 categorization power. While mediated receptor binding is

18 generally agreed to be the primary initiating event for ER and

19 possibly for AR-related endocrine disruptor, TR binding may not

20 be the primary means of chemical alteration for thyroid hormone

21 systems. Thus, the scheme developed or the ER pathway as

22 described in this SAP may not be directly applicable to the

23 thyroid disruptors.

24 Thyroid hormone homeostasis in mammal systems

25 is important in developing the central nervous system, general

26 growth of individuals and play a role in metabolism. So you
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1 already have multiple AOPs because you may have multiple final

2 adverse outcomes that you're trying to deal with. 

3 Therefore, it would be greater burden on the

4 Agency to justify why the AOP that they select to begin work on

5 is likely to be the most important one. That is, is the AOP

6 will have the greatest chemical space for active compounds as

7 you want to put your initial effort where you're going to get

8 the most coverage of the chemical universe.

9 Thyroid disruptions stimulations are

10 inhibitions -- disruptor stimulation inhibitions of thyroid

11 through multiple mechanisms as a pathway was demonstrated in the

12 FIFRA SAP consultation we had in 2011. It was entitled,

13 "Integrated Approaches to Testing and Assessment Strategies for

14 Using New Computational Molecular Tools."

15 We strongly advise the Agency to revisit that

16 and build on the work that was presented there and reviewed by

17 an expert panel. Obviously what came out of that was an Adverse

18 Outcome Pathway for the molecular initiating event was PX

19 activation of the PXR receptor and the CAR receptor in liver

20 ligand to the final adverse effect of impairment of neural

21 development in children. I'm listing this as a specific example

22 where the Agency has always made a ready-made progress in that. 

23 To demonstrate the complexity of that, it was

24 proposed that the activation of PXR and CAR leads to up

25 regulation of hepatic Phase I and Phase II enzymes and hepatic

26 transporters. The up regulation of these enzymes and
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1 transporters results in increased metabolism of T4 and the

2 resulting decrease and circulating tissue T4 and tissue T3. So

3 we're already seeing an Adverse Outcome Pathway that has been

4 proposed by the Agency in a different context that is much more

5 complicated than the pathways that we have demonstrated with the

6 ER.

7 Taking this one step further, of course, it

8 should be noted that the activation of PXR and CAR and the

9 indirect stimulation of enzymes is only one pathway. Thyroid

10 disruptors can exert their action directly on thyroid by

11 stimulating and inhibiting thyroid receptors or stimulating and

12 inhibiting thyroid enzymes that inactivate the thyroid hormone

13 or displace it from binding proteins in the blood.

14 Thyroid receptors Alpha 1, Alpha 2, and Beta 1

15 are also possible molecular initiating events, but they are

16 unlikely to be the most important molecular initiating event. 

17 Ligand binding domains of those receptors impact by recruitment

18 and release of co-activation and core reflective factors in the

19 receptor and our potential key events.

20 Lastly, T3 and T4 binding, competitive

21 inhibition, paraprotein characters, thyroxin with carriers,

22 several are mentioned are also possible MIEs. I won't go

23 through the whole thing, but the point is that the Panel is

24 aware that there is a lot more mechanisms involved with the

25 thyroid. So the Agency needs to be clear to define that they

26 are aware of this, which one they select, why they selected it
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1 to move forward on that type of a situation. 

2 The Panel recommends that the Agency explore

3 all the possible molecular initiating events, establish a

4 priority list of pathways and summarily move that as time and

5 resources permit. It was also noted that because the pathways

6 are more complex, have different apical endpoints that assays

7 that may be included and established in the Expert System may be

8 very different in design than those that were used from the ER

9 and AR binding systems. 

10 For example, human neural progenitor cell

11 systems exposed to potential thyroid disruptors, and then in

12 vitro measurements of proliferation, migration and

13 differentiation are all in vivo aspects that may or may not be

14 low-throughput or high through put, but could be used to measure

15 possible key cellular events that were demonstrated in the

16 Adverse Outcome Pathways.

17 It is likely that ex vivo assays such as the

18 zebra fish larvae or the xenopus larvae, which studied very

19 early stages in development, may also be relevant for screening

20 for thyroid disruptors for higher level of effects. Zebra fish

21 assay basically evaluates the impairment of T4 synthesis, while

22 the xenopus assay evaluates agonist and antagonist activities. 

23 In summary, the Panel feels that moving

24 forward into the thyroid area is going to be a much greater

25 challenge to the Agency than moving into the AR realm and that

26 they're going to be a greater burden of proof on transparency
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1 and mechanistic plausibility, et cetera, et cetera, and why the

2 tests are selected in order to build the Expert System. Thank

3 you. 

4 DR. DANIEL SCHLENK: Dr. Meek?

5 DR. BETTE MEEK: Nothing further. Thanks.

6 DR. DANIEL SCHLENK: Dr. Burris?

7 DR. THOMAS BURRIS: Nothing further.

8 DR. DANIEL SCHLENK: Dr. Perkins?

9 DR. EDWARD PERKINS: I just wanted to second

10 what Terry said and I happen to put together a slide right

11 during his talk. This sort of outlines everything that Terry

12 was talking about and the problems that you might run into. 

13 It's a very complex system and what's also not included in there

14 and it was alluded to binding to something, it just doesn't

15 necessarily indicate effects, is a feedback control system that

16 also is in there. 

17 So at some point, whether what tier it's at

18 that has to be understood, at what point do you exceed the

19 threshold for compensation?

20 But one of things that I wanted to point out

21 in the challenges that you have is currently, there's only a few

22 high through put assays that I'm aware of that ToxCast is using

23 right now and that's the thyroid receptor binding assays and

24 assays for the liver cellular transporters. So a number of

25 assays that cover these different potential molecular initiating

26 events really haven't been developed. So there is a lot of work
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1 going on there. I know people are working on that.

2 From my understanding, one of the most

3 reliable indicators of disruption of thyroid metabolism is

4 decreases in serum T3 and T4 concentrations and there have been

5 assays in them using zebra fish embryo larvae, non-animal

6 models, so to speak, that have been adapted for high through put

7 areas, but these are all new tests, so they're going to require

8 a lot of validating and things like that.

9 Some of the other areas of integrating more

10 complex organisms, more fish larvae or frog embryo tests may

11 have to be adapted to more high through put methods because they

12 have very nice phenotypes, very easy to assay and they integrate

13 multiple MIEs. So we're possible to sort of capture complexity

14 without expanding your universe of potential assays, which is

15 going to be a problem in the future, especially if everything is

16 defined very narrowly. You're going to have to have a universe

17 of AOPs to capture the universe of chemicals. 

18 One consideration would be how can you do a

19 high through put assay that would use a more complex integrative

20 assay such as an embryo test in a more facile manner and use

21 that as a screen for either a prescreen for sensitivity to

22 something or a general screen.

23 The slide right before that, Slide 4 -- can

24 you go back one? There we go. This is just to illustrate one

25 result with several different chemicals. You have more direct

26 effect chemicals that are causing, potentially, through a really
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1 recognized MIE and then you have some that are more indirect,

2 like PFOS and PFOA. Some of the embryo assays, they don't

3 really see those because they're not able to get some of the

4 more subtle effects. 

5 Many of the other in vitro mammalian assays

6 don't either, but more complex ones like alternative species

7 like frogs looking at morphogenesis and also embryo

8 developmental slides can start capturing that. And those could

9 be more consistent with the actual AOP, with the actual adverse

10 outcome. So that's something that may be a consideration where

11 you have a complex and subtle effects you want to capture at the

12 same time. So that's all I have. Thank you. 

13 DR. DANIEL SCHLENK: Great. Dr. Cronin.

14 DR. MARK CRONIN: No further comment.

15 DR. DANIEL SCHLENK: Dr. Jacobs.

16 DR. MIRIAM JACOBS: This is probably

17 unnecessary, but I just want to flag up the thyroid DRP work

18 that was led by the U.S. EPA in 2006 and then the current

19 ongoing scoping work on the thyroid again. It's going on now

20 under the auspices of the OECD, but just to acknowledge that the

21 U.S. EPA are playing a very significant role.

22 DR. DANIEL SCHLENK: Dr. Keiser.

23 DR. MICHAEL KEISER: No further comment.

24 DR. DANIEL SCHLENK: And Dr. Safe.

25 DR. STEPHEN SAFE: No further comment.

26 DR. DANIEL SCHLENK: Any other Panel
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1 members?

2 (No response.) 

3 Okay. Does the Agency have any questions or

4 clarification regarding 9.3?

5 MS. MARY MANIBUSAN: No additional comments. 

6 Thank you.

7 DR. DANIEL SCHLENK: Okay. So this

8 concludes the questions that have been proposed in terms of the

9 charge questions. What is sort of customary at this point is

10 that we have one last movement around the Panel to present final

11 comments, maybe issues that you feel that were not addressed in

12 the questions. This is your opportunity to get them on the

13 record so it can be included into the written document.

14 I'll go ahead and start to my left with Dr.

15 Klaine.

16 DR. STEPHEN KLAINE: I have no other comments.

17 DR. MARTHA SANDY: I wanted to thank the EPA

18 staff for a very clear documents and presentations. Thank you.

19 DR. JAMES MCMANAMAN: I also want to thank the

20 EPA for nice presentations and bearing with us in our questions.

21 DR. PRAKASH NAGARKATTI: I want to thank EPA

22 as well. I have no additional questions or comments.

23 DR. KATRINA WATERS: I have no further

24 comments.

25 DR. MARK CRONIN: I have just a couple of

26 comments. Firstly, thank you again for some very, very nice
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1 work. I would emphasize the importance to the European Union

2 issues with a number of pieces of legislation that have gone

3 through or are going through, and being enacted at the moment. 

4 And I would encourage this approach, particularly based around

5 the AOP and categories to be publicized, certainly outside of

6 this forum, as possible. One aspect is through the OECD and the

7 advisory group on molecular biology.

8 I would also commend a number of novel

9 approaches. They've been covered, we needn't go over them

10 again, and I would just suggest, from a personal point of view,

11 we shouldn't get too hung up over how we use categories,

12 definitions of categories and so forth. The category approach

13 is there to group chemicals together to get information within

14 that group and to apply that information. It's been used fairly

15 flexibly within the European Union for reach and I can see

16 things here that we can take and possibly cross over. I would

17 just mention that. Thank you. 

18 DR. DANIEL SCHLENK: Dr. Schultz.

19 DR. TERRY SCHULTZ: Yes. I would like to

20 congratulate the Agency in making a step out into the realm of

21 actually discussing the paradigm shift. We've been hearing

22 about this for a number of years, mostly in conceptual papers

23 with boxes and arrows. The seeing, the effort to make the high

24 through put screening data applicable to the real world

25 questions that you are faced with and also to match that up with

26 the efforts that you have done over the past decade when these
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1 types of data weren't available.

2 I think this is a very good start. You have

3 forced us to ask some really tough questions and I think that's

4 a good mix of a Panel served well. Thank you.

5 DR. DANIEL SCHLENK: Dr. Chambers.

6 DR. JANICE CHAMBERS: I'll just say that

7 having sat through the previous SAPs on this topic, you've made

8 a lot of progress and I commend you for the progress that you've

9 made.

10 DR. BETTE MEEK: I really think that it's

11 exciting that what you're doing is really exciting, as you're

12 kind of moving this forward to have pragmatic application. I

13 think you face a number of challenges as the methodology

14 evolves, and we saw that, you know, at this meeting. 

15 I just have one small suggestion in terms of

16 so that the Panel in the future can kind of position this as a

17 component of a larger agenda. It would be really helpful -- I

18 recognize that you're learning while doing, but it would be

19 really helpful to contextualize it from the perspective that

20 this is one AOP for one aspect; we are looking at other aspects.

21

22 That, and also, if you can be thinking about

23 those other components we talked about, like exposure, where

24 those might fit in and we recognize that that's going to change

25 over time, but it's really just to kind of force some thinking

26 at the very outset to provide you with some of the more
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1 discriminating other potential influences, recognizing your

2 regulatory constraints, of course.

3 DR. DANIEL SCHLENK: Dr. Perkins.

4 DR. EDWARD PERKINS: I'd like to thank the

5 Panel and their insights into the problems and also EPA and all

6 the hard work they've done to try to develop something useful

7 out of the high through put screening efforts and all the work

8 that's gone into this. 

9 I agree totally with what Bette said. There

10 was some vision given on what this context is and how this is

11 serving as a model for development of future ones, but given the

12 complexity of the entire universe of chemicals and sort of the

13 universe of AOPs, I know it's a relatively new concept applying

14 in a regulatory context, but it builds on modes of action and

15 pathways. So it's not totally foreign. 

16 Some ideas or concepts on how this approach

17 may be applied in parallel: Are there going to be 10,000 AOPs

18 to analyze, 10,000 chemicals? Or are you going to find you've

19 got two -- that could potentially can bog you down so much in

20 developing protocols to so accurately define things initially,

21 when you're really just trying to prioritize for things that can

22 potentially cause problems. So some thought might be needed on

23 just how much specific prioritization is needed. 

24 I think the ideas on trying to identify and

25 prioritize of sort of relative hazard weighting of different

26 pathways and mechanisms is one good route to do that, but that's
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1 something I'm looking forward to seeing as you evolve and see

2 what kind of strategy to expand this. 

3 You don't want to expand it infinitely, you

4 want to have some management set as a goal to have 10, 20, 100

5 AOPs to act as a pre-filter for everything. Conceptually, it's

6 potentially possible, given a pathway-based approach with using

7 sort of the ToxCast as it's evolving. But other than that, I

8 think everything went nicely. 

9 DR. DANIEL SCHLENK: Dr. Burris.

10 DR. THOMAS BURRIS: No other comments.

11 DR. DANIEL SCHLENK: Dr. Keiser.

12 DR. MICHAEL KEISER: First, I would like to

13 join the others in thanking the EPA, the Agency, and also the

14 Panel, too, for this, and especially the very clear

15 presentations. 

16 Additionally, I'd also like to thank the

17 public commenters for a lot of hard work that obviously went

18 into comments and the data and information sent to us. And just

19 more generally, I'm quite excited about this direction. I'm

20 looking forward -- and I like that it was phrased earlier by Dr.

21 Bradbury, this idea of physical and computational and other

22 models that contribute to the prioritization step. 

23 So computational, Expert System, physical,

24 things like HTP that then eventually take us to higher levels of

25 testing and confirmation as we move forward. So to that point,

26 just to reiterate, that I think it's very important with these
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1 models that we assign weighting or confidences to them so that

2 we can take them all together and find out what sorts of

3 aggregate priorities we might want to take from them. Thank

4 you. 

5 DR. DANIEL SCHLENK: Dr. Safe.

6 DR. STEPHEN SAFE: No further comment.

7 DR. DANIEL SCHLENK: Dr. Denslow.

8 DR. NANCY DENSLOW: I really like the approach

9 that you're taking. I think the high through put assays are

10 really there and I would second what Dr. Meek said that, you

11 know, where I got bogged down I think was in the statement that

12 we're looking at all estrogenic activity and you're not really;

13 you're looking at one specific piece. So just focusing it

14 smaller made a difference. Thank you.

15 DR. DANIEL SCHLENK: Dr. Jacobs.

16 DR. MIRIAM JACOBS: I would like to thank the

17 U.S. EPA and the Panel and the audience as well, as everyone has

18 done before me. I really appreciated the presentations and the

19 critical eye that was presented by the U.S. EPA on their

20 analysis of the work in the documents, and the public

21 commenters, the work that was presented was really helpful and

22 useful to see.

23 I think we got a little bit stuck this

24 afternoon, a bit circular. I think that's really because we,

25 perhaps, the Panel had been looking much more holistically at

26 the estrogenic activities because biology isn't linear. I mean,
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1 there are other factors coming in and there are other factors

2 that are coming into play with high through put assays,

3 especially for example. 

4 So with respect to that also, I want to echo

5 Bette's comments on the ways forward. And thank you. Thank you

6 very, very much.

7 DR. DANIEL SCHLENK: Well, final comments

8 from my own personal evaluations of the meeting. Again, the

9 Agency, as Dr. Chambers mentioned, having seen this kind of move

10 through the ranks over the years, I think, without a doubt,

11 every person that I know and have talked with in the Panel is

12 very positive in terms of the direction that you're moving. I

13 think this is the way to go. 

14 There are challenges, obviously, and I think

15 that came out in some of our discussions. Hopefully that will

16 be beneficial to you and not something that hinders you in your

17 progress, moving forward. But I look forward to the next

18 meetings with additional data. As scientists, you know, we're

19 always asking for more data. So it would be very interesting to

20 see what the next round brings. So I'm very on pins and needles

21 for that. So I think that will be very exciting.

22 At this time, I would like to thank my fellow

23 Panel members for doing a fantastic job of meeting together and

24 actually going above and beyond the call of duty, in terms of

25 putting together your responses. I think you've really done a

26 good job in trying to beat out the best out of these questions
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1 as best as you can. So I'm appreciative of that. 

2 I'm thankful to the Agency for the

3 presentations that you made and the hard work that went into

4 those presentations. Obviously, the tremendous amount of work

5 that's gone forth, not only in the high through put areas, but

6 also the low through put areas. I love that. 

7 The public commenters, thank you for your

8 comments throughout the week. Obviously, you heard, they were

9 definitely influencing some folks' opinions as well. So those

10 words were not wasted. Thank you for that. 

11 I would also like to thank Sharlene and the

12 FIFRA SAP staff for getting us here and providing all of the

13 materials that we had, through numerous emails and physical

14 copying and change of flights and all the other good stuff that

15 has to happen through that process. So we're very grateful for

16 that. 

17 With that, I'll turn it over to the Agency for

18 final word.

19 MS. MARY MANIBUSAN: On behalf of Agency, I

20 would like to extend our thank you and appreciation. Over the

21 last three days we've received excellent and very constructive

22 and very specific recommendations and advice on how to move

23 forward. Your positivity and your energy is very, very helpful

24 in supporting the Agency as we look forward to the next decade

25 of EDSP. 

26 I'm a timeline person, so I think about
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1 timelines as I've talked with you. We recognized where we've

2 been. We know where we are today and we know where we'd like to

3 go. I believe all of us can share in this moment that this will

4 be the new inflection point on our timeline. This is quite

5 revolutionary. We're very excited about moving forward,

6 accomplishing our mission very effectively and efficiently. And

7 those are the things that will bring us forward in 2013.

8 For the subsequent SAPs that will be coming,

9 there will be some reiterating themes that the Agency will be

10 asking from this SAP Panel, in terms of recognizing the Agency's

11 goals and programmatic accomplishment in terms of what is the

12 question we're seeking to answer. 

13 What is the tolerated uncertainty that we're

14 willing to accept in making that decision and how do we anchor

15 our decisions by toxicity pathways and biological mechanisms of

16 understanding? But doing this in a very, very transparent way

17 so that we're bringing our entire community together; not only

18 our scientific community, but the public community and our

19 stakeholders at large, together, as we move forward because the

20 endocrine program certainly is indeed a community-based program.

21 So I would like thank the public commenters as

22 well for their insightfulness and taking the time to provide

23 their intelligent information and comments before this Panel. 

24 I'd like to extend my appreciation for our designated federal

25 official, Dr. Sharlene Matten, for organizing us, making us fit

26 within the timeframe and accomplish, again, our very large goal
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1 in front of us. So thank you. Thank you very much, and I look

2 forward to 2013. 

3 And our next meeting will be focused on some

4 operational aspects of data reviews for the Tier 1 assay and

5 battery performance as we bring before the SAP our analysis of

6 the List 1 chemicals that we had issued test orders on, and then

7 moving forward again on finishing up our task of validating the

8 Tier 2 methods so that we can make timely decisions on chemicals

9 that would advance for additional testing and then coming back

10 to the SAP with our -- follow our evaluation of the complete

11 data set of Tier 1 data, the publicly available information,

12 making sure we tie in all the literature that speaks to per

13 chemical, as well as our 158 information that gives us

14 information already on reproductive development effects, chronic

15 effects, any repeat studies that will bring to bear in our

16 weight-of-evidence determinations.

17 So thank you in advance for those SAPs as

18 well. Dr. Bradbury.

19 DR. STEVEN BRADBURY: Just real quick so you

20 can all catch your planes. I just wanted to thank the Panel

21 again and Sharlene and the staff that helped put this together,

22 and the public comments. I want to also thank our colleagues in

23 the Office of Research and Development working with us, as well

24 as colleagues in the Office of Water, and also some of the

25 comments made by the Panel in terms of we're like a little fish

26 in the big ocean, and there are a lot of other fish in the
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1 ocean. 

2 So collaboration across the globe in how these

3 new concepts and new technologies are brought to bear and we'll

4 try to do it at SAP meetings to make sure we're providing

5 context in what we're trying to take on, but also want to

6 acknowledge how we're all collectively tapping into real neural

7 nets, biological neural nets, as well as the physical and

8 mathematical models we're using. 

9 So I just want to acknowledge that want to

10 keep reaching out through the SAP and through other venues to

11 make sure and try to bring everybody's ideas together as we go

12 forward. So thanks again. Chairman Schlenk, turn it back to

13 you.

14 DR. DANIEL SCHLENK: All right. We'll turn it

15 over to Dr. Matten to close us out. 

16 DR. SHARLENE MATTEN: I'd like to thank all of

17 the members of the Panel for their very intensive work for

18 staying within our requirements for the Federal Advisory

19 Committee Act and preparing, doing so very well in preparing for

20 this meeting and articulating your points. I hope the report

21 will be as clear as we've ended in putting things in the right

22 organizational structure to make it very simple for the Agency

23 to understand all the different points. 

24 I appreciate the Agency and its different

25 components, Office of Pesticide Programs, Office of Research and

26 Development, Office of Science and Coordination Policy, and any
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1 other offices that have contributed to the good and welfare of

2 developing this program; all of the members of the public who

3 did very well in providing their comments in written form in a

4 timely way so they could be distributed and encompassed by the

5 Panel, and the presentations that we were able to provide.

6 We are trying to move towards less paper, so

7 perhaps, there was a little confusion, probably because I was

8 trying to do multiple things, but we have resolved those things

9 for future. I think the Panel really thought the memory sticks

10 with which we are trying to provide presentations in advance, so

11 I appreciate my colleagues for being able to move forward with

12 that. This is our second time using them. 

13

14

15

16

17

18

I would like to thank my office group, first

and foremost, Laura Bailey, the executive secretary; my other

colleagues who are designated federal officials, Joe Bailey and

Fred Jenkins, the entire administrative team, Shirley Percival,

Joyce Coates, and Don Woods and the contractors from EnDyna, who

do a lot of the meeting logistics and travel logistics.

19 I'd like to thank the presenters from EPA for

20 really responding well to comments to really make their

21 presentations very precise and informative. I think that we all

22 tried to appreciate all of the hard work that everyone has done.

23 So I thank you very much from me, personally. For those who

24 were the direct contacts in the material, it was all done

25 extremely well, on time, early and I can say that I appreciate

26 that quite a lot. 
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1 So with that, before I adjourn officially, I'd

2 like the Panel members to meet in the room adjacent to this. 

3 Thank you very much. So I'll close the meeting. 

4 (Whereupon, at 1:55 p.m., the 

5 FIFRA SAP meeting was adjourned.) 

6 * * * * * 

7

8

9

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12

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A

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195:21 224:20 252:14 261:7 441:22 498:9,17

500:10

advanced 22:7 289:19

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advancer 253:17

advances 27:20 35:9 476:9

advancing 20:4 299:26 439:10

advantage 317:14 336:9 416:20 426:6 447:10

advantages 364:19 459:3

adverse 7:9 13:13,16,20,25 19:13 29:4,23 36:6

42:10 47:22 49:13 52:23 54:26 55:4 56:26 82:21

145:11 165:23 182:5,15 221:18 250:12 252:12

253:22 254:5,16 255:3,12 270:24,25 299:7

305:10 310:19,21,25 336:22 343:8 344:24 354:18

361:3,7 385:14 399:4 439:20 440:4,23 468:18

471:2,17,19,21 472:2,7,14 475:1,5 478:6,8,15,18

483:2,17,20 484:3 485:16 488:9

adversity 58:14

advice 3:1,5,9 11:7 14:14,18 16:23 29:9 48:16 49:5

49:8 90:6 122:8 215:25 221:2 223:12,22 303:20

402:16 410:18 430:15 433:24 496:22

advise 90:7 234:4 483:15

advised 312:23

advisory 1:1 2:4,13,16,23,24,25,26 3:4,9 4:2 5:8

10:12,17 11:17 18:20 213:3,4 246:9 247:16,23

262:16 390:3,8 490:7 499:18

advocated 28:21 29:3

affect 6:17 80:16 156:24 157:6 162:18 172:8 181:7

181:8 187:26 217:13 255:12 272:6 344:1 353:7,8

363:2 364:14 365:23 366:13 373:18

affective 289:22

affiliation 5:15 390:16

affiliations 213:20

affinity 33:25 56:7 58:22 64:22 68:4 80:24 85:4,5

89:25 151:18 152:14 153:15 155:8,10,12,12

164:9 165:2,11,15 166:26 167:1 168:5 169:17

172:25 173:6 174:9,12 182:25 183:1 185:4,4

216:25 223:4 267:7,11,12 268:15,23 269:4,5

308:9,15,17 310:9 334:7 352:10 394:12 397:26

406:22 446:15 452:9 453:20

affirmative 67:20 72:13 84:19

afford 96:21

afternoon 84:7 114:6 167:22 494:24

afternoon's 83:24 87:17

age 167:23

agencies 4:4 264:11

agency 1:12 2:11 3:1,5,9,13 4:10 6:5 9:7 10:16

18:9 21:12 22:23 25:9 27:15,17 34:3,18 37:26

47:18 48:13 101:19 124:20 125:1,22 133:26

135:5 140:21 216:15 225:11 233:6 234:20 249:5

250:5,13,18,19 251:13 262:9,21 263:17,20,20,24

269:24 271:1,3,9 272:3,7,17,23 273:20,26 297:23

300:1 303:5 318:9 319:24 320:21 325:7 328:21

332:19 335:8,10,24 342:11 354:9 359:14,17

360:1 370:23 374:22 380:26 384:13 389:1 392:13

392:23 393:9 394:21 395:16 398:13 399:1 400:24

406:21 407:16 412:17 413:13 415:14 418:26

425:7 427:13,22 428:9,16 429:21 433:5,24 435:1

438:26 444:3 445:3 446:11 451:10 454:18 455:8

455:18 456:6,18,26 457:15,24 462:2 466:10

468:9 469:1,5,23 470:18 471:21 476:22 480:19

482:1 483:4,15,22 484:4,25 485:2,25 489:3

490:20 493:13 495:9 496:2,17,19,24 497:9

499:22,24

Agency's 320:13 326:8 382:14 393:5,16,25 394:7

440:6 443:14 497:10

agenda 4:9,15 211:14 213:9 491:17

agent 44:4

agglomerative 375:18

aggregate 314:24 315:13 375:14 385:25 453:1

494:3

aggregation 138:2,3

ago 39:5 129:23 218:22 284:25 294:1

agonism 148:11 163:5 220:15

agonist 19:23 113:10 116:12 132:25 133:10 135:1

136:26 137:10 141:26 147:11,13 162:3 277:20

477:11 485:22

agonists 114:16 135:26 136:4 137:9 334:13 349:18

363:19 377:9 384:21 411:13 477:18

agree 119:6 121:12 140:12 166:16 169:4,7,15

180:6 233:14,18 274:4 275:18 316:23 317:21
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319:24 320:14 323:23 328:13 330:5 331:20

339:25 346:1 370:19 374:14 407:18 421:9 442:16

472:26 474:7 475:21 492:9

agreed 28:12 115:6,7 166:20 328:20 334:3 373:2

482:18

agreement 84:4 155:4,13 164:16 166:15,22 171:25

172:15 173:2 320:3 336:21 396:20 450:18 470:25

472:10,12

agrees 322:15 323:5,13 325:26 326:23 327:7,24

470:19

Ah 279:11

ahead 38:21,22 52:13 76:12 91:3 159:20 185:17

188:8 195:21 203:11,17 247:3,9 248:14 251:11

285:10 286:2 302:18,20 332:25 342:16,19 371:21

393:22 425:8 426:21 433:2,11 444:1 455:23

466:14,21 476:26 482:4 489:14

air 324:18

airports 2:18

al 256:22 278:1 282:3 368:6,11 387:21,22

ALAS 475:3

albeit 267:6 280:4

albumin 464:15

alcohol 337:19

alcohols 192:10 193:3

aldehyde 199:15 200:4

aldehydes 76:10,13 79:24 199:7,10,23 200:1 203:1

alert 211:20 462:2

alerts 267:2 330:10,11,13,16 336:1 434:19

algorithm 59:26 108:7 116:15 179:24 266:16

382:24 428:3

algorithms 46:6 108:9 234:20,22 250:21 266:12,13

266:14 272:19,20 326:10,15 341:5

aligned 317:17 405:26

aliphatic 337:19 459:19

alkanes 202:22

alkoxyphenol 72:7

alkoxyphenols 226:10

alkyl 177:22 178:5 187:19

alkylaniline 68:22 194:4

alkylanilines 61:7 66:3 194:5,8 205:15 226:12

alkylation 160:6

alkylbenzenethiol 157:25

alkyloxy 72:21

alkylphenol 81:9

alkylphenols 61:3 65:22 66:2 165:5,11,14 226:10

227:2 386:18

allow 58:19 140:19 222:13 225:10 228:7 238:2

249:18 252:1 272:4 273:21 305:14 375:10,11

426:6 434:16 443:14

allowed 27:13 92:18 290:7

allows 64:18 65:5 222:5,25 227:3 345:7 462:13

Alltech 134:2

allude 403:10

alluded 240:6 401:21 486:14

alluding 477:23

allusion 331:4

alongside 125:8

alpha 64:4 171:3,4,13 357:22 358:1 363:23,26

364:1,15 371:8,9 484:14,14

alphabetical 13:6

alter 55:19 414:5

alteration 482:20

altered 57:19

altering 55:22 478:3

alternate 48:5,14

alternative 129:4 233:20 262:2 278:8 307:12 318:4

329:25 336:15 349:15,20 351:5,11 353:14,17

411:10 452:3 488:6

Alternatively 368:3

alternatives 262:3 352:22 411:17

alters 386:11

ambient 33:1

ambiguity 115:11

amean 201:15

amenable 461:12 463:10

amendable 308:7

amended 215:15

Amendment 35:11

amendments 18:14 296:8 297:13 300:2

America 247:20 260:5 275:16 279:5 282:25

American 247:7 282:26 283:2 296:4

amiable 372:20

amino 74:10 203:8 459:24 479:17

amino-acids 46:18

ammonia 41:2,7,8 43:21

ammonium 41:6,8

amount 9:22 52:6 65:26 92:1 105:15 148:22 153:4

163:9 164:1 171:10 236:5 283:9,10 398:15

467:23 479:20 496:4

amounts 26:26 144:14 170:2

Amphibian 20:16

ample 206:7

amplified 405:17

anabolic 257:17

analog 196:8

analogs 75:21 76:5 196:8

analogy 325:9 449:2

analyses 34:9 92:8 121:22 220:9 251:24 269:15

341:12 346:17 372:10 373:22,22 375:14,19

384:18 386:22 396:7 465:19,21
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analysis 23:1 34:9,14,15 36:13,16 86:12,18 92:1

102:21 103:12 121:8 128:20 129:10 136:7 142:18

143:4 144:4 161:20 166:23 167:12,13,14 175:11

220:21 221:1,3 232:15 247:22 251:10,14 256:18

274:12 275:25 277:3 279:16 285:6 290:12,18,20

292:3 309:3,8 314:1,9 315:7,13 335:13 338:23

339:3,5,8,9 362:26 365:17 368:16 375:8 376:13

376:14 386:1,20 387:17,25 396:1 397:6 398:5,8

408:2 409:21 413:14 414:8,16 437:5,7,12 439:7,8

449:17 469:11 494:20 498:5

analytical 103:13 120:10 209:2 278:13 388:1

analyze 91:15 103:24 121:7 122:5,6 492:18

analyzed 198:26 329:22

analyzing 93:19 121:12 375:5 398:13

anchor 310:20 460:7 497:14

anchored 17:13 30:21 47:22

anchoring 24:9

androgen 19:3,10,23 29:26 30:2 92:13,21 94:7

242:19 246:5 257:11 259:15,17 467:2 473:21,25

474:1,4 475:2,3,8,17 477:6 479:5,11,15,15,19

480:5 481:13

androgens 257:9

and/or 4:11 319:4 343:11 399:18 455:25 478:13

angle 70:20

angstroms 337:20

anhydride 44:9

anilin 85:6,7

aniline 69:14 194:22 205:12

anilines 69:10 194:3 226:12

anilins 84:25 345:21 359:5

animal 31:15 95:10 134:18 241:9 246:9 464:12

animals 31:18 209:19 236:7 238:11 305:1

animal's 307:4

animation 150:20

annotated 273:13

annotations 414:15

announced 235:13

anomalous 184:13

answer 12:3,4,8 13:18,20,24 14:4,8 19:8 65:10

67:20,20 84:19 89:18 121:8 124:13 129:8 136:11

156:4 168:23 179:16 180:3,24 183:20 208:18

216:8 218:20 219:19 221:15 235:18 294:23

339:11 344:7 354:6 357:16 365:13 381:8,8

417:23 428:8 434:21 447:14 497:12

answerable 244:14

answered 169:2 298:3 383:18 412:15

answering 37:8 168:22 219:8 291:12

answers 12:11,17 89:20 113:24 194:10 208:6

227:4 248:25 396:7 402:24 428:15 429:10,11

443:8 472:17

antagonism 133:13 148:11 162:8,12 163:8,11

antagonist 19:23 114:16 132:23,25 133:1,5,9,10,10

137:7,12,26 139:16,18,21,23 147:12,13 162:6,13

163:6,7,8 353:8 379:25 477:12 485:22

antagonists 132:20 133:7 138:14 363:19 477:18

anthracenediones 74:10

Anthraquinones 200:24

anticipate 50:10

anticipation 32:21

antimicrobial 61:15 334:7

antimicrobials 33:13 36:8 53:18 59:16 65:12 70:1

84:5 143:17 201:5

anti-androgen 475:13,17

anti-androgens 475:10,18

anybody 95:7 106:6 211:8 225:18 250:18 342:10

481:16

anymore 105:18 115:21 467:23

anytime 50:10

anyway 97:16 100:15 132:8 285:11 464:13

AOP 47:26 56:14 58:12 59:4 186:3 189:4 190:1

222:6 223:6 224:5,24,25,25 238:17 253:26 270:5

270:24 305:15,21 307:17 315:17 322:7 336:26

342:25 343:7,11,25 344:3,4 345:6 346:10 352:24

361:17,21 362:2,2 363:17 364:13,18,23,25

365:12 371:24,25 373:12 378:20 385:8 386:13,21

399:3,3,12,12 415:16,17 420:18,19 446:18

451:18 453:19 460:17 468:6,11,15,25 470:8

471:11 473:15 474:25 477:22 478:24 481:19

483:4,5 488:9 490:5 491:20

AOPs 246:5,10 252:7,21 467:1 469:12 483:1

487:17 492:13,17 493:5

AOP-based 461:11 469:4,21

AOP-related 398:7

AOP-specific 416:13

AP 246:25

apart 138:12 337:20

apical 19:12 24:14 30:20 221:26 477:24 485:6

Apologies 279:12

apologize 284:10 377:19 411:22

apparent 39:11 82:7 164:11 396:15 438:13

appealing 253:21

appear 33:6 132:19 237:4 378:16 403:22 423:26

441:5

appearance 263:6 394:18

appeared 394:12,14 453:8

appears 85:3 304:10 373:15 385:9 396:23 397:5,6

403:23 416:4 452:10 469:23 477:9

Appendix 94:11,13 235:3,9 338:7,9,13 339:10

340:12 435:12 436:6,11
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apples 64:15,15 401:3,4,4,26,26

applicability 26:1 59:7 219:7 251:19 321:20

324:13 325:25 331:5

applicable 32:24 61:13 219:3 240:16 246:13 262:9

262:17 335:1 461:16 466:25 470:21 476:3 477:16

482:22 490:24

application 18:6 25:24 39:20 48:2 80:15 261:25

319:3 341:15 365:5 491:12

applications 251:24

applied 47:13 51:24 63:21 72:10 80:17 94:26

200:3 246:12 253:4 320:18,19 329:21 338:7

375:22 471:4 492:17

applies 296:6

apply 26:10,11 35:17 64:9 70:13 73:11 80:14 154:5

196:1 197:8 223:24 245:6 262:10 322:12 330:17

425:4 436:14 473:1 474:13 478:20 490:14

applying 44:19 51:8 58:1 245:18 468:17 492:13

appreciate 2:17 9:21,26 10:5 206:26 209:6 212:3

236:9 243:12 258:22 260:9 263:2,25 272:20

303:19 304:1 318:11 324:8 392:19,24 405:12

420:6 424:8 434:2 499:24 500:11,22,25

appreciated 310:16 346:18 367:20 369:12 381:5

396:4 433:10 494:18

appreciates 240:1 303:21,25 304:3,16 368:21

434:11

appreciation 496:20 497:24

appreciations 16:19

appreciative 333:22 353:26 393:4 496:1

approach 15:11 20:7 24:17 28:8 29:23 30:3 31:15

31:18,21 35:1,2 36:7,22 47:21 48:17 52:24 53:7

53:25 55:4 64:18 65:5,11 82:20 86:12 88:9 138:1

177:10 188:16 191:19 193:20 200:21 203:18

204:9,13 207:4,5,11 208:24 209:4 210:3,15 216:1

216:11 217:4 218:19 219:4,12 220:5,25 221:8,10

221:11 222:25 223:13 224:23 233:20 239:3

240:11 241:25 242:20 244:24 250:8,10 254:26

275:18 276:5 278:8,17,26 279:3 289:21 302:25

303:2,7,9,24 304:19 307:19 308:21 311:8,16

318:24 320:17,22,23 326:25 329:13 331:6 333:4

333:7,11 335:8,11,12 336:11 337:22 338:3

345:10,19 346:15 349:16 371:5,23 372:1,3,25,26

373:2 385:10 386:4,16 395:23 396:4,5 398:20

408:11 422:7 425:19 427:9 431:21 433:15 434:10

435:6 436:8,24,26 437:3 440:20 441:11 443:2

447:11 452:15 462:24 464:12 474:10,14,19

476:14 480:22 481:1 490:4,12 492:16 493:6

494:8

approached 29:11 231:1 431:20

approaches 11:18 12:9,11,17,25 29:15 88:11,14,20

96:15 104:3,4 186:3 187:9 210:8 221:6,12,16

222:14 224:22 240:1 249:25 271:19 277:26

304:13,23 311:1 315:2 317:10,17 325:2,14

330:24 337:26 338:5,11 339:3 340:13,18,23

341:3,4,6 352:14 375:5,9 388:1 395:25,26 397:8

398:23 414:11 426:12 436:1,6,9 442:24 446:6

461:8 474:18 476:3 481:7 483:13 490:9

approaching 11:11 13:22 89:2

appropriate 3:12,13 18:10 26:13 39:11 49:5,6

59:18,20,20 105:20 108:5 202:10 210:13 232:26

240:1 241:1,2 243:21 245:7,8,23,25 246:17

264:23 290:8,8 301:23 320:24 323:20 326:2,3,25

328:21 331:13 332:3 387:20 388:1 437:26 441:11

441:15 443:15 452:12 474:18

appropriately 12:20 362:23

appropriateness 243:22 302:24 372:10 373:22

appropriations 236:22

approved 211:16

approving 211:16

approximately 4:3 27:14 39:7 44:21,22 309:11

433:16

approximation 448:12

aptly 187:8

AP-1 128:7

AP1 350:26

aquatic 5:20,24,25 462:19 463:16

aquifer 297:25

AR 186:10 223:25 470:1,15 476:2 477:9,16 478:2

478:3 479:12 481:16,20 482:14 485:9,25

arbitrary 44:6 50:17,18

arching 253:14

area 2:18 5:16 6:16 7:22,25 8:2,6,10 27:26 56:22

57:16,20 85:9 128:25 192:6 217:5 250:13 260:9

268:5 329:16 343:20 402:7 414:1 419:16 450:2

463:15 485:24

areas 181:3 188:17 204:2 264:1 289:4 315:3 331:6

336:10 402:19 463:23 487:7,9 496:5,6

are-you-awake 109:9

argue 441:9

argument 259:7 337:14 407:2

ARLINGTON 1:16

Army 7:21 214:18 391:17

aromatase 387:6

aromatic 199:16 205:24

aromatics 201:16

aromatizable 257:11 259:17

aromatization 257:13

aromatize 257:18

aromatized 257:12 259:13

arose 303:26

arranged 3:25
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array 349:7,7 387:26

arrive 204:15

arrow 34:3 147:17

arrows 490:23

art 326:2

article 278:1,2

articulated 25:5 32:9,13

articulates 21:12

articulating 499:20

artifact 110:20 161:19

artifacts 383:11 415:9

artificial 182:10 348:14

AR-binding 477:23

AR-related 482:19

ascribed 170:16

aside 108:7,7,11 300:8,10 448:16

asked 22:22 54:10 67:10,21 91:12 94:15 121:4

206:22 234:4 289:10,25 295:16 329:8 362:23

392:10 409:2 448:24

asking 31:20 37:2 90:15 121:5 122:1,2 128:5 129:2

131:22 168:22,25 180:18 183:17 207:19 261:11

294:18 341:17 358:25 360:2,14,26 438:10 449:5

449:7,7 495:19 497:10

aspect 207:14 216:26 219:20 222:23 268:16 311:11

337:9 353:6 363:22 403:21 479:5 490:6 491:20

aspects 197:2 227:19 248:2 258:11 270:3 334:13

363:26 381:15 382:16 386:13 403:8 415:25

430:18 440:13 485:13 491:20 498:4

Aspirin 44:11

assay 20:1 22:18 31:21 33:19,21 34:16 61:23,24,26

62:4,11,20 63:1,12,14,14,15,15,19 64:1,14 69:2

71:21 73:12 91:11,23 92:8,23 94:8,10 95:3 96:11

98:4,13,14,14,17,19,19,22 99:4 100:2,3,4,15

101:7,8,11,12 104:7 105:14,17 106:10,14,16

107:3,17,19 108:1 109:2,2,4,18 110:3,12,14,20

110:22,23 111:4,9,12 112:17,25 113:12,13 115:5

115:6,7,8,9,14,14,15,18,18,20 116:2,9 117:15

121:3,8,21 122:15,18,21 123:6,7,16 128:2 130:8

130:23 131:16,22 133:16,18,19,20 135:8 137:11

137:13,21 139:17 141:16,19 142:21 144:7 145:2

145:2,8,8,13,21 146:26 147:1,2,5,9,14,16,25

148:10,10,15,18,19,19,21,21,22,24 149:8,9,10,11

149:13,18 150:9 151:4,7,9,10,10,13,14,20 152:20

153:4,9,15,18,19 154:7,12 155:18,23 156:11,15

156:15,17 157:5 158:8,22,23,24,25 159:9,10,11

159:22,23 160:10,15,19,23 161:15 162:2,21,22

163:6,19,24,25 164:3,3,5 165:8,12,20 166:10,15

166:24 167:18,25 168:10,17,19,20,20 170:10,10

170:14,19,24,25 171:10,23 172:4,16,16,19,22,24

174:6 175:2,25 176:18,18 178:21 180:13,13,17

181:16,19 183:3,4,8,9 184:1,9,9 185:1 197:6

201:12 203:14 209:2 218:10 221:14 222:22

237:25 243:7 244:19,24 258:19 267:1,2 268:7,9

268:10 270:2,6,17 272:6,12 273:2 275:12,12,19

276:11,19,19,25 277:1,5,6,10,11,25 278:10,14,14

278:16,18,21,22,26 279:17 281:1,6 285:19,20

291:10 309:4,5,12 316:1 321:18 342:25 343:25

348:2,12,19 349:1,10,12,22 354:24 358:14,14,15

360:24,25 361:6,8 362:25 363:2,4 365:1,6,8,16

366:3,8,9,14 368:7 369:4,9 372:6 373:10,11,14

373:18 377:9 378:18,22,25 379:4,5,7,9,25 382:19

383:1,22 384:23,24 385:11,25,26 387:7 388:8,14

388:15 394:8,9 395:25 396:12 397:3 398:6 399:2

400:15 402:13 404:5,7,10 405:14,17,24 408:1,9

408:20,21 409:10,17,18 410:2 411:12,16,16

413:5,16,23 414:4 415:9 416:10 417:22 423:20

427:6 429:2,3,5 439:7 441:2,20 443:23,24 444:23

445:1 448:26 452:17 453:1,6,14,24 455:1 460:22

461:22 463:1 468:10 469:19 475:3,11,13,16

478:16 479:7,8,11,12,22 480:10 481:12 485:21

485:22 487:12,19,20 498:4

assays 19:16,17,18 20:14 23:5,18 24:6 28:17 29:16

33:14,18 34:11,12 36:13 55:2 56:13 57:15 58:16

58:16,18,24 61:19 62:3 63:11,25,25 64:13 88:24

91:24,25 92:20,21 93:1,4,5,6,12,15,17,20,23 94:3

94:4,6,9,17,20,22 95:2,17,18,18,19,19,21,22 96:4

96:10,18,19,19,23,24 97:5,20,26 98:2,3,17 99:8,9

99:14 100:9,9,12 101:13,15 103:23,26 106:24,24

107:14 109:19,22,25 110:11,25 111:11 112:26

113:4,8,9,16 114:3,6,9,13,16,19,21,22,26 115:6

115:22 117:9,15,26 118:1,2,3,15,18 119:1,3,14

120:14,15 121:6,18,19,23,24,25,25,26,26 122:20

123:6,8,8,12,18,18,18 124:8,22,26 127:4,23

128:2 129:12,20 130:4,13,15,16,16,17,19 131:4,4

131:8,9,12,13,14,15,20,24 132:2,2,5,7,19,23,24

133:1,2,3,6,10,21 134:4 135:3,4,14 136:17,21,21

136:21 137:12 138:11,12,13,19 139:4,16,19,23

140:2 142:26 143:1,5,7,8,10,11,13,13 144:1,3,5,5

144:8,13,13,15,15,17,18,20,23 145:2,8,16,19,23

145:25 147:14 148:8,17 149:22,26,26 150:11,12

150:13,19,21,25 151:3,21 152:4,10,11,13,15,17

152:19,22,24 153:3,13,14,17 154:6,6,16,20,21,26

155:4,11,14,18,25,25 156:8,10,11,13,14,20,22,24

157:3,6,7,7,9,10,17,19 158:1,2,17,20 159:8,21

160:8,9,13 161:8,11,12,25 162:5,19 163:7,15,22

164:6,8,26 165:3,6,17,23,26 166:1,1,2,4,11,18,25

166:25 167:6,9,14,15 168:1,4,6,8,15,16,23,24,26

168:26 169:10,16,18,23,24 170:3,5,17,23 171:2

171:20,23,25 172:16 174:1,1,5,23 175:13,14,18

175:21,26 176:6,7 178:14,15 181:15,17,23 182:7
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182:16,17 183:7,18,19,25,25,26,26 184:1,1,2,3,6

184:8,23 185:8 186:4 189:4,6,26 190:1,2,3,3,18

190:19,24,25 191:13,14 192:22,23 195:6,8

199:25 202:19 203:15 205:7 207:9 209:17 219:17

220:10,13,18,22,23,24 221:1 222:6,6 224:2

228:23 229:8,15 231:7 234:16,16 237:2,25 238:7

238:8,10,12,24 239:6 241:5,13 242:8 243:9,10,23

243:25,26 244:4,12 245:9 246:3,6,11,12 248:18

249:4,9,12,12,18 250:4 251:3,15 256:12,12

257:12 258:7,8,12,14,17 259:16 267:19,23 268:2

268:3,8 269:12,25 270:1,3,11,13,14,16,23 271:4

271:10,11,13,15,15,25 272:9,10,25 273:6,7,9,17

273:23 274:6,7 275:8,23,24 276:3,17,17,23 277:9

277:13,15,17,19,22 278:3,6 279:19,20,21,24

280:4,5,22 281:3,10,13,14,15,17,18 282:2 284:8

285:5,6,7,13,25 289:8 290:5 291:6,8,15,16,23

300:20 301:12 308:2,3,4,5,23,24 309:14,15 310:4

310:6 313:23 314:13 323:15 324:13 334:9,24

337:6,16 343:14 348:1,2,9,12,25 349:2,3,16

350:20 355:4 356:10,23 357:1,3 358:8,25 359:21

362:24 363:12,20,21 364:4,4,6,10,17 365:11,15

365:18,21,23,26 366:1,5,7,13,18,18,20,21,24

367:1,5,10,22,24,25 369:3,18 370:26 371:1,2,6,8

371:11 375:22,26 376:2,14 377:11 378:6,8,19

379:2,4,17 382:5,15,16 383:5,9,12 384:20,21

385:5,13,19,19,26 386:3,23 387:18 393:12,26

394:11,13,15,16,17,19 396:10,20 397:2,9,9,11,12

397:15,21,22 398:7,10,10,14,25,26 399:8,10,11

399:15,17,19 400:8,14 401:6,7,9,22 402:9,26

403:3,8,18,19 404:5,7,11,12,13,14,15 405:9,19

406:15,18,23,25 407:22,24 408:7,13,16,23,26

412:22,26 413:5,10,14,17,19,24 415:3,15 416:7,8

416:13 418:3 420:2 428:5,20 429:12 437:10,17

440:7 441:5 443:21,22 444:1 445:13 447:3,7

448:24 449:14 450:4,7,7,12,15,17,19,21,22,26

451:21,24 452:13,18 453:13,19,21,22 454:9

455:4,10 456:23 460:16 465:8 468:12,12,14,15

470:9 471:6,7 478:13 479:6 480:5,10 485:6,17

486:22,23,24,25 487:5,14 488:2,5 494:9 495:2

assay-specific 399:20

assemble 207:16

assess 189:17 203:19,21 204:9 275:19 278:10,26

289:7 323:13 337:1 377:8 385:25 434:14 440:24

452:4

assessed 276:11 305:26 306:1 338:22 365:2 366:10

382:16 440:21

assessing 53:10 188:25 189:8 310:26 434:21 435:2

449:8

assessment 5:26 6:4 7:16,18 11:18 12:18,24,25

16:16 19:13 23:23 25:11 31:4 56:5 58:12 80:14

166:23 214:1 221:6 246:19 274:11 276:22 292:22

305:14 310:22 345:10 390:25 393:25 394:7

439:14 440:9 483:13

assessments 12:26 30:9 216:18 440:6

assessors 24:3

asset 385:22

assign 90:21 146:15 346:24 352:3 494:1

assigned 75:20 90:15

assigning 89:26 164:18 317:9 347:4

assimilating 356:1 362:15

assist 305:21 316:15 456:25 457:15,24 458:2,4

460:4 461:7

assistance 2:24 264:10

Assistant 8:19

associate 6:24 7:16 307:22 319:17 369:25 380:9

associated 2:7 70:25 158:12,13,14 159:2 161:5

213:5 227:13 303:1 327:10 435:20 436:17 439:24

469:13 470:10 473:8 477:20

association 283:3 352:3 415:4

assume 46:10 297:9 329:9,14,14 479:11

assumed 151:23 234:5 254:24 284:13 322:10

329:13 379:25

assuming 97:17 146:3 150:11,13 157:10 171:13

283:26 430:12

assumption 69:19 322:12,18 385:5 477:15

assumptions 284:9

assure 406:26

asterisk 161:3

asterixes 158:26

ATG 462:26

atom 74:17

atomic 74:16 191:8 226:3,6,13 476:10

atoms 460:10

attached 315:5

Attagene 95:22 100:10 109:1 115:15 117:9 118:1,2

118:17 120:16 128:10 133:16 138:13 140:25

141:16,19 176:7 229:11 272:26 284:15 291:6,7

attain 472:6,10

attempt 200:13 248:20 407:21 419:23 422:11

424:9 444:11 448:11

attempted 76:7 199:22

attempting 325:3 335:13 448:6

attempts 66:9

attention 37:2 234:9 239:2 257:10 258:22 330:22

attenuating 74:5 75:2

attenuation 72:26 73:6

attribute 171:21 175:1,4

attributes 215:22,24

attributing 167:22

Atul 346:16
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audience 494:17

augment 341:1 453:15 463:21

augmentation 340:20 424:14 444:16 446:1

augmented 378:11 424:15 425:1,9

augmenting 338:22

auspices 488:20

author 134:3 281:16

authorities 26:23

authority 3:8 8:14 35:15 37:13 215:2 230:9 296:16

296:18,19 297:3,9,11,14 298:7,22,23 299:11,15

299:26 300:5,8,10,12,15,25 301:21,26 392:2

autofluorescence 108:4 111:6

automate 53:20

automated 66:25 69:22 81:13 103:25 339:7 473:24

automatically 44:24

automating 66:9,11

automation 372:20

autopilot 44:14

availability 188:2 260:26 309:18,21

available 4:6,14 10:19,20 20:13 67:3 98:25 104:17

125:25 134:17,18 140:15 142:9 144:4 148:24

153:3,10 189:3,24 192:13 215:7 238:14,18

242:13 244:17 245:16 251:6 260:14 261:14

264:25 267:21 269:15 271:16 272:21 273:9,11,21

274:13 284:1,18,21 285:16 290:6 309:20 325:23

326:1 338:21 341:12 346:6 364:5,11 366:19

414:17 417:20,24 418:17 421:17,18 425:6 433:6

433:7 437:7 443:18 444:6 448:15,19 457:12

462:17 465:7,9,24 481:24 491:1 498:11

avenue 313:2

average 40:2 46:25 47:3 452:26

Avian 20:14

avoid 39:25 322:23 362:6 427:4 473:3

aware 124:26 262:26 484:24,26 486:22

awesome 360:18

awful 310:15

axes 19:25

axis 119:9,9

Aylward 280:6

A&M 8:6 214:24

A-B 360:4

A-mode 225:21

A-ring 72:15

A-type 74:16

a.m 211:24 212:6 466:18

B

B 62:24 66:20 116:11 128:7 177:1 189:21,22 190:7

191:9 196:17 226:11,12,19,26 227:10,12 228:12

228:12 266:11 336:10 338:7,9,13 339:10 340:12

345:24 358:26 368:7 427:19 431:4,7,16 435:12

436:11 459:21 460:4

baby 91:17 121:16

back 13:15 14:15,25 15:2,14 16:22 22:19,24 23:11

28:9,9,15 33:11 38:11 41:8 47:16 52:16,22,25

53:2 63:4 70:22 72:23 75:12 76:11,22 80:10 83:8

86:15 88:8 91:4 94:12,12 100:1 103:19 104:19

105:5 112:3 116:17 120:24 129:22 136:12 139:16

142:11,19 148:16 149:11,17 154:21 156:26

169:26 172:1,3 173:21 177:19 178:7,23 181:24

182:11 194:10 198:24 199:24 205:11 210:14

215:25 218:15,23 227:21 228:5 231:2 247:4

249:15 253:17 255:26 259:6 266:21 267:16

276:13 284:23 295:14 301:15 302:14 318:9

319:21 320:6 323:4 342:16,17 345:19 354:9

362:10 365:8 389:1,10,20 393:9 400:24 401:2

421:8 424:1 425:22 427:12 428:25 431:22 432:24

437:20 438:26 466:15,16 471:18 473:20 478:23

487:24 498:9 499:12

backbone 10:15

background 4:4,21 7:19 8:14 16:12 53:5 91:23

162:4 276:13 315:4,6 334:16 361:14 367:10,12

372:4,8 373:3,4,20 382:17 384:25 433:26 434:3

463:1

backgrounds 118:8 367:16

backwards 341:18

bad 106:1 115:18 116:20 118:18 126:23 391:6

baffled 419:2

Bailey 500:14,15

balance 244:7 304:7 379:18

balanced 274:8 378:14

balancing 459:6

ball 257:8,19

bandwidth 132:4

bang 469:25

bar 110:4,5

barbituric 42:21 43:2

base 15:1 39:19 41:2,3 42:1 97:9 135:20 200:1

245:25 282:6 345:11 372:14,15 471:22

based 10:19 18:25 20:1 27:22 33:19 40:8,9,14,15

43:10 51:12,20 59:14 64:17 69:20 70:1 88:9

111:4 113:3 124:9 134:14 135:25 156:3 158:23

160:17 161:7,12 164:5 167:1 170:26 172:20

173:13 177:21 187:14 189:18 191:17 207:20

217:4 221:11 222:21,24 225:22 234:15 236:12

237:22 240:1 244:17,18 246:14 265:7,8 266:26

267:10,17 268:2,14 269:3,12 270:11,15,16

273:11 276:16 291:23 292:22 308:8,22 312:11

314:21,23 326:15,25 327:22 330:24 334:18

336:15 337:25 341:15 350:4 354:26 364:22

373:12 375:12 377:22,23 383:9 384:18 385:5
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388:6 396:26 402:4 403:23 405:9 412:9 415:21

415:23 419:13,15 422:6,7,19,20 423:19,24 424:1

424:9 428:3 431:5,7 436:25 439:6 444:17 445:11

445:12 446:20 447:18 448:18 451:22 457:3

458:20 462:18,24 467:9,15 468:12 473:23 481:13

490:4

baseline 106:14,19

bases 40:3,11,18 41:10,13,17 42:8,20 43:23 50:22

50:24 264:17

basic 32:2,11,16 43:4,5 44:21,24 97:24 126:1 316:7

320:4 327:13

basically 53:4 54:8 65:23 89:10 123:4,5 169:5,12

187:3 191:7 193:20 205:1 275:10 290:14 291:16

293:23 301:25 308:11 359:7 366:22 370:7 414:13

438:10 448:11 449:15 456:5 459:22 461:23 462:5

467:7,11 468:17 470:23 472:17 485:21

basion 338:2

basis 22:10 42:6 45:2 50:12 54:25 61:10 69:18

217:24 235:5 238:8 310:20,26 336:22 378:3

393:13 413:15 426:5 427:20 436:19 481:5

batch 68:7 113:18

batches 113:23

batteries 131:16 387:17

battery 19:5,5,16,18,20,22 20:1,6,24 22:18,25

23:21 24:25 25:3 91:23 92:19 93:17 121:21

131:14 135:6,14,19 137:14 183:18 237:9,20

238:11 241:26 359:21 386:16 498:5

Bayer 260:2 279:12,15

Bayesian 436:7

beam 87:14

beans 196:21

bear 29:10 236:18 498:15 499:3

bearing 489:20

beat 302:13 432:21 466:15 495:26

beautiful 112:26

Becker 247:7,8,10,10,11 253:3 254:8 259:3 289:26

beginning 19:20 62:21 91:13 106:7 143:1 176:26

216:3 233:14 297:4 302:1 318:1

begins 240:23

begs 355:18

behalf 233:12 260:5 282:17 296:3 496:19

behave 194:5,8 203:9 394:22

behaved 62:23,25

behaving 396:14

behavior 55:22 61:8 113:10 396:13,17 402:26

believable 117:17

believe 26:9 29:21 66:16 68:15 81:13 84:3 85:4

105:17,22 108:5,6 110:1,5,19,20 111:3 112:9,14

113:5 121:19 127:15 128:21 136:18,25 137:15

139:22 172:14 182:7 184:20 215:4 234:1,12,19

235:1,4,6,11,14,15 238:1 258:26 300:4 301:1

302:19 308:8 309:26 310:3 316:16 358:24 381:3

392:4 393:23 398:4 407:10 418:15 420:1 448:13

456:8 497:3

believes 330:18 396:10 470:24

belong 192:1

belongs 84:10 149:1 344:5

bench 460:16

benchtop 124:22

bend 155:7

beneficial 425:19 495:16

benefit 277:22,23 306:4 313:2 314:7 413:7

benzene 200:2

benzenes 201:5

benzenethiol 159:5,25

benzine 177:14 199:16

benzoates 192:8 193:7

benzophenone 143:22 151:15 173:21

benzophenones 150:6

benzopyrene 206:3

Benzothiazoles 201:1

benzotriazole 73:21 160:4

benzotriazoles 160:19

best 10:19,20 71:25 73:14 76:5 86:19 210:20 262:8

284:10 304:21 336:22 347:5 377:20 405:15 471:2

471:18 478:24,24 495:26 496:1

bet 410:15

beta 144:23 170:12 171:5 257:3 357:23 358:2

363:25 371:8,8,9 484:14

betalactamase 107:3 110:12,13,22 115:9,14 116:8

116:8 118:16 119:3 122:15 139:21

betalactamases 120:26

beta-estradiol 97:26

beta-lactamase 98:3

bets 174:11

Bette 214:16,16 229:18,25 230:3,3 231:13 310:14

391:15,15 430:17 433:20 439:13 446:4 452:1

472:21 479:1 486:5 491:10 492:9

better 12:17 13:5 24:24 26:3 34:18 86:23 89:10

122:8 195:25,26 197:24 210:8,15 228:15 237:14

243:12 256:10 261:11 293:8 295:10 300:16 305:8

306:8 314:25 322:26 323:16 330:11 331:7,14

344:10 366:5 367:1 369:20 370:13 398:10 403:2

413:17 417:10 425:10 437:11 443:1 458:17,21

468:19

Bette's 495:5

Betty 7:15,15

beyond 18:24 19:3 36:8 69:17 187:20 197:23

202:21 209:16 295:1 333:17 341:5 355:20 476:8

495:24
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BG-1 98:16 257:5

BG1 98:19 100:4 111:12 115:7,14 118:4,16 119:4

139:17,21 144:23 156:16 157:17 158:22 159:11

160:23 161:13,14 164:2 171:4 190:3 268:9

284:19 285:16 366:3 445:1 447:1,21 448:16

BG1s 120:26

BG1-Luc 245:2 446:19

bias 105:24

biased 394:12 396:9

biases 317:16 436:25

bid 131:15

big 91:21 93:3 96:12 100:24 109:6,23 111:19,20

120:19 139:5 155:24 169:16 183:18 186:13

199:10 289:16 352:5,16 402:5,14 410:15 413:10

414:11 498:26

bigger 229:15

biggest 186:8,20 360:6 402:2,19 469:25

bill 236:23 241:20

bin 241:15,15 473:15

binary 177:8 178:1,18 378:23 381:24

bind 15:4 33:10 51:17 57:1 65:7 72:12 79:18,18

82:15,25 83:3 108:24 141:17 149:14 182:3,13

185:26 264:25 327:11 345:23,24 347:23 350:5

354:21 358:26 360:24 368:2 401:17 422:21,22,23

427:19 435:18 464:14 478:24

binder 146:15 147:12,15,16,18,24 148:11,14 149:1

149:23 164:11,15 179:23 183:3 194:15 225:21,21

334:19 349:13 448:25 467:9

binders 26:3 74:16,16 79:19,25 80:3 82:8,17 84:12

169:17 243:24,25 346:9 348:7 349:14 352:13

358:10,12 397:16 399:14 400:16 403:11 406:12

407:1 420:21 437:13,14 441:8 450:2,16 452:8

453:20 456:14 459:18 460:3,18

binding 29:2 33:19,20,25 36:12,22 48:18,19,19

51:14 54:15,18,21 56:7,12,15,25 57:5,16,18,21

60:10 61:20 62:4,20,24 63:13,14,22 64:1,11,21

64:22 65:16,16 67:24 68:2,4 69:1,14 70:16 71:8

71:22 72:25 73:2,13 74:18 80:6,11,23,26 81:2

84:19 86:17 89:25 95:18,18,21 96:2,2,4,18 97:5

112:26,26 115:22 118:3,18 123:12,13 128:3

130:15,17,19,23 132:2 137:21 138:19 140:2

141:21 143:10 144:10,22 145:1,8,10,25 146:3,9

146:18,25 147:2,4,14 148:2,3,18 149:7,11,26,26

150:8,11,21,25 151:13,19 152:4,10,17,22 153:5,8

153:15 154:6,6,9,11,12,16 155:11,12,14,23,25,25

156:1,13,15 157:5 158:7,9,10 159:9,10 160:10,11

160:17 161:12,12,24,25 162:21,21,25 163:15,22

164:4,8,23,26 165:24 166:11,14,18 167:25

170:10 171:8,8 172:3 175:18,25 176:18 180:13

180:17 181:5,6,16,16,16,17,18,18,21 182:5,12,19

183:25,26 184:2,9 185:2 188:17 189:5,7 196:14

196:16 197:7,18 198:3 199:1,1 202:19 204:10

206:4 210:1 216:25 220:17 223:4 226:12 227:19

238:10 245:26 252:14,15 258:7 265:4 267:1

268:7,15,23 269:4,4,5 274:5 276:17,19 277:9,13

277:21 279:25 280:1,18,23 281:6,14 283:11

285:6,19 288:4,5 307:17,26 308:9,11,13,15,17

313:1 325:11 329:22,26 333:8 334:6,9,13 336:23

337:4,5,16 338:26 340:7 343:8,14 347:19,19,21

348:4,9 350:2,7,8,19 352:4,10 353:2,3,4,7,9

354:23,24,25 355:3,7 356:22 357:19,19,24,25

358:14 359:3,8,13,15 362:21 364:3,8,8,15 365:1

365:3 367:22 368:3,6 369:19 371:10 372:15

382:15 392:15 394:15 397:26 398:3 399:20

404:23 406:22 411:24 415:6 422:24 423:19

428:19 431:6 432:14 435:24 436:2,14 440:7,9

446:15,25 447:6,18,22,24 448:15,18 449:1,1,3,25

450:19,21 452:9 453:16,22 454:14 457:1 459:13

459:23 460:22 461:9 468:25 470:20 471:25

472:15 474:15,26 475:24 476:10 477:11 478:3

479:9,16,16,18 482:17,19 484:13,17,20 485:9

486:14,23

bindings 33:15 51:14 152:10

binding-focused 447:7

binding-related 428:5

binding-specific 447:3

binds 95:11,14 205:26 226:5 267:6 364:15 432:12

binning 243:5

bioactivated 464:13

bioactive 137:11 164:16

bioactivity 224:10

bioassay 58:9 429:25

bioavailability 63:13 65:19 156:2 305:24 321:12

324:6 367:26 368:3

bioavailable 321:17 327:12 328:14

biochemical 308:22

biod 265:9

biologic 185:6

biological 24:11 30:15,21 46:3 47:6 50:19 55:6

57:9 59:5 60:9,21 205:7 221:22 254:4 259:19,20

265:26 268:19,24,25 275:26 278:16,22 305:12

316:17 334:12 359:7 364:24 385:6,18 403:21

422:25 439:17 440:16 471:12 472:3,8 477:13

478:2 497:15 499:7

biologically 44:17 51:21 244:10,12,19

biologist 6:10,10

biology 7:22 58:11 59:2 89:11,13 140:10 184:11

255:15 403:25 414:14 415:20,25,25 490:7 494:26

biomarkers 250:9

Biomedical 275:1,3
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BioReliance 279:22

bipyridines 200:19

bisphenol 97:25 107:3 109:24 116:11 273:5

bisphenols 113:9

bit 22:8 38:21 40:19 42:7 44:6 50:1 52:22 53:3

60:6 61:12 64:24 66:19,22 70:22 83:24 85:5

87:16 88:8 91:22 92:7 98:5 99:8,19 103:1,26

107:1 108:15,21 113:18 115:12 116:7 117:19

119:1 127:25 145:7 146:21 152:2,7 157:2 160:26

163:15 171:21 188:15,24 190:11 199:23 201:3

202:10,24 205:15 216:20 227:7 231:22 239:18

241:5 242:9,23 244:3 250:5 252:6 256:11 260:18

268:26 270:18 285:26 293:8,21 297:8 335:16

346:24 348:19 400:8 416:18 428:16 432:21

433:26 445:24 452:2 459:12 480:10 494:23,24

BLA 144:22 156:11,15 157:17 158:20,25 159:11

160:19 164:3 165:12 190:3

black 97:24 107:4 109:12 110:3 221:10 251:5

273:3

Blah 481:13

blamed 383:11

blank 133:1

blend 34:20

blind 339:3 373:26

blinded 105:1,4 383:24

block 124:18

blood 463:17 464:14 484:13

blown 97:22

blue 98:7 110:25 111:3 115:10 116:23,24 117:11

blues 118:13

blunt 428:19

blush 171:12

board 262:16 279:14 432:16

body 257:17

bog 492:19

Bogart 247:9

bogged 494:11

boiler 467:6

bona 358:9

bond 61:8 226:15

bonding 196:19 438:1

border 120:21

borderline 119:26 453:7

Borger 267:8 268:19

Borgert 253:4,5

borrow 251:23 340:17

borrowed 408:2

bottleneck 386:20

bottom 66:5 75:22,26 95:17 110:15 119:12 143:21

150:19,26 151:8 153:7 155:1,9 163:26 165:10

166:13 172:11 174:3,9,12 189:19 198:2 201:26

357:9 375:3 401:25

bought 113:21,21,21

bound 159:18 185:12,12 204:14 313:3

boundaries 32:15 68:21 86:5 337:24

boundary 147:10 196:26

bounds 24:5 147:8

bovine 144:14,16 148:20 151:8 153:6,20 154:3,10

155:19 163:25,25 277:10,12,15,16,19,21 368:9

box 32:24 84:14 166:7 180:21 188:19 199:21

221:10 280:9 427:23

boxes 67:19,21 68:12 95:20 165:6,7,16 200:22

205:11 251:5 490:23

Bradbury 8:24 9:11,13,14,15 16:14,24,26 17:9

25:13 28:24 38:3,10,19 48:15 218:13 227:6,6

233:14 263:20 354:12 356:18 362:10 400:26

409:9 410:22,25 411:19 419:2,10 426:21,22

428:24 447:8 450:1 493:21 498:18,19

branch 68:18 194:1 445:6

branched 73:16 199:15

branches 67:15

breadth 425:4 439:15

break 38:22 52:14 91:3 138:12 142:11,12 190:7

205:10 223:2 247:3,5 333:1 342:16,18 343:5,18

432:23 433:4 466:14

breakdown 192:12

breaking 442:2

breakout 82:5 192:2 277:14

brevity 446:5

bridged 196:1 197:19,24

brief 5:15 88:7 247:12 277:24 334:3 433:1

briefed 3:15

briefly 8:23 91:24 213:19 282:18 390:15

bright 111:13,17 112:8,14

bring 12:12 20:18 22:16,26 23:5 47:20 48:4 53:20

93:1 94:2 108:4,18 131:25 140:20 143:4 144:3

152:21 162:14 184:25 187:11 225:17 239:1

247:26 252:2 350:14,17 370:26 371:5 376:5

393:11 396:5 447:7 497:7 498:5,15 499:11

bringing 17:1 23:2 36:12 47:16 108:3,19 124:19

125:6 277:22 355:23 414:21 497:17

brings 408:3 495:20

broad 63:9 143:14 352:10 378:20 393:1

broaden 367:2

broadened 364:9

broader 10:13 14:12 64:19 232:5 311:22 314:3

363:9 403:11

broader-based 88:19

broadest 363:20 458:7

broadly 49:3 219:2 434:18
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broken 193:7 199:23

brominated 202:23 208:14

bromine 74:6

bromophenols 201:13

brought 20:24 29:10 33:11 75:5 152:25 285:8

288:1 292:21 316:24 317:15 341:3 354:13 371:2

373:24 392:14 397:25 398:8 443:6,13 454:24

464:4 470:26 499:3

browse 104:17

bubble 38:8

buck 469:25

budgetary 418:14

buffer 409:14

build 34:5 55:12 56:13 59:8 61:19 64:1 77:24 78:7

86:11 179:9 191:22 199:24 203:19 207:5,9,25

208:4 218:15 219:26 224:22,22 276:5 333:5

345:10 375:19 394:2 453:4 483:16 486:2

builders 257:17

building 1:14 14:20 23:26 25:4 26:6 29:12 53:24

53:26 60:8 66:11 69:24 79:13 80:2 114:2 179:12

210:5,6 219:16 223:23,24 281:24 338:1 342:22

354:15 381:15 412:12 427:1 451:21 454:15

builds 492:14

built 26:9 33:18 34:13,17 53:17 59:17 60:25 61:6

77:13 78:26 79:2 218:8 334:8 353:2 356:26 378:7

378:7 422:5 426:15 446:15 447:23 473:14

Bulgaria 85:25

bullet 26:15 40:9 159:16 319:23 323:22 325:16

bullets 40:7

bunch 104:3 107:9 111:15 184:18

burden 483:3 485:26

Burgas 85:25

burning 142:10

Burris 7:24,24 124:5,6,6 125:18,19,19 135:21,22

214:20,20 225:18,19 290:11,12,22,25 291:14,21

292:2,11,14 307:14,22,23 342:3,4 349:25,26

354:22 370:2,3 374:6,7 375:23,24 380:14,15

384:3,4 388:17,18 391:19,19 395:5,6 400:3,4

404:2,3 416:26 417:1 422:17,18 428:17,18

472:23,24 479:3,4 486:6,7 493:9,10

burst 109:6,23 127:8

bursts 110:10 383:14

butanol 337:15

Butte 346:16

button 264:6 269:19

butylbenzenethiol 158:11,12,22,26 159:9,15

butylthiol 159:19

buy 102:16,16 125:13,14 126:2 131:9 228:11

Bye 212:7

bypass 129:3 463:19

B-mode 225:21

B-type 74:16 85:6

C

C 359:14,16 436:6

caco-2 368:9

Cal 390:25

calculate 106:9,11 257:2 326:3,5 378:14

calculated 226:8,13 324:3 372:4,26

calculates 75:8

calculating 326:12

calculation 43:1,7,8 75:7 226:17,25 369:14 372:9

373:21

calculations 369:13

calendar 104:15

calf 368:10

California 5:19 6:4 213:22 390:19

Caliper 229:12

call 31:5 41:1 62:23 75:20 98:8 104:12 106:13,16

106:17,21 107:20 108:1 111:6,10 112:10 113:15

115:6,12 116:10,26 117:11 127:25 135:23 136:16

136:20 147:16 148:6 149:2,23,23 155:13 158:8

164:10,11 173:1 174:5,6 177:24,24 250:14

258:20 259:4 271:26 285:4 301:8 351:9 475:2

495:24

called 58:9,9 71:4 92:25 100:5,10 108:22 116:21

147:12,24 151:24 164:12,15 173:8 174:13 175:20

226:23 238:15 278:7 281:2 322:9 324:21 354:22

354:22 373:7 386:10,15

calling 43:19 54:9 77:2 106:22 244:18

calls 108:5 121:13 151:21 156:8 164:22 165:1

166:6 175:17 179:10 233:1

Canada 7:18

cancer 6:5 8:6

candidate 32:26 311:20 312:25

candidates 467:17

canonical 363:23 364:16

capabilities 124:8 307:26 427:25 456:24 457:11

463:7

capability 87:25 145:18 426:3 440:16 456:22

capable 428:21

capacities 461:18

capacity 3:14 27:16 117:25 238:24 245:12 259:11

465:24

capillary 101:1

capped 72:17

capture 36:9 71:3 167:23 198:16 224:4 311:8

321:22 329:19 356:13 358:9 360:12 363:21 364:5

379:20 398:11 403:7,10 422:26 426:16 446:17

453:10 473:14 487:13,17 488:11

captured 31:11 87:2 321:13 364:4 371:10 446:6
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456:9 464:2 468:4

captures 303:3 382:2 462:5

capturing 313:14 381:24 460:24 488:8

CAR 483:19,24 484:8

carbon 460:10

carbons 72:24

carboxylic 71:1

Carcinogenesis 6:6

carcinogenic 343:11

care 211:25 217:11 268:20 369:12 457:20

cared 130:7

career 7:18

careful 114:26 362:7 413:8 451:1 453:3

carefully 103:2 256:2 258:18 276:9,21 326:3

carload 102:16

Carolina 6:15 214:5 391:5

carried 263:2 288:16

carriers 484:21

cartoon 95:9

cartoonish 104:23

CAS 69:5 70:11 206:16

cascade 477:13

case 48:18 55:10,15 57:19 60:10 62:12 63:20,22

68:22 71:20 72:4 73:7 95:6 107:5 108:23 110:6

125:23 137:20 160:7 161:10 177:5 179:18,21

187:10 199:17 200:1,16 216:5,19 219:25 238:7,8

245:6 265:12 285:7,24 293:23 294:16 295:5

329:13 336:23 337:4 341:19 366:2 379:11 386:5

386:7,16 406:24 407:2,19 408:8,12 417:22

421:15 436:15 445:7 447:5 449:24 455:4,6,6

461:22 464:23 470:2,15 472:13 476:1 477:25

cases 101:10 112:5 114:19,20 138:24 149:21 177:3

179:19 187:4 276:16,18 283:20 286:16 317:5,11

319:6 329:6 346:8 369:22 453:7 460:4,6 463:14

463:20,21 468:20 474:19 476:9

cast 13:5 101:24 111:13 364:11 404:14

catch 346:8 351:7 498:20

catching 195:6

categories 35:19 59:23 60:8,8 71:4,11 81:25 82:20

88:21 161:7,8 177:3 187:14 188:4,26 189:10,11

190:12 191:26 193:13 199:4 203:23 227:17 240:3

267:3 268:13 314:24 344:9,11,11 345:9 346:23

346:25 347:1 352:2,6,9,14 359:6 370:15 394:1,22

394:23 403:25 412:11 415:22,23,26 419:12,15

420:7,10,12 423:13,16 424:8 425:12 427:1,18

434:9,14,19 435:2,21 437:22 438:2,11,12,14

445:8,10 458:26 467:15 478:21 490:5,11,12

categorization 29:15 166:19 305:20 393:3 403:17

403:22,23 414:3 420:16 422:13 435:15 436:25

482:17

categorizations 317:5

categorize 417:10 435:18

categorized 415:21

categorizing 406:17 421:1

category 15:11 50:1 64:17 146:24 147:18 161:9

164:17 180:1,7,8 187:10 188:3 190:7,16,22 191:3

191:7,10 199:22 222:25 227:4 236:15 265:14

313:1,3 327:19,20,21 333:6 340:7,10 343:1 344:2

344:14 345:7,10,19 352:1,4 360:22 421:16

423:21,23,24,25 424:1 428:11 434:15,16 437:23

438:14 439:19 441:17 448:21 449:16 454:15

490:12

category's 437:2

category-based 65:5 188:15 221:16 433:14 435:6

435:11 466:26 470:21 474:10,14,19 480:21

Catherine 239:12,14

cationic 43:26

causal 254:23 255:15 471:16

causally 399:4

cause 43:12 56:25 96:9 110:15 112:25 257:18

327:25 354:17 383:16 406:13 414:8 473:4,9

492:22

caused 110:20 112:15

causes 272:14 351:18

causing 31:23 57:6 96:12 302:3 314:13 473:12

487:26

caution 202:14,21 315:18 321:21 368:22

cautionary 84:9

caveat 418:9 450:20 454:15

caveats 359:24

CCL3 299:2,6,16

cDNAs 141:11

cell 95:16 109:19,22 112:26 115:7 118:8 123:9,21

127:8 140:2,4,26 141:12 164:4 171:5 224:15

259:12 278:19 279:20 284:8 285:7,14 308:22

316:2 348:20 367:13,25 368:5 383:14 387:8,10

388:8,10 401:8,24 402:2,11 409:11,14,15,19

429:19 462:25,26,26 463:1,4,9 465:7 478:4

485:10

cells 95:10 100:21,25 101:5,10 109:20 110:25

117:23 144:15,16 171:4 182:7 279:21 325:5

355:9 365:7 367:19 368:9 463:11 465:22,24,26

cellular 222:9 367:9,12,16 396:14 399:7,10 485:15

486:24

cell-based 61:24 461:3

cell-free 96:18

center 1:13 17:6 91:7 93:11,22 229:14 233:9,13

480:20

centered 336:16 392:20 393:20

centers 87:7
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central 482:25

Centre 7:17

cents 361:24

century 11:22 12:1,6,16 216:11 218:16

certain 64:26 90:11 97:13 127:3 138:9 140:25

184:6 216:8 228:1 265:16 304:4,10 306:11,17

329:2 349:7 356:2 378:10 441:15 445:5 463:13

463:20,21 473:8

certainly 12:15 35:17 121:25 127:20 135:10 199:3

202:13 203:11 215:18 263:23 265:24 268:3

288:22 310:22 316:18 343:10 376:4 471:8 490:5

497:20

certainty 195:8 217:2,2 240:4

certificate 102:21 103:11

cetera 123:10 204:14 238:17 256:10 318:23 326:5

364:9 365:8 372:4,26 373:23 432:24 449:19,19

460:12,12 486:1,1

chain 72:21,23 73:17 159:19 177:22 178:5 187:19

306:20 424:19

chair 2:15 5:7 16:11 211:2 213:16 390:12 393:18

Chairman 499:12

Chair's 326:6

challenge 54:1 122:9 215:19 260:6 434:1,2 456:15

469:20 470:7,17 485:25

challenged 314:18

challenges 12:5,18 54:1 215:21 224:1 249:26 283:8

482:7 486:21 491:13 495:14

challenging 5:1 248:7 296:19

Chambers 7:11,11 37:6,7,7,18 50:14,15,15 214:14

214:14 231:21,22 303:12,13 391:13,13 417:17,18

463:26 464:1 491:5,6 495:9

chance 140:10 279:11 405:15 443:1

change 79:2 82:10 126:1 136:9 163:14 174:25

214:23 265:2 294:10,20 306:23 354:17 369:4

413:3,21 414:24 443:8 448:2 491:24 496:14

changed 242:13 273:16 290:4 476:13

changes 83:1 86:1 136:10 303:9 304:19 432:24

443:22 444:2

changing 12:24 55:23 270:7,11 377:1 459:12

channel 98:7,12 110:16,19 116:23,24 222:26

channels 98:7 110:14

character 205:4 459:22

characteristic 444:18 459:14

characteristics 55:23 191:8 227:20 256:8 318:19

323:23 324:4 325:7 368:12 403:24 437:23

characterization 262:10 463:2 465:9,23

characterize 40:25 42:17 70:6 245:11,12 338:9

463:6

characterized 41:9 242:9 243:10 368:23 382:18

434:18

characterizes 465:7

characters 484:21

charge 4:20 9:23 16:1 35:25 40:8,10,11,15 42:13

45:9 50:26 52:3 61:4,7 66:18 74:16 161:21

162:15 167:3 191:8 203:8 207:15 211:13 215:26

216:9 217:1,1 226:18 247:21 248:22,23 263:17

264:12,13 266:3,25 269:10 275:6,10 279:15

289:9 290:7 296:6 302:14,19,26 320:5 323:18

328:12,23,25 350:7,8 354:24 419:6,17 421:22,23

489:9

charged 4:5 42:6,8,26 44:22,24 45:14 50:2,17

71:21 73:8 74:26 201:12,23 264:17 319:11

328:13 478:11

charges 35:25 74:18 226:4,6,14 227:13

chart 38:16 153:15 296:26

charts 85:4

chased 401:21

check 98:14 206:6 217:10 274:21,24 301:15

checked 70:10

check-outs 432:24

chelators 138:20

chem 470:12

chemaxon 42:25,25 43:1,7 318:22

Chembl 346:6

chemical 7:16 8:20 10:13 14:23 15:17 16:3,23

18:15 19:9 20:3 26:2 27:4,20 28:6 29:15 30:22

31:21,22 32:2,15,22,23 33:4,10,12 34:6,22 35:10

35:13,19 36:3 39:19 40:9,11 41:11,18 42:1,13,18

43:10 44:3,15,20,23 46:3 47:13,13 48:1 53:23

55:5,9,17 56:16,25 57:1,8,9,12,17 58:20,23 59:4

59:23 60:1,6,7,7,10 61:10,23,25 62:5,9,19,20

63:9,13,16,18 64:7,11,17 65:13,18 67:18 69:7

72:11 74:22 78:4 81:24,25 82:20 83:19 84:18

86:20 88:21,21 89:13 90:8,10,18,19,21 93:11,21

93:24 94:5 97:24 99:16 100:12 101:22,24,25

102:4 103:3,12 104:7 105:2,5,14,17,25 106:16

107:4,22 108:2,13,19,23 111:2,22 112:10 113:3,3

113:5,18,23 116:20,21,25 117:20 122:1,2 124:18

125:14,14 126:7 127:20 136:18 139:10 141:20,24

141:25 142:4 143:14,26 144:1 145:7,10,20

146:16,17,23,24,25 147:4,6,9,10,11,21,24 148:5

148:7,13,24 149:1,23 150:5,24 151:17 152:14,18

152:18,23,26 153:3,10,24,26 154:1,13 156:2,24

156:25 157:1,24 158:1,3,6,20 159:6,7,17,20,24

160:8,22,23,25 161:7,8,12,14 162:1,16,17,20

163:17 164:9,13,17 167:3,11,11 168:18 170:2

173:22 175:26 177:3,17,20,21 178:4,7 180:7,9

181:10 182:3,12,20 183:11 184:19 187:4,10,14

187:15,22 188:1,3,26 189:10 192:15,20,20,23

193:14 195:12,14 202:13,26 204:12 208:25 209:1
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217:8,14 219:10,16,23,24 220:7,12 221:14 222:3

222:14,24 224:9 226:20 228:20,26 229:14 230:11

231:14,18 236:12,15 239:26 240:3,18,21 243:4

243:16,23 245:8 247:7 264:14,15,24 265:25

266:4,23 267:3 269:19,22 273:23 275:19 276:8

276:15 278:5,6 283:2 287:12,18 299:22 302:25

305:2 312:10,11 314:11,18,22 316:18 318:19

323:3,4,19,22 326:8,20,22 327:19 332:6 333:6,8

334:12,19 335:1,7,14 336:11 337:18,23,25

338:23 340:5,13 342:24 343:1,24 344:2 345:7

346:23 350:5 352:1,23 354:20 355:21 358:26

360:22 366:26 368:16,16,17 369:15 371:24,25

374:1 375:14 377:12 378:8 379:22 380:3 386:14

387:18 393:3,15 394:1 402:6 406:9,23 407:5

413:23 414:2,4 415:12 418:2,9 423:20 424:23

425:4,5 426:18 427:18 429:12 430:13 431:4,20

432:2 434:9,12,15,16,18,23 435:4,14,26 437:5,21

437:24 438:14,15,15 439:18,19 441:3,26 444:19

445:11 449:16 464:6 467:14 468:23,24 469:6,7

469:15,19 474:15,20 477:20 478:21 482:20 483:6

483:8 498:13

chemicals 1:5 2:8 5:13,20 6:16 9:9,19 13:3,6 15:2

15:3,12,19,20 18:2,11 19:26 20:26,26 21:2,14

22:5,17,20,23 23:8 24:19,20 25:18,26 26:19,20

26:22,26,26 27:4,10,13,24 28:8 29:20 30:6,24

31:10,24 32:7,10,19,20,25 33:8,22,24 34:2,7,10

34:10,23,24 35:3,5,7 36:14,21,23 38:4,6,13,18

39:10,18,22,26 40:1,4,12,13 41:20 42:17 43:11

43:12,14,17,19,22,24 44:2,5,19 45:14 47:23

51:12 52:8,10 54:4,15,18,21 55:13,15 57:24

58:19,21,22 59:6,11 60:5,14,15,18,20,24 61:1,13

61:14,16,18 62:5,8,14,15,22 63:9 64:20 65:3,6,16

68:18 70:14,15,18,24 71:3,9 72:2 73:22 76:7,14

76:18 77:21,23 78:1,11,18,21,25 79:8,17,26 80:6

80:12,17,21,23 81:3,4,6,26 82:6,15,18 83:3 86:17

88:15,17,18 89:6 90:20 91:26 92:3 93:5 94:3,18

94:21,22 95:26 96:22 97:5,10,12,18 98:26 99:2

101:17,18,21,22 102:3,15,23 103:17 105:2 109:5

110:26 114:1,19,20,20,24 115:7 116:3,8 117:10

117:12 119:10 120:9,12,20,22 125:10,12 128:25

129:3 130:13,14 132:21 134:13,16,26 135:2

138:16 140:8 141:16 142:26 143:4,9,12,17,19,23

143:25 146:8 147:18,23,26 150:3,7,15,17,26

151:1 152:4,5,8,12,24 153:22 154:15,17 155:2,7

155:9,10,12,19,21 156:12,13 157:11,20,26,26

159:19,21,24 160:1,2,5,10,14 161:9,21 163:21

164:2 165:1,6,7,15 166:3,9,14,17,26 167:1,15,16

168:5 169:18,20 171:24 172:19 173:6 174:3,10

174:10,12 180:18 181:10,14 183:20,24 184:7,12

184:13,18,18,21 186:9,11,20 187:5,14,16,23

188:16,22 189:10,22,25 190:6,16 191:2,10,11,14

191:18 192:1,22,26 193:5,11,21 194:7,9 195:6,10

195:13 198:3,5,7,19,21 202:4 203:15,24,26

204:13,21,25 205:2,19,24 207:13 208:1,21

209:17,20 210:12,15,21 213:6 215:15,17,20

216:5 219:13 220:2 222:16 223:6 224:3 226:8,18

227:16 228:25,25 229:19 230:5 231:3,15,23

232:4,5,8,9,14,19 237:6,6,10,26 238:2,3 240:16

241:15,15,23 242:26 243:3,4,13 245:22 246:18

249:13,16,17 253:11 254:3 256:20 263:5 265:5

265:14,17 267:12,12,12 274:2 275:9 276:1,10

277:14,18 278:4,15,26 280:19 283:13,14,24

284:3 285:18 286:6 288:14 295:7,12,15 296:6,20

297:4,14,16,17 298:24 299:9,10,12,19,26 300:5

300:15,16 301:3,5,6,17,20,22 302:2,4 303:18

304:4 305:22 306:14,18,18,24 307:13 311:1,17

311:20 312:12,25 313:19 314:1,12,20,25 315:5,6

317:1 320:4 322:13 325:3,8,11 327:19 334:19,23

335:4,5 336:7 338:11 339:4,8 345:12,22,24

351:20 356:9 359:3,4,6 361:5 363:19 365:2 367:3

367:15,17 368:22,23 369:1,20 377:7,9,13,21,26

378:2,10,18,20 379:9,12,14,20,22,23 382:4

383:11 385:13 386:17 387:15 390:5 392:23,26

393:1 394:1,14,22 396:17,24 397:1,11 399:20

402:3 403:12,12,19 405:13,15,23 406:7,11

407:11 408:22,26 409:5 413:18 414:6 415:5,23

416:5,6,10,12 417:9,13 418:4,13,16,17 420:3,8

420:12 422:14 423:26 427:7,18,25 429:5 430:9

431:10 432:18 433:15,16 434:26 435:7,24 436:13

436:18,19,21,23 437:6 439:7,11 441:21,23,24

442:2 450:4 451:17 452:4,8 453:11,17,23 456:13

456:19 459:20 460:21 464:18,24 469:16 470:7,14

473:4,7 487:17,25,26 490:13 492:12,18 498:6,8

chemical's 319:7 329:6 355:10

chemist 81:3 86:23 445:12

chemistries 26:4 81:8 393:1

chemistry 40:20 47:18 51:18 58:11,18 59:2 62:18

67:1 80:19 81:5 89:4 180:11,12 282:26 296:4

339:11 414:14

chemists 46:1 230:22,26

chemotypes 317:1

Chen 256:22

chief 6:5 8:1

children 483:21

chime 46:24 357:7

chimeric 479:17

chlorhexidine 153:25

chloride 43:25

chlorinated 201:2,10 202:23 208:15

chlorine 201:10
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choice 97:13,19 275:11 469:22

choked 425:18

choose 131:25 407:21

choosing 136:7

chose 68:1 102:1 134:14 153:25 280:2

chosen 51:3 265:24 271:26 325:26 385:13

Christopher 247:9 253:3,5

chronic 332:5,12 498:14

chugging 227:16

chunk 410:15

CI 43:4

circle 111:18,19

circular 494:24

circulating 484:2

circulation 322:20

circumstances 97:13

circumvent 316:7

cis 87:14 101:4,7,12 117:15 128:12,15 196:21

citation 465:14

citations 462:4

cite 324:23

cited 227:21 249:21 281:16

cites 236:20

citing 389:4

claim 183:20 210:19

clairvoyant 28:10

clarification 35:7 37:5 45:7,22 46:9 48:12 52:13

83:13 90:6 128:14 132:10 137:19 140:14 167:20

179:4 186:23 204:18 228:1 304:1 318:9 329:8

332:20 354:9 362:11 370:23,25 374:23 380:26

383:18 389:2 392:10,13 393:19 395:17 400:25

402:7 419:1,13 422:2 423:10 431:1 432:8 439:1

442:16 446:12,14 447:15 455:19 466:10 476:23

482:2 489:4

clarifications 45:9 175:10 332:22 462:10

clarified 383:6

clarifies 421:3

clarify 45:11 90:15 130:23 231:17 284:5,17 389:13

412:15 422:12,16 424:5 443:19 444:11 446:4

447:26 473:6

clarifying 187:3 188:6 317:23 421:6

clarity 25:10,17 216:3 305:8 306:5 318:6 328:10

328:15,26 356:3 392:19 401:1 402:19 410:26

class 35:21 78:3 81:26 90:19 131:12 143:15 146:12

146:13,15,16,22,24 147:6,7,7,10 149:2,3,5 150:7

159:6,17 173:22 177:8,17,21,25 178:4 179:10,13

179:14 180:18,19 187:15,23 190:9 191:23 192:19

192:20 193:6,14,20 199:22 201:17,25 228:26

259:18 313:1 375:14 383:24 386:14 387:15

406:12 415:4 431:8 434:26 436:22 465:19

classed 380:3

classes 70:19,24 78:14,24 89:13 138:17 139:10

143:14,19 159:7 164:25 177:3,7,20 179:8 181:3

187:4 188:7 189:17,18 192:2,4 193:2,14,21,22

198:10 204:3 226:20 340:5 379:10,18 386:17,18

387:18 403:12 411:8 414:4,7 415:5 421:2 435:5

classic 133:7 359:4

classical 89:12 128:26 350:2 351:10,13,16 353:5

354:22 387:12 400:15 411:9

classification 134:15 178:3 232:22 240:15 276:7

334:15 378:19 379:15 381:24 386:24 403:21

436:24

classifications 276:15,16

classified 158:1 179:23 378:21

classifier 437:11

classifiers 338:2 436:7,9

classify 178:6 403:18

classifying 350:22 403:18 436:18

clean 104:14 120:7

cleanest 107:19

Cleaning 283:2

clear 10:14 12:2,7 18:3 25:19 27:26 30:15 48:17

94:16 128:7 156:4 165:6 185:10 205:18 218:8,8

218:10 220:26 221:25 230:7 236:14,25 239:6

248:5,17 259:3 260:8 262:17 272:9,11,16 273:19

285:10 297:15 298:10 299:13 308:14 309:17

311:5 313:13 315:14 317:13 334:1 344:12 356:24

366:18 372:26 373:2 375:10 376:20,23,26 381:2

381:7 382:10 384:13 394:8 396:5 397:10 398:16

411:21 412:16 415:4 421:21 438:10 447:4 473:3

484:25 489:18 493:14 499:21

clearance 324:12

clearer 323:16 354:14 426:14 428:16

clearly 137:11,12 160:20 241:21 248:21 250:22

263:11 298:21,23 300:4 303:18 308:9,12 309:7

309:15 310:2 313:9 322:25 329:24 330:19 350:7

359:16 361:19 362:7 399:6 404:12 405:1 425:26

435:4,9 436:25 439:13 467:18,20 472:16

Clemson 5:23 213:24 390:21

clerical 21:11

climb 280:17

clock 52:14

close 4:23 120:4 196:8 261:4 333:18 360:6 480:7

499:15 501:3

closely 9:5 51:19 81:21,23 159:4

closer 194:14 332:7 340:1 399:9 415:17,26 417:11

closest 52:15

closing 15:25

cloud 448:16

cluster 119:13 352:14 376:14 386:20
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coach 36:25

Coates 500:17

coculture 463:9,13

code 85:20 106:5 341:12

coding 85:24

coefficients 138:4

cofactor 364:8

cofactors 95:13 367:12,13

coffee 142:9 342:3

coffee's 332:25

coherent 362:16 413:24 414:10,22,25

coined 251:23

collaboration 210:19 279:4 499:2

collaborations 340:25

collaborative 66:26

collaborators 85:25 93:10 98:25

collapsed 408:7

colleague 126:21

colleagues 2:22 16:5,5 17:5 316:12 498:22,24

500:11,15

collect 71:14

collected 283:11

collection 39:21 465:25

collectively 499:6

college 6:21 7:12

color 75:5 143:25

Colorado 6:9 214:3 391:1

colored 119:17

colors 74:21 115:4 119:16 254:16

column 99:11 151:6 166:2

columns 115:23 198:19

combination 21:3 226:25 314:22 336:25 388:15

398:10,15 408:6 421:19 444:22 453:13 469:11

combinations 398:12 413:17

combine 194:10 271:24 447:21 452:12 458:19

462:13

combined 122:10 385:21 398:14 442:21 458:17

465:17,23

combines 27:6

combing 458:20

combining 308:23 317:13 328:22 442:23 447:20

451:20

come 11:9,14 12:4,11,17 14:8,9 16:9 22:23 37:10

39:12 52:16 70:23 73:4 76:11 80:10 83:8 99:6

100:1 113:10 117:20 122:18 128:14 131:12 133:6

137:26 140:20 191:19 205:18 211:22 213:14

218:23 230:11 237:23 267:16 293:10 294:5

316:25 320:6 323:1 328:10 340:9,16 341:20

343:6,21 349:14 357:4 359:7,8 360:6 396:7

406:17 407:20 410:6 412:20 414:24 418:7 428:2

428:7 430:18 431:26 432:26 438:10 452:26

456:10 459:8 466:15 473:11 480:15

comes 13:11 90:9,17 100:25 109:1 129:17 131:17

153:12 181:23 253:22 265:17 283:2 311:13

373:16 410:23 415:10 428:4 454:7 465:15

comfort 178:11 376:6

comfortable 83:20 416:14

coming 2:17,19 12:3 13:9 16:22 28:14 71:23 92:23

93:12 94:2 112:2 131:2,15 133:7 136:15 139:16

233:8 291:17 293:26 309:3 315:11 324:21 335:9

346:19 361:12 381:11 390:10 407:17 409:3,8

421:1,7 425:10 445:13 495:1,2 497:8 498:9

commend 239:24 293:13 315:1 341:9 490:8 491:8

commendable 272:18

commended 303:16

commending 262:24

comment 3:24 4:2 85:16 124:1 127:11 139:26

185:16 206:21 211:21 213:12 233:17,26 261:4,4

261:19 263:25 268:1 272:5 275:17 296:2,5

302:24 304:17 310:1 315:17 325:21 329:1,4,23

330:5 332:1,17 333:4 341:23 342:11,23,26 347:9

349:26 350:11 352:21,22 353:21 354:4 360:21

362:24 365:14 370:14,25 371:13,14,23 374:14,19

376:8,10,12,20 377:7 382:14 384:19 392:24

393:25 395:22 397:25 403:7 404:19 407:17 409:7

416:18 427:13 431:2 433:14 437:12 439:9 442:8

451:19 454:2 466:24 473:1,16 474:6 476:7 480:2

480:14 481:16,26 488:14,23,25 494:6

commented 233:15

commenters 211:20 250:20 260:15 302:11 308:25

404:26 493:17 494:21 496:7 497:21

commenter's 233:8 305:17

commenting 233:7 261:12 296:3 302:12 307:23

351:26

comments 2:20,20 3:6,9,24 4:1,11,11,22 8:19 11:3

140:1 185:16 205:17 206:26 211:22 213:13,13

218:4 225:12 233:12 234:12 235:3,10 236:19

238:4 239:8,17,17,20 247:17,19,19 250:2 253:7

260:4 261:6,10 263:15 264:8 266:20 268:18

275:15 279:4,5,16 284:24 287:20 296:4 303:2

309:26 316:11 317:20 318:8,12,13 319:8,17

322:23 329:17 331:2,24 332:22,23 334:2 339:14

339:15,22 340:3,20 342:6 344:19,20 351:22

352:19 353:16,25 354:1 356:12 358:7 360:17

362:10,13 370:20 372:2 374:5,7,9,11,20 375:17

376:24 377:4,20 380:1,10,15,17,20,25,26 381:9

383:26 384:2,4,6,8,11,13,14 388:3,22 392:4,20

393:3 394:7 395:3 396:6 399:6 411:26 412:10

416:25 417:1,3,6 418:21,22 419:1 424:11 429:22

430:25 432:21 433:9 438:5 439:1,2 442:11,13
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443:5 445:14,17,18 446:2,12 451:11 454:5

455:15,20 456:8 465:2 466:11 467:5 473:19

474:23 476:20,24 479:26 481:10 482:3 489:5,11

489:16,22,24,26 493:10,18 495:5,7 496:8 497:23

498:22,25 500:3,20

comment's 375:24

commercial 336:13

Commission 480:9

commitment 2:23

committee 2:13,26 18:21 131:26 235:23,26 360:2

381:2 499:19

common 72:4 89:3 143:9 315:24 362:3 442:1

commonly 267:21

commons 72:4

communicate 261:21 263:6,11 283:7 420:14

communicated 310:18

communicating 261:19 262:25

communication 262:7,20 263:19

communications 226:1 283:5 301:11

community 56:19 377:3 497:17,18,18

community-based 497:20

Comp 233:19 234:4,14 235:16 262:16 279:23

companies 131:11,12,15 283:3

company 100:5,10 102:6,7 133:17 269:19,22

273:14 368:26

comparable 41:4 220:17 413:22 461:25 481:19

comparative 220:21 232:14 275:12 277:25 384:19

388:7 396:1 398:4 439:8

comparators 257:22

compare 167:26 169:24 173:13,19 174:17 243:16

245:21 281:22 282:1 284:4,19 294:9 304:11

367:16 401:26 413:1,4

compared 60:5 88:10 156:14 172:11 194:4 245:24

284:22 285:21 334:20 379:4 385:17 394:2 398:1

402:5 439:22 445:6 447:13

comparing 34:16 36:17 63:25,25 64:13,14 97:4

120:14 124:20 129:12 144:6,8 151:3 153:13

168:12 169:25 170:1,21 172:24 282:4 310:8

395:23 401:3 402:8,10 406:3 413:15 414:4

comparison 145:1 170:23 232:21,26 242:6 244:1

245:9,23 268:2,6,9 269:12 281:16 282:15 350:16

381:19 382:7 393:13 396:25 398:9 413:6,26

415:21 479:6

comparisons 64:14 65:15 169:7 170:3 176:3 242:8

249:14,19 254:14 269:3 281:12

compatible 162:25

compensate 313:12 324:1

compensated 183:1

compensating 443:1

compensation 486:19

competent 61:21 117:24 460:20

competently 370:13

competition 151:18

competitive 33:19 145:1 147:15,16 484:20

compiled 319:17

complaining 211:8

complement 371:1

complementary 250:10

complementation 130:16 133:3

complete 18:4 23:3 27:23,24 44:23 92:2 104:6

260:13 309:14 371:5 385:3 396:19,22 400:9

456:19 498:10

completed 4:23 7:7 21:17 30:5

completely 59:17 87:14 204:3 242:12 256:16

257:20 287:13 293:6 304:6 338:15 422:22 446:25

447:2 479:15

completes 35:23

completion 21:18 22:13

complex 182:1 255:15 336:1 396:6 399:16 416:1

485:6 486:13 487:10,19 488:6,11

complexity 358:13 435:3 482:15 483:23 487:13

492:12

complicated 71:2 86:3 181:2 313:8 484:5

complimentary 19:21 385:7 403:2,3

compliments 34:12

component 68:26 181:6 248:13 315:6 353:3,4

433:9 465:18 491:17

components 38:13 49:3 63:15 87:6 183:8 185:11

221:13 248:15 303:1,24 385:7 454:16 470:10

491:23 499:25

composition 77:12 379:11

compound 48:18 63:7,8 102:4 110:15,17,24

111:12 112:12,14 113:22 118:24 137:11 141:21

164:17 182:24 203:8 207:18 224:12 257:12

270:17 272:14 288:16 292:4,6 305:3 306:1 307:8

308:2,7 309:18,20 321:22 322:3 326:24 328:21

329:10 349:3 350:22 353:7 360:23 368:14 371:26

388:10 406:3 424:20 425:1 444:25 445:3 452:15

452:18 457:3,18

compounds 5:21 6:11 30:26 38:14 42:12 45:10

48:6,12 64:19 84:1 100:17 101:26 102:9,11 103:6

111:15,25 112:24 113:8,20,26 114:4,15 116:1,4

118:10,15,23,25 119:4,11,17,17 120:3,26 122:6

123:3,22 125:21,25 126:3,5 133:12 136:4,10

171:26 172:7,8 181:3,24 184:26 185:2,4,9,26

186:13 192:9,14 202:21 205:5,5,6,24 208:11,14

208:15 216:17,23,25 217:3 220:14,16 228:1,6,11

228:13,16,19,20 230:23 231:5,7,9 263:12 270:9

270:15 271:12 272:13 273:16 280:10,12 281:1,19

288:6 293:6 304:25 305:5 307:24 308:10,19
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309:12,18,22,22,25 310:4,5,8 317:24 318:3

320:16,24 321:4,7,15 322:17 323:8,10 324:17

327:7,13,24 328:9,13 329:26 330:21 338:25

340:11 347:15 348:22 350:3,5,9,24 352:4 358:9

379:23 381:19,25 385:22 388:14 400:12 401:13

401:15,16,17 404:12,22 405:3,7 407:19,19 411:8

415:21 417:19 422:21,22 423:1 425:8 428:22

435:9,10,18,20 436:3 444:6 451:19 453:5 459:19

460:2,9 483:6

comprehend 336:9

comprehensive 21:18,20 22:3,12 320:17 330:8

comprise 336:25 440:22

comprised 20:26 476:16

comprises 471:12

computation 465:17

computational 1:5 2:8 5:13 6:24,26 7:3 8:15 9:18

11:11,15,21 12:6,16 14:13 17:6,19 21:10 23:9

26:10 27:26 29:5 91:8 124:17 213:6 307:25 308:8

309:19,24 313:13 316:26 317:15 338:24 350:3

361:13 393:7 424:23 436:1 447:10,11,12 448:12

455:26 457:13 462:24 480:15 482:11 483:14

493:21,23

computationally 177:9 307:4 448:7

computer 8:3 108:7 308:26 421:10

computer-based 482:8

concentration 58:23 62:19 63:3,16,21 64:3,10,11

96:22 97:1,21 98:5,6 99:10,23 100:13,16 106:9

106:17 107:2,7,10,21,21 109:11,12,14,15 110:9

116:3,14 119:8 129:14 130:24,25 136:20 144:19

144:25 145:6,16 146:20 150:23 152:23 153:11

155:23 156:24 157:4,8,9,18 161:3 162:8 167:3

169:16 173:6,9,10,26 174:5,7,8,11,13,23,24

175:3,15 176:6 208:25 209:1 243:16 245:9

259:21 263:9 270:16 279:20,26 280:20,24,26

281:5 321:8 322:8,21 328:1 334:17 367:22 369:8

369:15,18 394:15,16,20 396:15,16 397:8,14

399:18,18 401:5,9 402:9,12 404:25 406:25 407:2

410:7,16 428:26 429:18 430:7 441:4 443:23

445:2 453:16 464:21

concentrations 33:2 62:10 63:19,26 109:7,21,26

118:7,7 141:20 148:25 150:16 151:19 157:21

158:7 162:24 163:7 168:5 169:9,23,25 171:22

172:6,20,26 173:16 175:23 176:11 273:3 279:19

280:9,15,16 307:7 310:11 324:1 325:4 349:18

369:7,7 370:11 397:10 401:11 411:13 429:13

453:18 487:4

concentration-response 105:10

concept 11:17 13:12 32:1,1 186:3 221:19 222:4

252:18 254:11,21 307:18 416:8 420:17 424:4

446:6 467:19,20 470:20 492:13

conception 477:10

concepts 11:24,25 14:1 29:14 218:17,22 223:15,23

256:9 321:24 466:24 470:26 492:16 499:3

conceptual 55:4 56:17 66:14,24 221:21 252:7

302:25 311:7,15 490:22

conceptually 249:25 250:6 289:18 493:5

concern 41:10 42:8 61:13 79:23 84:4 195:20 201:3

234:21 263:7 292:2 305:23 307:8,10 316:25

323:10 341:2 358:17 435:13 441:18 450:2 452:5

458:13 463:22 468:11

concerned 120:2 129:5 169:5 185:3 291:14 314:3

320:16 445:24 461:11

concerning 124:7 135:23 244:3,3 363:17 390:4

concerns 152:25 241:7 242:4,17 253:12 256:4

354:6

concert 476:17

concise 373:1

concisely 472:16

conclude 429:4

concluded 4:18 235:7

concludes 225:11 233:5 302:11 489:8

conclusion 73:4 234:26 278:25 289:5 296:13,15

301:16,24 307:16 331:12

conclusions 235:14 315:8

concordance 118:14,14 154:20 155:11,13 165:26

166:5 169:21 172:15 173:3 174:19 243:6 258:14

286:24,25 294:4,21 309:1,4,14 388:9,9 396:22,24

397:5 400:10,10,11 415:18

concordant 397:22

concrete 180:3 197:1

concur 316:11

concurrent 107:14

conditions 332:13 362:25 363:2 365:16 366:12

conduct 5:25 247:26 251:10,14 379:21 385:24

conducted 145:18 306:25 307:3 432:1

conducting 395:26

CONFERENCE 1:13

confidence 15:16,22 23:26 24:2,2,3,4 25:4,6 61:22

89:26 106:11 107:24 139:6,8 159:17 178:4 179:9

184:23 193:17,22 216:11,20 217:3,5,20 218:1

219:18,19,24 220:10 222:7,11,21 223:17 249:3,5

250:16 251:15 252:1 253:21,26 255:3 310:26

319:2 329:11 330:25,26 336:15,18 337:7,8,9,12

337:17,21,23 342:24 343:1 344:2,8,13 346:23

347:26 352:1,5,8,11,16,23 356:23 358:22 360:22

381:26 399:14 409:23,26 410:2 417:21 437:15

453:20 456:18 467:14

confidences 90:16 317:9 346:24 347:5 494:1

confident 49:22 81:22 85:26 178:17,19 179:10,13

195:7 196:5 244:5 355:10 357:8,8,9,11 401:19
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429:14

configured 337:20

confines 478:18

confirm 35:10 51:13,23 263:4

confirmation 113:16 135:3 137:20 147:13,26

148:14 493:25

confirmatory 453:6

confirmed 28:26 397:21 448:5 455:4

confirms 460:17

Conflict 3:15

conformational 210:6

confound 272:12 435:14

confounding 58:16 401:6

confused 171:21 231:23 239:15 356:14 378:16

409:10 419:3,16 422:4

confusing 93:24 410:19

confusion 54:7 316:7 322:24 351:18 414:8 424:10

427:4 500:7

congratulate 490:20

congratulated 335:10

Congress 297:18 298:8,8,9,15

conjugate 40:19 41:6,7,19,23 42:2,19

conjunction 416:16 420:5

Connect 389:17

connection 254:24

connectivity 415:17 465:26

connects 13:18

consensus 136:16,18 233:1 371:6

consensus-based 452:25

consequence 95:16 97:3 177:5 337:10

consequences 55:24 343:12 452:5

conservative 106:20,20 259:1,2,5 286:1 324:7

369:14 397:26 434:16

conserved 18:26

consider 2:6 34:19 44:1 90:3 124:11 138:25 237:25

248:14 250:10 252:23,26 261:10 262:8,14 277:18

279:2 304:4 310:10 318:23 319:8 325:7 328:22

330:6 335:24 349:21 352:13 356:20 397:24

398:13 403:17 405:5 411:17 433:5,25 436:5

476:16 477:17 482:12

considerable 313:2 328:23 363:16 366:6 368:20

373:6

consideration 31:18 128:8,25 129:6,14 170:4

202:9 280:7 306:6,14 311:21 312:20 318:12

319:26 327:5 328:18 331:14 346:4 351:3 352:5

444:4,13 448:14 459:13 471:18 487:18 488:10

considerations 25:9 237:22 240:12 265:8,8 303:5

307:1 312:18 333:11 335:19 375:5 395:26

considered 87:10 164:13 209:26 238:15 276:9,21

285:26 292:18 310:7,21 311:10 312:16 318:20

319:25 320:6 323:6,23 324:5,9 328:16 337:26

346:5 369:19 382:15,22 385:23 386:13 387:9,18

397:20 442:4 460:19

considering 4:21 35:18 37:22 51:8 185:11 188:19

277:18 305:19 428:22 453:3

consistence 470:4

consistency 135:18 180:17 262:21 365:11

consistent 32:25 56:23 130:18 140:4 185:23

275:25 278:12 279:1 305:14 335:11 339:9 372:17

407:4 436:4 488:9

consistently 136:22,23

consists 4:19 312:6

consortium 282:19

constant 40:16,24,26,26 41:1,3,4,5 324:3 413:6

constants 41:21

constituents 368:1,2

constituted 323:3

constitutes 75:19

constrain 462:12

constraints 330:19 359:8 367:26 368:1 418:13,14

418:14 437:3 450:25 459:16 492:2

constrictive 166:19

construct 100:22 251:5 310:19 439:19

constructed 394:24

construction 466:5

constructive 496:21

consultant 264:10

consultation 3:20 47:17 483:12

consulting 283:3

consume 238:11

contact 4:1,15

contacts 500:24

contain 352:17 415:5 422:14 435:13

contained 68:14 335:3

containing 80:3

contains 69:11 189:22 234:2 421:22,22,23

contaminants 21:4 299:2

contaminate 32:26

contamination 113:5 209:8

content 141:5 340:18 408:3

context 11:10 12:21 15:12 16:13 48:25 54:26 56:17

58:3,8,10 60:7,9 91:19,20 92:17,17,18 123:19,21

126:5 130:11 140:2,5 142:22 144:12 146:15

182:4,25 183:10 185:6,13 215:25 216:13 222:6

255:18 270:8 278:19 297:10 310:19 313:20

315:26 320:20 322:7 329:22 332:3 347:17 348:5

348:17,20 349:10 355:22 361:4 364:7 369:9

388:8 396:11,14 399:10 401:23 402:10,14 412:11

415:17 451:18 458:7 462:19 463:12 467:8 468:15

468:19,25 470:11 478:2 484:4 492:10,14 499:5
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contexts 25:25 123:9

contextualize 312:3 491:19

continually 270:7

continue 14:20 67:21 76:21 165:14 204:8,14 224:5

271:3 272:3 277:21 315:26 414:23,26 415:1

475:1

continued 14:25 415:1

continues 62:26 230:14 338:18 359:17

continuing 21:7 165:10 188:4

continuity 407:7

continuous 332:5

continuum 420:19

contract 93:20 229:9,11,12 230:22 305:4 418:2

contractor 279:23 383:2

contractors 383:7 500:17

contrary 286:22

contrast 281:22 338:10

contribute 143:11 343:15 351:5 493:22

contributed 2:21 167:2 500:1

contributes 343:9 453:1

contribution 250:12

control 62:8,13 91:16 93:19 96:13 126:19,23 206:2

241:4 369:22 372:5 373:4,5,7 374:15,15,16

383:13 405:25 413:2 471:23 486:15

controlled 376:1 440:15

controls 126:25,25 207:1 413:9,11 462:22

convened 304:16

convening 247:16

conversation 48:22 428:25

conversations 225:26

conversion 164:16

converted 458:3

convince 139:7

convinced 358:11

cooperativity 181:9,11,21 182:26 183:2

coordinate 388:13

coordinately 383:15

coordination 3:19,20 16:16,17 147:2 499:26

copy 239:8

copying 496:14

core 412:26 480:20,25 481:4,5 484:18

corner 142:20 151:16

Corps 7:21 214:19 391:18

correct 84:6 123:10 129:12 132:13,15,15,15

134:25 137:23,24 140:15 164:18 168:1,9,12,17

168:17,18 179:2 185:21 186:16 244:1 329:23

339:20 341:14 404:19

correctly 86:21 112:10 234:10 288:15 296:20

297:8,11 344:11 367:19 426:2 448:4

correlated 442:25

correlates 24:13

correlation 30:20 65:17 66:1 129:19 194:2 280:13

280:22 281:3,8,9 286:23

correlational 254:18

corresponding 115:2,14

corrosive 32:11 40:4,12 43:11,17,20,24 44:3 50:25

217:15 265:14,18 320:5 327:14

corrosively 265:16

corrosivity 32:17 81:14 305:3 330:6

corticosterone 117:13,14

Cosmetic 18:9

cost 31:15 231:4,5 418:8

cough 236:19

Council 247:8 296:4

Counsel 3:21

Counselm 283:1

count 136:17 270:1

countries 238:23 261:26

counts 104:26,26

couple 11:25 25:9 41:24 43:18 74:21 78:26 84:8

85:8 86:14 89:1 102:13 115:9,15,16 126:15 132:9

138:21 145:22 150:3 151:2 152:16,17 161:19

175:10 203:3 210:19 220:16 229:14,18 237:6,9

237:10 283:20 284:25 300:7 307:9 354:12 388:6

389:21 392:8 400:26 419:10 426:11 433:3 462:9

473:22 480:4 489:25

course 10:1 12:4 16:21 17:4,8 18:25 30:12 87:24

144:16 190:5 207:15 211:9 235:15 237:5 238:19

248:11 249:9 250:3 251:8 252:3,8 253:24 255:14

256:16 262:11 263:25 265:22 294:6 308:3 309:23

312:14 317:14 330:8,23 340:26 408:3 433:21

459:3 460:18 461:17 475:8 478:19 480:18 481:4

484:7 492:2

covalent 325:11

cover 34:7,22 53:1,17 54:11,17,20 59:10,15 65:11

74:12 77:19,22,24 79:1,5,13 82:23 143:15,16

188:16,18 189:10 191:19 193:6 196:4,10 198:7

205:1 220:8 240:16 338:15 364:18 403:8 438:14

439:10 441:2 446:20,26 468:24 474:19 480:26

486:25

coverage 33:12,15,24 34:6,25 35:21 53:10 68:19

73:25 74:11 75:4,16 77:3,5,10 78:17,19,20 79:3,7

79:7,10,14,15,16 82:7 83:1 84:5 190:23,23

191:22 192:11,18,21 194:1 196:7 197:16,23

201:26 202:2,6,26 204:7 220:7 223:19 335:7

336:4,6,7 340:4 393:15 435:24 458:20 481:4

483:8

covered 39:23 59:17 61:15 62:1 65:11 78:25 82:25

191:20 200:9 204:3 208:2 222:16 336:11 342:9

342:14 433:17 481:6 490:9
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covering 19:22,24 36:21 39:15 82:10 94:6 193:12

240:20 396:8 399:11 433:16 453:19

covers 54:14 83:10 188:21 190:26 203:22 324:14

cow 401:24

co-activation 484:18

co-chair 225:2

co-culturing 461:7

CPU 227:16

crack 209:10 402:22

crazy 209:18

create 99:13 177:4 425:12

created 237:4 394:23

creates 308:14

creating 273:2

credence 197:16

Creosol 265:17

cresol 81:10

crisp 411:26

criteria 14:3 15:14 29:17 32:8,9,17,24 37:16 39:13

40:15 41:17 43:10 44:15,25 45:3 46:11,19,21

47:14 49:13 68:21 74:15 117:3 121:24 135:23

147:2 166:22 196:26 217:17 226:26 231:3 270:6

278:5,17 285:3,12 288:2 290:2 292:10 293:9

311:6 319:2,3,9 320:2,10,14 321:13,17 322:7,13

323:14 324:10 325:1 327:11,16,22 328:22 329:15

329:21,24 330:6,18,23,26 350:22 354:26 365:22

366:19,25 373:5 399:1 449:14

criterion 42:15 43:6,6,9 44:11 47:13 50:2 321:20

331:1

critical 3:12 20:21,22 21:13 22:2,17 24:1 25:20,25

26:7 28:20,25 34:9 50:8 51:22 182:18 246:19

265:11 270:21 271:22 272:2 275:22,22 305:25

309:5 310:4 311:11 332:8,10 362:2 376:3 383:22

386:26 398:18 404:5,16 416:15 449:25 464:21

469:23 471:14,20 494:19

criticisms 365:25

Cronin 7:1,1 45:7,8,8,18,18,21 214:10,10 319:13

319:14,14 331:18,24 341:22,23,24 347:7,8,8

391:9,9 438:4,5 442:7,8 454:1,2 480:1,2 488:13

488:14 489:25

Crop 279:12

CropLife 247:20 260:5 275:16 279:4 282:25

CropScience 260:2 279:15

cross 126:18 133:26 209:7 321:8 339:5 399:2

413:26 490:16

crosstalk 128:16,19

cross-agency 16:7 17:2 93:9

crowds 302:13 466:15

crude 256:18,19

CRYSTAL 1:15

crystalized 210:2

crystallization 392:20

culture 162:10

cultured 368:9 465:25

cultures 367:25 463:5

curation 346:15

curious 87:5 89:5 90:2 136:2 138:1 141:1,5 177:2

177:7 179:18,22 225:21 270:2 273:12 294:3,9

current 17:17 19:15 23:16 27:8,22 28:7 29:12

31:21 44:11 204:3 233:20 235:2,5 237:2,8,20

239:6 242:7 244:2 289:21 298:21 299:9 314:6

324:10 330:2 335:13 337:26 338:10 340:2,9

346:2,7,12 372:18 422:25 424:14 457:4 480:18

481:7 488:18

currently 20:5,13 87:16 187:8 230:10 238:10

242:18 311:7 314:4 338:17 340:6,8 364:20

373:23 403:22,23 422:20 446:14 447:19 457:12

465:12 480:6 486:21

curve 64:6 97:24 105:12,13,22 106:3 107:4,8,13,18

109:13 110:3 119:9 138:26 139:3 146:1,2,9,10,19

148:3,3 150:10 151:14,19 153:5,8,16,16 158:7,10

158:13,14 160:11,16,17 161:4 177:6 293:8 372:8

373:20 402:12 410:7 430:22

curves 62:9 104:7,8 113:1 146:3,4,6 150:8,8,19,24

151:13 152:2,11 154:3 160:26 161:2,5,25 163:9

163:11 173:24 177:1 179:18 369:21 410:16 429:9

curve-fitting 104:4

custom 131:8

customary 390:15 489:9

cut 14:11 75:10 100:23 145:14 174:13 200:15

257:25 258:26 259:7,23 266:24 268:11,16,23

278:4,17 305:6 319:2,7 321:25 325:1 329:12

369:8 437:18

cutoff 44:6 50:16 51:2 65:7 66:21 67:26 68:25

89:25 106:14,18 107:17,22 150:10,17,25 151:12

151:17,20 152:12,15 153:7 154:22,23,23 155:16

155:23 157:4 173:9,26 174:22 175:15 177:23

199:3 256:15 327:24 332:2 407:3 452:9

cutoffs 36:4 45:25 50:18,19,23 167:3 255:24

321:19 322:10 324:2,14 326:3,20 329:20 330:2

331:3,8,10,12 369:8 384:24 409:22 429:10 430:8

cuts 469:25

cutting 239:5 481:2

cycle 200:6 415:1

cyclers 325:12

cyclic 83:19 201:2 459:1 460:2,9

cyclohexane 200:1

cyclohexanol 85:9,11

Cyclohexanones 199:8 200:12

cysteric 459:16
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cytochrome 461:14

cytosol 65:15,16 145:4 162:21,23 171:1 180:16

cytosolic 64:2 65:20,25 66:4 145:4 148:19,19

149:19 167:25 170:20,22 171:8 238:10 448:25

449:11 467:9

cytostatic 112:24

cytotoxic 109:5 110:15 112:24

cytotoxicity 61:24 98:10,11 107:15 108:4 109:5

110:12,17,21 128:23 133:12,12 145:17 156:25

162:9 272:8,9,11 334:17 365:22 397:4 440:16

C-H-E-M-B-L 346:6

D

d 369:6 373:19

daltons 32:3

Dan 2:14 5:8,17 87:3 213:16,22 260:4 390:17

dancing 253:23

Daniel 5:10 8:17 9:12 16:14 37:4,24 38:19,25 45:6

46:7 47:7 48:3 49:9,24 50:13 51:5 52:12,18 83:12

84:21 85:13 87:3,20 88:4 91:2,6 122:13,25 123:2

123:26 124:5,12 125:17 126:11 127:26 130:20,26

132:17 133:14 134:10,22 135:21 137:4,16 139:13

139:24 140:13,18 141:13 142:8,13 167:19 170:7

171:17 175:8 176:12,22 180:25 183:15 185:15

186:22 188:8 204:17 205:21 206:7,11 208:8

209:9,24 210:17,24 211:5 213:17 215:3 225:8

227:24 229:5,16 230:19 231:10,20 232:20 233:4

235:19,22 239:10 246:26 247:6 253:2 258:24

259:26 263:14 264:3 269:17 274:14,26 279:7,11

282:8,11 290:10 293:18 295:24 302:7,10,17

303:11 307:21 310:13 313:5 316:9,21 317:19

318:7,14,25 319:12 331:18,22,25 332:14,18,24

333:14,24 339:13,16,20,24 341:21,25 342:2,5,8

342:15,19 343:2 344:18,21 345:26 347:7,10

349:25 350:10,12 351:21 352:19 353:12 354:8

356:16 357:6,15 358:4,21 360:15 361:10,25

362:9 363:5 369:24 370:2,4,6,17,21 371:12,15,19

372:11 374:3,6,8,10,12,17 375:1,7,15,23 376:7,9

376:11,17 377:4,14 380:8,12,14,16,19,22,24

381:7 382:10,20 383:25 384:1,3,5,7,10,12,16,26

388:2,4,17,19,21,24,26 389:7,19 390:12,14 392:3

393:21 394:4 395:1,5,7,9,11,13,20 396:2 399:26

400:3,5,17,20,22 402:21 404:2,17 405:10 406:4

406:19 407:8,14 408:17 410:22 411:1 412:3,13

416:23,26 417:2,4,7,16,25 418:6,21 419:9,20

421:4 422:17 423:2,11,14,17 424:6 425:15

426:21 427:12 428:17 430:16,19,24 431:12,24

432:5,20 433:2,18 438:3,6,8,18,21,23 439:4,12

442:7,9,12,14 445:16,18,26 446:8 447:16 448:22

449:21 451:3,7,13,26 454:1,3,6,22 455:14,16,22

456:2 462:7 463:26 464:26 466:9,13,21 467:3

472:19,23,25 473:17 474:5,21 475:19 476:5,19

476:26 477:7 478:26 479:3,24 480:1,3,12 481:8

481:10,15,25 482:4,9 486:4,6,8 488:13,15,22,24

488:26 489:7 490:18 491:5 492:3 493:9,11 494:5

494:7,15 495:7 499:14

Danish 474:3,3

data 15:16 20:2,8 21:13 22:1,16 23:2,15 27:14,15

27:18 28:17 31:3,5 33:1 34:19,26 43:20 54:17,20

60:5 62:1 66:7 83:7,10 86:9 90:3 91:11,14,15,17

91:21 92:1,2,3,4,10,10,13,16,17,23 93:6,19 96:13

97:4 98:24,25 103:16,19,20,21,24 104:5,6,10,13

104:13,14,15,16,17,18,19,21,24 105:2,7,16,26

106:1,1 108:4,4,8,13,13 111:23,23,26 113:6

114:12 115:2,2 117:5 119:1 120:6,10 121:7,10,11

121:12,17 122:5,6,9 124:10,23,25 125:8,16

127:23 129:8,10 130:18 134:18 137:7,8 138:21

142:19,24 143:7,12 144:4 152:6 155:20 158:2

161:21 162:25,26 166:24 167:5,7 168:4 171:6

173:13,14,19,23,24 174:17,26 175:6 176:4

177:16,17 178:8,9,9,12 183:21 188:21 189:2,3,3

189:24,26 190:6,19,24,26 191:1,11,12,13,20,21

191:21,25 192:12,24 194:26 195:1,5 198:6 202:3

203:14 206:4,5,5 209:3 220:4,15,21,25 222:19

224:13 227:1 232:15,16 233:1 234:3,17 240:5

242:5,6,10 243:14,21,21 244:15 245:21,22,24

251:5,8,24 252:2 254:13,14 255:18 256:17,26

257:1,5 259:5,24 260:24 261:25 262:1,14,14,17

263:10,12 266:24 267:7,15,24 269:14 270:14

271:11,15,24 272:3,8,11 273:9,12,13,15,15,18,21

274:4 276:14,18 279:17 280:8 281:18,23 282:5

282:16,16 283:9,10,18,21,25,25 284:1,12,18,23

286:1,7,7 288:9,10,25,26 289:5,6,13,15 290:5

291:5 293:3,5 294:8,13,24 295:4,7,13,13 305:15

305:15 308:1,19 309:1,9 310:1 314:20 315:8

317:11,15 318:21,23 325:17 326:12,14,16,18,23

329:24 331:15 334:8 336:17 338:20 339:7 340:17

341:11,16,19 342:25 343:26 352:24 354:25 356:4

361:9 362:21,23,25 365:11,16 366:23 368:18

370:8 371:23,26 372:3,5,7,9,13,25 373:3,11,15

373:19,21,22 375:6,8 376:22,25 377:1,23 378:12

379:1,3 382:9,22 383:9,18,21,21,24 386:1 394:2

394:24 395:23,24 396:26 398:6 399:21 404:6,24

405:6,8 407:5 408:4 410:13 412:9,18,19,21,22,23

413:8,22,23 414:2,5,7,10,11,14,17,21,23,24

415:18,24 416:16,21 418:19,20 419:14,14,15,21

420:4,16 421:1,18 422:5,6,7,9,26 424:18,20,26

425:6,11,20,21 426:4,9 427:6,17 429:1,6,9,10

431:3,6 432:2,9,10,11,13,19 433:6 435:10,17

437:3 439:9 440:26 441:2,4,12,14 442:21,21

443:11 444:5 445:9 448:16,19 449:13,16,24
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451:17,20,21,24 452:6,12,14 453:8,16,24 455:2

459:17 465:9,21 466:1,3 467:13 469:7 472:9,12

478:14,20 482:14,16 490:24 491:1 493:18 495:18

495:19 498:4,11,11

database 104:11,11 346:6,7 470:13

databases 336:3 337:3 380:2 414:18 466:4

dataset 136:8 140:7 161:23 413:18

date 16:20 17:14 18:23 248:8 329:20 376:2 434:12

dated 238:22

dating 365:8

daughter 456:20

Dave 279:14

david 279:8,10,13 282:10

day 2:4 10:5 31:9 36:1 44:7 93:3 95:25 211:2,19

213:4 261:3 298:11 328:4,5,8 390:3 407:12

days 4:13,23 5:2 9:7,16 15:26 16:21 37:1 44:6,9,12

50:17 140:17 256:6 261:5,7 265:6 284:25 319:10

327:23 328:2,11 332:9 392:8 458:4 496:21

DBS 177:14

DC 202:17

deactivation 475:16

deal 48:13 96:13,14 99:20 102:14 103:25 104:20

106:25 112:23 113:6,25 114:2,4,6 117:7 134:8

137:1 138:8 169:19 205:14 208:15 252:20 289:16

314:13 319:19 323:8 327:3 343:14 365:21 373:14

376:1,4 431:19 432:23 456:16 471:14 483:2

dealing 110:8 121:16 124:8 138:1 141:20 169:5

209:7 216:22 296:8 316:5 354:5 381:15 382:24

383:23 392:8 401:16,16 402:3 407:18 457:6

475:24,25

deals 264:14 296:25

dealt 126:10 296:9 323:8 331:10

death 432:21

decade 20:23 490:26 496:24

decades 27:24 365:4

decanoate 257:16

decide 17:15 113:16 117:17 183:10 220:24 403:25

443:15 455:9

decided 27:12 95:5 108:21

decides 350:1

deciding 366:25

decision 3:6 14:3 15:14 21:14 25:25 26:14 27:20

29:6 56:19 59:12 66:15 67:7,22,24 75:12 86:20

88:9,22 89:6,23 90:2 111:8 124:17 129:23 145:23

176:25 177:7,8 178:1,18 179:1,20,23,24 190:20

193:19 211:21 216:18 222:24 233:25 234:3

235:16 250:22 264:2 290:2 334:11 335:10 340:2

341:1 346:3 347:1 354:26 370:12 372:14,23

376:3 404:9 415:13 416:21 421:12,16,20,25

422:15 424:19,21 425:13 443:12 444:15,21 447:7

497:14

decisions 11:22 22:2 24:10 39:19 178:16 184:23

186:12 217:25 218:11 223:10 249:7 270:18

335:20 497:15 498:8

decision-making 10:16 445:9

declaring 32:21

decorrelate 442:26

decrease 110:15 162:11 163:3 327:26 484:2

decreased 82:4,4

decreases 82:7 487:4

decreasing 55:23

dedicated 248:7

deduced 334:25

deem 89:24

deemed 283:21

deeper 156:4 297:8

default 154:24 173:1

defensible 333:7 340:3 344:12 468:16

defensibly 338:7

defer 40:4 87:18 419:7

deficiencies 223:16

definable 325:25

define 32:15 75:25 147:10 178:4 219:11 255:2

275:26 278:16 310:4 330:3,11 351:19 362:7

407:5 423:23 427:18 438:13 459:15 471:3 476:2

476:4 478:21 484:25 492:20

defined 25:19,24 26:22 30:14 58:7 86:5 136:2

169:11 177:7 178:24 189:12 193:1,24 198:4

215:19 219:21 222:4 298:4,23 308:9 323:3

325:25 329:25 330:13 334:19 344:11,13 360:1

364:14 372:13 399:13 421:13 434:19 435:4,9

437:22 443:4,8 444:22 445:3 477:20 487:16

defining 48:9 110:9 177:20 318:1 330:10 331:5

333:9 345:20 371:24 399:6 434:13

definite 240:13 299:14

definitely 39:17 77:16 87:20 127:15,19 130:12

188:7 229:23 291:24 496:9

definition 37:26 45:11 51:1 94:17 322:24 328:25

328:26 329:3 330:16 331:7,14 344:10 356:9

361:18,20 362:1 434:16

definitions 89:1 322:26 323:17 415:4 474:24

490:12

definitive 258:20

degradation 326:5 458:8

degraded 321:15 327:24

degrading 332:6

degree 15:16 203:19,21 216:16 281:3 311:12,24

394:19 410:1 451:25 452:13 456:16

degrees 251:18

dehydrogenase 162:10
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delete 111:23

deletion 341:14,15,18

deliberations 140:20 225:17

Dellarco 36:24 215:5,10 225:6,9 232:13

delta 85:19

delved 71:26

demonstrably 325:26

demonstrate 25:7 29:5 30:9,10 34:11 233:24 251:6

255:5 259:12 297:11 320:9 387:16 458:18 472:16

478:1 483:23

demonstrated 33:16,23 82:23 254:6 255:4 372:19

393:12 405:1 458:17 477:26 483:11 484:5 485:15

demonstrates 320:14 470:13

demonstrating 29:22 124:24 125:7 467:7

demonstration 29:24 124:19 187:8 234:2 257:7

298:1

denaturation 272:15

denominator 98:12

denote 67:21

dense 211:3

density 119:23

Denslow 8:8,9 132:17,18,18 133:15,16 141:14,15

141:15 170:7,8,8 214:25,25 230:20,21,21 339:13

339:14 344:18,19 357:17,17 363:6,7,7 372:11,12

372:12 374:3,19 375:7,8 391:25,25 408:18,19

481:11,12 494:7,8

Department 5:18 43:18 390:18

departure 202:17 322:9 442:1

depend 347:17

dependence 339:11

dependent 20:8 25:6 90:16 349:3

depending 146:2 159:25 178:25 192:8,19 194:8

199:24 310:24 320:22 348:5 355:26 357:21 368:4

369:3 409:1 412:19,23 444:1 452:23

deprioritized 237:22

Deputy 3:19 8:19

derivatives 367:18 434:14 435:2 438:12 460:14

derive 417:12 424:23

derived 28:16 225:22,25 234:17 365:11 377:22

422:14 437:15

dermal 314:15

describe 77:7 86:23 343:14 354:18 385:10

described 30:9 68:21 127:10 266:18 305:25 308:13

311:26 320:12 338:6 362:24 363:1 365:15,18,22

366:12 368:25 372:1,7 373:11,12,14 436:10

444:6 447:8 468:6 482:22

describes 22:4 191:19 303:4 319:15

describing 89:10 242:2 382:18 424:22

description 5:16 66:19 188:3 303:3 365:26 366:5

456:26

descriptions 366:16 376:26

descriptor 89:14

descriptors 89:8,17,19 324:2 325:19 336:8 437:26

438:1

deselection 301:8 302:4

design 97:12 129:23 367:24 458:1 460:1 485:8

designate 271:26

designated 2:3,10 3:11 213:2 390:9 497:24 500:15

designating 268:16

designed 55:2 82:21 186:21 210:12 300:21 308:12

308:16 309:5,6 355:4 356:11 428:19 439:16

designing 387:17 457:16

desire 446:24

desired 58:2 61:12

despite 99:5

destroys 49:2

destruction 41:12 42:8 43:12

destructive 305:3

detail 90:14 94:13 95:1 104:9 107:1 116:7 235:9

250:6 253:13 275:14 279:5 284:25 303:24 305:8

316:13 317:3 320:6 330:11 373:1 468:11

detailed 4:20 233:26 238:20 239:8 256:4 266:6

315:20 384:22 437:11 464:19 465:5

details 36:2 94:10,12 95:8 100:1 115:3 126:22

132:5 141:10 240:24 253:15 254:7 315:21 448:17

468:9

detect 61:20 111:21 291:22 334:24 349:17,17

385:12 399:16 404:15 441:21

detected 165:19 166:26 173:6 309:11,12 394:13

detecting 169:17 174:11 384:20 451:17

detection 105:11 128:3 365:20 383:14 385:11

detergent 43:26 138:11,25

detergents 138:4,19 175:20

determination 20:1,3 25:16 51:20 230:13 268:23

determinations 498:16

determine 19:11 20:8 39:13 46:2 54:17 88:18,20

145:20 147:15 159:26 167:6 194:10 230:5 241:14

271:19 296:15,22 298:11 299:21,25 301:4,11,20

306:1 311:12,17 428:23

determined 64:20 164:6 299:5 415:9 435:5 440:26

determines 297:3 300:14,24 301:26

determining 24:19 25:18 54:19 77:10 142:23

188:21 189:1 307:11 362:22

detoxified 464:15

detoxify 464:11

develop 18:9 19:7 35:2 70:4 204:11 207:8 224:2

239:25 242:19 248:1 251:12,14 252:26 303:16

313:22 333:5,12 337:23 361:8 462:25 466:24

492:6

developed 11:1 34:26 86:9 104:8 220:1 248:15
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268:13 269:23 271:22 279:4 299:3 310:18 312:7

322:5 331:13,16 335:23 465:18 468:2 469:16

473:23 477:16 478:12,17,21 479:6 480:7,9

481:22 482:21 486:26

developers 24:2 267:4

developing 87:6 91:23 92:8 96:15 164:17 179:8

188:4 197:1 201:17 204:20 245:4 246:10 272:18

299:6 331:9 332:10 336:1 376:25 470:20 471:19

474:2 481:18 482:7,11,25 492:20 500:2

development 7:8 8:7 16:6 17:5 20:16 53:3,14

82:20 209:13 246:5 251:24 252:20 325:6 333:4

370:9 377:21,24 386:12 416:17 426:5 427:8

469:12 470:8,9 477:3,5 483:21 485:19 492:11

498:14,23 499:26

developmental 265:11 488:8

developments 239:25 383:22 463:10

deviation 106:18,19 107:5,14,16 118:21 135:24

devise 469:3

devoted 94:22

de-selection 261:24

diacetate 153:25

diagonal 119:11,19,20 120:19

diagram 31:13 66:24 230:12 255:14 421:11 427:20

444:8,14

diamond 39:6 421:12 444:21,24 448:20

diamonds 444:14,17,20 445:9 446:17 447:8

diaquat 200:19

dictate 349:8 450:26

dictated 327:1

dictates 123:22 140:5

difference 44:8 65:25 101:4 254:10 297:18 328:4,6

410:16 461:17 494:14

differences 77:9 87:22,24 123:22 148:17 169:16

170:15 171:7,9 220:20,22 266:15 268:15 315:20

316:6 349:3,3 384:23,25 385:4 388:12 398:23

451:16

different 12:8,10,11,13,13,21,26 13:23 14:4 15:8

19:22 28:6 30:2 42:7 45:2 48:23,24,25 49:6 54:6

54:8,21 56:20 57:7,10,11,13,22 59:25 60:13,15

60:15 61:7,8,9 62:6,7,7,20 63:9,18,25 64:23,24

69:10 87:15 89:1,6 90:16 99:8,17,22 100:23,26

103:21,23 105:6 112:5,6 113:23,24,24 117:6,6

120:14,15,15 123:9,15,18,19,22,25,25 131:24

138:21 140:2 141:3,17,19,22 144:14 157:6,7

160:7 162:19 163:7 168:15 169:10,23 171:12,22

178:14 180:14 181:3 182:2,11,16,17 185:25

189:2,11 196:15 206:1 216:20 221:12,22 226:14

228:18 229:3 231:24 232:1,6 251:18,19 256:12

257:1 258:12,15 266:13,13 270:3,4,18 277:26

285:2,23 288:2 297:7 304:15 305:22,22 311:3

314:16 315:22,25 316:3 317:10,14 326:18 329:10

330:3 347:18 348:17,20 349:10 354:20,26 355:4

355:25 356:1 357:2,25 362:4 364:24 366:24

367:4,5,16 368:9 375:25 379:9 382:5 386:17,19

386:22 387:18 388:8,10,14 392:12 394:9 396:12

396:18 398:12,15 399:8 400:8,15 401:9,22,24

402:9,10,11,13 403:4,8 407:23 408:4,16,16,24,25

412:1,9,11,20 413:17,18 414:2 415:15,25 418:16

420:18 426:11 427:1 428:6,13,14 429:10,11,13

431:11 432:12 435:1 440:11 443:2 449:8 451:20

452:13 461:17,18 462:14 463:7,9 470:16 471:12

471:15 477:24 484:4 485:6,8 486:25 487:25

492:25 499:23,24

differential 173:15

differentiation 485:13

differently 117:7 146:4 221:4 254:17 330:3 384:21

408:6

difficult 27:23 242:8 267:5 271:6,17,19 273:10

284:6 298:22 313:22 366:1 368:21 376:4 401:12

431:19 440:17

difficultly 434:13

difficulty 48:9 345:9 460:5

dig 117:19,26 132:5 297:7

digest 100:25

digging 117:17 121:2 156:4 418:3

dihydroxy 337:19

Diisocyanates 201:16

dilute 99:26

diluting 99:24

dilutions 368:17

dimensional 227:11,14,17,18,20 336:8,8

dimensions 87:5,7 321:9 471:13

dimerization 95:18 137:8,13 181:8,12,22 183:26

364:7

dimerizes 95:12

dimers 133:6

diminishing 125:13

dioxin 369:2

direct 5:23 43:1,7 46:12 48:7,11 245:9 249:14

423:4 445:13 447:11 462:17 463:22 480:20

487:25 500:24

directed 18:9 83:25 236:23 335:14 440:19

direction 68:18 246:23 248:25 493:19 495:12

directive 56:23,24

directly 42:26 128:6 315:25 351:1 361:7 413:1

426:25 428:23 456:13 463:17 477:16 482:22

484:10

Director 6:24 7:16 9:14 16:15 263:21

directors 263:21

disadvantages 459:3
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disagree 121:13

disagrees 299:13

discard 108:12,13

discern 159:15 220:11 222:12

disclosure 3:18

discordance 143:11 155:6,24,26 157:6 164:11,15

165:20 168:16,25 169:22 171:20 172:11 173:4,15

175:2,5 176:15 182:24 187:6,24 244:2

discordant 120:25 159:7 164:22 165:1 167:15

169:18 245:13 395:25 398:3,23 420:9

discounted 323:11

discovery 352:8

discreet 237:10

discrepancies 369:17

discrepancy 113:19 169:9

discrete 38:13,18 70:2,7,23 71:2 78:1 335:3

discriminating 311:23 312:22 317:24 492:1

discrimination 311:12 317:24

discuss 143:10 157:2 183:6 205:16 216:12 241:5

250:1,11 264:12,16 319:6 329:5 467:22

discussant 303:12 307:22 319:13 333:25 356:17

363:6 369:25 394:4 433:19 456:2 467:3

discussed 57:8 63:24 135:16 137:8 144:21 145:6

218:22,23 220:6 235:3,9 246:6 316:13 323:18

348:13 349:6 363:3,10 366:14 373:16 393:1

419:18 443:4 467:19 474:10

discusses 155:5 245:18 246:15

discussing 47:21 134:9 243:2 411:21 490:21

discussion 4:12 17:10,22 51:9 57:3 90:14 107:9

133:24 168:14 187:4 234:11 240:26 241:4,10,16

241:24 244:20 264:19,26 265:3 279:18 285:9,14

294:1 303:14 308:4 329:19 340:16,24 343:5,6,18

343:21 353:14 356:25 357:4 362:10 363:14,16

365:19 366:6,16 367:8,15 368:20 369:11 371:7

373:6 377:24 379:23 387:20 390:11 392:11 401:2

408:1 419:4,5,11 422:12 433:22 442:22 443:20

443:23,24 447:26 450:10 452:2 465:16 473:2

discussions 5:1 11:23,26 13:12 14:17 99:7 240:6

242:14 319:18 381:10 495:15

displace 174:9 484:13

displacement 62:4,9 96:19 144:18 145:3 146:1,2,7

146:21 147:22,22,24,26 148:13 150:12,15,22,24

151:25 154:3 158:15,15,18 161:26 162:22 163:25

164:1,1 174:25

display 308:11

disproved 256:7

Disrupter 213:6

disrupters 238:21 262:12

disrupting 5:12,21 6:11 57:6 263:5 278:4 313:19

351:20

disruption 7:23 8:7,10 9:8 16:23 245:19 268:21

345:4,13 359:10,22 364:20 468:8 469:13 470:15

471:16 487:3

disruptions 471:15 483:9

disruption-related 470:22

disruptor 1:4 2:8 18:20 122:2 386:25 482:19

483:10

disruptors 8:16 56:24 263:1 271:10 482:23 484:10

485:11,20

dissemination 262:10

dissociation 40:16,24,26 41:3,4,5,8,21

distance 464:16

distances 227:13

distinct 278:19 320:26 385:7,14,20

distinction 37:16

distributed 80:8 500:4

distribution 188:25 189:18 305:4

distributions 336:7

diverse 56:26 65:16 185:26 304:11 338:7 380:3

403:7 404:12,15 407:19 435:13 436:2 456:7

468:24

diversity 16:8 201:24 403:11 404:4 405:13 407:6

divided 427:16

Division 6:24 16:16 142:15

DMSO 102:25 114:1 208:23 228:9 272:13 373:6

374:15,26

DNA 95:14,18,21 181:6,9,18,21 183:26 185:13

186:12 347:19,19,21,23 353:4,9 357:19,24

363:24 479:9,16,16,18

dockers 210:20

docket 1:7,9 4:8,16 139:1 211:12 213:8,8 261:15

263:23 267:26

docking 227:17 340:23 431:26 432:8,15 473:24

document 21:11,11,22 22:4,10 26:21,23 35:25

254:1 260:8,11 264:19,26 265:13 267:8 269:5,7,9

276:14 285:15 289:24 315:19 326:8 354:4,14

462:4 481:23 489:13

documentation 135:11 140:24 269:1 382:24

documented 325:24 364:23

documents 4:5,12 237:3 248:20 260:7 261:3

267:25 320:13 376:21 378:16 489:18 494:20

dodecane 337:19

dodecyl 177:15

dodecylbenzwnesulfonic 161:24

dodecyltrimethylammonium 43:25

doing 9:6,9 12:26 23:26 24:7 25:2 26:18 32:14

34:8 41:11 60:6 89:17 91:16 92:6 93:19 94:23

97:8 99:4 104:14 115:26 118:8 127:14 130:9

133:17,19 136:15,19 137:2 138:22 159:23 163:6

168:3,5 170:4 180:10 181:14,24 186:14 207:22
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208:3 209:2,14,21 221:3 224:26 233:20 240:17

245:10 250:7 253:11 255:3,17,20 258:6 283:8,19

284:6 301:23 302:2 316:1 323:5 339:19,21

347:13,26 348:1,2 354:23 359:11,19 387:25

407:2 408:21 414:5 417:19 426:13 428:9 431:20

431:21,26 451:2 457:7 465:21 467:12 474:26

475:4,6 491:11,18 495:23 497:16 499:19

domain 14:24 25:24 26:1 33:15 34:22 48:19 53:17

53:22 54:16 58:9 59:7,10,11,14,15,17 67:25

70:17 77:24 79:17 80:6 86:22 144:10,22 154:9,11

156:1,16 181:5,6,17,18 210:1 219:7 222:17

243:23 321:20 331:5 335:1 339:5 347:21 350:8

357:19,20,24 393:15 411:24 433:17 434:22

457:12 479:9,10,16,16,18

domains 53:16,16 210:13 323:19 325:25 330:4

357:25 474:20 484:17

Don 500:17

donating 61:8

donor 40:22 226:15

door 112:8

dose 62:22 112:12 129:16,21 137:21,22 150:10

173:26 209:19 252:21 256:5 280:3,7 281:26

369:21 394:20 397:13 429:9 430:22 441:4,8,10

449:17,26 450:11,12,18 453:21 464:6

dosed 95:10

doses 97:11

dose-response 19:14

dosimetry 282:2

dosing 321:7

dotted-dashed 107:16

DOT8 43:19,24

double 34:3 217:10

doubt 495:10

Dow 269:19,22

download 250:25 292:22,25

downloaded 273:14

downstream 24:13 30:17,20 47:25 57:10 95:16

96:4 182:5,14,14 291:16 343:9,16 364:23 404:11

445:10 446:17 477:12

dozens 105:6

dr 2:1,14 5:8,10,22 6:2,8,13,19,23 7:1,5,11,15,20

7:24 8:1,5,8,12,17 9:12,13,14,15 16:14,14,24,26

17:9 25:13 28:24 34:15 35:1 36:1,5,6,10,15,20,24

37:4,6,7,18,24,24,25 38:3,10,19,19,21,24,25,26

39:4 45:6,7,7,8,12,18,20,21,26 46:7,7,8,15,17,23

47:7,9,15 48:3,3,4,15 49:9,9,9,11,20,24,24,24,26

50:6,13,13,13,15,21 51:5,10,18 52:2,12,18,19,21

76:24 83:12,13,14 84:21,23 85:13,15 86:7 87:3

87:20 88:4,5,6,12,25 89:9,20 90:5,13,18 91:1,2,6

91:7,9 97:4 122:13,14,14,15,17,21,23,25 123:1,2

123:3,11,13,15,17,26,26 124:3,5,5,6,12 125:9,17

125:17,18,19,26 126:11,11,13,14,20 127:1,4,6,12

127:13,26,26 128:1,11,24 129:7,11,22 130:20,20

130:20,22,26,26 131:1,6 132:9,14,16,17,17,18,22

133:14,14,15,22 134:10,10,11,12,19,19,22,24

135:21,21,22 136:6 137:4,4,6,16,16,18,24,25

138:6,23 139:2,13,13,13,15,20,24,24,24,26 140:6

140:12,13,13,14,16,18 141:8,13,13,15,24 142:8

142:13,16,16 143:6 156:22 157:1 160:20 165:25

167:19,20,21 168:2,9,13 169:4,14,26 170:6,7,7,8

170:18 171:7,17,17,18 172:3,5,13,18,21 173:12

173:17,18,20 174:15,20 175:1,8,8,9 176:12,12,13

176:17,20,22,22,24 177:11 178:22 179:3,6,15,25

180:4,23,25,25 181:1,26 182:22 183:5,15,15,16

184:24 185:15,18 186:22,22,23,26 187:12 188:6

188:8,13,14 204:17,18,19 205:16,21,21,22 206:7

206:11,15,20,22 208:8,8,9,18 209:5,9,9,11,24,24

209:26 210:4,17,18,24 211:5,8,11 213:1,16,17,24

213:26 214:2,4,6,8,10,12,14,16,18,20,22,24,25

215:1,3,10 218:13,24 220:5 225:6,8,8,18,19,24

227:6,24,26 228:4,22 229:5,6,7,10,16,16,16,18

229:22,25,26 230:3,19,20,21,25 231:10,10,10,12

231:20,21,22 232:1,3,13,18,20,20,21,24 233:4,14

235:19,22 237:23 239:10,14 246:26 247:2,6,8,9

247:10,11 253:2,2,5 258:14,24,25,26 259:3,26

260:3 263:14,14,16 264:3,4,7 267:8 268:18,19

269:17,18,21 274:14,16,20,22,23,25,26 275:2

279:7,10,11,13 282:8,9,10,11,13 290:10,10,11,12

290:15,22,23,25 291:3,14,19,21,24 292:2,8,11,13

292:14,16 293:18,19,20 294:12,15,18,20,22,26

295:2,3,6,8,9,17,20,22,23,24 302:7,9,10,17

303:11,12,13 307:21,22,23 310:13,13,14 313:5,5

313:6 316:9,9,11,21,22,23 317:19,20,21 318:7,14

318:25 319:12,14 322:23 325:8 329:23 331:18,18

331:19,20,22,22,23,24,25,26 332:1,14,15,16,18

332:21,24 333:14,14,16,21,24,24,26 339:13,13

339:14,16,16,18,20,21,22,24,24,25 340:24

341:21,21,22,23,25,25,26 342:2,3,4,5,5,6,7,8,15

342:19 343:2,2,3 344:18,18,19,21,21,23 345:26

345:26 346:1 347:7,7,8,10,10,11 349:25,25,26

350:10,10,11,12,13,14,18 351:21,22,23,24

352:19,20,21 353:12 354:2,3,8,12,13,21 356:16

356:16,18,19 357:6,11,12,15,17 358:4,4,6,21,22

358:24 360:15,15,16,18,19 361:10,10,12,25,25

361:26 362:9,10,14 363:5,6,7 369:24,25,26 370:2

370:2,3,4,4,5,6,6,7,17,18,19,21,24 371:12,14,15

371:17,19 372:11,11,12 374:3,3,4,5,6,6,7,8,8,9

374:10,10,11,12,12,13,17,19,24 375:1,7,7,8,15

375:15,16,23,23,24 376:7,7,8,9,9,10,11,11,12,17

376:18,19 377:4,14,14,16 379:24 380:8,8,10,12
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380:12,13,14,14,15,16,16,17,19,19,20,22,22,23

380:24 381:1,7,10,15,17 382:1,8,10,20,20,21

383:25,25,26 384:1,1,2,3,3,4,5,5,6,7,7,8,10,10,11

384:12,16,26,26 385:1 387:24 388:2,2,3,4,4,5,17

388:17,18,19,19,20,21,21,22,24,24,25,26 389:7

389:12,19 390:1,12,14,20,23,26 391:3,7,9,11,13

391:15,17,19,21,23,25 392:1,3,9 393:21 394:4,5

394:6 395:1,2,3,5,5,6,7,7,8,9,9,10,11,11,12,13,19

395:20 396:2,2,3 399:26,26 400:2,3,3,4,5,5,6,17

400:17,19,20,20,21,22,26 401:8 402:21,21,23

404:2,2,3,17,17,18 405:10,11,11,12 406:4,4,5,19

406:19,20 407:8,8,9,14,14,15,16 408:5,17,18,19

409:9,12 410:22,22,25 411:1,3,4,19 412:3,7,13

412:13,14 416:23,23,24,26,26 417:1,2,2,3,4,4,5,7

417:7,8,16,17,18,25,25,26 418:6,6,7,21,23 419:2

419:9,10,20,23 421:4,4,5 422:10,17,17,18 423:2

423:7,9,11,12,14,15,17,18 424:6,6,7 425:15,15

425:17 426:21,21,22,24 427:12,14,15 428:17,17

428:18,24 430:16,16,17,19,19,20,24,25,26 431:1

431:12,12,13,24,24,25 432:4,5,5,6,7,15,17,20

433:2,13,18,19,20 438:3,3,5,6,6,7,8,8,9,18,18,19

438:21,21,22,23 439:4,12,12,13 442:7,7,8,9,9,10

442:12,12,13,14,14,15 445:16,16,17,18,19,20,21

445:25,26 446:3,4,8,14,22 447:8,16,16,17,25

448:22,22,23 449:21,21,22,23 450:1 451:3,3,4,7

451:13,26,26 452:1 454:1,1,2,3,3,4,6,6,7,22,22

454:23 455:2,14,14,15,16,22 456:2,3,4 462:7,7,8

463:26,26 464:1,26,26 465:1,2 466:9,13,21 467:3

467:4,5 472:19,19,21,23,23,24,25,25,26 473:17

473:17,18 474:5,5,6,8,21,21,22 475:19,20,21

476:5,5,6,19,26 477:7,7,8 478:26,26 479:1,3,3,4

479:24,24,25,26 480:1,1,2,3,3,4,12,12,13 481:8,8

481:9,10,11,12,15,21,22,25 482:4,9,9,10 486:4,4

486:5,6,6,7,8,8,9 488:13,13,14,15,15,16,22,22,23

488:24,24,25,26 489:7,14,16,17,19,21,23,25

490:18,18,19 491:5,5,6,10 492:3,3,4 493:9,9,10

493:11,11,12,20 494:5,5,6,7,7,8,10,15,15,16

495:7,9 497:25 498:18,19 499:14,15,16

draft 262:25

draw 29:25 32:25 33:1 201:22 234:8

drawback 350:23

drawbacks 313:13

drawing 10:24 133:1

drawn 26:23 47:18 64:24

drawn-up 66:24

drew 33:13

drill 256:2

drinking 18:14,16 21:3 26:24 27:3,5 31:9 35:11

37:10,15,19 54:4 77:20 78:15,21 79:8,26 208:1

215:14 296:7 297:13,20,24,25 298:2,5 299:4,20

300:2 377:13 416:6

drive 1:15 215:21 281:8 304:5

driven 306:7

driver 420:1

drivers 57:9,12 215:12

driving 190:15 191:4 226:24

drop 63:3 155:14

dropped 162:8,24 271:13,14

drove 394:18

DRP 488:17

Drs 307:14

drug 8:4,7 18:8

drugs 6:17 430:9

ducks 225:15

due 27:16 73:6 74:5 152:15,24 153:11 172:6

173:15 175:2,5 321:9,11 322:19 327:12,26

394:14 409:22 410:16

Duluth 103:2 108:20 132:1 169:12 232:17 265:24

duration 44:7

duty 495:24

dwellers 75:26

dye 43:5

dyes 138:24

dying 409:14

dynamic 99:3 379:7 459:14

dysfunction 478:4

Dysregulation 386:10

E

earlier 53:12 57:3 66:19 78:17 95:25 101:23

110:12 127:9 131:2,19 132:10 137:17,20 144:11

144:19 145:7 148:5 152:19,25 158:9 162:16

165:25 167:13 168:14 169:24 171:7 176:26

180:11,12 187:3 191:17 208:13,16 216:5 259:20

277:6 278:2 285:2 289:26 294:7 295:3 308:4

313:12 335:16 339:9 351:25 371:7 377:19 383:6

386:6 387:24 399:6 418:11 424:11 436:4 443:5

444:9 493:20

earliest 312:22

early 30:19 66:8 88:19 135:5 215:13 302:13

304:16,26,26 310:20,23 370:12 377:17 403:9

420:22 458:12,15 471:26 485:19 500:25

earth 232:9

easier 296:14 361:23

easily 67:14 213:18 266:21 290:3 364:25 444:6

456:14

easy 70:23 127:22 272:23 487:12

EC 257:2,6

ECCVAM 98:20 114:25 134:6 243:2

echo 310:15 313:6 389:3 495:4

eclectic 267:6
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eco 265:7

ecological 20:14 263:8

Ecology 142:15

ecosystem 5:26

ecotoxicologist 5:25

ecotoxicology 25:11

ECVAM 244:24

Ed 7:20 214:18 227:26 307:20 391:17 400:7,13

404:4 407:10 464:3

edge 187:24 481:2

EDP 390:4

EDSP 17:26 20:21 22:4 23:8,10 26:19 31:25 33:26

34:7 35:9 37:17 39:10 54:2 76:14 77:3 197:9,25

198:14,18 202:20 203:7,26 213:6 232:4 233:21

234:20 235:1,2,5,7 236:4 247:25 248:2,4 254:23

254:26 255:1 274:3 280:17 281:6 282:6,16,20,24

288:26 290:13 291:2,17,26 292:4 294:13 295:13

296:21 298:7 299:11,26 300:9,10,12 301:17,26

302:25 303:6 309:1,4,13,15 310:6 320:2 322:16

377:11 378:8 390:4 496:25

EDSPR 279:25

EDSP21 21:9 24:16 36:25

EDSTAC 18:20 28:10,15 32:1 38:11 39:24 320:23

EDSTAC's 20:22

educated 240:19

educating 356:17

EdWARD 7:20 214:18 227:26 232:21 313:6 332:1

361:26 369:26 374:5 375:16 380:17 384:6 388:20

391:17 394:6 396:3 402:23 406:5 412:14 418:7

419:23 425:17 426:24 438:22 445:17 449:22

455:15 462:8 472:26 479:26 486:9 492:4

effect 41:15 53:12 57:19 60:18 63:23 72:17,21 73:1

97:18 142:2 159:12 161:1,13,16 162:13 163:1,2

217:18 236:12 252:13 314:6 322:21 327:10,25

367:21 369:2 406:2 415:26 440:5 471:17 473:5

483:20 487:26

effected 164:25

effecting 440:23

effective 12:17 107:20 273:25 308:21 369:14 464:9

477:5

effectively 303:10 322:17 497:6

Effectiveness 233:10,13

effects 7:4 8:4 17:6 18:12 19:3 24:13 31:22,23

32:12 55:5 56:8,26 57:10,26 64:17 159:1 167:3,4

234:7 252:15 257:18 299:8 314:3,23 362:5

364:23 368:11 385:10 386:12,18 392:22 399:16

403:7 406:2,10,13 412:18 415:16 416:15,21

420:22 423:13,19 424:9 425:14 436:23 437:1

445:8 449:26 463:12 468:1 471:16 473:9,13

485:20 486:15 488:4,11 498:14,15

effects-based 29:15 35:19 60:7,8 82:20 236:15

394:24 403:17 416:9 418:20 419:12,21 423:15

effects-driven 415:21

effect-based 333:6 334:6 403:19 421:15 424:16

477:5

efficacy 292:11 310:5 349:17

efficiency 433:14

efficient 12:19 34:5 225:1 273:25 434:10 435:7

464:5 477:4

efficiently 17:20 303:9 307:3 323:9 398:5 497:6

effort 10:2,5 15:26 16:3,7 17:2 21:7 23:4 66:26

98:23 136:26 209:21 210:23 223:3 225:2 236:5,8

246:8 303:25 304:5 310:16 313:17 314:7 354:19

398:19 418:12 420:16 434:11 437:13 471:9 481:5

483:7 490:23

efforts 9:18,26 10:13 14:13,25 21:7 28:13 159:26

232:7 236:10 262:25 263:2 282:1 303:16 304:7

367:20 398:21 403:15 415:14 480:20 490:26

492:7

Ehrich 6:19,19 331:22,23,23 342:5,6 350:10,11

354:2,3,3

eight 34:11 51:15 74:12 80:12 83:9 91:24 93:16

94:9 104:22 105:19 112:23 115:6 121:18,26

123:8,18 131:4,25 132:7 134:14 156:12 189:3,26

190:2,18,21,24 191:13 192:23 203:14 229:8

271:25 273:6 278:6 285:13 360:13 366:18,19,21

370:26 371:1,2 376:2 377:25 379:12 383:9

384:20 385:4,13,18,21 407:24 428:4

Eighty 166:16

eight-level 104:8

either 74:6 93:20 94:24 108:2 112:10 116:10

158:21 159:20 160:14 180:19 184:9 198:23

226:20,22,26 234:3,24 255:1,2 257:1 258:16

276:16,18 286:13 288:3 314:21 317:15 357:26

358:26 399:4 407:24 412:16 414:23 420:24

425:25 427:6,8 450:23,25 463:5 479:14 487:21

488:6

elaborate 85:23

election 131:3

electronic 40:8,10,15 226:15 227:19

electronically 330:14

electrophilic 325:9,10

electrophoresis 101:1

element 122:16 348:15 364:16

elements 181:9 246:16 353:10 363:23 364:8 367:6

eleven 282:24 283:8

elicit 321:22 324:1 456:13

eliminate 258:17 264:18 318:3 327:17 350:9 406:1

eliminated 327:20 453:12

elimination 328:1
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Ellen 282:12,13 290:15,23 291:3,19,24 292:8,13,16

294:12,18,22 295:2,6,9,20,23,25

elucidate 378:15

eluded 205:12

email 211:25 239:7 333:17

emails 496:13

Emax 106:12

embedded 12:9

EMBL-14 470:12

embryo 487:5,10,20 488:2,7

emerging 12:9 23:16 28:7 29:12 337:26

Emeritus 7:6

emphasis 345:24

emphasize 36:6 217:23 348:24 359:17 388:7 400:7

417:8 418:12 469:14,24 490:1

emphasized 30:4 80:18 362:1

empirical 15:7,10,16 90:3 124:10,23 219:1 220:20

221:10 227:1 228:2 308:19 356:4 419:14,14,15

422:5,6,7 424:19,26 425:11 426:7 429:1 431:3,6

432:9,13 455:25 459:17 465:17

empirically 422:14

employ 13:7 217:20 220:6,25 222:5 308:22

employed 273:14 455:26 460:17

employing 10:19 28:22 221:13 304:26

empty 294:7

enable 222:14 323:7 398:10

enabled 39:21

enables 42:25 262:21

enacted 490:3

enactment 296:7

encapsulate 346:13

encodes 334:11

encompass 416:11

encompassed 500:4

encompassing 26:1

encounter 67:13 70:18 71:15 88:16 476:9

encountered 340:6 452:24

encountering 207:24

encourage 236:16 247:22 248:26 250:1,13 271:2,3

272:3 273:20,26 316:19 389:16 418:1 435:1

471:9 490:4

encourages 469:1

encouraging 255:16

encyclical 456:22

endeavor 9:6 11:10 18:4

ended 291:4 292:20 499:21

endgame 311:14

endocrine 1:4 2:7 5:12,21 6:11 7:23 8:7,10,15 9:8

16:23 17:10 18:2,11,20,26 19:10 25:18 28:3,8

31:5 32:5 33:7 56:24 98:20 121:17 122:2 124:14

129:24 213:5 215:16 234:7 238:21 240:9 242:18

245:19 260:23 262:12 263:1,5 268:21 271:10,12

278:3 282:17,19 289:18 297:16 305:22 311:18

313:19 345:4,13 347:14,25 351:20 359:10,22

364:20 386:8,25 392:22 398:6 437:1 456:26

465:4 466:6 468:8 469:13 470:2,22 482:19

497:20

endocrine-active 32:22 256:8

endocrine-related 92:20 94:4 281:18 473:26

endogenous 71:7 144:15,23 145:5,18 170:20 171:4

334:20 386:10

endogenously 101:6

EndoMet 386:15

endorse 332:17 445:21 451:5

endpoint 18:3 19:13 24:14 25:19 30:14,21 56:11

56:11 58:7,8,10,12 145:3 150:13 182:21 222:1,1

276:1 278:16 336:26 362:22,23 365:21 429:24,24

454:11,25,26 455:7,11,13 477:24

endpoints 66:8 109:6,16 364:10 454:10 461:4

468:3,15 470:23 471:15 473:25,26 485:6

ends 436:21

energies 240:25

energy 496:23

engaged 47:19

engineer 251:9

engineered 100:21

engineering 266:19

Engineers 7:21 214:19 391:18

England 7:3

enhance 368:3 426:3

enhanced 442:5

enjoyable 390:10

enormous 136:8 304:21

enriched 140:26 462:25 463:6

enrichment 141:9 463:4

ensemble 338:3 436:8

ensure 3:13,21 25:21 26:15 29:7 30:14 35:13 36:21

216:3 218:19 219:9 301:9

ensures 262:20

ensuring 2:12 10:18 26:7 217:22 234:21 313:17

enter 280:19 306:19,24

entering 4:8 306:19 320:18

enters 230:12

entertain 258:23

entire 34:22 35:21 68:6,14,14 103:12,19 175:2,5

236:26 253:24 292:22 299:15 308:6 339:23

363:21 437:5 492:12 497:17 500:16

entirely 211:4 230:7 442:18 448:2

entities 264:11 353:23

entitled 483:12
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entity 353:20,21,25

entry 31:23 32:12

envelope 197:5

environment 3:3 7:21 40:13 43:15,16 50:25

102:15 249:8 298:12 300:18 306:19,25 310:12

312:13 321:16

environmental 1:11 5:18,24 6:3,5,16 7:4 8:11 33:3

211:17 214:1 223:6 224:3 240:21 282:12 306:13

306:15 327:26 328:17 386:12 390:18,21,24 475:9

environmentally 263:8

envision 255:11 270:8

envisioned 364:19

enzymes 100:25 343:16 462:15,16 463:6 464:4

483:25,26 484:9,12

EPA 2:21 16:6 59:10 66:26 134:4 143:16 213:10

213:15 233:18,19,24 234:4,19,25 235:1,5,12,16

236:9,13,20,23 237:4 238:26 239:24 240:17

246:22 247:15,26 251:12 252:26 254:1 260:8,22

261:20,26 262:8,13,15,22,24 263:4,5,7,11 264:7

264:9 265:24 273:14 279:23,25 281:20 283:6,21

293:13 296:15,26 297:2,7,8,15,18 298:4,6,10,11

298:16,18,23,25 299:6,10,11,13,17,21,24 300:3,4

300:8,11,14,24 301:20,26 302:2 303:15,20 304:3

304:7,10 315:9 317:23 341:9 350:1 363:1,3

366:11,13 372:16 373:14 385:2 390:25 396:4

418:1 434:3 438:10 473:6 474:3 488:18,21

489:17,20,21 492:5 493:13 494:17,19 500:19

EPA's 213:11 233:16 235:14 289:21 296:19 298:20

298:22 299:9,14 303:3 395:23

EPA-HQ-OPP-2012-0818 1:7 4:8

EPA-only 92:26

EPA/ORD 230:25

Epidemiology 6:6

epidermal 46:20

epigenetics 473:12

EpiSuite 318:22

equal 150:18 154:18 157:10 174:1 345:23 379:17

equally 67:26 260:24 406:7 422:4

equals 39:7

equilibrium 40:23,25 265:2,2,4

equivalent 92:13 148:23 163:9 170:2 283:21

equivocal 161:11 268:17,26 286:2

equivocals 286:3 287:5

ER 28:26 29:1,23 30:2 33:9,10,23 34:6,6,12,17,24

36:7,12,17 47:26 51:11,13,15,17 52:1 56:7,11,15

56:25 57:1,5 63:13,14 82:19 92:9,14 93:6 128:14

135:1 142:18 143:8 145:11 155:18 162:21 163:25

164:9 185:1,12,12,20,22 186:2 217:19 219:26

220:17 222:4,23 223:4,18 224:3,18 244:22

245:20 248:9,17 249:12 250:22,25 265:1 267:1

268:7 269:4,4 270:1,2,4,16 271:11,13 273:6,22

274:1,5,5,10 279:16 280:1,18,23 281:6,13,14,23

283:11,11 284:22 285:19,19,22 288:14 307:17

318:4 319:5 322:18 329:25 333:3,5,8,12 334:5,6

334:8,13,13,19,24 336:23 337:4,5,7,16 342:25

343:8,14,17,25 344:3,4 345:12 349:18,19 350:3,5

350:8,19,24 352:24 353:1,2,5,9,15,16 356:21

357:13,14 359:10 360:12 361:21 362:21,21

363:17,17,18,26,26 364:13,15,18 365:3,12 367:6

371:8,24,25 372:15 377:9 380:4 382:15 384:21

385:8,14 386:13 392:15 393:10,14 403:9 404:16

415:16 416:13 420:19 428:19 435:18 437:1

439:10 440:7 441:22 442:5 446:14,16 451:18

452:16 453:15,19 454:13 456:1,13 457:1 459:13

459:14,18 460:3,17,22 466:25 467:8 468:25

469:2 470:20 471:25 472:15 476:2 477:10,16,19

479:7 481:20 482:14,18,21 484:6 485:8

ERE 123:7 128:3,3,15,26 181:19 182:26 348:12

349:20 350:25 388:7

EREs 182:6 350:19 405:18

ERG 454:14

Erich 214:6,6

err 127:18

error 287:14,19 442:25,26

errors 352:8 442:25 443:1

ERs 123:6 343:16

ERTA 135:8

ER-alpha 144:9,10,22,23 151:7 153:4,9 160:15

163:24,25 334:20

ER-focused 477:3

ER-interaction 60:13

ER-led 359:22

ER-mediated 82:21 468:5

ER-related 470:14

ES 243:26 323:14 335:3 337:7 338:1,1 339:2 340:2

356:25 381:20 443:21 452:16 453:18

especially 8:10 15:11 81:1 111:12 153:12 185:3

200:8 208:16 243:20 304:15 309:14 310:8 325:10

332:8 369:9 373:23 375:20 396:15 415:20 435:3

436:13 445:21 449:24 451:18 457:1,24 461:20

465:2 468:10,23 471:5 472:7 474:9 476:7 477:12

479:6 480:15 487:15 493:14 495:3

essence 310:24

essential 348:5

essentially 111:21 142:25 145:9 209:14 211:6

242:1 286:18 294:23 402:25 404:24 425:24 434:8

434:26

establish 159:17 249:3 281:25 372:5 373:4 377:10

378:5 379:6 399:1 485:3

established 29:18 36:3 321:25,26 322:2 365:4,6,21
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485:7

establishes 31:4

establishing 243:21 250:16 262:8

establishment 373:5

ester 257:16

esters 74:14 191:24

estimate 60:5 204:6 352:7 358:22

estimated 40:17 44:8

estimates 58:5 256:19 266:4,8,13 313:3 379:18

estradiol 60:23 61:5 62:5,14,15 71:7 85:3 105:9

107:11,13 126:16 225:26 226:4,5 257:3,12 353:7

369:21 378:26

estrogen 15:4 19:10,23 42:11,14 45:13,15,16 46:12

46:13,21 48:6,8,8,11,19,21 50:4 57:6 60:11,14,23

62:4 92:10,11,20 93:14 94:6 95:2,5,8,12,12,15

96:6,11 98:8 101:11 105:8 108:24 109:25 110:3

114:25 118:9 122:3 123:4,20,23 128:6 129:24

131:18 132:19,20 137:11 140:1 145:5,10 146:7

149:26 170:9,11,13,14 171:3,12 184:20 186:1

205:26 206:1 220:14 238:4 240:24 256:9,24

259:9,10 264:25 265:3 267:5,18 269:26 276:2

307:6 314:25 330:2 343:7,17,24 347:19,22,22

348:4,9,11,14 350:2 354:16,21 357:21,22 358:1,2

358:9 361:15 363:19,22,22,23,24,25 364:14,15

364:16,26 366:26 367:4,12,18 378:21 385:12

387:1 388:12 398:7 399:12 409:15 411:14 427:19

428:4 467:1

estrogenic 18:12 19:3 117:22 123:3 136:18 167:12

205:25 257:18 259:13 265:18 343:7,10,23 344:4

345:4 346:9 347:24 357:3 373:7,8 374:26 392:22

494:12,26

estrogenicity 56:23 185:24 306:20

estrogens 206:4 257:23,24

estrogen-signaling 412:2

ESv 338:10

ESv1 333:12 334:5,8 335:6,13 336:4 338:15

396:26 397:2,20 445:22

ESv2 333:5,11 334:10 335:7 338:15 416:20

ESv3 364:19 414:26 415:11 416:17 426:4 427:1

451:22

et 123:9 204:14 238:17 256:10,22 278:1 282:3

318:22 326:5 364:9 365:7 368:6,11 372:4,26

373:23 387:21,22 432:24 449:19,19 460:12,12

486:1,1

ethics 3:13,19,22 236:2

ethylparaben 117:2

EU 368:26 386:9

Europe 368:26

European 8:14 215:1 320:20 326:8 392:1 480:9

490:1,15

evaluate 86:19 124:24 125:7 215:15 239:25 242:8

242:15 243:17 251:9 270:3 271:11,17 308:26

378:25 474:18

evaluated 217:9 249:18 270:1 326:24 363:20

378:23 379:5 383:9,12 441:22,25 472:4

evaluates 485:21,22

evaluating 92:19 184:26 185:9 254:15 319:6

326:22 329:6 379:10

evaluation 136:3 185:14 242:22 260:21 275:8,22

276:9 280:8 378:16 379:8,17 381:21 382:14

383:8,20 385:4 467:17 498:10

evaluations 53:22 280:25 495:8

event 30:17,17 42:10 45:5 47:24 48:10,18,23 49:5

49:7 55:14 56:14 57:7,11,12,23 58:14 60:10,16

145:9 171:14 182:4 189:5 217:18 219:25 221:24

223:4 224:23 252:11,11 254:3 255:9,12 305:13

305:18 319:5 336:26 337:1,2 344:25,26 345:11

345:16 355:7 365:4,6 439:18 440:4,4,23,24,25

449:10 456:1 469:21,22 476:10,10,16 477:11

478:16,17 482:18 483:18 484:16

events 252:9,9,16,17,21,23 254:9,21,22,25 255:9

310:21,25 336:25 343:9 345:1,3,8,14,17 346:12

346:18 355:26 356:10 362:2 364:26 371:9 399:7

399:7,8,11 403:9 415:16 440:22 446:18,20 449:6

449:6,8,13 453:19 467:1 468:7,10 471:12,23,23

472:3,7,11,13 476:15 477:12,14 482:13 484:15

484:19 485:3,15 486:26

eventual 76:6 241:25 448:21

eventually 55:16 68:13 71:12 75:21,25,26 76:16

205:9,11,14 242:2 493:24

everybody 10:26 11:6 16:18 104:16 130:2 184:20

213:21 236:4 247:12,18,24 249:4 250:17,24

252:17 284:23 342:2,9

everybody's 251:26 499:11

evidence 19:12,26 21:11 23:1 47:21 51:20 56:26

96:3,5 142:1,4 149:3,4,6,6 161:26 162:11 175:17

175:21 176:3 184:19 185:25 186:14 266:3,9,10

266:17 306:18 313:14 315:13 318:24 322:19

326:25 327:9 330:24 336:20,24 346:3,5 347:20

375:11 428:4 440:18,21 467:14

evolution 442:4 463:8

evolutionary 17:21 28:1

evolve 414:24 415:19,23 425:23,25 493:1

evolved 433:22 452:2

evolves 413:20 491:14

evolving 271:8,19 416:20 426:8 493:7

ex 485:17

exact 76:2 195:18 196:11,26 198:12,24,26 241:13

263:24 338:11 367:2 403:5

exactly 28:18 77:13 133:26 211:6 226:24 259:17
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412:15 413:2 429:24 451:1

exaggerated 63:5

examination 80:19 452:6

examine 406:15 412:17

examined 365:6

examining 434:9 440:21

example 25:21 35:20 38:10 40:22 41:3,25 42:21

49:2 62:14 63:26 71:19,26 73:9,20 74:9 94:26

101:21 108:26 110:2,17 112:11 113:9 115:20

135:13 137:10 141:4 143:22 146:5 148:26 150:5

157:25 158:5 160:4 163:20,23 165:4 174:12

182:23 183:23 187:2,7 193:2,23 196:20 204:22

205:10,12 218:22 233:23 245:25 250:23 261:24

268:19 272:24 273:5 277:7 280:24 284:7 299:1,6

299:21 305:2 309:22 311:23 312:9,11,12,25

328:26 329:15 334:24 336:1,6,12,16 337:14

339:3 341:13 344:2 364:2,7,26 367:4 368:24

375:13 376:14 379:23 382:17,23 383:20 385:26

386:8,9,26 411:14 421:16,23 434:24 435:16

436:20,21 437:26 438:17 444:24 455:9 459:23

460:2,8 465:19,26 471:25 472:15 474:12 477:22

483:21 485:10 495:3

examples 41:24 43:21,22 44:8 84:8 85:9 89:24

109:1 116:12 145:25 146:6 150:1 151:2 156:20

157:23 187:5 189:13 190:14 206:13 276:12 317:1

326:1 465:19 470:12

exceed 43:9 486:18

exceeding 40:3

excellent 83:15 363:11 366:16 471:24 496:21

exception 131:7 140:1 459:19

exceptions 73:6

excess 47:3 141:26

excited 493:19 497:5

exciting 491:11,11 495:21

excludable 42:18,23 43:3,22 44:11,24 47:4 330:21

exclude 32:10 41:26 42:5 48:7 50:11 51:21 68:23

238:2 241:17 373:15

excluded 32:17 41:18 44:15,19,21 45:10 46:14,19

46:21 48:12 51:12 82:1 306:24 315:4 321:2

325:12 329:26 330:1 399:14 453:20

excluding 46:25 313:25 325:8 326:20

exclusion 32:8 40:7 42:15 46:10,11 48:24 49:4,14

49:15,17,18 51:4 83:18 208:13 241:18 319:2,3,8

320:1,10,11,14 321:13 322:6,7,14,16 323:14,24

327:16 329:15 330:26

exclusionaries 68:23

exclusions 40:8,9,14 45:22 312:11

excuse 38:25 138:3 236:18 306:12 333:14 425:13

425:18 447:1

execution 17:24

executive 500:14

exercise 80:20 86:8 135:2 200:10 464:17

exercises 326:16

exert 484:10

exhausted 126:6

exhaustive 377:24

exhaustively 465:7

exhibit 25:7

exist 56:10 242:18 317:12 329:26

existed 56:26

existing 28:16,22 203:21 218:17 221:8 238:6 240:5

263:10 298:24 311:26 399:19 424:8 453:22

expand 18:24 19:2 34:6 54:20 66:7 71:12 77:15

83:7 84:20 142:24 159:16 188:22 193:19 197:12

197:15,25 220:7 227:7 245:1 248:10 268:3 279:3

335:13 351:24 406:23 426:19 427:18 433:15

434:10 449:15 463:23 469:5 493:2,3

expandability 69:23

expandable 334:10

expanded 49:17 53:9 70:4 74:8 82:22 198:16

223:13 335:1 337:8 372:16 380:3

expandible 67:13

expanding 14:23 28:17 34:22 53:22 54:12 70:17

73:18 79:4 193:13 203:6 210:13 243:23 324:21

393:14 414:7 468:23 487:14

expands 319:24

expansion 36:7 52:24 53:7 198:12 202:12 338:14

372:22 413:22 435:8 437:9 446:20

expansions 196:12

expect 41:12 47:4 141:3 150:14,17,25 153:2,8,26

157:19 163:11 172:14 174:2 204:5 212:2 260:24

278:19 288:16 291:18 309:13 388:8,11 396:19

397:16,22 400:9 407:20

expectation 291:21

expectations 313:20 362:8 393:26 394:21 420:11

expected 79:12 115:3,6 117:12 151:4 152:14

220:19,19 258:15 301:12 312:22 340:11 385:17

435:23

expecting 211:24,26 398:19

expense 450:20

expensive 96:21 130:4 231:7

experience 83:20 102:12 178:13 216:22 243:2

265:25 282:23 312:24 336:17 418:3 464:7 479:20

experiences 244:23

experiential 428:3

experiment 121:9 405:26

experimental 234:15,16 272:6 276:18 326:15

363:2 366:12 367:24 469:8

experiments 145:19
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34:17,24 36:7,17 45:23 46:1 47:16,17 48:1 50:7

51:11,13,15,18 52:1,24 53:1,3,7,8,15 54:7,9,13

54:25 56:14,16 57:20 59:9,12,14,21,24 60:25

64:2 65:8,10 66:9,11,12 67:3 69:21,24,26 70:4,14

74:9 77:1,4,15 78:17 79:4 80:7,17,19 82:19 83:22

84:2 85:16,26 86:8 122:18 132:1 134:15 142:18

142:24 143:8 179:9 185:1 188:19,26 189:16,21

191:22 198:12,22 199:11 200:9 202:8,25 203:19

203:21 204:15 207:5 218:3,9 220:1,7 222:15,23

223:13,18 224:18 232:22 238:1,13 242:17,20

244:15 245:10 248:9,18 250:22,25 265:22 266:26

267:4,17,22 268:12 269:1,2 273:22 274:1 279:16

281:23 282:4 310:17 313:26 314:9 315:7 330:17

330:19 333:4,5,12 334:5,6 335:18 340:21 341:10

341:15 342:23 346:15 363:17,18 364:9,18,19,22

367:6 371:3 372:14,22 383:19 393:10,14 394:3

394:24 395:23 403:24 412:12 419:12 420:24

421:11 422:5 423:10 424:14,15 425:9 433:17

435:5,7,16,21,23 437:5,7,22 439:8,10,11 440:10

440:10 441:3,23,25,26 442:6,20 443:12 444:2

446:15,25 447:5,17 448:6,11,21,26 449:15

451:16 452:14 453:4,5 454:8,12 457:1 459:2

460:15 462:1 466:25 467:9,25 468:2 469:4,11,21

470:9,20 477:21 478:22 480:21 482:11 483:17

485:7 486:2 493:23

expertise 5:16,19 6:26 7:3,26 16:9 17:7 178:13

experts 105:21 330:22

expert's 45:24

explain 59:1 155:15,15,26 175:14 181:22 196:24

259:10,14 369:17 410:20 431:14

explained 58:6 456:14

explaining 244:4 384:23

explanation 257:11 306:8 328:10 373:17 376:13

398:3 431:16

explanations 382:22 394:8

explicit 252:19 468:9

explicitly 297:15 311:26 316:5 339:4

exploited 256:10

exploration 343:21

explore 25:1 466:2 485:2

explored 250:5 453:14,17

exploring 446:20 474:2

exposed 97:11 297:21 298:1 299:5 332:6 407:12

485:11

exposure 16:15 18:17 29:13 32:24 33:22 35:12

37:9,16,20,22 236:12 237:22 240:12 248:13,15

299:19,24,25 300:1,18 305:24,26 306:5 307:1

310:8,10 311:24,25 312:5,15,18,21,23 313:4

314:11,16,22,22,26 324:19 327:25 331:10 332:13

368:16 406:10,11,13,16 407:10 457:25,26 458:5

458:12 463:22 475:9 491:23

exposures 299:22 310:9 312:3 314:15,15 328:8

394:20 407:13 410:17

express 171:5 225:10

expressed 101:6 145:5 170:12 171:3 234:21 268:22

452:17

expressing 145:14

expression 95:19 355:2,8 356:23 360:24 396:12

454:14 461:24 462:13 465:21,25

extend 16:19 75:16 204:2 335:7 361:18,19 496:20

497:24

extended 20:11 43:13 85:17 340:4 416:9 476:14

extending 337:18

extension 199:26 323:24

extensions 27:16

extensively 386:4

extent 39:23 49:1 160:6 191:21 303:2 304:10

306:11 324:15 342:26 344:1 348:10 351:26

360:21 361:17 362:26 365:5,17 381:4 384:22

437:14 451:15

external 10:20,22,23 235:2

extra 239:18 316:25 405:2

extraordinarily 464:6

extrapolate 64:18 65:4 125:15 315:24

extrapolation 222:14 227:4 324:22

extreme 235:9 434:15

extremely 237:5 385:2 397:6 452:8 500:25

Exxon 275:3

ExxonMobil 275:1

eye 114:23 494:19

eyes 98:1 261:16

e-docket 4:6,13

e-publication 271:9

e.g 318:20,21 335:25 372:18 384:21,24 415:21

467:1

E2 113:11,11 133:7,8 144:18 145:3,14 146:10

147:22 150:12,24 157:17 158:15,18 160:17

161:26 162:11,22 163:8,10 174:9 397:19

F

F 94:11,13

face 213:21 283:8 341:17 491:13

faced 490:25

facile 487:20

facilitate 426:8 477:4

facilitated 311:21

fact 15:3 43:9 59:22 81:16 84:1 87:12 123:24 124:1

138:25 172:7 175:2 181:10 185:5 201:9,14

205:12 210:4,12 227:11 232:13 238:10 260:13

288:1 291:7 300:17 302:5 312:26 317:11 324:17
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facto 249:19

factor 46:20 74:5 75:2 95:14,22 98:3 100:9,26

128:2 130:16 175:5 208:13 305:2 352:5 406:10

439:24 440:12 449:25 451:2 479:18

factors 49:14,15 58:16 100:21 101:5,9 128:4,7,9,12

128:16,22 129:2 142:2,7 143:11 156:22 171:11

217:21 272:6,12,16 304:8,8 325:2 330:12 439:25

439:26 440:14,16,21 461:7 484:18 495:1,1

fail 116:22

failed 289:15

failing 331:6

failings 313:13

fair 105:15 292:19 464:16

fairly 146:17 154:20 170:22 196:5 236:19 243:6

313:8 381:18 396:4 400:8 420:3,8 432:23 490:14

fall 69:7 71:2,4,17 85:8 88:2 96:23 138:12 151:13

167:8 179:26 193:11 199:4 301:21 313:1 398:20

falling 259:20 320:1

falls 153:15 399:5 426:24

false 25:22,22 96:12,14 106:23 109:8 110:13

112:17,18 127:9,10,11,16,19,19,20,24 130:6

137:2 139:12 140:3,9 146:22,25 148:4,6 161:24

162:12,26 167:4,4 177:4,12,12,25 244:8 258:4

273:24 286:19 287:7,15,17,20 288:10,26 289:13

290:23 291:22,25 293:11 300:21,22,24 301:2,4,7

301:10,11 302:5 309:6,10 313:15,15,21 338:26

340:10 352:7 368:1 369:10,16 370:10,11 373:18

379:19 383:15,16 393:16,17 394:19 396:9 398:17

398:18 399:22,23 405:2 412:8,8 415:7 434:21

441:18,24 449:18 450:6 451:18 452:4,7,10,21,22

452:24 453:8,9,12 473:6,14

familiar 55:3 132:7 326:9

families 379:22

fantastic 495:23

far 27:8 46:25 68:19 76:12 84:10 85:6 87:21 162:6

172:25 197:21 198:11,19 199:11 212:2 265:5

269:6 317:2,22 320:16 325:26 339:26 359:23

361:17 363:8 378:17 385:3 389:10 410:18 461:21

471:11

farther 416:18

fashion 23:25 24:8 69:11 125:4 266:18 362:16

fast 464:9

faster 17:19 34:5

faults 244:7

favorite 257:16

FDA 93:11 121:11 309:2

feasible 468:16

feature 72:26 236:16 427:10

features 60:20 349:11 421:14 426:1

February 27:9

fed 361:5

federal 2:3,10,12,26 3:11,15 18:8 32:8 39:26 213:2

390:9 497:24 499:18 500:15

fee 153:2

feedback 15:9 48:26 92:5 205:6 355:24 486:15

feel 13:16 83:20 101:13 178:19 187:6 196:2,5

197:11,22 244:5 343:20 389:15 398:18 403:6

406:8 416:8 420:15 447:6 467:11,26 489:11

feeling 420:4,23 478:7

feels 260:5 482:10,15 485:23

fell 399:10

fellow 495:22

felt 51:1 85:25 132:6 344:6 356:19 370:7 371:4

378:11 381:11 385:1 394:19,21 419:25

fetal 368:9

fewer 404:14

FFDCA 26:24 37:13 215:14 296:8 297:12

fide 358:9

field 104:5 271:8 476:9 479:9

FIFRA 1:1,8 2:15,25 3:4 4:1 5:8 22:16 23:7 30:26

211:23 390:8,13 483:12 496:12 501:5

Fifth 386:9

Fifty-three 166:20

figure 13:3,8,22 14:2,2,7 31:8 38:14 60:22 103:5

131:20 162:12 225:19 280:6,8,11,23 292:9

293:16 294:22,24 296:2 298:18 328:10 372:13,16

399:9 402:6,12 457:25

figured 102:17

figures 225:20 280:11 372:20

filed 3:17

files 104:18,24 105:10

fill 481:1

filling 415:2 437:13

filter 29:20 30:6,7 31:19,20 52:5 70:23 81:5,13

82:7 110:6 120:8 128:23 162:15 163:18 223:10

265:16 272:11,25 284:8 298:15 299:24 305:5

309:9 319:7 329:7 457:25 458:5,12 473:8

filtered 80:18,21,23 81:12,15 82:16

filtering 52:7 53:13 82:8,16,24,26 96:14 105:16

108:16 110:9 111:24 120:24 163:17 175:22

273:11,18 285:8 286:4 287:3,17,19 288:18,24

290:18,19,20,21 314:12 370:9 376:23 377:2

405:2,6 406:9 408:8 453:11 473:9

filters 39:20,21 44:19 51:24 80:15,18 82:1 112:1

113:2 217:6,9,11 218:8 242:10 250:22 271:24

273:14 284:13,13,14 285:11,15 287:10,11,24

289:2 297:2 303:8 304:24 305:7 306:13 311:26
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final 25:1 89:21 100:8 104:10 120:6 122:10 136:12

210:25 211:13,14 232:26 258:2 271:23 295:25

315:15 317:7 336:26 360:17 406:5,6 418:25

440:4,23 470:22 471:17 477:14 483:1,20 489:10

495:7 496:18

finalized 304:2

finally 2:22 36:19 65:9 92:7 104:11 105:19 108:12

108:21 115:22 117:1 121:15 234:24 238:13,18

239:2 248:11 315:11 341:8 369:6 416:2 457:8

financial 3:18

find 65:6 74:21 126:7 138:18 167:25 171:26

172:10 175:7 197:18 210:20 220:16 228:24

241:19 267:11,21 284:18 304:23 346:8,17 351:5

360:21 366:2 382:3 405:23 412:26 413:16 418:4

430:2,3,12 457:8 459:18 492:18 494:2

finding 15:22 169:15 213:9 300:1 401:19 444:15

findings 142:18 286:1

fine 123:14 215:26 259:11 261:17 454:16

finer 255:7 256:3

fingerprint 349:12 408:6 428:3 444:22

fingerprints 89:8

finish 176:3 222:20 223:18 262:24 296:24 297:25

339:23

finished 221:1 353:1

finishing 94:1 498:7

fipronil 87:23

firm 24:10

firmer 32:14

first 2:4 13:8 14:8,11 18:24 20:22 23:15 24:17,17

24:20 25:10 31:19 38:8,12,18,22 39:6 40:14 53:2

54:6 58:5 59:17 61:25 65:13 73:7 78:18 83:17,18

83:21 98:3 107:21 124:18 125:24,25 129:14,18

129:20 130:5,9,15,16,25 134:2,26 136:11 137:23

145:24 146:5,8,11 151:16,18,22 152:9,21 157:14

157:15,24 166:2 171:12 172:18 176:26 178:24

184:18,22 185:8 188:24 192:26 194:16 198:19

200:14 207:5 215:9 216:1,12,18 218:23 219:8

223:5 224:23 225:25 231:7 233:8 237:17 239:24

245:26 247:15 252:10 253:6 264:17 269:11

270:23 271:2 276:6 293:5 295:12,15 298:19

300:8,13 301:16 307:21 309:21,25 310:14 316:23

319:21,23 320:17 323:21 334:18 341:9 343:23

346:1 364:22 365:15 369:24 395:17 397:7 402:7

402:10,17 405:22 408:20 410:6 428:2 429:23

433:26 442:15 443:5,13 444:5 446:22 448:12

456:10,10 458:25 469:24 470:23 474:7 493:12

500:13

Firstly 327:17 489:26

fiscal 305:2

fish 20:14 55:19 164:9 315:19,20,23 316:2,3

354:17 364:17 413:5 450:14 468:5,13 477:23

485:18,20 487:5,10 498:25,26

fit 59:18 90:24 103:26 106:12 107:24,24 147:5

178:2 210:10 225:25 226:4,9,19,20,22,26 230:7

240:26 246:11 250:11 251:17 298:25 308:16

340:14 344:3 422:24 491:24 497:25

fits 251:21 255:23 376:15 431:17

fitted 64:25

fitting 105:12 106:3 293:9 326:16 372:8 373:20

five 3:24 19:16 21:24 27:21 54:6 65:12 72:1 92:21

142:22,23 144:4 145:25 156:13 165:6 166:4,24

166:25 212:4 241:20 264:22 274:22 275:7 280:10

280:12 286:8,12 322:1 386:19,23 387:4,8 389:20

393:12 397:19 457:19 460:10

five-year 7:7

fix 373:17 398:24

flag 159:3 488:17

flagged 162:17 163:17

flagging 343:4

flare 111:11,13,22 112:11,11,15,19 113:5 159:1,12

160:26 161:5,13,15

flares 111:22 208:16 382:25 383:11

flarey 111:17

flare-related 111:26

flash 397:4 449:19

flashed 204:21

flat 146:2 148:26 149:11 160:11,16 179:18

flavor 16:8 196:2 197:12 198:15 199:18 200:13

202:1 203:4

flexibility 210:6 335:26

flexible 26:10 334:10

flexibly 490:15

flies 209:19

flight 432:23

flights 496:14

flip 409:20

flipside 359:26

Florida 8:9 214:26 391:26

flow 30:6 31:13 218:2 230:12

flows 104:11 335:25

fluid 426:8

fluorene 202:23

fluorescence 144:24 156:16

fluorescence-based 100:20 110:23

fluorescent 110:23

fluorescents 397:4

fluorophenols 201:13

fly 2:18

focus 12:19 13:9,25 14:26 15:21,21 21:24 23:7,17
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254:2 258:13 263:17 296:5,11 340:3 351:13,14

351:16 353:25 363:26 393:20 443:26 447:2

457:21

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221:25 223:3 273:23 307:17 353:16 361:21

403:15 404:21 408:15 411:23 415:15 446:25

447:6 455:6,13 498:3

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241:10 248:4,9 255:8 285:18 353:20 406:26

415:7 416:10 437:13 494:13

fold 11:2 135:24 155:8 339:5

folded 237:18

folding 72:23

folks 9:5 16:3 47:18 101:20,20 121:10,10,11

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follow 14:8 97:1,16 130:19 171:19 212:2 231:12

251:1,13 255:6 256:16 284:6,8 343:24 345:17

381:5 407:9 465:1 476:17 498:10

followed 195:17 449:3 455:12 469:8 479:17

following 33:20 36:1,5 38:17 49:12 83:25 130:7

267:24 319:8 321:6 327:25 392:16 421:12 454:19

471:18 476:7 478:5

followup 22:22 87:4 120:5 138:23

follow-up 125:20

food 8:14 14:16 18:8 27:2 33:12 36:8 53:17 54:3

59:16 61:14 65:12 70:1 77:13 78:4,7,24 79:21

84:5 143:16 201:4 215:2,15 297:12 306:15,19

392:2

food-using 334:7

fooled 110:24

foot 247:13 253:18

footnote 234:12

force 491:25

forced 491:3

forecasted 28:11

foreign 202:24 492:15

foremost 500:14

forever 96:20

forget 136:19 206:9 360:10

forgive 427:3

forgot 2:2 42:23 390:7

forgotten 231:3 288:13

form 3:18 25:8 27:6 40:16 42:3 58:23 68:22 74:7

81:18 205:11 265:23 292:22 340:19 345:7 346:21

362:14 393:13 415:22 464:25 500:3

formal 241:2 263:22

formalizes 240:17

format 103:21 105:6,7 414:22 418:24 441:4

formation 35:18 449:16

formats 103:23 105:6 123:15

formed 18:19,22 20:21 457:22 464:8

former 254:11 264:9 296:11

forming 75:17 158:13 200:26 345:9

forms 15:10 81:18 163:19 196:6 426:5

formulate 13:19 71:3 72:22

formulated 90:7 320:17

formulating 72:9

formulation 25:12

forth 52:22 94:12 263:3 324:25 325:5 464:15

471:21 490:12 496:5

Forty-three 190:15

forum 282:17,19 289:18 490:6

forward 4:26 9:1,17 10:9,19,24,26 11:3,7,10,15,23

12:12,15,20 13:17,26 14:1,20,22,24 17:11,15

18:21 19:5,21 20:4,22 21:2,6,9,10,15,25 22:1

23:12 24:20 25:7,21,23 28:20 29:1,26 30:11,15

31:8,12 32:7,23 33:5,8,26 36:20,26 37:23 51:25

52:2,5 90:2 123:14 124:15,19,20 125:3,6 188:11

188:12 213:14 215:26 219:12 220:24 225:17

230:16 233:8 240:9,14 242:23 244:21 248:1,10

248:13 249:16 252:2 260:25 261:18,20 270:26

273:1 283:6 308:25 309:1 316:24 318:12 332:26

340:25 341:19 359:19 360:2 371:2 393:2,7,17

398:24 410:21 448:10 468:21 469:1,21 470:26

474:9 476:7 480:17 485:1,24 491:12 493:1,20,25

495:5,17,17 496:23,24 497:5,7,19 498:2,7 499:12

500:11

forwards 246:4

found 18:15 58:19 64:21 65:5 70:19 71:7 76:13

86:16 89:14 163:1,13 171:24 172:9,19 193:13

198:4,25 203:2,3 207:7 210:14 219:13 227:3

279:5 284:6 286:5,23 288:21 291:18 292:5

297:20,24 298:2 338:17 450:16

foundation 10:15,17 12:24 21:6 23:12 56:13

227:22

foundational 11:19

founded 227:18 320:22

four 5:2 9:7,16 20:13,17 23:5 72:24 78:6 79:19,21

79:26 83:8 99:15 106:4 112:4 115:4 121:25

129:23 132:23 149:26 150:6,8,18 151:3 152:9

161:25 165:3 170:13 194:4,4,15,24 212:1 264:16

281:17 286:9 287:20,21 290:19 366:3 386:22

397:19

fourthly 478:19

four-day 2:5

four-step 273:1

FQPA 223:8
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fraction 63:20,23 64:8,12 148:23 153:10 187:26

fractions 170:1

fragment 68:22 72:4,7 85:17 421:23

fragments 68:24

fragrance 78:6

fragrances 27:2 54:3 76:14 77:19 78:7 79:5,22

200:8 203:3 314:12 335:4 377:12 378:9 435:22

framework 23:23 29:24 48:21 215:20,22,24

221:21 238:17 250:8 252:7 255:6 266:17 386:9

397:20 414:10 471:11

frameworks 218:17 254:22 278:12

framing 316:3

frankly 90:12

Fred 500:16

free 63:20,23 64:8,12 112:26 148:23 153:10,26

169:26 187:26 212:5 279:20

freely 325:23 326:1 346:6 465:24

frog 20:16 348:14,17 468:13 487:10

frogs 488:7

front 24:12 84:11 92:6 156:21 237:8,13,14 284:9

290:3 421:10 498:1

fronts 9:2

front-end 211:14

frozen 102:26

fruit 209:19

fruitful 390:11

full 19:17 34:25 62:9 96:22 130:25 137:22 141:26

144:15 146:1,7,21 147:24 154:3,9,10 156:1,12

181:13 265:13 275:9 294:8,8 331:16 366:4

369:21 371:1 407:2 426:14 429:9 441:4,8,10

448:9 450:12,15,18 452:16 463:7

fuller 427:11

fully 30:5 34:7 67:12 125:3 243:12 244:4 257:26

289:7 311:16 329:14 368:23 373:14 408:9

fun 188:5 362:15

function 22:19,19 43:26 106:4 109:14 367:13

386:21 476:1

functional 44:1 198:9 347:23

functionalities 71:15,16 85:7

functionality 71:6 72:18

functionally 283:21

functions 351:4,6

fund 297:26

fundamental 221:7 256:6 420:1

funded 282:24

funding 450:20

funnel 224:6

funny 120:12

further 53:22 80:19 84:9 103:13 147:9 160:3

167:12,14 178:10 201:11 202:13 233:4 238:2,3

239:5 277:3 291:16 313:20 314:2 325:5 327:22

330:1,25 331:9,9 337:14 338:18 339:15 341:23

346:21 347:8 350:11 368:13 378:15 387:13

398:10 404:11 405:24 407:5 416:9 418:1 435:18

437:11 438:5 442:8 445:14,17 451:9 453:2 454:2

455:15 456:21 457:14 460:15 461:1 463:4 467:17

470:6 479:1,26 480:2,10 481:9,23 484:7 486:5,7

488:14,23,25 489:23 494:6

furthered 76:6

Furthermore 338:20

fused 196:1,3 197:16 479:9

fusing 414:7

future 9:10 17:10,11,12,18 28:14 75:5 169:19

197:26 216:6 223:3 224:7 234:25 248:3,3 305:23

306:24 307:20 324:19 335:19 340:6 353:18 361:6

385:23 388:16 415:19 425:22 456:5 476:14

487:15 491:16 492:11 500:9

G

GA 337:6,16 372:15

gain 90:8 253:25

gaining 255:3 326:17

GAL 479:22

Gal-4 479:16

GAL4 122:16,16,23 123:6 128:17 479:8,9

game 298:10

gamma 171:5

gaps 415:2

garbage 473:15

gather 88:14 91:15

gathered 478:14

gathering 93:18 454:13

gears 155:22 156:9

gender 471:13

gene 30:18 33:21 36:13 61:21 62:12 145:13 156:10

156:14 157:7 163:2,23 166:11,15 258:8 280:5

281:14,15 285:6 334:9 348:15,17 349:7 355:2,8

356:22 360:24 365:1,6 367:5 387:26 392:17

423:20 431:6 440:7,9 446:19 449:1,4 452:17

461:24 465:21,25 467:10 471:25

general 3:21 22:5 25:5 26:10,20 29:7 37:6 92:12

106:23 177:2 185:9 220:5 236:19 248:23,23,24

253:11 275:17 290:25 313:8 319:22 321:24

329:17 330:5 338:12 368:19 369:10 381:3 389:3

394:11 420:4 465:6 473:16 474:6 480:14 482:25

487:22

generalization 269:2,6

generalizations 269:13

generally 15:5 164:25 226:15 294:3 295:1 300:21

301:1 323:16 339:1 466:25 470:21 480:23 482:13

482:18 493:19
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generate 27:14 56:18 91:21 103:20 122:5 182:26

234:6 308:19 372:3,25 420:2 451:21 452:14

generated 15:9 220:5 262:11 310:2 457:4

generates 233:25

generating 71:23 245:21 263:4 422:25 472:9

generation 262:10 316:15 456:25 460:25

generations 473:13

generic 30:3 217:12 307:19

genome 462:12

Genomic 93:11,22 229:14

gentlemen 279:14

Geter 279:10,10,13,14 282:9,10

Getter 279:9

getting 9:23 11:5 14:24 15:9,14 39:1 48:22,26

57:12 60:18 63:23 79:25 98:26 101:3 113:6

136:11 148:13 149:12,13 159:7 162:25 168:8

169:26 176:2 177:19,22 178:18 181:23 199:6

204:22 216:10 228:18 236:18 257:20 277:15

293:8,21 301:15 313:14 326:17 355:2,2,3,6,9,14

370:15 402:16 419:6 422:8 425:18 429:7 431:22

460:14 463:17 466:16 496:12

gills 463:17

give 14:17 30:7 37:26 42:3 53:2 68:17 78:12 84:16

110:13 138:12 145:25 146:6 147:13,23 150:1,3

154:18 156:20 164:3 165:4 168:17 174:24 179:25

180:2 182:23 186:5 197:11,15,23 198:15,17

200:12 202:1,6 203:4 208:6 210:21 215:5 223:1

228:13,14,21 244:15 258:1,15 259:13 260:15

261:11 272:24,26 281:21 299:1 306:16 307:5

321:2 325:1 334:3 366:23 376:15,15 396:11

427:10 432:25 447:13 465:9

given 39:10 43:1 44:24 53:11 90:24 91:19 107:15

124:7 125:20 177:13 199:21 215:17 224:1 236:9

246:25 259:16 265:6 280:23 281:21 282:2 330:7

336:4 339:10 340:10 341:10 362:21 366:1 396:18

399:12 412:7 415:5 416:7 440:3,25 443:15

447:17,23 450:20,24 452:22 492:10,11 493:6

gives 56:2 107:23 120:7 146:24 147:11,21 152:6

158:11 198:1 257:8 261:8 323:25 423:22 432:11

498:13

giving 62:16 256:12 258:13

glad 250:14 340:23

glancing 115:13

glassware 62:25

glimpse 51:9

globe 499:2

glow 111:2,3

go 5:14 9:21 11:3 12:12,20 13:15,17,26 14:9,22

15:19,26 16:7 17:24 23:26 25:5,7 38:22 49:16

52:13,14,25 53:2,23 55:11 62:3,10 70:11 72:8

75:6 91:3,24 93:21 94:12 96:23 97:16 99:26

102:5,6 104:9,12 106:26 107:1 108:2,15 109:10

111:7 116:17 117:21 120:24 124:20 125:14 126:7

129:3 130:19 131:9 133:8 136:12 143:23 146:4

147:3,4,19 148:4,8 149:5,8,17 155:11 159:15,20

159:20 163:15 169:12 172:1 173:20 176:6 178:7

179:12 182:11 183:22 184:21 185:17 186:3,6,9

186:12,19 187:1 188:8 190:20 195:21 197:12

201:15 205:4,22 210:14 213:12,19 215:9,23

219:5 225:12 227:20 228:11 245:18 247:3,8,9

249:15 250:7,24 253:11,16 255:12 256:17 259:6

264:18 266:21 267:21 276:13 283:6 287:3 289:17

292:18 293:2,22 295:10 297:2 299:8 301:16

302:13,18,20 308:24,26 310:5 314:2 317:3 318:8

327:3 332:19,25 333:1 341:5 342:11,15,19

345:19 350:1,25 354:8 356:16 361:18 362:10

370:22 371:21 374:22 380:25 382:3 389:1,21

390:15 393:22 395:16,21 400:24 405:24 407:23

410:20 418:25,26 424:1 426:21 427:12 433:2,11

433:23 434:5,25 438:26 446:11 451:13 455:18,22

464:16 466:14,21 469:26 473:20 476:3,22,26

482:1,4 484:22 487:24,24 489:14 490:9 495:13

497:3 499:11

goal 25:10,17 67:11 92:9 93:1 127:10 219:23

234:23 238:9 248:11 249:4 250:5 258:4 270:22

311:9,16 351:3 378:25 379:3 398:17 405:4

407:23 429:14 452:22 453:13 493:4 497:26

goals 54:6 93:10 142:22 289:22 362:6 497:11

goes 9:22,25 10:24 75:17 90:19 104:16 129:22

148:16 158:8 172:16 190:10 269:7 279:22 349:22

363:8 425:22 445:23 463:16,18

going 4:7 5:7 8:23,23 11:6 13:6 14:1,5,6 23:9 24:24

28:26 32:10 38:10 46:1,4,18 52:14,19,22,22,23

53:1,2,5 54:13,14,22,23 55:1 65:21 66:10,10 67:9

67:17 70:18 75:22,26 76:11,13,23 77:5,9 80:13

80:20 83:5 84:8 85:7,26 86:10 88:2,8,15,18 91:10

91:17,17,22 92:11,26 93:15,16 94:9,13 95:20

97:4,20 98:1 103:12,13,15,18 104:15 109:3,5,23

110:1,5 111:2 112:3 113:6 114:9,17 115:4,24

116:26 117:16,19 120:5,6,12 121:22 122:17

125:1 127:18,23 130:14 131:21 133:24 136:16,17

136:22,22,23,25 138:19 141:25 142:5,17 143:3,7

144:2 147:17,25 149:25 150:1 151:9 153:17

155:22 156:9,19 157:25 164:5 165:8,18 167:26

168:25,26 169:11 172:26 173:1,7,10 175:16

177:23 180:15,20 181:15,16,17,20 184:10 185:19

187:21 188:15,24 189:8,14,19 195:13,20 197:17

200:6 201:22 203:2,9 204:21,25 205:5,19 207:12

207:12,23,24 210:20 212:1 213:11,21 215:11,26

216:18 217:25 219:5 228:5 233:8 242:2 243:8
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258:8 260:25 264:11,16 266:6,25 267:16 270:14

270:15,18 271:24 274:11 275:6,10,13 276:11

281:19 282:14 284:2 286:21,21 288:21 293:15

296:4,15 297:6,7 298:16 300:5 301:14 306:9

307:12 313:22 314:12,14,16,17,25 315:12 316:3

318:26 319:22 322:22 330:13 332:6 334:1,4

335:15,17 340:25 341:18,19 343:3,4,5 344:16

345:9 346:20 348:6,7,16,21 349:9,14 350:8

351:19 352:2,4,10,12 355:14 356:13 357:3

359:10,22 360:11 361:18 363:10,14 373:17 376:3

378:7 379:25 383:3 389:17 390:11 392:6 396:4

402:8 405:6,16 410:14 411:25 413:18 414:6

424:15 431:1,20 432:1,3 433:20,23 434:5,25

438:12 444:24 449:2 450:3,9,23 451:2 452:2

457:8,10,21 459:8,8 468:20 470:3,17 472:9,10,11

473:3 474:11 475:10,11,12,18 476:7 478:10

480:23 483:7 485:24,26 487:1,7,15,16 488:19

490:3 491:24 492:17,18 495:24

gold 107:15 110:3,5,18 167:26 276:10 448:5

452:17

gonad 55:20

gonadal 19:24

good 2:1 5:10,22 6:2,8,13 7:5 8:8,12,22 11:4,5 12:3

12:3 14:16 16:18 29:9 36:26 38:24 48:15 50:6

51:22 57:2 65:17 76:15 79:9,15 80:20 87:11 92:6

92:15 93:23 99:5 111:24 113:9 127:15,16 134:20

137:9 146:9 154:20 155:10 162:25 166:5,15

170:26 177:16 184:19 186:18 191:22 193:19,26

194:2 196:5,6 200:14,21 204:1 207:1 213:1,18

233:11 237:24 240:19,22 243:1,4,5,6,13 244:13

246:23 249:22 250:23 253:8,9 255:5 256:1 260:3

268:19 275:2 279:13 282:13 283:5 296:1,3 297:5

300:11 301:2,10 302:13 309:10 315:22 322:12

326:1 328:15 329:15,20 335:9,19 345:15 348:10

359:26 360:1,4,10 362:10 365:24 377:24 381:18

390:20,26 391:3 396:23 398:3 409:5,5 427:13

463:2 491:2,4 492:26 495:26 496:14 500:1

goodness 59:18

goof 98:11

gotten 194:23 232:10 283:17 287:6 424:4

governing 322:6

government 3:18 222:26 264:11

governments 261:25 262:22

Goyak 275:1,2,3

grab 197:14 199:20 200:19

grabbed 72:1 74:10

grabbing 200:21

gradations 145:26

gradient 177:9

gradients 321:9

granularity 247:14

graph 256:18

graphic 93:23

graphically 336:6 399:7

graphs 256:26

grateful 496:15

great 88:4 91:9 126:13 140:7,18 161:8 196:13

271:2 279:2 289:20 295:25 302:22 313:7 315:18

318:6,16 330:18,18 332:24 333:3 342:21 361:8

362:19,20 380:24 389:21 395:20 408:21 411:20

426:6,6 430:19 450:14 455:22 463:8 488:13

greater 32:3 41:19,23 42:19 44:25,26 56:3 67:26

128:15 146:6 159:17 197:15,16 262:4 306:4

312:24 319:9,11 327:6,8 328:6,10,13,14,26

334:21 335:26 337:12,17 483:3 485:24,26

greatest 298:17 483:6

greatly 9:26 393:4 433:9

green 67:19 68:11 73:12 98:7 107:20 109:25

110:25 111:3,18,19 143:26 195:23 317:8

grid 69:11

grip 481:3

grips 410:6

ground 52:25 131:19 460:6

groundwater 33:2

group 15:12 36:10,25 44:1 46:1 47:18 60:18 66:21

69:4,15 71:2,3,26 72:18,25 73:2,6,15,24,26 75:1

75:17,19,19,19,25 76:2,19 78:22,22 80:19,25

81:2 85:3 87:16 90:19,20,22,24 91:12,18 92:7

108:20 119:20 120:19 122:8 132:6 133:26 138:14

138:14 139:6 142:25 148:5,7 155:8 158:1 159:24

160:1,6 165:5 177:16,24,26 178:2 190:8,11 191:4

191:15,16,18 192:8,15,20 193:24,25 194:6,11,11

194:22 195:11,12,14 198:10,11 199:15 200:3

201:3,14 206:26 226:6,13 227:2,8 232:8 236:1

246:9,9 256:23 275:4 282:25 295:26 321:24

334:2 337:25 339:3 344:6 348:13 387:13,25

389:13 397:24 422:14 445:14,25 447:19 457:9

473:23 474:3 490:7,13,14 500:13

grouped 60:20 200:24 286:2

grouping 72:2 84:26 85:12 89:6 187:10 199:15

200:10,14 287:4 336:1 367:3 436:13

groupings 445:13

groups 53:23 57:17 66:20 67:13 68:26 71:9,11,18

72:15 73:19 74:6,6 75:13,15 76:6,16,26 78:9

79:14,20,23 80:1,4,7 81:24 85:11 86:24,25 88:21

89:15 101:20 138:13,15 155:7 157:24 166:5

189:2,12,14 191:15 192:5,20 193:1 198:4,14

200:17 201:21,21 203:8,12 204:11,11,12 210:19

226:18 227:1 246:7 333:9,9 335:6,14 337:20,23
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338:14,16 347:15,16 359:4 428:13 431:4,5 435:9

436:15,18 439:11 441:3 459:23,24,24 465:20

477:20 479:22

grow 407:20 413:17

growing 336:21 470:24

grown 356:25

grows 441:14

growth 20:16 46:20 477:23 478:3 482:26

guanidine 42:1

guanidinium 42:2,3

guess 76:6 78:26 85:1,11 86:2 87:9 90:6 98:20

136:11 138:18 140:26 171:12 186:18,18 188:4

196:20 197:11 198:1 199:5,9,13,21 202:14

204:22 205:4,9 230:1,4 233:5 247:14 249:20

292:8 315:11 342:3 348:24 349:22 353:15,19

357:8 358:22 375:16 381:9 402:7 404:1 414:9

424:4 462:16 463:10

guesses 240:20

guidance 21:22 262:14 283:5 393:9 481:23

guide 22:6 111:1 251:1,13

guided 304:7,21

guideline 135:7 139:18 245:3 246:14 279:25 280:4

283:19 480:8 481:23

guidelines 135:7 245:5 251:1 262:8 283:19 377:22

guidepost 26:12

guides 251:24

guiding 30:13 481:5

guilt 352:3 415:4

gut 76:3

guy 199:10

guys 89:2 139:6 244:22 245:2 246:10 353:15

374:21 381:8 405:14 451:9

H

half 39:22 119:8 265:6

half-life 43:16 44:7,12 318:22 319:10 321:18

327:23 328:5 332:2 458:11

half-lives 44:9

hall 390:3

Halo 201:5,5

halogen 70:26

halogenated 202:22

haloperidol 117:13,15

Hamner 324:24

hand 42:1 43:4 106:6 137:17 153:17 187:9 196:10

253:19,21 299:15,16 311:13 317:11 326:7

handful 111:25 120:3,22

handle 88:1 129:9 181:25 208:20 244:15 246:24

417:10

handled 90:9 383:24

handling 76:1 195:18 316:6

handout 274:18

hands 481:14

hands-off 107:26

hang 201:10,21

hanging 211:21

happen 9:26 17:24 69:16 71:19 96:1 104:15

180:22 211:18 212:1 222:9 233:2 261:24 290:1

335:17 355:17,22 377:3 471:26 486:10 496:15

happened 69:13 82:2 224:14 233:3

happening 10:26 56:6 63:8 184:22 186:5 218:3

221:24 291:4 332:12 355:21 401:19 409:15

415:20 420:20

happens 61:25 62:26 67:17,24 68:8,13 69:4,10

70:24 95:26 113:12 200:3 210:23 355:9 409:12

happy 104:24 119:19 122:11 134:8 243:7 258:23

275:4 346:20 368:19 468:1

hard 103:4 116:18 125:15 160:15,17 242:15 248:6

249:13 260:8 261:16 284:8 291:4 341:20 346:8

346:24 374:20 401:25 409:3 473:3 492:6 493:17

496:3 500:22

harder 81:6 126:8

hardwired 364:24,25

harmonized 135:4,6

harsh 333:20

hate 76:11 203:20

hay 294:6

Hays 280:7

haystack 240:21 294:7 401:20 430:12

hazard 6:4 56:11 214:1 240:12 262:23 390:24

492:25

hazards 261:22

HBO 72:10

HDP 308:2

head 117:22 265:7 323:1

headed 67:1

heading 237:10,26

heads 117:23 357:11 411:3

health 3:3 6:4 7:4,18 17:6 18:1,24 214:1 249:8

263:8 265:8 269:3 298:12 299:7 300:17 390:24

hear 5:4 18:5 34:14 35:1 36:19 92:24 103:1 211:7

218:5 224:26 243:7 256:5 260:14 311:3 334:1

340:24 428:10 429:16 430:6

heard 54:26 125:25 128:23 137:20 215:11 219:6

242:5 243:24 244:6 251:16 260:22 266:19 289:23

311:18 359:14 389:4 417:20 424:11 432:16 433:4

440:13 442:3 467:7 481:16 496:8

hearing 17:1 51:26 312:2 357:1 382:3 421:7 424:3

490:21

heart 28:19

heavily 85:24 120:25
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heavy 50:2,3,9 260:7

hedging 174:11

Hello 239:14

help 5:6 9:21 13:10,20,25 14:4 16:9 18:4 22:6,21

24:19,23 34:18 38:4 63:10 89:14 90:12 93:23

108:21 129:9 147:9 167:16 173:22 197:20 207:21

213:9 215:21,26 218:18 221:15 224:24 251:12

252:3 282:1 283:6,6,7 293:16 306:8 316:7,18

317:5 351:14 353:24 370:14 371:3 375:13 394:20

399:19 403:10,21,25 405:8 406:14 410:20 411:20

412:24,25 413:25 414:17 419:18 423:2 425:23

426:23 453:22 457:10 462:14,15,17 463:22

helped 298:15 498:21

helpful 49:23 251:1,12 276:26 290:1 312:17

320:26 326:13 331:2 354:6 419:3,11 429:16

430:6 431:26 433:24 480:11 491:17,19 494:21

496:23

helping 34:5 252:26 331:5

helps 13:22 55:25 80:26 183:10 188:7 208:5

278:16 423:23 424:4

Henry's 324:3

hepatic 483:25,25

hepatocytes 364:17 461:24,25

HepG2 101:5 117:23

heralding 28:1

Hermens 63:17

heroic 98:22 232:7

Hershberger 258:20

hesitate 4:17

hexenol 337:17

Hi 134:24 228:4 235:25 351:23 376:19

hidden 211:15

hierarchal 375:13 376:13

high 21:1 24:18,22 25:3 28:12,17,23 29:16 31:16

34:4,9,11,16,19,21,25 36:11,17 54:17,19 62:10

83:6,9 91:11,13 92:9,16,17 93:1 94:8,15 97:17

99:3,9 100:11 103:5 104:5 109:7,11,12,14 110:9

112:12,20 113:1 115:19 118:6,7 121:16 124:21

125:3 126:14 141:5 142:18,23 143:1,6,13 144:5

150:10 154:25 155:3,10 157:9 158:7 164:9 165:8

165:16,18 166:1,4,6,9,11,16,20,25,25,26 167:14

167:17,26 168:6 169:10 172:23 173:5,26 174:10

183:19 188:20,21 189:2,9,24,25 190:16,17,18,19

190:23,26 191:2,11,12,14,20,24 192:7,9,16,17,21

192:23 194:24,25 195:2 198:5 202:2,3,5 203:13

203:16 205:3 208:10,17,19 209:8 216:25 217:3,5

219:2 220:4,18,24 224:13 228:8,14,21 229:8

231:5,6,25 232:15,16,23 233:1 238:13 243:20

244:26 246:18 249:26 251:2,4 267:11 274:6

275:8 278:3 279:17,21,24 280:22 281:2,10,12

282:15 286:23 308:9,17 310:9 314:26 322:17

340:18 349:17 357:1 362:20 368:22 370:8,14

375:6 377:8 379:2 381:12,20 384:20 385:5

393:12,15,25 394:7,11,12,14,18 395:24 397:8,12

401:8 403:15,19 405:14 406:11,13,23,25 407:4

407:10,12,21 408:3,6,13 409:23 410:4,10,12

413:19,23 414:21 415:8 416:3,8 419:14 420:2

421:18,19 422:8 424:13,25 425:21 427:6,17,24

428:2,5,20 429:3,4,8,9 430:6,14 431:21 432:2,9

432:11,18 433:21 435:10 437:4,9,10 439:6,9,16

440:5 442:4,20 443:11,16,22,25,26 444:5,26,26

446:20 448:24 449:3,3,10,14 450:17 451:16

453:8 456:23 461:2 463:15 464:6,9 469:6 471:8

472:3 478:9 485:14 486:22 487:6,11,19 490:23

492:7 494:9 495:2 496:5

higher 30:17 38:5 52:10 58:14 65:2 68:2 69:15

75:8 79:25 85:1,4,5 98:5 159:8 160:8 165:9,15,15

172:17,26 173:7 176:10,11 189:5,7 191:10 193:5

193:6 194:17 228:24 229:3,20 230:2,23 238:9

254:4 257:24 259:21 277:11,12 280:17 310:10

334:26 345:18 392:16 399:15 401:11 404:13,25

410:1 453:16,18 456:16 477:13 485:20 493:24

higher-level 399:15 468:1

highest 41:19,20,23 42:19 43:5 100:16 144:25

145:6,16 151:10 155:8 173:13 174:16 175:3

193:16 205:2 280:25

highlight 30:12 31:14 197:14 219:21

highlighted 21:16 80:2 163:17 285:13 398:23

445:25

highlighting 18:12 68:8

highlights 275:14

highly 33:14,21 40:3 49:1 103:25 109:2 323:25

327:13,24 337:6 399:4 408:10

high-affinity 401:15

high-level 101:14

high-potency 394:12

high-production 27:11

high-quality 471:2

hill 106:4,12 107:23 138:4

hinders 495:16

hindrance 72:25 73:7 74:7

histogram 109:13 273:2

historically 134:26

history 17:16 28:9 39:24 53:3,14 231:2

hit 105:18 129:26 271:26 286:9,14 288:3,3,4,7,14

291:8,11 334:15 433:21

hits 116:23 199:6 272:26 273:2,5,7 285:21,25

286:6,7 288:5,8,25 291:6

hitting 96:2 109:25 116:23 184:20 275:14

hit-calling 105:12
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hoc 267:10

hold 73:14 153:23 185:16 339:22 376:17 432:5

holds 176:6

holistic 51:8

holistically 494:25

home 126:22 204:8 266:22

homeostasis 482:24

homerun 413:16

hood 209:15

hope 22:11 49:8 205:9,14 211:17 213:17 227:4

234:1,9 237:14 239:21 252:24 253:13,15 279:2

376:4 432:26 433:24 499:20

hopeful 49:20

hopefully 12:18 14:8,16 76:21 83:6 186:7,24

205:11 208:6 211:18 242:24 465:12 480:8 495:15

hoping 276:4 363:18

horizon 21:24

hormone 46:18 47:6 181:5,16,18 242:19 387:3

467:2 482:8,12,20,24 484:12

hormones 46:13 55:20 475:26 476:1

Hornung 34:15 36:15 97:4 142:14,16 168:2,2,13

168:14 169:14,14 170:18,18 172:3,13,13,21

173:17,20 174:20,20 176:17,17 177:11,11 179:3

179:25 181:26,26 183:5,5 187:12,12 395:19

423:8,9,12,15,18

host 272:3

Houck 122:18,23 123:1,1,11,15 128:11,11 129:7,7

130:21,22,22 137:4,6,6 141:8,8,24,24

hour 133:23 180:13,14

hours 62:21 261:15 332:9 458:4

House 236:22

housed 24:15 26:21

HQ-OPP-2009-0322-0002 372:16

HTP 165:11 171:23 173:14 175:13,21,22 176:4,9

234:3 241:5 242:4 243:25 244:12 245:9 248:9,18

250:4 267:23 269:25 271:10 272:10,11 273:22

274:1,4 284:17 285:13,21,25 286:6,7,20,21 287:1

287:5 288:5,8,21,22,25 289:6,8,14,14 290:13,26

291:15,18,22 292:3,6,10,10 293:15 294:1,2,9,14

294:15,19,25 295:15 308:5,21 309:4,12,14 310:2

310:4 313:11 314:13 317:15 321:18 324:13

334:24 343:14 363:20 364:4,10,17 371:7 375:22

382:15 383:5 394:16,19 396:25 397:2,22 398:25

399:2 403:18 415:15,18,22 416:10,13 418:3

421:24 422:26 425:8 441:2,5,12,13,20 444:23

445:8 447:1,3,7,12 448:15,18 449:24 450:6,12

452:18 453:8,15 455:2 465:8 480:16 493:24

HTPC 242:5 243:22

HTPS 238:1

http 1:8 4:7

huge 103:21 207:14 223:9 236:5 283:9,10

huh 213:18

human 3:3 7:4 18:12,16,24 35:12 65:23,24 108:8

111:7 141:3 144:14 151:7 153:5,18 154:3,10

155:18 162:21 163:26 171:3 179:1 239:12,15

255:6 256:24 263:8 265:8 268:2 269:3,4,12

277:10,11 298:12 299:22 300:17 315:20,23 341:1

341:1 357:13 361:19 362:2 373:25 386:25 387:8

402:4 465:26 468:12 485:10

Humane 239:15

humans 387:9

human-based 267:19

hundred 220:16 237:6,9

hundreds 93:4 103:22 134:17 236:6

hung 490:11

hurry 464:25

hybrid 122:24 128:17 348:2

hydrocarbons 88:3 191:16 192:11 195:17 197:17

201:2 205:25 207:3 226:22

hydrogen 61:7 86:26 196:19 226:15 438:1 459:23

460:9

hydrolysis 44:8 50:16 319:10 327:23 332:2

hydrolyzable 44:5

hydrolyze 40:13 312:12

hydrolyzing 43:15

hydrophobic 45:19

hydroxybenzophenones 187:19

hydroxyl 72:14,25 74:6 86:24,25

hydroxylated 200:25

hydroxylation 72:16

Hydroxyquinoline 162:20

hyphenated 152:18

hypothalamic 19:24

hypotheses 56:18 71:24 81:20 227:15 256:5

hypothesis 71:24 89:12 194:14 197:16 221:11,15

320:8 322:6 375:11

hypothesis-based 471:5 478:23

hypothesize 161:14,16 435:19

hypothesized 323:26

I

IATA 221:12

ICCVAM 114:25 134:6 249:16 257:5 267:20

269:11 276:13

ICCVAN 268:4

idea 13:14 38:17 63:8 64:13 66:16,20 68:17 72:2,9

73:7,8 74:26 78:8,12 80:21 105:2 117:16 120:7

186:5 198:1,15,18 201:23 202:6 232:7 237:24

239:1 240:15 253:8 256:1 258:3 267:13 289:20

302:13 314:19 316:26 317:8 340:17,26 404:14

407:20 425:7 443:10,12 444:12 457:20 493:21
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ideal 398:16 436:15

ideally 119:7 417:23 420:19

ideas 10:25 11:1 13:21 134:7 138:26 209:18

253:11,14 265:12 408:2,5 433:8 474:17 492:16

492:24 499:11

identical 357:26 403:5

identification 372:8 373:20 398:2 404:21

identified 34:10 49:22 56:15 280:15 300:23 318:21

345:15,22 386:22 387:4 456:19 469:14 471:25

472:1

identifies 56:16 322:3

identify 33:14 38:4 42:17 54:14 149:22 155:20

243:24 271:10 278:3 319:3 325:3 344:25 379:22

383:21 386:20 405:6 406:11 415:11 457:2 471:16

492:24

identifying 33:25 47:23 61:10 77:11 217:3 240:20

369:15 372:6 373:10 379:24 394:12 460:3 467:16

470:7 478:16

identity 62:19

IDs 105:5

ignore 109:23 345:16

ignored 84:7 359:15 453:10

II 461:19 462:16 483:25

Illinois 227:8

illustrate 71:25 72:12 84:8 93:18 253:15 255:8

434:4 444:10 487:24

illustrating 336:10

illustrative 336:5 444:25

imagine 131:22 229:1 294:6 347:1

imagining 89:5

Immediate 437:9

immediately 202:16 450:3 476:12

immune 6:17

immunotoxicology 6:16

impact 3:2 30:7 129:19 171:24 172:6 255:24 363:3

366:14 484:17

impactful 19:7

impaired 58:13

impairment 56:11 483:20 485:21

impairs 71:22

impart 60:21 196:3 368:1

imparted 58:21

imparting 74:6

imparts 61:9

imperfect 124:26

implement 266:16 432:23

implementation 3:7 7:9 17:22 21:22 24:16 25:8

implemented 108:18

implementing 23:13

implication 446:24

implications 262:23

imply 254:5

implying 299:24

importance 62:17 71:5 233:15 243:19,20 345:2,6

362:22 440:18 456:10 460:18 477:21,26 478:8,12

478:19 490:1

important 8:26 9:2,7 10:11 11:26 13:11,17 14:21

15:8,11 17:13 21:8 22:9 24:8 25:23 27:7 28:1

29:1 40:23 44:13 48:16 49:1 53:6 57:3,8 58:12,25

63:12 76:9 77:8 86:14 89:15 93:7 96:8 99:6,14

100:1 104:2 106:13 121:20 127:22 175:11 182:18

183:13 184:16,19 187:16 200:7 215:24 216:14,26

217:7 218:5 219:6,10,11,20 220:20 222:23

223:12 224:10,11,16,17,25 234:13 238:5 245:12

245:23 246:16 247:17,26 248:8,14 250:21 251:20

251:25 252:22 254:11,12,20 255:2 260:9 261:20

262:6,13 263:1 270:20,21,25 280:24 287:16

298:6,10 312:4,18 313:15 316:1 319:25 320:13

335:20 338:21 355:24,25 356:3,8 358:19 363:1

366:12 379:21 393:6 402:10 414:23 430:3 440:2

459:9 460:24 461:13,20 470:3,4 473:20 479:5

480:25 482:25 483:5 484:16 493:26

importantly 14:24 300:20

imported 231:15

importers 231:19

imposed 359:18

impressed 98:26

impression 90:13

impressive 308:20

improve 167:16 217:10 223:13,17 224:20 248:2

251:26 260:26 367:23 385:21 456:21

improved 248:1 448:13

improvement 367:24

improvements 313:10 441:10 457:15

improving 247:25 457:23

impure 230:23

impurities 369:3

impurity 369:2

inability 260:17

inactivate 176:16 461:18 484:12

inactive 98:13 106:21 108:2,6 110:7 115:8,11

116:22 119:14 120:4,20,23 121:1 143:18,26

149:2,3,19 151:24 152:12,14 154:18,19 158:2,4

158:16 159:8,9,10 160:8 164:12,13 166:3,4,5,6,9

166:22 171:26 172:9,20 173:1,3 174:6,6 192:1,4

218:11,12 219:20 238:2 276:8 321:4 350:22

371:25 378:21,23 379:10,12,16 381:19,25 385:22

396:24 397:2 405:16,23 415:6 423:23 437:16

inactives 115:16 119:13 154:19 165:8,11 377:26

378:5,13 381:12
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inactivity 423:22

inadequate 435:11 441:5

inadvertently 356:8

inaudible 169:12 176:15 185:10 336:2

include 18:15,24 40:10 58:7 68:23,24 84:12 143:18

156:9 159:21 206:4 210:22 259:7 265:1 281:17

282:2 299:11 322:5 325:9 335:2,19 336:12 338:1

339:3 364:10 372:2 374:25 378:12 387:1 388:1

412:10 434:25 436:6 437:25 457:1,16 468:9

471:1 473:4

included 3:10 32:4 33:12 43:21 53:10 69:13 70:8

109:18 132:21 235:12 241:3 267:11 270:11 282:3

312:9 315:7 324:10 326:22 331:24 339:4 344:19

363:24 370:20 379:12 380:10,17,21,23 383:26

384:2,4,6,9,11 387:10,11 388:3,23 441:14 465:8

466:3,8 472:7 485:7 486:13 489:13

includes 37:14 231:19 238:22 240:9 241:13 298:21

458:26 473:24 477:21

including 40:2 78:14 104:18 178:20 236:2 240:10

299:16 324:11 343:16 380:3 407:11 440:14,22

442:26 456:20 465:7

inclusion 45:2 321:17 399:17 453:21

inclusive 19:3,16 29:18 32:18

incoming 240:5

incompatible 426:12

incomplete 271:21 370:9

incompleteness 449:17

incompletion 449:18

inconsistencies 397:7

inconsistency 348:7

incorporate 68:6 88:23 221:14 245:15 303:6

352:16 415:19 418:24 420:23 427:6 444:4,17

456:23 461:3

incorporated 65:8 75:14 84:2 102:5 268:22 303:20

339:15 360:17 439:15 441:12 455:3

incorporates 82:22 240:11 245:2 424:16

incorporating 87:26 210:5 413:11 416:21 418:19

422:8 425:11 447:11 448:15 465:3,10

incorporation 311:22 426:4 435:26 441:24

incorrect 277:15 337:11

incorrectly 187:11

increase 61:22 63:21 64:11 79:11 110:16 150:23

174:7 182:8 217:20 238:24 240:4 258:16 326:14

326:21 337:8 338:24 369:16 379:16 381:25

399:13 407:6 434:12 435:22,24 441:19 448:19

452:20 453:20 456:18 482:15

increased 64:26 183:2 399:17 406:24 453:21 484:1

increases 79:14 337:7 356:23 435:3,22 467:13,14

increasing 31:17 63:18 73:11 159:19 216:10

336:18,20 381:3 399:18 450:11

increasingly 11:19 240:18 421:18

incremental 23:25 124:16 125:6

incrementally 53:26

incubation 62:21

indentified 81:14

independent 2:26 117:2 273:21 414:11,15

independently 51:24 113:20 118:5 119:5 304:12

366:10

indicate 34:3 273:13 304:1 322:10 333:10,11

486:15

indicated 16:2,26 17:9 25:13 30:5,23 317:26 364:6

indicates 18:26 43:2 313:2

indicating 225:20 410:6

indication 32:2 61:20 158:18,21 159:12 160:11,22

180:14 458:12

indicative 315:21 407:22 420:20

indicator 56:8 349:4,5,5 386:24

indicators 324:11 487:3

indiction 63:5

indirect 484:9 488:1

indirectly 48:8

indistinguishable 259:15

individual 38:12,14,18 55:21 56:6 236:24,25 238:7

264:9 275:11,24 276:3,23 278:13,18 323:4

375:26 383:11 453:14 476:17

individually 81:11

individuals 16:8 482:26

induce 55:18

induces 206:1

inducing 388:10 475:16

induction 62:13,16 157:15,20 163:13 170:25

171:15 183:12

indulge 212:4

industrial 46:26 228:20

industry 101:21 134:6 321:6 386:5

inergetics 459:15

inert 21:1 27:1,3,11 31:10 33:12 36:9 37:14 53:17

77:20 78:4,24 322:18 334:7 335:2 377:12 378:9

inerts 36:8 37:22 54:3 59:16 69:25 70:1,6 76:7

77:13 78:10,11,12,14,20,22 79:4,7,8,12,21 82:3

84:5 143:16 208:2 237:21 416:5,11

inexpensive 130:17 416:6 471:6,7

inexpensively 427:26

infect 325:4

infer 362:5

infinitely 493:3

inflection 497:4

influence 181:11 201:20,22 461:26

influenced 181:10 199:3 439:25

influences 254:13 492:1
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inform 22:21 52:2 240:2,7 399:21 453:25 469:7

informatics 103:22,25 338:23 340:13 352:13 436:1

474:16

information 4:9,16 5:5 14:26 15:7,9,10 21:13,14

23:16,16 29:13 32:26 33:4 38:14 42:25 51:22

55:26 56:1 60:2 61:26 69:12,19 88:23 89:23

108:3,20 139:8,9,11 141:6,7 147:8 167:21 175:26

189:7 192:16,26 193:2,4 211:17 213:7,8,10 219:1

221:21,22 222:5 230:17 233:23 238:15,16,18

239:4,9 240:5,7,8 241:22,23 243:3,11,13 246:25

248:1 252:2 256:12 257:21,21,26 258:12 260:7

260:13,15,17,20 261:23 271:4 273:11 283:11,15

284:2,24 287:4 288:12 306:17 307:5 312:21

313:3 315:3,4 316:4 324:20 334:25 340:19 341:6

346:21 356:26 362:26 365:17 367:3,6,9 368:18

371:9 372:21 373:4 374:2 379:26 381:11,14

384:22 385:2 414:19 417:13 420:3 430:5 431:22

432:3 433:6 434:1 437:21 439:15,17 446:21

450:5 458:14 460:1,25 464:19 465:15 470:13

473:13 478:20 490:13,14 493:18 497:23 498:11

498:13,14

information-based 240:11

informative 446:7 500:21

informed 142:8 320:14 432:22

informs 58:12

ingredients 20:2 21:1,1,3 27:1,2,3,10,11 30:25

31:10 33:13 36:9 37:13,14 53:18 77:20 237:18

334:7 377:12 378:9

inhalation 314:15

inherence 217:13

inherent 221:13 224:15,17

inhibiting 475:17 484:11,12

inhibition 138:3 477:23 484:21

inhibitions 483:10,10

inhibitors 42:11 45:16

inhibits 272:14

initial 20:25 22:17 32:7 53:20,26 56:24 61:14

62:24 63:3,7 79:1,2,15 85:24 89:12 108:1 130:24

182:5,19 183:13 203:19 236:11 252:9,11,14,17

254:9 255:8,9 264:15 266:6 274:11 305:17 307:2

311:8 387:9 406:8 408:8,13 420:7 424:13 449:26

452:21 455:25 459:10 468:21 470:8 481:3 483:7

initially 102:2 116:22 168:3 311:10 348:18 383:4

404:21 423:20,25 425:20 482:16 492:20

initiate 48:20 55:22 56:16 60:16 145:10 182:20

183:11 219:24 222:3 252:10 342:25 343:25

352:23 356:11 371:24,25 451:17

initiating 30:17 42:10 45:5 47:24 48:10,18,23,25

49:5,6 55:14 56:14 57:7,11,12,23 58:14 60:10,16

145:9 171:14 182:4 189:5 217:18 223:4 224:23

252:8 254:2,9 255:11 305:13 319:5 343:9 344:25

344:26 345:1,3,8,11,14,16,17 346:12,18 355:7,26

356:10 365:4 456:1 467:1 468:7 469:21,22

476:10 482:13,18 483:18 484:15,16 485:3 486:25

initiation 55:9,10 222:8,9 385:8 396:17 476:15

initiator 48:10

inordinate 238:11

input 10:20,25 45:23 179:24 186:19 217:19 223:14

311:4 325:6 376:18 424:18 425:11 443:11,17

446:8

inputs 335:19

inserted 140:26 348:15

inside 60:22 262:14

insight 481:21

insightfulness 497:22

insights 10:9 15:15 307:5 492:5

insoluble 102:1

instance 46:20 55:19 61:3 63:16 74:10 102:1

175:19 180:9 181:4 184:1 193:7 256:15,25

320:19 321:4,7 324:5,17,23 325:2,22 326:26

327:19,21 328:18 329:10 330:15,25 398:6 431:8

458:18 480:21

instances 323:18 394:9 427:10

instantiation 424:24

Institute 5:24 7:25 214:21 283:2 391:20

institutions 306:9

insufficient 260:12 367:14 369:19 379:12

intact 305:1 464:7,12 479:14

integrate 238:5 255:20,22 315:2 376:2 420:15

487:12

integrated 248:21 377:18 439:14 440:20 448:20

483:13

integrating 28:21 124:16 221:22 442:24 443:11

487:9

integration 249:1 256:13 258:3 385:22 420:26

integrative 76:20 221:6,9,19 385:25 487:19

integrator 11:17

intelligent 497:23

intend 29:25

intended 19:21 30:7 251:16,17 262:1,18,18 272:24

327:17 355:5,5 356:7 358:6

intensive 31:14 260:24 305:1 499:17

intent 45:26 272:22 446:23

intention 236:26 241:20,21 302:2 371:5

interact 19:10 42:14 45:15 47:23 60:13,26 61:1,1,2

61:4 122:3 176:16 183:24 191:8,9 210:9 223:6

322:18 431:10,11 459:24

interacting 55:9 60:14 181:4 220:12

interaction 19:12 48:11 57:9 58:11 64:23 189:22
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interactions 61:16,17 181:7,8 353:9 358:16

interacts 60:24 61:5

intercorrelated 47:12

interest 3:15 6:10 7:13 34:21 72:9 73:19 74:14

87:5 196:13 234:21 283:1 310:25 324:20 338:10

interested 40:18 50:1 70:17 131:16 205:23 210:22

282:21 320:19 425:10

interesting 5:1 72:20 79:22 81:10 98:18 100:11

131:18 133:3,5 140:10 176:5 184:11 201:6,9

207:7 244:23 245:11,11 386:14 454:24 495:19

interestingly 293:3

interests 6:15

interfere 359:22

interference 96:11 98:15 100:2 111:4 112:17,25

127:7 138:10 184:10 372:6 373:11,12,16,18

383:14 397:3 398:2 415:9 453:10

interferences 141:21 156:25 373:14 382:18 384:24

interfering 470:1

interim 76:4

interlead 99:13,22

intermediate 436:23

internal 21:22 371:4

internally 305:14

internationally 218:18

interpret 15:6 34:18 63:10 254:15 429:16 430:5

445:12 465:17

interpretation 58:26 62:1 69:13,20 92:2 157:6

159:5 167:17 219:22 240:4 242:10 243:14,21

259:19 272:7 277:4,8 363:4 364:14 365:10

366:15 367:11 368:18 369:4 371:23,26 372:13

448:23

interpretations 245:24 395:24 398:22

interpreted 263:7 297:23 439:19

interpreting 45:25 58:15 156:22 157:3,24 170:5

interrelated 271:5

intersection 255:13

interspecies 65:15

intervals 106:11 107:25 139:6,8

intervening 255:13

intervention 105:21 373:25

inter-laboratory 20:17

intoxication 401:8

intricate 223:21

intrinsically 327:13

introduce 5:7,15 16:15

introduced 69:6 305:17 312:8 467:21

introduces 436:25

introducing 86:3

introduction 312:16,21

introductory 8:18

inventories 58:20 61:15 66:7 77:26 82:23 84:4

143:16 161:22 186:20 191:17 195:20 198:25

202:12 206:25 207:6,10 210:7,16 219:14 232:4

338:10 406:24 407:6 435:12 468:24

inventory 26:25 64:19 65:11 68:6,7,14,14 69:25

70:5 73:22 74:2,13 76:7,14 77:7,7 78:8,13 79:6

80:14 82:3,16,17 88:16 143:18 159:24 160:2

162:17 194:18 196:4 197:9,13 198:14 200:13

202:20 203:7,26 206:13 210:21 215:19 216:22

217:6 219:12 220:8 222:15 223:9,19 224:6

240:16 302:26 316:16 469:17 480:26 481:3

Invertebrate 20:15

invest 32:20 307:4

invested 434:12

investigated 114:11 458:24

investigating 114:7

investing 472:11

invite 46:23 52:4

invoke 74:23

involve 181:17 475:25

involved 45:4 72:26 83:15 144:21 145:23 266:12

282:21 345:3 468:8 484:24

involving 197:2

in-house 383:5

iodaphenols 201:13

IOM 250:8

ion 41:7,8 42:2,3

ionic 46:9 318:2,5

ionization 40:8,10,15 321:14 327:12 328:20 330:9

ionized 163:19 328:19

ions 50:2,3,9

iron 368:10

isobutyric 44:9

Isocyanates 201:14

isoforms 141:2

isolable 321:10

isolated 396:11

isomers 87:12,23

issuance 20:25 27:19

issue 18:25 32:6 35:15 36:3 66:18 85:10 96:12

102:14 103:22 112:19 117:5 125:10 126:1,9

134:1 137:1,1,26 139:11 175:6 197:4 208:23

209:12 216:10 219:21 224:7,8,20 230:6,9 231:17

234:11 235:8,12 296:11,12 307:25 308:18 309:17

330:12 331:9 345:12 346:10 370:10 398:18

409:21 413:10 452:4,7 454:7,20 458:15 462:19

463:3,20,21 469:23

issued 21:20 22:4,7 23:11 26:21 27:9 498:6

issues 2:7 3:2 16:23 27:17 32:12 98:9 100:2 106:25
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263:18,18,25 283:4 293:16 296:9,10 302:26

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370:13 399:22 403:26 422:18,21 468:20,22 469:2

469:14 470:25 471:20 489:11 490:2

issuing 262:14

italics 318:16

item 445:24

items 264:16

iteration 136:12

iterative 204:9,13 240:15

it'll 241:15

ivory 306:9

i.e 335:7,12 369:16 371:24,25 373:26 386:17

J

JA 453:6

Jacobs 8:12,13 130:26 131:1,1 132:9,11,16 139:14

139:15,15 208:8,9,9 209:5,5 215:1,1 332:15,16

332:16 333:24,26,26 339:16,18 342:7 343:2,3,4

356:16,19 360:16 362:14 370:18,19 374:12,13

376:11,12 377:14,16,16 381:10 382:8,20,21,21

384:26 385:1 392:1,1 430:19,20 431:24,25

432:15 445:19,20 464:26 465:1 473:17,18 474:8

479:24 480:3,4 481:21,22 488:15,16 494:15,16

jam 409:19

James 6:8 46:8,17 48:4 181:1 182:22 184:24

209:26 214:2 317:21 352:21 358:6 390:26 405:12

475:21 489:19

Jan 7:11 37:7 50:15 214:14 311:24 391:13

JANICE 7:11 37:7,18 50:15 214:14 231:22 303:13

391:13 417:18 464:1 491:6

JANUARY 1:17,17

Jan's 310:15

Japanese 196:14 480:6

Jenkins 500:16

jettisoned 243:8

jettisons 246:17

Jim 6:8 46:8 182:22 184:24 214:2 390:26

jitter 119:15

job 10:5 99:5 111:24 132:6 267:5 301:10 304:6

356:17 360:1,4,10 362:15 363:12 450:14 458:21

471:24 495:23,26

jobs 34:4

Joe 500:15

John 7:2 214:10 226:1 391:10

join 493:13

joint 239:17 247:19

Joop 63:17

Joyce 500:17

JRC 466:6

judgement 127:25 178:13

judgment 289:25 341:1

Judson 36:10 91:7,9 122:14,17 125:9,17,26 126:11

126:20 127:4,12,13 129:22 130:20 131:6,7

132:14,22 133:14,22,22 134:2,19 136:6,6 137:24

138:6 139:2,13,20,24 140:6,6,14,16 143:6 156:22

160:20 175:8,9,9 183:15,16,16 209:9,11,11

210:18 220:6 228:4,5 229:10,16 230:25 231:10

232:1,2,24,24 340:24 370:24,24 371:14,17

374:24 381:1,1 382:1 432:17

jump 76:12 193:15

jumped 156:5

June 21:21

justifiably 234:5

justification 359:9

justified 322:16,19 328:2 469:18

justify 320:11 483:4

K

Ka 41:5,9

Kaiser 338:4

kappa 128:7

Katie 275:2,3

Katrina 6:23,23 126:14,14 127:1,6 134:12,12

214:8,8 351:23,23 376:19 380:10 381:17,17

383:26 388:3 391:7,7 395:3 400:2 404:18 416:24

438:7 442:10 454:4 489:23

Katy 275:1

Katzenellenbogen 226:2 227:8

Katzenellenbogen's 256:23

keep 61:25 72:15 75:18 154:21 176:15 180:21

200:20 247:2,12 251:20 270:21,25 289:11,16

291:9 298:5 312:2 318:25 413:25 414:17,25

446:24 447:26 455:6 464:3 481:5 499:10

keeping 45:1

Keiser 8:1,1 88:5,6,6,25 89:20 90:13 91:1 137:16

137:18,18,25 138:23 176:23,24,24 178:22 179:15

180:23 186:23,26,26 188:6 214:22,22 237:23

293:19,20,20 294:15,20,26 295:3,8,17,22 316:22

316:23 339:24,25 341:21 345:26 346:1 351:24

361:11,12 387:24 391:21,21 407:14,15 421:4,5

422:10 424:6,7 432:6 438:18,19 442:14,15

445:21 446:14,22 447:25 451:3,4 454:22,23

474:5,6 476:5,6 480:12,13 488:22,23 493:11,12

Keith 122:18,23 123:1,1,11,15 128:11,11 129:7,7

130:22,22 137:6,6 141:8,8,24,24

Kent 36:2,5 38:21,24,26 39:4 45:7,12,20,26 46:15

46:23 47:9,15 49:20,24 50:6,13,21,21 51:18 52:3

157:1 332:21

kept 85:12 291:9

keratinocytes 368:8
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ketones 76:10,13 79:24 199:7 200:7,10 203:1

208:14

key 10:17 18:23 25:9 88:9 89:5 236:15 252:16,21

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310:25 365:6 399:11 403:9 415:16 440:3,4,22,23

440:24,25 446:17,20 449:6,6,8,9 452:22 453:19

468:10 471:13 472:6,11,13 478:16 484:19 485:15

keynote 16:25

keys 474:4

Ki 147:14,17 177:17

kibitz 390:2

kick 450:3

kicks 449:26

kidding 2:3

kinases 343:19 351:1

kind 11:6 39:15 49:2,7,21 51:8 52:7 54:22 61:18

63:2 68:17 75:6,10,21 78:24 81:6,16 83:8,23

86:12 87:16 88:17 92:12 97:26 98:9,14,22 106:19

106:21 120:21 132:7 136:26 138:14 145:26

154:15 155:5 161:20 164:21 166:18 169:25

175:17 185:9 186:6,11,17 188:19,25 189:19

193:20 194:7 196:24 199:11 200:17 209:18,19,22

210:11 237:24 242:7 246:19 248:13 249:13 251:8

251:18 252:18 253:22 256:18 258:6 261:16

274:12 275:24 287:24 289:7 293:1 311:11 312:4

312:17 355:23 359:15 381:24 404:19 419:25

420:12 427:22 434:6 463:7 464:2,3 467:24

491:12,16,25 493:2 495:9

kinda 178:1

kinds 12:21 13:10 47:5 92:7 112:25 131:16 138:21

141:17 184:15 251:25 252:15 276:24 278:20

475:26

kinetics 464:3

Klaine 5:22,23 213:24,24 390:20,21 489:15,16

Klassen 368:6

knew 164:23 185:22

KNIME 335:25

knock 347:21,22

knocking 402:19

know 10:4 13:4 14:19 15:19 18:3 25:19,19 26:8

49:21 50:3,8 56:10,20 63:4 75:7 76:26 81:17

85:16 86:18 91:12 95:8 102:14 105:25 108:8,10

109:20 115:24 116:25 125:24 126:6 128:25 130:1

138:8,9 140:21 146:14 147:19 148:5 162:1,18

169:1,2 173:21 174:25 177:15 178:11 179:4,11

179:11 181:24 185:9,18,21,24 186:9 187:15

191:9 193:10 201:7 203:9,10 205:15 207:11,12

207:15,17 208:21 212:1 216:22 220:13 223:6

225:15 228:12 232:9,11 238:19 240:16 242:11

243:26 244:9,11,16,20,23 249:22 250:24 251:7

255:15,16 258:13 259:11,14 260:5 271:14 273:7

273:8,12,19 278:14 291:3 292:6,9 293:1,16 295:9

295:11,11,12 300:20 301:13 308:12 314:4 316:5

348:26 350:23 351:8,11 353:19 355:11 356:8,14

360:22 371:20 374:21 381:8 401:14 404:20 405:5

408:22,23 409:20,21 410:5,11 411:4 414:22

419:19 421:6 424:4 429:24,24 430:5,10 431:18

448:8 450:1 459:22 475:2,5 481:17,21 487:1

491:14 492:13 494:11 495:11,18 497:2,2

knowing 15:1 146:16 447:21

knowledge 12:26 15:1 28:22 198:20 207:21,22

221:9 357:2 372:15 374:1 383:19 415:19 454:13

knowledge-based 448:26 449:15 454:13 477:21

known 45:16 46:12 51:14 56:25 70:19 75:15 92:15

128:5 146:11,13,22,24 147:6,6 149:1,3 177:4,12

177:16 181:11 192:1,4 205:25 257:15 321:5

325:18 339:11 341:14 346:9 365:22 380:4 458:20

knows 126:22 130:2 138:11

Kolanczyk 35:2 36:5,19 51:10 52:20 66:13 83:23

84:21,26 85:14,22,23 87:11,21 188:13 195:16

206:10,12,12,18

KOLANSZYK 204:26

Kow 73:24

KOWWIN 75:7

Kristie 235:23,25,25 239:16,20 241:12,19,26

245:15

Kristi's 239:18

kudos 236:9

L

lab 7:21 93:13 103:21 120:14 121:6 132:1 229:11

229:12

labeled 319:19

labor 305:1

laboratories 229:9

laboratory 6:25 17:7 27:16 31:16 67:1 214:9

265:24 307:13 391:8

labs 93:20,21 94:21 103:18,20 230:22 382:5

lack 84:26 268:25 281:16 338:12 358:22 364:21

398:4 400:11 443:15

lacking 321:25

lactate 162:10

ladies 279:13

laid 23:10 32:8 47:15 51:23 74:15 206:13 275:20

lamppost 111:20

landscape 270:7

language 223:8 236:22,26 237:1 241:20 297:17

317:23

languish 301:6 302:4

large 24:7 32:3 74:5 88:15 92:19 101:18 183:20
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434:20 474:3,4 497:19,26

largely 97:6 114:24 167:2 190:7 203:2 409:22

439:24 440:12,15 477:13

larger 82:25 189:14 191:18 192:20 195:11 199:22

204:4 210:21 240:18 243:9 295:18,19 333:8

377:3 398:5 417:13 418:18 427:6 491:17

largest 72:3,7 79:11 189:20 190:13,21 191:3,6

larvae 485:18,18 487:5,10

Larval 20:16

lastly 85:15 196:8 201:23 273:22 329:4 331:8

484:20

late 411:23

lately 199:11

lateness 167:22

laudably 234:21

Laura 500:14

law 295:26 324:3

laws 3:16 114:26 134:19,19,24,24 223:8

lay 15:17 23:11 253:23

layer 237:8 242:1

laying 21:6 32:14

layouts 368:17

lays 227:21

LBD 479:19

LDH 162:7

lead 54:23 57:25 61:6 66:3 152:7 156:7 182:14

183:9 226:13 301:7 303:11 319:12 333:25 334:4

356:17 358:10 363:5 369:15 377:15 379:23 394:4

404:7,10 433:18 438:15 456:2 460:11 467:3

482:16

leading 30:17 182:4 336:23 337:4 344:26 345:2

470:22 477:9 478:4

leads 24:13 97:14,14 182:21 483:24

leakage 162:9

learn 86:2,4 132:5 210:23 224:26 402:8 473:11

learned 35:16 224:24 242:22 269:25 477:3,19

480:14

learning 23:26 127:25 491:18

leave 27:12 84:11 147:20 178:3 211:9 344:16

441:17 455:8

leaves 190:5 347:4,6,12

leaving 190:19 191:14 192:23

LEC 107:20 369:15

led 20:25 67:7 232:11 488:18

left 2:14 33:6 56:3 64:8 66:15 73:11 82:11 118:2

119:4,12 120:16 143:23 146:1 150:16 151:5,14

153:24 154:1 157:12 162:3 163:4 165:9 184:2

215:4 260:12 265:6 293:2 489:14

left-hand 30:25 35:17 116:11

legal 296:9 300:8

legally 296:21

legislation 490:2

legislative 18:8,17 56:23,24 185:24 215:12,20

240:13 304:4

lends 176:8

length 101:1 265:9,10

Lentz 368:10

lesser 337:21 365:5

lessons 224:24 242:22 477:2 480:14

letting 89:11,13 282:14 415:24

let's 14:2,2 52:13,15 60:5 61:11 77:2,25 80:5 91:3

91:4 107:1 116:7 120:15 136:20 164:1 175:12

182:24 185:16 188:8 192:25,26 194:26 213:17

215:8 247:3,4 256:17 296:2 298:20 300:7,10,11

302:14,18 318:14 342:15,16,19 356:16 360:6,10

362:12,17 371:21 375:2 377:5 380:25 382:11

384:16 390:15 393:22 395:20 397:18 398:22

427:12 432:24,26 433:2,11 439:4 451:13 455:22

463:24 466:14,16,17,21,22 476:26 482:1

level 1:13 25:14 31:24 55:19 58:14 64:26 94:10

96:26 104:25 105:15,19,23 106:4 108:3,16,18

110:6,6,7 112:22,23 113:14 115:19 116:18,22

129:10 136:1 141:12 151:26 165:24 173:14

174:16,22 183:12 189:5,7 222:5,9,10 244:2

247:13 251:2 253:18 259:4 268:20 271:25 304:5

336:14 342:24 343:1 344:2 352:1,3,22 353:9

360:22 372:5 373:4 376:6 397:23 404:23 407:12

452:11 460:16 464:9 485:20

leveled 365:25

levels 55:5,6 57:14,22 64:26 129:14 141:1 165:23

189:4,26 254:4 264:20 268:11,16,24 270:5 305:7

305:16,26 310:2 314:26 345:18 364:24 392:17

399:15 407:11 443:23 471:12 472:3,8 477:13

478:2 493:24

leverage 218:14

liability 365:10 366:9

liaison 2:11

library 94:5 101:22 103:12 118:24 124:9 308:7

368:16

lies 434:13,21

life 199:11 265:6 332:8 471:13

lifeline 122:18

ligand 48:19 71:7 95:9,9,11,11 128:5 144:9,22

154:9,11 156:1,15 210:1 334:20 350:7 357:19,25

364:3 475:3 483:20 484:17

ligands 394:13

ligand-binding 479:9

light 96:10 101:13 111:20 200:8 252:12
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lighting 118:1

lights 96:4 113:3

likelihood 128:15 434:20

liken 25:10

likewise 352:9

limit 3:24 103:3,6 280:3 281:26 284:20 370:10

383:14 385:11

limitation 425:2 457:11 482:16

limitations 125:1,2,5 187:8 220:23 275:24 276:26

314:5 317:16 331:15 338:6 359:18 382:19 383:12

402:26 436:10 451:20,23 469:26 474:11

limited 24:21 74:2 132:4 152:26 155:19 156:6

164:1 198:14 201:1 289:7 308:22 309:21 385:14

387:15 425:20 439:24 450:20 459:23

limiting 298:5

limits 157:8,8,9 169:13 220:3 327:20 329:12

429:19

line 5:3 29:21 39:1,3 64:24,25 65:23 66:4,5 69:7

70:6 107:16 118:14 141:12 146:2 148:26 149:11

150:10 160:11 162:7 166:13 172:11,12,14 174:4

174:4 179:18 201:26 244:9 259:12 273:3 281:20

316:2 325:21 357:9 372:21,21 388:11 420:20

454:16 462:26 463:1 473:10 480:11

linear 494:26

lines 171:5 313:16 387:9,10 401:24 402:2,11

462:25,26 465:7 466:16

lineup 241:11

link 234:11 366:4 399:8 414:18

linkage 55:8,16 58:13 59:3 222:8

linkages 55:12 56:10 221:23 222:7

linked 399:4 472:3

linking 478:9

links 336:2

Lipinski 322:1 324:6,7,9

lipophilic 328:9

Lisa 260:1,3,4 264:5

list 20:26 21:2 22:17 30:10,24 32:7,26 33:6 35:9,25

38:5,9 40:1 43:19,19,24 51:11,19 59:11 68:6

69:11 77:14,16,19,23 78:5,6,11,15,18 79:1 81:24

82:25 101:23 112:21 114:23 188:18 203:22

207:11,18 208:1,2 230:15 231:23 233:7 249:15

262:25,25 263:6 267:20 268:4 269:11,13 270:9

270:10 274:2 275:19 282:20,20 283:1,13 295:26

299:2,2,6,10,16 301:6 302:4 306:22 316:16 326:6

330:7 378:3 426:19 457:4,20 459:7 485:4 498:6

listed 43:20 67:23 190:17 198:17 262:3,4 306:18

listening 10:8 39:2 403:13

listing 243:6 483:21

lists 4:15 27:5,5 33:4 77:21 216:6 231:24 232:2

246:20 262:26 263:4 298:24,25

literally 424:19

literature 13:15 18:25 20:3 50:3,9 149:4,7 207:2

227:21 238:17 315:6 341:6 346:3,5,7,9 378:12

379:15,22 380:4 381:14 382:3 386:9 473:20

498:12

little 16:12 22:8 38:20 40:19 42:7 44:8 45:24 46:9

50:1 52:22 53:3 60:6 61:11 64:23 66:22 70:22

75:4 83:23 85:4,5 86:3 87:16 88:8 90:6 91:16,22

92:7 93:23 94:13 97:21 98:5 99:8,19 103:1,26

107:1,20 108:15 109:21,25 112:14 113:18 114:13

115:12 116:7,18 117:19 119:1,15 125:26 127:24

128:18 146:21 150:3,19 151:17 152:2,7 155:17

157:2 158:25 159:2,4,12 160:16,26 161:3 163:15

171:21 179:16 188:15,24 190:11 192:15 199:23

200:4 201:24,25 202:10 205:10 216:20 227:7

231:22 239:17 241:5,17 242:9,23 244:2 250:5

252:6,19 253:13 254:17 255:4 256:11 257:1

270:2,18 280:17 283:16 284:6,8 285:26 291:3,8

293:15,21 296:5 297:8 298:22 314:3 315:3 318:6

322:19 328:4 335:15 339:26 348:18 354:1 356:14

361:13 363:13 400:8 416:18 418:3 428:16,19

431:5 432:21 434:20 459:12 475:7 477:14 494:23

498:25 500:7

liver 141:4 145:4,12,13,17 146:26 147:1 149:8,9,10

149:13 156:17 157:13,16 159:22,22 162:10,21,23

167:25 170:11,19,20,22 224:19 316:3 334:9,14

337:5,16 354:25 355:1 356:22 365:1,8 372:15

413:5 416:1 448:25 450:7,15 452:17 453:6 455:1

458:8 460:16,19,22 463:19 464:13 467:10 483:19

486:24

Liverpool 214:10 391:9

livers 145:6

lives 307:4

living 22:10

LLC 264:6

LMC 67:1 85:25

load 68:6

loading 67:18

LOAEL 280:10

LOBBY 1:13

local 59:13

localization 406:1

locate 237:15

located 60:23

log 41:1 157:14 163:12

logic 59:13,22 60:1 67:7 69:5 222:21,24 227:9,12

253:21,26 255:21,24 256:1,16 259:11,18 307:18

314:18 332:11 421:1,16 461:1

logical 57:2 84:24 88:22 185:13 204:24 223:25

224:5,7 249:1 254:24 303:8,16 304:24 305:10
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logically 185:13 223:10

logic-based 59:9

logistics 500:18,18

LogKow 64:22,22 66:21 68:25 69:5,18 70:20 73:11

73:13 74:11 75:4,6,7,8,16 89:16 90:23 143:15,23

146:14 147:8 159:8,20 160:8,9 164:23,24 165:15

165:15,18,18 187:15 193:4 194:2 195:25 197:5

198:8 199:3 203:22 204:4 205:1 227:3 265:22

421:14 436:22,26 437:25

LogP 65:1,7 189:13 192:6 324:14

long 39:9 44:6 84:11 177:22 187:19 211:2 242:21

257:16,17 270:22 313:25 321:6 328:2 359:17,24

477:17

longer 72:21,23 103:5 432:23

look 13:3 14:4 15:19 19:4,5,18,22 22:24 26:8 28:9

30:13 36:26 37:12 38:12,15 42:26 43:7 46:2

51:19 53:11 55:5,8 56:4 61:6,18,24 62:5,8,18

63:14 72:3,6 73:15 76:9 77:2,25 78:2,16 79:15,16

79:20 80:20 81:1,1,3,6,20,22,23 82:2 83:26 84:9

89:16 90:19,20 105:13,22 108:9,22 109:12 111:7

113:4 114:26 115:4 116:7,17 120:24 128:12

130:12 132:6 134:26 135:18 136:9 138:25 143:2

143:9 144:6,21 145:19 146:3 153:6 157:23 158:9

158:10 159:3 163:20 164:22 166:2 167:9 168:6

168:20,21,26 178:8 180:2,8,11,12 182:3,10,11

183:23 185:24 186:12 187:5,16 191:25 194:25,26

195:3,4,5 196:15 202:4,16,19 206:25 207:1,2

208:26 216:6,8 218:26 219:12 221:15,21 223:25

227:11 231:13 232:6 247:18,23 249:15 250:1,13

256:17,18 257:22 259:24 274:1,21 275:23 276:14

277:8 282:5 284:12 286:11,19,21 288:2,8 289:10

290:13,26 291:1,20 292:17 293:3,4 295:11,14

298:14,16 300:11 301:20 309:3 316:19 318:12

325:1 347:13 348:1 349:19 351:3 354:19 355:7

355:13 360:19 396:21 398:11 407:23 411:11

413:1,10,13 418:1,4 420:21 428:9 454:8,12

457:25 458:9,22 459:9,10,17 461:4 463:11,12,13

475:13,14,15 495:17 496:24 498:1

looked 14:11,12 63:17,19 65:13,14,18,22,22 77:19

81:13 87:12,23 89:7 101:25 121:18 132:20

143:12 144:3 151:22,23 155:17,18 156:20 161:23

162:19 174:21 193:5 196:13 207:26 210:5 220:13

228:8,26 256:23 259:5 268:13 280:25 284:21

285:23 286:7 293:1 299:6 355:8 375:26 407:24

427:16 428:7,14

looking 4:26 9:17 12:13 15:2,8,15 18:4 25:20

27:13 29:6,12 34:3 35:22 37:10,20 39:18 41:5,6,7

41:15,21 42:9 43:14 48:23 56:9,15 57:14,16,17

59:21,21,22 66:18 72:7,16 73:9 79:24 87:22 89:2

89:22,26 92:5,12 93:4 96:3 97:6,25 98:24 100:16

105:16 114:22 119:2 120:26 122:7 123:24 128:3

128:4,9 129:25 133:26 134:3 135:16,18 136:8

138:4 139:3 140:23 142:21,25 143:19 144:6,7

145:9 151:24 152:2 153:14 156:6,7 159:6,26

160:16 162:2 165:23,26 166:24 167:5,5,7 168:3

168:23 170:25 171:12,15 173:4 175:18,26 176:4

177:1,21 179:18 181:21 182:1 184:12,13 185:2

187:13,15 188:3 191:26 195:6 196:12,21 198:10

200:26 201:9,20 202:20,22,23 203:5,22 204:4,10

205:13 207:3 209:13 211:12 217:13 219:3 220:4

220:21 221:13 222:8 223:3,26 224:7,13,19 227:2

232:3,4 248:20,22 251:11 252:8 256:19 270:22

272:17,17 273:15 275:7 276:24 278:2,15 280:16

285:2 286:18 291:5 293:23 296:18 308:25 309:1

321:13 346:17 347:15 348:13,23 351:9,10,12

353:6 357:22 360:2 390:5 402:4,11 403:14 414:3

430:8,9,9 458:6 459:21 460:2,24 465:16 473:7

474:26 475:10,11,18 477:17,18,25 480:10 488:7

491:20 493:1,20 494:12,13,25

looks 19:16 82:10 98:12 105:14 110:16 146:9,20

148:3 153:16,18,22 160:16 237:14 238:25 292:6

320:2 353:1 422:1 423:26

lose 278:20,21,22 301:3 358:16 405:4

loss 62:24,26 208:26 324:2

lost 137:3 243:2 287:11 288:15 289:1

lot 9:1,1 10:4 14:13,14 15:17 30:8 41:13,20 46:4

50:9 52:8 61:26 64:9,10 65:6 77:16 78:9 81:6

82:17 83:2 87:8 88:14,23 89:16 96:8 102:15

112:16 114:21 115:1,19,23 116:1,4 117:5 118:19

129:8,25 130:7,18 133:24 136:7 139:7,7 148:7,18

155:15 160:1 165:2,6 167:21 169:20,22 171:9

180:14,18 182:7,11 183:21 185:25 186:14 191:1

191:1,17,18 192:5 193:2 195:13 207:7 223:20,20

226:2 227:11 228:19 240:6,9 243:2,24 244:4,14

248:6,6 253:9 255:13 256:5 257:10 282:1 289:23

298:17 303:18 310:15 314:20,20 315:3 316:7

321:1 322:26 344:9 347:23 348:7 349:1,18 350:4

350:15,24 353:13,17 354:6,10 356:1 357:4,11

366:15 376:21,24 377:20,24 381:11,13 402:8

403:13 410:11 411:2 413:7,25 418:17,19 420:2,2

420:9 421:13 429:5 450:4,4,6 456:7 463:8 464:19

464:25 470:3,5,13 484:24 486:26 487:8 491:8

493:17 498:26 500:18,26

lots 11:8 12:8 16:5 102:23 103:23,23 109:3,6,15,15

114:12 115:23 118:26,26 130:1,1,6 131:20,24

230:26 347:20 402:18

loud 334:1

loudly 333:18

loudness 333:22
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Louise 8:19,22 16:1,24

love 281:21 496:6

low 15:5 29:15 34:20 39:15,16 58:22 97:11 98:23

99:1 109:10,12 113:1 125:8 141:20 163:18,26

165:2,9,11,18 167:1 168:4 169:17 172:25 173:6

175:13,23 180:22 185:3,4 194:24 205:3 209:6

216:24 217:3 219:1,1 256:5 264:22 267:7,12

274:5 306:22 308:15 310:9,10,11 321:11 334:23

348:6 394:17 406:22 407:19 419:13 430:7 432:10

432:13 449:26 451:19 453:9,11,20 496:6

lower 33:6 37:26 38:6,7,17 41:26 65:3 68:3 99:4

141:5 142:20 143:23 148:22,24 150:16 151:17,19

152:9,14 153:11 155:11,12 160:9 173:8 188:18

194:20 202:5 208:20 222:5 228:23 277:19 322:13

361:24 394:16 397:14 404:23 472:8

lowering 44:7

lower-level 468:2

lowest 41:18,20,22 42:18,22 68:2,4 73:24 107:20

151:9 155:9 193:15 205:2 270:16 369:14

low-affinity 61:17 399:14,19 400:16 403:11 407:1

422:24 450:2,16 453:22

low-potency 394:13 401:13,16

low-throughput 485:14

luciferase 98:16 100:6 118:17 123:7 144:24 158:23

272:14 348:15

luminescence 156:16

lumping 321:1

lunch 83:5 91:3,5 302:13,16 466:14,16

luncheon 466:19

M

M 391:23

machine 101:2

MAD 107:17 136:9,10,10,10,13,13

Madden 333:15

magnitude 163:12 268:14,14

main 129:24 149:9 226:4 320:1 329:5 449:23

462:5

maintain 407:6

maintains 218:1

major 169:15 241:7 242:17 343:10 441:18 450:2

majority 26:3 71:8 80:9 190:13,25 289:14 430:11

459:18

makeup 78:13

making 3:6 17:24 22:1 24:7,11 25:25 26:14 27:20

29:6 47:17,21,24 56:19 65:25 124:17 156:8

178:15 179:1,24 184:23 202:14 218:11 259:3

260:4 262:26 264:2 268:1 281:12 335:19 341:1

345:12 360:16 402:11 414:10 471:9 490:20

497:14,25 498:12

malformation 478:4

mammal 482:24

mammalian 20:10 122:24 128:17 348:2 365:7

462:20 464:13 488:5

mammals 461:12,26

manageable 217:8 304:21

managed 270:14 313:21

management 21:18,20 22:4,9,12 25:15 215:19,21

246:9 263:19 264:2 493:4

managers 21:23 24:4

manages 232:8

mandate 185:24

mandates 18:8,17 240:13 304:4

Manibusan 9:3 16:12,18 37:12,21 38:2 47:7,9,10

51:6,7 124:13,14 211:1 225:5 230:8,20 231:16,20

302:23,23 318:11,18 319:1 333:3,19,20 342:13

342:22 362:20 371:22 375:4 377:6 382:13 384:14

384:18 389:3 392:9 393:24 395:22 412:7 422:3,3

433:13 439:2,6 446:13 451:11,15 455:20,24

466:11,23 476:24 477:2 482:3,6 489:5 496:19

Manibusan's 448:14

manifestations 362:4

manifold 265:24

manipulate 290:3

manipulation 398:16

manmade 322:25

manner 12:19 29:8 218:1 249:1 373:26 399:13

402:20 478:14 487:20

mannitol 368:8

manual 105:21 271:26 346:9,15 383:23

manually 152:2

manufactured 230:14

manufacturers 230:9 231:17

manufacturing 230:10

manuscript 121:14 282:4

map 24:15 54:23 83:4 105:4 142:20 237:16 251:12

290:1 343:19 465:26 468:7

mapping 105:1

maps 207:16,17

Marion 6:19,19 214:6,6 331:23,23 342:6 350:11

354:3,3

Mark 7:1,1 45:8,8,18,18,21 214:10,10 319:13,14

319:14 341:23,23 347:8,8 391:9,9 438:5 442:8

454:2 480:2 488:14 489:25

market 27:13 261:25

married 474:24

marry 248:20

Martha 6:2,2 213:26,26 229:7,7 231:12,12 357:12

380:23 384:11 388:25 390:23,23 395:12 400:21

407:9 417:8,26 489:17

Marty 269:19,21,21 274:17,20,23 285:1 292:21
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Mary 9:3 16:12,15,18 37:12,21,25 38:2 39:5,23

47:9,10 49:12 51:7 53:12,16 78:17 80:22 124:14

186:7 210:25 211:1 225:2,5 230:4,8,19 231:13,16

231:20 302:21,23,23 318:11,18 319:1 333:3,18

333:20,23 342:13,22 362:11,18,20 371:22 375:4

377:6 382:12,13 384:14,18 389:3 392:4,8,9

393:24 395:22 411:22 412:7 422:3,3 433:13

439:2,6 446:13 448:14 451:11,15 455:20,24

466:11,23 476:24 477:2 482:3,6 489:5 496:19

mask 86:22

masks 68:23 86:3

massive 303:17

master 99:21,24 103:14,14 359:20

match 51:3 76:2 164:24 172:1 178:25 195:18

196:12,26 198:12,24,26 220:17,19 265:19 274:17

288:12 304:11 317:2 338:11 490:25

material 43:20 218:25 319:18 323:20 325:15

353:17 368:4 500:24

materials 2:21 4:4,21 218:26 285:9 496:13

math 190:6 290:17

mathematical 67:1 430:1,4 499:8

Mathematics 6:25

matrices 306:15 463:11

matrix 294:8

Matten 2:1,2 211:11 213:1,2 263:14,16 274:16,22

274:25 333:16,21 389:12 390:1,8 497:25 499:15

499:16

matter 82:11 216:4 217:12,23 218:26 414:14

matters 216:8 354:13 448:16

mature 95:13

maximize 23:15 411:7

maximizing 129:16

maximum 106:10 369:7

MCF-7 465:22,24,26

MCF7 144:16 387:25

McLaughlin 7:17

McManaman 6:8,9 46:7,8,8,17 48:3,4 180:26

181:1 182:22,22 184:24,24 209:25,26 214:2,2

317:20,21 322:23 329:23 352:20,21 358:5,6

390:26 391:1 405:11,12 475:20,21 489:19

mean 45:19 46:3 50:24,25 55:11 94:16 106:16

123:7 127:1 141:25 174:19 179:21 181:15 183:17

219:7 224:4,12 230:1 240:16 241:15 242:5,13

244:22 245:26 257:2,6 263:7 264:23 265:9

273:18 277:7 289:20 291:19,25,26 292:16 293:13

294:23 295:6,10,11 296:17,18 298:8,9 327:17

338:18 355:11 359:11,12 361:17 402:23 409:13

411:25 471:8 475:11 494:26

meaning 106:20 297:22 475:26

meaningful 305:12

means 38:1,6 58:3 67:18 94:16 108:7 118:11

219:20 223:8 243:13 300:22 321:16 359:26

425:12 429:15 448:8 449:19 460:3,13,23 467:12

482:20

meant 29:18 329:9 355:5 443:7 448:10

measurable 26:13 397:21,23 437:16

measure 55:8 58:16 268:8 276:3 278:21 308:1

397:10 413:2 437:18 485:14

measureable 306:14,15

measured 145:15 162:9,23 219:17 305:26 386:18

440:26 449:13 472:4

measurement 127:7 383:13

measurements 58:15 59:8 363:2 366:13 420:19

485:12

measures 39:25 55:7 59:18,19 266:1 278:18

324:16 344:8 385:6 403:5 412:24

measuring 41:14 98:6 221:21 278:14,23 403:3

411:9 432:14

meat 68:20

mechanically 232:25

mechanism 24:11 29:2 30:15,21 168:24 240:1

305:14 368:4 385:8 422:23

mechanisms 5:19 56:4 239:26 355:4 357:3 364:20

470:17 473:11 483:11 484:24 492:26 497:15

mechanist 219:22

mechanistic 54:25 56:10 58:26 128:8 221:26 222:2

222:12 237:12 334:11,25 336:19,22 349:4 365:10

399:13 408:9 431:15 432:12 440:3 460:5 471:1

478:20 486:1

mechanistically 58:6 59:1 169:6 350:23 375:10

440:20 468:2

media 8:11 145:8 156:25 162:10 367:26

median 106:18 135:24

mediate 477:14

mediated 29:23 56:11 96:7 109:8 145:11 171:13

185:20 186:2 343:23 350:2 456:14 482:17

mediators 482:12

medical 6:21

medicinal 445:12

medicine 6:22 7:12 235:24 236:1,2

medium 209:14,21,22 368:5

Meek 7:15,15 214:16,16 229:17,18,25 230:3,3

391:15,15 430:16,17 433:19,20 439:12,13 446:3

446:4 451:26 452:1 455:2 472:19,21 478:26

479:1 486:4,5 491:10 494:10

Meeks 231:13 310:13,14

Meek's 445:25

meet 37:16 43:6 47:14 74:15,19 116:16 117:4

121:24 329:15 389:20 449:14 501:2

meeting 1:2 2:4,5,11,18,19,22 3:10,12 4:6,14,15,18
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4:19,19,22,24,26 9:25 10:3,9 132:8 212:9 213:3,4

213:15 218:15 247:16 261:15 389:22 390:4,9,10

390:11 477:9 491:14 495:8,23 498:3 499:20

500:18 501:3,5

meetings 2:24 9:21 495:18 499:4

meets 35:10 147:3 373:23

Mekenyan 67:2

MELN 244:24

melt 21:12

member 4:1 5:26 6:7,11,18,22 213:10 238:23

360:9 368:25 401:3 436:20 459:13

membered 460:10

members 2:16,19,21 3:14,23 5:15 74:24 213:15

247:23 261:6 283:12 316:22 331:26 332:14

333:17 342:9,10 350:13 357:13 363:17 365:26

370:22 374:21 380:25 384:12 388:26 395:13

400:22 405:7 417:17 427:16,23 438:23 451:5

455:16 461:6 462:6 466:10 470:6 475:20 478:7

489:1 495:23 499:17 500:2 501:2

membership 177:8

membrane 170:13 272:14 343:16 349:19 356:6

363:25 411:14,25

memory 500:9

mention 5:2 38:16 42:23 49:16 67:6 93:17,20

133:4 231:3 241:24,26 275:13 324:14 367:23

373:25 442:17 454:17 479:4 481:16 490:17

mentioned 50:16 53:12,16 66:20 69:23 70:7 84:15

110:12 126:15 127:7 131:19 135:25 169:24

196:15 197:10 199:7 203:1 208:13 215:13 218:13

221:5,20 241:12,19 242:21 245:15 254:8 255:19

258:14 264:16 265:15,22 268:18 269:14 270:4

271:25 276:23 277:6 278:2 290:16 311:24 313:11

323:24 324:6 325:9 340:12,13 341:13 346:13,23

350:20 351:25 389:4 390:17 398:7 401:8 410:8

413:15 426:7 484:22 495:9

mentioning 412:20

merely 29:24 196:16

merge 99:12 106:24 371:6

merging 23:1 29:11

merit 305:18

mess 100:24

messenger 268:8

met 3:22 117:3 131:16 225:15 226:11 374:18 471:2

meta 81:10 199:17

metabolic 114:9 117:21,25 140:24 145:18 161:17

164:16 207:16,17 238:24 245:12,14,15,16 259:11

440:16 456:22,25 457:15 460:1 461:5,12 462:3

462:12,15,16,18 463:5,7,23 464:4 465:23

metabolically 61:21 117:24 460:19

metabolism 7:14 63:6 114:5 145:19 149:12,17,21

157:3 163:22 164:5 167:9,10 180:15,20 207:14

224:8,18 282:3 324:11 367:15 386:11,21 387:11

398:4 456:15,23 458:2,6,7,13,15,16 460:14,24

461:7,19,23,26 462:23,24,25 463:2,12,13,19,20

465:10 466:7 482:26 484:1 487:3

metabolisms 461:3

metabolite 145:21 149:14,22 164:14,14 224:12

459:10,11 461:13 464:8,21,25

metabolites 179:22 207:19,20,23 456:1 457:2,4,7,7

457:8,14,18,24 458:20,24,26 459:1,7 460:14

461:18 464:19 466:2,8

metabolize 224:16

metabolized 95:11 149:14 164:7 259:10 367:18,18

metabolizing 238:20 465:4

metal 50:2,3,9

meta-analysis 414:12

method 29:11,11 98:24 99:1 219:9 336:15,17

337:2,3 371:26 372:13 373:13 397:7 440:2,3,25

441:1 452:25

methodologies 272:4 290:5 390:6

methodology 30:10 92:2 304:13 307:17 387:19

491:13

methods 18:10 20:5,24 104:3 216:12 217:24 218:2

233:19,21 234:5,14 235:16 236:24 238:9 243:14

249:25 266:23 285:17,25 287:8,17,23 288:19

289:20 304:11,26 308:26 325:26 326:4 329:11

333:7 337:1 338:2,24 387:19,23 396:24 402:13

413:3 429:25 436:7,14,16 439:16,22,23 440:5,12

440:24 449:8,9 452:3 458:17 465:10 472:10

482:7 487:11 498:8

method/data 472:13

methoxychlor 116:11

methyl 44:10 158:22 224:13

methylaniline 69:15

methylanilines 329:1

methylation 473:12

methylbenzenethiol 158:6,23

methylcholanthrene 205:26 206:3 438:16

methylestradiol 224:16

methylphenol 187:17

methylphenyl 160:24 161:2

methyltestosterone 163:23 257:10 259:8

methyl-substituted 196:6

metric 96:26

metrics 241:5 272:1

metro 465:21

metylphenyl 161:4

mg/mL 64:3 144:17 148:20

Michael 8:1,1 88:6,6,25 89:20 90:13 91:1 137:18

137:18,25 138:23 142:14,16 168:2,13 169:14
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170:18 172:3,13,21 173:17,20 174:20 176:17,24

176:24 177:11 178:22 179:3,15,25 180:23 181:26

183:5 186:26,26 187:12 188:6 214:22,22 293:20

293:20 294:15,20,26 295:3,8,17,22 316:23

339:25 346:1 361:12 391:21,21 395:19 407:15

421:5 422:10 423:9,12,15,18 424:7 438:19

442:15 446:22 447:25 451:4 454:23 474:6 476:6

480:13 488:23 493:12

microarray 465:21

microbial 458:8

microfluidics 463:10

microgram 102:16

micromolar 96:25 97:7,15,17 98:18 100:5,14

102:25 103:8,9,10 109:15 116:4,5 119:24,25,25

130:5,9 132:11 144:26 150:9,17,21 151:12,17,20

152:12,13 153:7,16 154:23,23 157:5,14,18

158:17 172:9,10,20 173:25 174:13,22 176:7

184:4 279:20,22,25,26 280:1,3 430:23

micromolars 98:5 369:19

micromole 129:15,17,18,21

micromoles 129:16

microphone 339:26 390:12

microsomal 141:4

microtiter 94:23

middle 65:3 151:7 162:7,12 163:5 166:8 239:21

267:13 268:26

middling 118:18

Mid-Continent 142:14

MIE 322:8 327:9 343:10 344:4 392:15,23 408:15

411:21,24 412:1,1 429:15 469:3,12 474:24,25

488:1

MIEs 330:3 343:15 385:14 392:11 411:26 463:23

470:22 484:22 487:13

might've 445:2

migration 485:12

Mihaich 282:12,13 290:11,15,23 291:3,19,24

292:8,13,16 294:12,18,22 295:2,6,9,20,23

mike 34:15 36:15 168:2,13 169:14 170:18 172:13

174:20 175:16 176:17 177:11 179:7 180:6 181:26

183:5 184:14 187:12 333:18,23

millimolar 103:14 132:12,15

million 104:6,7,7

MiLu 396:14

mimic 98:11 118:23 367:4

mind 25:12 38:21 48:13,14 154:22 178:26 213:20

216:2,3 239:23 251:20 252:18 254:16 265:17

270:26 289:11,17 297:18 298:5,9 299:3 302:20

318:16 360:16 464:3 476:13

mine 380:13,23

mineral 40:3

mini 209:15

minicell 89:4

minimal 47:17 121:24 131:23 137:10 244:7 398:15

minimize 127:9 129:13 273:24 300:21 301:10

309:6 340:10 393:16 394:21 398:17 452:10

minimized 379:20

minimizing 25:22 127:10 217:3 258:4 451:18

452:22 453:12

minimum 130:13 369:7

mining 466:3

minor 300:6 475:7

minus 114:8 163:10,10 259:1,21 294:25 397:19,19

minuses 315:15

minute 38:25 142:10 241:6 307:14 427:24

minutes 3:24 4:19,22 21:19 23:24 30:18 212:4

389:20 411:23 418:25 433:12 458:4 475:23

476:13

Miriam 8:12,13 131:1,1 132:9,11,16 139:15,15

208:9,9 209:5,5 215:1,1 332:16,16 333:26,26

339:18 343:3,4 356:19 370:19 374:13 376:12

377:16,16 381:10 382:8,21,21 385:1 392:1,1

430:20 431:25 432:15 445:20 465:1 473:18 480:4

481:22 488:16 494:16

mirrored 251:2

misallocating 302:1

misallocation 300:13

misconstrued 241:20

misinterpretations 362:6

mismatch 397:15

missed 115:9,15,19 136:5 287:13,18,20 290:18,19

290:20,21 291:1 369:20 415:11 420:24 441:24

442:2

missing 115:23 116:1 118:19 129:3 165:19 274:23

277:17,20 317:1 353:10 361:23 404:24 420:15

436:23 441:26

mission 17:20,22,26 18:1,5,7 25:8 497:6

Mississippi 7:12 214:15 391:14

misspoken 45:12

mistake 72:6

mistaken 206:16

misunderstood 432:19

mitigate 454:26

mitigated 452:25

mitigating 330:12

mix 170:21 269:13 345:8 491:4

mixed 71:4,5,9,11,12,18,26 72:1 74:4,8 75:22,23

76:1,2 80:9,10 84:24,24,25 85:1,2,10,11 189:14

189:14 190:8,10,13 191:3 198:9,10,10 200:17

203:13,13 204:6,6 333:10,10 335:4,5 338:15,16

345:20,21 436:22
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mixing 34:20 179:11 454:9,10

mixture 81:11 323:10

mixtures 38:12,13,15,16 40:4 70:9 78:1 102:2

322:15 323:2

MO 338:12

Mobil 275:3

mode 13:15 68:7,10 132:25 139:18,22,23 162:3,6

166:12 254:22 258:9 349:21 385:8 392:14,21

441:8,10 456:13

model 24:2 53:3,15 54:16 56:18 59:7,9,14 65:9

66:17 67:5,15,25 68:1 70:17,18,22 71:13 75:14

77:24 79:17 80:6 84:6 86:19,20,22 87:4 92:12

133:9 250:21 251:5,6,18,22,24 252:20 284:21

286:13,14 308:25 309:10 315:25 320:10 325:3

326:18,26 329:20 339:4,5,7 341:16,18 347:1

351:25 356:4 359:2,3,7 363:18 371:7 378:14

379:10 381:15 386:15 429:26 430:1,4,4 447:12

448:6 451:1 468:12 477:4,5 481:20 482:14

492:11

modelation 351:4

modelations 351:6

modeled 329:21

modeling 124:10 210:1,5 225:22 227:11,14 462:12

462:12 463:16

models 28:16,18 59:20 66:17 210:6,10 219:16

249:4,26 250:17 251:4,9,16 252:1,3 255:6 284:22

285:22,23 286:12,13,16 287:8 288:11 289:9

307:25 308:9,16 309:19,24 313:13,23 316:26

324:26 325:5,20,24 327:2 329:20 350:3 378:6,7

385:23 386:7 429:26 430:1 447:9,9,10 473:22

474:13 487:6 493:22 494:1 499:8

model's 326:16 339:10

moderate 115:25,26 166:26 359:4 377:26 381:23

moderately 379:6

modes 5:20 196:16 308:11,13 385:20 392:12

492:14

modest 79:14 400:10 435:21

modification 256:10 265:15 338:19 417:23

modifications 479:23

modified 333:13 335:6 479:13

modify 447:2

modulators 123:5,21,23 140:5 347:16,17 348:23

388:12

molar 157:14

molecular 6:10 7:22 8:15 30:16 40:2 42:9 45:4

46:25 47:3,24 48:10,10 49:15 56:14 57:7 60:10

60:16 88:26 89:7,14 145:9 171:14 182:4 217:18

219:25 220:12 223:4 224:23 239:25 246:9 252:8

252:9,14 254:2,9 255:8,11 289:19 305:12,17

306:2 316:17 319:5 321:9,14 322:17 343:9

344:25 345:3,8 355:7 365:4 399:6 435:3 456:1

466:26 468:7 469:21,22 482:13 483:14,18 484:15

484:16 485:3 486:25 490:7

molecule 41:16 138:2,3 178:25 444:7,18

molecules 87:8 308:16,17 424:15 474:14

moment 294:1 300:10 314:6,14 326:7 344:7

407:15 466:6 490:3 497:3

momentum 182:15

money 130:7 231:8

monitoring 33:1 383:1

monoisocyanate 201:16

mono-cyclic 192:11

months 132:4 271:12

Moores 7:2 214:11 391:10

moot 306:20

morning 2:1 4:3 5:11,22 6:2,8,13 7:5 8:8,12,22

16:18 38:23,24 188:10 202:8,11 203:2 207:26

211:4,5,25,26 213:1,18 215:13 233:11 253:7

260:3,4 275:2,5 279:13 282:13 296:1 298:10

299:18 300:3 376:24 377:17 389:11,13,14,18

390:20,26 391:3 392:4 405:1

morning's 196:23

morphogenesis 488:7

motivated 293:25

motivating 88:9

motivation 295:18 340:10

motivations 236:21

Mount 465:20

mouse 144:9,10 151:6,9 153:4,9,18 154:2,11

155:19 160:14 163:24 277:10,12,14,16,19,21

347:23 401:24 468:13

mouth 232:3

move 9:1 10:9,19,25 11:7,9,15,23 14:20 17:11,15

17:19 20:22 21:6,26 23:12 24:20 25:21,23 29:1

29:18,26 30:15 31:8,9,12 32:23,23 33:22 36:20

37:23 52:2,5,11 73:20 80:13 105:5 125:3 146:16

146:20,23 177:6,24 178:21 188:9 211:3 215:26

216:5 219:12 220:24 225:1 233:6 242:22 248:13

254:3 266:25 270:9,10,26 297:4 301:22 302:1

318:15 323:21 325:16 332:25 359:19 360:2

362:12,17 371:21 375:2 377:5 382:11 393:2,17

411:25 433:2 439:4 445:10 455:23 469:1 474:9

476:8 480:17 485:1,4 493:25 495:9 496:22

497:19 500:6,11

moved 30:11 33:26 35:8 69:24 70:1 146:25 467:18

movement 28:3 489:10

moves 33:5,8 37:17 51:24 124:15

moving 12:15,25 14:24 21:10 25:8 31:14 53:25

66:11,24 67:8 74:8 177:13 188:17 196:26 198:12

221:9 241:25 261:18,20 276:22 310:17 313:11
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328:12 360:14 393:7,10 469:20 480:19 485:23,25

491:12 495:12,17 497:5 498:7

MP 338:12

mRNA 145:15 157:13 396:13,17

MSDS 81:14 125:12

MSDSs 43:20

Mt 346:20 387:24

multiple 31:8 41:21 47:14 48:2 60:13 71:14 72:16

113:12 118:8 141:17 145:18 181:19 182:6 199:16

199:18 270:13,14 304:7 318:23 326:14,23 328:8

354:16 375:19 392:11 404:13 405:9,18 408:4

483:1,1,11 487:13 500:8

multiplex 94:25 100:11 128:13

multiplexed 109:2

multiplexing 100:18 141:19

multivariates 465:18

multi-brominated 202:24

multi-chlorinated 202:24

multi-cyclic 88:3 191:16 195:17 197:17 200:3

207:3 226:21

multi-disciplinary 23:23

multi-functionalities 70:25

Multi-Generation 20:15

multi-phased 24:16

multi-system 476:15

multi-target 476:15

multi-tier 23:22

multi-variant 387:16

mush 249:13

mute 5:3 39:1,2

N

N 39:7 152:3,4,5 194:1

Nagarkatti 6:13,14 127:26 128:1,24 129:11 171:17

171:18,18 172:5,18 173:12,18 174:15,15 175:1

214:4,4 350:13,14 380:19,20 384:7,8 388:21,22

391:3,4 395:9,10 400:18,19 417:4,5 489:21

naive 338:1 436:7

name 2:1 5:17 7:20 8:5,8,12 122:26 126:12 134:23

213:2,20,22 233:11 234:10 260:3 269:21 275:3

279:14 302:21 390:7,8,16,17

names 93:21

Nancy 8:8,9 132:18,18 133:15,16 141:15,15 170:8

170:8 214:25,25 230:21,21 339:14 344:19 357:15

357:17,17 363:7,7 372:12,12 375:8 391:25,25

408:19 474:25 481:12 494:8

nandrolone 257:15,16

nanomolar 98:4,17 120:4 184:3

naphthaline 196:6

narrow 258:11 315:3 351:15

narrower 364:14 365:12

narrowly 316:4 427:17 487:16

NAS 28:20

National 6:25 17:5,6 91:7 93:10 102:10 214:9

391:8

native 101:10 256:24 396:13

natural 42:10 45:13,15 305:26 397:15

naturally 328:19

nature 400:11

NCCT 36:10

NCGC 93:13 94:5 95:1,22 98:4,16 99:7 103:13

107:2 110:24 111:9 113:21 115:7 121:10 126:4

131:8 132:24 139:21 190:3

near 73:25 157:17 176:26 271:16 330:23

nebulous 312:5

necessarily 79:9 140:3 193:24 200:25 201:18

216:24 243:9,11 253:23 254:9 266:11 305:26

312:8 317:17 326:13,13 358:1,20 361:14 364:4

367:2 381:22 408:9 426:12 472:4 476:12 486:15

necessary 20:9 39:17 235:1 320:9 332:22 368:18

373:26 378:3 379:18 397:10 416:16 473:5

need 12:22 15:21 21:15 32:4,16,19,20 33:16 35:10

35:13 39:14 46:5 48:12 64:9 79:24 81:20 98:13

113:2 121:25,25,26,26 125:15 129:5 147:25

159:3 170:4 171:13 178:20 184:17,22 188:11

193:6 194:11 199:19 200:19 201:18 203:26

204:23 205:17 213:9 216:4 217:26,26 223:16

228:14 243:15 246:24 247:3 252:19 253:10

255:18,23 256:2 257:25 266:11 267:14,24 268:5

284:5,17 297:23 298:2 302:18 304:3,7 309:25

310:22 311:4 312:4 313:21 314:2 315:12 320:5

328:9 329:2 330:2 331:14 342:12 344:8,10,11

351:11 353:21 361:8 362:13 365:1 381:22 389:11

393:16 395:17 397:25 399:5 400:14 404:24

406:17 408:22 417:21 423:4 424:16,24,25 425:5

428:1 432:22 437:6 447:23 456:21 459:9 464:18

464:20 467:21,22 468:26 469:18,19 471:16 472:2

472:6 473:13,15 480:19 482:12

needed 248:25 260:13 284:3 304:12 320:16 324:1

377:10 378:5 476:17 478:17 479:22 492:22,23

needing 45:22

needles 240:21 401:20 430:12,13 495:20

needn't 490:9

needs 31:2 108:8 125:1 129:6 133:20 216:8 217:8

233:2 248:13 258:10 265:15 266:18 267:10 268:2

268:15 289:4 290:1,7 313:17 315:14 326:11

328:2 329:16 333:13 343:20 344:12 366:5 387:13

389:13 397:21 399:1 465:17 467:26 468:9 469:15

476:12 484:25

negative 25:21,22 41:1 62:5,14 69:14 71:17 72:21

73:2,26 74:4,5,25 75:2 84:13 87:24 90:11 115:26
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116:10 117:12 118:10 126:25 127:11 129:19

134:14,16,16 135:12,13,13,18 140:3 151:1 154:7

158:9 160:20,22 162:26 167:4 168:11,12 174:14

175:14,17 176:1,9 177:17 183:9 185:5 196:22,25

197:3,17 198:23 199:1,4 201:8 202:9,15 235:4,6

268:17 281:2 286:15,17 291:2 292:24,24 293:2

293:10,24 294:8,11,12,13,19 337:13,15,18 339:6

352:12 373:7,9 374:16 382:5 408:23,24 409:3,7

409:24 410:13 411:6 415:10 429:6 453:9,17

455:6 460:22,23 462:21

negatives 25:22 73:12 112:18 127:10,19,24 135:10

135:11,17,20 137:2 148:6 154:24 195:23 216:21

217:4 239:6 244:7 273:24 287:16,18,20 288:9

290:24 291:17 294:5,25 300:21 309:6,11 313:15

338:26 340:11 349:12 368:2 369:10,17 370:11

373:18 377:25 378:5 379:19 381:3,4 382:6

393:16 394:19 396:9 398:17 399:23 409:2,6,22

409:25 410:4,5 411:5,11,18 412:8 415:7 441:7,25

450:6 451:19 452:4,7,10,22 453:13 455:9 473:6

473:14

neglected 265:1

neighborhood 68:15

neighboring 111:13 112:2

neighbors 99:17 112:6

nervous 482:25

net 313:2 337:11 364:25 404:15

Netherlands 324:24

nets 499:7,7

network 8:3 346:17 436:8

networks 255:16

neural 436:8 483:20 485:10 499:6,7

neuronal 386:11 387:10

neurotoxicology 7:14

neutral 318:5

never 38:7 41:13 231:1 239:23 288:15 299:4

308:13 355:5,5 356:7 359:14 430:2 481:22

nevertheless 44:2

new 13:7 21:25 23:16 28:8 31:26 53:11 54:1,10,14

67:13,13 69:25 71:3,11,11 73:19 74:21 75:13,17

75:19 76:16,26 77:12,14,16,23 80:1 121:20

124:24 125:7 188:17 193:25 203:22 207:3 237:1

237:8 240:5 315:2 333:9 338:14,20 339:8 340:11

383:21 412:22,22 421:1,16 430:9 434:9 435:2,4

436:15 456:24 462:11 463:10,23 465:20 469:6,12

470:26 471:21 483:14 487:7 492:13 497:4 499:3

499:3

newer 77:7 393:11

NF 128:7

NFkB 343:18 350:26

NHEERL 142:15

nice 70:23 116:13 240:26 273:1 274:12 280:13

281:25 308:25 324:23 346:4 406:6 432:25 475:13

487:12 489:20,26

nicely 493:8

night 225:14,15

NIH 93:11,13,21 229:13

nine 264:22 271:11 278:6 281:14 285:4,25 286:6,6

287:1 288:9 360:13

nitro 201:16,20,21,21

NOAEL 280:10

nodding 117:22

node 69:10 90:11,17 197:25 338:22 421:20 426:8

nodes 67:8,10,14 68:17 69:3 84:12 89:7,23 90:1,4

90:8,26 197:26 255:13 335:9 415:12 422:7,9

noise 105:26 106:14,19,21 113:14 115:19 116:18

135:26 402:1,5,5,12 410:15

noisier 158:25

noisy 106:15 118:24

nominal 116:9 148:24 411:13

nominated 101:18,24 102:11 228:6

nomination 101:23

non 243:25 246:9 259:17

nonactive 369:16

nonbinders 81:24 82:9 83:2

nonclassical 129:1 350:1,25

noncyclic 83:19

nondiscrete 70:9

nonfood 27:2 36:9 54:3 69:25 70:2,5 76:7 77:14

78:5 79:3,12,21 143:18 201:4 335:2

nongenomic 363:25

nonpolar 42:12 45:10,11,11,13,19

nonspecific 63:22 64:11 109:16,23 110:1

non-animal 487:5

non-aromatizable 259:14

non-binder 146:23 147:17 148:6 149:1,2,4,5,12,24

164:10,14 193:14 334:21

non-binders 61:22 180:8 193:16

non-binding 177:6

non-intuitive 317:2

non-inventory 72:11

non-ligand 96:7

non-pesticide 297:16

non-predictable 358:17

non-toxic 337:13

normal 432:23

normalization 105:7 126:16 372:7 373:19

normalize 174:16 379:1

normalized 105:8 379:4

normally 48:7 109:10 367:17 374:14

nortestosterone 257:15
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Northwest 6:25 214:9 391:8

notations 159:1

note 21:8 22:9 35:26 81:26 151:22 155:17 163:16

211:17 258:2 276:7 277:24 319:15,16 320:26

368:11 397:1 420:9 471:7

noted 145:17 275:18 327:4 365:26 406:21 436:17

436:24 442:3 456:24 458:10 459:13 461:6,10,14

469:20 470:6 484:8 485:5

notes 69:13,20 435:12 457:14,23 460:15

notice 32:8 39:26 68:16 73:14 74:3,24 75:6 107:17

noticed 85:20 141:2

noting 285:15 302:26

Novascreen 95:21 144:5 145:2 148:20 150:11

151:3,6,10,13,20 152:13,15,24 153:17 154:20

155:14,25 158:17 160:13 163:24 165:17 174:1

229:12 268:7 277:9 447:3

Novascreens 150:1 159:10

novel 490:8

November 22:7 26:22

NRC 11:20,21 13:14

NTP 113:21 121:11 134:5 228:17

nuances 361:15

nuclear 7:26 142:1 354:23 387:12 406:1,2 411:24

479:10

nuclear-active 397:11

nucleophilic 325:9

nucleus 95:13

number 1:7 4:8 13:5 28:6 31:17 40:2 46:25 47:2

64:19 69:5 71:8 80:9 82:8,14 83:1,2,3,8,16 92:21

98:23 101:10 104:25 107:6 109:13 110:11 113:8

117:11 129:4 142:23 169:8 173:7 179:26 189:1

189:20 190:17,21 191:6 192:7 193:21 198:19

215:10,17 216:9 218:6 232:12 238:11 252:13

270:15 271:21 272:12,19 273:2,16,17 281:1

291:22 292:23 300:22 302:24 303:17,26 304:14

308:22 309:6 313:10 318:17,18 319:20 320:15

325:13 326:4,10 333:8 336:5,25 337:1 348:9,25

381:25 411:12 413:13 419:6,24,24 420:3 428:22

434:20 440:13,22,24 441:7 444:23 449:26 454:18

459:23 465:5 466:4 467:7 470:16 475:12 479:21

486:24 490:2,8,22 491:13

numbers 30:6 47:2 68:16 70:11 86:13 88:15

101:24 126:1 169:21 171:11 179:19 229:23 236:6

313:24 365:2

numeral 189:21

numerals 66:16

numerical 90:1 317:9 346:26

numerous 11:22 496:13

nutritionist 8:13

NVS 137:21 441:5

n-alkyl 73:17

N1 160:15

O

o 198:23

Oasis 284:22 285:22 286:13,14,14

objective 10:18 39:13 105:24 122:7 283:4 383:20

objectives 18:6 23:14

obligate 254:10

observation 310:20 420:1 441:15

observations 242:25 243:15 269:23 441:13

observed 195:22 369:18 384:24 425:14 435:17

478:1

observing 361:16

obtain 228:24,25 229:3 309:22 368:22

obtainable 228:2

obtained 437:6

obtaining 307:13 418:12

obvious 327:18

obviously 70:18 82:5 84:14 177:4 184:23 190:11

199:10 202:4 205:3 236:21 238:3 247:17,25

248:12 249:3 252:3 330:21 345:6 398:15 425:2

461:1,5 471:1 483:17 493:17 495:14 496:4,8

occasionally 346:3 421:14

occupitation 359:16

occur 5:1 180:5 249:1 252:17 266:20 364:3

occurred 249:2 293:1

occurrence 32:26

occurrences 412:8

occurring 63:6

occurs 19:12 150:15

OCD 238:19

ocean 498:26 499:1

OCED 377:22

octanol 337:15

October 22:11 27:9 271:8

octyl 177:15

octylbenzene 161:22

octylphenol 187:17

octylphenyl 160:25

odd 118:17 404:20

Odessy 229:11

Odyssey 95:23 100:5 115:17 118:17 132:26 139:22

284:20 285:16

OECD 7:7,8,10 20:12 26:8 29:6 30:12 58:1,6 66:26

67:4 84:18 98:19 101:20 114:25 134:6 218:24

238:23 241:3 245:3,4 246:8,8,13,19 250:4,24

275:25 279:1 330:16 372:18 373:24 429:22

454:19,24 457:5,6 465:6 468:19 481:17 488:20

490:6

OECD's 135:6
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offer 211:1 335:26

offered 11:1

office 3:19,20 6:3 8:20 9:14 10:13 16:3,6,16 17:3,4

17:4 211:16 214:1 230:17 263:20,21 390:24

498:23,24 499:25,25,26 500:13

Officer 3:19 8:2

offices 134:5 184:17 500:1

official 2:3,10 3:11 213:2 263:24 390:9 497:25

officially 230:13 299:26 501:1

officials 3:13 500:15

offs 257:26 266:24 319:2 321:25 369:8

off-target 8:4

off-the-shelf 131:9

oh 5:26 75:1 81:4 111:5 229:6,23 239:23 247:8

258:25 285:7 291:19,24 292:13 295:2 310:1

339:16 344:15 362:17 375:2 390:7 457:9 459:24

okay 16:11 38:20 45:6,21 51:5 52:13,18 58:1 61:11

65:9 66:13 77:1 80:13 87:21 91:2,6,9,14 92:4,9

92:19 93:14 94:8,15 95:4,26 96:17 98:2 99:6

100:3,8 101:17 102:5 103:7,17 104:22 105:13,24

106:3 107:25,26 108:21,26 110:11 112:1,16

113:24 114:12,23 115:6,13 116:23 117:9,22

118:12 119:23 120:13 121:3,4,15 122:11,20

124:3 127:6,15,18,26 130:26 131:12 132:17

133:2 134:10 135:21 136:9,17 137:16 138:17

141:13 142:8,13 167:19 173:14 174:17 177:23

179:15 185:18 186:22 188:8 198:9 210:25 211:7

213:1 225:6,8,9 227:24 228:17 229:5,25 230:3

233:4,6 235:20 239:11,22,23 242:25 243:15

244:21 245:20 246:2,22 247:8,8,10 253:3,5 254:8

254:11 258:25 259:26 263:14 264:3,5,7 269:9,18

273:4,5 274:25,26 279:8 282:9,13 285:17 286:18

290:22 293:18 294:12,18 295:24 296:3 298:20

301:1,19 302:8,10,11,18 311:19 316:9 318:7,8,14

319:12 329:13 332:18,19,24,26 333:24 339:21,24

342:2,11,19 344:21 345:26 347:7 349:25 350:10

350:12 351:15,21 352:21 353:12,13 354:8 358:4

358:21 361:10 362:9,11,16,17 363:5,8 366:11

370:18,21,22,22 371:20 372:11,24 373:19 374:17

374:22 375:1,15 376:17 377:4,5,16 380:12,25

382:11,20 384:13,16 389:1,7 393:21 395:1,7,16

395:20 396:2 400:17,24 402:21 405:10 412:3,6

412:14 419:9 423:14,17 427:12 430:16,24 431:24

432:6,20 438:3,26 439:4 445:16,18,26 446:8,11

448:22 451:7,13 455:18 466:13,22 476:5,19,22

476:26 479:24 481:15 482:1,4 489:3,7

old 198:24 243:2

omission 332:4

once 35:7 42:3 90:4 104:13 115:20 120:11,19,23

121:23 122:9 137:13 235:8 263:4 288:14 289:1

ones 15:5,22,23 37:19 38:8 39:13,14 64:21 65:4

80:5 81:1,20,21 112:22 114:7 115:1 117:2 119:21

120:5 125:23 130:2,8 137:22 138:18 147:23

152:9 155:9 158:11 176:8,9 185:4 186:6 194:17

210:9 216:18,19 224:5 246:3 262:3,4 282:23

291:1,2,26 294:1 313:25 317:2 346:25 349:13

358:11,18,19,20 396:6 406:11,12 407:23 408:24

418:11 443:25 447:4 457:21 462:22 469:24 472:9

480:6 488:6 492:11

One-Generation 20:11

one-size-fits-all 12:10 104:1

one-third 102:8

one-to-one 413:15

ongoing 111:9 347:24 488:19

onset 51:26

ontology 414:13

Oops 333:14

open 1:2 2:18 24:8 26:16 106:5 249:24 251:23

258:6,9 317:13 325:23 335:25 341:6,9 346:6

445:14 454:20

opened 421:11 427:23 456:7

opening 8:24 9:13 16:25

Operating 8:2

operation 27:8

operational 216:12 498:4

operationally 21:26

opinion 45:24 330:17,19 394:25 403:24 429:25

opinions 496:9

OPP 1:9 47:19

opportunities 35:22 389:15

opportunity 25:2 188:9 206:8 225:13 233:17

248:10 253:6 261:11 264:8 269:16 275:5 296:2

301:5,15 303:14 304:18 392:10 393:19 420:15

427:5 478:9 489:12

opposed 169:11 296:16 330:22 419:14 431:21

448:1,20 453:14 458:8

opposite 436:21

optimization 22:25 26:5 387:13

optimize 33:14 61:20

optimized 33:21 168:4,23,24 220:2

option 211:2,4 271:25

options 319:6 329:5 412:10

oral 2:20 4:11 261:12 274:17 321:7

oranges 401:4,4,4

ORD 9:4 47:19 91:8 92:19 132:3 134:4 142:15

order 13:6 18:11 25:18 27:10,19 31:3 35:26 106:16

126:18 129:13 169:6 224:6 241:14,16 243:17

251:9,26 252:15 268:14,14 271:11 275:22 282:20

282:21 285:4 290:26 306:1 311:17 319:15,16

352:7 383:21 407:4 415:16 453:10,17 456:9
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486:2

ordered 195:24 228:10

ordering 205:2 228:10 231:2

orders 20:25 35:15 163:11 230:9 231:17 236:25

241:22 282:23 498:6

organ 252:16 464:16 478:5

organic 190:8 230:22

organics 71:5,12 74:9 75:23 76:2 80:9 84:24 85:1

189:15 190:10,14 198:11 203:13 204:6 333:10

335:5 338:16

organism 252:16 332:9 357:24 358:3 464:7

organisms 5:20 306:2 321:5 463:16 487:10

organization 55:6,6 189:6 254:4 345:18 364:24

392:16 399:15 471:13 472:3,8 478:2

organizational 499:22

organize 238:18

organized 9:6 13:21,21 425:13

organizing 29:4,24 305:11,20 310:19 315:18

497:25

organs 478:5

orient 98:1

oriented 151:8

origin 477:13

original 60:25 366:1

originally 131:3

orphan 200:4

Ortego 260:2,3,4 264:4

ortho 81:10 165:20 194:4 199:17

orthogonal 121:18,19 317:17

OSCP 9:5

OSRI 21:14 23:2

Ottawa 7:17 214:17 391:16

ought 259:8

outcome 7:9 13:13,16,20,25 29:4,23 36:6 42:10

47:22 49:13 52:23 54:26 55:4 61:12 82:21 145:11

165:24 182:5 221:19 250:12 253:22 254:5,16

255:4 270:24,25 305:10 310:19,25 311:1 336:23

343:8 344:24,26 345:2 346:18 354:18 385:15

408:15 439:20 468:18 471:3,19,21 472:2,7,14

475:5 477:14 478:6,8,10,15,18 483:18 484:3

485:16 488:10

outcomes 58:2 60:3 270:23 274:8,10 310:21

361:22 362:3 399:5 439:22,23,25 483:2

outlier 105:11 372:7 373:20

outliers 105:11 369:13

outline 143:3 188:25

outlined 313:9

outlines 486:11

output 46:5 104:10 293:4 326:14 335:18

outreach 26:17

outset 491:26

outside 54:16 67:25 70:16,19 90:22,23 101:19

134:9 147:7 189:13 192:6 203:12 222:16 252:1

262:15 298:21 300:12 304:9 320:1 327:19 383:5

403:16 419:21 435:9 490:5

Ovanes 67:2

ovary 316:2

overall 26:6 78:10 79:6,10,18 80:4 82:17,18

114:17 180:21 192:18 206:22 207:4 239:4 247:24

253:8 256:1,1 276:24 277:4,5,11,22 278:24

289:14 294:21 302:25 303:7 304:23 305:11

307:18 311:15 385:25 396:3

Overlaid 17:21

overlap 44:23 77:16,21 102:11 239:17 328:23

330:8

overlapped 419:25 420:13

overlapping 208:2 419:25

overlaps 78:9,14 319:7 329:7 427:2

overlay 312:17

overlays 165:5

overly 355:14 397:26

overnight 140:19

override 306:26

overtraining 412:18

overview 17:16 35:23 36:11 37:6 94:8 101:15

215:6 303:22 334:4

over-fitting 412:18

ovotestes 55:21

owed 196:20

oxidase 387:5

oxidation 324:18 460:2,12

oxidative 109:19 127:8

oxidizing 44:4

oxygen 61:4 74:18 226:6

o'clock 389:13,17

P

pace 27:8,22 28:3,23

Pacific 6:25 214:8 391:8

package 414:16,16

packages 266:16 375:13

page 50:2 241:19 274:23 299:23 338:6,9,13 366:3

436:10

PAH 379:23

paid 257:10

pair 76:18 119:10 403:9

paired 461:4

pairs 104:7

Pandora's 427:23

panel 1:1 2:4,11,15,16,16,23,24,25 3:4,7,14 4:2,5

4:13,25 5:4,8,14 6:1,7,12,18,22 10:12,17 16:20
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37:2 38:20 48:16,26 49:21 50:7 51:18 83:13,25

84:3 90:7 95:7 96:24 100:15 124:19 125:4 128:12

132:24 140:19,21 142:9 143:21 151:5 162:7

163:4,5 202:15 212:3 213:3,4,19 215:7 225:10

235:20 237:23 247:16,23 257:5 258:25 260:5,18

261:6,8,10,11 263:17,23 264:4,8 271:2 274:15

279:8 282:9 302:8 303:15 316:12,21 317:20

318:8 322:15 323:5,12 326:23 327:24 328:21

330:18 331:25 332:14 333:17 342:8,10 344:23

346:24 350:12 353:14,19 354:2,13 356:19 357:9

357:13 360:8,9 361:16 362:23 363:17 365:26

367:4,14,20 368:21,25 369:12 370:21 371:4

373:1 374:21 376:18,20 378:1,11 380:25 384:12

385:1 386:23 388:26 389:9,19 390:4,9 392:11,14

393:3,11 394:19,21 395:13 396:10 400:22 401:3

402:8 404:5 405:7 408:14,18 410:6,19 411:1

412:17 415:14 417:16 418:22 421:6,24 423:2

424:7 427:15,16,24 428:7,8,10 429:16 431:2,3,11

433:4 434:10 436:20 438:9,23 445:19 451:5

455:9,16 456:7,12,24 457:14,23 458:10,23

459:12 460:15 461:6 462:2,6 466:10 467:6,7

469:1,15,20 470:6,19,24 472:17 473:2 475:20

477:9,23 478:7 479:7 481:11 482:10,15 483:17

484:23 485:2,23 488:26 489:10 491:4,16 492:5

493:14 494:17,25 495:11,23 497:10,23 498:20,25

499:17 500:5,9 501:2

panelists 307:9

panels 11:17 151:6

Panel's 52:4 186:19 239:1 318:12 370:11 401:1

Pap 387:6,6

paper 11:26 13:5 34:8 54:22 77:4 133:25 218:25

234:9 238:20 245:16 246:15 249:21,23 250:3,11

252:6 256:22 271:12 272:25 273:6 274:8 275:21

277:26 280:6,7,23 284:11,16 299:17,23 305:8

306:4 330:14 338:19 465:5,11,15 470:11 500:6

papers 206:3 227:9 429:11 441:6 490:22

Paperwork 233:24

para 165:21 199:18

parabens 226:10 359:5

paradigm 423:6 428:1 471:10 490:21

paragraph 319:23 329:5

parallel 146:10 339:8 425:8 442:19 444:1 446:6

481:26 492:17

parameter 40:17 106:4,12,13 107:12 162:20

318:21 319:7 329:7

parameters 48:24 49:4 59:3,4 107:24,24 127:17

136:13 144:7 157:2 251:5 311:25 312:22,24

322:4 324:12 329:10 372:4,26 386:19 435:19

paraprotein 484:21

paraquat 200:19

para-cresol 81:11

para-substituted 194:5

parent 63:7,8 145:20 149:18,23 161:11 163:21

164:13 167:11 207:18 224:12 457:3,17 464:6,24

parenthetical 303:23

park 257:8,19

parse 68:13 76:8 78:8,23

parsed 68:18

part 6:4 20:23 57:21 65:7 87:9 104:11 113:19

129:25 132:24 138:17 165:19 177:19 178:3,12

182:19 190:20 195:4 198:9 206:13 210:9 232:17

241:18 266:2 275:22 293:14 315:9 330:23 345:17

357:21 366:11 395:17 401:5 408:13 410:8,18

416:21 419:23 420:12,16 421:1 427:8 447:22

469:12 470:23 478:7

partial 113:10 116:12 135:26 136:4,26 137:9 164:1

partially 454:26

participant 3:17

participating 10:8

participation 4:26 218:4

particular 24:9 33:11,14 38:4 46:16 47:2,5,5,13

50:19 56:22 67:20 68:24 69:18 75:9 89:23 90:1,4

90:26 112:12 131:14,18 135:25 141:18 184:6

196:25 215:8 271:7 303:8 305:13 317:26 324:12

327:2 336:25 337:2 338:5 342:12 344:26 347:6

350:6 353:24,26 358:14 361:21 389:9 392:23

406:3 434:26 436:9 444:19 449:9,10 460:1

particularly 5:21 43:14 100:6,10 101:11 208:14

321:6 328:8 333:9 344:9 356:21,21 378:5,7,12

432:18 436:2 464:5 473:23 474:1 490:4

particulate 421:19

parties 282:21

partly 131:26 132:2 135:15

partners 121:9 126:2 134:5

parts 12:13 189:11 261:26 262:12 357:20 425:9

480:25 481:3

party 366:9

pass 38:3 137:23 289:25 330:26 338:23 340:12

435:26

passing 315:16 463:18

passive 321:8

Pat 35:1 52:21 66:20 67:7 74:15 76:23,24 84:20

86:7,7 88:12,12 89:9 90:5,5,18 144:11 145:6

148:16 158:9 179:4,6,6 180:4,4 185:18 188:14

197:10 205:11 206:20,20 208:18 210:4 225:24

227:7,22 228:22 229:19,23 231:25 232:2,18

path 90:2 346:3 398:24 443:15 477:9

pathway 7:9 13:13,13,16,20,25 15:18,20,21,23,24

18:3 19:24 29:4,23,25 30:2 36:6 42:10 46:13,22

47:22 48:5,14,21 49:13 52:23,26 54:26 55:10,11
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55:25 56:4,15,17,17,21,22 57:5,6,23,25 82:21

92:15 96:1,1,6,18 98:15 125:21 129:1,4 131:18

131:20,21 145:11 147:19 165:24,25 171:15

179:20 182:15,19 183:11,13,18 185:20,20 186:2

186:5,17 219:26 222:3,4,10 223:4 238:4 244:22

245:20 253:22,24 254:5,6,16 255:4 270:4 305:10

307:6 310:20 318:4 319:5 327:9 336:23 343:8

344:24 345:18 350:25,26 351:10,14,16 353:5

354:5,16,22,23 355:12 356:1,2,5 409:16 411:14

412:2 422:15 439:20 464:22 468:18,25 471:3,19

472:2,14 473:5 475:5 477:6,10,16,17,20 478:8

482:21 483:11,18 484:3,9

pathways 19:4,19,22 30:1,1 47:25 48:2,20,25 55:4

92:12,14 95:6 96:9,12 129:25 185:21 186:4

223:22 225:1 239:3 242:18 246:5 250:12 255:10

255:14 269:26 305:22 307:20 329:25 343:23

346:11,18 349:19 350:2,26 351:5,11 353:14,17

353:23 360:14 385:15 386:22,26 387:26 469:2

470:14,16 471:21 472:7 474:2 476:18 478:6,15

484:5 485:4,5,16 492:15,26 497:15

pathway-based 493:6

Patlewicz 234:9

PATRICIA 225:24 228:22 229:22,26 232:18

430:26 432:4,7 447:17

pattern 164:23 407:17 408:5

pattern-matching 466:3

Pat's 85:4

PBDE 465:22

PBPK 463:16 464:12

PCBs 368:25

peaks 349:8

peer 3:1 11:2,5 29:10 218:2,3 235:2,6 238:22 251:7

252:3 257:5 260:12,14 261:1 273:21 290:8

peer-review 10:21,22,23

pejorative 296:19

pentachlorophenol 201:8

pentylaniline 157:13,15

pentyl-cyclohexanone 154:1

people 5:6 9:24 97:10 98:23 103:23 104:18 116:25

134:6 209:16,22 211:18 212:1 216:12 224:1

238:19 289:23 297:26,26 301:1,13 313:10,17

315:25 350:15 354:20 412:20 414:18 421:9 473:2

487:1

peptide 46:12,18 47:6 475:26

peptides 318:4

perceived 74:17 337:10

percent 35:4 42:16,26 43:2,8,9 51:2,3,14,15 65:12

78:13,15,17,19,21,22,24 79:3,7,7,10,19,20,21,22

79:26 80:22 82:14,25 102:20,20,21,22,24 103:15

105:8 106:10 107:7,11 113:10,11,11,14,15

118:16,18 125:10,21 126:3,4,8,16 133:7,8 146:6

146:7 147:22 154:19 155:3,6 166:6,16,20,21

169:21,22 171:25 172:1,2,2 174:18,18,24,24,25

189:23,25,25 190:1,8,9,9,15,16,18,19,22,23,25

191:2,12,12,13 192:10,17,18,19,19,21,22 198:6

204:5,7 208:3 222:20 228:3,15,16,19 229:20,24

230:1 231:4,23,24 232:10,12 264:23 274:9,9

281:4 282:22 286:19,25 287:7 288:10,26 289:13

291:25 293:10 309:11 314:4 319:11 328:12,14,20

329:11 334:21,22 357:18,19,20,21,26 369:1

378:13 401:17 409:26 418:4,15 429:1 435:22

452:9 457:17

percentage 79:25 82:14 83:1 180:2,22 192:9

228:24 229:4

percentages 82:10 191:4 336:5

perception 328:5

Percival 500:16

perfect 75:23,24 121:21 122:7 127:23 225:5 324:7

413:16 448:8 473:4

perfectly 220:19

perflourinated 81:4

perfluorinated 81:3 83:26 459:19

perfluoro 192:14 202:21 203:5,8

perform 147:14 197:6 275:23 307:13 366:7 384:21

performance 22:18 92:8 114:22 135:2,20 220:10

243:18 246:2,14 251:6,9 267:14,22 271:23 272:1

272:7 275:7,12,12,20 276:24 277:3,19,25 278:10

278:11,13,21 279:1 331:3,4 338:21 339:2 365:23

366:8,9 377:8,22 378:19,22,25 379:8 382:14

383:8,10,19 384:20 385:22 386:2 388:7 394:1,8

420:11 439:7 441:20 453:14 498:5

performances 326:26

performance-based 245:3,5 413:1

performed 168:21 285:10 286:12 330:24 393:26

398:5 413:2

performing 470:21

performs 386:1

period 4:2 94:19 124:1 185:17 202:11 209:2

213:12 233:7 261:4,4,12 265:11 302:12 359:23

371:15

periodically 11:9

Perkins 7:20,20 214:18,18 227:26,26 232:20,21

307:14 313:5,6 331:26 332:1 361:25,26 369:25

369:26 374:4,5 375:15,16 380:16,17 384:5,6

388:19,20 391:17,17 394:5,6 396:2,3 401:8

402:21,23 405:11 406:4,5 412:13,14 418:6,7,23

419:23 425:15,17 426:24 438:21,22 445:16,17

449:21,22 455:14,15 462:7,8 465:2 472:25,26

479:25,26 486:8,9 492:3,4

permanent 2:15 6:1,7,12,18,22 468:6
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permanganate 44:3

permeability 368:8

permit 485:5

permits 373:1,5

peroxidases 461:8

peroxyoctanoic 44:9

person 134:21 232:8 249:22 333:21 495:11 496:26

personal 226:1 490:10 495:8

personally 242:26 403:6 414:9 500:23

perspective 75:13 89:4 208:4 241:8,8 242:1 271:18

280:2 291:9 312:3,15,19,23 347:14 359:12 360:9

361:13 393:6 421:7 422:19 491:19

pertain 238:4 443:7,20

pertaining 200:10

perturb 95:26 412:1

perturbations 48:20 272:15

perturbed 354:17

perturbing 467:1

pesticide 7:13 9:14 10:12 16:4 17:3 20:2 27:13

31:1 37:13 230:10,17 231:17 237:18 263:21

297:14,16 499:25

pesticides 3:1 334:8

pesticide-related 3:2

PFOA 202:16,18 203:10 488:2

PFOS 202:16,18 203:10 488:2

pH 40:11 41:14 42:6,9,13,16 43:1,2,9 44:22,24

45:14 50:22,23,24,25,25,26 71:20,20,21 156:25

162:20,23,24 163:1,3,14,19 167:3 201:12 318:5

328:13,16,17 350:7

Pharma 214:23

pharmaceutical 321:6,10 386:5

pharmaceuticals 8:2 321:26 391:22

pharmacological 256:7 257:7

pharmacologist 6:20

pharmacology 7:26 8:3 253:4 338:24 436:1

Pharmacy 7:2

phase 7:8 24:17,22,26 25:1,16 93:26 94:1,1 135:9

245:22 281:17 282:5 283:26 461:19 462:15,16

483:25,25

phases 93:26 311:8

phenol 70:26 71:6 72:6,18 160:6 421:23

phenolic 265:16 327:21

phenols 71:4,12 72:1,2 73:21 75:23 76:1 80:10

84:24 85:2,5 160:5 184:5 189:14 191:3 198:10

201:5,10,20 203:13 204:6 333:10 335:5 338:16

345:20 359:5 437:26

Phenolsulfonphthaleins 74:1

phenones 73:9,15

phenotypes 487:12

philosophy 322:7 335:12

phone 38:26 39:2 212:7 389:16

phones 5:4

phonetic 387:6,21,22 481:17

phos 74:23

phosphatases 351:1

phosphoric 41:24 43:21 74:14

phosphorus 74:19

photocopy 274:19

photon 104:26

phrase 421:8 422:16

phrased 493:20

phrases 297:23

pHs 75:9

phthalates 85:8,10 344:3 386:17

physical 32:15 36:2 48:1 101:25 236:12 264:14,15

265:23,25 266:23 312:10 314:11,18,22 317:23

350:4 356:4 406:9 421:15 424:24 429:26 430:4

435:13 437:24 444:18 447:9,12 493:21,23 496:13

499:7

physically 108:23

physicians 235:23,26 236:1

physico-chem 39:18 49:16 51:9,12,26 53:13 80:15

80:18

physico-chemical 29:13 31:19,26 39:7 45:25 47:11

228:7 320:24 327:16 368:12 403:20 415:22

physico-chemicals 306:12

physiologic 351:4,6

physiological 201:12 257:23 280:14

physiologically 257:23 281:26

physio-chemical 338:8

phys-chem 316:16 319:26 327:4

pH7 319:11

pick 137:14 155:20 157:11 188:16 195:2 197:21

205:2,5 254:7 258:2 288:19 349:1 355:4 356:11

367:1 405:18 425:7

picked 174:22 287:22 356:8 366:18,21

picking 118:9 155:18 358:18 366:19,24 404:9

407:15 408:4 464:12 474:8

picture 107:23 252:6 300:23

pie 196:18,18

piece 66:25 69:22 93:7 255:21 363:14 494:13

pieces 490:2

pier 235:4

pink 72:12 73:13 165:16 195:23

pins 495:20

pipeline 104:22

pit 75:22

pitch 475:22

pituitary 19:24

pK 50:18
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pKa 40:17,17,18 41:1,9,18,19,22,23,26 42:4,17,18

42:19 75:1 162:15,16 163:18,18 178:9 201:11,22

264:20,20,21 318:22 319:9 326:5 327:5,8,20,21

328:23 437:26

pKas 41:25 42:22,24,24 43:5

pKs 51:3

place 56:2 57:2 72:18 129:20 223:5 253:12 261:25

322:13 356:2 448:25 480:26

placed 275:9 323:4 329:16 469:11

places 222:6 324:24 464:15

plan 17:15,18 20:18 21:6,9,9,18,20,22 22:4,5,9,12

22:16,26 23:4,10,12,13,14,24 24:16 25:13 95:4

138:8 236:16 248:16 373:15,16 420:26

planes 498:20

planned 22:13 108:17 260:23 371:3

plans 21:12 184:26 367:23 432:17

plasma 280:9

plastic 459:14

plasticizers 386:12

plate 99:10,21,24 104:25 111:14,14,17,17 112:13

126:17,18,23,26 127:3 159:2 209:16 271:14,14

368:17 379:1 467:6

plates 94:23 99:12,25 102:26 103:14,14 104:26

105:3,9 111:11 112:4,4 114:3 126:21 161:5

209:16 368:17

platform 335:22 414:11 429:17,25

platforms 15:8 335:25

plausibility 55:17 305:12 486:1

plausible 44:17 51:21 55:16 58:13 471:1

play 71:17 75:20 88:2 155:24 167:3 169:16 187:23

191:19 203:9 230:11 256:13 398:20 482:26 495:2

played 156:2

playing 153:3 355:3 488:21

plays 154:12 157:3 187:16

please 3:24 4:1,16 5:3 17:25 39:1,2 122:26 134:22

187:1 206:17 213:10,14 236:18 247:4,6,18,23

264:6 275:14 302:18,24 303:2 319:5,8 329:5

333:4,10 342:23,26 352:22 360:21 362:16 371:23

372:2 374:26 377:6 382:13 384:17,19 389:5,6

390:1 393:24 395:22,25 412:10 432:26 433:5,12

433:13 439:5,9 451:14,19 455:24 466:23

plot 65:15 119:7,7

plots 97:21 107:2,25 109:10 280:9 336:8 372:3,25

plotting 119:14

plugged 84:18

plus 114:8 281:19 393:1

pluses 315:15

plus/minus 463:2

pocket 225:26 226:2,4 432:13

point 5:14 8:18 19:19 23:19 28:1 30:19 37:5 40:23

45:1 48:15 50:6 60:16 61:2,4,5 66:17 67:2,9,10

67:19 69:12 70:9,12 71:22 73:22 74:1,3,20 89:9

90:8 92:16 99:14 104:2 112:13 117:5 118:9

119:10 124:2 125:4,13 126:10 127:21 129:10

133:5,25 135:1 142:9 146:19 153:19 179:23

182:18 184:14 185:15 193:18 195:2,19 196:4

202:14 215:8 216:1,14 217:22 218:13 219:22

220:20 222:4,17 225:9,13 233:13 234:13 238:4

239:22 244:9,11,14 246:7 247:3 250:7 253:15

254:20,23,23 255:2,7 256:1,25 266:5,8 267:16

276:12,20 286:20,21 294:16 299:16 300:6,14

301:18 302:12 303:19 304:20 306:21,21,25 310:7

316:13,25 317:3,4,7 319:9 321:2 322:9,22 323:22

325:16 332:3,7,19 337:22 341:8,20 344:6,16

347:25 353:24,25 355:9,19,24 368:21 369:11

376:3 388:5 389:8,9 393:22 407:16 417:9 418:26

419:12 424:10,17,21 427:13 428:18 430:17,20

432:7 437:18 443:16 444:15 447:4 448:14 454:24

454:25 462:21 474:8 475:9 479:10 481:7 484:23

486:17,18,20 489:9 490:10 493:25 497:4

pointed 222:14 297:8 411:22 447:9

pointing 81:23 308:18

points 56:20 60:14,15,24 61:9 62:6 104:6 119:15

132:9 139:5 151:25 160:18 175:12 183:6 185:21

237:12 250:22 256:3 301:25 319:24 335:16 340:1

369:22 400:7 433:21 442:1 443:19,20 445:2,5

447:7 451:5 461:5 462:5 463:18 464:2 468:10

499:20,23

point's 49:7

polar 46:14 337:20 459:22

policy 3:6,20 16:16,17 263:18 264:2 269:2,6,7,13

282:17,19 283:4 289:18 296:10,12 300:11 499:26

Pollution 8:20 10:14 16:4 17:3 230:17

POLM 480:9

polycyclic 205:24

polycyclics 206:4

polymer 32:3 46:19 322:24

polymeric 46:10 318:5

polymers 40:2 46:14,15,24 47:1,5 70:9 78:2 318:2

322:15,16 323:18

polynuclear 206:6

pool 33:23

poor 148:2 289:6 321:10 322:3 324:5 382:22

383:10 386:2 397:6

population 18:13 35:12 55:24 56:6 170:26 243:25

297:21,26 299:5

populations 18:16

portal 31:23 32:11

portion 84:14

portrayed 376:24
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pose 197:17 217:14 262:4 298:12,16 300:17

posed 73:19 434:1

position 71:6 76:25 299:14 491:16

positive 62:8,13,15 69:16 71:16 72:13,15,17,19

73:5 74:4,25 84:16 90:11 105:25,26 111:19 116:9

116:26,26 117:10,12 118:3,3 126:18,19,23,25

127:2,16,20 129:15,18 134:15 136:17 138:18,20

139:12,12 140:3 146:23 148:4 149:22 150:18,26

154:7 155:2,21 162:1 165:18 167:4 168:11,11,18

168:19 175:13,17,20,22,23,25 176:9 177:15

183:2,3,9,24,25 185:6 194:16 196:22 198:23

199:1,6 202:18 203:10 206:2,26 224:14 239:20

239:21 244:18 255:26 268:17 270:16,17 273:4

278:5,7 284:14 285:4,5,25,26 286:6,7,9,12,15,19

286:26 287:7,11,25,26 288:7,8,10,20 289:1,1,13

291:22,25 292:18,24,24 293:2,6,9,11,23,25,26

300:24 301:4 309:12 313:21,25 320:2 334:15

337:12 339:6 350:21 352:12 369:21 383:13,16

397:5,20 401:11 404:6,7 405:3,7 408:21 409:5

410:11,14 415:8,10,11 421:25 441:18 445:4

452:18,21,24 453:9 455:4,6 460:22,23 462:21

470:7 495:12

positively 294:2

positives 25:23 73:13 96:12,14 106:23 112:17

115:25 118:20 127:9,19 130:6,19 140:9 177:4,13

177:25 195:24 239:6 244:8 258:5 281:7,9 286:2

287:4,5,25 288:9,21 290:26 294:24 300:23 301:2

301:6,7,10,12 302:5,5 309:3 313:15 349:12

370:10 383:15 393:17 396:8,9 398:18 399:22

405:3 409:4,17 410:9 412:9 434:21 449:19 453:1

453:6 455:12

positivity 496:23

possibilities 22:25 360:3

possibility 114:4 117:25 304:24 324:11 328:22

330:20 335:26

possible 32:19 175:7 180:15 237:1 245:21 250:8

321:21 323:13 324:2 325:18 327:15 341:11,12

344:7 345:14,17 359:21 385:18 418:5 424:12

425:8,22 432:25 434:22 444:12 457:4 458:25

459:11,26 484:15,22 485:3,15 487:13 490:6

493:6

possibly 54:17 202:3 215:6 265:2 327:16 453:12

482:19 490:16

post 249:19

posted 211:19 213:8

posting 211:18

post-MIE 403:9

Potassium 44:3

potatoes 68:20

potencies 220:14 259:16 334:23 377:10 378:4

potency 15:5 216:24 217:3 256:5,6,13,20,23,25

257:8,19,21,24 258:12 259:20 268:20,22 277:20

292:12 310:5 314:21 359:17 378:24 398:19

399:22 407:19 451:19 453:11,25 471:23

potent 15:3 88:17 97:12,18 119:12 130:9 137:9,11

141:26 152:18 184:9 216:19 314:25

potential 15:18 19:9 30:7 32:24 33:23 36:23 37:9

51:14 54:15,18,21 56:8,25 67:25 68:2,9 70:16

71:8,10 80:6,11,26 81:2 86:17 99:7 111:4 149:7

163:22 177:12 182:13,20 183:11 188:17 193:24

197:26 198:3 201:11 204:10 207:19 215:16

240:12 259:9 261:22 278:3 290:13 298:17 299:19

299:22 305:11,25 306:19 308:13 310:8,9,11

311:23,25 312:21,24 313:1 314:21 333:8 336:24

338:26 340:7 346:11 354:21 359:3 363:3 366:14

367:15 385:14 386:21,25 406:11,13,14,16 412:19

427:19 435:25 436:3,14 450:4 461:5,6 462:11,23

463:15,23 473:4 474:15 482:13 484:19 485:11

486:25 487:14 492:1

potentially 57:24,25 69:7 100:16 111:26 112:19

135:26 148:3 153:18 192:12 198:5 200:17 203:16

207:24 209:17 305:18 311:22 313:25 323:15

324:16 337:4 346:17 376:3 386:11,24 406:10

407:25 413:19 426:7,8 442:17 450:5 455:11

487:26 492:19,22 493:6

potentials 415:6

potently 129:26

POTOMAC 1:14

power 238:1 259:16 441:14 447:13 448:13,19

482:17

powerful 320:15

powers 308:19

practical 102:13 103:4 125:11 208:10 306:21

417:26 450:25 459:5

practicality 418:8

practically 241:15

practice 262:8 382:26 383:3

pragmatic 323:6 424:12 491:12

pragmatically 217:6

Prakash 6:13,14 128:1,24 129:11 171:18 172:5,18

173:12,18 174:15 175:1 214:4,4 350:14 380:20

384:8 388:22 391:3,4 395:10 400:19 417:5

489:21

preamble 370:25 372:12

precautionary 329:13

precedent 9:1,9

precedents 85:1

precipitation 158:13,14

precise 321:25 328:16 500:21

precision 145:14
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preclude 305:4

precursor 75:21

precursors 76:6

predicate 425:26

predicated 312:10 426:1

predications 441:23 458:11 467:15

predict 57:24 59:19 80:17 81:1 207:9 222:1 267:18

274:7 289:15 294:19 307:25,26 308:12 333:8

347:26 396:24 399:3 422:23 428:19,20,23 446:15

451:1 457:17 458:2,20 467:26 479:21

predicted 42:24 59:3 68:9 79:19,25 80:23 81:25

82:15,25 83:3 86:21 90:25 198:22 204:9 274:10

286:16 287:9,10 288:15 294:2 325:18 368:12

385:23 415:8 429:2 437:14 458:24 459:2

predicting 15:15 56:9 287:1 288:6,21 352:11

457:14

prediction 33:9 90:10,16,17 122:10 249:4 250:17

251:16,22 274:9 288:22 289:9 308:8,24 310:20

336:15,18,20,21,22 337:9,10,12,13,18,25 344:13

381:26 415:2 458:26 461:11

predictions 46:2 59:1 61:12 78:16 79:18 82:6,13

96:6 233:26 234:6 284:21 286:22 293:26 294:11

294:13,13 305:21 317:10 326:24 337:7 341:17

351:25 352:6,10 356:24 381:20 385:21 415:3

458:16,19

predictive 7:2 8:3 28:18 89:26 216:21 219:9

223:24 270:22,24 274:11 293:23,25 308:10

313:23,23 316:26 326:10 339:6 340:5 346:26

426:3 436:14,16 448:19

predictiveness 274:1 276:3

predictivity 52:9

predictor 325:20

predicts 278:14 334:6

predominant 142:3

predominantly 280:5

predominately 397:3

preface 443:5

prefaced 467:6

prefer 252:9

preferable 325:23

preliminary 30:8 36:16 92:5 96:15 142:17 166:23

167:13 175:11 203:12 242:5 311:9 347:6 385:3

444:4 469:9

prematurely 106:22

prep 162:23 170:20,22,22 354:25

preparation 64:2

preparations 243:17

prepare 4:19 10:3,4 32:6

prepared 4:15 14:14 88:1 121:14 145:4 271:7

preparing 499:19,19

preregistered 3:26

prescreen 97:15 152:13 157:5 158:17 449:26

487:21

prescreened 424:17,20 441:7

prescreening 319:25 441:9

prescriptive 26:11

presence 264:17 265:1 444:19

present 141:2 142:17 253:14 264:8,24 271:7 284:2

294:11 305:8 308:1 315:22 334:2 353:18 371:5

424:20 462:17 489:10

presentation 35:23 36:26 37:5 44:14 52:19 53:2

66:19 76:13 83:15,24 87:17 91:7 94:14 135:16

142:14 156:23 180:12 188:12,13 211:6,13 213:12

215:5 218:5 233:5 274:17,18 296:18,24 320:13

328:3 352:26 427:22 445:25 446:5 456:9

presentations 4:10,21 5:4 14:9,17 16:1,7 36:1

66:23 211:3,12,24,26 215:11 225:11,20 238:25

260:19 261:14 308:23 364:6 378:17 403:13,16

454:18 489:18,20 493:15 494:18 496:3,4 500:5

500:10,21

presented 52:19 131:5 195:5 213:7 215:18,22

229:8 237:8 250:18 267:8 307:10,17 309:9

335:23 351:13 368:20 372:2,21 376:22 396:6

399:9 419:22 446:2 462:5 467:8 468:25 470:26

473:19 477:22 483:16 494:19,21

presenter 233:9 235:23 239:12 253:3 260:1 264:5

269:18 275:1 279:8 282:11 295:25

presenters 37:8 215:7 275:18 500:19

presenting 91:21,24 233:12

preset 421:25 445:4

president 6:14 391:4

press 249:24 269:19

pressure 324:3

presumably 294:7

presuming 262:3

pretty 77:22 79:9,15 81:22 85:21 89:18 97:18 99:4

111:24 130:8 139:5 166:5 179:10 180:10,22

181:1 191:4 195:17 207:3 226:7 228:13 239:26

243:1 297:15 376:20 382:10 401:25 405:1 421:2

464:1 465:6

prevent 437:4

preventing 142:6

Prevention 8:21 10:14 16:4 17:3 230:17

previous 11:16,20 68:11 73:20 158:11 171:19

272:1 278:9 303:20 380:18 429:7 430:21 433:22

450:10 491:7

previously 3:25 32:14 322:23 452:3,26

pre-filter 493:5

pre-screen 394:15

primarily 5:19 20:26 40:7,16 76:11 116:2 242:19
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248:3 335:4 350:18 475:18

primary 21:24 142:4 346:13 354:5,23 366:3,4

392:21 460:14 477:9 482:18,20

primetime 114:10

principal 450:2 465:18

principally 312:10

principals 154:5

principle 11:18 13:17 29:4 30:13 96:3 97:9 305:11

305:20 315:18 429:23

principles 22:6 26:8,12,21 29:7 30:13 40:7 49:17

49:18 53:6 55:1 58:1,6 82:22 218:24 219:6,21

223:23 241:3 245:3,6 246:14 250:4 275:26 279:1

304:22 320:22 429:22 454:11,19,24 466:24

468:18,18 470:19 471:18 478:23,24,24

principle's 467:19

print 261:17

prior 261:5,15 296:16 299:26 312:16 437:5

priori 249:19 290:2 311:6 479:21

priorities 303:9 307:24 309:16,24 494:3

prioritization 13:2 28:13 29:11,17,22 30:4,22 33:5

35:8,24 37:22,23 38:9 39:24 51:23 68:1 93:2

121:17 124:7,9,18 125:23 168:7 216:13 217:18

218:21 221:11 236:8,12 240:2,10,11,13 241:14

247:25 248:3,12,15 253:19,25 254:25 255:21

256:14 264:19,26 270:9,10,21 273:23,26 275:21

277:5,23,26 281:26 289:16 296:16,17,24 297:2

297:10 298:7,15 299:23,25 300:5,20 303:1,4,6,17

306:6 307:11 311:4,9 313:18 330:21 377:12

378:9 390:4,6 392:18,26 398:19 399:21 404:12

416:16 441:11 452:21 453:25 456:20 466:26

467:26 468:3 470:21 471:22 472:5 477:4,5 482:7

492:23 493:22

prioritize 12:22 23:8 24:19 28:6 55:15 184:17

186:6,17 241:23 253:10 300:9,12,16 301:22

309:20 311:1 327:22 330:20 375:21 405:4,9

407:26 409:6 433:7 452:15 464:17,24 482:11

492:21,25

prioritized 30:11 35:9 65:13 82:18 241:12 270:15

408:22 453:2

prioritizing 1:4 2:7 5:12 9:19 28:8 33:26 61:14

213:5 254:3 302:25 314:1 315:16 398:21 435:2

448:9

priority 33:7 37:26 38:5,6,8,17 52:10 216:15

223:10,11 258:3 261:19,21 262:5,11 263:3,13

289:22 306:22 308:2 310:3,5,10 313:19 314:10

314:23 322:13 397:16 404:13 413:24 433:7

462:22 485:4

private 264:11

pro 460:3

probabilities 245:14

probability 326:20,21 345:23

probable 115:21 336:19 375:10 419:26 457:2

471:2

probably 15:17,19 19:7 52:14 77:22 79:1 102:16

107:18 109:8 166:14 170:11 198:6 199:6 202:17

232:10 243:8 244:16 247:13 283:16 289:7 302:12

303:15,26 311:2 322:26 323:7 331:9,10 353:18

360:13 399:19 402:1 403:2 409:4,14,22 410:1

414:7 416:19 418:4 419:3 443:14 450:5 454:14

481:21 488:16 500:7

problem 25:12 85:19 111:9 113:25 125:10 133:11

138:7 142:5 176:20 181:2 186:9,20 273:5 292:20

301:9 314:13,17 348:8,8,11 359:25 368:24

398:24 403:11 406:7 418:10 431:13 449:23

462:14 487:15

problematic 314:9 349:13

problems 139:2 248:22 249:11 360:6,7 399:22

403:14 426:17 453:26 478:15 486:12 492:5,22

procedure 296:10 309:5 383:23 441:9

procedures 243:15,22 271:18 383:20,23 459:1

proceed 200:23 221:3

proceeded 235:8

process 10:16,21,22,23 11:2,6 17:23 26:17 29:19

30:5,23 31:4,11 35:6,8,17,24 40:23 50:8 51:8,23

76:21 91:26 93:18 98:21 104:9,14 108:1 110:7,9

115:21 145:23 176:25 178:23 179:1 181:4,14

182:6,21 188:5 225:23 230:7 232:11,11 236:10

237:19 238:21 240:10,18,23 241:8 242:10,14

244:6,26 245:4,10,17 246:7,13 253:19 255:18

256:11 263:13 265:4 271:17,20 273:1 284:7

303:3,22 304:17,21,26 306:6 313:8 317:13 323:5

338:18 353:4 364:2 366:22,24 372:6 373:10

421:12 480:17 496:15

processed 143:7 146:4

processes 114:25 233:16 306:23 376:6

processing 22:1 92:4 372:9 373:21 383:10

procure 74:22 125:11 207:13

procured 113:20 117:3,7 118:5 119:5

produce 148:6 149:15 151:21 162:4,26 163:3

177:25 235:4 379:2 464:25

produced 35:14 95:14 135:17 161:24 230:5 231:14

242:7 312:15

produces 95:15 146:22 182:13

producing 167:4,11 231:18

product 301:8 302:3

production 21:1 33:3 230:16 299:7 306:17,23

334:14 406:16

productivity 327:1

products 249:7 262:2 283:3 456:20

profession 7:12
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professor 5:17,23 6:9 7:1,6,25 8:9 390:17,21 391:1

profile 67:18 116:14 140:24,25 141:6 463:5

profiles 136:21 465:25

profiling 289:19

profound 369:2

progenitor 485:10

progesterone 117:13,14,20 118:2,6 135:12

prognostic 349:4,5,11,15 361:6,8 387:19 388:16

408:10,14

program 1:5 2:8 5:13 7:10 9:19 10:12 11:12,20

16:4 17:10,14,17,23 18:10,22 19:8 20:14,20 21:5

21:26 25:20 28:4,9,25 29:1 32:5 75:8 85:17,21

86:1 92:25,26 93:3,8,9,11 97:9 98:21 101:19

102:10 103:19 113:19 121:17 124:15 129:23

131:7,10 184:17 204:24 213:6 216:6,17 236:4,5

236:21 242:1 260:21 262:13 263:21 269:23

271:23 274:3 283:6,14 298:9 313:16 314:6 315:1

316:8 404:20 456:26 457:16 497:20,20 500:2

programmatic 25:10,17 215:25 311:16 314:5

418:14 441:20 452:22 497:11

programmers 85:25

Programmes 386:10

programs 9:14 17:4 35:10 92:23 98:21 248:19

262:9 315:9 417:14 499:25

progress 76:20 108:25 117:7 118:11 248:6 289:24

303:26 304:18 347:3 375:18 483:22 491:8,8

495:17

progressed 455:10

progresses 445:5

progression 125:7 308:3 444:15

project 17:18 95:4 128:21 368:26 386:10 387:16

projected 22:19

projector 239:22

proliferation 95:16 184:1 485:12

prominent 345:16

promiscuous 138:2,2

promise 40:20 469:10

promised 202:7

promises 209:12

promising 233:20 388:14

promote 218:18 221:8 236:2 262:2 322:21

promoter 122:16 367:5

pronounced 234:10

proof 467:19 485:26

proper 413:9

properly 234:17 235:7,17

properties 29:13 31:20,26 32:16 36:3 39:7,18

47:11,12 48:2 49:15 51:9,12,26 58:20 60:15,19

62:7 63:10 217:14 219:13 221:14 228:7 236:13

264:14,15 265:18,21,23,25,26 266:4,5,7 312:10

314:23 316:16,17,18,18 317:24 319:26 320:24

321:11,12 324:9 325:3 327:4 331:15 338:8 350:5

352:17 403:20 415:12,22 435:14 437:24,25

441:26 444:7

property 41:15 101:25 266:1 421:15 439:17

448:20

proportion 119:18 128:26 141:12 229:19 311:10

312:26 322:11

proposal 28:5 340:9 424:11

proposals 131:11,15

propose 197:14,20 208:15,22 326:5

proposed 3:2 20:5 21:2 29:10 30:22 36:4 197:22

198:12,13 199:12 202:2 233:19 234:3 254:25,25

323:14 331:13 338:17 367:24 425:3 434:3 435:5

435:7 443:22 456:6 469:5 483:24 484:4 489:8

proposing 23:22 208:12 236:14 434:5,6

proprietary 133:18 234:20,22 335:23 366:7

proprieties 265:26

protect 18:1 378:19

protected 269:3

protecting 478:11

protection 1:12 6:5 215:15 249:8 297:12

protective 332:12

protein 57:20 63:13,18,26 64:3,5,10 65:25,26

95:15,19 130:16 133:3 144:12,14 148:17,18,23

151:9,10 153:2,5,11,23,26 171:10 252:14 272:15

325:11 343:10,16 367:21 368:2,3 396:16 398:2

461:8

proteins 55:20 144:16 156:3 176:16 318:4 484:13

protocol 103:2,7 116:2 121:8 169:12 228:5,11

344:13 383:1 440:12 469:8,16 481:1

protocols 94:10 365:7 370:9 376:26 469:2 492:20

proton 40:21 41:2,9 42:4

prototype 240:23,25,25

prove 139:10

proved 332:8

proven 335:24

proverbial 240:21

provide 3:26 11:7 16:12 18:23 35:21 36:11,25

104:18,21 232:17,19 240:19 247:14,21 260:17

266:13,15,22 269:12 271:3 276:4 304:18 307:19

312:24 325:5,14 333:7 346:20 362:13 364:11

366:25 386:7 389:5 392:10 393:19 394:8 414:19

416:15 418:23 423:10 437:10 439:17 455:25

460:13,23 467:13 491:26 497:22 500:5,10

provided 2:21 4:9,11,12 33:24 34:8 141:7 240:9

263:18,23 264:10 266:1 268:19 305:7 331:17

336:18,22 366:4 367:9 368:25 379:26 382:24

384:23 385:2,6 393:9 418:23 434:24 437:21

provides 2:26 3:5 21:21 56:17,18 222:7 234:10
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261:9 276:2 306:18 310:26 334:24 368:18 386:15

426:11 433:24 460:25 478:8

providing 2:19 10:9 26:16 248:24 276:23 315:4

316:4 341:9 356:4 468:11 496:12 499:4 500:3

provisions 2:12 3:15

proximity 310:24

public 2:19 3:23,26 4:2,6,11,22 5:3 10:7,23,25 18:1

20:3 24:6 26:17 33:1,3 67:3 211:20 213:12,13,13

218:1,4,4 225:12 233:7,7 247:17,19 249:7 261:3

261:15,22 262:21,26 263:11,25 266:21 267:15,25

269:15 271:18 272:3,4 302:10,11 305:17 308:25

328:5 376:24 396:6 397:25 404:26 457:12 493:17

494:20 496:7 497:18,21 498:22 500:2

publically 242:13 271:16 272:21 273:9 274:13

publication 238:22 465:6

publications 94:2 465:3

publicized 490:5

publicly 284:18 376:25 498:11

published 18:25 133:25 205:26 206:3 238:19

245:17 249:23 340:18 398:1 465:5,13,23 474:3

pull 75:18 76:5,16 89:19 184:13 194:13 201:16

253:14 339:26 375:18 444:8

pulled 84:17 197:9 256:26

pulling 396:8 413:7

purchase 444:6

pure 78:3

purify 230:23

purities 228:21

purity 102:17,20,22,24 228:14,15 230:23 368:16

368:22 369:1 417:21

purple 68:16 165:16

purplish-pink 143:24

purpose 33:17 55:2,3 75:10 219:8 220:1,2 233:22

236:14 237:7 241:1 242:7 243:23 244:13 246:11

251:17 258:10 311:12 363:12 390:5

purposes 26:2 38:5 40:21 119:14 236:3 251:19

392:17

pursue 160:2 207:7 312:4

pursued 437:3

pursuing 180:7,21 227:15 403:17

purview 304:9

push 158:7 162:24 264:6

pushed 38:5 187:19 196:24

pushing 168:5 172:16 197:5

put 10:2 18:21 19:21 21:2,8 24:18,23 25:3 28:12

28:17,20,23 29:16 31:16 32:7 33:6 34:4,9,11,16

34:19,21,21,26 36:11,17 54:17,20 67:14 83:7,9

85:1,20 91:11,14 92:9,16,17 93:1 94:8,13,16

98:24 99:1,3,4,9,26 101:23 102:26 103:5,24

104:5 112:20 113:1,1,2 119:15 121:16 124:21

125:3,8 131:11 134:7 142:18,19,23 143:1,6,13

144:5 151:11 155:4 159:25 162:15 165:8,17

166:1,4,6,9,12,16,20,25,25 167:14,17 168:1,6

169:10 175:13 182:3,7 183:19 185:6,13 188:20

188:21 189:2,9,24,26 190:16,17,18,19,24,26

191:2,11,12,14,20,25 192:7,16,18,21,23 194:25

198:5 202:1,2,3,5,5 203:13,16 204:20 205:14

206:17 208:11,17,19,20 209:6,8,14,15,21,22

211:14,15 219:2,2 220:4,18,24 224:13 226:9

228:8,23 229:8 230:15 231:6,26 232:2,2,15,16,23

233:1 236:5,22 238:9,13 241:11 243:20 244:26

246:18 249:16 251:3,4 255:7 265:13 269:8 274:5

275:8 276:11 278:3 279:17,21,24 280:22 281:2

281:10,13 282:16 295:15 296:26 300:7,10 310:15

313:17 314:7 315:19 316:14 320:6 331:9 340:19

345:24 357:1 362:21 367:21 370:8,14 375:6

377:9 381:12,21 384:20 385:5 393:12,15,25

394:7,11,17 395:24 396:11 397:9,12 401:9

403:15,19 405:14 406:23,25 407:5,21 408:7,14

409:23 410:4,10,12,24 413:18,19,23 414:21

415:8 416:3,8 418:12 419:13,14 420:2 421:5,18

421:20 422:9 423:3,21 424:13,25,26 425:1,20,21

427:6,17,25 428:2,5,20 429:3,5,9,10 430:6,7

431:22 432:2,9,10,11,13,18 435:10 437:4,9,10

439:7,9,16 440:5 442:5,20 443:11,16,22,25,26

444:5,26,26 446:21 448:16,24 449:3,4,10,14

450:17 451:17 456:23 458:15 461:2 463:15

464:18 468:21 469:6 471:8,21 478:9 483:7

485:14 486:10,22 487:6,11,19 490:24 492:7

494:9 495:2 496:5,6 498:21

puts 213:21

putting 38:6 187:22 188:26 254:15 280:1 298:15

314:9 317:12 335:11 374:2 414:21 415:24 425:7

442:19 464:6 495:25 499:21

PX 483:18

PXR 483:19,24 484:8

pyrethroids 35:20

pyridines 200:16,18 204:22

p-chem 82:24 157:2 167:7 178:9 217:6,9 218:8

319:7 325:6 329:7

P-fluor 83:26 84:15

P-par 92:14

P-phos 83:26 84:15

p-toluene 158:5

p.m 91:4 501:4

P450 140:25 461:14

P450s 462:13

Q

QA 273:18 412:24

QC 103:13,15 108:13 120:10 412:24
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QPCR 62:13 145:15 156:18

quadrants 99:16

qualify 44:10 284:5 288:13

qualifying 250:9

quality 91:16 93:19 96:13 119:2 215:15 233:23

241:4 297:12 326:14 333:22 379:3

quantified 344:14 441:16

quantify 339:10 440:7

quantifying 19:13

quantitative 324:22 339:2

quantitatively 101:2

quarter 466:14

queried 341:7

queries 60:1

query 87:2

question 12:2,2,4 13:2,18,19,24 14:2,3,12,25 19:9

25:13 31:2,20 37:8 44:16 51:16 54:19 59:3,10

60:4 65:10 67:20 72:9,14 84:23,24 86:15 87:4,12

88:26 89:18,21,21 90:15 95:25 117:16 120:13

124:7,20 125:20 127:7 128:1 129:8,12,20 133:16

134:13 135:10,22 137:26 140:23 141:16 146:11

146:14 153:12 154:9 159:12 168:22,23 169:2,8

171:19,20 172:5 174:16 175:16 176:14 177:2,12

179:7,16 180:24 183:12,13,21 184:25 187:3

188:11 193:16 194:2,6 202:11 206:21 207:8,15

207:19 208:10 216:2,7 219:19 221:16 227:5

230:4 231:13 244:13 258:22 264:13 266:2,3,25

269:10 275:7 277:18 279:15 287:15 291:13 292:7

292:19 294:23 295:18 296:7 302:21,23 303:12,22

316:4 317:12,26 318:8,15,18 319:15,19 329:5,18

331:11 332:19,26 333:3 334:3 336:14 337:1

339:12,23 342:12,22 343:22 344:7,16 345:1,21

347:3 350:15 354:24 355:6,13,19 357:7,12 358:7

358:25 360:20 361:21 362:18,20 363:8 365:13,15

366:17 368:15 371:12,22 373:13,24 374:25 375:2

375:4 376:15 377:5,6,17 380:6 382:13 383:6

386:6 388:6 393:22,23,24 394:5,26 395:18

399:25 402:15 404:4 405:13 406:21 416:19

417:19 419:3,17 422:2 423:7 425:22 426:23,25

426:26 427:2,2,8 428:8,10,12,12,15,25 429:7

430:18 432:25 433:3,12,13,23 437:21 440:5,24

443:4,6,7,8 444:9,11 445:1,7 446:14,24 447:14

448:4,24 449:5,7,12 454:20 455:23,24 456:5

461:19 466:15,23 474:7 476:18 497:12

questionable 116:10 148:3

questioning 178:19

questions 4:16,20 5:5 9:23 12:7,8,10,22 13:9 14:4

14:6,7,8 16:1 37:5 38:20 45:6 52:3,13 67:9 71:23

83:12,17 88:5,7 90:9 91:2 94:11 122:1,12,13,20

124:2 126:15 131:2,23 137:19 140:14,20 142:10

146:18,22 148:4 152:6,20 154:4 155:26 167:20

168:25 169:19 170:3,9 176:22,25 177:18 180:18

180:25 183:17 184:15,16,17 185:17 186:23 188:9

194:3 204:17 207:15 210:24 211:13 213:18 215:8

215:26 216:4,4,9 217:1,1,9 218:6,20 219:8

223:12 225:10,13,14,16,18 227:10,25 229:5,18

233:5 235:18,20 239:10 246:26 247:1,2,21

248:22,24 250:14 253:2 258:24 260:1 261:11

264:4,12 269:17 274:14 275:10 279:7 282:8

289:9 290:10 293:19 295:25 298:2 300:8 302:7

302:14,19,26 303:26 304:14 318:9 331:16 332:20

335:18 343:7 354:9,11 356:25 360:13 361:16,19

361:20 362:11 370:23 371:16 374:22 377:19

380:26 381:19 382:11 383:17 389:1 392:7 395:16

395:19 400:25 409:1 419:1,6,24 421:6 430:21,22

439:1 446:11 449:18 450:9 451:9 455:18 462:20

466:10 467:23 474:17 476:22 482:1 489:3,8,9,12

489:20,22 490:25 491:3 495:26

queue 138:7

quick 20:19 45:9 50:16 88:7 101:14 116:5 137:19

137:19 140:23 143:3 144:3 244:22 444:8 498:19

quicker 17:19 23:19 34:5 179:17

quickly 84:1 120:11 143:4 146:17 180:10 223:19

242:23 290:17 307:3 420:3 427:26 464:11 469:1

quiet 411:19 412:2

Quill 295:26,26 296:1,3 302:9

quinoline 163:3

quinolines 200:16,18 204:22

quite 9:6 17:7 21:16 30:1 41:13 50:3 85:24 96:21

98:10,26 99:3 102:14 108:20 109:4 130:4 144:14

169:3 187:5 192:8 208:12 244:5,5 248:23 260:6

309:9 376:3 381:2 420:24 422:8 429:1 493:19

497:4 500:26

quote 28:15 254:1

quotient 257:3

Q)SAR 7:8 26:8,9 28:16 55:1 57:20 58:5,6,7 82:22

88:10,19 124:7,11 207:20 218:24 241:3 248:9

250:4,25 284:22 285:22 330:16 338:6 341:4

422:6,20,25 425:19,23,25 426:5,15 427:9 428:19

429:22,26 436:10 454:11,19 468:19 478:25

Q)SARs 454:9

R

R 87:22 106:5

RAAship 16:5

radar 199:8

radioligand 96:19

rainbow 33:19,20 69:2 73:10,12 145:12,13 150:8

167:24 168:10 170:9,12,19 173:23,24 195:22

220:15 232:15 267:1,1,18 268:3,7 269:4 279:26

280:18,22 281:1,10,13 284:22 285:22 288:13
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334:8 337:5,16 353:2 354:25 356:22 357:13

359:1 364:17,26 372:14 392:17 394:13,17 397:15

401:7 429:6 448:25 449:11 452:16 460:16 461:11

461:16,22 467:9

raise 81:19 154:4 172:9 417:18

raised 32:1 95:25 175:12 350:16 406:21

raises 146:21 159:2,12 170:3 184:25 343:17

raising 345:1

raloxifene 137:9,10

rambling 205:15

ran 51:13 68:3 84:16,18 86:11 94:3 95:2,21 116:3

132:25,26 197:4 198:26 291:26

random 341:14,15,18

range 17:7 58:20 62:23 63:9 69:18 70:20 81:25

90:23 99:4 143:14,15 146:14 148:20 173:2 174:8

175:4 189:13 193:5,6 205:1 219:13 220:14 226:5

226:6,7,9,11,13,18 243:4 245:8 257:4 280:19

311:22 319:7 329:7,10,12 352:10,17 377:10,25

378:4,21 379:7 385:12 397:14 399:18 401:5

407:2 429:1,18 430:7 445:2 450:11,12,15 453:21

463:7

ranged 204:12

ranges 189:9,11 192:6 198:8 203:23 204:4,14

226:3 243:16 396:15 399:18 401:9 402:9 428:26

rank 237:26 277:6 290:26 375:21 405:8,22

ranking 311:20 312:25 313:18 314:19,21,25 359:5

398:20 405:15 408:7

ranks 409:5 495:10

rapid 324:11 327:26 416:7 471:5,7

rapidly 321:16 323:9 332:7 416:9 458:3

rat 65:15 171:7 387:8 468:12

rate 241:24 286:19 287:7 288:10 289:1,14 293:11

313:21 452:21 453:9,9 458:2 473:6

rated 408:10

rates 294:10,21 311:8 338:25 352:8 406:13 452:24

ratio 98:8,12 110:14,16

rational 240:10 335:13

rationale 10:25 47:16 56:19 88:9 131:3,24 141:1

141:11 218:7 259:9 265:12,13 266:23 270:2

304:13,15 305:6 325:20 336:19 337:24 372:1

408:9 431:15 441:13 469:17

rationales 97:8

rationally 320:15

ratios 382:17

raw 104:19,24 121:10,11,12 256:17 257:1 259:24

379:1

Ray 36:1 38:24 39:4 45:12,20,26 46:15,23 49:20

50:6,21,21 66:18 80:18 162:15 332:21

RBA 67:26 154:18 155:7,8 165:9,9 227:3 244:11

334:19,22

RBAs 165:2

reach 67:22 69:19 151:25 306:2 310:11 322:11

464:21 490:15

reached 90:4 173:2 239:7 322:10 334:18

reaching 499:10

reactions 460:11

reactive 32:11 40:4 43:11 44:1 49:2 201:14 217:16

265:5 320:4,25

reactivities 458:11

reactivity 40:9,12 43:11 45:3 49:18 186:13 312:12

325:10,10

read 29:14 35:18 64:18 101:2 107:25 115:3 116:21

119:7 203:20,20 236:26 240:4 261:17 264:14

267:8 284:16 302:18 333:6 342:20 363:10,13

374:19 382:11 384:16 393:22 412:6 433:11

466:22 477:1 482:5

readily 137:15 469:3

reading 242:12,25 249:23 284:11 301:24 302:21

318:16 334:4 353:1 403:14 475:23

readout 100:6,19,19,20 110:13 144:24 156:16,17

156:18

readouts 94:25 100:13,14 109:3,13,18,24

ready 28:13 104:13 114:10 115:24 136:11

ready-made 483:22

read-across 221:16

real 50:15 94:19 113:13 116:24 120:25 121:16

125:11 137:2 145:15 156:3 184:11 218:22 301:10

365:10 366:9 427:4 470:17 490:24 498:19 499:6

realistic 75:10

reality 430:2

realize 191:1 223:21,26 294:8 345:14 356:12 431:4

450:14 457:11

realized 210:7

realizes 344:23 359:18

realizing 12:5 14:5 15:3 48:19 401:12

really 9:17 10:5 12:1,7 14:21,26 15:7,11 17:2,13

18:6,22 19:11 21:9,21,23 22:18 23:10,17 24:1,12

26:7 28:1,25 30:19,21 32:20 33:15 37:8,10 39:11

44:16 47:11,20 51:22 52:3,3,8 53:25 55:7 56:4

57:23 58:2,22 68:26 69:12 71:17 75:20 76:18,19

81:22 88:17 91:13 92:10 95:6 96:8 97:5,10 103:4

103:4 104:4,20 105:23 106:15,15 108:22 111:3,8

111:18 112:8,14,18,20 113:16 116:17,20,25

117:17 118:23 119:12,24,26 120:12,22 121:19

123:9,17,18 125:15 126:5 127:22 128:21 129:10

129:26 130:7 131:2 135:12 136:25,25 137:2

139:5 140:9 155:10 156:4 158:21 160:5 170:14

175:11 176:10 178:6,19 179:13 180:18 181:24,25

184:16,19 185:3,5,11 191:4 196:10 199:5 200:8

200:17 201:26 203:9 206:24 207:1,4 212:3
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221:11 222:25 223:18 228:19 236:4,6,9 242:11

242:11 243:5,12,17,26 244:1,9,14,22 248:9

252:10 254:5 256:2 258:10,14,21 259:11,15

260:9 266:14 267:5 268:19 269:6 270:5 271:7,17

271:22 272:2,2,18,18,19 275:13,18,21,22,25

276:3,8,10,20,25 277:2,11,17,20 278:12,25 283:4

284:2 285:10 286:24 289:6 290:17 291:4,8

292:16 293:13 295:20 296:20,24 297:12 299:5,8

299:9,14 301:10 304:2 310:16,17 311:13 312:18

312:23 314:7,13 315:1 319:23 325:23 332:12

343:20 348:1,2 349:6 351:14 352:11,17 353:20

353:21 356:19 357:4 365:19 373:8 374:13 376:26

377:3 380:2,4 381:14,23 385:16 391:5 393:8

394:23 396:19 398:18 403:17 404:20 409:2,7

418:9 420:26 430:3 434:4 436:12 437:20 444:8

445:22 449:6 450:9 456:6 457:19 460:6 464:2,18

464:20 467:26 474:25 475:4,6 486:26 487:26

488:3 491:3,10,11,17,19,25 492:21 494:8,10,12

494:18,21,24 495:25 500:9,20,20

realm 178:18 245:19 446:26 447:26 485:25 490:20

rearrange 195:25

reason 48:4 50:7,19 65:17 81:7,15 127:15 128:4

129:2 168:16 183:17 193:25 207:10,25 208:4

237:21 291:7 297:9 326:1 344:24 355:23 404:10

428:11 440:1,11

reasonable 42:12 51:2 59:5 60:4,4 92:1 94:19

97:19 130:9 132:6 217:2 223:5 236:13 308:24

311:10 359:23 382:6

reasonably 319:3 474:4 480:7

reasons 46:10 111:15 112:17,18 182:8 228:1

259:23 273:17 297:5 300:7,11 319:26 321:1

384:23 411:7 439:21

reassessed 441:9

rebuilding 339:7

recalcitrant 436:2

recalibration 28:2

recall 15:2 296:26

recap 27:9 211:3 215:12

recapitulate 448:7

receive 35:15 282:23

received 318:10 496:21

receiving 20:21

receptor 15:4 33:10 42:11,14 45:13,15,17 48:8,11

50:4 51:17 55:9,18 56:12 57:16,21 60:11,14,17

60:23,24 61:2 62:4 64:7 65:26 66:4 72:12 73:2

92:10,11,13,20,21 93:15 95:2,5,8,12,12,15 96:7

96:11 98:8 101:11 105:8 108:24 109:8,25 110:3

114:26 118:9 122:3 123:4,21,23 128:6 129:24

131:18 132:19,20 137:7,13 140:1,5 141:18,22

144:10 145:10 146:7 147:23 148:12 149:15,20,26

150:21 154:10,11 156:1,2 158:19 160:10 161:13

161:26 162:22 170:9,13,26 171:3,8,8,11,13,16

181:5 182:3,25 184:20 186:1,10 191:9 196:16

205:26 210:10 217:19 223:7,26 224:3,4 227:19

240:24 252:14 256:24 264:25 265:3 267:6,18

276:2 307:6,26 330:2 343:7,24 347:16,17,19,22

347:22 348:4,9,23 353:2,3,8 354:21,23 356:6

357:22,23 358:1,2,10 359:1,2,2,13,15 363:23,24

363:25 364:3,7,8 365:1 378:21 385:12 388:12

392:15 394:15 396:11,13 398:7 399:12 401:17

404:23 411:15,24 420:21 422:21 427:20 431:10

431:18 432:3 446:16 449:24 450:19,21 459:14

467:2 470:2 473:22,25,25,26 474:1,4 475:2,9,24

477:11 479:5,10,11,15,16,17,20 480:5 481:13

482:17 483:19,19 484:19 486:23

receptors 7:26 50:5 64:2 65:20,20 85:6 141:17,19

142:1 144:9,21 145:4,5 170:13,16,20,21,23 171:5

343:17,18 361:15 367:12 387:12 396:20 406:1

475:25 484:11,14,17

receptor-alpha 170:11,14

receptor-based 397:9

recess 52:17 433:1 466:19

recipients 282:20,21

recognize 27:8 39:4 44:6,22 50:11 137:1 248:8

278:18 310:16 353:22 359:25 367:5 392:21

480:19 491:18,24

recognized 39:9 50:22,23 134:26 272:8 323:9

326:11 488:1 497:1

recognizes 303:21 434:11

recognizing 48:1 393:15 433:21 492:1 497:10

recombinant 64:4,7 65:20,22,23,26 66:4 144:9

148:12,15,18 149:18

recommend 239:4 252:26 261:2,13 278:24 304:9

335:21 348:25 375:5 395:26 414:9 447:20

recommendation 19:2,6 20:22 22:21 28:19 38:18

49:21 262:15 269:9 277:2 278:11 328:1,7 419:17

436:12 448:2,3 455:2,12

recommendations 3:5,7 16:24 18:21,22,23 29:25

36:4 37:1 52:4 83:25 238:26 247:22 249:15

260:25 262:7 276:4,6 279:3 339:10 392:7 429:8

436:5 496:22

recommended 38:12 39:25 249:15

recommends 485:2

recomputed 426:10

reconfirm 392:25

reconsider 257:25

reconstruct 362:25 365:16

record 134:23 234:2 302:19 342:20 360:17 363:9

365:14 382:12 390:16 412:6 448:1 466:22 489:13

recount 20:20
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recovery 338:25

recreate 266:21 272:23 273:10 430:2

recruited 95:13

recruitment 484:17

recurring 311:2

red 21:16 67:21 68:12 97:26 107:13 110:24 111:3

115:7,23 158:26 161:3 162:7 174:4 205:10

redefining 195:12,14 445:8

redox 325:12

reds 118:13

reduce 166:22 217:7 320:15 368:2,7 383:15 406:14

470:25

reduced 176:15 326:20 434:17

reducing 31:17 316:15 405:5 473:7

reduction 233:24 313:24

redundancy 388:11

redundant 385:6 386:2

reemphasis 314:19

reevaluate 204:11

reevaluated 453:12

reexamine 397:26

reexamined 467:22

refer 275:15 361:7

reference 34:10,10 36:14 98:26 114:15,24 117:10

118:10,15,22,25 119:17,17 134:13 152:4 220:14

242:26 243:13 246:18 249:17 275:9,10,19 276:1

276:8,9,15 277:17 278:15,26 312:2 323:19

324:13 333:13 366:2,3 368:10 369:5 374:26

377:7,9,21 378:1,18,20 379:9 380:2 383:9 384:21

389:5 437:20 439:7 441:21 465:25

referenced 366:5 385:13

references 275:11 323:20 325:14 331:17 338:4,4

364:11 366:1 368:14 387:21,26

referred 294:6 386:6

referring 94:12 296:21 338:9,12 450:12

refine 330:25

refined 327:21 343:22 425:25

refinement 427:9

refinements 424:2

refining 458:5

reflect 424:10 464:7

reflected 304:14 394:25 441:17

reflection 221:12

reflective 484:18

reflects 21:23 346:7 439:18 475:4

refocused 21:5

regard 38:11 45:9,25 228:23 234:8 298:23 321:10

321:18,26 322:14,24 323:2,12 324:5,19 329:4

330:3,17 333:9 354:1 368:24 457:1 461:6 468:5

regarding 3:2 306:5 307:10,13 317:23 329:1

336:14 343:7 346:10 353:14 382:24 395:24

398:22 400:25 419:1 439:1 446:12 489:4

regardless 442:23

regards 457:13

regime 474:15

region 111:16 359:14

regions 111:17

register 32:8 39:26

registrants 27:14 230:10 231:18 282:22

registration 30:26 31:11 237:19

regression 59:20

regular 359:15

regulate 298:13

regulated 27:5 215:18

regulation 244:11 387:3 483:25,26

regulations 3:14 306:7,26

regulatory 3:3,6,6 7:18 18:6 25:25 26:1,14 28:13

29:6 33:16 35:10 53:16,17 56:19 58:20 59:3,10

59:11,15 61:13 65:10 124:17 134:5 218:20

219:14 221:16 232:4 233:10,13,22 236:3 263:18

263:19,19 264:1,2 267:10 270:8 282:12 283:4

296:9,11 310:22 478:10 492:2,14

reinforce 317:18 404:4

reinforced 354:13

reiterate 203:25 239:16 313:11 340:2 362:1 374:18

400:13 433:3 450:10 465:2 473:20 493:26

reiterating 184:14 497:9

rejection 369:13

relate 215:24 217:17 221:23 319:23 345:4 355:21

363:14

related 4:5 131:1 171:9 227:9 262:6 264:1 268:12

270:1 276:2 311:25 325:10 336:19 343:17 345:13

358:7 359:10 360:12 386:8 390:6 415:16 427:2

468:7 470:14

relates 86:8 218:15 305:11

relating 265:25 328:8,17 344:7 354:3 469:15

relation 362:2 383:21 414:9 433:22 436:22 437:12

445:22 457:24 458:7 460:15 465:15 466:7

relationship 19:14 59:2,4 64:21 65:1 73:13 74:26

89:16 151:8,11 153:23,26 201:23 205:8 220:11

222:12 224:10 227:2 356:6 417:12 434:22 481:17

relationships 63:2 159:18 164:24 222:19 240:3

375:19 420:6 426:5,16,18 468:14 471:17

relative 33:25 64:22 89:25 107:8,9 130:17 256:19

256:25 257:3,8,19 259:15 268:15,23 280:10

311:20 312:25 326:26 331:3 334:6 359:5 378:24

427:26 446:15 452:9 492:25

relatively 43:16 97:11,12 109:7,14 118:6 129:26

177:6 195:19 243:24 307:3 388:10 396:23 416:6

427:26 465:3 492:13
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relaxed 258:9

relaxing 258:5

relayed 424:4

release 33:3,3 104:14 260:11 281:18 377:1 484:18

released 261:3 376:25

releases 299:7 377:1

relevance 56:5 136:5 255:6 259:19,20 268:19,25

268:25 336:26 337:2 377:11 378:6 440:3,4,23,25

456:10

relevant 169:6 208:10 238:16 244:10,12,19 246:6

263:9 269:26 281:26 305:13 312:26 325:2 330:6

337:6,25 343:15 349:21 382:16 385:24 387:9

436:6 437:24,25 440:8 449:7,9,10 468:3 485:19

reliability 249:9 289:8 337:3 377:11 378:6 412:23

413:9 440:26 449:13,16

reliable 228:16 233:25 234:6,18 487:3

reliably 222:13

reliant 450:21

relied 85:24 198:11 298:24

reluctant 42:3

rely 230:16 249:6,7 338:11 367:25

relying 301:3 350:19

remain 82:8 238:9 338:25 340:9 435:25 455:12

474:14 480:19

remainder 137:22 190:5 192:22

remained 34:2 51:15 335:2

remaining 35:3 52:8 54:15 77:11 190:1 191:25

192:1 198:7 385:19 435:9 436:13

remains 30:8

remarks 8:25 9:13 233:15 307:16 310:15

remember 102:6 107:10 141:9 149:11 150:11

156:12 160:20 207:26 226:7 252:7 401:3 471:11

475:12

remet 361:17

remind 38:26 126:12 140:19 236:4 263:16 284:23

362:12 418:22

reminder 148:22 156:11 285:18

reminding 65:19

remit 443:14 447:2

remote 302:20

remove 32:10 304:24 305:5 319:25

removed 82:7 230:15 246:3 284:15 329:2 379:3

removing 32:19,20

render 71:16 75:2

repeat 178:22 322:22 498:15

repeated 299:17 300:3

repeating 322:25 348:19 376:16

repertoire 364:5

repetition 335:16 385:16

replace 237:2 340:26 420:17 448:10 479:15

replaced 442:5 445:23

replacement 24:26 25:2 93:3 241:26 445:22

replacing 238:7,8 308:4 442:17

replicate 119:9,10,22,25 120:1,4 152:26 160:21,23

replicated 450:17

replicates 99:15 107:6 112:5,7 118:22 127:2

152:22 243:19

replication 152:3,3,19 153:12 156:7 377:2

report 3:10 4:19,19,22 10:4 11:20,21,22 13:14

32:1 38:11 100:26 257:6 322:5 323:17 358:1

374:25 410:23 426:14 427:11 434:25 465:13

471:24 499:20

reported 102:23 108:14 257:6 368:6

reporter 100:22 123:7 281:14,15 285:6 348:15

440:8

reporters 100:22

reports 3:21 235:5,6

represent 143:14 198:20 199:21 219:16 282:22

377:10 378:4,20 385:7,13,17,20

representation 74:2 191:11 195:19 198:18 204:2

377:25

representative 196:7 197:10,13 200:21 334:18

436:19

representatives 76:18 134:4 203:3 236:22

represented 195:23 386:17

representing 219:10 322:9

represents 68:5

reproducibility 114:18 119:2 378:25

reproducible 113:12 116:13 146:3 233:26 234:6

234:18 383:13

reproducibly 379:2

reproduction 20:10,11,15 55:24 58:13 468:6

reproductive 6:10 8:16 56:8 336:24 337:5 343:11

362:3 386:11 391:1 498:14

reputation 228:13

request 234:24 389:4

requested 103:1 232:6

requesting 3:23 31:3

requests 131:11

require 52:8 202:13 233:24 296:26 297:19 305:1

311:19 330:21 367:12 456:20 487:7

required 37:14 234:2 265:19 283:19 304:6 305:21

311:12 323:17 328:26 331:7,17 373:1

requirement 7:7 35:11 283:23 299:20

requirements 3:22 102:19 215:14 286:8 299:8

383:21 499:18

requires 52:5 379:9 383:19

requiring 102:22

rerun 121:6

research 5:25 6:14,15 7:25 8:6,6,10 16:6 17:5,7
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18:4,5 93:3 126:5,6 136:26 184:15,16 209:13,20

210:23 214:21 220:5 226:2 227:8,12 232:11

247:26 248:18 260:9 298:9 391:4,20 433:8

498:23 499:25

resend 274:18

reside 75:14

resided 70:16

resides 3:8 33:4 35:24 67:25

resolve 139:5 197:24 462:14 469:2

resolved 468:26 500:8

resource 31:14 208:4 466:2

resources 12:19 24:21 31:17 32:21 52:6,8 206:23

222:26 236:7 239:5 281:22 282:12 300:14,16

302:2 304:5 309:21 437:4 443:13,16,18 452:23

458:15 485:5

respect 17:18 59:1 72:2 187:3 195:25 197:8,18

202:20 203:14 204:3 208:16 220:13 222:15

240:20 251:23 334:12 346:22 371:23 376:13

380:1 393:10,26 430:22 441:19 443:10,21 465:3

473:21 495:4

respiratory 314:14

respond 47:8 126:13 142:26 145:14 161:8 182:17

185:19 186:8 206:10 419:20 448:3

responded 443:9

respondents 319:17

responding 183:7 407:16 443:6 446:23 454:23

500:20

response 62:23 63:21 96:22 97:2,21 98:6 99:10,23

106:14,17,18 107:2,16,22 116:3,14 122:16

129:16 130:25 136:20 137:22 147:12 148:11

150:10,12 153:3,9,25 154:12 157:13,17 158:12

163:2 165:13 168:17,18 173:26 183:1,3,9 256:9

259:13 263:22,24 266:5 287:2,12,21 288:25

289:15 305:12 321:23 324:1 350:15 355:15 357:8

357:10 358:16 361:3 363:8,23 364:8,16 369:21

383:13 388:13 395:15 400:23 401:1 402:12

406:20,25 408:17 412:5 422:6 429:9 430:18,22

438:25 441:4,8,10 446:10 447:2 449:17 455:17

456:5 460:23 476:21 489:2

responses 4:20 145:26 150:2 153:14 161:11,15

164:3 167:6,7,15 168:8 171:13 206:1 252:22

318:10 347:24 361:7 401:10 415:25 433:5 434:8

436:22 476:23 478:1 495:25

responsibility 3:12

responsible 2:12 7:8 72:25 74:17 235:24,26 257:13

responsive 348:14 397:11

rest 98:2 117:26 120:23 192:3 236:19 237:15 455:8

restate 358:7

restrict 328:7 460:8,11

restricted 341:16 479:14

restricting 295:5

result 72:21 90:1 117:18 204:10 205:7 270:17

288:23 337:11 341:4 355:3 364:25 368:1 385:25

394:18 396:12 397:3,7 403:1 404:6 423:22,23

436:22 445:10,13 487:25

resulting 484:2

results 20:2 34:16 36:16,17 69:1 73:11 74:4 82:17

82:24 86:10 95:1 103:16 117:6 128:13,14 143:9

144:6,8 146:23 150:9 151:3 159:6 167:18 170:5

178:14 187:6 194:9 232:23 234:15 237:25 238:6

245:13 258:15 260:16 261:20,21 262:11 267:18

268:7 271:4 272:4,7,11,13,20,23 273:4,16,19

274:2,10 277:7,9,11 278:20 280:12,14 282:6

286:22 294:9 301:14 304:11 313:12 323:26 335:6

350:20 363:4,4 366:14,15 367:11 368:19 369:9

374:1 375:14 382:22 394:9,25 395:25 396:25,25

397:5 398:4,11,13,23,25 399:2 403:14 404:7

405:8 414:4 416:10 417:22 420:10 421:19 425:10

426:4 439:22 440:11,19 444:5,23 484:1

resume 389:17

resurrected 179:20,20,21

rethink 185:7

retinoids 368:7

retract 371:17

retrospective 346:26

returns 125:14

reveals 452:7

reversal 55:22

reverse 184:7 251:8 266:19 290:14

review 2:6 3:1 11:2,5 20:25 22:1,18,22 23:6 27:17

30:26 31:11 53:5,6,19 58:9,24 59:24 64:17 66:6

66:15 144:3 217:23 218:2,3 234:25 235:1,2,6,12

235:14,16 237:20 238:20,22 245:24 246:2 251:7

257:5 260:13,14,18,24 261:1,3,9 272:4 273:21

276:14 290:8,9 346:9 371:4 465:5,12

reviewed 3:21 22:23 28:25 53:15 483:16

reviewers 252:3

reviewing 376:21

reviews 22:17 29:10 235:4 245:16 462:3 498:4

revisit 31:1 175:6 332:11 483:15

revisited 332:2 350:4

revisiting 340:22

revolutionary 497:5

Rich 179:4

Richard 36:10 91:9 122:17 124:26 125:9,26

126:20 127:4,12,13 129:22 131:6,6 132:14,22

133:22,22 134:19 136:6,6 137:24 138:6 139:2,20

140:6,6,16 152:1,25 175:9,9 183:16,16 190:3

209:11,11 210:18 228:4,4 229:2,10 230:25 232:1

232:2,24,24 246:15 247:7,11 259:3 370:24,24
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371:14,17 374:24 381:1,1 382:1

Rick 35:2 36:5,19 51:10 52:19,21 53:23 66:10,13

76:25 77:6,14 79:23 80:1 83:23 84:21,26 85:13

85:22,22 86:23 87:11,21 143:22 177:20 187:18

188:13 190:14 191:19 195:15,16 204:26 206:10

206:12,12,18 254:8 289:26

rid 194:23 287:6 386:2 405:2

rifling 72:15

right 9:23 14:18,19 31:7,9 56:5 62:15,26 63:4 64:6

73:11 78:3 84:11 86:2 89:12 91:3 97:13 99:1

103:15 116:14,16 118:16 119:4,11,13,21 123:7

127:1,24 128:24 129:21 142:20 143:24,26 146:2

151:16 152:11 153:1,7,15,17 155:22 157:21

162:6 163:4,16 165:9 168:20 169:3 171:2 173:19

177:18 179:7,23 188:11,19 193:15 194:16 195:24

200:5 215:3 218:3 221:2 222:19 230:12 246:23

247:9 253:26 260:1 264:4 271:1 275:2 284:9

291:13 293:22 317:8 318:14 342:8,15 346:23

347:13 350:17 351:10,17 354:19 356:15 362:12

371:20 373:3,15 375:1 389:8 402:15,18 410:10

411:23 416:19 419:8 426:1,17 439:13 448:18

466:14,16 486:10,23 487:23 499:14,21

rightly 424:17

right-hand 35:16

rigor 26:16

ring 187:20 196:1,3,18,18 197:17,24 199:16,16,17

200:1,2 444:19 457:9 460:10

rings 72:17 201:10

rise 403:25

risk 5:26 7:16,18 12:18,24,25,26 19:13 24:3,4

25:11,15 31:3 56:5 58:12 215:18,21 216:18

217:14 262:3,4,23 263:8 298:12 308:7,9,14,15,17

309:7 310:22

risks 261:22

risk-based 248:12

Riverside 5:19 213:23 390:19

RNA 95:14,19 100:22,25 157:16 268:8

road 24:15 54:23 83:4 142:20 251:12 289:26

433:25 468:7

roadmap 251:13 252:26

roads 312:4

robot 99:26

robotic 209:22

robots 94:24

robust 39:17 51:1 103:24 236:14 325:26 326:1

335:8,24 340:3 358:19 379:21

robustness 59:19 303:7 336:17

Rockville 93:22

role 154:12 155:24 156:2 167:4 169:16 187:16,23

263:26 482:26 488:21

roles 441:20

rolled 74:20

rolling 103:16

roman 66:15 189:21

room 5:14 111:18 212:4 238:19 389:20 390:15

501:2

root 72:10 347:2

roster 211:14

rotate 322:4

rotely 81:5 86:18

Rotroff 271:9,11 274:8 278:1 285:1,3,5,24 286:23

287:8,17,25 288:19 290:18

rough 68:17 179:19

roughly 39:22 198:21

round 495:20

rounds 131:10

route 314:16 324:18 333:2 492:26

routes 415:1

row 99:10 165:10 166:7,8 225:15

rtER 64:4 443:23 448:5,12 453:6 454:26 455:5

rule 59:9 109:1,21 202:14 335:6 341:14,14 344:12

421:17,25 424:8,16 443:12

rules 59:13,14,22 60:1 65:8 66:21 83:18 85:17

191:7 197:1 220:8 222:21 226:23 230:6 312:16

314:18 322:1,1,3 323:24 324:6,8,9 338:12 381:4

416:21 421:12,13 422:15,19,20,25 423:18 426:9

431:8,14,17 439:10 444:15 451:22 455:3

rules-based 67:7

rule-based 232:22 334:11 442:1 458:19

run 50:10 62:5 63:15 68:7 70:3,24 72:22,24 75:22

75:26 76:17 85:10 93:20 94:5,21,23,24 96:20,21

96:22,23,25 98:4,4,5,16,17 99:2,8,14,15,17 100:4

100:5,10,12,14,15 101:1,4,6 103:18 110:24 114:3

114:8,13,19,20 116:2 125:14 130:5,13,14,14,15

130:24 131:10,22 132:10 139:20 149:21 152:23

152:26 157:7 162:5,19 176:10 184:18 198:21

220:3 229:13,22 231:6 232:16 249:12 250:26

283:13 295:12 304:12 308:19 383:5 408:25 410:3

417:24 428:21 429:13 441:8 443:19 444:1 459:2

474:11 478:15 486:12

running 74:26 111:11 121:9 139:17 145:25 163:7

164:8 178:13 186:24 279:24 282:24 401:7,7,11

409:10,11 428:22 441:10

runs 139:21 152:23 251:6

S

S 87:22 387:6

SAB 28:10

SAB/SAP 22:22 38:11 39:24

safe 8:5,5 18:14 26:24 31:9 35:11 37:10,15,19

49:10,25,26,26 54:4 77:20 78:15,21 122:14,15,21
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123:3,13,17,26 124:3 139:25,26 140:12,13

176:12,13,13,20 205:21,22,22 206:15 208:1

214:24,24 215:14 258:14 296:7 298:5 299:10,20

300:2 341:25,26 345:12 347:10,11 350:18 360:18

360:19 370:4,5 374:8,9 376:7,8 377:13 379:24

380:12,13 384:1,2 388:4,5 391:23,23 395:7,8

400:5,6 408:5 409:12 411:3,4 416:5 417:2,3

474:21,22 481:8,9 488:24,25 494:5,6

safety 8:14,20 10:13 16:3 43:20 215:2 263:12

392:2

salicylates 69:4 226:10

sample 118:5 119:5,6 141:4 352:15

sampled 117:4

samples 95:2 102:20 113:21 117:2,6,7 118:5

232:16

sampling 352:7

Sandy 6:2,3 213:26,26 229:6,7,7 231:11,12,12

357:11,12 380:22,23 384:10,11 388:24,25 390:23

390:23 395:11,12 400:20,21 407:8,9 417:7,8,25

417:26 489:17

SAP 1:1,8 2:6 4:18 5:11 9:5 10:3 11:9,23 14:11,12

14:14,16 16:20,22 20:18,24 22:16,24,26 23:3,5,7

26:18 28:10,25 29:3,25 33:11 36:12 38:4 53:4

54:8 66:15 70:8 142:22 161:22 211:23 227:21,23

233:15 234:2,4,25 235:12,14,16 236:16 240:8

251:12 261:5 271:1,13 285:12,15 287:23 290:9

290:19 303:21,25 304:3,9,16,20 306:26 307:17

325:26 327:7 333:12 339:9 354:15 372:16 390:13

393:9 402:4 407:1 409:11 467:21 468:6,26

472:17 482:22 483:12 496:12 497:10 498:5,10

499:4,10 501:5

SAPs 11:20 12:13 22:13 233:16 234:24 248:3

260:23,24 303:21 491:7 497:8 498:17

sat 491:7

satisfied 3:14

satisfy 283:22 286:8

save 124:1 239:5

saw 61:17 68:11 79:22 137:16 150:2 152:17 157:4

161:20 162:9 164:26,26 167:1 169:20 174:26

208:16 223:2 267:7 295:3 435:21 491:14

saying 37:18 50:7 84:6 123:19 168:9,10 174:21

186:16 204:23 205:4 224:21 251:20 253:8,23,25

254:17 266:11 296:22 297:4 303:15 313:18 316:5

351:14 354:4 359:7 360:8,9 374:19 380:5 402:25

407:10 409:26 410:13,14 411:16 416:15 420:16

420:17 425:24 428:11 431:9 432:8 443:6 445:21

469:18

says 39:7 109:21 110:7 115:5,8,10,11 116:15

119:26 127:2 162:7 289:24 292:10 297:15,18

299:15,21 303:23 314:9 352:22 448:26 457:26

scale 38:7 338:13 378:23 416:14

scaled 364:25

scales 221:22

scenario 326:19 345:20 434:17 437:6 457:26 461:2

468:13 471:2

scenarios 360:3,12 468:8

scenes 9:22

schedule 30:26 38:21

scheduled 235:13

schematic 421:11 444:21

scheme 204:20 218:21 221:11 236:13 277:5 290:25

303:4,6,17 311:4 314:6 320:3 325:22 375:22

381:3 423:3 440:22 450:24 472:5 477:15 478:3

482:21

schemes 469:3 471:22

Schlenk 2:14 5:8,10,17 8:17 9:12 16:14 37:4,24

38:19,25 45:6 46:7 47:7 48:3 49:9,24 50:13 51:5

52:12,18 83:12 84:21 85:13 87:3,3,20 88:4 91:2,6

122:13,25 123:2,26 124:5,12 125:17 126:11

127:26 130:20,26 132:17 133:14 134:10,22

135:21 137:4,16 139:13,24 140:13,18 141:13

142:8,13,16 167:19 170:7 171:17 175:8 176:12

176:22 180:25 183:15 185:15 186:22 188:8

204:17 205:21 206:7,11 208:8 209:9,24 210:17

210:24 211:5 213:16,17,22 215:3 225:8 227:24

229:5,16 230:19 231:10,20 232:20 233:4 235:19

235:22 239:10 246:26 247:6 253:2 258:24 259:26

263:14 264:3 269:17 274:14,26 279:7,11 282:8

282:11 290:10 293:18 295:24 302:7,10,17 303:11

307:21 310:13 313:5 316:9,21 317:19 318:7,14

318:25 319:12 331:18,22,25 332:14,18,24 333:14

333:24 339:13,16,20,24 341:21,25 342:2,5,8,15

342:19 343:2 344:18,21 345:26 347:7,10 349:25

350:10,12 351:21 352:19 353:12 354:8,13 356:16

357:6,15 358:4,21 360:15 361:10,25 362:9 363:5

369:24 370:2,4,6,17,21 371:12,15,19 372:11

374:3,6,8,10,12,17 375:1,7,15,23 376:7,9,11,17

377:4,14 380:8,12,14,16,19,22,24 381:7 382:10

382:20 383:25 384:1,3,5,7,10,12,16,26 388:2,4

388:17,19,21,24,26 389:7,19 390:12,14,17 392:3

392:9 393:21 394:4 395:1,5,7,9,11,13,20 396:2

399:26 400:3,5,17,20,22 402:21 404:2,17 405:10

406:4,19 407:8,14 408:17 410:22 411:1 412:3,13

416:23,26 417:2,4,7,16,25 418:6,21 419:9,20

421:4 422:17 423:2,11,14,17 424:6 425:15

426:21 427:12 428:17 430:16,19,24 431:12,24

432:5,20 433:2,18 438:3,6,8,18,21,23 439:4,12

442:7,9,12,14 445:16,18,26 446:8 447:16 448:22

449:21 451:3,7,13,26 454:1,3,6,22 455:14,16,22

456:2 462:7 463:26 464:26 466:9,13,21 467:3
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488:26 489:7 490:18 491:5 492:3 493:9,11 494:5

494:7,15 495:7 499:12,14

Schmieder 35:1 36:6,20 51:10 52:19,21 76:24 86:7

86:7 88:12,12 89:9 90:5,5,18 165:25 169:26

171:7 179:6,6 180:4,4 185:18 186:23 188:13,14

206:20,20 208:18 210:4 218:24 225:24,24 228:22

228:23 229:22,23,26 232:18 430:25,26 432:4,6,7

447:16,17

Schmieder's 232:3

school 6:21 7:2

Schultz 7:5,6 37:24,25,25 49:9,11,11 83:13,14

84:23 85:15 167:20,21 168:9 169:4 170:6 204:18

204:19,19 205:16 206:22 211:8 214:12,12 316:11

325:8 331:19,20 339:22 344:22,23 358:23,24

370:6,7 374:10,11 376:9,10 391:11,11 406:19,20

427:14,15 431:12,13 438:8,9 442:12,13 448:22

448:23 449:23 454:6,7 456:3,4 467:4,5 477:7,8

482:9,10 490:18,19

Schultz's 407:16 431:2

science 3:19 8:3 9:8 10:15,18,20,24 11:1,4,17,22

16:17 31:3 217:24 219:8 236:2 247:15,23 269:7

269:13 279:12 324:21 499:26

sciences 5:18 6:24 275:1,3 390:18 391:2

science-based 268:16

scientific 1:1 2:4,6,15,23,24,25,26 3:4,9 5:8 10:12

10:16 26:7,15 127:15 133:5 149:6 213:3,4,4

215:21,22,24 219:4 225:22 248:1 249:3,5 250:16

251:15 262:16 263:18 303:5,7 304:8,21 342:24

346:7 352:23 390:3,8 467:11 497:18

scientifically 39:17 44:16 217:21 238:16 333:7

468:16

scientist 7:22

scientists 17:2 236:1 248:7 281:20 495:18

scope 26:23 134:9 270:11 296:16,21 301:26

scoping 246:8 488:19

score 126:25 408:8 453:1

scores 90:1

Scott 233:9,11,12 235:21 249:21

scour 232:9

scratch 104:20 393:8

scratching 265:7

screen 18:11 21:17 25:18 37:14 52:11 67:16

129:18 130:6,9,24 185:8 199:14,20 237:4,5,15

239:7 244:17 266:6 311:17 318:15 325:17 372:19

387:7 406:17 408:3 423:3 424:26 434:22 443:16

444:5,26 445:1,4 449:3,4 487:21,22

screened 32:19 134:17 237:20 274:2 325:13

screening 1:4 2:8 5:12 12:23 18:1,10,20 19:8,8,15

19:17,18,20 24:20 27:23 30:11 31:18,25 32:4,7

33:7 34:1 35:9 37:17 38:5,7 91:14 92:16,18

112:21 130:1 133:9 135:6 187:8 208:11 213:6

230:13,16 231:6 236:24 237:13,16,18,22,26

238:3,6,14,14 239:5 240:10 241:13 243:20

244:26 245:25 246:10 253:20,24 255:1,22 256:14

258:4 262:13 264:15,20 266:23 289:19 295:4

297:19 300:1,25 301:12,22 303:17 304:6 305:16

306:25 307:2 308:6 311:20 320:3,18 323:5

325:12 340:18,23 370:8,14 381:21 397:8,13

404:22 405:22 406:9 416:3,5,9 418:8 424:13,18

428:2 431:22 434:17 437:4,10 442:5,20 445:1

450:6 456:26 464:18 469:6 485:19 490:24 492:7

screens 283:8 284:18 286:10 340:17 408:3 421:24

443:19

screw 409:15

Scripps 7:25 391:19

Scripts 214:20

scrutinized 368:13

scrutiny 365:9

SDWA 32:25 77:23 78:5,11,14 82:4 202:26 335:4

378:9

se 12:1 252:10

Sea 214:22

SeaChange 8:2 391:21

search 68:21 379:22 466:5

searches 414:13 473:15

seat 390:2

seats 247:6 302:17 390:2

second 24:22 34:14 36:16 40:7 72:18 73:8 86:25

88:26 118:14 127:6 137:25 159:16 165:10 169:7

179:16 190:21 192:2 213:3 242:24 316:14 321:15

325:16 327:18 330:5 337:9 338:5,23 340:12

346:19 365:3 366:11 397:18 402:17 432:5 435:26

436:9 441:18 459:1,4 460:25 462:8 470:7 486:9

494:10 500:12

secondary 55:23 70:21 142:3 360:7

secondly 300:19 302:3 346:10 358:15

seconds 39:5 458:4

secretary 500:14

section 6:6 18:15 35:24 62:2 80:12 83:9 180:11

275:20,20 278:1 284:16 288:1 296:8 319:15

320:12 322:14 330:7

sections 54:21 384:19

sector 264:11

secure 47:24

seductive 436:26

see 9:25 10:26 11:6 12:15 14:17,23,25 19:4 20:8

31:8 32:12 37:8 38:16 39:6 46:5 52:15,26 54:21
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111:17,18,19 113:8 114:8 115:13 116:5,12,19,24

117:22 118:23 119:23 120:19,25 129:2 138:15,19

142:1,3 144:13 145:26 147:22 148:13 150:15,24

151:12,16,20,25 152:10 153:1,9 154:16,23 155:6

155:9 157:12,14,20 158:23,24,26 159:3 160:7,26

161:10,21,25 162:3,6 163:12,24 165:2,10,16

168:15 172:14,15,23,26 176:8 180:13 181:2,25

184:5 194:21 197:25 199:24 207:21 208:24

210:22 211:22 213:10 217:7 231:14 246:3 257:22

258:15 260:19 263:2,3,5,11,24 267:19 273:4

274:12,21 276:14 277:11,15 281:7,9,21 295:4,19

296:2,14 320:19 321:17 347:2 349:10,16,22

355:1,16 357:10 359:18,25 368:15 370:12 375:2

382:2,26 385:26 397:18 398:22 411:2 413:7

416:1 422:1 423:24 425:10 429:8 431:1 444:14

445:7,23 463:24 471:20 473:21 488:3 490:15

493:1 494:22 495:20

seeing 24:12,13 30:16,19,20 31:23 88:20 110:18

114:13 121:12 142:6 149:16 153:5,6 157:22

176:5 178:14 216:21 237:7 280:14 403:15,26

421:8 484:3 490:23 493:1

seek 258:13 374:22

seeking 497:12

seen 43:22 83:16 87:23 91:10 118:7 147:19 150:21

158:10 163:16 201:3 202:25 216:26 237:4 248:19

249:11 273:4 317:2 343:13 346:25 378:17 397:11

404:6 423:19 450:16 479:21 495:9

segment 315:15

select 23:20 181:20 192:26 194:9 203:24 204:13,25

245:22 408:13 416:7,13 433:15 435:6 436:19

437:10 483:4 484:26

selected 34:11 91:26 114:15 117:23 141:8 143:14

143:15 205:23 218:9 266:7,8 270:3 320:10 335:5

366:22 386:16,19,23 484:26 486:2

selecting 189:10 204:21 205:19 222:19 434:14

435:1 438:11 469:16,24

selection 219:18 270:2,6 309:5 325:20 377:7 420:8

436:20 469:15

selective 123:4,20,23 256:9 347:16,17 348:23

388:12

selector 140:5

self 282:24

self-context 400:9

self-proliferation 95:20

self-rebinding 96:24 138:10,13

seminal 477:11

send 103:17,18 300:25

sends 103:21

senior 7:22

sense 46:3 47:14 48:24,25 59:5 81:5 88:22,24 89:3

154:8 160:2 173:3 195:26 217:12 239:3 296:19

306:16 307:18 315:13 341:11 349:1,9 355:21

363:20 370:16 400:7 421:13 476:10

sensible 339:1 435:8 453:5

sensitive 128:18 133:11 153:10,18 163:14 243:26

244:19 291:15 332:8 334:23 348:26 349:1 356:21

405:20 406:15,18 414:2

sensitivity 136:7 149:19 174:2 244:18 249:10

251:10,19 276:25 277:12 278:10 289:8 291:23

293:24 294:4 327:1 339:5 349:24 378:14 379:5

379:19 385:11 411:8 437:17 441:16,21 451:25

452:13 487:21

sent 493:18

sentence 351:26

sentiment 313:7

separate 45:1 123:13 194:11 248:18 398:14 433:8

471:23

separated 71:5 163:9 185:13 269:8

separately 379:5

September 23:11 39:26

sequence 303:8 307:10 312:8

sequencing-based 100:19

sequentially 319:20

Seri 196:14

series 16:22 49:13 59:24 61:6 62:24 65:22 66:3

67:8,9 71:23 73:21,23,24 74:11 75:3,18 85:6

87:17 90:9 98:6 99:10 138:20 183:23 195:24

225:20 226:9 283:19 310:1 358:25 454:8

serum 280:9 368:9,10 487:4

serve 2:11 456:10 458:12

served 491:4

serves 4:22 196:5

serving 2:15 258:10 492:11

session 196:23 318:1

set 34:17 36:18 40:14 45:2 57:15 59:8 61:20 64:19

68:3 69:5,10,13 70:1,11 73:10,23 74:15,24 87:13

90:20 91:21,25 94:3 98:24,25 100:9,9 101:18

104:5 105:21 108:7,7,11 111:26 114:15 121:24

124:24 125:7 130:8,13 131:23 133:2 134:13

137:3 141:9 142:19,24,26 143:6,8,13,25 144:1

145:23 146:11 147:7,7 150:7 152:22 154:15,18

155:2,7,13,20,25 156:12,17 158:1 164:12 165:1,3

165:7,14 166:1,2,7,9,17,21,24 167:2,13,18

171:22,25 172:19,21 173:3,5 179:9 181:18

183:20 188:22 193:26 195:22 197:1,3,12,15,23

201:1,8 220:15 222:19 228:18 229:15 231:4

232:5 243:9,10 249:16,17 254:16 255:24 256:21
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352:24 353:1,11 354:24 358:8,8,25 363:21 371:6

376:5 378:26 379:9,17 380:2 381:22 382:6

393:13 394:2,10 395:23 398:6,9 404:12 412:9

413:22,23 414:2 415:18 420:7 423:25 433:16

436:19 437:3,8,12 439:8,24 441:14 443:12

451:21,24 452:14,16 453:18 455:3 493:4 498:11

SETASIDE 111:7

sets 54:20 59:13 150:5 218:9 219:15,18,26 222:12

249:12 317:11 334:18 341:11,14,14 379:11

412:21 413:8 414:5,24 415:18 424:8,16 434:10

setting 136:12 210:18 216:15 223:10,11 258:3

261:19,21 262:5,11 263:3,13 289:22,26 290:2

293:9 307:23 308:2 310:3 311:4

settle 68:13

seven 62:2 66:16,16 98:21 105:15,24 108:3,16,19

110:6,7 112:22 116:22 274:20 278:1 281:7 288:9

seventh 259:1,21

Seventy-seven 198:4

sex 55:22,23 354:17

shadow 111:22

shaking 117:23 357:11 411:2

shape 146:9 148:2 415:25 438:1

share 231:25 244:23 269:22 497:3

shared 272:21

sharing 60:20

Sharlene 2:1,2 5:10 9:20,24 211:10,11 213:1,2

263:16 274:16,22,25 333:16,21 389:12 390:1,8

390:14,16 496:11 497:25 498:21 499:16

sheet 81:14

Sheets 43:20

shift 296:5 428:1 471:10 490:21

shifted 64:8 184:2 397:14

shifts 201:11

shipped 102:26

Shirley 500:16

shopping 457:20 459:7

short 43:16 75:4 150:20 223:20 238:9 240:2

372:19

Shortly 18:19 21:5

shot 67:16

shots 372:20 407:22

shoulder 153:8

show 28:2 65:21 68:13 97:20,22 108:26 145:24

146:5 150:8 165:16 192:2 195:21 201:2 249:22

276:12 280:9 292:4 315:23 321:10 336:6 404:25

405:3 421:25 428:21

showed 39:5 85:5 87:1 115:17 143:22 148:16

152:20 158:9,18 165:25 171:7 187:18 196:14

224:12

showing 63:7,20 119:2 121:15 153:20 154:24

160:10 161:4 399:9

shown 89:24 122:5 138:21 151:4 163:3 225:20

234:18 277:8,16 289:4 328:24 337:15 348:6

359:3 386:26

shows 43:8 118:4,4 137:12 143:21 147:17 150:20

152:8 155:1 166:7 194:13 280:12 342:26 343:26

352:24 429:1,11

shuffled 99:16 112:4

Shultz 258:25,26 316:10 339:21 360:15

sic 440:8

sick 126:22 355:9 409:20

side 24:12 30:25 35:16,17 56:3,5 58:18 62:15 74:6

78:3 86:26 87:1 96:9 116:11 124:21 127:18

153:17 159:20 169:12 175:24 176:4,5 208:20

220:3 226:11,12,14 249:14,14,19,19 315:23,23

360:4 361:14 409:20 428:10 480:16,16

sidebar 457:6

sides 194:22

sidetrack 86:14

sieve 258:6

sieves 29:19

sifted 163:11

Sigma 102:6 228:12

signal 99:18 133:9 138:12 161:16 162:4,12 182:8

244:10 278:22 334:16 349:1 406:1

signaling 48:21 182:14 223:21 269:26 270:4

343:10,16 349:19 354:16 409:16

signals 161:19

signal-to-noise 382:17

significance 153:20

significant 22:13 24:1 26:13 106:7 129:4 157:15

157:16 165:13 263:7,8 292:23 300:7,22 307:25

312:26 324:18 334:16 375:17 416:20 434:2,11

469:10 479:19 488:21

significantly 368:7,8 442:5

silico 24:18,23 25:3 28:12,22 29:14 336:16

similar 57:11 59:6 60:5,17,20 61:5 65:18 78:20,23

142:25 144:18 145:2 148:20 150:3,14 157:18,19

157:26 158:3 160:5 161:9,24 164:26 174:23

187:5 201:1 242:20 243:16 245:1 248:14 258:7

350:16 357:18,19,24 375:25 400:8 451:25 452:13

461:4 477:10

similarities 461:12

similarity 60:6,7,9,17 61:10 72:3 88:26 171:6

177:19 219:16 337:23 338:2,3 357:26 436:6,7,8

similarly 150:13 161:9 325:11 394:22 434:18

Simon 264:5,6,7 269:18
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simpler 85:11 312:14 323:7 336:9

simplified 460:13

simplistic 13:4 255:15

simply 68:1 75:15 94:15 114:2 118:21 120:9 122:2

179:21 217:15 221:8 258:17,19 296:25 317:12

326:12 340:4 361:15 373:15 379:4 407:21,22

408:14 424:14 425:11 443:21,24

simulation 457:15 460:1

simulations 456:22 460:8 482:8

simulator 457:5 458:9

simulators 456:25 457:16 458:1 460:26 462:3,18

Sinai 346:20 387:25 465:20

single 44:15 67:18 68:8,10 73:15 75:25 100:22

129:21 130:23 137:21 193:19 249:16,17 323:6,7

323:11 331:1 332:3,4 345:11 346:4 383:10

394:14 397:8,13 404:6,10 408:7,15 421:25

422:14 439:17,24 441:4 453:1,16 455:7 459:11

476:9

single-plex 94:25

singular 344:25

sit 105:22 122:19

site 1:8 74:18 96:2 170:2 188:2 189:21,22 190:7,20

191:8 196:18 211:15 227:9,10,12,13 297:26

305:3 322:12 345:23,24 354:25 358:26,26 359:15

359:16 421:23 427:19 431:4,7,16 459:21,21

460:3,4 461:20,21,23

sites 64:23 100:23 226:5 359:1 361:20 383:2 397:4

463:22

sitting 112:7,8,13 119:15

situation 50:10 87:13 88:1 345:5 367:17 401:6

404:20 456:11 457:10 458:3 459:16 485:1

situations 359:20 396:18 428:14 474:9

six 19:17 92:26 98:4 105:12 106:4 110:6 132:4

192:9 193:11 205:5 275:20,20 279:15 281:14,15

283:24,25 285:6 287:7 397:19 460:10

size 39:22 178:25 217:8 251:21 321:14 322:19

344:9 352:4 379:11,16 438:1

sizes 352:15

ski 158:6

skin 458:8

skip 238:3

skipped 479:25

Slaughter 233:9,11,12 235:19,21 249:21

slice 61:22 62:11 145:12 146:26 147:1,5,9 148:8,10

148:10 149:8,9,10,13 156:17 157:13 159:22,22

162:2,5,11 167:25 170:19,24,25 176:18 178:21

334:9 337:6,16 355:1 356:22 365:1,8 372:15

416:1 448:25 450:7 452:17 453:6 455:1 460:17

460:19,22 467:10

slices 145:13,14,17 149:13,15 157:16 163:2,14

224:19 334:14

slide 17:24 39:5 40:23 44:13 66:14 68:5,11 76:9,17

76:23 91:10 104:9 132:11,12,14 148:16 151:5,16

152:8,9 156:10 163:16 165:22 177:1 187:1,7

198:2 199:26 200:20 202:1 203:4 205:23,25

206:2 223:1 226:1 252:5 259:6 261:14 268:1

278:9 286:4 300:6 318:26 319:21 327:3 486:10

487:23,23

slides 75:18 87:18 91:20 132:10 144:2 145:22

151:5 152:16 154:14 176:26 188:12 203:20,20

223:2 261:17 274:19 488:8

slightly 120:15 146:4 155:21 278:19 396:12 403:4

slop 99:18,18

slope 138:4,26 139:3,3,5

slope-shaped 158:6

slots 231:6

slowly 103:16

small 93:7 124:16 138:2,3 167:13 180:1 188:7

195:12,19 281:3 311:24 352:6 374:13 386:5,14

416:14 420:8 444:23 491:15

smaller 59:25 64:19 89:15 243:9 352:15 436:18

470:15 494:14

smart 35:5 52:5 108:9,10 222:26 330:15

smarter 23:19 393:2

SMARTS 436:5

smiles 87:26 436:5

snapshot 20:19

snuck 375:3

Society 239:12,15

soft 325:10

software 66:25 69:22 85:18,21 87:25 106:6 266:11

266:11,15 335:22 375:12 414:15 466:3

solely 410:14 424:22,23

solid 273:3 354:25 460:6

solubilities 62:7

solubility 61:23,26 103:3,6 145:7,16 156:25 160:12

172:22,23 173:14,24 174:3,17 175:3 188:1 197:4

208:23 220:3 306:11 321:19 322:3 324:12 334:17

355:8 401:7 409:11 429:18 440:14

solubilize 103:8

solubilized 102:25

soluble 62:9 103:8,9 114:1 228:9 358:2

solution 12:10 197:6 201:15 323:7 437:11 469:5

solutions 14:8 62:22

solve 13:10 112:9 301:9

solved 113:25 138:6 213:18

solvents 368:17

solving 360:6

somebody 104:19 121:7 126:9 133:19 196:20
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somewhat 43:13 50:18 229:3 250:1 252:25 267:10

313:21 402:24 427:2,15

Song 387:21

soon 245:21 281:17

SOP 70:8 366:4

sophisticated 85:21 87:26 136:14,15 416:4

sorry 85:2 92:18 132:10 157:8 229:6 231:16 236:3

246:4 247:11 274:21 279:12,12 293:17 294:26

319:1 329:7 339:18 343:4 375:2 391:5 405:11

427:13 479:25

sort 38:16 39:15 42:12 43:13 44:18 50:22,23,25

88:7 97:9 102:17 109:16 113:14 116:12 117:8

120:13 126:24 132:4 133:4,12 138:11,16 141:22

154:13 176:2,25 179:24 183:22 197:20 205:23

209:6 211:6 220:9 221:10 224:5,6 227:15 240:17

240:25 241:20 258:9 264:1 266:22 268:13 284:15

289:25 295:14 313:21 314:5 315:11,12 316:4

345:5 349:4,23 353:20 355:18 361:3 362:6 386:4

401:6,21 404:18 410:6 419:18,25 421:6,12

426:23 430:15 433:8 457:10 458:3 459:16 461:2

474:23 486:11 487:13 489:9 492:12,25 493:7

sorted 73:10

sorts 230:26 256:6 347:18 387:26 408:4 432:17

494:2

sought 254:24

sound 217:21 300:6 446:16 467:12

soundness 26:7,16 219:4

sounds 133:17 140:26 167:23 169:8 296:25 445:26

source 42:24 106:5 251:23 297:24 306:16 332:4,5

332:7 341:6 346:4,6,16 387:5 412:19 424:25

442:25 450:6

sources 43:18 160:21,24 161:14 266:7,24 297:20

297:24 299:4 317:15 318:21,23 325:17,18 326:15

346:14 412:9,20

South 1:14,15 6:15 214:5 391:5

so-called 48:5

space 93:24 196:11 197:22 198:7 217:18 219:10

227:20 229:2 240:18 243:4 295:19 314:3 316:5

401:15 402:6,14 407:5,20 416:11 418:18 425:21

426:18 427:7 434:12,23 437:5 468:23 473:7

483:6

spaced 139:5

spaces 413:6 469:6,7

span 273:20

spawning 332:10

speak 36:2,7 51:10 84:7 225:16 227:25 256:21

282:14 295:6 303:15 333:18 487:6

speaker 247:7

speakers 255:19

speaking 17:8,12,23 23:10 46:15 47:10 51:18 84:4

91:11 275:11 282:16 294:26 323:16 480:13,23

speaks 35:25 51:25 341:2 442:25 498:12

special 7:13 66:21 96:20 98:9 100:6 191:7 226:23

431:8,14,17 460:4,6

specialty 6:7 283:2

species 18:26 40:11 42:6,9,16,26 43:8 45:9 47:2

51:2 65:24 66:1 71:21 73:8 161:21 162:15 201:12

264:18,24 265:4 315:19 319:11 320:5 323:18

328:12,25 329:2 402:2 410:15 461:15,17,17

462:14,18 468:11 488:6

specific 14:15,15 18:2,3 49:5,13 55:2 70:5 71:15,16

87:2 90:8 109:24 111:10 112:20 114:14 126:1

128:20 133:8 139:9 141:2,11,18 164:24 170:15

175:3,12 179:17 242:4 251:14 254:7 258:19

265:26 278:4,18 303:1 314:2 319:7 323:19 329:6

346:22,25 353:20,21 354:10,19 355:7,13,23

361:15 363:12,21 364:2 372:24 379:26 387:17

392:14,15 406:2 411:24 413:11 427:10 429:15

444:14,17 448:17 453:24 462:4 470:12 483:21

492:23 494:13 496:22

specifically 18:9 53:17 77:19,24 79:6,9 93:15

98:18 118:24 135:7 143:16 232:3 245:18 268:12

275:7,26 285:8 289:6 317:8 345:22 346:14 365:8

381:15 383:23 392:13 393:11 407:18,26 415:7,16

421:21 424:9 437:16 445:10,21 448:15 455:5

466:7

specificity 248:26 249:10 251:19 273:24 276:25

278:9 289:8 293:24 327:1 339:6 378:15 379:19

specifics 14:22 80:11 260:15

specified 40:2 180:19 463:5

specify 229:21

spectrum 39:16 436:21

speech 124:4

speed 143:5 236:10

spend 9:17 10:6 21:19 105:15 130:6 231:8

spending 10:8

spent 7:17 139:6 223:20 236:8 467:23

Spigen 481:16,20

spiky 109:13

split 30:24 73:15

splitting 414:7

spoke 101:23 190:4

spoken 213:14 342:7

Spotfire 336:13 375:13

spots 14:19

spread 192:5

spreading 111:20

spring 22:15 23:4

SP1 350:26
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square 83:21

squelching 142:2

squiggle 85:19,20

stability 306:12,13 324:16 341:17 461:13,19

stable 272:13 281:20 458:24

stack 294:6 436:8

stacking 259:18

staff 2:20,23 4:2 9:20,24 21:23 211:23 213:10,15

489:18 496:12 498:21

staffer 264:9

stage 70:13 135:15 312:22 327:18 420:22 448:10

467:20 471:14

stages 270:10 310:23 332:8 406:9 485:19

stakeholder 261:5 290:8

stakeholders 24:7 101:19 232:5 249:6 260:17

261:9 282:26 497:19

stand 69:22 306:21

standard 3:18 96:2,19 103:7 104:4 105:6,7 106:19

107:5,14,16,26 118:21 121:3 126:17 127:3

167:26 210:8,10 228:20 244:25 267:14,15 276:10

376:26 379:3 382:26 383:1,20,22 414:25 448:5

452:17 472:12

standardize 243:14 440:17

standardized 440:6 478:13

standards 373:23 378:26 379:6 413:25

standpoint 103:4 126:6 431:15

stand-alone 66:25

Stanford 346:16

start 11:12 13:6,19 14:3 15:18,18,20,21,23,23

25:11 38:2 55:13,14 57:2,24,25 58:10 60:19

66:14 67:19 76:15,23 85:17,17 99:11 100:16

101:17 104:19 106:6 108:3,19 110:17 114:13,22

119:22 131:6,22 140:8 149:25 157:20 169:5

171:14 177:21 178:24 180:18 182:6 186:4 187:22

189:19 193:12 195:25 196:2 199:13,14 200:14

201:9,17 204:26 205:1 212:6 236:20 239:20

246:21 253:7 261:5,15 275:23 277:3,6 278:20,21

278:21 279:2 280:19 281:7,8 296:13 297:1

302:14 303:14 313:7 327:5 334:4 335:9,15

342:20 343:3,4 345:15 347:2 362:18 375:21

389:12 454:9 471:9 474:2 488:8 489:14 491:2

started 13:4,8 94:7 134:25 144:4 159:23 163:6

185:20 186:2 200:26 210:7 232:25,25 296:23,23

302:18 357:4 392:4 466:22

starting 4:3 13:14 17:11 25:12 30:23 55:12 67:8,19

76:16 92:15 96:17 122:11 124:18 135:1 178:18

194:21 223:5 280:17 294:16 301:18 393:8,8

starts 21:9 272:26 273:1 275:26 278:13 392:15

state 7:13 10:11 28:2 31:2 68:2,25 87:25 122:25

126:12 134:22 213:20 214:15 261:25 302:21

326:2 389:11 390:16 391:14 442:18

stated 69:26 236:21 289:22 300:2 311:16 320:21

322:25 327:11 331:21 338:19 339:26 346:2

356:20,24 373:24 385:3 442:16 450:1 474:8

477:8

statement 331:24 333:15,16 363:9 375:11 380:11

380:18 382:1 427:5 494:11

statements 319:22 364:12

states 1:11 35:14 230:14 231:19 234:26 239:13

300:4 441:6

static 39:1

stating 375:25 421:6

statistical 96:26 104:3,3 116:15 136:1,3 259:16

309:8 338:2 372:10 373:22 378:14,24 379:8,17

381:21 385:9 436:7 441:14

statistically 334:16 385:17 399:4

statistical-based 440:1

statistics 114:21 118:21 169:6 276:24 277:4 278:9

278:11,25 293:7 331:3,5 338:22

status 146:15 473:12

statute 32:25 298:26

statutes 18:19 215:18

statutory 37:12 230:8 296:9 299:8

stay 211:8 247:1

stayed 70:8

staying 421:8 499:18

steady 208:26

stealing 83:23 87:16

steep 146:19

steered 202:15

step 70:22 76:8 87:9 105:4,12 108:17 145:25

240:14 253:17 258:6 271:2 277:3 301:19,21,25

337:14 367:2 376:1 402:17,17 452:21 461:1

484:7 490:20 493:22

Stephen 5:22 8:5 49:26 213:24 341:26 347:11

354:12 360:19 370:5 374:9 376:8 380:13 384:2

388:5 390:20 391:23 395:8 400:6 411:4 417:3

474:22 481:9 488:25 489:16 494:6

steps 10:24 11:3 24:1 53:20,26 70:3 84:9 91:13,18

104:23 106:3 121:16 124:16 126:21 179:13 181:4

182:16 240:9 250:22 252:13,22 272:1 303:4

307:10 315:23 364:2,5 366:22,24 420:18 422:13

445:9

step-wise 125:6

stereochemistry 87:22 196:23 197:2

stereoisomer 87:6,14

steric 72:24 73:7 74:7 227:19

steroid 60:26 216:25 386:10,21 387:11

steroidal 61:16 344:4

steroidogenesis 92:14,22 94:7
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steroids 186:21 256:24 257:17 359:5

Steve 5:22 8:5,24 9:3,11,13 16:24 49:26 186:7

205:22 213:24 214:24,24 227:6 263:20 390:20

391:23

Steven 9:15 38:10 48:15 122:15,21 123:3,13,17

124:3 139:26 140:12 176:13,13,20 205:22 206:15

227:6 400:26 409:9 410:25 411:19 419:2,10

426:22 428:24 498:19

stew 140:19

stewing 225:14

sticks 500:9

stimulated 252:25

stimulating 484:11,11

stimulation 334:13 483:10 484:9

stimulations 483:9

stock 62:22

stomach 328:19

stop 62:6 174:4 194:18 280:26 386:1 389:15

445:14

stopped 150:20

stopping 389:8

story 117:21

straddles 329:12

straddling 29:21

straight 73:17 123:14 159:19 255:12 273:1 463:18

straightforward 435:14

strategic 21:21 34:26 35:2 36:22 72:8 200:21

222:18 393:2 469:8

strategically 71:10 83:11 188:16

strategies 221:20 240:7 414:3 435:11 439:14

453:15 458:23,25 469:15 473:15 483:13

strategize 184:26

strategy 26:7 28:21 124:11 134:25 194:26 195:7,8

200:2 204:1 205:18 206:22 237:13 239:5 326:11

398:24 401:22 403:2 408:20 459:3,4 493:2

stream 464:14

streaming 27:15

streamline 241:9 246:11,13,16

streamlined 242:3

strength 119:16,18 275:24

strengthen 221:19

strengths 125:1,2 220:23 276:26 382:19 439:14

451:20,23

stress 20:6 25:4 109:19,19,22 127:8 175:11 225:4

272:25 273:7 284:12,13,14 285:7,11,14 286:4

287:3,10,11,17,19,24 288:24 289:2 291:9,10

383:14

stressed 436:20

stressing 124:15 200:20

strictly 399:12 443:3

string 46:18

stringency 258:5,9,16,18 405:5

strings 330:15

striving 105:23

strong 40:3,10 41:10,12,22 42:1,7 43:23,26 44:4

50:24 66:2 115:25,25 135:11 138:15 149:4,5

158:18 160:22,24 161:15,26 164:3,4 177:6

328:18 335:12 349:14 358:11 359:4 377:26 378:3

381:23

stronger 241:18 281:9

strongly 378:22 469:1 483:15

structural 57:9,17 60:8,20 68:22,26 72:3,4 138:17

177:19 185:26 267:2 336:1 337:24 403:24 421:14

434:19,23 435:19 436:5 437:23

structurally 157:26 160:5 187:25 425:12,14

468:24

structure 67:23 68:8 69:4,16 70:25 71:16 72:10

75:9 113:3,23 159:18 164:19 182:13 187:26

194:1 196:1,8,11,25 197:21 198:7 199:10 205:8

221:9 224:10 229:2 240:3 330:7 355:16,16

383:24 401:15 402:6,14 417:11,11 420:5 423:18

424:24 425:5 426:4,15,17 436:26 444:19 445:11

451:21 457:3 476:1 499:22

structured 325:14

structures 22:20 56:16 57:1 68:15 69:6,15 70:2,6,7

70:10 71:14 72:1,5 76:10 77:17 197:10,21 199:14

199:19 200:7,22 202:20 203:5 220:8,12 222:3

319:25 323:3,4 338:8 356:6 435:13

structure-based 164:18 431:20 455:3 478:22

struggling 410:8

strung 181:19

stuck 348:25 349:20 494:23

student 63:17

studied 192:5 485:18

studies 20:11,15 23:20 24:24 28:2 128:8 238:16

246:20 251:14 282:24 283:12,20 284:15,19

286:26 288:5 292:1 295:14 353:18 359:2 365:5

369:19 386:6 432:1,8,15 458:18 498:15

study 20:16 83:6 280:15 285:1 320:12 386:7,16

stuff 62:25 110:1 119:13 239:18,21 241:10 245:25

376:23 409:14 496:14

sub 303:23

subdivided 470:16

subdivision 339:7

subgroup 374:18

subgroups 435:18

subject 23:5 31:24 76:12 80:19 178:26 339:2

367:26 375:9 453:5

subjected 235:2 452:19

subjective 374:2
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submitted 239:16,19 247:17 275:15 283:22 319:18

subpocket 459:15

subpockets 459:25

subsequent 31:6 32:6 35:26 38:9 69:15 151:5

270:10 302:26 305:15 334:10 433:9 473:12 497:8

subsequently 108:13 467:13

subset 22:17,23 23:3 91:25 93:16,22 94:4 197:11

346:4 371:3

substance 45:19 268:17 297:21 300:23 322:11

368:13 387:14

substances 18:15 70:10 256:8 263:10 267:6,20

297:20 298:7,11,18,21 299:4 300:9,12 312:26

318:2 319:4 320:11 321:10 322:15,25 323:6,11

323:12,13,25 327:18 335:2,4 377:25 466:1,6

480:7

substantial 18:16 35:12 297:21,26 299:5 379:15

substantially 258:7

substeal 86:26

substitute 194:5 202:5

substituted 86:25 194:3 203:7

substitution 194:22 196:18 199:17,18 460:10

substrate 42:11 45:13

substrates 45:16

substructure 178:24

substructures 89:3,11,13 178:6

subtle 399:16 488:4,11

subtraction 382:17

subtractions 372:8 373:20

subunits 47:1,2,3 322:26

sub-groups 338:16

sub-models 59:23,25

sub-nanomolar 121:1 349:18

sub-questions 319:20

success 349:8

successes 472:15

sudden 180:9 252:12

suddenly 184:7,8

Sue 269:18,21,21 274:16,20,23 285:1 292:21

sufficient 283:22 322:20 372:17,22 377:8 430:23

435:17 437:17

sufficiently 354:17 362:24 363:1,3 365:15 366:12

366:14 372:2 384:22 385:10 412:16 441:22

suggest 149:7,12 252:18 308:21 332:11 404:6

458:14 490:10

suggested 144:19 202:18 328:3 341:3 431:21 437:2

suggesting 125:4 259:22 324:26 426:13 446:16

suggestion 206:18 281:11 307:11 308:1,6 327:15

416:2 417:9,19 422:8,10 444:3,13 448:1 455:2

491:15

suggestions 52:4 209:7 237:15 304:18 307:12

411:20 431:26 446:1 465:9

suggestive 149:17

suggests 164:5 412:17 415:14 429:6 458:23

suit 195:17

suitable 320:25

sulfate 162:20 163:3

sulfide 387:4

sulfonamides 199:9 201:24

sulfonate 73:1

sulfonic 71:1,20,20 161:22 177:14,22 178:5

sulfotransferases 387:5

sulfur 343:17

Sullivan 235:23,25,26 239:11

sulphate/PAP 387:2

sulphonation 387:1

sults 387:5

sum 394:26

summarily 485:4

summarize 118:12 154:15 164:21

summarized 28:24 250:19

summarizing 227:22 228:5 329:22

summary 4:20 21:9 44:18 82:19 94:10 112:16

116:6 118:21 121:15 155:23 156:21 192:17 198:2

203:18 204:8 211:3 213:11 215:6,9 269:9 278:9

278:24 336:16 434:6 470:23 485:23

summation 150:3

summer 22:15 23:4

summing 392:5

summits 272:3

super 297:25

superimposed 150:9 173:25

superior 243:9

supervision 179:1

supplant 443:7

supplement 22:3 370:15

supplemental 218:25 285:9

supplemented 28:16 368:10

suppliers 418:2

supply 323:19

support 236:16,17 239:1,2 247:24 253:10 276:15

303:7 315:8 320:8 372:22 379:15 396:21 449:22

supportable 44:16

supported 378:1

supporting 60:2 243:3 336:21 337:3 365:5 372:21

435:26 496:24

supportive 289:19

suppose 418:10

supposed 105:25 115:8 120:10 135:12 356:13

363:18 371:20

supposedly 113:22 257:13
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supraphysiological 280:20

sure 9:21 14:19 17:13 24:7,11 29:19 30:19 41:13

47:21,24 70:11 74:12 115:1 124:10,23 136:3

146:8 178:23 179:12,25 206:11 207:12 211:7

242:14 257:20 263:26 264:13 286:3 289:21

291:12 326:9 342:10 360:16 364:13 381:17

382:25 389:14 393:6,19 402:11,23 409:2,4,4,6

410:9 411:6,26 412:23 424:3 427:7 447:4,19

498:12 499:4,11

surface 33:2

surfactant 113:2,4

surfactants 112:25 175:19,24

surprise 190:26 349:22

surprised 134:16

surprising 78:25 163:2

surprisingly 43:23 80:10

surrogate 74:23 76:20 196:5

surrogates 228:26 311:23

survey 385:14

Susan 114:26 134:19,24,24 243:1

susceptible 321:7 338:6 436:10 478:4

suspect 201:15

SWDA 416:12

sweep 410:9

sweeping 410:12

Swiss 473:23

switch 155:22 156:9 252:12

switched 293:6

symbols 97:23 109:26 280:11

symptoms 471:24

synchronize 453:17

synthese 387:6

synthesis 36:25 125:22 268:9 364:17 387:1,1,11

485:21

synthesize 125:24 419:4

synthesized 126:5 309:23,25 369:1

synthesizing 314:8

SysTax 368:26

system 6:17 12:26 15:1,7 18:26 19:10 28:26 33:9

33:23 34:6,7,13,17,25 36:8 48:1,6,8 51:11,13,15

52:1,24,24 53:1,3,7,8,15,20,22 54:7,9,11,13,25

56:14,16 57:20 59:9,12,14,17,21,24 60:1,26 61:6

61:14 64:2,4 65:8,10 66:9,11,12 67:3,11,13,17

69:21,24,26 70:4,14 74:9 77:1,4,13,15 78:17 79:1

79:2,4 80:7,8,16,17 82:19 83:22 84:2,16 85:16,26

86:5,9 88:13 103:25 104:12 120:16 122:24

128:13,15,17,17,18 132:2 142:18,24 143:8 179:9

185:22 188:19 189:1,16,21 191:22 198:13,15,22

199:11 200:9 202:8,25 203:19,22 204:15 207:21

208:17 211:14 216:21 218:9 220:1,7 222:15,24

223:14,18,23 224:1,15,18 232:22 238:1,14

242:20 244:15 245:10,13 248:9,18 250:23,25

252:16 265:23 266:26 267:5,14,17,20,23 268:12

269:2,2 273:22 274:1 281:23 282:4 310:18 312:7

313:26 314:9 315:7 328:21 333:4,5,12 334:5,6

335:18,23,24 340:2,21 341:10,10,15 342:23

348:14,16 361:5 362:7 363:17,18 364:9,18,22

365:20 367:7 371:3 372:14,22 383:19 386:11

393:10,14 394:3 395:23 412:12 419:12 420:24

421:11 422:5 423:10 424:15,15,21,22 425:1,3,9

429:26 431:7 433:17 435:6,8,16,21,23 436:1

437:5,22 439:8,11 440:8,10,10 441:3,23,25,26

442:1,6,17,20 443:12 444:2,16,20 446:2,15,25

447:5,12,18 448:2,6,11,21,26 449:15 451:16

452:14 453:4,5 454:8,12,13 457:1 458:19 459:2

460:10,15,20 461:25 462:1 464:5,10,13 466:25

466:26 467:9,25 468:2 469:11,22 470:9,20 471:3

473:4 475:3,14,16 477:21 478:22 480:21 481:1

481:13,26 482:8,11,12,25 485:7 486:2,13,15

493:23

systematic 23:25 105:16 122:7 312:20 385:24

402:20 478:14

systematically 203:24

systemic 31:22 32:12 217:14 305:4 322:20

systemically 304:25 305:6 321:5

systems 18:11 19:11 29:14 33:1 34:20 65:19 66:4

81:17 142:21 145:24 182:10 198:24 207:5,9,25

208:5,19,19 209:14 217:19 218:10 221:15 222:22

223:24 238:20 242:18,19 245:15,16 315:25

328:18 332:10 338:24 364:20 388:15,15 416:1

442:19 461:3 465:4 467:2 469:4 474:9,13 475:11

475:24,25 476:8 478:5 480:24 482:21,24 485:9

485:11

System's 439:10

S9 114:8 286:14,14

T

T 106:12 387:6

TA 245:25

table 16:9 107:23 118:12,20 134:7 154:16 155:1,5

164:20 165:22 166:8 172:1,4 173:21 176:3

198:17 243:1 328:24 354:7 452:6

tables 150:4 154:14 156:21

tackle 12:23 204:5

tackling 203:11

tag 52:22 100:22

take 13:3 19:19 23:8 27:24 38:12,22 52:13 61:19

70:22 71:26 72:26 78:3 81:6 85:17 90:3 91:17

99:21 100:24 103:13 104:26 107:11 108:18

111:24 121:7 122:11 128:8,24 129:14 130:18

136:20 138:16 142:10 145:22 170:4 175:12 177:5
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178:8 193:3 194:20 204:8 209:10 211:25 223:9

223:22 230:22 237:11 238:24 242:20 245:5 247:3

247:6,18 265:9,16 266:22 277:2 291:20 294:4,24

301:25 302:17 309:23 322:22 333:1 337:13

342:16 351:2 390:1 398:11 402:22 413:24 416:20

433:25 444:3 490:16 493:24 494:2,3 499:5

taken 28:19 29:9 49:7 52:17 82:12 83:2 91:5

124:11 129:6 142:12 172:22,22 173:5,23 175:25

247:5 302:16 306:5 342:18 346:3 369:12 433:1

444:13 466:19 467:18

takes 151:2 217:22 224:21,22 459:13

talk 22:8 24:1 30:18 36:15 52:23 53:5,9,15 54:2,13

55:1 57:15 58:21 60:6 61:11 63:24 64:22 66:10

70:22 77:5,10 80:5,11 83:6,9 87:18 91:22,25 92:7

92:11 93:15,16 94:9,26 95:23 96:9,15 97:4,4,23

98:9 99:19 100:1 101:15 104:8,24 106:8 108:19

113:18 114:5,17 115:12,17 118:20 127:13,14

132:23 133:4,23 134:8 143:3 152:16 172:2 177:3

177:13 182:8 188:15,24 189:8 190:11,14 192:25

193:1 195:13 206:8 213:14 242:23 245:26 249:24

250:3,15 252:5,20 254:6 275:5 280:21 296:11

298:20 347:11 356:5 419:26 427:7 429:17 486:11

talked 11:16,20 12:12 45:22 53:19 58:13,24 59:23

62:17 64:16 65:14 66:6,6,8 77:6,10,15 79:23 80:1

80:22 112:3 113:5 114:2 143:6 144:11,19 152:1

152:19 157:1 160:21 161:22 162:16 167:12

169:26 175:20 177:20 196:23 202:10 211:23

226:21 230:11 278:8 285:1 294:25 358:13 401:3

414:20 420:11 450:8 474:16 491:23 495:11 497:1

talking 9:18 21:19 23:24 36:20 39:6 40:6 44:20

46:24 47:11 51:25 53:1 57:5 58:8 60:25 69:1

71:18 76:25 77:26 80:13 86:2,24 93:7,12 94:20

105:15 114:7 149:25 168:7 179:7 189:16 190:2

192:10 218:16 229:2 233:19 244:10 246:10

247:13 248:2 273:6 275:6 282:15 284:1 296:20

347:4 353:5 360:4 407:10 412:21 419:21 422:4

426:19,26 450:11 470:12 486:12

talks 54:24 83:5

tan 74:21

tandem 337:7 356:23 445:24

tapping 499:6

target 23:17 24:24 26:3 67:23 69:4 79:6,10 80:26

90:21 142:4 220:12 306:2 310:17 311:8 387:10

463:18,22 464:10,16 480:19

targeted 20:7 206:24 207:10 375:12 467:12

targeting 23:20 207:6 404:16 415:15

targets 129:26 130:1 306:3

task 196:10 392:26 434:2 498:7

tasked 126:7,9

teaches 6:21

team 14:23 16:2 52:22 356:14 500:16

tech 6:20 39:15,16 214:7

technical 5:5 88:7 99:7,18 107:6,19 118:22 122:19

152:22,26 260:7,20 283:5 296:10 303:4 479:5

technique 62:13

techniques 11:15 13:7 307:2 317:4 372:9 373:21

375:9,18 455:26 457:13 473:24 474:16 480:18

technologies 13:8,10,24 14:5,6 21:26 23:17 24:18

24:23 28:7 29:12 36:11 120:15 131:14 132:1

330:15 393:11 481:2 499:3

technology 11:24 12:9 22:7 25:4 28:12,23 34:4

111:10 113:12 138:13 244:17,17 413:19

technology-specific 138:10

Ted 264:5,5,7

teleconference 5:3

Telephone 1:9

tell 45:23 53:21 83:4 89:11 91:12 138:7 160:17

178:9 208:5 226:17,26 271:16 282:18 298:22

299:14 356:9

telling 154:6 220:23 348:20

tells 78:26 82:16 182:5 257:4 348:16

template 210:10

ten 231:4 259:21

tenant 256:7

tend 138:19 169:2 184:2,4 458:21

tends 99:16,17 155:14

Tennessee 7:6 214:13 391:12

term 23:26 45:10 99:19 223:20 238:9 240:2 251:23

252:9 270:22 374:15 404:11 427:26

terminology 305:16

terminus 479:17

terms 9:19,22 14:24 15:17 19:20 21:13 23:15 26:4

30:16 32:11 34:20 35:11,17,18 36:12 37:1 45:19

47:15 49:1 51:1,16 52:9 87:6,7 88:26 89:2,22

107:19 128:20 138:4,24 179:19 185:8 195:26

217:10 222:7 224:7 227:16 236:5 240:8 243:4,22

244:21 245:20 246:4,11 267:19 268:11,21 297:23

298:5,26 306:13 311:6 312:3 323:17 328:6,11

330:10,14 331:8 336:4,7 340:4,5 341:7 347:11

351:25 352:11 353:14 358:13 367:11 392:5,7

393:2 400:9,13 404:9 412:1 419:21 421:15

423:11,12 429:17 433:5,8,16 435:4 437:23 446:5

464:20 466:16 475:22 481:19 489:8 491:15

495:12,24 497:10,11 498:25

terribly 97:17 117:24

territory 470:1

Terry 7:5,5 37:25,25 49:11,11 83:14 84:23 85:15

167:21 168:9 169:4 170:6 204:19,19 205:16

214:12,12 258:26 295:26 296:1,3 302:9 316:11

331:20 339:22 344:23 358:24 370:7 374:11
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442:13 448:23 454:7 456:4 462:8 464:1 467:5

477:8 482:10 486:10,11 490:19

tertiary 194:1 360:7

test 18:10,11,11 19:11 20:5,23,25 21:8 27:10,19

35:4,4,15,20,20 37:15 39:12 61:23 62:8 65:2,2

71:10 73:23 74:12 76:3 81:18 85:19 86:10 95:6

97:24 101:26,26 102:2 103:2 107:4 109:9 114:23

125:12 135:7,7 139:18 140:10 147:9 148:12

152:5,24 156:24 159:21,22 162:17 172:24 193:18

193:21,22 194:9,16,17,18,23,24 195:2,9 198:6

199:19 200:20 201:18,18 203:26 204:5,11,13,24

205:20 207:23 208:5,7,12,22,23,25 209:17

222:17,20 230:9 231:17 234:26 235:2,5,11,12,15

236:24,25 241:22 245:3,5,8,13 246:14 256:20

267:9,14,15,19,22 282:20,20,23 304:4 324:17

328:17,21 356:21 366:22,24 367:2 368:12,25

369:1,6 370:10 372:19 377:21,22 386:23 404:11

417:9 418:5 424:25 425:5 427:25 436:15 438:12

439:18 468:23 469:6,7,19 480:6,8 481:23 487:20

498:6

testability 41:11 45:3 312:11

testable 31:21 56:18 81:16

tested 39:13,20 69:8,17 74:25 77:18 81:16 91:26

93:6 101:21 103:3 144:25 145:6,7,16 146:26

154:25 155:2 164:2 169:23 174:3,10 178:12

197:13 198:23 199:2,7 201:7 204:12 228:2,7

283:14 309:25 335:6 337:15 364:1 365:21 366:8

368:14 369:7 379:14 382:4 387:14 406:23 416:11

430:14 434:22

testing 11:18 18:2,20 19:8 20:4,7 23:17,22 28:3,24

31:15,15 32:4 34:26 36:22 39:14,25 41:13 52:6,9

58:19 61:25 64:20 71:24 72:8 74:23 75:6 85:16

86:18 88:15 93:2 102:17 107:21 131:18 154:25

160:1 189:10 192:26 193:9,12 197:22 202:4,6,13

203:24,25 206:24 207:22 208:3 209:18 221:6,20

222:5 228:2 230:6 236:3 240:7 241:14,23 246:18

248:4 255:22 297:16,19 300:26 304:25 305:5

306:22 308:7 311:2,11 312:16 313:12,20 314:2

320:1,3,10,16,25 321:21 323:25 324:19 327:14

327:22 333:6 335:14 339:2,8 345:10 356:7

375:12 387:14 393:2 418:13 423:21,21 426:7

429:18 433:15 434:17 435:7 439:14 450:19

452:15,23 453:2 456:21 465:17 467:12 469:8,17

471:5 478:23 482:11 483:13 493:25 498:9

testosterone 224:14 256:25 257:4,14

tests 31:6 41:12 233:21 234:15,17 235:7 241:10

243:18 307:13 334:26 343:13 350:16 386:19,20

387:4,8,10,11,13,17 439:23,23,25 465:4 478:10

486:2 487:7,10

Texas 8:6 214:24 391:23

text 275:9 285:8 322:5 331:17 434:3

TG 377:23

TG457 139:18

thank 2:14,16 4:25 5:8 8:17,25 9:2,5,10,12,20,24

10:1,7 15:25 16:21,24 37:2,4 39:3 45:5,21 49:11

52:12,21 66:13 83:12,14 88:25 89:20 91:1,4,9

122:13 129:11 132:16 142:16 167:19 170:6

179:15 180:23 188:7,14 204:15,17 206:19 209:24

210:25 212:7 215:3 227:24 233:6,16 235:17,19

235:21,22 239:10,11 246:26 247:2,10,11,15

253:1,3,6 260:1 263:13 264:3,7 269:15,17,18

274:13,14 279:6,7,8 282:7,8,9 295:22 296:1

302:6,7,8,9 303:11,13,13 307:21 310:13 313:5

316:9,20,21 317:18,19 318:7,13 319:14,21

331:18,25 332:22 334:1 339:13 341:20,21 343:2

344:17 347:6 360:14,15 362:8 369:24 374:3

375:23 376:7 377:14 384:15 388:2 389:9 390:9

392:3,9 393:18 394:6 409:9 438:3,19 439:3 442:7

445:14 449:20 451:4,6,12 454:21 455:21 456:4

462:6 463:25 466:8,12 467:5 472:19 476:25

486:2 488:12 489:6,17,18,19,21,26 490:17 491:4

492:4 493:16 494:3,14,16 495:5,5,22 496:7,10,11

496:20 497:21 498:1,1,17,20,22 499:16 500:13

500:19,23 501:3

thankful 496:2

thankfully 242:21

thanking 493:13

thanks 5:10 8:16 9:15 16:14 88:4 123:2 126:13

134:10 141:13 186:22 188:6 206:8 213:17 225:8

253:6 264:5 269:20 274:25,26 282:10,14 290:9

295:23,25 319:12 332:24 341:25 344:21 354:12

374:17 382:12 390:14 393:21 412:2,3 433:18,20

451:3 452:1 466:9 472:22 473:17 474:21 475:19

479:2 481:15 486:5 499:12

theme 11:19 13:11 221:7 311:2

themes 497:9

theoretical 306:20

theory 176:9 195:18 225:25 434:9 459:15

Thera 95:23 100:5 115:18 118:17 132:26 139:22

229:11

thereof 324:7 326:2

thing 63:12 64:16 82:26 86:20 97:14,24 99:6 106:1

120:8 126:15 127:16 136:15 146:8 149:9 154:21

156:19 162:14,18 163:16,20 172:7 193:19 195:3

206:17 211:5 219:9 221:5 226:11 228:17 240:22

242:2 243:8 244:16 248:5 260:26 296:25 301:2

315:17 346:2 349:6 359:26 403:3,5 407:16

408:25 410:6 412:25 413:4 417:15 475:7,8 476:7

480:14 484:23
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80:16 86:14,16 89:7,24 104:4 105:5 109:3 112:20

117:11 119:19,24 121:1 124:1 127:17 137:14

138:11,24 144:7,12 148:9 152:17 154:4 156:5,20

156:23,26 157:1 159:3,16 160:3 162:23 163:6

164:21,21,23 167:24 168:6 170:1 178:9,26 181:9

181:20 182:2,11 183:8 187:13,26 200:14,26

201:17 207:7 211:15,18 224:6 228:11,15 231:1

240:2 241:11 246:17 248:17 249:13 250:11 251:7

251:25 252:15 253:12 254:17 255:24 256:6

257:24 260:16 261:24 262:6 271:6,21,23 272:2,8

272:13 289:17 290:1,3 292:23 293:6 298:16

299:3,7,17 301:8 304:15 314:8,8,17,21 315:16

316:19 321:1 347:12,18 352:12 359:25 360:5

388:6 389:21 392:6,6 397:17 402:18 406:14,17

410:11 413:21 414:12 419:11,18,19,26,26 421:14

428:6 433:4 443:6 446:26 450:22,24 457:21

458:3,21 462:9 467:21 468:3,21 471:26 474:25

481:6 486:20 487:8 490:16 492:20,21 493:24

497:7 499:21 500:8,8

think 10:11 11:25 13:11 17:12 24:23 25:20 27:7

31:13 32:18 38:21 39:9,11 45:26 46:4,23,26

47:20 50:8,21,22 51:22 66:9 70:21 71:25 72:5,24

74:25 76:22 77:6 78:16 80:22 84:7,11 85:8 86:13

86:21 87:13,13,21 88:1 89:10 91:15 93:1 95:6

100:8 105:26 108:1 109:24 112:1 116:22 121:4,4

123:26 126:24 127:16 129:9 130:11 134:3,20

135:19 136:14 139:8 143:22,24 144:2,11 148:21

149:5 152:4,21 153:19 155:5 156:26 159:26

160:15 161:17 164:20 165:22 166:13,17 167:5

168:13,14,19,21 169:15,20,22 171:4,7,13,14

173:17,20 174:21 177:19 178:7,12,20 179:3

180:1 181:13,20,22 182:1,2,11 183:6,6,9,12

184:25 185:23 186:7,8,10,13,19 187:13 188:2,11

196:17,24 197:4,5 200:14 201:8 205:17 206:16

207:23 208:4 209:16 216:11 225:2 226:6 229:26

236:8,11,15 237:17,21 238:5 239:3,19 240:19

241:17 244:3 245:11 246:19,22,23,24 247:12,24

248:5,7,19,21,25,26 250:4,12,17,18,20 251:1,22

251:25 252:1,17 253:8,9,10,11,18 254:11,12,12

254:14,17,20 255:2,11,17,22,23 256:1,2,10,11

257:25,26 258:10,17 259:18 260:18,20 261:8

262:13,16 264:21 266:12 267:5,7 268:4 269:7

270:20,21,25 272:17,22 275:21 276:7,20 278:25

279:2 284:16,24 286:20,24,26 287:16 288:1

289:2,4,10,16,20,23,26 290:7 292:19 293:13,14

293:24 298:8 299:9 301:9 302:12 303:14 304:13

304:23 305:25 306:8,21 307:24 308:14 309:2,4

309:13,15,19 310:15,17 311:2,3,5,11 312:6,17,20

313:7,8,16,20,26 314:7,12,17,20,23 315:2,6,13

315:18,22 316:6 317:23 318:2 319:16 325:8,20

327:17 328:9 329:18,22 330:7,18 331:3,10

335:16 341:26 342:9 344:26 347:25 348:4,9,10

350:23 351:2 353:16,22,26 354:4,5,21 355:24

356:3 357:7,15 358:7,8,19 360:3,8,20 362:5

363:11 366:2 370:22 375:18,24,26 376:20,23,25

381:18,21 382:2 388:13 389:7,8 394:7 396:3

398:1 399:24 402:16,24 403:16,18 404:3,4,5,14

405:1,4,18,20,25 406:8 409:3,7,12 410:5,23

411:1,4,6,12,22 413:10,12,23 414:1,26 416:19

417:14 419:3,5,7,17,24,26 420:4,14 422:4,18

424:10,17 425:22 426:14,22 427:5 428:1,7 429:7

429:21 431:3,17 432:21 433:24 434:4 435:1

438:9,16 442:18,21,22 443:13 446:6 447:8

449:13 450:17 451:1 462:11,23 463:8,24 464:20

467:16,22 471:24 472:14 473:5 474:1 475:12

476:11,14 480:10,14 481:14 491:2,3,10,13

492:24 493:8,26 494:9,11,23,24 495:10,13,14,21

495:25 496:26 500:9,21

thinking 13:16,20 46:4,11,17 52:9 96:5 127:14

144:7 164:17 177:14 218:7 231:2 248:14 252:19

252:25 253:9 268:21 372:18 421:7 461:23 491:22

491:25

thiol 158:5,16

thiols 176:14,16,21

thiophosphate 191:24

third 40:9 89:21 191:6 202:14 366:9 390:3

thirdly 346:22 478:12

Thirty 166:6 189:25

Thomas 7:24 124:6 125:19 135:22 214:20 225:19

290:12,22,25 291:14,21 292:2,11,14 307:23

349:26 370:3 374:7 375:24 380:15 384:4 388:18

391:19 395:6 400:4 404:3 417:1 422:18 428:18

472:24 479:4 486:7 493:10

thorough 260:12

thought 14:16 49:14 66:14 80:20 89:18 127:9

204:20 210:2 242:26 243:5 253:13 255:18 258:11

265:19 306:16 309:9 335:11 339:18 350:17

365:18 366:16 367:14 369:10 406:5,6 416:2

417:20 492:22 500:9

thoughtful 434:11

thoughtfully 310:18

thoughts 13:21,26 224:19 236:11 354:1 355:15

393:14 400:26 445:22 455:25 456:7

thousand 35:3,4 237:10 281:19

thousands 13:3 94:21 122:6 216:17 227:16 236:6,7

427:25

threat 298:17 300:17

three 5:2 9:7,16 41:25 71:6 74:11,11 79:26 87:7

88:7 91:20 95:5 96:18 99:15,22 103:20 107:6
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158:17 160:19,21,23 161:14 165:3 171:23 180:12

193:10,12 195:20 196:7 198:13 199:12 203:6

204:15 205:6 227:10,14,17,18,20 229:13 264:12

266:26 269:10 271:12,13 272:25 273:7 277:9

281:7,14 284:14,20 285:6,14 286:9,13,15 287:4

291:10 320:26 327:4 330:25,26 336:8 382:4

397:18 428:20 435:22 445:2,5 457:19 496:21

three-dimensional 88:10 463:11 473:24

threshold 68:4,4 110:4 116:15,16,16 117:4 126:17

135:24 136:7,24 137:3 151:26 153:20 369:14

397:20,24 421:25 437:19 445:4 486:19

thresholds 106:8 383:10 385:11 437:16 453:10,11

threw 75:18 203:4 224:8

throat 391:5

throughput 249:26 274:7

throw 71:19 122:17 126:26 127:17,21 138:20

177:25 178:2 353:13 450:4

throwing 126:21 199:13

thrown 354:10

thumb 322:1,4

thumbs 85:23

thunder 83:24 87:17

thyroid 19:4,11,23,25 29:26 30:1 223:26 224:1,4

242:19 246:5,8 387:3,10 467:2 470:2,2 471:15,16

473:25 476:2,8 482:8,12,20,23,24 483:9,10 484:9

484:10,11,12,12,14,25 485:11,20,24 486:23

487:3 488:17,19

thyroids 470:15

thyroxin 484:21

tick 107:20

tickle 136:24

tie 498:12

tied 425:14

tier 19:4,5,8,11,15 20:1,4,5,6,8,9,23 21:8,13,15

22:16 23:1,3,20 24:25 25:2 27:20 30:11,17 31:6,6

33:26 35:9 52:11 135:5 230:12,16 235:2,5,7,11

235:12,15 237:2,9,10,20,26 238:6,8,14 239:6

241:11,13,21,22,23,26 245:22 246:3 248:4

254:26 255:1,22,22 258:3 274:6 282:6,16,20,24

283:20 286:1,8,9 295:13 300:1,25,25 301:3,4,22

308:3,4 396:25 397:11,21 398:25 399:2 404:7

415:3 448:9 486:17 498:4,8,11

ties 68:26

tight 106:16 107:18 226:7

tightly 139:4

till 52:16 142:11

time 2:17 3:23,25 5:14 8:18 9:17 10:2,5,7 11:14

21:23 22:11,15 27:19 28:11 30:19 33:12 38:7

46:10 59:16 68:9 73:19 84:3 86:2 91:3,22 92:6

94:19,23 101:25 105:15 108:12 109:3 110:18

116:24 123:24 130:25 139:7 144:4 145:15 152:21

195:20 202:10 205:17 209:1 210:8,14 224:21,22

225:9,17 226:23 227:16 240:17 242:21 246:1

247:3 253:25 255:21 258:23 260:12,18,26 261:8

261:9 265:9,10 266:5 273:20 274:9 281:16,21

282:7 283:16 289:22 290:7 291:5,6 292:20 293:5

293:21 302:12 309:23 320:17 321:6 323:8 326:21

330:19 348:12 349:10,14 353:24 359:23 365:9

372:19 389:9 403:16 414:25 418:13 432:25

435:17 437:4 465:13 467:23 474:17 481:7 485:4

488:12 491:25 495:22 497:22 500:12,25

timeframe 497:26

timeframes 25:6,6

timeline 17:21 20:19 496:26 497:4

timelines 497:1

timely 12:19 221:2 498:8 500:4

times 31:8 182:7 242:5 243:24 279:18 286:13

287:21 291:15 331:15 382:4 403:4 454:18 467:7

timing 260:11,26 444:2

tiny 129:25 322:11

tip 157:21 270:23

tips 146:19

tired 370:15

tissue 31:24 41:12 42:8 43:12 45:3 49:2 55:19,20

61:22 62:11 222:9 358:17 463:9 484:2,2

tissue-level 57:19

tissue-specific 462:12,13

title 280:7

today 13:1 14:10 16:7,10 17:1,8,9,23 18:5 23:7,10

24:17 26:4,18 27:25 28:5,11 29:22 31:8 32:14

36:12 39:16 40:6 47:21,26 54:24 93:7,12 114:7

134:9 137:8 138:22 169:1 183:21 209:21 211:12

213:3 233:12,17 247:12 248:2,19 249:2 260:20

264:12 268:22 275:6 276:4 282:14 309:9 340:16

383:6 389:10 421:7 474:10 476:11 497:2

today's 36:26 94:13

token 86:4

told 221:18 228:14 232:7,8

tolerate 216:16 473:7

tolerated 497:13

toluene 158:16

Tom 7:24 124:6 125:19 214:20 342:4 391:19

tomorrow 4:3 94:11 97:23 140:15 188:10 211:24

389:11,12

tonight 211:25

tool 29:1 33:11 120:11 253:20

toolbox 7:9 67:4 84:18 250:25 330:16 457:5,7

tools 1:5 2:9 5:13 8:4 11:11 14:4,5,7 21:10 22:7
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124:17 136:3 213:7 236:13 237:1 239:25 248:1

309:15 336:2,12,13,13 432:18 436:6 483:14

top 17:21 30:23 65:23,24 66:4 73:25 95:17 96:17

99:24 106:12 113:14 115:25 116:14 119:13,15,21

150:14,17,26 151:7,22,25 155:9 161:3,4 172:11

172:14 199:8 200:25 213:9 279:20,26 281:20

323:1 433:7 457:19,19

topic 8:26 14:15 217:24 247:17 261:18 271:7 491:7

topics 11:9 53:1 169:1

total 63:12,16,18,26 64:3,5,10 78:22 79:16 91:25

93:5 101:23 104:5 131:14 144:12,13 148:17

151:9,10 153:2,4 284:2 313:24 467:18

totality 35:6

totally 304:2 492:9,15

totals 30:24

touch 11:13 17:16 142:19

touched 156:23 355:24 387:24

touches 153:19 464:3

touching 66:22 476:11

tough 133:13 491:3

tougher 201:25

tower 306:9

tox 17:6 23:9 26:10 29:5 124:17 233:19 234:5,14

235:16 262:16 279:23 280:5 364:11

ToxCast 92:25,25 93:8,10,23,26 97:9 101:19

103:19 131:9 190:2 236:23 248:18 271:10 273:12

273:13,15 279:19 280:8,11,13,14,26 281:13,18

282:5 283:25,25,26 291:5 292:23 293:5 380:2

404:20 418:15 469:7 479:12 486:22 493:7

toxic 305:11 337:12 471:23

toxicity 13:13 93:2 157:8,8 160:11 220:3 336:24

337:5 351:4,6 362:4 383:15 409:11 429:19 452:4

456:12 473:16 497:15

toxicological 56:3 85:19 136:5 305:13

toxicologist 6:3,20 7:13 8:13 275:4 390:24

toxicologists 46:2 47:20

toxicology 1:5 2:9 5:13,24,25 6:6,26 7:2,4,19,22

8:15,15,16 9:18 11:11,15,21 12:6,6,16,16 14:13

17:19 27:26 91:8 93:11 102:10 213:7 253:4

264:10 390:22 393:7 439:18

toxics 17:3 47:19 230:18

ToxRef 282:5

Tox21 21:7 28:21 92:25 93:9 113:19 118:15 119:3

121:9 126:2 134:4,5 156:14 191:1 364:5 366:19

469:7

TR 482:19

tracking 105:5

tracks 401:2

trading 227:15

traditional 130:2 234:16 341:4

traditionally 310:21

trail 169:2

train 132:1 371:3

trained 281:20 396:26

training 34:17 36:18 54:20 57:15 59:8,13 61:19

64:19 68:3 69:5,9 73:10,23 74:24 87:13 90:20

142:19,24,26 143:6,8,13,25 144:1 145:23 146:11

147:6,7 150:7 152:22 154:15,18 155:2,7,13,24

156:17 164:12 165:1,3,7,20 166:1,2,7,8,17,21,24

167:2,17 171:22,25 172:19,21 173:5 179:8

188:22 193:26 195:22 197:15 201:8 210:1 218:9

219:15,18,26 220:15 222:11,19 267:9,11 279:16

334:18 337:8 342:23,25 343:25 352:24 353:1,11

354:24 358:8 393:13 394:2,10 395:23 398:9,11

412:9 414:2 415:18 420:7 433:15 434:10 436:19

437:3,7,12 439:8 441:14 451:21,24 452:14,16

453:18 455:3

trajectory 27:22

trans 87:14 101:4,8,12 120:16 128:13,17 196:21

transaction 144:20

transactivation 100:4 115:18 123:16 132:2 140:2

143:10 156:11 165:17,24 166:18 170:10 171:2

175:19 183:26 184:3,8 206:5 220:18 258:8 268:8

274:5 283:12 285:19 288:3,4 348:12 358:10,15

362:21 364:1 367:10 400:15 432:14 447:21,24

450:22 480:5

transactivators 358:12

transcription 95:14,22 98:3 100:9,20,26 101:5,9

128:2,4,6,9,12,16,22 129:1 130:15 142:2,6

479:18

transcriptional 276:17,19 371:10 396:20 420:22

transcripts 101:3

transdisciplinary 24:4

transfect 101:8,9 141:10

transfectant 396:12

transfer 343:17

transferability 121:4,5

transferred 469:3

transformation 63:6 117:21

transformed 457:18

transiently 101:9

translate 57:18 62:12

translation 239:2

translational 85:18

translocates 95:13

transmitted 261:6

transparency 11:4 26:6 58:2 69:23 89:4 217:22

218:6,7,19 234:22 250:17,20,21,23 251:2,22

265:13 289:4,6 292:26 293:15 304:12 305:9
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transparent 10:26 24:8 26:17 29:8 67:11 88:13

89:19 217:21,26 242:12 250:26 266:18 269:14

290:6 315:14 317:13 325:24 334:10 336:19

366:20 375:10 414:10 438:10 440:20 467:12

468:14 469:17 471:1 497:16

transparently 303:3

transport 368:4

Transportation's 43:18

transporters 483:26 484:1 486:24

transports 321:8

travel 9:23 500:18

treated 144:18 145:3 321:20

treating 448:4 450:26

treatment 349:8

tree 59:12 60:1 66:15 67:8,22 68:12 75:12 76:2

84:10,14,17 86:20 88:9,22 89:6,23 90:2,4,10,26

190:21 222:24 233:25 234:3 334:11 335:10 347:2

347:2,3,3 372:14,23 404:9 421:20 425:13 444:16

trees 347:6,12 415:13

tremendous 496:4

trend 176:6 184:6

trending 28:1

trends 176:5

TRI 299:7

triangle 83:21

tricky 118:10

tried 54:16 73:25 77:19 103:26 126:8 183:6 205:10

230:24 237:15 247:21 329:18 500:22

trigger 128:26 319:4 327:9 392:22 456:1 464:21

triggered 322:8

triplets 414:12

triplicate 112:9

tritiated 150:12

trivial 129:8

trout 33:19,21 64:2 65:16,21,23,24 69:2 73:10,12

145:1,12,13,24 148:12,15,19 150:1,8,20 151:3,4

152:9,11,18 154:25 156:14 157:12 159:9,21,22

160:10 162:2,21 163:26 164:6 167:25 168:4,10

170:9,12,19 171:8 173:23,24 174:5 175:18,24,25

176:9,10 195:22 201:8 220:15 232:15 267:1,18

268:3,7 269:4 280:1,18,23 281:1,10,13 284:22

285:22 288:14 334:8 337:5,16 353:3 354:25

356:22 357:14 359:1 360:25 361:4 364:17,26

372:14 392:17 394:13,17 397:15 401:7,24 402:4

409:17,26 410:2,12,13 429:2,6 448:25 449:11

452:16 460:16,19 461:11,16,22 467:9

trout's 267:1

TrSet 443:21 448:12 451:16 455:4,10

true 76:19 101:13 110:19 111:16 118:10 139:12

147:16 163:8 257:7,14,22 268:24 273:24 294:5

294:10,12,13 301:14 417:11 477:15

truly 110:19 147:15 159:14 294:19 302:5 352:7

403:17 470:7

truncated 75:7

trust 103:11 111:23 120:8

trusted 102:7

truth 379:11,16 381:22

try 11:23 12:12 13:3 14:20 38:22 52:25 55:8,14

56:9 62:19 76:4 77:9,24 89:16 99:20 102:1 103:9

112:1 115:1 147:9 154:5 167:6,10 180:21 182:8

186:12 193:21 206:24 207:2,25 210:13 235:17

241:9 255:5 291:13 293:16 301:11 348:24 349:10

352:14 356:13 359:20 418:11 420:5 421:21

423:10 424:1 426:14 427:10 462:26 476:3 492:6

499:4,11

trying 11:19 12:3,7 13:18,19,24 29:19 38:14 39:12

48:17 49:8 57:24 60:19 75:18 87:13 89:19 90:22

122:4 125:14 133:12 139:7 154:5 159:15 175:7

179:12 182:2 183:7 185:10 186:11,17 188:18,20

197:11 200:12 207:16 210:2,11 221:14 222:1,2

223:1 224:2,4 230:4 242:6,15 247:2 252:18

258:13 259:23 267:11 276:12 283:6 292:9 294:22

295:14 304:11 311:14 315:1 347:12,24 351:13,15

351:16 355:19 356:5 396:5 398:17 402:6 403:7

406:7 410:26 411:7 416:11 420:14 425:20 426:10

430:1 483:2 492:21,24 495:26 499:5 500:6,8,10

tumor 316:2 349:7

tune 215:26

turn 16:11 44:14 66:10 67:19 76:22 105:9 108:5

109:19 136:13 178:17 183:13 195:14 211:9

213:15 327:4 388:15 390:11 467:14 478:22 481:6

496:17 499:12,14

turned 199:11 286:8 298:6

turning 8:24 9:10 109:6,16,22 252:11 355:2 443:3

turns 60:12 108:14 112:13 129:15,18 300:24

364:16

tweak 127:17 223:17

tweaking 383:18

twenty 231:4

twice 258:6,9 287:18 293:4 327:16

two 18:7,17 27:14 33:13,18 34:8,12,20 35:21,24

40:7 45:8 53:10,24 54:13 57:14,22 60:24 64:23

65:3,18 66:12 69:24 70:5 71:4 73:19,23 74:9,24

76:18 77:2,6,11,15 78:17 79:4,12,13 80:2,7 81:12

81:12,15,15 82:14,24 83:14,19 86:1,11,24 87:5

91:20 92:23 93:26 94:1,1 95:5 98:7 100:12,12

102:7 105:4 109:25 110:14 113:23,24 115:16

117:2,6,7 118:3 119:6 120:14,14 121:26 123:5,8

123:9,15,18,18 129:20 132:26,26,26 133:6
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137:18 139:23 140:16 142:21 144:2,20 150:15,17

150:19,26,26 152:8,9,12,22,23 154:14 156:13

157:23,26 159:18 160:5,9,10,17 161:2 163:7,11

163:12 164:20 165:12,23 166:11 167:24 174:2

176:24 184:14 185:11 187:14,16,18,23 189:1,4,6

189:11,17,26 190:1,2,19,25 191:13 192:22 193:8

194:3,4,8,14 198:9,13,19 199:12,20 200:9,17,22

202:17,25 203:15 211:17,19 223:14 226:16

229:11,13 260:22,23 262:25 263:21 264:12,13

265:12,19 266:3,15 268:14 271:14 273:8 276:17

277:25 278:5,6 281:15,17 282:5 283:2 284:14,15

284:17 285:2,3,15,20,23,25 286:15 287:4,17,25

288:2 290:16,17,20 291:6,7,10 297:26 310:8

336:7 337:20 338:5 348:2 361:24 365:4 369:22

378:15 382:4 388:10 395:25 396:24 397:7,18

400:14 406:3 407:21 413:6 418:2 428:6 429:12

436:9 442:19,24 445:5 449:6 452:18 458:25

492:19

Two-Generation 20:10

two-tiered 19:7

type 60:18,26 61:8,16 66:19,20 86:19 87:22 128:18

136:2 142:2,21 143:19 147:16 151:11 153:9,23

158:11 171:10,16 177:1 178:7 182:12 189:3,22

195:5 196:16,17,17 197:7 202:26 226:5,13,19,19

226:26,26 290:18,19,26 309:3 314:14 328:9

348:19 349:13 366:23 367:13 376:5 408:25 427:9

456:11 460:25 462:15 463:3 485:1

types 58:19 60:13 61:9 62:20 66:8 80:21 144:8

145:26 150:2 161:19 167:6,7 168:8 170:1 178:15

210:15 219:13 316:19 352:14 359:20 375:19

378:15 396:7,7 404:15 413:11 414:22 450:24

454:16 463:9,9 473:13 478:4 491:1

typical 88:17,19 349:12 478:1

typically 40:25 46:26 94:24 99:9 104:25 105:8

111:6 126:24 252:13 322:17 351:19 439:16 440:7

440:21 479:8

typology 444:18

t-butylphenol 165:21

T3 484:2,20 487:4

T4 484:1,2,20 485:21 487:4

U

ubiquitous 407:13

ultimate 54:1 351:3

ultimately 11:2 106:24 135:19 234:14 271:20

315:12

umbrella 29:4

unable 319:4 422:23 480:26

unactive 321:11

unambiguous 59:26 373:5

unanimously 373:2

unavailable 289:5

Unbiased 383:19

uncertainly 329:11

uncertainties 124:25 191:21 281:24 402:19 412:11

427:1

uncertainty 24:5,5 25:14 56:2 147:5 148:7 152:7

156:7 216:10,16 310:26 319:6 329:6 341:18

351:25 352:18 436:17 456:16 470:25 473:8

497:13

uncharged 42:16,26 43:2,8 51:2 264:24 265:4

unclassifiable 335:3

unclear 270:13,18 383:2,4 422:24 437:14

uncovered 460:9

undefined 78:2 322:15 323:12

undergoing 20:17

underlies 15:7

underlying 13:17 14:26 234:16 341:12 448:2

understand 24:11 25:26 26:2 27:25 34:18 55:12,13

55:25 57:4 58:10 60:19 62:19 77:8 93:24 118:11

125:2 127:21 154:5 167:10 178:23 179:7 183:7

184:22 187:1 192:12 219:11 220:21,22 224:11,17

224:17 226:23 242:15 244:4 245:13,14 249:9

253:20 257:7 262:22 263:20 275:23 276:26 280:2

283:7 295:17 306:7,9,26 312:5 349:23 359:24

360:20 393:5 399:7 402:25 410:20 411:5 418:13

420:6 422:11,12,20 426:15 428:24 431:9,11

443:20 447:19 448:4 456:15 462:15 499:23

understanding 15:6,10 24:5,10,21 30:1,16 47:25

53:7 56:3,20 58:15,23 59:12 62:18 92:16 124:21

131:19 163:21 181:13 182:18 186:4 220:20

221:23,26 222:2,13,18 224:9 226:3 227:18

251:26 271:23 292:7 311:13 320:23 328:6 334:12

357:2 367:21 401:22,23 411:2 432:12 434:1,4

448:18 460:5,5 487:2 497:16

understands 261:22 304:20 467:8

understood 28:14 129:21 217:26 263:26 295:8

352:26 431:1 432:1 486:18

undertake 297:15

undertaking 9:19 11:10 12:14 262:23

unesterified 75:1

unexpected 308:11 346:17

unfiltered 176:4

unfortunate 260:19

unfortunately 126:21 283:16 288:11 289:3 298:4

ungrouped 435:10

unhappy 109:20

uniform 170:23

unintended 261:24

unintentionally 262:2

Union 490:1,15
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unique 26:26 81:5,7 101:24 333:11

United 1:11 35:14 230:14 231:19 239:13

units 163:12 476:17

universal 36:21 50:23 319:24

universe 1:4 2:7 5:12 15:2 22:5 23:8 24:19 25:26

26:2,19,20,22,25 28:6 30:23 33:24 34:7,22 39:10

53:11 54:2,10,11,14 77:3,12 186:13 197:25

198:18 202:13 213:5 296:6 297:1 298:20,21

299:9,15,19,22 301:17,19 309:19 336:11 390:5

406:8,14,23 436:18 456:19 483:8 487:14,16,17

492:12,13

universes 417:13

University 5:18,23 6:9,15 7:3,6,13,17 8:6,9 63:17

213:22,25 214:2,5,11,13,15,16,25 227:8 324:23

390:18,21 391:1,5,10,12,14,16,24,25

unknown 36:22 54:15,18,20 67:24 70:16 71:8 80:6

80:11,26 82:26 86:17 90:24,25 188:17,21,26

198:3 204:10 313:1 317:5 338:25 340:7 352:3,12

435:24 436:2,14 437:13 474:15

unknowns 77:11 81:2 82:9 83:2,10 189:1,18,20,23

189:24 190:7,22 191:26 192:5 193:11 194:7

195:11 196:4 204:14 344:8

unlikelihood 361:23

unlimited 206:23

unnecessarily 307:5

unnecessary 488:17

unnoisy 118:25

unreasonable 309:13 394:23

unregulated 27:4

unrelated 437:1

unreliable 352:11

unspecified 311:7

unstable 43:14 265:5 320:5 324:17

unsuitable 327:14

unsupervised 352:13 436:13

untestable 319:4 327:18

untested 189:9

update 22:11,12 245:17

updated 22:10 31:2 135:7 465:11

updates 211:11

updating 22:24

upgrade 252:2

upper 143:24 151:15 154:16 264:22

uptake 158:13 368:4,7

urge 247:18 317:22

urgency 418:8

usable 49:3

use 11:12,19 13:24 17:18 21:22 22:5 25:14 26:13

27:2 29:3 33:12 36:8,9 40:25 45:9 51:2 53:17

54:3,3,19 56:9 59:16 61:15 65:12 67:17 69:25

70:1,2,6 74:23 77:13,14 78:4,7,23,24 79:3,12,21

83:7,10 84:5 86:10 95:5 97:1 98:20 106:4 111:1

115:21 121:20 124:21 127:23 131:20 136:22

143:16,18 144:17 148:12,21 171:26 173:8 176:14

176:15,18 185:1 188:22 192:25 198:5 202:3,21

207:1 209:3 216:12 217:11,25 221:8 226:17

227:17 234:3,20,22,26 235:8,17 236:24 237:1

238:17 241:22 242:6 244:10 249:5,6 250:8

251:17 253:22 254:13 255:24 258:7,9,19 262:2

262:14 263:13 264:20 265:26 267:2,22 277:21

278:2,4,11 279:24,26 281:5 285:16 299:24

306:17 313:1,3 314:24 320:9 322:7 326:9,12

327:15,16 330:19 335:2 349:2 354:20 355:1

366:7 369:18 371:3 377:11 378:1,8 382:6 386:16

394:14 397:8 405:8,20,22,26 406:22 407:4

410:10 414:12 415:20 425:25 429:8,9,22 431:3,4

436:12 437:11 449:2 458:13 459:26 460:21,26

470:8 476:2 478:10 481:19 487:19,20 490:11

useable 414:17 450:10

useful 234:10 236:14 237:5,6,17 244:9 250:10

256:8 277:5,21 336:5 361:6 363:1 365:18 377:3

378:24 383:2 385:2 386:7 396:26 397:1 400:16

441:2 450:5,19 466:2,5 473:22 492:6 494:22

usefulness 58:2 61:12

USEPA 35:26

user 250:26 251:24

users 67:11 266:20

uses 251:16

usually 56:3 327:13

uterine 65:15 144:16 148:20

uterotropic 258:19 274:7,10 283:12 285:20 286:26

287:11 288:19 289:1

utility 218:19 219:3 385:25

utilization 21:25 24:10 29:5,8

utilize 34:4 304:19

utilized 308:2 326:11 375:20 379:25 387:8

utilizes 145:13

utilizing 23:23 24:18 148:22

Utrecht 63:17 324:24

U.S 7:21 214:18 230:6,11 231:14,15 236:22 238:23

312:15 385:2 391:17 488:18,21 494:17,19

V

valid 46:6 376:6

validate 21:7 22:6 133:18 234:4 269:11 363:19

398:25 405:24 415:3 470:9

validated 18:10 20:12,12 98:19,22 135:5 233:21

234:17,26 235:7,17 274:5 282:6 290:6 295:14

376:1 377:23 416:14 437:15 478:22

validating 20:23 98:23 134:3 487:8 498:7

validation 20:17 22:6 23:4 26:8,12,20 29:7 30:13
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234:11,14 235:11,14 240:26 241:2,3 244:26

245:1 246:8,12,16,20 249:26 251:18 252:20

256:20 257:5 267:20 275:25 278:12 279:1 376:5

378:18 383:1 415:2 429:23 480:5,9

valuable 125:23 260:21 270:6 273:25 314:1 447:6

valuation 456:21

value 169:22 198:22 201:11,22 206:18 264:22

265:6 293:23,25 328:20 339:6 398:1 434:20

442:19,26 452:10

values 257:6 317:9 318:22,22 319:9 325:19 326:13

327:5,8 398:1

vapor 324:3

variability 107:19 181:23 348:21,22 401:23 402:2

402:3,14 439:21 440:11

variable 403:20

variant 424:14

variation 107:17

varies 192:7,18 461:14

variety 22:20 136:3,19 185:26 336:2 365:7 482:13

various 67:15 103:18 106:11 107:24 112:25 126:20

171:20 181:4 256:20,24 257:22 258:12 308:26

310:2 311:1 343:6 401:5

variously 312:9

vary 337:24 343:1 344:2,9 352:1 360:22 367:13

varying 338:8 412:8 435:13 455:25

vast 80:9 430:11 459:18

vehicle 374:15

vein 239:20,21 248:14

veins 313:26

vendor 102:4,4 228:12,12 366:7,10

vendors 102:7,13,26 105:1 113:24 228:14,17,18,20

229:14

venues 499:10

verbal 356:12

verbally 362:13

verification 70:10 161:18 167:10 170:24,26

verified 61:21 437:17 447:18,24

verify 62:11 149:18 167:16 178:8,21 449:1

verifying 57:16,18 472:13

version 53:4,10,24 54:7,9,13 66:12 67:3,4 69:22,24

70:4,5,14 73:18,18 74:9 77:2,2,3,4,5,10,15 78:18

79:4,11,12,13 80:2,7 82:24 84:2,17 85:26 86:9,11

86:11 189:1,17 198:13,13 200:9 202:8,25 203:6

204:15 223:14,16 250:25 281:25 333:5,12 334:5

335:3 338:1,1 339:2 342:23 356:26,26 381:20

412:12 423:5,5 433:17 435:6,8,16,21,23 439:11

440:10,10 441:3,25 444:2,2 452:14 453:5 479:13

versions 54:8 99:22 338:10 394:3 453:4

versus 15:20,23 44:25 45:4 65:20,26 87:22 120:26

129:1 139:12 154:7,9 166:22 168:20 171:23

172:2 179:11 194:3,12 196:1,21,22 197:18

205:13 226:15 227:17 316:2 368:9 370:26 371:8

379:10 396:13 401:24 403:12 412:1 414:4 429:2

432:13 450:7 458:4,4 462:19 477:12

vested 283:1

veterinary 6:22 7:12

viability 461:13,20

vice 6:14 391:4

Vicki 36:24 215:5,9,10 225:6 232:13,13 242:21

view 68:10 69:9 75:10 311:15,21 321:3 347:14,25

459:12 475:10 490:10

viewpoints 408:16

violate 455:11

violated 454:25

violating 454:10,20

violet 43:4

Virginia 1:16 6:20 214:6

virtual 340:23

visible 21:16

vision 17:22 25:8 28:20 218:17 492:10

visit 11:14

visually 23:12 444:10

vitellogenin 55:18 57:19 62:12 145:12,15 147:25

149:16 156:18 157:13,16 162:2,4,5 163:4,13

170:10,25 267:2 268:10 334:14 360:24 364:16

443:24 446:26 447:18 448:5,13 452:16 455:1,5

461:24 475:6 481:20

vitro 14:26 15:9 19:16 24:12,25,25 25:3 29:16

33:14,18 34:12,16 36:18,18 41:11 43:13 55:2

57:15 58:18 59:13 61:19 112:21 114:26 115:2

122:3 135:3 142:21 143:5 167:17,18 182:7

203:24 218:10 219:2,2,17 221:14 222:6 224:2

238:7,12,21,24 258:17 270:22 274:10 275:8

276:11 278:3 279:17 280:4 289:14 320:10,25

321:18,21 323:15,25 324:13,19,22 325:4 328:17

336:16 342:25 343:25 352:24 356:21 361:22

382:5 386:20,23 393:26 394:2 397:14 399:2

429:17,25 437:17 439:16,22,23 440:2,3,11

460:16 465:4 472:12 478:10,13 481:12 482:7

485:12 488:5

vitros 285:20

vivo 19:17 23:20 24:24 41:11 43:12 56:9 115:2

135:3 206:5 222:1 238:7 258:19 270:23 274:7,10

285:20 286:22 287:1,13 288:23 289:15 309:15

310:6 321:22 324:5,12,22 327:25 328:15 334:26

336:26 361:22 367:17 399:3 404:7 439:18,23,23

439:25 478:13 485:13,17

vocal 333:22

volatile 114:4 208:12,21 209:1,20 228:9 323:25
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volatiles 101:26

volatility 208:12,23 209:12 265:23 318:20 323:23

325:4 440:15

volume 21:1 27:11 299:7 406:16

W

wait 239:22 341:26 359:20 410:23 427:24

waiting 2:17 247:12 311:6 335:21

walk 17:17 58:3 104:23 190:20

walking 246:23 454:17

want 2:14,16 5:2 8:25 9:15,20,24 10:1,7 14:7 15:25

16:2 23:20 30:12 31:13 35:7,26 47:7 54:1,7 66:17

67:2,6,10 68:25 69:12 70:21 71:22 74:3,12,20

78:2 83:14 84:9,19 86:5 87:25 94:11,20 101:12

104:18 113:16 124:2,12 125:12 127:21 133:23

138:25 145:24 146:5 159:25 161:17 164:18 165:4

175:9 178:7 182:17 184:17,21 193:15 195:19

199:5 203:21,25 205:1,2 206:1,5 209:9 211:1

215:12,23 216:20 217:5 218:14,23 219:9,15,21

221:25 223:14,19,22 224:26 225:3 233:13 234:13

239:16 241:9 245:26 247:15 252:5 253:7,17

256:25 264:7 269:8 273:23 284:9,25 286:20

287:14 291:13 295:10 296:10 298:12 318:25

342:11 345:11 347:12,13 349:1,20,21,24 353:24

354:18 355:20 357:6 360:11 361:26 363:13

365:13,14 370:18 375:20 393:19 402:22 406:22

410:9 411:10,17 412:6 422:16 423:5 426:19

428:9 429:17 430:12 443:5 444:3,10 447:4,26

458:14,21 459:6 462:2 475:22 479:4,10 483:7

488:11,17 489:19,21 493:3,4 494:3 495:4 498:22

499:5,9,9

wanted 70:14 72:3 88:13 101:21 104:19,20 131:21

144:6 151:22 152:20 164:22 171:19 176:13 187:2

196:4 211:20 212:3 220:16 236:4,20,25 239:1

246:7 263:16,26 269:22 274:16 276:7 282:17

316:24 317:7 340:15 342:9 350:14 351:24 360:20

363:9 388:5 400:6 425:16 451:4 458:9 462:9

464:2 476:6 486:9,20 489:17 498:20

wanting 405:22

wants 225:3 298:11 342:2

warm 16:19

warrants 20:4

Washington 2:17 9:16

wasn't 65:25 126:22 141:11 185:10 223:7 230:6

242:7 271:26 284:1 285:8 383:9 397:13 415:3

420:24 426:25 456:5

waste 123:24

wasted 496:10

watch 288:22 289:2

water 17:4 18:14,16 21:4 26:24 27:4,5 31:10 33:1

33:2 35:11 37:10,15,19 54:4 77:20 78:15,21 79:8

79:26 114:2,3 188:1 208:1 215:14 296:7 297:13

297:20,24,25,25 298:2,5 299:4,20 300:2 306:11

306:12 377:13 416:6 458:11 498:24

waters 6:23,23 126:13,14,15 127:1,6 134:11,12,12

214:8,8 327:26 328:17 351:22,23,24 376:18,19

380:8,10 381:16,17,17 383:25,26 388:2,3 391:7,7

395:2,3 400:1,2 404:17,18 416:23,24 438:6,7

442:9,10 454:3,4 489:23

way 13:22,25 22:19 31:16 33:16 35:5 52:5 55:11

55:12 71:25 76:1 78:7 81:19 85:11,12 89:2,3,5,10

89:18 93:23 96:20 99:8 100:18 104:23 108:23

109:11 112:3 119:8 120:21 122:7 123:20 127:9

127:14 131:10 133:19 135:13 136:19 141:6 146:1

172:8 173:12 177:6 182:16 184:4 186:15 193:25

195:18 208:24 209:3 217:21 221:9,19 222:26

223:17 224:7 225:1 240:19 243:14 244:21 246:4

254:13,16 261:22 264:18 287:26 288:15 298:14

301:17,23 312:7 315:14 317:16 325:1 341:18

345:4 356:11 358:24 359:9,19,19 375:25 388:10

398:16 403:4 407:26 409:19 410:3 411:9 412:15

413:3 421:8 422:13 425:17,18 428:9 430:15

435:25 438:10 445:11 467:16 471:14 474:12

475:13 481:3 495:13 497:16 500:4

ways 57:6 89:8,26 103:23 122:8 136:19 167:5

180:5 231:8 246:15 285:2 288:2 325:13 340:3

354:16 356:1 359:11,13,21 392:22 435:1 470:1

495:5

weak 26:3 33:15 42:2,4 113:26 115:24,26,26 116:1

117:3 118:19 119:24 136:4,26 147:26 148:11

182:25 183:1 243:24 257:23 277:17,17,20 291:1

291:17 337:22 349:13,18 359:4 373:8 377:26,26

381:22 397:16 406:12 411:13 452:8,8

weaker 176:8 358:12,12,18,20 482:16

weakest 100:17

weakly 120:20 373:7 378:22 379:6,20 404:21

441:21,23

weakness 441:16 452:20,24

Wearing 387:21

WEB 1:8

webcast 5:3

website 4:7 43:1,8 104:16,17

weeds 176:2

week 4:14 12:5 13:1,9,12 95:2 99:2 133:25 202:17

362:17,17 496:8

weekly 292:5

weigh 448:17

weight 19:26 21:11 23:1 40:3 46:26 47:3,20 51:20

90:21 96:3 175:17,21 176:3,8 266:3,9,10,17

311:3 315:12 318:23 326:24 330:24 336:20,24

375:11 440:18,20 467:13
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weighted 408:6 441:13 450:24 452:26

weighting 90:3,8,11,25 237:25 315:9 375:22

399:20 408:2 412:19,22 415:12 453:24 468:23

492:25 494:1

weightings 317:9 347:5 441:17

weights 322:17

weight-of-evidence 471:4 498:16

welcome 5:11 8:23 9:16 122:8 329:23 351:8

welcoming 211:22

welfare 500:1

wells 100:12 111:14

well-defined 58:8 362:22 429:23 454:11,25 455:7

455:11

well-developed 477:22

well-documented 468:16

well-established 440:6

well-know 44:3

well-known 41:25

well-plates 99:13

well-run 97:5

well-studied 192:6

went 18:15,23 54:5 66:18 68:10 69:5 70:7 71:15

77:4 82:3 84:17 93:26 102:3,12 132:3 157:18

180:10 196:12 200:9 202:16 228:10 284:7 285:10

285:17 286:2 493:8,17 496:3

weren't 81:15 88:17 126:7 143:19 166:19 208:2

228:2 356:5,7,10 491:1

Wetmore 282:3

we'll 9:9 12:11 13:2 14:19 16:22 17:1,11 22:12

23:24 30:18 31:7 35:21 36:19 37:1 38:21 40:6

51:26 53:9,15,20 58:21 66:21 73:19 80:10 83:6,8

83:9 88:3 93:6 94:12 95:23 96:14 103:7 104:8

105:20 114:5,12 120:7,24 127:17,24 135:17

137:14 142:10,11 146:15,23 147:8,16 149:2

157:2 161:21 177:24 188:9 196:2 199:9 205:16

210:20,22 217:25 225:12 248:3 252:1 279:2

302:14 308:12 317:3 318:12 323:19 342:16

346:20 354:8 364:11 376:17 381:18 389:12,17

419:19 426:14 427:7,10 429:8,10 430:2 499:3,14

we're 10:18,24 11:10,19 12:7,13 13:1 14:5,6 15:8

17:10,13 18:3 19:22 20:5,13 23:9,19,19,22,25

24:7,12,13 25:20 26:11,17 27:13,25 28:2,5 29:19

30:16,19,20 31:14,16 32:14,18 34:3,15 36:12,21

38:6,20 39:1 40:18,22 41:4,7,17,21 42:9,16,23

43:13 44:20 45:1 46:1,4,24 47:10,11,21,22,26

48:17,21 49:7,20,22 50:7 51:7,25 52:23,26 53:2

54:9,13,14,23 56:8,9,14 57:5,14,16,16,17,18,22

57:23 58:4,7 60:6,25 61:11 69:1 70:18 71:4 72:7

74:26 75:4 77:2,26 79:25 81:21,23 83:5 86:10

88:15,18 89:10,11 91:3,16,17,21,23,24 92:5,5,8

92:10,11,12 93:4,5,15,16 94:1,20 95:20 96:5,15

97:10,24 102:21 105:23 106:5 110:8,8 112:9,22

113:6 114:7,7,17 115:4 116:26 117:17 118:19

120:1,5 121:2,5,9,12,15,22 122:1,2,3,7,10 124:15

124:15,19,23 125:5,11 126:2,3 127:12,14,23,25

128:15,21 129:9,25 130:1,11 133:13 134:9

136:11,15,15,16,16,19,22 138:22 142:20,23

143:1 144:7 145:9 153:13 157:21 159:6,6 165:23

168:3,7,25,25,26 171:15 176:4 177:23 179:8,9,11

179:13,18 182:2 186:11,17,24 187:13 188:14,18

188:18,20 189:8,16,18 192:9 193:1 195:13

196:15 197:4 198:2 199:6 203:5 205:8 207:3,23

207:24 208:19,25 209:2 210:18,20,22 211:22

213:11 216:21,22 217:4,25 218:11 219:15,25

220:4,26 221:13,25 222:1,2,8,18 223:9 224:19

225:6 227:15 228:18 233:6 236:1 237:7 240:9

246:25 248:20 251:20 252:8,18 253:21,22,25

254:14,15,17 255:2,3,8,17,19,20 256:19 257:20

258:5,8 270:7 274:6 282:23 283:25 284:2,5,6

286:21 289:21 293:8,22,26 295:14 296:3,8

298:16 302:1,3 311:6,19 316:6 321:1 324:26

335:15 339:20 353:5 359:22 361:18 389:17

392:25 393:6,7,8,20 398:19 401:13,15,16 402:2,6

402:11,25 407:18 409:3,4,4,6 410:25 411:23

412:21 419:6 420:14,16,17 425:24 426:13,18

428:22 430:8,9,9 431:9 449:5,7 461:10 466:15

468:1,20 471:18 476:11 477:9,17,18 484:3

487:13 494:12 495:18 496:15 497:5,12,13,17

498:25 499:4,5,6,8

we've 9:4 11:8,13,16,20,23 12:12 13:12,22 14:13

14:13 17:14,15 28:19 29:9,11 30:5 34:26 36:3

39:12 43:17,21 49:22 63:3 64:1 68:3 69:13 71:5,7

72:10 75:6,10 76:1 79:6 82:23 87:22 91:26 93:5

94:6 103:26 104:8 108:17 114:2,19,20 115:20

122:5 126:6 131:11 134:7 137:13 156:5 157:26

164:6 168:4 175:20 193:3,13 198:11,21 200:18

201:7 212:2 218:8,9,14 220:9 223:13,14,15,23

231:1 237:3 242:4 244:6 247:20 248:19,22

249:11 250:5 251:16 254:5 255:4 266:19 283:5

283:10 287:11 289:23 295:7 317:2 323:18 324:14

329:19 343:5,18 356:20,24 357:1 363:10,14

378:17 381:13 392:7 393:12 404:5 407:24 410:13

432:21 440:13 442:3 443:4 447:8 450:8 474:10

477:19 490:21 496:21 497:1 499:21

whichever 334:17

whilst 319:18 321:21

whiskers 107:5,15

white 13:5 34:8 54:22 77:4 115:5 165:7 271:12

272:24 273:6 275:21 277:26 280:23 284:11,16

299:17 305:8 306:4 338:19 429:11 441:5
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whittle 39:21

wholesale 25:2

wide 17:7 81:25 128:25 139:8 185:25 258:6,9

336:2 352:16

widely 320:18 325:18 338:8 435:13

wider 22:20 29:20

wild 265:10

wildlife 18:1,24

Willet 268:18

Willett 239:12,14 247:2

willing 231:8 497:14

wise 8:19,22 16:24 80:9 82:14

wish 3:26 4:25 328:22

wishy-washy 94:19

withhold 385:26

withstood 365:9 372:18

woman's 349:7

wonder 45:23 87:8 288:20 330:20

wondered 131:3 132:12 139:16

wonderful 133:17

wondering 129:17 141:18 170:15 175:5 423:9

Woods 500:17

word 250:17,20 428:11 496:18

words 41:6 42:2 70:26 73:15 74:22 75:1,20 76:3,10

84:12 198:21,25 232:2 241:1 254:12 258:5

297:22 301:6 302:4 320:9 321:8,13,19 322:25

323:4 354:20 428:26 458:13 468:17 496:10

work 3:12 9:3,22,25 10:4,17 16:20,26 17:11 18:17

21:9,17 23:9,10,13,14 24:16 30:8 36:25 46:5 57:7

63:16 76:20,21 92:11 108:24 117:7 118:11

126:24 133:19,26 135:5 186:18 199:9 201:25

203:11,17 204:1 217:25 218:10 223:20 224:24

227:7,14 234:22 236:17 238:2 240:24 248:3,6

255:5 268:5 289:24 293:12,13 303:18,25 304:17

324:23 329:16 335:25 346:16,20,26 365:20

389:10,16 401:22 411:8 445:23 453:3 462:11

467:18 477:19 479:11,19 481:14 483:4,16 486:26

488:17,19 490:1 492:6,7 493:17 494:20,21 496:3

496:4 499:17 500:22

worked 14:23 77:8 98:23 134:20 210:15 254:21

260:8

workflow 31:4

workgroup 132:3,3

working 9:4 20:5,13 54:12 57:22 94:17 198:24

205:8 209:22 210:7 250:23 270:7 311:19 334:2

374:20 377:17 386:1 431:22 442:19 474:20

479:20 487:1 498:23

works 170:24 255:15 481:13

world 75:23,24,24 106:6 188:10 210:20 401:13

490:24

worldwide 133:26

worried 50:1 97:10 348:19 350:6 411:5,11,18

worries 474:23

worrisome 292:5 293:8

worry 98:15 103:10 119:23 132:8 184:15 360:7

366:21 400:11

worrying 126:3 136:8

worse 288:25 322:11

worse-case 326:19

worth 92:3 184:13 244:20 305:18 340:22 474:1

worthwhile 185:7 417:15

wouldn't 129:19 130:14 141:25 194:18 213:20

228:9 278:19 300:15 302:20 318:16 360:4,16

400:9 410:12 426:2 479:20

would've 450:16

wrap 76:25 211:6

Wrapping 36:24

write 87:1 374:25 389:5

writing 356:13

written 2:20 4:11 138:8 233:26 234:12 235:3,9

239:8,17,19 261:4 263:22,22 274:18 275:15

279:5 284:24 287:19 318:13 340:19 346:21

353:16 354:14 360:17 362:14 381:9 389:5 418:24

434:25 445:3 456:8 489:13 500:3

wrong 129:13 168:21 284:10 289:3 293:21 329:24

337:11 366:2 410:19 452:5

wrote 86:1 211:17 218:25 311:18

www.epa.gov/scipoly/sap 1:8

www.regulations.gov 4:7

X

X 119:9

xenoestrogens 123:4

xenopus 485:18,22

Y

Y 119:9

YARD 1:14

yeah 81:4 291:24 292:16 302:20 371:15,19 402:5

408:18 410:23 426:22

year 16:22 20:21 21:23 27:17 94:6,21,22 104:15

218:22 235:13 340:12

yearly 22:10

years 11:8 21:24 27:14,21 31:1 83:20 92:26 95:5

98:22 103:20 129:23 223:20 322:2 416:4 480:18

480:24 490:22 495:10

yeast 123:6 365:7 479:18

yellow 75:5 95:21 115:11 117:1 159:2 197:15,22

206:17

yellows 118:13

yesterday 92:3 213:8 215:4,7,11,13 216:1 218:14
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219:6 220:26 221:5,20 222:15 223:2 224:9

227:10 233:15 237:24 238:25 243:7 248:19

269:25 270:4 279:18 280:21 297:1,7 299:18

300:3 320:13,21 328:3 329:1,23 331:4 340:16

379:24 401:2 404:26 408:5 431:26 432:16 438:16

465:16,19 474:11,16

yesterday's 392:11

yes-no 335:9,20

yes/no 67:9 90:9,16

York 465:20

young 261:16

Z

Z 126:24

zebra 485:18,20 487:5

zero 62:21 154:19 174:24 180:13

zeros 165:3

0

0.000001 244:12 334:22 452:9

0.00001 334:21 407:3

0.01 64:4

0.02 44:9

0.085 43:2

0.28 144:17

1

1 19:4,8,15 20:1,7,8,23,26 21:13 22:16 23:1,21

24:25 25:2 30:11 31:6 32:7 33:26 35:9,25 42:15

43:9 51:1,3 52:11 91:4 122:24 135:5 152:3,6

184:4 230:13,16 235:2,5,7,11,12 237:2,9,11,20

237:26 238:6,8,14 239:6 241:11,13,21,22,23,26

245:22 246:3 248:4 254:26 255:22 258:3 274:6

282:6,16,20,24 283:20 286:1,8,9 295:13 300:1,25

301:3,22 308:3,4 318:8 331:11 333:12 334:5

335:3 338:11 342:23 381:20 394:3 396:25 397:11

397:21 398:25 399:2 404:7 415:3 421:24 435:16

440:10 448:9 453:4 484:14,14 498:4,6,11

1st 4:24

1,000 279:26

1,700 418:17 433:16

1.0E-07 256:15

1.5 104:7,7

1.65 43:8

1.8 41:25

1.84 162:16

1:00 302:14

1:55 501:4

10 79:22 92:21 103:9,9 106:18 107:17 119:25

135:24 136:9,10,13,21 155:8 163:10,10 174:24

184:3 191:24 198:6 202:4 204:5,7 205:19 397:19

397:19 457:17 493:4

10,000 27:7,24 39:22 94:22 99:2 101:22 102:3,9,11

103:6,12 110:26 114:20 130:14 215:19 228:6

237:5 247:13 253:18 280:1,3 297:26 393:1

418:15 492:17,18

10,341 26:26 30:24

10.2 337:20

10:15 247:4

10:45 432:24,26

100 92:3 97:6 100:5 102:16 105:8 109:15 116:5

119:25 126:16 130:5 133:7 144:25 146:7 157:14

380:3 382:3 409:26 493:4

100,000 94:18

1000 32:3 40:3 47:4 94:3,18 113:20 119:4 136:10

101 387:5

11 19:17 52:16 192:10 286:25 319:9

11.5 41:19,23 44:25 50:23 319:10 327:6,8

11:45 466:18

112 337:19

12 50:23,25 136:9,21 205:5 288:25 466:14

12.5 42:4

12.9 41:26

12:50 466:17

124 155:2 166:8

125 203:14

13 211:26 385:19

134 166:3,7

14 43:4 191:24 273:5,7 338:14

1400 119:4 189:22

143 132:11

1457 18:15

15 27:12 31:1 35:4 94:6 99:25,25 103:19 136:21

142:10 222:20 261:6 322:2 423:26

15,000 466:1

15-point 98:6

1500 27:1 70:2,13

1536 99:13,14,22

1536-well 111:11

158 20:2 21:13 23:2 498:13

16 192:15 198:13 288:7,21 294:24,25

165 82:18,25

17 75:8 97:26 257:3

17beta-estradiol 334:20

17B-estradiol 437:18

17-estradiol 465:22

17-methylestradiol 164:7,10

17-methyltestoserone 164:12

17-methyltestosterone 164:7,10

17-metylestradiol 164:8

1700 34:2 44:20 189:23 222:16,20
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1704 80:7 82:26 190:9

172 154:17 166:3 172:19

174 177:1

175 190:17

177 179:17

1800 93:5 114:20

19 257:14 357:21

197 187:1

198 187:2

1990 28:15

1996 18:8,14,19 300:2

1998 18:21 20:20 28:9

1999 20:20 22:22 28:9 368:6

2

2 7:8 19:5,11 20:4,5,9 21:2,8,15 23:3 27:20 31:6

41:19,22,26 44:25 50:18,22,24 152:5 235:15

255:1,22 300:26 301:4 318:15,18 319:9,9 327:5,8

329:18 332:19 333:5 338:1 339:2,5 356:26 394:3

421:24 423:5,6 433:17 435:21 437:21 440:10

441:25 444:2 453:4 484:14 498:8

2E1 141:5

2,6 194:20

2,6-substituted 194:7

2-alkyl 194:12

2-alkylanilines 193:23 205:13

2-chloropropinate 44:10

2-hydroxy 143:22 150:6

2-hydroxybenzophenone 72:5

2-hydroxybenzophenones 73:5,21 173:22

2-methyl 194:15

2-methylanilines 195:1

2.1 319:19

2.2 143:23 319:16 320:12

2.2.1 328:24

2.2.2 319:16 322:14

2.2.3.4 330:8

2.21 457:25

2:25 342:17

20 52:16 62:21 94:6 102:25 103:8,14 113:14

132:11,12,15 142:11 174:24 180:13 198:6 202:4

261:5 493:4

20,000 101:24,24

200 97:6 100:14 133:8 176:7 279:22,25 382:4

2000 208:1 267:20 268:4 368:11

2004 365:9

2005 39:26 401:14

2006 401:14 488:18

2007 11:21 13:14 28:20

2008 20:24 22:20 238:20 245:17 465:6

2009 11:21,22 14:11,15 15:2 27:9 28:24 33:11 53:4

53:15,20 54:8 59:24 66:15 70:8 76:7 83:16,17,25

161:22 227:21 273:15 292:23 293:5 333:13

338:15 354:15 372:16 393:9 401:14 402:4 407:1

409:11 467:19 472:17

2010 21:2 27:9 292:23

2011 14:12,16 21:8 23:11 28:24 29:3 218:16

483:12

2012 21:17,21 22:8,11 27:15 77:14,16 79:2 236:21

271:8 282:3 292:25 387:21,22

2013 1:17 16:22 20:18 22:14,16 23:4 27:15 260:23

273:15 497:7 498:2

203 198:3

2080 82:4

21 92:20 121:18,25 131:4,15,17,25 132:22 270:1

366:18 376:2 398:6,6 399:9 407:25 428:4

21st 11:22 12:1,6,15 216:11 218:16

211 68:14

22 113:14

22202 1:16

223 335:3

23 386:19 387:10

230 96:24

239 78:6

24 209:15,16 338:6 436:10

25 96:25 97:14 113:11,15 129:15,18 130:9 152:12

154:23 157:5 158:17 169:11 172:9,20 204:7

205:19 369:18 430:22 457:17

2500 49:19

26 86:25

2600 78:10

27 192:10 338:9

274 205:23

2777 1:15

28 104:6

29 100:14 101:3

29TH 1:17

290 232:14

294 203:15

296 143:12 154:17 155:1,20 179:26

3

3 42:21 43:5 152:5 332:26 333:3 338:1,11 339:7

356:26 412:12 421:24 435:6,8,23 439:11 441:3

444:3 452:14 453:5

3D 210:5 431:26

3PE 196:13

3,000 418:16

3-hydroxyl 85:3

3-methylcholanthrene 206:15

3-phosphoadenosine 387:2

3.0 193:9
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3.1 333:1,4 334:3 336:14 339:12,19,21 342:11

3.1.2 372:14

3.1.3a 372:20

3.1.3b 372:20

3.2 333:1 339:19 342:16,20,22 352:22

3.2a.1 372:17

3.4 193:4

3.95 42:22

3:30 370:22

30 103:15 125:10,20 155:3 169:21 191:14 228:3,16

228:18 229:20 231:23 232:12 314:4 418:15 429:1

30,000 95:2

300 133:8 223:2

3000 27:6

305-5805 1:9

31 1:17 192:11

33 166:10

34 166:10 284:3 285:18 286:5 288:14 290:17

294:25 295:7 338:13

35 171:25 172:2 281:4

36 190:24

36-well 99:25

3600 27:3

37 34:10 114:15,24 115:7 134:14

384 99:21

384-well 99:9,12

4

4 64:3 148:19 157:14 344:16 362:18,20 487:23

4PE 197:3

4-aklylanilines 194:12

4-alkylanilines 193:26 205:13

4-methoxy 72:18

4-methylaniline 194:15

4.1 362:18,21

4.2 371:21,22

4.3 193:9 375:2,4 377:5

40 44:6 50:16 113:11 190:23 192:21 228:16,18

265:6 283:13,15,25 284:3 314:4 319:10,10

327:23 328:2,5,11

4000 49:19

41 191:12

414 190:18

43 78:13 166:14 190:16

44 189:25 192:17,18 286:19 289:13 291:25 309:11

45 126:3

455 135:7 377:23

457 377:23

46 86:21

48 261:15

5

5 42:18,22 44:25 51:3 319:10 350:15 375:2 376:15

377:5 384:19 389:13

5% 33:25

5,000 416:5,5,11

5-phosphosulfate 387:2

5.1 377:6 380:9

5.2 380:6 382:11,13

5.3 384:16,18

5.6 144:17 151:10

50 86:21 97:16 106:10 107:7,11 113:10 116:4

126:3,4 129:16,17,21 146:6 147:22 150:9,16,21

151:12,16,19 152:11 153:7,15 154:22 157:5

166:21 169:11 172:10 173:25 174:13,22 229:24

257:2,6 279:20 281:18 287:7 293:10 378:13

50/50 279:11

500 198:21 201:19

5000 27:1 39:8 49:17 82:3 126:3 130:14 203:26

308:19 309:18

5000-compound 124:8

509 192:21

52 27:13 100:13 101:3 141:19

54 283:14

55 65:16

56 288:10

56-49-5 206:16

57 78:14 166:9 190:19

576 203:15 340:8 435:10,25 436:13

58 27:10 191:11 288:26 289:13 291:25

589 190:16

5975 82:3

6

6 98:17 163:10 384:19 394:5 428:8,12 443:6 444:9

467:23 474:17

6th 397:19

6.1 393:23,24 395:14

6.2 395:21,22 400:25 408:18 412:4

6.21 452:6

6.3 402:24 412:6,7

6.95 41:25

60 191:2,13 192:21 232:10 261:3 357:18,20,25

6000 78:18

61 44:12

611 189:24

62 166:17

642 372:21

66 191:12 192:1,18

67 20:26 27:10 195:11 197:10

68 78:18
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0818 

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7

7 40:11 42:6,9,13,16 43:3,9 44:22,24 45:14 50:2,26

71:20 162:24 189:21 284:16 288:1 328:13 350:7

428:10,12,15 430:18 432:25 433:3 443:8 467:23

474:17

7th 397:19

7.1 433:12,13 438:2,24

7.2 439:5,6 445:19 446:9 451:8

7.2.1 172:1

7.3 143:24 451:14,15 455:19

7.63 73:25

70 155:5 169:21 190:1,18 192:22 232:10 357:25

418:4

700 44:21,22

703 1:9

71 78:17,21 79:7,10 82:25 208:3

71% 33:24

72 78:21,22 79:6

75 126:8 457:17

76 71:21

789 190:6

8

8 43:19 75:6,7,10 136:10,13 163:10 211:24 419:3,6

419:17 421:23 427:8 455:23

8.1 455:24

8.85 43:5

80 102:20 118:18 190:8 230:1 231:24 232:10

800 94:4

82 189:23

8200 94:5

86 79:3

862 372:21

88 172:1,2 174:18 191:10

89 274:9,9

890 283:18

9

9 27:10 42:19 43:6 44:26 51:3 212:6 389:13,17

466:15

9.1 466:22,23 472:18 476:20

9.2 473:2 477:1,2 481:26 482:2

9.3 473:2 482:5,6 489:4

9:15 4:3

90 4:23 102:19 118:17 154:19 174:18 228:15

457:17

900 93:4,22

9000 49:16

92 51:13 80:22 98:4,18 157:18

94 118:16

95 102:21,24 282:22 329:11 357:18 401:16

950 82:6

96 99:9

96-well 102:26

960 192:23

97 78:24 465:5

98 190:8,9 369:1

99 319:11 328:12,14,20

99.99 102:22