FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

 OPEN MEETING

 REEVALUATION OF THE HUMAN HEALTH EFFECTS

 OF ATRAZINE:

 REVIEW OF NON-CANCER EFFECTS AND

 DRINKING WATER MONITORING FREQUENCY

 DOCKET NUMBER: EPA-HQ-OPP-2010-0481

 UNITED STATES ENVIRONMENTAL

 PROTECTION AGENCY

 CONFERENCE CENTER LOBBY LEVEL

 ONE POTOMAC YARD (SOUTH BUILDING)

 2777 SOUTH CRYSTAL DRIVE

 ARLINGTON, VIRGINIA 22202

 SEPTEMBER 14, 2010

 8:40 A.M.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 2

1 FIFRA SCIENTIFIC ADVISORY PANEL

2 MEETING

3 SEPTEMBER 14, 2010

4 DR. JOSEPH BAILEY: We'll get started

5 here in just a few minutes as soon as everyone gets

6 settled.

7 Okay, good morning, everyone. I'm Joe

8 Bailey, and I want to welcome everyone to this FIFRA

9 Scientific Advisory Panel meeting. This is our third

10 meeting this year on atrazine, and the topic for this

11 particular meeting is the Review of Non-Cancer Effects

12 and Drinking Water Monitoring Frequency.

13 I particularly want to welcome the Panel

14 and thank them for agreeing to participate in this

15 meeting. And I just have a word, a few quick routine

16 administrative remarks to make. I am serving as the

17 DFO for the meeting, and I will be serving as liaison

18 between EPA and the Panel.

19 And this is a Federal Advisory Committee

20 within the Federal Advisory Committee Act, which means

21 that the recommendations and advice that the Panel

22 provides to EPA is purely recommendations and advice on

23 science issues, and all regulatory matters are strictly

24 the Agency decisions, of course, taking into account

25 the recommendations and advice that the Panel gives.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 3

1 As far as the ethics regulations go, we

2 kind of review the contradict disclosure information

3 that the Panel members have provided to ensure that all

4 of our ethics regulations are complied with.

5 There is tomorrow on the Agenda,

6 beginning I believe about mid-morning, an opportunity

7 for public comment. That's one of the requirements for

8 FACA held meetings is to provide an opportunity for

9 public comment.

10 We do have a number of commenters who

11 have requested time on the Agenda to hear their

12 remarks. If there is anyone else who wishes to give

13 remarks during the public-comment opportunity tomorrow,

14 please let me know or one of the other people here from

15 the SAP office. If you have not made arrangements

16 prior to the meeting for public comments, we will ask

17 that you limit your comments to five minutes on the

18 Agenda.

19 As usual, we have a Public Docket

20 created for this meeting, and the number should be at

21 the top of everyone's Agenda. All of the background

22 documents that have been provided to the Panel so far

23 are in that document.

24 Some of them are sensitive materials

25 that you must contact the docket and sign an
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 4

1 affirmation of nondisclosure as to those; but

2 everything else should be available in the docket, and

3 that will include all of the slide presentations that

4 are made by EPA or public commenters. They are

5 enclosed in the docket very shortly after that

6 presentation, so they will be available as well.

7 And I think that covers everything I

8 wanted to bring up. Again, thank you all for coming

9 this morning, and I will now turn the mic to Chair of

10 this meeting, Dr. Steve Heeringa. Thank you.

11 DR. STEVEN HEERINGA: Thank you very

12 much, Joe, and welcome everyone to this 4-day meeting

13 of the FIFRA Science Advisory Panel on the topic of Re14 Evaluation of the Human Health Effects of Atrazine:

15 Review of Non-Cancer Effects and Drinking Water

16 Monitoring Frequency.

17 As so indicated, I am Steve Heeringa of

18 the University of Michigan. I am currently in for a

19 short period as the chair of the FIFRA Scientific

20 Advisory Panel.

21 I will be serving as Chair of these

22 proceedings with no specific substantive expertise to

23 contribute; but we have a very highly qualified Panel

24 of experts, and so we look forward to this meeting and

25 I thank them in advance and probably throughout this
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 5

1 meeting for their contributions.

2 But at this point, let's meet the Panel,

3 beginning here on my left with Ken Portier.

4 DR. KENNETH PORTIER: Good morning, I'm

5 Ken Portier, Director of Statistics at the American

6 Cancer Society National Office in Atlanta. I am a

7 biostatistician and member of the current Panel.

8 DR. JANICE CHAMBERS: I'm Jan Chambers;

9 I'm a Professor in the College of Veterinary Medicine

10 in Mississippi State University on pesticide

11 toxicology, neurotoxicology and metabolism expertise,

12 and I'm a member of the current Panel.

13 DR. CAREY POPE: I'm Carey Pope; I'm a

14 Professor of Toxicology at Oklahoma State University;

15 I'm a neurotoxicologist, and I'm a member of the

16 permanent Panel.

17 DR. JOHN BUCHER: I'm John Bucher. I'm

18 the Associate Director of the National Toxicology

19 Program at NIEHS. I'm a member of the current Panel.

20 DR. DANIEL SCHLENK: Good morning, my

21 name is Dan Schlenk; I'm a Professor of Environmental

22 Toxicology at the Department of Environmental Sciences,

23 University of California Riverside. My expertise is in

24 fate and effects of pesticides on aquatic organisms,

25 and I'm a member of the current Panel.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 6

1 DR. RICHARD GREENWOOD: I'm Richard

2 Greenwood, a current Professor of Environmental Science

3 at the University of Portsmouth in the UK. My

4 expertise is in the area of pharmacokinetics and

5 toxicology.

6 DR. KANNAN KRISHNAN: My name is Kannan

7 Krishnan; I'm Professor of Occupational and

8 Environmental Health from the University of Montreal in

9 Canada. My expertise is in pharmacokinetics and

10 cytotoxicology.

11 DR. MOIZ MUMTAZ: Hi, I'm Moiz Mumtaz,

12 ATSDR, CDC. I'm interested in chemical mixtures.

13 DR. NELSON HORSEMAN: Nelson Horseman

14 from the University of Cincinnati. I'm a physiologist

15 and endocrinologist, specific expertise in mammary

16 gland biology.

17 DR. JAMES McMANAMAN: I'm Jim McManaman,

18 I'm from the University of Colorado; I'm Professor of

19 Obstetrics and Gynecology, and I'm an expert in mammary

20 gland biology.

21 DR. KATHERINE ROBY: I'm Kathy Roby from

22 the University of Kansas Medical Center in the

23 Department of Anatomy and Cell Biology, and my area of

24 expertise is female reproduction.

25 DR. BARRY DELCLOS: Barry Delclos from
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 7

1 the FDA National Center for Toxicological Research and

2 work in the Department of Toxicology there.

3 DR. SANDRA LEGAN: Good morning. I'm

4 Sandy Legan from University of Kentucky; I'm a

5 Professor of Physiology, and my expertise is female

6 reproduction and control of the LH surge.

7 DR. WESLEY STONE: I'm Wes Stone with

8 United States Geological Survey; I'm a hydrologist,

9 work with chemical transport.

10 DR. RICHARD COUPE: Richard Coupe, also

11 with U.S. Geological Survey from Mississippi.

12 DR. HERBERT LEE: Herbert Lee,

13 Department of Applied Mathematics and Statistics and

14 Associate Dean for Graduate Studies in Research in

15 Baskin School of Engineering. I'm at the University of

16 California Santa Cruz; I'm a statistician.

17 DR. BETTE MEEK: And I'm Bette Meek. I'm

18 at the University of Ottawa interchange from Health

19 Canada. I have a background in toxicology, but I've

20 worked in regulatory risk assessment in Health Canada

21 for a rather long time.

22 DR. SUSAN AKANA: Susan Akana from the

23 University of California San Francisco; I'm a research

24 physiologist. I specialize in hypothalamic-pituitary25 adrenal axis and its interaction with energy balance.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 8

1 DR. PENELOPE FENNER-CRISP: Penny

2 Fenner-Crisp; I'm a private consultant, and I live in

3 Charlottesville, Virginia. My area of expertise is

4 toxicology and regulatory risk assessment like this

5 over here in the U.S.

6 DR. ELLEN GOLD: I'm Ellen Gold, I'm a

7 Professor of Epidemiology and Chair of the Department

8 of Public Health Sciences at the University of

9 California at Davis, and I have an interest in women's

10 reproductive health, endocrinology.

11 DR. SHELLEY HARRIS: Good morning, I'm

12 Shelley Harris; I'm a scientist at Cancer Care Ontario

13 and Associate Professor at the University of Toronto,

14 and I'm an epidemiologist with expertise in pesticide15 exposure substances.

16 DR. JOHN BAILAR: I'm John Bailar,

17 retired from the University of Chicago. I am a

18 physician, a Ph.D. statistician, mostly an

19 epidemiologist, and I'm currently a Scholar in

20 Residence at the National Academies here in Washington.

21 DR. GERALD LEBLANC: Good morning, I'm

22 Gerry LeBlanc, and I'm a Professor and Head of the

23 Department of Environmental and Molecular Toxicology at

24 North Carolina State University, and my area of

25 expertise is endocrine toxicology and I'm a member of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 9

1 the permanent Panel.

2 DR. STEVEN HEERINGA: Again, thank you,

3 everyone on the Panel, obviously a very diverse and

4 deep set of scientific expertise that the Staff of

5 FIFRA SAP has again assembled for us. I acknowledge

6 their role in this, as well.

7 At this point, I'd like to open the

8 proceedings. I would like to turn to Dr. Steven

9 Bradbury, who is Acting Director of the Office of

10 Pesticide Programs at EPA.

11 DR. STEVEN BRADBURY: Chairman Heeringa.

12 I want to welcome the Panel members to this week's

13 meeting, as well as the public, who will be

14 participating tomorrow in our proceedings. We want to

15 thank you first for all your hard work in leading up to

16 the meeting.

17 There's a lot of information I know you

18 have been reviewing, and we appreciate the effort that

19 you've invested thus far and the investment of time and

20 your expertise during the proceedings this week and as

21 you prepare the Report in the coming months.

22 The activities of a peer-review Panel

23 such as the Panel that you're on is really critical to

24 the business of the Agency in ensuring that we got the

25 best possible scientific advice in the decision-making
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 10

1 that we have to do. It's really critical to have this

2 kind of peer-review process in place.

3 It's also important to ensure that

4 there's an opportunity for the public to provide you

5 information and insights as we proceed in evaluating

6 whatever scientific issues we have before us; it

7 supports our regulatory decision-making.

8 I just want to reaffirm the very

9 important role we play in the work of the Environmental

10 Protection Agency, in particular in the work of the

11 Pesticide Program as we not only deal with some

12 scientific issues around the pesticide atrazine but

13 more broadly around our risk-assessment approaches and

14 specific scientific challenges we take on as we go

15 forward.

16 Science is always evolving and, hence,

17 our regulatory approaches need to evolve with the

18 science. And some of the topics that we've been

19 discussing around atrazine in the past and will be

20 discussing this week and into the future tap into a

21 number of areas that are interesting from a scientific

22 perspective and also during reporting for informing

23 where we are with the science on atrazine in

24 particular.

25 Some of the discussions we've had with
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 11

1 over the last several meetings have included

2 epidemiology and how we should be trying to advance our

3 ability to use epidemiology studies in conjunction with

4 external toxicology studies and problem-formulation

5 stuff through the various stages of doing a risk

6 assessment.

7 And we'll be getting some very important

8 advice on some non-cancer issues regarding atrazine

9 during this week's meeting, which will be very helpful;

10 but it will also give us insights more broadly in terms

11 of how to use epidemiology studies as we advance our

12 risk-assessment approaches.

13 We'll also be spending some time with

14 you discussing toxicity pathways and looking at

15 different intermittent pathways and how different Modes

16 of Action can give us insights into adverse outcomes

17 and try to understand those relationships; it's very

18 important for understanding atrazine and its Mode of

19 Action and understanding adverse outcomes and how they

20 relate to different --across different levels of

21 biological immunization, very critical to ensure we

22 have the most up-to-date information around atrazine as

23 we take a look at risk assessment.

24 But those insights will be more broadly

25 applicable as we try to take advantage of the NAS
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 12

1 report on 21st Century toxicology testing and

2 integrative assessment approaches on risk assessments

3 and how it to look across different levels of

4 biological immunization and understand Mode of Action

5 that are mechanisms of action, which in turn is

6 critical for improving our ability to look at dosimetry

7 in our risk assessments and to better define what's the

8 appropriate dosimetric to use for different Modes of

9 Action at different levels of biological organization

10 and how to link this information together, both in

11 terms of the dose, delivered dose, perhaps from

12 exposure but also the duration of exposure, and our

13 understanding of Mode of Action and how that relates to

14 the timing of exposure.

15 Again, very important for understanding

16 the science around atrazine and informing our current

17 risk assessment and whether or not we need to revise

18 the current risk assessment with the insights that we

19 got from previous peer reviews and future peer reviews

20 will give us some insights more broadly as we advance

21 on risk-assessment techniques.

22 And then, of course, looking at

23 dosimetry and Mode of Action and understanding risk

24 potential from epidemiology studies or experimental

25 toxicology studies, and how do you relate that
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 13

1 information to environmental exposures.

2 In this case with atrazine, how to take

3 a look at drinking-water concentrations and

4 understanding how different changes in space and time

5 of atrazine concentration in the drinking-water sources

6 relate to that understanding we have about dosimetry

7 and temporal exposure with those and Mode of Action and

8 adverse outcomes, and how do you bring that all

9 together so that we can take a look at what's going on

10 out in the world and be confident that the current use

11 of atrazine or any pesticide down the road is safe in

12 the context of our risk assessment, what we understand

13 about dosimetry, and how that relates to temporal and

14 spatial exposure on the pesticide question, in this

15 case atrazine.

16 So a number of really important issues

17 that are very central to understanding the science on

18 atrazine, but also very informative more broadly if we

19 take a look at our risk-assessment tools in general.

20 The peer reviews that we've had thus far

21 and the ones that we'll have, the peer review we've

22 having this week and the peer review that we'll have in

23 2011 are very important in taking a look at our

24 regulatory position with atrazine and ensuring that the

25 current state of the science is still reflective of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 14

1 what we knew about atrazine back in 2003.

2 It has been about seven years' time

3 since we did the re-registration decision for atrazine,

4 and there has been a lot of epidemiology studies that

5 have come over that time frame along with a number of

6 experimental toxicology studies done over that time

7 frame. And those studies are very important as we take

8 a look at now about five, six, seven years of intensive

9 drinking-water monitoring data that's been accumulated

10 as a condition of re-registration of atrazine.

11 And so in 2010, looking back at 2003 and

12 many years of monitoring now, the Agency felt it was

13 time to sit back, take a look at the state of the

14 science both in terms of experimental toxicology,

15 epidemiology, dosimetry in the context of sampling

16 designs for drinking water which are including making

17 sure we've got the most current science and that's

18 where we stand. I want to thank you very much at this

19 time for the group that met here in February.

20 What I would like to do now is turn over

21 the mic to Dr. Tina Levine, who is the Division

22 Director for our Health Effects Division to introduce

23 the other members of our team that have prepared for

24 the risk assessment.

25 We have had a number of folks from
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 15

1 across multiple Divisions in the Pesticide Program

2 participating in this effort, as well as colleagues

3 from our Office of Research and Development and from

4 NIEHS who have been involved in our efforts to date,

5 and I will turn it over to Dr. Levine to introduce the

6 team members and acknowledge their contributions

7 DR. STEVEN HEERINGA: Thank you very

8 much, Steve.

9 DR. TINA LEVINE: Thank you very much.

10 I'd also like to extend my welcome to the Panel and

11 echo Steve's appreciation for your time and efforts.

12 Your feedback is an important component of improving

13 the scientific foundation on regulatory decisions.

14 I would also like to thank Joe Bailey

15 and the SAP Support Team for their help in making sure

16 these meetings run smoothly and putting together these

17 wonderful Panels.

18 Let me acknowledge the OPP Team: Ms. Anna Lowit; Ms.

19 Mendez who we're very happy to see back, even though we

20 tried to keep her away; Chester Rodriguez; John

21 Liccione; Marquita King; Jessica Kidwell as well as

22 many other toxicologists in HED, along with Nelson

23 Thurman and Mary Frankenberry from the Environmental

24 Fate and Effects Division, who contributed to the

25 current Issue Paper.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 16

1 I would also like to acknowledge the help of

2 Melanie Briscoe from PRD, who has provided a

3 significant amount of support to this effort.

4 And finally, I would like to thank the ORD Team: Ralph

5 Cooper; Susan Law, Tammy Stoker; Danielle Roddell; and

6 Jerry Goldman. Our colleagues at ORD are vital to this

7 ongoing re-evaluation, and we truly appreciate their

8 time and talents.

9 As Joe mentioned and Steve talked about, we

10 have conducted three SAP reviews this year. In

11 February we presented a Draft Framework for

12 incorporating epidemiology and human incident data into

13 our risk assessments. The February meeting included

14 two case studies involving atrazine. One of these case

15 studies looked at epidemiology studies involving

16 developmental outcomes. The same epidemiology study

17 included in the February case study will be discussed

18 today in the larger context of the total weight of

19 evidence across the non-cancer epidemiology database.

20 The second atrazine case study discussed in

21 February involved an ongoing collaborative project with

22 the principal investigators of the Ag Health Study. To

23 keep the Panel and the public up-to-date on that

24 collaborative project, we provided a short update in

25 the Appendices of the Issue Paper.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 17

1 In preparation for April, we reviewed

2 approximately 100 experimental toxicology studies that

3 were published involving atrazine, that were published

4 since 2003, and considered a wide variety of toxic

5 effects including immunotoxicity, neurotoxicity and

6 effects on steroidogenesis.

7 At the April meeting we placed a strong

8 emphasis on Mode of Action. This involved updating and

9 reaffirming the key events related to the hypothalamic10 pituitary-gonadal axis, what we typically call the HPG

11 axis, which were previously supported by the SAP in

12 2000.

13 The April Issue Paper also described emerging

14 data on the effects of atrazine on the hypothalamic15 pituitary-adrenal axis, what we call the HPA axis. At

16 that meeting, the Panel agreed with us that the HPG

17 axis remains the major pathway for atrazine toxicity.

18 With respect to the HPA axis, we heard from you that

19 these new studies provided a biologically plausible

20 hypothesis, but that a little work needed to be done to

21 clarify existing uncertainties.

22 At the April meeting, we also presented some

23 proposed approaches for evaluating drinking-water

24 monitoring data. As you'll see from today's

25 presentation, we've taken your advice on using
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 18

1 monitoring datasets based upon more intensive sampling,

2 and we'll present a general framework for developing a

3 monitoring program tailored to the endpoints we are

4 discussing this week.

5 This week's meeting brings the topics from

6 February and April together in a single integrated

7 analysis. We'll be discussing a wide array of topics

8 such as non-cancer epidemiology, statistical approaches

9 for evaluating drinking-water monitoring and

10 pharmacokinetics. We will also hear from Dr. Sue

11 Fenton, formally of EPA and now with NIEHS, about her

12 work on atrazine and the developing mammary gland.

13 The literature review contained in the Paper

14 is current up to July 15. There have been new atrazine

15 studies published since July 15. These new papers are

16 not in the Agency's Issue Paper and will not be

17 discussed this week. We expect to hold another SAP on

18 human health effects of atrazine in 2011 to cover

19 remaining issues regarding non-cancerous risk

20 assessment and cancer epidemiology. The 2011 meeting

21 has not been scheduled. When it is scheduled, we will

22 review all studies that become available from this past

23 mid-July until approximately two months prior to the

24 meeting.

25 We look forward to your thoughtful
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 19

1 deliberations over the next few weeks, and now I'd like

2 to turn the microphone over to Dr. Anna Lowit, who will

3 begin our scientific presentations. Thank you.

4 DR. STEVEN HEERINGA: Thank you. Dr.

5 Levine.

6 Dr. Lowit.

7 DR. ANNA LOWIT: Good morning. I would

8 like to reiterate Steve and Tina's appreciation for all

9 of your time and thank you for coming. Some of you are

10 here for your third atrazine this year, and some of you

11 are on your first.

12 A couple of years ago, the NRC published

13 a couple of new reports, NAS reports. One of those

14 reports, science and physicians' reports, encourages

15 the Agency to do a couple of things and one of those in

16 particular is to make our risk assessments more useful

17 and to have more utility.

18 And the primary way to do that is

19 through problem formulation by better linking the

20 purpose and scope of the risk assessment more in line

21 with risk-management goals and needs.

22 And as you will see from the next few

23 days with atrazine, this is really an excellent case

24 study of doing that sort of thing: of focusing on

25 problem formulation across a broad spectrum of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 20

1 scientific issues, and to think about the needs of the

2 Risk Managers as it relates to the drinking-water

3 monitoring, and to align those things in a way to

4 answer two straightforward although very complex

5 questions: do we need to do any risk assessment; and

6 to evaluate the drinking-water monitoring frequency.

7 So all those questions may be relatively

8 straightforward.

9 As you can tell from the complexity of

10 this Panel, it's a very complex set of issues.

11 You'll hear a combination. In order to

12 align the risk assessment with the risk-management

13 needs, we have a very multi-disciplinary interactive

14 team, as you will see from the presentations.

15 You will see a discussion of hazard

16 assessment around sensitive life stages, endpoints,

17 duration of exposure, but also a very detailed analysis

18 of statistics around drinking-water exposure.

19 But then at the end of the week, we'll

20 take all of those topics and will try to overlay them

21 on top of each other and think about them in an

22 integrative way of how to take durations of exposure,

23 Mode of Action, life-stage sensitivity and think about

24 drinking-water exposure and monitoring and how those

25 things come together to help think about the drinking-
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 21

1 water monitoring frequency question.

2 Prior to the 2010 review, atrazine has

3 been through the previous risk assessment has been

4 through a significant amount of peer review, starting

5 with the SAP 1988 on mammary gland tumors in rats.

6 That was followed up about 12 years later with the 2000

7 SAP which you heard Tina and Steve refer to, where the

8 key events and Mode of Action were evaluated.

9 And at that time it was really related

10 to development of the mammary gland tumor. One of the

11 outcomes of that meeting was that although those

12 mammary gland tumors in the adult rat may not be

13 relevant for human, the reproductive and developmental

14 findings from atrazine related to the Mode of Action

15 certainly are relevant for human.

16 In 2003 there was an evaluation of

17 prostate cancer epidemiology, and we anticipate in 2011

18 when we do our SAP review on cancer epidemiology that

19 we will build on that 2003 analysis.

20 You have heard quite a bit from Dr.

21 Levine. We have in this year, this is our third

22 meeting. And not to reiterate her points, but I think

23 it's worth noting that the meetings are really intended

24 to build on each other; that the February meeting was a

25 very broad meeting about framework, of how to think
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 22

1 about bringing epidemiology and experimental toxicology

2 data together into an analysis for our risk assessment

3 and to really focus on problem formulation in bringing

4 those things together.

5 We did at that time have two atrazine

6 case studies, one on developmental epidemiology

7 studies, which you will hear Dr. Christensen talk

8 about. The other one is a collaborative effort with

9 principal investigators at the Agricultural Health

10 Study. It's a project we're very excited about that's

11 moving on at a nice, steady pace that I expect you will

12 hear more about in 2011.

13 In April we focused entirely on

14 experimental toxicology data in drinking-water

15 analysis. And so today and this week, we are going to

16 bring all of it back together and talk about it in one

17 integrative week.

18 So to focus a little bit on April, just

19 to lay some of the groundwork of how we got to what's

20 in the Issue Paper and the presentations you'll hear

21 today, one of the important outcomes of the April

22 meeting was a reaffirmation of the key events related

23 to the HPG axis. Certainly we're going to I think

24 probably talk a great deal about LH, luteinizing

25 hormone, in the next few days.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 23

1 The other important part that was

2 discussed at the April meeting was emerging data around

3 the HPA axis, and as Dr. Levine indicated, it was the

4 consensus of the Panel that although they're very

5 plausible biological hypotheses, there are still some

6 existing uncertainties in those data. And as a result,

7 the current Issue Paper in our plan right now is to

8 focus on the HPG axis as it relates to selecting

9 endpoints for risk assessment.

10 Another important point that came out of

11 the April meeting as it relates to endpoint selection

12 and thinking about durations of exposure for risk

13 assessment is that the April Panel provided the

14 conclusion that seemingly effects from corticosterone

15 and ACTH were not relevant for risk assessment.

16 And I guess the last important point

17 from April around the hazard assessment brings us to

18 the focus on LH; that at that meeting we reviewed close

19 to 100 studies on many different effects and that even

20 as of right now we're unaware of any studies around

21 immunotoxicology, neurotox, steroidogenesis or a

22 variety of other things that provide in vivo doses and

23 those relevant things eliciting attenuation of LH. And

24 that's important from a risk-assessment point of view,

25 because you want to be regulating at the most sensitive
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 24

1 level.

2 Also in April on the drinking-water

3 monitoring side, we provided a few examples of how to

4 think about evaluating data, and we proposed some

5 modeling approaches. We did hear strongly from the

6 Panel that we needed to be using datasets that had

7 higher frequency monitoring, and that has been

8 certainly you'll hear a lot about that today.

9 Okay, so how does that move us into what

10 we're going to do this week? We have reviewed a number

11 of non-cancer epidemiology studies that includes the

12 studies that were evaluated for the February meeting,

13 along with a number of other outcomes.

14 The feedback that we got from the Panel

15 in April on our reviews on how to think about those

16 studies, how to think about those studies in respect to

17 thinking of our risk assessment, was very helpful as we

18 broadened that analysis to add another influence.

19 As Dr. Levine said, Dr. Suzanne Fenton

20 will be here first thing in the morning to give her

21 presentation on her own work on the development of the

22 mammary gland. So you will not hear a presentation

23 from the Agency today on that, though she will be

24 presenting her own data.

25 There are reviews of both Dr. Fenton's
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 25

1 work and the newer study submitted by Syngenta, which

2 is one of the Coder studies, are contained in Appendix

3 A. Particularly the Rayner and the Coder studies, the

4 effects that are seen are very high doses at around 100

5 milligrams per kilogram, which is something about 50

6 times higher than the value that we have estimated for

7 our point of departure for LH.

8 The other study out of the Fenton lab is

9 a mixture study. It is what we often call the Enoch

10 study. It is unique among those mammary gland

11 development studies, in that the doses are quite low

12 and they actually include a dose lower than the LH

13 endpoint.

14 We do have some concerns about the

15 design of that study. We have concerns about how the

16 design affects the interpretation. We also have

17 concerns about how the mammary glands are evaluated and

18 how we evaluate the different dose-responses. So we

19 are really looking forward to the comments from the

20 Panel on the study design and interpretation of dose21 responses there.

22 Okay. So the current Issue Paper is, as

23 far as we know, and we believe this to be true, the

24 literature review is up-to-date as of July 15th. That

25 analysis, the new studies are from January 30th to July
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 26

1 15th, which is essentially our cutoff from the February

2 meeting -- excuse me, our cutoff from the April meeting

3 our cutoff from the April meeting up until the cutoff

4 for the current meeting.

5 All of those reviews are contained in

6 Appendix A, and for those of you who were here in April

7 and you looked at Appendix A and it seemed a lot of it

8 seemed the same. Well, it was actually intentional.

9 We wanted to, partly as a tool for transparency, partly

10 because we knew there would be some new members on the

11 Panel, we decided to include all of the reviews from

12 both April and the new ones in September. But

13 hopefully we had a table at the beginning, so you were

14 able to figure out what was the new part.

15 One of the really important parts of the

16 new Issue Paper is our proposals for dose-response

17 assessment. As we move out of caudal formulation into

18 a, sort of a new analysis phase, we have reviewed many,

19 many new experimental toxicology studies.

20 We've reviewed quite a few epidemiology

21 studies. And as you'll hear from Dr. Christensen, our

22 view is that those epidemiology studies, although very

23 informative for human relevance and qualitative

24 characterization, don't provide the quantitative

25 support for using in a risk assessment, so that our
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 27

1 proposal is to rely on the animal data.

2 We heard you loud and clear also in

3 April to think about doing internal dosimetry and doing

4 benchmark dose analysis, so you will certainly hear

5 about that today.

6 Another area that you'll hear from

7 Nelson Thurman and Mary Frankenberry is around the

8 drinking-water exposure analysis. That will be another

9 area where certainly the Panels from April will hear

10 that we heard your advice loud and clear and that we

11 have moved forward with that. We're proposing a

12 framework of how to think about the water monitoring.

13 Later in the afternoon we're going to

14 have two presentations on two what I like to think of

15 as integrative topics, the first one being the analysis

16 for the FQPA safety factor, and that's an integrative

17 analysis because it by statutory requirement has to

18 include both hazard and exposure together, and we'll

19 see an evaluation.

20 It is important to remember that that

21 analysis is still going on. We have not proposed an

22 update to the FQPA factor used back in 2003 in the red,

23 and we are going to wait to propose that factor until

24 there are some important toxicology studies ongoing and

25 until the drinking-water analysis is much further
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 28

1 along.

2 But we are soliciting comment from all

3 of you on the important scientific factors around

4 drinking water and hazard to think about as we move

5 forward in the coming months to complete that analysis.

6 The second multidisciplinary area, which

7 is really the crux of a lot of this, is pulling it all

8 together in one bucket, I guess, for lack of a

9 scientific word, is the implication of the toxicology

10 in the Mode of Action on the critical duration of

11 exposure.

12 And that is a very important part of

13 this whole re-evaluation, the reason being is that

14 critical duration of exposure then goes into the

15 drinking-water analysis to determine the averaging

16 time, and you'll see some from some of the

17 presentations we will rely on.

18 As we move forward in the next few

19 months and the next year, we do have some issues that

20 we will be talking with the SAP on. We will be having

21 an SAP on human health in 2011. As Dr. Levine noted,

22 it has not been scheduled yet. But at that meeting, we

23 will pick up our external toxicology literature review

24 from July 15th forward; so things that have come out in

25 the last month will be included in that 2011 review.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 29

1 And we will follow up with proposals for

2 the duration of exposure. That's important as it

3 relates to drinking water monitoring frequency. We

4 will discuss with you our more complete analysis around

5 life-stage sensitivity as it relates to the FQPA

6 factor. And obviously, we will have a very detailed

7 analysis of drinking water at that time.

8 We'll also be talking extensively about

9 cancer, particularly epidemiology, as we await the

10 findings of a new study from the Agricultural Health

11 Study on atrazine. And at that point, we hope that

12 we'll be able to do an integrated weighted analysis

13 around cancer doing the epidemiology and the toxicology

14 more in light in the way that we've done the non-cancer

15 for this meeting.

16 So with that, this will sort of give you

17 a bird's eye view of the presentations. They are

18 largely intended to build on each other. We'll start

19 with non-cancer epidemiology, which is in my mind a

20 continuation of problem formulation. As we largely

21 focused on that in April and on experimental

22 toxicology, this is our epidemiology review in thinking

23 about how to use those data in risk assessment.

24 That presentation will be followed by

25 Dr. Chester Rodriguez, who will have a fairly lengthy
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 30

1 presentation on our proposals for dose-response

2 assessment, and many of you were here; you gave us some

3 recommendations, and we certainly took them to heart

4 and have done quite a lot of work on that.

5 After that, you will hear -- the end of

6 Dr. Rodriguez's presentation will talk about how to

7 think about the possible dosimetrics to link them to

8 drinking-water monitoring. So the natural presentation

9 after that is actually the presentation on drinking10 water monitoring.

11 After Nelson and Mary give their

12 presentation, I will come back and give the two

13 integrative presentations around the sensitivity, the

14 life-stage sensitivity, the FQPA factor and the

15 implications, pulling it all together to think about

16 durations of exposure.

17 And just on a personal and a

18 professional note, Dr. Elizabeth Mendez is not giving a

19 presentation today, but she has been instrumental in

20 every single one of them. And we are glad she is back,

21 even if it's just for a couple of hours.

22 DR. STEVEN HEERINGA: Thank you, Dr.

23 Lowit.

24 And just for the Panel and for the

25 audience, too, in terms of the proceedings today, we
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 31

1 have this set of presentations by the Scientific Staff

2 of the EPA that are scheduled for today and then

3 another tomorrow morning to start from Dr. Fenton.

4 There's plenty of time in today's schedule I believe

5 for exchange on questions and clarification.

6 It's not going to be an intense day.

7 Proceedings will become much more intense and time8 limited as we move into the period of public comment

9 and Charge Questions later on; it's just the way the

10 meeting is structured, and we probably can't change

11 that.

12 But at this point before we move on to

13 Dr. Christensen, are there any questions or forwarding

14 questions or clarification for Dr. Lowit based on her

15 discussion of the proceedings from February and April

16 in terms of this meeting?

17 Okay, Dr. Christensen.

18 DR. CAROL CHRISTENSEN: Thank you, and

19 good morning. Again, my name is Carol Christensen, I'm

20 an epidemiologist with the Health Effects Division, and

21 over the next several minutes I'll be reviewing with

22 you our evaluation of the atrazine non-cancer

23 epidemiology literature.

24 So to provide a little bit of background

25 about how we got here, previous our discussions we've
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 32

1 had with the Panel with reference to epidemiology data,

2 as well as review the purpose of our current

3 evaluation.

4 I'll touch upon in this presentation our

5 methodology or the way in which we selected these

6 studies included in our review, and I'll briefly

7 summarize those studies. Mainly we're focusing on the

8 results of those investigations and some of the major

9 strengths and limitations. Our full analysis is of

10 course presented in the written materials.

11 Looking across this database, we

12 discussed the information available to inform our, our

13 informal causal inference, including a discussion of

14 other non-causal explanations for the associations

15 observed. In synthesizing and integrating across both

16 the observational and the experimental datasets to

17 inform risk assessment, I will conclude with our

18 summary of the two, the two data streams and our

19 proposed next steps.

20 So by way of background as has been

21 mentioned, in February of this year EPA presented its

22 Framework for incorporating epidemiologic research into

23 our pesticide resistance process, and at that time we

24 articulated our attention to explicitly consider these

25 kinds of observational epidemiologic data in the public
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 33

1 formulation phase of risk assessment.

2 Very briefly, as has been mentioned and

3 just to be very clear, several studies related to both

4 fetal and perinatal outcomes were presented to the

5 Panel in February of this year and for the sake of our

6 comprehensive evaluation they are also included this

7 morning as well as the written materials.

8 And as Anna just mentioned, this review

9 reflects our assessment of the non-cancer epidemiology

10 literature as our evaluation of atrazine potential for

11 carcinogenic effects will occur in 2011.

12 So the purpose of our review again of

13 course is to identify non-cancer epidemiology studies

14 to inform risk assessment; so looking at investigations

15 of atrazine exposure in association with several

16 different health outcomes within the human population.

17 Our evaluation included assessment of the strengths and

18 limitations of each study individually.

19 This information is presented in

20 Appendix B2, as well as synthesizing and integrating it

21 across the observational epidemiology databases in

22 Section 3.0. So the results of the non-cancer epi

23 studies have been integrated with the experimental

24 database to formulate our overall conclusions and

25 proposed next steps in Section 4. Of course, these
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 34

1 will be modified by certain criteria and scientific

2 judgment as we look forward to the Panel in February of

3 this year.

4 So again back in February, the Panel

5 provided several very helpful comments to the Agency as

6 far as elements to consider for good-quality

7 epidemiology studies that are to be used to inform our

8 risk-assessment process. This wasn't by any means

9 exhaustive, but some of the key points mentioned by the

10 Panel are clearly articulating whether the study is

11 hypothesis generating or hypothesis testing in nature;

12 of course, the assessment of the validity and

13 reliability of the exposure assessment; and outcome

14 ascertainment was identified as a key factor.

15 The measurements of potentially

16 confounding variables, particularly when those

17 variables are measured in the same way between

18 comparison groups is also mentioned. And sample size

19 and statistical power, particularly the statistical

20 power to look at sub-analyses, sensitivity analyses --

21 for example, the influence of a third variable -- or

22 effect modification in the association under study was

23 also mentioned.

24 So, again, by no means exhaustive. The

25 Panel's recommendations were certainly very, very
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 35

1 helpful in the completion of our evaluation about these

2 non-cancer health effects.

3 So our literature review methodology,

4 the way in which we identified these studies, of course

5 looking for evaluations of non-cancer adverse health

6 outcomes in the open literature. We went to common

7 major/minor databases: Pubmed, Science, limited use of

8 Google Scholar.

9 We identified search criteria,

10 delineated in Appendix B1 of that material, and as well

11 as we utilized some citation mapping available through

12 Web of Science, we had hand searching of key reference

13 points and so on.

14 So we identified several hundred studies

15 initially that could potentially be included in this

16 review; however, upon review of both the title abstract

17 and the full text realized that not all of them were

18 appropriate to this question.

19 For example, some of them were really

20 exposure-only studies that did not measure association

21 with the non-cancer health outcomes, so they were

22 excluded. Several studies did not measure atrazine and

23 triazine specifically, so of course were excluded from

24 our consideration of the question today.

25 There were a handful that reflected
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 36

1 essentially case reports of acute pesticide poisoning,

2 and all very important in the literature review and one

3 instance in which the full typed document was not

4 available and several in which the studies were not in

5 fact original research but reflected editorial or human

6 opinion pieces.

7 So applying those criteria, we

8 identified 19 studies of non-cancer health effects for

9 our epidemiology literature review. So very briefly,

10 in contrast to the studies brought to the Panel's

11 attention in February of this year, the 19 studies

12 included in our lit review: several different study

13 designs, cohort case control, master case control, as

14 well as the ecologic studies presented back in

15 February.

16 In addition, it's notable that several

17 of these investigations took place within ongoing long18 term epidemiology studies: the Agricultural Health

19 Study; the Ontario farm family study; the Study for

20 Future Families; now they are using a vested or hybrid

21 study on childhood deficiencies and good control

22 device.

23 However, looking across the database, in

24 many instances exposures have been most limited,

25 particularly for our consideration of these in
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 37

1 quantitative risk assessments.

2 So these 19 studies can generally fall

3 into the following categories of male and female

4 reproductive health, fetal and perinatal outcomes, as

5 well as respiratory health. And the next several

6 slides kind of delineate the study results by major

7 health effects topic area.

8 So with regards to female reproductive

9 health, we identified three studies, all of which took

10 place within the Agricultural Health Study. Very, very

11 briefly, the Ag Health Study is a long-term,

12 prospective, cohort investigation of private and

13 commercial pesticide applicators in Iowa and in North

14 Carolina.

15 The study also asked spouses of licensed

16 pesticide applicators to participate in this study, and

17 in many instances the spouses are female; so that

18 comprised largely the sample population for these

19 investigations of female reproductive health hazards.

20 Some authors published an increase --

21 they observed an increased odds of long and missed

22 menstrual cycles in association with atrazine exposure

23 to atrazine or lindane users - and it should be quite

24 clear in this investigation authors were not able to

25 make an estimate of atrazine only in association with
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 38

1 menstrual-cycle characteristics; they grouped atrazine

2 or lindane users and compared that to non-users of any

3 pesticide.

4 In addition, a similar group of authors

5 a few years later published a study in which they

6 observed a delay in the timing of menopause in

7 association with ever use of atrazine over a lifetime;

8 and I should clarify that exposure was assessed by a

9 questionnaire, and the exposure metric of ever/never

10 use over a lifetime was used in these studies.

11 And excuse me, lastly, authors published

12 an elevated odds of gestational diabetes among women

13 who reported ever using atrazine over a lifetime who

14 also reported engaging in direct application of

15 pesticides during the first trimester of pregnancy. So

16 I want to be very clear that this observation was only

17 among women who reported using any pesticides during

18 the first trimester of pregnancy.

19 So looking across these three

20 investigations of female reproductive health outcomes,

21 there are several strengths as well as limitations,

22 these studies did measure atrazine exposure on an

23 individual level versus an aggregate level, all taking

24 place within the Ag Health Study, which is a large,

25 relatively highly exposed sample and is appropriate to
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 39

1 investigate this question.

2 However, as I mentioned, particularly

3 with respect to menstrual-cycle characteristics,

4 authors were not able to evaluate atrazine

5 specifically. In addition, the measurement of timing

6 of exposure is somewhat limited, again using that

7 ever/never exposure metric over the course of a

8 lifetime.

9 The frequency of assessment of menopause

10 was minimal in that investigation. Menopause is not a

11 one-time event. It occurs sometimes over several years

12 and, as the authors acknowledge, a more frequent

13 assessment of the occurrence of menopause would have

14 aided that investigation.

15 And the potential for residual

16 confounding by physical activity, both the menstrual17 cycle paper and the menopause paper used occupational

18 physical activity; but authors acknowledged the

19 potential for residual confounding by physical activity

20 as well.

21 So overall, these three studies we feel

22 are supportive of a hypothesis that atrazine may affect

23 the hormonal review in such a way as to possibly

24 influence reproductive health outcomes.

25 So with regard to male reproductive
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 40

1 health; we just found one study. Authors looked at

2 semen parameters in association with urinary

3 concentration of atrazine mercapturate. They never

4 calculated purely for atrazine.

5 Authors reported an eleven-fold elevated

6 odds of poor semen quality in association with atrazine

7 use, and authors defined "poor semen quality" as "sperm

8 concentration below the population median". However,

9 as you know, the observations are significantly

10 elevated but with a very large confidence interval,

11 indicating lack of precision with regard to the small

12 sample size.

13 In additional analysis within the study,

14 authors reported that individuals with more than one

15 different pesticide analyte measured in the urine were

16 more likely to have "poor semen parameters", which were

17 lack of sperm concentration, morphology and motility.

18 So in this study, the use of biomarkers

19 of both atrazine exposure and semen parameters is

20 considered a strength. The analytic techniques, both

21 the statistical and the laboratory-based techniques,

22 are sufficient to maximize the information gained from

23 this evaluation.

24 However, it does reflect a relatively

25 small sample size for some of the comparisons served,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 41

1 and it's been noted subsequent to the publication of

2 this study that atrazine mercapturate, indeed likely

3 underestimates total atrazine exposure, researchers

4 with CDC laboratories suggest the measurement of

5 additional urinary metabolites would be fruitful for

6 future epidemiologic evaluations. And the potential

7 for confounding by other pesticides or other

8 environmental exposures is also possible.

9 So overall this one study that we

10 identified in reproductive health is suggestive of a

11 possible association; however, we need replications to

12 fully inform the nature of this association.

13 Several studies looked at fetal and

14 perinatal outcomes, two studies within the Ontario farm

15 study. The long-term investigation looked at

16 miscarriage or spontaneous abortion. Across these two

17 studies, authors recorded a 20 to 40% non-communicative

18 elevated odds of miscarriage in association with

19 herbicide and/or atrazine use.

20 These two studies, the strength of these

21 two studies include the fact that to be eligible to

22 participate in the Ontario farm family study, both male

23 and female partners in the pregnancy had to live and

24 work on the site, so it is a highly exposed population

25 to investigate this question. And as a case control
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 42

1 study, design is highly efficient and it significantly

2 reduces the possibility of recall bias.

3 However, limitation, the main limitation

4 is the essentially probabilistic nature of the exposure

5 assessment. In this evaluation, participants were

6 asked individually whether they engaged in a farm

7 activity that could have included a pesticide

8 application and separately the specific use of

9 pesticides was assessed at the farm level, not at the

10 individual level.

11 So authors assumed that those who

12 reported engaging in a pesticide activity and reported

13 use of specific pesticides were in fact the same

14 people; but it's essentially a probabilistic method of

15 exposure assessment, as acknowledged by the authors.

16 So largely because of this method of

17 exposure assessment and the fact that both the male and

18 female partners were living and working on the farm,

19 it's very difficult to isolate the influence of either

20 male or female exposures specifically in relation to

21 this fetal outcome. Also, the period of recall for

22 birth characteristics or pregnancy characteristics is

23 quite lengthy in some instances.

24 So overall, we feel these investigations

25 reflect initial evaluations of this question. Both
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 43

1 studies published over ten years ago. However, there

2 are several major limitations to consider in the

3 incorporation of these data into our assessment

4 process.

5 Similarly, we identified several

6 epidemiology investigations about birth defects in

7 association with atrazine exposure. And just to note

8 for the Panel, many of these studies or several of

9 these studies were proffered in February of this year:

10 for instance, the study by Mattix, et al., which is an

11 ecologic study we discussed back in February but very

12 briefly.

13 These authors, again using an ecologic

14 study, compared the rates of abdominal-wall defects in

15 the State of Indiana to the U.S. as a whole, reported

16 that the rates were higher in Indiana as compared to

17 the U.S. in an area of high use of atrazine.

18 Authors also reported they observed a

19 statistically significant correlation between monthly

20 concentration of atrazine in surface water and monthly

21 rates of abdominal-wall defects in the State of

22 Indiana.

23 Another study that looked at abdominal

24 wall or a type of abdominal wall defect specifically

25 was published recently in this year. In February of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 44

1 this year Waller, et al., reported a statistically

2 significant 40% to 60% increased odds of gastroschisis.

3 Gastroschisis is one of the two major types of

4 abdominal wall defects in association with maternal

5 residence being close to an area of a high atrazine

6 USGS monitoring site. Authors define "high" as being

7 greater than the EPA limit.

8 Authors in this study also reported a

9 positive long-term exposure response relationship

10 between maternal distance from the high USGS monitoring

11 site and the gastroschisis.

12 Two other studies, also both of these

13 brought to the SAP in February of this year, looked at

14 atrazine exposure in association with several different

15 types of birth defects, so not just abdominal wall

16 defects. Winchester, et al., in 2009 reported a

17 statistically significant correlation between 11 and 22

18 different birth defects that they were able to evaluate

19 in association with the estimated time of conception

20 during the spring months versus all other seasons of

21 the year.

22 I won't recite the 11 to 22 here, but I

23 will note that the authors did not observe a

24 significant difference between the rates of

25 omphalocele, which is a second major type of abdominal
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 45

1 wall defect, in association with the estimated time of

2 conception in the spring.

3 In a second evaluation, authors reported

4 a 20% increase of lumen abnormalities among women who

5 resided closer versus further away from corn and

6 soybean fields. So atrazine exposure was estimated by

7 proximity of maternal residence to corn and soybean

8 fields in this study.

9 These authors also looked at several

10 different types of birth defects, and I will just note

11 that they observed a non-significant 50% increased odds

12 of abdominal cavity defects -- not quite the same as

13 abdominal wall defects or gastroschisis or omphalocele,

14 but I'm trying to kind of tie together across the

15 studies for you.

16 So the evaluation of these birth defect

17 investigations, several of which are hypothesis18 generating to suggest associations in a similar

19 direction; however, the ecologic nature of these

20 studies or these exposure surrogates which were not

21 validated are significant limitations to this dataset

22 under reporting of birth defects which we examined back

23 in February and has been given consideration in the

24 observational work on this type of outcome and

25 certainly after, after here.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 46

1 So in conclusion, several of the

2 hypothesis-generating studies suggest that atrazine may

3 play a role in developmental outcomes; however, there

4 are several uncertainties, largely having to do with

5 the study design and the method of exposure assessment.

6 We also identified a number of

7 investigations that had adverse birth outcomes. The

8 effects were small for gestational age. Two studies

9 which looked at this question reported no association

10 when they modeled atrazine exposure over the entire

11 pregnancy period; however, when authors were able to

12 isolate atrazine exposure during specific periods of

13 the pregnancy, two investigations reported that a 20%

14 and 50% increased odds respectively if the third

15 trimester overlapped a period of high atrazine use,

16 essentially that spring/summer period in which atrazine

17 is typically used at peak levels.

18 And for the sake of completeness, I'll

19 note an ecologic study published over ten years ago

20 which reported a significant correlation between

21 residents in a county in which high atrazine levels

22 were measured on USGS sites with intrauterine growth

23 retardation.

24 Intrauterine growth retardation is

25 exactly an effect of that gene, but we suspected they
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 47

1 are probably measuring a very similar outcomes on

2 grouping them here together.

3 Some of these same authors also looked

4 at preterm-delivery and low-birth-weight adverse birth

5 outcomes in association with atrazine exposure, three

6 of which reported no significant association. One

7 study by Savitz, et al., in 1997 is within the Ontario

8 farm family study, that same study I mentioned with

9 reference to miscarriage.

10 In that same study authors reported a

11 two- to fourfold increased odds of the male partner who

12 was exposed in the preconception period. Again, this

13 is utilizing that probabilistic method of exposure of

14 living on the farm site.

15 And Villanueva also reported a 30%

16 increased odds of preterm delivery if the first

17 trimester overlaps the period by half a season,

18 essentially the spring-type period, and that was based

19 upon a sub-analysis.

20 So for low birth weight, three

21 investigations reported no association; and that same

22 Villanueva study also looked at low birth weight,

23 reported a non-significant 20% increased odds of low

24 birth weight in association with atrazine exposure.

25 And I should note both Villanueva and the Ochoa-Aku
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 48

1 study were able to measure atrazine as treated in

2 water, so getting a little bit closer to atrazine

3 exposure and water monitoring.

4 So the strength of these studies, again,

5 the two that I just mentioned, were fine measures of

6 atrazine exposure, i.e., atrazine in drinking water,

7 are somewhat consistent for the outcomes of small for

8 gestational age. Looking across these studies, both

9 the main analyses and the sub-analyses kind of give a

10 hint to critical windows of exposure. Those studies

11 are supposed to overlap at first trimester or third

12 trimester, for example.

13 However, there is still likely exposure

14 measured in air, keeping the challenges of measurement

15 exposure for this outcome, and the possibility of

16 unmeasured confounding other environmental effects that

17 can probably play a role in the association between

18 atrazine and birth outcomes.

19 So in conclusion, you can see it is just

20 a possible association with small for gestational age;

21 however, the evidence for preterm delivery and low

22 birth weight are more limited.

23 And finally, we identified one

24 investigation within the Ag Health Study. Every

25 atrazine in association with wheeze, wheeze had some
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 49

1 association associated with asthma and considered to be

2 sort of a tighter health outcome to try to measure via

3 questionnaire. Authors reported a statistically

4 significant 20% increased odds in association with ever

5 use of atrazine over a lifetime sorry, no, I'll

6 correct myself.

7 One of the strengths actually of this

8 investigation is that authors asked participants about

9 both atrazine use or all pesticide use and episodes of

10 wheeze within a similar recent time period. So this

11 comes up, these data were collected in a follow-up

12 questionnaire within the Agricultural Health Study, so

13 the questions were asked the last year or the last year

14 that you engaged in pesticide application, what

15 pesticide did you use and how many episodes of wheeze

16 did you experience. So that was a strength of the

17 study.

18 In addition, I have given you large

19 amounts of data collected in this cohort. Authors were

20 able to control for corn and grain dust and the

21 association between atrazine and these.

22 And collaborative -- that's essentially

23 a hypothesis-generating study -- atrazine was not among

24 the a priori hypotheses identified by the authors, and

25 at this time there is no biological mechanism proposed
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 50

1 to inform the nature of the observed association.

2 So this really reflects the initial

3 investigation. There are other studies and other work

4 ongoing on atrazine in specific and pesticides in

5 general within the Ag Health Study.

6 So looking across all of these data,

7 what does it say or how can it help us inform the

8 nature of the association or inform our ability to make

9 causal inference?

10 As we stated in the written materials,

11 at this time EPA cannot conclude that the associations

12 identified in the epidemiological database are indeed

13 causal in nature. The reason for this? Largely

14 because other non-causal explanations cannot be ruled

15 out or eliminated at this time.

16 As I suggested over the course of the

17 presentation, we made several biases, including the

18 possibility for exposure measurement error leading to

19 exposure. Misclassification is likely across these

20 studies; however, we feel in general the nature of this

21 misclassification would most likely be non22 differential.

23 With respect to the potential for

24 confounding measurement error, there are some

25 instances, particularly the measurement of physical
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 51

1 activity in the recurrent count outcomes, as well as

2 the measurement of other pesticides and other

3 environmental exposures in the analysis of these

4 particular associations that would have enhanced the

5 precision of the respective studies.

6 In addition, some of the studies, as

7 well as the sub-analyses reported within the studies

8 that are relevant to our question of use of this data

9 in atrazine risk assessment. Our sample size is

10 relatively small, relatively low; cannot rule out the

11 possibility of chance in some of our observations.

12 So synthesizing within the active

13 database of these crops to the experimental toxicology

14 database, what can we learn from this evaluation? It

15 was notable that among the comparatively stronger

16 studies within the observational epi database, there is

17 some support from the toxicology database, as we note

18 on the next slide.

19 Just within the epidemiology database,

20 investigations of female reproductive cycle

21 functioning, timing of menopause were comparatively

22 stronger studies in our opinion. Our reasons for this

23 are the authors had a priori hypotheses regarding their

24 definition of hormone-reactive pesticides, which did

25 include atrazine, so they sort of came in with prior,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 52

1 prior knowledge or prior ideas about the nature of the

2 association.

3 The fact that in other methodological

4 work, the self-reported menstrual-cycle characteristic

5 was found to be relatively reliable, enhancing

6 measurable outcome.

7 As well within the Ag Health Study,

8 accuracy of the self-report information regarding very

9 specific chemicals have been shown to be relatively

10 higher on or at the top.

11 I'm looking at male reproductive

12 outcomes, the study by Swan, et al., in 2003. It had

13 biomarker exposure. As I mentioned, both statistical

14 and laboratory analytic methods were good.

15 And looking at adverse birth outcomes,

16 the two studies that measured atrazine exposure in

17 treated water, so closer to the actual exposure

18 profile, resulted in a relatively consistent estimate

19 for this outcome. I recall a 20% to 50% increased odds

20 in association with atrazine use if the third trimester

21 overlaps the period of higher atrazine use.

22 In addition, these studies were able to,

23 as I just mentioned, look at different periods of the

24 pregnancy, so inform a little bit better the timing of

25 the exposure, and in general the reporting of birth
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 53

1 characteristics or birth weight, the many factors that

2 go into defining this outcome have been shown to be

3 relatively reliable as well.

4 So as I mentioned, there is some

5 consistency in the experimental observations, as we've

6 heard with the female reproductive effects, male

7 reproductive effects, semen parameters and small for

8 gestational age. For example, the observation of

9 reduced pup weight in the toxicology data.

10 However, for several of the reasons that

11 I articulate here and identified throughout the

12 presentation, we feel these epidemiologic data are not

13 sufficient quality to include in our quantitative

14 assessment of atrazine. The lack of an exposure

15 response.

16 Measurement for many of these studies

17 used the ever/never use of atrazine over a lifetime or

18 atrazine concentration above or below the limit of

19 detection; plus they extended the exposure metric; lack

20 of individual-level exposure measurement in some of the

21 studies; and lack of validation for use of surrogates

22 for individual exposure is also a factor within our use

23 in quantitative risk assessment.

24 As noted, the use of the atrazine

25 mercapturate biomarker has been shown to underestimate
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 54

1 total atrazine exposure; other biomarkers are now

2 recommended for epidemiology studies. The ever/never

3 reporting of birth defects and the lack of precision in

4 most sample size were some main questions as well as

5 the sub-analyses were also important factors supporting

6 that statement.

7 So in conclusion, we feel the use of

8 these non-cancer epidemiology results will support and

9 inform our hazard characterization. As Dr. Lowit

10 mentioned, our use of these data are to be qualitative

11 and not quantitative in nature at this time.

12 These data do provide support for the

13 human relevance and the critical effects found in

14 graphs; however, on the biological plausibilty, we

15 cannot link the influence of atrazine on the

16 hypothalamic-pituitary-adrenal axis specifically to the

17 recorded outcomes and the classification of the data.

18 So for these reasons and others, we will

19 continue to rely upon the external toxicology data in

20 our quantitative risk assessment.

21 So that concludes my presentation. I'll

22 be happy to take any clarifying questions. I did,

23 however, want to of course acknowledge the good work of

24 many different people on the atrazine team for the

25 review and specific integration of these data, and I
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 55

1 particularly acknowledge my colleague, Dr. Tinelle

2 Logdall, who is here with us from our Office of

3 Research and Development, who also helped respond to

4 these clarifying questions. Thank you very much.

5 DR. STEVEN HEERINGA: Thank you, Dr.

6 Christensen.

7 At this point, I'll put it out to the

8 Panel for questions of clarification in Dr.

9 Christensen's presentation or really the content of the

10 Issue Paper and other supporting material that we've

11 had. We got our epidemiology studies to consider. Are

12 there any questions of clarification from the Panel?

13 Dr. Bucher?

14 DR. JOHN BUCHER: I'm John Bucher. This

15 isn't so much clarification, but I was wondering if EPA

16 has ever attempted any kind of exposure reconstruction

17 for any of these studies.

18 DR. CAROL CHRISTENSEN: No. We have

19 reviewed what's available to us in the published

20 literature; we've not engaged in that kind of original

21 work ourselves at this point in time.

22 DR. STEVEN HEERINGA: Dr. Harris?

23 DR. SHELLEY HARRIS: Thanks for your

24 presentation. I just had a question on how many people

25 reviewed the individual epidemiologic studies? Was it
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 56

1 one person that reviewed all of them, or was there a

2 team?

3 DR. CAROL CHRISTENSEN: No, as I

4 mention in my acknowledgments, it was certainly a team

5 effort, both in the selection or the application of the

6 exposure criteria and the selection of the individual

7 studies, the 19 studies that I just went over.

8 And it was a team effort in both the

9 review of the individual studies and some of this

10 across the database and in the tox database. Typically

11 how it worked one person would sort of draft the

12 initial review, and many others would review and

13 provide comment; we had several meetings along the way.

14 So it was certainly a group effort.

15 DR. SHELLEY HARRIS: Was there any

16 thought given to developing some kind of scoring system

17 for those papers, the ones they reached and passed the

18 inclusion criteria?

19 DR. CAROL CHRISTENSEN: No, we did not

20 apply any kind of quantitative criteria for the

21 selection of these.

22 DR. STEVEN HEERINGA: Any other

23 questions or clarifications or exploration in regard to

24 the logic of the work that's been done?

25 Dr. Portier.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 57

1 DR. KENNETH PORTIER: Dr. Bucher, this

2 is kind of an observation. As I was reading Section 3

3 in the Appendix, I kept thinking, "Well, you're telling

4 me what's really not good about these epi studies".

5 It would have been nice to see or to

6 think about what criteria would have represented a

7 really good, strong environmental epi study. I can

8 think of a lot of occupational studies where you get a

9 lot of good quantitative information that myself and a

10 number of other people at ACS, our epidemiologists have

11 this discussion all the time:

12 Well, what would make a good kind of

13 broad population study that would provide strong

14 evidence for the kind of risk assessment we're doing

15 here? And I don't get that in the report. I get a

16 feeling of what's not there, not what you would have

17 liked to have seen.

18 DR. STEVEN HEERINGA: Dr. Christensen, I

19 don't know if there's an answer to that. Dr. Portier

20 admitted that himself. But, feel free.

21 Any other questions or clarification

22 this morning?

23 Well, I think that again the pace today

24 will be a little more leisurely, which is good,

25 actually. It probably is a good pace, and so let's
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 58

1 take a break and at this point let's give ourselves

2 about 25 minutes, reconvene at 25 after 10:00. Thank

3 you.

4 (WHEREUPON, a recess was taken.)

5 DR. STEVEN HEERINGA: Welcome back,

6 everyone, to the second half of our first morning

7 session. To be precise, Science Advisory Panel meeting

8 on the Re-Evaluation of the Human Health Effects of

9 Atrazine: Review of the Non-Cancer Effects and

10 Drinking Water Monitoring Frequency".

11 Prior to the break we had heard from Dr.

12 Christensen in sort of an overview on the epidemiologic

13 assessment that had been done based on literature

14 review, and at this point I think we're to hear from

15 Dr. Chester Rodriguez on Proposed Updates to the Dose16 Response Assessment.

17 DR. CHESTER RODRIGUEZ: So thank you

18 very much.

19 So like he was saying, I'm going to be

20 talking about the Proposed Updates to the Dose-Response

21 Assessment for atrazine. Just to give you a little

22 background and to put in context, you know, what I'm

23 going to be talking about.

24 In the previous risk assessment to

25 support up here in the red, the approach was based on a
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 59

1 NOAEL/LOAEL. The previous study was a 6-month rat

2 laboratory study, and the key event was hormone

3 attenuation. This current re-evaluation examines new

4 science and more sophisticated approaches.

5 That includes internal dosimetry and

6 benchmark of the dose model, two of the recommendations

7 from the April 2010 report.

8 So this is what the outline looks like.

9 I'm first going to be talking about the support for

10 internal dose-response assessment. Then I'm going to

11 be moving on to the temporal aspects of plasma

12 triazines, which will then lead to a bit to a

13 comparison of LH attenuation studies of different

14 repeated dose and durations.

15 Then I'm going to move on to propose a

16 daily steady-state area under the curve as an internal

17 dosimetric. And finally, I will conclude with

18 benchmark dose modeling and some indications for water

19 monitoring.

20 So there is good reason to actually do

21 an internal dose-response assessment. In a report

22 published by the National Research Council, it was

23 cited that the dose at a target site of the internal

24 dose is the ultimate determinant of risk.

25 As we move on to apply a Mode of Action
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 60

1 approach to risk assessment, we need to be paying

2 attention to the pharmacokinetics, because that will

3 determine what internal dosimetry would that in turn

4 will lead to the Mode of Action of the key events which

5 ultimately would lead to the observed toxicity.

6 So my presentation, then, is mostly

7 concentrated on this part, the pharmacokinetics in

8 dealing with internal dosimetry. Now, talking

9 specifically about atrazine, okay, atrazine is quickly

10 and extensively metabolized as soon as it enters the

11 body through the oral route.

12 It first undergoes a first round of

13 dealkylation, mediated by the cytochrome of the P450

14 enzymes. And the result of that is the deviated two,

15 two mono-dealkylated metabolites. One is the

16 deethylatrazine, DEA; the other one is deisopropyl17 atrazine, DIA.

18 So the difference basically is that you

19 remove, okay, this ethyl group for this metabolite, and

20 then you remove this isopropyl group for the other

21 metabolite.

22 Another round of cytochrome P450 of

23 dealkylation then leads to the ultimate metabolite of a

24 Diaminochlorotriazine, or DACT.

25 So atrazine can also undergo a
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 61

1 glutathione conjugation by which that single chlorine

2 on the triazine ring gets substituted with a

3 glutathione residue, okay, and is conjugated, actually

4 can become part of this pathway as well.

5 So the fast metabolism of atrazine as a

6 parent chemical leads to a very short half-life in

7 vivo.

8 Now, there are no direct half-life

9 measurements for atrazine; but we can get a sense of

10 how short the half-life is from pharmacokinetic

11 metabolism studies.

12 For example, in a report by McMullin in

13 2003, it was reported that after dosing rats by oral

14 gavage with 90 mg/kg of atrazine, 30 minutes post dose

15 the parent chemical accounted for only 4% of total

16 plasma chlorotriazines, whereas that was really at more

17 than 50%.

18 Twenty-four hours post dose, the parent

19 chemical was very detectable in plasma, whereas that

20 actually was more than 98% of total chlorotriazines.

21 So these certain indications are very transient levels

22 of atrazine as a parent chemical.

23 There is another reason why there is

24 support for moving to an internal dose, dose-response

25 assessment. And that is that at least some of the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 62

1 metabolites are also active in attenuating LH. It has

2 been demonstrated by at least two different groups that

3 that actually has intrinsic activity at intramolar

4 levels of atrazine.

5 DEA and DIA are presumed to be active at

6 the base on the intact chlorinated structure, the

7 single chlorine. Now, glutathione conjugates are

8 presumed to be inactive in this process; however, the

9 only disposition studies that we have are actually

10 based on core of the radiolabel, a radiolabel that is

11 actually from the triazine ring. Therefore,

12 glutathione conjugates need to be included in the

13 internal dosimetry.

14 So in summary, in addition to just being

15 a good thing to do to do an internal dose-response

16 assessment, we have a very short in vivo half-life for

17 the parent chemical atrazine, and the activity of the

18 chlorinated metabolites are actually good reasons to

19 use internal dosimetry in dose-response assessment.

20 So based on this, then, we're actually

21 proposing to use an internal dosimetric that is based

22 on all triazine species without having to distinguish

23 between the parent and the active metabolites. We feel

24 that this dosimetric is conservative in case none of

25 the metabolites are active. Like I said before, it's
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 63

1 actually convenient because the only pharmacokinetic

2 data that we have is actually based on this position of

3 a core of the radiolabel; that is, they don't

4 distinguish between the parent and the metabolites.

5 So now moving on, I'm going to be

6 talking then about the temporal aspects of plasma

7 triazines. For this, we paid careful attention to the

8 results of this study from the Cooper Lab, where they

9 dosed rats with a single dose of atrazine via oral

10 gavage, and the single dose was as high as 300 mg/kg.

11 And the result of the high single dose was actually

12 more this attenuation of the LH.

13 In contrast, when they tested in much

14 lower dose of 6 or lower and say 50 mg/kg/day given

15 once per day over 3 days, they almost got complete

16 attenuation with the exception of the 6-hour time

17 point. And as you can see from the results of these

18 studies, there was no NOAEL. There is no real dose19 response, because you get incomplete attenuation of the

20 LH response.

21 So from this study, then, we can

22 conclude that the effect is not governed by peak levels

23 -- that is Cmax -- and that it represents really a

24 repeated dosing effect.

25 So based on these two proposals that we
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 64

1 should be using total triazines for the reasons I cited

2 before and the additional observation that the

3 attenuation is not really a single dose effect, we are

4 proposing then to use as dosimetric the area under the

5 plasma concentration time curve that includes all

6 triazine species.

7 And the rationale for this is that this

8 dosimetric is really the product of concentration times

9 duration and this is a hypothetical view of a repeated

10 dosing event. So the area under this curve, then,

11 would be our proposed dosimetric.

12 So what does the plasma profile looks

13 like for triazine for repeated daily dosing? For that,

14 we turned our attention to Thede of the 1987 study that

15 was based on a C14 radiolabel of a triazine ring. And

16 as you can see, this is the triazine ring right here,

17 and all the carbons are actually carbon 14; so you have

18 the radiolabel at three different places.

19 So female rats were dosed daily for 10

20 days with a wide range of doses that included 1, 3, 7,

21 10, 50 and 100 mg/kg/day of atrazine. But actually

22 more importantly, though, plasma levels, were actually

23 monitored very frequently.

24 I think it was nearly every 24 hours.

25 And it included the elimination phase. So by far, this
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 65

1 study provides the most thorough plasma concentration

2 time profile for triazine from repeated dosing with

3 atrazine, and we just want to make that clear.

4 So now this is what the plasma profile

5 looks like. And as you can see, then, dosing started

6 time 0, and plasma levels were actually monitored

7 almost every 24 hours. So one of the things that we

8 noted right away from this profile is that the plasma

9 levels do not change much up to 96 hours; that is after

10 4 days.

11 And that is for up for the dose groups

12 1, 3, 7 and 10. For the high dose groups of 50 and

13 100, plasma levels do not change much starting at 72

14 hours; that's 3 days. And they stay pretty much

15 constant during this period of continuous daily dosing.

16 They also monitor the elimination phase

17 for each of these dose groups. This is where daily

18 dosage stopped, and these plots here represent the

19 elimination phase.

20 So I am getting more evidence of this

21 notion of pseudo-steady state, because that's really

22 what we're talking about. This quad actually looks

23 like this. When you repeat a dosing, it goes up and

24 down, up and down, and they build up until you reach

25 what we are terming pseudo-steady state.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 66

1 So we wanted to get more evidence for

2 that. We came across this new study by Stoker, et al.,

3 where pregnant Wistar rats were dosed daily with either

4 5 or 25 mg/kg/day of atrazine either for 3 or 7 days.

5 So basically we're comparing a 3-day exposure to a 7-

6 day exposure. And plasma levels were analyzed for

7 chlorotriazines at the end of the dosing period.

8 So these are the two groups right here.

9 So for this group, then, daily dosing took place from

10 gestation day 18 through 20. For the other group,

11 daily dosing was done from gestation day 14 through 20.

12 So as you can see from the different columns, the

13 individual chlorotriazines actually vary for the two

14 exposure groups, as you can see for that for DACT, for

15 DIA and DEA. But the sum of all of these in the last

16 column, this is total chlorotriazines.

17 Now, this is the 3-day exposure group.

18 This is the 7-day exposure group. As you can see, the

19 plasma levels actually should remain the same. And if

20 you want to take into account the variability, these

21 two numbers compared to these two numbers will be the

22 same. So basically there is no difference between the

23 3-day and the 7-day exposure groups. And that is

24 consistent with this notion of pseudo-steady state of

25 the plasma levels or triazines.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 67

1 So now I've been talking about pseudo2 steady state. What does it mean exactly? Well, for

3 pseudo-steady state plasma levels, plasma levels

4 actually increase upon repeated dosing until you get up

5 into a mean value, and then after that plasma levels

6 should remain pretty much the same so that they

7 oscillate around the mean value. So the magnitude of

8 this oscillations actually depends on the dose rate.

9 So the more frequent you dose, the

10 smaller the oscillation. And the oscillation will be

11 the smallest when you have an IV infusion, where this

12 would just be a line that would go like this. So as

13 you now, once you stop dosing them, you have an

14 elimination phase. But the critical aspect of pseudo15 steady state, at least for us, is that once you reach

16 these levels, plasma levels will remain fairly

17 constant, regardless of how long this repeated dosing

18 continues. And that is a powerful statement, at least

19 for us, because of the next slide coming up.

20 So now moving through the slide then,

21 I'm going to talk about the comparison of different

22 studies of different repeated dosing durations. So at

23 this point, then, we came up with a hypothesis. We

24 hypothesized that the level of LH attenuation will be

25 similar for studies that achieve the same level of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 68

1 pseudo-steady state plasma levels of triazines.

2 So that's the hypothesis, and in order

3 to proceed in investigating this hypothesis, what we

4 did is we compiled all these studies where daily dosing

5 with atrazine was done for at least four days, because

6 after four days you seem to get these pseudo-steady

7 state plasma levels.

8 We expressed the attenuation effect as

9 percent control to account for the difference in rat

10 strains and inter-lab variability. We only examined

11 doses that were less or equal to 30, because after that

12 the level of the effect does not really change; you

13 seem to get a plateau after that.

14 So we concentrated on the dose range

15 after 30 mg/kg/day atrazine.

16 So these are the studies that we

17 complied. At first and foremost, we had a Cooper, et

18 al., a brand-new study, doesn't publish. It is a 4-day

19 study in duration. The mode of oral dosing was by

20 gavage, and the atrazine doses that were evaluated was

21 as follows: 1.5, 3.12, 6.25, 12.5 and 25. And the

22 NOAEL of 3.12 was established with a LOAEL of 6.25.

23 We also came across this McMullin 2004

24 study with a repeated daily dosing duration was for 5

25 days. And the doses evaluated were 0 of course, 30,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 69

1 100 and 300. We only examined, like I mentioned

2 before, only the 30 of it, because the 100 and 300 were

3 high and they don't provide any additional information,

4 where this here is close to a plateau. There was no

5 NOAEL identified in this study.

6 We also looked at a summary of a 2001

7 study, which actually was for 1 month and covered doses

8 of 0, 2.5 and 5. We only looked at these two doses,

9 2.5 and 5, because of the same reasons that I cited

10 before. And the NOAEL for this study was actually 5.

11 Last but not lest, we looked at the critical 6-month

12 study that was used in the last risk assessment.

13 So the duration of this study is

14 actually 6 months, and the doses that were evaluated

15 were as follows: 0, 1.8, 3.65 and 29.4. The NOAEL and

16 LOAEL were 1.8 and 3.65. I just want to make a note of

17 that. because that was a critical study that was used

18 in the last risk assessment for a point of departures,

19 I think.

20 So when we plotted all these studies as

21 percent control well, actually, before getting to

22 that, let me just say a few things about the 4-day

23 study, because we're actually proposing to use it as a

24 critical study.

25 Basically, it was aimed at identifying
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 70

1 the NOAEL or LOAEL for the effect following repeated

2 daily dosing with atrazine. They used rats that were

3 regularly cycling. And the effect was evaluated over

4 the course of one full estrous cycle, which in the rat

5 is 4 consecutive days.

6 So dosing was performed via oral gavage

7 once per day beginning at 900 hours on the day of

8 vaginal estrus, and it continued on the day of diestrus

9 I and II, and it ended on the day of proestrus, at

10 which point the effect on LH was analyzed, evaluated.

11 So when we compared this 4-day study to

12 the other studies, they differed drastically.

13 Integration, this is what we saw. First of all,

14 though, this is the dose-response for the 4-day study

15 at the 1800-hour time point. That is the peak of the

16 LH surge. And like I said before, the NOAEL was set at

17 3.12 and the LOAEL 6.25 at this time point.

18 So then this is what we saw when we

19 compare the four different studies that actually differ

20 drastically, like I said before, in repeated daily

21 dosing durations. The thick line is the 4-day study,

22 and as you can see once again, you have a well-defined

23 dose-response.

24 But actually we were ecstatic when we

25 saw this, because basically you cannot differentiate
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 71

1 the NOAELs of the study. They're hard to tell, even

2 though, like I said before, they differ drastically in

3 repeated daily dosing durations.

4 And actually more importantly, I should

5 say that the single dose that we examined from the

6 McMullin 2004 5-day study was nearly on top of the 29.4

7 dose group for the 6-month study. So to me, this is

8 very remarkable that these studies are so similar.

9 So now moving on, so we covered, then,

10 the support for doing internal dose-response

11 assessment, the temporal aspects of plasma triazines,

12 and I just talked to you about comparing different

13 studies of different repeated daily dosing durations.

14 So now we're going to move on to what we're proposing

15 as an internal dosimetric. and that's a daily steady16 state area under the curve for total triazines.

17 So this is summary of the internal

18 dosimetric, of the internal dosimetry. I suppose that

19 it has to be based on total triazines. And the reason

20 for that is that we have a very short in vivo half-life

21 for atrazine as a parent chemical.

22 I also talked about the activity of the

23 chlorinated metabolites and the uncertainty about

24 glutathione metabolites. From this, then, we are

25 actually proposing to use the area under the plasma
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 72

1 concentration time curve as the dosimetric, based on

2 the observation that the effect is not really a single3 dose effect, a Cmax effect, but a repeated dosing

4 effect.

5 So the grounds, then, for selecting this

6 dosimetric is that it accounts for levels as well as

7 duration of exposure. And when you add to that the

8 steady-state condition strongly associates with the

9 effect of attenuation of the ledge.

10 And when you include, then, the

11 following repeated daily exposure, you get what we call

12 pseudo- steady state of the plasma levels of triazines

13 by the fourth day in the rat. So when you add all of

14 this up, we propose an internal dosimetric that will

15 based on the daily steady-state area under the curve.

16 So what would this dosimetric look like

17 graphically? It would actually be one of these little

18 triangles that you see in the steady-state area of the

19 plasma profile. So it will be the area under each of

20 these rectangles.

21 But this is not really rectangles. What

22 they are, I actually try this always, and that leads me

23 to the next slide. For this analysis, we used a

24 classical trapezoidal rule, okay, in a non25 compartmental analysis, and we also used the linear
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 73

1 elimination phase assumption.

2 So the way you do this analysis, by the

3 way, if you've never done one of these, is that you

4 estimate the area under the curve to the last time

5 point. And we have a software package that will do

6 that for us.

7 Then you take these three data points,

8 which represents the elimination phase in your product,

9 and from the slope of this line, you can estimate the

10 elimination rate constant. So then you have the area

11 to the last time point; you have an estimate of the

12 remaining area, which will be from here all the way to

13 infinity.

14 With the assumption of linear kinetics

15 then, this area for the remaining part, is actually the

16 ratio of the plasma time point here, or the elimination

17 rate constant. So basically if you want to get an

18 estimate of the area from zero all the way to infinity,

19 you basically add these two up, and that is the basis

20 of the so-called trapezoidal rule in compartmental

21 analysis.

22 So we did this for each of those groups.

23 The one that I am showing you is for the 1 mg/kg/day of

24 the dose group; but we did them all.

25 Now, when we plotted the AUC for total
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 74

1 triazines as a function of the atrazine dose -- excuse

2 me -- what we saw was a very linear relationship. I

3 mean, I could not believe that this was real data,

4 actually, after being so linear.

5 The dash line represents the 95%

6 confidence interval.

7 When we also plotted the pseudo-steady

8 state plasma levels as a function of atrazine dose, we

9 also saw a very nice linear relationship.

10 I should mention that the pseudo-steady

11 state of the daily area under the curve is just the

12 product of the steady-state serum levels times twenty13 four. So, these two plots are pretty much the same.

14 Except that they differ by only 24.

15 But the take-home message from this is

16 that we're seeing a linear pharmacokinetics within the

17 dose range of 1 all the way to 100. That suggests to

18 us that there are no dose-dependent changes in the

19 pharmacokinetics or total triazines that may preclude

20 the use of the daily area under the curve as internal

21 dosimetric.

22 So now, now I'm going to move on to

23 talking about benchmark dose modeling. This part of

24 the work, by the way, was done by Joanie Shione of our

25 group. And then I'm going to talk a little bit about
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 75

1 the implications on water monitoring.

2 So Joan, she did the same thing, okay,

3 that I did by compiling all the studies that could be

4 analyzed by benchmark dose model, and they started

5 studies and they came up with once again, the Cooper

6 4-day study was on top of the list on the basis that it

7 has a well-defined dose-response curve.

8 There was also a 1-month study by

9 Danelle, et al., a 1-month study by, authored by

10 Morseth, and the critical study that was used in the

11 last active risk assessment. I should, I should point

12 out that we use the Benchmark Dose Software, the latest

13 version.

14 The models that were analyzed were those

15 - okay, that are used for continuous data, like the

16 effect that we're seeing, less attenuation. These

17 other models that were evaluated -- exponential, Hill,

18 power, polynomial, linear -- the details of the

19 analysis that includes, okay, the basic criteria are

20 all in Appendix C of the Issue Paper.

21 But I'm going to talk -- but the main

22 point of this presentation, by the way, will be on the

23 Cooper of the 4-day study, since we're proposing it to

24 use it as a critical study. So like I've been

25 mentioning throughout, this study has a well-defined
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 76

1 dose-response relationship.

2 I guess you can see from the plot that I

3 showed you before, the 1800 of the time point where the

4 search of the LH actually takes place. And this study

5 also had less data variability when compared to the

6 other datasets.

7 So as for the selection of the benchmark

8 response, BMR, this is a critical issue and we'd like

9 to get input from the Panel on this. As to the BMR, it

10 is selected generally on the basis of biological and/or

11 statistical rounds.

12 I don't know what happened. Sorry, I

13 don't know what is going on there.

14 So in the absence of information

15 regarding the level of LH attenuation that could be

16 considered associated with an adverse effect, in the

17 absence of this information, then we use a BMR that's

18 based on one standard deviation from the control mean.

19 So that's a default approach in the absence of any

20 other information, and we would appreciate to get

21 feedback on that from the Panel.

22 So, BMD modeling was performed based on

23 the external dose of atrazine. The best-fit model for

24 that was exponential. The same analysis was then based

25 on steady state of the triazine levels, which you can
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 77

1 derive from the linear regression analysis that I

2 showed you before. And the best-fit model for that was

3 the Hill model. And the details of all this analysis

4 is in Appendix C of the Issue Paper.

5 So these are the results. When you do

6 the analysis based on the external dose of atrazine,

7 you come up with a BMDL of 1.96 mg/kg/day of atrazine.

8 When you do it based on steady-state levels of all

9 triazines, you come up with a BMDL of 0.65 mg/L.

10 Just keep in mind that the units of this

11 are different. And when you do the analysis based on

12 the daily steady-state area under the curve for total

13 triazine, you come up with a BMDL of 15.56 mg/L-h,

14 that's sort of the units of the area.

15 So these steady state dosimetrics, by

16 the way, can be converted back to an atrazine exposure

17 based on the linear regression analysis that I showed

18 you before. And if you do that, you come up with a

19 dose of atrazine of 1.86, and you get the same results

20 for these two because they're pretty much the same,

21 like I mentioned before; they just differ by 24. So

22 these dosimetrics, then, may be used to establish a

23 point of departure, and we'd like to get input from the

24 Panel regarding this analysis.

25 Now so as of the whole point behind this
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 78

1 work, one of the main points I'm just saying is to try

2 to refine the drinking water monitoring frequency.

3 Now, this is a hypothetical water

4 chemograph. It's not pretty-looking; I mean, it's

5 hypothetical, but this is realistic based on the shape.

6 So how will you use, then, the results of our analysis

7 to analyze something like this?

8 Well, on the next slide, we came up with

9 two approaches that you can use. First, you can use a

10 drinking water of the rolling average value for a time

11 period of concern, and that can be compared to a point

12 of departure that is based on external dose of

13 atrazine.

14 This is in line with the current

15 approach, actually, that's in place right now. It's

16 set to the time period; it's actually 90 days. So

17 you're seeing a 90-day rolling average.

18 As a second approach, though, you can

19 compare an average daily concentration of triazines to

20 steady-state levels of triazines, or you can even

21 compare the area under this water chemograph over a

22 period of concern and you can compare that to a daily

23 steady-state area under the curve.

24 So those are two possible approaches

25 that we came up with, and we'd like to get input from
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 79

1 the Panel whether that's good or bad, I guess.

2 So given the linear relationship between

3 steady-state triazine levels and external dose of

4 triazine, the potential levels of concerns can be

5 related back to an atrazine exposure. So the

6 observation that we're seeing, linear kinetics, is a

7 very good thing that will simplify the analysis.

8 So this is just a summary slide, a

9 conclusion slide, if you will. So I talked to you

10 about the basis of doing an internal dose-response

11 assessment that includes all plasma triazines, parent

12 as well as metabolites.

13 I talked about the temporal aspects of

14 plasma triazines and the effect, LH attenuation. Both

15 of these actually support the use of a daily steady16 state area under the curve for triazines. We use

17 benchmark dose modeling that was based on steady-state

18 dosimetrics of total triazines as well as external dose

19 of atrazine.

20 And we feel that our analysis will give

21 very valuable perspectives for refining water

22 monitoring frequency, and of course we'd like to get

23 feedback from the Panel on the impact of this analysis.

24 So with that, I will conclude my

25 presentation. Thank you.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 80

1 DR. STEVEN HEERINGA: Thank you, Dr.

2 Rodriguez.

3 At this point, I'd like to open it up to

4 the members of the Panel for any questions or

5 clarification either on Dr. Rodriguez's presentation or

6 the corresponding material in the Issue Paper.

7 Dr. Bailar?

8 DR. JOHN BAILAR: There's a great deal

9 of information here. It's a kind of information that

10 I'm not really familiar with in a direct personal way,

11 so I may have missed something.

12 But I am very much impressed by the

13 regularity of the dose-response curve, even at the

14 lowest positive dose, which was not statistically

15 significantly different from the control.

16 This suggests to me that an approach

17 might be developed that is closely related to how we

18 deal with carcinogens, which I know a good bit more

19 about; that is, in the process of considering

20 regulation, to consider what would be an de minimis

21 risk and then proceed from that, rather than from what

22 appear to be the NOAEL and the LOAEL.

23 DR. STEVEN HEERINGA: Thank you, Dr.

24 Bailar.

25 Others?
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 81

1 Dr. Schlenk.

2 DR. DANIEL SCHLENK: Yeah, just a point

3 of clarification. Are you through this presentation

4 moving away, then, from a PBPK model? Is that kind of

5 the idea that I'm getting, because it seems that you're

6 kind of going with this AUC sort of thing, and I'm just

7 curious. Can you just fill me in a little bit more on

8 why you are deciding to go away from the PBPK?

9 DR. CHESTER RODRIGUEZ: Okay. So the

10 reason why we're moving away from a PBPK model is

11 because there is no reliable PBPK model. So the ideal

12 approach here would actually involve a PBPK model; but

13 that's not the only pharmacokinetic analysis that you

14 can do.

15 You can use non-compartmental analysis,

16 which is what we use, and actually derive actually very

17 useful information from that. So you don't have to

18 have a full PBPK model to actually make use of

19 pharmacokinetic information.

20 DR. DANIEL SCHLENK: Just to follow up;

21 so, again, will you be pursuing a PBPK model in the

22 future, or is that sort of a dead end, I guess?

23 DR. CHESTER RODRIGUEZ: If one becomes

24 available absolutely, because, you know, when you have

25 a PBPK model you can extrapolate across different
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 82

1 routes of exposure, across different species, high2 dose, low-dose, absolutely. So if one actually becomes

3 available, absolutely we'll use it.

4 DR. STEVEN HEERINGA: Dr. Lowit?

5 DR. ANNA LOWIT: Yes, just one point I

6 have on the timing issue. Dr. Rodriguez has actually

7 reviewed the PBPK models and our reviews of that are in

8 the Appendix. There's a timing issue just to keep in

9 mind. One of the things that we always like to say is

10 that because of the nature of atrazine we're always

11 cognizant of what is going on in the literature, and we

12 always keep up with the literature.

13 But there's a goal within 2011 to create

14 whether or not to make a choice around the drinking

15 water monitoring and whether we need to do any risk

16 assessment, and it appears extremely unlikely in that

17 time frame for that existing PBPK model to be upgraded,

18 and peer-reviewed for that matter, to use.

19 So the hope is to have a less

20 sophisticated but still informative approach to looking

21 at internal dosimetry.

22 DR. STEVEN HEERINGA: Dr. Chambers and

23 Dr. Lee.

24 DR. JANICE CHAMBERS: I'm kind of

25 confused about one of the things you alluded to several
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 83

1 times. You're talking about a dose-response curve on

2 the Cooper, et al., study of 2010 actually in slides 56

3 and 57

4 DR. CHESTER RODRIGUEZ: Yeah.

5 DR. JANICE CHAMBERS: and I just don't

6 see where you're saying dose-response with those

7 standard errors or standard deviations. What dose8 response have you.

9 DR. CHESTER RODRIGUEZ: What dose10 response?

11 DR. JANICE CHAMBERS: There's no-effect

12 and there's effect, and it just looks like the no13 effect levels are all together with the levels of

14 standard errors and the effect levels, all of it like

15 they're not different from one another. So where is

16 the dose-response curve?

17 DR. CHESTER RODRIGUEZ: The dose18 response is actually based on that 4-day study. I'm

19 sorry, I guess I'm not following your question closely.

20 Are you confused about why you compare so well to the

21 other studies?

22 DR. JANICE CHAMBERS: Look at graph 56

23 so I can see it.

24 DR. CHESTER RODRIGUEZ: Okay, give me a

25 second.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 84

1 Okay, it's this one; 56, yeah.

2 DR. JANICE CHAMBERS: Oh, okay, all

3 right. I don't quite see what you're talking about is

4 the dose-response there, because the standard errors

5 suggest that there's the 3.12 and the low was all the

6 same statistically, and the 6.25 above is all the same,

7 right?

8 DR. CHESTER RODRIGUEZ: No, actually.

9 Based on the statistics that was done on this, the 3.12

10 excuse me, the 6.25 is actually very different from the

11 control. So that's why the NOAEL was set at 3.12 and

12 the LOAEL at 6.25. And so these statistics that was

13 done I believe was on a 0.05 confidence okay, p-value

14 if you will. But the benchmark dose modeling is based

15 on one standard deviation from the control mean.

16 Okay, does that answer your question?

17 DR JANICE CHAMBERS: No, not at all.

18 Let me try again.

19 DR. ANNA LOWIT: Dr. Chambers, let me

20 try. I think it may be helpful to look at slide 57

21 again, because that's the overlay of the four studies

22 on top of each other. I think if you look at the

23 pattern, you sort of look from the bend in Cooper data

24 around the doses around six and to the left, and then

25 six and higher.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 85

1 I can see your point, actually; as you

2 think about below six, there across those four studies

3 there's actually across four studies it's remarkable

4 that similar dose is that low; but I think it's in the

5 nature of this kind of data that you see those kind of

6 error bars. But it is also clear from this plot that

7 the dark black line from the Cooper data has much

8 stronger dose-response characteristics than did the

9 other studies.

10 But then if you look at you start to

11 get in the medium doses because we haven't really

12 plotted the really high ones on the graph, it's six to

13 higher; you can see it, you can really begin to see a

14 plateau. And if you continue the if Dr. Rodriguez had

15 continued to plot the doses greater than 30, because

16 several of these studies have that, you would see that

17 the plateau is almost completely flat at or around the

18 25% attenuation.

19 DR. CHESTER RODRIGUEZ: Right. And

20 actually, one of the reasons why, I mean, I didn't

21 include the higher doses is because you will not get to

22 see them at this region in the low dose, which I think

23 is critical. When you have different studies that

24 differ up dramatically in dosing but yet the NOAELs and

25 the LOAELs are hard to distinguish, that to me is
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 86

1 remarkable and

2 DR. STEVEN HEERINGA: Dr. Bailar?

3 DR. JANICE CHAMBERS: I'm sorry.

4 Dose-response implies to me that you get

5 more response with higher doses and all, and everything

6 from 6.25 across there with those standard errors looks

7 like it's the same and the things that you're looks

8 like the dose-response curve below that, you're saying

9 is all NOAELs and therefore not an effect. So I really

10 just don't understand.

11 DR. STEVEN HEERINGA: Dr. Bailar and Dr.

12 Portier on this one, and then we'll switch.

13 DR. JOHN BAILAR: Oh. This PowerPoint

14 reinforces my concern about the use of NOAELs. There

15 are, what, six points there that are labeled NOAEL?

16 Every one of them was below control.

17 What's the probability that would occur

18 by chance alone, even though the individual points may

19 not be statistically significantly different than

20 control? I think there's, you know, a real indication

21 that there's something going on in that area that's

22 labeled NOAEL.

23 DR. STEVEN HEERINGA: Dr. Portier and

24 then--

25 DR. KENNETH PORTIER: Well, I just
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 87

1 wanted to respond to Dr. Chambers' point. I think what

2 EPA is doing here is model-fitting and then join a

3 conclusion from the model-fitting that there's a dose4 response pattern, and this is given in Figures 5.9 and

5 5.10 in the white paper.

6 So they're not doing pair-wise

7 comparisons, you're right. If you look at the 25 and

8 you look at the 15, they overlap and you say there's no

9 difference. But when you step back and actually fit a

10 curve to this; there is a significant fit.

11 The Hill curve fits, and the Hill curve

12 suggests that a small change in dose produces a small

13 change in response, and I think that's what they're

14 basing their risk assessment on a more model-fitting

15 exercise rather than a dose-comparing, and that's why

16 they're trying to get away from this NOAEL, you know,

17 kind of making this point, this dose is significantly

18 different from this other dose. The model says a small

19 change produces a small response, and the Hill model is

20 the one that they are basing it on.

21 DR. CHESTER RODRIGUEZ: Thank you.

22 DR. STEVEN HEERINGA: Dr. Legan.

23 DR. SANDRA LEGAN: Thank you, Sandra

24 Legan.

25 With regard to the comments that have
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 88

1 been made and a couple of other things that I'm aware

2 of about LH surges, I'd like to just ask a couple of

3 questions.

4 Yes, there's a large degree of

5 variability around these data points, and that is

6 probably almost certainly due to the fact that these

7 data were taken from one time point, the 1800-hour time

8 point. And that is generally over a group of animals,

9 the peak time of the LH surge under these photoperiodic

10 conditions.

11 However, it's important to remember that

12 if you look at any LH surge data in any species, the

13 peak time at the peak is very variable in terms of the

14 levels. So this is simply the nature of the data;

15 that's our problem, especially with small changes in LH

16 peaks that you're talking about in terms of their

17 attenuation at low doses of atrazine or any common

18 disruptor.

19 In having said that, though, I'm a

20 little bit concerned with the fact that you see a

21 plateau, as you've pointed out, at higher levels. And

22 no dose there, shown at least up to 30 mg/kg/day, has

23 suppressed the amplitude of the LH surge more than

24 about, what, 40%, 50%, let's say; is that right? Maybe

25 60% at most. It turns out that physiologically, well,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 89

1 only maybe 20% of the LH-surge total amplitude is

2 required for fertility in the female rat.

3 So the LH surge is a very large release

4 of pituitary hormone that is overkill as far as the

5 fertility of the animal is concerned. So I think we

6 need to keep that in mind, because as one of the

7 previous Panel members mentioned, the Hill equation or

8 the analysis is going to talk about very small changes

9 in the amplitude of a physiologic hormonal release, and

10 these small changes in amplitude will have essentially

11 no effect on the reproductive function of the female

12 rat. Even down to 50% or 60% of the loss of this

13 released hormone, they'll still ovulate their full

14 complement of ova, and they will be fertile. So I

15 don't--

16 The other thing is that if you're

17 talking about at doses of 30 milligrams and beyond, out

18 to 100 I think was on the subsequent graphs with the

19 linear relationships for the benchmark dose analysis.

20 If there's no further suppression of the

21 LH surge -- and as Dr. Chambers pointed out so well, it

22 plateaued if they're all the same after that, that I'm

23 not sure I understand how more atrazine exposure could

24 be detrimental, even to the rat.

25 I mean, there's no dose, all the way out
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 90

1 to 100 mg/kg. Well, that I can see from attenuating

2 the LH surge, is going to affect the fertility of the

3 rat.

4 And then having said that, the last

5 comment is if you have a plateau beyond 30 mg/kg and

6 you didn't use the third--you didn't use those two

7 higher doses in the analysis, the first analysis of the

8 dose-response that we just saw slide 55 or 56; but

9 those doses are added on to the benchmark analysis.

10 And they're not part -- I don't think they're part of

11 the dose-response, what dose-response you have up to

12 30.

13 So could you address those issues a

14 little bit, especially the latter one?

15 DR. STEVEN HEERINGA: Dr. Cooper, I see

16 you've come to the microphone. I assume that you've

17 been singled out to address this.

18 DR. RALPH COOPER: As soon as the

19 questions get hard and somebody throws something at me.

20 Ralph Cooper, USEPA.

21 I think some of the points you make are

22 right spot-on; but what I think is a little confusion

23 here and Chester put this slide up, they stopped in the

24 previous slide. I don't know how you can go back to

25 the one we had where you did your analysis where you
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 91

1 stopped at 30 and you have the plateauing take place,

2 okay? Then you're focusing on, if you look at that, it

3 stopped at 30. If you look at the data that we

4 submitted right here, just stop here a moment -- you

5 see we stopped at 75.

6 What you're seeing there is I don't know

7 what the difference is between 6.25 and 25; but as you

8 increase the dose beyond 75 -- and that's what I think

9 this slide that Chester brought up with the blocks on

10 it shows that 100 dose does bring it down statistically

11 different.

12 If you compared the 100 to the 12you

13 would see it was lower to the point where the higher

14 doses are even right at baseline to the analyst. So

15 you can get back down to baseline with the higher

16 doses.

17 In this 4-day exposure, though, that's

18 the pattern that was observed. And I guess it's mostly

19 where it was shallow dose-response that's there, it's

20 just that it was locked off in his analysis. And

21 there's been --

22 SPEAKER: Well, have we seen is that 75

23 through their data?

24 DR. RALPH COOPER: That right there is

25 the same.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 92

1 SPEAKER: 25.

2 DR. RALPH COOPER: Oh, I'm sorry, it's

3 25. We don't have it on here now.

4 SPEAKER: So there wasn't any data like

5 that in the Issue Paper, either.

6 DR. RALPH COOPER: Yeah, 75 isn't here.

7 We eliminated it.

8 SPEAKER: In this one.

9 SPEAKER: Figure 5.9, Figure 5.9 shows

10 the 75 dosimetric.

11 DR. CHESTER RODRIGUEZ: Yeah. So we

12 were actually concentrating on doses that were below

13 30. And the rationale for that is that that was the

14 highest dose in the 6-month study.

15 So just to be able to compare studies,

16 then we concentrated on that, on the range. And we

17 also felt like the higher doses, I mean, the human

18 relevance actually goes down, you know, because it's

19 unlikely that a human would get exposed to 50 or 100

20 mg/kg. So I think this is more relevant in the context

21 of human health of the risk assessment.

22 DR. STEVEN HEERINGA: Okay. Now we go

23 to Dr.--

24 DR. RALPH COOPER: I didn't hear his

25 answer.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 93

1 DR. STEVEN HEERINGA: Oh, sure.

2 DR. RALPH COOPER: The second part of

3 your question, Dr. Rodriguez. These studies were done

4 with young adult female rats that have amplitude, LH

5 surge amplitudes at, in our lab anyway, at 20, 27, 30

6 milligrams per mil, and that's somewhere probably. as

7 you mentioned, the 80% excess in the amount of LH

8 that's, quote, unquote, "needed" for ovulation. So the

9 females will in this particular instance, you wouldn't

10 anticipate there would be any disruption of ovulation.

11 We chose the 75 as our highest dose

12 based on previous work that showed that 2 weeks of

13 dosing with 75, they stopped cycling. So, again, it is

14 this cumulative effect kind of thing that we were

15 working with, that in one cycle you may not see an

16 adverse outcome but continued dosing may eventually

17 manifest itself.

18 The second thing is that this is perhaps

19 in the 2000 SAP, the concern about atrazine and LH was

20 brought up because of the extended exposure bringing

21 about premature reproductive senescence.

22 And you know in the middle-aged female

23 that amplitude of that LH peak is not at 30. It's

24 down; it could be as low as 10. So now a small

25 decrease might actually drop them below the ovulatory
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 94

1 quota. So that's what the framework that we were

2 working is in those studies.

3 So you're correct in saying that I

4 wouldn't anticipate an adverse outcome of a single5 cycle exposure like this. And we're looking at that,

6 because one of the things is as you raise up the dose,

7 you saw fewer animals showing the expected proestrus

8 here. So there's something going on there but we just

9 don't know what, we haven't got the values yet.

10 But these lower doses here are just, as

11 we said in my thing, predictors for changes that are

12 occurring that are, that bode some type of adverse

13 outcome if dosing was extended.

14 DR. STEVEN HEERINGA: Dr. Legan, follow15 up?

16 DR. SANDRA LEGAN: Thank you.

17 I agree with your comments about the

18 fact that you might not see this effect over a single

19 cycle. Have you done in regard to what you said just

20 now, have you done a comparison of when the LH surge

21 actually drops below a certain amplitude, like below

22 about an 80% suppression, and the timing of when the

23 cycles stop, because in order for the effect on the LH

24 surge to be meaningful in terms of the treatment or the

25 effect of atrazine on it, that has to coincide.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 95

1 They can't just miss cycles when the LH

2 surge is suppressed after 75. What was it, 2 weeks at

3 75 mg/kg, I mean those things, the timing has to be

4 just right to be able to make that conclusion.

5 DR. STEVEN HEERINGA: Dr. Lowit.

6 DR. ANNA LOWIT: I think there was one

7 more piece of your question that hadn't been answered

8 yet, the part around when we did our benchmark dose

9 modeling as part of what's in Appendix C. The 75 mg/kg

10 dose was used in that analysis, so even though it's not

11 on this graph, the 75 dose was used in those

12 calculations.

13 DR. SANDRA LEGAN: It's on slide 62 and

14 63, 50 and 100 were used, not 75 I think; am I right?

15 And I don't know where -- that was part of the

16 question, why you're including those and where I guess

17 those doses were in the total atrazine where you

18 labeled disappearance data.

19 DR. RALPH COOPER: But the doses --

20 DR. STEVEN HEERINGA: Dr. Cooper?

21 DR. RALPH COOPER: The doses that were

22 submitted in that internal report that's in the Docket

23 had the 75, and it had the full day of proestrus

24 changes of the characterization of surge in all of the

25 dose groups at all the time points, and this one was
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 96

1 pulled out of that quota dataset; so I don't know.

2 DR. ANNA LOWIT: Dr. Legan, I think it's

3 important to keep in mind there are two sets of

4 quantitative calculations here. There is the set of

5 calculations that Dr. Rodriguez has the lead on around

6 pharmacokinetics, particularly with the Thede study,

7 looking at--what place--around slide 60 that that

8 pseudo-steady state graph.

9 Then there is a separate set of

10 calculations, primarily done by Dr. John Liccione, that

11 are contained in Appendix C that are benchmark dose

12 modeling estimate calculations, and they're important

13 distinctions, because the T-study is looking at plasma

14 concentrations over a wide range and then the benchmark

15 dose modeling is intended to derive a point of

16 departure for assessment purposes. They're certainly

17 interrelated as you think about using the internal

18 dosimetrics, but they are two separate sets of

19 calculations.

20 But in the benchmark dose modeling, the

21 75 dose was used?

22 DR. STEVEN HEERINGA: Figure 5.9 on the

23 Issue Paper shows that one.

24 Dr. Krishnan, and then Dr. Greenwood.

25 DR. KANNAN KRISHNAN: Just a couple of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 97

1 clarifying questions, too. This relates to slide 63.

2 Can I see slide 63, please? Yeah.

3 I was looking at the model part of it. I

4 just want to make the point that zero dose doesn't

5 correspond to zero AUC here, it's like that's suggested

6 by the equation more rather than the details and

7 origin. So is it like it takes a 0.7 mg/kg or

8 something to have zero AUC, or am I misreading

9 something here?

10 SPEAKER: I have no idea, actually.

11 DR. CHESTER RODRIGUEZ: I think you're

12 right, actually. One way of actually doing this thing

13 of integration is to force it through zero. I didn't

14 do that. I just felt like, you know, we may lose some

15 information when you force it to zero, and I just

16 wanted to get a sense of what the Y intercept would be

17 without modifying the data too much by forcing it

18 through the origin.

19 DR. KANNAN KRISHNAN: And the second q

20 of clarification is that the total dose or the

21 equivalence that you calculated, was it always adding

22 milligrams to milligrams, or in any of the cases would

23 you do an equivalence based on millimoles? In other

24 words, if you have data on the metabolites, one would

25 do millimoles using their molecular weights, add them
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 98

1 up, and then once you have total millimole then

2 multiply it with the atrazine molecular weight to have

3 the atrazine equivalence.

4 So my question is has any attempt of

5 calculations and computation based on the molecular

6 weights of the individual metabolites and atrazine

7 done, or was it just simply adding milligrams all the

8 time straight?

9 DR. CHESTER RODRIGUEZ: It was simply

10 adding actually milligrams. We just felt that the

11 molecular weights, you know, are so similar, and what

12 you're really looking are actually radiolabeling

13 equivalence. So it really doesn't matter what the

14 species is as long as it has the radiolabel. So that

15 was the basis for just adding milligrams.

16 DR. STEVEN HEERINGA: Dr. Greenwood, do

17 you want to talk?

18 DR. RICHARD GREENWOOD: I may be missing

19 something here, but if you're giving the same dose for

20 4 days, given that you've got an almost perfect

21 regression line between dose and area under the curve,

22 is there really that particular area under the curve?

23 I mean, I may be missing something here;

24 but it's easier to measure the dose than the area under

25 the curve, so why would you use area under the curve?
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 99

1 I mean, you said it's more useful; but I don't see why

2 if you're going to give the same dose 4 days.

3 DR. CHESTER RODRIGUEZ: Right, so

4 DR. RICHARD GREENWOOD: Sorry.

5 DR. CHESTER RODRIGUEZ: No, that's okay.

6 That goes back to the figure that I showed you before,

7 what a single high dose had a very modest effect,

8 whereas a much smaller dose, but even for at least 3

9 days, had a very pronounced effect. So that actually

10 suggests to me at least that duration of exposure is

11 actually critical, okay? And on that basis, then the

12 area under the curve is justified as an internal

13 dosimetric.

14 DR. STEVEN HEERINGA: That would be

15 slide 46 that shows that.

16 Dr. Lowit?

17 DR. ANNA LOWIT: Adding to what Dr.

18 Rodriguez said, I think the observation you've made

19 about the linearity of the lines at steady state,

20 because I think what Dr. Rodriguez basically just added

21 to that was that steady state is the required event for

22 these linear lines. We've asked ourselves that

23 question, too, Dr. Greenwood, and I think there's a

24 couple of ways to think about this. It's certainly

25 something that we'd like to hear your feedback on.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 100

1 From a simplicity standpoint, it would

2 appear from those lines that doing the sort of matching

3 of rolling-average water concentration to an

4 administered dose of atrazine is actually a pretty

5 reasonable thing to do; that's what those graphs would

6 suggest.

7 As we think about, as you hear Nelson

8 Thurman's presentation later, one of the important

9 questions that we'll be asking in the coming months is

10 the adequacy of the current monitoring data. And to

11 understand peaks that we don't have in the current

12 dataset, data peaks, we have peaks in certain datasets;

13 but had you had more monitoring, theoretically they

14 could have been higher.

15 Something that we've talked about is

16 that these area-under-the-curve metrics may help

17 provide conceptual help interpretation of some of that

18 work that Nelson will be doing in the coming months.

19 And so by using the AUC metrics in those

20 I don't know the right term, because Nelson is not

21 sitting here to tell me the right term; but as we move

22 through those statistical analyses to evaluate that,

23 the AUC metric may help us interpret missing piece in a

24 theoretical way easier than will be rolling averages

25 that can be a little bit hard to get your mind around
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 101

1 what exactly are the rolling average of a week or two

2 weeks or three months, or six months for that matter.

3 So that's something to think about.

4 DR. STEVEN HEERINGA: Dr. Akana.

5 DR. SUSAN AKANA: I'm interested in the

6 idea of the pseudo-steady state, and this is a

7 measurement of all the radiolabeled core. But is it

8 correct that there are different bio efficacies of

9 atrazine versus some of its metabolites like that?

10 DR. CHESTER RODRIGUEZ: To my knowledge,

11 there is no information on the relative activities of

12 the metabolites compared to the parent. So in the

13 absence of that information, I think it's justified to

14 use total plasma triazines.

15 DR. SUSAN AKANA: I'm not sure, but I

16 thought I recollected SAP to earlier this year that

17 that had a very different effect on the HPA axis and

18 some of the other metabolites and atrazine itself. So

19 I'm not pointing to LH. It may have some other effects

20 on other hormone systems that are not being recognized.

21 DR. STEVEN HEERINGA: Dr. LeBlanc.

22 DR. GERALD LEBLANC: Gerry LeBlanc.

23 Continuing on that line concerning

24 pseudo-steady state, based upon the information we see

25 and I think it's reasonable to assume that a pseudo-
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 102

1 steady state atrazine is not predominant triazine and

2 that among the dealkylated products, that is the

3 dominant triazine. I was wondering if you know

4 anything about GSH conjugates, whether to what

5 proportion they exist as pseudo-steady states?

6 DR. CHESTER RODRIGUEZ: I can say that

7 based on mass-balance pharmacokinetic studies,

8 glutathione conjugates can account up to 30% of the

9 metabolism, and of course you'd only have one, right?

10 You have at least four glutathione conjugates. They

11 are presumed to be not active because of the lack of

12 the chlorine on the triazine ring. But, actually,

13 there is no information actually to support that that

14 I've seen.

15 So by using, then, total triazines we

16 feel we are being conservative, just in case some of

17 the metabolites are active or not active. But in the

18 absence of relative activity information, I think this

19 is the best approach.

20 DR. STEVEN HEERINGA: Dr. Bailar.

21 DR. JOHN BAILAR: I'd like to hear a

22 little more about why you consider this a conservative

23 approach. I can imagine situations in which it might

24 be quite the opposite.

25 DR. CHESTER RODRIGUEZ: Quite the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 103

1 opposite in what sense, I mean?

2 DR. JOHN BAILAR: In the sense that if

3 it's a single complement of the total that's the bad

4 actor, you could be diluting that effect by throwing in

5 all the others.

6 DR. CHESTER RODRIGUEZ: Right, that is a

7 possibility. But in the absence of that information, I

8 think this is the best approach. This is the current

9 state of the science.

10 DR. JOHN BAILAR: I think that's, you

11 know, a reasonable position, but I'd be happier if you

12 said something more about it.

13 DR. CHESTER RODRIGUEZ: You have a very

14 good point, thank you.

15 DR. STEVEN HEERINGA: Dr. Lowit.

16 DR. ANNA LOWIT: I think it's important

17 to keep in mind there have been a couple of comments

18 now about the metabolites that there are normal

19 metabolites, and there is very little dose-response

20 data for anything but the administered dose to

21 atrazine.

22 There's...Susan Mollis, who sits behind

23 me, has done some outcome data with some of the

24 metabolites. There is certainly some DACT data out of

25 the McMullin lab and there has been a couple of other
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 104

1 groups. But it's, it's sparse, the dose-response is

2 generally very poor. So we don't want to over3 interpret that data.

4 DR. STEVEN HEERINGA: Dr. LeBlanc?

5 DR. GERALD LEBLANC: I don't know if it

6 needs to be said, but just to follow up on John's

7 comment, you referred to your approach being a

8 conservative approach, and I see it as a conservative

9 approach. I mean, you're looking at total triazine

10 internal dose without any consideration of which

11 component is toxic, which component isn't toxic; so it

12 seems reasonable to me, as well.

13 DR. STEVEN HEERINGA: Additional

14 questions or clarification for Dr. Rodriguez's

15 presentation before we conclude?

16 I think Dr. Portier has one question.

17 DR. KENNETH PORTIER: Oh, I have the

18 naive question of the day. When you talk about liter

19 there, is that the liter plasma internal dose or is

20 that liter water administered dose? It's not clear in

21 the documentation and you switched back and forth in

22 the discussion, so I got lost.

23 SPEAKER: Right.

24 DR. STEVEN HEERINGA: Dr. Rodriguez.

25 DR. CHESTER RODRIGUEZ: Are you talking
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 105

1 about the units?

2 DR. KENNETH PORTIER: On the --

3 DR. CHESTER RODRIGUEZ: Yeah. So these

4 are liters of blood, right, so that's actually the

5 units of the area under the curve are units of

6 concentration times time, right? It's easy to account

7 levels, that's more or less duration, and that's why

8 you see that hour at the end. But, yeah, we're talking

9 about plasma levels.

10 DR. STEVEN HEERINGA: Dr. Lowit.

11 DR. ANNA LOWIT: At this point in time,

12 we have not actually taken any of the internal

13 dosimetric data and linked it to any of the water data;

14 so I'm sorry if we weren't explicit in some of those

15 metrics. We haven't actually made that connection yet.

16 DR. STEVEN HEERINGA: Okay. Well, it's

17 been I think a productive start, and we've got a long

18 way to go. What I'd like to do at this point unless

19 there are additional questions from Panel members for

20 Dr. Rodriguez, Dr. Lowit or Dr. Cooper, why don't we

21 take a lunch break, and we're scheduled to return here

22 at 1:15.

23 So we'll see everybody back at 1:15 for

24 two additional sessions this afternoon, and again I

25 think that the pace of these sessions will be very
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 106

1 favorable. We'll have time to ask questions and make

2 sure everybody is clear on the content and look forward

3 to the afternoon.

4 Everybody, see you at 1:15.

5 (WHEREUPON, a luncheon recess was taken.)

6 DR. STEVEN HEERINGA: Good afternoon,

7 everyone. Welcome back to the afternoon session of our

8 first day of our FIFRA Science Advisory Panel Meeting

9 on the topic of the Re-Evaluation of the Human Health

10 Effects of Atrazine: Review of Non-Cancer Effects and

11 Drinking Water Monitoring Frequency.

12 At this point we are in the process of

13 hearing the formal scientific presentations that

14 accompany the Issue Paper, and we heard this morning

15 from Dr. Christensen and Rodriguez, and I think at this

16 point we are going to be turning to the issues related

17 to the evaluation of water-sampling strategies and the

18 frequency of monitoring, and I think Nelson Thurman and

19 Mary Frankenberry.

20 MR. NELSON THURMAN: Okay, so we're

21 transitioning from toxicity to exposure, and the reason

22 we're focusing on the drinking water is that for

23 atrazine drinking water is the major contributor to

24 aggregate exposure of human health. So it's important

25 for us to have a reliable estimate of that drinking-
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 107

1 water exposure.

2 And while I'm the one talking here, Mary

3 Frankenberry was instrumental in helping put all the

4 report together and putting this together; so it's a

5 team effort, so it's not just the guy talking.

6 Some of the conditions of re7 registration for atrazine included some monitoring

8 programs. One focused on community water systems for

9 human health, and the requirement was to monitor those

10 community water systems that through quarterly Safe

11 Drinking Water Act monitoring were above a certain

12 concentration. Those are considered to be the more

13 vulnerable systems for monitoring.

14 The program was designed to provide an

15 exposure estimate for a 90-day period of concern. So

16 there was weekly monitoring during the time frame when

17 you most expect to find atrazine in the waters,

18 generally beginning sometime in April and running

19 through August more or less, depending on the time and

20 the area.

21 There was a second monitoring program

22 that's focused on ecological exposure and particularly

23 impacts of atrazine on aquatic plant communities. That

24 monitoring tend to be up toward the headwater streams.

25 You're going to hear me refer to information and
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 108

1 lessons we've learned from both of those programs.

2 We've had, this is the second SAP that has focused on

3 the human-health part of the community water-system

4 monitoring and how that relates to the human health

5 assessment. Most of this focus of that SAP and this

6 has been on the frequency of monitoring: how frequently

7 do we have to monitor to capture the exposures of

8 concern.

9 There were two other SAPs, one in

10 December of 2007 and a followup in May of 2009, that

11 looked at the ecological effects exposure. The focus

12 in that one was primarily more of a spatial rather than

13 temporal, but there was some sampling frequency

14 involved in that, but it was more or less looking at

15 what contributes to vulnerable systems.

16 And so you'll hear me refer to both, but

17 those are the SAPs that we have addressed before.

18 And so as a quick recap of some of the

19 monitoring issues that we raised at the SAP. Like I

20 mentioned, the original monitoring design looked at was

21 based on providing exposure estimates for a 90-day

22 period of concern. It looked at weekly sampling during

23 that.

24 You've heard some of the presentations,

25 and we may end up with a different duration of concern
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 109

1 as a result of our deliberations, and it leads to the

2 question: Weekly sampling was adequate for a 90-day

3 duration of concern. If we have a shorter duration,

4 how adequate is that existing monitoring? Do we need

5 more monitoring, or can we provide some type of

6 confidence bounds or possibly even a safety factor that

7 could account for the differences, based on the

8 monitoring?

9 And I will point out that these

10 questions, where we're focusing on atrazine, these

11 questions have broader implications for other

12 pesticides as well. How much can we derive from

13 existing less frequent sampling, and how frequently

14 does sampling have to be to adequately characterize the

15 various exposures?

16 The main points we heard from the

17 September SAP regarding monitoring, the biggest one

18 was, well, to design a study you really need to know

19 the duration of concern, because honestly once you know

20 that it's easy to design a study, given whatever

21 confidence bounds you need.

22 For shorter durations, the estimates of

23 the peak exposures become more critical. For longer

24 duration, obviously it's less critical. We haven't

25 determined what that duration of concern is yet, but
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 110

1 we're looking at approaches that will work fast once we

2 have that in place. And we realize that one approach

3 may work better for short duration of exposure and

4 other approaches may be adequate for longer duration,

5 so we may be looking at more than one approach,

6 depending on where we end up.

7 The SAP was concerned that some of the

8 presentations we were looking at community water

9 systems that had weekly sampling intervals, and the

10 concern was those weekly sampling intervals may be

11 missing some short-term peaks and may be excluding out

12 some of the variability and providing a biased

13 representation of the actual concentration profiles.

14 The best set of more intensively sampled

15 data, which would include either daily or near daily

16 sampling during the time when we are likely to find

17 atrazine, is actually for ambient waters not

18 necessarily associated with the source water, the

19 community water system. Some of them we are looking

20 primarily at Heidelberg College's data.

21 Some of them are on rivers and streams

22 that are large enough to support a community water

23 system, if they don't. Some of them, like some of the

24 atrazine ecological exposure, are in the headwaters and

25 generally farther upstream than most of the community
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 111

1 water systems, although there are some smaller systems

2 that are not too far off from that.

3 So we'll talk a little bit more about

4 what we're looking at in that regard and our proposal

5 for how we think we would use that. And one of the

6 other things, the SAP recommended combining a

7 regression-based model such as USGS's watershed

8 regression on pesticides with creating random function

9 models.

10 They also suggested looking at extreme

11 value theory. We've taken a closer look at some of

12 those approaches, and we're going to follow up on that.

13 We provided some information in the background paper on

14 that.

15 For today, we're going to focus on three

16 main issues that we're bringing for you related to

17 monitoring, and I want to point out that I can't count

18 that should be Questions 4.1, 4.2 and 4.3 up there.

19 So we're briefly going to touch on a

20 framework for monitoring studies based on

21 recommendations from the SAP and some of the lessons

22 we've learned.

23 We're going to talk to you a little bit

24 more about what intensively sampled datasets we think

25 we can use and how we propose looking at them, and then
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 112

1 we're going to follow up with some of the approaches

2 interpreting monitoring data based on the feedback we

3 have from the April SAP.

4 So to begin with, this is, I guess when

5 you work on it for a while and work on some of these,

6 some of this starts to seem like common sense; but it's

7 probably a pretty good idea to capture down in writing

8 so that we have a chance to take a look at it.

9 We're looking and what we provide is a

10 framework for what we would describe more as targeted

11 monitoring study. You are not likely to be able to

12 monitor everywhere a pesticide occurs. So what we want

13 is to have a monitoring study that focuses on the area

14 where the pesticide is most likely to be found.

15 Models like WARP or other spatial data

16 layers can be used to identify vulnerable sites, not

17 just in pesticide use but also folding in some of your

18 hydrologic soil, weather factors that would drive

19 exposure.

20 In the same manner you're not

21 necessarily likely to sample throughout the year, you

22 may not need to sample throughout the year; but what

23 you want to do is target your intensive sampling during

24 the period where you're mostly likely to find the

25 pesticide in water, which is going to be at or around
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 113

1 the time when it's likely to be applied and then for

2 several, depending on the mobility and the half-life of

3 the pesticide, for a period of time afterwards.

4 For something like atrazine, which is a

5 pre-emergent herbicide, it's pretty easy to define that

6 time frame. For other pesticides, it may be a little

7 more difficult; but it is a way to focus that

8 monitoring.

9 The sampling frequency needs to be based

10 on the toxicological exposure duration of concern,

11 sampling more frequently if you've got a short duration

12 and less frequently if longer duration. What you want

13 to see here is you need to balance the cost of the

14 study with the needed accuracy that you need.

15 And one of the reasons we're looking

16 closely at the intensively sampled data is we want to

17 be able to, depending on your sampling frequency, can

18 we provide some type of confidence bounds that you're

19 likely to get in a monitoring study or, alternatively,

20 can we provide some type of a safety factor that can be

21 used to address with less frequent monitoring.

22 One of the recommendations came out in

23 the last SAP was consider using auto-samplers to

24 collect the data for exposure periods of interest.

25 We're seeing more use of auto-samplers; it's still not
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 114

1 commonly used for at least the pesticide monitoring we

2 see, but we do see it being used a little bit more

3 often as the technology improves.

4 There are a couple of ways you could do

5 that. One is to use it to fill in for events in

6 between your regularly sample intervals. For instance,

7 you might have a flow triggering, so if you have a

8 runoff event that it triggers increase in flow.

9 Another way may be to use auto-samplers to collect

10 regular intervals over your time period of concern so

11 that you end up with a time-integrated average over

12 that time period. So there's a couple of ways of

13 looking at that.

14 And the idea, the concept of possibly

15 using auto-samplers to integrate over the time period

16 of concern plays into what Dr. Rodriguez was talking

17 about in terms of looking at how do we relate

18 monitoring to an area under the curve approach where we

19 may not need to capture every single peak, but what we

20 need to do is capture what that exposure is over that

21 duration of concern.

22 Now, targeted monitoring isn't new, and

23 I think what we've seen with atrazine is a pretty good

24 example both in the community water system and in the

25 ecological exposure, a pretty good example of how that
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 115

1 can be applied.

2 For the ecological exposure monitoring,

3 we used WARP to identify the watersheds that were most

4 vulnerable for atrazine exposure, based on WARP

5 estimates. The dark-blue watersheds you see here were

6 the ones that were the most vulnerable based on WARP.

7 And then what we did was have a

8 spatially balanced random selection process to identify

9 candidate watersheds for monitoring. And the results

10 of that study and through some of the followup

11 monitoring that is going on now are helping us better

12 define those vulnerable areas and how to target and

13 pinpoint those areas.

14 For the community water systems, the

15 approach was a little bit different, in that the

16 community water systems were identified based on Safe

17 Drinking Water Act monitoring on a quarterly basis.

18 The interesting thing is that most of those community

19 water systems that were identified -- and they're shown

20 as dots on the, on the map -- happened to fall into

21 that most vulnerable tier of watersheds; so that's more

22 reinforcement in terms of identifying vulnerability

23 based on something like WARP. So it does show that

24 there's ways to target the monitoring and that would be

25 more efficient in the way that where we have to target.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 116

1 Both the models also targeted in time

2 with more intensive monitoring during the time period

3 coinciding with when atrazine was likely to be applied,

4 in this case to corn or sorghum, and continuing mostly

5 through the summer months in that regard.

6 For the ecological exposure monitoring

7 study, a subset of those sites included auto-samplers

8 to complement the regular 4 days' grab samples that

9 were taken at those sites, and that subset of

10 monitoring sites -- and they're not shown on there, but

11 they spread from Ohio, Indiana, Illinois and Missouri -

12 - that subset of sites are one of the subsets that

13 we're looking at because it provides daily or near

14 daily monitoring during the actual atrazine use area.

15 And that leads us to the second point,

16 which is the need for a set of intensively sampled

17 datasets. And we agree with the SAP's recommendation

18 that this is important.

19 For community water systems, the most

20 intensively sampled monitoring data we can find is

21 basically the weekly samples that we see that Syngenta

22 has been doing for the last several years with

23 community water systems here, and then there are a few

24 other precursor programs that also sampled weekly

25 during that regard.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 117

1 We do know of some monitoring data that

2 is sampled more intensively. Heidelberg College, which

3 is now Heidelberg University, has been doing intensive

4 sampling in several streams of various sizes in Ohio

5 for a number of years, and so that provides a wealth of

6 data over time in one location where at least during

7 the likely times, times you're likely to find atrazine,

8 you have daily or near daily monitoring.

9 As I mentioned, the atrazine ecological

10 exposure monitoring program also had its subset of

11 sites that included daily or near daily sampling,

12 ranging primarily from April through the end of August.

13 That was spread out more over from Ohio westward to

14 Missouri and I think actually into Nebraska as well.

15 So it covers a little broader cross-section of the

16 atrazine use area. Some of those will have from two to

17 five years of data in that regard.

18 We think these datasets are critical for

19 determining confidence bounds in monitoring estimates

20 that help us to evaluate less frequent monitoring, and

21 what we're proposing to do is to use those datasets to

22 simulate different sampling frequencies to evaluate

23 what kind of confidence bounds we get in that with the

24 idea of possibly saying if you have, depending on what

25 your duration of concern is, a sampling frequency of X
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 118

1 days or X weeks might have a safety factor involved

2 associated with that.

3 We also think those are important if

4 we're looking at ways of estimating exposure in between

5 monitoring data points, and so this is a way for us to

6 evaluate how well those work.

7 As I've been talking about, we mentioned the Heidelberg

8 monitoring data and the atrazine ecological exposure

9 monitoring. Those are monitoring data for streams and

10 rivers.

11 We don't really have the same type of

12 daily or near daily monitoring for reservoirs. We're

13 looking at a couple of approaches: one, the streams and

14 rivers may use as a surrogate. We'd expect them to be

15 able a little more flashy in nature than what you'd see

16 in reservoirs.

17 The other is to use a model, pesticide

18 root zone model exposure analysis modeling system,

19 which is what we use to estimate our drinking water

20 exposures for reservoirs. It gives us daily

21 concentrations over typically 30 years of weather data.

22 So that's something we are considering as an

23 alternative for the reservoirs.

24 One issue that was raised at the last

25 SAP is whether drinking water treatment smooths out
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 119

1 those atrazine peaks so that treated water would be

2 less variable than the source water. What we know

3 about atrazine is that your conventional drinking water

4 treatment, which would be a process of sedimentation,

5 flocculation and chlorination, does not remove atrazine

6 from the water.

7 In general, you need an activated

8 charcoal treatment to remove atrazine from the water.

9 You can have, and activated charcoal can be used to

10 remove things like odor, it can remove things like

11 other organics other than atrazine. So the

12 effectiveness depends on how much you need and whether

13 you're targeting atrazine or something else.

14 What we did is we went back and looked

15 at 44 of the community water systems that had detects

16 of total chlorotriazines of 15 ug/L, and there's

17 nothing magical about that other than we wanted to

18 have a cutoff of looking at the more vulnerable systems

19 based on higher exposures, and that just seemed to be a

20 cutoff that worked given the time we had. So don't put

21 too much weight in that actual number; it was just to

22 help us get a manageable set of community water system

23 data.

24 We compared the paired source and

25 treated water samples for that, and what we found is
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 120

1 that for a third of those community water systems,

2 there was no difference in concentrations between the

3 source water and the treated water. For roughly half

4 of the systems, we saw some reduction in total

5 chlorotriazines as it went from source to treated

6 water; but it wasn't a complete removal. And roughly

7 about a sixth of the sites did we see complete removal

8 of the triazines as a result of treatment.

9 This plot here is an illustration of one

10 of the sites where the total chlorotriazines were

11 similar in both the source water, which you see in

12 blue, the blue and blue dots, and the treated water,

13 which is the magenta dots. So the bottom line, as you

14 see, it very much follows the same pattern.

15 The bottom line is that if we focus on

16 source water, it emphasizes, first of all, the

17 importance of protecting source water. Secondly, it

18 removes another potential source of variability that

19 you would have to account for whenever you're doing the

20 analysis.

21 But thirdly, it also reflects the type

22 of community water systems we see right here. So we

23 believe it's reasonably protective without being an

24 overkill. In those systems where the treatment does

25 knock out atrazine altogether, those can be addressed
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 121

1 separately in the risk assessment.

2 For terms of looking at variability,

3 though, this gives us a chance to use some ambient

4 water data and provide what we think is a reasonable

5 assessment of the type of variability you might see.

6 In the background document, we propose

7 assessing the uncertainty in the sampling frequency by

8 matching the weekly community water systems with the

9 more intensively sampled monitoring datasets.

10 And I want to try to explain what we

11 mean, because this is a comment we've wrestled with and

12 we still are asking ourselves: how well is this going

13 to work? But the idea is that for the community water

14 systems you have what we describe as a chemograph

15 shape, which looks at the number, the duration, the

16 spacing, the magnitude of your peaks or spikes that

17 occur during the monitoring season.

18 And the question we have is: can we

19 provide some general characterization of chemograph

20 shape or some classifications of chemograph shape where

21 we might be able to match a community water system with

22 weekly samples to one of the more intensively sampled

23 datasets.

24 And the idea is that if we can more

25 closely match these monitoring datasets based on
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 122

1 chemograph shapes, it may give us a bit more confidence

2 in analyzing the uncertainty bounds in that.

3 I do want to caveat this. We can start

4 with an intensively sampled dataset where you've got

5 daily or near daily samples, and we can create a

6 smoother chemograph based on, for instance, weekly

7 sampling. But you can't necessarily start with weekly

8 sampling and build in your daily peaks in between.

9 What we can do is that we can use that

10 intensively sampled dataset to provide some confidence

11 bounds of good exposure estimates we might see in

12 between.

13 So when we're talking about that, that's

14 where our thinking is. It is not to take a 7-day

15 sample and create this out of air but to provide some

16 confidence bounds in what we may have missed with the

17 sampling frequency, in this case the weekly sampling.

18 So we've done some preliminary analysis

19 on the intensively sampled Heidelberg and ecological

20 exposure monitoring datasets. We provided that

21 analysis in Appendix D2. I just want to touch on a few

22 things. I just point out what we did in this, we were

23 focused on that April to August time frame when we have

24 the most intensive sampling going on, and just for a

25 preliminary analysis we sampled those chemographs at 7-
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 123

1 day fixed intervals.

2 So we had seven separate fixed-interval

3 samples on that. And that's just to give us a feel for

4 whether we can work with that. If we were to do

5 further analysis then what we would really need to do

6 to simulate the way Syngenta did the monitoring on this

7 is to do bootstrapping analysis within weekly sampling

8 intervals, so it could occur at any time within that

9 regard.

10 Then what we also did is we compared the

11 number of spikes, the maximum detections. We also

12 looked at maximum 4-day average and the number of days

13 when those 4-day averages exceeded 20 to 40 ug/L.

14 These were numbers we just picked to work with.

15 These are not, once we determine the

16 magnitude and duration of concern, that's what we'll

17 ultimately work with; but this was just to give us an

18 idea of how we might work with that.

19 The numbers exceeding that period - as

20 a matter of fact, let me just go to the next slide.

21 Our thinking here is, particularly if

22 you're looking at something we know is not that one

23 peak exposure where you so you may have one day that

24 triggers the effect but it may be the period has to go

25 long enough until you get to your, as Dr. Rodriguez
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 124

1 explained, in a pseudo-steady state, then you probably

2 need more than one day to do that. So that's why we

3 are looking at the days where you might be exceeding

4 your, your, the threshold value, whatever it turns to

5 be.

6 This is a sampling from one year from

7 the Maumee River, part of the Heidelberg monitoring.

8 It's a relatively simple chemograph as far as those go.

9 You've got one large short-duration peak that's pretty

10 much driving your exposure, and you have a few smaller

11 peaks.

12 Just to get you oriented, the blue line

13 you see here is the daily concentrations measured at

14 this site. The dashed lighter-blue line are your

15 rolling 4-day averages. The red arrows point to the

16 peaks or spikes that we've identified in that

17 monitoring dataset.

18 Now if we are to take sampling at weekly

19 sampling intervals -- and what you see here, the red

20 dots show the weekly sampling intervals for one of the

21 seven potential intervals. The red line you see there

22 is just to help to see the shape of the chemograph.

23 What you do see is you start to see a little bit of

24 data-smoothing going here; you see some of the smaller

25 peaks are cut off, some of the valleys in between those
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 125

1 peaks are cut off, you get some smoothing.

2 If the sampling is timed right, though,

3 and this timing happened to hit your peak exposure, you

4 still get a shape that is fairly reflective of the

5 actual chemograph shape; however, if you were to take

6 those samplings, start those sampling intervals a

7 couple days later, you're likely to miss this short8 duration peak altogether, and in this regard, this is

9 what's driving your exposure. So if we were looking at

10 a short duration of concern, this would be missed

11 altogether.

12 And so this is one of the questions we

13 were asking in terms of doing this analysis: Can we

14 provide some type of a confidence bound around the

15 exposure estimates you get from weekly sampling; or

16 another way is some estimate of what's the probability

17 of missing the peaks of a certain duration of certain

18 exposure concentration?

19 I'm going to move to a little bit more

20 complex chemograph. This happens to be when you've got

21 two fairly large very short-duration spikes and a

22 number of smaller short-duration spikes that hit in

23 between, do not, you know, for now these actual

24 concentrations measured here are higher than what we've

25 seen in any of the community water system monitoring.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 126

1 So we're not really concerned at this point about the

2 magnitude of the exposures; what we are concerned about

3 is how well we capture the actual exposure involved in

4 that.

5 In this particular area, you start

6 seeing, the more short-duration spikes you get, you

7 start seeing, even when you start capturing some of

8 these, there is still a lot of really small spikes,

9 peaks and valleys that are missed. And if you time it

10 right, it is possible even with 7-day intervals, which

11 we tend to think is a very robust monitoring data for

12 most pesticide, even 7-day intervals you can miss every

13 one of them, every one of those spikes.

14 This is the reason why we want to look

15 at more intensive monitoring datasets to get a feel for

16 what confidence we might have in exposure estimates

17 from less frequent sampling.

18 And I also point out that this is a good

19 example of one of the reasons that I think the April

20 SAP recommended that when you start looking at exposure

21 estimation methods, you need to look at methods that

22 have the possibility of estimating exposures that are

23 greater than the maximum that you measure. And so

24 these are some of the things that are driving where

25 we're going now.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 127

1 So if we take a look at preliminary

2 analysis just to kind of sum up that section, the

3 chemograph shape, and particularly the duration of

4 frequency of the peaks and how much of an overlap we

5 get between peaks, whether they're separate or closer

6 together, it's very critical to sampling analysis that

7 we do.

8 As we look at this even with the more,

9 with 7-day fixed intervals, you can start, you can see

10 the effect of the data-smoothing, which comes back to

11 reinforce what the April SAP said and expressed as a

12 concern.

13 All that said, we believe that with this

14 analysis supports more strongly than before the need

15 for using intensive monitoring with daily sampling so

16 that we can get the expected during that expected

17 exposure period; so it's a critical to evaluate less

18 frequent monitoring samples and strategies in that

19 regard.

20 I want to close with an update on

21 approaches we've been looking at for estimating

22 exposures between sampling points, because we still

23 have weekly sampling. We've got a lot of years of

24 monitoring with weekly sampling. And we may find that

25 that weekly sampling interval is adequate if we can
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 128

1 provide some means of estimating exposures in between,

2 how best to estimate the exposures in between that.

3 We have a lot of pesticide monitoring

4 data for other pesticides where the intervals are even

5 longer, and a lot of monitoring 2-week intervals are

6 pretty good. A lot of NACWA monitoring tends to be in

7 2-week and some of the times you do get weekly, but 2-

8 week is more common.

9 One of the things that we understand and

10 the comment made by the SAP is the common methods we

11 use for interpreting between sampling intervals, which

12 is a linear interpretation or a stairstep-type

13 approach. They're likely to underestimate the peaks,

14 especially for the short-duration exposures.

15 We presented some ideas on using

16 artificial neural networks as a way of estimating

17 exposures. We haven't given up on that. It's possible

18 that they may still be too complicated for easy use,

19 but it hasn't been something we've given up on. We've

20 looked at some of the other recommendations in that

21 time frame.

22 The April SAP suggested looking at

23 extreme value theory. From what we've read and looked

24 at, it works best where we have a lot of measurements

25 over long periods of time.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 129

1 We did find a paper by Huang and

2 Batterman that used both deterministic and still3 casting modeling to generate 1,000 years of data, and

4 then provided somewhat characterizing potential

5 exposures based on that. That may have some

6 application to what we're doing. It may also be

7 complex, but we are taking a look at that.

8 As far as kriging methods, kriging is

9 generally used for geospatial assessments, although it

10 does lend itself to temporal assessments because you

11 still have a, you have a similar autocorrelation-type

12 approach. It assumes a stationary mean and variance

13 that's critical.

14 The SAP recommended that we might

15 estimate the correlation structures across the pooled

16 systems, and your other recommendation which we're

17 looking at in more detail because we think there is

18 some real promise there is to combine it with a

19 regression model such as WARP.

20 So we've been looking at that. And

21 we've been looking at some of the USGS modeling

22 efforts, and there's some promise there that we're

23 following up on.

24 We've done some exploratory kriging

25 analyses using some of the intensive monitoring dataset
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 130

1 since we put out the background paper. And by "we", I

2 mean primarily Dr. Jim Hetrick and EFED did some of

3 this analysis, so I want to give him credit for doing

4 this work. I get to just be the talking head in this

5 regard.

6 I'm not going to try through the next

7 slide or two document what we did, but I want to use

8 what we've done to illustrate where our thinking is in

9 terms of next steps.

10 So we did a variogram analysis on log

11 transform data, and we found that the Gaussian

12 spherical models provide the best description of the

13 semi-variance structure. This is an illustration of

14 one year, but we looked over multiple years.

15 For those of you who may not be familiar

16 with the variogram analysis, the range refers to the

17 temporal scale where you have an autocorrelation within

18 the concentrations. So once you get beyond the range,

19 then we don't see a temporal relationship; it's more of

20 a random process.

21 But we did see in all the years, we saw

22 a strong temporal autocorrelation; it ranged from 35 to

23 83 days. And once again, this is using more

24 intensively sampled data than I think Dr. Lee looked at

25 with the weekly sampling at the last SAP.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 131

1 We then estimated a time series using

2 one-dimensional, ordinary-point kriging, and this is

3 what you see here.

4 I'm going to step on to the next one,

5 because we then used a Gaussian sequential simulation

6 to assess the uncertainty associated with the missing

7 data. And we're only showing you the 50th, the 75th

8 and the maximum in this particular slide, which is I

9 think the one graph that does show up in the handouts

10 in that regard. So some of the lower percentiles, you

11 don't see plotted on here.

12 The conditional simulations generally

13 trace the actual monitoring data, and we would like the

14 fact that they do provide us a means of estimating

15 confidence bounds of data.

16 One issue that's critical not just to

17 kriging but to other exposure estimation methods is how

18 much information you lose when you go from frequent to

19 less frequent monitoring data, and what I'm going to

20 show you is a series of kriging data that started with

21 roughly the 4-day sampling, roughly weekly sampling and

22 then biweekly sampling, and you can see that by the

23 time you get to biweekly, this exposure profile really

24 didn't look anything like this.

25 At some point, that information loss
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 132

1 impacts not only our capabilities of estimating short2 term exposures, but it also impacts how well we can

3 estimate long-term exposures as well. So that's one of

4 the concerns we have as we move forward.

5 So to kind of wrap up what we're looking

6 at in terms of exposure estimation options, one of the

7 things we've talked about is the kriging looked

8 interesting, but we may get more information if we were

9 to do some type of co-kriging your concentrations with

10 something like daily stream flow. That may be helpful

11 in that regard.

12 The conditional simulations that are

13 based on monitoring data structure, particularly in

14 percentiles or some temporal structure, are very

15 critical in terms of providing some type of confidence

16 bounds that we can use to assess the monitoring

17 frequency.

18 We're looking very closely not just at

19 some of the updates of the WARP model. At the 2009 SAP

20 on atrazine ecological exposure, the SAP recommended

21 that we explore developing a corn-belt version of the

22 WARP model that incorporates more of the data, the

23 information available on a much more detailed scale

24 than on the national scale model.

25 I know there's efforts in USGS now on
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 133

1 looking at that corn-belt version of WARP, and so

2 that's something we are keeping our eyes on and keeping

3 in touch on with USGS, because we believe it has

4 applications not just for the ecological exposure but

5 also for community water-system assessments.

6 Even now, the WARP provides percentile

7 estimations that can be used in combination with

8 conditional simulations and we think there is some

9 promise there. One thing that came out, I think we may

10 have referenced this in the April SAP but we've taken a

11 little bit more look at this and talked to the person

12 in USGS who developed the SEAWAVE model, which was a

13 way of combining WARP with or, they're looking at

14 combining WARP with this seasonal variability model.

15 They use this model to assess trends

16 in assay concentrations over years, taking into account

17 the seasonal variability you get in rainfall and

18 runoff. They are looking at the potential of combining

19 this with WARP to provide more detailed monitoring

20 estimates.

21 One thing I will point out is that is

22 probably a little longer-term effort than we may be

23 looking at for a 2011 turnaround, so part of what we've

24 got to consider as we go forward is what can we do in

25 the immediate future.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 134

1 So I promise this is the last slide.

2 So for drinking water monitoring portion

3 in this SAP, our questions are focusing on these main

4 issues. We've proposed a general framework for

5 designing a monitoring study that could be used to

6 estimate drinking-water exposures for range of exposure

7 durations of concern.

8 We've also made a proposal of what we

9 would like to do in terms of using intensively sampled

10 monitoring datasets to evaluate both various sampling

11 frequency strategies as well as other exposure

12 estimation methods. And we've updated our

13 considerations in terms of methods for estimating

14 exposure from less frequently sampled monitoring data.

15 So at this point I'm going to open

16 things up for questions.

17 DR. STEVEN HEERINGA: Thank you very

18 much, Nelson.

19 Questions or clarification from the

20 Panel? And first, Dr. Lee.

21 DR. HERBERT LEE: It's Herbert Lee, I

22 got two sets of questions. The first set is about the

23 variogram estimation you put up. What was the raw data

24 used to estimate that variogram?

25 MR. NELSON THURMAN: I think that was
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 135

1 actually one of the ecological exposure monitoring

2 profile sites. So it was one that had the

3 DR. HERBERT LEE: It was daily data?

4 MR. NELSON THURMAN: Daily data.

5 DR. HERBERT LEE: Looking at things like

6 the Heidelberg datasets, those don't look like Gaussian

7 correlation functions to me from ones I've seen,

8 certainly not a stationary Gaussian correlation

9 function itself. They're a lot less smooth. So I'm

10 surprised to hear you say that Gaussian looks

11 appropriate.

12 DR. NELSON THURMAN: Okay, and let me

13 clarify that we only looked at a small group. In fact,

14 I'm pretty sure I only provided Jim with the ecological

15 exposure datasets.

16 DR. HERBERT LEE: Okay.

17 MR. NELSON THURMAN: So that may very

18 well change when you look at the Heidelberg.

19 DR. HERBERT LEE: My second question is,

20 what do you mean by chemograph matching?

21 DR. NELSON THURMAN: What I mean is --

22 let me see if I can go back to that. I mean, naively I

23 thought that we could just say number of peaks with the

24 peaks might be a way of doing this; but what you soon

25 learn is that, what I've learned by this exercise is
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 136

1 when you start taking a look at weekly sampling, you

2 cut out a lot of the smaller peaks.

3 So I'm looking at more of a generalized

4 shape-type matching now rather than and probably more

5 generalized shape in terms of what is the duration of

6 your high exposure and whether there, how much overlap

7 do you see in that.

8 DR. HERBERT LEE: It seems like an

9 important thing to take into account there is the fact

10 that you may be missing peaks altogether when you're

11 matching those. But it's something that can be done.

12 DR. NELSON THURMAN: We learned that

13 very quickly, and if you have any suggestions on how we

14 might do this better, we're open to that, because the

15 more we looked at that, the more we saw holes in that

16 approach.

17 DR. STEVEN HEERINGA: Dr. Bailar.

18 DR. JOHN BAILAR: You're proposing to

19 collect some very extensive data, which will be

20 subjected to a quite sophisticated analysis. I'm

21 interested in the use of the output of that.

22 I see two broad categories of possible

23 use: one is what you could call scientific, trying to

24 understand the relationship between exposures and

25 health outcomes.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 137

1 The second has to do more with

2 monitoring and surveillance over the very long run, and

3 I can see a use here of these data in deciding what

4 kind of monitoring and surveillance will be needed

5 during a long period of what might be regulation. Do

6 you see further uses of these data in the first of

7 those, the scientific category?

8 DR. NELSON THURMAN: Yes, I do see, and

9 I do want to clarify. We're proposing using extensive

10 data that has already been collected so we're

11 DR. JOHN BAILAR: Yes.

12 DR. NELSON THURMAN: In that regard, but

13 yes. And what we're proposing to do is take what can

14 we learn from that data. Our focus more is on what you

15 talk about the second part: how much monitoring, how

16 frequently do you have to monitor to do that; but at

17 the same time, there are scientific lessons that we're

18 going to learn from this that have a broader

19 application.

20 And the other thing I want to point out

21 is that we're actually requiring the registrant to

22 collect the data, so it's not EPA out there.

23 DR. JOHN BAILAR: Right, I understand

24 that and appreciate it; but I think this document might

25 be improved by having some further explanation of the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 138

1 intended uses of these data and how you expect these

2 uses to play out over the years to come.

3 I have a second question that has to do

4 with sampling strategy. I understand how less frequent

5 sampling may miss the peaks; but still, if you have a

6 lot of samples, the peaks should be represented with

7 their frequency in the general population of possible

8 samples. So it's not clear to me why over a large

9 dataset you would miss the 1% kind of peak; it ought to

10 show up in 1% of your samples. Am I being clear?

11 DR. NELSON THURMAN: Yes, you are, and

12 one thing I want to say at this point, what we're

13 focusing on at this point, we're looking at individual

14 community water systems. So the concern is: is there

15 exposure on those community water systems that may be

16 exceeding what we, what would be determined to be a

17 level of concern in a year-by-year basis.

18 For the 90-day exposure period for those

19 sites we haven't had an exceedance in any of these

20 community water systems, but we've been looking closely

21 at that. If you look at this regard, our focus has

22 been on and once again, this is a condition of the re23 registration, because our assessment in general is, and

24 what you've explained, we have not been seeing

25 concentrations in general that have exceeded what we're
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 139

1 seeing at our existing level of concern; but at the

2 same time, we haven't seen some of the monitoring data

3 that is sampled more intensively.

4 So what we wanted to do as a condition

5 is have Syngenta go to those community water systems

6 that had higher estimate exposures from quarterly

7 samples, three of which are not likely to be in the

8 atrazine use period, and do more intensive sampling to

9 see whether that, those exposures are higher and how

10 much higher they are.

11 So that's where we have been going with

12 that. But our focus has been on the individual

13 community water system method.

14 DR. JOHN BAILAR: I think this aspect of

15 the sampling could also use some further explanation.

16 I'm not objecting to it.

17 DR. STEVEN HEERINGA: Other questions

18 and clarifications?

19 Dr. Lee, did you get all of your

20 questions in?

21 Yes, Dr. Krishnan.

22 DR. KANNAN KRISHNAN: In some of the

23 slides like 83 and other places, when you refer to TCT,

24 yes, it's atrazine as well as some of its

25 transformation products; and what other triazines are
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 140

1 also included and what's the approximate percentage?

2 Is there some idea of the range that you can give or

3 infer from historical data?

4 DR. NELSON THURMAN: All right. First

5 of all, it includes atrazine, simazine and the chloro6 degradates of --

7 DR. KANNAN KRISHNAN: DIA, DEA and

8 DR. NELSON THURMAN: Yes. So it

9 includes the chloro-degradates as well. I've been

10 working with basically the total TCT. The monitoring

11 data particularly in the last few years has not

12 analyzed for the individual components as well as the

13 total chlorotriazines, so we could go back and make

14 that estimate. One of the things we find is that it

15 tends to vary, depending on the timing and such and how

16 much degradation has occurred in there.

17 So it's not an easy straightforward,

18 "Here's what the percentage is". It's more of a range

19 over time. But we have not done that, but we have a

20 capability of doing that.

21 DR. KANNAN KRISHNAN: Also what I am

22 trying to understand is the atrazine assessment if it

23 is based on atrazine numbers at the end of the day,

24 would that be compared with the TCT monitoring data or

25 one liter atrazine, or would you add atrazine plus its
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 141

1 metabolites, if you can clarify that?

2 DR. NELSON THURMAN: We're focusing on

3 the TCT.

4 DR. STEVEN HEERINGA: Dr. Portier.

5 DR. KENNETH PORTIER: Maybe this thought

6 is not fully formed, but I'll ask it anyway. You

7 mentioned that in moving forward in designing a

8 monitoring plan, we needed to know about the dose and

9 duration, right, the area under the curve and how long.

10 It strikes me there's another aspect of sampling that

11 you haven't talked about, and that's the tolerance for

12 uncertainty in a decision.

13 You know, we use the phrase

14 "monitoring", and monitoring usually means at some

15 point I want to be able to answer the question kind of

16 with certainty that we have exceeded a certain level.

17 So we monitor to see when we exceed, right, and then

18 you can implement some kind of intervention.

19 An alternative is to measure and then be

20 able to come up with a probabilistic statement that

21 says, "Given what I've seen in these monthly

22 measurements, my chances of having exceeded this

23 criteria is such-and-such", right?

24 Now you haven't monitored, you haven't

25 measured that exceedance; you've measured something,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 142

1 and through some kind of modeling or estimation

2 procedure you were able to make a probabilistic

3 statement.

4 Within the EPA scheme of things, is that

5 kind of second statement about a probabilistic chance

6 of exceeding, does that fit into the regulatory scheme?

7 Is that something that's thought about when you're kind

8 of designing the sampling scheme?

9 And the reason I'm talking about it is

10 because I think this is a key example of where you may

11 never be able to monitor enough, measure enough, to be

12 able to say with certainty that you've exceeded.

13 But you may be able to answer a

14 probabilistic statement with enough sampling to be able

15 to be confident that you exceeded a certain likelihood

16 of the event happening. I hope I'm clear enough.

17 DR. NELSON THURMAN: Yeah, and actually

18 we've talked about that in terms of that as an

19 approach. We didn't flesh it out much in the

20 background document and obviously didn't talk about it

21 too much other than, you know, one of the things I made

22 very brief mention and probably a lot briefer than it

23 should be in terms of our focus has been on: can we

24 try to find confidence bounds or some type of safety25 factor approach which plays in easier to what we've
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1 been doing at FQPA.

2 But the other option is what's the

3 likelihood that we actually missed something of this

4 magnitude and this duration.

5 So it's something we've talked about in

6 some of the approaches, and I think extreme value is an

7 example of one where it may be easier to use it that

8 way.

9 So we have thought about that; we just

10 didn't flesh it out too much in that.

11 DR. STEVEN HEERINGA: Yes, Dr. Coupe.

12 DR. RICHARD COUPE: I'm Richard Coupe.

13 I just wanted to follow up a little bit on the

14 discussion we had the question before when you were

15 talking about mixtures of total chlorotriazines, and

16 this kind of brings up a little something I was going

17 to talk about later is that you'd have trouble going

18 back I think and looking at the total chlorotriazine

19 totals in historical data, because simazine was such a

20 big factor in days gone by and we no longer use

21 simazine. And so you wouldn't be able to do the ratios

22 very well.

23 The point that I wanted to bring up is

24 though, is that sometimes we seem to think that this is

25 all -- no offense to statisticians and whatnot -- but
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 144

1 it's not the same dataset every year. Things change

2 through time with market forces, and pesticides come

3 in, come out.

4 Sometimes it's not really easy to use

5 historical data. It's what you have right now which

6 you need to start with, but you need to always keep in

7 mind that these things are changing and the use

8 patterns and how you do it, adjuvants change; a lot of

9 things change that could mean every year you have a

10 different set of conditions out there that lead to an

11 exposure problem.

12 So you need to sample every minute of

13 every day.

14 DR. STEVEN HEERINGA: Any comments or

15 questions at this point?

16 I think if they come up, we'll have the

17 opportunity, of course, to address them again. Thank

18 you, Nelson and Mary, for this.

19 At this point, I think we're really

20 right on schedule with the program, which as I said is

21 an easy thing to do today; it'll be more difficult in

22 days to come. Forewarned is forearmed, I guess.

23 At this point in time, I think we return

24 back to Dr. Lowit for a presentation on Scientific

25 Considerations and Potential Sensitivity of Infants &
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 145

1 Children and Implications of the Mode of Action on

2 Water Monitoring Strategy.

3 DR. ANNA LOWIT: Well, if you'll give us

4 a minute, we're going to do some Musical Chairs.

5 DR. STEVEN HEERINGA: You can certainly

6 take your time.

7 DR. ANNA LOWIT: Ralph? I think it's

8 settled. I have a question for Dr. Heeringa. My

9 slides are, I have basically two short presentations,

10 about 10 or 12 slides. There's a natural stop in the

11 middle. I can either just do them all, or I can stop

12 in the center; either one.

13 DR. STEVEN HEERINGA: Why don't we stop

14 in the center and take a short break? No matter what

15 you do, we're going to be out of here early today, and

16 I don't want to drag this on unnecessarily today; but

17 why don't we go ahead and take a break in the middle

18 just for questions on it?

19 DR. ANNA LOWIT: Okay. Well, it's not

20 that many; it probably won't take that long.

21 So I did ask some members of the team to

22 come back up, because neither of these presentations

23 are heavy in data; they're more conceptual

24 intentionally. And so the people who know the answers

25 to the detail questions can be close at hand when those
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 146

1 come up.

2 Joe? Joe? Can you help me with working

3 the slides? It's like 20 of them or something.

4 So there are two presentations, and

5 they're coming up and we'll stop in between; but

6 they're both integrated, which is where we thought we

7 could sort of put them together.

8 DR. STEVEN HEERINGA: Go ahead and put

9 them together

10 DR. ANNA LOWIT: It doesn't matter.

11 DR. STEVEN HEERINGA: You're making a

12 convincing argument.

13 DR. ANNA LOWIT: Okay. Both topics have

14 both a hazard-assessment component and an exposures15 assessment component, and largely the issues as you

16 think about evaluating life stages and sensitivity are

17 not too far removed from how you think about the

18 critical duration. A lot of the same points come up,

19 particularly on the water. So it's moderately logical

20 to do this together.

21 So I'll start with the FQPA analysis.

22 FFDCA, as amended by the FQPA, or what should read the

23 Food Quality Protection Act, makes some unique

24 requirements of the Agency. And one of those is to pay

25 special attention to infants and children by thinking
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 147

1 explicitly about the availability of information on the

2 toxicology and the exposure of a particular pesticide.

3 And this relates to the FQPA safety

4 factor, the 10X factor. And for those of you who are

5 not familiar with our regulation it's a unique statute,

6 in that the Congress requires that we apply an extra

7 factor and that that factor can be removed in the event

8 that you have sufficient information for toxicology and

9 exposure.

10 At the present time, we have not

11 proposed a new FQPA factor. We have not revised the

12 factor from the red. We are reserving that decision

13 pending things that are still ongoing.

14 The chapter in the paper, in the Issue

15 Paper, has a lot of detailed information about

16 experimental toxicology studies, a short summary of

17 epidemiology which you heard from Dr. Christensen this

18 morning and is really missing in the water section

19 largely because, as you have heard from Nelson and

20 Mary, a lot of that work is still ongoing and still

21 being worked out.

22 So what I'm going to do in just these

23 few slides is instead of digging into the details of

24 the data, to talk to you more about the way we'll think

25 about the FQPA analysis and the kinds of things that
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 148

1 we'll think about. And so the question we're asking

2 the Panel is more around the framework of thinking

3 about those things, are we on target with the right

4 things, are we missing some things, that sort of

5 information.

6 So with respect to hazards, I'm going to

7 separate this. The bulk of this is going to be the

8 hazard considerations, because we've just spent, you

9 know, a good 45 minutes talking about the water so that

10 we don't have to duplicate that. So we'll circle back

11 around to hazard.

12 With respect to the hazard

13 considerations, the important issue to consider is the

14 availability of data to assess critical life stages.

15 And as I'll talk about a little bit more in detail in a

16 couple of minutes, there are some key studies that are

17 still ongoing that are pending.

18 But at the end of the day, we're going

19 to consider everything that we have available to us:

20 we're going to think about Mode of Action; we're going

21 to look at the animal toxicology database, the human

22 relevance of that data; we're going to look at the

23 dose-response relationships; and we're going to think

24 about the epidemiology findings.

25 So as we consider all the information
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 149

1 with respect to the Mode of Action, as I very quickly

2 gleaned over this morning, the key events involving the

3 neuroendocrine Mode of Action for atrazine have been

4 well established.

5 In 2000, the SAP supported those. It

6 starts with the effect of the pulsatile release of

7 GnRH, leading to changes in LH attenuation and

8 ultimately in the rat to mammary tumors, but in the

9 human we think more about development and reproductive

10 outcomes.

11 As you heard from Dr. Christensen this

12 morning, there is epidemiology data that is relevant

13 for thinking about infants and children. And we think

14 that this information provides qualitative evaluation

15 of the human relevance of some of the animal findings.

16 I think in particular, as you heard Carol talk about,

17 they're small for gestational age because we certainly

18 see changes in pup weight in the animal toxicology

19 base.

20 The two Farr studies on the menstrual

21 cycle effects as it relates, I think it's easy to think

22 about changes in LH attenuation affecting menstrual

23 cycle and, maybe to a little bit lesser extent, the

24 semen.

25 On the animal data, we did a fairly
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 150

1 extensive evaluation about it in the Issue Paper, which

2 is really the beginning of what will become a very

3 extensive sensitivity analysis.

4 We have right now a number of studies on

5 specific life stages: those from gestational exposure,

6 lactational, early postnatal and peripubertal. And

7 there are also two very recent high-quality tissue

8 dosimetry studies in fetuses and lactating pups, and

9 both of those studies actually come from EPA labs. One

10 of them is out of the Stoker lab.

11 The second one is a brand-new study,

12 which is a collaborative effort between Tammy Stoker of

13 NHEERL and one of the OPP analytical labs, so I think

14 that's a case where you collaborate across the Agency

15 to get very high-quality data.

16 We are putting a lot of focus on LH

17 attenuation as you've heard today, because it is well

18 documented with respect to dose and across lots of

19 different time courses from one day all the way out to

20 many months of exposure and, as Dr. Rodriguez

21 discussed, both the plasma concentrations and the LH

22 data we have. And you can correlate those very highly.

23 The LH attenuation is the most sensitive

24 endpoint of the database. No other high-quality study

25 provides endpoints lower than those from LH
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 151

1 attenuation, and that includes outcome data related to

2 delayed puberty onsets among other things, in addition

3 to the more standard guideline kind of studies we see

4 on systemic toxicity, such as dogs and rats and

5 everything else.

6 There is strong biological plausibility

7 with respect to LH attenuation, which makes it a good

8 endpoint for thinking about assessing human risk.

9 Okay. But one of the points of why we

10 are reserving the decision or a proposal around the

11 FQPA factor is there are three experimental toxicology

12 studies that are still ongoing -- and I mean ongoing,

13 still in the lab, animals still being exposed.

14 Two of these are at the ORD lab, the

15 first two of the bullets on the slide. The first one

16 is looking at hormonal changes and outcomes from

17 gestational exposure, and this will provide a nice

18 dataset. Right now there isn't a strong dataset

19 looking at hormonal exposure from gestation.

20 The ORD labs are also looking at

21 behavioral changes in male rats from gestational

22 exposure. The SAP report from 2000 on the key events,

23 as that Panel thought about the human relevance for

24 developmental and repro outcomes, there's a statement

25 in that report that neurobehavioral findings turn out
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 152

1 to be a sensitive endpoint. So ORD is looking at that

2 to see if that turns out to be true.

3 And then the third bullet is a study

4 being conducted by Syngenta, which I expect that or I

5 assume they'll talk about tomorrow, although I don't

6 know. But they're looking at the latent effects in

7 adults from both gestational/lactational exposure, and

8 that study is actually a direct result of some

9 uncertainty the Agency had identified in 2003.

10 And the statement is actually in the

11 Charge Question that one of the in the previous risk

12 assessment the Agency had identified multi-life

13 exposure studies as an area of uncertainty.

14 As I talked about a minute ago we have

15 gestational exposure, lactational, peripubertal, a very

16 isolated, but none that cover multiple life stages.

17 And as you think about people who don't move or a farm

18 family, let's say, that lives in Iowa and does that for

19 many years, that would be a multi-life stage exposure.

20 So the study that Syngenta's conducting

21 is, I believe, intended to fill that need of multi-life

22 stage. I expect them to talk about it tomorrow, so I'm

23 not going to spend a lot of time on it. We just

24 thought we'd let you know sort of how we saw the

25 status. It's a very complicated study. It has
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 153

1 multiple cohorts and multiple subsets. They're also

2 looking at recovery, which is very nice.

3 As of right now, as of July the 15th,

4 excuse me, two of the subsets have been submitted,

5 what's called Cohort I, Subset A, and Cohort II, Subset

6 D, which is essentially the gestational through

7 lactation and a postnatal through necropsy. So a great

8 deal of that study is still ongoing. I believe we'll

9 probably hear more about it tomorrow.

10 We are reserving our larger view of that

11 study, simply because so much of it is not in; but we

12 have made one observation so far that in the animals in

13 those two subsets that we've seen that there's a lack

14 of effect on LH.

15 The dose is less than 50 mg/kg, and as

16 you saw from Dr. Chester Rodriguez's presentation this

17 morning, that's actually a relatively unusual finding,

18 because even some of the other Syngenta data show

19 effects on LH at doses much lower than 50.

20 So that would be the hazard, the things

21 we'll think about for hazard: Mode of Action, dose22 response, epidemiology, human relevance. The other

23 part of the FQPA analysis is, it would be the drinking

24 water exposure.

25 And as you heard Nelson and Mary's
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 154

1 presentation, there's quite a bit of work to do in this

2 area. Part of it is because they're waiting for the

3 tox team to tell them what the duration is, and part of

4 it is it's just a very difficult issue that we're

5 working through.

6 But we are asking Questions 4.1 to 4.3,

7 and the answers to those will help us think about the

8 FQPA analysis.

9 So in the coming months, the Agency is

10 going to work, we're going to work towards completing

11 the scientific analysis for the FQPA factor. And what

12 we'd like from you in Question 5 is to think about

13 those factors I just talked about: the Mode of Action,

14 dose-response, human relevance, the findings and the

15 epidemiology, along with the thinking that will be the

16 responses to Questions 4.1 to 4.3: are we missing some

17 factors; does that seem like the right set of things to

18 think about; that sort of stuff.

19 Okay, so that's the FQPA issues. So if

20 we move on to thinking about putting it all together in

21 the water monitoring strategy, if we take a step back

22 and go back to the beginning.

23 The current drinking water program,

24 monitoring program that Syngenta conducts as a

25 requirement of registration that the Agency requires,
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 155

1 right now they're doing weekly monitoring during the

2 application and roughly the growing season, which is

3 roughly the spring to the summer, and biweekly for the

4 rest of the year.

5 In the last risk assessment, the Agency

6 conducted 90-day rolling averages of monitoring data,

7 and those averages were derived from interpolating

8 between the weekly monitoring points.

9 And I think the issue of interpolating

10 has been addressed in other comments; I'm just trying

11 to make sure everyone is on the same page of what was

12 done in the last assessment. So these 90-day rolling

13 averages were then compared against a level of concern

14 derived from the 6-month Morseth LH data.

15 So the question is: in this matching of

16 the 90-day rolling average to 6-month Morseth study,

17 given the current knowledge of atrazine, particularly

18 its temporality of the toxicology, should the critical

19 duration of exposure be revised and, if so, how?

20 That's the essence of this question.

21 So if we take another step back, it's

22 important to just say explicitly that the atrazine

23 database is lacking in human-specific information that

24 we can use to quantitatively extrapolate between rats

25 and humans.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 156

1 Because of that, because of the lack of

2 real chemical-specific quantitative information, what

3 we have to do is to infer generic knowledge across

4 multiple disciplines. And so that's what we have done

5 and what we are asking for feedback on, because I

6 expect and I hope that there are more things that we

7 haven't considered, and that is what we ask from you.

8 So what we've done is we've looked at a

9 couple of different things. We're looking at the

10 empirical effects from animal studies and also

11 toxicodynamic and toxicokinetic information.

12 So if you go back to the toxicology

13 dataset for atrazine, there are a number of endpoints.

14 Delayed puberty onset is one of the major ones

15 throughout the developmental and reproductive dataset.

16 And in the rat, those studies are 4 days or longer; but

17 in humans, as we know, puberty occurs over a long

18 period of time.

19 So linking the 4-day exposure in the rat

20 from the LH study to the puberty in humans that lasts a

21 long period of time, there's a little bit of a mismatch

22 there.

23 As it relates to prostatitis, another

24 one of the endpoints measured in the atrazine database,

25 in the rat, exposure to the dam, when the dam was
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 157

1 exposed to atrazine there's an inhibition of prolactin,

2 transmission in the milk.

3 And it is this milk exposure to the pup

4 that affects the development of the TIDA neurons in the

5 offspring. And it is those TIDA neurons that in turn

6 cause the effect of the prostatitis in the male pups,

7 but it is derived from exposure to the dam.

8 In humans, prolactin plays a role in

9 development and maintenance of the prostate; but the

10 critical periods of development and the hormonal

11 involvement is far less known, particularly the

12 temporality of that.

13 Okay. So as we think about LH

14 attenuation, as the Cooper data have shown that we saw

15 from Dr. Rodriguez earlier, a single day of dosing is

16 not sufficient at low doses; at extreme high doses, you

17 may see some attenuation but not too much.

18 And to reach its maximal effect, we see

19 that at or around pseudo-steady state, which we believe

20 at the lower doses occurs around 4 days in the rat, and

21 that there is a very nice matching of this LH

22 attenuation beginning at 4 days to the pseudo-steady

23 state tissue levels also after 4 days. But the

24 question here is matching, matching this 4 days of

25 exposure from the LH in the pseudo-steady state in the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 158

1 rat to the humans.

2 Pharmacokinetically, we much prefer to

3 use a PBPK model, as was alluded to earlier. McMullin,

4 there is a McMullin model out that we have reviewed

5 that we find has some important shortcomings. It does

6 not do a very good job of capturing the rapid kinetics

7 of atrazine and under-predicts the plasma

8 concentrations of the chlorotriazine metabolites.

9 Dr. Rodriquez did do a series of

10 calculations looking at and taking the elimination-rate

11 values from the Thede study and doing some allometric

12 scaling to see if that can't help inform durations of

13 exposure that would be relevant to humans.

14 So I won't do his calculations justice,

15 because he did them much better justice this morning;

16 but in essence what he has done is taken the

17 elimination-rate constants from the lower dose groups,

18 which are pretty constant across the 1- to 10-mg group,

19 and performed allometric scaling for an average female

20 body weight of 60 kilograms.

21 And he also assumed that there would be

22 three to five half-lives required to get steady state.

23 And if you look at this, the last column in here, you

24 see that the values range from something in the order

25 from about two-and-a-half weeks to about a month, maybe
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 159

1 four or five weeks, which all, despite the

2 uncertainties in the calculations, are all shorter than

3 the 90-day rolling averages currently being used.

4 So we also asked ourselves:

5 What is known about the LH surge in

6 humans that might inform critical periods of duration?

7 Dr. Mendez, who is not here now but was here this

8 morning, did some research into this area and found

9 that information from the pharmaceutical literature

10 might give us a qualitative handle on thinking about

11 windows of susceptibility in the human as it relates to

12 the menstrual cycle. I won't go into this in detail;

13 if someone wants to ask questions, they can ask Dr.

14 Cooper.

15 But our look at that literature is that

16 you can conceive from that IVF literature two different

17 periods of possible susceptibility. First would be the

18 follicular phase of the menstrual cycle, or in other

19 words approximately the first half of the female cycle,

20 or more specifically the second half of the late

21 follicular cycle, which would only last four or five

22 days.

23 It's very important as you think about

24 these IVF drugs and how they would relate the atrazine,

25 you have to be very careful not to put too much weight
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 160

1 on the absolute findings. IVF drugs are very potent.

2 They're given for very specific reasons. Most of them

3 are given by injection, they're not oral.

4 So there are some uncertainties around

5 making those comparisons. But we think qualitatively

6 they do suggest that the follicular phase, maybe even

7 the late follicular phase, is a potential window of

8 susceptibility.

9 So we have tried to think about this in

10 a multidisciplinary way, allometric scaling from the

11 pharmacokinetics; we've thought about the relevance of

12 the LH from rats to humans as it relates to the

13 menstrual cycle.

14 We've also thought about the

15 experimental toxicology data and the outcomes you see:

16 the delayed puberty, the prostatitis and how you would

17 relate those windows in the rat to the windows in the

18 human, and there really is no absolute finding. You do

19 see a range of possible values from that analysis of

20 just a few days, from four or five days up to something

21 in the order of for four or five weeks, maybe

22 approximately a month.

23 There are a couple of things you can

24 take from that. First, all of those are shorter than

25 the 90-day rolling average being used right now. There
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 161

1 may be other things we haven't thought of that are as

2 long as the 90-day rolling average. If there are, we

3 are looking for that feedback.

4 Another thing you can take from that is

5 that there is a lack of precision around the estimate,

6 and there is going to be a lack of precision in the

7 rat-to-human extrapolation. So what we'd like to do is

8 to have a multidisciplinary approach that thinks about

9 this from multiple points.

10 So as we think about answering Charge 6,

11 the Question No. 6, we'd like for you to comment on our

12 analysis -- I mean our preliminary conclusions -- but

13 we are also hoping that you have some alternatives and

14 some additional things for us to think about.

15 I think that may be the last slide in

16 this set, but both of these areas that I went over just

17 now, the FQPA analysis for the life stage sensitivity

18 and also the critical duration of exposure, not only

19 are they important as we think about the water

20 monitoring; but as we think about the 2011 SAP, they

21 will be the most likely two major areas on the hazard22 assessment side that we address at the next meeting.

23 DR. STEVEN HEERINGA: Thank you, Dr.

24 Lowit.

25 At this point, members of the Panel, any
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 162

1 questions of clarification on the material?

2 Dr. Fenner-Crisp.

3 DR. PENELOPE FENNER-CRISP: Most of the

4 studies that are still ongoing both in the Agency and

5 outside are focusing on the non-adult life stages. In

6 order to make conclusions about whether or not the

7 younger life stages are more sensitive than the adult,

8 do you have enough adult data against it to compare?

9 DR. ANNA LOWIT: I believe so, yes.

10 DR. PENELOPE FENNER-CRISP: For all of

11 the endpoints of concern?

12 DR. ANNA LOWIT: Yeah, I think that, I

13 mean, atrazine has a very strong database from really

14 top to bottom. I think when you add these datasets in,

15 they provide a very solid package from which to make

16 those findings, yes.

17 DR. PENELOPE FENNER-CRISP: What's the

18 timeline for finishing up the undone studies? Are they

19 going to be available by the time, your target time for

20 completing your reassessment?

21 DR. RALPH COOPER: Actually, there's two

22 papers in draft form at the moment, one addressing the

23 female and one addressing the male, wherein the dams

24 were treated from gestation day 14 through 21, they

25 were allowed to give birth, and then we followed the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 163

1 offspring out, looking at a number of different

2 measures from body weight, most of your male

3 reproductive parameters.

4 There was some behavioral observations

5 in there, and we've gone on to look at some of the

6 parameters in the female that we look at typically in

7 cyclicity in that.

8 That study was run in four blocks, so

9 some of those offspring are getting up there in age and

10 we're waiting for them -- as a matter of fact, the

11 controls are undergoing reproductive senescence at the

12 moment. So this was a gestational only exposure;

13 that's the one that EPA was responsible for, or ORD.

14 And my answer to your question now is

15 imminent.

16 DR. PENELOPE FENNER-CRISP: That's good

17 news.

18 DR. RALPH COOPER: I hope that these two

19 papers are going through internal clearance within the

20 next couple of weeks, if I'm not overestimating how

21 hard those people are working.

22 DR. ANNA LOWIT: You can ask Syngenta

23 the same question on their study tomorrow.

24 DR. PENELOPE FENNER-CRISP: I will.

25 And the other question I have is, are
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 164

1 you open to the possibility there may be more than one

2 critical duration of exposure to have to decide what

3 kind of monitoring strategy you may have?

4 DR. ANNA LOWIT: We're open to your

5 feedback.

6 DR. PENELOPE FENNER-CRISP: Or you

7 wouldn't answer this question back in April. Is this

8 BMR going to be applied to all risk assessments from

9 acute through chronic, unlike the current one which has

10 a different dataset driving the acute?

11 DR. ANNA LOWIT: Just make sure we have

12 the terms right. The BMR is the response, make sure we

13 have the acronyms right. The BMR is the Benchmark

14 Response, which is the magnitude of the value in the

15 benchmark dose. So I think --

16 DR. PENELOPE FENNER-CRISP: It

17 represents a particular dataset.

18 DR. ANNA LOWIT: Yeah, so the benchmark

19 dose estimates that are in Appendix C, I believe, focus

20 on LH. As you accurately have said, the last risk

21 assessment, there were different endpoints for

22 different durations of exposure.

23 I think, I believe that what Dr.

24 Rodriguez has very elegantly shown in his overlay of

25 the temporal, the plasma data and the LH data is that I
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 165

1 think we need to rethink the durations of the risk

2 assessment, because if steady state or pseudo-steady

3 state around plasma levels are driving what the

4 responses are, particularly around LH, I think we have

5 to take a step back and look at those standard

6 durations that we normally use and maybe do something

7 or focus as it relates to the atrazine Mode of Action

8 from the toxicology data.

9 DR. STEVEN HEERINGA: Yes, Dr. Meek.

10 DR. BETTE MEEK: This is a really simple

11 question. It's just you mentioned that you weren't

12 proposing any change in the FPQA factor -- FQPA, sorry

13 -- at this time; but it was applied in the previous

14 assessment, the tenfold factor was applied? I just

15 want to make it clear.

16 DR. ANNA LOWIT: In the previous

17 assessment, it's in the Charge Question if you look at

18 the very beginning of the Charge Question. The

19 previous assessment for those systems included in the

20 community water system, the 10X was reduced to 3X,

21 because the drinking water monitoring was sufficient to

22 evaluate the 90-day rolling average.

23 DR. STEVEN HEERINGA: Additional

24 questions at this point?

25 I have one while others are thinking;
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 166

1 it's for Nelson. The auto-samplers, they're

2 accumulating samplers; they're not specific aliquot3 type

4 MR. NELSON THURMAN: Yes, that's true.

5 DR. STEVEN HEERINGA: Yes, Dr. Schlenk.

6 DR. DANIEL SCHLENK: I just had a

7 question. Maybe I should have asked it during the epi

8 presentation, but there were I guess some birth defects

9 that were associated with some of the epi studies, and

10 in reading the background documents, I guess there

11 wasn't a Mode of Action association with that.

12 I'm curious with the gonadotropin13 releasing hormone antagonist, has there ever been any

14 association with any birth defects associated with that

15 just to see if there's a common Mode of Action through

16 that pathway, and if that has or has been evaluated,

17 just--

18 DR. ANNA LOWIT: Well, I can add I'll

19 add the first part, and Ralph can add the second part.

20 To my knowledge that with respect to the

21 experimental toxicology database, there is some

22 standard rat in vivo developmental studies, and none of

23 those abdominal-wall defects have been seen. And they

24 can be seen in the rat, and they weren't. And some of

25 those doses in those studies are very high. But I
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 167

1 think Ralph had something to add.

2 DR. RALPH COOPER: Well, I was just,

3 with the GnRH antagonist, you can get pregnant, if I

4 understood the question.

5 DR. DANIEL SCHLENK: No, I'm just saying

6 with those particular compounds; I'm just saying

7 experimentally if you use those in a lower dose

8 obviously, they're very, very potent -- but could you

9 potentially generate a system that would allow you to

10 look at that potential endpoint and whether or not

11 those gut-wall issues are related to LH reduction or

12 not, if anybody's ever done that.

13 DR. STEVEN HEERINGA: Yes, Dr. Horseman,

14 you look like you're --

15 DR. NELSON HORSEMAN: This might relate

16 to the previous question. As I understand, all these

17 toxicology studies in gestation have been done in late

18 gestation. Has there been work done earlier in

19 gestation to look at outcomes of the pups?

20 DR. RALPH COOPER: To my knowledge,

21 early gestation alone actually, for a completely

22 different reason there was a study where the animals

23 were exposed for a little bit of different time on the

24 atrazine and they looked at outcome, and it was body

25 weight and blood pressure and these kinds of things.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 168

1 I don't believe that work has been

2 published yet. It was actually done by another group

3 in RTP. But the Syngenta study that Anna showed up

4 there would be one where you would expose throughout

5 gestation; they started on gestation day 0, right?

6 And then there's multi-gen studies where

7 they do if there is a multi-gen on this, and I don't

8 know, I don't believe there was any such signals in

9 that study about effects in the pups there of that

10 nature.

11 And then of course there is the rat

12 developmental study that's required wherein the animals

13 are dosed from gestation day 6 to 16 or 6 to 20 I

14 guess, depending on the study; but 6 to 16 probably

15 given the age of the study, and they, for

16 teratogenicity there.

17 DR. STEVEN HEERINGA: Okay. What I

18 would like to suggest, Dr. Lowit, is that we take a 15-

19 minute break, and I know you have one wrap-up

20 presentation.

21 But talking with Joe Bailey, I think one

22 thing that would be productive, given that we have the

23 time today, we can't move on, unfortunately. I think

24 it would be nice to cover this in a little bit of time

25 this afternoon.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 169

1 But what we can do is I'd like to go

2 sort of systematically through the Charge Questions,

3 not to discuss them but just to make sure that we

4 understand exactly what you're asking. And so we might

5 just click them off and say clear enough and we'll move

6 on; but we'll do that afterwards, and that'll benefit

7 our discussants, too, to make sure that, again, we

8 don't want to get into a discussion where we might sort

9 of influence your thinking, because I think that but

10 just to make sure that we understand the question

11 correctly and what you may be driving at there.

12 So we'll do that after the break. Let's

13 plan to reconvene at five minutes after 3:00.

14 (WHEREUPON, a recess was taken.)

15 DR. STEVEN HEERINGA: Okay, welcome

16 back, everyone. We'll wrap up our first day of the

17 FIFRA SAP meeting.

18 At this point, I mean, I turn the floor

19 back to Anna Lowit for the last of her presentations;

20 but I also think she had one point she wanted to

21 address from a break conversation we just felt that

22 should be brought out in the public discussion here,

23 too.

24 DR. ANNA LOWIT: And Dr. Krishnan and I

25 had a conversation; he approached me at the break
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 170

1 asking for clarification around how the total

2 chlorotriazines, i.e., the TCTs, are handled in the

3 exposure assessment. I thought it made more sense for

4 Nelson to do that than it did for me.

5 DR. STEVEN HEERINGA: Dr. Cooper is not

6 here, so it's Nelson.

7 MR. NELSON THURMAN: Sure. The water

8 monitoring data measured atrazine, simazine and each of

9 the chloro metabolites. And for what we were providing

10 in terms of exposure estimates has been on the sum of

11 the total chlorotriazines.

12 So that's how it's measured. If we find

13 at some point one metabolite is more toxic than the

14 other, we can go back and break that out, if necessary.

15 At this point, it makes sense just to do exposure

16 estimates on total chlorotriazines. Does that answer

17 your question?

18 DR. KANNAN KRISHNAN: Yeah. Thanks.

19 Based on I think where we are headed, I think that was

20 a critical question. I wanted to be sure that I I

21 know I had heard you almost around 2:30, but so I

22 wanted be sure that I have correctly get that

23 classification sort of thing.

24 DR. ANNA LOWIT: So just so we close

25 that loop, there is an implicit assumption there that
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 171

1 the Chloro metabolites are equipotent to parent

2 atrazine, and in the absence of really robust data to

3 do something more quantitative, it seems like a fairly

4 reasonable thing, particularly what's little - we know

5 a little bit about DACT and equal molar potency seems a

6 pretty reasonable assumption.

7 DR. STEVEN HEERINGA: Yes, Dr. Horseman.

8 DR. NELSON HORSEMAN: One tiny little

9 question, because I think it will come up tomorrow.

10 Hydroxyatrazine, which is apparently a bacterial or

11 plant metabolite, is it in any of these TCT

12 measurements?

13 MR. NELSON THURMAN: I was going to look

14 for Mary Frankenberry, who's dealt in it. I don't

15 recall that it is.

16 DR. KANNAN KRISHNAN: In the list of

17 components of TCTs listed in the document, it wasn't

18 listed.

19 DR. STEVEN HEERINGA: That was Dr.

20 Krishnan.

21 I guess if we can confirm -- I mean, if

22 we get information to the contrary, we'll

23 MR. NELSON THURMAN: Okay, yeah. If

24 it's not a chlorotriazine, then it would not have been

25 included, so .
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 172

1 DR. STEVEN HEERINGA: Dr. Lowit, I think

2 if we could have your last presentation, and then I

3 would like to go through this step of just walking --

4 DR. ANNA LOWIT: Okay, it's just a

5 couple slides to pull it all back together; there are

6 just three or four slides.

7 Just to go back to the very beginning,

8 we've covered a lot of very diverse topics today and

9 we'll have one more in the morning before the public

10 comments with Dr. Suzanne Fenton. We heard a very

11 detailed presentation from Carol Christensen on non12 cancer epidemiology today, and we'll have a couple of

13 questions on that.

14 We are proposing to continue to

15 emphasize LH attenuation in any future risk assessment,

16 as we believe this is a biologically plausible key

17 event in the Mode of Action for atrazine. As we see

18 the database, it's the most sensitive endpoint and

19 appears relevant for reproductive and developmental

20 outcomes, which are throughout the database.

21 We have conducted a number of series of

22 calculations since the April meeting around internal

23 dosimetry and benchmark dose analysis that you heard

24 about from Dr. Rodriguez.

25 And then drinking water, we have updated
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 173

1 the approaches. We provided some examples that you

2 heard from Nelson quite a bit ago.

3 The last two-part presentation -- I

4 don't know why that text is red -- that I gave a little

5 bit ago integrating the exposure and the hazard,

6 thinking about life stage sensitivity and the critical

7 duration of exposure. And both of those are components

8 for which we're still working and we're looking for

9 your feedback on, and will be a large bit of the focus

10 in 2011.

11 So thinking about the Next Steps around

12 non-cancer, pending the outcome of this meeting, we

13 anticipate a number of things in the coming months. We

14 anticipate selecting one or more or a range for that

15 matter of critical durations of exposure. And so

16 pending SAP -- put that S up there before you ask me

17 about the multiple ones. So certainly, we're thinking

18 about that.

19 And we'll be further developing the

20 drinking water analysis as you heard from Nelson, and

21 we'll be completing the FQPA analysis. We do expect an

22 SAP on these issues, along with any new studies that

23 come out in the literature, including those that have

24 come out since July the 15th, and we'll talk about

25 those in 2011.
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 174

1 Also in 2011, we'll move back to cancer

2 and cancer epidemiology. We are expecting sometimes in

3 the coming months a new study from the Agricultural

4 Health Study on cancer, and that will be integrated

5 along with other cancer epidemiology studies in the

6 experimental toxicology database on cancer, and we'll

7 also cover that at the 2011 meeting.

8 And I think that's it, yep.

9 DR. STEVEN HEERINGA: Okay. Thank you

10 very much.

11 At this point, what I would appreciate

12 and I think it would be useful; we often do it

13 concurrently just but I think since we have a little

14 time, if we could bring up the Charge Questions and

15 just step through those.

16 I don't think we'll spend a lot of time.

17 But if you would, Dr. Lowit, just sort of try to

18 paraphrase essentially what in the intent of that

19 question is. Most cases it will be very obvious, but I

20 think there are some times where we've had situations

21 where what you intended and what our people read into

22 it were slightly different.

23 So if you could do that now, it

24 certainly will prepare us for tomorrow afternoon or

25 Thursday morning as we begin to address the Charge
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 175

1 Questions.

2 So, again, you can make this as short

3 and sweet as you'd like. If you think it's very clear

4 what you're asking for, just .

5 DR. ANNA LOWIT: Well, we always think

6 it's clear; but sometimes it turns out to not be that

7 way. So this is probably a very good idea. And I've

8 asked the major leads on each particular subsection to

9 come up; so if I misspeak on our question for that or

10 if there's anything to add, we'll do this as a team

11 effort.

12 The epidemiology, we have two sub13 questions. The first one is largely on our reviews and

14 that to give us feedback on whether or not our reviews,

15 particularly in the Appendix, do an adequate job of

16 identifying the major strengths and limitations of

17 those studies. So I think the source of that

18 information is predominantly in the Appendix.

19 DR. STEVEN HEERINGA: Dr. Gold and her

20 discussants are prepared to address that, I'm sure.

21 Sure, if you'd like to.

22 DR. ELLEN GOLD: So one thing we were

23 wondering is if you want comments on each specific

24 study or if you want more generic kind of comments

25 about strengths and limitations that might not have
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 176

1 been touched on.

2 DR. ANNA LOWIT: I think it depends; I

3 think that's largely up to you. We're not going to

4 tell you how to answer the question. There are

5 multiple ways to cut it.

6 DR. STEVEN HEERINGA: Right. I think we

7 have the detail and that will be in the written report,

8 and the question is whether we go individually through

9 the studies; but we can discuss that more, but I think

10 the intent of that question is quite clear.

11 DR. ANNA LOWIT: Go ahead.

12 DR. STEVEN HEERINGA: Dr. Christensen.

13 DR. CAROL CHRISTENSEN: Yeah, this is

14 Carol Christensen, so just very briefly. I guess

15 specifically with those five evaluations that I had

16 sort of characterized as being somewhat stronger across

17 the database; so comments individually on those studies

18 in particular would be helpful.

19 In my slide presentation and also within

20 the written evaluation, sort of identified those on

21 female/male reproductive health and small for

22 gestational age.

23 These are the ones that we are

24 characterizing as being relatively stronger across the

25 database. So it's the two Farr studies, the State of
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 177

1 Illinois and the Villanueva - I'm sorry, it's Ochoa2 Akuna. It's in my slide presentation.

3 DR. STEVEN HEERINGA: So from this

4 morning, there are five studies in particular that you

5 would appreciate individual comments, okay. I think

6 there are 14 other studies total that you considered?

7 Maybe Question

8 DR. ANNA LOWIT: Okay, Question 1.2 has

9 two subparts, and admittedly there is some overlap

10 between A and B. But they're intended to get at

11 specific sections in the Issue Paper. The latter part

12 of Section 3 describes the utility of the epidemiology

13 database as it relates to risk assessment, and that's

14 what's in Part A.

15 And then, Part B focuses primarily on

16 what's in Section 4 of the Issue Paper, which is

17 thinking about bringing the strengths or similarities -

18 - the strengths, the differences and the uncertainty in

19 both of the experimental toxicological and the

20 epidemiology databases together in one integrated

21 analysis.

22 So admittedly, there is some overlap

23 between A and B; but they are separate sections of the

24 Paper, so we had separate sub-questions.

25 DR. STEVEN HEERINGA: John, are you
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 178

1 fairly comfortable with Part 1.2?

2 Dr. Gold?

3 Okay, thanks. So, Question 2.1.

4 DR. ANNA LOWIT: Okay, the large

5 subheading of Question 2 relates to mammary gland

6 development, and there are also two parts here.

7 The first one relates primarily to the

8 two Rayner papers and the Coder paper, which is one of

9 the Syngenta-supported papers. The Rayner, the two

10 Rayner publications come out of the Fenton lab.

11 And we find as we look at those there

12 are some similarities; but there are also some

13 differences, and we are looking for some feedback on

14 what could lead to some of those, the differences in

15 those findings, and similarities for that matter.

16 DR. STEVEN HEERINGA: Dr. McManaman,

17 fairly comfortable with those questions as you

18 understand them?

19 DR. JAMES McMANAMAN: Yeah.

20 DR. STEVEN HEERINGA: Good, thank you.

21 Yeah, a confident response, that's good. I like that.

22 DR. ANNA LOWIT: Question 2.2 is

23 explicitly about the Enoch mixture paper. As I noted

24 very briefly in my introductory remarks, if you look at

25 our review in Appendix A, we have some concerns about
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 179

1 that study and are having some trouble interpreting

2 that study.

3 And so that's what Question 2.2 is

4 getting at: not only a review of the strengths and

5 limitations, but in your view how the study design

6 impacts the interpretation. So it's predominantly a

7 mixture question, but I think some of the mammary gland

8 development issues are also implicit in that.

9 DR. STEVEN HEERINGA: Dr. Mumtaz?

10 DR. MOIZ MUMTAZ: That should be good.

11 You want to focus only on the mammary glands, is that

12 right, Anna?

13 DR. ANNA LOWIT: Just the Enoch paper.

14 DR. MOIZ MUMTAZ: Yeah, right. And then

15 look at the experimental design. I'm comfortable with

16 that.

17 DR. STEVEN HEERINGA: Good, thank you,

18 sir.

19 Question 2.3.

20 DR. ANNA LOWIT: Yes, and then there is

21 one more as it relates to mammary gland development.

22 It is thinking about integrating the mammary gland

23 development papers into the weight of the evidence for

24 atrazine.

25 We are, as you heard earlier, choosing
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 180

1 to emphasize more of the LH attenuation and use the

2 mammary gland development more in hazard

3 characterization as opposed to hazard identification,

4 and we're looking for feedback on that proposal.

5 DR. NELSON HORSEMAN: If Jim and Moiz

6 make it easy, it will be easy.

7 DR. STEVEN HEERINGA: Okay, Dr.

8 Horseman.

9 Okay, Question 3.1.

10 DR. ANNA LOWIT: I keep forgetting to

11 advance the slides.

12 Okay. Then we have a series of smaller

13 questions intended to get at particular components of

14 the tissue dosimetry and the proposed updates to the

15 dose-response assessment. And we had hoped that we had

16 written them so that there was a logical order that one

17 would build on the other, but I'm sure there's a good

18 bit of overlap.

19 The first one has to do with simply the

20 non-compartmental analysis conducted by the Agency that

21 you heard from Dr. Rodriguez this morning to estimate

22 internal dose of atrazine and its metabolites.

23 DR. STEVEN HEERINGA: Dr. Greenwood?

24 DR. RICHARD GREENWOOD: No, I think

25 that's clear. Obviously, there will be some overlap
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 181

1 with other things; but I think that that's clear, thank

2 you.

3 DR. ANNA LOWIT: So I think that the

4 difference between A and B, A is more the actual

5 analysis and the mathematical procedure behind the

6 analysis, and Part B is a related question asking about

7 our interpretation of what is Figure 5.5 in the Issue

8 Paper which we saw a couple of times today, which is

9 the figure from the Thede paper of the tissue dosimetry

10 data of the ramping up, and the values stay fairly

11 constant until they stop dosing and they're eliminated

12 and our interpretation of what that figure shows.

13 DR. STEVEN HEERINGA: We're set there;

14 Dr. Krishnan indicates that we're all set on that one,

15 too, in terms of our understanding of what you're

16 after.

17 DR. ANNA LOWIT: Okay. And then we have

18 two sub-questions on the benchmark dose analysis. The

19 first one is akin to the first one of 3.1; it's more of

20 a technical review of the actual analysis, the things

21 contained in Section 5.3 in Appendix C.

22 And then the Part B of that one is more

23 of a conceptual subpart around establishing a benchmark

24 response as it relates to calculating benchmark doses.

25 So one is a technical mathematical question, and the
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 182

1 other one is mostly conceptual.

2 DR. STEVEN HEERINGA: Parts A and B, Dr.

3 Meek, are you, and then Dr. Roby?

4 Okay, great. Okay. I guess 4.1, Dr.

5 Lowit.

6 DR. ANNA LOWIT: Okay. Then we have the

7 next one in that wait a minute. Now, I'm confused.

8 The slides are like out of order or something.

9 DR. STEVEN HEERINGA: I think I jumped

10 you over.

11 SPEAKER: 3.2 is 2.

12 DR. STEVEN HEERINGA: Yeah, we did 3.1.A

13 and B, and I think we did not do we did C and then I

14 jumped ahead here. So, Dr. Akana on 3.1.C.

15 DR. ANNA LOWIT: So we, the Agency, we

16 need to fix these slides. C is missing from the slides.

17 Okay, so it's just out of order. Okay.

18 So this subpart, whichever number it is

19 I'm moving too fast to figure out, but this is a

20 subpart question. I think it's the one after Figure

21 5.5, but it doesn't matter I guess for right this

22 second.

23 We do have a sub-question around our

24 interpretation of what is Figure 5.5, which was

25 discussed at length this morning after Dr. Rodriguez's
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 183

1 presentation, and it is the overlay plot of the LH

2 attenuation data from I believe it's four studies --

3 the new Cooper data; the Morseth 6-month data; and a

4 couple of other studies -- and that the analysis that

5 went into that plot and our interpretation of it.

6 DR. STEVEN HEERINGA: Yeah, we have this

7 question as 3.1.C, and Dr. Akana is our lead

8 discussant.

9 DR. ANNA LOWIT: Okay, we'll have to fix

10 these tonight, the slide numbering.

11 So I believe that covers all the dose12 response, even though they're out of order. And I'll

13 let Nelson cover his.

14 MR. NELSON THURMAN: Okay, Question 4.1

15 is basically focusing on what we propose as the

16 framework for the monitoring study design; so more or

17 less wanting to get your feedback on strengths,

18 weaknesses, anything else you'd want to recommend on

19 that.

20 DR. STEVEN HEERINGA: So Wesley Stone on

21 Part A, are you okay with that, Wesley, and then on

22 Part B

23 MR. NELSON THURMAN: Okay, and Question

24 4.2 is

25 DR. STEVEN HEERINGA: Oh, Dr. Coupe on
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 184

1 4.1; yes, I'm sorry. 4.2, I'm sorry.

2 MR. NELSON THURMAN: Okay. 4.2 is

3 basically questions relating to our plan to use the

4 intensively sampled monitoring data.

5 And the question on chemograph shapes,

6 we already heard some question about what we're

7 intending, and I hope I clarified that a little bit.

8 But that is something the more we put together, the

9 more difficult the concept and application is. So

10 we're interested in your feedback on that.

11 And then the second's on the strengths

12 and weaknesses of the data that we propose using.

13 DR. STEVEN HEERINGA: I think Wes is

14 indicating he's good with Part A.

15 DR. WESLEY STONE: On Part B, can you

16 say that again in terms of what you're looking for?

17 MR. NELSON THURMAN: On Part B, we're

18 looking for the strengths and weaknesses of the

19 datasets themselves. The Heidelberg data, the

20 ecological exposure monitoring data. And then I

21 touched on it briefly, but the idea of using prism

22 exams for reservoirs, as opposed to the --

23 DR. WESLEY BROWN: Okay, so it's about

24 the data themselves, because it wasn't clear. It's

25 using them for something in particular or just using
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 185

1 them in general or--

2 MR. NELSON THURMAN: Well, using them

3 for evaluating the sampling frequency and the model

4 estimation.

5 DR. STEVEN HEERINGA: And there is a

6 Part 4.3, too.

7 MR. NELSON THURMAN: Yes, there is, and

8 4.3 is what we had recommend, their update on base

9 basically how we might estimate the exposure

10 estimation, what methods we'd use to estimate exposures

11 in between.

12 DR. STEVEN HEERINGA: Dr. Lee indicates

13 that he comprehends and we addressed that.

14 Question 5, then, I guess we go back.

15 DR. ANNA LOWIT: Questions 5 and 6 are a

16 little different than I think all the other questions;

17 they're more conceptual and a little bit more esoteric

18 than some of the other ones that are far more

19 technically oriented.

20 The first, Question 5 on the scientific

21 considerations for potential sensitivity, that was the

22 first half of my presentation this afternoon. And what

23 we're asking you to provide us feedback is on the sorts

24 of scientific factors we should think about as we move

25 forward with an updated FQPA 10X analysis, keeping in
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 186

1 mind that we're going to be that there's particular

2 focus under FQPA to infants and kids, and we'll be

3 thinking about things like that I had in my slide: the

4 drinking water, sufficiency of the drinking water data;

5 the Mode of Action; the epidemiology; the human

6 relevance; all the things that were in my slides and

7 that are in that chapter of the Issue Paper. So it's

8 predominantly a bigger-picture question around the

9 factors to think about.

10 DR. STEVEN HEERINGA: Carey, Jan, Bette,

11 Penny, are you okay with that in terms of the way it's

12 okay.

13 DR. ANNA LOWIT: And then Question 6 is

14 the one that's intended to bring everything together as

15 we think about the critical windows of exposure or the

16 duration of exposure for moving forward with a risk

17 assessment or as we think about the monitoring

18 frequency.

19 We've thought about it in a number of

20 ways I talked about earlier, and we're asking you to

21 not only comment on what's in our Paper that we've

22 already done and the sorts of things that we're

23 thinking about, but also to provide alternatives and

24 additional things to think about, because this will be

25 one of the areas that we put a good bit of emphasis on
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 187

1 in the coming months.

2 DR. STEVEN HEERINGA: Dr. Bucher,

3 associate discussants? Do you feel comfortable with

4 it?

5 Dr. Coupe?

6 Okay. I think it appears that we're all

7 set there. Anything from the Panel? I appreciate

8 taking the time to do that. I think that hopefully

9 will be helpful.

10 Okay, at this point in time, in the

11 Agenda we have completed the scientific presentations

12 from the EPA. We'll have an opportunity first thing

13 tomorrow morning to I think Dr. Lowit and the team, if

14 you have anything that occurs to you overnight you want

15 to present, you'll have an opportunity first thing.

16 But we'll also have a presentation tomorrow morning by

17 Suzanne Fenton of NIEHS.

18 And after that presentation and our

19 questions of clarification, we'll move to the period of

20 public comment. If there's anyone in the audience who

21 wishes to make a public comment, please see Joe Bailey,

22 the Designated Federal Official, to sign up for a time.

23 One other comment I guess, public

24 comment I think will take a substantial share certainly

25 of the late morning and afternoon, I believe. There
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 188

1 will be a number of presentations, and several of them

2 will be fairly lengthy and in-depth.

3 So we'll spend the time we need to focus

4 on that. But for public commenters, slides, make sure

5 that they are brought to Joe or to the SAP Staff to be

6 loaded up, hopefully before your presentations, or if

7 you have handout material see that they get to Joe also

8 so that he can arrange to have them copied and

9 distributed.

10 There is one handout that the Panel has

11 received. It's a preliminary view and statistical

12 analysis of data associated with Cooper, et al., study

13 by Dr. Silken and I think this may be relevant to some

14 of the public comment tomorrow; but just note that the

15 Panel has received it, and a copy of this will also go

16 into the Docket.

17 Okay, at this point, are there oh, Dr.

18 Lowit.

19 DR. ANNA LOWIT: Just one issue about

20 the first thing in the morning, we're still working to

21 get Dr. Fenton's slides. Our hope is that by 8:30, we

22 have 30 copies of them. They may appear while she's

23 talking if we don't get them in time, but we're doing

24 our best.

25 DR. STEVEN HEERINGA: That's fine; we
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 189

1 understand.

2 DR. ANNA LOWIT: She just came back from

3 Spain a couple of days ago, so

4 DR. STEVEN HEERINGA: I'll have to use

5 that one, just came back from Spain.

6 SPEAKER: For you it's Russia.

7 DR. STEVEN HEERINGA: Yeah, Russia.

8 Okay. And just a note from Joe Bailey, too, we will

9 start tomorrow morning at 8:30. Barring any other

10 questions from the Panel this afternoon again, you'll

11 have a chance tomorrow morning first thing, too, and

12 probably throughout the session tomorrow -- but I think

13 we'll bring the afternoon's proceedings to a close.

14 Panel members, if we could just meet

15 briefly in the breakout room just to discuss any sort

16 of small group sessions that you may want to plan in

17 preparation for addressing the Charge Questions,

18 probably later tomorrow afternoon or certainly first

19 thing Thursday morning.

20 So, if not, good afternoon, everybody,

21 and we'll see you 8:30 tomorrow morning.

22 (WHEREUPON, the MEETING recessed for the day at 3:32

23 p.m.)

24

25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 190

1 CAPTION

2 The foregoing matter was taken on the date, and at the

3 time and place set out on the Title page hereof.

4 It was requested that the matter be taken by the

5 reporter and that the same be reduced to typewritten

6 form.

7 Further, as relates to depositions, it was agreed by

8 and between counsel and the parties that the reading

9 and signing of the transcript, be and the same is

10 hereby waived.

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FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 191

1 CERTIFICATE OF REPORTER

2 COMMONWEALTH OF VIRGINIA

3 AT LARGE:

4 I do hereby certify that the witness in the foregoing

5 transcript was taken on the date, and at the time and

6 place set out on the Title page hereof by me after

7 first being duly sworn to testify the truth, the whole

8 truth, and nothing but the truth; and that the said

9 matter was recorded stenographically and mechanically

10 by me and then reduced to typewritten form under my

11 direction, and constitutes a true record of the

12 transcript as taken, all to the best of my skill and

13 ability.

14 I further certify that the inspection, reading and

15 signing of said deposition were waived by counsel for

16 the respective parties and by the witness.

17 I certify that I am not a relative or employee of

18 either counsel, and that I am in no way interested

19 financially, directly or indirectly, in this action.

20

21

22

23

24 MARK REIF, COURT REPORTER / NOTARY

25 SUBMITTED ON SEPTEMBER 14, 2010
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 192

0

0 65:6 68:25 69:8

69:15 168:5

0.05 84:13

0.65 77:9

0.7 97:7

1

1 64:20 65:12

69:7 73:23 74:17

158:18

1% 138:9 138:10

1,000 129:3

1.2 177:8 178:1

1.5 68:21

1.8 69:15 69:16

1.86 77:19

1.96 77:7

1:15 105:22

105:23 106:4

10 64:19 64:21

65:12 93:24

145:10

10:00 58:2

100 17:2 23:19

25:4 64:21 65:13

69:1 69:2

74:17 89:18 90:1

91:10 91:12

92:19 95:14

10-mg 158:18

10X 147:4

165:20 185:25

11 44:17 44:22

12 21:6 145:10

12.5 68:21

12you 91:12

14 2:3 64:17

66:11 162:24

177:6

15 18:14 18:15

87:8 119:16

168:18

15.56 77:13

15th 25:24 26:1

28:24 153:3

173:24

16 168:13 168:14

18 66:10

1800 76:3

1800-hour 70:15

88:7

19 36:8 36:11

37:2 56:7

1987 64:14

1988 21:5

1997 47:7

1-month 75:8 75:9

2

2 93:12 95:2

128:7 178:5

182:11

2.1 178:3

2.2 178:22 179:3

2.3 179:19

2.5 69:8 69:9

2:30 170:21

20 41:17 66:10

66:11 93:5

123:13 146:3

168:13

20% 45:4 46:13

47:23 49:4 52:19

89:1

2000 17:12 21:6

93:19 149:5

151:22

2001 69:6

2003 14:1 14:11

17:4 21:16 21:19

27:22 52:12

61:13 152:9

2004 68:23 71:6

2007 108:10

2009 44:16 108:10

132:19

2010 2:3 14:11

21:2 59:7 83:2

2011 13:23

18:18 18:20

21:17 22:12

28:21 28:25

33:11 82:13

133:23 161:20

173:10 173:25

174:1 174:7

21 162:24

21st 12:1

22 44:17 44:22

24 64:24 65:7

74:14 77:21

25 58:2 58:2 66:4

68:21 87:7

91:7 92:1 92:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 193

25% 85:18

27 93:5

29.4 69:15 71:6

2-week 128:5

128:7

3

3 57:2 63:15

64:20 65:12

65:14 66:4

99:8 177:12

3.0 33:22

3.1 180:9 181:19

3.1.A 182:12

3.1.C 182:14

183:7

3.12 68:21

68:22 70:17 84:5

84:9 84:11

3.2 182:11

3.65 69:15 69:16

3:00 169:13

3:32 189:22

30 61:14 68:11

68:15 68:25 69:2

85:15 88:22

89:17 90:5 90:12

91:1 91:3

92:13 93:5 93:23

118:21 188:22

30% 47:15 102:8

300 63:10 69:1

69:2

30th 25:25

35 130:22

3-day 66:5

66:17 66:23

3X 165:20

4

4 33:25 65:10

70:5 98:20

99:2 116:8

156:16 157:20

157:22 157:23

157:24 177:16

4% 61:15

4.1 111:18

154:6 154:16

182:4 183:14

184:1

4.2 111:18 183:24

184:1 184:2

4.3 111:18

154:6 154:16

185:6 185:8

40 123:13

40% 41:17 44:2

88:24

44 119:15

45 148:9

46 99:15

4-day 4:12

68:18 69:22

70:11 70:14

70:21 75:6 75:23

83:18 91:17

123:12 123:13

124:15 131:21

156:19

5

5 66:4 68:24 69:8

69:9 69:10

154:12 185:14

185:15 185:20

5.10 87:5

5.3 181:21

5.5 181:7

182:21 182:24

5.9 87:4 92:9

92:9 96:22

50 25:5 63:14

64:21 65:12

92:19 95:14

153:15 153:19

50% 45:11 46:14

52:19 61:17

88:24 89:12

50th 131:7

55 90:8

56 83:2 83:22

84:1 90:8

57 83:3 84:20

5-day 71:6

6

6 63:14 69:14

161:10 161:11

168:13 168:13

168:14 185:15

186:13

6.25 68:21

68:22 70:17 84:6

84:10 84:12 86:6

91:7

60 96:7 158:20

60% 44:2 88:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 194

89:12

62 95:13

63 95:14 97:1

97:2

6-hour 63:16

6-month 59:1

69:11 71:7 92:14

155:14 155:16

183:3

7

7 64:20 65:12

66:4 66:5 122:25

72 65:13

75 91:5 91:8

91:22 92:6 92:10

93:11 93:13 95:2

95:3 95:9

95:11 95:14

95:23 96:21

75th 131:7

7-day 66:18 66:23

122:14 126:10

126:12 127:9

8

8:30 188:21 189:9

189:21

80% 93:7 94:22

83 130:23 139:23

9

90 61:14 78:16

900 70:7

90-day 78:17

107:15 108:21

109:2 138:18

155:6 155:12

155:16 159:3

160:25 161:2

165:22

95% 74:5

96 65:9

98% 61:20

A

abdominal 43:23

43:24 44:4 44:15

44:25 45:12

45:13

abdominal-wall

43:14 43:21

166:23

ability 11:3 12:6

50:8

able 26:14

29:12 37:24 39:4

44:18 46:11 48:1

49:20 52:22

92:15 95:4

112:11 113:17

118:15 121:21

141:15 141:20

142:2 142:11

142:12 142:13

142:14 143:21

abnormalities

45:4

abortion 41:16

absence 76:14

76:17 76:19

101:13 102:18

103:7 171:2

absolute 160:1

160:18

absolutely

81:24 82:2 82:3

abstract 35:16

Academies 8:20

accompany 106:14

account 2:24

66:20 68:9 102:8

105:6 109:7

120:19 133:16

136:9

accounted 61:15

accounts 72:6

accumulated 14:9

accumulating

166:2

accuracy 52:8

113:14

accurately 164:20

achieve 67:25

acknowledge 9:5

15:6 15:18

16:1 39:12 54:23

55:1

acknowledged

39:18 42:15

acknowledgments

56:4

acronyms 164:13

across 11:20 12:3

15:1 16:19 19:25

32:11 32:15

33:21 36:23

38:19 41:16

45:14 48:8

50:6 50:19 56:10

66:2 68:23 81:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 195

82:1 85:2 85:3

86:6 129:15

150:14 150:18

156:3 158:18

176:16 176:24

ACS 57:10

Act 2:20 107:11

115:17 146:23

ACTH 23:15

Acting 9:9

action 11:16

11:19 12:4

12:5 12:9

12:13 12:23 13:7

17:8 20:23

21:8 21:14 28:10

59:25 60:4 145:1

148:20 149:1

149:3 153:21

154:13 165:7

166:11 166:15

172:17 186:5

activated 119:7

119:9

active 51:12 62:1

62:5 62:23 62:25

75:11 102:11

102:17 102:17

activities 9:22

101:11

activity 39:16

39:18 39:19 42:7

42:12 51:1

62:3 62:17 71:22

102:18

actor 103:4

actual 52:17

110:13 116:14

119:21 125:5

125:23 126:3

131:13 181:4

181:20

actually 25:12

26:8 30:9 49:7

57:25 59:20 61:3

61:20 62:3

62:9 62:11 62:18

62:20 63:1

63:2 63:11 64:17

64:21 64:22 65:6

65:22 66:13

66:19 67:4

67:8 69:7

69:10 69:14

69:21 69:23

70:19 70:24 71:4

71:25 72:17

72:22 73:15 74:4

76:4 78:15 78:16

79:15 81:12

81:16 81:16

81:18 82:2

82:6 83:2

83:18 84:8 84:10

85:1 85:3

85:20 87:9 92:12

92:18 93:25

94:21 97:10

97:12 97:12

98:10 98:12 99:9

99:11 100:4

102:12 102:13

105:4 105:12

105:15 110:17

117:14 135:1

137:21 142:17

143:3 150:9

152:8 152:10

153:17 162:21

167:21 168:2

acute 36:1

164:9 164:10

add 24:18 72:7

72:13 73:19

97:25 140:25

162:14 166:18

166:19 166:19

167:1 175:10

added 90:9 99:20

adding 97:21 98:7

98:10 98:15

99:17

addition 36:16

38:4 39:5

49:18 51:6 52:22

62:14 151:2

additional

40:13 41:5

64:2 69:3 104:13

105:19 105:24

161:14 165:23

186:24

address 90:13

90:17 113:21

144:17 161:22

169:21 174:25

175:20

addressed

108:17 120:25

155:10 185:13

addressing 162:22

162:23 189:17

adequacy 100:10

adequate 109:2

109:4 110:4

127:25 175:15
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 196

adequately 109:14

adjuvants 144:8

administered

100:4 103:20

104:20

administrative

2:16

admitted 57:20

admittedly

177:9 177:22

adrenal 7:25

adult 21:12

93:4 162:7 162:8

adults 152:7

advance 4:25 11:2

11:11 12:20

180:11

advantage 11:25

adverse 11:16

11:19 13:8

35:5 46:7 47:4

52:15 76:16

93:16 94:4 94:12

advice 2:21

2:22 2:25 9:25

11:8 17:25 27:10

Advisory 2:1

2:9 2:19 2:20

4:13 4:20 58:7

106:8

affect 39:22 90:2

affecting 149:22

affects 25:16

157:4

affirmation 4:1

afternoon 27:13

105:24 106:3

106:6 106:7

168:25 174:24

185:22 187:25

189:10 189:18

189:20

afternoon's

189:13

afterwards

113:3 169:6

Ag 16:22 37:11

38:24 48:24 50:5

52:7

against 155:13

162:8

age 46:8 48:8

48:20 53:8

149:17 163:9

168:15 176:22

Agency 2:24

9:24 10:10 14:12

19:15 24:23 34:5

146:24 150:14

152:9 152:12

154:9 154:25

155:5 162:4

180:20 182:15

Agency's 18:16

Agenda 3:5 3:11

3:18 3:21 187:11

aggregate 38:23

106:24

ago 19:12 43:1

46:19 152:14

173:2 173:5

189:3

agreed 17:16

agreeing 2:14

Agricultural 22:9

29:10 36:18

37:10 49:12

174:3

ahead 145:17

146:8 176:11

182:14

aided 39:14

aimed 69:25

air 48:14 122:15

Akana 7:22 7:22

101:4 101:5

101:15 182:14

183:7

akin 181:19

Akuna 177:2

al 43:10 44:1

44:16 47:7 52:12

66:2 68:18

75:9 83:2 188:12

align 20:3 20:12

aliquot 166:2

allometric 158:11

158:19 160:10

allow 167:9

allowed 162:25

alluded 82:25

158:3

alone 86:18

167:21

already 137:10

184:6 186:22

alternative

118:23 141:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 197

alternatively

113:19

alternatives

161:13 186:23

altogether 120:25

125:8 125:11

136:10

am 2:16 4:17 4:18

5:6 8:17 65:20

73:23 80:12

95:14 97:8

138:10 140:21

ambient 110:17

121:3

amended 146:22

American 5:5

among 25:10 38:12

38:17 45:4 49:23

51:15 102:2

151:2

amount 16:3

21:4 93:7

amounts 49:19

amplitude 88:23

89:1 89:9

89:10 93:4 93:23

94:21

amplitudes 93:5

analyses 34:20

48:9 100:22

129:25

analysis 18:7

20:17 21:19 22:2

22:15 24:18

25:25 26:18 27:4

27:8 27:15 27:17

27:21 27:25 28:5

28:15 29:4

29:7 29:12

32:9 40:13

51:3 72:23 72:25

73:2 73:21 75:19

76:24 77:1

77:3 77:6

77:11 77:17

77:24 78:6

79:7 79:20 79:23

81:13 81:15 89:8

89:19 90:7

90:7 90:9

90:25 91:20

95:10 118:18

120:20 122:18

122:21 122:25

123:5 123:7

125:13 127:2

127:6 127:14

130:3 130:10

130:16 136:20

146:21 147:25

150:3 153:23

154:8 154:11

160:19 161:12

161:17 172:23

173:20 173:21

177:21 180:20

181:5 181:6

181:18 181:20

183:4 185:25

188:12

analyst 91:14

analyte 40:15

analytic 40:20

52:14

analytical 150:13

analyze 78:7

analyzed 66:6

70:10 75:4 75:14

140:12

analyzing 122:2

Anatomy 6:23

and/or 41:19

76:10

animal 27:1

89:5 148:21

149:15 149:18

149:25 156:10

animals 88:8 94:7

151:13 153:12

167:22 168:12

Anna 15:18 19:2

19:7 33:8 82:5

84:19 95:6

96:2 99:17

103:16 105:11

145:3 145:7

145:19 146:10

146:13 162:9

162:12 163:22

164:4 164:11

164:18 165:16

166:18 168:3

169:19 169:24

170:24 172:4

175:5 176:2

176:11 177:8

178:4 178:22

179:12 179:13

179:20 180:10

181:3 181:17

182:6 182:15

183:9 185:15

186:13 188:19

189:2

answer 20:4 57:19

84:16 92:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 198

141:15 142:13

163:14 164:7

170:16 176:4

answered 95:7

answering 161:10

answers 145:24

154:7

antagonist 166:13

167:3

anticipate

21:17 93:10 94:4

173:13 173:14

anybody's 167:12

anyone 3:12

187:20

anything 102:4

103:20 131:24

175:10 183:18

187:7 187:14

anyway 93:5 141:6

apparently 171:10

appear 80:22

100:2 188:22

appears 82:16

172:19 187:6

Appendices 16:25

Appendix 25:2

26:6 26:7

33:20 35:10 57:3

75:20 77:4

82:8 95:9

96:11 122:21

164:19 175:15

175:18 178:25

181:21

applicable 11:25

application 38:14

42:8 49:14

56:5 129:6

137:19 155:2

184:9

applications

133:4

applicators 37:13

37:16

applied 7:13

113:1 115:1

116:3 164:8

165:13 165:14

apply 56:20 59:25

147:6

applying 36:7

appreciate 9:18

16:7 76:20

137:24 174:11

177:5 187:7

appreciation

15:11 19:8

approach 58:25

60:1 76:19 78:15

78:18 80:16

81:12 82:20

102:19 102:23

103:8 104:7

104:8 104:9

110:2 110:5

114:18 115:15

128:13 129:12

136:16 142:19

142:25 161:8

approached 169:25

approaches

10:13 10:17

11:12 12:2 17:23

18:8 24:5 59:4

78:9 78:24 110:1

110:4 111:12

112:1 118:13

127:21 143:6

173:1

appropriate

12:8 35:18 38:25

135:11

approximate 140:1

approximately

17:2 18:23

159:19 160:22

April 17:1 17:7

17:13 17:22 18:6

22:13 22:18

22:21 23:2 23:11

23:13 23:17 24:2

24:15 26:2

26:3 26:6

26:12 27:3

27:9 29:21 31:15

59:7 107:18

112:3 117:12

122:23 126:19

127:11 128:22

133:10 164:7

172:22

aquatic 5:24

107:23

area 6:4 6:23 8:3

8:24 27:6 27:9

28:6 37:7

43:17 44:5 59:16

64:4 64:10 71:16

71:25 72:15

72:18 72:19 73:4

73:10 73:12

73:15 73:18

74:11 74:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 199

77:12 77:14

78:21 78:23

79:16 86:21

98:21 98:22

98:24 98:25

99:12 105:5

107:20 112:13

114:18 116:14

117:16 126:5

141:9 152:13

154:2 159:8

areas 10:21

115:12 115:13

161:16 161:21

186:25

area-under-thecurve 100:16

argument 146:12

arrange 188:8

arrangements 3:15

array 18:7

arrows 124:15

articulate 53:11

articulated 32:24

articulating

34:10

artificial 128:16

ascertainment

34:14

aspect 67:14

139:14 141:10

aspects 59:11

63:6 71:11 79:13

assay 133:16

assembled 9:5

assess 131:6

132:16 133:15

148:14

assessed 38:8

42:9

assessing 121:7

151:8

assessment 7:20

8:4 11:6 11:23

12:2 12:17 12:18

13:12 14:24

18:20 19:20 20:5

20:12 20:16 21:3

22:2 23:9

23:13 23:15

23:17 24:17

26:17 26:25

29:23 30:2 32:17

33:1 33:9

33:14 33:17

34:12 34:13 39:9

39:13 42:5 42:15

42:17 43:3

46:5 51:9

53:14 53:23

54:20 57:14

58:13 58:16

58:21 58:24

59:10 59:21 60:1

61:25 62:16

62:19 69:12

69:18 71:11

75:11 79:11

82:16 87:14

92:21 96:16

108:5 121:1

121:5 138:23

140:22 146:15

152:12 155:5

155:12 161:22

164:21 165:2

165:14 165:17

165:19 170:3

172:15 177:13

180:15 186:17

assessments

12:2 12:7

16:13 19:16 37:1

129:9 129:10

133:5 164:8

associate 5:18

7:14 8:13 187:3

associated 49:1

76:16 110:18

118:2 131:6

166:9 166:14

188:12

associates 72:8

association 33:15

34:22 35:20

37:22 37:25 38:7

40:2 40:6

41:11 41:12

41:18 43:7

44:4 44:14 44:19

45:1 46:9 47:5

47:6 47:21 47:24

48:17 48:20

48:25 49:1

49:4 49:21

50:1 50:8 52:2

52:20 166:11

166:14

associations

32:14 45:18

50:11 51:4

assume 90:16

101:25 152:5

assumed 42:11

158:21
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 200

assumes 129:12

assumption 73:1

73:14 170:25

171:6

asthma 49:1

Atlanta 5:6

atrazine 2:10

4:14 10:12 10:19

10:23 11:8 11:18

11:22 12:16 13:2

13:5 13:11 13:15

13:18 13:24 14:1

14:3 14:10 16:14

16:20 17:3 17:14

17:17 18:12

18:14 18:18

19:10 19:23 21:2

21:14 22:5 29:11

31:22 33:10

33:15 35:22

37:22 37:23

37:25 38:1

38:7 38:13 38:22

39:4 39:22

40:3 40:4 40:6

40:19 41:2

41:3 41:19

43:7 43:17 43:20

44:5 44:14

45:6 46:2

46:10 46:12

46:15 46:16

46:21 47:5 47:24

48:1 48:2 48:6

48:6 48:18 48:25

49:5 49:9

49:21 49:23 50:4

51:9 51:25 52:16

52:20 52:21

53:14 53:17

53:18 53:24 54:1

54:15 54:24 58:9

58:21 60:9

60:9 60:17 60:25

61:5 61:9

61:14 61:22 62:4

62:17 63:9 64:21

65:3 66:4 68:5

68:15 68:20 70:2

71:21 74:1

74:8 76:23

77:6 77:7

77:16 77:19

78:13 79:5 79:19

82:10 88:17

89:23 93:19

94:25 95:17 98:2

98:3 98:6

100:4 101:9

101:18 102:1

103:21 106:10

106:23 107:7

107:17 107:23

109:10 110:17

110:24 113:4

114:23 115:4

116:3 116:14

117:7 117:9

117:16 118:8

119:1 119:3

119:5 119:8

119:11 119:13

120:25 132:20

139:8 139:24

140:5 140:22

140:23 140:25

140:25 149:3

155:17 155:22

156:13 156:24

157:1 158:7

159:24 162:13

165:7 167:24

170:8 171:2

172:17 179:24

180:22

ATSDR 6:12

attempt 98:4

attempted 55:16

attention 32:24

36:11 60:2

63:7 64:14

146:25

attenuating

62:1 90:1

attenuation 23:23

59:3 59:13 63:12

63:16 63:19 64:3

67:24 68:8

72:9 75:16 76:15

79:14 85:18

88:17 149:7

149:22 150:17

150:23 151:1

151:7 157:14

157:17 157:22

172:15 180:1

183:2

at--what 96:7

AUC 73:25 81:6

97:5 97:8 100:19

100:23

audience 30:25

187:20

August 107:19

117:12 122:23

authored 75:9

authors 37:20

37:24 38:4 38:11

39:4 39:12 39:18

40:1 40:5 40:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 201

40:14 41:17

42:11 42:15

43:13 43:18 44:6

44:8 44:23

45:3 45:9

46:11 47:3 47:10

49:3 49:8

49:19 49:24

51:23

autocorrelation

130:17 130:22

autocorrelationtype 129:11

auto-samplers

113:23 113:25

114:9 114:15

116:7 166:1

availability

147:1 148:14

available 4:2 4:6

18:22 32:12

35:11 36:4 55:19

81:24 82:3

132:23 148:19

162:19

average 78:10

78:17 78:19

101:1 114:11

123:12 155:16

158:19 160:25

161:2 165:22

averages 100:24

123:13 124:15

155:6 155:7

155:13 159:3

averaging 28:15

await 29:9

aware 88:1

away 15:20 45:5

65:8 81:4 81:8

81:10 87:16

axis 7:25 17:10

17:11 17:15

17:15 17:17

17:18 22:23 23:3

23:8 54:16

101:17

B

B1 35:10

B2 33:20

background 3:21

7:19 31:24 32:20

58:22 111:13

121:6 130:1

142:20 166:10

bacterial 171:10

bad 79:1 103:3

Bailar 8:16

8:16 80:7 80:8

80:24 86:2 86:11

86:13 102:20

102:21 103:2

103:10 136:17

136:18 137:11

137:23 139:14

Bailey 2:4 2:8

15:14 168:21

187:21 189:8

balance 7:25

113:13

balanced 115:8

Barring 189:9

Barry 6:25 6:25

bars 85:6

base 62:6

149:19 185:8

based 18:1

31:14 47:18

58:13 58:25

62:10 62:20

62:21 63:2 63:25

64:15 71:19 72:1

72:15 76:18

76:22 76:24 77:6

77:8 77:11 77:17

78:5 78:12 79:17

83:18 84:9 84:14

93:12 97:23 98:5

101:24 102:7

108:21 109:7

111:20 112:2

113:9 115:4

115:6 115:16

115:23 119:19

121:25 122:6

129:5 132:13

140:23 170:19

baseline 91:14

91:15

basic 75:19

basically 60:18

66:5 66:22 69:25

70:25 73:17

73:19 99:20

116:21 140:10

145:9 183:15

184:3 185:9

basing 87:14

87:20

basis 73:19

75:6 76:10 79:10

98:15 99:11

115:17 138:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 202

Baskin 7:15

Batterman 129:2

become 18:22 31:7

61:4 109:23

150:2

becomes 81:23

82:2

begin 19:3

85:13 112:4

174:25

beginning 3:6 5:3

26:13 70:7

107:18 150:2

154:22 157:22

165:18 172:7

behavioral 151:21

163:4

behind 77:25

103:22 181:5

believe 3:6 25:23

31:4 74:3

84:13 120:23

127:13 133:3

152:21 153:8

157:19 162:9

164:19 164:23

168:1 168:8

172:16 183:2

183:11 187:25

benchmark 27:4

59:6 59:18 74:23

75:4 75:12

76:7 79:17 84:14

89:19 90:9

95:8 96:11 96:14

96:20 164:13

164:15 164:18

172:23 181:18

181:23 181:24

bend 84:23

benefit 169:6

best 9:25

102:19 103:8

110:14 128:2

128:24 130:12

188:24

best-fit 76:23

77:2

Bette 7:17 7:17

165:10 186:10

better 12:7 19:19

52:24 110:3

115:11 136:14

158:15

beyond 89:17 90:5

91:8 130:18

bias 42:2

biased 110:12

biases 50:17

bigger-picture

186:8

biggest 109:17

bio 101:8

biological

11:21 12:4

12:9 23:5

49:25 54:14

76:10 151:6

biologically

17:19 172:16

biology 6:16 6:20

6:23

biomarker 52:13

53:25

biomarkers

40:18 54:1

biostatistician

5:7

bird's 29:17

birth 42:22

43:6 44:15 44:18

45:10 45:16

45:22 46:7

47:4 47:20 47:22

47:24 48:18

48:22 52:15

52:25 53:1

54:3 162:25

166:8 166:14

bit 21:20 22:18

31:24 48:2 52:24

59:12 74:25

80:18 81:7 88:20

90:14 100:25

111:3 111:23

114:2 115:15

122:1 124:23

125:19 133:11

143:13 148:15

149:23 154:1

156:21 167:23

168:24 171:5

173:2 173:5

173:9 180:18

184:7 185:17

186:25

biweekly 131:22

131:23 155:3

black 85:7

blocks 91:9 163:8

blood 105:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 203

167:25

blue 120:12

120:12 120:12

124:12

BMD 76:22

BMDL 77:7 77:9

77:13

BMR 76:8 76:9

76:17 164:8

164:12 164:13

bode 94:12

body 60:11 158:20

163:2 167:24

bootstrapping

123:7

bottom 120:13

120:15 162:14

bound 125:14

bounds 109:6

109:21 113:18

117:19 117:23

122:2 122:11

122:16 131:15

132:16 142:24

Bradbury 9:9 9:11

brand-new 68:18

150:11

break 58:1

58:11 105:21

145:14 145:17

168:19 169:12

169:21 169:25

170:14

breakout 189:15

brief 142:22

briefer 142:22

briefly 32:6 33:2

36:9 37:11 43:12

111:19 176:14

178:24 184:21

189:15

bring 4:8 13:8

22:16 91:10

143:23 174:14

186:14 189:13

bringing 22:1

22:3 93:20

111:16 177:17

brings 18:5 23:17

143:16

Briscoe 16:2

broad 19:25 21:25

57:13 136:22

broadened 24:18

broader 109:11

117:15 137:18

broadly 10:13

11:10 11:24

12:20 13:18

brought 36:10

44:13 91:9 93:20

169:22 188:5

BROWN 184:23

Bucher 5:17

5:17 55:13 55:14

55:14 57:1 187:2

bucket 28:8

build 21:19 21:24

29:18 65:24

122:8 180:17

bulk 148:7

bullet 152:3

bullets 151:15

business 9:24

C

C14 64:15

calculated 40:4

97:21

calculating

181:24

calculations

95:12 96:4

96:5 96:10 96:12

96:19 98:5

158:10 158:14

159:2 172:22

California 5:23

7:16 7:23 8:9

Canada 6:9 7:19

7:20

cancer 5:6 8:12

18:20 21:17

21:18 29:9 29:13

172:12 174:1

174:2 174:4

174:5 174:6

candidate 115:9

capabilities

132:1

capability 140:20

capture 108:7

112:7 114:19

114:20 126:3

capturing 126:7

158:6

carbon 64:17

carbons 64:17
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 204

carcinogenic

33:11

carcinogens 80:18

Care 8:12

careful 63:7

159:25

Carey 5:13 5:13

186:10

Carol 31:18 31:19

55:18 56:3 56:19

149:16 172:11

176:13 176:14

Carolina 8:24

37:14

case 13:2 13:15

16:14 16:14

16:17 16:20

19:23 22:6

36:1 36:13 36:13

41:25 62:24

102:16 116:4

122:17 150:14

cases 97:22

174:19

casting 129:3

categories 37:3

136:22

category 137:7

caudal 26:17

causal 32:13 50:9

50:13

cause 157:6

caveat 122:3

cavity 45:12

CDC 6:12 41:4

Cell 6:23

center 6:22 7:1

145:12 145:14

central 13:17

Century 12:1

certain 34:1

61:21 94:21

100:12 107:11

125:17 125:17

141:16 142:15

certainly 21:15

22:23 24:8

27:4 27:9 30:3

34:25 45:25 56:4

56:14 88:6 96:16

99:24 103:24

135:8 145:5

149:17 173:17

174:24 187:24

189:18

certainty

141:16 142:12

chair 4:9 4:19

4:21 8:7

Chairman 9:11

Chairs 145:4

challenges

10:14 48:14

Chambers 5:8

5:8 82:22

82:24 83:5 83:11

83:22 84:2 84:17

84:19 86:3

87:1 89:21

chance 51:11

86:18 112:8

121:3 142:5

189:11

chances 141:22

change 31:10 65:9

65:13 68:12

87:12 87:13

87:19 135:18

144:1 144:8

144:9 165:12

changes 13:4

74:18 88:15 89:8

89:10 94:11

95:24 149:7

149:18 149:22

151:16 151:21

changing 144:7

chapter 147:14

186:7

characteristic

52:4

characteristics

38:1 39:3

42:22 42:22 53:1

85:8

characterization

26:24 54:9 95:24

121:19 180:3

characterize

109:14

characterized

176:16

characterizing

129:4 176:24

charcoal 119:8

119:9

Charge 31:9

152:11 161:10

165:17 165:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 205

169:2 174:14

174:25 189:17

Charlottesville

8:3

chemical 6:12 7:9

61:6 61:15 61:19

61:22 62:17

71:21

chemicals 52:9

chemical-specific

156:2

chemograph 78:4

78:21 121:14

121:19 121:20

122:1 122:6

124:8 124:22

125:5 125:20

127:3 135:20

184:5

chemographs

122:25

Chester 15:20

29:25 58:15

58:17 81:9 81:23

83:4 83:9

83:17 83:24 84:8

85:19 87:21

90:23 91:9 92:11

97:11 98:9

99:3 99:5 101:10

102:6 102:25

103:6 103:13

104:25 105:3

153:16

Chicago 8:17

childhood 36:21

children 145:1

146:25 149:13

chlorinated

62:6 62:18 71:23

chlorination

119:5

chlorine 61:1

62:7 102:12

chloro 140:5

170:9 171:1

chloro-degradates

140:9

chlorotriazine

143:18 158:8

171:24

chlorotriazines

61:16 61:20 66:7

66:13 66:16

119:16 120:5

120:10 140:13

143:15 170:2

170:11 170:16

choice 82:14

choosing 179:25

chose 93:11

Christensen

22:7 26:21 31:13

31:17 31:18

31:19 55:6 55:18

56:3 56:19 57:18

58:12 106:15

147:17 149:11

172:11 176:12

176:13 176:14

Christensen's

55:9

chronic 164:9

Cincinnati 6:14

circle 148:10

citation 35:11

cited 59:23

64:1 69:9

clarification

31:5 31:14

55:8 55:12 55:15

57:21 80:5

81:3 97:20

104:14 134:19

162:1 170:1

187:19

clarifications

56:23 139:18

clarified 184:7

clarify 17:21

38:8 135:13

137:9 141:1

clarifying

54:22 55:4 97:1

classical 72:24

classification

54:17 170:23

classifications

121:20

clear 27:2

27:10 33:3 37:24

38:16 65:3

85:6 104:20

106:2 138:8

138:10 142:16

165:15 169:5

175:3 175:6

176:10 180:25

181:1 184:24

clearance 163:19

clearly 34:10
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 206

click 169:5

close 23:18

44:5 69:4 127:20

145:25 170:24

189:13

closely 80:17

83:19 113:16

121:25 132:18

138:20

closer 45:5

48:2 52:17

111:11 127:5

Cmax 63:23 72:3

Coder 25:2 25:3

178:8

cognizant 82:11

cohort 36:13

37:12 49:19

153:5 153:5

cohorts 153:1

coincide 94:25

coinciding 116:3

co-kriging 132:9

collaborate

150:14

collaborative

16:21 16:24 22:8

49:22 150:12

colleague 55:1

colleagues 15:2

16:6

collect 113:24

114:9 136:19

137:22

collected 49:11

49:19 137:10

College 5:9 117:2

College's 110:20

Colorado 6:18

column 66:16

158:23

columns 66:12

combination 20:11

133:7

combine 129:18

combining 111:6

133:13 133:14

133:18

comes 49:11

127:10

comfortable 178:1

178:17 179:15

187:3

coming 4:8 9:21

19:9 28:5

67:19 100:9

100:18 146:5

154:9 173:13

174:3 187:1

comment 3:7 3:9

28:2 31:8

56:13 90:5 104:7

121:11 128:10

161:11 186:21

187:20 187:21

187:23 187:24

188:14

commenters 3:10

4:4 188:4

comments 3:16

3:17 25:19

34:5 87:25 94:17

103:17 144:14

155:10 172:10

175:23 175:24

176:17 177:5

commercial 37:13

Committee 2:19

2:20

common 35:6 88:17

112:6 128:8

128:10 166:15

commonly 114:1

communities

107:23

community 107:8

107:10 108:3

110:8 110:19

110:22 110:25

114:24 115:14

115:16 115:18

116:19 116:23

119:15 119:22

120:1 120:22

121:8 121:13

121:21 125:25

133:5 138:14

138:15 138:20

139:5 139:13

165:20

comparatively

51:15 51:21

compare 70:19

78:19 78:21

78:22 83:20

92:15 162:8

compared 38:2

43:14 43:16

66:21 70:11 76:5

78:11 91:12

101:12 119:24
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 207

123:10 140:24

155:13

comparing 66:5

71:12

comparison

34:18 59:13

67:21 94:20

comparisons 40:25

87:7 160:5

compartmental

72:25 73:20

compiled 68:4

compiling 75:3

complement

89:14 103:3

116:8

complete 28:5

29:4 63:15 120:6

120:7

completed 187:11

completely

85:17 167:21

completeness

46:18

completing 154:10

162:20 173:21

completion 35:1

complex 20:4

20:10 125:20

129:7

complexity 20:9

complicated

128:18 152:25

complied 3:4

68:17

component 15:12

104:11 104:11

146:14 146:15

components 140:12

171:17 173:7

180:13

compounds 167:6

comprehends

185:13

comprehensive

33:6

comprised 37:18

computation 98:5

conceive 159:16

concentrated 60:7

68:14 92:16

concentrating

92:12

concentration

13:5 40:3 40:8

40:17 43:20

53:18 64:5

64:8 65:1 72:1

78:19 100:3

105:6 107:12

110:13 125:18

concentrations

13:3 96:14

118:21 120:2

124:13 125:24

130:18 132:9

133:16 138:25

150:21 158:8

concept 114:14

184:9

conception

44:19 45:2

conceptual 100:17

145:23 181:23

182:1 185:17

concern 78:11

78:22 86:14

93:19 107:15

108:8 108:22

108:25 109:3

109:19 109:25

110:10 113:10

114:10 114:16

114:21 117:25

123:16 125:10

127:12 134:7

138:14 138:17

139:1 155:13

162:11

concerned 88:20

89:5 110:7 126:1

126:2

concerning 101:23

concerns 25:14

25:15 25:17 79:4

132:4 178:25

conclude 32:17

50:11 59:17

63:22 79:24

104:15

concludes 54:21

conclusion

23:14 46:1 48:19

54:7 79:9 87:3

95:4

conclusions 33:24

161:12 162:6

concurrently

174:13

condition 14:10
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 208

72:8 138:22

139:4

conditional

131:12 132:12

133:8

conditions

88:10 107:6

144:10

conducted 16:10

152:4 155:6

172:21 180:20

conducting 152:20

conducts 154:24

confidence

40:10 74:6 84:13

109:6 109:21

113:18 117:19

117:23 122:1

122:10 122:16

125:14 126:16

131:15 132:15

142:24

confident 13:10

142:15 178:21

confirm 171:21

confounding 34:16

39:16 39:19 41:7

48:16 50:24

confused 82:25

83:20 182:7

confusion 90:22

Congress 147:6

conjugated 61:3

conjugates 62:7

62:12 102:4

102:8 102:10

conjugation 61:1

conjunction 11:3

connection 105:15

consecutive 70:5

consensus 23:4

conservative

62:24 102:16

102:22 104:8

104:8

consider 32:24

34:6 43:2

55:11 80:20

102:22 113:23

133:24 148:13

148:19 148:25

consideration

35:24 36:25

45:23 104:10

considerations

134:13 144:25

148:8 148:13

185:21

considered 17:4

40:20 49:1 76:16

107:12 156:7

177:6

considering 80:19

118:22

consistency 53:5

consistent 48:7

52:18 66:24

constant 65:15

67:17 73:10

73:17 158:18

181:11

constants 158:17

consultant 8:2

contact 3:25

contained 18:13

25:2 26:5

96:11 181:21

content 55:9

106:2

context 13:12

14:15 16:18

58:22 92:20

continuation

29:20

continue 54:19

85:14 172:14

continued 70:8

85:15 93:16

continues 67:18

continuing 101:23

116:4

continuous

65:15 75:15

contradict 3:2

contrary 171:22

contrast 36:10

63:13

contribute 4:23

contributed 15:24

contributes

108:15

contributions 5:1

15:6

contributor

106:23

control 7:6 36:13

36:13 36:21
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 209

41:25 49:20 68:9

69:21 76:18

80:15 84:11

84:15 86:16

86:20

controls 163:11

convenient 63:1

conventional

119:3

conversation

169:21 169:25

converted 77:16

convincing 146:12

Cooper 16:5

63:8 68:17

75:5 75:23

83:2 84:23

85:7 90:15 90:18

90:20 91:24 92:2

92:6 92:24

93:2 95:19 95:20

95:21 105:20

157:14 159:14

162:21 163:18

167:2 167:20

170:5 183:3

188:12

copied 188:8

copies 188:22

copy 188:15

core 62:10 63:3

101:7

corn 45:5 45:7

49:20 116:4

corn-belt

132:21 133:1

correct 49:6 94:3

101:8

correctly

169:11 170:22

correlate 150:22

correlation 43:19

44:17 46:20

129:15 135:7

135:8

correspond 97:5

corresponding

80:6

corticosterone

23:14

cost 113:13

Council 59:22

count 51:1 111:17

county 46:21

Coupe 7:10 7:10

143:11 143:12

143:12 183:25

187:5

couple 19:12

19:13 19:15

30:21 88:1

88:2 96:25 99:24

103:17 103:25

114:4 114:12

118:13 125:7

148:16 156:9

160:23 163:20

172:5 172:12

181:8 183:4

189:3

course 2:24 12:22

32:10 33:13

33:25 34:12 35:4

35:23 39:7 50:16

54:23 68:25 70:4

79:22 102:9

144:17 168:11

courses 150:19

cover 18:18

152:16 168:24

174:7 183:13

covered 69:7 71:9

172:8

covers 4:7 117:15

183:11

create 82:13

122:5 122:15

created 3:20

creating 111:8

credit 130:3

criteria 34:1

35:9 36:7 56:6

56:18 56:20 57:6

75:19 141:23

critical 9:23

10:1 11:21

12:6 28:10 28:14

48:10 54:13

67:14 69:11

69:17 69:24

75:10 75:24 76:8

85:23 99:11

109:23 109:24

117:18 127:6

127:17 129:13

131:16 132:15

146:18 148:14

155:18 157:10

159:6 161:18

164:2 170:20

173:6 173:15

186:15
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 210

crops 51:13

cross-section

117:15

crux 28:7

Cruz 7:16

cumulative 93:14

curious 81:7

166:12

current 5:7

5:12 5:19 5:25

6:2 12:16

12:18 13:10

13:25 14:17

15:25 18:14 23:7

25:22 26:4

32:2 59:3

78:14 100:10

100:11 103:8

154:23 155:17

164:9

currently 4:18

8:19 159:3

curve 59:16

64:5 64:10 71:16

72:1 72:15

73:4 74:11 74:20

75:7 77:12 78:23

79:16 80:13 83:1

83:16 86:8 87:10

87:11 87:11

98:21 98:22

98:25 98:25

99:12 105:5

114:18 141:9

cut 124:25

125:1 136:2

176:5

cutoff 26:1

26:2 26:3 26:3

119:18 119:20

cycle 39:17 51:20

70:4 93:15

94:5 94:19

149:21 149:23

159:12 159:18

159:19 159:21

160:13

cycles 37:22

94:23 95:1

cyclicity 163:7

cycling 70:3

93:13

cytochrome

60:13 60:22

cytotoxicology

6:10

D

D2 122:21

DACT 60:24

66:14 103:24

171:5

daily 59:16 64:13

64:19 65:15

65:17 66:3

66:9 66:11

68:4 68:24

70:2 70:20

71:3 71:13 71:15

72:11 72:15

74:11 74:20

77:12 78:19

78:22 79:15

110:15 110:15

116:13 116:14

117:8 117:8

117:11 117:11

118:12 118:12

118:20 122:5

122:5 122:8

124:13 127:15

132:10 135:3

135:4

dam 156:25 156:25

157:7

dams 162:23

Dan 5:21

Danelle 75:9

DANIEL 5:20

81:2 81:20 166:6

167:5

Danielle 16:5

dark 85:7

dark-blue 115:5

dash 74:5

dashed 124:14

data 14:9 16:12

17:14 17:24 22:2

22:14 23:2

23:6 24:4

24:24 27:1 29:23

32:1 32:18 32:25

43:3 49:11 49:19

50:6 51:8 53:9

53:12 54:10

54:12 54:17

54:19 54:25 63:2

73:7 74:3

75:15 76:5 84:23

85:5 85:7 88:5

88:7 88:12 88:14

91:3 91:23

92:4 95:18 97:17

97:24 100:10

100:12 103:20
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 211

103:23 103:24

104:3 105:13

105:13 110:15

110:20 112:2

112:15 113:16

113:24 116:20

117:1 117:6

117:17 118:5

118:8 118:9

118:21 119:23

121:4 126:11

128:4 129:3

130:11 130:24

131:7 131:13

131:15 131:19

131:20 132:13

132:22 134:14

134:23 135:3

135:4 136:19

137:3 137:6

137:10 137:14

137:22 138:1

139:2 140:3

140:11 140:24

143:19 144:5

145:23 147:24

148:14 148:22

149:12 149:25

150:15 150:22

151:1 153:18

155:6 155:14

157:14 160:15

162:8 164:25

164:25 165:8

170:8 171:2

181:10 183:2

183:3 183:3

184:4 184:12

184:19 184:20

184:24 186:4

188:12

database 16:19

32:11 33:24

36:23 50:12

51:13 51:14

51:16 51:17

51:19 56:10

56:10 148:21

150:24 155:23

156:24 162:13

166:21 172:18

172:20 174:6

176:17 176:25

177:13

databases 33:21

35:7 177:20

dataset 45:21

96:1 100:12

122:4 122:10

124:17 129:25

138:9 144:1

151:18 151:18

156:13 156:15

164:10 164:17

datasets 18:1

24:6 32:16

76:6 100:12

111:24 116:17

117:18 117:21

121:9 121:23

121:25 122:20

126:15 134:10

135:6 135:15

162:14 184:19

data-smoothing

124:24 127:10

date 15:4

Davis 8:9

day 31:6 63:15

66:6 66:10 66:11

70:7 70:7 70:8

70:9 72:13 95:23

104:18 106:8

123:1 123:23

124:2 140:23

144:13 148:18

150:19 157:15

162:24 168:5

168:13 169:16

189:22

days 19:23

22:25 63:15

64:20 65:10

65:14 66:4

68:5 68:6

68:25 70:5 78:16

98:20 99:2

99:9 116:8 118:1

123:12 124:3

125:7 130:23

143:20 144:22

156:16 157:20

157:22 157:23

157:24 159:22

160:20 160:20

189:3

de 80:20

DEA 60:16 62:5

66:15 140:7

dead 81:22

deal 10:11

22:24 80:8 80:18

153:8

dealing 60:8

dealkylated 102:2

dealkylation

60:13 60:23

dealt 171:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 212

Dean 7:14

December 108:10

decide 164:2

decided 26:11

deciding 81:8

137:3

decision 14:3

141:12 147:12

151:10

decision-making

9:25 10:7

decisions 2:24

15:13

decrease 93:25

deep 9:4

deethylatrazine

60:16

default 76:19

defect 43:24 45:1

45:16

defects 43:6

43:14 43:21 44:4

44:15 44:16

44:18 45:10

45:12 45:13

45:22 54:3 166:8

166:14 166:23

deficiencies

36:21

define 12:7

44:6 113:5

115:12

defined 40:7

defining 53:2

definition 51:24

degradates 140:6

degradation

140:16

degree 88:4

deisopropyl 60:16

delay 38:6

delayed 151:2

156:14 160:16

Delclos 6:25 6:25

deliberations

19:1 109:1

delineate 37:6

delineated 35:10

delivered 12:11

delivery 47:16

48:21

demonstrated 62:2

Department 5:22

6:23 7:2 7:13

8:7 8:23

departure 25:7

77:23 78:12

96:16

departures 69:18

depending

107:19 110:6

113:2 113:17

117:24 140:15

168:14

depends 67:8

119:12 176:2

derive 77:1 81:16

96:15 109:12

derived 155:7

155:14 157:7

describe 112:10

121:14

described 17:13

describes 177:12

description

130:12

design 25:15

25:16 25:20 42:1

46:5 108:20

109:18 109:20

179:5 179:15

183:16

Designated 187:22

designed 107:14

designing 134:5

141:7 142:8

designs 14:16

36:13

despite 159:1

detail 129:17

145:25 148:15

159:12 176:7

detailed 20:17

29:6 132:23

133:19 147:15

172:11

details 75:18

77:3 97:6 147:23

detectable 61:19

detection 53:19

detections 123:11

detects 119:15

determinant 59:24

determine 28:15

60:3 123:15
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 213

determined 109:25

138:16

determining

117:19

deterministic

129:2

detrimental 89:24

developed 80:17

133:12

developing 18:2

18:12 56:16

132:21 173:19

development

15:3 21:10 24:21

25:11 55:3 149:9

157:4 157:9

157:10 178:6

179:8 179:21

179:23 180:2

developmental

16:16 21:13 22:6

46:3 151:24

156:15 166:22

168:12 172:19

deviated 60:14

deviation 76:18

84:15

deviations 83:7

device 36:22

DFO 2:17

DIA 60:17 62:5

66:15 140:7

diabetes 38:12

Diaminochlorotria

zine 60:24

diestrus 70:8

differ 70:19 71:2

74:14 77:21

85:24

differed 70:12

difference

44:24 60:18

66:22 68:9

87:9 91:7

120:2 181:4

differences 109:7

177:18 178:13

178:14

different 11:15

11:15 11:20

11:20 12:3

12:8 12:9 13:4

23:19 25:18

33:16 36:12

40:15 44:14

44:18 45:10

52:23 54:24

59:13 62:2 64:18

66:12 67:21

67:22 70:19

71:12 71:13

77:11 80:15

81:25 82:1 83:15

84:10 85:23

86:19 87:18

91:11 101:8

101:17 108:25

115:15 117:22

144:10 150:19

156:9 159:16

163:1 164:10

164:21 164:22

167:22 167:23

174:22 185:16

differential

50:22

differentiate

70:25

difficult 42:19

113:7 144:21

154:4 184:9

digging 147:23

diluting 103:4

direct 38:14 61:8

80:10 152:8

direction 45:19

Director 5:5 5:18

9:9 14:22

disappearance

95:18

disciplines 156:4

disclosure 3:2

discuss 29:4

169:3 176:9

189:15

discussant 183:8

discussants 169:7

175:20 187:3

discussed 16:17

16:20 18:17 23:2

32:12 43:11

150:21 182:25

discussing

10:19 10:20

11:14 18:4 18:7

discussion

20:15 31:15

32:13 57:11

104:22 143:14

169:8 169:22

discussions 10:25

31:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 214

disposition 62:9

disruption 93:10

disruptor 88:18

distance 44:10

distinctions

96:13

distinguish 62:22

63:4 85:25

distributed 188:9

diverse 9:3 172:8

Division 14:21

14:22 15:24

31:20

Divisions 15:1

docket 3:19

3:25 4:2 4:5

95:22 188:16

document 3:23

36:3 121:6 130:7

137:24 142:20

171:17

documentation

104:21

documented 150:18

documents 3:22

166:10

dogs 151:4

dominant 102:3

done 14:6 17:20

29:14 30:4 56:24

58:13 66:11 68:5

73:3 74:24

84:9 84:13

93:3 94:19 94:20

96:10 98:7

103:23 122:18

129:24 130:8

136:11 140:19

155:12 156:4

156:8 158:16

167:12 167:17

167:18 168:2

186:22

dosage 65:18

dose 12:11

12:11 25:12

25:20 27:4 58:15

59:6 59:14 59:18

59:23 59:24

61:14 61:18

61:24 63:9 63:10

63:11 63:14

63:18 64:3 65:11

65:12 65:17 67:8

67:9 68:14

71:5 71:7 72:3

73:24 74:1

74:8 74:17 74:23

75:4 75:12 76:23

77:6 77:19 78:12

79:3 79:17 79:18

80:14 82:2

83:7 83:9

83:17 84:14 85:4

85:22 87:3 87:12

87:17 87:18

88:22 89:19

89:25 91:8 91:10

92:14 93:11 94:6

95:8 95:10 95:11

95:25 96:11

96:15 96:20

96:21 97:4 97:20

98:19 98:21

98:24 99:2

99:7 99:8

100:4 103:20

104:10 104:19

104:20 141:8

150:18 153:15

153:21 158:17

164:15 164:19

167:7 172:23

180:22 181:18

183:11

dose-comparing

87:15

dosed 63:9

64:19 66:3

168:13

dose-dependent

74:18

dose-response

26:16 30:1 58:20

59:10 59:21

61:24 62:15

62:19 70:14

70:23 71:10 75:7

76:1 79:10 80:13

83:1 83:6

83:16 84:4

85:8 86:4 86:8

90:8 90:11 90:11

91:19 103:19

104:1 148:23

154:14 180:15

dose-responses

25:18

doses 23:22

25:4 25:11 64:20

68:11 68:20

68:25 69:7

69:8 69:14 84:24

85:11 85:15

85:21 86:5 88:17

89:17 90:7

90:9 91:14 91:16
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 215

92:12 92:17

94:10 95:17

95:19 95:21

153:19 157:16

157:16 157:20

166:25 181:24

dosimetric 12:8

59:17 62:21

62:24 64:4

64:8 64:11 71:15

71:18 72:1

72:6 72:14 72:16

74:21 92:10

99:13 105:13

dosimetrics

30:7 77:15 77:22

79:18 96:18

dosimetry 12:6

12:23 13:6 13:13

14:15 27:3

59:5 60:3 60:8

62:13 62:19

71:18 82:21

150:8 172:23

180:14 181:9

dosing 61:13

63:24 64:10

64:13 65:2

65:5 65:15 65:23

66:7 66:9

66:11 67:4 67:13

67:17 67:22 68:4

68:19 68:24 70:2

70:6 70:21

71:3 71:13

72:3 85:24 93:13

93:16 94:13

157:15 181:11

dots 115:20

120:12 120:13

124:20

Dr 2:4 4:10

4:11 5:4 5:8

5:13 5:17 5:20

6:1 6:6 6:11

6:13 6:17 6:21

6:25 7:3 7:7

7:10 7:12 7:17

7:22 8:1 8:6

8:11 8:16 8:21

9:2 9:8 9:11

14:21 15:5

15:7 15:9

18:10 19:2

19:4 19:4 19:6

19:7 21:20

22:7 23:3

24:19 24:19

24:25 26:21

28:21 29:25 30:6

30:18 30:22

30:22 31:3 31:13

31:14 31:17

31:18 54:9

55:1 55:5 55:5

55:8 55:13 55:14

55:18 55:22

55:22 55:23 56:3

56:15 56:19

56:22 56:25 57:1

57:1 57:18 57:18

57:19 58:5 58:11

58:15 58:17 80:1

80:1 80:5 80:7

80:8 80:23 80:23

81:1 81:2 81:9

81:20 81:23 82:4

82:4 82:5 82:6

82:22 82:22

82:23 82:24 83:4

83:5 83:9

83:11 83:17

83:22 83:24 84:2

84:8 84:17 84:19

84:19 85:14

85:19 86:2

86:2 86:3

86:11 86:11

86:11 86:13

86:23 86:23

86:25 87:1 87:21

87:22 87:22

87:23 89:21

90:15 90:15

90:18 91:24 92:2

92:6 92:11 92:22

92:23 92:24 93:1

93:2 93:3

94:14 94:14

94:16 95:5

95:5 95:6

95:13 95:19

95:20 95:20

95:21 96:2

96:2 96:5

96:10 96:22

96:24 96:24

96:25 97:11

97:19 98:9 98:16

98:16 98:18 99:3

99:4 99:5

99:14 99:16

99:17 99:17

99:20 99:23

101:4 101:4

101:5 101:10

101:15 101:21

101:21 101:22

102:6 102:20

102:20 102:21

102:25 103:2

103:6 103:10

103:13 103:15
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 216

103:15 103:16

104:4 104:4

104:5 104:13

104:14 104:16

104:17 104:24

104:24 104:25

105:2 105:3

105:10 105:10

105:11 105:16

105:20 105:20

105:20 106:6

106:15 114:16

123:25 130:2

130:24 134:17

134:20 134:21

135:3 135:5

135:12 135:16

135:19 135:21

136:8 136:12

136:17 136:17

136:18 137:8

137:11 137:12

137:23 138:11

139:14 139:17

139:19 139:21

139:22 140:4

140:7 140:8

140:21 141:2

141:4 141:4

141:5 142:17

143:11 143:11

143:12 144:14

144:24 145:3

145:5 145:7

145:8 145:13

145:19 146:8

146:10 146:11

146:13 147:17

149:11 150:20

153:16 157:15

158:9 159:7

159:13 161:23

161:23 162:2

162:3 162:9

162:10 162:12

162:17 162:21

163:16 163:18

163:22 163:24

164:4 164:6

164:11 164:16

164:18 164:23

165:9 165:9

165:10 165:16

165:23 166:5

166:5 166:6

166:18 167:2

167:5 167:13

167:13 167:15

167:20 168:17

168:18 169:15

169:24 169:24

170:5 170:5

170:18 170:24

171:7 171:7

171:8 171:16

171:19 171:19

172:1 172:1

172:4 172:10

172:24 174:9

174:17 175:5

175:19 175:19

175:22 176:2

176:6 176:11

176:12 176:12

176:13 177:3

177:8 177:25

178:2 178:4

178:16 178:16

178:19 178:20

178:22 179:9

179:9 179:10

179:13 179:14

179:17 179:20

180:5 180:7

180:7 180:10

180:21 180:23

180:23 180:24

181:3 181:13

181:14 181:17

182:2 182:2

182:3 182:4

182:6 182:9

182:12 182:14

182:15 182:25

183:6 183:7

183:9 183:20

183:25 183:25

184:13 184:15

184:23 185:5

185:12 185:12

185:15 186:10

186:13 187:2

187:2 187:5

187:13 188:13

188:17 188:19

188:21 188:25

189:2 189:4

189:7

draft 16:11 56:11

162:22

drag 145:16

dramatically

85:24

drastically 70:12

70:20 71:2

drinking 2:12

4:15 14:16 20:25

28:4 29:3 29:7

30:9 48:6

58:10 78:2 78:10

82:14 106:11

106:22 106:23

106:25 107:11

115:17 118:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 217

118:25 119:3

134:2 153:23

154:23 165:21

172:25 173:20

186:4 186:4

drinking-water

13:3 13:5 14:9

17:23 18:9

20:2 20:6

20:18 20:24

22:14 24:2

27:8 27:25 28:15

30:8 134:6

drive 112:18

driving 124:10

125:9 126:24

164:10 165:3

169:11

drop 93:25

drops 94:21

drugs 159:24

160:1

due 88:6

duplicate 148:10

duration 12:12

20:17 28:10

28:14 29:2

64:9 68:19 68:24

69:13 72:7 99:10

105:7 108:25

109:3 109:3

109:19 109:24

109:25 110:3

110:4 113:10

113:11 113:12

114:21 117:25

121:15 123:16

125:8 125:10

125:17 127:3

136:5 141:9

143:4 146:18

154:3 155:19

159:6 161:18

164:2 173:7

186:16

durations 20:22

23:12 30:16

59:14 67:22

70:21 71:3 71:13

109:22 134:7

158:12 164:22

165:1 165:6

173:15

during 3:13

9:20 10:22

11:9 38:15 38:17

44:20 46:12

65:15 107:16

108:22 110:16

112:23 116:2

116:14 116:25

117:6 121:17

127:16 137:5

155:1 166:7

dust 49:20

E

earlier 101:16

157:15 158:3

167:18 179:25

186:20

early 145:15

150:6 167:21

easier 98:24

100:24 142:25

143:7

easy 105:6 109:20

113:5 128:18

140:17 144:4

144:21 149:21

180:6 180:6

echo 15:11

ecologic 36:14

43:11 43:13

45:19 46:19

ecological 107:22

108:11 110:24

114:25 115:2

116:6 117:9

118:8 122:19

132:20 133:4

135:1 135:14

184:20

ecstatic 70:24

editorial 36:5

EFED 130:2

effect 34:22

46:25 63:22

63:24 64:3

68:8 68:12

70:1 70:3

70:10 72:2

72:3 72:3 72:4

72:9 75:16 76:16

79:14 83:12

83:13 83:14 86:9

89:11 93:14

94:18 94:23

94:25 99:7

99:9 101:17

103:4 123:24

127:10 149:6

153:14 157:6

157:18

effectiveness

119:12

effects 2:11 4:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 218

4:15 5:24

14:22 15:24 17:5

17:6 17:14 18:18

23:14 23:19 25:4

31:20 33:11 35:2

36:8 37:7 46:8

48:16 53:6

53:7 54:13

58:8 58:9 101:19

106:10 106:10

108:11 149:21

152:6 153:19

156:10 168:9

efficacies 101:8

efficient 42:1

115:25

effort 9:18

15:2 16:3 22:8

56:5 56:8

56:14 107:5

133:22 150:12

175:11

efforts 15:4

15:11 129:22

132:25

either 42:19 66:3

66:4 80:5 92:5

110:15 145:11

145:12

elegantly 164:24

elements 34:6

elevated 38:12

40:5 40:10 41:18

eleven-fold 40:5

eliciting 23:23

eligible 41:21

eliminated

50:15 92:7

181:11

elimination 64:25

65:16 65:19

67:14 73:1

73:8 73:10 73:16

eliminationrate 158:10

158:17

Elizabeth 30:18

Ellen 8:6 8:6

175:22

else 3:12 4:2

119:13 151:5

183:18

emerging 17:13

23:2

emphasis 17:8

186:25

emphasize

172:15 180:1

emphasizes 120:16

empirical 156:10

enclosed 4:5

encourages 19:14

endocrine 8:25

endocrinologist

6:15

endocrinology

8:10

endpoint 23:11

25:13 150:24

151:8 152:1

167:10 172:18

endpoints 18:3

20:16 23:9

150:25 156:13

156:24 162:11

164:21

energy 7:25

engaged 42:6

49:14 55:20

engaging 38:14

42:12

Engineering 7:15

enhanced 51:4

enhancing 52:5

Enoch 25:9 178:23

179:13

ensure 3:3 10:3

11:21

ensuring 9:24

13:24

enters 60:10

entire 46:10

entirely 22:13

environmental

5:21 5:22 6:2

6:8 8:23 10:9

13:1 15:23

41:8 48:16

51:3 57:7

enzymes 60:14

EPA 2:18 2:22 4:4

9:10 18:11

31:2 32:21

44:7 50:11 55:15

87:2 137:22

142:4 150:9

163:13 187:12

epi 33:22 51:16

57:4 57:7

166:7 166:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 219

epidemiologic

32:22 32:25 41:6

53:12 55:25

58:12

epidemiological

50:12

epidemiologist

8:14 8:19 31:20

epidemiologists

57:10

epidemiology

8:7 11:2 11:3

11:11 12:24 14:4

14:15 16:12

16:15 16:16

16:19 18:8 18:20

21:17 21:18 22:1

22:6 24:11 26:20

26:22 29:9 29:13

29:19 29:22

31:23 32:1

33:9 33:13 33:21

34:7 36:9

36:18 43:6 51:19

54:2 54:8

55:11 147:17

148:24 149:12

153:22 154:15

172:12 174:2

174:5 175:12

177:12 177:20

186:5

episodes 49:9

49:15

equal 68:11 171:5

equation 89:7

97:6

equipotent 171:1

equivalence 97:21

97:23 98:3 98:13

error 50:18 50:24

85:6

errors 83:7 83:14

84:4 86:6

esoteric 185:17

especially

88:15 90:14

128:14

essence 155:20

158:16

essentially

26:1 36:1 42:4

42:14 46:16

47:18 49:22

89:10 153:6

174:18

establish 77:22

established 68:22

149:4

establishing

181:23

estimate 37:25

52:18 73:4

73:9 73:11 73:18

96:12 106:25

107:15 118:19

125:16 128:2

129:15 132:3

134:6 134:24

139:6 140:14

161:5 180:21

185:9 185:10

estimated 25:6

44:19 45:1

45:6 131:1

estimates

108:21 109:22

115:5 117:19

122:11 125:15

126:16 133:20

164:19 170:10

170:16

estimating

118:4 126:22

127:21 128:1

128:16 131:14

132:1 134:13

estimation 126:21

131:17 132:6

134:12 134:23

142:1 185:4

185:10

estimations 133:7

estrous 70:4

estrus 70:8

et 43:10 44:1

44:16 47:7 52:12

66:2 68:17

75:9 83:2 188:12

ethics 3:1 3:4

ethyl 60:19

evaluate 20:6

25:18 39:4 44:18

100:22 117:20

117:22 118:6

127:17 134:10

165:22

evaluated 21:8

24:12 25:17

68:20 68:25

69:14 70:3 70:10

75:17 166:16

evaluating 10:5

17:23 18:9

24:4 146:16
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185:3

evaluation 4:14

21:16 27:19

31:22 32:3

33:6 33:10 33:17

35:1 40:23

42:5 45:3

45:16 51:14

106:17 149:14

150:1 176:20

evaluations

35:5 41:6

42:25 176:15

event 39:11

59:2 64:10 99:21

114:8 142:16

147:7 172:17

events 17:9

21:8 22:22

60:4 114:5 149:2

151:22

eventually 93:16

ever/never 38:9

39:7 53:17 54:2

everybody

105:23 106:2

106:4 189:20

everyone 2:5

2:7 2:8 4:12 9:3

58:6 106:7

155:11 169:16

everyone's 3:21

everything 4:2

4:7 86:5

148:19 151:5

186:14

everywhere 112:12

evidence 16:19

48:21 57:14

65:20 66:1

179:23

evolve 10:17

evolving 10:16

exactly 46:25

67:2 101:1 169:4

examined 45:22

68:10 69:1 71:5

examines 59:3

example 34:21

35:19 48:12 53:8

61:12 114:24

114:25 126:19

142:10 143:7

examples 24:3

173:1

exams 184:22

exceed 141:17

exceedance 138:19

141:25

exceeded 123:13

138:25 141:16

141:22 142:12

142:15

exceeding

123:19 124:3

138:16 142:6

excellent 19:23

Except 74:14

exception 63:16

excess 93:7

exchange 31:5

excited 22:10

excluded 35:22

35:23

excluding 110:11

excuse 26:2 38:11

74:1 84:10 153:4

exercise 87:15

135:25

exhaustive 34:9

34:24

exist 102:5

existing 17:21

23:6 82:17 109:4

109:13 139:1

expect 18:17

22:11 107:17

118:14 138:1

152:4 152:22

156:6 173:21

expected 94:7

127:16 127:16

expecting 174:2

experience 49:16

experimental

12:24 14:6 14:14

17:2 22:1

22:14 26:19

29:21 32:16

33:23 51:13 53:5

147:16 151:11

160:15 166:21

174:6 177:19

179:15

experimentally

167:7

expert 6:19

expertise 4:22

5:11 5:23 6:4

6:9 6:15 6:24
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7:5 8:3 8:14

8:25 9:4 9:20

experts 4:24

explain 121:10

explained 124:1

138:24

explanation

137:25 139:15

explanations

32:14 50:14

explicit 105:14

explicitly

32:24 147:1

155:22 178:23

exploration 56:23

exploratory

129:24

explore 132:21

exponential 75:17

76:24

expose 168:4

exposed 38:25

41:24 47:12

92:19 151:13

157:1 167:23

exposure 8:15

12:12 12:12

12:14 13:7 13:14

20:17 20:18

20:22 20:24

23:12 27:8 27:18

28:11 28:14 29:2

30:16 33:15

34:13 37:22 38:8

38:9 38:22

39:6 39:7

40:19 41:3

42:4 42:15 42:17

43:7 44:9

44:14 45:6 45:20

46:5 46:10 46:12

47:5 47:13 47:24

48:3 48:6

48:10 48:13

48:15 50:18

50:19 52:13

52:16 52:17

52:25 53:14

53:19 53:20

53:22 54:1 55:16

56:6 66:5 66:6

66:14 66:17

66:18 66:23 72:7

72:11 77:16 79:5

82:1 89:23 91:17

93:20 94:5 99:10

106:21 106:24

107:1 107:15

107:22 108:11

108:21 110:3

110:24 112:19

113:10 113:24

114:20 114:25

115:2 115:4

116:6 117:10

118:4 118:8

118:18 122:11

122:20 123:23

124:10 125:3

125:9 125:15

125:18 126:3

126:16 126:20

127:17 131:17

131:23 132:6

132:20 133:4

134:6 134:11

134:14 135:1

135:15 136:6

138:15 138:18

144:11 147:2

147:9 150:5

150:20 151:17

151:19 151:22

152:7 152:13

152:15 152:19

153:24 155:19

156:19 156:25

157:3 157:7

157:25 158:13

161:18 163:12

164:2 164:22

170:3 170:10

170:15 173:5

173:7 173:15

184:20 185:9

186:15 186:16

exposure-only

35:20

exposures 13:1

36:24 41:8 42:20

51:3 108:7

109:15 109:23

118:20 119:19

126:2 126:22

127:22 128:1

128:2 128:14

128:17 129:5

132:2 132:3

134:6 136:24

139:6 139:9

146:14 185:10

expressed 68:8

127:11

extend 15:10

extended 53:19

93:20 94:13

extensive

136:19 137:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 222

150:1 150:3

extensively

29:8 60:10

extent 149:23

external 11:4

28:23 54:19

76:23 77:6 78:12

79:3 79:18

extra 147:6

extrapolate 81:25

155:24

extrapolation

161:7

extreme 111:10

128:23 143:6

157:16

extremely 82:16

eye 29:17

eyes 133:2

F

FACA 3:8

fact 36:5 41:21

42:13 42:17 52:3

88:6 88:20 94:18

123:20 131:14

135:13 136:9

163:10

factor 27:16

27:22 27:23 29:6

30:14 34:14

53:22 109:6

113:20 118:1

142:25 143:20

147:4 147:4

147:7 147:7

147:11 147:12

151:11 154:11

165:12 165:14

factors 28:3 53:1

54:5 112:18

154:13 154:17

185:24 186:9

fairly 29:25

67:16 125:4

125:21 149:25

171:3 178:1

178:17 181:10

188:2

fall 37:2 115:20

familiar 80:10

130:15 147:5

Families 36:20

family 36:19

41:22 47:8

152:18

farm 36:19

41:14 41:22 42:6

42:9 42:18

47:8 47:14

152:17

Farr 149:20

176:25

farther 110:25

fast 61:5 110:1

182:19

fate 5:24 15:24

favorable 106:1

FDA 7:1

February 14:19

16:11 16:13

16:17 16:21 18:6

21:24 24:12 26:1

31:15 32:21 33:5

34:2 34:4

36:11 36:15 43:9

43:11 43:25

44:13 45:23

Federal 2:19 2:20

187:22

feedback 15:12

24:14 76:21

79:23 99:25

112:2 156:5

161:3 164:5

173:9 175:14

178:13 180:4

183:17 184:10

185:23

feel 39:21

42:24 50:20

53:12 54:7 57:20

62:23 79:20

102:16 123:3

126:15 187:3

feeling 57:16

felt 14:12

92:17 97:14

98:10 169:21

female 6:24 7:5

37:3 37:8

37:17 37:19

38:20 41:23

42:18 42:20

51:20 53:6 64:19

89:2 89:11

93:4 93:22

158:19 159:19

162:23 163:6

female/male

176:21

females 93:9

Fenner-Crisp
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8:1 8:2 162:2

162:3 162:10

162:17 163:16

163:24 164:6

164:16

Fenton 18:11

24:19 25:8

31:3 172:10

178:10 187:17

Fenton's 24:25

188:21

fertile 89:14

fertility 89:2

89:5 90:2

fetal 33:4 37:4

41:13 42:21

fetuses 150:8

fewer 94:7

FFDCA 146:22

fields 45:6 45:8

FIFRA 2:1 2:8

4:13 4:19 9:5

106:8 169:17

figure 26:14 92:9

92:9 96:22

99:6 181:7 181:9

181:12 182:19

182:20 182:24

Figures 87:4

fill 81:7 114:5

152:21

finally 16:4

48:23 59:17

finding 153:17

160:18

findings 21:14

29:10 148:24

149:15 151:25

154:14 160:1

162:16 178:15

fine 48:5 188:25

finishing 162:18

first 9:15

19:11 24:20

27:15 38:15

38:18 47:16

48:11 58:6

59:9 60:12 60:12

68:17 70:13 78:9

90:7 106:8

120:16 134:20

134:22 137:6

140:4 151:15

151:15 159:17

159:19 160:24

166:19 169:16

175:13 178:7

180:19 181:19

181:19 185:20

185:22 187:12

187:15 188:20

189:11 189:18

fit 87:9 87:10

142:6

fits 87:11

five 3:17 14:8

117:17 158:22

159:1 159:21

160:20 160:21

169:13 176:15

177:4

fix 182:16 183:9

fixed 123:1 127:9

fixed-interval

123:2

flashy 118:15

flat 85:17

flesh 142:19

143:10

flocculation

119:5

floor 169:18

flow 114:7

114:8 132:10

focus 22:3

22:18 23:8 23:18

108:5 108:11

111:15 113:7

120:15 137:14

138:21 139:12

142:23 150:16

164:19 165:7

173:9 179:11

186:2 188:3

focused 22:13

29:21 107:8

107:22 108:2

122:23

focuses 112:13

177:15

focusing 19:24

32:7 91:2 106:22

109:10 134:3

138:13 141:2

162:5 183:15

folding 112:17

folks 14:25

follicular 159:18

159:21 160:6

160:7

followup 108:10
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115:10

follow-up 49:11

Food 146:23

force 97:13 97:15

forces 144:2

forcing 97:17

forearmed 144:22

foremost 68:17

Forewarned 144:22

forgetting 180:10

form 162:22

formal 106:13

formally 18:11

formed 141:6

formulate 33:24

formulation 19:19

19:25 22:3 26:17

29:20 33:1

forth 104:21

forward 4:24

10:15 18:25

25:19 27:11 28:5

28:18 28:24 34:2

106:2 132:4

133:24 141:7

185:25 186:16

forwarding 31:13

foundation 15:13

fourfold 47:11

fourth 72:13

FPQA 165:12

FQPA 27:16

27:22 29:5 30:14

143:1 146:21

146:22 147:3

147:11 147:25

151:11 153:23

154:8 154:11

154:19 161:17

165:12 173:21

185:25 186:2

frame 14:5 14:7

82:17 107:16

113:6 122:23

128:21

framework 16:11

18:2 21:25 27:12

32:22 94:1

111:20 112:10

134:4 148:2

183:16

Francisco 7:23

Frankenberry

15:23 27:7

106:19 107:3

171:14

free 57:20

frequencies

117:22

frequency 2:12

4:16 20:6 21:1

24:7 29:3 39:9

58:10 78:2 79:22

106:11 106:18

108:6 108:13

113:9 113:17

117:25 121:7

122:17 127:4

132:17 134:11

138:7 185:3

186:18

frequent 39:12

67:9 109:13

113:21 117:20

126:17 127:18

131:18 131:19

138:4

frequently

64:23 108:6

109:13 113:11

113:12 134:14

137:16

fruitful 41:5

full 32:9 35:17

36:3 70:4

81:18 89:13

95:23

fully 41:12 141:6

function 74:1

74:8 89:11 111:8

135:9

functioning 51:21

functions 135:7

future 10:20

12:19 36:20 41:6

81:22 133:25

172:15

G

gained 40:22

gastroschisis

44:2 44:3

44:11 45:13

Gaussian 130:11

131:5 135:6

135:8 135:10

gavage 61:14

63:10 68:20 70:6

gene 46:25

general 13:19
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18:2 50:5

50:20 52:25

119:7 121:19

134:4 138:7

138:23 138:25

185:1

generalized 136:3

136:5

generally 37:2

76:10 88:8 104:2

107:18 110:25

129:9 131:12

generate 129:3

167:9

generating

34:11 45:18

generic 156:3

175:24

Geological 7:8

7:11

geospatial 129:9

GERALD 8:21

101:22 104:5

Gerry 8:22 101:22

gestation 66:10

66:11 151:19

162:24 167:17

167:18 167:19

167:21 168:5

168:5 168:13

gestational 38:12

46:8 48:8

48:20 53:8

149:17 150:5

151:17 151:21

152:15 153:6

163:12 176:22

gestational/

lactational

152:7

gets 2:5 61:2

getting 11:7 48:2

65:20 69:21 81:5

163:9 179:4

given 45:23 49:18

56:16 63:14 79:2

87:4 98:20

109:20 119:20

128:17 128:19

141:21 155:17

160:2 160:3

168:15 168:22

gives 2:25 118:20

121:3

giving 30:18

98:19

glad 30:20

gland 6:16 6:20

18:12 21:5 21:10

21:12 24:22

25:10 178:5

179:7 179:21

179:22 180:2

glands 25:17

179:11

gleaned 149:2

glutathione

61:1 61:3 62:7

62:12 71:24

102:8 102:10

GnRH 149:7 167:3

goal 82:13

goals 19:21

Gold 8:6 8:6

175:19 175:22

178:2

Goldman 16:6

gonadotropin

166:12

gone 143:20 163:5

good-quality 34:6

Google 35:8

governed 63:22

grab 116:8

Graduate 7:14

grain 49:20

graph 83:22 85:12

95:11 96:8 131:9

graphically 72:17

graphs 54:14

89:18 100:5

great 22:24

80:8 153:7 182:4

greater 44:7

85:15 126:23

Greenwood 6:1 6:2

96:24 98:16

98:18 99:4 99:23

180:23 180:24

grounds 72:5

groundwork 22:19

group 14:19

38:4 56:14 60:19

60:20 66:9 66:10

66:17 66:18 71:7

73:24 74:25 88:8

135:13 158:18

168:2 189:16

grouped 38:1
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grouping 47:2

groups 34:18 62:2

65:11 65:12

65:17 66:8 66:14

66:23 73:22

95:25 104:1

158:17

growing 155:2

growth 46:22

46:24

GSH 102:4

guess 23:16

28:8 76:2 79:1

81:22 83:19

91:18 95:16

112:4 144:22

166:8 166:10

168:14 171:21

176:14 182:4

182:21 185:14

187:23

guideline 151:3

gut-wall 167:11

guy 107:5

Gynecology 6:19

H

half 47:17 58:6

120:3 159:19

159:20 185:22

half-life 61:6

61:8 61:10 62:16

71:20 113:2

half-lives 158:22

hand 35:12 145:25

handful 35:25

handle 159:10

handled 170:2

handout 188:7

188:10

handouts 131:9

happened 76:12

115:20 125:3

happens 125:20

happier 103:11

happy 15:19 54:22

hard 9:15 71:1

85:25 90:19

100:25 163:21

Harris 8:11

8:12 55:22 55:23

56:15

haven't 85:11

94:9 105:15

109:24 128:17

138:19 139:2

141:11 141:24

141:24 156:7

161:1

having 13:22

28:20 46:4 62:22

88:19 90:4

137:25 141:22

179:1

hazard 20:15

23:17 27:18 28:4

54:9 148:8

148:11 148:12

153:20 153:21

161:21 173:5

180:2 180:3

hazard-assessment

146:14

hazards 37:19

148:6

head 8:22 130:4

headed 170:19

headwater 107:24

headwaters 110:24

health 4:14 6:8

7:18 7:20 8:8

8:10 14:22 16:22

18:18 22:9 28:21

29:10 31:20

33:16 35:2

35:5 35:21

36:8 36:18

37:4 37:5 37:7

37:9 37:10 37:11

37:19 38:20

38:24 39:24 40:1

41:10 48:24 49:2

49:12 50:5

52:7 58:8

92:21 106:9

106:24 107:9

108:4 136:25

174:4 176:21

hear 3:11 18:10

20:11 22:7 22:12

22:20 24:5

24:8 24:22 26:21

27:4 27:6 27:9

30:5 58:14 92:24

99:25 100:7

102:21 107:25

108:16 135:10

153:9

heard 17:18

21:7 21:20

27:2 27:10

53:6 58:11
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106:14 108:24

109:16 147:17

147:19 149:11

149:16 150:17

153:25 170:21

172:10 172:23

173:2 173:20

179:25 180:21

184:6

hearing 106:13

heart 30:3

heavy 145:23

HED 15:22

Heeringa 4:10

4:11 4:17 9:2

9:11 15:7 19:4

30:22 55:5 55:22

56:22 57:18 58:5

80:1 80:23

82:4 82:22

86:2 86:11 86:23

87:22 90:15

92:22 93:1 94:14

95:5 95:20 96:22

98:16 99:14

101:4 101:21

102:20 103:15

104:4 104:13

104:24 105:10

105:16 106:6

134:17 136:17

139:17 141:4

143:11 144:14

145:5 145:8

145:13 146:8

146:11 161:23

165:9 165:23

166:5 167:13

168:17 169:15

170:5 171:7

171:19 172:1

174:9 175:19

176:6 176:12

177:3 177:25

178:16 178:20

179:9 179:17

180:7 180:23

181:13 182:2

182:9 182:12

183:6 183:20

183:25 184:13

185:5 185:12

186:10 187:2

188:25 189:4

189:7

Heidelberg 110:20

117:2 117:3

118:7 122:19

124:7 135:6

135:18 184:19

held 3:8

help 15:15 16:1

20:25 50:7

100:16 100:17

100:23 117:20

119:22 124:22

146:2 154:7

158:12

helped 55:3

helpful 11:9

24:17 34:5

35:1 84:20

132:10 176:18

187:9

helping 107:3

115:11

hence 10:16

Herbert 7:12 7:12

134:21 134:21

135:3 135:5

135:16 135:19

136:8

herbicide 41:19

113:5

Here's 140:18

he's 184:14

Hetrick 130:2

Hi 6:11

high 25:4 43:17

44:5 44:6

44:10 46:15

46:21 63:10

63:11 65:12 69:3

82:1 85:12

99:7 136:6

157:16 166:25

higher 24:7

25:6 43:16 52:10

52:21 84:25

85:13 85:21 86:5

88:21 90:7 91:13

91:15 92:17

100:14 119:19

125:24 139:6

139:9 139:10

highest 92:14

93:11

highly 4:23 38:25

41:24 42:1

150:22

high-quality

150:7 150:15

150:24

Hill 75:17 77:3

87:11 87:11

87:19 89:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 228

hint 48:10

historical

140:3 143:19

144:5

hit 125:3 125:22

hold 18:17

holes 136:15

honestly 109:19

hope 29:11

82:19 142:16

156:6 163:18

184:7 188:21

hoped 180:15

hopefully 26:13

187:8 188:6

hoping 161:13

hormonal 39:23

89:9 151:16

151:19 157:10

hormone 22:25

59:2 89:4

89:13 101:20

166:13

hormonereactive 51:24

Horseman 6:13

6:13 167:13

167:15 171:7

171:8 180:5

180:8

hour 105:8

hours 30:21 61:18

64:24 65:7

65:9 65:14 70:7

HPA 17:15 17:18

23:3 101:17

HPG 17:10 17:16

22:23 23:8

Huang 129:1

human 4:14

16:12 18:18

21:13 21:15

26:23 28:21

33:16 36:5 54:13

58:8 92:17 92:19

92:21 106:9

106:24 107:9

108:4 148:21

149:9 149:15

151:8 151:23

153:22 154:14

159:11 160:18

186:5

human-health

108:3

humans 155:25

156:17 156:20

157:8 158:1

158:13 159:6

160:12

human-specific

155:23

hundred 35:14

hybrid 36:20

hydrologic 112:18

hydrologist 7:8

Hydroxyatrazine

171:10

hypothalamic 17:9

17:14

hypothalamicpituitary 7:24

hypothalamicpituitaryadrenal 54:16

hypotheses 23:5

49:24 51:23

hypothesis

17:20 34:11

34:11 39:22

45:17 67:23 68:2

68:3

hypothesisgenerating

46:2 49:23

hypothesized

67:24

hypothetical 64:9

78:3 78:5

I

i.e 48:6 170:2

I'd 9:7 15:10

19:1 80:3 88:2

102:21 103:11

105:18 169:1

idea 81:5 97:10

101:6 112:7

114:14 117:24

121:13 121:24

123:18 140:2

175:7 184:21

ideal 81:11

ideas 52:1 128:15

identification

180:3

identified

34:14 35:4

35:9 35:14

36:8 37:9

41:10 43:5
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46:6 48:23 49:24

50:12 53:11 69:5

115:16 115:19

124:16 152:9

152:12 176:20

identify 33:13

112:16 115:3

115:8

identifying 69:25

115:22 175:16

II 70:9 153:5

I'll 31:21 32:4

32:6 46:18

49:5 54:21

55:7 141:6

146:21 148:15

166:18 183:12

189:4

Illinois 116:11

177:1

illustrate 130:8

illustration

120:9 130:13

I'm 2:7 5:4 5:8

5:9 5:12 5:13

5:13 5:15 5:15

5:17 5:17 5:19

5:21 5:25 6:1

6:7 6:11 6:12

6:14 6:17 6:18

6:18 6:19 6:21

7:3 7:4 7:7

7:8 7:15 7:16

7:17 7:17 7:23

8:2 8:6 8:6 8:11

8:12 8:14 8:16

8:19 8:21 8:22

8:25 31:19 45:14

52:11 55:14

58:19 58:22 59:9

59:10 59:15 63:5

67:21 74:22

74:25 75:21 78:1

80:10 81:5

81:6 82:24 83:18

83:19 86:3

88:1 88:19 89:22

92:2 101:5

101:15 101:19

105:14 107:2

125:19 130:6

131:4 131:19

134:15 135:9

135:14 136:3

136:20 139:16

142:9 142:16

143:12 147:22

148:6 152:22

155:10 163:20

166:12 167:5

167:6 175:20

177:1 179:15

180:17 182:7

182:19 184:1

184:1

imagine 102:23

immediate 133:25

imminent 163:15

immunization

11:21 12:4

immunotoxicity

17:5

immunotoxicology

23:21

impact 79:23

impacts 107:23

132:1 132:2

179:6

implement 141:18

implication 28:9

implications

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109:11 145:1

implicit 170:25

179:8

implies 86:4

importance 120:17

important 10:3

10:9 11:7

11:18 12:15

13:16 13:23 14:7

15:12 22:21 23:1

23:10 23:16

23:24 26:15

27:20 27:24 28:3

28:12 29:2

36:2 54:5

88:11 96:3 96:12

100:8 103:16

106:24 116:18

118:3 136:9

148:13 155:22

158:5 159:23

161:19

importantly 64:22

71:4

impressed 80:12

improved 137:25

improves 114:3

improving 12:6

15:12

inactive 62:8

incident 16:12

include 4:3 25:12

26:11 27:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 230

41:21 51:25

53:13 72:10

85:21 110:15

included 11:1

16:13 16:17

28:25 32:6

33:6 33:17 35:15

36:12 42:7 62:12

64:20 64:25

107:7 116:7

117:11 140:1

165:19 171:25

includes 24:11

59:5 64:5

75:19 79:11

140:5 140:9

151:1

including 14:16

17:5 32:13 50:17

95:16 173:23

inclusion 56:18

incomplete 63:19

incorporates

132:22

incorporating

16:12 32:22

incorporation

43:3

increase 37:20

45:4 67:4 91:8

114:8

increased 37:21

44:2 45:11 46:14

47:11 47:16

47:23 49:4 52:19

indeed 41:2 50:12

in-depth 188:2

Indiana 43:15

43:16 43:22

116:11

indicated 4:17

23:3

indicates

181:14 185:12

indicating

40:11 184:14

indication 86:20

indications 59:18

61:21

individual

38:23 42:10

53:22 55:25 56:6

56:9 66:13 86:18

98:6 138:13

139:12 140:12

177:5

individuallevel 53:20

individually

33:18 42:6 176:8

176:17

individuals 40:14

infants 144:25

146:25 149:13

186:2

infer 140:3 156:3

inference 32:13

50:9

infinity 73:13

73:18

influence 24:18

34:21 39:24

42:19 54:15

169:9

inform 32:12

32:17 33:14 34:7

41:12 50:1

50:7 50:8

52:24 54:9

158:12 159:6

informal 32:13

information 3:2

9:17 10:5

11:22 12:10 13:1

32:12 33:19

40:22 52:8

57:9 69:3

76:14 76:17

76:20 80:9

80:9 81:17 81:19

97:15 101:11

101:13 101:24

102:13 102:18

103:7 107:25

111:13 131:18

131:25 132:8

132:23 147:1

147:8 147:15

148:5 148:25

149:14 155:23

156:2 156:11

159:9 171:22

175:18

informative 13:18

26:23 82:20

informing 10:22

12:16

infusion 67:11

inhibition 157:1

initial 42:25

50:2 56:12
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 231

initially 35:15

injection 160:3

input 76:9

77:23 78:25

insights 10:5

11:10 11:16

11:24 12:18

12:20

instance 36:3

43:10 93:9 114:6

122:6

instances 36:24

37:17 42:23

50:25

instead 147:23

instrumental

30:19 107:3

intact 62:6

integrate 114:15

integrated 18:6

29:12 33:23

146:6 174:4

177:20

integrating 32:15

33:20 173:5

179:22

integration 54:25

70:13 97:13

integrative

12:2 20:22 22:17

27:15 27:16

30:13

intended 21:23

29:18 96:15

138:1 152:21

174:21 177:10

180:13 186:14

intending 184:7

intense 31:6 31:7

intensive 14:8

18:1 112:23

116:2 117:3

122:24 126:15

127:15 129:25

139:8

intensively

110:14 111:24

113:16 116:16

116:20 117:2

121:9 121:22

122:4 122:10

122:19 130:24

134:9 139:3

184:4

intent 174:18

176:10

intentional 26:8

intentionally

145:24

interaction 7:25

interactive 20:13

intercept 97:16

interchange 7:18

interest 8:9

113:24

interested 6:12

101:5 136:21

184:10

interesting 10:21

115:18 132:8

inter-lab 68:10

intermittent

11:15

internal 27:3

59:5 59:10 59:16

59:21 59:23 60:3

60:8 61:24 62:13

62:15 62:19

62:21 71:10

71:15 71:17

71:18 72:14

74:20 79:10

82:21 95:22

96:17 99:12

104:10 104:19

105:12 163:19

172:22 180:22

interpolating

155:7 155:9

interpret

100:23 104:3

interpretation

25:16 25:20

100:17 128:12

179:6 181:7

181:12 182:24

183:5

interpreting

112:2 128:11

179:1

interrelated

96:17

interval 40:10

74:6 127:25

intervals 110:9

110:10 114:6

114:10 123:1

123:8 124:19

124:20 124:21

125:6 126:10

126:12 127:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 232

128:4 128:5

128:11

intervention

141:18

intramolar 62:3

intrauterine

46:22 46:24

intrinsic 62:3

introduce 14:22

15:5

introductory

178:24

invested 9:19

investigate

39:1 41:25

investigating

68:3

investigation

37:12 37:24

39:10 39:14

41:15 48:24 49:8

50:3

investigations

32:8 33:14 36:17

37:19 38:20

42:24 43:6 45:17

46:7 46:13 47:21

51:20

investigators

16:22 22:9

investment 9:19

involve 81:12

involved 15:4

16:21 17:8

108:14 118:1

126:3

involvement

157:11

involving 16:14

16:15 17:3 149:2

Iowa 37:13 152:18

isn't 55:15

92:6 104:11

114:22 151:18

isolate 42:19

46:12

isolated 152:16

isopropyl 60:20

issue 15:25 16:25

17:13 18:16

22:20 23:7 25:22

26:16 55:10

75:20 76:8

77:4 80:6 82:6

82:8 92:5

96:23 106:14

118:24 131:16

147:14 148:13

150:1 154:4

155:9 177:11

177:16 181:7

186:7 188:19

issues 2:23

10:6 10:12

11:8 13:16 18:19

20:1 20:10 28:19

90:13 106:16

108:19 111:16

134:4 146:15

154:19 167:11

173:22 179:8

it'll 144:21

it's 10:1 10:3

11:17 20:10

21:23 22:10

30:21 31:6

31:9 36:16

41:1 42:14 42:19

62:25 78:4

78:4 78:15 78:16

80:9 84:1 85:3

85:4 85:12

86:7 88:11 91:18

91:19 92:2 92:18

93:23 95:10

95:13 96:2

97:5 98:24

99:1 99:24

101:13 101:25

103:3 103:16

104:1 104:1

104:20 105:6

105:16 106:24

107:4 107:5

109:20 109:24

112:6 113:1

113:5 113:25

120:23 124:8

127:6 127:17

128:17 130:19

134:21 136:11

137:22 138:8

139:24 140:17

140:18 143:5

144:1 144:4

144:5 145:7

145:19 146:3

146:19 147:5

149:21 152:25

154:4 155:21

159:23 165:11

165:17 166:1

170:6 170:12

171:24 172:4

172:18 175:3

175:6 176:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 233

177:1 177:2

179:6 181:19

182:17 182:20

183:2 184:23

184:24 186:7

186:11 188:11

189:6

IV 67:11

I've 7:19 67:1

75:24 102:14

118:7 135:7

135:25 140:9

141:21 175:7

IVF 159:16 159:24

160:1

J

JAMES 6:17 178:19

Jan 5:8 186:10

JANICE 5:8

82:24 83:5 83:11

83:22 84:2 84:17

86:3

January 25:25

Jerry 16:6

Jessica 15:21

Jim 6:17 130:2

135:14 180:5

Joan 75:2

Joanie 74:24

job 158:6 175:15

Joe 2:7 4:12

15:14 16:9 146:2

146:2 168:21

187:21 188:5

188:7 189:8

John 5:17 5:17

8:16 8:16

15:20 55:14

55:14 80:8 86:13

96:10 102:21

103:2 103:10

136:18 137:11

137:23 139:14

177:25

John's 104:6

join 87:2

JOSEPH 2:4

judgment 34:2

July 18:14

18:15 25:24

25:25 28:24

153:3 173:24

jumped 182:9

182:14

justice 158:14

158:15

justified 99:12

101:13

K

Kannan 6:6 6:6

96:25 97:19

139:22 140:7

140:21 170:18

171:16

Kansas 6:22

KATHERINE 6:21

Kathy 6:21

Ken 5:3 5:5

KENNETH 5:4

57:1 86:25

104:17 105:2

141:5

Kentucky 7:4

key 17:9 21:8

22:22 34:9 34:14

35:12 59:2

60:4 142:10

148:16 149:2

151:22 172:16

kids 186:2

Kidwell 15:21

kilogram 25:5

kilograms 158:20

kinds 32:25

147:25 167:25

kinetics 73:14

79:6 158:6

King 15:21

knew 14:1 26:10

knock 120:25

knowledge 52:1

101:10 155:17

156:3 166:20

167:20

known 157:11

159:5

kriging 129:8

129:8 129:24

131:2 131:17

131:20 132:7

Krishnan 6:6

6:7 96:24

96:25 97:19

139:21 139:22

140:7 140:21

169:24 170:18

171:16 171:20

181:14
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 234

L

lab 25:8 63:8

93:5 103:25

150:10 151:13

151:14 178:10

labeled 86:15

86:22 95:18

laboratories 41:4

laboratory

52:14 59:2

laboratorybased 40:21

labs 150:9 150:13

151:20

lack 28:8 40:11

40:17 53:14

53:19 53:21 54:3

102:11 153:13

156:1 161:5

161:6

lacking 155:23

lactating 150:8

lactation 153:7

lactational 150:6

152:15

large 38:24 40:10

49:18 88:4

89:3 110:22

124:9 125:21

138:8 173:9

178:4

largely 29:18

29:20 37:18

42:16 46:4 50:13

146:15 147:19

175:13 176:3

larger 16:18

153:10

last 11:1 23:16

28:25 49:13

49:13 66:15

69:11 69:12

69:18 73:4 73:11

75:11 90:4

113:23 116:22

118:24 130:25

134:1 140:11

155:5 155:12

158:23 159:21

161:15 164:20

169:19 172:2

173:3

lastly 38:11

lasts 156:20

late 159:20 160:7

167:17 187:25

latent 152:6

later 21:6

27:13 31:9

38:5 100:8 125:7

143:17 189:18

latest 75:12

latter 90:14

177:11

Law 16:5

lay 22:19

layers 112:16

lead 59:12 60:4

60:5 96:5 144:10

178:14 183:7

leading 9:15

50:18 149:7

leads 60:23

61:6 72:22 109:1

116:15 175:8

learn 51:14

135:25 137:14

137:18

learned 108:1

111:22 135:25

136:12

least 61:25

62:2 67:15 67:18

68:5 88:22

99:8 99:10

102:10 114:1

117:6

LeBlanc 8:21 8:22

101:21 101:22

101:22 104:4

104:5

ledge 72:9

Lee 7:12 7:12

82:23 130:24

134:20 134:21

134:21 135:3

135:5 135:16

135:19 136:8

139:19 185:12

Legan 7:3 7:4

87:22 87:23

87:24 94:14

94:16 95:13 96:2

leisurely 57:24

lend 129:10

length 182:25

lengthy 29:25

42:23 188:2

less 68:11

75:16 76:5 82:19

105:7 107:19
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 235

108:14 109:13

109:24 113:12

113:21 117:20

119:2 126:17

127:17 131:19

134:14 135:9

138:4 153:15

157:11 183:17

lesser 149:23

lessons 108:1

111:21 137:17

lest 69:11

let's 5:2 57:25

58:1 88:24

152:18 169:12

level 24:1

38:23 38:23 42:9

42:10 67:24

67:25 68:12

76:15 138:17

139:1 141:16

155:13

levels 11:20 12:3

12:9 46:17 46:21

61:21 62:4 63:22

64:22 65:6

65:9 65:13

66:6 66:19 66:25

67:3 67:3 67:5

67:16 67:16 68:1

68:7 72:6

72:12 74:8 74:12

76:25 77:8 78:20

79:3 79:4

83:13 83:13

83:14 88:14

88:21 105:7

105:9 157:23

165:3

Levine 14:21 15:5

15:9 19:5

21:21 23:3 24:19

28:21

LH 7:6 22:24

23:18 23:23 25:7

25:12 59:13 62:1

63:12 63:20

67:24 70:10

70:16 76:4 76:15

79:14 88:2

88:9 88:12 88:15

88:23 89:3 89:21

90:2 93:4 93:7

93:19 93:23

94:20 94:23 95:1

101:19 149:7

149:22 150:16

150:21 150:23

150:25 151:7

153:14 153:19

155:14 156:20

157:13 157:21

157:25 159:5

160:12 164:20

164:25 165:4

167:11 172:15

180:1 183:1

LH-surge 89:1

liaison 2:17

Liccione 15:21

96:10

licensed 37:15

life 20:16 146:16

148:14 150:5

152:16 161:17

162:5 162:7

173:6

life-stage

20:23 29:5 30:14

lifetime 38:7

38:10 38:13 39:8

49:5 53:17

light 29:14

lighter-blue

124:14

likelihood 142:15

143:3

likely 40:16 41:2

48:13 50:19

50:21 110:16

112:11 112:14

112:21 112:24

113:1 113:19

116:3 117:7

117:7 125:7

128:13 139:7

161:21

limit 3:17 44:7

53:18

limitation 42:3

42:3

limitations

32:9 33:18 38:21

43:2 45:21

175:16 175:25

179:5

limited 31:8 35:7

36:24 39:6 48:22

lindane 37:23

38:2

line 19:20

67:12 70:21 73:9

74:5 78:14

85:7 98:21

101:23 120:13

120:15 124:12
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 236

124:14 124:21

linear 72:25

73:14 74:2

74:4 74:9

74:16 75:18 77:1

77:17 79:2

79:6 89:19 99:22

128:12

linearity 99:19

lines 99:19 99:22

100:2

link 12:10 30:7

54:15

linked 105:13

linking 19:19

156:19

list 75:6 171:16

listed 171:17

171:18

lit 36:12

liter 104:18

104:19 104:20

140:25

literature

18:13 25:24

28:23 31:23

33:10 35:3

35:6 36:2 36:9

55:20 58:13

82:11 82:12

159:9 159:15

159:16 173:23

liters 105:4

little 17:20

22:18 31:24 48:2

52:24 57:24

58:21 72:17

74:25 81:7 88:20

90:14 90:22

100:25 102:22

103:19 111:3

111:23 113:6

114:2 115:15

117:15 118:15

124:23 125:19

133:11 133:22

143:13 143:16

148:15 149:23

156:21 167:23

168:24 171:4

171:5 171:8

173:4 174:13

184:7 185:16

185:17

live 8:2 41:23

lives 152:18

living 42:18

47:14

loaded 188:6

LOAEL 68:22 69:16

70:1 70:17 80:22

84:12

LOAELs 85:25

location 117:6

locked 91:20

log 130:10

Logdall 55:2

logic 56:24

logical 146:19

180:16

long 7:21 36:17

37:21 67:17

98:14 105:17

123:25 128:25

137:2 137:5

141:9 145:20

156:17 156:21

161:2

longer 109:23

110:4 113:12

128:5 143:20

156:16

longer-term

133:22

long-term 37:11

41:15 44:9 132:3

loop 170:25

lose 97:14 131:18

loss 89:12 131:25

lost 104:22

lot 9:17 14:4

24:8 26:7 28:7

30:4 57:8 57:9

126:8 127:23

128:3 128:5

128:6 128:24

135:9 136:2

138:6 142:22

144:8 146:18

147:15 147:20

150:16 152:23

172:8 174:16

lots 150:18

loud 27:2 27:10

low 25:11 47:20

47:22 47:23

48:21 51:10 84:5

85:4 85:22 88:17

93:24 157:16

low-birthweight 47:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 237

low-dose 82:2

lower 25:12 63:14

63:14 91:13

94:10 131:10

150:25 153:19

157:20 158:17

167:7

lowest 80:14

Lowit 15:18

19:2 19:6 19:7

30:23 31:14 54:9

82:4 82:5

84:19 95:5

95:6 96:2

99:16 99:17

103:15 103:16

105:10 105:11

105:20 144:24

145:3 145:7

145:19 146:10

146:13 161:24

162:9 162:12

163:22 164:4

164:11 164:18

165:16 166:18

168:18 169:19

169:24 170:24

172:1 172:4

174:17 175:5

176:2 176:11

177:8 178:4

178:22 179:13

179:20 180:10

181:3 181:17

182:5 182:6

182:15 183:9

185:15 186:13

187:13 188:18

188:19 189:2

lumen 45:4

lunch 105:21

luncheon 106:5

luteinizing 22:24

M

magenta 120:13

magical 119:17

magnitude 67:7

121:16 123:16

126:2 143:4

164:14

main 42:3 48:9

54:4 75:21

78:1 109:16

111:16 134:3

Mainly 32:7

maintenance 157:9

major 17:17

32:8 37:6 43:2

44:3 44:25

106:23 156:14

161:21 175:8

175:16

major/minor 35:7

male 37:3 39:25

41:22 42:17

42:20 47:11

52:11 53:6

151:21 157:6

162:23 163:2

mammary 6:15 6:19

18:12 21:5 21:10

21:12 24:22

25:10 25:17

149:8 178:5

179:7 179:11

179:21 179:22

180:2

manageable 119:22

Managers 20:2

manifest 93:17

manner 112:20

map 115:20

mapping 35:11

market 144:2

Marquita 15:21

Mary 15:23 27:7

30:11 106:19

107:2 144:18

147:20 171:14

Mary's 153:25

mass-balance

102:7

master 36:13

match 121:21

121:25

matching 100:2

121:8 135:20

136:4 136:11

155:15 157:21

157:24 157:24

material 35:10

55:10 80:6 162:1

188:7

materials 3:24

32:10 33:7 50:10

maternal 44:4

44:10 45:7

mathematical

181:5 181:25

Mathematics 7:13

matter 82:18
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 238

98:13 101:2

123:20 145:14

146:10 163:10

173:15 178:15

182:21

matters 2:23

Mattix 43:10

Maumee 124:7

maximal 157:18

maximize 40:22

maximum 123:11

123:12 126:23

131:8

may 20:7 21:12

39:22 46:2 74:19

77:22 80:11

84:20 86:18

93:15 93:16

97:14 98:18

98:23 100:16

100:23 101:19

108:10 108:25

110:3 110:4

110:5 110:10

110:11 112:22

113:6 114:9

114:19 118:14

122:1 122:16

123:23 123:24

127:24 128:18

129:5 129:6

130:15 132:8

132:10 133:9

133:22 135:17

136:10 138:5

138:15 142:10

142:13 143:7

157:17 161:1

161:15 164:1

164:3 169:11

188:13 188:22

189:16

maybe 88:24

89:1 141:5

149:23 158:25

160:6 160:21

165:6 166:7

177:7

mcmanaman 6:17

178:19

McManaman 6:17

178:16

McMullin 61:12

68:23 71:6

103:25 158:3

158:4

mean 67:2 67:5

67:7 74:3

76:18 78:4 84:15

85:20 89:25

92:17 95:3 98:23

99:1 103:1 104:9

121:11 129:12

130:2 135:20

135:21 135:22

144:9 151:12

161:12 162:13

169:18 171:21

meaningful 94:24

means 2:20 34:8

34:24 128:1

131:14 141:14

measurable 52:6

measure 35:20

35:22 38:22 48:1

49:2 98:24

126:23 141:19

142:11

measured 34:17

40:15 46:22

48:14 52:16

124:13 125:24

141:25 141:25

156:24 170:8

170:12

measurement

39:5 41:4

48:14 50:18

50:24 50:25 51:2

53:16 53:20

101:7

measurements

34:15 61:9

128:24 141:22

171:12

measures 48:5

163:2

measuring 47:1

mechanism 49:25

mechanisms 12:5

median 40:8

mediated 60:13

Medical 6:22

Medicine 5:9

medium 85:11

Meek 7:17 7:17

165:9 165:10

182:3

meet 5:2 189:14

meeting 2:2 2:9

2:10 2:11 2:15

2:17 3:16 3:20

4:10 4:12 4:24
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 239

5:1 9:13 9:16

11:9 16:13

17:7 17:16 17:22

18:5 18:20 18:24

21:11 21:22

21:24 21:25

22:22 23:2 23:11

23:18 24:12 26:2

26:2 26:3 26:4

28:22 29:15

31:10 31:16 58:7

106:8 161:22

169:17 172:22

173:12 174:7

189:22

meetings 3:8 11:1

15:16 21:23

56:13

Melanie 16:2

member 5:7 5:12

5:15 5:19 5:25

8:25

members 3:3

9:12 14:23

15:6 26:10

80:4 89:7 105:19

145:21 161:25

189:14

Mendez 15:19

30:18 159:7

menopause 38:6

39:9 39:10 39:13

39:17 51:21

menstrual 37:22

39:16 149:20

149:22 159:12

159:18 160:13

menstrual-cycle

38:1 39:3 52:4

mention 56:4

74:10 142:22

mentioned 16:9

32:21 33:2

33:8 34:9

34:18 34:23 39:2

47:8 48:5

52:13 52:23 53:4

54:10 69:1 77:21

89:7 93:7 108:20

117:9 118:7

141:7 165:11

mentioning 75:25

mercapturate 40:3

41:2 53:25

message 74:15

met 14:19

metabolism 5:11

61:5 61:11 102:9

metabolite

60:19 60:21

60:23 170:13

171:11

metabolites

41:5 60:15

62:1 62:18 62:23

62:25 63:4 71:23

71:24 79:12

97:24 98:6 101:9

101:12 101:18

102:17 103:18

103:19 103:24

141:1 158:8

170:9 171:1

180:22

metabolized 60:10

method 42:14

42:16 46:5 47:13

139:13

methodological

52:3

methodology

32:5 35:3

methods 52:14

126:21 126:21

128:10 129:8

131:17 134:12

134:13 185:10

metric 38:9

39:7 53:19

100:23

metrics 100:16

100:19 105:15

mg/kg 61:14 63:10

90:1 90:5

92:20 95:3

95:9 97:7 153:15

mg/kg/day 63:14

64:21 66:4 68:15

73:23 77:7 88:22

mg/L 77:9

mg/L-h 77:13

mic 4:9 14:21

Michigan 4:18

microphone 19:2

90:16

middle 145:11

145:17

middle-aged 93:22

mid-July 18:23

mid-morning 3:6

mil 93:6

milk 157:2 157:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 240

milligrams 25:5

89:17 93:6 97:22

97:22 98:7 98:10

98:15

millimole 98:1

millimoles

97:23 97:25

mind 29:19

77:10 82:9

89:6 96:3 100:25

103:17 144:7

186:1

minimal 39:10

minimis 80:20

minute 144:12

145:4 152:14

168:19 182:7

minutes 2:5

3:17 31:21

58:2 61:14 148:9

148:16 169:13

miscarriage 41:16

41:18 47:9

misclassification

50:19 50:21

mismatch 156:21

misreading 97:8

miss 95:1 125:7

126:12 138:5

138:9

missed 37:21

80:11 122:16

125:10 126:9

143:3

missing 98:18

98:23 100:23

110:11 125:17

131:6 136:10

147:18 148:4

154:16 182:16

Mississippi

5:10 7:11

Missouri 116:11

117:14

misspeak 175:9

mixture 25:9

178:23 179:7

mixtures 6:12

143:15

mobility 113:2

mode 11:18 12:4

12:13 12:23 13:7

17:8 20:23

21:8 21:14 28:10

59:25 60:4 68:19

145:1 148:20

149:1 149:3

153:21 154:13

165:7 166:11

166:15 172:17

186:5

model 59:6 75:4

76:23 77:2

77:3 81:4

81:10 81:11

81:12 81:18

81:21 81:25

82:17 87:18

87:19 97:3 111:7

118:17 118:18

129:19 132:19

132:22 132:24

133:12 133:14

133:15 158:3

158:4 185:3

modeled 46:10

model-fitting

87:2 87:3 87:14

modeling 24:5

59:18 74:23

76:22 79:17

84:14 95:9 96:12

96:15 96:20

118:18 129:3

129:21 142:1

models 75:14

75:17 82:7 111:9

112:15 116:1

130:12

moderately 146:19

Modes 11:15 12:8

modest 99:7

modification

34:22

modified 34:1

modifying 97:17

Moiz 6:11 6:11

179:10 179:14

180:5

molar 171:5

molecular 8:23

97:25 98:2

98:5 98:11

Mollis 103:22

moment 91:4

162:22 163:12

monitor 65:16

107:9 108:7

112:12 137:16

141:17 142:11

monitored 64:23
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 241

65:6 141:24

monitoring 2:12

4:16 14:9

14:12 17:24 18:1

18:3 18:9 20:3

20:6 20:24

21:1 24:3 24:7

27:12 29:3

30:8 30:10

44:6 44:10

48:3 58:10 59:19

75:1 78:2

79:22 82:15

100:10 100:13

106:11 106:18

107:7 107:11

107:13 107:16

107:21 107:24

108:4 108:6

108:19 108:20

109:4 109:5

109:8 109:17

111:17 111:20

112:2 112:11

112:13 113:8

113:19 113:21

114:1 114:18

114:22 115:2

115:9 115:11

115:17 115:24

116:2 116:6

116:10 116:14

116:20 117:1

117:8 117:10

117:19 117:20

118:5 118:8

118:9 118:9

118:12 121:9

121:17 121:25

122:20 123:6

124:7 124:17

125:25 126:11

126:15 127:15

127:18 127:24

128:3 128:5

128:6 129:25

131:13 131:19

132:13 132:16

133:19 134:2

134:5 134:10

134:14 135:1

137:2 137:4

137:15 139:2

140:10 140:24

141:8 141:14

141:14 145:2

154:21 154:24

155:1 155:6

155:8 161:20

164:3 165:21

170:8 183:16

184:4 184:20

186:17

monodealkylated

60:15

month 28:25

69:7 158:25

160:22

monthly 43:19

43:20 141:21

months 9:21 18:23

28:5 28:19 44:20

69:14 100:9

100:18 101:2

101:2 116:5

150:20 154:9

173:13 174:3

187:1

Montreal 6:8

morning 2:7 4:9

5:4 5:20 7:3

8:11 8:21 19:7

24:20 31:3 31:19

33:7 57:22

58:6 106:14

147:18 149:2

149:12 153:17

158:15 159:8

172:9 174:25

177:4 180:21

182:25 187:13

187:16 187:25

188:20 189:9

189:11 189:19

189:21

morphology 40:17

Morseth 75:10

155:14 155:16

183:3

mostly 8:18

60:6 91:18

112:24 116:4

182:1

motility 40:17

move 24:9 26:17

28:4 28:18

31:8 31:12 59:15

59:25 71:14

74:22 100:21

125:19 132:4

152:17 154:20

168:23 169:5

174:1 185:24

187:19

moved 27:11

moving 22:11

59:11 61:24 63:5

67:20 71:9

81:4 81:10 141:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 242

182:19 186:16

multidisciplinary

28:6 160:10

161:8

multidisciplinary

20:13

multi-gen 168:6

168:7

multi-life 152:12

152:19 152:21

multiple 15:1

130:14 152:16

153:1 153:1

156:4 161:9

173:17 176:5

multiply 98:2

Mumtaz 6:11

6:11 179:9

179:10 179:14

Musical 145:4

myself 49:6 57:9

N

NACWA 128:6

naive 104:18

naively 135:22

NAS 11:25 19:13

national 5:6 5:18

7:1 8:20 59:22

132:24

natural 30:8

145:10

nature 34:11

41:12 42:4 45:19

50:1 50:8

50:13 50:20 52:1

54:11 82:10 85:5

88:14 118:15

168:10

nearly 64:24 71:6

Nebraska 117:14

necessarily

110:18 112:21

122:7

necessary 170:14

necropsy 153:7

neither 145:22

Nelson 6:13

6:13 15:22

27:7 30:11 100:7

100:18 100:20

106:18 106:20

134:18 134:25

135:4 135:12

135:17 135:21

136:12 137:8

137:12 138:11

140:4 140:8

141:2 142:17

144:18 147:19

153:25 166:1

166:4 167:15

170:4 170:6

170:7 171:8

171:13 171:23

173:2 173:20

180:5 183:13

183:14 183:23

184:2 184:17

185:2 185:7

networks 128:16

neural 128:16

neurobehavioral

151:25

neuroendocrine

149:3

neurons 157:4

157:5

neurotox 23:21

neurotoxicity

17:5

neurotoxicologist

5:15

neurotoxicology

5:11

newer 25:1

news 163:17

NHEERL 150:13

nice 22:11 57:5

74:9 151:17

153:2 157:21

168:24

NIEHS 5:19 15:4

18:11 187:17

NOAEL 63:18 68:22

69:5 69:10 69:15

70:1 70:16 80:22

84:11 86:15

86:22 87:16

NOAEL/LOAEL 59:1

NOAELs 71:1 85:24

86:9 86:14

no-effect 83:11

non 50:21 72:24

172:11

non-adult 162:5

non-cancer 2:11

4:15 11:8
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 243

16:19 18:8 24:11

29:14 29:19

31:22 33:9 33:13

33:22 35:2

35:5 35:21

36:8 54:8 58:9

106:10 173:12

non-cancerous

18:19

non-causal

32:14 50:14

non-communicative

41:17

non-compartmental

81:15 180:20

nondisclosure 4:1

none 62:24 152:16

166:22

non-significant

45:11 47:23

non-users 38:2

normal 103:18

normally 165:6

North 8:24 37:13

notable 36:16

51:15

note 30:18 43:7

44:23 45:10

46:19 47:25

51:17 69:16

188:14 189:8

noted 28:21

41:1 53:24

65:8 178:23

nothing 119:17

noting 21:23

notion 65:21

66:24

NRC 19:12

O

objecting 139:16

observation 38:16

53:8 57:2 64:2

72:2 79:6

99:18 153:12

observational

32:16 32:25

33:21 45:24

51:16

observations 40:9

51:11 53:5 163:4

Obstetrics 6:19

obvious 174:19

obviously 9:3

29:6 109:24

142:20 167:8

180:25

occupational

6:7 39:17 57:8

occur 33:11 86:17

121:17 123:8

occurred 140:16

occurrence 39:13

occurring 94:12

occurs 39:11

112:12 156:17

157:20 187:14

Ochoa 177:1

Ochoa-Aku 47:25

odds 37:21

38:12 40:6 41:18

44:2 45:11 46:14

47:11 47:16

47:23 49:4 52:19

odor 119:10

offense 143:25

office 3:15 5:6

9:9 15:3 55:2

Official 187:22

offspring 157:5

163:1 163:9

oh 84:2 86:13

92:2 93:1 104:17

183:25 188:17

Ohio 116:11 117:4

117:13

okay 2:7 24:9

25:22 31:17 60:9

60:19 61:3 72:24

75:2 75:15 75:19

81:9 83:24

84:1 84:2

84:13 84:16 91:2

92:22 99:5 99:11

105:16 106:20

135:12 135:16

145:19 146:13

151:9 154:19

157:13 168:17

169:15 171:23

172:4 174:9

177:5 177:8

178:3 178:4

180:7 180:9

180:12 181:17

182:4 182:4

182:6 182:17

182:17 183:9

183:14 183:21

183:23 184:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 244

184:23 186:11

186:12 187:6

187:10 188:17

189:8

Oklahoma 5:14

omphalocele 44:25

45:13

one-dimensional

131:2

ones 13:21

26:12 56:17

85:12 115:6

135:7 156:14

173:17 176:23

185:18

one-time 39:11

ongoing 16:7

16:21 27:24

36:17 50:4

147:13 147:20

148:17 151:12

151:12 153:8

162:4

onset 156:14

onsets 151:2

Ontario 8:12

36:19 41:14

41:22 47:7

open 9:7 35:6

80:3 134:15

136:14 164:1

164:4

opinion 36:6

51:22

OPP 15:18 150:13

opportunity 3:6

3:8 3:13 10:4

144:17 187:12

187:15

opposed 180:3

184:22

opposite 102:24

103:1

option 143:2

options 132:6

oral 60:11

61:13 63:9 68:19

70:6 160:3

ORD 16:4 16:6

151:14 151:20

152:1 163:13

order 20:11

68:2 94:23

158:24 160:21

162:6 180:16

182:8 182:17

183:12

ordinary-point

131:2

organics 119:11

organisms 5:24

organization 12:9

oriented 124:12

185:19

origin 97:7 97:18

original 36:5

55:20 108:20

oscillate 67:7

oscillation 67:10

67:10

oscillations 67:8

others 54:18

56:12 80:25

103:5 165:25

Ottawa 7:18

ought 138:9

ourselves 55:21

58:1 99:22

121:12 159:4

outcome 34:13

42:21 45:24

48:15 49:2

52:6 52:19

53:2 93:16

94:4 94:13

103:23 151:1

167:24 173:12

outcomes 11:16

11:19 13:8 16:16

21:11 22:21

24:13 33:4 33:16

35:6 35:21

37:4 38:20 39:24

41:14 46:3

46:7 47:1 47:5

48:7 48:18

51:1 52:12 52:15

54:17 136:25

149:10 151:16

151:24 160:15

167:19 172:20

outline 59:8

output 136:21

outside 162:5

ova 89:14

overall 33:24

39:21 41:9 42:24

overestimating

163:20

overkill 89:4
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 245

120:24

overlap 48:11

87:8 127:4 136:6

177:9 177:22

180:18 180:25

overlapped 46:15

overlaps 47:17

52:21

overlay 20:20

84:21 164:24

183:1

overnight 187:14

overview 58:12

ovulate 89:13

ovulation 93:8

93:10

ovulatory 93:25

P

p.m 189:23

P450 60:13 60:22

pace 22:11

57:23 57:25

105:25

package 73:5

162:15

page 155:11

paid 63:7

paired 119:24

pair-wise 87:6

Panel 2:1 2:9

2:13 2:18 2:21

2:25 3:3 3:22

4:13 4:20 4:23

5:2 5:7 5:12

5:16 5:19 5:25

9:1 9:3 9:12

9:22 9:23

15:10 16:23

17:16 20:10 23:4

23:13 24:6 24:14

25:20 26:11

30:24 32:1

33:5 34:2 34:4

34:10 43:8

55:8 55:12

58:7 76:9

76:21 77:24 79:1

79:23 80:4

89:7 105:19

106:8 134:20

148:2 151:23

161:25 187:7

188:10 188:15

189:10 189:14

Panels 15:17 27:9

Panel's 34:25

36:10

paper 15:25 16:25

17:13 18:13

18:16 22:20 23:7

25:22 26:16

39:17 39:17

55:10 75:20 77:4

80:6 87:5 92:5

96:23 106:14

111:13 129:1

130:1 147:14

147:15 150:1

177:11 177:16

177:24 178:8

178:23 179:13

181:8 181:9

186:7 186:21

papers 18:15

56:17 162:22

163:19 178:8

178:9 179:23

parameters 40:2

40:16 40:19 53:7

163:3 163:6

paraphrase 174:18

parent 61:6 61:15

61:18 61:22

62:17 62:23 63:4

71:21 79:11

101:12 171:1

participants 42:5

49:8

participate

2:14 37:16 41:22

participating

9:14 15:2

particular 2:11

10:10 10:24

19:16 51:4

93:9 98:22 126:5

131:8 147:2

149:16 164:17

167:6 175:8

176:18 177:4

180:13 184:25

186:1

particularly 2:13

25:3 29:9

34:16 34:19

36:25 39:2 50:25

55:1 96:6 107:22

123:21 127:3

132:13 140:11

146:19 155:17

157:11 165:4

171:4 175:15

partly 26:9 26:9
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 246

partner 47:11

partners 41:23

42:18

passed 56:17

past 10:19 18:22

pathway 17:17

61:4 166:16

pathways 11:14

11:15

pattern 84:23

87:4 91:18

120:14

patterns 144:8

pay 146:24

paying 60:1

PBPK 81:4 81:8

81:10 81:11

81:12 81:18

81:21 81:25 82:7

82:17 158:3

peak 46:17

63:22 70:15 88:9

88:13 88:13

93:23 109:23

114:19 123:23

124:9 125:3

125:8 138:9

peaks 88:16

100:11 100:12

100:12 110:11

119:1 121:16

122:8 124:11

124:16 124:25

125:1 125:17

126:9 127:4

127:5 128:13

135:23 135:24

136:2 136:10

138:5 138:6

peer 12:19

12:19 13:20

13:21 13:22 21:4

peer-review

9:22 10:2

peer-reviewed

82:18

pending 147:13

148:17 173:12

173:16

PENELOPE 8:1

162:3 162:10

162:17 163:16

163:24 164:6

164:16

Penny 8:1 186:11

people 3:14 42:14

54:24 55:24

57:10 145:24

152:17 163:21

174:21

per 25:5 63:15

70:7 93:6

percent 68:9

69:21

percentage

140:1 140:18

percentile 133:6

percentiles

131:10 132:14

perfect 98:20

performed 70:6

76:22 158:19

perhaps 12:11

93:18

perinatal 33:4

37:4 41:14

period 4:19

31:8 42:21 46:11

46:15 46:16

47:12 47:17

47:18 49:10

52:21 65:15 66:7

78:11 78:16

78:22 107:15

108:22 112:24

113:3 114:10

114:12 114:15

116:2 123:19

123:24 127:17

137:5 138:18

139:8 156:18

156:21 187:19

periods 46:12

52:23 113:24

128:25 157:10

159:6 159:17

peripubertal

150:6 152:15

permanent 5:16

9:1

person 56:1 56:11

133:11

personal 30:17

80:10

perspective 10:22

perspectives

79:21

pesticide 5:10

8:14 9:10

10:11 10:12

13:11 13:14 15:1
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 247

32:23 36:1 37:13

37:16 38:3 40:15

42:7 42:12

49:9 49:14 49:15

112:12 112:14

112:17 112:25

113:3 114:1

118:17 126:12

128:3 147:2

pesticides 5:24

38:15 38:17 41:7

42:9 42:13

50:4 51:2

51:24 109:12

111:8 113:6

128:4 144:2

Ph.D 8:18

pharmaceutical

159:9

pharmacokinetic

61:10 63:1 81:13

81:19 102:7

Pharmacokinetical

ly 158:2

pharmacokinetics

6:4 6:9 18:10

60:2 60:7

74:16 74:19 96:6

160:11

phase 26:18

33:1 64:25 65:16

65:19 67:14 73:1

73:8 159:18

160:6 160:7

photoperiodic

88:9

phrase 141:13

physical 39:16

39:18 39:19

50:25

physician 8:18

physicians 19:14

physiologic 89:9

physiologically

88:25

physiologist 6:14

7:24

Physiology 7:5

pick 28:23

picked 123:14

piece 95:7 100:23

pieces 36:6

pinpoint 115:13

pituitary 89:4

pituitary-adrenal

17:15

pituitary-gonadal

17:10

place--around

96:7

placed 17:7

places 64:18

139:23

plan 23:7 141:8

169:13 184:3

189:16

plant 107:23

171:11

plasma 59:11

61:16 61:19 63:6

64:5 64:12 64:22

65:1 65:4 65:6

65:8 65:13

66:6 66:19 66:25

67:3 67:3 67:5

67:16 68:1

68:7 71:11 71:25

72:12 72:19

73:16 74:8 79:11

79:14 96:13

101:14 104:19

105:9 150:21

158:7 164:25

165:3

plateau 68:13

69:4 85:14 85:17

88:21 90:5

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85:15 120:9

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131:11

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23:10 23:16

23:24 25:7 29:11

31:12 55:7 55:21

58:1 58:14 63:17

67:23 69:18

70:10 70:15

70:17 73:5 73:11

73:16 75:11

75:22 76:3 77:23

77:25 78:11 80:3

81:2 82:5 85:1

87:1 87:17

88:7 88:8

91:13 96:15 97:4

103:14 105:11

105:18 106:12

106:16 109:9

111:17 116:15

122:22 124:15

126:1 126:18

131:25 133:21

134:15 137:20

138:12 138:13

141:15 143:23

144:15 144:19

144:23 161:25

165:24 169:18

169:20 170:13

170:15 174:11

187:10 188:17

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89:21

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35:13 73:7

78:1 86:15 86:18

88:5 90:21 95:25

109:16 118:5

127:22 146:18

151:9 155:8

161:9

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poor 40:6 40:7

40:16 104:2

Pope 5:13 5:13

population

33:16 37:18 40:8

41:24 57:13

138:7

Portier 5:3 5:4

5:5 56:25 57:1

57:19 86:12

86:23 86:25

104:16 104:17

105:2 141:4

141:5

portion 134:2

Portsmouth 6:3

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63:2 103:11

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80:14

possibility

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51:11 103:7

126:22 164:1

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30:7 41:8

41:11 48:20

78:24 126:10

128:17 136:22

138:7 159:17

160:19

possibly 39:23

109:6 114:14

117:24

post 61:14 61:18

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153:7

potency 171:5

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167:8

potential 12:24

33:10 39:15

39:19 41:6 50:23

79:4 120:18

124:21 129:4

133:18 144:25

160:7 167:10

185:21

potentially 34:15

35:15 167:9

power 34:19 34:20

75:18

powerful 67:18

PowerPoint 86:13

PRD 16:2

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precision 40:11

51:5 54:3

161:5 161:6

preclude 74:19

preconception

47:12

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predictors 94:11

predominant 102:1

predominantly

175:18 179:6

186:8
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pre-emergent

113:5

prefer 158:2

pregnancy 38:15

38:18 41:23

42:22 46:11

46:13 52:24

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167:3

preliminary

122:18 122:25

127:1 161:12

188:11

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17:1 189:17

prepare 9:21

174:24

prepared 14:23

175:20

present 18:2

147:10 187:15

presentation

4:6 17:25

24:21 24:22

29:24 30:1

30:6 30:8 30:9

30:12 30:19 32:4

50:17 53:12

54:21 55:9 55:24

60:6 75:22 79:25

80:5 81:3

100:8 104:15

144:24 153:16

154:1 166:8

168:20 172:2

172:11 173:3

176:19 177:2

183:1 185:22

187:16 187:18

presentations 4:3

19:3 20:14 22:20

27:14 28:17

29:17 30:13 31:1

106:13 108:24

110:8 145:9

145:22 146:4

169:19 187:11

188:1 188:6

presented 16:11

17:22 32:10

32:21 33:4 33:19

36:14 128:15

presenting 24:24

pressure 167:25

presumed 62:5

62:8 102:11

preterm 47:16

48:21

pretermdelivery 47:4

pretty 65:14 67:6

74:13 77:20

100:4 112:7

113:5 114:23

114:25 124:9

128:6 135:14

158:18 171:6

pretty-looking

78:4

previous 12:19

21:3 31:25 58:24

59:1 89:7

90:24 93:12

152:11 165:13

165:16 165:19

167:16

previously 17:11

primarily 96:10

108:12 110:20

117:12 130:2

177:15 178:7

primary 19:18

principal 16:22

22:9

prior 3:16

18:23 21:2 51:25

52:1 52:1 58:11

priori 49:24

51:23

prism 184:21

private 8:2 37:12

probabilistic

42:4 42:14 47:13

141:20 142:2

142:5 142:14

probability 86:17

125:16

probably 4:25

22:24 31:10 47:1

48:17 57:25 88:6

93:6 112:7 124:1

133:22 136:4

142:22 145:20

153:9 168:14

175:7 189:12

189:18

problem 19:19

19:25 22:3 29:20

88:15 144:11

problemformulation 11:4

procedure 142:2
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181:5

proceed 10:5 68:3

80:21

proceedings

4:22 9:8 9:14

9:20 30:25

31:7 31:15

189:13

process 10:2

32:23 34:8

43:4 62:8

80:19 106:12

115:8 119:4

130:20

produces 87:12

87:19

product 64:8 73:8

74:12

productive 105:17

168:22

products 102:2

139:25

proestrus 70:9

94:7 95:23

professional

30:18

Professor 5:9

5:14 5:21 6:2

6:7 6:18 7:5 8:7

8:13 8:22

proffered 43:9

profile 52:18

64:12 65:2

65:4 65:8

72:19 131:23

135:2

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program 5:19

10:11 15:1

18:3 107:14

107:21 117:10

144:20 154:23

154:24

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116:24

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16:24 22:10

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157:8

promise 129:18

129:22 133:9

134:1

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111:4 134:8

151:10 180:4

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29:1 30:1 63:25

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59:15 72:14

111:25 121:6

183:15 184:12

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24:4 27:21 32:19

33:25 49:25

58:15 58:20

64:11 134:4

147:11 180:14

proposing 27:11

62:21 64:4 69:23

71:14 71:25

75:23 117:21

136:18 137:9

137:13 165:12

172:14

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157:9

prostatitis

156:23 157:6

160:16

protecting 120:17

Protection

10:10 146:23

protective 120:23

provide 3:8

10:4 23:22 26:24

31:24 54:12

56:13 57:13 69:3

100:17 107:14

109:5 112:9

113:18 113:20

121:4 121:19

122:10 122:15

125:14 128:1

130:12 131:14

133:19 151:17

162:15 185:23

186:23

provided 3:3 3:22

16:2 16:24 17:19

23:13 24:3

34:5 111:13

122:20 129:4

135:14 173:1

provides 2:22

65:1 116:13

117:5 133:6

149:14 150:25

providing

108:21 110:12
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132:15 170:9

proximity 45:7

pseudo 67:1 67:14

72:12 101:25

pseudo-steady

65:21 65:25

66:24 67:3

68:1 68:6 74:7

74:10 96:8 101:6

101:24 102:5

124:1 157:19

157:22 157:25

165:2

puberty 151:2

156:14 156:17

156:20 160:16

public 3:7 3:9

3:16 3:19 4:4

8:8 9:13 10:4

16:23 31:8 32:25

169:22 172:9

187:20 187:21

187:23 188:4

188:14

publication 41:1

publications

178:10

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3:13

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published 17:3

17:3 18:15 19:12

37:20 38:5 38:11

43:1 43:25 46:19

55:19 59:22

168:2

Pubmed 35:7

pull 172:5

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30:15

pulsatile 149:6

pup 53:9 149:18

157:3

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157:6 167:19

168:9

purely 2:22 40:4

purpose 19:20

32:2 33:12

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pursuing 81:21

putting 15:16

107:4 150:16

154:20

p-value 84:13

Q

quad 65:22

qualified 4:23

qualitative 26:23

54:10 149:14

159:10

qualitatively

160:5

quality 40:6 40:7

53:13 146:23

quantitative

26:24 37:1 53:13

53:23 54:11

54:20 56:20 57:9

96:4 156:2 171:3

quantitatively

155:24

quarterly

107:10 115:17

139:6

question 13:14

21:1 35:18 35:24

39:1 41:25 42:25

46:9 51:8

55:24 83:19

84:16 93:3

95:7 95:16

98:4 99:23

104:16 104:18

109:2 121:18

135:19 138:3

141:15 143:14

145:8 148:1

152:11 154:12

155:15 155:20

157:24 161:11

163:14 163:23

163:25 164:7

165:11 165:17

165:18 166:7

167:4 167:16

169:10 170:17

170:20 171:9

174:19 175:9

176:4 176:8

176:10 177:7

177:8 178:3

178:5 178:22

179:3 179:7

179:19 180:9

181:6 181:25

182:20 183:7

183:14 183:23

184:5 184:6

185:14 185:20

186:8 186:13

questionnaire
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38:9 49:3 49:12

questions 20:5

20:7 31:5 31:9

31:13 31:14

49:13 54:4 54:22

55:4 55:8

55:12 56:23

57:21 80:4

88:3 90:19

97:1 100:9

104:14 105:19

106:1 109:10

109:11 111:18

125:12 134:3

134:16 134:19

134:22 139:17

139:20 144:15

145:18 145:25

154:6 154:16

159:13 162:1

165:24 169:2

172:13 174:14

175:1 175:13

178:17 180:13

184:3 185:15

185:16 187:19

189:10 189:17

quick 2:15 108:18

quickly 60:9

136:13 149:1

quite 21:20 25:11

26:20 30:4 37:23

42:23 45:12 84:3

102:24 102:25

136:20 154:1

173:2 176:10

quota 94:1 96:1

quote 93:8

R

radiolabel

62:10 62:10 63:3

64:15 64:18

98:14

radiolabeled

101:7

radiolabeling

98:12

rainfall 133:17

raise 94:6

raised 108:19

118:24

Ralph 16:4

90:18 90:20

91:24 92:2

92:6 92:24

93:2 95:19 95:21

145:7 162:21

163:18 166:19

167:1 167:2

167:20

ramping 181:10

random 111:8

115:8 130:20

range 64:20 68:14

74:17 92:16

96:14 130:16

130:18 134:6

140:2 140:18

158:24 160:19

173:14

ranged 130:22

ranging 117:12

rapid 158:6

rat 21:12 59:1

68:9 70:4

72:13 89:2 89:12

89:24 90:3 149:8

156:16 156:19

156:25 157:20

158:1 160:17

166:22 166:24

168:11

rate 67:8 73:10

73:17

rates 43:14 43:16

43:21 44:24

rather 7:21 80:21

87:15 97:6

108:12 136:4

ratio 73:16

rationale 64:7

92:13

ratios 143:21

rats 21:5 61:13

63:9 64:19

66:3 70:2 93:4

151:4 151:21

155:24 160:12

rat-to-human

161:7

raw 134:23

Rayner 25:3 178:8

178:9 178:10

re 4:13 107:6

138:22

reach 65:24 67:15

157:18

reached 56:17

reading 57:2

166:10

reaffirm 10:8

reaffirmation
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 253

22:22

reaffirming 17:9

real 63:18 74:3

86:20 129:18

156:2

realistic 78:5

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really 9:23

10:1 13:16 19:23

21:9 21:23

22:3 25:19 26:15

28:7 35:19

50:2 55:9 57:4

57:7 61:16 63:23

64:3 64:8

65:21 68:12 72:2

72:21 80:10

85:11 85:12

85:13 86:9 98:12

98:13 98:22

109:18 118:11

123:5 126:1

126:8 131:23

144:4 144:19

147:18 150:2

160:18 162:13

165:10 171:2

reason 28:13

50:13 59:20

61:23 71:19

81:10 106:21

126:14 142:9

167:22

reasonable

100:5 101:25

103:11 104:12

121:4 171:4

171:6

reasonably 120:23

reasons 51:22

53:10 54:18

62:18 64:1

69:9 85:20

113:15 126:19

160:2

reassessment

162:20

recall 42:2 42:21

52:19 171:15

recap 108:18

received 188:11

188:15

recent 49:10

150:7

recently 43:25

recess 58:4 106:5

169:14

recessed 189:22

recite 44:22

recognized 101:20

recollected

101:16

recommend

183:18 185:8

recommendation

116:17 129:16

recommendations

2:21 2:22 2:25

30:3 34:25

59:6 111:21

113:22 128:20

recommended

54:2 111:6

126:20 129:14

132:20

reconstruction

55:16

reconvene 58:2

169:13

recorded 41:17

54:17

recovery 153:2

rectangles

72:20 72:21

recurrent 51:1

red 27:22 58:25

124:15 124:19

124:21 147:12

173:4

reduced 53:9

165:20

reduces 42:2

reduction 120:4

167:11

re-evaluation

16:7 28:13

58:8 59:3 106:9

refer 21:7 107:25

108:16 139:23

reference 32:1

35:12 47:9

referenced 133:10

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refine 78:2

refining 79:21

reflect 40:24

42:25

reflected 35:25
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36:5

reflective

13:25 125:4

reflects 33:9

50:2 120:21

regard 39:25

40:11 56:23

87:25 94:19

111:4 116:5

116:25 117:17

123:9 125:8

127:19 130:5

131:10 132:11

137:12 138:21

regarding 11:8

18:19 51:23 52:8

76:15 77:24

109:17

regardless 67:17

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region 85:22

registrant 137:21

registration

107:7 138:23

154:25

regression 77:1

77:17 98:21

111:8 129:19

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regular 114:10

116:8

regularity 80:13

regularly 70:3

114:6

regulating 23:25

regulation

80:20 137:5

147:5

regulations 3:1

3:4

regulatory 2:23

7:20 8:4 10:7

10:17 13:24

15:13 142:6

reinforce 127:11

reinforcement

115:22

reinforces 86:14

reiterate 19:8

21:22

relate 11:20

12:25 13:6

114:17 159:24

160:17 167:15

related 17:9 21:9

21:14 22:22 33:3

79:5 80:17

106:16 111:16

151:1 167:11

181:6

relates 12:13

13:13 20:2

23:8 23:11

29:3 29:5 97:1

108:4 147:3

149:21 156:23

159:11 160:12

165:7 177:13

178:5 178:7

179:21 181:24

relating 184:3

relation 42:20

relationship 44:9

74:2 74:9 76:1

79:2 130:19

136:24

relationships

11:17 89:19

148:23

relative 101:11

102:18

relatively 20:7

38:25 40:24

51:10 51:10 52:5

52:9 52:18

53:3 124:8

153:17 176:24

release 89:3 89:9

149:6

released 89:13

releasing 166:13

relevance 26:23

54:13 92:18

148:22 149:15

151:23 153:22

154:14 160:11

186:6

relevant 21:13

21:15 23:15

23:23 51:8 92:20

149:12 158:13

172:19 188:13

reliability 34:13

reliable 52:5

53:3 81:11

106:25

rely 27:1 28:17

54:19

remain 66:19 67:6
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67:16

remaining 18:19

73:12 73:15

remains 17:17

remarkable 71:8

85:3 86:1

remarks 2:16 3:12

3:13 178:24

remember 27:20

88:11

removal 120:6

120:7

remove 60:19

60:20 119:5

119:8 119:10

119:10

removed 146:17

147:7

removes 120:18

repeat 65:23

repeated 59:14

63:24 64:9 64:13

65:2 67:4

67:17 67:22

68:24 70:1 70:20

71:3 71:13

72:3 72:11

replications

41:11

report 9:21

12:1 57:15

59:7 59:21 61:12

95:22 107:4

151:22 151:25

176:7

reported 38:13

38:14 38:17 40:5

40:14 42:12

42:12 43:15

43:18 44:1

44:8 44:16

45:3 46:9

46:13 46:20 47:6

47:10 47:15

47:21 47:23 49:3

51:7 61:13

reporting 10:22

45:22 52:25 54:3

reports 19:13

19:13 19:14

19:14 36:1

represent 65:18

representation

110:13

represented

57:6 138:6

represents

63:23 73:8

74:5 164:17

repro 151:24

reproduction 6:24

7:6

reproductive 8:10

21:13 37:4

37:8 37:19 38:20

39:24 39:25

41:10 51:20

52:11 53:6

53:7 89:11 93:21

149:9 156:15

163:3 163:11

172:19 176:21

requested 3:11

required 89:2

99:21 158:22

168:12

requirement 27:17

107:9 154:25

requirements

3:7 146:24

requires 147:6

154:25

requiring 137:21

re-registration

14:3 14:10

research 7:1 7:14

7:23 15:3

32:22 36:5

55:3 59:22 159:8

researchers 41:3

reserving

147:12 151:10

153:10

reservoirs 118:12

118:16 118:20

118:23 184:22

resided 45:5

residence 8:20

44:5 45:7

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residual 39:15

39:19

residue 61:3

resistance 32:23

respect 17:18

24:16 39:3 50:23

148:6 148:12

149:1 150:18

151:7 166:20

respective 51:5
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respectively

46:14

respiratory 37:5

respond 55:3 87:1

response 44:9

53:15 58:16

63:19 63:20 76:8

83:8 83:10 83:18

86:5 87:4

87:13 87:19

153:22 164:12

164:14 178:21

181:24 183:12

responses 25:21

154:16 165:4

responsible

163:13

rest 155:4

result 23:6 60:14

63:11 109:1

120:8 152:8

resulted 52:18

results 32:8

33:22 37:6

54:8 63:8

63:17 77:5 77:19

78:6 115:9

retardation 46:23

46:24

rethink 165:1

retired 8:17

return 105:21

144:23

review 2:11 3:2

4:15 13:21 13:22

18:13 18:22 21:2

21:4 21:18 25:24

28:23 28:25

29:22 32:2

32:6 33:8

33:12 35:3 35:16

35:16 36:2

36:9 36:12 39:23

54:25 56:9 56:12

56:12 58:9 58:14

106:10 178:25

179:4 181:20

reviewed 17:1

23:18 24:10

26:18 26:20

55:19 55:25 56:1

82:7 158:4

reviewing 9:18

31:21

reviews 12:19

12:19 13:20

16:10 24:15

24:25 26:5 26:11

82:7 175:13

175:14

revise 12:17

revised 147:11

155:19

Richard 6:1 6:1

7:10 7:10

98:18 99:4

143:12 143:12

180:24

ring 61:2 62:11

64:15 64:16

102:12

risk 7:20 8:4

11:5 11:23

12:2 12:7

12:17 12:18

12:23 13:12

14:24 16:13

18:19 19:16

19:20 20:2

20:5 20:12

21:3 22:2 23:9

23:12 23:15

24:17 26:25

29:23 32:17 33:1

33:14 37:1

51:9 53:23 54:20

57:14 58:24

59:24 60:1 69:12

69:18 75:11

80:21 82:15

87:14 92:21

121:1 151:8

152:11 155:5

164:8 164:20

165:1 172:15

177:13 186:16

risk-assessment

10:13 11:12

12:21 13:19

23:24 34:8

risk-management

19:21 20:12

River 124:7

rivers 110:21

118:10 118:14

Riverside 5:23

road 13:11

robust 126:11

171:2

Roby 6:21 6:21

182:3

Roddell 16:5

Rodriguez 15:20

29:25 58:15
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58:17 80:2

81:9 81:23

82:6 83:4 83:9

83:17 83:24 84:8

85:14 85:19

87:21 92:11 93:3

96:5 97:11

98:9 99:3 99:5

99:18 99:20

101:10 102:6

102:25 103:6

103:13 104:24

104:25 105:3

105:20 106:15

114:16 123:25

150:20 157:15

164:24 172:24

180:21

Rodriguez's

30:6 80:5 104:14

153:16 182:25

Rodriquez 158:9

role 9:6 10:9

46:3 48:17 157:8

rolling 78:10

78:17 100:24

101:1 124:15

155:6 155:12

155:16 159:3

160:25 161:2

165:22

rolling-average

100:3

room 189:15

root 118:18

roughly 120:3

120:6 131:21

131:21 155:2

155:3

round 60:12 60:22

rounds 76:11

route 60:11

routes 82:1

routine 2:15

RTP 168:3

rule 51:10

72:24 73:20

ruled 50:14

run 15:16 137:2

163:8

running 107:18

runoff 114:8

133:18

Russia 189:6

189:7

S

safe 13:11 107:10

115:16

safety 27:16

109:6 113:20

118:1 142:24

147:3

sake 33:5 46:18

sample 34:18

37:18 38:25

40:12 40:25 51:9

54:4 112:21

112:22 114:6

122:15 144:12

sampled 110:14

111:24 113:16

116:16 116:20

116:24 117:2

121:9 121:22

122:4 122:10

122:19 122:25

130:24 134:9

134:14 139:3

184:4

samplers 166:2

samples 116:8

116:21 119:25

121:22 122:5

123:3 127:18

138:6 138:8

138:10 139:7

sampling 14:15

18:1 108:13

108:22 109:2

109:13 109:14

110:9 110:10

110:16 112:23

113:9 113:11

113:17 117:4

117:11 117:22

117:25 121:7

122:7 122:8

122:17 122:17

122:24 123:7

124:6 124:18

124:19 124:20

125:2 125:6

125:15 126:17

127:6 127:15

127:22 127:23

127:24 127:25

128:11 130:25

131:21 131:21

131:22 134:10

136:1 138:4

138:5 139:8

139:15 141:10

142:8 142:14

185:3
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 258

samplings 125:6

San 7:23

Sandra 7:3

87:23 87:23

94:16 95:13

Sandy 7:4

Santa 7:16

SAP 3:15 9:5

15:15 16:10

17:11 18:17 21:5

21:7 21:18 28:20

28:21 44:13

93:19 101:16

108:2 108:5

108:19 109:17

110:7 111:6

111:21 112:3

113:23 118:25

126:20 127:11

128:10 128:22

129:14 130:25

132:19 132:20

133:10 134:3

149:5 151:22

161:20 169:17

173:16 173:22

188:5

SAPs 108:9 108:17

SAP's 116:17

Savitz 47:7

saw 70:13 70:18

70:25 74:2

74:9 90:8 94:7

120:4 130:21

136:15 152:24

153:16 157:14

181:8

scale 130:17

132:23 132:24

scaling 158:12

158:19 160:10

schedule 31:4

144:20

scheduled 18:21

18:21 28:22 31:2

105:21

scheme 142:4

142:6 142:8

Schlenk 5:20 5:21

81:1 81:2

81:20 166:5

166:6 167:5

Scholar 8:19 35:8

School 7:15

science 2:23 4:13

6:2 10:16

10:18 10:23

12:16 13:17

13:25 14:14

14:17 19:14 35:7

35:12 58:7

59:4 103:9 106:8

Sciences 5:22 8:8

scientific 2:1

2:9 4:19 9:4

9:25 10:6

10:12 10:14

10:21 15:13 19:3

20:1 28:3 28:9

31:1 34:1 106:13

136:23 137:7

137:17 144:24

154:11 185:20

185:24 187:11

scientist 8:12

scope 19:20

scoring 56:16

search 35:9 76:4

searching 35:12

season 47:17

121:17 155:2

seasonal 133:14

133:17

seasons 44:20

SEAWAVE 133:12

second 16:20 28:6

44:25 45:3

58:6 78:18 83:25

93:2 93:18 97:19

107:21 108:2

116:15 135:19

137:1 137:15

138:3 142:5

150:11 159:20

166:19 182:22

Secondly 120:17

second's 184:11

section 33:22

33:25 57:2 127:2

147:18 177:12

177:16 181:21

sections 177:11

177:23

sedimentation

119:4

seeing 74:16

75:16 78:17 79:6

91:6 113:25

126:6 126:7

138:24 139:1

seem 68:6 68:13
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 259

112:6 143:24

154:17

seemed 26:7

26:8 119:19

seemingly 23:14

seems 81:5 104:12

136:8 171:3

171:5

seen 25:4 57:17

91:22 102:14

114:23 125:25

135:7 139:2

141:21 153:13

166:23 166:24

selected 32:5

76:10

selecting 23:8

72:5 173:14

selection 23:11

56:5 56:6

56:21 76:7 115:8

self-report 52:8

self-reported

52:4

semen 40:2 40:6

40:7 40:16 40:19

53:7 149:24

semi-variance

130:13

senescence

93:21 163:11

sense 61:9

97:16 103:1

103:2 112:6

170:3 170:15

sensitive 3:24

20:16 23:25

150:23 152:1

162:7 172:18

sensitivity 20:23

29:5 30:13 30:14

34:20 144:25

146:16 150:3

161:17 173:6

185:21

separate 96:9

96:18 123:2

127:5 148:7

177:23 177:24

separately 42:8

121:1

September 2:3

26:12 109:17

sequential 131:5

series 131:1

131:20 158:9

172:21 180:12

serum 74:12

served 40:25

serving 2:16 2:17

4:21

session 58:7

106:7 189:12

sessions 105:24

105:25 189:16

sets 96:3 96:18

134:22

settled 2:6 145:8

seven 14:2 14:8

123:2 124:21

several 11:1

31:21 33:3 33:15

34:5 35:14 35:22

36:4 36:12 36:16

37:5 38:21 39:11

41:13 43:2

43:5 43:8

44:14 45:9 45:17

46:1 46:4

50:17 53:10

56:13 82:25

85:16 113:2

116:22 117:4

188:1

shallow 91:19

shape 78:5 121:15

121:20 121:20

124:22 125:4

125:5 127:3

136:5

shapes 122:1

184:5

shape-type 136:4

share 187:24

Shelley 8:11 8:12

55:23 56:15

she's 188:22

Shione 74:24

short 4:19

16:24 61:6 61:10

62:16 71:20

110:3 113:11

125:7 125:10

132:1 145:9

145:14 147:16

175:2

shortcomings

158:5

short-duration

124:9 125:21

125:22 126:6
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 260

128:14

shorter 109:3

109:22 159:2

160:24

shortly 4:5

short-term 110:11

showed 76:3

77:2 77:17 93:12

99:6 168:3

showing 73:23

94:7 131:7

shown 52:9 53:2

53:25 88:22

115:19 116:10

157:14 164:24

shows 91:10

92:9 96:23 99:15

181:12

sign 3:25 187:22

signals 168:8

significant

16:3 21:4

43:19 44:2 44:17

44:24 45:21

46:20 47:6

49:4 87:10

significantly

40:9 42:1

80:15 86:19

87:17

Silken 188:13

simazine 140:5

143:19 143:21

170:8

similar 38:4

45:18 47:1 49:10

67:25 71:8

85:4 98:11

120:11 129:11

similarities

177:17 178:12

178:15

Similarly 43:5

simple 124:8

165:10

simplicity 100:1

simplify 79:7

simply 88:14 98:7

98:9 153:11

180:19

simulate 117:22

123:6

simulation 131:5

simulations

131:12 132:12

133:8

single 18:6 30:20

61:1 62:7 63:9

63:10 63:11 64:3

71:5 72:2 94:4

94:18 99:7 103:3

114:19 157:15

singled 90:17

sir 179:18

sit 14:13

site 41:24 44:6

44:11 47:14

59:23 124:14

sites 46:22

112:16 116:7

116:9 116:10

116:12 117:11

120:7 120:10

135:2 138:19

sits 103:22

sitting 100:21

situations 102:23

174:20

six 14:8 84:24

84:25 85:2 85:12

86:15 101:2

sixth 120:7

size 34:18

40:12 40:25 51:9

54:4

sizes 117:4

slide 4:3 51:18

67:19 67:20

72:23 78:8

79:8 79:9

84:20 90:8 90:23

90:24 91:9 95:13

96:7 97:1 97:2

99:15 123:20

130:7 131:8

134:1 151:15

161:15 176:19

177:2 183:10

186:3

slides 37:6

83:2 139:23

145:9 145:10

146:3 147:23

172:5 172:6

180:11 182:8

182:16 182:16

186:6 188:4

188:21

slightly 174:22

slope 73:9

small 40:11 40:25
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 261

46:8 48:7

48:20 51:10 53:7

87:12 87:12

87:18 87:19

88:15 89:8 89:10

93:24 126:8

135:13 149:17

176:21 189:16

smaller 67:10

99:8 111:1

124:10 124:24

125:22 136:2

180:12

smallest 67:11

smooth 135:9

smoother 122:6

smoothing 125:1

smoothly 15:16

smooths 118:25

so-called 73:20

Society 5:6

software 73:5

75:12

soil 112:18

soliciting 28:2

solid 162:15

somebody 90:19

someone 159:13

sometime 107:18

somewhat 39:6

48:7 129:4

176:16

somewhere 93:6

sophisticated

59:4 82:20

136:20

sorghum 116:4

sorry 49:5

76:12 83:19 86:3

92:2 99:4 105:14

165:12 177:1

184:1 184:1

sort 19:24

26:18 29:16 49:2

51:25 56:11

58:12 77:14 81:6

81:22 84:23

100:2 146:7

148:4 152:24

154:18 169:2

169:8 170:23

174:17 176:16

176:20 189:15

sorts 185:23

186:22

source 110:18

119:2 119:24

120:3 120:5

120:11 120:16

120:17 120:18

175:17

sources 13:5

soybean 45:6 45:7

space 13:4

spacing 121:16

Spain 189:3 189:5

sparse 104:1

spatial 13:14

108:12 112:15

spatially 115:8

SPEAKER 91:22

92:1 92:4 92:8

92:9 97:10

104:23 182:11

189:6

special 146:25

specialize 7:24

species 62:22

64:6 82:1

88:12 98:14

specific 4:22

6:15 10:14

42:8 42:13 46:12

50:4 52:9

54:25 150:5

160:2 166:2

175:23 177:11

specifically

35:23 39:5 42:20

43:24 54:16 60:9

159:20 176:15

spectrum 19:25

spend 152:23

174:16 188:3

spending 11:13

spent 148:8

sperm 40:7 40:17

spherical 130:12

spikes 121:16

123:11 124:16

125:21 125:22

126:6 126:8

126:13

spontaneous 41:16

spot-on 90:22

spouses 37:15

37:17

spread 116:11
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 262

117:13

spring 44:20 45:2

155:3

spring/summer

46:16

spring-type 47:18

Staff 9:4 31:1

188:5

stage 152:19

152:22 161:17

173:6

stages 11:5 20:16

146:16 148:14

150:5 152:16

162:5 162:7

stairstep-type

128:12

stand 14:18

standard 76:18

83:7 83:7

83:14 84:4 84:15

86:6 151:3 165:5

166:22

standpoint 100:1

start 29:18

31:3 85:10

105:17 122:3

122:7 124:23

125:6 126:5

126:7 126:7

126:20 127:9

136:1 144:6

146:21 189:9

started 2:4

65:5 75:4 131:20

168:5

starting 21:4

65:13

starts 112:6

149:6

state 5:10 5:14

8:24 13:25 14:13

43:15 43:21

65:21 65:25

66:24 67:2

67:3 67:15

68:1 68:7

71:16 72:12 74:8

74:11 76:25

77:15 79:16 96:8

99:19 99:21

101:6 101:24

102:1 103:9

124:1 157:19

157:23 157:25

158:22 165:2

165:3 176:25

stated 50:10

statement 54:6

67:18 141:20

142:3 142:5

142:14 151:24

152:10

states 7:8 102:5

stationary 129:12

135:8

statistical

18:8 34:19 34:19

40:21 52:13

76:11 100:22

188:11

statistically

43:19 44:1 44:17

49:3 80:14

84:6 86:19 91:10

statistician 7:16

8:18

statisticians

143:25

statistics 5:5

7:13 20:18

84:9 84:12

status 152:25

statute 147:5

statutory 27:17

stay 65:14 181:10

steady 22:11 67:2

67:15 71:15

72:12 76:25

77:15 79:15

99:19 99:21

102:1 158:22

165:2

steady-state

59:16 72:8 72:15

72:18 74:12 77:8

77:12 78:20

78:23 79:3 79:17

step 87:9 131:4

154:21 155:21

165:5 172:3

174:15

steps 32:19 33:25

130:9 173:11

steroidogenesis

17:6 23:21

Steve 4:10 4:17

15:8 16:9 19:8

21:7

Steven 4:11 9:2

9:8 9:11 15:7

19:4 30:22

55:5 55:22 56:22
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57:18 58:5

80:1 80:23

82:4 82:22

86:2 86:11 86:23

87:22 90:15

92:22 93:1 94:14

95:5 95:20 96:22

98:16 99:14

101:4 101:21

102:20 103:15

104:4 104:13

104:24 105:10

105:16 106:6

134:17 136:17

139:17 141:4

143:11 144:14

145:5 145:13

146:8 146:11

161:23 165:9

165:23 166:5

167:13 168:17

169:15 170:5

171:7 171:19

172:1 174:9

175:19 176:6

176:12 177:3

177:25 178:16

178:20 179:9

179:17 180:7

180:23 181:13

182:2 182:9

182:12 183:6

183:20 183:25

184:13 185:5

185:12 186:10

187:2 188:25

189:4 189:7

Steve's 15:11

Stoker 16:5

66:2 150:10

150:12

Stone 7:7 7:7

183:20 184:15

stop 67:13 91:4

94:23 145:10

145:11 145:13

146:5 181:11

stopped 65:18

90:23 91:1

91:3 91:5 93:13

straight 98:8

straightforward

20:4 20:8 140:17

strains 68:10

strategies 106:17

127:18 134:11

strategy 138:4

145:2 154:21

164:3

stream 132:10

streams 32:18

107:24 110:21

117:4 118:9

118:13

strength 40:20

41:20 48:4 49:16

strengths 32:9

33:17 38:21 49:7

175:16 175:25

177:17 177:18

179:4 183:17

184:11 184:18

strictly 2:23

strikes 141:10

strong 17:7

57:7 57:13

130:22 151:6

151:18 162:13

stronger 51:15

51:22 85:8

176:16 176:24

strongly 24:5

72:8 127:14

structure 62:6

130:13 132:13

132:14

structured 31:10

structures 129:15

studies 7:14 11:3

11:4 11:11 12:24

12:25 14:4

14:6 14:7

16:14 16:15

16:15 17:2 17:19

18:15 18:22 22:6

22:7 23:19 23:20

24:11 24:12

24:16 24:16 25:2

25:3 25:11 25:25

26:19 26:21

26:22 27:24 32:6

32:7 33:3

33:13 33:23 34:7

35:4 35:14 35:20

35:22 36:4

36:8 36:10 36:11

36:14 36:18 37:2

37:9 38:10 38:22

39:21 41:13

41:14 41:17

41:20 41:21 43:1

43:8 43:9

44:12 45:15

45:20 46:2

46:8 48:4 48:8

48:10 50:3 50:20

51:5 51:6 51:7
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 264

51:16 51:22

52:16 52:22

53:16 53:21 54:2

55:11 55:17

55:25 56:7

56:7 56:9 57:4

57:8 59:13 61:11

62:9 63:18 67:22

67:25 68:4 68:16

69:20 70:12

70:19 71:8 71:13

75:3 75:5

83:21 84:21 85:2

85:3 85:9

85:16 85:23

92:15 93:3

94:2 102:7

111:20 147:16

148:16 149:20

150:4 150:8

150:9 151:3

151:12 152:13

156:10 156:16

162:4 162:18

166:9 166:22

166:25 167:17

168:6 173:22

174:5 175:17

176:9 176:17

176:25 177:4

177:6 183:2

183:4

stuff 11:5 154:18

sub 175:12

sub-analyses

34:20 48:9

51:7 54:5

sub-analysis

47:19

subheading 178:5

subjected 136:20

submitted 25:1

91:4 95:22 153:4

subpart 181:23

182:18 182:20

subparts 177:9

sub-question

182:23

sub-questions

177:24 181:18

subsection 175:8

subsequent 41:1

89:18

subset 116:7

116:9 116:12

117:10 153:5

153:5

subsets 116:12

153:1 153:4

153:13

substances 8:15

substantial

187:24

substantive 4:22

substituted 61:2

such-and-such

141:23

Sue 18:10

sufficiency 186:4

sufficient

40:22 53:13

147:8 157:16

165:21

suggest 41:4

45:18 46:2

84:5 100:6 160:6

168:18

suggested 50:16

97:5 111:10

128:22

suggestions

136:13

suggestive 41:10

suggests 74:17

80:16 87:12

99:10

sum 66:15 127:2

170:10

summarize 32:7

summary 32:18

62:14 69:6 71:17

79:8 147:16

summer 116:5

155:3

support 15:15

16:3 26:25 51:17

54:8 54:12 58:25

59:9 61:24 71:10

79:15 102:13

110:22

supported 17:11

149:5

supporting 54:5

55:10

supportive 39:22

supports 10:7

127:14

suppose 71:18

supposed 48:11

suppressed

88:23 95:2
FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 265

suppression 89:20

94:22

sure 14:17

15:15 89:23 93:1

101:15 106:2

135:14 155:11

164:11 164:12

169:3 169:7

169:10 170:7

170:20 170:22

175:20 175:21

180:17 188:4

surface 43:20

surge 7:6 70:16

88:9 88:12 88:23

89:3 89:21

90:2 93:5

94:20 94:24 95:2

95:24 159:5

surges 88:2

surprised 135:10

surrogate 118:14

surrogates

45:20 53:21

surveillance

137:2 137:4

Survey 7:8 7:11

Susan 7:22 7:22

16:5 101:5

101:15

susceptibility

159:11 159:17

160:8

suspected 46:25

Suzanne 24:19

172:10 187:17

Swan 52:12

sweet 175:3

switch 86:12

switched 104:21

Syngenta 25:1

116:21 123:6

139:5 152:4

153:18 154:24

163:22 168:3

Syngenta's 152:20

Syngentasupported 178:9

synthesizing

32:15 33:20

51:12

system 56:16

110:19 110:23

114:24 118:18

119:22 121:21

125:25 139:13

165:20 167:9

systematically

169:2

systemic 151:4

systems 101:20

107:8 107:10

107:13 108:15

110:9 111:1

111:1 115:14

115:16 115:19

116:19 116:23

119:15 119:18

120:1 120:4

120:22 120:24

121:8 121:14

129:16 138:14

138:15 138:20

139:5 165:19

T

table 26:13

tailored 18:3

take-home 74:15

taking 2:24 13:23

38:23 129:7

133:16 136:1

158:10 187:8

talents 16:8

talk 22:7 22:16

22:24 30:6 67:21

74:25 75:21 89:8

98:17 104:18

111:3 111:23

137:15 142:20

143:17 147:24

148:15 149:16

152:5 152:22

173:24

talked 16:9 71:12

71:22 79:9 79:13

100:15 132:7

133:11 141:11

142:18 143:5

152:14 154:13

186:20

talking 28:20

29:8 58:20 58:23

59:9 60:8 63:6

65:22 67:1 74:23

83:1 84:3

88:16 89:17

104:25 105:8

107:2 107:5

114:16 118:7

122:13 130:4

142:9 143:15
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148:9 168:21

188:23

Tammy 16:5 150:12

tap 10:20

target 59:23

112:23 115:12

115:24 115:25

148:3 162:19

targeted 112:10

114:22 116:1

targeting 119:13

TCT 139:23 140:10

140:24 141:3

171:11

TCTs 170:2 171:17

team 14:23 15:6

15:15 15:18 16:4

20:14 54:24 56:2

56:4 56:8

107:5 145:21

154:3 175:10

187:13

technical

181:20 181:25

technically

185:19

techniques

12:21 40:20

40:21

technology 114:3

temporal 13:7

13:13 59:11 63:6

71:11 79:13

108:13 129:10

130:17 130:19

130:22 132:14

164:25

temporality

155:18 157:12

ten 43:1 46:19

tend 107:24

126:11

tends 128:6

140:15

tenfold 165:14

teratogenicity

168:16

term 36:18 100:20

100:21 132:2

terming 65:25

terms 11:10 12:11

14:14 30:25

31:16 88:13

88:16 94:24

114:17 115:22

121:2 125:13

130:9 132:6

132:15 134:9

134:13 136:5

142:18 142:23

164:12 170:10

181:15 184:16

186:11

tested 63:13

testing 12:1

34:11

text 35:17 173:4

thank 2:14 4:8

4:10 4:11 4:25

9:2 9:15 14:18

15:7 15:9

15:14 16:4

19:3 19:4 19:9

30:22 31:18 55:4

55:5 58:2

58:17 79:25 80:1

80:23 87:21

87:23 94:16

103:14 134:17

144:17 161:23

174:9 178:20

179:17 181:1

thanks 55:23

170:18 178:3

that'll 169:6

that's 3:7 14:9

14:17 22:10

23:24 27:16 29:2

49:22 56:24

65:14 65:21 68:2

71:15 76:17

76:19 77:14

78:15 79:1 81:13

84:11 84:21

86:21 87:13

87:15 88:15 91:8

91:17 91:19 93:6

93:8 94:1

95:22 97:5

99:5 100:5 101:3

103:3 103:10

105:4 105:7

105:7 107:22

115:21 118:22

122:13 123:3

123:16 124:2

124:9 129:13

131:16 132:3

133:2 139:11

141:11 142:7

150:14 153:17

154:19 155:20

156:4 163:13

163:16 166:4

168:12 170:12
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177:13 178:21

179:3 180:25

181:1 186:14

188:25

Thede 64:14

96:6 158:11

181:9

themselves 184:19

184:24

theoretical

100:24

theoretically

100:13

theory 111:11

128:23

therefore 62:11

86:9

there's 9:17 10:4

31:4 57:19

80:8 82:8

82:13 83:11

83:12 84:5

85:3 86:20 86:21

87:3 87:8 88:4

89:20 89:25

91:21 94:8 99:23

114:12 115:24

119:16 129:22

132:25 141:10

145:10 151:24

153:13 154:1

156:21 157:1

162:21 166:15

168:6 175:10

180:17 186:1

187:20

There's...Susan

103:22

they'll 89:13

152:5

they're 23:4 71:1

77:20 83:15 87:6

87:13 87:16

89:22 90:10

90:10 96:12

96:16 115:19

116:10 127:5

128:13 133:13

135:9 145:23

146:5 146:6

149:17 152:6

153:1 154:2

155:1 160:2

160:3 166:1

166:2 167:8

177:10 181:11

183:12 185:17

thick 70:21

third 2:9 19:10

21:21 34:21

46:14 48:11

52:20 120:1

152:3

thirdly 120:21

third--you 90:6

thorough 65:1

thoughtful 18:25

threshold 124:4

throughout 4:25

53:11 75:25

112:21 112:22

156:15 168:4

172:20 189:12

throwing 103:4

throws 90:19

Thurman 15:23

27:7 106:18

106:20 134:25

135:4 135:12

135:17 135:21

136:12 137:8

137:12 138:11

140:4 140:8

141:2 142:17

166:4 170:7

171:13 171:23

183:14 183:23

184:2 184:17

185:2 185:7

Thurman's 100:8

Thursday 174:25

189:19

thus 9:19 13:20

TIDA 157:4 157:5

tie 45:14

tier 115:21

tighter 49:2

time-integrated

114:11

timeline 162:18

Tina 14:21 15:9

21:7

Tina's 19:8

Tinelle 55:1

tiny 171:8

tissue 150:7

157:23 180:14

181:9

title 35:16

today 16:18 22:15

22:21 24:8 24:23
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27:5 30:19 30:25

31:2 35:24 57:23

111:15 144:21

145:15 145:16

150:17 168:23

172:8 172:12

181:8

today's 17:24

31:4

tolerance 141:11

tomorrow 3:5 3:13

9:14 31:3

152:5 152:22

153:9 163:23

171:9 174:24

187:13 187:16

188:14 189:9

189:11 189:12

189:18 189:21

tonight 183:10

tool 26:9

tools 13:19

top 3:21 20:21

52:10 71:6

75:6 84:22

162:14

topic 2:10 4:13

37:7 106:9

topics 10:18 18:5

18:7 20:20 27:15

146:13 172:8

Toronto 8:13

total 16:18

41:3 54:1

61:15 61:20 64:1

66:16 71:16

71:19 73:25

74:19 77:12

79:18 89:1 95:17

97:20 98:1

101:14 102:15

103:3 104:9

119:16 120:4

120:10 140:10

140:13 143:15

143:18 170:1

170:11 170:16

177:6

totals 143:19

touch 32:4 111:19

122:21 133:3

touched 176:1

184:21

toward 107:24

towards 154:10

tox 56:10 154:3

toxic 17:4 104:11

104:11 170:13

toxicity 11:14

17:17 60:5

106:21 151:4

toxicodynamic

156:11

toxicokinetic

156:11

toxicological 7:1

113:10 177:19

toxicologists

15:22

toxicology 5:11

5:14 5:18 5:22

6:5 7:2 7:19 8:4

8:23 8:25 11:4

12:1 12:25

14:6 14:14

17:2 22:1

22:14 26:19

27:24 28:9 28:23

29:13 29:22

51:13 51:17 53:9

54:19 147:2

147:8 147:16

148:21 149:18

151:11 155:18

156:12 160:15

165:8 166:21

167:17 174:6

trace 131:13

transform 130:11

transformation

139:25

transient 61:21

transitioning

106:21

transmission

157:2

transparency 26:9

transport 7:9

trapezoidal 72:24

73:20

treated 48:1

52:17 119:1

119:25 120:3

120:5 120:12

162:24

treatment 94:24

118:25 119:4

119:8 120:8

120:24

trends 133:15

triangles 72:18

triazine 35:23
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61:2 62:11 62:22

64:6 64:13 64:15

64:16 65:2 76:25

77:13 79:3

79:4 102:1 102:3

102:12 104:9

triazines 59:12

63:7 64:1

66:25 68:1 71:11

71:16 71:19

72:12 74:1 74:19

77:9 78:19 78:20

79:11 79:14

79:16 79:18

101:14 102:15

120:8 139:25

tried 15:20 160:9

triggering 114:7

triggers 114:8

123:24

trimester 38:15

38:18 46:15

47:17 48:11

48:12 52:20

trouble 143:17

179:1

true 25:23

152:2 166:4

truly 16:7

try 11:17 11:25

20:20 49:2 72:22

78:1 84:18 84:20

121:10 130:6

142:24 174:17

trying 11:2 45:14

87:16 136:23

140:22 155:10

T-study 96:13

tumor 21:10

tumors 21:5 21:12

149:8

turn 4:9 9:8 12:5

14:20 15:5

19:2 60:3 151:25

157:5 169:18

turnaround 133:23

turned 64:14

turning 106:16

turns 88:25 124:4

152:2 175:6

twenty 74:12

Twenty-four 61:18

two-and-a-half

158:25

two-part 173:3

type 43:24

44:25 45:24

94:12 109:5

113:18 113:20

118:11 120:21

121:5 125:14

132:9 132:15

142:24 166:3

typed 36:3

types 44:3

44:15 45:10

typically 17:10

46:17 56:10

118:21 163:6

U

U.S 7:11 8:5

43:15 43:17

ug/L 119:16

123:13

UK 6:3

ultimate 59:24

60:23

ultimately 60:5

123:17 149:8

unaware 23:20

uncertainties

17:21 23:6

46:4 159:2 160:4

uncertainty 71:23

121:7 122:2

131:6 141:12

152:9 152:13

177:18

underestimate

53:25 128:13

underestimates

41:3

undergo 60:25

undergoes 60:12

undergoing 163:11

under-predicts

158:7

understand

11:17 12:4 13:12

86:10 89:23

100:11 128:9

136:24 137:23

138:4 140:22

167:16 169:4

169:10 178:18

189:1

understanding

11:18 11:19

12:13 12:15

12:23 13:4
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13:6 13:17

181:15

understood 167:4

undone 162:18

unfortunately

168:23

unique 25:10

146:23 147:5

United 7:8

units 77:10 77:14

105:1 105:5

105:5

University 4:18

5:10 5:14 5:23

6:3 6:8 6:14

6:18 6:22 7:4

7:15 7:18 7:23

8:8 8:13 8:17

8:24 117:3

unless 105:18

unlike 164:9

unlikely 82:16

92:19

unmeasured 48:16

unnecessarily

145:16

unquote 93:8

unusual 153:17

update 16:24

27:22 127:20

185:8

updated 134:12

172:25 185:25

updates 58:15

58:20 132:19

180:14

updating 17:8

upgraded 82:17

upon 18:1 32:4

35:16 47:19

54:19 67:4

101:24

upstream 110:25

up-to-date

11:22 16:23

25:24

urinary 40:2 41:5

urine 40:15

useful 19:16

81:17 99:1

174:12

USEPA 90:20

users 37:23 38:2

USGS 44:6 44:10

46:22 129:21

132:25 133:3

133:12

USGS's 111:7

usual 3:19

usually 141:14

utility 19:17

177:12

utilized 35:11

utilizing 47:13

V

vaginal 70:8

validated 45:21

validation 53:21

validity 34:12

valleys 124:25

126:9

valuable 79:21

value 25:6 67:5

67:7 78:10

111:11 124:4

128:23 143:6

164:14

values 94:9

158:11 158:24

160:19 181:10

variability 66:20

68:10 76:5

88:5 110:12

120:18 121:2

121:5 133:14

133:17

variable 34:21

88:13 119:2

variables 34:16

34:17

variance 129:12

variety 17:4

23:22

variogram

130:10 130:16

134:23 134:24

various 11:5

109:15 117:4

134:10

vary 66:13 140:15

version 75:13

132:21 133:1

versus 38:23

44:20 45:5 101:9
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vested 36:20

Veterinary 5:9

via 49:2 63:9

70:6

view 23:24

26:22 29:17 64:9

153:10 179:5

188:11

Villanueva

47:15 47:22

47:25 177:1

Virginia 8:3

vital 16:6

vivo 23:22 61:7

62:16 71:20

166:22

vulnerability

115:22

vulnerable 107:13

108:15 112:16

115:4 115:6

115:12 115:21

119:18

W

wait 27:23 182:7

waiting 154:2

163:10

walking 172:3

wall 43:24

43:24 44:4 44:15

45:1 45:13

Waller 44:1

WARP 112:15 115:3

115:4 115:6

115:23 129:19

132:19 132:22

133:1 133:6

133:13 133:14

133:19

Washington 8:20

wasn't 34:8

92:4 120:6

166:11 171:17

184:24

water 2:12 4:15

14:16 21:1 27:12

28:4 29:3 29:7

30:10 43:20 48:2

48:3 48:6

52:17 58:10

59:18 75:1

78:2 78:3

78:10 78:21

79:21 82:15

100:3 104:20

105:13 106:11

106:22 106:23

107:1 107:8

107:10 107:11

110:8 110:18

110:19 110:22

111:1 112:25

114:24 115:14

115:16 115:17

115:19 116:19

116:23 118:19

118:25 119:1

119:2 119:3

119:6 119:8

119:15 119:22

119:25 120:1

120:3 120:3

120:6 120:11

120:12 120:16

120:17 120:22

121:4 121:8

121:13 121:21

125:25 134:2

138:14 138:15

138:20 139:5

139:13 145:2

146:19 147:18

148:9 153:24

154:21 154:23

161:19 165:20

165:21 170:7

172:25 173:20

186:4 186:4

waters 107:17

110:17

water-sampling

106:17

watershed 111:7

watersheds

115:3 115:5

115:9 115:21

water-system

108:3 133:5

ways 99:24

114:4 114:12

115:24 118:4

176:5 186:20

weaknesses 183:18

184:12 184:18

wealth 117:5

weather 112:18

118:21

Web 35:12

we'd 76:8 77:23

78:25 79:22

99:25 118:14

152:24 154:12

161:7 161:11

185:10

week 9:20 10:20
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13:22 18:4 18:17

20:19 22:15

22:17 24:10

101:1 128:8

weekly 107:16

108:22 109:2

110:9 110:10

116:21 116:24

121:8 121:22

122:6 122:7

122:17 123:7

124:18 124:20

125:15 127:23

127:24 127:25

128:7 130:25

131:21 136:1

155:1 155:8

weeks 19:1

93:12 95:2 101:2

118:1 158:25

159:1 160:21

163:20

week's 9:12

11:9 18:5

weight 16:18

47:20 47:22

47:24 48:22 53:1

53:9 98:2 119:21

149:18 158:20

159:25 163:2

167:25 179:23

weighted 29:12

weights 97:25

98:6 98:11

welcome 2:8

2:13 4:12 9:12

15:10 58:5 106:7

169:15

we'll 2:4 11:7

11:13 13:21

13:22 18:2

18:7 20:19 27:18

29:8 29:12 29:18

82:3 86:12 100:9

105:23 106:1

111:3 123:16

144:16 146:5

147:24 148:1

148:10 153:8

153:21 169:5

169:6 169:12

169:16 171:22

172:9 172:12

173:19 173:21

173:24 174:1

174:6 174:16

175:10 183:9

186:2 187:12

187:16 187:19

188:3 189:13

189:21

well-defined

70:22 75:7 75:25

we're 15:19 22:10

22:23 23:20

24:10 27:11

27:13 32:7 57:14

58:14 62:20

65:22 66:5 69:23

71:14 71:14

74:16 75:16

75:23 79:6 81:10

82:10 94:5 105:8

105:21 106:20

106:22 109:10

110:1 111:4

111:12 111:15

111:16 111:19

111:23 112:1

112:9 113:15

113:25 116:13

117:21 118:4

118:12 122:13

126:1 126:25

129:6 129:16

129:22 131:7

132:5 132:18

136:14 137:9

137:10 137:13

137:17 137:21

138:12 138:13

138:25 141:2

144:19 145:4

145:15 148:1

148:18 148:20

148:20 148:22

148:23 154:4

154:10 156:9

163:10 164:4

173:8 173:8

173:17 176:3

180:4 181:13

181:14 184:6

184:10 184:17

185:23 186:1

186:20 186:22

187:6 188:20

188:23

Wes 7:7 184:13

Wesley 7:7 183:20

183:21 184:15

184:23

westward 117:13

we've 10:18 10:25

13:20 13:21

14:17 17:25

26:20 29:14

31:25 53:5 55:10

55:20 99:22

100:15 105:17
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108:1 108:2

111:11 111:22

114:23 121:11

122:18 124:16

125:24 127:21

127:23 128:19

128:19 128:23

129:20 129:21

129:24 130:8

132:7 133:10

133:23 134:4

134:8 134:12

138:20 142:18

142:25 143:5

148:8 153:13

156:8 156:8

160:11 160:14

163:5 172:8

174:20 186:19

186:21

whatever 10:6

109:20 124:4

whatnot 143:25

wheeze 48:25

48:25 49:10

49:15

whenever 120:19

whereas 61:16

61:19 99:8

wherein 162:23

168:12

WHEREUPON 58:4

106:5 169:14

189:22

whether 12:17

34:10 42:6

79:1 82:14 82:15

102:4 118:25

119:12 123:4

127:5 136:6

139:9 162:6

167:10 175:14

176:8

whichever 182:18

white 87:5

whole 28:13 43:15

77:25

who's 171:14

wide 17:4 18:7

64:20 96:14

Winchester 44:16

window 160:7

windows 48:10

159:11 160:17

160:17 186:15

wishes 3:12

187:21

Wistar 66:3

women 38:12 38:17

45:4

women's 8:9

wonderful 15:17

wondering 55:15

102:3 175:23

work 7:2 7:9 9:15

10:9 10:10 17:20

18:12 24:21 25:1

30:4 41:24 45:24

50:3 52:4

54:23 55:21

56:24 74:24 78:1

93:12 100:18

110:1 110:3

112:5 112:5

118:6 121:13

123:4 123:14

123:17 123:18

130:4 147:20

154:1 154:10

154:10 167:18

168:1

worked 7:20 56:11

119:20 147:21

working 42:18

93:15 94:2

140:10 146:2

154:5 163:21

173:8 188:20

works 128:24

world 13:10

worth 21:23

wrap 132:5 169:16

wrap-up 168:19

wrestled 121:11

writing 112:7

written 32:10

33:7 50:10 176:7

176:20 180:16

Y

year-by-year

138:17

yep 174:8

yet 28:22 85:24

94:9 95:8 105:15

109:25 168:2

you'll 17:24

20:11 22:20 24:8

26:21 27:6 28:16

108:16 145:3

187:15 189:10
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young 93:4

younger 162:7

you've 9:19

73:3 88:21 90:16

90:16 98:20

99:18 108:24

113:11 122:4

124:9 125:20

138:24 141:25

142:12 150:17

Z

zero 73:18 97:4

97:5 97:8

97:13 97:15

zone 118:18