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FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

SCIENTIFIC REVIEW OF THE ENDOCRINE DISRUPTOR 

SCREENING PROGRAM (EDSP); TIER I SCREENING 

ASSAY AND BATTERY PERFORMANCE 

DOCKET NUMBER: EPA-HQ-OPP-2013-0075

FIFRA SAP WEB SITE 

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

OPP Docket Telephone: (703) 305-5805

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL

ONE POTOMAC YARD (SOUTH BUILDING)

2777 SOUTH CRYSTAL DRIVE

ARLINGTON, VIRGINIA 22202

MAY 21 - 23, 2013 
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1 DR. FRED JENKINS: If I could ask everyone to

2 please get seated, we're going to go ahead and get started.

3 Just give us one second to get the panel

4 members all seated.

5 Good morning again, everyone. I want to

6 welcome everyone to this FIFRA Scientific Advisory Panel

7 meeting. The topic of this meeting is the Endocrine Disruptor

8 Screening Program: Tier 1 Screening Assays Battery Performance.

9 My name is Fred Jenkins, and I am your

10 Designated Federal Official for this meeting. Before we get

11 started, I want to take just a couple of minutes to go over a

12 few administrative items. As the DFO, I serve as the liaison

13 between the panel and the Agency, and I'm responsible for

14 ensuring that the provisions of the Federal Advisory Committee

15 Act are met. I want to extend my thanks to the panel for

16 agreeing to serve on this committee and as well to the public

17 for attending this meeting.

18 The FIFRA Scientific Advisory Panel Committee

19 provides independent scientific peer review and advice to the

20 Agency on pesticide issues. It's important to note that the

21 panel only provides advice and recommendations to the EPA and

22 that all regulatory decision making and implementation authority

23 remains with the Agency. We have worked with appropriate Agency

24 officials to ensure that all appropriate ethics regulations are

25 satisfied for this meeting. Panel members have been provided

26 the provisions of the Federal Conflict of Interest laws, and
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1 each participant has filed a financial disclosure report. I,

2 along with our Deputy Ethics Officer and in consultation with

3 the Office of General Counsel, have reviewed those reports to

4 ensure all ethics requirements are met.

5 This meeting provides for an opportunity for

6 public comments. We will have several people who will be

7 speaking during the public comment period, if you have not made

8 prior arrangements and wish to make public comments at this

9 meeting during the public comment period, please let me know or

10 someone else on our SAP staff, who will be seated here to my

11 right, know that you would like to provide public comments if

12 you haven't made arrangements with me. Now it's important to

13 note that without having made prior arrangements to provide your

14 oral comments, we ask that you please limit your comments to no

15 more than five minutes.

16 There is a docket for this meeting. The

17 docket number is listed on the agenda. All background materials

18 and other related dockets are available in the docket. Slides

19 of EPA presentations that you will see should be available in

20 the docket very shortly.

21 At this point, I want to introduce and extend

22 my thanks to Dr. Daniel Schlenk, to my left, who is the FIFRA

23 Scientific Advisory Panel Chair who is chairing this meeting for

24 this week. Thank you.

25 DR. DANIEL SCHLENK: Thanks, Fred. Good

26 morning, everyone. Welcome to our panel this bright Tuesday,
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1 titled Scientific Review of the Endocrine Disruptor Screening

2 Program: Tier 1 Screening Assay and Battery Performance. My

3 name is Dan Schlenk. I'm a professor of environmental sciences

4 at the University of California Riverside, and I'll be chairing

5 the panel this week.

6 What I'd like to do at this particular point

7 in time is to go around the panel and have you state your name,

8 your affiliation and a brief description of your expertise so

9 that everyone can be familiar with you. So let me begin with my

10 left with Dr. Klaine.

11 DR. STEPHEN KLAINE: I'm Steve Klaine. I'm a

12 professor at Clemson University, and I direct the Institute of

13 Environmental Toxicology and the Aquatic Ecoducts Colleges. And

14 I'm a member of the permanent panel.

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

16 Virginia Tech. I teach pharmacology and toxicology to

17 veterinary and medical students, general toxicology. I'm a

18 member of the permanent panel.

19 DR. JAMES McMANAMAN: Hi. Good morning. I'm

20 Jim McManaman. I'm a professor of reproductive biology at the

21 University of Colorado, School of Medicine and a member of the

22 permanent panel.

23 DR. BARRY DELCLOS: I'm Barry Delclos. I'm

24 from the National Center for Toxicological Research, the FDA. I

25 work in reproductive toxicology and carcinogenesis, and I am

26 also on the permanent panel.
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1 DR. GEOFFREY SCOTT: I'm Geoff Scott with the

2 National Oceanic and Atmospheric Administration, Director of the

3 Center for Coastal Environmental Health & Biomolecular Research.

4 My expertise is in aquatic toxicology.

5 DR. DANIEL SELVAGE: I'm Dan Selvage. I'm an

6 associate professor at the University of New England, and I

7 specialize in endocrinology and neuroendocrinology.

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

9 Schultz, professor emeritus, Department of Comparative Medicine

10 in the College of Veterinary Medicine at the University of

11 Tennessee. My expertise is in adverse outcome pathways and

12 screening protocols for endocrine disruption, among other

13 things. Thank you.

14 DR. DONALD TILLITT: Morning. My name is Don

15 Tillitt. I'm with the U.S. Geological Survey, Department of the

16 Interior in Columbia, Missouri. My expertise is contaminant

17 effects on fish development and early life stages.

18 DR. KATHERINE ROBY: Hi. I'm Kathy Roby from

19 the University of Kansas Medical Center and Department of

20 Anatomy and Cell Biology, and I'm a reproductive

21 endocrinologist.

22 DR. ALDERT PIERSMA: Good morning. My name is

23 Aldert Piersma. I work in the Netherlands in the National

24 Institute of Public Health as a reproductive and developmental

25 toxicologist, and I'm a professor in Utrecht University.

26 DR. REYNALDO PATINO: Reynaldo Patino. I'm
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1 with USGS Texas Cooperative Fish and Wildlife Research Unit at

2 Texas Tech University. My expertise is in aquatic toxicology,

3 endocrine disruption in fish and amphibians primarily.

4 DR. DEBORAH MacLATCHY: I'm Deborah MacLatchy.

5 I'm a professor of biology at Wilfrid Laurier University in

6 Waterloo, Ontario, Canada and a comparative endocrinologist and

7 ecotoxicologist, particularly in fish.

8 DR. JACK LEONARD: I'm Jack Leonard from the

9 University of Massachusetts Medical School. I'm a professor of

10 microbiology and physiological systems, and I'm an expert in

11 thyroid and steroid hormone action.

12 DR. NELSON HORSEMAN: Good morning. Nelson

13 Horseman. I'm at University of Cincinnati in the College of

14 Medicine, a professor there, endocrinologist. Over the years,

15 I've worked on everything from fish and frogs to pigeons and

16 people and currently work primarily in the area of lactation and

17 breast milk.

18 DR. SHERRIL GREEN: Good morning. I'm Sherril

19 Green. I am a professor in comparative medicine at the Stanford

20 University School of Medicine, and my expertise is in the health

21 and disease of laboratory Xenopus laevis.

22 DR. DANIEL SCHLENK: Thank you, everyone. 

23 Let's begin this morning with some opening remarks. And our

24 first presenters are Dr. David Dix. He's the Acting Director of

25 the Office of Science Coordination and Policy.

26 DR. DAVID DIX: Thank you, Dr. Schlenk. 
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1 Welcome to the panel. I appreciate you all being here very

2 much. I'm the newly appointed Acting Director of OSCP. I'm

3 coming over from the Office of Research and Development within

4 EPA. I've been involved in toxicology, reproductive toxicology,

5 toxicogenomics, and most recently I was the Acting Director of

6 the National Center for Computational Toxicology prior to this

7 latest appointment.

8 But within OSCP, the Office of Science

9 Coordination and Policy, our focus is on providing coordination,

10 leadership, peer review and ensuring that sound scientific

11 decisions are being made across the Office of Chemical Safety

12 and Pollution Prevention. Our goal is to coordinate across new

13 and emerging scientific assessments such as within the EDSP

14 program, the Endocrine Disruptive Screening Program, and to

15 continue to bring the best science from within and without the

16 Agency to bear on meeting EPA's mission.

17 Today's Science Advisory Panel is the second

18 EDSP-related meeting this calendar year and is focused on

19 evaluating performance of the EDSP Tier 1 assays, as we well

20 know, and looking across the battery at the performance of these

21 assays for the initial List 1 of EDSP chemicals. 

22 This review, I think, is informed very much by

23 the 2008 SAP review on the Tier 1 battery for selective

24 reference compounds. So today we have information from the

25 initial list of chemicals to evaluate the performance of the

26 Tier 1 battery in the context of these actual test chemicals
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1 with unknown endocrine modes of action.

2 So on behalf of OSCP and OCSPP, I acknowledge

3 and thank your scientific expertise here on the panel as well as

4 the scientific expertise and hard work that has been put forth

5 by members of the Office of Pesticide Programs, the Office of

6 Pollution Prevention and Toxics and the Office of Research and

7 Development here within EPA. I thank you for the time you've

8 committed this week and to look across this work and provide us

9 your insights. I hope the meeting is successful and enjoyable

10 for all of you, and we look forward to your feedback and your

11 input in this important process. Thank you very much.

12 DR. DANIEL SCHLENK: Thank you. Additional

13 opening remarks from Dr. Steve Bradbury who is the Director of

14 the Office of Pesticide Programs.

15 DR. STEVEN BRADBURY: Good morning, everyone. 

16 I'm pleased to see some of you again from our meeting last

17 January as well as some other faces I remember from other

18 peer-review meetings that we've had over the years. And I want

19 to stress the importance of the SAP, the FIFRA SAP panel, to the

20 work that we do not only in OPP but as this peer review

21 reflects, work across the Agency and how we move forward in our

22 scientific processes that underlie our decision making. 

23 And the role of the Science Advisory Panel

24 most in terms of external peer review but having a public

25 transparent process throughout the peer-review process is really

26 fundamental to the work we do and the business we do. Our
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1 regulations are science based, and having said that, peer review

2 is an important part of that process.

3 So I just want to thank you again for the hard

4 work leading up to getting here today, the hard work you'll have

5 over the next three days here and the hard work it takes to put

6 the report together. So I want to thank you in advance for all

7 your efforts.

8 Also want to thank the public for

9 participating in the process. As indicated before, all these

10 peer reviews reflecting on a White Paper that scientists, my

11 colleagues across the Agency have put together, we also greatly

12 value the input from the public in terms of scientific

13 approaches and views that the Agency has, and we look forward to

14 you taking a hard look at and a good look at those comments and

15 weave that into your thoughts as we move forward with the

16 program.

17 I also want to thank Fred and all his

18 colleagues in the SAP office and Designated Federal Officials

19 for all the work it takes to put on these meetings, everything

20 from logistics to all the ethics stuff you all had to go through

21 and just sort of getting everything organized and the work it

22 takes to put the report together. Without Fred and his

23 colleagues in the SAP office, it would be almost impossible to

24 pull a meeting like this off and the many meetings that we have.

25 I'd like to spend just a couple of minutes

26 following up on some of David's points for context before we get
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1 into the real meat of the next three days. 

2 And as you know from the background materials

3 and the SAP we had in January and at least from historical

4 perspective previous SAPs looking at the endocrine program, this

5 effort flows out of two Acts, the Federal Food, Drug and

6 Cosmetic Act when it was amended in 1996 by the Food Quality

7 Protection Act; which basically said to the Agency take a look

8 at all pesticide chemicals, and make a determination as to

9 whether or not these chemicals have the potential to interact. 

10 It said estrogen in the Statute, but subsequently, through peer

11 review and advice, we expanded that to a broader interpretation

12 of endocrine systems. 

13 But take a look at pesticides, and determine

14 whether or not they have the potential to interact with

15 endocrine systems, and if so, gather the appropriate information

16 so that you can make an informed risk assessment and an

17 appropriate regulatory decision. The lawyers will be all upset

18 as to how I synthesized that, but that's essentially the

19 message. And the Safe Drinking Water Act had sort of comparable

20 updates to it in 1996 in terms of taking a look at chemicals

21 that could be in drinking water, and Agency, make sure you're

22 regulating these chemicals properly using a sound scientific

23 approach to do so.

24 And through the years from 1996 on, peer

25 review, public participation input has been critical. The

26 EDSTAC, Endocrine Disruptor Screening and Testing Committee of
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1 FACA that provided advice to the Agency -- and I think it was

2 around 1998 -- it provided some advice on a tiering process to

3 go through this evaluation of all these compounds. 

4 And then in 1999, there was a combined

5 scientific advisory board, Science Advisory Panel peer review on

6 basically the framework of going forward. And the framework

7 essentially laid out a two-tiered process. Well, actually it

8 laid out more than that. It had a prioritization process which

9 I'll run through in a second. 

10 But related to this specific SAP, it laid out

11 two tiers. And it said the first tier use a set of validated

12 assays to determine whether or not a chemical has the potential

13 to interact with the estrogen, androgen or thyroid system. And

14 it concurred with the EDSTAC to say take a look across a variety

15 of taxa and take a look across various assays, some which may be

16 very diagnostic, some which may be more in terms of frank

17 effects, apical outcomes. In those days, adverse outcome

18 pathways wasn't jargon we used but basically the concept of an

19 adverse outcome pathway were reflected in the logic of the

20 framework.

21 And so then in 2008, the Agency brought to the

22 SAP 11 assays that had gone through validation processes through

23 OECD, Organization for Economic Cooperation and Development, and

24 other national and international groups, to get feedback on

25 those 11 assays and get advice as to whether or not these assays

26 were ready to go forward. 
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1 And the SAP concurred but gave us a really

2 great framework to use as data would start to come in to

3 evaluate how well those validated assays worked as laboratories

4 started to undertake the efforts. And that was consistent with

5 the SAB/SAP review on 1999 which said Agency, after you've

6 issued test orders for maybe 50 to 100 chemicals and you start

7 to gets some of that data coming in, it would be good to have a

8 check in and see how well those assays are working as we move

9 forward.

10 And that's essentially what this first SAP in

11 spring/summer is about, is getting feedback on how well those 11

12 validated assays were executed looking at a cross-section of the

13 chemicals that we're issuing test orders three years ago; and

14 also to get some advice on how well the battery is performing as

15 a whole, consistent with the 2008 SAP recommendations and how to

16 sort of get your head around that and think those things

17 through.

18 So the development of those assays, as David

19 indicated, was the result of a lot of hard work not only in EPA

20 but EPA with colleagues in NIH, with colleagues in Europe who

21 are doing the research on some of these same methods, colleagues

22 in Japan who are working on a number of these assays. And so in

23 many ways, these assays reflect an international effort in terms

24 of their development and ultimate validation. The Organization

25 of Economic Cooperation and Development, is a sort of

26 international body that lots of member countries work together
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1 on harmonized methods, was a body that sort of helps us go

2 through that validation process back in the 2000s.

3 So as we go through this SAP, we're hoping to

4 get feedback on how well those assays perform and how to take a

5 look at the battery as a whole. Later this summer, we'll have

6 another SAP that will give us advice on our proposed method of

7 how to take that Tier 1 information, combine it with other

8 information that may be available, and then integrate that

9 information to determine whether or not a chemical has the

10 potential to interact with the estrogen, androgen or thyroid

11 pathway.

12 So we sort of broke up getting feedback from

13 you all in two steps. First step is give us some feedback on

14 how we're thinking about how well the assays are working, how

15 well they seem to be working together, and kind of absorb that

16 feedback from you all. And then see again -- maybe not all of

17 you -- but see the panel again at the end of July and give us

18 advice on how we're taking that information and integrating it

19 with other information to determine whether or not a chemical

20 has the potential to interact. We kind of broke up in two steps

21 the recommendations from the 2008 SAP.

22 And just to wrap up quickly how this

23 information then feeds into our overall program, it gets back to

24 what I discussed in terms of the statute. So we have this

25 charge to take a look at pesticide chemicals, certain chemicals

26 that can be in drinking water, and make a determination as to
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1 whether or not they can interact with an endocrine system, and

2 if so, do the proper risk assessment and make the appropriate

3 regulatory decision.

4 So the way this tiered process is working, is

5 first, does the chemical have the potential to interact? If no,

6 done; realizing as science evolves there may be reasons to come

7 back and revisit that question, but these are based on today's

8 science. 

9 If yes, then how to take that information,

10 combine it with other information to determine if it does and if

11 so what kinds of additional tests would be needed to get the

12 dose response curve to do the risk assessment to make the

13 appropriate regulatory decision. 

14 And in the case of FFDCA is if it's a

15 food-used pesticide is there reasonable certainty of no harm;

16 same kind of risk assessments we do all the time in the

17 pesticide program, but making sure we're taking a look at this

18 specific toxicological pathway appropriately. And in the case

19 of ecological issues, whether or not there could be unreasonable

20 adverse effects associated with this chemical, but making that

21 risk assessment we need that dose response curve to go forward.

22 So Tier 1, does it have the potential? If no,

23 done. Keep track of the science so you may come back and

24 revisit that. If yes, how? How do you make that determination,

25 and if so, then what kind of information do you ask afterwards. 

26
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1 But this week's SAP is focusing on how well

2 does that Tier 1 battery work as it was executed on these

3 chemicals in List 1? And then how the battery as a whole seem

4 to be hanging together. In July we'll talk about how to

5 integrate that information to make a determination if a chemical

6 can interact.

7 So with that, I thank you all again. I also

8 want to thank all the members of the pesticide program team and

9 the OSCP because it's a lot of hard work, as you can imagine,

10 pulling this together. And I want to thank the team. Mary

11 Manibusan, as the Director of the Endocrine Program, will give

12 you a little more background and be introducing the team members

13 as well.

14 With that, just want to thank you all again

15 for your time and effort in this review.

16 DR. DANIEL SCHLENK: Thank you, Dr. Bradbury. 

17 So to give an overview of the EDSP program, let me introduce

18 Mary Manibusan who is the Director of Exposure Assessment

19 Coordination and Policy Division, Office of Science Coordination

20 and Policy.

21 DR. MARY MANIBUSAN: So good morning

22 everybody, and a warm welcome to everyone on the permanent

23 panel, welcome back, and to those who are new to the panel, we

24 welcome you and thank you for all your efforts that you've

25 already done so far in preparation for today's meeting.

26 Let me start by thanking the opening remarks
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1 provided by Dr. Dix and Dr. Bradbury, welcoming Dr. Dix, and we

2 are very much excited that his expertise and his background will

3 be a warm welcome to the endocrine program in moving us forward

4 in the area of computational toxicology as well as reproductive

5 toxicology.

6 So with that, my job this morning is to

7 provide a really brief overview of the Endocrine Disruptor

8 Screening Program. How I'd like to do that is by focusing on

9 the mission and vision of the program and then how that mission

10 and vision is implemented in the program. So I'll provide a

11 roadmap in terms of how a chemical moves from prioritization to

12 screening and testing. And as Dr. Bradbury indicated, we do the

13 work of risk assessment in our programs, so I'll focus on a

14 little bit of that. And overlaid on top of that roadmap, I'll

15 give you a sense of where we're headed in terms of future

16 Scientific Advisory Panel reviews.

17 The EDSP program has a unique mission, and

18 that is to protect public health and wildlife by screening and

19 testing chemicals. But we don't stop there in terms of hazard

20 identification. If indeed there is hazard, we do take

21 appropriate actions for those chemicals in terms of our risk

22 assessment and put forward risk mitigation options if they're

23 found to have endocrine effects and be protective again of

24 public health and wildlife.

25 How we operate is under two legislative

26 mandates. The 1996 Federal Food, Drug and Cosmetic Act requires
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1 the Agency to have developed a screening program, and then

2 underscoring the word "validation" here, using validated test

3 systems and other scientifically relevant methods to determine

4 certain substances may have an effect on humans and effects that

5 produce and actually create estrogen and other endocrine effects

6 as the administrator may designate. Just want to focus on

7 validated test systems focusing on humans as well as estrogenic

8 pathway.

9 Under the FFDCA, the Agency is required to

10 screen all pesticide chemicals inclusive of active ingredients

11 as well as inert compounds. Different from the FFDCA is the

12 1996 Safe Drinking Water Act Amendment. This is in Section

13 1457. Here it explicitly states that the endocrine program is

14 allowed to screen chemicals only if they meet the exposure

15 finding, and that is that they're found in sources of drinking

16 water and if there are substantial human populations of

17 exposure, very different, again, from FFDCA, but certainly we've

18 looked to that to establish our Universe of Chemicals.

19 Shortly after the 1996 amendments, the Agency

20 had looked to scientific expertise through development of the

21 Federal Advisory Committee. This was called the Endocrine

22 Disruptor Screening and Testing Advisory Committee. This

23 committee provided some really critical recommendations that

24 really formed the look and feel of our program to date. Certain

25 key recommendations are highlighted here, and that is the first

26 being the expansion of protection to be not only inclusive of
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1 human health but also of wildlife, that being more sensitive

2 sometimes to human adverse effects. And you'll see that

3 demonstrated in the range of Tier 1 assays as well as the

4 proposed Tier 2 assays as we move forward.

5 We also look to expand beyond the estrogen

6 pathway to be inclusive of the androgen and the thyroid pathways

7 because those pathways were clearly most common and most known

8 at that time. Again, we look to the advancement of science to

9 be inclusive of other modes of action. 

10 But more critical of those recommendations is

11 this development of a two-tiered screening and testing program. 

12 The founding members of the EDSTAC group actually had a

13 conceptual framework that produced chemicals that led from

14 prioritization into a Tier 1 screen, and that is to answer the

15 question of whether that chemical even has the potential to

16 interact with the endocrine system, specifically estrogen,

17 androgen and thyroid hormone, allowing for some tolerated

18 uncertainty. 

19 As chemicals are evaluated, and a weight of

20 evidence indicates that it warrants additional testing through

21 Tier 2, the Tier 2 tests are really to determine whether that

22 interaction actually occurs in the endocrine system, and if so,

23 the ability to quantify that dose-response relationship is

24 really important as we move it forward into our risk assessment

25 paradigm.

26 Here is what the endocrine program Tier 1
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1 screening battery looks like. It's comprised of five in vitro

2 assays and six in vivo assays. And the thought here is that it

3 forms a battery of assays, and so they're intended to be

4 complementary. And I put forward this visual of a puzzle piece

5 because they're meant to interconnect. They're not duplicative,

6 but they're meant to inform each other in terms of the certainty

7 in which they interact with the endocrine system. 

8 Of course, the in vivo assays, because they're

9 multi-parameter, can cover many more modes of action, even those

10 that are not yet known in a wholistic, whole-animal system where

11 you're capturing not only the toxicodynamics but also the

12 toxicokinetic features of metabolism and compensatory

13 mechanisms; while the in vitro assays give you a lot more

14 specificity in terms of mode of action, in terms of binding

15 potential and activation assays as well as steroidogenesis and

16 aromatase assays.

17 Here's just another look at the Tier 1 assays

18 together, and here, while in 1998 the terms of mode f action or

19 adverse outcome pathway were not common at the time, you can see

20 just looking at the estrogen agonist pathway that that's how

21 it's laid out in terms of toxicity and our understanding of

22 molecular initiating events occurring early on in terms of ER

23 binding and transactivation occurring subsequent to that. 

24 We're also looking at organ responses in terms

25 of uterotrophic assays; and again, in the whole animal, do we

26 see apical endpoints that match up to what we would expect to
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1 see when you do have the estrogenic pathway triggered? And that

2 is exemplified in the pubertal female and the fish reproductive

3 for the estrogen agonist.

4 We look to the three pathways, estrogen,

5 androgen and the thyroid HPT, but you'll note here when you look

6 across the battery that that redundancy, that understanding of

7 adverse outcome pathway, is not clearly laid out as for estrogen

8 as compared to the thyroid. So thyroid, we really just have the

9 rat pubertals and the amphibian metamorphosis assay. And that

10 was clearly noted in the 1998 report from EDSTAC as well as the

11 2008 SAP report.

12 Combining that Tier 1 data alongside with

13 other relevant scientific information, if we conclude that

14 chemical warrants the movement forward to Tier 2 testing, these

15 are the Tier 2 tests that are being proposed. Starting from the

16 top, we have the mammalian two-generation reproduction study

17 that's already a validated study as well as the extended

18 one-generation reproduction study. 

19 What we're currently working on in terms of

20 validation effort, and what we plan to bring before the SAP next

21 month in June, are four remaining ecological Tier 2 test

22 methods. They run across a broad range of taxa, as you can see

23 here, starting with the avian, moving forward to the larval

24 amphibian growth and development, the fish multi-generation

25 study and the invertebrate multi-generation reproduction study. 

26
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1 Similar to the mammalian studies, we'll look

2 for opportunities, and we'll ask for SAP's recommendations on

3 moving from a multi-generation reproduction study protocol to a

4 single-generation reproduction study but not losing the

5 sensitivity in terms of identifying sensitive endocrine

6 endpoints.

7 Here's just a quick snapshot. I think it's

8 really important as the EDSTAC committee had focused on, there

9 is a clear distinction between Tier 1 screening and Tier 2

10 testing. So Tier 1 screens are intended to be shorter term

11 assays and done at a very much lower cost. It is intended

12 because it service as a screen to minimize false negatives but

13 also balancing false positive, so the ability to really operate

14 and function as an adequate screen.

15 The real question for the screen is again does

16 that chemical have the potential to interact with the endocrine

17 system, and is this customarily a binary response, yes, no. And

18 because of that, you'll see that a lot of the Tier 1 assays have

19 limited doses. Some of the assays have only two doses, and

20 that's intended, again, to push the system so that we're seeing

21 an effect of the screen but not chase lower-dose effects as you

22 would in a Tier 2 test.

23 The combination of the in vitro/in vivo assays

24 are intended, again, to complement and create that power in

25 terms of the screen and intended to work together. And that's

26 why we ask for all 11 assays in a Tier 1 screening battery so
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1 that we can get information in order to decide whether that

2 chemical can and should be moved forward to Tier 2, or we can

3 decide that it does not exemplify the potential to interact with

4 the endocrine system, and we put it in a hold box.

5 If we move forward to Tier 2 testing, these

6 are again similar to definitive studies that we use in typical

7 risk assessments of longer term. They have many more doses,

8 many more animals per dose so that you have the statistical

9 power to identify any subtle changes or effects for apical

10 endpoints upon which to establish your risk assessment. 

11 Here the question to be answered is again

12 whether that chemical has, indeed, the ability to interact with

13 the endocrine system, and then more importantly, what is the

14 dose at which you want to set your point of departure for risk

15 assessment. The collection of Tier 2 tests are primarily in

16 vivo for that reason, but they cover multiple taxa, so they

17 cover the rat for the human risk assessment and then the four

18 ecological assessments intended to capture the most sensitive

19 ecological species.

20 And these differences from the Tier 1 screen

21 are not intended to be a battery but intended to be a targeted

22 testing approach. So again, asking for only that Tier 2 study

23 that we need in order to conduct our risk assessment to be

24 protective of human health and wildlife.

25 So how do we put the Tier 1 and Tier 2 assays

26 into an operational program? This slide is intended to do that,
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1 to capture how a chemical moves from prioritization to screening

2 and testing, and then overlaid on top of that is our projected

3 SAP schedule for all these fundamental steps. 

4 So starting from the left, step one is the

5 prioritization of chemicals. For the initial list of chemicals,

6 we prioritize that pesticide chemical list by exposure pathways.

7 So based on exposure, they were put onto our initial list.

8 Going forward to the second step, which is

9 where we are today, when the data is submitted to the Agency,

10 the Agency conducts a very thorough review inclusive of

11 performing a primary, secondary, as well as a tertiary view for

12 consistency across the assays and across chemicals. And that is

13 what we're bringing forward to you today.

14 Step three involves bringing that Tier 1 assay

15 data and its conclusions collectively with integration of other

16 scientifically relevant data. This is inclusive of the

17 pesticide submission information in terms of the developmental

18 studies, if there's existent multi-reproduction assay that's

19 been conducted as well as the chronic assays and any repeat

20 systemic dosing study, is very informative to the weight of

21 evidence in terms of balancing what is biologically plausible to

22 what we're seeing in the Tier 1 assays. 

23 If the determination from that weight of

24 evidence allows the agency to decide that we need to move that

25 chemical forward for additional testing, for example, Tier 2,

26 then we would move that forward based on the effects that we're
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1 seeing and the potential effects that we're acknowledging in the

2 Tier 1 assays. That would move into the multi-generation

3 reproduction studies and then embed it into the risk assessment.

4 In terms of the SAP timeframe, in January we

5 brought before the panel a prioritization method that was

6 inclusive of physicochemical properties as well as (Q)SAR and

7 high-throughput computational toxicological methods in order to

8 strategically prioritize chemicals. 

9 We are at the second step, and the second step

10 is really again meant to anchor down the Tier 1 assays. Are the

11 Tier 1 assays performing as intended? Is the battery as a whole

12 operating as it was intended? As Dr. Dix had indicated, this

13 time we're bringing back to the panel some real-world chemicals

14 with unknown endocrine modes of action. So really testing the

15 systems and asking the question can we do better; can we

16 optimize the assays and still answer the question without

17 sacrificing any certainty.

18 And then, of course, the third step which is

19 weight of evidence we're going to bring back to the panel in

20 July, and that will be bringing forward some specific case

21 studies exemplifying, again, the methodology of weight of

22 evidence. How do we bring forward the Tier 1 alongside with the

23 158 data alongside with published information, and put together

24 an integrated assessment to help us decide whether that chemical

25 moves forward?

26 Next month, in June, we'll be bringing forward
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1 the work that our Office of Science Coordination and Policy in

2 conjunction with Office of Research Development and Office of

3 Pesticide Program have been working on for probably over the

4 last decade now in completing the validation effort for the

5 remaining ecological Tier 2 assays. And these are, again,

6 primarily multi-generation reproduction studies.

7 So that's to just quickly give you a sense of

8 where we're headed, where we are today and where we'd like to

9 be.

10 Just as a quick reminder, back in January,

11 that chemical prioritization methodology was brought before the

12 SAP. At that time, the focus was to prioritize the Universe of

13 Chemicals using the estrogen receptor adverse outcome pathway as

14 a specific example or model for other pathways, and in

15 particular, focusing on the use of computational toxicology

16 tools to make our program much more efficient and effective as

17 we move forward. 

18 We sought to obtain the SAP's input or

19 recommendations on the scientific concepts and the principles

20 and processes used to prioritize chemicals for EDSP screening. 

21 At the beginning of this month, we received the final report. 

22 We thank the panel very much for their recommendations. They

23 will be very critical as we move forward to prioritizing our

24 10,341 chemical comprised Universe of Chemicals.

25 Here's just a quick snapshot, just again as a

26 reminder that what we brought before the panel in January was a
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1 methodology that looked to prioritize strategically our Universe

2 of Chemicals using a combination of various filters. On your

3 right, it's demonstrated the different filters, the use of

4 physicochemical properties such as acidity, whether a chemical

5 is too basic, whether it's a long-chain polymer and not able to

6 be bio-available systemically to introduce any endocrine

7 activity. That's a filter that could preclude chemicals from

8 endocrine testing.

9 The next being a very important filter, the

10 exposure potential filter, and their chemicals would then filter

11 into the expert system and high-throughput box which is our

12 computational toxicology tools in parallel. And as chemicals

13 move forward, based on its potential through this particular

14 prioritization, it moves into a list for EDSP Tier 1 screen. 

15 This is where the January SAP ended, and this

16 is where our SAP today starts. And that is the focus on the

17 data that is received having gone through the screen. Here the

18 focus for the SAP panel is not only on the performance of the

19 Tier 1 individual assays but how laboratories are able to

20 execute these study method protocols and whether they can do it

21 efficiently.

22 The second primary area of focus is the

23 performance of the EDSP Tier 1 battery. As we break apart the

24 Tier 1 assays and click it back together, is the battery

25 performing as it was intended to? Does it give us information

26 on thyroid, androgen and estrogenic pathway? Can we do the same
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1 job with less assays; and if so what would we be missing when we

2 remove some of the critical assays which we move forward? 

3 And this was, again, at the impetus of the

4 SAB/SAP panel back in 1999 that was pretty clairvoyant in

5 suggesting that we convene a panel of independent experts to

6 review all the screening data for approximately 50 to 100

7 compounds but, more specifically, with an eye towards revising

8 the process and eliminating those methods that just don't work.

9 This SAP, of course, is not a revalidation of

10 the Tier 1 assays, but it's intended to look at the Tier 1

11 assays with real-world chemicals. As exemplified in all SAPs,

12 this is an open and transparent public process intended to give

13 the Agency insights from an independent, external scientific

14 peer review. And based on their findings in 2008, the current

15 state of science at that time indicate that the Tier 1 assays

16 and batteries were indeed capable of identifying potential

17 estrogenic, androgen and thyroid endocrine-active compounds. 

18 And more importantly, it's an appropriate

19 starting point to detect potential endocrine disruptors,

20 acknowledging that the state of the science was advancing very,

21 very quickly and that we could supplement the battery with

22 additional test methods. 

23 But the Tier 1 battery was, in fact, pointed

24 out to be very, very informative because it include multiple

25 taxa and different modes of action endpoints and provide a broad

26 range of metabolism and needed corroboration so that we can
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1 control for any false negative and false positive results as we

2 move forwards.

3 Today we are starting with the initial list of

4 EDSP chemicals that was submitted for Tier 1 screening back in

5 2009. That data has started rolling in, submitted to the Agency

6 beginning 2011, and we received the last set in 2013. We hand

7 selected among the 52 pesticide chemicals 21 unique chemicals

8 for the reasons stated here, that they really represent a range

9 of physicochemical properties inclusive of volatility, a broad

10 range of log Kow values as well as solubility. 

11 We also looked to establish a subsample that

12 had a broad range of biological activity and inclusive of

13 various classes of pesticide chemical. So includes chemical

14 classes representative of emides, azols, organophosphates and

15 pyrethroids, et cetera. These pesticide chemicals are designed

16 with clear biological activity, and that's why as an Agency we

17 felt these selected chemical would be the way to test the Tier 1

18 assays more robustly.

19 So the Tier 1 assays from our initial review

20 has demonstrated again, as 2008 had concluded, to be useful

21 information to evaluate the potential interactions of E, A and T

22 signaling pathways. There were no major problems identified in

23 Tier 1 assays, and again, we demonstrated here in our evaluation

24 that laboratories as a whole were able to execute each of the

25 assay protocols in accordance with the respected test

26 guidelines, and they were able to achieve specific performance
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1 criteria noting that there were some minor deviations from the

2 performance criteria. Those differences were not considered

3 substantial.

4 The second half of our evaluation was to look

5 and focus on the battery, whether the battery performed as

6 intended and whether there were opportunities to reduce the

7 battery and not sacrifice any sensitivity. We looked at the

8 battery in terms of the specific pathways that was intended to

9 be informative for. So starting at the top for thyroid, what we

10 found was that the amphibian metamorphosis assay and the rat

11 pubertal assays were indeed able to give us different types of

12 information across the taxa and reinforce the importance of

13 having both taxa in the screening battery to reduce the

14 potential for false negatives.

15 For the androgen pathway, we found that the in

16 vitro assays weren't able to consistently exhibit significant

17 effects when effects were observed in the in vivo assays, and

18 this could be due to potentials in differences in metabolism and

19 different whole animal systems that are not able to be

20 demonstrated in in vitro assays. These possible redundancies

21 across in vivo assays in terms of the Hershberger, male pubertal

22 and the fish assay, were observed, albeit there were noted some

23 overt toxicity.

24 For the estrogen pathway, we found again that

25 the female pubertal and fish assays are indeed very important in

26 evaluating the estrogenic pathway across taxa. The uterotrophic
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1 here we note was consistently negative for all chemicals, but

2 that's not surprising because there was no representative

3 estrogen agonist in our subsample. I want to again confirm that

4 this estrogen pathway and all of the Tier 1 assays associated

5 with it had been validated in 2008. And it's particularly

6 important to note that although the uterotrophic assay was

7 consistently negative, it was never positive while the in vivo

8 system assays were negative.

9 So the fundamental charge to the panel today

10 is really to receive your overall comment on the assay, the

11 laboratory performance and advice on how to address some of the

12 technical deviations and other issues encountered with specific

13 assays. We noted in our White Paper examples of overt toxicity,

14 cytotoxicity and difficulties in solubility of different

15 compounds.

16 The second area is to receive your comment on

17 Tier 1 battery performance and whether there are unnecessary

18 redundancies among the 11 assays and whether we can do the job

19 of answering the question of potential interaction with fewer

20 assays.

21 I leave you with this quote from Aristotle

22 which to me punctuates the review that we have before us, and

23 that is, "The whole is greater than the sum of its parts." 

24 Certainly the Tier 1 eleven assays are indeed very informative

25 and they're a good tool. But it's intended to provide us an

26 answer to the question and the question is whether the chemical
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1 has potential to interact with the endocrine system. If we can

2 do that job with less assays, it's important to understand what

3 information we would be missing if we were to reduce the

4 battery.

5 And again, I want to acknowledge the OCSPP

6 team that worked on this evaluation. They're listed here, Jess

7 Rowland, Greg Akerman, John Liccione, Amy Blankinship, Catherine

8 Aubee, Tom Steeger, Vicki Dellarco, Patience Browne, Don

9 Bergfelt and Calvin Powell, all just very important contributors

10 to the scientific evaluations and some who will be speaking

11 during the course of today.

12 Here I leave you with this last slide, and

13 this is just the order of the presentations for today. 

14 Following me is Dr. Les Touart who will be providing some

15 background on the current validated Tier 1 screening assays and

16 going into the history a little bit more on the conceptual

17 framework. 

18 Dr. Greg Akerman will be going over the in

19 vitro assays, Dr. Liccione the in vivo mammalian assays. Amy

20 Blankinship will be going over the fish assay, Catherine Aubee

21 the frog assay, and Tom Steeger, Catherine and Amy will

22 collectively go over the battery performance, and then we'll

23 close out with an overall summary provided by Dr. Tom Steeger.

24 Thank you very much, and I welcome any

25 questions.

26 DR. DANIEL SCHLENK: Thank you, Mary.
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1 Any general questions regarding the overview?

2 Yes, Dr. Ehrich.

3 DR. MARION EHRICH: Could I ask you to put up

4 that slide again that had all 11 batteries just to take another

5 look at? Thank you.

6 DR. DANIEL SCHLENK: Did you have a question? 

7 No? Okay.

8 Dr. Klaine.

9 DR. STEPHEN KLAINE: Thanks for the

10 presentation. I'm sure this is a lot of work in terms of

11 analyzing this. I'm still puzzled as to the choice of the 21

12 chemicals out of the 52, and was the strategy to minimize or,

13 you know, to use your effort wisely in terms of analyzing the

14 data? In other words, why didn't you just do all 52?

15 DR. MARY MANIBUSAN: So that's a very good

16 question, and what we've written into the White Paper is our

17 selection process. We were very specific in choosing these 21

18 chemicals for the reasons articulated, that they represent a

19 broad range of pesticide chemicals within that 52-chemical

20 category.

21 It also had very clearly a broad range of

22 physicochemical properties that we again wanted to demonstrate

23 that the Tier 1 assays were able to perform for. And it had a

24 broad range of biological activities so inclusive of herbicides,

25 rodenticides and fungicides; it even includes an inert compound.

26 This subsample is really representative of the
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1 remaining chemicals within that 52 class, but as you said, we

2 would like to receive the panel's recommendation and advice

3 before completing and dedicating resources to completing all of

4 the remaining 52 chemical assessments.

5 DR. DANIEL SCHLENK: Dr. Scott.

6 DR. GEOFFREY SCOTT: Just a follow-up question

7 to Dr. Klaine's question. Of those compounds that were

8 screened, what proportion of total pesticide use do you think

9 those represent? It may not be a precise number, but is it a

10 major proportion of pesticide use or is it in the middle or is

11 it the smaller portion? I guess what I'm saying, out of about

12 1,000 plus compounds, what percentage of total use does that

13 possibly represent?

14 DR. MARY MANIBUSAN: Maybe I can start and

15 pass it over to Dr. Bradbury.

16 So these initial lists of pesticide chemicals

17 were prioritized based on exposure. So it had to meet four

18 particular exposure pathways in order to be moved into the list.

19 So it had to be used in a residential setting, occupational, be

20 used for food and water. So because of that, this was

21 prioritized in a higher category for the initial list to be used

22 for screening.

23 Let me pass it to Dr. Bradbury for additional

24 information.

25 DR. STEVEN BRADBURY: As Mary indicated, it

26 was back 2007'ish, I think, when we put out for public comment a
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1 process to get that first 50 to 100 compounds. And we laid out

2 that we would try and get a good cross-section of

3 active-ingredient pesticides as well as a cross-section of

4 high-production volume inert ingredients.

5 And so the process that we put out for public

6 comment wasn't to get as quantitative, as you've implied in your

7 question, Dr. Scott, but we've qualitatively capture chemicals

8 that have a higher potential for exposure. So we looked at

9 whether or not a compound could be used, as Mary said, in a

10 residential setting. Was it detected in water? Was it a

11 food-use pesticide which is an indirect measure that could be in

12 the human food chain, those kinds of parameters, and we made

13 sure that every chemical in that list of 61 was in at least

14 three of the four exposure pathways, so all of these compounds

15 have at least three of the four exposure pathways.

16 Having said that, the compounds themselves, if

17 you can see in the public, the list of 67, many of them

18 represent, for example, insecticides and herbicides are very

19 commonly used.

20 The other point of sort of how we're moving

21 forward is in the one graph that Mary put up which showed sort

22 of the pesticide process of how we're going to go through an

23 evaluation and the non-pesticides. So for the pesticide active

24 ingredients, we're using our registration review program which

25 is our 15-year cycle for reevaluating pesticides. 

26 And so once we kind of know how all the
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1 machinery is going to work, we'll use that process to

2 systematically go through the active ingredients, although we

3 may use some read-across and other techniques not to have to

4 test every single one or screen every single one, so a

5 qualitative approach to the exposure potential; and as you'll

6 see the list, many of them are pretty common.

7 DR. DANIEL SCHLENK: Dr. Scott.

8 DR. GEOFFREY SCOTT: Thank you all for that

9 clarification. What you're telling us is it's a good

10 representative group, and that was really the intent of the

11 question. It wasn't clear before exactly without following all

12 the details, so thank you for that explanation.

13 DR. STEVEN BRADBURY: And if I could, to

14 follow up on Dr. Klaine's question which was related and Mary's

15 response, is that yeah, we did take a look, you know, at peak

16 log Kows, some very soluble compounds, some, if you look at the

17 list of 67 there are plenty of pyrethroids on there, and those

18 of us who have done experiments with pyrethroids know what that

19 entails. 

20 So we knew we had a pretty good cross-section

21 to add some other P Chem properties and then also taking a look

22 at a fairly representative cross-section of insecticidal mode of

23 action, herbicidal mode of action. Again, as Mary pointed out,

24 part of the challenge of this group is how to get enough dose,

25 just enough, knowing that if you go too high that insecticidal

26 and herbicidal mode of action will start to kick in and so we
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1 figured that would be a pretty robust group, and to get some

2 feedback partway through the effort.

3 So, Dr. Klaine, it was a bit of a balancing

4 act. One option would have been to do all fifty-some and then

5 if you come back and go well, you all really got it wrong, that

6 would be an option. But we felt a more prudent option would be

7 to take a good cross-section and get some initial feedback from

8 you, because you may have some suggestions to go back and

9 rethink a few things on these twenty one and we can also use

10 that knowledge as we go forward with the remaining

11 thirty-something. And so it's kind of balancing getting

12 feedback and using our resources efficiently.

13 DR. DANIEL SCHLENK: Dr. Piersma.

14 DR. ALDERT PIERSMA: Just to follow up on

15 these questions, one more detail is from the report I understand

16 that the Tier 1 data for 50 compounds were submitted, and after

17 that the 21 were selected. Was the Tier 1 data in any way

18 influencing the selection of the 21 compounds?

19 DR. MARY MANIBUSAN: No. In fact, we

20 understood which 52 were going to be submitted to the Agency and

21 we specifically chose those 21 chemicals, because they again

22 represented that broad range of physicochemical properties and

23 pesticidal mode of action.

24 DR. DANIEL SCHLENK: Dr. Patino.

25 DR. REYNALDO PATINO: I may be missing

26 something but why not all 52? I mean the more information the
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1 better. Or is it that it may be an effort-related problem here

2 that you didn't have time to do all 52? I mean in my opinion,

3 the more information the better.

4 DR. STEVEN BRADBURY: It isn't more is better

5 -- not to say more isn't better -- but it's also balancing time

6 and time in terms of when to get some feedback as we go forward.

7 From a scientific standpoint it's get back to (Q) SAR and

8 read-across and chemical classes. Do we have a sense of that

9 universe of fifty-some chemicals with the 21 and our conclusion

10 was yes. 

11 It's reasonably representative of the

12 insecticidal mode of action in the group of 67. It's reasonably

13 representative of the herbicidal mode of action in the group of

14 67, the fungicides in the 67, the log Kow range. If somebody

15 was going to ask me are the 52 representative of all 1,500

16 active ingredients, the answer would be no. Inherently it can't

17 be.

18 The SAP said get 50 to 100 and see how it's

19 going. So what we decided to do to balance resource and good

20 timing to get input from peer-review panel, let's take a look at

21 21 that are reasonable, very reasonable cross-section of

22 fifty-some, and get some feedback. Because part of your advice

23 may be, I hope, yeah, the assays are generally executed

24 reasonably well, but here's a couple of things to think about as

25 you look at this assay or that assay.

26 Okay, good. We can go back and touch up the
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1 21, and we'll know that going into the remaining 30 as we take

2 your advice and move forward.

3 DR. DANIEL SCHLENK: Dr. Klaine.

4 DR. STEPHEN KLAINE: I think you answered

5 this, but just to kind of follow up. So in the relatively near

6 future we could expect the rest of the analysis of the other

7 compounds? Okay. 

8 DR. STEVEN BRADBURY: We're nodding our head

9 yes.

10 DR. DANIEL SCHLENK: Thanks. Dr. Nelson.

11 DR. NELSON HORSEMAN: This sort of touches a

12 similar question, but assume from this list that the EDSP has

13 10,000 compounds for which this set of assays is being applied. 

14 And understanding that it's not this committee's purview or

15 responsibility, those non-FIFRA compounds, is there useful

16 information about the performance of these individual or battery

17 tests in the rest of EDSP that this panel could be informed by?

18 DR. MARY MANIBUSAN: So again, we're going

19 back to the issue of how representative is the subsample of 21

20 chemicals that we're bringing before this panel for review? And

21 again, the focus is on the performance of the assays and battery

22 and not on the chemical risk assessments. So how they perform

23 for the chemical, what decisions we make is not subject to this

24 review. This review is just primarily looking at methodology. 

25 And that's the reason why the subsample is so important.

26 Among that 21 subsample is an inert ingredient
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1 that would be in parallel with the Safe Drinking Water Act,

2 would be consistent and similar to those chemicals. But in

3 terms of the broad range of SDWA chemicals, again, the initial

4 list was focusing on pesticide chemicals as a start. But we

5 feel that that is a robust, you know, list of chemicals upon

6 which to challenge the Tier 1 assays and the battery.

7 DR. DAVID DIX: If I could follow up. I think

8 as we move forward in the SAP processes, the upcoming weight of

9 evidence SAP will bring in some additional data that I think

10 will satisfy some of these questions and comments. And then

11 moving forward as we expand the program and take a more

12 integrated computational approach to analysis, I think that will

13 address these issues even more so. 

14 We see that as the future, and just to

15 emphasize, I think that getting this intermediate feedback sort

16 of midway through this dataset in the first round of Tier 1

17 testing will be extremely valuable to us, and that's why we set

18 up the schedule the way it is.

19 DR. DANIEL SCHLENK: Thanks. Okay, we ready

20 to move on for background presentation? 

21 Okay. Presenter is Leslie Touart who is with

22 the Exposure Assessment Coordination and Policy Division, Office

23 of Science Coordination and Policy. Dr. Touart.

24 DR. LESLIE TOUART: Thank you, Dr. Schlenk,

25 and I thank the panel for being here and good morning.

26 My purpose here is to talk a little bit about
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1 the background of the Tier 1 battery, to discuss a little bit

2 about how the assays address elements within the pathways that

3 are utilized. 

4 Going back and just remember the endocrine

5 system, recognizing that it's an integrated system of organs and

6 tissues, chemical messengers, to maintain homeostasis within an

7 organism primarily through a system of negative feedback

8 signaling. The estrogen, androgen and thyroid subpart of these

9 endocrine axes is really just a subpart, it's only part of that.

10 The focus of the endocrine screening program is on these

11 hormones because they were considered to be primary and suitable

12 for the available science at the time to develop assays to look

13 at these. And so this is what was targeted and developed.

14 The endocrine system as we know can be

15 perturbed at multiple sites and by multiple mechanisms. So we

16 wanted to develop a battery of assays that we would be able to

17 pick up and screen for all of the known mechanisms at the time.

18 The considerations that were given were that

19 there's complex interactive nature of the endocrine system, and

20 it's clear that these should be screened for apical activity in

21 addition to some of the more molecular or in vitro kinds of

22 things. Since the main indications in terms of reproduction,

23 growth, development, you know, these are the key things that the

24 Agency is concerned about in terms of its risk assessment. So

25 we want to be able to be sure that we're looking and screening

26 for those kinds of activities because these are going to be more
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1 comprehensive and inclusive.

2 So the battery itself was designed to ensure

3 detection of estrogen, androgen, thyroid hormonal system

4 interactions. It fulfills the EDSTAC recommendations that a

5 range of taxonomic groups be included, and it includes

6 sufficient diversity of endpoints to maximize sensitivity and

7 minimize false negatives. 

8 Again, we wanted to emphasize the need for

9 apical assays to provide a more comprehensive assessment in the

10 context of what was being done for the screening. So in general

11 it meets the overall objective of detecting potential for

12 endocrine-mediated effects regardless of the particular

13 mechanism of action.

14 Again, as already have been mentioned, it's a

15 two-tiered approach that we're utilizing and what was

16 recommended by the EDSTAC, with Tier 1 designed to detect

17 potential to interact with the endocrine system and the pathways

18 we're focused on are estrogen, androgen and thyroid. 

19 This Tier 1 includes both in vitro and in vivo

20 assays, and if the context is a positive then you would consider

21 need for Tier 2.

22 Tier 2 then is the higher-level design to

23 corroborate interactions that may have been seen in Tier 1, to

24 characterize adverse effects if these occur, and provide

25 dose-response information from these. 

26 In general, the Tier 2 tests consist of
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1 lifecycle and multigenerational-level studies and cover a broad

2 range of taxa including the mammals, birds, frogs, fish, and

3 even including invertebrates for the connection that they might

4 have in terms of actions that may be seen.

5 The assays that are included in Tier 1 are

6 basically these, and we've seen the similar slides before, but

7 the in vitro assays include steroidogenesis, the estrogen

8 receptor binding and a transcriptional activation assay, an

9 androgen receptor binding and aromatase. 

10 There are a couple of in vivo assays that have

11 the HPG axis interrupted, and these are uterotrophic and

12 Hershberger. Then there are the whole-animal assays which are

13 pubertal female, pubertal male and amphibian metamorphosis and

14 then a fish short-term reproduction.

15 These assays are designed to work together in

16 an integrative fashion to maximize sensitivity and minimize

17 false negatives. The in vitro assays as a group provide

18 sensitivity and mechanistic clues in terms of what may be going

19 on or what an initiating event may be. The in vivo assays

20 provide for more integrative responses and include metabolism

21 and distribution considerations.

22 The battery itself relies on similarities of

23 endocrine systems across taxa and includes redundancies in these

24 assays as well as some assay-unique endpoints in terms of being

25 able to cover and interpret what may be going on to allow for

26 conclusions that there is indeed potential interactions with the
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1 hormone or endocrine system.

2 And it's interpreted by a weight of evidence

3 of the battery in addition to other relevant information that

4 may exist, other toxicologic information, literature information

5 that may be available. And it's this weight of evidence rather

6 than the sum of, you know, any positive or negatives in terms of

7 responses of the assays themselves.

8 This is another familiar slide in terms to

9 identify across the assays the coverage with the various

10 hormonal pathways in terms of which assays are reacting to

11 elements of perturbations along the pathway for estrogens, the

12 androgens. In the steroid synthesis, you know, the androgen

13 steroid and estrogen steroid syntheses themselves and really

14 towards the right we have the HPG, HPT axes, and these are

15 really going to be comprehensive in terms of interactions and

16 include both the mammalian as well as fish and amphibian.

17 To get into a little bit more specifics for

18 the individual pathways and the variety of assays that relate or

19 speak to these, for the estrogen pathway first, we have ER

20 binding which detects chemicals that bind to the estrogen

21 receptor. This is critical since that would be an initiating

22 activity for what would ultimately become the outcome within

23 that particular pathway.

24 There's the estrogen receptor, the

25 transcriptional activation assay which detects the receptor

26 interaction. And with this particular assay, we're able to
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1 differentiate ER agonist from antagonist. The uterotrophic is

2 designed to detect chemicals that act through the ER. It has

3 the HPG interrupted so will be using ovariectomized or immature

4 individuals, and it incorporates metabolism as well. But it

5 would be able to indicate any activity that would be determined

6 by an estrogen signal independent of the hypothalamus and

7 pituitary interaction.

8 The pubertal female is designed to detect

9 chemicals that act on the mammalian estrogen system and there

10 are key endpoints within this particular assay to respond to

11 that from an integrated or comprehensive assessment. And the

12 same with the fish; it's designed to detect chemicals that act

13 with the fish estrogen system. 

14 The one key with the fish is it's fish during

15 the reproductive portion of the lifecycle. So for compounds

16 like estrogen as well as the androgen, which are reproductive

17 hormones, we have the ability to detect what might be going on. 

18 And although reproduction is somewhat nonselective in terms of

19 being endocrine-specific, it does pick out endocrine activities

20 and with combinations of other endpoints and other information

21 within the battery, we're able to determine if it's primarily

22 through endocrine mechanism or not. At least that's the

23 consideration that we have.

24 Within the androgen pathway, a similar set

25 where we've got in vitro assay with the AR binding which is

26 designed to detect compounds that bind to the androgen receptor.
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1 There is a Hershberger which is an in vivo assay which detects

2 compounds that act through the androgen receptor. It can

3 distinguish between agonist and antagonist, you know, the aspect

4 of this particular assay. It incorporates metabolism and likely

5 uterotrophic. It is independent of the hypothalamus and

6 pituitary interaction because these would be castrated

7 individual.

8 The pubertal male is another in vivo assay

9 designed to detect chemicals that act on the mammalian androgen

10 system through a variety of pathways or tissues that would be

11 responsive to this and developmental cues so the indications

12 would be from a kind of comprehensive interaction. And fish,

13 you know, likewise, in vivo detecting chemicals that act through

14 the fish androgen system and specifically with endpoints like

15 secondary sex characteristics in reproduction. There would

16 indication of activity that would be prevalent and able to

17 compare that with information gleaned from some of the other

18 assays within the pathway in terms of the action overall.

19 Thyroid pathway is limited to the three

20 assays, all in vivo. We weren't able to identify or at least

21 have recommended the in vitro thyroid assays. That's still an

22 area of active research. But for now we're limited to in vivo

23 activities which would be comprehensive. 

24 With both the pubertal female and pubertal

25 male, these are designed to detect hormonal changes through

26 measures of things like T4, TSH and then thyroid histology. For
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1 the amphibian metamorphosis, this detects changes in thyroid

2 histology but also developmental changes that would occur

3 through the metamorphosis which is a good indicator and a

4 general indicator for vertebrate thyroid controls.

5 Steroidogenesis is another aspect that's

6 considered within the battery. We have aromatase assays

7 specific to detecting chemicals that would inhibit aromatase

8 action in an in vitro system. There is steroidogenesis which is

9 a little bit more comprehensive in terms of any interactions or

10 perturbances in steroid metabolism that could inhibit enzymes in

11 the ultimate steroid synthesis. 

12 The in vivo assays of the pubertal male,

13 female as well as the fish screen, because these are

14 comprehensive in terms of the estrogen/androgen pathways, would

15 detect any interferences within the steroid metabolism as well.

16 So in trying to consider the real strength of

17 the battery overall in terms of the various kinds of activities

18 and the number of assays that speak to these that either

19 compliment or potentially provide a little redundancy in terms

20 of action, you know, this table kind of compares that.

21 For estrogenic activity, it's generally done

22 fairly well of the five assays that are looking at various

23 components across the pathway with ER binding, transcriptional

24 activation, the uterotrophic, the female pubertal and fish. 

25 And going down the line for things like

26 anti-estrogenic, there are three assays that would be able to
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1 detect along those lines. There is the androgenic, we've got

2 four assays; and anti-androgenic, the same four assays.

3 In compounds that modulate steroidogenesis,

4 we've got five assays with two in vitros as well as the three

5 key in vivos that deal with the estrogens and thyroids. 

6 For aromatase, we're back with really only

7 four because the male pubertal doesn't provide, you know, really

8 good information in terms of aromatase interactions in terms of

9 the kinds of endpoints that are going to be more androgen

10 specific.

11 For hypothalamic pituitary function in terms

12 of the HPG axis overall, we have to deal with the integrated in

13 vivo assays where we have the pubertal, the fish and the

14 amphibian on the HPT axes. Anti-thyroid activity, male, female

15 pubertals are directed at this as well as amphibian

16 metamorphosis primarily through the histopathology as well as

17 the hormone measures that would be made in the rat pubertal.

18 For thyromimetic activity, we're limited to

19 the amphibian metamorphosis. And this is done through the kind

20 of development, either accelerated development or things like

21 asynchronous development that would seen, would be indicators

22 for this particular type of activity.

23 In terms of looking at battery overall and

24 trying to interpret, you know, we do this through weight of

25 evidence review. That uses professional judgment. Some

26 endpoints are more diagnostic and specific than others. Effects
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1 seen in multiple endpoints and across multiple assays will carry

2 the most weight. 

3 You know, we will be considering weight of

4 evidence review on a forthcoming SAP later this summer, and so

5 we'll focus here just in terms of how the battery performs in

6 providing information that would be utilized within kind of a

7 weight of evidence context.

8 Really we anticipate two possible outcomes. 

9 You know, either there is sufficient potential to identify

10 within the review of the assays in terms of E, A or T acuity

11 which could warrant additional analysis in Tier 2 testing. The

12 other outcome would be the potential to consider either low or

13 nonexistent.

14 We went back and looking at previous 

15 SAP in 2008 in terms of its review of the Tier

16 1 screening battery, we had a charge question at that time, are

17 there any unnecessary redundancies for MoAs across the battery? 

18

19 The panel assessed that, considered each assay

20 in reference to different modalities for the EAT pathways,

21 examined degrees of complimentarity or concordance across the

22 assays and examined any degree of redundancy or concordance. 

23 And their conclusion was that in vitro assays convey specificity

24 and sensitivity but provide no complementarity or redundancy

25 within the battery for these in vitro assays alike. But the in

26 vivo assays provide both complementary and redundancy within the
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1 battery.

2 The overall recommendations were based on the

3 current state of science. This proposed set of Tier 1 assays

4 are an appropriate starting point, but EPA should consider this

5 as a work in progress and expects to continue to develop, refine

6 and review. That brings us here in terms of providing feedback

7 on some of the initial outcomes of the battery performance in

8 terms of how well these assays are working and to what degree we

9 see redundancies or complimentarity in terms of how the assays

10 function.

11 So kind of to summarize, multiple assays are

12 required to comprehensively screen E, A and T hormone systems. 

13 You know, there is not a one size fits all. In vitro assays are

14 good for well understood mechanisms such as receptor binding. 

15 The in vivo assays with intact HPG or HPT axes are good for

16 efficiently screening complex processes to make sure we are

17 identifying outcomes that may be acting through mechanisms that

18 we might not be as secure about. 

19 Multiple taxa and mode of action endpoints

20 provide a range of metabolism and needed corroboration. And

21 then the complete battery is needed to support a weight of

22 evidence finding of low or no potential for EAT activity.

23 So with that, I thank the panel for your

24 attention and can respond to any specific questions or

25 clarification on the battery itself.

26 DR. DANIEL SCHLENK: Any questions for Dr.
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1 Touart? Yes, Dr. Delclos.

2 DR. BARRY DELCLOS: One question. Before your

3 grand summary slide, is there an anti-estrogenic component in

4 the guideline for the uterotrophic assay? Because in the White

5 Paper I think it said that there is, but nobody did it.

6 DR. LESLIE TOUART: For the uterotrophic assay

7 as part of the validation effort, there was a component part

8 that dealt with the antagonism in terms of what was used. But

9 that didn't perform so well, so it wasn't included in the final

10 guideline. So it's something that could still, you know, be

11 developed, but it didn't perform well enough for us to consider

12 using it at the time.

13 DR. DANIEL SCHLENK: Any other questions? 

14 Yes, Dr. McManaman.

15 DR. JAMES McMANAMAN: In your steroidogenesis

16 assays, are those gene assays, or are they based on actual

17 measurements of products in the adrenal?

18 DR. LESLIE TOUART: Yeah, for the

19 steroidogenesis as well as the aromatase, these are in vitro

20 assays. They're not gene-based. They are actual

21 component-based to look at actual products in terms of

22 productions of the particular hormones like testosterone or

23 estradiol. So they are measured in terms of that context.

24 And I should mention that the assays

25 themselves, that there is discussion in later presentations so

26 there will be a little bit more detail in terms of the actual
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1 assays and some of the functional elements of those.

2 DR. DANIEL SCHLENK: Yes, Dr. Piersma.

3 DR. ALDERT PIERSMA: I have a general question

4 about the level of depth of the presentation of the data. 

5 Basically the way you present the data is either it gives us a

6 response or not in the various assays. And in order to

7 appreciate the results, it might even help for you to go into a

8 little more depth, but this has not been done purposely on

9 things like directionality, potency within the different assays

10 and how they compare among the assays. 

11 What particular endpoint within

12 multiple-endpoint assays that was affected that drove the either

13 plus or minus within the different tests, that kind of level? 

14 Have you thought about presenting it at that level and why

15 haven't you done it that way?

16 DR. MARY MANIBUSAN: So in terms of the degree

17 of detail inclusive of directionality and endpoint analysis

18 across the different assays, that will be the composition of the

19 July SAP. So we've purposely refrained from moving in that

20 direction because we wanted to hold the methodology standardized

21 so that we can look at just the performance of the assay and

22 battery and not draw any conclusions for chemical assessments

23 and its ability to initiate particular endocrine activity. That

24 will be the subject of the July meeting.

25 DR. DANIEL SCHLENK: Dr. Dix to add?

26 DR. DAVID DIX: Yeah, just to add to Mary's
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1 response, I think, Dr. Piersma, your comments could very well

2 lead to some very useful suggestions from this panel in how we

3 might revise, not just our analysis but our presentation of

4 those analyses and communication.

5 DR. DANIEL SCHLENK: Thanks. Dr. Green?

6 DR. SHERRIL GREEN: I have another general

7 question about the metamorphosis assays that I'll hear more

8 about later in Xenopus, but I was wondering, you know, the

9 Xenopus FETAX test already exists; and are there not elements of

10 that very standardized, very well-used, documented test that

11 would be applicable to the Tier 1 assay here and be cheaper,

12 faster? Just maybe some elaboration on that would help me.

13 DR. LESLIE TOUART: In determining the context

14 of how the amphibian metamorphosis assay was selected, again,

15 the context was that the Xenopus developmental model was

16 considered to be well understood and could be adapted in terms

17 of a thyroid activity screen fairly easily. The initial context

18 was perhaps a tail resorption assay, but that was felt, well

19 that's usually coming a little bit after the big peak within the

20 thyroid level.

21 The FETAX was considered, but FETAX doesn't

22 really provide us good information in terms of thyroid activity

23 to the context of the histopathology that's done on the gland

24 from what we can see. And then the developmental cues that are

25 identified in terms of accelerated development and, you know,

26 things like asynchronous development that come later than the
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1 FETAX window would allow for. 

2 So FETAX was discounted, you know, kind of

3 early in the considerations, and then the effort was moved to

4 develop metamorphosis which went through the validation process

5 and for the thyroid active compounds that we used to challenge

6 it were determined to be effective on those.

7 DR. DANIEL SCHLENK: Any other questions for

8 Dr. Touart?

9 Okay. Thank you. Let's go ahead and take a

10 first break. And I'm going to go by this clock on the wall

11 here, so let's be back at 20 'til 11.

12 (Whereas a break was taken)

13 DR. DANIEL SCHLENK: Our next presentation on

14 in vitro assays is by Gregory Akerman who is with the Health

15 Effects Division, Office of Pesticide Programs.

16 DR. GREGORY AKERMAN: Good morning. My name

17 is Greg Akerman. I'm from the Health Effects Division, Office

18 of Pesticide Programs. I would like to first begin by

19 presenting an overview of the EDSP assay performance

20 presentations.

21 As been previously mentioned, this slide shows

22 the order of the presentations. I will begin by presenting the

23 results of the Tier 1 in vitro assays. John Liccione will

24 present the results of the in vivo mammalian assays. Amy

25 Blankinship will follow with presentation of fish short-term

26 reproduction assay, and Catherine Aubee will complete the assay
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1 performance session with a presentation of the amphibian

2 metamorphosis assay.

3 The goals of the assay performance are listed

4 on this slide. The primary goal for this analysis is to

5 evaluate how well the Tier 1 assays can be executed by the

6 testing laboratories based on defined performance criteria and

7 the test guidelines. The second goal of this exercise to

8 identify any issues associated with the interpretation of

9 responses within each of the assays.

10 As mentioned previously, there are a total of

11 21 chemicals that were selected as case studies for this

12 analysis, and these chemicals represent chemical space of 52

13 pesticide actives that were included in the first group of

14 chemicals to receive EDSP Tier 1 test order. This subset of

15 chemicals represented a broader range of physical-chemical

16 properties, chemical classes and pesticide activities.

17 Please note as the presenters discuss the

18 results of the assays that the number of chemicals evaluated for

19 each of the assays may differ as some of the assays were

20 satisfied by other scientifically relevant information commonly

21 referred to as O-S-R-I or OSRI. OSRI is defined as the existing

22 data used to satisfy guideline requirements.

23 This table shows the number of case study

24 chemicals evaluated for each of the 11 Tier 1 assays. As

25 previously mentioned, some of the study requirements are

26 satisfied by OSRI. Please also keep in mind that although some
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1 of the assays have the same number of case studies, for example,

2 AR binding, uterotrophic, Hershberger and pubertal female all

3 have 17 case-study chemicals. This does not necessarily mean

4 that they are the same 17 chemicals for all four assays. Fish

5 short-term reproduction assay is all 21 chemicals evaluated for

6 that assay.

7 This slide outlines the data review process

8 that was followed for evaluating the Tier 1 assays. The Agency

9 has developed a standard set of standard evaluation procedures,

10 or SEPs, for each of the 11 Tier 1 assays. These SEPs were used

11 in conjunction with the appropriate EDSP Test Guidelines to

12 review the studies. 

13 The Agency also developed data evaluation

14 records or DERs. The DERs capture the data to determine how

15 well the assay conforms to the guideline. It also documents

16 information on materials and methods, study results, made strong

17 conclusions as well as the Agency's conclusions.

18 The Agency used a multi-tiered approach to

19 review the Tier 1 data. Primary reviews were performed either

20 by the Agency or contract scientists. Secondary reviews of the

21 DERs were performed by Agency scientists. And then finally, an

22 additional level of review was conducted by an interdisciplinary

23 committee to ensure consistency in the interpretation of the

24 assay results across all sets of chemicals.

25 Before discussing individual assays' results,

26 I'd like to also mention two topics that will be discussed
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1 throughout the talks today. One is performance criteria. Most

2 of the Tier 1 in vitro assays and some of the in vivo assays

3 have performance criteria established for controlled chemical

4 performance and/or specific assay parameters or conditions. And

5 some of the performance criteria are related to the second

6 topic, overt toxicity. 

7 Overt toxicity was observed in some of the in

8 vivo assays, and some examples of overt toxicity including

9 bodyweight loss in rats and possibly in fish studies, clinical

10 signs or erratic behavior in swimming or aquatic studies. For

11 the purpose of this analysis, we note where overt toxicity was

12 seen, and we indicate if potential endocrine-related effects

13 were seen in the presence of overt toxicity.

14 Next we're going to move into the discussion

15 of the in vitro assays. One this slide are the five in vitro

16 assays that we've mentioned previously, the ER binding assay,

17 the ER transcriptional activation assay or ERTA, AR binding

18 assay, steroidogenesis assay and aromatase assay.

19 This slide shows an overview of how we'll

20 present the five in vitro assays. First, I will provide

21 background information on each assay. Then I will discuss the

22 evaluation of the study including performance criteria, general

23 observations from the selected studies and the results. 

24 Finally, I will provide concluding statements

25 of how well the assay was executed.

26 Starting with the ER binding assay, this
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1 assay, as Dr. Touart had mentioned, measures the ability of a

2 test chemical to interact with ER. Rat uterine cytosols is the

3 source for the estrogen receptor and both ER alpha and ER beta

4 are expressed in this tissue. The assay cannot distinguish

5 between ER agonism and ER antagonism.

6 A total of 18 chemicals were evaluated for the

7 ER binding assay preliminary analysis. ER binding assay has two

8 components, the saturation binding experiment and the

9 competitive binding experiment. I will talk about each one

10 individually.

11 Starting with saturation binding, the purpose

12 of the saturation binding experiment is to determine if the ER

13 binds estradiol correctly as indicated by a dissociation

14 constant, or Kd value, and are the ER estrogen receptors present

15 in reasonable numbers in the cytosol preparation as indicated by

16 the Bmax. Saturation binding experiment is performed by using a

17 constant concentration of receptor with increasing

18 concentrations of the radiolabled estradiol. The amount of

19 radioactivity bound to the receptor is measured.

20 In the competitive binding experiment, the

21 objective is to determine whether the test chemical interacts

22 with the ER. In this case, the concentration of the radiolabled

23 ligand, estradiol and the estrogen receptors are held constant

24 and incubated with increasing concentrations of the test

25 chemical. If the test chemical does interact with the estrogen

26 receptor, increasing concentrations of the competitor will
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1 result in a descending concentration response curve.

2 The next slide, I show an example of a

3 competitive binding curve. The competitive binding assay is

4 composed of three independent runs. Each run includes a strong

5 positive control which is unlabeled estradiol, a weak positive

6 control norethindrone as all the studies we evaluated used 19

7 norethindrone as the weak positive control. And it also

8 includes a negative control. The test chemical is included and

9 tested over a range from 10 to -10 to 10 to -3 molar or up to

10 the limit of solubility. In this example, the test chemical did

11 not interact with the estrogen receptor.

12 Binding classifications are listed on this

13 slide. The classifications of a chemical is based on the result

14 of three independent binding runs. The three classification

15 categories are interactive, not interactive and equivocal. 

16 A run is classified as interactive if more

17 than 50 percent of the radiolabeled estradiol has been displaced

18 from the receptor. The run is classified as not interactive if

19 less than 25 percent of the radiolabeled ligand has been

20 displaced. And a run is considered equivocal if it does not

21 meet the criteria for interactive or non-interactive.

22 Shown here are the performance criteria for ER

23 saturation binding. The parameters include Bmax, Kd and

24 nonspecific binding. For the 18 chemicals evaluated, all

25 saturation binding performance criteria were met. I'd also like

26 to point out that the 18 chemicals evaluated represents the
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1 results from six different testing laboratories. It was common

2 for some laboratories to evaluate multiple test chemicals used

3 in the same cytosol prep.

4 This slide shows performance criteria for

5 estradiol for the competitive binding experiment. The left

6 column lists the parameters for the response curve, and the

7 middle columns show the expected values for each criteria. The

8 column on the right show the actual ranges of values observed

9 across the 18 chemicals evaluated.

10 This graph summarizes the performance criteria

11 for the positive control 17 beta estradiol. The dark gray boxes

12 indicate the performance criterion was met. Although it may be

13 difficult to distinguish the shades of gray, the important point

14 emphasized here is that the large majority of the chemicals

15 performance criterion were met.

16 This slide shows the binding classifications

17 of the 18 chemicals evaluated. Please note, this table was

18 incorrectly labeled on page 27 of the White Paper. That's Table

19 4. The table on this slide shows the correct result of the 18

20 chemicals. None of the 18 chemicals classified as interacted

21 with the ER. Two were classified equivocal, and 16 were

22 classified as not interactive.

23 For the ER binding assay, we conclude that

24 although the number of the studies evaluated for this analysis

25 did not meet all of the performance criteria, in general, when

26 the performance criteria were not met, the values fell slightly
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1 outside the expected ranges. Overall, the laboratory

2 performance of the ER binding assay was generally acceptable.

3 Next I'd like to move to the ER

4 transcriptional activation assay. This system evaluates

5 potential of a chemical to bind and activate the ER using a

6 luciferase reporter gene system. 

7 Some general background information is

8 provided on this slide. The assay uses the HeLa-derived cell

9 line containing human ER alpha expression construct and an

10 estrogen response luciferase construct.

11 Test chemical binds as an ER agonist,

12 luciferase enzyme is produced. When the substrate luciferin is

13 added, it caused the light-emitting reaction that we measured. 

14 This assay measures ER alpha agonist activity only. Nineteen

15 chemicals were evaluated in this analysis.

16 A chemical is considered positive in this

17 assay if inducive response is at least 10 percent of the maximum

18 response of the positive control which in this assay is one

19 animal estradiol. The positive response must be observed in at

20 least two of the three experimental runs.

21 Test guide recommends that concurrent

22 reference chemicals be included in each experiment to ensure

23 that the test system is running properly. Listed here are the

24 four reference chemicals with the expected ranges of values for

25 each parameter. 17 beta estradiol is used as a strong positive

26 control. 17 alpha estradiol is included as a weak positive
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1 control. Methyltestosterone is included as a very weak positive

2 control, and corticosterone serves as a negative control count.

3 For 17 beta estradiol, all performance

4 criteria were met for 10 of the 19 chemicals. 17 alpha

5 estradiol, all parameters were met for 5 of the 19 chemicals. 

6 Testing facilities did appear to have difficulty meeting the

7 guideline performance criteria for 17 alpha methyltestosterone. 

8 However, in most cases for the referenced chemicals, the values

9 that did not meet the criteria fell slightly outside of the

10 expected ranges.

11 This table shows the classification of the 19

12 chemicals evaluated with ERTA. There were 5 positives, 13

13 negatives, and one study was considered inconclusive based on

14 cytotoxicity that was observed in the reference chemicals that

15 compounded interpretation of the study.

16 In conclusion, the ERTA, all the performance

17 criteria for the referenced chemicals were not met for the

18 majority of the chemicals. However, in most cases, the values

19 were close to the guideline ranges and did not affect the

20 interpretation of the assay. Most chemicals had difficulty

21 meeting performance criteria for the very weak agonist, 17 alpha

22 methyltestosterone. And all but one chemical was classified as

23 either negative or positive in this assay.

24 Next I'll move to the AR binding assay. I

25 will not spend a lot of time going into the background

26 information since it's the same principle as seen in the ER
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1 binding assay. This assay detects the ability of the chemical

2 to interact with the androgen receptor.

3 Seventeen chemicals were evaluated for this

4 assay. As with the ER binding assay, there were two components

5 to the assay, saturation binding and competitive binding. The

6 AR saturation binding experiment is similar to ER binding with

7 the exception that AR binding assay uses the synthetic androgen

8 R1881 as a reference androgen ligand and rat prostate cytosol as

9 a source of the AR.

10 The objective interpretation of this assay is

11 similar to the ER binding assay. The assay uses constant

12 concentration of the radiolabeled ligand, constant concentration

13 of receptor with increasing concentrations of the test chemical.

14 If the test chemical competes with the ligand, a descending

15 concentration curve is produced as shown in the next slide.

16 Here we see an example of an AR competitive

17 binding curve. The assay is composed of three independent runs

18 with each run containing unlabeled R1881 as the strong positive

19 control, dexamethasone as a weak positive control, and the test

20 chemical tested in the range from 10 to -10 to 10 to -3 molar or

21 up to the limit of solubility. 

22 In this example, the test chemical showed a

23 positive response producing a descending concentration response

24 curve. The binding classifications have shown, on this slide,

25 for classification purposes the term "binder" and "non-binder"

26 are equivalent to the terms that are used in the ER binding
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1 assay which are interactive and not interactive.

2 This slide shows performance criteria of the

3 Kd and nonspecific bindings for the AR binding assay. 

4 And here, we see the results of the saturation

5 binding performance criteria. The Kd values were not met for 4

6 of the cytosol preparations that were used for a total of 8

7 chemicals. All laboratories met the nonspecific binding

8 criteria.

9 Performance criteria for the competitive

10 binding experiments is shown here with the curved parameters

11 from the bottom, top plateau levels and the hill slope. The

12 expected values of strong positive and weak positive controls

13 are shown.

14 This graph shows how well the assays met the

15 competitive binding performance criteria for R1881. In nearly

16 all cases, the performance criteria were met for the strong

17 positive control. And for the weak positive control, all

18 performance criteria were met for all chemicals.

19 This table shows the classifications for the

20 17 chemicals evaluated in the AR binding assay. Three chemicals

21 were classified as binder. Ten were classified as non-binder. 

22 Three were classified as equivocal, and one was considered

23 untestable because it was not soluble above concentrations of 10

24 to 6 molar in the assay system.

25 In conclusion for the AR binding assay,

26 although the performance criteria were not met for all
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1 chemicals, particularly with regards to saturation binding;

2 however, when the cytosols were used in the competitive binding

3 criteria, nearly all performance criteria were met. Overall,

4 the laboratory performance of the AR binding assay was generally

5 acceptable.

6 Next I'll move to the steroidogenesis assay. 

7 This assay uses the adrenal corticocarcinoma cell on H295R. The

8 cell line expresses all enzymes needed to synthesize

9 testosterone and estradiol. Eighteen chemicals were evaluated

10 in this analysis.

11 This diagram shows the basics of how the assay

12 is performed. The assay is generally conducted in 24 well

13 plates, and the endpoints measured are estradiol and

14 testosterone production. Three independent runs are conducted,

15 and each run includes the positive control for inducer and

16 inhibitor of hormone production.

17 This slide shows a test chemical that resulted

18 in estradiol inhibition on the left panel, and the graph on the

19 right shows an example of test chemicals that inhibit

20 testosterone production. Positive responses are indicated by a

21 statistically significant increase or decrease of estradiol/

22 testosterone production.

23 Performance criteria for basal-level

24 production of testosterone, estradiol and the cell system are

25 shown here as well as the criteria for the minimum level of

26 hormone induction inhibition for the positive control chemicals
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1 for both estradiol and testosterone.

2 The graph on this slide shows how well the 18

3 chemicals performs in this assay. Although it may be difficult

4 again to read all the shades of gray, the overall message is

5 that most of the performance criteria are met for most of the

6 parameters. Again, in cases where the performance criteria were

7 not met, they were typically close to the expected values and

8 did not impact the interpretation of the study.

9 This table shows the results of the 18

10 chemicals. Six chemicals were a negative in this assay, and ten

11 chemicals showed some effect on either estradiol and/or

12 testosterone production. Two chemicals were classified as

13 equivocal.

14 In conclusion for this steroidogenesis assay,

15 laboratory performance in the steroidogenesis assay was

16 generally consistent across the 18 chemicals evaluated. 

17 Although not all performance criteria were met for every

18 chemical, the experimental values were close to the expected

19 values, and the deviations did not affect the interpretation of

20 the study.

21 Finally, the last in vitro assay I'll discuss

22 is the aromatase assay. This assay is used to determine if a

23 test chemical can inhibit Step 19 aromatase enzyme activity

24 which is the enzyme that converts androgens to estrogens. The

25 components of the reaction are shown here on this slide. A

26 radiolabeled estrogen dione substrate is added to microsomal
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1 complex containing the aromatase plus the test chemical,

2 concentrations of .1nM - 1.0mM. The endpoint measurement in

3 this assay is the production of tritiated water.

4 Some additional background information on this

5 assay measures the rate at which tritiated water is produced

6 with increasing concentrations of test chemical. Three

7 independent runs are conducted. Each run has a full activity

8 control, background activity control, and each run also includes

9 eight concentrations of the positive control and eight

10 concentrations of the test chemical. Eighteen chemicals were

11 evaluated for this analysis, and all 18 studies evaluated used

12 human recombitant microsomals as the source for aromatase.

13 Classification categories for this assay are

14 shown here. A chemical is classified as an inhibitor if there's

15 a 50 percent or more reduction in aromatase activity. Chemicals

16 that allow 50 to 75 percent activity are classified as

17 equivocal, and non-inhibitors do not fit the inhibition model or

18 they do not reduce activity by 25 percent.

19 Performance criteria for this assay are shown

20 here. Top half of the table show the criteria for experimental

21 parameters, and the bottom half show the performance criteria

22 for the positive controlled response curves.

23 This slide shows the performance criteria

24 results. The performing laboratories were able to meet most of

25 the criteria for most of the chemicals. In the case of the

26 criteria not met, values predominantly fell slightly outside
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1 expected ranges and generally did not impact the overall

2 interpretation of the study.

3 For the 18 chemicals evaluated in this assay,

4 two chemicals were classified as inhibitors, five chemicals were

5 classified as equivocal with those chemicals showing some

6 limited inhibition at the highest test concentration. Eleven

7 chemicals were classified as non-inhibitors aromatase activity.

8 The laboratory performance aromatase assay was

9 generally consistent across the test chemicals evaluated. As

10 with the other in vitro assays, most of the performance criteria

11 were met, and in cases where they were not met, the results

12 slightly exceeded or fell outside the expected range of the

13 performance criteria.

14 Finally, I'd like to briefly discuss a few

15 additional observations in this initial set of chemicals. One

16 is proficiency testing. Proficiency testing for the

17 laboratories was not consistently reported across chemicals and

18 in some cases proficiency data were not included in the study

19 reports, or in other cases, proficiency testing was not

20 mentioned in the study report.

21 The second observation has to do with

22 solubility and cytotoxicity. Test chemical and solubility and

23 cytotoxicity was observed with many of the chemicals at the

24 highest test concentrations. When test chemical solubility

25 issues were encountered some, but not all laboratories followed

26 EPA guidance by adding additional half-log concentrations as
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1 recommended in many of the in vitro test guidelines.

2 These additional half-log concentrations

3 helped to better define a lower portion of the response curve

4 and aided the reviewers in interpreting the results. We

5 encourage the use of half-log concentrations when appropriate.

6 And the last observation was for saturation

7 binding data, the saturation binding data from the binding

8 assays, the ER and AR binding assays. These data were not

9 always submitted within the study report, although they were

10 submitted subsequently upon request by the Agency. We

11 encouraged laboratories to include those data within the study

12 reports for reviewing purposes.

13 Thank you for your attention.

14 DR. DANIEL SCHLENK: Any questions for Dr.

15 Akerman?

16 Dr. Scott.

17 DR. GEOFFREY SCOTT: Across the different

18 assays, the word "slight" is used. Is that a consistent

19 quantitative deviation from the norm across the assays?

20 DR. GREGORY AKERMAN: We use that word just to

21 characterize when the values fell barely outside that we didn't

22 really use the same criteria. Of course, there were some

23 instances where the values fell considerably outside the

24 obvious. But for the most part, if they were very close to the

25 expected ranges we used the word slightly.

26 DR. GEOFFREY SCOTT: I mean is it a percentile
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1 or, you know, is it within 10 percent, 5 percent? You know, I

2 think that was the problem I had in reading through this

3 section, is not having that quantitative. 

4 I understand when a value is pretty close and

5 we want to help you with this. So I think one of the things

6 that would help us in helping you with defining this is, you

7 know, I happen to kind of agree with most instances if

8 something's just slightly outside of the norm, you make a value

9 judgment as to whether or not that's affecting the assay or not.

10

11 So I think the panel will probably want to

12 deliberate and comment on that. So any information you can

13 provide us on that would be helpful.

14 DR. DANIEL SCHLENK: Okay. Dr. Schultz.

15 DR. TERRY SCHULTZ: Would it be safe to say

16 that the Agency has more experience with the ER-related tests

17 than the AR-related tests? And I only refer to that because

18 there has been a couple of Scientific Advisory Panels that have

19 kind of focused on that area within the prescreening scheme.

20 DR. MARY MANIBUSAN: As the statute is focused

21 on estrogenic pathway, that has been our primary focus with the

22 extension to the androgen and thyroid.

23 In terms of our experience, I don't believe

24 our experience is that different across ER and AR. It's a

25 matter of bringing before the panel our experience with this

26 initial list and how this initial list performs for the Tier 1
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1 assays as a whole.

2 DR. DANIEL SCHLENK: Dr. Schultz, follow?

3 DR. TERRY SCHULTZ: Yes, as a follow-up. Has

4 the Agency looked at revising the performance guidelines so that

5 they can do a better job of capturing what is going to be the

6 inherent inter-laboratory rare-ability in some of these test? 

7 I, like Dr. Scott, am concerned about using the word "slight"

8 and things like that. That doesn't sound very scientific and it

9 does lead to perception that there's something going on that is

10 varied in the report or the information and the data.

11 DR. DANIEL SCHLENK: Dr. Touart, going to

12 respond to that?

13 DR. LESLIE TOUART: The context with the

14 various assays in terms of the performance criteria that were

15 imposed were part of the validation program originally. And so

16 those fell within the context of what we perceived performance

17 was being achieved in the number of iterations that we have. 

18 I think the context here in a broader, you

19 know, test of the assays in context in terms of how these are

20 done through, you know, routine laboratory testing, you know,

21 there is the indication that in data they were seeing some fall

22 short of these criteria. And so the consideration is well, are

23 we still able to make determinations, you know, in terms of

24 determining that the assay was conducted satisfactorily, and we

25 can form a conclusion, in which case the performance criteria

26 could, you know, conceivably be modified to accommodate that. 
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1 In those cases where it leads to more ambiguities or concerns.

2 When we set performance criteria, these things

3 are also more or less recommendations, they are targets in terms

4 of what we're trying to achieve to ensure quality, you know,

5 studies to be performed. So they're not established as bright

6 lines in terms of, you know, kind of a pass/fail situation. 

7 But there would be situations where you fall

8 short enough in enough of the criteria that you might deem the

9 test unsatisfactory and would need to be repeating. So I think

10 there would be some room for going back and revisiting these

11 based upon this broader context to determine if we need to

12 modify or at least loosen some of these criteria to allow better

13 interpretation and at least more successes in terms of

14 laboratory conduct of these assays.

15 DR. DANIEL SCHLENK: Okay. Dr. Maclatchy.

16 DR. DEBORAH MACLATCHY: Similarly along those

17 same lines, there were a number of places in the White Paper

18 where I've seen that there was missing information coming from

19 the lab. And one of the questions I have is how iterative is

20 EPA when they're getting the information from the labs as far as

21 whether or not the labs might have done things, and they didn't

22 report them so the data's available or whether or not they

23 didn't actually do that aspect and the data's not available?

24 DR. GREGORY AKERMAN: Yeah, most cases the

25 data was available; we just had to go back and ask them and then

26 they sent it us. It wasn't like the data wasn't available. 
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1 It's just not included within the study report for some reason.

2 DR. DEBORAH MACLATCHY: Okay. So when it's

3 not available, it clearly is not available?

4 DR. GREGORY AKERMAN: Correct.

5 DR. DEBORAH MACLATCHY: Okay. And I guess as

6 a supplement to that, in some cases it looks like the criteria

7 really hadn't been met. And did the labs redo the assay saying

8 okay, this batch didn't work, and so I'm going to test the

9 chemical again and then send in other data, or how did that

10 process happen within the lab?

11 DR. GREGORY AKERMAN: No. I mean there were

12 instances, and you can tell from the study report that they had

13 failed runs and they didn't submit and they had to repeat it and

14 explain why there was failed runs for a particular experiment. 

15 But the data was submitted and they can come back in and submit

16 additional data later if their criteria were not met.

17 DR. DANIEL SCHLENK: Any other questions for

18 the panel?

19 Yes, Dr. Tillitt.

20 DR. DON TILLITT: You know, aside from the

21 performance criteria or really probably performance guidelines,

22 you report that proficiency testing was not consistently

23 reported. How comfortable are you with not getting all of that

24 information and evaluating that performance criteria without

25 having that proficiency testing?

26 DR. GREGORY AKERMAN: Many of these labs have
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1 tested multiple chemicals now so I think they've shown that they

2 are proficient in testing using these assays. There's only a

3 few cases where the proficiency testing information is not

4 reported. Other times they would make statements saying that

5 the proficiency testing results were what they expected. They

6 just didn't submit the data necessarily, but they showed the

7 report and describe in the report that they were able to conduct

8 the assay. 

9 They had performed the proficiency testing,

10 but they just didn't include the actual data in the study

11 report.

12 DR. DANIEL SCHLENK: Could you state your

13 name, please?

14 DR. JOHN LICCIONE: John Liccione from HED. 

15 To give you an example, the steroidogenesis assay, when the

16 proficiency test wasn't included, we carefully looked at the

17 quality control plate that were run concurrently, that gave us

18 another way of looking at proficiency.

19 DR. DANIEL SCHLENK: Any other questions?

20 (No audible response) 

21 DR. DANIEL SCHLENK: Okay. Let's go ahead and

22 move on for in vivo mammalian assays. That will be Dr.

23 Liccione.

24 DR. JOHN LICCIONE: Thank you. Welcome

25 everyone. In this presentation, I'm going to discuss the

26 available in vivo rodent endocrine screening assays. And I will
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1 start with a brief description of each individual assay

2 including its purpose and study design, how well it adhered to

3 performance criteria, any technical issues that had been

4 identified, and finally, a conclusion for each assay.

5 You've already seen these assays in a number

6 of slides, but just quickly to go over them one more time, we

7 have four mammalian in vivo assays, the uterotrophic,

8 Hershberger, male pubertal and the female pubertal assays.

9 To begin with, we'll start with the

10 uterotrophic assay. This assay evaluates the ability of a

11 chemical to elicit an agonist estrogen response, and the

12 important metric is an increase in uterine weight. It is a

13 rapid screen and it incorporates first pass metabolism if the

14 oral route is used. It has a mechanistic sensitivity and

15 specificity, and there are two models that are available, the

16 immature female and the adult ovariectomized female rat models.

17 In the immature model, postnatal day 18 rats

18 are treated for three days with dosing by either the oral or

19 subcutaneous routes, and then the rats are subjected to a

20 necropsy. In the adult ovariectomized model, which is the

21 preferred model, adult females, ages six to eight weeks, are

22 dosed for at least three days and then the rats are necropsied.

23 Endpoints that are pertinent for both models

24 include uterine weights, both wet and blotted, and other things

25 such as bodyweight, food consumption, clinical signs and

26 mortality. Optional endpoints include histology of the uterus,
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1 cervix and the vagina. 

2 A positive estrogenic response is defined as a

3 significant increase in uterine weight. A positive control, 17

4 alpha ethynyl estradiol is included in the assay to verify

5 sensitivity in performance of the assay. 

6 From a baseline study with the positive

7 control, an effective dose of 70 to 80 is selected for use in

8 the assay. The performance criteria for this assay is that the

9 blotted uterine weights should be .09 percent of the final

10 bodyweight for the immature model and .04 percent of the final

11 bodyweight for the adult ovariectomized model.

12 In regard to the summary of the performance of

13 this assay, all 17 chemicals were negative for the estrogenic

14 component that was measured. And the positive control did

15 increase uterine weight as expected. There were no major

16 technical issues with the assay. Baseline control studies were

17 not reported for two test chemicals and overt toxicity was noted

18 at the highest dose tested for one chemical and this included

19 decreased bodyweight and some clinical signs.

20 In conclusion, the assay performance was

21 consistent across all 17 test chemicals. The assay performed as

22 expected, and dosing was considered adequate.

23 Moving on to the Hershberger assay, the

24 Hershberger assay detects androgenic agonists, antagonists as

25 well as 5 alpha-reductase inhibitors. It is also a rapid screen

26 and incorporates first pass metabolism if an oral route is used.
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1 It has mechanistic specificity and sensitivity. In this assay,

2 postnatal day of rats from 42 to about 48 male rats that had

3 been castrated are dosed for 10 days. Neither the oral or the

4 subcutaneous route can be used, and the high dose tested is

5 selected at or below a maximum tolerated dose not to exceed a

6 limit dose.

7 The primary endocrine-related endpoints that

8 are measured are based on the weights of five known

9 androgen-dependent tissues in the castrated male rats. These

10 include the ventral prostate, seminal vesicles, the levator ani

11 and bulbocavernosus muscles -- herein I will just refer to it as

12 LABC -- Cowper's glands and the glans penis. Other endpoints

13 include clinical signs, bodyweight mortality, weights of the

14 liver, adrenal and the kidney. Optional endpoints could include

15 testosterone, luteinizing hormone as well as follicle

16 stimulating hormone.

17 A positive androgenic response is defined as

18 an increase in two or more of the androgen-dependent weights. A

19 positive anti-androgenic response is identified as a decrease in

20 two or more androgen-dependent weights. This assay includes

21 performance criteria for the organ weights, specifically the

22 maximum recommended percent coefficient of variations for the

23 sex accessory tissues.

24 For the summary of the performance of this

25 assay, none of the 17 chemicals showed an androgenic response. 

26 Seven did show an anti-androgenic response. Four of the seven
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1 were in the presence of overt toxicity. Effects included a

2 decreased bodyweight, food consumptions and clinical signs. 

3 There were no major issues identified in the assay. The percent

4 of coefficient variation in excess of performance criteria was

5 noted in 14 of the 17 studies, but this did not have a major

6 impact on study interpretation.

7 In conclusion, the assays conformed to the

8 guidelines. The assay performance was consistent across the 17

9 chemicals tested, and dosing was considered adequate based on

10 the studies that were submitted.

11 Moving on to the female rat pubertal assay,

12 the purpose of the female rat pubertal assay is to identify

13 pubertal development and thyroid function in the intact female

14 juvenile rats. It has the ability to detect antithyroid,

15 estrogenic, anti-estrogenic chemicals or alterations in the

16 hypothalamic pituitary gonadal axis.

17 In the study design, female rats are treated

18 orally daily from postnatal days 22 to 42. The weights and age

19 of attainment of vaginal opening are recorded. Statistical

20 analysis recommended include an ANCOVA an analysis of covariance

21 with bodyweight at weaning as covariate for the age and

22 bodyweight at vaginal opening as well as for organ weights. 

23 Endpoints measured include the

24 endocrine-related endpoints, specifically age at vaginal

25 opening, first estrus, changes in estrus cyclicity, weights and

26 histology of the ovary, uterus, thyroid and levels of T4 and
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1 TSH. Other endpoints include clinical signs, mortality,

2 bodyweight and clinical chemistry, weights of the liver,

3 pituitary, adrenal and the kidney.

4 For dose selection, the high dose is selected

5 at or just below the maximum tolerated dose but not to exceed

6 the limit dose. The toxicity of the profile of the chemical is

7 also considered. The second dose level is spaced to produce a

8 lesser degree of toxicity relative to the high dose tested. 

9 With regards to some strength and limitations

10 of the assay, this assay involves a sensitive period of

11 development, the pubertal period. It has a well-established

12 knowledge base of estrogen and thyroid-dependent endpoints. 

13 There is variability of organ weights associated with the

14 ovarian cycle. And the assay has an inability to identify

15 specific mechanisms of action.

16 In assessing the results in terms of what a

17 positive response is, the direction and magnitude of the changes

18 in the following endpoints are considered, particularly any

19 alterations in the vaginal estrus, vaginal opening, first

20 estrus, estrus cyclicity, weights and histology of the ovaries

21 and the uterus, changes in thyroid weight and histology and the

22 levels of T4 and TSH.

23 Performance criteria for the vehicle-control

24 includes mean coefficient variation and range of values for a

25 number of endpoints including the weights of uterus, ovaries,

26 thyroid, adrenal, kidneys and pituitary, T4 levels, final
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1 bodyweight and age and weight of vaginal opening. However,

2 performance criteria are available only for the Sprague-Dawley

3 rat.

4 For the summary of the performance of this

5 assay, 6 of the 17 chemicals displayed a response in at least

6 one estrogen-dependent endpoint. Four of the six were in the

7 presence of overt toxicity the effect were generally decreased

8 bodyweight, food consumption and clinical signs. 

9 Eleven of the seventeen chemicals showed a

10 response in the thyroid-related endpoints, and four of the

11 eleven were in the presence of overt toxicity.

12 There were no major issues that compromised

13 the conduct and/or interpretation of the study. There were some

14 minor deviations in percent coefficient outside the limits of

15 the performance criteria. But this was considered not to have

16 any major impact on the interpretation of the study.

17 In conclusion, the assays were conducted in

18 accordance with the guidelines, and the assay performance was

19 consistent across all of the chemicals tested, and dosing was

20 considered adequate.

21 Moving on to our last assay, the male rat

22 pubertal assay, the purpose of the male pubertal assay is to

23 identify effects on pubertal development and thyroid function in

24 the intact male juvenile rat. It detects chemicals with

25 antithyroid, androgenic, anti-androgenic activities for any

26 perturbations in the HPG axis.
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1 In terms of the study design, male rats are

2 treated orally daily from postnatal day 23 to 53. Rats are

3 examined for preputial separation beginning on postnatal day 30.

4 Statistical analysis also include an ANCOVA, a bodyweight at

5 weaning as to covariate, for age and body weight preputial

6 separation and also organ weights.

7 Endpoints again are measured including the

8 endocrine-related endpoints, age and weight at preputial

9 separation, the weights of ventral prostate, dorsal prostate,

10 LABC, seminal vesicle, epididymides, testes, thyroid, histology

11 of the epididymides, testes, thyroid, T4 and TSH and

12 testosterone levels.

13 Other endpoints include the clinical signs,

14 mortality, bodyweight, weights of liver, kidneys, pituitary and

15 adrenal and also histology of the kidney and some clinical

16 chemistry measurements.

17 For dose selection, very similar to the female

18 rat pubertal assay, the high dose is selected at or just below

19 the maximum tolerated dose but not to exceed a limit dose. The

20 toxicity profile is also considered in the dose selection. The

21 second dose level is spaced to produce a lesser degree of

22 toxicity relative to the high dose tested.

23 With respect to strengths and limitations, the

24 assay involves a sensitive pubertal period of development and

25 includes metabolism, and it has a well-established knowledge

26 base of androgen- and thyroid-dependent endpoints and has an
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1 inability to identify specific mechanisms of action.

2 And similarly to the female rat pubertal assay

3 in terms of how do we assess a positive response, we look at the

4 direction and magnitude of the changes and the important

5 endpoints that would include alterations in preputial

6 separation, changes in the androgen-sensitive organ weights,

7 testosterone levels, thyroid weight and histology with T4 and

8 TSH levels.

9 In regard to performance criteria, the

10 vehicle-control also includes criteria for the mean coefficient

11 and variation and range of values for various endpoints, the

12 important endpoints relating to accessory tissues, epididymides,

13 testes, thyroid, adrenal, kidneys, liver, pituitary as well as

14 testosterone levels, age at preputial separation, the levels of

15 T4 and TSH, weaning weight and final weight.

16 For summary of performance, 9 of the 16

17 chemicals showed a response in at least one androgen-dependent

18 endpoint. Two of the nine were in the presence of overt

19 toxicity. Once again, decreases in bodyweight, food

20 consumption, clinical signs. 

21 Seven of the six chemical showed a response in

22 thyroid-related endpoints. Two of the seven were in the

23 presence of overt toxicity. There were no major deficiencies

24 that compromised the conduct or interpretation of the studies.

25 And in conclusion, the assay was conducted in

26 accordance with the guidelines. Assay performance was
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1 consistent across all 16 chemicals, and dosing was considered

2 adequate.

3 DR. DANIEL SCHLENK: Okay. Any questions for

4 Dr. Liccione?

5 Dr. Selvage.

6 DR. DANIEL SELVAGE: Hi. Dan Selvage. Other

7 than overt toxicity signs and adrenal weight, I may know the

8 answer to this, but I just want to ask; did you guys measure any

9 HPA axis parameters, like corticosterone or anything like that?

10 DR. JOHN LICCIONE: Oh, no the stress

11 hormones?

12 DR. DANIEL SELVAGE: Yeah.

13 DR. JOHN LICCIONE: Stress is obviously one of

14 the concerns that they can affect and that's why one of the

15 covariants are generally included in these assays. We know that

16 that could -- but none of these assays measured in general

17 corticosterone.

18 DR. DANIEL SELVAGE: Okay. So no ACTH or

19 anything like that?

20 DR. JOHN LICCIONE: No.

21 DR. DANIEL SCHLENK: Dr. Liccione, can I just

22 ask you to speak up in the mic a little bit closer? Thanks.

23 DR. DANIEL SCHLENK: Yeah. Dr. Green.

24 DR. SHERRIL GREEN: I wanted to know what were

25 the strains, the genetic backgrounds, of both the male and the

26 female rats, and was that consistent across all these studies?
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1 DR. JOHN LICCIONE: The Sprague-Dawley rat was

2 used in the male and female pubertals except for one chemical

3 that used the Wistar rat.

4 DR. SHERILL GREEN: And male and female

5 Sprague-Dawley? 

6 DR. JOHN LICCIONE: Right. Sprague-Dawley,

7 Wistar.

8 DR. SHERRIL GREEN: And I have a second

9 question, if that's all right, related to the housing and the

10 standardization across the study. Were they all in the same

11 kind of cages, similar or same food, bedding, water? Just to

12 rule out the possibility of androgenic, estrogenic effect?

13 DR. JOHN LICCIONE: Right, exactly. Right. 

14 And they adhered to the guidelines in terms of randomizing the

15 bodyweight they grew -- for the female pubertal assay obviously

16 it's very important to present the final estrogen content of the

17 diet. They all conformed and we didn't see any major problems

18 with housing in range of the bodyweight and content of the diets

19 in the female pubertal were critical.

20 DR. DANIEL SCHLENK: Dr. Leonard.

21 DR. JACK LEONARD: Was there any comparison

22 made between the route of administration of the test chemicals? 

23 I noticed sub-q was suggested as well as oral gavage. Was a

24 comparison made between those two routes of administration?

25 DR. JOHN LICCIONE: In terms of the

26 guidelines?
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1 DR. JACK LEONARD: In terms of testing the

2 efficacy or the impact of the compound on any of the in vivo

3 assays you were talking about.

4 DR. JOHN LICCIONE: Oh, okay. They came from

5 the validation studies, the recommendation for the uterotrophic

6 assay to use the subcutaneous, more came based on the validation

7 as a screening assay to enhance the response. And for the

8 Hershberger, both oral or subcutaneous can be used, and I think

9 in most cases they used oral. All cases for Hershberger used

10 the oral. I think there was one subcutaneous for the

11 uterotrophic. Most elected to use the oral which is okay with

12 the guidelines, the profile of the chemicals considered. Of

13 course, for the pubertals they were all oral.

14 DR. DANIEL SCHLENK: Dr. Delclos.

15 DR. BARRY DELCLOS: Since you mentioned

16 phytoestrogen, in the validation studies, I know that the OACD

17 in the uterotrophic assay didn't test the phytoestrogen levels. 

18 Did they do that in the pubertal assays as well? Was there any

19 effect on the thyroid hormones?

20 DR. JOHN LICCIONE: Oh, you mean in terms of

21 validating it?

22 DR. BARRY DELCLOS: When it was validated, did

23 they do varied phytoestrogen contents in the pubertal assays as

24 well?

25 DR. LESLIE TOUART: This is Les Touart

26 responding. During the validation programs, as part of the
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1 development of the assays, that was something that was looked at

2 in terms of phytoestrogens, and even looking at

3 phytoestrogen-free type diets, and that's difficult because

4 within the rats they still need some sterile components within

5 the diet itself.

6 But in conduct I think that the

7 recommendations that were made were to kind of limit the amounts

8 to the extent and try to use some standardized diets, you know,

9 to do that. And as long as the diets were, you know, kind of

10 uniform or standard, I think that was considered to be

11 sufficient. But more details would be available in the actual

12 validation reports for those assays as they were developed.

13 DR. BARRY DELCLOS: I was just wondering, I

14 mean the range is quite large, up to 350 parts per million, I

15 think. I'm familiar with the uterotrophic assays. I don't

16 remember much about the other assays or whether that was tested

17 and if thyroid hormone levels would be something. I would be

18 interested in knowing if they were varied across. That's okay. 

19 I understand.

20 DR. DANIEL SCHLENK: Yes, Dr. Piersma.

21 DR. ALDERT PIERSMA: In those cases where you

22 had a specific effect on the endpoint in the assay in the

23 presence of overt toxicity, did you always consider the specific

24 effect as the primary effect or the primary mechanism affected? 

25 Or were there cases where you would consider the overt toxicity

26 mediating the other endpoint? I mean have you made those
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1 assessments in each specific case?

2 DR. JOHN LICCIONE: Yeah. We did consider the

3 overt toxicity as whether that's driving the endocrine-related

4 effect. So that's where we noted those cases where

5 endocrine-related endpoints that were changed were in the

6 presence of overt toxicity in cases where they were not at the

7 lower doses. We didn't conclude mechanistically but we did know

8 that that's a concern.

9 DR. ALDERT PIERSMA: Okay. So to be

10 absolutely clear, where you stated that there was overt toxicity

11 also present, it is not clear whether the compound had a

12 specific effect on the specific endpoint?

13 DR. JOHN LICCIONE: Exactly. Exactly. And

14 actually, one of the charge questions related to the overt

15 toxicity.

16 DR. ALDERT PIERSMA: Thank you.

17 DR. DANIEL SCHLENK: Dr. Horseman?

18 DR. NELSON HORSEMAN: Well, back in Dr.

19 Touart's talk he presented a very nice slide showing the

20 construction of the feedback loops and such, which there's a lot

21 of information inherent in that. I noticed the only assay in

22 which the LH and FSH variables may be optional is in the

23 Hershberger assay. Is there information in that LH and FSH data

24 that -- was that done so that's optional, and is there

25 information in there that would help to increase the information

26 content?
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1 DR. JOHN LICCIONE: Are you referring to

2 during the validation of the assay the LH and FSH?

3 DR. NELSON HORSEMAN: I'm mostly referring to

4 the conduct of this Tier 1 screening.

5 DR. JOHN LICCIONE: Oh, no, no.

6 DR. NELSON HORSEMAN: None of those were?

7 DR. JOHN LICCIONE: The FSH or LH was not

8 included.

9 DR. DANIEL SCHLENK: Okay. Any other

10 questions?

11 Dr. Scott.

12 DR. GEOFFREY SCOTT: Geoff Scott. Just a

13 little clarification on the selection of the second dose. Could

14 you just go back and re-amplify how you do that again, and in

15 terms of design? I understood the top dose and how you did

16 that.

17 DR. JOHN LICCIONE: Okay. What the guidelines

18 specify is you have a high dose that should not exceed the

19 maximum tolerated dose. And for the female and male pubertals

20 it's half that. 

21 DR. GEOFFREY SCOTT: Oh, so 50 percent?

22 DR. JOHN LICCIONE: But keep in mind the

23 toxicity profile of the chemicals considered in all of these

24 labs did provide dose selection rationale for selection of the

25 doses, which we found adequate in terms of the range findings

26 and sometimes other, actually in Hershbergers and uterotrophic
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1 assays, the results of dose to inform on these apical assays.

2 DR. GEOFFREY SCOTT: You answered my -- what I

3 really want to know is there are specific guidelines?

4 DR. JOHN LICCIONE: Yeah. That's correct.

5 DR. GEOFFREY SCOTT: Okay. Thank you.

6 DR. DANIEL SCHLENK: Dr. Klaine.

7 DR. STEPHEN KLAINE: Just to follow up on

8 that, so in every case when you did find overt toxicity at the

9 higher dose, and you reported that, you saw no effects at the

10 lower dose?

11 DR. JOHN LICCIONE: No. In some cases, there

12 were endocrine-related effects that were in the absence of overt

13 toxicity. We wanted to dissect it out clearly without making

14 any conclusions about the overt toxicity or mechanism knowing

15 that there were some effects at the lower doses.

16 DR. STEPHEN KLAINE: Okay. And they were

17 scored positively?

18 DR. JOHN LICCIONE: Right. Correct. It's

19 based on the performance criteria and guidelines.

20 DR. DANIEL SCHLENK: Any other questions,

21 clarification?

22 Yes, Dr. Roby.

23 DR. KATHERINE ROBY: Just then to clarify,

24 when there was toxicity at a higher dose, the guidance was not

25 to repeat two lower doses but only one?

26 DR. JOHN LICCIONE: Well, what the guidelines
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1 say is you can do a limit dosing if there's no toxicity or

2 you're really not exceed the maximum tolerated dose, and then

3 you'd go to a lower dose; in the pubertal assays it's halfway

4 where you don't have any evidence of any adverse effects. 

5 DR. KATHERINE ROBY: So then to clarify, if

6 there was toxicity at a higher dose, then only a single lower

7 dose would be included in the analysis?

8 DR. JOHN LICCIONE: Yes. Often there was only

9 one. Occasionally, they did two, but most of the time was only

10 one dose.

11 DR. DANIEL SCHLENK: Okay. Dr. Horseman.

12 DR. NELSON HORSEMAN: So this obviously is the

13 favorite question about the toxicity. And it relates to the

14 guidance to give the compound at or near the maximum-tolerated

15 dose which seems to me that invites toxicity. Given that these

16 assays depend upon a healthy system, are you comfortable with

17 that piece of the guidance, to use the maximum tolerated dose as

18 the place to start and then only use one lower dose?

19 DR. JOHN LICCIONE: Yeah. Well, in terms of

20 the screening assay, of course, you want to make sure you're

21 testing high enough so that you can demonstrate -- this is in

22 the screening assay so you want to give some kind of a

23 challenging dose. But yet, at the same time, you want to try to

24 get at that maximum -- not to exceed that maximum tolerated dose

25 which sometimes is a challenge. 

26 And from that, you want to go to a lower dose
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1 where you know you can back off, or you can have a dose that

2 will -- where if there's an endocrine-related effect, that

3 you'll be able to pick it up. So that's the intent.

4 DR. DANIEL SCHLENK: Dr. Scott.

5 DR. GEOFFREY SCOTT: Just to follow up on

6 that, when you look at our exposure diagram at the maximum

7 tolerated dose, what percent of the coefficient or variance are

8 you seeing above that with the measurements of that?

9 DR. JOHN LICCIONE: It depends on which

10 parameter you're talking about, if I understand correctly.

11 DR. GEOFFREY SCOTT: Well, what I'm trying to

12 find out is if you're at the maximum tolerated dose, and 20

13 percent of the time you're above it by your variance, you're

14 above the maximum tolerance which is getting back to Dr.

15 Horseman's question. I'm just trying to understand is that a

16 slight deviation, going back to semantics here?

17 I'm just trying to get my arms around this

18 because it would seem like trying to go back to what Nelson

19 pointed out. You want to be right at that -- I understand where

20 you're coming from too, you want to be there where your

21 challenging the animal --

22 DR. JOHN LICCIONE: Right.

23 DR. GEOFFREY SCOTT: -- but you don't want to

24 kick him over the edge. So I'm just trying to figure out are we

25 kicking him over the edge a little bit of the time, you know, or

26 what percentage of the time?
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1 DR. JOHN LICCIONE: We'll come back there. We

2 didn't analyze it in that regard. In some cases, we noticed --

3 we were trying to get at the overt toxicity, but we can revisit

4 that.

5 DR. GEOFFREY SCOTT: If I can follow up, it

6 would be interesting to look and see when you had those

7 deviations, at what percentage of the time did you have to go to

8 that second dose? That might be very helpful at least in seeing

9 how it all fits.

10 DR. JOHN LICCIONE: Okay. 

11 DR. DANIEL SCHLENK: Okay. And we can get

12 that report tomorrow? Is that possible? Yeah? Okay. Great. 

13 Yes, Dr. Leonard.

14 DR. JACK LEONARD: Were there any guidelines

15 provided for dietary content of iodine? I know the thyroid is

16 the key component we're talking about here and was wondering

17 whether or not you had some guidelines as to what the iodine

18 content of the commercial diet were?

19 DR. JOHN LICCIONE: Not in the available

20 guidelines that we had that I'm aware of.

21 DR. DANIEL SCHLENK: Okay. Any other

22 questions before we take our EAT activity?

23 Sorry. I've been holding off on that all day.

24 Okay. Let's go ahead and break for lunch, and let's reconvene

25 at 1 p.m. Thanks.

26 (Whereas a lunch break was taken)
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1 DR. DANIEL SCHLENK: Okay good afternoon

2 everyone. We're ready to reconvene; just a couple of

3 announcements. If you are a public commenter and would like to

4 make your presentation today we do have time available on the

5 agenda for that this afternoon. So if you have your materials

6 if you could get them to the EPA staff before this afternoon's

7 break that would be great. 

8 Right now I believe Dr. Dourson is going to be

9 presenting today rather than tomorrow. We still will have the

10 public commenting period tomorrow morning as well. 

11 But if you have your materials ready we can do

12 that this afternoon just to sort of save a little bit of time

13 there if you're up for that. Yeah we will have the full comment

14 period tomorrow so that is not going away it's just if we could

15 get some folks in this afternoon that would be great. Okay? 

16 So before we get started I believe we had some

17 clarification comments from the Agency in regards to some of the

18 overt toxicity questions. And could you just state your name?

19 Thanks.

20 JESS ROWLAND: Thank you. I'm Jess Rowland. 

21 I want to give some context to the dose selection for the in

22 vivo assays. For most of the studies they ran a range finding

23 studies with the three or four dose levels.

24 And based on the results of those studies they

25 selected the high dose and the lower dose. If they didn't run a

26 range finding study they used the existing knowledge from the
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1 other standard toxicology studies to see where the NOAEL and

2 LOAEL fell in the 14-day studies and the 28-day studies and used

3 that.

4 So for other chemicals that we felt that the

5 high dose selected were appropriate, except for like one out of

6 17 in the uterotrophic had a kind of a short overt toxicity, 3

7 out of the 17 from the Hershberger, five out of the 17 for the

8 female pubertal, and three out of 16 for the male pubertal.

9 For a few studies that the high dose showed

10 some overt toxicity, and even though toxicity was very -- we

11 usually consider the criteria and decrease of more than ten

12 percent body weight, clinical signs for acceleration in some

13 cases decrease in food consumption, so those doses were run

14 concurrently. And for the Hershberger or the androgenic phase

15 they tested two doses, for the antiandrogenic they used three

16 doses. 

17 On some of the pubertal we had two doses for

18 some clinicals and three for the others. So the overall picture

19 of the 67 studies may be right on target because most of the

20 studies were selecting the high dose and the lower dose. 

21 DR. DANIEL SCHLENK: Okay, any questions,

22 clarifications of panel members regarding that? Okay thank you.

23 Let's move ahead and start talking about our fish assays and

24 presentation there will be from Amy Blankinship from

25 Environmental Fate and Effects Division, Office of Pesticide

26 Programs. 
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1 AMY BLANKINSHIP: Good morning. I will be

2 discussing the fish short-term reproduction assay, which is

3 referred to hereafter as the FSTRA.

4 Shown in the photo taken from the Agency's

5 webpage on the FSTRA is a test species fathead minnow. I would

6 like to start by giving an overview of the presentation today. 

7 First I will provide a brief background on the assay. Second I

8 will discuss the evaluation of the available studies as it

9 relates to the execution of the assay and performance criteria

10 as well as some general observations on the conduct and results

11 of the studies. And finally some concluding remarks.

12 The FSTRA is intended to be a screening assay

13 which provides information on reproductive fitness and the

14 potential to interact with the endocrine system. It can be used

15 to detect potential estrogen and androgen agonist and antagonist

16 as well as chemicals that affect steroidogenesis and other

17 interactions along the HPG axis.

18 The assay contains endpoints that are

19 reflective of changes associated with interactions with the HPG

20 axis such as plasma vitellogenin regimens in male fish. The

21 FSTRA is conducted using an aqueous exposure and the test

22 species is a fathead minnow. The fish are distributed into

23 breeding groups of two males and four females. 

24 The breeding groups are monitored for 14 days

25 prior to exposure to evaluate reproductive output, which will

26 allow for a systematic method of distributing the breeding
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1 groups to the control and treatment groups, after which the fish

2 are exposed to the chemical for 21 days. 

3 Typically there are three test concentrations

4 in a negative control. And if a solvent carrier is used, a

5 solvent control, there are four replicates for treatment. This

6 assay examines many endpoints both during the study and at test

7 termination.

8 Shown here are the data endpoints or

9 parameters that are evaluated in the FSTRA which are recorded in

10 the data evaluation record or DER. The individual parameters

11 are listed in the left hand column and the type of information

12 recorded for each endpoint is listed in the right hand column.

13 The endpoints include parameters such as

14 survival and growth along with other reproductive endpoints such

15 as daily measurements, fecundity and fertilization success. 

16 Other endpoints indicative of reproductive fitness are measured

17 at test termination for each sex and include measurement of

18 secondary sex characteristics including nuptial tubercles,

19 gonadal somatic index, or GSI, and gonadal staging and

20 histopathology.

21 The biochemical marker of plasma vitellogenin

22 or VTG is included as well. Optional endpoints include

23 measurement of sex steroids including testosterone and

24 estradiol. It is noted that alterations in some endpoints such

25 as vitellogenin may provide more diagnostic utility in

26 evaluating a potential endocrine interaction. Whereas
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1 disruption in other endpoints, such as a decrease in fecundity,

2 may be due to nonendocrine or nonspecific mechanism. 

3 There are six performance criteria identified

4 in the FSTRA guideline. These criteria are referred to control

5 performance for the endpoints of percent fertility, percent

6 survival and fecundity rate. 

7 The three other criteria are reflective of

8 test chemical exposure and environmental conditions including

9 the maximum variability and chemical exposure for a treatment

10 group as well as the minimum percent dissolved oxygen

11 concentration and range of water temperature in the test

12 chambers.

13 As with any of the identified criterion it is

14 anticipated that slight deviations will occur and the conduct

15 and result of each study are evaluated as a whole to determine

16 the degree of confidence in the ability to discern the potential

17 for endocrine interaction.

18 Twenty-one FSTRA studies with unknown

19 endocrine potential were evaluated for their ability to adhere

20 to the six identified performance criteria.

21 This figure summarizes the number of studies

22 out of the 21 evaluated that met each of the six performance

23 criteria. The number of studies that met each criterion is

24 identified by the green bars and the studies that did not are in

25 orange.

26 As illustrated 75 percent or more of the
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1 studies met each criterion except for the test concentration

2 variance, which may be anticipated with some of the more

3 difficult to test substances due to their physical chemical

4 properties such as low water solubility and absorption

5 properties. 

6 All of the studies were able to maintain water

7 temperature within the guideline range and all but one study met

8 the control survival criterion. 

9 Shown here is another summary figure, but this

10 figure summarizes how many of the six criteria each study met. 

11 Four of the 21 studies met all of the six criteria with another

12 11 studies meeting all but one. For the 11 studies that met all

13 but one, the table at the bottom of the slide identifies which

14 one of the six criteria the study did not achieve.

15 Most of the 11 studies did not achieve either

16 test concentration variance or the control fecundity followed by

17 the dissolved oxygen, survival and fertility. If the study did

18 not meet two or more of the six criteria typically the unmet

19 ones were test concentration variation along with the control

20 fecundity and/or dissolved oxygen concentration.

21 The first of the six criteria that I will

22 discuss in greater detail is the measured test concentration

23 coefficient variance or CV. A CV for each test concentration

24 was calculated using the 21-day treatment mean or time weighted

25 average. Twelve out of the 21 studies were able to maintain

26 test concentrations within the recommended 20 percent variance
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1 with another four CVs ranging up to 30 percent.

2 For some of the 21 evaluated chemicals their

3 physical chemical properties could have made maintaining test

4 concentrations in the water column more difficult. In an effort

5 to maintain test concentrations some laboratories increased the

6 test solution to an overt rate in the test system.

7 Additionally some laboratories did utilize the

8 solvent carrier to help facilitate dosing. Furthermore in an

9 effort to better characterize exposure some laboratories took

10 additional measurements during the 21 days. 

11 The second criterion is control fecundity. 

12 The fecundity is monitored in both the pre-exposure and exposure

13 phase. During the pre-exposure phase fecundity is measured to

14 ensure adequate spawning in a breeding group prior to exposure

15 and to allow for distribution of the breeding group for the test

16 concentration using a randomized block design.

17 While not a performance criteria, the

18 guideline has a recommended fecundity rate during the

19 pre-exposure phase and 20 of the 21 studies met this

20 recommendation. During the exposure phase the criterion for

21 control fecundity is that each of the four replicates in a

22 control should achieve either 15 eggs per female per

23 reproductive day or spawn at least every four days.

24 If the study used a solvent carrier then these

25 criteria were applied to the solvent control replicates as well.

26 All control replicates in 15 of the 21 studies met one of these
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1 two conditions. However, if evaluating fecundity using an

2 average rate, in 17 of the 21 studies had mean control fecundity

3 rate of 15 eggs per female per day or greater.

4 The third criterion is controlled

5 fertilization success. As in fecundity fertilization success is

6 assessed daily. The guideline states that on average 95 percent

7 or more of the eggs produced in the controls should be

8 fertilized. Seventeen of the 21 studies met this and the mean

9 fertility in the other four studies range from 90 to 94 percent.

10

11 For the fourth criterion of control survival

12 90 percent or greater survival of controlled fish over the

13 21-day is desired as there are 16 females and eight males in a

14 control for a total of 24 fish, this allows for two mortalities.

15

16 All but one study was able to maintain

17 survival at 90 percent or greater during the test duration. The

18 control survival in the remaining study was 83 percent. The

19 study authors in that one study stated that mortality during the

20 pre-exposure phase was within the allowable US EPA guideline

21 mortality for an acclimation period and that all other control

22 performance criteria in the study were met.

23 The remaining two criteria referred to

24 environmental conditions during the test. The guidelines states

25 that dissolved oxygen or DO should be maintained at 60 percent

26 saturation or greater during the 21-day exposure. Per the
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1 guideline, DO should be measured in each test replicate at least

2 weekly. Fifteen of the 21 studies were able to maintain DO at

3 60 percent or greater. In the other six studies at least one DO

4 measurement was below 60 percent with recorded minimums of 45 to

5 58 percent.

6 Given the reported frequency of measurement of

7 DO, it was not always possible to know the duration under which

8 the DO may have been less than 60 percent. However, once a

9 decrease in dissolved oxygen below 60 percent was measured, the

10 laboratories added aeration or in some cases increased aeration

11 if it was already present. 

12 Decreases in DO were observed in studies that

13 used a solvent carrier as well as studies that did not. For

14 water temperature all 21 studies were within the guideline

15 criterion of 25+/- 1 degree Celsius. 

16 In addition to DO in water temperature Table 1

17 of the guideline has reported maximum limits on other water

18 quality parameters such as total organic carbon and residual

19 chlorine.

20 While not all of the 21 studies recorded

21 measurements for these parameters, based on historical water

22 quality information for these laboratories the agency deemed

23 that the dilution water used in these studies was adequate to

24 support survival, growth and reproduction of fathead minnows.

25 Overall the control performance for the 21

26 evaluated studies was generally within or close to the guideline
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1 criteria with four of the studies meeting all six criteria and

2 another 11 meeting all but one. In the cases of the dissolved

3 oxygen and test concentration variation, two of the most missed

4 criteria laboratories appear to be responsive to these criteria

5 by taking measures to increase dissolved oxygen and/or reducing

6 variation in chemical exposure.

7 Now I'd like to discuss some general

8 observations in a FSTRA after evaluating a 21 study, with one of

9 these being the occurrence of incorrectly sexed fish from the

10 FSTRA. In this assay fish are distributed to test chambers by

11 two males and four females. This sorting occurs at the

12 beginning of the pre-exposure phase. As the study is generally

13 initiated using newly mature males and females, the guideline

14 recognizes potentials for misidentifying the sex of a fish

15 during this distribution process and recommends adding

16 additional tanks during the pre-exposure phase.

17 As the thought is that once a fish starts to

18 actively reproduce during the pre-exposure phase it will become

19 apparent that a fish was misidentified and this breeding group

20 can be excluded. However, upon evaluation of the 21 studies, 6

21 studies or 30 percent had at least one replicate with an

22 incorrectly sexed fish. In all cases it was a male mistaken for

23 a female, which was typically not realized until test

24 termination.

25 The figure in this slide represents how many

26 replicates contained an incorrectly sexed fish for each of those
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1 six studies. Half of the six studies had only one replicate

2 with an incorrect sex ratio whereas one study or 25 percent had

3 an incorrect ratio. For the studies that recorded the missed

4 sexed fish, the study author suggested the fecundity value of

5 that replicate to account for the additional male.

6 However, for the other gender specific

7 endpoint such as GSI and vitellogenin some laboratories excluded

8 the missed sexed male while others did not. The Agency thinks

9 it is appropriate to remove the missed sexed fish from all

10 endpoint analyses. As laboratories gain experience with this

11 assay, the occurrence of missed sexed fish may diminish.

12 The second topic I would like to discuss is a

13 selection of test concentrations in the FSTRA and the appearance

14 of potential overt toxicity in the reported results. The

15 guidelines suggest that the highest test concentration in the

16 study should not result in increased overt toxicity compared to

17 the control. The guidelines provide some suggestions for

18 studying the highest test concentration which include using 1/3

19 of the 96 hour LC50. The use of the limit of solubility or a

20 maximum concentration of 100 parts per million may also be used.

21 Based on the table in this slide, about half

22 of the 21 studies used, at least in part, 1/3 have an acute

23 LC50, but the few other studies using a limited solubility or

24 the necessary maximum concentration as the high dose. 

25 This slide discusses the observation of

26 potential overt toxicity in the FSTRA. Potential overt toxicity
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1 in this evaluation is defined as (1) a reduction in survival,

2 (2) the reporting of clinical signs of toxicity in a treatment

3 group such as abnormal swimming, lethargy or decreased feeding

4 and these affects were only evaluated qualitatively and in some

5 cases the number of fish affected was not always reported for

6 clinical signs of toxicity; and (3) a statistically significant

7 reduction in body weight compared to the control.

8 For reductions in body weight it is noted that

9 reductions may or may not be due to potential endocrine

10 interactions, and that changes in body weight could be affected

11 by alterations in other endpoints such as fecundity. The top

12 figure shows the number of studies that recorded an effect one

13 or more of the three main categories of overt toxicity.

14 Please keep in mind that a study may have

15 reported more than one effect which is why the three categories

16 add up to more than 21. Thirteen studies reported effects on

17 one or more of the three categories with another four studies

18 reporting a decrease in body weight only. 

19 Generally the observed overt toxicity was

20 reported only in the high concentration. This is illustrated in

21 the bottom figure in which 70 percent of the studies that

22 reported a potential overt toxicity only reported it in the

23 highest treatment group. 

24 There are seven main endpoints evaluated in

25 the FSTRA with four of them being gender specific. These

26 endpoints are presented in the figure on the slide. Changes
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1 and/or alterations in these endpoints compared to the control

2 were evaluated for the 21 studies. However, only three of the

3 21 studies measured sex steroids.

4 An effect was reported at least once for all

5 seven endpoints. However, an effect in one of these endpoints

6 is often reported at the same concentration where the presence

7 of potential overt toxicity was also reported. In this figure

8 the red bars represents where an effect was observed only in the

9 presence of potential overt toxicity. 

10 The orange bars represent where an effect was

11 observed both with and without accompanying overt toxicity. For

12 example, an effect on fecundity was observed at both the high

13 and mid-test concentration but abnormal swimming was only

14 reported at the highest test concentration. 

15 And finally the green bars indicate that an

16 effect was reported without any associated overt toxicity. 

17 From the graph, changes or alterations in

18 fecundity in female gonadal histopathology were reported most

19 often. Other general observations observed in these 21 studies

20 were that for control male or female fish, the GSI values were

21 within the guidelines reported range.

22 Additionally, male control fish were reported

23 to have the appropriate secondary sex characteristics of nuptial

24 tubercles. Whereas female control fish did not, which is

25 normal. These observations further indicate that the fish used

26 were sufficiently appropriate for use in this study. 
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1 For the endpoint in vitellogenin the

2 appearance of potential outliers was observed in several

3 studies, primarily for male fish. As normal male fish do not

4 express vitellogenin, and the measurement is often at or around

5 background levels, variation of individual male fish may be

6 expected. The next slide will further illustrate this.

7 And finally while an optional endpoint, as

8 stated, sex steroids were only measured in three of the 21

9 studies. Inclusion of these measurements could potentially be

10 quite useful in interpreting results when evaluating for a

11 potential endocrine interactions. 

12 As indicated this slide shows two examples of

13 a measurement of male plasma vitellogenin for two different

14 chemicals. For the figure on the left, Chemical L, while the

15 range of values for the negative control or NC and the lowest

16 treatment group, or L1, are close together, there was one value

17 for both groups that is must greater than the other values as

18 noted by the red circles.

19 For the highest treatment group, or L3, it is

20 statistically different from the control. However this may be

21 due to the two values near the top of the box plot whiskers. 

22 For the figure on the right, or Chemical A,

23 none of the treatment groups are statistically different from

24 the control and for the mid-treatment, or L2, one value with

25 orders of magnitude greater than the other values in this study,

26 which may be hard to see, but it definitely is. 
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1 In these examples these outliers were visually

2 observed with no statistical test performed. In the 21 studies

3 some laboratories removed perceived outliers while other

4 laboratories included all data. In this evaluation the Agency

5 included all reported data unless it was clear that the reported

6 value was due to a technician or instrument error. However

7 plotting the data like as shown here can be quite useful when

8 evaluating whether a chemical is truly affecting vitellogenin in

9 fish for a study.

10 Overall for the FSTRA the assay is genially

11 being conducted as expected with a majority of the evaluated

12 studies meeting all or most of the performance criteria. 

13 Observations of decreased survival, body weight and/or other

14 clinical signs of toxicity were observed in many of the

15 evaluated studies. 

16 Therefore revisiting or additional guidance on

17 dose selection may be warranted. However, the observation of

18 overt toxicity was generally isolated to the highest treatment

19 group which would allow for evaluation potential endocrine

20 interaction in the two remaining test concentrations. However,

21 having three concentrations available for evaluation would be

22 optimal. 

23 Additionally the Agency recommends excluding

24 fish from the data analysis that were incorrectly sexed. And

25 finally although the measurement of plasma sex steroids is an

26 optional endpoint, the endpoints are endocrine specific and are
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1 anticipated to provide a very useful line of evidence and

2 whether a chemical is potentially impacting the HPG axis. 

3 Therefore the Agency emphasizes the importance

4 of measuring sex steroids when the blood sample volume is

5 sufficient. And I'll take any questions.

6 DR. DANIEL SCHLENK: Any questions from the

7 panel? Yes Dr. Patino.

8 DR. REYNALDO PATINO: I'm curious to know a

9 more specific rationale for your recommendation to exclude the

10 incorrectly sexed males from analysis of endpoint sex with

11 vitellogenin and nuptial tubercles. What is your rationale for

12 excluding that one fish?

13 AMY BLANKINSHIP: The thought would be that

14 prior to pre-exposure those fish were identified by the

15 technician as a female. So they probably weren't expressing

16 normal male secondary sex characteristics and so they may not be

17 acting as normal male fish. And so having them carry through,

18 through all the evaluation, may not be appropriate if they

19 weren't necessarily acting normally or exhibiting normal

20 secondary sex characteristics prior to any chemical exposure.

21 DR. DANIEL SCHLENK: Okay Dr. Leonard. Oh do

22 you have follow up?

23 DR. REYNALDO PATINO: Follow up. Yeah I can

24 see the rationale for nuptial tubercles but how about

25 vitellogenin where all the levels should be close to being on

26 the table?
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1 AMY BLANKINSHIP: Yes and so that's a good

2 point and so I recommend that anything like that you put in the

3 record for consideration, but that is a good point.

4 DR. DANIEL SCHLENK: Dr. Leonard.

5 DR. JACK LEONARD: So I want to make sure I

6 follow up a little and understand so that when a fish with

7 missed sex that was discovered at the end of the study?

8 DR. AMY BLANKINSHIP: Yes, typically.

9 DR. JACK LEONARD: So you had a dataset for

10 that fish up until the end of the study.

11 DR. AMY BLANKINSHIP: So that fish, if it was

12 caught at test termination, should have a full suite of

13 endpoints.

14 DR. JACK LEONARD: Right. If you evaluated

15 those endpoints in that missed sex fish, did they differ

16 significantly from the correctly sexed male?

17 DR. AMY BLANKINSHIP: In the evaluations that

18 I looked at we did try; because like I said some laboratories

19 excluded them and some did not. So we removed them. Sometimes

20 it did not make a difference in the overall endpoint, especially

21 for vitellogenin where there can be some variation; so generally

22 we're starting with an end of 8 if we have all 8 male fish

23 available.

24 So the thought of with the end of that value

25 each fish could contribute significantly to that mean value and

26 statistical analysis. But in some of the cases I looked at it
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1 did not appear to change the overall sort of effect level.

2 DR. DANIEL SCHLENK: Dr. Horseman.

3 Dr. nelson horseman: A point was made about

4 the coefficient variation and measured test concentrations, were

5 those variances normally distributed or typically skewed? And

6 if not normal how were they skewed?

7 DR. AMY BLANKINSHIP: I did not look at that

8 in that detail. I do know that the coefficience of variance for

9 each treatment group did vary quite a bit. 

10 In this assay there is a 10x spread between

11 concentration; so if you're approaching near the limit of

12 solubility, or a chemical that has an absorption properties, a

13 10x factor between concentrations could result in significant

14 differences between how the chemicals are measured compared to

15 the nominal concentrations for that treatment group. So there

16 was quite a range of CVs even within a study for each of the

17 three treatment group.

18 DR. DANIEL SCHLENK: Dr. Scott.

19 DR. GEOFFREY SCOTT: Along those same lines on

20 the coefficient of variance of dose, you talked about the

21 variance relative to dose in terms of the range of doses. What

22 about the effect of time in the assays as conditions change in

23 the test chambers and the size of organisms get bigger over

24 time; have you looked at the time variable on variance, whether

25 the coefficient of variance increases over time? Is that part

26 of the problem? I'm just trying to get to the source of the
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1 variance.

2 Amy Blankinship. Sure. Yeah as expected they

3 can change over time so in some cases the measured

4 concentrations were time-weighted averages just to sort of help

5 account for that, especially if they took additional

6 measurements over time. But generally in terms of my

7 understanding it the coefficient variance just was sort of based

8 on all the measurements that were taken over 21 days for each

9 individual treatment growth.

10 DR. GEOFFREY SCOTT: Follow up. The

11 guidelines do allow for adjusting, you said several of labs did

12 adjust flow rate I believe?

13 Amy Blankinship: Correct.

14 DR. GEOFFREY SCOTT: The guidelines do allow

15 for that?

16 Amy Blankinship: Maybe not directly but the

17 goal is to help try and maintain test concentrations. So in

18 terms of common practice for aquatic toxicity testing it would

19 be common to increase the turnover rates in the system to help

20 maintain test concentrations.

21 DR. GEOFFREY SCOTT: And what would be the

22 allowances of increases. Is that a ten percent increase? What

23 rates are in the guidance?

24 AMY BLANKINSHIP: There are no particular

25 defined rates in the guidance; generally it's sort of dictated. 

26 Sometimes if you don't use a solvent in terms of how much of
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1 your test dosing solution you can get through the system in

2 terms of your solubility of your highest test concentration

3 stock solution. But this study asks for a minimum of five

4 turnovers. So I wouldn't suspect that you can have turnovers of

5 at least maybe 2/3 of that or greater. To me that would still

6 think that the fish would still sort of operate normally and

7 still behave properly.

8 DR. GEOFFREY SCOTT: One last comment. Had

9 any of the studies -- I assume the studies were measuring the

10 exposure in aquaria with fish. 

11 AMY BLANKINSHIP: Right.

12 DR. GEOFFREY SCOTT: Have you had any blank

13 determinations where you have an empty chamber to look at the

14 delivered dose so that you know whether or not the variance is a

15 function of the dose or is a variance of what is going on in the

16 tank? Because that is getting to your source of variance, it

17 helps your endpoint. 

18 So I just ask if there are considerations of

19 those criteria; because if you know you're delivering the dose

20 and it's just not there because of a lot of intrinsic factors in

21 the bioassay itself, uptake absorption of organics in the

22 system. I think at least you know the dose was there and I

23 think that's the real key thing everybody is trying to lean in

24 on.

25 AMY BLANKINSHIP: Sure. So laboratories would

26 do a sort of a pre-exposure to make sure that at least a test
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1 initiation of the dose is sort of there or where they're at. 

2 But there is not an abiotic tank in this test system, which is

3 not common -- I'm going to say -- for regulatory aquatic

4 toxicity testing for fish.

5 DR. GEOFFREY SCOTT: I can only say a lot of

6 the sub-legal work we've done, we've always done that just so we

7 can be sure we're delivering the dose. And that way you know at

8 least you're delivering the dose because everything else is

9 subject the variance and you don't know what that source of

10 variance is. If it's in the delivery then you need to adjust

11 the delivery.

12 If it's not then you need to think about what

13 it is that you can do about it.

14 AMY BLANKINSHIP: I will say further on that,

15 some laboratories do measure their stocks at each sampling point

16 so they will so -- I say there is not an abiotic, but they will

17 necessarily sometimes measure their dosing solution to help

18 characterize proper dosing at least.

19 Dr. Jeff Scott: And I think that is a good

20 QAQC requirement because they're probably making the volume

21 measurements too. So if you know the volume and you know your

22 dosing stock, that's an indirect way of measuring it without

23 having to measure.

24 AMY BLANKINSHIP: You're absolutely right they

25 do monitor and check the delivery system generally twice a day

26 and so that they have a weekly dosing of stock solutions each
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1 time. You're correct, that is a good way to check for that.

2 DR. GEOFFREY SCOTT: Well that might be worth

3 putting in the reporting as well to say that's a way of not

4 having to measure but reporting what you already did.

5 DR. DANIEL SCHLENK: Let's make those comments

6 when we get to the question that would be great. Dr. Tillitt?

7 Dr. Donald Tillitt: I'm still not exactly

8 clear in terms of the high CV. Are those mainly in the high

9 concentration or were they mainly in the low concentrations, do

10 you know that?

11 AMY BLANKINSHIP: I don't know that off the

12 top of my head, but I will say that for some of them the high

13 group was fine and then as we sort of went that 10x factor,

14 especially if they had a high absorption property, those CVs

15 tended to increase with the lower concentration just because

16 they tended to absorb to surfaces. So I don't know off the top

17 of my head sort of where the distribution amongst the low middle

18 and high concentrations for the 21 assays.

19 DR. DONALD TILLITT: Nor if it was close to

20 the limit of protection for the assay for the chemical analysis?

21 AMY BLANKINSHIP: Correct. I would say that

22 probably the measurements of what the LOQs and LOD were probably

23 appropriate on these assays.

24 DR. DONALD TILLITT: I have one more question

25 to follow up. Have you done covariate analysis at all in terms

26 of the males that showed out of range for unexpected high
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1 vitellogenin? Have you done covariate analysis with the

2 estrogen concentrations on the females from the same tank?

3 AMY BLANKINSHIP: No we have not and I will

4 say that I only have estrogen levels right now in maybe in those

5 three of the 21 studies. So that's an endpoint that is optional

6 and often not done.

7 DR. DONALD TILLITT: Thank you.

8 DR. DANIEL SCHLENK: Any other questions from

9 the panel? Okay thanks. Let's go ahead and move on to our next

10 presentation. This will be by Catherine Aubee, who is from

11 Environmental Fate and Effects Division, Office of Pesticide

12 Programs. 

13 And again let me remind the panel that we will

14 have the Agency back up tomorrow just if you have any questions

15 that you've been pondering over the night or when you're meeting

16 together, to bring back up, you can do that tomorrow.

17 CATHERINE AUBEE: So thank you Dr. Schlenk,

18 thank you everybody. As Dr. Schlenk introduced, my name is

19 Catherine Aubee I am a biologist in the Environmental Fate and

20 Effects Division along with Amy and Dr. Steeger.

21 Our division has the primary responsibility

22 for reviewing pesticide studies submitted under both the FSTRA

23 guideline and the amphibian metamorphosis assay guideline which

24 I will be presenting this afternoon.

25 My presentation follows the general structure

26 of the AMA chapter in the white paper if you've had the pleasure
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1 of looking at that already. I'll first give a brief overview of

2 the intended purpose of the AMA and its general experimental

3 designs. 

4 I will introduce some of the quality control

5 elements mentioned in the test guideline and discuss how well

6 the studies we've reviewed so far have informed those criteria.

7 I'll then discuss in a little bit more detail,

8 specific observations from our first round of studies. You'll

9 note that some of these issues have been identified in the

10 charge questions to the panel, which we will discuss more I

11 believe again tomorrow.

12 The amphibian metamorphosis assay, as its name

13 implies, uses frog metamorphosis as a model for evaluating

14 potential affects with a hypothalamic-pituitary-thyroid or HPT

15 axis and it's downstream thyroid hormonal pathway.

16 It is one of three assays as discussed earlier

17 within the two Tier 1 EDSP battery that looks at this potential

18 for interaction with the HPT axis. The other two are the male

19 and female pubertal rat assays, which was presented by Dr.

20 Liccione.

21 Like the fish short term reproduction assay

22 the AMA uses a 21 day aqueous exposure method. The test species

23 is the African quad frog or the Xenopus laevis which is a very

24 well characterised model species, which is fully aquatic

25 throughout its life cycle.

26 That assay is initiated with tadpoles at
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1 Nieuwkoop-Faber or NF Stage 51. Depending on environmental

2 conditions, this stage is usually reached within 17 days post

3 fertilization. Tadpoles are assigned to test substance

4 treatment levels at one of at least three treatment

5 concentrations or to a concurrent control. A solvent control is

6 also included if the solvent is used in the study.

7 However, the guideline recommends against the

8 use of a solvent whenever possible. Each treatment level

9 includes at least four replicates containing at least 20

10 tadpoles per rep. 

11 If you are unfamiliar with xenopus this is

12 just an image taken from the test guideline which shows the hind

13 limb bud of the tadpole at stage 51; so this is what it looks

14 like when we begin the test.

15 This is just to give you a sense of the

16 morphological markers used in determining the NF stage of the

17 tadpoles. Hind limb morphology is one of the key biomarkers

18 during this period. Others include forelimb emergence and tail

19 resorption.

20 For the AMA, endpoints are evaluated at two

21 time points, day 7 and day 21. At day 7, endpoints are expected

22 to be particularly sensitive to potential thyroid agonism as

23 background levels of thyroid hormone are essentially negligible

24 or nonexistent at this stage of development.

25 By day 21, at test termination, endpoints are

26 expected to be more sensitive to thyroid antagonisms as thyroid
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1 hormone levels are peaking as the tadpoles approach metamorphic

2 climax. Day 7 endpoints are evaluated in a subsample of

3 tadpoles which are not returned to the test population.

4 The same endpoints plus thyroid histopathology

5 and individual observation with clinical signs of toxicity are

6 evaluated in the remaining tadpoles at test termination. The

7 key endpoints of those sampling points, which you see here

8 include NF stage as we mentioned, hind limb length which is

9 normalized to snout-vent length to account for growth. 

10 We analyzed snout-vent length separately and

11 also body weight. Most test guidelines include quality control

12 criteria which may be identified as validity elements,

13 performance criteria or some combination of the two. The AMA

14 test guideline includes both of these. 

15 In the AMA the validity elements address the

16 minimum number of viable individuals, replicates and treatments

17 needed to provide confidence in determining whether there is a

18 treatment related affect in the test.

19 This is generally associated with the

20 statistical power of the test design. By viable in this case we

21 are talking about units that are not compromised by mortality,

22 other obvious overt toxicity or human or technical errors, and

23 those do happen from time to time.

24 In general a reviewer will not automatically

25 discount the results of the study if one or more validity

26 elements are not met, but will carefully assess all aspects of
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1 the study before reaching a conclusion as to its validity or

2 usefulness.

3 Performance criteria in the AMA are included

4 more as an indicator of whether environmental and exposure

5 conditions are adequate and consistent. These are intended to

6 ensure the health of the test organisms and to promote

7 successful performance of the study, or in other words to

8 minimize potential confounding factors. 

9 These criteria relate to the consistency of

10 exposure concentrations, either 21 day coefficient variation

11 each treatment level, NF stage distribution at test termination

12 and water quality characteristics. It is helpful to the

13 reviewer when each of these criteria is addressed explicitly in

14 the study report. 

15 This figure gives an overview of how many of

16 the 18 studies included in the preliminary review of the AMA

17 satisfied each of the performance criteria. The black bars

18 which are largest all the way through here represent the studies

19 which met the criteria.

20 The white bars indicate studies which did not

21 meet them and the grey bars -- which are not really grey on this

22 slide, I apologize -- represent studies which did not provide

23 enough information for the reviewer to determine whether or not

24 the criteria were met.

25 So for example the fourth bar down -- I'll

26 attempt to use the pointer here, I'm not allowed to use the
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1 remote at home so bear with me. The fourth bar down represents

2 the performance criteria for NF stage distribution and controls

3 at test termination. The guideline recommends that this should

4 include less than or equal to four stages between the tenth and

5 90th percentiles.

6 So most of the studies we looked at reported

7 that they did meet this criterion. However, three studies only

8 reported median stage values and did not report individual level

9 data or report the stage distribution; so we weren't able to

10 really determine if they met the criteria or not. 

11 Another example is with replicate mortality. 

12 In some cases mortalities were only reported on a per treatment

13 basis. Individual replicates were not identified. With these

14 studies it was unclear, and you can see those here on the bottom

15 -- not quite the bottom but the next two rows up. In these

16 studies it was unclear whether the mortalities were spread

17 across replicates or occurred in a separate single replicate so

18 it could not be determined whether a given replicate was

19 compromised.

20 The 21-day CV, this is right up here in the

21 middle, was the performance criterion which was most likely to

22 be missed. This was not surprising as Amy discussed earlier

23 with a longer term test of aquatic exposure. 

24 The deviations from the recommended CVs were

25 usually minor. In this case I'll say roughly within five

26 percent of that 20 percent or occurred in only one treatment
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1 level leaving two treatments still for analysis.

2 In such cases as long as measured

3 concentrations for different treatment levels did not overlap

4 time weighted averages could be employed to make more accurate

5 conclusions relating exposure levels and any effects observed in

6 the study.

7 However, in one case from this sample the 21

8 day CVs were extremely high, up to 60 percent, and were coupled

9 with extremely poor recovery of the test substance despite the

10 use of the solvent. 

11 Aside from the one major deviation in that

12 case, overall study performance for the AMA was fairly

13 consistent with the validity and performance criteria in the

14 guidelines. 

15 So we discussed that a couple of the

16 performance criteria are related to NF stage and controls at

17 test termination. The guideline recommends that the minimum

18 median stage at day 21 be NF stage 57, that's for control

19 tadpoles. 

20 However, individuals which have reached NF

21 stage 61 or greater are generally excluded from the analysis of

22 continuous endpoints related to growth, such as snout-vent

23 length and body weight, because morphometric changes during

24 metamorphic climax would skew the distribution and might reduce

25 the chance of detecting a treatment related affect.

26 Therefore performing laboratories have to
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1 strike a careful balance between attaining the minimum

2 recommended median stage and controls at test termination, but

3 not having too many tadpoles which progressed beyond NF stage

4 60. 

5 While no laboratories have reported problems

6 in meeting the minimum recommended stage, some laboratories have

7 reduced feeding during the end-life portion of the study to

8 reduce the likelihood of control tadpoles progressing beyond NF

9 Stage 60.

10 Dietary considerations such as these are also

11 of interest when reflecting on the incidence of spinal curvature

12 in Xenopus Laevis tadpoles in these studies. Spinal curvature,

13 which is also referred to variously as bent tail, crooked tail,

14 scoliosis or I think tail flex in the comments that we'll hear

15 tomorrow. This is a musculoskeletal deformity, which may

16 resolve itself naturally in Xenopus through metamorphic

17 progression. 

18 This table which you should see here and which

19 follows on the next slide shows some of the factors which have

20 been proposed as possibly being linked to the incidence of

21 spinal curvature; it includes the quantity and type of feed and

22 iodide availability. 

23 Genetic factors may also play a role, although

24 the spinal curvature that we are discussing here should not be

25 confused with the bent tail mutations, which is a recessive

26 mutation described by Drouin (phonetic) in 1992. That is
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1 actually a lethal mutation associated with internal

2 haemorrhaging, and so we don't want confusion on the terminology

3 there.

4 As discussed in the White paper experimental

5 work by our colleagues in the Office of Research and Development

6 suggests that the prevalence of spinal curvature, at least

7 within their test populations of Xenopus Laevis and Xenopus

8 Tropicalis, is strongly linked to the type and quantity of feed.

9 I should make a couple of clarifying remarks here regarding

10 statements in the Paper. 

11 The principal investigator for the Olmstead et

12 al. Manuscript which is cited in the Paper has since left the

13 Agency, but he clarified for us that none of the tadpoles in

14 those studies were intentionally overfed, so that is

15 mischaracterized perhaps in the paper. Although they do see

16 associations with different types of feed and things perhaps as

17 protein content.

18 Also we cited the Paper as in prep because

19 that's how it was cited on an EPA webpage, but that is no longer

20 current. I don't think it's currently being reviewed by any

21 journal so we just wanted to make those clarifying remarks.

22 So back to this lovely busy table, I think

23 there is an even busier version in the Paper that you have. You

24 can see that we're tracking on several variables here. I'm

25 going to skip ahead a little bit for time but you can see we

26 looked at the different types of feed and they're only a few
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1 different kinds that are being used.

2 Whether or not feed was reduced compared to

3 the guideline recommendations and if it says yes that this means

4 that the laboratory reported reducing feeding by 50 percent

5 compared to the guideline. We also looked at the quantity of

6 iodide in food, if that was reported or if it was reported by

7 the manufacturer.

8 Background levels of iodide in water. This is

9 hugely based on periodic analysis just historical data from the

10 lab and not necessarily during a given study. And then whether

11 or not iodide supplementation was given during the study. 

12 So in only one case of the 18 was

13 supplementation provided, and I think the guideline recommends

14 that if it's below .5 micrograms per liter in the water that

15 supplementation should be given. 

16 So you don't see a clear pattern here you also

17 see very high variability in the incidence and prevalence of

18 spinal curvature. It was reported in almost all of the studies.

19 A few of the studies didn't really say if they saw it or not,

20 but if they did describe it they usually provided numbers. 

21 And it could have occurred anywhere from a few

22 tadpoles to as much on this side you see as much as 87 percent,

23 this is in controls up to 92 percent in treatments. So there

24 doesn't seem to be any indication that this is treatment related

25 in this studies but it does occur sometimes at relatively high

26 proportions of the test pathogens. 
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1 At this time the initial industry data as we

2 just described in the table do not suggest any single factor

3 that appears to be predicted of the spinal prevalence of spinal

4 curvature. However, again, given that the abnormality has been

5 documented in up to 87 percent of control tadpoles we do seek

6 advice on what may be done to reduce the occurrence of this

7 abnormality and regarding whether, and at what point, the

8 prevalence of spinal curvature maybe so great as to render the

9 result of the study unreliable; at least for the purposes of

10 screening for thyroid-related effects, which is what we are

11 interested in here. 

12 So if everybody is tired of that table we'll

13 switch gears a little bit and talk about thyroid histopathology

14 in the AMA. This is the one endpoint that overlaps with the

15 male and female pubertal rat assays. 

16 Because histological presentations are

17 expected to change throughout metamorphosis the test guidelines

18 recommends that the test specimen selected for thyroid

19 histopathology at day 21 be stage matched to the median NF stag

20 in controls. So in other words if the median stag in controls

21 is 58 then you want your specimens from the other treatment

22 levels selected for thyroid histopathology also to be stage 58

23 if possible.

24 And this way we control for effects on

25 development and improve the sensitivity of the thyroid

26 histopathology analysis. The test guideline identifies several
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1 diagnostic and additional thyroid histopathology criteria. In

2 our review of the data we summarized the incidence and severity

3 of these various diagnoses and incorporate any other relevant

4 narrative descriptions such as cell shape for context.

5 Based on the initial data submissions the

6 thyroid histology analysis appears to be fairly straightforward.

7 However in some cases the shift toward decreased incidence in

8 severity of a particular diagnosis such as follicular cell

9 hypertrophy were reported when treatments were compared to

10 concurrent controls.

11 This was only, however, identified as a

12 potential treatment-related effect by one histologist even

13 though it was seen in multiple studies. This histologist noted

14 that it might suggest a potential treatment-induced

15 developmental delay because thyroid follicular cells normally

16 increase in height during tadpole development.

17 This example of the data here on the slide

18 shows how similar observations in different studies may lead to

19 different conclusions by different histopathologists. Therefore

20 that Agency seeks advice on general topic of interpreting

21 histological indicators of mild developmental delay during

22 metamorphosis and particularly any indicators which may present

23 as decreases in incidence or severity when compared to controls.

24 These may be counterintuitive to reviewers

25 with extensive training and traditional toxicology or

26 ecotoxicology, but perhaps not in endocrinology or
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1 histopathology. 

2 I mentioned before that the guideline

3 recommends against the use of a solvent in test substance

4 delivery whenever possible. However solvents continue to be

5 frequently relied upon in aquatic toxicity tests and the AMA is

6 no exception. Solvents may interfere with the test directly or

7 indirectly by increasing microbial growth or affecting uptake of

8 the test substance.

9 When a solvent is used, concurrent solvent

10 controls are required and are compared to the negative, which is

11 solvent free or a dilution water only control, to determine

12 whether the solvent is causing any effect. Solvent effects were

13 noted in six of all the 18 AMA studies which we evaluated, and I

14 believe ten of those used a solvent. 

15 Acetone was used in one case as the solvent

16 and the remaining five of those six cases Dimethylformamide was

17 used, or DMF. In all cases where a solvent was used the

18 concentration was equal to or below the maximum limit of 0.1

19 ml/l which was recommended in guidelines for standard aquatic

20 toxicity tests. 

21 In one case where solvent effects were still

22 observed the solvent concentration was .02 ml/l. This is

23 consistent with the even lower maximum concentration suggested

24 for studies of potential endocrine effects, which we cite in the

25 standard evaluation procedures for both the FSTRA and the AMA

26 test guidelines.
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1 Even in these cases where a relatively low

2 solvent concentration is used, the presence of the solvent

3 effect makes it challenging to determine whether and to what

4 extent the test substance itself is affecting endpoints in the

5 AMA.

6 In this example statistically significant

7 effects on day 21, normalize timeline length were observed in

8 the solvent control and in all treatment levels we see this here

9 in green and red if it shows up for you.

10 You'll note that we identify the statistical

11 test in these tables for each comparison -- that's just a side

12 note -- consistent with the guideline if you see a monotonic

13 trend just based on visual observation, and the results we'll

14 use the Jonckheere-Terpstra test. Otherwise, we typically use

15 Dunnet's test if the data satisfies assumptions for normality

16 and homogeneity of variance. Or we'll use the nonparametric

17 Mann-Whitney test.

18 In the same study - moving to the next slide -

19 a slight promotion of day 7 snout-vent length was observed in

20 the solvent control in intermediate treatment level while a

21 significant decrease was observed at the highest treatment

22 level. 

23 With respect to body weight marginal solvent

24 effects were also observed on day 21. Ultimately solvent

25 effects on growth and other parameters complicate the

26 interpretation of data and they in some cases reduce confidence
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1 in the studies ability to detect the treatment related effect.

2 The studies can also be difficult to evaluate

3 when they show nothing. In cases where there is confidence in

4 actual exposure conditions based on the analytical chemistry

5 throughout the test, the absence of an effect is just that, no

6 effect.

7 However the test substance that demonstrates a

8 very poor recovery and has measured concentrations which

9 fluctuate or decline considerably throughout the course of the

10 study may not be amenable to an aqueous exposure method. In

11 such a case the absence of an effect in the AMA may reflect an

12 absence of exposure rather than providing confidence that the

13 substance does not interact with the HPT axis in amphibians.

14 The AMA test guideline points to an OEC

15 guidance document which provides numerous techniques for testing

16 successfully with difficult test substances, which is what they

17 are termed very appropriately. However, there may be cases when

18 a substance is so poorly soluble or highly lipophilic that an

19 aqueous exposure is unrealistic and uninformative. 

20 In such cases the guidelines suggest

21 considering a dietary exposure protocol as an alternative to the

22 aqueous exposure in the AMA. However the Agency is currently

23 unaware of any validated dietary exposure protocols for testing

24 effects of the substance on frog metamorphosis. 

25 The figure you see here just represents the

26 measured concentrations from the one case earlier that I
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1 mentioned where there was a major deviation from the 21 day

2 exposure CVs for the study and you can see this is the high

3 treatment level.

4 It drops down so that the day 21 levels

5 basically overlap with the other ones. This is the intermediate

6 and the low treatment level. And the other thing just to note,

7 other than the major drop at the high level, is that their

8 actual achieved concentrations are about 1/3 of what their

9 nominal concentrations were. So this was difficult on many

10 levels. 

11 For the most part the challenges which I

12 discussed here and which are discussed in more detail in the

13 White Paper are the exception rather than the rule. Laboratory

14 performance of the AMA has generally met expectations based on

15 the general scientific soundness of the studies and the validity

16 and performance criteria which are detailed in the test

17 guidelines.

18 The assay appears to be an integral and

19 informative part of the Tier 1 battery in that it provides data

20 regarding the affects of the test substance on frog

21 metamorphosis, which is an HPT dependent process. 

22 The high quality of most studies reviewed thus

23 far provides the assurance that the method is reproducible

24 across laboratories as initially shown in the assay validation,

25 and that the test guideline and other guidance materials

26 generally provide sufficient information for a laboratory to
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1 successfully conduct the test.

2 That said, as we continue to gain experience

3 with these studies and these datasets there may be specific

4 areas where additional targeted guidance could be useful to both

5 laboratories and reviewers. Such areas may include additional

6 guidance for difficult to test substances for example when or

7 how to perform dietary exposure test, minimizing the background

8 occurrence of spinal curvature in xenopus tadpoles and ensuring

9 consistent interpretation of thyroid histopathology indicators,

10 which may be diagnostic of mild developmental delay.

11 And so that concludes my presentation and I

12 believe all of the individual assay presentation. I'm happy to

13 take any questions.

14 DR. DANIEL SCHLENK: Any questions from the

15 panel. Dr. Patino.

16 DR. REYNALDO PATINO: I have a number of

17 questions but I'll start with two.

18 CATHERINE AUBEE: Okay.

19 DR. REYNALDO PATINO: In regards to the bent

20 tails and what that might mean in terms of the validity of the

21 other endpoints or how that may affect, did you actually try to

22 determine whether the individuals with bent tails were apart

23 from the others in terms of the other influence of growth or

24 other endpoints?

25 CATHERINE AUBEE: No we haven't performed any

26 analysis like that. The data are not presented to us in such a
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1 way that we could figure that out. In other words we don't

2 usually get data that say for this individual snout-vent length

3 was this and this individual also showed bent tail.

4 DR. REYNALDO PATINO: It would be useful to

5 have that information, it would be easy to determine.

6 CATHERINE AUBEE: Right.

7 DR. REYNALDO PATINO: The other question I

8 have is one of the concerns or one of the questions you had is

9 about the interpretation of the thyroid histopathology and what

10 that means.

11 CATHERINE AUBEE: Right.

12 DR. REYNALDO PATINO: Also I guess your

13 previous answer may have answered the second question I have. 

14 So you have that some compounds suggested that there was a

15 reduced overall ranking of the thyroid condition. Was that

16 associated also with reduced development in the same individual?

17 CATHERINE AUBEE: What I showed was just

18 intended to be an example. The type of question that you're

19 asking is what we would anticipate then looking at when we're

20 looking at the weight of evidence for an individual chemical. 

21 So those are the types of questions that we're thinking about. 

22 And this one case I don't think that that was the case. 

23 So in the one case that I showed you I think

24 we would be able to sort out on our own looking at the other

25 data and saying no it's not really consistent.

26 However, our concern is more that we're seeing
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1 that sort of trend and those diagnoses across several studies

2 and we just don't know in terms of the interpretation of that on

3 its own.

4 DR. REYNALDO PATINO: It's difficult to

5 interpret on its own if you don't always develop the

6 developmental stage.

7 CATHERINE AUBEE: Right. We do have data on

8 developmental stage usually for each organism. Although it is

9 not required in the guideline, we do recommend that data be

10 reported to us on an individual level with individual

11 identifiers when possible, so that we can track the end-life

12 measurements with the histopathology analysis, which is usually

13 done separately and after the fact. But it's not something we

14 get all the time.

15 DR. REYNALDO PATINO: Alright, thank you. 

16 DR. DANIEL SCHLENK: Dr. Green?

17 DR. SHERRIL GREEN: I have a couple of

18 questions for you as well. Would it be possible for the panel

19 to see images of the thyroid histopathology with examples of

20 your grading system, which I think was zero to three, so we

21 could have some perspective on what's being evaluated?

22 CATHERINE AUBEE: Sure.

23 DR. SHERRIL GREEN: Maybe tomorrow or later

24 sometime.

25 CATHERINE AUBEE: Okay, we don't routinely

26 receive images for these studies. We ask that they keep them on
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1 file. That's something we would have to make a special request

2 for from the company. What we do have and what we have

3 referenced, I think, within the body of the charge question

4 document, are the guidance documents which are available to

5 industry and to reviewers which have some representative

6 micrographs of those severity grading scales.

7 DR. SHERRIL GREEN: Okay. Thanks, wonderful.

8 CATHERINE AUBEE: So that may be useful.

9 DR. SHERRIL GREEN: Along that line, how do

10 you control between company reviewers or the contract labs for

11 intra and inter observer agreement? Because this is fairly

12 subjective and it's difficult if the changes are slight and you

13 don't use a software system to measure.

14 CATHERINE AUBEE: Right. I think we recognize

15 that as a limitation. One of the things which Director

16 Manibusan may be mentioning later is that a histopathology

17 workshop or training session is anticipated sometime in the near

18 future.

19 DR. SHERRIL GREEN: Okay that's good.

20 CATHERINE AUBEE: But other than that the

21 guidance documents are sort of our work in progress and we'd

22 like to build on that.

23 DR. SHERRIL GREEN: Right. Would we be able

24 to see for bent tail possibly? Do you have any images of the

25 bent tail samples of what it looks like?

26 CATHERINE AUBEE: I don't have any from the
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1 specific studies.

2 DR. SHERRIL GREEN: Okay.

3 CATHERINE AUBEE: I think we have a public

4 commenter who again did some work while with the Agency and I

5 don't know if he intends to present it or not. Dr. Touart did

6 you want to say something?

7 DR. DANIEL SCHLENK: Dr. Touart?

8 DR. LESLIE TOUART: Just a follow up; in terms

9 of the kinds of guidance that is provided in doing the

10 histopathology, there is a histopathology guidance document that

11 goes into a lot of detail with a lot of images and analysis to

12 demonstrate to help insure kind of uniformity across

13 pathologists that would be looking at these.

14 DR. SHERRIL GREEN: Okay great. I have a

15 couple of more.

16 DR. DANIEL SCHLENK: Sure.

17 DR. SHERRIL GREEN: With regards to the bent

18 tail, you know, I'm aware that there is a mutation that is

19 lethal, but there are also some naturally occurring mutations

20 that are not lethal. And when labs breed and they use wild

21 caught frogs, sometimes they introduce bent tail into their

22 group from breeding.

23 Have you attempted any mutagenesis screening

24 on some of these sample cohorts so you could see if you have it

25 in your family lines?

26 CATHERINE AUBEE: We have not within our
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1 program. I cannot state for the Office of Research and

2 Development. Unfortunately I don't know if they're on the line

3 remotely with us right now. I know that they have looked at

4 some of the other environmental and genetic factors. I don't

5 know what type of screening they may have done, however.

6 DR. DANIEL SCHLENK: Is it possible to contact

7 them during the week so that we can get that information?

8 CATHERINE AUBEE: Yes. Absolutely. We will

9 ask for it.

10 DR. SHERRIL GREEN: Another question related

11 to the tails. One Slide 8 there was no comment on tail

12 appearance or parameter as one of the things that you would

13 test, that you would evaluate one of the criterion; I was

14 wondering if you meant not to include that and why not? Or is

15 this something that came up after this?

16 CATHERINE AUBEE: In terms of the historical

17 perspective on what is in the test guidelines, Dr. Touart may be

18 able to talk about that a little bit more. However, this is

19 something that we've recognized as an issue as people are

20 starting to do the studies. 

21 We discussed it in a corrections and

22 clarifications document that was published after the test

23 guidelines came out and basically said keep an eye on this, make

24 sure other performance criteria are being met, and we are

25 definitely going to pay attention to whether or not it appears

26 to be a treatment related effect.
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1 Dr. Touart, I don't know if you want to speak

2 to the guideline a little bit more.

3 DR. LESLIE TOUART: Just a follow up as

4 Catherine said, the context is if we see some type of a

5 concentration related, you know, response to that that would be

6 reported, but the occurrence, because it is somewhat prevalent. 

7

8 One thing to note with the kind of tail

9 curvature, it's really more of a muscular anomaly than it is a

10 skeletal anomaly within the tail at that time. It appears that

11 what you have is an asymmetry within the muscles of the tail in

12 terms of how they grow.

13 Recognize that within the metamorphosis of

14 these tadpoles, the tail is growing but also being resorbed over

15 the time course; so that the context of how the tail could

16 presumably get (inaudible) and stuff is there, but there are

17 these factors whether their mutations are possible to enter in. 

18

19 But the general observation for these tadpoles

20 in terms of once the tails are absorbed or once they go through

21 metamorphosis, there doesn't seem to be any correlation between

22 the survivability of the individual beyond that.

23 But other than that kind of general context

24 the actual assay itself isn't looking at specific outcomes of

25 that unless there is something that appears to be concentration.

26 DR. SHERRIL GREEN: Okay that's all I have.
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1 DR. DANIEL SCHLENK: Okay. Dr. Schultz you

2 had your hand up.

3 DR. TERRY SCHULTZ: Because question five and

4 six directly apply to two very specific components of your talk,

5 don't feel that the committee or panel is picking on you but we

6 kind of need this kind of information if we're going to provide

7 the components back. So I know I'm not supposed to make

8 comments just questions I'm going to list a series of questions

9 and just get a yes or no and get a feel for this.

10 The curvature of the spine is that a

11 dorsal/ventral kinking? Does it have flexure that goes down or

12 is it a lateral flexure?

13 DR. LESLIE TOUART: Primarily lateral.

14 DR. TERRY SCHULTZ: Does it curve primarily in

15 the anterior region of the tail?

16 CATHERINE AUBEE: Yes. 

17 DR. TERRY SCHULTZ: Does appearance tend to be

18 related to a specific stage. In other words because you get to

19 a certain stage you can start to see it and it might not be

20 recorded there or not?

21 CATHERINE AUBEE: Sure. I'll speak on that

22 for a moment if that's all right. In most cases what we're

23 seeing is it is not reported at test initiation with NF stage

24 51. However at some point during the study it begins to appear

25 and it generally seems to increase when we get that information;

26 it appears that it increases over the duration of the study. 
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1 So for example the study may report starting

2 to see it in tadpoles around day seven or eight and then you get

3 this really high prevalence by test termination.

4 DR. TERRY SCHULTZ: Is there any anatomical

5 pathology studies that you're aware of that's been associated

6 with this lesion?

7 CATHERINE AUBEE: Not that I know of but I

8 don't know for certain.

9 DR. TERRY SCHULTZ: Is there any biochemical

10 or other information to suggest that it's associated with

11 connective tissue defects?

12 CATHERINE AUBEE: Not that I'm aware of.

13 DR. TERRY SCHULTZ: Okay. 

14 DR. DANIEL SCHLENK: Piersma?

15 DR. ALDERT PIERSMA: One very general question

16 after all these details is what I noticed in the report is that

17 you state that the reviews you've done of the data is of a

18 preliminary nature. Both in the beginning and the end of the

19 text I find that. What do you mean by that? Does it mean that

20 this test has been analyzed so far in less steps than other

21 tests, or do I need to weight these results differently from

22 other tests? What do you want to say?

23 CATHERINE AUBEE: What that means is that we

24 are waiting for feedback from you before we finalize the

25 reviews. As Dr. Bradbury mentioned earlier we're very

26 interested in your feedback and if there is something that we
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1 should perhaps adapt or look at differently we would like to be

2 sure to incorporate that before finalizing anything that then

3 becomes a final public record for these chemicals.

4 DR. DANIEL SCHLENK: Dr. Leonard.

5 DR. JACK LEONARD: Has there been an attempt

6 to make a relationship between the molecular cell height and the

7 snout/tail limb in your data sets?

8 CATHERINE AUBEE: To my knowledge no one has

9 looked at that specifically although some of the

10 histopathologists do appear to have had access to the actual

11 study data. And then in some cases they will discuss that in

12 more of a qualitative sense.

13 DR. DANIEL SCHLENK: Dr. Scott?

14 DR. GEOFFREY SCOTT: In the report itself you

15 did not cover this in your remarks, but in Table 32 there is a

16 mention of the suppliers. And I didn't hear you mention that in

17 your talk. I'm sure you all probably did look at that and maybe

18 you can comment on what your interpretation is in looking at the

19 data on suppliers if you all had looked into that with greater

20 detail.

21 CATHERINE AUBEE: Thank you Dr. Scott. I was

22 thinking of that also when Dr. Green asked her question earlier

23 about screening for mutations and specific populations. 

24 I don't know if that's been done at the

25 laboratory level either. Something that, as you pointed out, we

26 took out of the slide just because it really didn't fit
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1 space-wise onto the slide itself, was that we are tracking on

2 the supplier for test organisms when that is reported. It

3 usually is. 

4 But again basically that is just keeping track

5 of the name and does it seem like we see more bent tail in this

6 case when it's all coming from the supplier.

7 Generally, again this is a relatively small

8 sample in that we have only a few labs that we're looking at

9 with a few different suppliers; so it's hard to say

10 qualitatively what is going on there. But you can tell, if you

11 imagine that this supplier is pretty much aligned with what the

12 lab here is, you can see there does seem to be at least visually

13 some difference across labs and potentially suppliers. Does

14 that get at your question?

15 DR. GEOFFREY SCOTT: Yeah that's what I was

16 asking. Thank you.

17 DR. DANIEL SCHLENK: Dr. Patino?

18 DR. REYNALDO PATINO: I have another question

19 in concerns to the thyroid histology. And that is, what is the

20 rationale for using a published guide of rankings versus using

21 the internal controls for this study?

22 CATHERINE AUBEE: Okay. Again, the published

23 guide was actually created during the validation of the test

24 itself. So it was sort of work that went hand in hand and

25 because there wasn't really anything out there before looking at

26 amphibian thyroids during metamorphosis. So they did a lot of
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1 work and they catalogued it as well as possible. And that is

2 what is resulted then in the published guide.

3 Now there are a couple of different ways to

4 evaluate the histopathology criteria for the amphibian thyroid

5 described in the guide. There are certain things, which you may

6 know or not, that are diagnosed: We assigned the incidence, the

7 number of observations, and severity based on a grading scale of

8 0 not remarkable to 1, 2, 3 being the most severe.

9 In some cases those are based on the normal

10 micrographs which, for example, you might see in the guide that

11 could be used as a reference case. For other specific regions

12 it's intended to be based on what you see in the control

13 specimen.

14 However, what I should clarify is that even

15 when something is diagnosed based on the control micrographs in

16 the published guide, I think how it should be interpreted is

17 then based on what you see in the controls for the study. 

18 So for example even if we go to the particular

19 diagnosis that we were discussing here, you can see an incidence

20 of ten individuals diagnosed as grade 1 follicular cell

21 hypertrophy in the controls. 

22 So this is based on what the normal diagnosis

23 or what the normal micrograph would be. But then what we're

24 thinking is for the treatment levels we would see how these line

25 up compared to the controls. So when interpreting a treatment

26 related affect we are looking at the concurrent controls.
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1 DR. REYNALDO PATINO: Okay.

2 CATHERINE AUBEE: Does that answer your

3 question? It's very confusing.

4 DR. REYNALDO PATINO: Somewhat. I do have a

5 follow up.

6 CATHERINE AUBEE: Okay.

7 DR. LESLIE TOUART: Just to add a little bit

8 more clarification with that. In context the controls, you're

9 using the histopathology guidance document to read controls as

10 well as treatments. So in the lesions and instance and severity

11 grades that are used would be matched to what the guidance

12 document in terms of criteria are listed.

13 So if you have a severity grade of 2, you know

14 that would be identified, it could be in control, it could be in

15 treatment and those kinds of things. The pathologist himself

16 would be going back and kind of comparing it. Once they've

17 graded the variety of slides and stuff to try to determine okay

18 is there some type of a concentration related response, are they

19 able to sort the slides in some capacity in that context.

20 But in general it's not that you're comparing

21 an effect, you know, in the atlas versus what your actual

22 control will be because that's being compared within the same

23 context.

24 DR. REYNALDO PATINO: I understand. You are

25 comparing and indirectly the control is compared against an

26 external standard so to speak.
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1 DR. LESLIE TOUART: Correct. It's just a

2 matter of once everything has been read and you've got all your

3 gradings then you can go back and compare, okay did the actual

4 instances or follicular hikes that have been measured, is that

5 different from control to the treatments in that sense. But in

6 terms of the actual grading, that's based upon kind of the

7 standard that would be provided within the guidance documents.

8 DR. REYNALDO PATINO: So a follow up

9 concerning thyroid histopathology. This measures are kind of

10 semi quantitative, qualitative your ranking the severity of the

11 lesion or whatever you call it. Have you considered using a

12 more continuous variable such as in this case would be thyroid

13 side height? 

14 Have you any thoughts about doing that and

15 having a continuous variable for the thyroid histopathology? 

16 Currently with the guideline we don't, it's all ranking. 

17 DR. LESLIE TOUART: I think the context, I

18 mean, we did have a histopathology workshop, whatever they

19 consider that in terms of considerations of how these things

20 might be considered, but one aspect given that this is a screen,

21 we move more towards keeping it somewhat qualitative. 

22 I think if one moves into evaluations that

23 would be more say in the realm of Tier 2, definitive type

24 testing, the need for evaluating a little bit more qualitatively

25 in context, using continuous measures and using your statistical

26 elements in terms of determination, would be perhaps
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1 appropriate. But in this context it was decided to try to keep

2 it simple because histopathology is one of the more expensive

3 aspects of these assays and so we want to keep it as concise and

4 simple as is practical in the context.

5 So that was the, I think, the determining

6 factors in terms of what went into the guidance.

7 DR. REYNALDO PATINO: So this would be

8 something for Tier 2? Thyroid side height?

9 DR. LESLIE TOUART: Conceivably, and we look

10 forward to further recommendations that you may have on this.

11 DR. REYNALDO PATINO: Follow up question. 

12 Concerning controls, what is your rationale -- not just for this

13 but the other endpoints in the AMA. What is your rationale for

14 comparing against the native control when there is a solvent as

15 opposed to the solvent control?

16 CATHERINE AUBEE: I'm being looked at because

17 this is a policy of our division. This is just the standard

18 practice for evaluating eco-toxicity data within our division. 

19 I believe it ties back to what is in the regulations, the 40 CFR

20 which actually have a statement on the laboratory practices that

21 say if there is solvent interference then the study really

22 should be thrown out and invalidated.

23 And so based on that our practice in the past

24 has been to compare to the negative control but to test to see

25 if there is a solvent effect using a t-test or something like

26 that, and if there is then we look at things a little bit more
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1 carefully and make some decisions as to the overall validity of

2 the study.

3 Now this is a new paradigm for us and, of

4 course, we recognize that it is quite common practice in many

5 cases to compare it to the solvent control. However, for the

6 reasons that we've discussed earlier it does sometimes -- even

7 if we do that -- it does raise some questions. In some cases

8 it's just a matter of how much uncertainty you're willing to

9 deal with. How much a solvent may be masking an effect in the

10 study.

11 DR. REYNALDO PATINO: Okay.

12 DR. DANIEL SCHLENK: Okay Dr. Horseman.

13 DR. NELSON HORSEMAN: Back to the thyroid

14 histology. I understand the discussion of the comparison made

15 to controls on this. Is there an effort to determine whether

16 the thyroid histology is concordant internally with the

17 metamorphic stage of those animals?

18 It seems that the more informative question is

19 that, whether thyroid histology corresponds to what one would

20 expect with that metamorphic staging.

21 CATHERINE AUBEE: I think we do that perhaps

22 indirectly by recommending that at least the specimen selected

23 the stage matched. Ideally we would be looking at individuals

24 which all have the same developmental stage, and although then

25 we don't necessarily look to see okay are these characteristics

26 of the thyroid, are they representative of what we would see at
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1 that stage, we operate under the assumption that if they are all

2 at the same stage then they should all have similar thyroid

3 characteristics; and if there is a difference then that could be

4 a potential effect.

5 DR. NELSON HORSEMAN: Just a follow up. If

6 there is then a treatment effect on stage how does that affect

7 your selection of histopathology specimens?

8 CATHERINE AUBEE: This sort of bridges over to

9 what I will show in another presentation where we're showing the

10 decision logic for the AMA test guideline, but it is in the test

11 guideline. 

12 The first thing I would say is if we see an

13 effect on developmental stage that it appears to be advancing

14 developmental stage according to the test guidelines that could

15 be a hit for the screens. So we might consider that a positive

16 finding for the screen and so even if there is uncertainty as to

17 the thyroid diagnoses because there is variability there we

18 already know that there is some sort of effect.

19 Now where it gets a little bit more

20 complicated is, as we mentioned, if we see a developmental delay

21 and then we are unable to stage match those specimens. The

22 guidance recommends that you get as close as possible. So for

23 example if there is a developmental delay and you can't get the

24 same stage you would select the next closest one down.

25 So we try to get it close but it does

26 introduce some more uncertainty, I think, into the analysis.
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1 DR. DANIEL SCHLENK: Dr. Green.

2 DR. SHERRIL GREEN: I have a different line of

3 question. I was looking on slide number nine, the test

4 concentration CV = 20% over 21 days and in the report there it

5 looks like only 12 out of the 18 studies met that performance

6 criteria.

7 And I listened very carefully to your

8 explanation but just for my own edification, could you reiterate

9 that one more time? I'm struck by how many of the studies

10 didn't meet that performance and it seems to me like test

11 concentration is so critical to this entire body of work. 

12 So if you could explain one more time the

13 discrepancy there between the only six labs having met that

14 criteria and the others not. Thank you.

15 CATHERINE AUBEE: Sure. As you pointed out I

16 think it was about maybe 10 studies. I can't tell exactly on

17 here and I don't remember the exact number, but I think that ten

18 studies met the criteria and something like six to eight did not

19 meet the criteria for 21 day CVs. 

20 In most cases the criterion is = 20% so those

21 cases may have been where at the high treatment level where it's

22 really close to the limit of solubility they got slightly

23 outside that. Or conversely, as the case was brought up before,

24 we did see the opposite also where at very low concentrations

25 close to the limit of detection it was slightly outside that.

26 The point that I was making was that except
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1 for the one case where I showed you the different figure -- and

2 I'll bring that up again here. This was pretty drastic, but this

3 is uncharacteristic for most of the chemicals at least in the

4 subset that we analyzed.

5 So we knew based on the physical chemical

6 characteristics of this chemical that it was going to have

7 issues. But in most cases even if they did not meet that

8 criterion -- so say for the other five chemicals that missed it,

9 it was a relatively minor deviation, so maybe 5 percent or less,

10 that's a rough approximation. Or it was at only one treatment

11 level and so one of the other criteria within the guideline is

12 that you have to have two uncompromised treatment levels. You

13 start with three, so if you have less confidence in one because

14 of something weird like this you may still be able to get enough

15 information out of that study based on the remaining two.

16 DR. SHERRIL GREEN: Just so I recall

17 correctly, tissue levels of the test substance were not measured

18 in the tadpoles themselves, only in the water.

19 CATHERINE AUBEE: Correct.

20 DR. SHERRIL GREEN: Okay thank you.

21 DR. DANIEL SCHLENK: Dr. Scott.

22 DR. GEOFFREY SCOTT: Yeah I think this figure

23 is really illustrative of the problem with getting to this

24 dosing conformance because if you look in this data that you

25 have posted here, at least in the lower two doses, at the start

26 of the test and well in beyond the doses seem pretty
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1 consistently at I think you said about 30 percent of the

2 nominals.

3 So the real question is what is causing that,

4 where is that other 70 percent? And I think that's why this

5 kind of information is real useful and I guess I assume you all

6 are looking at those kinds of differences. I think this is a

7 good example of having this kind of data to be able to look at,

8 to sort out where the variance may be.

9 DR. DANIEL SCHLENK: Okay. All right one

10 more. We're about ready to break. Yeah Dr. Patino:

11 DR. REYNALDO PATINO: Okay last question. So

12 in the report you mentioned that you normalized hind limb length

13 by snout-vent length. And I think tomorrow in the public

14 comments there may be a question about that procedure. But my

15 question for you is have you determined that the relationship

16 between these two variables is proportional or not? And that

17 would probably address questions about the validity of doing

18 that. 

19 CATHERINE AUBEE: We do not test to see if

20 there is a relationship between snout-vent length and hind limb

21 length prior to doing the analysis where it's simply operating

22 under the recommendations of what was put into the guideline. I

23 had the chance to look back at the validation reports that came

24 out in three phases and they did look at this specifically.

25 They did different types of normalizations. 

26 They also tried normalizing to body weight and they decided at
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1 least based on the subset of reference chemicals that they used,

2 that normalizing the hind limb length was the most appropriate. 

3 They didn't necessarily recommend doing any sort of other tests

4 for relationships between that and snout-vent length beforehand.

5 I will say that in our first set of chemicals

6 when we do our analysis it's sort of a batch process for the

7 statistics, so we run all the data through; so non-normalized

8 hind limb length is something that is analyzed although it's not

9 one of the recommended endpoints. And then pretty much all of

10 the cases where we saw an effect on normalized hind limb length,

11 it was also significant for the non-normalized. We just didn't

12 then report that because it wasn't a guideline.

13 DR. REYNALDO PATINO: May I follow up? Is

14 there any way you can provide the rationale on the development

15 of the guideline? Is there some document where we can see why

16 they recommended that? I was not involved in that so I don't

17 know and I'd like to know.

18 CATHERINE AUBEE: Sure. I can pull the

19 relevant sections of the earlier reports if that is helpful and

20 I don't know if Dr. Touart wants to speak to that.

21 DR. REYNALDO PATINO: Thank you.

22 DR. LESLIE TOUART: There was some discussion

23 within the integrated summary report that was developed for the

24 AMA that went for the peer review and the validation activity to

25 consider that.

26 One thing to remember though is that the hind
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1 limb length is measured at day seven and at day seven there is

2 not a lot of other measures that are being made since the test

3 is being carried throughout. 

4 So the snout-vent length is an indicator of

5 size so it can normalize to a size that would seem to be

6 appropriate for context. So that is a situation for providing

7 it in that particular case because weight or other indications

8 are not as effective.

9 DR. DANIEL SCHLENK: Okay. If it's okay with

10 Dr. Steeger we're going to take a break now and then we can

11 combine your presentations at the end of that is okay. And

12 there will be ample time because this will be a combined

13 presentation. If you have other questions that come up during

14 the break you can ask them at that time as well. So let's go

15 ahead and take a 15 minute break and be back at 2:45.

16 (Whereas a break was taken)

17 Okay. I have another announcement again for

18 public presenters. If you have a presentation please get that

19 to Fred either after the meeting today or before 8:30 tomorrow

20 morning if you're up for tomorrow morning. Again, public

21 commenters if you have a presentation please get that to Fred

22 Jenkins after the meeting today or before 8:30 in the morning. 

23 Okay. Let's go ahead and continue with our presentations and

24 Dr. Steeger if you're up on the battery performance.

25 DR. TOM STEEGER: All right thank you very

26 much. 
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1 Before I move into the battery performance

2 section I just want to make one comment and that is that over

3 the past several presentations talking about the individual

4 assay performances, the agencies highlighting issues that have

5 been associated with some of the performance of the assays

6 potentially high CVs in some of the assays where measured

7 concentrations may not have been close to nominal. 

8 I think that the important thing to keep in

9 mind here is that while those might occur in a test they did not

10 necessarily occur across all treatments within the test.

11 So the Agency considers the performance

12 criteria as guidelines; they aren't meant to be hard and fast

13 rules; that if you exceed the criteria then it automatically

14 discounts the utility of the study. Most of these studies as

15 has been indicated in the presentations have met the performance

16 standards while some replicates within the treatment groups may

17 not have. 

18 In general, I think that what we're seeing is

19 that across the assays and across the different measurement

20 endpoints, including the chemical/physical and environmental

21 conditions of the tanks themselves, there tended to be

22 relatively good consistency across the different chemicals that

23 were tested.

24 With that said there are some chemicals that

25 are very difficult to test, and those were used as actual

26 examples where solubility and high organic carbon partition
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1 co-efficient really do challenge any type of aquatic testing

2 with those chemicals.

3 So moving into battery performance in the

4 following presentations; battery performance will be discussed

5 with respect to each of the endocrine modalities. 

6 Catherine Aubee will be discussing battery

7 assays associated with the thyroid pathway. Amy Blankinship

8 will be providing an overview of the assays associated with the

9 androgen pathways and Greg Ackerman will provide an overview of

10 the assays associated with the estrogen pathways.

11 In evaluating the performance of assays and

12 the battery, the goal of the analysis was to evaluate the extent

13 of complementarity of responses within individual assays and the

14 extent of concordance of responses across assays. That is the

15 concept of redundancy for each of the three endocrine pathways. 

16 In considering the extent to which there is

17 redundancy across assays, the analysis also examines whether or

18 not it may be possible to reduce the number of assays and still

19 adequately identify potential interactions with the E A or T

20 hormonal pathways.

21 As discussed during the presentation by Dr.

22 Touart, in 2008 the SAP concluded that the EDSP Tier 1 assay

23 served as an appropriate screen for disrupters of the E A and T

24 axis and that the ability of the assays to identify endocrine

25 active substances is enhanced through what was considered by the

26 SAP to be adequate complementarity within assays and redundancy
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1 across assays to provide safeguards against false negatives or

2 false positives.

3 This analysis used the 2008 FIFRA SAP report

4 is a guide in terms of evaluating endpoints within assays for

5 complementarity and the consistency of responses across assays

6 as indicators of potential effects on specific endocrine

7 pathways.

8 The concept of complementarity and redundancy

9 is used in this document, in the White Paper as defined by the

10 2008 SAP, complementarity describes the concordance of endpoints

11 within assays and redundancy describes the concordance of

12 endpoints across assays that are intended to evaluate an

13 interaction in a particular E A or T pathway.

14 As will be discussed in the following

15 presentations for each of the hormone pathways evaluated,

16 redundancy is evaluated across assays that are intended to

17 provide evidence that a particular modality is potentially

18 affected.

19 For example six Tier 1 assays are considered

20 capable of detecting chemicals with androgenic or antiandrogenic

21 like activity that may disrupt the androgen hormonal signaling

22 pathway, while three Tier 1 assays are capable of detecting

23 chemicals that may disrupt the thyroidal hormone signaling

24 pathway, and seven Tier 1 assays are capable of detecting

25 chemicals with estrogenic or antiestrogenic like activity that

26 may disrupt the estrogen hormonal signaling pathway.
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1 Within and across assays complementarity and

2 redundancy are evaluated in the context of different modalities

3 that span multiple levels of biological organizations, including

4 molecular interactions and cellular organ and whole organs and

5 responses for each hormonal class that is the E A or T hormonal

6 systems that the battery was expected to assess.

7 The 2008 SAP defined these biological levels

8 or organizations consisting of receptor/ targeted binding, cell

9 responses, enzyme activity, organ responses, male and female

10 system integrity, developmental such as thyroid and comparative

11 system integrity. As such, the battery of assays provides a

12 means of integrating information across multiple levels of

13 biological organization and across multiple taxa. 

14 As noted previously the first set of Tier 1

15 test orders was issued for 67 chemicals by EPA in 2009. Fifteen

16 chemicals were voluntarily cancelled or the manufacturer or

17 importer agreed to cease all sales and distribution of chemicals

18 in the pesticide market.

19 Tier 1 assay data were actually submitted on a

20 total of 52 chemicals. To facilitate timely feedback, a

21 subsample consisting of 21 chemicals, which were considered to

22 have adequately representative chemical space of the 52

23 chemicals, were selected for this analysis. 

24 Of these, 18 case study chemicals were used in

25 the preliminary analysis of the performance of the battery for

26 the HPG estrogen hormonal pathway screening. Seventeen case
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1 study chemicals were used in the preliminary analysis of the

2 performance of the battery for the HPG androgen hormonal pathway

3 screening.

4 Sixteen case study chemicals were used in the

5 preliminary analysis performance battery of the hpt thyroid

6 hormonal pathway screening. 

7 When positive Tier 1 findings are observed in

8 the presence of overt toxicity the agency is aware there is

9 uncertainty as to whether or not the responses are mediated

10 through an endocrine pathway.

11 The Agency, however, will evaluate the degree

12 of confidence for endocrine responses that co-occur with

13 systemic toxicity in future weight of evidence determination,

14 and plan to do so on a case by case basis to maintain

15 transparency. Cases where overt toxicity was observed are

16 clearly identified in the section reviewing each assay and are

17 noted in all tables including the battery review section. 

18 So we're ready to move into each of the

19 individual data reach. 

20 DR. DANIEL SCHLENK: Okay we'll just hold

21 questions at the end for everybody if that's okay.

22 CATHERINE AUBEE: Hello again everybody. I

23 will be presenting a very high level overview of the performance

24 of the Tier 1 battery with respect to the detection of potential

25 interactions with the HPT axis or downstream thyroid hormonal

26 pathway.
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1 We tend to use those terms interchangeably

2 within this presentation for better or worse. The important

3 thing to note is that we currently lack truly mechanistic assays

4 for identifying potential thyroid interaction in the Tier 1

5 battery.

6 You have seen this slide or some version of

7 this table several times today -- we just love it's on the

8 website. As Dr. Steeger mentioned multiple in vitro and in vivo

9 assays provide more extensive complementarity and redundancy for

10 detecting affects on the estrogen and androgen hormonal

11 pathways, but the current battery provides much less

12 complementarity and redundancy for detecting potential thyroid

13 effects.

14 What we do have are three fairly comprehensive

15 in vivo assays which we have discussed in detail here today;

16 with male and female pubertal rats and with metamorphosing

17 amphibians. These work together to cast a wide net to detect

18 chemical-induced perturbations which could be thyroid related.

19 Within each assay multiple endpoints are use

20 to screen for affects. The thyroid hormone levels including

21 thyrotropin or thyroid stimulating hormones, TSH, and thyroxine,

22 T4, are measured in the male and female pubertal rats.

23 The 2008 FIFRA SAP recommended, "A significant

24 affect of test substance on serum T4 must be considered a

25 positive finding whether or not serum TSH is affected and

26 whether or not the AMA is positive." That is from page 31 of
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1 their report.

2 Therefore in this preliminary analysis of Tier

3 1 battery performance, the agency considers a treatment related

4 affect on TSH, T4 or thyroid weight or histology in the male or

5 female pubertal rat to indicate a potential interaction with the

6 thyroid hormonal pathway in those assays.

7 The amphibian metamorphosis assay evaluates

8 downstream affects on developmental biomarkers such as

9 normalized hind limb length. 

10 These two endpoints are analyzed separately

11 but are intended to be complimentary to one another within the

12 AMA. They are grouped together in this presentation. 

13 The AMA overlaps with the pubertal rat assays

14 in that it also evaluates thyroid histopathology and gross

15 pathology. Although in the AMA thyroid weights are usually no

16 reported. 

17 Whereas the Agency does not currently assign

18 any particular relative weight to the thyroid related endpoints

19 in the male and female pubertal rat assays, as compared to one

20 another, the AMA test guidelines provides a methodical framework

21 for evaluating the potential significance of responses in

22 metamorphosis in frogs. 

23 This figure is from the test guideline and was

24 developed during the international validation at the AMA. The

25 decision framework is not a hard and fast rule, but it is

26 proving very useful for reviewers who are evaluating data from
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1 test substances already known to be bioactive, in our case

2 mostly pesticides, but with unknown potential for endocrine

3 activity.

4 Following this logic, significant indications

5 of advanced development or the presence of a synchronous

6 development are considered more diagnostic of potential thyroid

7 interaction; whereas developmental delay may be considered an

8 affect of generalized toxicity unless corroborated by thyroid

9 histological findings.

10 So what I'm going to show you now in this

11 table is a very high level overview of the overall responses in

12 each of the three thyroid relevant assays by case study

13 chemical. You should note that at this phase of the review,

14 where we are just broadly considering performance, we are not

15 presenting detailed information on the direction and magnitude

16 of affect of the different endpoint responses; or whether these

17 responses when taken together tell a consistent story about what

18 type of interaction may be occurring.

19 We will definitely be considering and

20 addressing those issues when we move into the more interpretive

21 weight of the evidence evaluation for test chemicals. That

22 again is the subject of a future meeting. 

23 Here we are simply showing whether there was a

24 response and whether we saw responses in multiple endpoints or

25 assays. That relates to the concept of complementarity and

26 redundancy, which Dr. Steeger introduced earlier.
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1 In this table an X indicates a significant

2 effect on one or more thyroid related endpoints in that assays. 

3 A blank cell indicates no effect. A footnoted "a" as you see

4 here indicates that thyroid relevant effects occurred only in

5 the presence of overt toxicity.

6 For the amphibian metamorphosis assay where

7 you see a footnote of "b" that indicates there was potential

8 solvent interference in this study, and in this case this was

9 the case we saw where there was a question as to whether the

10 exposure levels in the study were adequate because the test

11 substance appeared to be unstable.

12 It's important to note that the number of Xs

13 in this table should not be interpreted as the number of

14 chemicals which may be thyroid active. We need to refine our

15 analysis through the weight of evidence process before making

16 any such determinations. There is no single clear pattern of

17 responses in the assays which suggested that any one assay or

18 endpoint was consistently more or less sensitive than in others.

19 In the next slides I'm going to show you a

20 handful of examples of the different combinations of responses

21 that were seen.

22 Something to note here is that all of these

23 chemicals, again, have unknown endocrine potential. So we were

24 not necessarily predicting any specific pattern of responses

25 within the case study chemicals.

26 In the first example which you see here, three
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1 of the 16 case study chemicals showed at least one endpoint

2 response in all three assays. For these three chemicals, T4 was

3 affected in all three cases in both male and female pubertal

4 rats. 

5 TSH was also affected in only one case in the

6 female rat. Amphibian development was affected in all three

7 cases and was corroborated by effects on thyroid histology in

8 two of those cases. However, effects on rat thyroid histology

9 were observed only in one case in females and in the presence of

10 overt toxicity.

11 In the second example, three chemicals

12 elicited a response in both male and female pubertal rats, but

13 did not cause an effect in the frog. In one case here the

14 affect in rats was observed only in the presence, again, of

15 overt toxicity noted by an "a".

16 And the other two endpoint responses were

17 fairly consistent between male and female rats in once case, but

18 differed -- in other words different endpoints were affected

19 between sexes in the other case. Detectible differences in TSH

20 were accompanied by changes in thyroid weight two out of three

21 times in this study. 

22 In the third example the affects were seen in

23 the frog, but not in the rat. Again this type of response was

24 seen in three of 16 cases. Potential solvent interference was

25 noted in two of these three cases. In one case where solvent

26 affects were noted however the developmental affect was
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1 corroborated by affects on thyroid histology which were not

2 present in the solvent controls.

3 Here is example number four. No affects were

4 seen in any of the guideline assays in these studies. For one

5 of these three case study chemicals the footnote however,

6 indicates that the test order requirements were satisfied by

7 other scientifically relevant information. 

8 Interestingly in one of these submissions a

9 significant effect was seen on T3, which was not a guideline

10 required endpoint, however, it was not corroborated by any

11 affects on T4 or TSH.

12 The next example -- this is the last one, this

13 is just a catchall for all the remaining four of the 16 case

14 study chemicals and which responses varied across endpoints and

15 assays. 

16 Here we have affects on T4 in female rats in

17 two cases, corresponding to effects on thyroid histology on both

18 female rats and in the frog in one case.

19 We see effects on male TSH also corresponding

20 to effects on thyroid histology in one case. And we have one

21 chemical, which affected female rat thyroid weight but only in

22 the presence of overt toxicity. No other effects were seen but

23 there is a question as to whether exposure levels were adequate

24 in this last AMA study that you see at the bottom.

25 So what does all this suggests with respect to

26 performance of the battery? We know that there is less
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1 complementarity and less redundancy built into the battery for

2 the thyroid pathway as compared to the estrogen and androgen

3 pathways. With that in mind we did not go into this review

4 anticipating a certain pattern of responses from chemicals with

5 unknown potential for interaction and we didn't get one.

6 If anything the broad types of responses were

7 distributed remarkably evenly across possible combinations. 

8 Some of this is expected because of inherent difference between

9 the assays, the rat and amphibian assays focus on different

10 portions of the thyroid pathways and there maybe be sex specific

11 differences between male and female rats.

12 The rat and amphibian assays also use

13 different exposure methods. Tissue responses in the rats are

14 more likely to incorporate effects due to first-pass metabolism.

15 On the other hand tissue responses in the amphibian may reflect

16 effects on peripheral tissue diodinations. 

17 Individual study requirements as described

18 earlier in the review of Tier 1 assays also plays an important

19 role in study interpretation. Given the widespread in responses

20 and the general lack of complementarity and redundancy built

21 into the battery for the thyroid pathway, the Agency concludes

22 that it is premature to consider dropping any one of these three

23 assays from the Tier 1 screen at this time. 

24 This conclusion is consistent with that of the

25 2008 SAP which advised that all three assays, and particularly

26 the amphibian metamorphosis assay, were critical to effectively
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1 screen for thyroid interaction. And that concludes my

2 presentation and Amy Blankinship will be up next.

3 AMY BLANKINSHIP: Hello again good afternoon

4 and I will be discussing the evaluation of the androgen pathway

5 for the EDSP Tier 1 battery performance. Again I would like to

6 start by giving an overview of the presentation today. 

7 First I will provide a brief background on the

8 assays that inform of the androgen pathways. Second I will

9 discuss the concept of complementarity responses within an assay

10 as well as redundancy across assays. Third I will present some

11 examples of the different types of results across the assays and

12 finally some concluding remarks.

13 There are six assays that inform the androgen

14 pathway. Three in vitro assays and three in vivo assays. The

15 information on the slide is also found in table 38 of the White

16 Paper. 

17 Seventeen chemicals were used to evaluate the

18 androgen pathways Tier 1 battery performance. In regards to the

19 inclusion of the in vitro aromatase and steroidogenesis assays

20 and the androgen pathway evaluation, the 2008 SAP panel stated

21 that androgens could also influence enzyme activities in these

22 two assays and as such were including in this evaluation.

23 The endpoints listed in this table encompass

24 several different levels of biological organizations as well as

25 the nine modalities presented by the 2008 SAP panel.

26 For example the AR binding assay represents
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1 receptor or target binding while the aromatase and

2 steroidogenesis assays represent enzyme activities. A change in

3 the age at PPS or VO in the male and female rat pubertal assays

4 represent a change at the organism level and correspond to the

5 modalities of the male and female system integrity respectfully.

6 Similarly an alteration in fecundity in female

7 fish or nuptial tubercles in male fish would represent a

8 potential alteration in female or male system integrity

9 respectfully. The criteria that constitutes a response to each

10 of these six assays for the androgen Tier 1 battery evaluation

11 varies depending on the type of assays and what guidance is

12 provided in the Agency guidelines.

13 For the three in vitro assays, AR binding,

14 aromatase and steroidogenesis, there is one endpoint for each of

15 those assays that are used to inform the androgen pathway. In

16 the analysis of the Tier 1 battery for the AR binding and

17 aromatase assays a positive response is elicited when the

18 chemical's activities surpasses a thresholds that is established

19 in the guidelines.

20 For the steroidogenesis assays, a positive

21 response is obtained when the chemical's response is

22 statistically different from the control.

23 Additionally there are three in vivo assays

24 for the androgen pathway. A Hershberger rat assay, the male rat

25 pubertal assay and the fish reproduction assay.

26 For the Hershberger assay, the guideline
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1 defines a response when any two of the five measured androgen

2 dependent weights in the treated animals are different from the

3 control. The response can be an increase or decrease in the

4 tissue weights.

5 In the male pubertal assay, a change in any

6 one of the four endpoint categories listed in this table could

7 be considered as indicative of a response for the androgen

8 pathway for the purposes of the battery evaluation discussed

9 here. 

10 And finally for the FSTRA, if a statistically

11 significant difference from the control was found for one of the

12 seven endpoints listed in the table, it could also be considered

13 to be indicative of a potential response for the androgen

14 pathway.

15 As shown in the evaluations for thyroid, again

16 this table shows the assay results for all 17 chemicals used to

17 evaluate the androgen pathways, which is also presented at table

18 39 in the White Paper. 

19 The Xs denotes that a response, as defined in

20 the previous slide, was reported for that assay. 

21 Blank spaces indicate that no response was

22 reported. In a few cases, equivocal results for the in vitro

23 assays were also reported. An equivocal result is obtained when

24 the chemicals response does not reach threshold established for

25 a positive result, but is greater than the maximum allowable

26 limit used to define a negative response.
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1 For the AR binding assay for Chemical C, the

2 classification of untestable is defined by the guidelines as the

3 test chemical not being soluble at the minimum concentration

4 tested in the assay.

5 For chemical T results from several Tier 1

6 assays were not available as other scientific data were used to

7 satisfy those assays.

8 For the FSTRA for Chemical T, while there were

9 not statistically significant endpoint alterations reported,

10 decreasing trends were observed for several endpoints and as

11 such at this time this study is classified as inconclusive.

12 Additionally two other FSTRA assays results

13 were deemed questionable due to potential solvent interferences

14 or overall study conduct. 

15 In several cases for the in vivo assays a

16 response was only observed in the presence of overt toxicity as

17 identified in this table with an "a" or "b" superscript.

18 Also please note in this table that if a

19 response was observed in the Hershberger assay it was

20 accompanied by a response in either the male pubertal or the

21 FSTRA assays. 

22 Now I'd like to discuss the concept of

23 complementary responses within an assay. As defined by the 2008

24 SAP reports, complementarity is a concordance of endpoints

25 within an assay.

26 As there is only one endpoint evaluated for
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1 each of the three in vitro assays, evaluation of this concept to

2 those assays is not appropriate. However, in the male pubertal

3 assay there are four defined androgen responsive endpoints in

4 that assay. Age at PPS, weight of androgen dependent

5 reproductive tissue, histopathology of those tissues and plasma

6 testosterone levels. 

7 As presented in the top table in the slide,

8 none of the 17 chemicals elicited a response in all four

9 endpoints. Additionally no alterations in the four defined

10 endpoints were reported for seven chemicals.

11 In the remaining chemicals two reported

12 alterations in three of the four endpoints and four in three

13 studies reported alterations in two or one of the four

14 endpoints. 

15 For example, Chemical E resulted in changes in

16 testosterone levels and androgen dependent organ weight for a

17 total of 2 complimentary responses.

18 For the Hershberger assay the endpoint is a

19 change in two of the defined androgen dependent tissue weights,

20 for which seven chemicals reported such a change. As a response

21 for this assay is gauged by the change in tissue weights only,

22 the ability to evaluate complementary responses is limited. 

23 In the FSTRA there are seven defined

24 endpoints. Fecundity, fertilization success, secondary sex

25 characteristics including nuptial tubercles, GSI, histopathology

26 of gonadal tissues, plasma vitellogenin and the optional
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1 endpoint of plasma sex steroid concentration.

2 None of the 17 chemicals elicited a response

3 in all seven endpoints. Only one study reported no response

4 whereas another study was inconclusive. Eight of the 17 studies

5 reported altering three of the seven defined endpoints whereas

6 additional four studies each reported alterations to five or two

7 of the seven endpoints. For example, Chemical J reported

8 alterations in five complimentary responses, which are listed on

9 the slide. 

10 Now I'd like to discuss the concept of

11 redundancy in modalities across the assays. Again, as defined

12 by the 2008 SAP reports, redundancy is the concordance of

13 endpoints across the assays within a modality that are intended

14 to evaluate an interaction with a particular pathway, in this

15 case the androgen pathway. 

16 Since the in vitro assays are specific in

17 their endpoints and have different mechanisms of action for each

18 assay, they have limited redundancy between them. However,

19 while the AR binding assay is different from the aromatase and

20 steroidogenesis assay, both the aromatase and steroidogenesis

21 assays evaluate enzyme activity such as interactions with

22 estradiol or testosterone, although via slightly different

23 mechanisms. 

24 The table on this slide illustrates how for

25 different chemicals modalities are conserved across the

26 different assays. For some chemicals a redundancy was reported
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1 for one of the nine modalities as is shown for chemical E and G

2 for alterations in organ response. While redundancies in three

3 modalities were observed for Chemical S and include enzyme

4 activity, organ response and male system integrity.

5 Again, please be aware that some of the

6 responses were only observed in the presence of potential overt

7 toxicity, which are flagged here in the table. However, for the

8 purposes of evaluating the battery performance, they are

9 retained in this analysis.

10 For chemical G, redundancy and organ response

11 was reported for all three in vivo assays, which is

12 characterised by a change in the androgen dependent tissue

13 weights for both the Hershberger and the male pubertal assays,

14 and as a change in vitellogenin in fish. For chemical J, a

15 response in enzyme activity was reported for the male pubertal

16 and the fish assay as well as the aromatase and steroidogenesis

17 assays, which represents an alteration in sex steroid for all

18 four assays.

19 I'm sorry if the table is a little hard to

20 read. 

21 Now I'm going to highlight some examples of

22 the type of results reported across the six assays for the

23 androgen pathway, regardless of whether their responses were

24 redundant within a defined modality or that concordance was

25 observed among endpoints regarding things such as direction of

26 effect for a particular alteration for androgen pathway.
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1 Rather this table merely shows that some type

2 of response, as defined by the table earlier, was reported for

3 an assay. These results are from the larger overview table

4 presented near the beginning of this talk and the footnotes are

5 the same as in that table.

6 In example one, these chemicals represent

7 cases where there were multiple in vitro assays which were

8 reported as a positive or a binder. There are three chemicals

9 listed here. This list does not include chemicals where the in

10 vitro results were reported only as equivocal or negative. 

11 In example 2 these chemicals represent cases

12 where there were responses in multiple in vivo assays, but no

13 positive or binding responses in the in vitro assays. 

14 The lack of in vitro response may provide

15 insight into whether the response observed in the in vivo assays

16 was mediated via an endocrine interaction or if it occurred

17 along a different part of the pathway or if metabolism was

18 involved.

19 In both of these examples please notice that

20 if a response was reported for the Hershberger assay a response

21 was also reported for either the male pubertal or the FSTRA. 

22 In the final example, these chemicals

23 represent cases where there were responses in both the in vitro

24 assays and the in vivo assays. Please note that for some of the

25 in vitro assays equivocal results were reported. 

26 In conclusion, complementary responses were
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1 observed within the in vivo assays as well as redundancy across

2 the different modalities when evaluating across assays.

3 However, the observation of overt toxicity

4 needs to be considered when evaluating for potential endocrine

5 effect. 

6 And finally it was observed that when a

7 response was reported for the Hershberger assay it was

8 accompanied by a response in the male pubertal and/or the fish

9 assays. And I think we'll go to Greg Akerman.

10 DR. GREGORY AKERMAN: Good afternoon this is

11 Greg Akerman again. I will be discussing the evaluation of the

12 estrogen pathway which is the final pathway in our EDSP Tier 1

13 battery analysis.

14 Following a similar overview is what Amy just

15 presented; I will be following that similar format she used to

16 describe the androgen pathway. I will first provide an overview

17 of the assays and then the endpoints that were used to evaluate

18 the estrogen pathway. Next I will describe the complementary

19 responses within an assay and redundancy of responses across the

20 assay to the E pathway. 

21 I will then summarize our observations by

22 presenting examples of results across the assays and then

23 providing a concluding statement for the E pathway battery

24 performance.

25 This slide shows the seven assays that are

26 designed to identify potential interactions with the estrogen
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1 pathway. They're comprised of four in vitro assays, the ER

2 binding, ERTA, aromatase and steroidogenesis assays. And three

3 in vivo assays, uterotrophic, female pubertal and fish

4 reproduction.

5 Eighteen chemicals were evaluated for this

6 preliminary analysis for the battery performance for the E

7 pathway. The endpoints shown for each of the assays are

8 categorized based on the nine modalities of the levels of

9 organization as identified by the 2008 SAP. 

10 For example, ERTA represents cell signaling

11 modality, aromatase and steroidogenesis represent enzyme

12 activity modality.

13 Whereas endpoints such as a change in age of

14 vaginal opening in the female pubertal assay both fecundity and

15 the fish reproduction assay represent a change at the organism

16 level and corresponds to the modality of female integrity

17 system. 

18 The criteria for determining a positive

19 response for a particular endpoint is specific for each assay

20 according to the Agency's test guidelines. The four in vitro

21 assays in the uterotrophic assay each represented a single

22 endpoint to inform the estrogen pathway. The ER binding, ERTA,

23 aromatase assays positive response is based on the chemical's

24 percent activities surpassing the specified threshold as

25 established in the test guidelines.

26 In the steroidogenesis assay specifically
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1 significant alterations in estradiol levels indicate a positive

2 response.

3 In uterotrophic assays a response is indicated

4 by a specifically significant increase in uterine weight

5 representing an organ response modality.

6 For the female pubertal assay a change in any

7 of the three endpoint categories shown in the slides indicate a

8 positive response for the estrogen pathway. And finally for the

9 fish a statistically significant difference in the control for

10 any of the seven endpoints listed could be considered a response

11 indicative of an estrogen pathway interaction.

12 This slide show an overview of the results

13 from the 18 chemicals evaluated from the seven assays that

14 informed the estrogen pathway. This table is also presented in

15 the White Paper on page 151. The Xs in the table indicate a

16 response was observed as defined in the test guidelines. In

17 some cases an equivocal response was reported as indicated by an

18 "E".

19 In other cases a response was observed only in

20 the presence of overt toxicity. These responses are denoted

21 with an X followed by the superscript "a" or "b" which

22 corresponds to footnote at the bottom of the slide to indicate

23 the type of overt toxicity that was observed. 

24 The superscript "c" in the fish assay for

25 chemical B indicates the results were deemed questionable due to

26 potential solvent interference in footnote "d" for the fish
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1 assay.

2 Chemical G indicates that the study's conduct

3 was questionable and the data may not be reliable.

4 As recognized by the 2008 SAP, the in vitro

5 assays in general have few or no complementary endpoints. 

6 Therefore the analysis for complementary responses within assays

7 will focus on the three in vivo assays for the estrogens

8 pathways.

9 As described in the previous presentation,

10 complementarity is defined as a concordance of endpoints within

11 an assay. In the upper panel this slide shows the number of

12 reported alterations in three endpoint categories; sexual

13 development characteristics, weight of estrogen dependent

14 reproductive organs and histopathology of the estrogen dependent

15 reproductive tissues for the female pubertal assay.

16 As shown here no chemicals show complimentary

17 responses in all three endpoint categories in the female

18 pubertal assay. Three chemicals reported complementary

19 responses in two endpoint modalities and three chemicals showed

20 response in a single modality. Of the 18 chemicals, 11

21 chemicals showed no response.

22 Note the table at the bottom of the slide that

23 no chemical induced a response in the uterotrophic assay. 

24 This table shows the complementary responses

25 observed in the fish assays. There are seven identified

26 estrogen response endpoints for this assay; fecundity,
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1 fertilization success, secondary sex characteristics,

2 gonado-somatic index, gonadal histopathology and plasma

3 vitellogenin and the optional endpoint of plasma steroid

4 concentrations.

5 None of the 18 chemicals evaluated for the

6 estrogen pathway showed a response at all seven endpoints. And

7 one study was considered inconclusive.

8 As shown in this table a majority of the

9 chemicals showed a response in three or more of the estrogen

10 responsive endpoints in this assay. 

11 In the example provided on the screen Chemical

12 V showed three complementary responses for the estrogen pathway,

13 those responses included fertility, secondary sex

14 characteristics and fecundity. However, these effects were seen

15 in the presence of potential overt toxicity in the form of

16 decreased body weight.

17 Next we looked at redundancy and modalities

18 across the assay. As stated in previous presentations,

19 redundancy is the concordance of endpoints across the assays

20 within a modality that are intended to evaluate potential

21 interactions with specific pathways. 

22 Here we look at the redundancy of responses

23 across assays with the estrogen pathways. The in vitro assays

24 are designed to be more mechanistic in nature and therefore have

25 limited redundancy built in across the assays.

26 Chemical S shows that organ response in the
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1 female pubertal assay and fish assay as well the equivocal

2 response from aromatase inhibition. Chemicals M and Q show

3 response in two modalities, enzyme activity and female system

4 integrity in both the female pubertal and the fish assay.

5 Chemical K showed a response in three

6 modalities, enzyme activity in aromatase assay and both cell

7 response and female system integrity responses in the female

8 pubertal and fish assays. 

9 Next we looked at chemicals that showed

10 multiple responses across the seven assays for the estrogen

11 pathway. This includes results regardless of whether or not

12 there was redundancy within a defined modality. These results

13 are taken from the larger overview table that was presented

14 earlier and did not include chemicals where only equivocal

15 results were observed.

16 In example 1 the top of the panel in the

17 slide, there were seven cases where responses were seen in

18 multiple in vitro assays. Please note that in the three cases

19 where there were positive responses in the estrogen receptor

20 transcriptional activation assay, there were no responses in the

21 ER binding or the uterotrophic assays. All seven cases showed a

22 response in the steroidogenesis and fish assays. 

23 Example 2, Chemical G and Q represent two

24 cases where there were multiple responses in the in vivo assays

25 which showed no corresponding responses in the in vitro assay. 

26 Note that the ERTA was considered inconclusive for Chemical Q. 
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1 In Example 3 we show ten cases where the

2 response was observed in in vitro and in vivo assays. None of

3 these responses were in the steroidogenesis and fish assays. 

4 In conclusion complementary responses were

5 observed within the in vivo assays and redundancy observed

6 across the Tier 1 assays. The observation of overt toxicity

7 needs to be considered when evaluating potential endocrine

8 interactions. Finally for all chemicals evaluated for this

9 analysis, there were no positive responses in the uterotrophic

10 or the ER binding assays. 

11 DR. TOM STEEGER: Hi. This is Tom Steeger and

12 I am going to be summarizing the information that has been

13 provided throughout the day.

14 The intent of this effort was to evaluate

15 whether laboratories were able to consistently execute EDSP Tier

16 1 battery of 11 assays, and to highlight issues that were

17 identified in the conduct of those assays, particularly those

18 that may impact the interpretation of the study.

19 Additionally this effort aimed to evaluate

20 whether there was a concordance of information that is

21 complementary within assays and to evaluate whether there was a

22 concordance of information that is redundancy across assays. 

23 The Agency is committed to reducing the number

24 of animals involved in testing and toward lowering the costs

25 associated with testing; recognizing that the battery of Tier 1

26 tests in combination with testing already required to support
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1 chemical registrations is high in terms of both the number of

2 animals used and the costs of conducting and reviewing such

3 studies.

4 Therefore the review of the data from the EDSP

5 Tier 1 battery of assays also considered whether or not a

6 modified number of assays within the battery could adequately

7 ensure the identification of potential interactions with the

8 estrogen, androgen or thyroid hormonal pathways.

9 The Tier 1 tests required data on 52 chemicals

10 and a subset of these were selected for this analysis. The

11 subset of chemicals represents a range of chemical classes as

12 well as a range of biological activities in terms of pesticidal

13 modes of action including an inert chemical.

14 As has been discussed in the preceding

15 presentations with respect to the ability of laboratories to

16 consistently conduct the Tier 1 battery of assays, performance

17 was typically consistent across the assays in performing

18 laboratories. While some performance standards were not met,

19 the deviations were not considered significant and it did not

20 impact the interpretation or reliability of the data.

21 In general the Agency's review of the studies

22 using the subset of 21 chemicals indicated that the assays were

23 providing useful information for evaluating potential

24 interactions with the E A and T signaling pathways. 

25 Based on the information provided in the

26 preceding series of presentations regarding assay and battery
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1 performance, some of the issues identified in the performance of

2 the in vivo assays included dose selection and overt toxicity.

3 With respect to the question of whether

4 individual assays provided complementary results within the

5 assays and whether there was a concordance of information across

6 assays, the individual assays measuring multiple parameters did

7 demonstrate complementarity; that is a concordance of measuring

8 endpoints within an assay. And the battery of assays identified

9 for a particular pathway demonstrated a concordance of

10 measurement endpoints across assays that is redundancy.

11 As discussed in the preceding slide

12 presentations for the estrogen and androgen pathways there were

13 complementary responses for two or more endpoints within the in

14 vivo pubertal and FSTRA assays. Additionally, a few of the case

15 studies exhibited complementary responses in the in vivo male

16 and female pubertal and/or FSTRA along with the relevant in

17 vitro assays for the estrogen and androgen pathways.

18 However, there were some cases where

19 concordance was not observed between the in vitro and the in

20 vivo assays. These situations emphasize the importance of the

21 multiparameter in vivo assays and the potential differences

22 between these pathways across taxa.

23 With respect to the thyroid pathway the

24 available assays were designed with less complementarity with a

25 redundancy across assays. This emphasizes the importance of the

26 in vivo amphibian and the rat pubertal assays in measuring the
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1 potential effects of the thyroid pathways. 

2 Overall based on review of the EDSP Tier 1

3 assays and test results using the subset of chemicals,

4 complementarity was demonstrated within assays and redundancy

5 across assays consistent with their intended design for

6 evaluating potential interactions with the E A or T signaling

7 pathways across taxa.

8 With respect to the question as to whether

9 there may be some redundancy that could be reduced in terms of

10 the number of assays required as the Tier 1 battery, the

11 preceding presentations indicate the chemicals which elicited a

12 response in the Hershberger assay also elicited a response in

13 the male pubertal assay and/or the fish short term reproduction

14 assay.

15 Also, the uterotrophic assay did not show a

16 response with any of the chemical subset used to evaluate the

17 battery performance. The SAP is being asked to provide input

18 based on the White Paper and information presented during the

19 preceding presentations whether there may be an opportunity to

20 reduce the number of assays while still ensuring adequate

21 complementarity and redundancy.

22 This concludes the presentations on the

23 overall analysis of the assay and battery performance and the

24 extent to which there was a concordance of information within

25 assays that is complementarity and across assays, redundancy. 

26 Several assays within the Tier 1 battery may represent
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1 unnecessary redundancies. SAP input is being sought on the

2 analysis and the extent of necessary redundancy.

3 In closing I would like to emphasize what was

4 stated by Dr. Manibusan during her opening presentation and that

5 is that this SAP is not focused on the weight of evidence

6 analysis that will be used in evaluating whether chemicals

7 transition to higher tier testing.

8 The Agency is aware that the magnitude and

9 direction of an effect, the consistency of responses within and

10 across assays, the route of exposure, bioactivation and

11 metabolism, existing effects and other scientifically relevant

12 information, or OSRI, all have a role in determining the extent

13 to which the studies discussed during this SAP will be used in

14 decision making.

15 However, these aspects of the decision making

16 process will be discussed during subsequent SAPs. The focus of

17 this SAP is on assay and battery performance. Thank you and

18 we're open for questions. 

19 DR. DANIEL SCHLENK: Any questions from the

20 panel? Let me just remind the Agency and panel to direct your

21 question to the specific person and then if that person can just

22 state your name when the question comes in that would be great

23 thanks. Dr. Green?

24 DR. SHERRIL GREEN: I have a question for Dr.

25 Steeger. This is a very broad general question but of those 21

26 different chemicals that were selected from that set of the 52,
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1 were any of those chemicals already known to have effects that

2 are well documented on any of the EAT pathways?

3 DR. TOM STEEGER: The Agency has, as a policy,

4 determined that prior to this point to the development of the

5 EDSP testing battery that we did not have the capacity to

6 identify chemicals that were acting on endocrine related

7 processes. 

8 With that said there are certainly open

9 literature studies and there are lines of evidence that will be

10 used in evaluating which studies move to higher tier testing

11 among the chemical domain that was used in this subset that

12 could be used to support the conclusion that they may be acting

13 on endocrine mediated processes. 

14 That may sound like a roundabout answer, but

15 I'm reflecting the Agency's policies that up until this point we

16 didn't have the capacity for making that determination. We are

17 aware of studies and there are endpoints in our other existing

18 158 studies that could be useful in making such determinations,

19 though.

20 DR. SHERRIL GREEN: I have a follow up

21 question to that. So I guess right to the heart of my question

22 would be how would you validate that this Tier 1 collection of

23 three assays actually does predict what you think it would

24 predict, and indicate that a compound should go on to Tier 2

25 testing?

26 DR. TOM STEEGER: I think that the capacity of
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1 the battery to serve as a means for detecting whether a chemical

2 is acting on E A or T pathways has already been established

3 through the use of known standards in prior SAPs and through

4 ring testing of the battery. So the capability has already been

5 established. 

6 The difficulty that I think that the panel is

7 faced with here and one that the Agency, those of us that are

8 seated here, are faced with is making sense of what the

9 responses from unknown chemicals are, and patterns that you

10 might expect given the 11 assays in terms of complementarity

11 within an assay or redundancy across assays, do you see those

12 patterns starting to be illuminated. 

13 We believe that based on the information that

14 we've presented that indeed the individual assays are working

15 together as expected, however, we're not talking about standards

16 here, we're talking about unknowns. But there seems to be a

17 concordance of information that will ultimately have to be

18 weighed in light of other lines of evidence and that would be

19 part of the weight of evidence analysis.

20 DR. DANIEL SCHLENK: Questions? Dr. Schultz.

21 DR. TERRY SCHULTZ: Were any of the 52

22 chemicals run through any of the pre-screening components in

23 your program?

24 DR. TOM STEEGER: The answer to that is yes.

25 DR. DANIEL SCHLENK: Dr. Horseman.

26 DR. NELSON HORSEMAN: I believe this is
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1 probably for Dr. Steeger, maybe if somebody else wants to chime

2 in. 

3 Am I correct in assuming that all 21 of these

4 example compounds, or for that matter all 52 of the ones in the

5 larger set, are chemicals that have been toxicity tested

6 previously in ordinary toxicology screens, and for which there

7 are safe levels defined or virtue that non-ED set of modalities.

8 DR. TOM STEEGER: As we indicated during the

9 presentation the chemicals that were selected as part of the

10 subset and the 52 that were identified, our pesticide mode of

11 action, one of which is an inert, and to support the pesticide

12 registrations they are required under FIFRA to have a testing

13 that is conducted both human health and ecological tests, a full

14 suite of chemicals -- both exposure and effects data are

15 required to support the registration of pesticides.

16 So yes there are a battery of 158 studies that

17 are required to support the registration of pesticides.

18 DR. NELSON HORSEMAN: A follow up. Excuse me

19 go ahead.

20 DR. DANIEL SCHLENK: Actually why don't you

21 follow up first and then we'll go.

22 DR. NELSON HORSEMAN: Well the follow up to

23 that would then be, given the inherent theory behind the

24 endocrine disrupter mode of action, would you be interested in

25 seeing tests presumably at this level rather than Tier 2 of

26 these compounds at something like 2X the safety levels defined
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1 by these other toxicological tests as opposed to maximum

2 tolerated dose and these sort of doses that we are seeing here,

3 as a more relevant insight into their possible endocrine

4 disrupter modality as opposed to the normal toxicological

5 modalities.

6 DR. TOM STEEGER: The EDSP battery of tests as

7 was indicated by previous presenters, the intent is to try and

8 develop the highest dose that goes up to a point where you know

9 that you're starting to stress the animal, or test the animal,

10 relative to the chemical itself and then back off that dose so

11 that you have suitable understanding that you've tested a large

12 enough concentration, but a range of doses that we feel that if

13 it passes the screen we have covered both sides if we tested up

14 high enough and we've tested down low enough.

15 So I think that that is the general rationale

16 that Dr. Touart can add to that if he wishes.

17 DR. DANIEL SCHLENK: I believe he had some

18 comments for something else as well right?

19 DR. LESLIE TOUART: Yeah a couple of

20 observations I mean just to go back a little bit. The battery

21 of assays was selected based on the validation principles. And

22 we were required to develop a validated set of assays to

23 evaluate for endocrine modalities, so it's that context that is

24 used to try to identify. Shouldn't the findings or other

25 information that is within the literature be somewhat suspect

26 until they are considered in light of the validated assays in
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1 context? 

2 They will be considered as part of weight of

3 evidence considerations in terms, but in terms of how existing

4 data may inform, I think that's the purpose of why we were

5 mandated by Congress to establish an endocrine screening

6 program, was concern that existing studies that are based on the

7 toxicological endpoints and stuff we are looking at may not

8 actually cover all particular endocrine activities and concerns.

9 Methodologies like on the mammalian side, but

10 the rat (inaudible) had been recognized early on and endocrine

11 relevant endpoints added to that, and that's why that particular

12 assay is already considered acceptable. We are evaluating on

13 the ecological side, new methodologies that would be able to

14 focus on that. 

15 So those are all aspects of consideration and

16 with the 52 compounds that are part of this initial battery,

17 they were selected based upon exposure criteria not on existing

18 toxicological information, although for some of these there is

19 literature that may reflect and that will be incorporated or

20 considered as part of weight of evidence when the battery of

21 assays are considered altogether.

22 DR. DANIEL SCHLENK: Thank you. Any other

23 questions? Dr. McManaman?

24 DR. JAMES MCMANAMAN: So did you look at the

25 concordance between your assays? Let me get specific here to

26 help out the question. Is that on the thyroid assay it shows
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1 Compound A, Chemical Code A had a female pubertal effect, but on

2 none of the other assays did it have a female pubertal effect.

3 So if it's having an effect on one why isn't

4 it having an effect on the others? So just to see how closely

5 -- what we are looking at -- is Compound A the same for each of

6 these assays or did you mix them up in terms of the codes on

7 them? 

8 It just seems kind of -- if it's having an

9 effect in one assay it should be having the same effect in

10 another assay.

11 DR. TOM STEEGER: The codes are not mixed up

12 across these assays. The chemical codes are consistent across

13 all the assays. The information that we have and that the panel

14 does not have is the magnitude and direction of an effect that

15 is being measured in any one of the assays. And I think that

16 that needs to be considered in looking at the fact that you

17 might have an effect on one in vivo assay and not on another.

18 It could also represent that there might be

19 differences between taxa that is one of the reasons that

20 redundancy has been incorporated into the assays; if Catherine

21 would like to add more on her understanding of the thyroidal

22 assays?

23 CATHERINE AUBEE: I have nothing to add. 

24 Thank you.

25 DR. JAMES MCMANAMAN: So it's presumably the

26 same taxa so that wouldn't be the explanation because it's the
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1 rat assay right or the pubertal?

2 DR. TOM STEEGER: That's correct, but there

3 can be differences between sex.

4 DR. JAMES MCMANAMAN: But if it was a female

5 pubertal assay, though, so the sex wouldn't be the explanation

6 there either. I guess.

7 CATHERINE AUBEE: Could you please clarify and

8 can you restate your specific question about Chemical A. I'm

9 not sure I understood it.

10 DR. JAMES MCMANAMAN: Yes, so the female

11 pubertal assay -- I'm asking about the female pubertal assay; it

12 shows a positive result in the T but negative results for the E.

13 CATHERINE AUBEE: So across pathways is what

14 you're asking? Okay.

15 DR. JAMES MCMANAMAN: Yes, right, yeah.

16 CATHERINE AUBEE: Okay, well I think that Amy

17 said she would like to speak to that.

18 AMY BLANKINSHIP: If I understand your

19 question correctly it's because the thyroid for the female

20 pubertal has very specific endpoints like in terms of thyroid

21 histopathology, TSH, T4. Whereas looking for the E or the A,

22 while it's the same assay we only evaluated for like the

23 estrogen sort of a change in VO or estrogen dependent weights or

24 androgen dependent weights. So they won't necessarily overlap

25 in terms of looking at a response for the E, the A and the T

26 because they have two different endpoints they're looking for
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1 those pathways.

2 DR. JAMES MCMANAMAN: So is it possible to try

3 to identify some concordance across assays? I mean because like

4 you said they're using one specific assay for E, for instance,

5 and another one for the G. Is there a way to combine those two

6 so that you could get a full spectrum or full view of what's

7 going on with a particular compound?

8 AMY BLANKINSHIP: They are the same single

9 assay so within that single assay they are looking at VO

10 estrogen dependent weights and the thyroid endpoints in that;

11 they've just been broken up for this analysis of the different

12 pathway. 

13 So as I understand it for the weight of

14 evidence we would look at the totality of all the endpoints

15 affected in that single female pubertal assay for that chemical

16 and how it fits into each of the E the A and the T pathways for

17 an interaction for that single chemical.

18 DR. JAMES MCMANAMAN: So in other words the

19 question should be held until you get the weight of evidence

20 component of it? Okay.

21 DR. DANIEL SCHLENK: Dr. Bradbury you have

22 something to add?

23 DR. STEVE BRADBURY: Just for clarification

24 just in case because I was getting a little confused too. 

25 So in a pubertal assay, say a female rat and

26 female pubertal assay, that same rat is giving information on E
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1 and T from the same animal. So it's not like three separate

2 pubertal assays. It's one pubertal assay. But those rats are

3 giving us male information on A and information on T; it's

4 female information on E and information on T. So you could see

5 a response for T because TSH changed and T4 changed, but none of

6 the E related endpoints did anything. So show a negative for E

7 but positive for the T. But it's the same information content

8 for that rat that is giving us information on two pathways. 

9 DR. JAMES MCMANAMAN: I have a second

10 question. It looks for the estrogen binding assay that none of

11 the assays appear to be positive. And the same thing for the

12 uterotrophic assay. So this relates to the question of whether

13 you can reduce the number of animals.

14 Is it your contention that none of these

15 compounds would have been positive for these assays because it

16 seems odd that there was no positive result in any of these

17 compounds for any of these basic binding or the uterotrophic

18 assays. Or are these assays just so insensitive that they would

19 not have picked up? Do you feel that there is a possibility

20 that these assay are so insensitive it wouldn't have picked up a

21 positive response?

22 DR. TOM STEEGER: The assays have been

23 validated against known standards. So we know that they are

24 capable of detecting a response if it is actually binding to the

25 receptor. So I don't think that that's in question. Whether

26 these chemicals are actually binding to the receptor, the data
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1 suggests that based on the assay responses that they are not.

2 So the fact that you have within the domain of

3 the chemical space that was used in this subsample, nothing

4 that's binding to that particular -- or positive in those

5 particular assays does not speak to the utility of the assay. 

6 It just tells us something about the chemical space in which

7 we're looking.

8 DR. DANIEL SCHLENK: Dr. McManaman?

9 DR. JAMES MCMANAMAN: So in the universe of

10 the chemicals you think you just happened to select 19 chemicals

11 or 21 chemicals that just didn't have any estrogenic activity in

12 terms of being able to bind to the estrogen receptor or activate

13 the uterotrophic response?

14 DR. TOM STEEGER: I think that based on the

15 preliminary analysis of the assay results by themselves, without

16 taking into account other scientifically relevant information

17 that would be suggested of what we have. 

18 DR. DANIEL SCHLENK: Dr. Horseman?

19 DR. NELSON HORSEMAN: With regard to this

20 question on concordance -- and I believe it does relate to this

21 discussion -- have you applied any sort of a logic tree to these

22 concordances? In other words, for example, something showing

23 agonist activity in the ERTA assay ought to be predicted to have

24 a positive effect in the uterotrophic assay, absent some oddball

25 metabolic circumstance or something. 

26 Therefore that particular concordance ought
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1 maybe to have a weight different than, you know, happenstance of

2 something being positive, aromatase and ER binding or something.

3 DR. DANIEL SCHLENK: I think we're going to

4 get in that in July perhaps.

5 DR. NELSON HORSEMAN: We probably are. So

6 maybe that's just a warning shot.

7 DR. DANIEL SCHLENK: Just not to jump the gun

8 a little bit there.

9 DR. NELSON HORSEMAN: Maybe that's a warning

10 shot.

11 DR. DANIEL SCHLENK: Let's just kind of focus

12 on these particular assays here. Yeah. Any other questions? 

13 Dr. McManaman.

14 DR. JAMES MCMANAMAN: Sorry I have one other

15 clarification question. So for the ER binding assay, the

16 equivocal assays; were they equivocal because they truly gave

17 equivocal results or were they equivocal -- and I may have

18 missed it when you explain it -- but were they equivocal because

19 some other component of the assay failed in terms of that they

20 didn't meet standards; you know, certain numbers of the samples

21 didn't or the controls didn't meet the standards that you set

22 for them. 

23 So was it a true equivocality or was it some

24 other technical reason why they were given an E-value?

25 DR. TOM STEEGER: The definition of equivocal

26 for that assay is very well defined. And I will let my
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1 colleague here who is biting his lips speak.

2 DR. GREGORY AKERMAN: They were true

3 equivocal based on the response curve for those chemicals, so it

4 had nothing to do with the assay. They're actually classified

5 by the test value classifications.

6 DR. DANIEL SCHLENK: Dr. Shultz.

7 DR. TERRY SCHULTZ: I have a question to the

8 chair. We can't move forward to weight of evidence, can we move

9 backwards to pre-screening?

10 DR. DANIEL SCHLENK: Well since those minutes

11 have just gone out I believe I guess it's if we have enough time

12 you can elaborate if you like if you have a question. I'll

13 leave that up the Agency if they wish to answer that.

14 DR. TERRY SCHULTZ: This will be a question to

15 the Agency, but it comes from the fact that a number of the

16 panel members have been surprised that a number of the

17 chemicals, of the 51 or 21 chemicals, so few of them are ER

18 binders when we feel we know a great deal about ER binding

19 domain. 

20 And so when you start to ask questions is

21 there redundancy and assays and these other things, the question

22 that seems to be leading from the panel is, were the 51

23 chemicals evaluated in your ER binding pre-screen so that there

24 weren't any of those in there, because that would explain why

25 you don't have any ER binding at all or was it just luck of the

26 draw.
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1 DR. TOM STEEGER: The selection of the 52

2 chemicals was based on exposure and not on activity.

3 DR. DANIEL SCHLENK: Dr. Bradbury did you want

4 to add something?

5 DR. STEVE BRADBURY: I'm glad Dr. Schultz

6 brought it up because I was thinking about his earlier questions

7 and I'm glad we did this. 

8 As Dr. Steeger said, the first 67 actually

9 that got test orders was based purely on an exposure metric. 

10 And because in part the Tier 1 assay were still under

11 development, in part the expert system and related technologies

12 done in previous SAPs are still a work in progress, it really,

13 at that point in time back in 2005/2006 we didn't really have an

14 effective base toolbox to help inform us. 

15 I think it's a fair point, especially as we to

16 into weight of evidence, taking a look at for example would be

17 what do we know about receptor binding domain, how many

18 (inaudible) between two pockets, what's the Log Kow of these

19 things? And I think that kind of information would be helpful

20 to bring in to bear as we take a look at the in vivo responses

21 and in vitro responses. 

22 And as we discussed in the January SAP,

23 clearly as we look at the non pesticidal compounds and the inert

24 ingredients, we're going to be using the physical chemical

25 properties for exclusion. We're going to be looking at

26 computational toxicology or expert systems to help hone in on
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1 which ones that go through testing and bringing exposure ideas

2 as you all reported out in the panel.

3 And even with the active ingredients, although

4 we didn't go into great detail, we had that box about read

5 across, which was meant as a placeholder to use other kinds of

6 information to help move forward. 

7 Clearly the July SAP is an important step

8 along that process to give us advice on how to use other kinds

9 of information along with Tier 1 data. It could be

10 pre-screening data; it could be post -- Tier 2-ish data to help

11 inform that.

12 DR. DANIEL SCHLENK: So it sounds like the

13 entire slide will be present for our July meeting then it sounds

14 like. Dr. Roby.

15 DR. KATHERINE ROBY: Kind of continuing on the

16 same idea, given that apparently none of the test chemicals

17 under review were estrogenic and given the history behind the

18 uterotrophic assay and understanding the sensitivity of that

19 assay, what's the rationale behind questioning that assay within

20 the battery?

21 DR. MARY MANIBUSAN: I'm going to take a stab

22 at answering your question. The question that we're posing to

23 the panel is not only that of battery performance as performance

24 against this subsample of chemicals that we've chosen among the

25 52. It's asking a question of whether there is unnecessary

26 redundancies within the battery.
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1 And with that I only want to share a point

2 made in the White Paper; and that is for the uterotrophic,

3 certainly they were all negative for the chemicals that we've

4 chosen to evaluate. But they were never a case where the

5 uterotrophic was positive and in vivos were negative.

6 And so the question to the panel is, if we

7 remove the uterotrophic assay what valuable information would we

8 be missing? What type of information would we be missing to

9 introduce additional uncertainty that we could not accept in

10 order for us to answer the question of whether the chemical has

11 the potential to interact with E, A and T? Understanding again,

12 as we've all said, the Tier 1 battery of assays have been

13 already validated against referenced compounds, both very strong

14 agonist, very strong antagonist, negative and weak binders.

15 So we know that they work. The question to

16 the panel is for today bringing forward real world chemicals

17 that have not been preselected because of their activity, but

18 preselected based on exposure, based on biologic activity and

19 representative of the 52 chemicals that we have, there are

20 pesticide chemicals. Were challenging the Tier 1 assays and

21 asking that question. Can we do better; can we do the same with

22 fewer assays.

23 DR. DANIEL SCHLENK: Any other questions? Dr.

24 Horseman.

25 DR. NELSON HORSEMAN: Just a follow up on

26 this; there is the issue of savings or eliminating unnecessary
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1 redundancies of FSTRA. There is also the issue of you making

2 sure that this level provides adequate screening to avoid moving

3 things in the second level unnecessarily. 

4 So could maybe a couple of people, maybe you

5 Mary, primarily, comment on the balance between those two values

6 in making this particular piece of advice that we're asked to

7 make.

8 DR. MARY MANIBUSAN: I'll start. I think what

9 we've been doing over the course of today is looking back at the

10 foundation placed down by the EDSTAC founding members in terms

11 of the purpose of the Tier 1 assays and its intention to serve

12 as a screen.

13 And having said that they recognize even back

14 in 1998 when they had written their report, that to serve as an

15 adequate screen you need to maximize sensitivity. Certainly

16 we're more accepting of false positive as a screen than false

17 negatives. But it also goes on to state very clearly that we

18 need to balance the false positives in order for it to serve as

19 an appropriate screen.

20 We as an agency have always acknowledged the

21 cost and price of all the required studies that we've asked for.

22 I believe that was the intention behind the EDSTAC conceptual

23 framework, that we not move chemicals forward for higher

24 definitive, highly costly long-term studies without ensuring

25 that that chemical even has a potential to interact with the

26 endocrine system.
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1 So the task before this panel is not an easy

2 one and that's why we're bringing it before you for

3 consideration and brining this clear representative subsample

4 that is intended to be robust enough to challenge the Tier 1

5 assays, and demonstrate again whether or not it can perform for

6 the intention of answering the question that we have before us.

7 We're not answering the question of does it

8 interact and what dose does it do it so we can integrate that

9 into our risk assessment; because certainly we understand that

10 the level of uncertainty there is a lot lower. We're looking at

11 the Tier 1 again as a screen within that conceptual framework

12 with the original intent of the EDSTAC members at that time,

13 which I believe is still relevant. But it's not an easy task

14 and we acknowledge that.

15 DR. DANIEL SCHLENK: Dr. Green.

16 DR. SHERRIL GREEN: This is for Mary. I think

17 I have it straight now. So my earlier question about validation

18 retrospectively on chemicals that are known; so as I understand

19 it now each of these assays have been validated individually

20 with specific chemicals that are known and that's well

21 documented. 

22 The task for this panel is to, if we can,

23 determine if we can streamline these tests so that maybe some

24 would not be necessary or reasons why some must be included. 

25 But the July -- the next session -- would go on to develop that

26 algorithm and say what if, then this.
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1 So that maybe some of the say AMA, the

2 amphibian metamorphosis tests, would not be germane and wouldn't

3 be part of the Tier 3 unless there is some criteria that was

4 identified by the other two. Is that the sort of direction?

5 DR. STEVE BRADBURY: I'll give it shot and

6 then you can ask me to clarify again. Just to back up, each of

7 the assays went through their individual assay validation other

8 folks have described. And then in 2008 we came to the SAP not

9 so much to ask them to redo their validation exercise that

10 already had been done through OECD or other peer review

11 processes. But we did ask just overall did we kind of do it

12 right, assay by assay, and the answer was yes. 

13 The big part of that 2008 SAP was to start to

14 get some feedback on this issue of how to think about redundancy

15 across assays and how to look for complementary responses within

16 an assay if it has multiple endpoints. And that was an

17 important -- in our opinion -- an important framework that the

18 SAP in 2008 provided us. And you see variations on that table

19 from the 2008 SAP that was getting at, in 2008 what do you all

20 think? Do you think this 11 is reasonable in terms of E, A and

21 T and redundancy across assays that would look at E, look at A

22 and look at T? Do you think it's reasonable in terms of assays

23 that have multiple endpoints or some coherency or some

24 concordance we should expect? 

25 And the SAP said yes. Eleven assays seem to

26 be the right way to get started in terms of E, A and T, looking
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1 at what goes on within an assay and looking at what goes across

2 an assay. But at that point that approach was based on

3 chemicals that weren't necessarily the same across all those

4 validation exercise. 

5 So now we've got 20 something chemicals that

6 we brought to sort of test out the rationale that the SAP had in

7 terms of, for this assay -- for this SAP -- when you take a look

8 at assays looking E, assays looking at A and assays looking at

9 T, are we seeing patterns that the 2008 SAP and we all talked

10 about in 2008 and sort of seeing it play out the way it seemed

11 it should play out; realizing there would be some noise in

12 there, but is it sort of playing out the way you would expect. 

13 And what we're chewing up today is some

14 initial input from you all, at least, in terms of -- you have

15 the 2008 SAP that said you need 11 to get enough redundancy and

16 complementariness type of word -- I'll blame you guys for that. 

17

18 Is it playing out the way the 2008 SAP figured

19 it would, and/or is there some indication that we may be able to

20 say there is more redundancy than you might need? And that's

21 reflected in the charge questions we're asking you in terms of

22 here is what we knew in 2008 based on the data we had in 2008,

23 but it all came from chemicals designed to validate the assays. 

24

25 Now we're looking at 21 assays handpicked for

26 any hypothesis ahead of time is the E, A or T potential. Here
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1 is what we're seeing, is the pattern of redundancy and

2 complementariness playing out like in 2008? Steady as she goes

3 or maybe there is some indication that we may have some

4 additional redundancy in there that you could pull away but

5 still not erode your ability to look at potential for E, A and

6 T. That's the step we're at. 

7 DR. DANIEL SCHLENK: Mary?

8 DR. MARY MANIBUSAN: And just to follow up on

9 Dr. Bradbury's response to Dr. Green's question, the other half

10 of your question deals with what we're forecasting for the July

11 SAP, the weight of evidence.

12 So that weight of evidence will be bringing

13 forward the Tier 1 assay evaluation and integrating it with

14 other scientifically relevant information so that we can answer

15 the question of whether that chemical has no potential to

16 interact with the endocrine system -- we can put those aside

17 --or whether we need to advance that chemical because there is

18 clear indication of potential interaction with the endocrine

19 system, that it needs to move forward for Tier 2 testing. 

20 That's the critical assessment that we'll be

21 bringing forward to the July SAP. It will not be revamping any

22 tiered screening or testing program but more brining the

23 information to bear so that we can look at it in an integrated

24 fashion in a weight of evidence approach and make a decision of

25 whether that chemical has an interaction with E, A or T.

26 DR. STEVE BRADBURY: And part of the reason of
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1 breaking it out into a couple of SAPs would be, you know, if we

2 had concluded the assay didn't work or looking at the assays

3 together, just throwing our hands up and there is no rhyme or

4 reason of what's going on. It's going on as we told you, but

5 getting that kind of feedback rather than try to roll it all up

6 into one period with you all. Because if we were seeing things

7 that were strange with the assays, it would be kind of good to

8 get some feedback from you at this stage and then carry, you

9 know, insights into the weight of evidence methodology that

10 we're putting together and getting some feedback on that

11 methodology.

12 DR. DANIEL SCHLENK: Thank you Dr. Bradbury. 

13 Yes Dr. Patino.

14 DR. REYNALDO PATINO: I have a related

15 question but going back a little bit. I think this is a

16 question for Dr. Ackerman and it relates specifically for the

17 binding assay. You said -- so each assay had a weak interacter

18 as a reference control right? And my understanding is that

19 maybe sometimes that weak compound may not perform as within the

20 guidelines. Would that have affected your designation in that

21 assay, the compound of interest, either a designation as

22 equivocal or negative, if in the assay itself a weak binder

23 cannot bind as expected.

24 DR. GREGORY AKERMAN: Certainly it would

25 have given us less confidence in those results if we saw a weak

26 binding controlled chemical that didn't respond properly. I
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1 don't think that was the case in any of these examples. Again,

2 we saw no corresponding response in the uterotrophic assay to

3 suggest that.

4 DR. DANIEL SCHLENK: Dr. Scott.

5 DR. GEOFFREY SCOTT: Just one question from me

6 and sometimes I'm a little slow. When you look at your results

7 where you have assays that are based upon the percent

8 displacement versus those that have comparisons with the control

9 -- an inherent control. We always say the best control gives

10 you the best ability to discriminate whereas inherent we really

11 control those variables. 

12 And so one of the questions I have as you look

13 through these data, have you all looked at that comparison of

14 those assay regardless of which part of the endocrine system

15 they're looking at in terms of their performance across the

16 complementarity in all of these redundancies, in terms of

17 whether or not those differences in the ability to discern an

18 effect may be underlying there. I'm just curious; I think some

19 of these assays I expected to perform -- particularly on the in

20 vitro assays -- better. So I just wanted to ask if you all had

21 a look at that. 

22 DR. TOM STEEGER: Again, the individual assays

23 and the full battery has already been evaluated through ring

24 testing and we know that they are capable of detecting the

25 chemicals. For some of those assays, even today, you're running

26 internal standards and you sort of have that assurance that they
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1 are functioning as expected. 

2 But you're right not all the assays are

3 running standard. But we have to rely on the effort that has

4 gone into preparing these batteries as part of the validation

5 effort as having been successful and that these assays are

6 performing as expected.

7 DR. GEOFFREY SCOTT: I just remember in the

8 '97 meeting when we were looking at this whole cascading among

9 the pathway, looking at that chapter and helping write that

10 chapter. I just remembered we were talking about how when an

11 organism integrates that may give you a more reliable estimation

12 and I'm really surprised with some of the results with the

13 binding assays not predicting more.

14 And I'm just trying to sort that out so I

15 apologize for thinking out loud but I'm trying to help you get

16 some more information. So anyway I just thought I would add

17 this question. I know we had a lot of discussion a long time

18 ago about how this would play out. So I thank you for your

19 response. 

20 I recognize, in fact, when I look at the

21 figures in the report on the assays and look at those positive

22 controls, weak controls and the negatives, I mean every one of

23 those assays is ready to get at. So I'm just trying to sort

24 things out.

25 DR. TOM STEEGER: Dr. Scott we appreciate your

26 input and I think you raised valid concerns. I think that the
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1 assays you need to keep in mind that they don't stand alone but

2 they will be considered in the context of other scientifically

3 relevant information. And again what you see giving in these

4 presentations is a very high level view of the responses;

5 underlying that are the components of those assays and what

6 direction each one is moving in and what kind of response did

7 you have across the full range of doses or concentrations that

8 were tested.

9 So hopefully with knowing that the battery has

10 been validated and then complimented with the idea that this

11 information doesn't exist in and of itself, that it's considered

12 in a weight of evidence context, that we will avoid making type

13 2 errors.

14 DR. DANIEL SCHLENK: Okay. Dr. Ehrich.

15 DR. MARION EHRICH: A very simple question on

16 the chemicals for table 3. Are they all high production

17 chemicals widely used?

18 DR. TOM STEEGER: For table 3?

19 DR. MARION EHRICH: That's the 21 you chose

20 based on tubercle chemical properties and so forth; were they

21 also high production chemicals, highly likely for exposure?

22 DR. TOM STEEGER: As indicated initially, the

23 chemicals that were selected for testing in the first tier of

24 test orders were based on the likelihood of exposure. So the

25 tests that were part of the subsamples, the chemicals that were

26 part of the subsample, indicated they represent a broad range of
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1 chemical domain and many of them are pesticides and one has

2 actually an inert.

3 So the inert would certainly be a high

4 production volume chemical.

5 DR. DANIEL SCHLENK: Okay any other questions?

6

7 Okay I think what we are going to do is I

8 think we're going to call it a day for today and Dr. Dourson

9 will be so kind as to make his presentation tomorrow. I didn't

10 want to throw you under the bus. Most people, I think, are

11 pretty much overwhelmed at this point.

12 So what I'd like to do is we'll conclude the

13 Agency's presentations today, but if possible in the morning if

14 we can have you back at the beginning and then have any final

15 comments that you may have. And then we'll conclude your

16 comments at that point and then begin the public commenting

17 period after that.

18 So with that let me just state to the panel we

19 need to meet in the room over here after a quick break. We can

20 meet over here after adjournment to discuss what you need to do

21 tonight. And so Fred has a couple of comments to say.

22 DR. FRED JENKINS: If I could have everybody's

23 attention really quick. I have a few quick notes for the public

24 commenters for tomorrow morning. 

25 First I want to note that the public comment

26 period is a very important time and we want to make the most out
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1 of it for everyone. So I ask number one, for the public

2 commenters if you are providing a power point presentation

3 please see me either right after this meeting or first thing in

4 the morning no later than 8:30 so that I can make sure that I

5 have enough time to load your presentations on the computer.

6 Also very important note, public commenters

7 please it's important that you arrive on time. We don't want

8 you to miss your opportunity to provide your public comments

9 because once the public comment period is closed and the panel

10 starts their deliberations on the charge questions, there will

11 be no more opportunity to provide public comments. So please

12 make sure you're here and available to provide your comment when

13 it's your time to go. 

14 Also, very important as well I ask that each

15 public commenter please stay within your time that was allotted

16 for you, that you initially requested. If you asked for 10

17 minutes please don't speak any more than 10 minutes. If you

18 asked for an hour or so or an hour and ten minutes to be

19 specific, please don't speak any more than that particular time.

20

21 We only have a couple of days left and we want

22 to make sure that we get everything done that needs to happen,

23 including having all the public commenters get their time and

24 also make sure that we have ample time for the panel to

25 deliberate on the charge questions.

26 So thank you everybody here for a very
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1 successful first day and we'll see you all early tomorrow

2 morning. The meeting starts at 9 am. Thank you everybody good

3 bye.

4 (Whereas the meeting was adjourned for the

5 day)

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1 DR. DAN SCHLENK: Good morning everyone. 

2 Today is day 2 of our panel. It's entitled Scientific Review of

3 the Endocrine Disruptor Screening Program, Tier 1 Assay and

4 Battery Performance. 

5 As is customary, we'll just go around the room

6 real quick to introduce the panel. Just state your name and

7 your affiliation. I'll begin. My name is Dan Schlenk. I'm a

8 Professor in Environmental Toxicology at University of

9 California, Riverside.

10 DR. STEPHEN KLAINE: Good morning. I'm Steve

11 Klaine and I'm a Professor of Ecotoxicology at Clemson

12 University.

13 DR. MARION EHRICH: Marion Ehrich, Professor

14 of Pharmacology and Toxicology at Virginia Tech where I teach

15 veterinary medical students and I'm a member of the Panel.

16 DR. JAMES MCMANAMAN: Hi, Jim McManaman,

17 Professor of Reproductive Biology, University of Colorado.

18 DR. BARRY DELCLOS: Barry Delclos. I'm with

19 the FDA National Center for Toxicological Research in Arkansas.

20 DR. GEOFFREY SCOTT: Jeff Scott, National

21 Oceanic & Atmospheric Administration, Center for Coastal

22 Environmental Health & Biomolecular Research in Charleston. I'm

23 an Aquatic Toxicologist.

24 DR. DANIEL SELVAGE: Dan Selvage. I'm an

25 Associate Professor of Pharmacology and Toxicology at the

26 University of New England and I specialize in endocrine and
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1 neuroendocrine system.

2 DR. TERRY SCHULTZ: Terry Schultz, Emeritus

3 Professor, Department of Comparative Medicine, College of

4 Veterinary Medicine, the University of Tennessee.

5 DR. DONALD TILLITT: Don Tillitt. I'm an

6 Environmental Toxicologist with the Department of Interior, U.S.

7 Geological Survey, Columbia, Missouri.

8 DR. KATHERINE ROBY: Kathy Roby, Reproductive

9 Endocrinology, University of Kansas Medical Center.

10 DR. ALDERT PIERSMA: Aldert Piersma, Professor

11 of Reproductive Toxicology, Utrecht University, and National

12 Institute of Health in the Netherlands. 

13 DR. REYNALDO PATINO: Reynaldo Patino. I am

14 with the USGS Texas Cooperative Fish and Wildlife Research Unit

15 at Texas State University and I specialize in endocrine

16 disruption in fish and amphibians. 

17 DR. DEBORAH MACLATCHY: Deborah Maclatchy,

18 Professor of Biology at Wilfrid Laurier University in Waterloo,

19 Ontario, Canada, and I am an Comparative Endocrinologist and

20 Ecotoxicologist.

21 DR. JACK LEONARD: I'm Jack Leonard. I'm a

22 Molecular Endocrinologist, Professor of Microbiology and

23 Physiological Systems at UMASS Medical School in Worcester,

24 Massachusetts.

25 DR. NELSON HORSEMAN: Nelson Horseman at the

26 University of Cincinnati, a Professor in Physiology.
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1 DR. SHERRIL GREEN: Sherril Green. I'm a

2 Professor in Comparative Medicine at Stanford University School

3 of Medicine.

4 DR. DANIEL SCHLENK: Thank you everyone. As

5 mentioned yesterday, you've had a chance to sort of dwell a bit

6 upon some of your questions and some of the charge questions. I

7 believe the Agency has some additional information to provide

8 you as well, so at this particular time, let's go ahead and, if

9 you guys want to make that presentation then hold your questions

10 until after that presentation for final clarifying questions at

11 that point. 

12 DR. CATHERINE AUBEE: Good morning everyone,

13 this is Catherine Aubee. I'm going to be following up with a

14 couple of clarifying remarks in response to specific questions

15 from the panel yesterday regarding aspects of the amphibian

16 metamorphosis assay, specifically regarding the occurrence of

17 spinal curvature or bent tail, and then also briefly touching on

18 hind limb length normalization.

19 Some of you requested images of what spinal

20 curvature looks like in these specimens. I think it may have

21 been Dr. Green yesterday. 

22 These are images from the unpublished Olmstead

23 et al. manuscript. This is actually in Xenopus tropicalis but

24 the ORD labs work with both species and we believe they're

25 seeing similar types of phenomenon in both species, so I think

26 this is representative. 
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1 And the top one here, A, this is considered a

2 mild form or essentially absent. And B, it's posterior to the

3 hind limbs and considered to be a moderate curvature in this

4 manuscript. And C, it's anterior to the hind limbs and much

5 more severe, could possibly be retained following metamorphosis.

6 So that's just to give you a quick overview of what it looks

7 like.

8 I want to reemphasis that the initial industry

9 data that we've looked at suggests that these abnormalities are

10 not treatment-related, at least in these cases. If they were

11 treatment-related, we would be more concerned about whether or

12 not, specifically, it is a thyroid-related affect that we're

13 seeing, whether or not the curvature resolves itself through

14 metamorphosis and perhaps what implications that might have for

15 different amphibian species, which may or may not resorb their

16 tails. 

17 However, the generally similar prevalence of

18 spinal curvature in control and treatment tadpoles in each given

19 study that we've reviewed, that's shown in the table that you

20 have in the White Paper, makes this more an issue of study

21 performance and not treatment effects. 

22 Therefore, our interest is in whether or how

23 much the incidents of spinal curvature may introduce uncertainty

24 into the interpretation of the AMA, with respect to screening

25 for potential thyroid related affects. We also seek advice on

26 what may be done to reduce the occurrence of the abnormality. 
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1 I wanted to briefly summarize some of what's

2 in the Olmstead et al. manuscript because we just had one

3 sentence about that. I don't know if Dr. Olmstead will be

4 speaking more about this today in his public comments. 

5 But it does show correlations between several

6 factors and the prevalence of spinal curvature and the Xenopus

7 tropicalis specimens, which they studied. Those factors

8 included the type of diet, body size and developmental stage,

9 which ORD believes are especially important, and the strain of

10 the tadpole used. 

11 The highest are squared observed, I believe,

12 for any single one of these factors was 0.66. They did not

13 identify any significant differences between spawns within a

14 string. One of the diets tested included sera micron

15 co-formulated with other elements such as Brine Shrimp. 

16 However, the study did not evaluate sera micron alone, which is

17 the recommended diet in the AMA test guideline.

18 Our ORD colleagues have indicated that they

19 have reduced the prevalence of spinal curvature in their test

20 populations to less than 10 percent by careful regulation of the

21 diet with respect to both formulation and amount and generally

22 avoiding overfeeding. 

23 They emphasize the importance of also

24 considering flow rate in the tank, so this is study-specific and

25 an absolute amount of feeding, therefore, cannot be given in the

26 guideline. That needs to be considered by the labs with respect
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1 to flow-rate in the tanks.

2 However, industry submissions in which the

3 sera micron feeding regime was reduced by 50 percent did not

4 show a lower prevalence of spinal curvature. Therefore, simply

5 reducing the amount of feed does not appear to resolve this

6 problem. 

7 The Agency is proposing a modified diet for

8 the proposed Tier 2 frog assay, which will be brought before the

9 SAP next month. That information may be used to help inform the

10 Tier 1 test guideline in the future. 

11 Ultimately, the Agency does not believe that

12 this phenomenon in itself compromises the utility of the AMA. 

13 However, it would be beneficial to identify any circumstances

14 under which a high prevalence of spinal curvature might

15 compromise the performance of the study or complicate the

16 interpretation of results, in other words, how much is too much.

17

18 It would also be helpful to identify other

19 steps which could be taken to minimize the occurrence of spinal

20 curvature in different test populations; therefore, the Agency

21 seeks advice on these issues.

22 One other question yesterday, I think it was

23 from Dr. Schultz as to whether there was a particular

24 developmental window which appeared to be more sensitive to the

25 development of scoliosis. In conferring with our ORD

26 colleagues, they indicated that NF stage -- and I'm sorry, we're
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1 not on this one yet -- NF stage 51 to 55 appeared to be the most

2 sensitive stages.

3 This slide here just shows an example of the

4 different normalization approaches that were considered for

5 evaluating the hind limb length measurements. This is in

6 response to Dr. Patino's question yesterday. And this is from

7 the OECD Phase 3 validation report for the AMA. They considered

8 three different normalization approaches normalizing to body

9 weight, to whole body length, which is HLL-W, which you see

10 here, or to snout-vent length, which is the last column HLLS. 

11 Generally, they determine that normalizing to

12 body weight was not appropriate. Based on these results, they

13 didn't necessarily see a significant difference between -- I

14 don't want to say statistically significance but they didn't

15 determine that one approach, either whole body length or

16 snout-vent length, was better than the other. But they did

17 indicated that as tadpoles approach metamorphic climax because

18 of the changes in the morphometric ratios, whole body length

19 would probably not be as appropriate. Therefore, normalizing to

20 snout-vent length was sort of more a safe bet.

21 In this one example, you see the comparisons

22 from the same datasets. This is one lab. This is data

23 generated with the chemical BP2. And you can see that when

24 evaluating day 21 hind limb length alone, not normalized, you

25 get significant differences at two treatment levels compared to

26 controls. You get it in only one treatment level when
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1 normalizing to whole body length and at no treatment levels when

2 normalizing the snout-vent length.

3 In another dataset, you see significant

4 differences at the top two treatment levels non-normalized. 

5 When normalizing to whole body length, you see it only at the

6 top treatment level. 

7 I think the most noteworthy thing here is that

8 for day 7, hind limb length, which we believe is really the most

9 useful time point for this measurement, you identify significant

10 difference at four treatment levels, whereas you only identified

11 them in two when using the non-normalized endpoint. However, at

12 day 21, again, you only detect it at the top treatment level. 

13 The bottom line from the OECD Phase 3

14 validation report was that they felt the snout-vent length

15 normalization approach was probably the best way to go. They

16 did believe it was appropriate to normalize for growth in some

17 manner, but they also recognized that it would probably be

18 useful to reevaluate that normalization approach once more data

19 were available, and so this is just a little bit more

20 information for you on that point. 

21 And that concludes my remarks.

22 DR. DANIEL SCHLENK: Thank you. Any

23 questions? Yes, Dr. Green?

24 DR. SHERRIL GREEN: Thank you for the images. 

25 I have a question. Would the water quality analysis that I

26 assume would be performed weekly from the various contract labs
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1 be available to the Agency and the Panel members? Meaning that,

2 particularly, in the group with the 92 percent occurrence of

3 bent tail, it would be really important to look at that water

4 quality, everything from ammonia to temperature to pH. All

5 those things can be impactful on the development of this kind of

6 finding.

7 DR. CATHERINE AUBEE: So those data are

8 available to the Agency at this point and we have considered

9 those in the review of the data. Generally, all of the studies

10 met performance criteria for water quality so they were at least

11 within those limits. 

12 Now, within that limit, we have not evaluated

13 to see if there are potentially differences between studies

14 related to the differences in the spinal curvature. Are there

15 specific measurements from, for example, the highest prevalent

16 study that would be useful to you?

17 DR. SHERRIL GREEN: Most of what I know about

18 in bent tail has been reported anecdotally. So as you can

19 imagine, it would be the place I would start, with the group I

20 would start because the number is so aberrant from any other

21 population. 

22 And it might be other conditions related to

23 water quality; the type of housing, the flow-thru, ambient

24 temperature in the room or something that's used to clean things

25 with. You know they are all other things that could impact

26 that. 
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1 So nothing specific, to answer your question,

2 but if there was an outlier in a particular parameter, it would

3 be useful to see that. And also understanding the methods in

4 which the various components of the water chemistry were

5 determined. Were they tank-side dip sticks or were they

6 spectrophotometric? Because the accuracy and the difference

7 there, depending on the test that you use, can be pretty wide

8 and some are more accurate than others. 

9 So understanding how they measured what they

10 measured would be useful too.

11 DR. DANIEL SCHLENK: Any other questions on

12 this particular point or any other final questions or

13 clarification that you have for the Agency? 

14 Okay. So at this point, I guess we'll

15 conclude the information presented by the Agency and move on to

16 our public commenting period. 

17 And let me remind the public commenters and

18 any speaker, for that matter, please to speak into the

19 microphone so that we can hear you properly.

20 Okay. According to my list, our first speaker

21 is Michael Dourson from Toxicology Excellence for Risk

22 Assessment. And I'll just remind the Panel that handouts for

23 Dr. Dourson's presentation were provided yesterday, it's the

24 blue one. It looks like this.

25 DR. MICHAEL DOURSON: Top of the morning to

26 you all. I thank Dr. Jenkins and Schlenk for the invitation to
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1 present before the esteemed Panel. My name is Mike Dourson and

2 I am a Board Certified Toxicologist and an expert in risk

3 assessment. I'm not an expert in your area and it would

4 probably be apparent if you ask me questions.

5 TERA is an NGO, 501(c)(3) neutral science

6 group and our mission is to provide information so others can

7 protect public health. 

8 Two-thirds of our work is government,

9 one-third is industry and we like to build coalitions and TERA

10 is supporting my trip. And the comments that you're going to

11 get are from a workshop that we've done but there are TERA

12 comments on it.

13 About eight months ago, I was approached by

14 Daland Juberg and Susan Borghoff -- Dr. Juberg was with Dow

15 AgroSciences and Susan Borghoff with Integrated Lab Systems --

16 with the idea of putting together a workshop. And when we first

17 heard about the workshop, we knew EPA was doing some effort in

18 this area so we thought, well why do we need to do this? And

19 what they wanted to do very much was to bring all this industry

20 information and experience to bear into one spot so we can share

21 experiences and then give that information to anybody who needed

22 it, including the EPA. 

23 So we thought okay, that's fine. That makes

24 sense. And they wanted to very much make it open, and so we

25 agreed to the task and it was going to be lots of people

26 invited. And we also asked them to consider working the task
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1 through the Alliance for Risk Assessment, which as some of you

2 may know, it's a consortium of other nonprofits that put things

3 together.

4 And the operating thing here is through an

5 open transparent multi-stakeholder approach. So if you go into

6 the Alliance for Risk Assessment, you have to be open and

7 transparent and everything is available to everybody else. And

8 there is a group that looks at these incoming projects with that

9 in mind. Is it going to be open and transparent and everybody

10 can work together. And you can see the group up here, mostly

11 government people, travel representative. 

12 And so this group doesn't actually do the work

13 but they do past judgment and say, "Hey, this is worthy of going

14 into the Alliance and the work can proceed along those lines." 

15 And so it did. It went to the Alliance and you'll be able to

16 find anything that happened at this conference at the Alliance

17 website.

18 Okay, now into the technical information, here

19 is the organizing committee. One of the things to be obvious

20 with this organizing committee is that it's a multi group. We

21 have some nonprofits in here, we have some industry, and we have

22 some government. And the intent, again, is to bring as many

23 parties to the table as possible that wants to participate. 

24 And a couple of things that were obvious, when

25 I finally understood this area a little bit more, was that the

26 focus is giving back lessons learned at the workshop. And part
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1 of the reason for this is that the Tier 1 studies are costly. 

2 You know, if you run all of them, the rough cost is somewhere at

3 a million dollars. I mean the people that actually do them will

4 know. I don't. And it's costly in terms of animals, and so

5 people wanted to not step forward in an unintelligent way.

6 But there was another thing behind it. It was

7 a lot of experience gained here and the scientists that actually

8 did the studies, wanted to bring that and share it with people. 

9 And quite independent of their own group, they wanted to be able

10 to say, hey, we did this and here is the successes we had, or

11 not. And of course, challenges and lessons learned, this is

12 what you're group's all about. It does this Tier 1 information

13 give EPA what it needs. 

14 So workshop objectives, I mean this is

15 probably to be true of any workshop; provide knowledge, identify

16 best practices, discuss insights, interpreting data, explore

17 biological mechanisms, engage perspectives. So that was the

18 workshop objectives. 

19 And the workshop was designed into four

20 blocks. And the first block was on performance and that's a lot

21 of what you folks are talking about now, and the EPA has

22 summarized nicely some data that's come from this industry and

23 others' experiences. EPA summarized data on 21 or so chemicals,

24 22 chemicals, and of course the industry brought information in

25 for all of their chemicals and other people, not just industry. 

26
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1 And not only were these presentations given,

2 but there were 20 abstracts given where there's a lot of

3 detailed information at the workshop. And you can see that

4 three people spoke at the workshop. We actually have two or

5 maybe all three of these people here today, so again, if there

6 are questions that are detailed, I'll just probably defer those

7 to one of you. 

8 I think that was important from my perspective

9 is some of the perspectives. And some of the perspectives are,

10 first of all, Tier 1 is not designed for dose response. As Dr.

11 Manibusan said yesterday, it really is hazard only.

12 There were several technical difficulties

13 identified in the in vitro assays. Meeting reference chemical

14 performance was one of the highlighted ones. Laboratory

15 constraints for ER and AR competitive binding assays was another

16 technical difficulty with some of these in vitro assays. 

17 Meeting performance criteria for steroidogenesis was another

18 technical difficulty that several people mentioned. 

19 On the in vivo assays, many of the people that

20 were doing the tests had to do additional pilot studies, and

21 therefore additional animals, in order to maximum the

22 sensitivity as required under the criteria. So there were more

23 animals used than intended and that's because of this idea of

24 maximizing sensitivity.

25 And a theme that came up routinely -- now

26 realize we're only talking hazard identification -- but this
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1 idea of testing at such high concentrations; why do this? If

2 the exposures are low and the potency is low such that the

3 margin of exposure at the risk term is ten thousand, why are we

4 doing these studies? Now again, we were doing it for hazard

5 identification, but this kept coming back, you know somehow get

6 in the idea of exposure and potency.

7 Session two, practical applications. Again,

8 you can see the cast of characters here. Dr. Gray is not here,

9 I don't believe, but Dr. Marty and Dr. Borgert are here and they

10 actually may be speaking later today. I actually don't know

11 that, but in any event, those are the speakers. 

12 Here are some perspectives. Development of

13 critical systematic evaluation; again, Tier 2. Tier 2 now is

14 even more expensive than Tier 1, especially in terms of animals

15 and money. There was a couple of posters that showed where --

16 there was some angst in this meeting. Again, when you bring all

17 these people together, there are differences of opinion. 

18 We had some posters where it showed lots of

19 testing data for these 11 bioassays and no effects. And the

20 poster presenter said, "Well we told EPA to expect this. Why? 

21 Because we have tested this chemical with multiple million

22 dollars of studies and we know there are no endocrine effects. 

23 But we did this anyhow and see, you know, we found what we told

24 them we'd find." So there was that tension in the room. So the

25 idea of going to Tier 2 where there is even more money and

26 animals spent, needs to be done in a systematic way.
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1 One of the interesting ideas that came out was

2 a Tier 1.5 screening strategy. Well what's Tier 1.5? Well it's

3 some additional tests that you do before Tier 2, and you do that

4 because the current scheme strives to avoid false negatives,

5 which of course maximizes false positives. So do you want to

6 really launch into Tier 2 with false positives? The answer is,

7 well, maybe Tier 1.5. 

8 Another thing that came up was this two

9 gatekeeper bioassays, Tier 0.5. It was kind of a nice

10 presentation. Actually I was quite taken by it. This is Dr.

11 Earl Gray his colleagues' presentation.

12 Basically, you'd say two gatekeeper bioassays,

13 the fish short-term reproduction study, the male rat pubertal. 

14 You do these two studies, and if they're both negative, you put

15 the chemical in a hold testing box. You don't stop it. You

16 just say, "Well we're going to put this in a hold and go to

17 something else." 

18 And then finally the assessment application of

19 alternate data, highly relevant, it replaced the Tier 1 assays. 

20 Now part of this is this high-throughput screening data. And

21 the fact that Dr. Dix is now part of the OSCP is remarkable. I

22 mean I didn't know about this but he comes from leading that

23 group, this computational toxicology group, so I think that's

24 going to be very important.

25 Session three was the future, and we had all

26 sorts of perspectives here, including David Dix, he gave a
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1 presentation. Mel Anderson and some of these people you know. 

2 We have Catherine Willett. She's in the audience as well, so

3 again we have one or two of these people here today. So if we

4 have questions about that that would be great.

5 The perspectives here -- well again, the

6 high-throughput screenings and compu-toxicology and In silico

7 assays don't have the in vivo characteristics, we all know that.

8

9 So if it doesn't have metabolism, cell-to-cell

10 communication or tissue interactions; so that we have to think

11 about validating these things before we actually use them. This

12 idea of considering human and ecological exposure and especially

13 this idea of exposure is valuable in prioritizing -- I actually

14 didn't know until Dr. Manibusan's presentation yesterday, that

15 EPA has already done this exposure cut. That's really

16 important. 

17 Health Canada did their domestic substances

18 list a couple of years back where they did 23,000 chemicals. 

19 They screened them on exposure. And then after they screened

20 them on exposure, then they went to hazard. So it looks like

21 EPA is doing something similar, and we need to emphasis that,

22 you know, screen on exposure first. 

23 This idea of current collaborations focused on

24 improving -- you know, we have human and ecological

25 biomonitoring now. And if we can biomonitor the chemical and

26 compare it to the critical effects point of departure -- that's
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1 another risk term. So the critical affect is a sentinel adverse

2 effect and its point of departure. 

3 If we can match those two then we can start to

4 winnow out on the basis of exposure. We don't have to do that

5 chemical. Why? Because we biomonitored it in the human

6 population and you can't even hardly even find it. And its

7 point of departure -- it's not very potent so its point of

8 departure is very, very high, so maybe that's a way to screen.

9 And then this idea came up more than once,

10 this idea of collaboration, coordination, communication amongst

11 regulatory community. Well this is one reason TERA is here, we

12 want to communicate the results of this. 

13 And several times people said, "You know we

14 need to pause and reflect on current findings." Well of course,

15 I mean we need to do this and this is what you folks are doing. 

16 We're pausing and we're reflecting on current findings. And I'm

17 glad to be able to bring this to you folks as part of your

18 deliberations along that line.

19 We had a final session which was like "Hey,

20 anybody can grab the microphone and add or subtract from what

21 they heard." And a number of things came up; this idea of

22 critical review and update to the guidelines for the conduct of

23 individual assays. Boy, there was a lot said there, and again,

24 your input on these various assays are important.

25 Biomonitoring and exposure-based

26 prioritization. If we know the existing human eco-exposures, we
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1 need to use that information. We test chemicals that have small

2 margins of exposure and we do that first. You maybe even

3 prioritize it. If you could, small margins of exposure, we're

4 on it. Large margins of exposures, to the back of the line.

5 Reduction in the number of animals in testing

6 used; redesign the studies to avoid pilot studies. If we could

7 do that, that would be great. Consider the gatekeeper bioassays

8 that Dr. Earl Gray and his colleagues suggested. Hey, that

9 might work, that's just one way. 

10 This idea of robust weight of evidence

11 approach using potency exposure; again, the potency and exposure

12 comes up -- I don't need to tell you folks this but dihydrogen

13 monoxide is a neurotoxin, and yet its exposure, in order to

14 cause neurotoxin, you need a lot of water. So therefore, why

15 worry about that. And that's what this whole idea of potency

16 exposure is, it keeps coming back. Again, it's this

17 hazard-based system so we have to integrate that desire with the

18 fact that it's hazard-based.

19 And finally, I think we have -- this is my own

20 perspective. I really was taken with this two-assay gatekeeper

21 approach, maybe in part because Dr. Gray is such an entertaining

22 speaker. But it makes a lot of sense to look at these two

23 studies first, and your thoughts on that would be highly

24 valuable to EPA as they struggle with this. 

25 From my point of view, from a risk

26 perspective, it seem to benefit all stakeholders that were in
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1 the audience at this workshop, and those who were not. We did

2 not have many environmental NGO colleagues there. But my sense

3 is the gatekeeper approach would be valuable to them as well.

4 Okay. So the summary was insight of

5 practitioners and labs, so it was a lot of good information

6 there, multiple stakeholders. And I think the most important

7 thing here is all the presentations and workshop materials are

8 available at the workshop website. 

9 And the final slide is the sponsors we had, a

10 bunch of sponsors here including nine NGOs. 

11 So with that, I will thank you very much for

12 your time and attention and maybe take one or two questions and

13 we also have a four-page summary of the workshop that just came

14 out a couple of days ago, so you guys get to see it first. 

15 Thank you.

16 DR. DANIEL SCHLENK: Thank you. Any questions

17 from the Panel? Okay. 

18 DR. MICHAEL DOURSON: All right, very good.

19 DR. DANIEL SCHLENK: Okay. Our next

20 presentation will be a series of presentations which will be

21 from the Endocrine Policy Forum. And according to my list here,

22 our first speaker is going to be Ellen Mihaich. And you have a

23 host of materials, I believe, that I believe are in order of the

24 speakers, or they're not? 

25 DR. ELLEN MIHAICH: They should be in order. 

26 Good morning. I'm Ellen Mihaich and I just have one quick
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1 slide. I'll be back later, but I just wanted to let you know

2 what the next little bit is about. So these next presentations,

3 nine or so, are from the Endocrine Policy Forum. The Endocrine

4 Policy Forum is a consortium of more than 95 percent of the Tier

5 1 test order holders. So we're the ones that gave you all the

6 data after EPA did it for you, but we're the ones that generated

7 this data. 

8 This group is the test order recipients and

9 other interested stakeholders. And we, as I said, are

10 responsible for all of the testing of the majority of the List 1

11 chemicals. 

12 So I just wanted to set the stage so that you

13 knew we're going to be going through each one of the screens and

14 then have some overarching issues. And this is the culmination

15 of 50 plus million dollars and a whole lot of years of

16 experience and work. And as Mike Dourson just talked about, we

17 pulled this together about a month ago and you're going to get

18 it in an hour and 10 minutes, so good luck.

19 DR. DANIEL SCHLENK: Thanks for the warning. 

20 Okay. Our first speaker is Steve Levine.

21 DR. STEVE LEVINE: Good morning everybody. 

22 I'd also like to thank Mr. Jenkins and Dan for allowing me to

23 provide comments this morning. I'm going to provide comments on

24 three of the in vitro assays, the ER binding and the AR binding

25 and the estrogen receptor transcriptional activation assay and

26 I'll call it the ERTA assay as it was referred to yesterday.
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1 Again, I'm Steve Levine. I'm a Senior Science

2 Fellow at Monsanto and I lead our ecotoxicology function. This

3 is an area that I've been following for about a decade. I've

4 had the opportunity to participate on several review committees

5 of these in vitro assays. I've participated on the EDMVAC,

6 which you heard about yesterday, between 2004 and 2006. I've

7 also had the opportunity to participate in OECD expert groups

8 and validation review committees, and most recently on an NIH

9 review committee, looked at the BG1 ERTA assay.

10 This is a quick overview of what I'm going to

11 cover in the next five minutes or so; comments on AR and ER,

12 approaches and challenges to the ERTA HeLa-assay, advantages of

13 the new BG1 assay, which is functionally equivalent to the

14 HeLa-assay but the methodology has several advantages, and the

15 EPA is to be commended for allowing us to use that improved

16 methodology for the ERTA assay; talk about ERTA assay

17 performance in the context of the White Paper as well as the

18 battery, comments on cell line assays, specifically with impacts

19 of cytotoxicity and then finish up with a few closing remarks.

20 So here are some general comments on the ER

21 and the AR binding assays. Currently the assays are very large

22 and cumbersome because they require many test-tubes with

23 cytosolic preparations which require a large number of animals

24 to be sacrificed. And currently we have advanced recombinant

25 technology which can be used to replace this in a

26 high-throughput format, and those are advances we should look
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1 for in the future.

2 Some of the issues with these assays center

3 around solubility issues at high concentrations. And in my

4 opinion, the assay should be limited to a maximum concentration

5 of 100 micromolar rather than 1000 micromolar. And in fact,

6 many of the validations only went up to 100 micromolar, not a

7 1000 micromolar. Those were added later during test guideline

8 preparation. 

9 There is a desire across the testing

10 facilities to harmonize both the AR and ER assays from a

11 methodology standpoint as well as from a test guideline

12 standpoint. 

13 The AR guideline seems more like an SOP and it

14 really reflects Bill Kelsis (phonetic) SOP from the ER binding

15 assay. The ER is more consistent with the modern test guideline

16 providing guidance and is not as prescriptive.

17 The screens generally appear to be

18 cyto-specific but some compounds can result in false positives,

19 and this is again because of the high concentrations that are

20 required at the top end of the dose range where the assays and

21 some compounds seem to crash. 

22 These assays could eventually be replaced with

23 reliable and validated transcriptional activation assays. Those

24 are functionally equivalent and have the advantage of telling

25 the difference between agonist as well as antagonist. However,

26 there's some watch-outs with those assays which we'll talk more
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1 about on the coming slides.

2 And finally, we heard of EPA's SAR models back

3 in January. And a SAR model was brought to the SAP back in

4 2009, which is very predictive for potential estrogenic

5 activity, and that should be used as a Tier zero screen as well

6 as OSRI or Other Scientifically Relevant Information.

7 This is the ERTA model and I'm not going to go

8 into this, but I just showed this. Again, this is looking at

9 more than just receptor ligand binding. Again, you can look at

10 agonism versus antagonism. 

11 A brief slide on ERTA methodology. One of the

12 issues with methodology in the HeLa-test guideline is the cells

13 are treated only three hours after plating, and that's not a

14 common practice. In the BG1, they're allowed to go overnight

15 before treatment. This shows up as the inability of the cell

16 line only three hours after plating to tolerate levels of DMSO

17 beyond .1 percent. If they go overnight and they're allowed to

18 recover, you can achieve higher DMSO levels up to one percent

19 and that allows you to reach some of those higher test

20 concentrations.

21 Activity and cytotoxicity are assessed after

22 24 hours and I'll have some more comments on cytotoxicity.

23 The other key issue with the assay is the

24 criterion for positive, which is the PC10 value, and we heard

25 about that yesterday in the presentation, what the PC10 value

26 is. 
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1 This is a look at some of the validation data.

2 These are the E2 curves from the validation and we can see

3 that we're only able to achieve approximately 4X to 16X at very

4 high concentrations of E2, the highest concentration being one

5 nanomolar. 

6 The PC10 represents 10 percent of the maximal

7 induction level. And even at the highest induction level at

8 16X, that's only 1.6X. And typically with assays with these

9 levels of inherent variability, experience in many labs, they

10 don't see these as biologically significant until you're over a

11 2-fold response and, you know, this improvement could be

12 achieved by reaching higher levels of induction.

13 The OECD validation review Panel had several

14 reservations about the reliability of the PC10 value as a

15 criterion for a positive. And in fact, in the OECD validation

16 review, it was stated that if the PC10 value were to be used as

17 an indicator of a positive response, it may lead to

18 identification, a lot of false positives when testing a broad

19 array of environmental compounds. And this was born out in the

20 White Paper, and I'll speak more about that in a moment.

21 ICCVAM, which made a publication in the late

22 90s, in Appendix B7 it stated "An efficacy of 25 percent of the

23 positive control should be considered as a positive response for

24 the test chemical in that assay." So they're really looking at

25 about a PC25. The V2 is the positive response.

26 Consistent with that ICCVAM recommendation,
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1 the BG1 ERTA assay settled on a PC20 as the endpoint for a

2 positive response. That was based on empirical information and

3 that was what led to a reliable response and limited the number

4 of false positives that could potentially come out of this

5 screening assay.

6 Assay Performance; overall, in my opinion

7 there was poor concordance between the ER binding and the ERTA

8 results using the PC10 as a criterion for a positive. In the

9 ERTA assay there were five positives out of the 19 compounds

10 evaluated. However, there were zero for the ER binding assay

11 and also zero for the uterotrophic assay, and those are all

12 mechanistically functionally equivalent assays. However, with

13 the nanomole model you do get metabolism or transformation. 

14 In the BG1 assay, concordance between the ER

15 binding assay and the ERTA assay was greater than 95 percent in

16 their evaluation. And there was also very high concordance

17 between receptor binding assay, the ERTA assay and the

18 uterotrophic, and we would expect that for most compounds. 

19 And in fact, if you pull out some of the ones

20 that were positive in the ERTA assay between the PC10 and the

21 PC20, it gets you very close to 95 percent concordance, which is

22 what you predict based on these earlier analysis, for the HeLa

23 validation and the BG1 validation.

24 Some comments on cytotoxicity, ICCVAM also

25 made a recommendation on cytotox and it shouldn't be greater

26 than 10 percent. And this is very important for all cell line
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1 assays, not only the ERTA assay. This test guideline permits up

2 to 20 percent cytotoxicity, which is high, particularly if the

3 assay is used to assess antagonism. We're not using it for that

4 now but it certainly could be used for that in the future.

5 And the reason for that is, if you have up to

6 20 percent cytotoxicity you could have 20 percent inhibition of

7 a response just due to cell death and that could flag as a

8 positive. And the guidelines right now don't allow for

9 correction of cytotoxicity, and they should. 

10 Cytotoxicity assessment should be quantitative

11 rather than just purely visual ratings; that's an option in the

12 BG1 just to do a categorical rating of cytotox. But in my

13 opinion it really needs to be quantitative so you can correct

14 for cytotox, particular if it's pushing near 20 percent.

15 So a few closing remarks; a retrospective

16 analysis is needed for studies using the PC10 value rather than

17 the PC20 value in the weight of evidence evaluations for

18 compounds judged to be positive with a value greater than a PC10

19 but less than a PC20. 

20 And three of those five values, as I said, hit

21 in that range. And one of those five is also considered to be

22 likely a false positive. That gets you down to one out of 20,

23 95 percent concordance. That gets you back to what has been

24 looked at in the validations under HeLa and BG1.

25 Further consideration needs to be given to

26 cytotox and shouldn't be greater than 10 percent, in my opinion.
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1 And in any case, there should be a correction allowed for

2 cytotoxicity, particularly when you're looking at an antagonism,

3 and this will come up again in the steroidogenesis assay, H295R

4 cell line assay.

5 So with that, I'll close my comments. And if

6 there's any questions, please. Thank you.

7 DR. DANIEL SCHLENK: Thanks. Any questions

8 for Dr. Levine? 

9 Okay, thanks. Moving along to our next

10 presenter, this will be Sue Yi. 

11 DR. SUE YI: Good morning. My name is Sue Yi

12 and I'm a Senior Toxicologist at Syngenta Crop Protection

13 speaking on behalf of the EPF. I'd like to take this

14 opportunity to thank you for the opportunity to speak, and what

15 I'll do is just go through the Steroidogenesis and the Aromatase

16 Assay. 

17 So just a quick overview. So mainly what I

18 will focus on is the cytotoxicity issue, issues with solubility

19 of the test compounds at high concentrations and the suitability

20 of the assays themselves.

21 So in terms of the steroidogenesis assay, I

22 won't go into the details of the protocol, but one of the issues

23 is the performance criteria, and that was discussed by EPA quite

24 a bit. 

25 In these cell lines, the H295R cells, there's

26 actually quite a bit of variability in cell behavior, and that
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1 is the hormone production at basal level. 

2 So in many of the cases, assays had to be

3 repeated to get to or get close to the performance criteria, so

4 there's quite a bit of variability. So in that respect, this

5 decreases the usefulness of this assay. Not only does it have

6 to be repeated many times to meet the criteria, so it's no

7 longer rapid, nor is it actually cost-effective because it has

8 to be repeated, but most importantly, this decreases the

9 confidence of the data that's generated.

10 There is also a 24-hour treatment period which

11 is a bit arbitrary time point. As we've shown in several

12 different publications that this is really an assumption that

13 there is an increase or a decrease in the hormones that are

14 being tested that that will be sustained. What we have is that

15 there could be a time course effect in which maximal induction

16 or inhibition could occur at 48 or 72 hours, but then decrease

17 back to basal level. So it could be an adaptive or a quick

18 response that returns to normal. 

19 And the other aspect that confounds the

20 performance criteria is the method of analysis for the estrogen

21 and the testosterone. So the gold standard for these analysis

22 actually are LC/MS/MS method. RIAs are the second method of

23 choice, and then ELISAs or EIAs, and that's because they're

24 actually not quite as reliable. 

25 The reason being is that the different assay

26 kits from different companies and within companies can be highly
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1 variable. These antibody assays are actually, especially at low

2 concentrations of estradiol and testosterone, are plagued with

3 non-specific binding cross-reactivity and matrix interference.

4 So to increase the confidence if you're using

5 an EIA immunoassay approach, the estradiol or the testosterone

6 should be separated by the chromatography before running these

7 analyses. And there's some general references to the

8 validations and the experiments that were done for the

9 cross-comparison of these assays. 

10 So I'll go back to the cytotoxicity issue. 

11 This actually has a major effect; it can have a major effect on

12 hormone production. What we know is that estrogens have been

13 demonstrated to be up-regulated to protect the cells in cases of

14 cytotoxicity, especially oxidative stress mediated cytotoxicity.

15 The solubility of test chemicals as well as

16 the hydroxycholesterol which is used to bump up the basal

17 production of E2 and testosterone, have cytotoxic and solubility

18 issues. 

19 And a visual assessment of solubility is not

20 quite scientific because it can actually be missed. Test

21 compounds could precipitate out at actually very low

22 concentrations even at about one micromolar that you can miss

23 because of the volume at which you're using these assays. 

24 So I'll now move on to the aromatase assay. 

25 And some general comments about the aromatase assays is that the

26 aromatase assay is only just looking at one half of the
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1 aromatase activity, so inhibition is the only assessment that is

2 being made so induction is not even considered in this assay. 

3 It is a measure of tritiated water rather than

4 estrone or estradiol production, so you could have nonspecific

5 affects at high concentrations.

6 The inhibition of aromatase assay can be

7 actually assessed in something like the steroidogenic assay or

8 other cell-based assays which can also detect induction of this

9 enzyme. There is an issue of some of the solvent control and

10 this sort of relates to the solubility. And it's

11 test-tube-based so therefore it's actually quite labor intensive

12 and probably can be miniaturized dramatically to a multi-well

13 format and up to about 96 well.

14 The aromatase assay uses the recombinant

15 variant; a single recombinant variant of the CYP19 gene, the

16 aromatase gene. So there's actually high variability of CYP19

17 across tissue and across species. 

18 The result is that the aromatase activity that

19 is being assessed may not be a true reflection of what happens

20 in vivo. And in addition, because it is a single variant, it

21 does not address individual or population variances. 

22 So there are issues in terms of the high

23 concentration insolubility. There are issues with the enzyme

24 denaturation that leads to false positives. There is also the

25 lack of xenobiotic metabolizing capability, and so you are not

26 assessing for what happens when these compounds are actually
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1 metabolized in vivo. 

2 There is some question about the longevity of

3 the activity of the enzyme preparation because specific activity

4 of those enzymes actually decrease with time and dependent on

5 temperature, as well. So in this assay, protein concentration

6 is the assessment, as opposed to the specific activity of the

7 enzyme itself. And I've already touched upon the tritiated

8 water being the measured endpoint rather than the estrone

9 itself. 

10 So just as a wrap-up, what I'd like for you to

11 take away from this presentation really is the key issues which

12 are the cytotoxicity issues. So this confounds the

13 interpretation of the data. And in vivo we know that

14 cytotoxicity or some type of an insult to the system that could

15 lead to cell death will actually induce aromatase or increase

16 production of estradiol because estradiol is involved in

17 protection of cells, especially in systems like the neurons

18 and/or the cardiomyocytes. 

19 The solubility of the test compound at high

20 concentrations, again, this is really important because we're

21 using a solvent to go beyond the limit of solubility. And this

22 is something that was touched upon by the EPA when discussing

23 the AMA or the FSTRA. But it's interesting that in vitro and/or

24 in vivo that we don't talk about the limit of solubility. And

25 then the necessity of these assays, whether the aromatase assay

26 is actually absolutely necessary or can we actually fold that
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1 into the steroidogenesis assay. Thank you.

2 DR. DANIEL SCHLENK: Any questions? Dr.

3 Schultz got one over there.

4 DR. TERRY SCHULTZ: This is the second

5 presentation we have heard the issue of cytotoxicity, yet one of

6 the criticisms that we've had at earlier SAPs was the fact that

7 we weren't taking the concentrations high enough. 

8 How do you rectify the issue of covering the

9 issue of water solubility for a particular chemical and the

10 tradeoff that you end up having in the fact that you had the

11 potential of becoming near or inducing the cytotoxicity issue?

12 DR. SUE YI: Right. So there are concerns

13 about going up to extremely high concentrations in order to

14 determine an effect of lets say estrogen binding or

15 steroidogenesis. 

16 One of the things that do not get discussed

17 actually is the solubility and the exposure at which the

18 population, whether it's eco or human population get exposed to.

19 And so it makes sense actually to take a look at the exposure,

20 the limit of solubility, in order to determine what the highest

21 concentration should be tested for that particular compound. 

22 DR. DANIEL SCHLENK: Okay, thanks. Any other

23 questions? Okay, thank you. Our next presentation will be by

24 Barbara Neal.

25 MS. BARBARA NEAL: Good morning. My name is

26 Barbara Neal. I'm a Toxicologist with ExPonent and I'm pleased
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1 to present comments on behalf of the EPF, which is the Endocrine

2 Policy Forum. 

3 I want to briefly provide comments on the

4 technical performance and reliability of the Uterotrophic and

5 Hershberger assays and also comment on how these assays may add

6 to the Tier 1 test battery either as a primary element or as

7 secondary phased in elements.

8 I think it's very clear from the literature

9 that the Uterotrophic assay is well validated. It's

10 scientifically sound. It's easy to interpret, the agonist

11 option in particular reliably detects estrogenic agents, and

12 both the ovariectomized mode and immature model appears

13 reliable. 

14 There are a few difficulties with the assay. 

15 Range-finding studies are generally required for dose selection.

16 And because the assay has a very short exposure duration, it's

17 difficult to determine -- in any case, sometimes the MTD

18 criteria are hard to meet clearly.

19 Other issues; sometimes the external suppliers

20 don't provide age of animals and the dates for ovariectomy. And

21 in some cases we encountered incomplete ovariectomies. 

22 With immature model the primary limitation is

23 the animal must be pre-pubertal. But EPA in its review

24 identified no difficulties that were considered a compromise,

25 the assay interpretation of reliability. 

26 Positive control data met the criteria in all
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1 cases, and I think the primary question stemmed on the doses of

2 the 17 alpha-ethinylestradiol, or EE, where excessive doses

3 could lead to non-dose related responses.

4 Essentially, though, it is a gold standard

5 assay, I think it's premature before establishing a weight of

6 evidence strategy to remove it from Tier 1 and make that

7 decision at this point. And I think that it can reliably help

8 distinguish compounds that interact via the estrogen receptor

9 from compounds that may cause sporadic or non-specific findings,

10 for example fecundity in the fish study or in published

11 literature in OSRI, in which cases group sizes may be small. 

12 And in either case, I think the utility may be greatest when

13 there is consideration that the testing may have been done at

14 overtly toxic doses.

15 And I think the main animal welfare

16 consideration is if the data are available some reproductive

17 toxicity studies incorporate weanling uterine weights. And

18 assuming the necropsies are done prior to puberty, these can be

19 used in lieu of the uterotrophic assay and mimic the immature

20 model.

21 The Hershberger assay is, again, a reliable

22 assay. I think it can be confounded by overt toxicity, as will

23 be discussed for the pubertal assay. Marked decreases in body

24 weights can lead to changes in some of the accessory sex organ

25 weights. And it can also be confounded by hepatic enzyme

26 inducers in Phase 2 of the assay, and that's where exogenous
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1 testosterone is added and you've got an increased clearance with

2 enzyme induction.

3 I think the assay guideline is complex as

4 written. I work with several sophisticated contract labs and

5 two of the three had real problems interpreting the protocol or

6 developing a protocol based on the guideline.

7 Again, the dose range-finding study is

8 generally necessary. And even though hormonal analysis is an

9 option in this assay, I think the group size of six per group is

10 too small to reliably develop hormone data. 

11 Again, it's the subject of artifacts from very

12 high doses and multivariate analysis which is recommended in the

13 guideline may lead to an increased number of false positives,

14 and that's based on the analyses of the validation studies.

15 That assay is a gold standard assay. It's

16 relatively specific for identifying androgenic, anti-androgenic

17 or the five alpha hydroxide reductase mechanisms. And again, it

18 provides additional information on activity and mode of action

19 for compounds that show sporadic or less specific indications,

20 and it may be particularly useful again in cases where there's

21 high dose suggestions in the applicable assays. 

22 So I think it's premature, again, before

23 developing the weight of evidence strategy and analyzing all of

24 the data available to say we should remove the assay. I do

25 think we could consider removing the androgenic phase or Tiering

26 that later if there's an interaction with AR binding, since
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1 androgenic compounds seems to be very rare. And that concludes

2 my comments.

3 DR. DANIEL SCHLENK: Thank you. Any questions

4 from the Panel? Okay, thanks a lot. Our next presenter will be

5 Sue Marty.

6 DR. SUE MARTY: Good morning. My name is Sue

7 Marty. I'm a Toxicologist with the Dow Chemical Company. What

8 I'd like to do is present some observations as a practitioner of

9 the Tier 1 EDSP mammalian assays specifically related to the

10 male and female pubertal assays.

11 First I'd like to talk about the performance

12 criteria. I understand and perfectly agree with the Agency's

13 decision to include performance criteria on the pubertal assays.

14 They're multimodal assays so they're designed to detect a

15 variety of endocrine activity. Because of that, it's difficult

16 to select one positive control, so this gives you a way to look

17 at assay performance.

18 I will say though that when you're looking at

19 performance criteria coefficient of variation or CVs, I think

20 it's important to remember that there are two contributors. Not

21 only variance in assay performance, what we call proficiency,

22 but also inherent biological variability. 

23 And based upon the Tier 1 data that were

24 submitted, I think there's an opportunity to adjust some of the

25 performance criteria that are listed in the test guidelines. 

26 So for instance, in the female pubertal assay,
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1 what we noted -- you can see some of the performance criteria

2 that were commonly missed. What we noticed was that terminal

3 body weight had CVs above the acceptable range. Now this is a

4 body weight collected on an animal at a specific age. If we're

5 missing performance criteria it suggests that maybe we didn't

6 account for sufficiently biological variability.

7 And we see the same thing when we look at the

8 male pubertal assay. There's 32 performance criteria that are

9 included in the assay. This list most commonly missed

10 parameters for CVs outside the performance criteria on the left

11 or means outside the performance criteria on the right. And

12 again we see that we're missing CVs for things like final body

13 weight. Again this suggests that maybe there is insufficiently

14 accounted for the inherent biological variability, not that it's

15 proficiency of a laboratory to collect the body weight.

16 You'll note that there are footnotes for a

17 number of the parameters in this table. These were parameters

18 for which the performance criteria were missed in the

19 inter-laboratory validation of the pubertal male assay. So this

20 suggests that, in fact, over time, labs continue to miss the

21 same performance criteria across labs. 

22 This is just a personal observation. I'm not

23 sure about the accuracy of all the performance criteria that

24 were reported in the white paper. I can only refer to the

25 subset of data that came from my laboratory, but I noted that

26 there were omissions in our datasets from the CVs for both the
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1 male and the female pubertal sections of the document. They're

2 probably minor but, in fact, there were some omissions.

3 Overall for lab performance criteria, I think

4 there's an opportunity for the Agency to consider whether or not

5 they should re-examine those criteria. And I think it would be

6 great if they could give us some additional guidance on how

7 those criteria are going to be used.

8 This is just a quick slide on logistics for

9 conducting the male and female pubertal assay related to the

10 money, resources and time that are needed to do these assays. 

11 And I just wanted to call your attention to the second bullet

12 point which is the animal use that's required for these assays. 

13

14 We're required to have 15 litters to conduct

15 each the male and female pubertal assay. We try to conduct them

16 together whenever possible. But the 15 litters are to control

17 for litter effect, which is scientifically valid. However, what

18 happens is that we actually are euthanizing more animals without

19 any data collections, so 135 animals versus only 90 that we need

20 to run the assay, so actually euthanizing more animals than

21 we're using for data collection.

22 So I was a little bit disappointed in the

23 White Paper because I was hoping that it would discuss more of

24 the issues that we had related to the pubertal assays. For

25 instance, there's something on hormone measurement. The

26 document acknowledges that changes in hormone measurements in
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1 the absence of any corresponding changes in other assay

2 endpoints are meaningful, and this is a little bit problematic

3 from the perspective that the hormone measurements are highly

4 variable. They represent a value at a single point in time and

5 they're subject to rapid stress-related changes. So these are

6 sensitive assays. If hormone changes are sustained, I would

7 expect to see changes in other endpoints over the 20- to 30-day

8 dosing period. So it would've been helpful for me to see some

9 more discussion about the Agency's thinking behind this point. 

10 For statistical analyses, we're required to

11 analyze some data points three different ways, ANOVA, ANCOVA and

12 linear trend test. This increases the potential for type 1

13 error rate, so it would've been great if we had some

14 understanding as to which of the statistical analyses are

15 preferred and why.

16 I would've liked to have seen some discussion

17 on endpoint whose utility is unclear, and for this I'll highly

18 the estrous cycle data. So estrous cycle can be highly variable

19 in animals at its initiation. And we all have a limited

20 monitoring period in the female pubertal assay for this

21 endpoint, so it would be great if we had more description from

22 the Agency on how they intend to use these data.

23 In the pre-validation study, there was one

24 pre-validation lab that failed to detect regular cycles in 12

25 out of 14 control females in the pubertal assay. So with such a

26 variable endpoint, it would be great to understand what the
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1 utility is in the eyes of the Agency.

2 Someone asked yesterday whether or not diet

3 can affect female pubertal assay endpoints, and it can. Dietary

4 phytoestrogen requirement from a test guideline is actually 300

5 micrograms per gram genistein equivalent. We're required to be

6 below that level and most of our standard rodent diets are

7 slightly above that level. 

8 So because of that most labs use low

9 phytoestrogen diets. And in actuality, that means that the age

10 at puberty onset vaginal opening in the females is actually

11 about two days later than what we see in our multi gem studies,

12 so it does have an impact.

13 Complementarity across assays, this is the

14 male and female pubertal assays. If you remove the studies for

15 systemic toxicity, what we see is that approximately 50 percent

16 of the assays are finding some indication of endocrine activity.

17 Now the pubertal assays detect both endocrine and non-endocrine

18 modes of action that are not seen in other Tier 1 assays, so if

19 you'll offer differences in species, that could be meaningful,

20 differences in gender, that may be meaningful. This can be very

21 difficult to dismiss any positive findings in pubertal assays.

22 I'm not going to go into this too much but

23 this addresses a section in the report that talked about the

24 aromatase assay contributing to understanding of androgen

25 pathways. I would like to see a little bit more information on

26 this because what we know is two of the mammalian in vivo assays
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1 that look at the androgen pathway, both the Hershberger and the

2 pubertal male, they don't reliably detect aromatase inhibitors,

3 so I would like to see a little more discussion on that. 

4 Our redundancy across assays, this is when you

5 combine all the assay data and you're looking for concordance

6 among the endpoints. Again, when you take out systemic

7 toxicity, what we're seeing is 50 to 80 percent of the chemicals

8 showing an indication of endocrine activity in either the

9 estrogen, androgen or thyroid pathways.

10 For me, the report lacked in sufficient detail

11 for me to put any of this information into context. I can't

12 tell whether or not these changes fit a pattern of responses

13 that I would expect to see across assays. So for example, in

14 the steroidogenesis assay, we had compounds that at higher

15 concentrations would increase estrogen production in the H295R

16 cells, but I never saw any indication of estrogenicity in any of

17 the female pubertal assays. So for me, that kind of information

18 is important to understand when I'm looking at assay

19 performance. 

20 So in conclusion, the male and female pubertal

21 assays are highly sensitive to detect potential endocrine

22 activity. We see a high proportion of positives, but they're

23 reliance on apical endpoints makes it difficult to determine the

24 mode of action for endocrine related findings. The assays

25 detect compounds that interact with estrogen or androgen

26 receptor, affects steroidogenesis, alter the
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1 hypothalamic-pituitary-gonadal or thyroid axis, induce hepatic

2 enzymes and enhance thyroxin or steroid hormone clearance, but

3 they also detect nonspecific effects due to systemic toxicity

4 and stress. Variability in some of the assay endpoints like

5 hormone measurements and estrous cyclicity can complicate data

6 interpretation.

7 From my perspective, the specificity and

8 toxicological relevance of these assays is somewhat unclear. 

9 And if gender and species differences are meaningful, it's going

10 to be difficult to dismiss a Tier 1 pubertal assay positive

11 because the assays detect different modes of action, both

12 endocrine and non endocrine that are not looked at by other Tier

13 1 assays. Thank you.

14 DR. DANIEL SCHLENK: Any questions? Okay,

15 thanks. The next presentation is by John Brausch.

16 DR. JOHN BRAUSCH: Thank you very much. Good

17 morning. My name is John Brausch. I'm an Aquatic

18 Ecotoxicologist at the BASF Corporation. Today I'll be

19 presenting the Amphibian Metamorphic Assay on behalf of the

20 Endocrine Policy Forum.

21 As we heard yesterday, there are a number of

22 performance criteria with the amphibian metamorphic assay. One

23 of the criterion that was most difficult to maintain was on test

24 concentrations. Discussions with both the performing

25 laboratories as well as in the EPA White Paper, this was common

26 throughout in that only seven of 18 studies actually maintained
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1 the appropriate test concentration. 

2 It's important to note that these are

3 mean-measured concentrations and the performance criteria

4 indicate that you need to maintain less than 20 percent

5 variation throughout the entire study. But it's important that

6 the concentration is known throughout the study and is monitored

7 throughout the entire 21-day assay. So what the organisms are

8 actually being exposed to is in fact well known, it's just that

9 it varies slightly or it varies from the nominal concentration. 

10 What the nominal concentration is set at is virtually

11 irrelevant; it's what the organism is actually exposed to.

12 The compounds that had the most difficulty in

13 maintaining these test concentrations where the lipophilic or

14 the highly biodegradable compounds. The tadpoles grow about

15 10-fold during this 21-day study, so you have basically a

16 10-fold increase in biomass from the beginning to the end, which

17 really makes maintaining these concentrations very difficult.

18 The next criteria that is difficult or had

19 difficulty with the AMA is a synchrony among tadpoles. We heard

20 yesterday that some tadpoles in the controls were exceeding

21 stage 60. Stage 60 is important because this is where the

22 tadpoles go from pro-metamorphosis to metamorphic climax. 

23 A number of things happened during metamorphic

24 climax such as tail resorption and apoptosis, as well as major

25 changes within the tadpole itself causing these indices to

26 change very dramatically. One thing that happens is the
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1 tadpoles tend to start to lose weight after stage 60, therefore,

2 making changes in comparisons between the controls after stage

3 60 and the tadpoles that are before stage 60 very difficult.

4 In 16 of 18 studies that were reviewed in the

5 White Paper, controlled tadpoles exceeded stage 60, and actually

6 four of these studies had greater than 20 percent of the

7 controls exceeding the stage making additional statistical

8 evaluation needed and very difficult.

9 As we've heard, there's been a number of

10 complications in dose setting. Dose setting was very difficult

11 in the AMA mostly because there's a lack of historical data on

12 amphibians for many of these compounds.

13 The guidance indicated that a third of the

14 LC50 or the maximum tolerated concentration should be used as

15 its highest concentration. When using this concentration, 12 of

16 18 studies showed overt toxicity at the highest concentration. 

17 One thing that was used in some studies was

18 that a 10th of this LC50 was used as the highest concentration. 

19 This would still allow for thyroid affects to be observed, but

20 also minimize the amount of overt toxicity.

21 The AMA is actually the most animal-intense

22 study in the 11-study battery. As many as 16,000 tadpoles were

23 produced when only 320 tadpoles are actually needed for the

24 study. This is additionally an expensive study costing upwards

25 of $100,000, and it's a fairly long study for a screening assay.

26
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1 So when you look at the whole battery, there's

2 the three studies that can identify thyroid affects, the male

3 and female pubertal as well as the AMA. 12 of the 16 studies

4 reviewed showed and demonstrated thyroid activity, and seven of

5 the 16 demonstrated thyroid activity in the AMA.

6 There's only actually one instance where the

7 AMA suggested thyroid activity that was not observed in the

8 pubertal rat. Yesterday we heard there were three. And while

9 three is true, the other two studies also had significant

10 solvent affects. So without all the data, it's very hard to

11 determine where this "thyroid activity" would have occurred,

12 whether it be from a solvent or from the actual compound itself.

13 This data is actually supported by a study

14 from 2010 by Pickford et al. that was commissioned by the OECD

15 in which he actually has in his abstract. There is little

16 convincing evidence that an amphibian screening assay would

17 detect significant numbers of thyroid active compounds that are

18 not picked up in the mammalian assays. In fact, we saw five

19 pubertal studies observe thyroid activity when the AMA did not,

20 but this could also be due to nonspecific overt toxicity, but

21 with the data that's provided, we are unsure.

22 There are a number of strengths with the AMA. 

23 First it's important to know that histopathology was by far the

24 most sensitive endpoint and very specific for thyroid activity. 

25 We saw the decision tree yesterday presented and it only

26 indicates that the histopathology should be done for thyroid
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1 antagonist and not thyroid agonist. However, there is evidence

2 that thyroid agonist can be observed in histopathology through

3 what appears to be thyroid atrophy. 

4 Other strengths of the AMA is that it's the

5 only assay that actually examines a developing thyroid versus a

6 developed thyroid, and it examines in ecological receptor in the

7 frog. It can also incorporate many different routes of exposure

8 which is very important to try to get a full picture of

9 potential thyroid activity. 

10 There are a number of limitations of the AMA. 

11 Most of these are actually going to be covered in the next

12 couple of presentations you hear, so I will just gloss over

13 those very quickly.

14 Thyroid histopathology, although one of the

15 benefits of the AMA, it also is currently a limitation as well

16 because it is very important to have stage matched thyroids. 

17 The stage matched thyroids are actually very difficult to get

18 because you have the lack of synchrony among the control as well

19 as the exposed organisms. 

20 We've heard a bit about the normalize hind

21 limb length, also this statistical analysis of the stage 60

22 tadpoles; it makes statistical analysis extremely difficult. 

23 And then we've heard earlier today some more about the spinal

24 curvature.

25 There are a number of different alternatives

26 to the AMA and we can talk about this for a long time. But if
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1 you refer to the public comments, there's about 18 pages for the

2 amphibian metamorphic assay and all three of these are discussed

3 much further. 

4 One alternative would be to use a stage fixed

5 design, so basically saying that we would continue the assay

6 until the tadpoles reach a certain stage, for example, stage 58

7 versus this time fix. At 21 days, you terminate the assay

8 regardless of where you're at. This is actually similar to the

9 proposed Tier 2 LAGDA study in which is it more based on a stage

10 versus a time. This would also assist in stage matching the

11 thyroids and having much greater strength in the histopathology.

12 Two other options would be to look at an 8-day

13 amphibian assay, which was proposed in 2010, and this actually

14 focuses primarily on biochemical endpoints, also a 14-day

15 metamorphic assay similar to what EPA initially recommended

16 could also solve some of the problems that we observed in the

17 21-day assay.

18 As currently designed, the AMA did not

19 significantly add to the battery. The rat pubertal identified

20 six or seven compounds that indicated thyroid activity. We say

21 that it did not significantly add to the battery but it's as

22 stated currently in the guidelines. A number of modifications

23 could be made. It could really increase its utility. This

24 could eliminate some of the growth issues that we've discussed

25 or the statistical issues. It can also increase the power for

26 the histology and have a little bit greater sensitivity and be
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1 more sure of what compounds actually do have thyroid activity. 

2 Thank you.

3 DR. DANIEL SCHLENK: Any questions from the

4 Panel? Yes, Dr. Green?

5 DR. SHERRIL GREEN: I'd be interested in your

6 opinion. Would a staged fixed design either result in an

7 overall shorter assay time and/or the use of fewer animals?

8 DR. JOHN BRAUSCH: It could definitely reduce

9 the amount of time. Stage 58 would usually occur slightly

10 before that 21-day that is currently established. I think that,

11 even if it was the same length and you used the same amount of

12 animals, there would be benefits in it because of the additional

13 power that you would have in the histopathology.

14 DR. DANIEL SCHLENK: Any other questions? 

15 Okay. Thanks Mr. Brausch. Our next presenter would be Allen

16 Olmstead.

17 DR. ALLEN OLMSTEAD: Good morning. My name is

18 Allen Olmstead and I am an Ecotoxicologist at Bayer CropScience.

19 My comments today are going to focus on the two charge

20 questions that dealt specifically with the AMA.

21 This abnormality, this condition that we've

22 been discussing quite a bit, different people call it different

23 names. You'll find it's called different things in the

24 literature and people have sort of different definitions to it. 

25 So I think it's important -- I find these pictures really

26 helpful in describing what it is that we're talking about and
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1 having a consistent identification so that people are onboard

2 with what this is.

3 This was already presented and described. It

4 would like to add that if you look at the fore limbs and the

5 hind limbs, you can tell that these tadpoles are at different

6 stages. And in fact, the one that has the most severe

7 abnormality is actually the least developed of these tadpoles. 

8 As was mentioned, you start to see these signs

9 around stage 51, 54 although you can see them in older animals

10 that didn't have them at that stage. And the severity of this

11 condition tends to progress as the animals get larger and as

12 they advance in the stages.

13 So the middle panel there, at that stage you

14 may have that level of abnormality, but at a further stage, that

15 condition may progress and become worse. 

16 Another point I'd like to make is that this

17 definition, sort of between the two different severities in

18 terms of whether or not this curvature occurs anterior or

19 posterior to hind limb lengths, I think it's important because

20 when it occurs anterior to the hind limb lengths after the

21 animal metamorphosis, you still sometimes see this condition in

22 the adult frog. In addition, that severity of this condition

23 may lead to inability of the tadpole to maintain itself in the

24 water column which would impair its ability to feed as well as

25 grow.

26 Certainly, I think across the board we can say
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1 that any improvements in diet and husbandry for any animals that

2 we work needs important considerations in things that we would

3 want to improve; both we want thriving healthy animals in our

4 research and for the applicable considerations there.

5 One of the points that I think is important is

6 that, does the development of this condition actually impair the

7 ability of the assay to detect thyroid active compounds. And to

8 my knowledge, we don't have any evidence that this would be the

9 case. 

10 So when this research was done, to try and get

11 a handle on this, the top panel was ascribed a score of zero,

12 the middle one was ascribed a score of one and the last one was

13 ascribed a score of two. So I bring that up to mention this

14 figure, which is meant to address one of the questions from the

15 Panel yesterday in terms of growth in relation to development of

16 this abnormality. 

17 So these data points don't represent

18 individual frogs, they represent means among the tanks. And

19 there are two strains of -- this is Xenopus tropicalis

20 illustrated here. You can see that one strain clearly develops

21 this condition more than the other strain. That strain also

22 actually grows larger than the other strain. 

23 And when this research was done, it wasn't

24 done just to reduce this degree of abnormality, but also

25 considerations were made to survivability as well as growth of

26 the animal. So we really wanted to have large healthy animals
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1 but not have these conditions. And so one of the

2 interpretations is over-feeding, sort of comes from this

3 relationship. The more you push growth the more you tend to see

4 this development. 

5 Personally, I feel that what we're looking at

6 here is some sort of nutritional deficiency, and this is based

7 on some other work that was done. Because we also looked at

8 Rana Sylvatica and Rana Pipiens that were brought in from the

9 field.

10 With regard to the other charge question and

11 thyroid histology, the important thing about where you make your

12 baseline or where you put your baseline or your reference, is

13 that you're always comparing to the control in these assays. 

14 So you can have an external baseline -- and

15 this would be the Grim et al. guidance -- or you can have the

16 internal baseline, which would be the controls themselves. But

17 in the interpretation, it has to be made between the controls

18 and the treatments. 

19 The last little cartoon here is that you want

20 to know if the rabbit is finishing the race differently from the

21 tortoise. It doesn't matter if you measure it from the start or

22 the finish or some arbitrary point in between, you still come to

23 the same conclusion.

24 Thyroid activity in these animals is changing

25 as you go through metamorphosis. This is a fixed time design,

26 so at the end of it, we're getting a snapshot. Different assays
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1 will have different snapshots of what's going on, and so that's

2 the important thing to consider in terms of interpretation,

3 especially in terms of this idea or this observation of lower

4 incidents, severity, hypertrophy compared to the controls. 

5 And so if you think of a thyroid antagonist

6 activity, we would expect to see developmental delay along with

7 increased hypertrophy compared to the controls. That's as the

8 test is typically designed where your animals have not reached

9 metamorphic climax. 

10 Certainly after you pass that metamorphic

11 climax, then this indication of lower incidence severity of

12 hypertrophy could be consistent with the developmental delay. 

13 Another explanation here is that the animals weren't properly

14 stage-matched with the controls. 

15 And so, again, going back to my cartoon, I

16 sort of think that if you don't have properly stage-matched

17 controls then your point of reference is different for your

18 controls and your treatments, and that's important. 

19 And another thing is that when you do have

20 developmental delay it's important to realize that you are

21 sub-sampling from these tanks from slightly different

22 populations. So that's another consideration, even if you are

23 stage-matching, there are other factors involved there.

24 The stage-matching, the issues here with how

25 the stage development of the animal impacts other endpoints is

26 important considerations as well because the stage here is the
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1 confounding factor for statistical analysis of all the other

2 endpoints, with the exception of developmental delay. And if we

3 moved to a stage-fixed design, I believe that would alleviate a

4 lot of the issues that we had. And with that, I thank you for

5 your time.

6 DR. DANIEL SCHLENK: Any questions? Dr.

7 Green?

8 DR. SHERRIL GREEN: So I just need some

9 clarification here because your observation of bent tail in

10 Xenopus laevis or Xenopus tropicalis -- lets start with that

11 species -- you alluded to over-feeding. And I'm wondering if in

12 your lab you've tested that hypothesis by purposefully

13 over-feeding and inducing the condition in the Nigerian and the

14 golden trouts.

15 DR. ALLEN OLMSTEAD: The work that was done

16 there was not focused on over-feeding. So none of the work in

17 that Olmstead et al. manuscript actually dealt with different

18 levels of feed. They were different diets, different components

19 of the diet. In particular, one of the observations that was

20 made was that when you included things that tend to have more

21 protein than fat, we tended to see higher rates of scoliosis. 

22 You also tended to see increased birth rates. So I'm thinking

23 of things like trout-starter, things that have, like, fishmeal.

24 DR. SHERRIL GREEN: Right, which have been

25 used for decades to feed those species in laboratory

26 environments. These reports are relatively recent, including
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1 the one with the high frequency of bent tail, the 92 percent

2 from one of the test laboratories. 

3 So it's really hard for me to get my head

4 around this information when it hasn't been published yet and

5 it's anecdotal. I see the pictures and I know that it's

6 happening. But whether it's overfeeding or feeding too much

7 protein, or it could be related to the changes in water quality

8 as a result of the amount of excrement in the water on a

9 particular change turnover system that isn't changed to

10 accommodate the waste that's produced from that. You follow me?

11

12 So there are other down-stream affects of

13 overfeeding. So would you agree it's still a little murky to

14 say -- no pun intended -- that overfeeding can consistently

15 produce these findings?

16 DR. ALLEN OLMSTEAD: I personally do not agree

17 with that this as an overfeeding issue, but that is an

18 interpretation.

19 DR. SHERRIL GREEN: Okay, so let me interject

20 here. All right. Thank you. Now I want to move to the second

21 comment that you made about nutritional deficiencies. Okay. 

22 So that was on work that you did in your lab

23 in wild-caught Rana Pipiens, which are not fully aquatic. They

24 are semi-terrestrial.

25 DR. ALLEN OLMSTEAD: It was based on similar

26 work that was done mainly with Rana Sylvatica although a little
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1 bit of work was done with Rana Pipiens. And so that work was

2 sort of done in collaboration with the University of Minnesota

3 Duluth. We had mesocosms, typical mesocosms as they normally

4 grow and we also tried to raise these in the labs.

5 And we tried different types of diets and what

6 we found is we had even worse severities of those conditions

7 than what we saw with the Xenopus. 

8 Now one of the things in terms of husbandry of

9 native amphibians is that when most people raise these in the

10 lab, they get really tiny metamorphosis. There're like 10

11 percent of what we actually were seeing in the lab. 

12 And so the animals that we raised in the lab,

13 you know, they were smaller but they had high levels of this

14 condition; whereas what we had in the mesocosms where it was a

15 more natural type food, they grew to the size that we're seeing

16 in the field but they did not have this condition.

17 DR. SHERRIL GREEN: So what were you feeding

18 them, Rana, in the lab when you were studying bent tail?

19 DR. ALLEN OLMSTEAD: We tried sera micron, we

20 tried trout starter. We tried fish plate food. We also had

21 boiled lettuce, boiled spinach. It was a mix of diets that we

22 looked at with Xenopus.

23 DR. SHERRIL GREEN: Thank you.

24 DR. DANIEL SCHLENK: Dr. Patino?

25 DR. REYNALDO PATINO: I have a follow-up on

26 the bent tail question. So just focusing on Xenopus and
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1 focusing on lab studies, do you have or do you know evidence

2 that would show or suggest that the appearance or the incidents

3 of bent tails is associated with or causes these problems in

4 performance, such as growth or development? 

5 I mean is there evidence that would say, okay,

6 this is a problem in terms of the other endpoints that are being

7 measured in the assay that they will affect those animals?

8 DR. ALLEN OLMSTEAD: Somewhat problematic in

9 that the animals that tend to grow larger, the ones that tend to

10 develop this, you know, you don't know if there is production in

11 growth because of that; sort of putting the horse before the

12 cart type of thing. 

13 The observations that I made was that when

14 this condition gets really severe the animal is clearly not

15 behaving normally. They have an impairment in how they move

16 through the water column, and I would assume that that would

17 impair their ability to feed. 

18 And so there are concerns with the compounding

19 factor with the measurements there that are in the assay that --

20 I mean from what I observed, as long as the animals are able to

21 move as an unaffected animal, I don't see how the endpoints that

22 are measured in the assay would be impacted by that, which is

23 why I would advocate -- I'm not just saying these animals have

24 this condition, but do they have it anterior or posterior to the

25 hind limbs.

26 DR. DANIEL SCHLENK: Dr. Leonard?
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1 DR. JACK LEONARD: How tight was the

2 population distribution in your tanks that showed bent tail? 

3 Did you find that the staging was broad, in terms of the

4 distribution of the tadpole within a given tank? And was there

5 relationship between the distribution and the incidence of bent

6 tail?

7 DR. ALLEN OLMSTEAD: So trying to rephrase

8 your question; are you asking if those tadpoles that tended to

9 reach metamorphosis earlier had higher incidences?

10 DR. JACK LEONARD: No. I'm asking exactly how

11 broad of an NF scale do you have in the tank when you have the

12 bent tail.

13 DR. ALLEN OLMSTEAD: The studies that were

14 done with this were actually done two-stage as opposed to at any

15 given time. 

16 So we went in and every time a tadpole entered

17 stage 58 or 62, you pull it out and assess it then. 

18 Those particular studies were not done under

19 the AMA guidelines. With the AMA guidelines, you have everybody

20 that starts at stage 51 on a certain date, so you have a really

21 tight variability there. These were done based on when they

22 respond, so the variability there was much more spread out than

23 what we would see in the AMA.

24 DR. DANIEL SCHLENK: Dr. Horseman?

25 DR. NELSON HORSEMAN: I'm going to get off the

26 bent tail. So I want to ask about the logic. You make a strong
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1 argument for stage matching, and we heard this earlier from EPA,

2 in the thyroid histology studies. 

3 I want to ask about the logic of that because

4 just as a hypothetical, a treatment that accelerated the rate of

5 metamorphosis, it seems to me if you stage matched those

6 measurements of histology, you'd be selecting for animals that

7 were less responsive to the testing factor. So I'm trying to

8 figure out the logic in this thing.

9 DR. ALLEN OLMSTEAD: I have to admit I didn't

10 come up with this guideline and my experience was more dealing

11 with Tier 2 test. And so I would be in favor of running this to

12 a stage as opposed to a time. And I think what you're

13 describing is what I refer to as when there is a developmental

14 delay, we were basically sub-sampling for those animals that

15 would least likely be affected. 

16 So again, this process of stage matching, we

17 have to be aware of what sort of factors might be involved in

18 there. I think the opposite or the other side to that is that

19 when we look at validation exercises, when we look at those

20 compounds that have anti-thyroid activity, what we see is we see

21 the delays in development, but we see these thyroid changes at

22 much lower concentrations as well. 

23 So it's sort of an adverse outcome pathway

24 thing here. You see changes in the thyroid gland concentrations

25 before you start seeing affects on development. So even though

26 we are doing this sort of sub-sampling here, there is a question
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1 of thyroid histology is such a sensitive endpoint that, based on

2 the validation work, I would argue that we should see evidence

3 in changes in thyroid, even though we are purposely sampling

4 here.

5 DR. NELSON HORSEMAN: So you stick by your gun

6 once it's stage-matched.

7 DR. ALLEN OLMSTEAD: The other issues with the

8 endpoints that were measured in the statistical analyses

9 consistently, from looking at the AMA reports that I had, those

10 were the big concerns, especially when you start dealing with

11 overt toxicity; because that does have an impact on -- it could

12 have an impact on development, it could have an impact on the

13 size of the animal.

14 The other nice thing about looking at time to

15 a certain stage is that's a much more concrete measurement

16 that's less abstract. When you say there's a stage difference

17 between, you know, the treatment here is NF57 versus NF56, how

18 you put that into ecological context? Whereas I think when you

19 say the time to stage 58 was delayed by two days, I think that's

20 more meaningful.

21 DR. DANIEL SCHLENK: Dr. Patino?

22 DR. REYNALDO PATINO: I have a question on

23 stage matching and asking for your opinion here. What would you

24 think of instead of matching the treatment to the subsample of

25 control animals, matching the controls to what is most common in

26 treatment, if the stages are there to match? 
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1 DR. ALLEN OLMSTEAD: I think that's the other

2 side of the coin if you are going to stage match. I think

3 you'll still run into the same issues where you're still

4 sub-sampling from these two different populations, and the

5 possibility of having a compounding factor there still exist.

6 DR. DANIEL SCHLENK: Dr. Scott?

7 DR. GEOFFREY SCOTT: Just one question. Had

8 there been any studies looking specifically at all of the

9 ingredients in foods themselves that are used? Has anybody

10 really done a detailed chemical analysis of the active

11 ingredients in the food? Particularly, I'm thinking about the

12 antioxidants that are used to stabilize the food. Has anybody

13 done anything?

14 DR. ALLEN OLMSTEAD: That sort of

15 consideration was only done as sort of food fiber, ash protein

16 that you typically get in these diets, but nothing to that

17 equal.

18 DR. DANIEL SCHLENK: Okay. Any other

19 questions? All right, thanks. At this point, let's go ahead

20 and take a break, if it's okay with Dr. Mihaich. We'll

21 reconvene at 11:00.

22 (Whereas a break was taken)

23 DR. DANIEL SCHLENK: So I've been informed by

24 our technical staff that the speakers need to sit as close as

25 you can to that mic. Apparently we've been having some

26 difficulties hearing in the back. 
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1 Okay? All right, let's continue with our

2 public commenting period by the Endocrine Policy Forum, and our

3 next speaker is Ellen Mihaich.

4 DR. ELLEN MIHAICH: Thank you. And I realize

5 that other than giving my name, I didn't even tell you who I

6 actually was earlier. I was just whisking in and whisking out. 

7

8 I am a consultant. I have my own consulting

9 company and I am the Scientific Coordinator for the Endocrine

10 Policy Forum, which is why I got to put all of this together for

11 all of these people. I also have an adjunct appointment at Duke

12 University teaching risk assessment, so I'm an environmental

13 toxicologist and risk assessor. 

14 And right now I am going to be presenting on

15 the FSTRA study. Dr. Katie Coady, with Dow Chemical, was not

16 able to be here, but she put these slides together, so I will do

17 my best to do her proud.

18 It's a very similar setup to what you just

19 heard from John Brausch on the amphibian metamorphosis study. 

20 There were difficulties with the performance criteria. Actually

21 only four out of the 21 studies that were examined met all of

22 the identified test validity criteria. It would be really great

23 to actually have all 52 studies to understand what it was across

24 the board. 

25 One of the biggest problems that we've had,

26 and I think we can see that -- we saw that with the AMA, was
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1 this issue of mean measure test concentrations and the

2 performance criteria for the aquatic. I mean these are

3 difficult to test. A lot of these are difficult to test

4 compounds. And as John mentioned, we do know at least what

5 they're being exposed to because we do analytical confirmation

6 of dose. 

7 But in the 21 studies that you have in your

8 White Paper, nine of them did not meet this performance

9 criteria, so 43 percent were not able to meet that performance

10 criteria.

11 The labs actually do a lot to try to meet

12 this. It is a huge amount of up-front work that goes into this

13 to try to get concentrations meeting the criteria that we have. 

14 They increase turn-over rates. They increase cleaning of the

15 tanks; just all sorts of things in order to try to meet that

16 criteria.

17 In many cases or in some cases, we did have to

18 use solvents. And then that came up yesterday, I believe, in

19 some of the discussion that there is a bit of a lack of guidance

20 on the issue for how to look at solvent controls versus food

21 controls versus negative controls. And within the OECD, in

22 fact, the standard practice within OECD is if your solvent and

23 your negative controls don't match, you then compare it to your

24 solvent control which is opposite of what is in the guideline;

25 so I think having a little bit more understanding of why that

26 is, we should probably take a better look at that too.
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1 We also had some difficulties in meeting the

2 performance criteria for the fecundity endpoint. Of course

3 fecundity is one of those apical endpoints not very specific to

4 an endocrine mode of action, or not always specific to an

5 endocrine mode of action. 

6 Six of the 21 examined studies in the White

7 Paper did not achieve the 15 eggs per female per reproductive

8 day, so it's 29 percent that didn't match it. Again, labs did a

9 lot of work to try to meet this. In particular, they added

10 collection trays underneath the spawning tubes so that they

11 could try to catch all of the eggs, any eggs that might've

12 fallen to try to up that to meet that performance criteria. 

13 I think we've also heard a lot about dose

14 setting, and it's not just on the aquatic studies. It's done

15 with the others, this overt toxicity. The guideline says that

16 it should be a third of the acute LC50. That's one of the

17 criteria and also based on solubility too, but this practice did

18 actually lead to some overt toxicity. I mean think about it;

19 you're doing a 96-hour LC50 typically to get that acute LC50 and

20 you're taking that to a 21-day study. 

21 So it's really you're very much pushing the

22 envelop as far as your maximum tolerated concentration. And out

23 of the studies that were evaluated in the White Paper, 20 out of

24 the 21 showed overt toxicity or some sort of sublethal affect. 

25 So I think that's something that we really do

26 need to think about as far as, again, thinking about where we
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1 set our doses. Our recommendation is that 1050 acute LC50 seems

2 to be a more appropriate place to put it. But also in trying

3 to, again, meet this and trying not to have to repeat these

4 studies over and over and over again to have doses that didn't

5 have a lot of overt toxicity, a lot of registrants did prolonged

6 range-finders, 7 to 14 days, before starting the study, and so

7 that just adds more time and expenses to doing these. And we

8 did also actually use the maximum dose separation in many of

9 them so that you would at least have two treatments that didn't

10 have the overt toxicity. 

11 Not quite as bad as the frog study but still

12 pretty high in animal use, 400 to 500 fathead minnows that are

13 usually put in here, but we also have range finders that could

14 be an issue. And there is some trouble with meeting the weight

15 requirements, and so you end up having to have a little more

16 fish to make sure that you met the weight requirements.

17 How this study is set up is you set up with

18 extra tanks to go into this preexposure time to make sure that

19 you have groups of fish that are reproducing well; so you over

20 setup in order to be able to pick the ones to make sure that you

21 have enough that are reproducing. 

22 So it's also a very expensive study. It's

23 about $150,000, so it is quite labor-intensive and you need to

24 know what you're doing. So it's high in animal use, high in

25 cost, high in time, not really what I would consider to be a

26 screen.
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1 There are some difficulties in endpoint

2 interpretation, especially things like the apical endpoints in

3 here that we always have to keep track on to make sure that

4 things across the battery. I mean that's why this was setup as

5 a battery and, you know, that was really smart that we can use

6 all of the studies. 

7 But things like fecundity, it's sensitive but

8 it's not really specific to an endocrine mode of action. 

9 Decreases in sex steroids, GSI fecundity, even vitellogenin. I

10 mean vitellogenin is a great endpoint for an increase in male

11 vitellogenin being exposed to an estrogen. But if you have, for

12 example, a decrease in female vitellogenin, it could potentially

13 be a path of toxicity; which has required us to all keep our

14 fish just in case, depending on what you find in the end so

15 that you might have to do some additional histopathology to try

16 to understand what the affect is that you're seeing.

17 We also have had troubles with bacterial

18 infections, microsporidia, things like that, and that can in

19 fact affect interpretation of the study. I just recently I had

20 a study where I got about two weeks into the pre-exposure and

21 they just stopped reproducing, and then you lose all those fish.

22 While you can check them ahead of time, and that's good,

23 sometimes it's not at the level to really see to know if it's

24 going to cause a problem until you really get into it and start

25 stressing through the reproduction. 

26 So there are some strengths of FSTRA. There
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1 are some very good endpoints, things like, again, increase in

2 male vitellogenin, it's a great one. Presence of tubercles in

3 females, another good one that make this study -- you can get

4 some good information out of it. 

5 It is another one where you can see exposure

6 with the aquatic route, so that's good. It gives us a little

7 bit more information. It is useful I think in the battery to

8 corroborate other findings, but you do, again, have to be very

9 careful about the apical endpoints. It's an in vivo system with

10 a species that we do know an awful lot about. And it actually

11 is possibly a little superior than the pubertals in detecting

12 steroids and synthesis inhibitors. 

13 But there are limitations, as we've seen with

14 all of these studies that we talked about. But again, apical

15 endpoints, lots of animals that we're using. There are just

16 huge issues with getting material into water. It's just not one

17 of the more fun things that we do. Long, expensive, and it is

18 probably a little bit more limited than the male pubertal and

19 the detection of anti-androgenic chemicals.

20 So just some proposed alterations to the

21 guideline, as you guys are thinking about this. Changing the

22 dose setting to maybe a 10th of the acute LC50 could help us

23 avoid some of these problems with overt toxicity. 

24 I think eliminating the fecundity and

25 fertility endpoints, they're really not that helpful. And there

26 is an OECD guideline study, the 230, that actually -- as we've
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1 done a lot of work through OECD and we ended up with two

2 guidelines, the 229, which is the same as the FSTRA study here,

3 the 890 guideline which has the fertility and fecundity

4 endpoints, and then the 230 which does not; so we could consider

5 maybe making some sort of a change towards that.

6 I think, again, if we had all of the data and

7 were able to do some sort of sensitivity analysis on some of

8 these endpoints to see how much information they do bring to the

9 table, it would be very helpful. And I think if we could have

10 some clarity on statistical issues, maybe go back and revisit

11 the issue of food controls versus comparing to the solvent

12 control; that could be very helpful. 

13 And overall, it is a valuable assay in the

14 battery. And again, because this is a battery, we can get some

15 good information out of it. It has good sensitivity for

16 estrogen and steroidogenesis inhibition. It is a waterborne

17 exposure and it's an aquatic species, so that's another good

18 plus for it. But certain modifications could really help us

19 improve the conduct interpretation of the screen. I think

20 that's it.

21 DR. DANIEL SCHLENK: Okay, any questions? Dr.

22 Scott?

23 DR. GEOFFREY SCOTT: Ellen, we've heard from a

24 number of folks this idea, attempted the LC50 with the frog and

25 the fish, and one of the questions I had since y'all are

26 conducting range-finding assays oftentimes with both of those
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1 assays; if you look at that attempt of the LC50, where does that

2 put you in terms of the NOEC and LOEC if you were to calculate

3 it in those range finders? Has anybody looked at that?

4 DR. ELLEN MIHAICH: I haven't.

5 DR. GEOFFREY SCOTT: Yeah, I know you're

6 presenting but it just -- I should've asked it earlier, but

7 since several folks have asked it and my brain cells have -- 

8 DR. ELLEN MIHAICH: I was just going to say,

9 can I just turn to my friends? No? I think we'd have to get

10 back to you on that.

11 DR. GEOFFREY SCOTT: Well to me, that would be

12 worth looking at. And it would give you some additional insight

13 since we've heard this repeated recommendation.

14 DR. ELLEN MIHAICH: Right. Thank you.

15 DR. DANIEL SCHLENK: Any other questions? Don

16 Tillitt?

17 DR. DONALD TILLITT: Ellen, you recommend

18 removing the fecundity. I'm somewhat surprised because that's

19 the ecological relevant endpoint. I know it doesn't provide,

20 you know, mechanistic, but it is an apical endpoint.

21 DR. ELLEN MIHAICH: Yes, but you got to

22 remember what the Tier 1 is. Tier 1 is a battery to look at the

23 potential to interact with the endocrine system, it's not risk

24 assessment. So there will be other studies, I'm sure. If there

25 was some concern about an endocrine mode of action in this with

26 good redundancy, good corroboration, and as long as the battery
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1 is working well, you would then go on to find that apical

2 endpoint, which is the population-relevant endpoint. 

3 So you're not doing a risk-assessment on this.

4 And because it's not mechanistic and can easily be perturbed by

5 other modes or mechanisms of action, I mean think about it, you

6 don't feel well, I think reproduction is going to go down, but

7 it may have nothing to do with an endocrine mode of action,

8 right? 

9 So I think that it just muddies the water from

10 that perspective. Let's keep these things more mechanistic,

11 more specific. Yes, it can provide some additional information,

12 but I think if you were looking at the FSTRA study, what would

13 be more important to me are things like male vitellogenin to

14 indicate that there's some estrogenic component with the

15 compound.

16 DR. DONALD TILLITT: Even with the cost

17 benefit being there?

18 DR. ELLEN MIHAICH: Well the thing with the

19 fecundity and fertility endpoints, it does add cost to the study

20 because you have to do a lot of counting. So the study is a lot

21 more resource-intensive and it is oftentimes -- when you can't

22 meet that criteria, you take the study down and you start again

23 because you don't want a study that's going to be rejected. 

24 So there are a lot of things that come in here

25 and it may be that it has nothing to do with understanding

26 whether or not there's a mechanism that is pertinent to the
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1 endocrine.

2 DR. DONALD TILLITT: Along those same lines

3 then would you recommend adding, as non-optional but required,

4 the steroid analysis based on your same logic?

5 DR. ELLEN MIHAICH: And I think Lisa is going

6 to talk about that in just a minute. I know that there are

7 issues and that's why it was not required. It's a blood issue,

8 you know, small fish not enough. But I was just talking with

9 someone else yesterday and there are methods that probably could

10 look at that. I'm sure that there are some issues kind of

11 similar to what Sue Marty just mentioned about a one-time point

12 measuring a hormone and what that might mean; but I think we

13 could probably get past that with some work. 

14 I think it's something to think about it, but

15 you need to make sure that it was valid within the study.

16 DR. DONALD TILLITT: Thank you.

17 DR. DANIEL SCHLENK: Any other questions? 

18 Okay. I think you have another presentation, yes? Round two.

19 DR. ELLEN MIHAICH: I do. You have to listen

20 to me again. 

21 Okay. So as he's pulling that up, I just

22 wanted to mention, I think you all got our 64 pages of comments.

23 I meant to mention that when I first got up there. If you want

24 additional information from what we're talking about here, I'm

25 sure you can find it in there or catch any one of us after the

26 fact.
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1 So the other presentation I just wanted to

2 make was on the guidelines themselves. And I've been doing this

3 endocrine stuff for a really long time. I was an EDSTAC

4 groupie. I followed it around the world, it seems like. And

5 I've also worked a lot through OECD to start working on some of

6 these guidelines. 

7 And one of the problems that we've had, and I

8 think you can probably get a sense to some extent from some of

9 our discussion, was when these 890 series guidelines -- and

10 please, if you haven't had a chance to page through them, you

11 really should because you can see that there's some different

12 quality to them. But at the time, there was no stakeholder

13 review prior to their publication. And so what you see is some

14 of the guidelines are done very well and others are really more

15 like a protocol than a guideline. They're very prescriptive. 

16 If you've been involved with the SAP at all before, you probably

17 have heard us talking about the corning hot plate. One of them

18 says that you should have a Corning hot plate. You know, that's

19 not really a guideline. 

20 Now, that being said, we have addressed some

21 of that. But the problem is, is that the test orders that these

22 registrants get is you have a requirement to follow the

23 guideline. If they say you have to test the guideline, then you

24 have to follow the guideline. So if you don't follow the

25 guideline and don't use your Corning hot plate -- and I'm just

26 being a little funny on that one. Like I said, that's not going
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1 to be a problem, I know that's not a problem, but there are

2 other things in these guidelines like that, all right? 

3 You have to consult with the Agency. So for

4 us, it's a bit of an efficiency issue, and for the Agency. And

5 if you don't, then you probably have a protocol deviation in the

6 study because, again, all of these studies are done under good

7 laboratory practices and you have to be very cognizant of

8 following guidelines. And if it says you're following the

9 guideline, but you don't really follow it in your protocol,

10 that's a protocol deviation. Okay?

11 So the Endocrine Policy Forum -- again,

12 because we are this consortia of all the test holders, we were

13 able to get a lot of guidance on a lot of these points. So the

14 Agency has worked very well with our questions to determine

15 whether or not some of these things are necessary. And you will

16 find on the endocrine website that there's a corrections and

17 clarifications document, which is very helpful. So it's been

18 very good to try to make sure that we have things like

19 flexibility on software and lab equipment because, again, many

20 of these guidelines really read just like a protocol. 

21 And there were also some errors in the

22 guidelines that needed to be corrected. So some of the

23 equations were wrong, some of the concentrations were wrong. 

24 You know, we've been working through these for the last few

25 years and I think we're moving forward very well, but the

26 guidelines haven't been formally updated. One of the things
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1 that I want to encourage you in your response back to the Agency

2 is to say that it would be a really good idea to update these

3 guidelines as you go forward.

4 The other thing that we have that gets filled

5 out -- and I think you had some -- someone mentioned from

6 yesterday there are standard evaluation procedures and study

7 profile templates that are also more or less called data

8 evaluation records, DERs. And there are a number of

9 inconsistencies that we have between the guidelines and those

10 documents also; things like in the uterotrophic study and the

11 standard evaluation procedure for the uterotrophic study, it

12 talks about doing a study for both estrogen agonist and

13 antagonist, but it's only the agonist portion that's supported

14 in the guideline. So there is a disconnect between the

15 evaluation procedure and the guideline. 

16 In the Hershberger Study there is inconsistent

17 concentrations for the testosterone propionate between the test

18 guideline, the SEP, and the DER. So I feel like we're at a

19 point where we can start to try to make sure that all of these

20 things come together. 

21 And it's important because, not only do

22 contract research organizations and the laboratories doing this

23 work need to know what the real answer is, you know, if there's

24 a problem in an equation, but the Agency contractors and

25 reviewers also need to know that too because they're filling out

26 these study profile templates based on the study and it has that
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1 chance of having more of a question come up of whether or not

2 we've met the guidelines.

3 You have a number of charge questions on

4 performance criteria. We've certainly talked about performance

5 criteria and some of the challenges we've had. As Sue Marty

6 said, there are 32 performance criteria in the male pubertal and

7 all 15 studies that were evaluated in the White Paper had at

8 least two out of acceptable range. In the AMA, seven out of 18

9 were out of range. 

10 So from what John mentioned earlier, you're

11 being asked to evaluate this. If you have changes in some of

12 these performance criteria, now is, again, a great time to make

13 sure that it gets put into the guidelines and the guidelines are

14 updated. 

15 Again, you are only getting a chance to look

16 at about 21 studies and a lot of the information is not

17 completely transparent in trying to be able to understand what

18 you're looking at. So here is a good opportunity to really try

19 to get more information out of what the industry has already

20 done.

21 So I'm just making a recommendation that EPA

22 should take the opportunity now to amend the guidelines and

23 other related documents before issuing new test orders on which

24 were likely to come around the corner pretty soon. And also, we

25 would recommend that EPA allow for stakeholder comment of the

26 Tier 2 guidelines which are going to be massive, and you'll get
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1 to see a month from now -- well not the guidelines, but at least

2 the validation reports -- before requiring testing so that we

3 can maybe circumvent some of these problems that we're bringing

4 to the table right now. And with that, I'm done.

5 DR. DANIEL SCHLENK: Any questions from the

6 Panel? Dr. Green?

7 DR. SHERRIL GREEN: Thank you for that

8 presentation. I have a question. In your travels as a

9 consultant and with the groups that you represent, would you say

10 in your opinion that most of the contract labs are 1) AAALAC

11 accredited where animals are used, and 2) GLP labs that are

12 regularly inspected?

13 DR. ELLEN MIHAICH: Yes to both of those

14 things. These studies have to be done under GLP, so there's no

15 option there. So the industry would only work with GLP labs for

16 providing the data to EPA for this program.

17 DR. SHERRIL GREEN: One of the topics that

18 continually comes up in the discussion of bent tail in the

19 metamorphic frogs and tadpoles, for example, is the leaning that

20 there is a general idea that husbandry is a significant player

21 in that result. And if these labs are abiding by the

22 guidelines, you would think that that wouldn't be as prominent

23 an occurrence as it appears to be in the reports that we've seen

24 right now. Could you weigh in on your opinion about how that

25 might happen? 

26 I've looked at the conditions in the husbandry
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1 guidelines. They're within a range that is generally practiced

2 but I'm suspecting that perhaps there is a bigger deviation

3 amongst labs.

4 DR. ELLEN MIHAICH: Okay. So you have to

5 think about this whole program. When this all started and

6 EDSTAC came forward with the studies that you should be looking

7 at, a lot of people, industry, EPA went off to start to really

8 work on validating these assays. Many of them didn't exist and

9 many of them changed greatly from what was suggested by EDSTAC. 

10

11 So you then start down this path of validation

12 and I know the Agency gets a lot of grief for how long it took. 

13 It just takes time, you know? They had a lot of work to do. We

14 had a lot of work to do to try to understand how best -- you

15 know, husbandry things can take forever to really get at, and

16 unfortunately a lot of times you don't have that luxury to

17 really get at it. 

18 The other thing is, when validation is

19 occurring, it's only being done in a couple of labs. So then

20 when you get into the regulatory part of this, you're scaling up

21 to have lots of these studies done in contract labs.

22 Now I don't think the contract lab is doing

23 something wrong. I think that it's just that we didn't see the

24 effect as much during the validation as we're now seeing it when

25 we're trying to push the system to get these done in a two-year

26 timeframe, which was the requirement, although there was a lot
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1 of help from the Agency from the standpoint of offering

2 extensions. 

3 I mean, this was all new for everybody and I

4 think we're just learning. And I think that what that means is,

5 this is what you guys are here to do, is to look at some of

6 these problems and make some suggestions for maybe making some

7 changes. But I think it would also be really important to look

8 at all 52 studies. I mean that's what the SAP said in 2008, 50

9 to 100 studies. You've got 52. 

10 It would really be a good opportunity to be

11 able to look at all of the data and to start teasing apart some

12 of these endpoints, sensitivity analyses, that sort of thing. 

13 You know, I think it's worth taking the time to stop and really

14 put this program together well.

15 DR. DANIEL SCHLENK: Okay. Dr. Schultz? 

16 DR. TERRY SCHULTZ: So the forum, would it

17 prefer to look into changing the protocols now based on the

18 information that's available on 21 or continue as was originally

19 intended in the 2008 to do the 52 -- I think the 2008 said more

20 than 50 but less than 100 chemicals. There were 67 and -- don't

21 worry about the numbers as being exact, but start to change the

22 protocols now based on the information you have on the 21 or

23 wait till all the information is available on the 52 and then

24 address that issue?

25 DR. ELLEN MIHAICH: I think we would prefer to

26 wait and do all 52. I mean, I think the best thing is you have
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1 a lot of data. But there are other things you can be doing at

2 the same time. The EPA has a great program going right now on

3 high-throughput. That was the January SAP. There are things

4 that can continue to move forward there, be able to find useful

5 information, perhaps. 

6 But I really think that it is very important

7 to make use -- again, this is over $50 million just in testing

8 cost, and that has nothing to do with other resources. And this

9 is EPA's resources, this is industry's resources, so there's

10 been a lot of time and money spent on this program and I think

11 it really behooves us all to step back and look at the data that

12 we have.

13 DR. DANIEL SCHLENK: Okay. Any other

14 questions? Okay. Our next presenter will be Lisa Ortego.

15 DR. LISA ORTEGO: Hi, I'm Lisa Ortego. I work

16 for Bayer CropScience. I'm an Ecotoxicologist there. I also

17 Chair the Technical working group of the Endocrine Policy Forum.

18

19 My comments are going to focus now on offering

20 some considerations to the Panel and to EPA for ways we might

21 reorganize the Tier 1 battery, the T1B to enhance its

22 efficiency, to reduce the use of resources and perhaps the

23 decision-making.

24 So in EPA's white paper for this SAP, they

25 proposed or at least suggested for the Panel to consider perhaps

26 elimination of the Uterotrophic and the Hershberger assay. This
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1 consideration seems at least partly based on the fact that the

2 pubertals are flagging more endpoints than the Uterotrophic and

3 the Hershberger. But as you heard my colleague Sue Marty talk

4 earlier, a number of the endpoints in the pubertals are

5 susceptible to non-endocrine specific activity. And I think

6 what the Uterotrophic and the Hershbergers bring, they inform on

7 those endpoints as to whether or not we should suspect an

8 endocrine mode of action or if there's maybe a non-endocrine

9 specific mode of action at work.

10 And as you heard Barb Neal discuss, the

11 Uterotrophic and the Hershberger assays are among the more

12 specific and extensively validated assays. They've undergone

13 testing and regulatory use years before the rest of the T1B, so

14 we have a lot of experience with them. We understand how they

15 work. We understand their specificity and the information that

16 they are telling us.

17 So our opinion is that the recommendation to

18 eliminate these assays from the T1B is probably not in the best

19 interest in maintaining specificity and mechanistic information

20 in the battery. 

21 However, there are some alternatives to

22 consider for reorganizing how we do the Tier 1 battery. And the

23 alternatives I'm going to present today, none of them recommend

24 elimination of any of the tools in the battery. It's just a way

25 to stage the battery in a different way such that we can

26 facilitate a faster resolution for questions and also get at
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1 reduction of resources.

2 So a couple of those alternatives, OECD and

3 the European Center for Ecotoxicology and Toxicology of

4 Chemicals both have framework. Both of these frameworks are

5 heavily weighted toward evaluation of all existing data prior to

6 beginning of any testing such that that information, whether

7 it's industry generated or it's from the peer group literature

8 can inform what test you might need to do. And those frameworks

9 progress from tests in less organized levels of biology to

10 higher levels of biological organization. So those are just a

11 couple of examples. 

12 These alternatives are fully referenced in the

13 comments that were submitted on behalf of Endocrine Policy

14 Forum, so you can find more information about them there. 

15 In 2011, there was a paper that was published

16 by Borgert et al. with some suggestions about how you might go

17 about organizing the Tier 1 battery instead of just testing all

18 11 at the beginning. Instead of a requirement, you just test

19 all -- to use all 11 assays. Perhaps you start with the

20 androgen receptor binding assay, Hershberger, the aromatase,

21 steroidogenesis or female pubertal. And if you don't see any

22 activity in any of those assays, maybe you don't need to go any

23 further. 

24 And if we do an assessment like this and we

25 supplement with other relevant information, so peer review

26 literature, other industry data, then we can gather a lot of
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1 information about which of these tests are really necessary for

2 us to run to understanding the effects that we may see.

3 Another alternative -- and this is the

4 proposal that you heard Mike Dourson talk about from the TERA

5 workshop that Earl Gray presented there. It's similar in

6 concept to the Borgert 2011 et al. paper. And in his paper,

7 they proposed that the FSTRA and the male pubertal assay are

8 sensitive and specific enough to stand alone as initial assays

9 in the battery. And they based this on an analysis of 12 model

10 compounds and they found that at least one fish and one rat

11 assay indicated a positive finding with high concordance. 

12 So the FSTRA and the male pubertal could be

13 performed first. If you have an effect in one of those assays,

14 then you chose from the rest of the T1B what you need to

15 characterize that affect and make a determination on whether you

16 need, again, risk-assessment data from Tier 2 at the end of that

17 evaluation. If no effects are observed in those two assays,

18 then that chemical goes to a hold status essentially, and no

19 further endocrine screening is necessary at that time.

20 Now in their paper, they did not appear to use

21 the fecundity endpoint. I haven't confirmed this with either

22 Gary or Earl and we would need to do that, but it's not cited in

23 the paper that the fecundity was a decision-maker. And if

24 that's the case, that might be another way that we could reduce

25 the amount of labor and cost and time associated with the FSTRA.

26 However, the optional endpoints would need to be added to use
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1 the Ankley/Gray approach. 

2 So I think that if we're going to evaluate

3 whether or not this is an appropriate way to go forward, we want

4 to consider whether reproduction or fecundity is necessary and

5 we'd want to consider that the optional steroid measurements may

6 be a requirement instead of an option, going forward.

7 Now in the Ankley/Gray paper, they focus on

8 estrogen and androgen activity, but thyroid activity is also

9 covered under this approach. The male pubertal assay includes

10 thyroid evaluation or you can modify the Hershberger to get that

11 information if somebody is more interested in pursuing the

12 Hershberger as the male assay here.

13 And I also wanted to say that the Pickford

14 paper, which you heard John Brausch reference earlier, supports

15 the idea that the thyroid data in the mammals is predictive of

16 what's happening in the frogs. 

17 So they compared a number of chemicals, I

18 think it was 20 to 30 chemicals across several thyroid modes of

19 action and found very good concordance and that the mammal

20 responses were predictive of the amphibian responses.

21 In addition to this data, all pesticides have

22 other studies in which thyroid evaluation are done. So this is

23 not the only thyroid data for pesticides available, so you would

24 have other mammal-based thyroid data to include.

25 So just to conclude, wanted to say that there

26 are a number of proposals that we could consider as an
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1 alternative to the elimination of the Uterotrophic and

2 Hershberger. None of these approaches would propose eliminating

3 any of the studies on the battery unless we just felt like they

4 weren't working.

5 Now the alternatives we think carry a high

6 potential for reducing time and the resources, if you're looking

7 at a gatekeeper type approach, you're talking about two assays

8 and possibly done so that you can get chemicals through the

9 process much faster. 

10 So we think that a staged approach like this

11 transforms this from just a requirement to use all 11 assays

12 into more a targeted assessment programs; so we're going to

13 target what we need to do based on the data that we have and

14 based on the new data that we don't. I'll be happy to take any

15 questions that you have.

16 DR. DANIEL SCHLENK: Questions from Dr.

17 Patino.

18 DR. REYNALDO PATINO: Do you know is there

19 information on differences in the sensitivity -- you had various

20 suggestions on all these stage approach to the Tier 1 assays to

21 eliminate either option that you proposed, eliminate the

22 amphibians metamorphosis assay. 

23 Do you know if the pubertal assay, I guess, is

24 the male that you are proposing -- has anybody looked at the

25 difference in sensitivity between the male pubertal assay when

26 it comes to thyroid endpoints and the amphibian assay?
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1 DR. LISA ORTEGO: Well the only information

2 that I have would be the information that was presented in the

3 Pickford paper in which that -- I don't know if sensitivity per

4 se was the exact endpoint they were using. But what they

5 reported was when it was positive in the mammal, it was positive

6 in the frog. There was one chemical that was an exception, it

7 was the methoxychlor, was the exception. It was positive in the

8 AMA and it didn't register in the rat data that they had.

9 What I would suggest is there is a lot of data

10 that's now been collected from the Tier 1 battery for these

11 chemicals, for 50 chemicals, and that's the kind of evaluation

12 that we would encourage to happen to make a determination on

13 whether or not the AMA could be a second line to understand the

14 response we saw based on the gatekeeper approach. I think the

15 analysis needs to be done and I think we have the data to do it.

16 DR. DANIEL SCHLENK: Dr. Horseman?

17 DR. NELSON HORSEMAN: Let's see. This

18 question is going to be asked from the perspective of having

19 thought endocrinology to medical students for 20 years or

20 something. 

21 In these pubertal assays, the tropic hormone

22 assays or the LH/FSH/TSH in particular, I would also add ACTH to

23 this. But those are generally optional, described as optional

24 assays and apparently aren't done ordinarily. 

25 The reason I ask about this is that

26 determining an endocrine mode of action ultimately relies on
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1 having that information. A patient comes in with delayed

2 puberty, the only way you'd know whether that's an endocrine

3 mode of action or endocrine cause of disease is by measuring the

4 gonadotrophic hormones. It could be nutritional, genetic, could

5 be lots of other things. 

6 So from that perspective, I'd like you to

7 comment on the fact that that information isn't being captured

8 in these assays.

9 DR. LISA ORTEGO: Okay. I'm going to have to

10 maybe defer to Sue Marty, but I believe thyroid hormone

11 measurements are required for the pubertals.

12 DR. REYNALDO PATINO: I'm specifically not

13 asking about thyroid hormone. I'm asking about the tropic

14 hormones. TSH could be the relevant ones here.

15 DR. LISA ORTEGO: Yeah, okay. So I really

16 don't have a response for you but Sue may want to. 

17 DR. SUE MARTY: Just a couple of things. TSH

18 is a requirement, both for male and female pubertal, as is T4. 

19 Testosterone is measured in the males. We don't measure

20 estradiol in the females because they're all cycling and we're

21 not required to take in necropsy at specific stage in the cycle.

22 As far as other tropic hormones, we don't

23 measure them. One of the issues I think with LH and FSH is

24 because of pulsatile release. It's really difficult to get a

25 good handle on those hormone levels.

26 DR. LISA ORTEGO: Thanks Sue.
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1 DR. DANIEL SCHLENK: Any other questions? 

2 Okay, thanks. So our last presenter for the Endocrine Policy

3 Forum would be Chris Borgert.

4 DR. CHRIS BORGERT: Thank you very much. I

5 know it's been a long morning. I'll try to be brief. I am a

6 consultant, as is Ellen Mihaich, and I serve as the Assistant or

7 Associate Scientific Coordinator. We haven't really resolved

8 that. I think the acronym is probably the same in either case.

9 I want to take you to sort of a 50 thousand

10 foot level now to think about some of what we've heard this

11 morning to emphasize the need for a thorough review of the EDSP

12 List 1 data. And I'll describe for you some work that the EPF

13 is undertaking. I'll give you then a very specific example that

14 I'm not really qualified to answer questions about because I'm a

15 pharmacologist. I'm going to give you an ecotox example, from

16 one that we've already heard probably more than you wanted to

17 hear this morning, and then make some general points.

18 Okay. So at this point, we now have data on

19 the 50 to 100 chemicals that in 2008, the SAP recommended be

20 reviewed to determine assay and battery performance. And so

21 having that data available, I want to emphasize that EPA has all

22 of the data. So far, what we have been able to collect, we're

23 calling our control data project. 

24 And the number of datasets that we have for

25 each assay is shown on the slide. So the number of datasets

26 that we have is over here. It's probably similar to the numbers
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1 that EPA is showing you that have been analyzed, but this is far

2 from the full dataset. We don't have the ability to issue test

3 orders to our members, so we can only sort of beg and ask, and

4 we have confidentiality issues. But we've been able to collect

5 a substantial amount to this point in time and more is coming

6 in.

7 Now what we do with those data, and as I say,

8 our goal here is to do a deeper dive because I think that's what

9 the 2008 SAP recommended, and we've shared with you a number of

10 concerns in assays and we think that requires then a deeper dive

11 in many of these data. 

12 But for confidentiality reasons, we then code

13 some of the parameters in the information that's submitted to

14 us. And those that are listed here, we code them, we don't just

15 get rid of them because many of these may be important

16 parameters to evaluate in the performance of the assays, if you

17 want to know something about inter and intra-lab variability,

18 variability over time as well as a number of other parameters.

19 So the next thing we do is make all the data

20 uniform for ease of analysis by the statisticians. And these

21 next couple of slides are just giving you a brief listing of

22 some of the data information in addition to the simple data

23 printouts of the control and test chemical responses. There is

24 a lot of other information with these assays that may affect

25 assay performance that we think needs to be taken into account. 

26 You've heard about some of those things already. I won't go
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1 through this list, but we've listed them for each assay.

2 Now I want to turn quickly to an example

3 you've already heard a lot about, and I'm just giving this as an

4 example. I don't mean to imply that we've resolved any

5 questions here, but just to give you an example.

6 So for instance, in the amphibian

7 metamorphosis assay, the test guideline talks about

8 normalization of hind limb length with snout-vent length. 

9 Presumably those recommendations in the test guideline are based

10 on what came out of 14 studies that were used in the validation

11 in the OECD program.

12 When data started coming into us, we noticed

13 that there may be some questions around this procedure, so we

14 tried to use our data collection project to do a more thorough

15 evaluation. Here is the dataset for snout-vent versus hind limb

16 length for -- I think this was just a subset of four that showed

17 poor correlations. This is being used for normalization. It

18 raises some questions. 

19 Just going to fly through the next slide or

20 the next series of slides to give you a flavor of what we

21 started to do. So going back then and looking at 18 studies

22 we've collected, plus the 14 studies used in the OECD and

23 plotting all those data. And I'm not the statistician who did

24 that. I'm not a statistician. He's unable to attend the

25 meeting today so I can't answer specific questions here. But

26 just to go through and point out that when developmental stage
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1 was used as a covariate, at least at this level of analysis,

2 there did appear to be a fairly tight relationship. So all the

3 statistical parameters are listed here but this is only for a

4 subset of control data. 

5 We don't think this is a necessarily answered

6 question, and of course we haven't looked at test data. EPA has

7 all of those data for the test article and the controls, and so

8 this is the type of analysis that we think needs to be done in

9 order to resolve these kinds of questions. So overall, we're

10 continuing to cull through these data and trying to do a deep

11 dive. We have a little bit of test article data. We have a lot

12 of control data. Like I said, EPA has all the data.

13 I think the lessons we're learning is that,

14 first of all, potential problems are not going to be identified

15 if you don't look for them. So doing some concordance analysis

16 and showing that things lineup is nice, but we think there's a

17 lot deeper dive that may need to be done. And now we have a

18 broader dataset that can be used for that thorough analysis.

19 You know, I actually served on EDSTAC. I was

20 on the screening and testing advisory committee so I have to

21 take some of the blame for the fact that we may have what we

22 have. But then in 2008, 10 years after EDSTAC made its

23 recommendations the SAP recommended this deeper dive after

24 spending all of these resources. And remember that 50 million

25 plus and the tens of thousands of hours is just since the test

26 orders were issued. There was a decade of work that preceded
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1 that. 

2 So at this point, it really seems like it

3 would be worth wild to step back and take a close thorough look

4 at all of the data. For example, as I gave you, the test

5 guideline for the AMA may need some revision to correct

6 normalization procedures. But as we've also heard, that raises

7 all kinds of confounding issues as well. I'm not expert enough

8 to answer those questions, others are. But it certainly is

9 going to take a more in-depth analysis of all the data, at least

10 in my view. We need to do this kind of an analysis I think for

11 all of the performance criteria. 

12 You've heard about how many performance

13 criteria there are in the pubertal assays, for instance. And

14 this kind of an analysis can help, first of all, understand what

15 the overall endpoint variance is across all of the assays, and

16 then may help tease out some of these issues related to the test

17 article where you can get a response that may or may not be

18 hormone-mediated. It may be a non-specific response. 

19 And I think it will also help in the

20 interpretation issues for eventually getting into a weight of

21 evidence because we'll know what the overall variance in these

22 endpoints is across laboratories within laboratories. We'll get

23 a better handle on the true biological variability in these

24 endpoints so we can better discern which ones are giving us real

25 information and which aren't. 

26 And so I think this has some real benefits to
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1 the Agency. This kind of an analysis, of course, is a lot of

2 work. We understand that. But a lot of work has already been

3 done. It would seem to be a shame, at least in my view, not to

4 take advantage of that. That would help the Agency really

5 assure people that assay performance and battery performance are

6 there. It would help them then to improve the test guidelines

7 that has been alluded to. It would help focus the refinement of

8 this battery. 

9 You know, looking at the data on this in-depth

10 sort of bases would help that you could actually then dry-run

11 some of these alternative suggestions for staging the battery. 

12 You could actually use those test data to figure out then, first

13 of all, which endpoints are really informative, and then dry-run

14 some of these alternative approaches to see if they might

15 actually be as or more useful and yet more efficient. 

16 So I'll conclude with that and just sort of

17 pose a question for the Panel. How comfortable are you making a

18 pronouncement on the performance of the assays themselves or on

19 the battery as a whole with only a subset of the data that may

20 represent only up to say 40 percent of the chemicals that have

21 been tested? 

22 As a scientist, that would give me some pause,

23 but I would encourage you to mull that question over and

24 consider the comments that you've heard this morning. So thank

25 you very much.

26 DR. DANIEL SCHLENK: Okay. Any questions? 
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1 You can stick around for questions if you want, if there are

2 any.

3 DR. CHRIS BORGERT: We've got Dr. Schultz over

4 here.

5 DR. TERRY SCHULTZ: So which would you

6 recommend first; evaluating all 51 chemicals based on the

7 protocols we have or starting to look at altering the protocols?

8 You've offered both options in your discussions this morning.

9 DR. CHRIS BORGERT: Well I may be

10 misunderstanding. You know, I get confused easily so maybe I'm

11 misunderstanding, but I don't see them as an either/or. 

12 It seems to me that you first have to look at

13 endpoint performance, endpoint variance, across the assays,

14 through laboratories, look at the effects, the possible effects

15 of all these different conditions in the various assays to

16 really understand what the performance of these endpoints is for

17 determining the various hypotheses that are posed.

18 You'd have to know something about that before

19 you -- see, I think you have to have that baseline information,

20 those baseline comparators before you start to then evaluate how

21 you could streamline the battery. 

22 So I'm not saying it so much as an either/or. 

23 Maybe I'm naive, but it seems to me that it's first necessary to

24 make those baseline comparisons with a deep dive on all the

25 endpoints that we have, unless there are assays that people

26 think that we can simply dispense with, a priori and not even
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1 bother evaluating those data, but I don't think that -- I

2 haven't heard that suggestion yet, so I would think one, then

3 the next.

4 DR. DANIEL SCHLENK: Mr. Scott?

5 DR. GEOFFREY SCOTT: In the analysis you

6 presented, you all started looking at the control. And I guess

7 one of the things I firmly believe that if you're going to use

8 as a test or a battery test in this case, being intimately

9 familiar with your controls is a starting point, and I assume

10 that was some of the logic you all used in starting with that

11 analysis, correct?

12 DR. CHRIS BORGERT: That's correct. But to be

13 candid and thorough in the answer, you know, there are

14 confidentiality issues here too, so we thought it might be a

15 much tougher sell to convince companies to release somewhat

16 sensitive test article data. So we felt more confident that we

17 could collect more of the control data but you're exactly right,

18 we felt that that was the starting point, and so both factors

19 played into it.

20 DR. GEOFFREY SCOTT: Okay, thank you.

21 DR. CHRIS BORGERT: As a scientist, I'd love

22 to have all the control data and all the test data. I only know

23 one group that has that, it's EPA.

24 DR. DANIEL SCHLENK: Dr. Green?

25 DR. SHERRIL GREEN: Just so I can have some

26 perspective on how long it took you to do that; so you had 18
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1 studies. How long did it take you once you got the data from

2 the various companies to go through each of them, pull up the

3 control tables and enter those data points in? Because doing

4 that at this point, of course, would require the Agency's

5 additional resources and take more time. But just to know, how

6 long, roughly, for those 18 studies did it take you to prepare

7 those slides that you showed today?

8 DR. CHRIS BORGERT: The statistician got them

9 a little more than a week ago.

10 DR. SHERRIL GREEN: Okay, thanks.

11 DR. CHRIS BORGERT: Now what took us the most

12 amount of time is collecting the data and standardizing the

13 spreadsheets, but I think EPA has those data. They can tell you

14 exactly what they have. But once you actually have the data --

15 and this was done by a couple of people, to be fair. You've

16 already heard from Allen Olmstead. He was one of the people

17 that was involved in this evaluation, and another statistician

18 John Green from DuPont, but they were both busy with many other

19 things while they were doing this so it wasn't even a solid week

20 of time, so I think this is trackable.

21 DR. SHERRIL GREEN: Thank you.

22 DR. DANIEL SCHLENK: Mr. Patino?

23 DR. REYNALDO PATINO: Just a quick question in

24 the plot that you show of hind limb length and snout-vent

25 length. Are those data just for the control or including all

26 the tadpoles, treatments included?
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1 DR. CHRIS BORGERT: Those are only controls. 

2 Those are only controls.

3 DR. RENALDO PATINO: Thank you.

4 DR. DANIEL SCHLENK: Anyone else? Okay. 

5 Thank you, Dr. Borgert.

6 DR. CHRIS BORGERT: All right, thank you.

7 DR. DANIEL SCHLENK: At this point let's go

8 ahead and take our lunch break and let's be back here at 1pm and

9 we'll continue our public commenting period at that time.

10 (Whereas a lunch break was taken)

11 DR. DANIEL SCHLENK: Okay. As we continue our

12 public comment period, our next presenter will be Clare Thorp

13 who represents CropLife America.

14 DR. CLARE THORP: Good afternoon. CropLife

15 America appreciates being able to address the Panel and the time

16 that you have given towards evaluating all the information that

17 you have been provided with.

18 My name is Clare Thorp. I'm the Senior

19 Director of Human Health Policy within CropLife America and I

20 administer all committees within CropLife America. I administer

21 the Endocrine Policy Forum but they are not actually part of

22 CropLife America, they are truly independent, as you've probably

23 realized. So my job in that regard is more like providing

24 administrative support and backup. 

25 I also administer the Human Health and

26 Risk-Assessment Committee with draws from the toxicologist
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1 risk-assesses, epidemiologists, medical toxicologists,

2 ecotoxicologists and scientists that reside with an industry. 

3 I'm represented on the committee that I administer, and it is

4 their comments and those of the EPF that I am reflecting here.

5 Just to give you a little bit about CropLife

6 America, CropLIfe America is a not-for-profit national trade

7 organization. So by way of background, CropLife America members

8 have played a critical role in providing input into the Agency's

9 development of the Endocrine Disruptive Screening Program. 

10 They have participated since EDSTAC was

11 established in 1996, and specifically, it is our member

12 companies who have been the recipients of the Tier 1 test orders

13 and their products were chosen precisely because of the rich

14 dataset that they had in the fact that they could therefore

15 provide some comparisons between the high level testing and the

16 screening test.

17 But essentially it is CropLife America member

18 companies which have been the laboratory facilities and the

19 research arm of the EPA for the Endocrine Disruptive Screening

20 Program. And the members have conducted this work at their

21 expense and they were requested to test the 52 compounds. 

22 And this data will be used for further

23 evaluation but these compounds are already data-rich. And it

24 seems unfortunate that due to the time-constraints that we feel

25 that there has been very short timeframe for the SAP to

26 adequately consider the material that was placed on the Docket. 
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1 And we are disappointed that all 52 compounds have not been

2 reviewed.

3 I want to specifically address the charge

4 question, question 8, which is to look at the minimal number of

5 screens and tests necessary to make sound decisions that by

6 reducing the time needed to make the decisions around these

7 screens, and to also establish whether or not the current

8 battery of 11 assays can be modified to reduce animal usage or

9 lower cost while adequately ensuring the EPA's ability to answer

10 the question of whether a chemical has the potential to interact

11 with the endocrine system. And I chose that word, interact,

12 that is important here.

13 And so from that regard, I want to discuss in

14 my talk the testing that pesticides already go through, and so

15 the data which is already available, the context as you would

16 have it within which these Tier 1 tests can be evaluated.

17 So on average, a new pesticide product takes

18 about 10 years to develop. And regulatory testing, which is for

19 the purposes for registration involves approximately 110 tests

20 per compound to meet the data requirements necessary for

21 registration, which is extremely extensive. And in addition to

22 the test required for registration, the EPA can require further

23 testing when more data is required. 

24 But there is also pre-market development

25 testing which identifies potential hazards, and it also guides

26 whether or not that project will ultimately be put forward for
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1 testing for registration purposes. So it's a little surprise to

2 our member companies here, for example, that there were not

3 positive hits on the uterotrophic assay because that would've

4 already been pre-screened prior to even conducting the test

5 required for registration.

6 So when we look at what actually happens,

7 pesticides come up for review, and then every 15 years they also

8 come up for registration review, which can require further

9 testing. And as noted in our submission to the Office of

10 Management and Budget, which is required to look at the cost of

11 all of these things, we calculated that it's approximately a

12 million dollars per chemical to complete all the 11 assays. And

13 it also takes about more than two years to actually complete

14 these assays. And this needs to be taken into consideration in

15 terms of not just financial costs but man-hours and the number

16 of experimental animals that are involved.

17 Again, this is just to show you what the data

18 requirements already cover, and this speaks to the presentations

19 that were made earlier by Ellen Mihaich and by Lisa Ortego and

20 by Chris Borgert around the data that is already provided on

21 these chemicals when the information is submitted for

22 registration.

23 So you have information on the product

24 performance and the physical and chemical characteristics and on

25 exposure. And you already have chemicals that are extensively

26 tested in multiple mammalian and wildlife species for potential
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1 affects on development, reproduction, normal growth and

2 including the structure and function of reproductive organs and

3 all other vital organs controlled by hormones.

4 So the question that we really want to pose is

5 whether or not the Tier 1 and 2 tests are in addition to all the

6 existing tests or whether or not we should be looking at an

7 integrated and complementary testing approach when it comes to

8 pesticide compounds.

9 Again, current toxicological testing, and this

10 addresses some of the comments that were made earlier, looks at

11 both acute, sub-chronic and chronic exposure of very wide range

12 of doses and a long duration, including multigenerational

13 testing on reproduction and fertility.

14 If we actually pull the mammalian toxicology

15 data requirements and the global requirements, and these

16 companies are multi-national, so generally speaking, if it's

17 required in one jurisdiction, it's applicable across all

18 jurisdictions. If they have to test for one country then it

19 doesn't matter if another country doesn't need it, they still

20 have to do that testing. And so this is the information that

21 already currently exists.

22 And for example, the two-generation

23 reproduction test covers animals prior to conception, throughout

24 pregnancy, the off-spring at birth. The off-spring are

25 continued through the testing and then they're mated and then

26 they're dosed throughout their pregnancy and then their
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1 off-spring from them, the second generation are also examined.

2 So throughout this study, there is an

3 opportunity to examine what's happening through production and

4 development from pre-pregnancy, through pregnancy and throughout

5 the development of the off-spring in two generations. There is

6 a lot of data there.

7 And so this test is also listed and one of the

8 Tier 2 assays, and results from this test also cover some of the

9 Tier 1 endpoints obtained from a pubertal assay. Now, okay,

10 this test was mandated in 1998, but before that there was,

11 nonetheless, reproductive data available on pesticide compounds.

12 So we are concerned about the automatic

13 testing of pesticides in the Tier 1 which could lead to

14 excessive, unnecessary and wasteful practices that offer no

15 additional benefits to the protection of human health, the

16 environment or knowledge of these compounds from an ability to

17 interact with the endocrine system already. 

18 This is the ecotox stage of requirements. 

19 Again, there's rather a lot of them. These are the global ones.

20 So therefore, when evaluating the potential of pesticide

21 products for their potential to interact with the endocrine

22 system, CropLife America would like to encourage that the

23 following approach be adopted moving forward in the future.

24 The Tier 1 and possibly Tier 2 tests for

25 registered products should only be required where existing test

26 results combined with physical-chemical characteristics,
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1 absorption, distribution, metabolism, and elimination

2 characteristics, and exposure data indicate data needs with

3 respect to the potential estrogenic, androgenic and

4 thyroidogenic activity.

5 The requirement to conduct specific Tier 1

6 assays should be tailored to fulfill only those areas where

7 there are additional data needs. And we support the idea of

8 gatekeeper assays for identifying potential endocrine activity

9 as outlined in the paper by Ankley and Gray from the Office of

10 Research and Development and the EPA, and using this approach to

11 determine the necessity for further, if any, endocrine-specific

12 testing is required. I actually have a copy of the paper here

13 but I've only got one, so if you want to see it, I can make it

14 available.

15 And again, as already elaborated on by the

16 previous speakers, we would like to see a move towards an

17 integrated testing and evaluation program, one that doesn't

18 require test to be conducted by rote or by requirement, and

19 which references all the existing data, the OSRI that we

20 referred to earlier to identify potential endocrine activity and

21 so that we can guide the selection of the endocrine-specific

22 test. 

23 And this means that the need for additional

24 testing should be determined on a case-by-case basis, selecting

25 from the available Tier 1 screening assays that will complement

26 and fill the data needs in the existing information. 
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1 And so on this basis, screening for endocrine

2 potential of chemicals can be done effectively with a reduced

3 number of assays. But as you can tell from the previous

4 presentations, there are some issues with some of the assays and

5 some of them fulfilled certain requirements and others do not,

6 so again, it requires this sort of case-by-case approach where

7 you look at what you need.

8 And again, we also support the idea of staging

9 so the decision can be made on specific assays that may not be

10 required, and therefore, may be waived. That waiving would be

11 based on, again, OSRI or Other Relevant Scientific Data,

12 including, again, exposure and also physical chemical properties

13 and so on and so forth. This was gone into in a lot more detail

14 by the Endocrine Policy Forum. 

15 And our other comment relates to selecting the

16 dose level. If this is to be an effective screen and we are to

17 actually understand whether or not the compounds are actually

18 interacting with the endocrine system, then we need to get the

19 dose level correct. 

20 And if the dose level is too high and it

21 elicits overt toxicity, then we'll have confounding affects and

22 we won't be able to elaborate whether or not this is truly

23 something that's interacting with the endocrine system or

24 whether or not something else is going on. Because stress on

25 the model system, be it the live animals or the cells in

26 culture, can actually impact on the results. 
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1 And moving forward, these are the suggestions

2 that we would like to make. And these are probably more policy

3 rather than scientist-oriented but they reflect and they are

4 based on the previous presentations. 

5 CLA, CropLife America, strongly recommends

6 that the EPA does not issue new Tier 1 screening orders for

7 additional substances which is on the List 2, before the Agency

8 has evaluated the lessons that have been learned from the

9 initial List 1 substances. 

10 And because this is such an important program,

11 because this is a definitive defining program, this program is

12 being looked at by the eyes of the world. This is not going to

13 be confined purely within the boarders of the United States, but

14 Europe, Brazil, Japan, all these other countries will be looking

15 to this program to see how it works and how it's structured, and

16 they will also be evaluating the data. 

17 And so we feel it's very important that it's a

18 very robust and very strongly science-based program. And this

19 is why we would like to have more time and also to make sure

20 that all 52 compounds are properly considered. Because then you

21 can truly stand on this program and say this is it, this is the

22 gold standard and we have evaluated it thoroughly.

23 And again, CropLife America strongly

24 recommends that the EPA deliberately evaluate the Tier 1

25 screening results alongside the assays, the functionally

26 equivalent data and the other scientific relevant information to
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1 identify the most effective and efficient path forward, and that

2 the experiences from the contract research organizations and the

3 test order recipients, as articulated in the previous

4 presentation, have made it clear that there is a need to closely

5 evaluate the test method procedures and endpoints with an eye

6 towards improving the program.

7 And so certain methods need to be upgraded and

8 their corresponding test guidelines revised before EPA proceeds

9 with issuing additional Tier 1 test orders for List 2

10 substances. And so obviously, we support the comments that are

11 made by the Endocrine Policy Forum.

12 But we'd also like to direct you to the TERA

13 workshop that was spoken about by Mike Dourson earlier which was

14 an independent workshop, and it has a wide variety of

15 stakeholders, not just industry. And some of the lessons

16 learned and the messages came out of that were very clear and

17 consistent across all the various representations that were

18 there.

19 My final point is around communication and,

20 again, around the global implications of this program. 

21 As you move forward to evaluate the Tier 1,

22 it's very clear that these are intended to screen and they are

23 intended to look at the interaction with the endocrine system. 

24 The further studies and the ones that I've already articulated

25 that are already done look at those level and look at

26 information that can then be put forward into a risk-assessment.
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1

2 And we commend the EPA for very carefully

3 articulating the purpose of the Tier 1 tests, and we encourage

4 them to continue with this as the results are released because

5 we have a lot of concern that this data will be misused to

6 incorrectly conclude that substances with positive results in

7 one or more Tier 1 assays are endocrine disrupters when in fact

8 they have an endocrine activity. And an endocrine disrupter by

9 definition is a compound that causes an adverse affect, and this

10 is not what the Tier 1 screens are there to identify.

11 And so we encourage EPA to explicitly state

12 that the results of Tier 1 screening do not indicate an

13 impending hazard or risk to humans or ecological species. Thank

14 you very much for your time.

15 DR. DANIEL SCHLENK: Thank you. Any questions

16 from the Panel? Dr. Horseman?

17 DR. NELSON HORSEMAN: If I might ask, so I've

18 characterized the history of the whole notion of endocrine

19 disrupter. It arises out of observations about effects that

20 are, let's say quasi physiological, because they're happening in

21 animals that aren't overtly suffering toxicologically

22 life-threatening sorts of events, and generally with substances

23 that had gone through at least some sort of testing as similar

24 to what you're describing for these registered chemicals. So I

25 think we're all having to deal with the issue of where do you

26 set that window so that you're not looking at things that are
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1 just toxicologically active versus things that may have these

2 endocrine effects. 

3 So if you could just comment on the fact that

4 endocrine disrupters have been discovered amongst compounds that

5 had already been screened for overt toxicity and how that plays

6 into this, or are these tests that you're describing here modern

7 enough that they would've picked up these compounds if they

8 didn't know about them.

9 DR. CLARE THORP: Well that's a big question

10 and one in which I think I would rather prefer to have addressed

11 by the Endocrine Policy Forum. 

12 But I think what you're talking about is the

13 difference between something that interacts with an endocrine

14 system and gives an effect but does not necessarily produce an

15 adverse effect or is not at the potency or the dose level that

16 would cause an adverse effect to be the lead toxic effect,

17 because the risk assessment that takes place takes account of

18 which is the most sensitive endpoint and which is the most

19 sensitive species. 

20 So if you're going to get an issue with a

21 different adverse effect that kicks in way before an endocrine

22 affect, you're not going to regulate based on the endocrine

23 affect, you're going to regulate based on the other one. But I

24 would prefer Katie to answer that.

25 Ms. KATIE HOLMES: Hello, I'm Katie Holmes. I

26 work for BSF and I am the Policy Chair of the Endocrine Policy
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1 Forum. That's a really good question and I believe Clare did

2 much better with the answer than I thought she was going to --

3 not in a bad way, but -- 

4 Our older testing -- and I'm not talking about

5 testing done in the '50s, but '80s, '90s into 2000s, we were

6 under a risk assessment policy so any of the data that were

7 generated, we were looking for the most sensitive endpoints. So

8 we did see affects possibly on endocrine-related mechanisms but

9 those weren't necessarily identified as such, if it wasn't the

10 most significant endpoint. So what we're looking at now is just

11 sort of defining a specific mode of action rather than looking

12 for the most sensitive endpoint for the driving of the

13 assessment. 

14 When you do a risk-assessment, if you have an

15 effect that is well above any potential exposure, then that's

16 where it stopped. So that's why maybe the endocrine mode of

17 action wasn't identified specifically in some of the compounds.

18 DR. DANIEL SCHLENK: Thank you. Any other

19 questions? Okay, thank you. Let's move on. Our next presenter

20 will be Scott Slaughter from the Center for Regulatory

21 Effectiveness.

22 DR. SCOTT SLAUGHTER: My name is Scott

23 Slaughter and I am commenting today on behalf of the Center for

24 Regulatory Effectiveness and I'd like to make two points in my

25 comments. 

26 First, do not use the amphibian metamorphosis
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1 assay in the EDSP. There are too many unexplained problems with

2 the test. 

3 Second, I assume everyone knows that EPA

4 intends to abandon the EDSP and use ComTox and high-throughput

5 screening if and when the Agency can validate ComTox and

6 high-throughput screening. We think that the use ComTox and

7 HTS, if they could be properly validated, is a very good idea,

8 and we recommend that the Agency do that sooner rather than

9 later, and do that rather than issue another round of test

10 orders for the EDSP that the Agency no longer even what's to

11 use. 

12 The amphibian metamorphosis assay -- which

13 will be known as AMA because I have a hard time pronouncing

14 amphibian metamorphosis assay -- we have a history with it, we

15 being CRE. In 2008, CRE filed a request for correction under

16 the Information Quality Act regarding the AMA. This request for

17 correction asked EPA to correct the Agency's publicly

18 disseminated statement that the AMA had passed peer review and

19 was properly validated. 

20 CRE's request for correction pointed out that

21 the AMA had received a generally negative peer review report and

22 asked EPA not to use the AMA in the EDSP Tier 1 test battery.

23 Pages 2 to 4 of our written comments that you

24 have, I hope in front of you, list some of the peer review

25 comments back in 2008, 2007, 2009 on the AMA. 

26 Just a few of them were -- and I'm quoting,
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1 "With regard to whether the AMA is reproducible, one peer

2 reviewer stated in the peer review report, 'This is a major flaw

3 of the material provided.'" He also explained that

4 inter-laboratory inconsistencies obviously just one table of the

5 AMA validation study "Would convince any reviewer or a reputable

6 scientific journal to recommend rejection" of the validation

7 study. 

8 He further stated, "The conclusions regarding

9 inter-laboratory validity are not warranted and it -- the AMA

10 test protocol -- fails as a method for accomplishing the stated

11 goal of the assay to be part of the endocrine disruptive

12 screening program." 

13 There are more peer review criticisms of the

14 AMA before it became a part of the EDSP, which are included on

15 pages 2 to 4 of our written comments, and there are also links

16 to our request for correction on the AMA and EPA's response,

17 which was basically to make a few minor changes to the AMA test

18 guidelines and go ahead and use it anyway.

19 Now it looks like CRE and the peer reviewer

20 may have been right. EPA now admits that there are serious

21 problems with the AMA based on the EDSP Tier 1 test data that

22 the Agency had reviewed. What's worse, I think, and probably

23 more disturbing is that no one has an explanation of why these

24 problems are occurring. 

25 These problems include the bent tails, which

26 had been discussed extensively today, and also the more rapid
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1 development in the controls. Until and unless somebody can

2 explain and hopefully solves these problems with the tests,

3 we've asked this SAP to advise EPA not to use the AMA test data

4 for any purpose. There are just simply too many unexplained

5 problems in the assay resulting in the generation of flawed and

6 questionable data at this point. 

7 We also asked this SAP to advise EPA not to

8 use the AMA as part of the EDSP Tier 1 test battery until and

9 unless these unexplained problems are explained and hopefully

10 solved by someone. 

11 With regard to our second point, I must say

12 that this all seems to maybe a waste of time and money and

13 animal lives because EPA plans to abandon the current EDSP Tier

14 1 test anyway. 

15 EPA told congress, and I'm quoting from

16 several agency documents, "In fiscal year 2012, EPA will begin a

17 multi-year transition from the EDSP to validate and more

18 efficiently use computational toxicology methods and

19 high-throughput assays that will allow the Agency to more

20 quickly and cost-effectively assess potential chemical

21 toxicity." 

22 We think that's an excellent thing for the

23 Agency to do. We recommend that they do it sooner rather than

24 later, and we recommend that they do that than spend their time

25 and scarce resources and valuable resources on that process

26 rather than sending out more Tier 1 test orders. 
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1 Thank you for the opportunity to comment and

2 I'll try to answer any questions you might have.

3 DR. DANIEL SCHLENK: Thank you. Any questions

4 from Panel? Dr. Green?

5 DR. SHERRIL GREEN: I have a question for you

6 concerning the AMA test. To my knowledge, there aren't many

7 toxicological tests that are required to be performed on

8 amphibians that are aquatic that go through metamorphic climax

9 like these frogs. And as you are probably aware, there is a

10 worldwide amphibian decline. 

11 So whether or not this test would be useful as

12 a gatekeeper, clearly, you know, we have some discussion to

13 think more about that -- but if not this test, could you

14 recommend from your perspective what else the Panel might

15 consider that would protect this species, in particular, in

16 addition to the many others in case they are exposed to -- 

17 DR. SCOTT SLAUGHTER: Xenopus -- I can't

18 pronounce that one either. You're talking about the one that

19 was tested in the AMA, the one that begins with X?

20 DR. SHERRIL GREEN: Xenopus laevis.

21 DR. SCOTT SLAUGHTER: I believe that Syngenta

22 Corporation under the supervision of EPA and the supervision of

23 another SAP, in fact several other SAP, has extensively tested

24 Xenopus using test protocols and looking primarily at -- you

25 know. I'm not sure necessarily thyroid but certainly endocrine

26 effect, and developed a series of test results and a series of
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1 test guidelines which prior SAP endorsed. So yeah, there are

2 alternatives out there that have been developed that I'm aware

3 of.

4 DR. SHERRIL GREEN: Which company?

5 Mr. SCOTT SLAUGHTER: I believe it's Syngenta.

6 They're represented here and they can speak for themselves, but

7 I think that they were cited in the White paper and I believe

8 EPA commented on that particular SAP in that particular test. 

9 Once again, I am not sure, in fact, I'm not

10 sure at all that it will address the thyroid issues, but yes,

11 other amphibian tests have been developed. 

12 And we're talking about initial screenings to

13 begin with. And I think the HTS and the ComTox, I think most

14 people would agree that if they could be properly validated and

15 standardized and prove to be of sufficient quality for

16 regulatory purpose, then that is a preferable alternative to the

17 current EDSP Tier 1 test. If people in the audience disagree

18 with what I just said, I'm sure they will come and tell you

19 that.

20 But, you know, they use far less animal lives.

21 They're far less expensive in general, I think, and they're

22 easier and they hopefully would not have some of the problems

23 that we're seeing in the AMA. And once again, the disturbing

24 part of these problems is -- well not to mention that we told

25 you so back in 2008, but no one really has an explanation as to

26 why they're occurring.
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1 You have a bunch of crippled tadpoles swimming

2 around and no one can explain why the test that crippled them

3 crippled them, you know? And until those questions are answered

4 satisfactorily, I would think that it would -- this test should

5 not be used for regulatory purposes. Anything else?

6 DR. DANIEL SCHLENK: Anybody else? Okay,

7 thank you. Our next presenter will be Patricia Bishop

8 representing PETA.

9 Ms. PATRICIA BISHOP: Okay. Good afternoon

10 everyone, I'm Patricia Bishop. I'm a research associate in the

11 regulatory testing division of People for Ethical Treatment of

12 Animals, otherwise known as PETA. 

13 This is kind of a summary of written comments

14 that I already submitted, so you should have everything that I'm

15 going to talk about here already. And also, these comments were

16 submitted both on the behalf of PETA as well as the Humane

17 Society of the U.S. and of Physicians Committee for Responsible

18 Medicine. 

19 So just from an animals' protection

20 perspective, these are some of our main concerns and I'll go

21 through them a little bit more in detail.

22 First of all, the large numbers of animals

23 using the battery, we've been following this program for pretty

24 much since its inception due to the potentially massive numbers

25 of animals that could be used with all the chemicals that need

26 to be tested.
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1 Also additional animals used in the

2 range-finding studies that have come to our attention after

3 reading the White Paper, animals killed in the culling process,

4 the fact that the in vitro binding studies actually do use

5 animals as they rely on the rat cytosol, the uterine and the

6 prostate cytosol that are collected. 

7 I just want to touch again -- I think one of

8 the other speakers has already mentioned this, but the

9 replacement for the ERTA assay using the BG1 Luc which might

10 have some advantages, elimination of the Hershberger and

11 Uterotrophic and tying the results back to mechanistic

12 interpretation. 

13 So again, the large numbers of animals; if we

14 just look at the test protocols alone we can see that based on

15 some estimates for the binding assays that we got from one of

16 the labs, looks like it's about, in their case, 13 rats used in

17 the ER binding to test one chemical. Again, just to test the

18 chemical. AR binding they use of about 10 rats, uterotrophic

19 18, Hershberger doing both the agonist and antagonist 48, 45

20 each in the pubertals and the fish, 96, 320 tadpoles, so a total

21 of 595 animals to test one chemical in just the protocols alone.

22 So for the 52 List 1 chemicals that we are

23 talking about here today, if you tested them all, in the entire

24 battery you would use about 31,000 animals, that's 31,000. Some

25 of the chemicals did receive waivers based on submittal of the

26 OSRI, and that ended up saving about 3300 out of 31,000.
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1 So the range-finding studies to estimate the

2 maximum tolerated dose; so it appears that -- and I'm not sure

3 about this because I couldn't really tell from reading the

4 document, but it appeared that potentially there were more than

5 one range-finding study done for the same chemical due to maybe

6 differences in the ages and gender differences of the various

7 tests. 

8 For example, the pubertals used animals that

9 are quite a bit younger. The Hershberger/Uterotrophic uses I

10 think eight-to ten-week old animals and the other ones use maybe

11 three-week old animals. 

12 So for the pubertal, the male pubertal, there

13 was 12 out of 16 chemicals were evaluated in range-finding

14 studies. A few of the others used the part 158 pesticide

15 studies to determine the maximum tolerated dose. But again, for

16 the example for the pubertal, 144 animals would have been tested

17 upon before the actual assay was even performed. 

18 So you start multiplying that by many, many

19 chemicals. You can see, beyond the test protocols you're using

20 even more and more animals to get this maximum tolerated dose.

21 The three-week animals in the pubertal versus

22 the eight- to ten-week old adults in the ovariectomize

23 uterotrophic. 

24 So we're thinking is there some other way to

25 determine the maximum tolerated dose because, obviously, there

26 was many cases of overt toxicity that you've all been hearing
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1 about. So however, these range-finding studies we're working,

2 it doesn't seem like in all cases they were coming up with

3 concentrations where animals were not still being subjected to

4 overt toxicity. 

5 So we're recommending perhaps development of

6 some kind of standardized method that hopefully does not have to

7 subject more animals to testing that could draw fully upon the

8 toxicity data from the part 158 studies and any reliable

9 literature studies that might be available and try to use that

10 to develop these higher doses rather than subjecting animals to

11 more toxicity studies.

12 So the issue of culled animals, I think Sue

13 Marty in her presentation touched on this a little bit. But

14 it's obvious that the culling of animals in many cases, or at

15 least in some cases, may actually use more animals than the test

16 itself is using. So again, using the pubertals as an example,

17 45 animals are what's actually used in the test protocol. But

18 to get there, you end up culling out about 200 animals and this

19 just gives you a quick rundown on how that's done. 

20 You need apparently at least 15 litters which

21 consist of about 13 or 14 pups. You usually get a few more dams

22 just because you want to make sure you get 15 that meet your

23 needs. Then these litters are culled after about three or four

24 days, I believe, discarding about 130 animals. And then

25 finally, to get down to your 45, you get rid of another 60 plus

26 the dams. 
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1 And interestingly, you can't use any of the

2 excess animals usually to run another compound because the

3 necropsies all have to be done the same day or two and this runs

4 into personnel issues to be able to do all these necropsies.

5 So the in vitro binding studies, as I

6 mentioned, ER and AR binding also use animals because you need

7 to collect the cytosol to run test, and again 13 rats for the ER

8 -- and that was just to run the chemical. And apparently I was

9 told that other rats were needed to optimize method and

10 standardize things and kind of get it working. Same issue with

11 the AR binding, that even though 10 were needed to run the

12 chemical, we had to use additional animals to get the method up

13 and running.

14 Our suggestion here is to do away with the

15 cytosol ER and AR binding methods. There's human recombinant ER

16 binding assays that are available that don't use animals at all.

17

18 I guess EPA has been participating in an

19 OEC-sponsored validation study and a test guideline is expected

20 in 2014. So we would urge the EPA to start looking toward

21 switching over to something like that and getting rid of these

22 animal so-call in vitro studies. 

23 The ERT assay, again, somebody did already

24 mention the BG1 LUMI-CELL ERTA assay that was recently

25 validated. It's an OECD test guideline now. It worked

26 apparently just as well as the current STTA ERTA assay that's in
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1 the Tier 1 and it also has the advantage of detecting

2 antagonists. 

3 What is interesting about this is, in the

4 validation study, this assay showed a 92 percent concordance

5 with the Uterotrophic. 12 out of the 13 reference chemicals

6 matched up. 

7 So we would like to propose that with the data

8 that are now available from the first round of List 1 chemicals

9 that perhaps we could do more comparisons with this particular

10 ERTA assay and hopefully strive for an elimination of the

11 Uterotrophic if this assay could give us essentially the same

12 information.

13 The Hershberger, certainly from an animals'

14 perspective, we would like to see some more animal tests be

15 eliminated. We think this could happen based on if the issues

16 with the fish and the male pubertal assay could be resolved. 

17 That was the proposal in the EPA's White Paper that perhaps this

18 particular test is giving redundant results. And assuming the

19 other two could have the issues worked out with them we would

20 certainly support elimination of Hershberger once all the issues

21 of overt toxicity and those types of things are resolved.

22 And then just finally, again, we've been

23 hearing this of tying the sort of apical affects that have no

24 supporting binding TA or steroidogenesis responses; what do we

25 do with these? 

26 For example, there was a chemical B, it had a
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1 positive Hershberger with an overt toxicity. It had a positive

2 fish assay but there was potentially solvent interference and a

3 positive male pubertal but it had negative in vitro responses. 

4 So the question is how do you interpret something like this? Is

5 it just an artifice due to the experimental conditions? Is it a

6 non-androgen mediated affect? Is it androgen-mediated but it's

7 not covered under Tier 1 or is it a metabolic activation thing? 

8

9 So by trying to get away from the more apical

10 effects in vivo and, you know, moving on to an adverse outcome

11 pathway or mechanism of action mode, which I think is -- you

12 know, again, we've heard moving from ComTox and high-throughput

13 assays, I think this is really something the program needs to

14 push toward and maybe get rid of some of these questionable

15 results that we're seeing with the in vivo assays.

16 This would also, I think, help to explain some

17 of the odd patterns of complementarity and redundancy that we've

18 seen and also the nonconcordance between what were supposed to

19 be redundant assays. And that's it.

20 And just to remind you all, you do have the

21 Panel's charge question number 8 which is how to potentially

22 minimize or reduce the use of animals in the Tier 1 assay, so

23 hopefully I've given you a little bit of insight on maybe some

24 responses to that. Thank you.

25 DR. DANIEL SCHLENK: Thank you. Any

26 questions? Okay. Let's move on to our next presenter. This
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1 will be Catherine Willett from the Humane Society.

2 DR. CATHERINE WILLETT: Okay. Thank you very

3 much for the opportunity to comment. I'm Catherine Willett. 

4 I'm currently at the Humane Society where I work on regulatory

5 testing, risk-assessment and alternatives. And I also

6 coordinate something called the Human Toxicology Project

7 Consortium, which is a group of companies and other stakeholders

8 who are interested in moving through a more predictive

9 mechanism-based paradigm for toxicity testing.

10 Well one of the benefits or hazards of going

11 later in the commenting round is that a lot of what you have

12 intended to talk about has already been spoken of. So some of

13 this I can go through rather quickly. Let's just get straight

14 to what I want to talk about.

15 So let's talk about the animal use. Pat went

16 through this in great detail so I don't need to do that. But

17 roughly, there's about 600 animals per chemical and that, to

18 remind you, does not count range-finding, repeats and anything

19 else. That's just the bare minimum number of animals in each

20 protocol.

21 And the reason that's important is maybe 600

22 animals doesn't sound like a lot to people, but the results of

23 that test will lead to Tier 2 testing which can start to really

24 impact the body count in a serious way. I don't know exactly

25 what the numbers are yet because the protocols are not

26 finalized, but this is a sort of gross approximation of how many
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1 animals will be used in the Tier 2 tests.

2 And again, as Pat said, so far in the program,

3 the 52 chemicals, about 30,000 animals have already been used. 

4 And when you consider that these are for evaluating pesticides

5 in HPB chemicals, there's probably going to be very little

6 actual information that's going to be used in a regulatory

7 nature that comes out of this program. The body count starts to

8 look like a tragedy in some respects. And I said this only to

9 emphasize that I think it's critically important that we use

10 this information to the maximum extent possible to help improve

11 this program. 

12 And there is ample evidence that a better

13 approach is warranted, one, some of them are listed on this

14 slide. This program as we've been hearing is extremely

15 resource, labor and animal intensive. It provides a lot of

16 data, but only some of this data may have any regulatory

17 applicability.

18 The current structure of the program provides

19 insufficient opportunity for chemically-relevant tailoring. We

20 heard Clare talk about that extensively, and other people have

21 suggested some other tiering approaches that might be more

22 efficient.

23 And critically, also, it does not link the

24 mechanism to adverse outcome. You know, this idea really wasn't

25 prevalent when the program was first conceived, but in

26 intervening a long time a lot has been learned about this, and
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1 also, the value of moving toward this approach in terms of being

2 able to create a predictive paradigm for toxicological

3 assessment.

4 And this current structure is not predictive. 

5 And in that way, we will never be able to progress in our

6 ability to assess a chemical using less and less information,

7 but we can if we have a different structure create something

8 that will help us learn how to be predictive.

9 Okay. So the first step toward doing this

10 might be to use a more refined tiered system that we've heard

11 suggested, and there are many different structures. I'm going

12 to emphasis one particular structure. It would be nice if the

13 initial tier took into account all existing information

14 including physiochemical and exposure. 

15 A second tier would assess potential

16 mechanisms of action. That's kind of where we are. I mean

17 that's sort of what the Tier 1 currently intends to do but

18 doesn't fully do. And then subsequent tiers would address the

19 potential affects in more complex systems or on perhaps multiple

20 modes of action. And then finally, the highest tiers would

21 begin to address adverse outcomes and dose response from which

22 you would be able to do a risk-assessment.

23 And this tiered system would also apply a

24 weight of evidence with clearly articulated criteria at each

25 tier, so you can define positive and negative cut-offs for each

26 tier level and then use the results to modify and design a
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1 strategy for further testing. And this way, the strategies

2 designed to address information needs as well as the chemical

3 activities.

4 This has been articulated already by the OECD

5 in the OECD conceptual framework, which is also been brought up

6 previously today, and this was not conceived as a testing

7 framework. However, it provides a logical approach to how one

8 organizes information in a decision context. So depending on

9 what level of decision you make, you need a certain level of

10 information to make that decision. 

11 And this just puts into table form what I was

12 talking about and it adds also the existing test guidelines into

13 each one of these levels. And the good thing about using this

14 kind of conceptual framework is that, as methods are developed

15 and validated, you can add them to this conceptual framework. 

16 So it's sort of a way to phase in transition between where we

17 are with these, like where we are now with this, and a future

18 move away from the higher level testing. 

19 Okay. And then this is something that can be

20 done relatively quickly, if not immediately; just a different

21 way of organizing your testing procedure.

22 We ran through how you might do this in a

23 publication that we published in Toxci using Atrazine as an

24 example, because at the time Atrazine had a full Tier 1 dataset.

25 So we were able to sort of pretend that maybe you didn't have

26 the information to go through this process and then make
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1 decisions based on the different levels of information and see

2 what kind of testing you may have been able to eliminate using

3 this approach.

4 Okay, next. So this is where I think we're

5 going. I think everybody agrees that this is where we're going

6 to go more toward a pathway-based approach. And this helps with

7 interpretation at every single level, would help with

8 interpretation of the information you currently have to deal

9 with now in the Tier 1. 

10 So adverse outcome pathways are basically

11 conceptual construct that organizes the information in such a

12 way that you would describe it from the direct molecular

13 initiating event all the way to an adverse outcome in all of the

14 intervening steps in between the different degrees of detail,

15 depending on what you have. I'm just going to go quickly

16 through some of the stuff that already exists.

17 So EPA has been working on an ER immediate and

18 reproductive impairment AOP for a number of years, which they

19 have integrated into development of an integrated testing

20 strategy, which I'll show here. This was mentioned earlier

21 today. This is the ER SAR that someone mentioned earlier. They

22 have a really very good SAR model for looking at chemicals that

23 have weak to no binding. 

24 So this is the AOP and these are the kinds of

25 tests right now that we have that can query the different levels

26 of organization in the adverse outcome pathway. This is our
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1 current state of science. Of course the goal would be to, as

2 you get more information and you can add more precision to these

3 early steps and start to add linkages between these steps, you

4 will progressively become more dependent on these early steps

5 and less dependent on these later steps. 

6 But right now the way it goes is you do in

7 vitro measurements or predictions of the binding. You can look

8 at gene activation, vitellogenin, and you can start to look at,

9 in liver slices, ex-vivo. You can look at changes in proteins

10 and gonadal histopathology. And then you can also look in vivo

11 in whole fish to see what's occurred and also the behavior and

12 whether or not you get an effect on fecundity.

13 In terms of ER and AR pathway efforts, there

14 are a number in a variety of different organizations. So US EPA

15 has worked on this to a large degree. As I mentioned, the ER

16 AOP and high-throughput and other in vitro assays that they're

17 looking that might satisfy the same pathway. 

18 The Hamner Institute has a project called Tier

19 1 and Done, which is an estrogen receptor pathway their looking

20 more globally at estrogen receptor. 

21 CAAT has a project called Pathways of

22 Toxicity, similar thing. They're looking at bioinformatics and

23 how that informs estrogen receptor pathways. The OECD

24 Validation Management Group, Non-Animal is looking at the

25 validations, so they're much more into the validation of assays

26 themselves. And there's another group at OECD, an Advisory
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1 Group on Molecular Screening and Toxicogenomics which is also

2 involved in AOP developments.

3 Thyroid pathways are less well-developed and

4 more complex. However, there are still some efforts that are

5 designing these. Several things going on at the OECD. And

6 also, the US EPA has a couple of case studies where they've been

7 able to design AOPs based on chemicals to help in the

8 risk-assessment for those chemicals.

9 Okay. So I'm not going to go through this in

10 detail but to get from where we are to creating this more

11 predictive paradigm based on pathway understanding, there are a

12 lot of things that are needed. But my point here is that all of

13 these things are in development. This is moving forward. This

14 is where we're going eventually and it would be nice if the EDSP

15 could be structured in such a way that it could both feed into

16 this process and allow the flexibility that it could be adapted

17 to this process readily as it moves forward.

18 So the current design needs to be improved. 

19 Let's change it using a more refined tiering system, combine

20 with weight of evidence and then eventually allow progression to

21 a pathway-based approach for all of this. 

22 DR. DANIEL SCHLENK: Okay. Quick questions

23 from anyone? Okay, thank you. Our next presenter will be

24 Colleen Toole representing CeeTox.

25 DR. COLLEEN TOOLE: Good afternoon. My name

26 is Colleen Toole. I'm a Director of Project Management at
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1 CeeTox. CeeTox is a small contract research organization

2 located in Kalamazoo, Michigan.

3 We've had lots of experience with these EDSP

4 assays, specifically the five in vitro assays because we are an

5 in vitro-only laboratory. We have also been doing endocrine

6 screening and endocrine in vitro assays for 10 years since the

7 company was first founded. We've run over 90 of these in vitro

8 studies following the EDSP guidelines and we run approximately

9 30 of these test materials that were on List 1 through our labs.

10 So I want to touch on a couple of subjects

11 that have sort of been tossed around in the group and bring some

12 clarity to some of the things that were in the White Paper. One

13 is the performance standards. So these are the performance

14 standards for the ER binding assay. We're looking at a

15 test-tube based assay, radioactive gold standard for in vitro

16 binding assays. It's been around for decades. 

17 You can see estradiol on the left, and then on

18 the right is the norethindrone in the weak positive. And you

19 can see the shift in the IC50.

20 When we talk about small changes in the white

21 paper that went on with these performance standards, what you

22 might see is the top plateau level at 112 versus 111. You might

23 see the hill slope being minus 1.2 versus 1.1. Those are the

24 types of small changes that they're talking about. If it was

25 larger in magnitude, what we would have done is repeated the

26 runs. So that just sort of outlines where I was going with the
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1 ER and those performance standards, and I'll get into the ERTA

2 one soon.

3 But comparison of the AR and the ER binding

4 assays -- because this was brought up by Steve a little bit

5 earlier -- there is differences in the guidelines because

6 they're so specific, so they're both test-tube based assays. In

7 the ER, you're going to use 100 micro liters of your cytosol

8 prep. In your AR, you're going to use 300 micro-liters. And

9 what happens is you're making up your assay into 300

10 micro-liters and then you're removing a hundred micro-liters,

11 putting that into a new test-tube, and you're discarding your

12 200 micro-liters, so there's a lot of waste going on. 

13 And so what we'd like the guidelines to be is

14 a little bit more free with the science, so that if we wanted to

15 miniaturize this assay and put it into -- not test-tubes but

16 maybe some 96 well-glassed tubes as something that's higher

17 thru-put, we have the capability and the science behind it. We

18 could still follow the performance criteria and allow us the

19 freedom as a lab to do a little bit of that and upgrade the

20 assays ourselves.

21 Another thing that comes out of this is

22 storage conditions for these assays. And this is, again,

23 getting into the specificity of the guidelines. 

24 For the ER you can store the cytosol at minus

25 80 for up to 90 days and then you have to re-characterize. But

26 for AR, the guidelines say you can store and you have to discard
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1 after six months. Well that was a lot of work in characterizing

2 the cytosol. You're running a protein determination, and then

3 you're running three runs of your saturation binding and then

4 you're going in to running competitive binding assays. 

5 So it's not trivial to prep this cytosol, and

6 we certainly don't want to waste any of it because of the animal

7 use too. And so these are just a couple of things to consider

8 as we're looking at the guidelines.

9 So one of the things that come out in the

10 binding assays is these sharp decreases at these high

11 concentrations, and I bring this up because the high

12 concentrations have been mentioned a couple of times today, how

13 relevant are they. And when you're looking at an in vitro

14 system, you're going to start to get artifacts when you push the

15 system out to these higher levels and concentrations of test

16 materials.

17 Susan Laws put out a paper a few years ago

18 where you can look at these sharp decreases of these high

19 contractions that fall like this in these non-monotonic curves

20 and you can perform these Ki experiments. And I'm not going to

21 go into the Ki experiments but it does go into talking about

22 some of the issues that we face as a lab, even with the negative

23 controls. 

24 And so basically we're going to determine

25 these false positives by increasing concentrations of your

26 tritiated E2 in the presence of your test material. We're going
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1 to construct Lineweaver-Burke plots and then we're going to

2 replot these versus concentration of the test material. 

3 So I just wanted to show you what a true

4 binding event looks like. You can see that all the trend line

5 intersect at the same point. When you replot the data you get a

6 nice linear slop. For a false positive what we see is this

7 doesn't intersect at the same point and you get a nonlinear slop

8 here.

9 So when we start looking at things like the

10 negative control that sometimes will fall out of solution

11 because we have solubility issues here, it will look like a

12 binder but we know it's not. There's going to be some variation

13 in lab day to day. There's going to be some temperature

14 changes, and we can see that we have solubility issues at this

15 high concentration. And this is just a comparison of the curves

16 for the atypical response with the negative control versus the

17 false positive response. 

18 The next assay I want to talk about a little

19 bit is the ERTA because we did have some challenges when we look

20 especially at the 17 alpha-methyltestosterone. 

21 These are the performance criteria, so you

22 have a strong positive, a weaker positive, your negative and

23 then your weak positive. If you're looking at the logPC50,

24 PC10, EC50, your hill slop, and these are the test range.

25 So I just went back through the data and

26 picked 10 runs at random and plotted it for E2. You see a nice
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1 dose-response curve. But what happens with 17

2 alpha-methyltestosterone that makes it hard to reach these

3 criteria is you basically have no response and a response, so

4 it's not even a dose-response, it's all or nothing. And so you

5 get small changes in that line and you're going to fall outside

6 of these criteria.

7 So what we do as an in vitro lab is we always

8 keep track of all our historical data. We track this. And so

9 what I would suggest is now we have lots of data from multiple

10 labs and from multiple compounds. We're running these three

11 times for each compound usually, and so we've got a lot of data

12 that we could then go back and look at and build some more

13 robust criteria around these controls, and I think that would

14 really help the assays. The assays are not bad themselves. I

15 think they just need more historical data and to firm up some of

16 these points that we're looking at. 

17 So I know the BG1 assay has been talked about

18 a couple of times now. Four our contract research lab we're

19 looking at $10,000 to purchase that cell line and just get it up

20 and running in the lab. So that differs a lot from the HeLa

21 cell line which we can purchase for $500 and get up and going.

22 When you're looking at in vitro assays you

23 have to know your assay system. This system with the HeLa cell

24 line is geared towards detecting false positive. And the reason

25 I say that is because we have an introduced ER alpha as a

26 plasma; so you've got high concentrations of ER alpha and the
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1 machinery of the cell just can't shut things down. The

2 co-activators and co-repressors are just not there in levels

3 that are going to really truly represent an in vivo response. 

4 And so that might be too wide, we see some

5 discrepancies in the data. But I'll also remind you we're just

6 looking at agonist and agonist activity here. So what's being

7 picked up by an ER binding event has ER alpha and ER beta

8 isolated from these cytosol preps. They're not always going to

9 match. And if it's antagonism that's going on causing the

10 binding event, we're not going to pick that up in this assay

11 because we don't run antagonist.

12 Stability of the cell line, what we're looking

13 at here is this red line, a four-fold induction for the positive

14 control. And so what I just wanted to show you is, is this is

15 pretty stable over time. We can get induction up to 80 and

16 normally we're probably around 20. 

17 But we're well over signal Denoise with this

18 assay. It is a robust assay but there's limitations to it. And

19 with every in vitro system, there's going to be limitation and

20 in every in vivo, and we just have to understand this systems.

21 Steroidogenesis can also be a challenge and

22 this was discussed a little bit earlier today too. But in the

23 guideline, 22R hydroxy cholesterol, which feeds in early in the

24 pathway can be used according to the guideline at concentrations

25 between 20 and 40 micromolar. 

26 You can see at 40 micromolar we've got a huge
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1 cytotoxicity issue. We've lost 60 percent in viability. Even

2 at 20 micromolar, you can see that when we look at gene

3 expression, you can see at 10 micromolar right before we're

4 having viability issues, we see some increases in transcription.

5 And so we'd backed off to 10 micromolar because this is good

6 science and this is where we run our assays.

7 So as far as laboratory proficiency goes, I

8 want to give people a little bit of comfort in that, as a lab we

9 do take great care and pride in what we're doing for every

10 client that we have. These are GLP studies. We've been audited

11 not only by the EPA but by our clients. 

12 And so our laboratory staff are proficient. 

13 They have to be to run these assays. They have to show they're

14 proficient by running the proficiency compounds through the

15 specific assays, and these are documented and put into their

16 training records. There was no indication from the guidelines

17 that we had to report those proficiency data in the reports that

18 we were giving for our test chemicals.

19 The DESTs were only available after our

20 studies were completed. So at that point we were onto the next

21 compounds, because there's five in vitro assays -- we're a small

22 lab -- we didn't have time to go back at that time to finish up

23 the DESTs. And we have now finished the DESTs for some of our

24 clients who wanted to pay for that service.

25 So there are some remaining challenges. One

26 thing that would be helpful, especially if we're looking forward
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1 to List 2, is to have some sort of technical support available

2 at the EPA for individual questions; and whether they can triage

3 that to someone who can answer the question. It would just be

4 helpful for us as a laboratory conducting these assays. 

5 Alternative vehicles are going to be a bigger

6 issue on the second list. We had some compounds that were not

7 soluble in any of the solvents listed in the guidelines, and

8 those are the things that we would like to have more rapport

9 with the EPA, more timely rapport to figure out how we should

10 handle those, because it's not just running it into an alternate

11 vehicle. And what we chose to do is, was now we have to run all

12 the reference compounds in both DMSO and the alternative

13 vehicle. What if we don't pass the performance criteria in the

14 alternative vehicle? So these are things, as you know, as a lab

15 doing laboratory work, we're always going to face.

16 Outlier analysis is another thing that I

17 wanted to briefly touch on because this could be why we fell

18 outside of performance criteria by these small amounts. We're

19 not removing any outliers. The validation study from the major

20 lab that did the work for ERTA didn't have any edge effects. We

21 do. We can see them, but we don't know really what freedom we

22 have to remove outliers. We know they're there. We know it's

23 edge effects, and we've reformatted our plates to address this,

24 but we still see it, and how to acknowledge that and what we

25 need to do that would be acceptable from the EPA.

26 I touched on this before but revise the
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1 performance criteria. We now have more labs, more data. You

2 know, can we build 95 percent confidence limits around the

3 curves that we have, and then miniaturization of the assays

4 because we feel pretty confident we could miniaturize some of

5 these assays and get higher throughput and still get good data. 

6 And so with that, I'll take any questions.

7 DR. DANIEL SCHLENK: Any questions from the

8 Panel? Okay, Dr. Scott?

9 DR. GEOFFREY SCOTT: Yes. I just wanted to

10 follow-up on your comment about proficiency testing. You do

11 maintain those records in your laboratory, I understood you.

12 DR. COLLEEN TOOLE: Yes. 

13 DR. GEOFFREY SCOTT: But what you said is that

14 was not always requested initially; is that correct?

15 DR. COLLEEN TOOLE: It was not requested in

16 any of the guidelines. However, when the DESTs came out

17 afterwards they did ask for that information, but that was after

18 we had finished a lot of studies for our laboratory.

19 DR. GEOFFREY SCOTT: I thought that's what you

20 said, I just wanted to make sure. Thanks.

21 DR. DANIEL SCHLENK: Dr. McManaman?

22 DR. JAMES MCMANAMAN: So can you briefly

23 comment about the variability in the ERTA assays? It looks like

24 you've got a maximum stimulation that ranges from 75 percent to

25 over 200 percent. And so how would you assess a compound with

26 that much variability in response?
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1 DR. COLLEEN TOOLE: It's all based upon

2 positive control and relative to positive control. At that

3 point, we have a robust signal. We're not downing the noise. 

4 In these transactivation assays, you'll always see some

5 variability in that direction. It's not a perfect system. I

6 don't make too much of it because it's just kind of some

7 differences across the plate. 

8 DR. JAMES MCMANAMAN: But if you're looking

9 for a 10 percent change to be a positive response and your

10 maximum on that particular assay is 75 percent, that's going to

11 be difficult to see. I think that's what the performance

12 standards were for, for the ERTA assays, is looking at 10

13 percent of the maximum response.

14 DR. COLLEEN TOOLE: Oh, the maximum response,

15 which is a hundred percent. So that is actually running one of

16 the reference. So your positive control is just run at the one

17 nanomolar, E2. And then you get your response curves from your

18 dose response on the rest of the plate, so that's just the

19 little bit of difference between the size of the plate from the

20 positive control.

21 DR. JAMES MCMANAMAN: Yeah, it looks like it

22 could make a big difference in how much the signal is going to

23 be, which could lead to some -- I could foresee that could lead

24 to some problems with assessing reliability of an assay. 

25 DR. COLLEEN TOOLE: Well as long as your fold

26 induction is high enough and you're not lost in the noise -- to
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1 Steve's point, what he was thinking is if you're just looking at

2 a four-fold induction or a four-fold change, that's pretty low. 

3 So looking at 10 percent of that is pretty small.

4 DR. JAMES MCMANAMAN: Right, so it could be

5 problematic.

6 DR. COLLEEN TOOLE: It could be.

7 DR. DANIEL SCHLENK: Okay. Any other

8 questions? Okay, thank you. 

9 DR. COLLEEN TOOLE: I'm sorry, I have one more

10 comment. So as you all have the difficult job of next steps,

11 what I would encourage you to evaluate is, looking at all the

12 test chemicals -- 

13 DR. DANIEL SCHLENK: I'm sorry, you've kind of

14 already exceeded your ten minutes.

15 DR. COLLEEN TOOLE: All right, thank you.

16 DR. DANIEL SCHLENK: Yeah, thanks. Our final

17 presenter is Leah Zorrilla from Integrated Laboratory Systems.

18 DR. LEAH ZORRILLA: I'd like to thank you for

19 the opportunity to participate in this discussion. My name is

20 Leah Zorrilla. 

21 I'm representing Integrated Laboratory

22 Systems. I contract laboratory organization that participated

23 in the conduct of the List 1, Tier 1 EDSP in vivo mammalian

24 assays. 

25 So my objective is to really quickly discuss

26 the in vivo conduct and performance from a CRO perspective. 
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1 Our recent experience involved running 18

2 chemicals in the Uterotrophic assays, 21 chemicals in the

3 Hershberger bioassays, 11 chemicals in the female pubertal and

4 10 chemicals in the male pubertal assays, and most of which of

5 those were submitted to the Agency for evaluation. In addition,

6 we have conducted over 30 range-finding studies to select a high

7 dose according to the individual assay definitions for the

8 maximum tolerated dose levels.

9 For all test substances, more time and

10 resources were required than originally anticipated to select

11 and evaluate the appropriate test substance vehicles, along with

12 performing dose formulation stability studies. This extended

13 the timeframe of the studies by approximately 20 days prior to

14 the initiation of the assay conduct. In addition, choosing the

15 appropriate maximum tolerated dose for each of these assays took

16 time and additional resources, which you've heard, which

17 involved conducting range-finding studies that were not

18 initially anticipated in the original timeline of the study

19 conduct. And this really became difficult for the CROs because

20 there weren't many of us that were able to conduct these assays,

21 and the deadlines were pretty strict for submission of these

22 reports.

23 The range-finding study that I had last

24 performed used age-matched animals and obtained daily body

25 weights at clinical observations. And for the pubertal assays,

26 we also evaluated liver and kidney weights to set those maximum
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1 tolerated dose levels.

2 There were still studies in which the high

3 dose level selected did exceed the maximum tolerated dose as

4 ascribed and that has been well discussed here at this meeting. 

5 And as discussed in the white paper, though, it did make some of

6 these findings difficult to interpret when there was a greater

7 than 10 percent body weight loss.

8 However, overall, the pubertal assays were

9 conducted with ease. I did want to bring up several

10 considerations that might be helpful in clarifying and refining

11 the assays. 

12 As discussed, the performance criteria for the

13 pubertal assays were overall met. However, now that several

14 laboratories have historical controlled data from the

15 performance of these assays, it would really be important to

16 utilize the facility historical controlled data ranges, in

17 addition to those available from the EPA guidelines.

18 Another point, the liver is weighted in the

19 pubertal assays, but it would be helpful to retain the liver in

20 the event that the test chemical induces the enzyme activity

21 which may help provide clarification and interpretation of

22 selected endpoints. 

23 In terms of cost-savings, analysis of the

24 clinical chemistry panel, which is a full clinical chemistry

25 panel, adds costs to the conduct of these pubertal assays. And

26 it's not clear that this has really been of value to study
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1 interpretation when range-finding studies have been performed. 

2 In our experience, these data did not provide any additional

3 information to be observed, the body weight loss or clinical

4 observations for the selection of the maximum tolerated dose

5 and/or the definitive study interpretations. 

6 And finally, it would be useful for the

7 pubertal guidelines -- if clarifications could be provided in

8 regards to the appropriate covariate for the ANCOVA analyses,

9 either the date of weaning, which is postnatal day 21 or the

10 actual first day of dose substance administration which is PND

11 22 for the females and 23 for the males. The guidelines, the

12 DESTs and the SEPs have different days, so clarification would

13 be great.

14 In regard to the Uterotrophic assay, currently

15 the maximum tolerated dose in the Uterotrophic assay guidelines

16 is defined as a dose to ensure animal survival without

17 significant toxicity or distress. And this is how maximum

18 tolerated dose levels were chosen for this first test level, at

19 least in our laboratory. And this is different from how it was

20 defined in the white paper, as it suggests that greater than 10

21 percent body weight loss should be taken into consideration when

22 evaluating the study.

23 Although the other three in vivo assays do use

24 this criterion as a greater than 10 percent body weight loss in

25 consideration in choosing the maximum tolerated dose, the

26 Uterotrophic assay guidelines do not. And this really needs to
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1 be clarified moving forward as choosing the MTD based on a 10

2 percent body weight loss or animal survival are different

3 things.

4 An additional consideration in the

5 Uterotrophic assay is the oral gavage level of ethinyl

6 estradiol, the positive control. The effective dose level is

7 much higher in adult OVX animal than it is in the immature

8 model. However, overall the performance criteria for this assay

9 is easily met for both the immature and OVX models.

10 The Hershberger bioassay was also easily

11 conducted. However, there are two study considerations from our

12 laboratory's experience. 

13 One would be to ensure the animals are

14 sexually mature prior to castration and place and placed on to

15 study so that all the tissues can be accurately evaluated. 

16 And the second is to weigh and retain the

17 liver in these Hershberger assays to assess hepatic enzyme

18 induction, particularly if the need arises for a follow-up study

19 to interpret potential anti-androgenic effects. Overall, the

20 performance criteria were easily met for these assays.

21 So I'd like to summarize by saying that the

22 four mammalian in vivo assays were easily conducted and

23 performance criteria overall were met.

24 The major challenges really involved meeting

25 timelines for study conduct and submission that did not consider

26 the time involved in selected the formulation, conducting
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1 formulation stability studies, which are required for GOP

2 submission, and the dose level selections involve conducting

3 these range-finger studies. And again, really, that all added

4 time with the limited amount of time that we were given in order

5 to submit these studies to the Agency. Thank you.

6 DR. DANIEL SCHLENK: Thank you. Dr. Selvage,

7 question?

8 DR. DANIEL SELVAGE: I have a couple of

9 questions particularly in running the pubertal assays. I didn't

10 notice in the guidelines where it said that during necropsy, you

11 were looking at the gut at all. I mean I'm a little concerned

12 these (inaudible) are being introduced by gavage. So did you

13 look at the stomach at all when you're doing necropsy?

14 DR. LEAH ZORRILLA: For particular chemicals

15 in which -- 

16 DR. DANIEL SELVAGE: Yes.

17 DR. LEAH ZORRILLA: Yes, we did.

18 DR. DANIEL SELVAGE: You have, okay.

19 DR. LEAH ZORRILLA: But that was really test

20 substance specific. So depending upon the information that our

21 clients had prior to, you know, they would say "This might be a

22 stomach irritant, potential irritant, so we would specifically

23 have to follow just necropsy to look at the potential effects

24 that were present.

25 DR. DANIEL SELVAGE: Okay, but it's not

26 standard? 
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1 DR. LEAH ZORRILLA: It's not standard and it's

2 not in every -- that's correct.

3 DR. DANIEL SELVAGE: Another question in terms

4 of the female pubertal assays, and this is kind of an opinion

5 question, do you think something could be gained by, instead of

6 taking the rats at day 42 or day 43, taking them for instance on

7 their first estrous or synchronizing estrous cycles and taking

8 them all the same day of the cycle? Do you think that might

9 give you more stable results?

10 DR. LEAH ZORRILLA: Right. I think it could

11 add value to the study. It depended how long you wanted to

12 continue the dose, which would also add cost to the study. But

13 depending on the affects, in theory, you'd be able to -- you

14 know, if you necropsied on the first day of diestrous, for

15 example, it would make measuring hormone levels a lot easier I

16 think. 

17 You would get rid of some of the variability,

18 I think. And then you'd also have a longer timeframe to

19 evaluate potential estrous cycle disruptions if there were. I

20 actually published a paper on that a couple of years ago, but

21 hormone levels, we didn't really see any significant differences

22 when we did necropsy on the same day of the cycle.

23 DR. DANIEL SELVAGE: Okay, thanks.

24 DR. DANIEL SCHLENK: Dr. Green?

25 DR. SHERRIL GREEN: Do you know what the

26 original intent was for drawing blood to evaluate clinical
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1 chemistries, to look for what, liver and kidney enzymes?

2 DR. LEAH ZORRILLA: Yes. So originally it was

3 for BUN and creatine evaluations. And I think it makes sense if

4 you're not doing a range-finder, and perhaps you don't see a

5 body weight loss but there might be some toxicity. 

6 But then really, when you got into what the

7 guidelines requested, it actually requested a full panel, and so

8 it quickly added -- I mean a significant amount of money for

9 what perhaps might not be adding a lot of scientific value.

10 DR. SHERRIL GREEN: And there were no hormone

11 analysis that were added to it or part of a -- because what a

12 typical screen is can vary from lab to lab and what it includes,

13 you know, T3, T4 or estradiol or things like that.

14 DR. LEAH ZORRILLA: Well for the female

15 pubertal, T4 and TSH are required, and any other hormone would

16 be optional. Most of our clients decided not to do the optional

17 hormone analyses. And in the males, it was T4, TSH and

18 testosterone were required.

19 DR. SHERRIL GREEN: Okay, thanks.

20 DR. DANIEL SCHLENK: Any other questions? 

21 Okay, thank you.

22 DR. LEAH ZORRILLA: Thanks very much.

23 DR. DANIEL SCHLENK: Okay. This concludes

24 our oral presentations of public comments. I do have an email

25 that was received today that we'll read into the record real

26 quick. 
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1 "Dear Dr. Jenkins. I've just been scanning

2 the proposed agenda for this week's SAP meeting, would

3 like to contribute a remark relating to charge question

4 8, how to minimize animal usage. In our view, a

5 significant reduction in animal numbers and animal

6 welfare could be achieved by allowing a different

7 approach to the ER and AR binding assays allowing the

8 use of recombinant cell lines in place of the current

9 requirement for cytosol from rat uterus or rat

10 prostate. 

11 The people we have working on these studies at

12 Hunting Life Sciences at CRO are finding that for an AR

13 assay, they are using the cytosol from about 60 male

14 rats, all of which have to undergo surgical removal of

15 testes 24 hours before necropsy to remove prostate. We

16 consider this number of animals per in vitro test,

17 ex-vivo in effect, highly undesirable when there are

18 good cell line based assays available. 

19 I have seen no mention of this issue in EPA

20 commentaries seen so far so I think it is worth drawing

21 attention to it. 

22 Now that there is an extensive database of

23 compounds tested with the current ER and AR ex-vivo

24 designs, it would seen an opportune point at which to

25 do a comparative validation with selected compounds in

26 the cell line assays. Thanks for your attention and I
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1 hope the meeting goes well." 

2 And this was from Will Davies (phonetic) who's

3 a consultant at LSR Associates in the U.K. 

4 Okay. With that, this concludes the public

5 commenting period and let's take a 15-minute break. We'll be

6 back here at twenty till.

7 (Whereas a break was taken)

8 DR. DANIEL SCHLENK: Okay. Mary, you want to

9 go ahead and read into the record question 1?

10 DR. MARY MANIBUSAN: Charge question 1: Based

11 on analyses of the data presented in Section III: Please

12 comment on the proficiency of the contributing laboratories to

13 execute each assay in accordance with the test guidelines and

14 achieve the performance criteria.

15 DR. DANIEL SCHLENK: Thank you. The lead

16 discussant for question 1 is Dr. Roby.

17 DR. KATHERINE ROBY: Okay. Each test has an

18 established guideline, and the guidelines define test

19 parameters, include criteria for assessing testing proficiency

20 and also include criteria defining acceptable limits for various

21 positive and negative controls to be included in most tests. 

22 Thus, general proficiency can be monitored at several endpoints.

23 Based on the information presented in Section

24 III, and overall assessment at a high level would indicate that

25 the testing laboratories demonstrated proficiency in carrying

26 out the battery of tests. However, there were variations across
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1 laboratories in the ability to achieve proficiency criteria

2 raising some concerns. 

3 At least some of the variation observed across

4 testing laboratories is likely due to slight variation in

5 methodology, interpretation or followthrough of testing guidance

6 such as when solubility became an issue, laboratories not

7 shifting to the recommended half dose log increment but instead

8 shifting to a full dose and adding an additional dose at the

9 lower end of the dosing series. 

10 Additional variation may also be introduced

11 based on individual laboratory conditions such as the ability to

12 maintain temperature control in the fish test. In addition,

13 variation within a laboratory may occur based on the proficiency

14 of individuals performing the test, and thus, individual

15 performance criteria for each analysis should be documented and

16 recorded over time.

17 With the data that was provided in Section

18 III, it was difficult at times to determine what laboratory

19 tested what chemicals. In addition, within the description of

20 the test given within the description of each individual test,

21 at places there was discrepancy with regard to the number of

22 testing labs that were included in that test and the numbers of

23 chemicals tested; there are places within the text that do not

24 agree that made some interpretation of the information

25 difficult.

26 Further, it appears that each chemical was
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1 tested by only one lab, possibly having more than one lab test

2 the chemicals or even just a subset would provide an additional

3 monitor for proficiency. 

4 With many of the in vitro assays, statements

5 were often made within the white paper indicating that the

6 testing laboratories did not provide complete proficiency data

7 to the Agency. Incomplete datasets limit the ability of the

8 Panel to carefully assess the proficiency in these instances. 

9 For example, with the steroidogenesis and the aromatase assays. 

10 If this is not clear within the guidance, it needs to be made

11 clear.

12 With the in vivo assays, it is not clear how

13 many laboratories participated in each test, and thus, comments

14 about multiple laboratories, proficiency cannot be made. 

15 Further, the apparent variation around established test criteria

16 together with the limited data provided within the White Paper

17 make interpretation of the outcomes difficult, and then

18 interpretation of laboratory proficiencies sometimes difficult.

19 In regard to the aquatic tests, data again is

20 not presented by lab, and thus, variation across laboratories or

21 within laboratories cannot be judged. As with the other Tier 1

22 tests, the aquatic test had variation around performance

23 criteria. Similar to the other tests, a clear definition of the

24 acceptable limits are needed for these tests. Although it is

25 understood, especially with the aquatic tests and across tests,

26 the acceptable deviation may differ. 
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1 To the point of variation around testing

2 criteria; in many cases, the established criteria were not met,

3 but they were not met by what would be considered a slight

4 reminder variation. If analyses indicated, as stated in the

5 White Paper, slight deviation from the established criteria did

6 not affect interpretation of the study outcomes, the guidance

7 performance criteria should be revisited.

8 It's further suggested that the Agency

9 establish a quantitative definition for the variation around

10 specific performance criteria, thus avoiding the more

11 non-descript slight and minor terminology.

12 An example might be defining minor and major

13 nonconformities and then define quantitatively what these two

14 ranges of deviation from the norm include. This may be based on

15 a percentile deviation such as a minor being less than 10

16 percent and a major being greater than 10 percent. It may also

17 be a statistical measure such as plus or minus one or two

18 standard deviations.

19 Some specific criteria should be established

20 to aid in the identification of unreliable test runs. In

21 addition, more specifically in regard to the CVs reported in the

22 in vivo studies, criteria defining acceptable performance CVs

23 may need to be revisited in order to account for the variations

24 existing in controlled populations.

25 In addition to further defining

26 nonconformities, it's considered that a decision tree approach
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1 would allow for a clear assessment of when test criteria have or

2 have not been met and when a given test can be accepted or

3 rejected or should be rerun. 

4 For example, when negatives controls included

5 in a test run are read as positive, as indicated in at least one

6 instance in the data summary that was provided, the run should

7 likely be rejected. In addition, guidance on how to consider

8 data in the presence of cytotoxicity and toxicity should be

9 included. 

10 Although the majority of the deviation from

11 proficiency criteria was not considered to impact the

12 interpretation of data, there were some deviations that may be

13 cause for concern. In regard to the fish short-term

14 reproductive bioassays, there are major deficiencies in dosing

15 conformance. If this is due to analytical limitations,

16 absorption or biodegradation needs to be determined. 

17 There is some concern that if laboratories

18 alter the rate of volume replacement, the total volume of

19 chemical delivered per aquaria per day will be different. This

20 would introduce a source of variation and effect. The

21 parameters of the protocol should assure that the initial target

22 nominal dose is being delivered. An additional measure in an

23 aquaria not containing fish may be recommended, although it is

24 understood this would add additional cost. 

25 In questions earlier with the EPA, it was

26 mentioned that there are routines stock concentration
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1 measurement requirements, as well as daily flow determinations. 

2

3 This combination of measurement should be

4 reported. It can also then be used to predict estimated nominal

5 concentration being delivered throughout each bioassay, and this

6 would at least provide an accurate estimate of the delivered

7 nominal dose and without having to directly measure it. Also,

8 it's important to use only one stock for dosing purposes. 

9 The second concern with the fish assay relates

10 to the cumulative affects of several non-conformities, including

11 low dissolved oxygen levels, elevated exposure temperatures, and

12 elevated levels of chlorine. 

13 Fish will respond to these changes with

14 increased ventilation, which may damage fish gill and may also

15 result in increased up-take of the chemical being tested. These

16 three non-conformities for performance criteria are easily

17 corrected and participating labs should be taking steps and

18 should have taken steps to assure compliance within these

19 criteria. In addition, there are a couple of concerns raised

20 with the amphibian metamorphosis assay. Again, we've discussed

21 these today. 

22 First, related to the tail curvatures, the

23 high rates of this observed in controls and exposure groups does

24 raise concern as to the health of the animals being used in the

25 bioassay. There are obvious issues with the abnormality being

26 tied to diet as well as the source of the test organisms. EPA
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1 should examine the underlying health of the frogs post

2 metamorphosis since the tail is resorbed during the

3 developmental process. 

4 If the abnormality does not affect the growth,

5 health and survival later in life, this issue may not be as

6 significant as it would be if the animals are affected later in

7 life. Also, maintaining accurate records of the source of the

8 frogs is a critical performance criteria to maintaining good

9 recordkeeping.

10 The second issue is related to the potential

11 affects of carriers used in relation to chemical solubility. 

12 The performance criteria presented clearly demonstrate that it

13 is possible to discern the affects of carriers used within the

14 assay. The question, however, is how to use the data or how not

15 to use the data when a carrier effect is observed, and the EPA

16 should formally address this within the performance criteria. I

17 think that's it for me for now.

18 DR. DANIEL SCHLENK: Thank you. Our first

19 associate discussant is Dr. Ehrich.

20 DR. MARION EHRICH: I think everything I had

21 is already in what she said. I have no further comment.

22 DR. DANIEL SCHLENK: Thank you. Dr.

23 Maclatchy?

24 DR. DEBORAH MACLATCHY: I have no additional

25 comments.

26 DR. DANIEL SCHLENK: And Dr. Scott?
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1 DR. GEOFFREY SCOTT: All of my comments were

2 well captured by the lead discussant.

3 DR. DANIEL SCHLENK: Thanks. Other Panel

4 members for question 1? Dr. Green?

5 DR. SHERRIL GREEN: I would like to second the

6 recommendations of the working group about the achievement of

7 appropriate doses in the aquatic environment. The same would

8 hold true for Xenopus laevis tadpoles in their tanks. Thanks.

9 DR. DANIEL SCHLENK: Any other comments for

10 question 1? Okay. Let me go to the Agency. Any questions or

11 clarifications regarding the comments of the Panel? 

12 DR. CATHERINE AUBEE: This is Catherine Aubee.

13 Thank you for your comments and advice. I'd just like to ask

14 Dr. Roby to clarify your last recommendation regarding the

15 interpretation of effects when there is apparent solvent

16 interference in this study. I believe that's what you were

17 discussing.

18 Could you just clarify what exactly you were

19 recommending? Are you recommending that additional criteria be

20 added to the guideline or are you just recommending that some

21 sort of formal analytical framework be set forth? I would

22 appreciate more information on that. Thank you.

23 DR. DANIEL SCHLENK: Dr. Scott?

24 DR. GEOFFREY SCOTT: If I could, I'll answer

25 that but I'm not sure if I could. I think what we were asking

26 is if it's in the protocol already, how to address that, that's
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1 fine. I could not discern from the document if that information

2 is in the protocol in a prescriptive way or in a general

3 guidance way. 

4 If it's in a general guidance way, then I

5 think you may want to make it a little more prescriptive as to

6 how you address that and that was really the intent there. It's

7 not that you guys are ignoring it; we know you're looking for

8 it, you reported it. 

9 So I think what we're really asking is if you

10 haven't given more specific criteria, you may want to think

11 about that just to help. Because I think we heard from several

12 of the labs today about the issues on some of this.

13 DR. CATHERINE AUBEE: Thank you.

14 DR. DANIEL SCHLENK: Thanks. Dr. Patino, you

15 have something to add?

16 DR. REYNALDO PATINO: Yes, follow-up.

17 DR. DANIEL SCHLENK: Okay.

18 DR. REYNALDO PATINO: We had a little bit of

19 discussion on this yesterday and I expressed an opinion that I

20 wanted to reiterate today, that if there is a solvent effect, I

21 think the proper comparison of effects of the compounds is

22 against the solvent control, not the native control. I just

23 want to repeat that comment. Thank you.

24 DR. DANIEL SCHLENK: All right. Let me go

25 around again one more time. Anybody else have any other

26 comments for question 1? Okay. Ready to move on to question 2?
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1 DR. MARY MANIBUSAN: Charge question number 2.

2 The performance criteria for each in vitro assay are clearly

3 stated in the test guidelines for the ER binding, AR binding, ER

4 alpha Transcriptional Activation, H295R steroidogenesis and

5 aromatase human recombinant assays. 

6 Although contributing laboratories did not

7 always demonstrate that results were within the specified

8 boundaries of the performance criteria, the majority of the

9 deviations were still close to the performance criteria. In

10 this regard, the EPA concluded that the data was still adequate

11 for use.

12 Please comment on the EPA's conclusion. 

13 Please comment on when a deviation from the recommended

14 performance criteria would render the study unreliable.

15 DR. DANIEL SCHLENK: Lead discussant for

16 question 2 is Dr. Schultz.

17 DR. TERRY SCHULTZ: Thank you. Clearly the

18 answer to question 2 has to be linked to the comments that came

19 out of question 1, so we asked the Agency to look at the answers

20 as being provided to question 2 in the context of what has been

21 provided already in question 1.

22 It was felt by the Panel that question 2

23 related to the first purpose of this review of Tier 1 assays,

24 specifically the evaluation of how well each Tier 1 assay test

25 guideline can be consistently executed based on defined

26 performance criteria and to highlight any issues associated with
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1 the interpretation of responses within each assay.

2 So what the Panel has done is addressed each

3 of the five assays separately, and then we have some additional

4 comments which are kind encompassing all of those. The Panel

5 has added some boilerplate to define the understanding of that

6 and I will briefly refer to that and then remind you to read

7 that in more detail. This is simply to note that the Panel

8 understands how the question was being asked. 

9 So the Panel understands that the endocrine

10 disruptive screening program White paper on Tier 1 screening

11 assay and battery performance -- hereafter I would approach that

12 just as the White Paper -- consists of a battery of 11 assays,

13 however, only five of these assays are defined as in vitro

14 assays, and we list those five. 

15 The Panel understands that the Tier 1

16 screening by plan consist of a battery of less complex in vitro

17 and in vivo assays designed to effectively and efficiently

18 detect the potential of a chemical to interact with any of the

19 three hormonal pathways noted, that is estrogen, androgen and

20 thyroid. And that based (inaudible) analysis will determine

21 what chemicals will be candidates for the Tier 2 testing.

22 In contrast to the Tier 2 testing, which

23 involves more complex in vivo tests, the Tier 1 basically is the

24 screening that lies between a T0 or the prescreen or a

25 prioritization step, and the more definitive Tier 2 that's

26 associated with the risk-assessment, so that there are some
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1 trade-offs associated with that. 

2 Specifically, that the Tier 1 assays by design

3 are shorter-term, lower cost -- that's a relatively term

4 obviously -- designed to minimize false negatives, basically

5 report the potential to interact in a yes or no format and use

6 limited dose regimes, especially at the higher end. With that

7 statement, basically the Panel is trying to couch our responses

8 in the way that we think that the question was being presented.

9 Each one of the Tier 1 assays which formed the

10 basis for this scientific Panel was previously validated with an

11 onset of referenced compounds and I believe that was reported in

12 2009, and that each one of these has gone through a methods

13 development, a pre-validation and some sort of inter-laboratory

14 validation. 

15 So that all of this was basically put together

16 and culminated in the panel of 2008, which basically reviewed

17 this and came up with the proposal of using these 11 assays, and

18 then to identify this in this two-step process looking at some

19 chemicals that have known mechanisms of affecting endocrine

20 disruption. And then the second step is screening a series of

21 chemicals that fit under the legislative mandate from FIFRA

22 where the effect on endocrine pathways is unknown. So the Panel

23 clearly understands how that came about.

24 The Panel also understands that in evaluating

25 the performance of the assays within this SAP that it is

26 difficult to separate that from the weight of evidence, since in
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1 the document that laid this out from 1998 it talked about the

2 different steps. However, we realized that the weight of

3 evidence is not part of this SAP.

4 With all of that said, I'm sure that is

5 agreeable for all the questions. We just want to get that on

6 the record. 

7 The Panel would like to congratulate the U.S.

8 Environmental Protection Agency on providing the summary the

9 results for the 21 compounds brought forth as part of this SAP. 

10

11 The Panel also thanks the public commenters

12 for their timely input because it clearly, in many cases,

13 address specific issues associated with the 11 assays, and these

14 were being provided by people that had on-hand experience with

15 these assays, and that's a great help.

16 The Panel realizes that the 21 test compounds

17 represents the 50 active and 2 inactive or inert ingredients

18 that's been screened as part of the Tier 1 battery as an

19 intermediate step towards the recommendation of 2008 that

20 basically somewhere between 50 and 100 chemicals be screened in

21 that sort of situation. And that basically this is following

22 the recommendation of the 2008 SAP.

23 While we understand what the Agency said, that

24 the 52 chemicals were selected from the Tier 1 test to be

25 representative in regards to the physical chemical properties in

26 modes of toxic action from the specific pesticides, we're
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1 unclear why these 21 were selected out of the 52. Were they the

2 first ones that information came in on, and those types of

3 situations? But we believe that, as the Agency said, it is

4 likely that the 21 chemicals reflect the spectrum of issues that

5 you would have with all 52.

6 The Panel realizes that while the assays in

7 total were designed to identify whether a chemical has potential

8 to interact with an estrogen, androgen or thyroid hormone

9 system, the in vitro assays, because of the nature of their

10 endpoints, focus only on the estrogen and androgen systems. 

11 Furthermore, the Panel understands that not all in vitro data

12 and in vivo data are of equal value. And the in vitro data may

13 or may not be informative of results from the in vivo assays.

14 Specifically, the Tier 1 in vitro assays are

15 intended to provide some mechanistic data for a single known

16 pathway for endocrine disruption while the in vivo Tier 1 assays

17 are more amenable to detecting multiple mechanisms of action. 

18 So whether a particular in vitro assay had a negative or a

19 positive or non-response compared to another assay was less

20 critical at this point.

21 As noted in Table 1 of the white paper, the in

22 vitro assays in the current Tier 1 test batteries addressed only

23 the ANE systems, but they quantify key endpoints that are most

24 likely to be mode of actions that are associated with the

25 receptor binding and steroidogenesis, so it's much more narrowly

26 defined. 
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1 I'm going to skip reading the discussion that

2 is in here about the specific assays that basically is in the

3 White Paper, and I'm not sure I can say all the letters and

4 number combinations correctly and it'll just cause you

5 confusion. 

6 But basically we're talking about the estrogen

7 system, binding and the gene expression, and we're talking about

8 the androgen system, aromatase and steroidogenesis. All of that

9 is rather obvious.

10 Okay. I was just scrolling through all this

11 stuff to make sure that I kind of captured all that. Obviously

12 there are several paragraphs in there, but they're just pulled

13 out from the documents.

14 ER binding. As noted in the White Paper, 19

15 neurethrone (phonetic) -- did I say it correctly? Somebody over

16 here should nod. They know what I'm talking about -- okay --

17 which is basically replacement for the negative weak positive

18 control ligand. And that was used in the assay. 

19 Since the guideline doesn't contain any

20 performance criteria for this particular weak positive, the

21 Panel recommends the Agency establish performance criteria for

22 this chemical since this chemical seems to be used universally

23 in place of the initially recommended positive weak control. 

24 The Panel agrees that ER binding assay

25 performance was, by and large, consistent across all 18

26 chemicals tested and that general issues were consistent with
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1 individual runs. 

2 In addition, while a number of the evaluated

3 assays did not meet all the performance criteria, overall, the

4 laboratory performance of the ER binding assays were acceptable.

5 Again, it was noted the use of the word slight would better be

6 served if performance guideline could be readdressed to

7 particular values in there. And as was mentioned earlier, the

8 idea of 10 percent or less being acceptable and greater than 10

9 percent being unacceptable was listed as an example.

10 One Panel member felt that the most

11 problematic aspect of the ER binding assay is use of a highly

12 complicated and labor-intensive protocol for the extraction of

13 the uterine cytosol or different laboratories and had varying

14 success, and recommended that the agency look into standardizing

15 this parameter for the assay. And this kind of leads into the

16 email that was mentioned that might look into replacing this

17 with a recombinant assay.

18 The Panel also feels the results and

19 conclusions of this assay are supported in the weight of

20 evidence by the Agency's work on their prioritization, in

21 particular, their in silico methods which involved work on the

22 rainbow trout ER binding assay, and that the work here on the

23 cytosolic assay should not be looked at just in isolation, but

24 should be looked at in regards to all the information that's

25 available on this. 

26 Again, and in particular, look at the report
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1 from the January 2009 SAP on the prioritization.

2 ER alpha transcriptional assay, obviously this

3 assay as noted in the White Paper basically looks at the

4 activation of transcription estrogen dependent reporter gene

5 protocol. It's obviously valid for detection of estrogen

6 agonist.

7 As noted in the White Paper, the response

8 curve parameters for the weak estrogen/androgen were met in only

9 five of the 19 chemicals. Since the outer range of values were

10 very close to the test guideline ranges for the majority of

11 these outliers, the Panel recommends the Agency look into

12 revising the guideline ranges for these control concomitant with

13 reviewing whether the issue are within laboratory performance,

14 and that's minimizing the need to revise the guideline and focus

15 more on the laboratory compliance within the guidelines.

16 The Panel generally agrees that the majority

17 of the deviation for the transcriptional activation assays can

18 be considered minor. However, some Panel members raise the

19 issue concerning that cytotoxicity observed with a couple of the

20 laboratory experiments. 

21 I think this is, again, a reflection of a

22 general comment that we see that's putting the Agency between a

23 rock and a hard place because one time we're asking them to do a

24 full range of solubilities to the testing and at the same time

25 we ask them to back off when they get into the cytotoxicity. 

26 And it's hard to do one without the other, but perhaps the
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1 weight of evidence issue will give us some guidance or clearance

2 on this.

3 Moving on to AR binding; the AR binding assay

4 as noted by the Agency is basically using radioactive labeling

5 and is considered to not be able to distinguish between agonist

6 and antagonist. 

7 The Panel agrees with the Agency with the

8 findings that overall the laboratory performance and the AR

9 binding assays were generally acceptable, and that the data for

10 the 17 chemicals that were tested were reliable. The Panel

11 agrees the results demonstrate that AR binding assay as

12 performed can distinguish between chemicals that bind to the

13 androgen receptor in vitro. That was about all.

14 Steroidogenesis; again, steroidogenesis

15 pathway, looking at production of testosterone and estradiol and

16 that sort of aspect, it measures changes in these two hormones. 

17 The Panel agrees with the agency that the laboratory's

18 performance of the assays was in demand consistent across all 18

19 test compounds and the performance criteria were by and large

20 generally met for all compounds. 

21 In the majority of cases where the performance

22 criteria were not met, the values only slightly exceeded the

23 expected values and slight deviations did not impact the

24 interpretation of the reliability. Again, perhaps the best way

25 of addressing this would be to revise the performance criteria

26 and add a quantitative component to that and get away from
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1 slight changes and things like that.

2 The Agency reported in the White Paper that

3 proficiency testing for both ER binding and the steroidogenesis

4 in vitro assays were often omitted from the laboratory reports. 

5 If the Agency feels that these are important, the Panel would

6 suggest that these proficiency tests be routinely run and

7 reported because it's going to add to the competence in the

8 result that you had. 

9 And the Panel agrees that the overall results

10 demonstrate that steroidogenesis as performed can distinguish

11 the (inaudible) and alter and do not alter testosterone and

12 estrogen levels in vitro.

13 Moving on to aromatase, the Panel concurs with

14 the Agency that, overall, the aromatase assay as performed by

15 the testing laboratories was able to distinguish between

16 inhibitors and non-inhibitors. And the performance for the

17 aromatase assay was mainly consistent across all 18 compounds

18 that were reported. 

19 Moreover, the Panel agrees with the Agency

20 that the performance criteria for the assays were generally met

21 in each study and the deviation noted were minor. Again, if we

22 can quantify what is met by minor, it's going to be a lot easier

23 for acceptance of these results.

24 As we move on to other comments, as noted in

25 the White Paper, the laboratory performance of each of the Tier

26 1 assays were evaluated across 15 to 21 compounds. Although
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1 there were situations where performance criteria were not met,

2 the Agency considered these deviations did not matter and did

3 not impact the interpretation or validity of the data. 

4 In summary, the Panel is in basic agreement

5 with the Agency's findings that overall laboratory performance

6 for each of the Tier 1 in vitro assays met expectations and the

7 submitted Tier 1 assay data provided the expected useful

8 information to evaluate potential interactions for test

9 chemicals with the A and the E signaling pathway.

10 The Panel notes that while the number of test

11 chemicals, 21 or less, is small, they Agency should look to

12 adjust the performance criteria for the individual assays to

13 better reflect the experience gained since 2008. In other

14 words, as the sciences move forward there's no reason for the

15 Agency to keep themselves handcuffed by what was considered the

16 state-of-the-art of 2008 when we know it's a lot better than

17 that in 2013.

18 Specially, the Panel recommends more guidance

19 on how much deviation from the norm is appropriate for an

20 individual run to be acceptable, and it should be included in

21 the Tier 1 guidance. This type of guidance should alleviate

22 conclusions of cases where there are questions about assay

23 parameters. 

24 The Panel further notes that this guidance

25 could be to take the form of a decision tree that would assist

26 laboratories in deciding when to redo an individual run which
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1 has not met the criteria. 

2 Additionally, the Panel recommends the Agency

3 rephrase from using such terms as slight and rather report

4 percent differences in the performance criteria. 

5 In general, the Panel feels that small

6 deviations less than 10 percent are likely to be insignificant. 

7 Generally, the Panel feels that deviations from the recommended

8 performance that would render the study unreliable are likely to

9 vary between the specific assays, but as a rule of thumb,

10 greater than 10 percent and maybe up to 20 percent on the assay

11 that the Panel seems to be comfortable with.

12 The Panel understands that the protocols used

13 in the Tier 1 assay represents the state of the science in 2008

14 when the initial Tier 1 testing order was issued. The Panel

15 agrees with the Agency of not altering the protocol until the

16 first round of testing was complete. 

17 While this SAP is based on data for only 21 of

18 the 52 chemicals being tested in the order, data for the

19 remaining 31 chemicals will soon because available. The Panel

20 recommends that the Agency make these data available as soon as

21 possible as they will impact any weight of evidence

22 deliberation.

23 With that said the Panel feels there is

24 sufficient evidence with selected assays to suggest the

25 reevaluation of the test protocols may be in order. And I guess

26 the poster child for that would be the AMA, based on the
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1 discussions that we've had.

2 Based on both report in the January 2013 FIFRA

3 SAP on a prioritization scheme and public comment, some Panel

4 members question the redundancy of the ER binding and ER alpha

5 transcriptional assay used in the Tier 1 in the context of the

6 rainbow trout, ER binding and rainbow trout liver/gene

7 activation assays that were used by the program in the

8 development of the computational toxicological tools that are

9 used in the prioritization of prescreening.

10 Specifically, it's recommended in light of the

11 fact that "The hormone binding domain of the ER is highly

12 conserved across species." And I believe that comes out of the

13 document. 

14 The agency may want to look at what value the

15 Tier 1 in vitro assay has in relation to the ER pathway and

16 adding the information that is already available from the

17 screening exercise. 

18 This recommendation for the Agency to look at

19 ER binding and ER transcriptional activation assays in total to

20 reinforce its interest in using high-throughput screening data

21 to expand the structural domain of prescreening activities. 

22 This is consistent with some of the recommendations that were

23 made in the open comment session is that we can use all relative

24 information to make these decisions for what we end up doing in

25 Tier 1 testing and moving on to Tier 2. 

26 And that, while very long, summarizes all of
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1 the comments that were put together. And I may add that the

2 reason that these were a little bit long is that we had six

3 members responding on this particular question, so there's a lot

4 of comments to integrate. Thank you.

5 DR. DANIEL SCHLENK: Thank you, Dr. Schultz. 

6 Dr. Ehrich, anything to add?

7 DR. MARION EHRICH: Well I question the value

8 of aromatase assay since the steroidogenesis assay covers some

9 of the same things, but that would be a minor, since the comment

10 was made that you're going to look at all of them any way. I

11 think that's included in what Dr. Schultz said.

12 DR. DANIEL SCHLENK: Okay. Dr. MacLatchy?

13 DR. DEBORAH MACLATCHY: I just have one

14 additional comment that I just want to make sure is highlighted,

15 in that the Panel does agree that the results demonstrate that

16 the AR binding assays performed can distinguish between chemical

17 that bind or do not bind the AR in vitro. 

18 And I just want to note that I think this

19 conclusion is supported by the result that the control compounds

20 tested appropriately in the assay even if the assay preparations

21 do not fully meet all the assay criteria; and that that really

22 demonstrates the robustness of the assay. That's noted in the

23 White Paper and I think it's a point that shouldn't be lost as

24 we move forward. And I think it gets to this issue around the

25 guidelines and the preparations, and then the value of the

26 controlled substance and what information that adds to on the
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1 efficacy of the assay and its interpretation. So I just didn't

2 want that to be lost.

3 DR. DANIEL SCHLENK: Dr. Roby?

4 DR. KATHERINE ROBY: I have nothing more to

5 add. Thank you.

6 DR. DANIEL SCHLENK: Dr. Selvage?

7 DR. DANIEL SELVAGE: I have nothing to add

8 either.

9 DR. DANIEL SCHLENK: Dr. Tillitt?

10 DR. DONALD TILLITT: I have nothing more to

11 add. Thank you.

12 DR. DANIEL SCHLENK: Other Panel members want

13 to weigh in? Dr. Scott?

14 DR. GEOFFREY SCOTT: Just one comment. Today

15 we had a presentation in the public comment section regarding

16 cytosol fractions and we heard a lot of testimony about the cost

17 of the assays and the number of animals involved. 

18 We're using performance criteria assuring that

19 that fraction is active and useful. Let's use those performance

20 criteria and I would recommend consideration of a storage study

21 of some sort to look at extending the time requirements that are

22 currently in place since we do have performance criteria to

23 assure that the fractions are active. That will reduce costs

24 and also reduce the number of animals.

25 DR. DANIEL SCHLENK: Thanks. Dr. Ehrich?

26 DR. MARION EHRICH: One of our questions was
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1 to say should any of the data be discarded based on you

2 performance criteria. But you had good positive control in all

3 these assays, and therefore underneath Dr. Schultz's, they were

4 saying that the date is okay to include.

5 DR. DANIEL SCHLENK: Okay. Any other input? 

6 Dr. Delclos?

7 DR. K. BARRY DELCLOS: Just one minor comment

8 based on the public comments and the comments about the cytosol

9 assays. I'm pretty sure the 2008 SAP also recommending using

10 recombinant, at least the androgen receptors, so that's been

11 pretty well support that that should be looked into.

12 DR. DANIEL SCHLENK: Okay. Any other input? 

13 Okay. Let me go to the Agency. Any questions or clarification?

14 DR. GREGORY AKERMAN: This is Greg Akerman. 

15 When you're referring to the performance criteria for the

16 transcriptional activation assay and potentially revisiting or

17 looking at the performance criteria, were you referring to all

18 of the referenced chemicals or just one specific reference

19 chemical, the very weak positive control? The transcriptional

20 activation, ERTA, that's probably about three pages back.

21 DR. TERRY SCHULTZ: The White Paper mentions

22 cytotoxity observed with two particular chemicals done with

23 laboratory one, whether that's of any help to you, and how that

24 toxicity might impact the interpretation of the assay. So the

25 performance criteria that were talked about in that situation;

26 that wasn't it? 
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1 DR. GREGORY AKERMAN: No. I thought you

2 reference -- you know, it was like the four reference group the

3 study used. I may have misunderstood you.

4 DR. DEBORAH MACLATCHY: Yeah, it was with

5 corticosterone and methyltestosterone in those labs, and the

6 issues with the cytotoxicity with some of those labs.

7 DR. GREGORY AKERMAN: So it was just the issue

8 with cytotoxicity and not the actual performance criteria

9 values? 

10 DR. TERRY SCHULTZ: No. But the issue of

11 cytotoxicity is part of the performance evaluation, because

12 where you're being asked to raise the concentration of the test

13 and you're bumping in to the cytotoxity. Now how the Agency is

14 going to address that, I don't know, but it's going to have to

15 obviously be addressed in those cases where we're getting into

16 those issues. Whether you just drop out the highest

17 concentration because it overlaps with cytotoxicity, I don't

18 know if the performance criteria states those things.

19 DR. GREGORY AKERMAN: Yes. I mean if there's

20 20 percent cytotoxicity, unusually they don't consider that

21 concentration because that concentration is considered

22 cytotoxic. 

23 DR. TERRY SCHULTZ: Right. 

24 DR. GREGORY AKERMAN: I guess I thought you

25 were talking about the performance criteria for those specific

26 reference chemicals because some of them had trouble meeting
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1 performance criteria, especially for the very weak positive

2 controls. I thought you were referring to revisiting that. 

3 Maybe I just misunderstood. Were you just talking about

4 cytotoxicity only? 

5 DR. TERRY SCHULTZ: No. I'm talking about

6 relation to cytotoxicity.

7 DR. GREGORY AKERMAN: Okay. Okay. Thanks you.

8 DR. DANIEL SCHLENK: Okay. Mary?

9 DR. MARY MANIBUSAN: I have a couple of

10 clarification questions. Dr. Schultz, you did mention the

11 difficulties, especially within a screening approach to follow

12 the recommendations of running an in vitro assay up to limits of

13 solubility then bumping against cytotoxity. 

14 If there are any explicit recommendations to

15 the Agency from you or the entire Panel, we would certainly

16 welcome that today. 

17 And another clarification on the mention of

18 the prescreening, the rainbow trout, ER binding and gene

19 activation assays that were used to develop the (Q)SAR expert

20 system, and then mentioning the high-throughput data. 

21 Is your recommendation to look for that

22 information if that data is available and not be redundant in

23 requesting the Tier 1 ER binding and gene trans-activation

24 study?

25 DR. TERRY SCHULTZ: As one I would make that

26 recommendation, but until I contact the rest of my cohorts on
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1 that, it wouldn't go so forward on that.

2 I think that, in light of all the information

3 that you're getting from other sources on ER binding, that there

4 is a great redundancy in those two Tier 1 tests than there is in

5 any of the other test. And I would go so far as to say I

6 wouldn't have expected that those things should not have been

7 positive in your Tier 1 test because those things should've been

8 selected out a long time ago. No one would be developing a

9 pesticide that had an overt ER binding activity. 

10 So in this case, some of those things where

11 you get negatives are a good thing. It's what you would expect.

12 And I don't know if you want to look at that as a confirmation

13 or what. It gets to be an expensive type of conformation.

14 DR. DANIEL SCHLENK: Do we went to go around

15 to our associates to see if they concur on that one? Would that

16 help? Mary?

17 DR. MARY MANIBUSAN: I think that would be

18 very helpful as the Agency is looking towards building

19 efficiency in the Tier 1 assays. And if there's opportunity to

20 do so, with respect to what we've learned from the January SAP

21 to build into this conversation, I think will be helpful to have

22 that on record.

23 DR. DANIEL SCHLENK: Okay. Dr. Ehrich?

24 DR. MARION EHRICH: Well if it was the same

25 compound that was tested in the fish assay that we talked about

26 in January and the one that you're proposing to run through Tier
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1 1, why not just use that data?

2 DR. DANIEL SCHLENK: So that would be a yes. 

3 Okay. Dr. MacLatchy?

4 DR. DEBORAH MACLATCHY: I would agree. If

5 you've got data on ER binding and in some system that you've got

6 data that's solid, then you don't need to do it in multiple

7 systems.

8 DR. DANIEL SCHLENK: Dr. Roby?

9 DR. KATHERINE ROBY: I agree.

10 DR. DANIEL SCHLENK: Dr. Selvage?

11 DR. DANIEL SELVAGE: Yes.

12 DR. DANIEL SCHLENK: Dr. Tillitt?

13 DR. DONALD TILLITT: Yes, I agree. I think

14 that, as you move forward, you'd want to go to these more in

15 silico approaches to do the screening.

16 DR. DANIEL SCHLENK: Is there anyone who does

17 not agree with that on the Panel? Okay. I think you got your

18 answer there.

19 DR. MARY MANIBUSAN: Thank you very much.

20 DR. DANIEL SCHLENK: Okay. Let's go ahead and

21 move on to question 3. 

22 DR. MARY MANIBUSAN: Charge question 3, unlike

23 the Hershberger and Uterotrophic assays, a positive control is

24 not required in the male and female pubertal assays. For these

25 in vivo assays in rats, coefficient of variation limits are

26 specified in the test guidelines for most endpoints. 
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1 Submissions from different laboratories sometimes fell short of

2 meeting all the test guideline-recommended CV limits for the

3 endpoints evaluated. However, in most cases these shortcomings

4 were considered of minor importance to the overall results, and

5 EPA concluded that the data are still adequate for endocrine

6 screening.

7 Please comment on the EPA's conclusion. 

8 Please comment on when a deviation from the recommended CV

9 limits would render the study unreliable.

10 DR. DANIEL SCHLENK: Lead discussant for

11 question 3 is Dr. Selvage.

12 DR. DANIEL SELVAGE: Okay. I've revised a

13 little bit of what I was going to say, so I'll have to try and

14 read my own writing here, so forgive me if it's a little rough. 

15

16 First, just for the record, so we can put it

17 in the minutes, I wanted to just briefly describe the assay. So

18 the male and female pubertal assays, these rats are undergoing

19 puberty, which is a period that is highly depended on normal

20 endocrine function to transition to early adulthood

21 successfully, and so this is used to test the disrupting affects

22 of various compounds that are found in the environment. The

23 tests used are very relevant administrative paradigm, gavage, so

24 metabolism, et cetera, is going to happen, which is good.

25 It is noted, and it was noted earlier today

26 that these tests do use a significant number of animals to
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1 ensure proper study population control and that sort of thing,

2 and if that could be limited in some way, shape or form, that

3 would be a good thing, obviously. 

4 Okay. Just to get into the variations and

5 their acceptability, I think overall, the CVs were frequently

6 exceeded in the assays -- well assays for 16 out of 17

7 chemicals, but most of these were considered minor. And the EPA

8 concluded that that did not really overall affect

9 interpretation. And I think the Panel in general agrees with

10 that evaluation. I do, however, want to get into specifics for

11 a little bit on each of the assays. 

12 You invited an HPA axis guy to the table, so I

13 do have a little bit of concern about the CVs and what's

14 acceptable, and also about developing CVs with randomly cycling

15 female rats, in particular.

16 We'll go to the female rat assays. So these

17 have relatively high acceptable CVs for a number of parameters

18 that are presumably due to cyclicity affects that aren't

19 accounted for in the assays. 

20 It is noted that the cyclicity data were not

21 included in the White Paper and so we can't really see if that

22 was taken into account with the data. It would be useful to

23 know the day of estrous at the time of sacrifice for these

24 animals if they are indeed cycling for the treated ones.

25 I think the overall thought is that even the

26 minor deviations were acceptable. However, I'm going to be a
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1 little bit cautionary in some of my comments here regarding

2 proceeding, in part because I'm an HPA axis guy and I think you

3 might have a stress parameter going on with some of these

4 studies that you could influence them.

5 So briefly, female reproductive parameters

6 that are being measured here, they're highly subjected

7 stress-related effects, and that could also affect the

8 variability of the data that you see. Thus, high CVs that are

9 found in treatment groups should be really interpreted carefully

10 when they're considered acceptable because you already have the

11 parameter of relatively large acceptable variation because

12 you're not cycling the rats or checking them on the same day of

13 the cycle for your measurements; so that could be an issue that

14 you'd want to take into account. 

15 But also just to suggest to the Agency that in

16 doing these studies, either during the range-of-finding studies

17 or the studies themselves, you look at some HPA activation

18 parameters that might influence your results down the road. 

19 For instance, if you're getting low chronic

20 stress activity that you're not picking up at the overt

21 observation, behavioral observation et cetera, that could be an

22 issue. And these could contribute to the higher CVs that you're

23 seeing. And one worry is that you get a higher CV, get higher

24 variability, and you might be getting a negative that you're not

25 picking up with those, so that is a concern.

26 Also, I would say there is a bit of a way
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1 around estrous cycling problem and I would just like to suggest

2 that you could account for some of the variation that you're

3 seeing, for instance, in ovarian weight by either synchronizing

4 the estrous cycles of the rats prior to sacrificing them, so

5 that they're on the same day of the cycle. And then your values

6 would be much more likely to be at a lower variation, and then

7 that would be easier to tell if the chemicals that you're

8 testing are having an effect. 

9 Also, if you cycled the rats using a GnRH

10 agonist that would also, with these rats, test the integrity of

11 the pituitary in terms of responding to GnRH and then the

12 ovaries in terms of responding to LH and FSH. So I think you

13 might gain something from doing that, in addition to being able

14 to sacrifice the rats on the same day of the cycle and at the

15 same age.

16 Or, alternatively you could use "reproductive

17 age" as your target for sacrificing the rats, i.e., as a woman

18 said earlier today, the first day of diestrous and use that as

19 the parameter rather than just absolute age. So you might be

20 able to tighten up your numbers that way.

21 Okay. That's really about all I'm going to

22 say for the female part of the assay. I think, once again,

23 mostly what was said was acceptable, and the right

24 interpretations, I think, were being made. 

25 For the males, there were also a fair number

26 of CVs that were out of range, but interpreted as acceptable and
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1 I think the Panel overall agreed with that. 

2 Once again, just as an HPA axis guy, though, I

3 would really caution about stress and stimulating stress in

4 these animals which the chemicals that you're testing and having

5 that go undetected. Because in particular, one of your measures

6 is of course testosterone which bounces all over the place in

7 rats, that could be down to about a zero in a perfect fine rat. 

8 And if you're only taking one measure, you know, you might see

9 that. 

10 What I would personally do if I wanted to

11 measure a testosterone, which obviously is going to drive your

12 other measures to a very large extent, would be to -- you could

13 give the rats, for instance, and HCG challenge where you give

14 them human chorionic gonadotropin and stimulate testosterone

15 release. 

16 And so you can get a much more uniformed view

17 of what's happening with testosterone production and also the

18 responsiveness of the testes to LH. If you did that, I don't

19 think you would affect your other parameters, your weights of

20 various testes, et cetera, by doing that, and you would get a

21 better idea of what's happening with testosterone itself.

22 Other than that, in terms of the overall Panel

23 in general, I think, once again there was concern about the high

24 doses that were being used in the interpretation from those high

25 doses and what that might do the CV criteria and it's caution

26 that a weight of evidence approach needs to be utilized to
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1 determine if the compound moves forward. You can't really take

2 either one of these assays on their own because they both have a

3 big likelihood of giving you a false positive.

4 Also it's noted that, to what extent you can

5 -- supplier of rats. I mean I realize these rats are raised in

6 house by each of the labs that make them. Sorry, I can't ask

7 you questions, can I? 

8 The rats are raised inhouse so, you know,

9 there's all kinds of parameters that should go into the

10 variations between labs, and I'm sure you guys are aware that

11 you need to look out for that and communication between labs

12 would be good so that they could account for that. And what

13 else do we have? I believe that's about it. Hopefully my

14 colleagues on the Panel can add more to what I missed.

15 DR. DANIEL SCHLENK: Okay. Dr. Delclos?

16 DR. K. BARRY DELCLOS: I don't have much to

17 add. I'll just say that, from the discussions yesterday, it

18 seems that, between the EPA, it was indicated that the test

19 guideline recommended CV limits were recommendations rather than

20 set assay. I think that needs to be clarified and perhaps

21 revise the guideline, or whatever. 

22 And as Dan just mentioned, while

23 Sprague-Dawley rats were used for most of the pubertal assays

24 reported, it wasn't clear that the rats were from the same

25 supplier and factors such as substrain or diet can certainly

26 attribute to variability and the means and variances among labs
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1 and from the guidance that was given in the document. 

2 In any case, as stated, the deviations or

3 recommended CV limits does not occur in all endpoints for a

4 given chemical in the most part. On the assays for chemicals

5 that did have CV limits that deviated from recommendations are

6 listed as showing positive effects, and the concern with the

7 large CVs of course is that the extent of variability weakens

8 statistical power and make the assay unreliable for the

9 detection of biologically significant effects. 

10 So I thought in the event that other assays,

11 uterotrophic or the Hershberger that are complimentary or

12 redundant for these endpoints, that are removed from the

13 battery, perhaps the inclusion of positive controls should be

14 considered, or at least periodic laboratory proficiency assay

15 with positive control. That's all.

16 DR. DANIEL SCHLENK: Thank you. Dr. Horseman?

17 DR. NELSON HORSEMAN: Have captured most of

18 our consideration, I think. However, I did want to add a couple

19 of thoughts. 

20 This is related to a performance criteria for

21 a coefficient of variation, which is really a statistical issue.

22 And the charge questions ask whether the fact that these

23 sometimes fell short of meeting the guidelines was meaningful. 

24 And I'm sure a lot of this is going to come up later in the

25 weight of evidence discussion. 

26 But I think, overall, the challenge with
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1 accepting evidence from experience that fall short of meeting

2 this performance criterion, it relates to the weight one assigns

3 to committing either type 1 or type 2 errors in this stage in

4 this Tier 1 assessment. So higher coefficient of variation will

5 tend to increase the likelihood of committing a type 2 error, in

6 other words, failing to detect a true difference or having a

7 false negative. 

8 And if there's a bias at Tier 1, one might

9 suggest that you would be more willing to accept a type 1 error

10 than a type 2 error. So that would likely increase the number

11 of compounds moving forward, but it would minimize the chances

12 of failing to move a compound forward that was impacted from

13 endocrine disrupters. So there is a balance to be met here, and

14 the tighter you get these coefficients of variation, the easier

15 it's going to be to meet that balance. That's the point.

16 The problem is that there's a potential for

17 piling type 2 errors on top of one another in this situation so

18 that the battery admits multiple assays that don't meet that

19 coefficient variation performance criterion, you're adding type

20 2 errors on top of one another, so it's a challenge.

21 So the other side of this is a consideration

22 related to whether the criterion CVs are set at the correct

23 level or not, or whether they need to be revisited so they can

24 be met without compromising utility of this Tier 1 screening. 

25 And this question is relevant not just to the two types of

26 assays in this charge question, but across the spectrum of this.
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1

2 Ultimately the acceptable level of variance in

3 the screening assay needs to be sufficiently broad to capture

4 the range of biological variability, but not so large as to

5 allow for careless execution to assays. 

6 And it'd be very helpful if the Agency could

7 provide an explicit rationale for how these criteria for assay

8 variances are actually set and documentation regarding how well

9 the variance criteria captures biologically meaningful spectrum

10 while maintaining the high standards that are necessary for the

11 testing.

12 DR. DANIEL SCHLENK: Okay. Dr. Roby?

13 DR. KATHERINE ROBY: Just to continue on to

14 the thought of the CVs, and that I think some of the comments

15 with regards to source of animals and large variation across

16 source. If some consideration could be given to assessment of

17 inhouse CVs or historically inhouse CVs, this might reduce the

18 deviation from what you consider to be the criteria parameters. 

19

20 So within a given lab, their own CVs may

21 differ from what you consider would reduce, if you were allowing

22 the individual CROs or testing labs to give consideration to

23 their inhouse historical CVs for these test parameters.

24 DR. DANIEL SCHLENK: Okay. Other panel

25 members, question 3? No? Okay. Let me go back to the Agency. 

26 Any questions or clarification? 
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1 DR. JOHN LICCIONE: John Liccione from EPA. I

2 want to thank Dr. Selvage for his comments. We appreciate the

3 complexity of the HPG axis. I just wanted to make sure I

4 understood one of your comments on the female pubertal and the

5 male pubertal. 

6 I understand the suggestion about the

7 synchronizing the cyclicity for necropsy. So are you suggesting

8 also the possibility for amending the assay to include GnRH

9 agonist? Is that something to explore for us? 

10 And just to finish up, because it's related to

11 the other suggestion on gonadal is that the HCG challenge to

12 stimulate testosterone would lead to then also to look at the

13 responsiveness of the testes to LH. So these are things you are

14 recommending as things that we should look at to incorporate in

15 the assay, because we do know the testosterone is a variable

16 thing that could be influenced by stress.

17 DR. DANIEL SCHLENK: Dr. Selvage?

18 DR. DANIEL SELVAGE: Yes. If I were running

19 these studies, I would look at both of those as ways to try and

20 stabilize the data that you're seeing, and also as something of

21 the test of the integrity of the HPG axis of the females or the

22 males; because, as it is now, it doesn't seem like you have a

23 really good feel for that. The females are young and they're

24 not going to be cycling very regularly anyway.

25 So yeah, I would look at that if I were you. 

26 I think it could help. It would need some analysis though.
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1 DR. JOHN LICCIONE: Thank you.

2 DR. DANIEL SCHLENK: Okay. So we have one

3 more question. How about a 10-minute break before we move on

4 just to take a breath and regroup a little bit for our last

5 question in? So let's be back at four o'clock. 

6 (Whereas a break was taken)

7 DR. DANIEL SCHLENK: Let's go ahead and read

8 question 4 into the record.

9 DR. MARY MANIBUSAN: Charge question number 4,

10 The test guidelines for the six in vivo assays, Hershberger,

11 Uterotrophic, the male pubertal, female pubertal, the fish

12 short-term reproductive assay and the amphibian metamorphosis

13 assay offer some guidance on setting the dose/concentration

14 range when testing for specific effects on the E, A or T

15 signaling pathways. 

16 In some of the in vivo assays, overt toxicity

17 was noted based on the effects on growth, other sub-lethal

18 effects and even mortality at the highest dose/concentration

19 tested. Positive Tier 1 findings indicating the potential for

20 endocrine activity can be difficult to interpret in the presence

21 of overt toxicity. 

22 Please comment on the nature and severity of

23 overt toxicity that would render study results unreliable for

24 the purpose of Tier 1 screening of a chemical for endocrine

25 activity. 

26 Please comment on what, if any, additional
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1 guidance is needed to minimize overt toxicity while ensuring

2 adequate dose selection for these screening assays. 

3 Specifically comment on the validity of

4 including treatment concentrations with apparent body weight

5 effects in the analysis for potential endocrine interaction for

6 the fish short-term reproductive assay.

7 DR. DANIEL SCHLENK: The lead discussant for

8 question 4 is Dr. Selvage.

9 DR. DANIEL SELVAGE: Okay, you've got me

10 again, but I'm going to be fairly short with this one. What

11 I've decided to do because I'm an in vivo person doing mostly in

12 vivo research, I'll do a quick summary and then we've all pretty

13 much focused on our own areas, so what I'm going to do is turn

14 it over then to the other discussants and let everybody say

15 their piece, so I'll be pretty quick here.

16 I think everybody on the Panel understands

17 that the use of high doses when investigating potential

18 environmental endocrine disrupting affects of different

19 compounds is of great importance and we need to test the range

20 of doses, including high doses. 

21 However, overt toxicity reported during

22 testing can really complicate the interpretation of data in all

23 the in vivo assays that are employed by the EPA for testing

24 compounds for the endocrine disrupting affects. And while the

25 use of high doses are important, it's also important for the EPA

26 to take into account the high potential for confounding affects
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1 elicited by test substances, their dose at levels resulting in

2 toxicity, you know, for instance, in the rat, the overt toxicity

3 (inaudible), et cetera.

4 Okay. So this is obviously true for all the

5 in vivo assays performed in the endocrine disruptor program that

6 are being discussed at this meeting. So as when activation of

7 stress parameters are involved, it affects all endocrine

8 systems. And once again, they brought an HPA axis guy to the

9 table, and when you activate the stress axis that overrides

10 pretty much everything and a lot of the parameters you're

11 looking at. And I know you're aware of that but I want to put

12 it out there.

13 So in my previous statement I talked a little

14 bit about potentially confounding stress axis related factors

15 that can be involved in mammalian studies and the pubertal

16 assays, in particular. This can also play an effect in the

17 Hershberger and uterotrophic assays; in other words, it you get

18 stress levels that are too high, whether you detect them or not,

19 you're going to have problems measuring your endocrine endpoints

20 for your reproductive parameters, and also thyroid.

21 I think, because of these stress issues and

22 other issues that are going to be noted by the other

23 discussants, it's highly suggested that every effort we made to

24 eliminate the use of doses that are going to cause overt

25 toxicity during initial testing just because of what this does

26 to all the parameters that you're looking at. So I think the
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1 general feeling of the Panel is that you really don't want to go

2 there where you're seeing really big overt toxicity when you're

3 doing a screening.

4 So just a couple other points that I picked up

5 from the Panel, then I'll turn it over to the rest of the

6 discussants. 

7 One thing that I felt was really important

8 that was brought up was that toxic compounds are going to

9 disrupt basic cellular machinery that are necessary for a normal

10 endocrine function. So this really needs to be taken into

11 account, and that was in part from a comment by Dr. Leonard. 

12 And also, when you have a dose that's

13 affecting body weight in any of these systems, I think you're

14 going to have a problem in terms of the endpoints you're

15 measuring.

16 And then finally -- well I was going to say

17 something about the fish, but I think I'll let that go to the

18 other discussants and I'll end right there.

19 DR. DANIEL SCHLENK: Dr. Delclos?

20 DR. K. BARRY DELCLOS: Well I think, as

21 already been said, in most cases the overt toxicity is going to

22 make it impossible to clearly link observed effects to an

23 endocrine-related mechanism. It might not always be the case. 

24 I could imagine, for example, a body weight being significantly

25 reduced and still seeing an accelerated vaginal opening. That

26 wouldn't interfere with the interpretation of that affect. 
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1 But it is going to be problematic,

2 particularly if it's a critical study. Affects are observed

3 only in the dose group of overt toxicity. If only one screening

4 assay shows a positive affect it would be difficult to weigh

5 that in to deciding whether compounds should go on to Tier 2

6 without a repeat experiment of a lower dose. 

7 It's been indicated in the White Paper and in

8 the discussions yesterday that sometimes two or three doses are

9 used. If it continues to be necessary to use a dose at or near

10 the MTD, to set the high dose, perhaps it should be required

11 that at least three doses be used in the screening assay to

12 increase the chance if there's a dose without overt toxicity or

13 even add an additional dose.

14 Personally I would like to see, where

15 possible, moving away from the MTD and using either an

16 exposure-based, larger exposure type setting for the high dose

17 or previous information, even if it's not exactly the same

18 exposure regimen. And I think I'm not going to comment on the

19 fish assay because I don't know much about it.

20 DR. DANIEL SCHLENK: Dr. Leonard?

21 DR. JACK LEONARD: I'll just amplify a little

22 bit on what Dan Selvage said about the basic machinery. Having

23 worked with rats for 30 plus years, I know that toxic compounds

24 will impact almost all the basic machinery in the cell, energy

25 utilization, (inaudible) precursors, genome stability, and

26 transcription. 
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1 And as such, if the impact of toxicity on the

2 organism of well-being is so large that you see weight loss or

3 overt symptoms, it's going to be very difficult then to sort out

4 whether we're talking about toxicity or endocrine affects. So

5 from that perspective, I think that an overt toxicity finding

6 will probably be a disqualifying attribute for that specific

7 data subset and a smaller dose is necessary, so your

8 range-finding becomes very important for looking at these

9 compounds that have an opportunity to be toxic. 

10 A good example of what I mean by that is

11 energy disruptors which will impact the mitochondria, will alter

12 energy dynamics led to really well characterize changes in

13 signal transduction pathways. Commonly, the Akt pathway which

14 is really one of the key elements looking at carbon utilization

15 in the cell, hypotrophy gets kicked in and all of those events

16 will impact negatively on any endocrine downstream effects that

17 take place. 

18 So that, at all costs, I think it's important

19 when overt toxicity is looked at to look at that as a toxic

20 event and not an endocrine.

21 Finally, with respect to body weight change, I

22 can refer most likely to rodents. Whenever you see a dramatic

23 loss of 10 percent or so, usually that's due to loss of glycogen

24 from the liver, as well as other sources. The liver is a source

25 of all the serum binding proteins that are necessary for your

26 EAT targets that you're using downstream. And in fact, when you
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1 make an animal sick, the liver of rats in particular make a

2 thyroid binding globulin that binds thyroxin much more avidly

3 than the serum does albumin.

4 And in that case, you're going to get an

5 increased longevity of thyroid hormone thyroxin and (inaudible)

6 in the serum and the circulation, so it's going to look like

7 you're going to have more thyroid hormone than you really do

8 have; and the animal is not really hyperthyroid, it's just

9 you've got them sick.

10 And so those are some events that I can

11 foresee might confound the issue.

12 DR. DANIEL SCHLENK: Thank you. Dr. Patino?

13 DR. REYNALDO PATINO: I'll make comments

14 specific to the metamorphosis assay, which is what I was asked

15 to do. 

16 So the metamorphosis amphibians is a

17 particularly well characterized process that is known. They're

18 well established; a primary regulator of that is thyroid

19 hormones, so that's pretty well established. It is also known

20 that the growth and development during pre-metamorphosis and

21 during pro-metamorphosis in xenopus are concurrent but not

22 necessarily associated events. 

23 For example, tadpole development can be

24 inhibited by a number of well-characterized (inaudible) without

25 considerable or significant effects on growth. On the other

26 hand, growth can be accelerated by compounds such as triclosan. 
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1 And by the way, I'm providing some references here; I'm sure you

2 have them, but just to backup my statements, so they'll be

3 references associated with this statement or this comment. 

4 So growth can be accelerated by compounds such

5 as triclosan without detectable affects. And there was another

6 perhaps more complicated or interesting example where tadpole

7 growth and certain aspects of development, such as hind limb

8 length, can be inhibited by environmentally relevant

9 concentrations of cadmium, but without notable effects on

10 thyroid and histopathology, particularly thyroid epithelial cell

11 height. And for full disclosure that's a study that my lab

12 conducted.

13 So although it is important to measure and

14 differentiate effects on growth from effects on development in

15 this assay, given the wide variety of responses that have been

16 already characterized in terms of growth and development to a

17 wide variety of chemicals, in my opinion, there is not a priori

18 reason to conclude that overt toxicity as defined by effects on

19 growth will render the assay results unreliable for the purposes

20 of Tier 1. 

21 So it is a concern, effects on growth, but in

22 my opinion they don't necessarily invalidate the results for the

23 purpose of Tier 1. 

24 A relevant issue in regards to growth and

25 growth measurements in the metamorphosis assay is the method

26 used for normalizing hind limb length in tadpoles of different
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1 size, and this has come up a couple of times yesterday in the

2 questions and during the questions today and several of the

3 public comments. 

4 In the White Paper, the Agency recommends

5 normalizing hind limb length based on snout-vent length mainly

6 by dividing one over the other. This method, however, assumes

7 that the relationship between the two variables is isometric. 

8 And generally speaking, an isometric scaling relationship is one

9 where, on a double-log plot of the data, you have a slope of

10 one. However, if the slope is different than one, either higher

11 or lower, the method which would render that relationship

12 allometric -- not isometric -- the method of correction could

13 lead to errors. That would depend on the deviation from one of

14 the slops and also the size range of tadpoles that you're

15 considering in population.

16 And it is well-known that the size of most

17 body parts across organisms, that relationship is not isometric.

18 I mean with some exceptions, it is allometric. So a suggestion

19 would be that the Agency should confirm what type of

20 relationship there is between those two variables. And it

21 indeed is allometric, then there's actually and alternative

22 method. I'm not a biometrician, so this is a suggestion from

23 just a practicing biologist. 

24 But there is a alternative method that could

25 be used to correct for tadpole size that would get around the

26 problem, and that would be to use an appropriate regression
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1 analysis for hind limb length against snout-vent length to

2 calculate residuals, and then to use the residuals as opposed to

3 the raw data for ratios in this statistical analysis or

4 comparison.

5 And that has been done by others. We've done

6 it in our lab. Again, I'm not a biometrician. You need to

7 consult with somebody who knows, but that's a method that has

8 been used and again it's published in the literature. 

9 So those are my suggestions on the topic of

10 growth or affects of growth in metamorphosis and impacts on the

11 utility of the assay. Thank you.

12 DR. DANIEL SCHLENK: Dr. Scott?

13 DR. GEOFFREY SCOTT: The overt toxicity may

14 affect the ability to discriminate endocrine affects in these

15 battery of assays. If there is actual mortality or lethality

16 itself, one of the things you're doing is you possibly are

17 removing some of your most sensitive organisms out of the

18 population, and they may be sensitive to a broad spectrum of

19 things including affects for endocrine disruption. So that is a

20 very significant thing to consider when we see actual lethality.

21 I think when overt toxicity is observed within

22 the guidelines going back and running the lower doses,

23 particularly at the half logs and things, that's a good way to

24 go back and discern that affect. And if you do not see those

25 affects in the absence of overt affects, then you either

26 conclude that the affect at the higher dose was a combination of
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1 overt stress and endocrine disruption or, if you do not see any

2 endocrine disruption at all in those lower doses, you may decide

3 it was only that overt stress. So I think the guidelines allow

4 you to make those determinations.

5 Now we were asked to comment on what would

6 render results unreliable, and I have a couple of suggestions,

7 at least that I would make to consider. Again, if there's

8 actual mortality/lethality in the assay, that's one. What is

9 the severity of the affect? Is it a 5 percent, 10 percent, 20

10 percent, 50 percent change in growth of whatever behavior or

11 whatever it may be? I think the severity is very important. 

12 Also the size of severity magnitude is the frequency that that

13 occurs across the number of animals in the test. 

14 For example, if it occurs in more than 50

15 percent of your animals -- I happen to believe 50 percent is

16 used in a lot of assay because it has a population connotation. 

17 And so I would say that that is something to really look at is

18 the proportion in the population of your bioassay at that

19 treatment effect is a very significant thing. 

20 So if you consider those three things, I think

21 it really gives you a way to look at when you may or may not

22 disqualify an assay.

23 Another thing we were asked to do is please

24 comment on any additional guidance as needed to minimize overt

25 toxicity. And I think the real guidance here is on the

26 selection of the maximum tolerated dose. We've heard a lot
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1 about range-finding assays, how we set those, and particularly

2 in the fish where we're setting that in the third of the LC50. 

3 We also have had a lot of information about

4 high coefficients of variation on exposure concentrations. Well

5 if you're testing on that threshold between effects and

6 non-effects and then you have an alteration in dose that can be

7 a deadly combination. 

8 And so one of the ways that we might want to

9 look at this -- and I'm just thinking out loud with you here and

10 giving you some suggestions. One thing that you could possibly

11 do to reduce the occurrence would be to better define the

12 maximum tolerated dose from the range-finders, and that may be

13 to use the correction that you're using when you have overt

14 toxicity in better defining using half-log doses in the

15 range-finder to help you better define with greater precision

16 the maximum tolerated dose. 

17 So if it can help you on one hand when you

18 have a problem, it may also help you in preventing the problems

19 we're trying to suggest to you.

20 The other thing to do would be to evaluate the

21 95 percent confidence limit from the range-finder and look at

22 that dose. And if you were to actually run a couple of

23 compounds and do half-log doses, if that more precise

24 range-finder number is closer to that lower 95 percent

25 confidence limit, that could be a potential way around dealing

26 with the confounding problems that you have with maintaining the
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1 dose. 

2 So those are just sort of suggestions of how

3 you can look at maybe solving some of this problem because I

4 think as we were presented information today from the public

5 comment, we asked some questions about no X and low X and really

6 where do those really fit? 

7 We want to be at a level to induce and

8 endocrine effect if it's going to be induced. At the same time

9 we want to maintain that fine line between not causing other

10 related toxicity that's nonendocrine related. That's a fine

11 line to walk, so I'm only saying the better we can define those,

12 the better we will probably prevent some of this.

13 We were also asked to comment specifically on

14 concluding body weight looking at fish assay, and I'm going to

15 just make a few comments on that.

16 Body weight determinations are easy to measure

17 and may be directly related to altered behavior and reduced

18 feeding rates that may be contributing to overt toxicity. So it

19 may be an integrated measure of a lot of things that may be

20 important. Body weight effects are directly measurable and are

21 not subjected as opposed to behavioral observations which may be

22 a little more subjective. So I really think that is an

23 important point to make. 

24 The percentage of feeding response is also

25 very important in addition to body weight because it is

26 quantitative. So I really think if you're going to use body
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1 weight, use a more quantitative endpoint, and the fish assay is

2 going to help you.

3 Just to read out of the report in the fish

4 assay, only one compound out of 21 compounds tested resulted in

5 significantly reduced lengths and weight determinations. And

6 there were some other results, 5 out of 21 compounds as

7 significant differences in male or female body weight at doses

8 where other sub-lethal effects occurs. 

9 So it seems like there was some concordance

10 where we wanted to see with this. We do recognize the changes

11 in the body weight of females were problematic and could lead to

12 some exclusion of some data at higher doses at some points in

13 time.

14 I think, despite this complication, I think

15 tracking the body weight is an important endpoint. 

16 At the Duluth meeting after the Raleigh

17 conference, we said there were three things you need to pay

18 attention to; growth, development, reproduction. And I still

19 think growth is one of those that we need to be paying attention

20 to, and body weight is a certain measure of that. Thank you.

21 DR. DANIEL SCHLENK: Dr. Tillitt.

22 DR. DONALD TILLITT: I think many of my

23 comments have been gone over, but I'll try not to be redundant.

24 So I'm going to start out with the fact that

25 the Panel agrees, or at least this Panel member agrees with the

26 Agency that the FSTRA test protocol is very robust in the sense
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1 of having study metrics at multiple endpoints of biological

2 organizations. You know, this really enhances the

3 complementarity of the assay in the system. It's a real

4 strength of the test and it really is, I think, an integral part

5 of the EDSP Tier 1 Panel.

6 I think it's already been discussed, the idea

7 of getting at this idea of overt toxicity, that the starting

8 initial dose at one-third 96 hours LC50 of a compound and

9 expecting not to see overt toxicity in a 21-day assay is

10 unreasonable, and that that dosing scheme needs to be addressed.

11

12 Now with regard to the signs of overt

13 toxicity, absolutely there's going to be potential confounding

14 factors with interpretation of endocrine related results. 

15 The Agency plans to evaluate results of the

16 assay and a systematic case-by-case stance will be required to

17 properly tease out which endocrine related affects are clear and

18 which ones are going to be confounded by overt toxicity. 

19 Overt toxicity including reduced survival and

20 body weight loss can confound the results of the study at

21 exposure concentrations where you see this expressed toxicity. 

22 It may not be possible in some cases, for example, the GSI to

23 discern these effects. However, there are certain cases where

24 even through the signs of overt toxicity, for example, ovotestes

25 or secondary sex characteristics where you can have clear

26 endocrine related effects through this overt toxicity. 
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1 But again, I think the idea here is that

2 you're going to have to go case-by-case looking for consistency

3 of endpoints with this assay to determine whether you can or

4 cannot determine whether there's an endocrine related effect,

5 and that's going to be addressed, I think, in this weight of

6 evidence Panel.

7 You know, solutions to how you address this

8 overt toxicity, I think, reducing the starting point. And a

9 selection has to be made and there have been some good

10 selections, ideas brought up already. You know, you could go to

11 the half-log idea that is used with binding assays, but I think

12 maybe a starting point that is determined in the range-finding

13 test at the beginning could give you a better starting point. 

14 That's it.

15 DR. DANIEL SCHLENK: Thanks. Other Panel

16 members? Yes, Dr. Schultz?

17 DR. TERRY SCHULTZ: I want to point out the

18 nature and severity of overt toxicity and what the definition is

19 there, and then down to the specific issue of apparent body

20 weight effects.

21 When you're dealing with fish at the

22 individual chronic endpoint, the most sensitive chronic endpoint

23 it's typically growth. And obviously body weight is going to be

24 a parameter that fits into that mix. So you can have chemicals

25 that cause chronic effects via endocrine pathway modes of

26 action, but you will also have chemicals that have chronic
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1 effects by other mechanisms. 

2 And so to say that the body weight is a sign

3 of overt toxicity and to disregard that with the endocrine

4 function, in some cases it may be valid. In other cases, it

5 won't be valid. So I just caution you in that type of situation

6 because that is a place where you have individual chronic

7 toxicity and aquatic species bumping into the endocrine thing,

8 so there are the changes that they're going to be chemical and

9 concentration schemes where the interpretation is going to be

10 very pleasant. I don't know if that helps you.

11 DR. DANIEL SCHLENK: Dr. Horseman.

12 DR. NELSON HORSEMAN: My comments may differ

13 in degree but not in kind from what the other Panelists

14 remarked. And probably because I'm old enough to remember when

15 there was no such thing as an endocrine disruptor, right? 

16 So the theory of an endocrine disruptor is

17 sort of a relatively new kid on the block, and it seems to me

18 that it's based on a very explicit notion that it is only useful

19 in absence of overt toxicity, because endocrine disruption was

20 discovered as a phenomenon related to exposures to things that

21 were not overt toxic, right? The feminization of fish; the fish

22 weren't dying. 

23 So if that's the theory behind endocrine

24 disruption, I think that this issue is really critical and

25 getting it right, whatever that is -- and I understand EPA's

26 interest in making sure the doses are always high enough, but if
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1 chemicals have been defined as toxic and therefore you're in a

2 concentration range at which they're already regulated, it seems

3 to me the information about endocrine disruption isn't very

4 useful there because it's not going to contribute anything new

5 to regulation at those concentrations. 

6 So getting data on endocrine disruption at

7 lower concentrations would seem in the long run to be more

8 useful. 

9 DR. DANIEL SCHLENK: Okay. Dr. Patino.

10 DR. REYNALDO PATINO: Just a follow-up to what

11 Dr. Tillitt said or was said earlier about, I think it was the

12 effects on growth in the fish assay, especially in the females,

13 could be an indirect effect on spawning, for example. If the

14 females are not spawning, they may still be producing eggs. So

15 I mean that probably can be captured in the GSI information, but

16 it needs to be considered. It would need to be considered in

17 order to really say it was an effect on growth. If you're using

18 mass as an indicator of growth and compensate for that, based on

19 the GSIs, or the gonad weight. I think that's all I want to

20 say. Thank you.

21 DR. DANIEL SCHLENK: Other Panel members? Dr.

22 Scott?

23 DR. GEOFFREY SCOTT: If I could just correct

24 one thing I said earlier. When I was mentioning the use of the

25 lower 95 percent confidence limit, I meant that within the

26 connotation of one-third of the lower 95 percent confidence.
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1 DR. DANIEL SCHLENK: Okay. Any other

2 comments? Okay, let me go to the Agency, any questions or

3 clarification regarding the comments?

4 DR. THOMAS STEEGER: This is Thomas Steeger. 

5 I think the discussion about how to define maximum tolerated

6 dose has been really helpful. I believe that the one-third of

7 the LC50 has been proven to be problematic. 

8 Using the guidance that was bulleted out that

9 maybe one way to approach this would be to say that you want to

10 have a dose that doesn't result in mortality with severity of an

11 effect, say, on growth would be less than 10 percent and that

12 the frequency of effects across animals would be defined.

13 Does the Panel agree that that's a reasonable

14 standard to apply in terms of setting a maximum tolerated dose? 

15 We would like to give some kind of guidance towards eliminating

16 or reducing this problem. As a follow-up to that, when you do

17 have an effect, and overt effect, am I correct in understanding

18 that the Panel is telling us that, yes, it would be considered

19 in the context of what's going on at lower doses, but it's

20 reasonable to consider dropping that dose and evaluating a

21 potential for an effect.

22 DR. DANIEL SCHLENK: Dr. Selvage, do you want

23 to lead off on that? 

24 DR. DANIEL SELVAGE: No.

25 DR. DANIEL SCHLENK: Do you want to pass that

26 to one of your associate discussants then? Dr. Delclos, do you
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1 have a comment? No? Dr. Leonard?

2 DR. JACK LEONARD: I think that when you have

3 two events taking place, you have overt toxicity and a presumed

4 endocrine disruptor event, that if the overt toxicity may

5 overweigh or overshadow so that dataset, that particular set of

6 points might be removed from the evaluation so that you were

7 then potentially looking at lower concentration. That's what

8 Dr. Scott was talking about, one-third of the LC50 to start with

9 so that you could sort out the difference between the stress

10 events and the endocrine disruptors events.

11 I think putting those two together makes it

12 difficult to decide which is the cause and which is the effect. 

13 So I think yes, if you dropped your dosage, that would be much

14 better. So I would encourage you to remove that high

15 concentration datapoint from your analysis and then reevaluate.

16 DR. THOMAS STEEGER: Could you give us a

17 specific example of that?

18 DR. JACK LEONARD: No. Since I haven't looked

19 at all of your data, no. But I can tell you an example from my

20 laboratory which we've looked at the thyroid action for 30 plus

21 years, and perchlorate is a very big endocrine disruptor there. 

22 You can give quite a bit of perchlorate to the animal without

23 having an overt effects, yet it will really (inaudible) the

24 thyroid gland very quickly.

25 But when you do raise it to a level that you

26 begin to see toxic events in the liver, then the stress events
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1 eliminate what you're sorting. So at that point, we would chose

2 to evaluate perchlorate levels at less than when we see body

3 weight loss. And that's just how operationally I would suggest

4 that would be something to look at.

5 DR. DANIEL SCHLENK: Okay, thanks. Dr.

6 Patino?

7 DR. REYNALDO PATINO: I don't have any

8 comment.

9 DR. DANIEL SCHLENK: Okay. Well I think he's

10 asking if -- I could be wrong here -- but you're asking if 10

11 percent is a good number, is that correct?

12 DR. REYNALDO PATINO: Oh, I'm sorry. Okay. 

13 Yeah. Like I said for the metamorphosis assay, I don't think

14 that an impact on growth is necessarily a sign of a problem with

15 the assay, so I don't know that a 10 percent -- that may be a

16 little too restrictive. So with that caveat, I can't comment on

17 the other -- you know, I'm not familiar with mammalian systems

18 or others.

19 With the fish assay, you know, they're 10

20 percent might be reasonable just because these fish are adults. 

21 I mean fish have an indeterminate growth which means that they

22 grow throughout life more or less until they die, but there is

23 not a whole lot in the growth rate that diminishes. 

24 So at that time, in a three week period there

25 is probably not a whole lot of difference in size, I would

26 suspect. And there may be some but not a whole lot. And the
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1 effects on body mass may be a different thing. They could

2 starve, you know, lose appetite and starve and be emaciated,

3 that sort of thing, but given a toxic effect. But I suspect

4 that would occur probably at very high doses of chemicals.

5 So 10 percent might be a reasonable estimate

6 for a fish but I think for frogs, it may be a little too

7 restrictive. And in the context of growth and development being

8 regulated by different -- they're joined at the hip a little

9 bit, but they're really regulated primarily by different

10 systems.

11 DR. DANIEL SCHLENK: Okay, great. Let me just

12 remind Dr. Selvage, I hope you're getting all this down. Okay. 

13 Dr. Scott?

14 DR. GEOFFREY SCOTT: Tom, let me just try to

15 address your question. 

16 I think I heard you considering a percent drop

17 in body weight in the extent of incidence. And those were the

18 two things I said I really felt were important. One suggestion

19 I would have would be to go back and look at the compounds

20 you've tested and see if you can generate that. I mean I don't

21 want to put too much onus on y'all but I think it would be nice

22 to know what that number is, because this might come up in the

23 weight of evidence discussion subsequent to this. 

24 And I would think if you could at least

25 provide that, I might have a better indication of whether 10 is

26 the right number or not. I will say that in differentiating
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1 between dose response effects in fish, a 10 percent difference

2 usually may not -- given the variance in the population -- may

3 not be significant. And so it would really depend -- and I

4 don't work -- I work with marine fish. You know, they do the

5 exact opposite with salts and things. I can only say that the

6 work I've done, that would be pushing your ability to discern

7 differences, so it may need to be higher than that, I'm not

8 sure.

9 DR. DANIEL SCHLENK: Dr. Tillitt?

10 DR. DONALD TILLITT: Yes. And my comments are

11 related to the fish assay. I would absolutely tend to agree,

12 I'm saying agree with Dr. Scott's comments. Ten percent may be

13 very difficult. You're talking about fish that are one gram,

14 two grams, and so that amount of difference can easily be seen

15 and so it may be greater than 10 percent as a number. But I

16 think, analyzing the data that you have and looking at variation

17 of that data would be maybe an appropriate step.

18 DR. DANIEL SCHLENK: Okay. Dr. Green?

19 DR. SHERRIL GREEN: So in terms of growth of

20 Xenopus through the metamorphic period, I don't believe

21 typically you're measuring snout to vent length in the tadpoles,

22 but it may depend on the lab. And clarification on what they're

23 measuring in terms of assessing growth rate will be important

24 because some labs report the time it takes to progress from one

25 stage to the next, and that's a growth rate over the number of

26 days, which you could compare to controls. 
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1 But turning tadpoles upside down and looking

2 for the vent, that may be another way to do it that some people

3 report, but typically you're not measuring from the snout to the

4 tip of the tail. And using that as a growth rate, or it could

5 be some combination in -- it wasn't clear to me in the

6 guidelines how exactly as we progress through stages, but that

7 would be something worth clarifying in that how are the labs

8 reporting growth rates. Is it how long it takes to get through

9 stage 4 through 57 or are they measuring snout to vent or snout

10 to tail, or is it body weight? Are they weighing the tadpoles

11 and looking at mass increase? 

12 DR. DANIEL SCHLENK: Okay. Yes, Dr. Roby?

13 DR. KATHERINE ROBY: I just want to double

14 check, you were also talking about the rodent assays here. I

15 think 10 percent is probably too restrictive, especially with

16 gavaging, certainly with the onset of dosing. There will

17 probably be at least a transient loss of easily 10 percent

18 weight. So maybe again looking at -- not to give you more work

19 -- but looking at some of the data you already have might be

20 insightful as to setting that criteria. I would guess 10

21 percent is too restrictive.

22 DR. DANIEL SCHLENK: Dr. Green?

23 DR. SHERRIL GREEN: And one more comment. I

24 apologize for talking about the xenopus on the rodent question. 

25 But with regards to the 10 percent, in terms of animal's welfare

26 and compliance, typically the IACUCs will have a body loss that
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1 they will approve of on a toxicity study. So some animal use

2 and care committees may recommend that it is no more than 10

3 percent. 

4 So if that is an estimate, we're within the

5 ballpark of what would be expected over a period of time when

6 exposed to a potential toxic substance, so no more than 10

7 percent. And I agree, I think 10 percent growth rate might be a

8 little fast for some specific strains.

9 DR. DANIEL SCHLENK: Okay. Dr. Leonard.

10 DR. JACK LEONARD: At our institution, the

11 IACUC has established an acute loss 10 percent body weight as an

12 overt toxic event. And in our studies we are obliged to

13 terminate the animal at that point, so that colors how I view

14 the weight gain or weight loss. 

15 It'll be really interesting to go back and

16 evaluate whether or not that loss was acute. Did it occur over

17 a very short time or was it gradual over the time period? That

18 would clearly change your view point what the event was taking

19 place specifically in the rodent. 

20 DR. DANIEL SCHLENK: Dr. Patino?

21 DR. REYNALDO PATINO: Yes. I think we

22 probably need to differentiate between animals that are not

23 growing in the different assays and animals that do. And I

24 don't know if that question there refers to weight loss or

25 difference between the treatments and the controls, which is a

26 different measure. So just clarifying that, we need to be clear
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1 on that. And that may be assay specific.

2 DR. DANIEL SCHLENK: Okay. Dr. Scott?

3 DR. GEOFFREY SCOTT: Yeah, one additional

4 comment. If indeed you have length measurements on the fish, it

5 would be good to use it conditioning -- in my opinion, and Don

6 you may comment on this. In my experience, normalizing its

7 length with weight changes is a condition -- actually we used

8 length and then you divide it by weight -- is a good metric. 

9 And if you're collecting both of those, it allows you to

10 normalize for differences in size. So if you have those -- I

11 wouldn't suggest adding those as additional measurements -- but

12 if you have them and can do that, it may be helpful.

13 DR. DANIEL SCHLENK: Okay. Anyone else want

14 to weigh in on this one? No pun intended. Don?

15 DR. DONALD TILLITT: I don't believe they take

16 the length. They do take lengths, so yeah. Okay, so that would

17 be useful, absolutely.

18 DR. DANIEL SCHLENK: Okay. Let me go back to

19 Dr. Steeger, did you get the information that you needed from

20 that?

21 DR. THOMAS STEEGER: Yes, I did. Thank you.

22 DR. DANIEL SCHLENK: Okay. All right. We are

23 done for the day, I think, so let's adjourn. We will reconvene

24 tomorrow morning at nine o'clock.

25 (Whereas the meeting was adjourned for the

26 day)
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1 DR. DANIEL SCHLENK: Good morning, everyone. 

2 We've got a long day ahead us. So just to sort of streamline

3 things a bit, we'll skip introductions this morning and kind of

4 get straight into the questions. 

5 A couple of things before we get started, a

6 couple of Panel members approached me and said that they had

7 talked with some folks during the break yesterday and I wanted

8 to put that on the record. I think Dr. Leonard had --

9 DR. JACK LEONARD: Yesterday afternoon Dr.

10 Manibusan and I had brief conversation that amplified slightly

11 on the percentage loss of weight during some of the in vivo

12 screening tests. And I made a clarification there that I want

13 to make sure it's on record that it was my opinion that an acute

14 loss of 10 percent weight should be viewed very differently than

15 a gradual loss of weight of 10 percent over the entire period,

16 which I considered less of a biological problem. Thank you.

17 DR. DANIEL SCHLENK: Great. Thanks. 

18 Anybody else have any comments on that? Dr. Green.

19 DR. SHERRIL GREEN: Yesterday, I spoke briefly

20 with a representative from Syngenic -- Syngenta, right? --

21 about an alternative assay to the AMA, and they clarified for me

22 that they did not have a different or more detailed approach to

23 the current AMA assay that's being used in Tier 1 testing. 

24 That's it.

25 DR. DANIEL SCHLENK: Thanks. Dr. Selvage.

26 DR. DANIEL SELVAGE: Sorry. Since we're doing
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1 this, I thought I would say I spoke to a gentleman from EPA

2 regarding the corticosteroid test in the rat species. So that

3 having be my comments.

4 DR. DANIEL SCHLENK: Great. Thanks. 

5 Anyone else? Any other comments. 

6 Fred has informed me that there are some

7 questions of clarification from the Agency for Question 4; is

8 that right? Do you want to put that on the record?

9 MS. CATHERINE AUBEE: Thank you. Good

10 morning, everyone. I just had a follow-up clarification

11 question for Dr. Patino regarding his discussion yesterday of

12 hind limb length normalization. You had made some

13 recommendations for testing for an isometric relationship prior

14 to doing any sort of normalization. 

15 I wanted to know, 1) if you're recommending

16 that we only do that in controls or across treatments. I want

17 to make sure I understand the recommendation. 

18 2) How much of a deviation from that one/one

19 isometric relationship would be too much that you would not

20 recommend doing the normalization? 

21 Additionally, I also wanted to request, if

22 possible, you said that you had some references to give to us. 

23 If it would be possible for you to read those into the record,

24 that would be very helpful since we won't actually get your

25 report for some time. Thank you.

26 DR. REYNALDO PATINO: Yes. That is something
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1 that I think needs to be, as far as the deviation from the slope

2 of one needs to be empirically determined. Also, that depends. 

3 It may not be a problem. Let me say that first. It depends on

4 the deviation from the slope of one and also on the range of

5 sizes that they're using because if you have a very short range

6 of sizes, the impact of a deviation of a slope of one may be

7 very limited. 

8 So I think it's something that needs t to be

9 empirically determined. I come mostly from the fish literature,

10 from the fish field, biology field, and it's not commonly

11 considered, but morphometricians do care about these things and

12 I've learned in the last few years about that. And the errors

13 -- if you don't consider those things, if you don't show that

14 there is not a problem -- then you may be introducing errors

15 into your data that actually does not exist before you do the

16 transformation. 

17 So I have some references. I'm allowed to do

18 it a little bit later because I'm going to have to find them. I

19 can read those into the record. Essentially, a colleague,

20 morphometrician, who taught me about this is, you know, some of

21 this papers is what I'm citing into the record.

22 MS. CATHERINE AUBEE: Thank you. 

23 DR. DANIEL SCHLENK: Dr. Steeger. 

24 DR. THOMAS STEEGER: I spoke briefly to Dr.

25 Selvage this morning and want to just make clear that what we're

26 looking for, in terms of the maximum tolerated dose, as I
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1 indicated yesterday, we appreciated the panel's comments on how

2 better to define that. 

3 To the extent that you can put boundaries

4 around that would be very helpful so that when we make

5 recommendations to the regulated community and how to go about

6 testing for maximum tolerated dose, we can be more specific. 

7 Thank you.

8 DR. DANIEL SCHLENK: All right. Let's go

9 ahead and move forward with Question 5. 

10 DR. MARY MANIBUSAN: Charge Question Number 5:

11 Spinal curvature usually manifesting as bent tail and Xenopus

12 laevis tadpoles was reported in 15 of 18 amphibian metamorphosis

13 assay studies reviewed thus far. The anomaly appears to be the

14 first observed several days after study initiation and

15 prevalence increases with time. 

16 Overall, the prevalence of spinal curvature in

17 these studies range from a "few per replica" to 92 percent of a

18 given treatment group by test termination. Experimental work by

19 the EPA Office of Research and Development suggests that certain

20 feeding regimes are associated with spinal curvature in their

21 Xenopus test populations; however, spinal curvature remains

22 prevalent in the range of 16 to 92 percent in the five industry

23 AMA studies in which feed was reduced by 50 percent, compared to

24 the guideline recommendations. 

25 Overall, the incidence of spinal curvature

26 appears to be highly variable. From a qualitative review of the
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1 data, there appears to be no consistent differences in the

2 incidence or variability of spinal curvature when studies using

3 guideline versus reduced feeding regimes are compared.

4 Please comment on whether the presence or

5 prevalence of spinal curvature in test species, including

6 controls, compromises the utility or validity of an AMA

7 submission. If so, when does the prevalence of spinal curvature

8 render the study unreliable?

9 What technical guidance may be useful for

10 laboratories in reducing the occurrence of spinal curvature and

11 determining if or at what point within the study a study may be

12 compromised because of this phenomenon?

13 DR. DANIEL SCHLENK: Thank you. Our lead

14 discussant for that is Dr. Schultz.

15 DR. TERRY SCHULTZ: Thank you. The Panel

16 realizes that there is a very high instance, 83 percent of the

17 studies that show this issue of bent tail or spinal curvature,

18 and they do feel that this has potential of impacting the assay.

19

20 I will not read into the record, but within

21 the report we will refer to basically Table 32 and the

22 information presented there. And from that came out, the Panel

23 agrees that the difference in iodine in the food and water, does

24 not seem to explain the syndrome, per se. And that the

25 examination of the data presented in the White Papers suggested

26 response seems to be somewhat laboratory dependent. 
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1 With that said, the Panel understands that the

2 AMA is a screening assay in the Tier 1 battery, intended to

3 empirically identify substances which may interfere with normal

4 function of the HPT axis. The AMA is a general vertebrate model

5 to the extent it is based on conservative structures and

6 functions of the HPT axis. As such, it's an important as an ex

7 vivo model or thyroid degenerative processes which respond via

8 the HPT axis.

9 The Panel further understands the AMA is

10 related to altered hypothalamic pituitary function antithyroid

11 activity and thermometric activity. In the case of the altered

12 hypothalamic pituitary function, the AMA is complementary to the

13 rat pubertal assays and the fish reproductive screening. In the

14 case of altered antithyroid activity, the AMA is complimentary

15 to the rat pubertal assay.

16 In the case of the thermometric activity, AMA

17 has no complimentary assays within the Tier 1 fabric. So there

18 is an overlap with other assays, other in vitro assays in the

19 Tier 1 battery, but there are some mechanistic or mode of

20 actions that are covered only by the AMA.

21 The general experimental design, of course,

22 comes from the work of Fort, et al. (2007). Briefly, the

23 Nieuwkoop and Faber Stage 51, Xenopus tadpoles are exposed to

24 treatment groups and minimum of three concentrations with a

25 negative control. The assay runs 28 days, there are replicates

26 per tank. 
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1 So basically, the density of 20 tadpoles per

2 tank with the replicates gives you 80 larvae that you're going

3 to be looking at. The observed endpoints include the hind limb

4 length, the snout to vent length, the Nieuwkoop and Faber

5 developmental stage at the end of 21 days, body weight, thyroid

6 histology or histopathology and daily observance mortality and

7 general clinical signs. 

8 The Panel feels that without an evaluation of

9 the weight of evidence, the AMA must today be considered

10 important as a Tier 1 screen. But the prevalence and

11 variability and the percent of clutches expressing this general

12 tail malformation or bent tail, does affect competence in the

13 utility of the assay. 

14 The Panel further understands that the

15 protocol represented the state of the science in 2008 when the

16 initial Tier 1 testing order was issued. The Panel agrees with

17 the Agency in not altering that protocol until the first round

18 of testing was completed. 

19 While this SAP is based on only 21 of the 52

20 chemicals being tested in this initial order, the Panel feels

21 there is sufficient evidence with the AMA that the protocol be

22 reevaluated as soon as possible.

23 However, the Panel also recommends not

24 changing the experimental design of the actual study outcomes

25 until all 52 chemicals are evaluated. The reason for the

26 discrepancy here, the Panel wasn't sure whether all 52 chemicals
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1 have been tested, and only 21 have been evaluated by the Agency

2 or there are others that are in the process.

3 We're not asking the Agency to change the

4 horse in the middle of the stream, but as soon as you get to

5 shallow water on the other side, start looking at another way to

6 get across the river.

7 Since they stopped the vent length as a key

8 measurement in the AMA, the Panel feels the bent tail issue has

9 the potential to impact both performance criteria for acceptance

10 of NGO test results and scientific competence in the utility and

11 the validity of the AMA. Therefore, the Panel recommends the

12 Agency determine the cause and possible mode of action for the

13 bent tail syndrome so guidance can be given to the laboratories

14 in how to eliminate it or at least reduce its morbidity response

15 to less than 10 percent of the population.

16 The Panel understands that the assay typically

17 begins with Stage 21 larvae that do not show tail curvature,

18 however, curvature is typically not observed until the larvae

19 are further along in the pro metamorphose stage. That is,

20 window of susceptibility for the AMA is basically Stage 51

21 through 55. 

22 Thus, laboratories cannot simply select the

23 effect out of the test; however, the Panel feels there may be

24 signs in the Stage 51 embryos they can use to assist the

25 laboratory in doing a better job of selecting against this

26 response. 
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1 The Panel further recommends monitoring a

2 portion of each clutch not used in a given study. It would be

3 prudentially useful to possibly report these results. The Panel

4 understands that summation of this competence is in the guidance

5 document, but maybe it needs to be elaborated.

6 In other words, once you set up the tests, you

7 had some things in the clutch monitoring you may identify early

8 on of whether you want to proceed to the full 21 days on that

9 test or make some decision at day 7 to discontinue that test

10 because clutch performance was so poor that it wouldn't be

11 useful.

12 The Panel further recommends that with the

13 acceptance of the AMA test results, overall, the clutch should

14 not have a rate of mortality or morbidity combined to be greater

15 than 20 percent.

16 Evidence presented in the White Paper and the

17 oral presentations at SAP does not allow for definitive

18 understanding of the bent tail syndrome. With that said, the

19 Panel acknowledges the presentation of Dr. Olmstead during the

20 public comment period was of real value.

21 The Panel believes that pathological

22 examinations of the tadpoles may reveal information that would

23 be more helpful. And in that regard we're referring not only to

24 gross anatomical pathology, but actually being some

25 histopathology of the lesion area. And the additional comments

26 will explain why we're interested in that.
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1 The Panel benefits from having members that

2 have expert, extensive experience with Xenopus husbandry. These

3 Panel members feel that the bent tail syndrome is mainly a

4 husbandry issue. The Panel believes that the potential causes

5 of this response are 1) genetic, 2) nutrient deficiencies, and

6 3) ionic imbalance.

7 The Panel further thinks that it is highly

8 probable that all three factors may come into play in some sort

9 of multifactorial causation scheme. In other words, it's very

10 difficult to tease out the genetics from the nutrition from the

11 (inaudible) because of the complexity of running those types of

12 assays, but there is both trend and anecdotal information that

13 says all three of those are particularly important.

14 In particular, as anecdotal information

15 leading to such syndrome is related to ionic balance which is

16 expected by both the composition of the water and the

17 nutritional deficiency. 

18 One panel member has extensive experience in

19 performing the frog embryo teratogenesis assay Xenopus, better

20 known as FETAX. Where this assay is different in protocol from

21 the AAM, the test species is the same. Work by this panel

22 member found that softer water, water with less calcium and

23 potassium caused a greater incidence in the lateral tail

24 flexures, and other general toxicity -- I'm putting "toxicity"

25 in quotes in here -- effects such as edema and abnormal gut

26 coiling. 
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1 We found edema, abnormal gut coiling and

2 lateral tail flexures to be general responses we see in a wide

3 variety of exposures to different toxicants. They are not a

4 specific teratogenic response that can be linked to a particular

5 mode of action.

6 Moreover, experience by selected Panel members

7 has shown that by keeping track of clutch performance,

8 specifically the number and the quality of the embryos from each

9 spawn, for high quality breeding pairs can assist in reducing

10 these general effects.

11 Moreover, breeding these selected pairs on a

12 regular schedule, regardless of the need for the embryo, does

13 improve clutch quality. So if people are people are purchasing

14 pairs, bringing them in, using them one or two times for this or

15 that, you get less overall clutch quality than if you bring in

16 matched pairs and keep track of them on the frontend and then

17 read them regularly so you're not getting older eggs in the

18 system. All improves clutch quality and all those things reduce

19 the general toxic observation.

20 The panel also notes that in fish, nutrient

21 deficiencies such as micronutrients, can lead to development

22 curvature of the spine. Also, excess of certain nutrients, for

23 example, selenium and copper, can also cause this lesion. 

24 Lastly, dissolved oxygen also leads to spinal

25 curvature in fish embryos. So there is evidence for a number of

26 aquatic species that the husbandry issue is very, very important
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1 in identifying this particular lesion. 

2 In relation to other comments, while not

3 directly asked in Question 5, the Panel strongly feels that the

4 issue of the bent tail in Question 5 and thyroid histology in

5 Question 6 and their interaction with other endpoints associated

6 with the AMA, leads to a recommendation that the Agency look at

7 the change in the experimental design of the AMA study. 

8 It is further recommended that the Agency

9 specifically look into the development of stage-dependent,

10 rather than time-dependent protocol. And I think this will be

11 addressed more specifically in Question Number 6, so I will

12 leave that right there and it will appear up again.

13 Since neither the White Paper, nor the

14 Agency's oral presentation have tried the histopathology of the

15 bent tail lesion or presents photomicrographs of the conditions

16 - this is looking at it with either a live microscope or the

17 electron microscope -- the Panel found it difficult to provide

18 meaningful guidance.

19 The Panel members with experience with the

20 Xenopus testing realize that the issue where tail mouth

21 (inaudible) lateral tail afflictions are typically considered

22 non-specific effect and associated with a muscular phenomenon

23 where malformation, such as the tail, a dorsal ventral kinking

24 tend to be more related to connective tissue or skeletal

25 phenomenon. And those are two very different mechanisms which

26 show different histopathologies, but show very similar growth
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1 pathologies. 

2 I will then like to read into the record that

3 we're noting the study of Schultz, et al. (1985), examining

4 osteolathyrogenic effects and the history of that and the study

5 of Hitch and Dale (2005), which talks about the lysyl oxidase

6 family and expression of frog which reports the importance of

7 lysyl oxidase and the copper co-factor in the formation of

8 elastin and collagen fibers.

9 Lastly, the Panel understands there is no

10 biochemical data to date that demonstrates there is alteration

11 in elastin and collagen formation. The Panel understands that

12 functionally, the tail bending that is reported does not

13 actually affect the reabsorption of the tail, but the Panel is

14 not convinced that the site of a lesion could not affect the

15 measurement of the snout to vent length. 

16 And since that is a key measurement that's

17 used in the total package compared to other components, it can't

18 be eliminated as that the tail bending does not have an impact

19 on the interpretation of the results of the AMA. And those are

20 the general comments, but there will be some other comments made

21 by some of the Panel members. Thank you.

22 DR. DANIEL SCHLENK: Thank you, Dr.

23 Schultz. Our next discussant is Dr. Green.

24 DR. SHERRIL GREEN: I'd like to elaborate,

25 particularly on some elements of information that we would need

26 in addition to assess the bent tail, as reported by some of the
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1 contract laboratories, and focus a little bit more on some of

2 the husbandry and the recommendations that I can make from

3 personal experience and also from what is published on Xenopus

4 and other species about the occurrence of bent tail.

5 Some of the additional information that would

6 be required, that would be helpful would be for the panel to

7 understand, in addition to photographs and histopathology of

8 these affected specimens, the source of the stock used in this

9 study -- and I'll explain why that's important in a moment --

10 and details regarding the husbandry conditions, especially water

11 quality; especially water quality -- I need to reiterate that --

12 under which these conditions occurred.

13 For someone like myself who is maintaining

14 large populations of adult Xenopus in laboratory conditions and

15 dealing with researchers who reared tadpoles, not on the

16 production scale that would be required in these situations for

17 laboratory testing. There are very few labs in academics that

18 do raise the large number of tadpoles. I can probably think of

19 three, maybe across the nation, and one or two vendors that do

20 it. 

21 The reason I bring this up is because the

22 raising of tadpoles is very much an art, as most of you who've

23 done it know, and part of it is science, but it really takes

24 experience. You learn a lot by trial and error. Somebody who

25 is new to the business of Xenopus, and particularly raising

26 Xenopus tadpoles beyond 96 hours, would be faced with some of
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1 these husbandry issues no matter what. So I wouldn't say that

2 they are entirely novel or unexpected. 

3 Moving forward, though, I think it's important

4 to point out that as a laboratory animal veterinarian in charge

5 of several thousand Xenopus laevis in a laboratory environment,

6 when we come across the occasional tadpole with a bent tail we

7 attribute it to infrequent recessive mutations that are

8 well-documented in this species.

9 In fact, the mutation load in Xenopus laevis

10 is 1.875, and that's slightly lower than what is actually seen

11 in humans. So it does occur in ideal conditions where husbandry

12 is carefully assessed every day and monitored continuously. So

13 they're not unusual, but they are considered rare. 

14 And usually, we can trace back those rare bent

15 tail tadpoles to matings with wild-caught stock. And this has

16 been published in a 2005 paper which I will supply to the

17 agency. Some of this information comes directly from this

18 paper. 

19 Again, as a laboratory animal veterinarian, if

20 I went into a room and somebody reported to me an entire tank of

21 tadpoles suddenly developed bent tail -- and this can happen,

22 literally, overnight. There are good hobbyist videos online

23 where you can see they've set up a webcam and they're watching

24 their tadpoles grow and then they'll report, "Oh my gosh, I came

25 in today and the entire tank of tadpoles has curved tails."

26 The first thing I would think of, as a
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1 laboratory animal veterinarian, diagnostically, is this is a

2 water quality problem. The sporadic morbidities and mortalities

3 in large colonies of Xenopus and tadpoles may be other causes,

4 infectious diseases, and you can usually look at your morbidity

5 reports and see that it happens over time, over weeks. But when

6 you have large numbers of animals affected, it's water quality

7 that we look to first.

8 Again, Noramly, et al. published that these

9 bent tail occurrences under these husbandry conditions are

10 considered epigenetic effects, usually due to less than ideal

11 husbandry conditions.

12 So what are those conditions? There are a

13 couple of them that had been reported in Xenopus. And I would

14 refer the Agency and the labs that they work with to that

15 Noramly paper published in 2005. It's from the Grainger Lab at

16 the University of Virginia, and they have vast experience and

17 they also refer to the Harland Lab at UC Berkeley.

18 The tadpole rearing conditions must be very

19 fine-tuned, as they report, especially during the first seven

20 days. They see bent tail then and then beyond and they feel

21 that it's largely due to water quality changes and issues in the

22 first seven days.

23 But other things affect the husbandry and they

24 go through in detail what they recommend for stock and density. 

25 One of the important points that they have

26 raised, particularly with tadpole rearing, and this is for
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1 Xenopus tropicalis, but I can infer by reading that paper that

2 probably for Xenopus laevis as well; pH seems to be very

3 important. And they have a very narrow pH range, as published

4 in that paper. On their website URL that they refer readers to

5 about dealing with bent tail.

6 Let me move through the document a little bit

7 more. Recommendations here, as Dr. Schultz pointed out and the

8 rest of the group that studied this on the panel, it will be

9 important to keep track of clutch performance. So you need to

10 know where your mating pairs came from. And some vendors,

11 wild-catch their Xenopus laevis and one big vendor has only

12 laboratory-reared and conditioned frogs. 

13 I might make the recommendation that that

14 should be considered that wild-caught frogs not be used in the

15 matings and that would eliminate some of the problem, not all of

16 the problem.

17 Usually, even if they are wild caught by two

18 or three generations, you can pick out the frogs that don't seem

19 to be showing, if it is a genetically related recessive mutation

20 and get rid of the problem significantly by doing that.

21 Another recommendation would be to closely

22 review the water quality conditions under which those bent tail

23 tadpoles were kept. And daily recording observations,

24 minimally, of pH, water temperature, ammonia content, water

25 hardness, DO. Those are pretty standard water quality tests,

26 with the YSI monitors that sit in the tanks that continuously
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1 read out which most systems use, or even by hand, would be

2 really important. 

3 Temperature, pH, some nutritional deficiencies

4 such as calcium and Vitamin C are reported to cause bent tail in

5 other amphibian species that are laboratory-reared. The

6 American bullfrog and the leopard frog, you know, be aware that

7 those are not fully aquatic species. They differ in eco-habitat

8 and in some of their nutritional requirements. But they're

9 close enough as an example of what can happen in a laboratory

10 environment without careful attention to some of these other

11 conditions.

12 Temperature and pH have been associated with a

13 condition in Xenopus tropicalis, as well as water softness,

14 which in some institutions, the water filters out certain ions:

15 calcium, magnesium, potassium, and can make the water softer. 

16 And that has been experienced by a panel member with Xenopus

17 laevis.

18 I studied carefully the overfeeding and there

19 is a different perspective, from a laboratory animal

20 veterinarian's review, that it isn't overfeeding and excessive

21 growth as a result of it that may be contributing to bent tail,

22 but in my opinion, overfeeding can really pollute the water. 

23 You have ammonia and pH changes that comes with the degradation

24 of the uneaten food and also the amount of feces and body wastes

25 that growing frogs will produce. So that all comes back to

26 water chemistry and scrupulous husbandry practices.
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1 Uneaten food and excess food, again, will

2 cause those changes and could potentially cause the bent tail

3 seen in many of these labs. So when they backed off by 50

4 percent, you could eliminate some of it, but not all of it

5 because you would still need to pay attention to what's

6 happening in the water. I would suggest that again, water

7 quality is a big issue here.

8 There are several papers that have been

9 published, maybe five years ago at this point, that show certain

10 herbicides and pesticides are known to cause bent tail in

11 Xenopus laevis tadpoles. So it makes sense that under these

12 testing conditions that we might screen both the water and the

13 food for herbicides and pesticides. Maybe not an entire

14 toxicological screen because that would be an added expense, but

15 certainly for the ones that we know cause bent tail. And that

16 too, I believe, has been recommended by Noramly in Robert

17 Grainger's paper. 

18 All in all, I think those recommendations

19 would take the Agency and the contract labs fairly far in

20 getting a handle on what's happening with bent tail in some of

21 these animals. 

22 As the Panel stated, morbidity and mortality

23 less than 20 percent is a typical acceptance rate, and bent tail

24 less than 10 percent is probably a reasonable target to try and

25 meet, I'd say. 

26 Again, even some of the research labs have
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1 recognized that some of these animals will go on with a bent

2 tail and go through metamorphosis and do quite well. In fact,

3 the webcam hobbyist had a tank of 15 tadpoles that literally,

4 overnight, developed bent tail. And if you look closely, you

5 also made a comment, "I noticed some brown fuzzy stuff growing

6 on one of them." 

7 Well, that's probably a water mold that's an

8 opportunistic pathogen, saprolegnia or some related. And that

9 doesn't usually appear unless there is a water quality problem. 

10 At any rate, with the exception of that tadpole, the other 15

11 progressed through metamorphosis quite fine and normally.

12 And again, if it is related to some of these

13 mutations where the kink affects the spine, then it will, as Dr.

14 Schultz pointed out, affect your ability to get an accurate read

15 on snout to vent length because the back is crooked. But if

16 it's beyond that and it's just the tail, many of those animals

17 will go on to develop just fine. 

18 Whether or not they should be included in a

19 study, ideally, I wouldn't include them and that's why it would

20 be important to get the effect rate, the prevalence of the

21 condition to 10 percent or below, and then you would still have

22 plenty of animals that are not affected and are developing

23 normally.

24 So I think that's all I have.

25 DR. DANIEL SCHLENK: Thank you, Dr. Green.

26 Dr. Scott.
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1 DR. GEOFFREY SCOTT: Just a couple of comments

2 to add to what Terry and Dr. Green had said. One of the issues

3 that I think is really important, we talked about, in this

4 assay, alterations in feeding, both overfeeding and reducing

5 feeding, and I want to talk, first of all, about the quality of

6 the food.

7 One of the things that I think is very

8 important in looking at that is to know the active ingredients

9 in the food. And I only say that from some experience dealing

10 with some fish assays. 

11 We were dealing with fish bioassays, looking

12 at multi-drug resistant transporter proteins expression and PGP,

13 which will prevent uptake of many things, chemicals as well as

14 other nutrients. And what we found was in some work we did,

15 taking fish from a reference area that had very little PGP

16 expression, after a week in the lab of feeding with tetramin

17 fish flakes, they suddenly had levels of that expression

18 comparable to what we found at EPA superfund sites in the same

19 species, which before, they had none.

20 We went back and looked; the only thing that

21 was different was the food and we found in the food ethoxyquin

22 which is a stabilizer that basically revs up the PGP and NDR. 

23 Now, the reason that's important, it prevents uptake of certain

24 things. So if you are reducing the uptake of essential

25 nutrients by that type of response that could -- and you're

26 overfeeding and giving more of those types of ingredients, then
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1 you're really compounding a situation. 

2 So one of the things that I would highly

3 recommend would be to look at the quality of the food and look

4 at those types of agents to see if any of those types of

5 compounds could play a role in some of this. It may not, but I

6 know that really surprised us that we could see those types of

7 gene expression get so increased dramatically.

8 The second point I would make about this, this

9 is not the only species we have seen problems with confounding

10 variables in testing. Those of us that have done molluscan

11 testing for FIFRA, we know, all of a sudden in the '80s, oysters

12 suddenly got a lot of oyster disease. 

13 We were getting annual mortality rates of 50

14 percent and we had to adjust our testing schedule to test around

15 that, but we did, as Dr. Green, I think suggested, limits of 10

16 percent. We did have to set limits control mortality and

17 bioassays, and you had to perform in order to do that. We've

18 had similar problems in shrimp assays with disease. 

19 So we have had those problems in other

20 species. One of the things, like I'll say in the oysters, we

21 did move towards a disease-free oyster. And I think Terry made

22 that in his comments, trying to get to those select strains so

23 we can minimize this is another approach to control of this

24 issue. 

25 I'd like to talk a little bit about how we can

26 improve QA/QC on the husbandry side. Dr. Green hit on numerous
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1 points and Terry did too, I just want to add, I think really

2 getting that information on clutch performance, the water

3 quality data Dr. Green talked about, and again, I would make

4 sure we have very good records on the food, again, the quality

5 of the food. 

6 And in that screening, as you mentioned, for

7 contaminates, I would add to that screening for biology toxins,

8 which could play a role in this. Possibly. Just to be sure

9 that that food is of good quality. And those should be

10 recordkeeping as part of this, in my opinion. 

11 The manufacturers are probably doing this,

12 asking for those records and keeping that information, it would

13 seem like would not be inordinate to ask and it would help us

14 move this along without increasing additional cost in testing.

15 Finally, one other suggestion. If we can

16 minimize this problem, but we still have some rate of bent back

17 that we're not happy with, one other option would be to look for

18 a positive control that would induce bent back that we know that

19 would do this. And I certainly can think of compounds for fish

20 that can do this and then see whether or not if you have -- what

21 you can do is take your data and use a large enough end in

22 developing some kind of positive control assay that referencing

23 this. 

24 If you do have ones that develop the syndrome,

25 you can compare the ability to discern an effect with or without

26 that syndrome. But I think using a positive control that would
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1 induce it will tell you whether or not you can discriminate and

2 whether with that confoundingness (sic) of that, you can still

3 discern an effect. At least that would allow you to standardize

4 and try to control for that. And I think that's all my

5 comments.

6 DR. DANIEL SCHLENK: Thank you, Dr. Scott. 

7 Dr. Tillit. 

8 DR. DONALD TILLIT: I have no further comment.

9 DR. DANIEL SCHLENK: Other Panel members? 

10 Dr. Green.

11 DR. SHERRIL GREEN: Following up on Dr.

12 Scott's comment about quality control of a clutch, I do agree,

13 too, that it takes some nominal technical expertise, but when

14 the female frogs lay eggs, the best way to get good control from

15 the get-go is have somebody go in there and put the Petri dish

16 under a dissecting scope and pick out all the ones that are

17 crinkled or misshapen, or have bacteria, or debris stuck to the

18 surface. And you can call a number of them that normally

19 wouldn't go on. 

20 After fertilization, you can go back and look

21 again, and with a pipette, suck out all the ones that look

22 deformed. That way you'll get a better quality hatch and

23 probably, at least the Grainger Lab has suggested that you can

24 eliminate some of the bent tail from the very beginning. 

25 DR. DANIEL SCHLENK: Any other comments? 

26 Okay. At this point, we'll go to the Agency. Any questions or
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1 clarification regarding Question 5?

2 MS. CATHERINE AUBEE: I have a clarifying

3 question for Dr. Scott. One of your recommendations was to add

4 the screening for biotoxins to the screening of the diet. Are

5 there specific biological toxins that you had in mind?

6 DR. GEOFFREY SCOTT: I can get back with some

7 suggestions on that. Generally speaking, you can extract by

8 different solvent techniques and get classes. And I think I can

9 come back some more detailed information on that later. It may

10 not be today, but I can get you that information.

11 DR. DANIEL SCHLENK: Okay. Ready to move

12 forward.

13 DR. MARY MANIBUSAN: Yes. No additional

14 comments. Thanks.

15 DR. DANIEL SCHLENK: Okay.

16 DR. MARY MANIBUSAN: Charge Question Number

17 6, (a) and (b): With the exception of thyroid gross pathology

18 findings, thyroid gland atrophy and hypertrophy in the AMA,

19 severity grades are generally assigned based on comparison to

20 normal Xenopus laevis thyroid findings depicted in the guidance

21 or based on the professional opinion of the pathologist

22 conducting the assessment; they are not assigned in comparison

23 to concurrent control findings from a given study.

24 In one study, the pathologist's report

25 identified a lower incidence in severity of follicular cell

26 hypertrophy when compared to the incidence and severity of this
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1 trait in control specimens. Similar trends have been observed

2 in other studies. 

3 In this case, the pathologist concluded that

4 the finding was potentially consistent with treatment-related

5 delay of metamorphosis because thyroid follicular cells normally

6 increase in height during tadpole development. 

7 Please comment on the validity of this

8 conclusion: What guidance may be given to better distinguish

9 between histological changes in the thyroid associated with

10 normal progression of metamorphosis and treatment-related

11 effects? 

12 Are there certain lesions or diagnoses which

13 may, by their absence or lessened severity as compared to

14 controls, be indicative of treatment-related HPT effects such as

15 delayed metamorphosis?

16 DR. DANIEL SCHLENK: Our lead discussant

17 is Dr. Patino. Let's handle (a) first if that's okay.

18 DR. REYNALDO PATINO: Thank you. The

19 discussants for this question met, or we emailed and exchanged

20 information and I'm going to try to represent the consensus

21 opinion of the group. I'm sure they will chime in later if I

22 missed or stated something incorrectly.

23 Before addressing these specific questions, I

24 would like to provide some general information to provide

25 context and also to maybe describe some of the issues that have

26 been raised in regards to the question. 
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1 The AMA test guidelines states that the assay

2 should begin with NF Stage 51 tadpoles and that the median NF in

3 controls at test termination, which is 21 days, should be

4 greater than 57, with the difference between the 10th and 90th

5 percentile, being no more than four stages. What this means is

6 that you can get the control stage at Day 21 to be less than

7 Stage 60.

8 As has been noted a number of times already

9 during this meeting, the activity of the HPT axis normally

10 increases during amphibian metamorphosis and this phenomenon

11 makes an assessment of treatment effects on thyroid

12 histopathology relatively difficult because the control

13 condition is literally a moving target. 

14 To compensate for this complication, or

15 potential complication, the AMA test guidelines recommends using

16 stage match individuals for histological analysis beyond

17 exposures. The guidelines states five tadpoles for treatment

18 per replicate for up to a total of 20 should be matched to the

19 median stage of full control tadpoles whenever possible.

20 Comments provided yesterday by the Endocrine

21 Policy Forum indicated that there is variability in the median

22 stage of developmental control tadpoles reported by the various

23 studies, and also included concern about the possibility of

24 inconsistency among the studies with respect to developmental

25 stage at which treatment effects are being compared.

26 Another point worth noting is that the test
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1 guideline calls for the use of a standard scheme to grade

2 thyroid lesion severity, but it's based on normal changes that

3 occur during metamorphosis in Xenopus.

4 The lack of quantitative endpoint of thyroid

5 activity was also a concern expressed in some of the written

6 statements provided by some of the public commenters. We will

7 explain in a few moments, this concern is also shared by the

8 panel. 

9 Specifically going through Question A, in the

10 opinion of this panel, the answer to Question 6a is "Yes." The

11 pathology's conclusion that a lower incidence and severity of

12 follicular cell hypertrophy is consistent with the treatment

13 recommendation of metamorphosis is valid, in our opinion.

14 And in this context, the finding is also

15 consistent with the potential disruption of the HPT axis;

16 however, this particular case also presents what may be an

17 atypical disruption of the HPT axis in that it is not based on

18 classical anti-thyroid mechanisms such as those observed after

19 exposure to thyroid synthesis inhibitors, but on a failure,

20 perhaps, of the axis to activate as it should, or normally.

21 A similar finding, as a reference, a similar

22 finding by the laboratory of a Panel member has been reported

23 for Xenopus tadpoles exposed to very high concentration of

24 (inaudible), and I will provide that reference in the comments.

25 Possible interpretations of these findings

26 include a reduced ability of thyrocytes to respond to TSH
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1 stimulation. That would be a thyroid lesion, and/or a reduced

2 ability of the pituitary to produce TSH. That would probably

3 represent the hypothalamic or pituitary lesion. 

4 And a better understanding of the pathology's

5 finding would require not only consideration of the actual

6 effects on development, which are also available. That would be

7 hind limb length in that stage, but also clarification of

8 mechanisms responsible for the apparent failure of the HPT axis

9 to activate properly, if in fact that is what happened.

10 These questions may be appropriately

11 considered more in-depth later, maybe when the Agency considers

12 mechanisms. Maybe even Tier 2 axis. 

13 As far as Question 6a, that's what I have.

14 DR. DANIEL SCHLENK: Dr. Green.

15 DR. SHERRIL GREEN: I have no further

16 comments.

17 DR. DANIEL SCHLENK: Dr. Leonard.

18 DR. JACK LEONARD: I would just like to

19 amplify one confounding issue that I think highlights what some

20 of the trouble here is. For context, the height of the

21 follicular cell epithelial is really a direct read out of tropic

22 hormone. So it is in the absence of TSH. The height is small

23 or thin, and in the presence of PSH, it's tall.

24 What pathologists here found was a flattened

25 epithelial, which suggests the silencing of the HPT axis

26 directly. A thyromimetic would do that. And a thyromimetic, in
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1 this instance, would silence the axis, but still allow a

2 metamorphosis to proceed. 

3 So in the absence of corroborating data,

4 pointing to the metamorphic state of the animal, this, in

5 isolation, just gives you insight into the HPT axis itself. So

6 it's consistent but not diagnostic. Thank you.

7 DR. DANIEL SCHLENK: And Dr. Schultz.

8 DR. TERRY SHULTZ: Yes. I would just like to

9 emphasize that change in the protocol to include software for

10 morphometric analysis would clearly eliminate some of the

11 subjectivity in looking at this follicular cell height, since

12 this is obviously a critical issue coming out of the assay. And

13 this eliminates them having to have a high level of experience

14 by the operator to do that. 

15 Many of these have gotten to the point where

16 they are fairly sophisticated, inexpensive and it kind of does

17 all the things for you, dump it in and do the analysis, so

18 really streamline the whole operation.

19 DR. DANIEL SCHLENK: Okay. Other Panel

20 members for 6a? Dr. Leonard.

21 DR. JACK LEONARD: Just one additional

22 amplification of Dr. Schultz's comments. When you look at the

23 histological preparation of a thyroid in cross-section, the

24 gland itself is functionally heterogeneous. So there are

25 portions of the gland that are quite active. Portions of the

26 gland that are actually silent. And it's evident; if you look
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1 at a lot of published images, you'll see large follicles, you'll

2 see small follicles. Partly it's because the follicles aren't

3 circled. But that confounds the issue of using -- it requires

4 the pathologist to make a very concerted effort to screen the

5 whole slide. And I think they are usually pretty good about

6 that. 

7 But at times when things are oppressed, those

8 screens can be more superfluous than statistical. So a

9 morphometric measurement is recommended.

10 DR. DANIEL SCHLENK: Sounds like a perfect

11 segue into (b), actually. Do you want to go ahead, Dr. Patino,

12 for 6b?

13 DR. REYNALDO PATINO: As we just mentioned,

14 the change in histological appearance of the thyroid during

15 metamorphosis complicates analysis and interpretation of

16 crippling effects. 

17 Unfortunately, this panel is unaware of any

18 other standard histological feature of the thyroid that does not

19 normally change during metamorphosis, but there may be one

20 histochemical feature, specifically in the Xenopus thyroid, but

21 maybe independent of development. 

22 A recent study involving collaboration with a

23 panel member reported that T4 immunoreactivity is concentrated

24 in the thyroid follicle, at the periphery of the colloid and

25 that the intensity of this ring remains fairly constant during

26 metamorphosis. It's fairly independent of development. 
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1 Moreover, the changes in the intensity of this

2 ring, following exposure to (inaudible) in this particular case

3 was recurring, had better sensitivity to detect anti-thyroid

4 activity than changes in standard histological features such as

5 thyrocyte height. 

6 This marker, obviously, it's only one study,

7 has not been validated for standard testing and is valued for

8 situations such as the one percent in Question 6, which is the

9 figure of activating -- an apparent failure to activate the HPT

10 axis. The applicability of this marker is uncertain. 

11 Nonetheless, the availability of a development

12 independent marker of thyroid activity will certainly avoid

13 most, if not all of the issues of concern to Question 6.

14 In addition, there are other assays that could

15 also be useful, such as an iodide optic assay that may provide

16 quantitative information. It may be worth, to the Agency, to at

17 least consider the possibility of modifications to this

18 guideline in the future to incorporate some of these assays, or

19 perhaps properly validate it to use them as replacement for some

20 of the current influence that have some of these questions.

21 There is another comment that has already been

22 partially mentioned in the preceding moments that is relevant to

23 Question 6 that this panel would like to offer and that is that

24 although categorical rank variables typically do not yield the

25 same statistical part of continuous variables, the current

26 amphibian metamorphosis test guidelines relies on the
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1 semi-quantitative grading scheme to assess thyroid effects.

2 Also, an additional limitation of grading

3 schemes is that they are subject to conditions biases and

4 therefore may yield inconsistent results between studies as well

5 as between laboratories. The Panel therefore advises the Agency

6 to consider the use of quantitative histological data to further

7 standardize the test. 

8 As has already been mentioned, there are a

9 number of computer-assisted image analysis procedures that could

10 be adopted for quantitative measurements. Many of these assays

11 are actually publically available for free. To some degree -- I

12 don't make the procedure -- and therefore avoid the bias that

13 has been of some concern to some people. Thank you.

14 DR. DANIEL SCHLENK: Dr. Green.

15 DR. SHERRIL GREEN: I have no further

16 comments.

17 DR. DANIEL SCHLENK: Dr. Leonard.

18 DR. JACK LEONARD: No further comments.

19 DR. DANIEL SCHLENK: And Dr. Schultz.

20 DR. TERRY SCHULTZ: I apologize for adding my

21 comment. It was meant for (b) and (a).

22 DR. DANIEL SCHLENK: Kind of jumped the gun a

23 little bit there. Any other panel input on Question 6?

24 Okay. At this point let me go to the Agency. 

25 Any questions or clarification for (a) or (b) for Question 6?

26 MS. CATHERINE AUBEE: So I have two questions.
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1 One is specifically for Dr. Patino and the other one is for

2 whoever might care to answer. First, could you just repeat a

3 little bit more slowly for me what you are recommending with

4 respect to potentially looking at immunoreactivity in the

5 thyroid?

6 I didn't quite catch what you were saying

7 something about T4 and the follicular cells. If you could just

8 repeat what you said for me, maybe a little bit more slowly.

9 DR. REYNALDO PATINO: About the procedure or

10 about the recommendations?

11 MS. CATHERINE AUBEE: About both.

12 DR. REYNALDO PATINO: Okay. Maybe because I

13 was reading that it was not very clear. So the procedure

14 involved doing immunocytochemistry for thyroid hormone

15 thyroxine. It has been shown in Xenopus, in the study that I

16 collaborated with, and also in fish that the immunoreactivity

17 concentrates at the periphery at the colloid so it kind of looks

18 like a ring. Both in fish and in Xenopus, this ring has been

19 shown to be a very sensitive marker of exposure to anti-thyroid

20 compound, such as for chloride. 

21 In xenopus, the study that I'm referring to,

22 also shows that the ring, that intensity of the ring, which we

23 don't understand the mechanisms fully, but seems to be fairly

24 constant during development. It doesn't increase or decrease

25 like the other histological features do; they kind of increase

26 in severity. This one seems to remain constant and potentially
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1 could avoid the complicated issues of a moving target. So the

2 suggestion was that you could consider that. That has not been

3 validated. 

4 I'm not saying you must do it, but potentially

5 could be a better marker for disruption of thyroid activity,

6 particularly of the anti-thyroid, those that you know are based

7 on anti-thyroid mechanisms. It could potentially be a better

8 marker of that than the current histological markers.

9 MS. CATHERINE AUBEE: Okay. Thank you. And

10 my other question was because we also look at thyroid histology

11 in the male and female pubertal rats and we don't really talk

12 about it because it's something that is more of a routine

13 measure, whereas with this, it's newer to us. We're getting

14 familiar with the data, as we mentioned, as the thyroid

15 undergoing metamorphosis. 

16 So your recommendations could be especially

17 useful there, especially in terms of the quantitative

18 morphometric evaluation. But my question then is whether you

19 think that approach would also be useful for the evaluation of

20 the mammalian thyroid in those assays?

21 DR. DANIEL SCHLENK: Dr. Leonard?

22 DR. JACK LEONARD: Yes.

23 DR. DANIEL SCHLENK: Dr. Shultz.

24 DR. TERRY SCHULTZ: Absolutely.

25 DR. DANIEL SCHLENK: Any other synonyms

26 out there? Dr. Green.
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1 DR. SHERRIL GREEN: I totally agree, yes.

2 DR. DANIEL SCHLENK: Okay. With that,

3 let's go ahead and take a break before we do Question 7. That's

4 a fairly extensive question. Oh, Dr. Scott.

5 DR. GEOFFREY SCOTT: If I could just respond

6 back on that toxin question on Number 6, please.

7 DR. DANIEL SCHLENK: Okay. Sure, if you

8 want to at this point.

9 DR. GEOFFREY SCOTT: At least on the algal

10 toxins. The question had been what biological toxins might you

11 want to consider to look at. I think I would like at the

12 microcystin toxin and the antitoxin, which are freshwater toxins

13 that might be worth looking at. But I will get you some more

14 detailed information. Those would at least be two that I would

15 get back on the microbial toxins.

16 DR. DANIEL SCHLENK: Okay. Let's go ahead

17 and break and be back at 15 after.

18 (Brief recess.)

19 DR. DANIEL SCHLENK: We're going to move

20 on to Charge Question 7. Dr. Patino wants to provide the

21 references for the record that Catherine asked for here. Do you

22 want to go ahead and do that?

23 DR. REYNALDO PATINO: I'll just read the

24 author names and year and maybe the journal or book so that you

25 can find it. More or less conceptual statements about the

26 procedure can be found in -- first author is Strauss, R.E., a
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1 chapter in a book called "Methods for Fish Biology." American

2 Fishery Society, also same author; this is a journal review

3 paper, Strauss, R.E., Systematic Zoology (1982), Volume 31. 

4 That's more conceptual. 

5 There is a paper that I found from another

6 colleague of mine too that has more specific procedures because

7 it was applied on the original research. The first author is

8 Hoverman, H-O-V-E-R-M-A-N, et al. Oecologia (2005), Volume 144.

9 Thank you.

10 DR. DANIEL SCHLENK: All right. Thanks. 

11 Okay, let's just read in 7a if we can.

12 DR. MARY MANIBUSAN: Charge Question 7: In

13 2008, the SAP acknowledged that the in vivo assays included in

14 the Tier 1 battery provide both redundancy and complementarity

15 for evaluating interactions with the E-, A-, or T- signaling

16 pathways. 

17 The Panel also noted that all of the Tier 1

18 assays and the broad range of endpoints appeared to be necessary

19 to "discriminate positive and negative results."

20 7a: Please comment on the battery

21 performance with respect to the anticipated complementary nature

22 of the more complex, multi-parameter in vivo assays in the

23 context of the observed responses with the case studies. Please

24 comment separately on the E-, A-, and T-related assays and

25 endpoints.

26 DR. DANIEL SCHLENK: Okay. Thank you. 
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1 Our lead discussant is Dr. Delclos.

2 DR. BARRY DELCLOS: Okay. Thanks. I hope not

3 to sound like a broken record going through here, but most of

4 the discussants listed on the question had a feeling that there

5 was a limited amount that we could do with this question, given

6 the level of information provided. 

7 So it would've been useful to have at least

8 directionality, that sort of thing, in terms of instruction, but

9 given that, we did come up with answer. First, I'll through the

10 estrogen. 

11 The current Tier 1 battery contains three in

12 vivo assays that are designed to detect estrogenic activity. 

13 The uterotrophic assay includes only one required endpoint,

14 uterine weight, but is a well-established and reliable assay for

15 the detection of estrogenic activity. 

16 The female pubertal assay includes vaginal

17 opening, time to first estrus, reproductive organ weights and

18 histology as complementary endpoints. The guideline also

19 includes monitoring the estrous cycle, but this was not

20 discussed and the contribution of this endpoint to the female

21 pubertal assay is not clear. 

22 The FSTRA assay has five complementary

23 endpoints. An optional endpoint of plasma concentrations of sex

24 steroids was stated by EPA to be useful and potentially should

25 be added as a requirement for the assay. Overt toxicity or

26 other issues cloud the interpretation of the effects observed in
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1 the majority of the FSTRA assays, nine of the 17 chemicals that

2 showed effects in those assays, and the female pubertal assay,

3 four of the seven chemicals showing effects. 

4 Overall, the results presented suggest that

5 the complementary performance of the multiparameter E battery

6 tests was relatively poor, although there were test chemicals

7 that affected 2 endpoints, both the FSTRA and the female

8 pubertal assay. 

9 Since the present exercise does not provide

10 the panel members with the magnitude or the direction of the

11 effects, it is difficult to say whether or not these two effects

12 will be determined to strengthen or weaken the case for the

13 estrogenicity of these compounds, but the fact that more than

14 one endpoint showed an effect should be helpful in the

15 evaluation and is encouraging.

16 The 2008 SAP indicated that the Tier 1 assays

17 have good coverage for the detection of estrogenic responses

18 mediated through ESR1, and relatively less ability to detect

19 anti-estrogenic effects or effects that might be mediated

20 through other potential mechanisms, such as through ESR2 or

21 membrane receptors, et cetera. This situation remains the same.

22

23 The 2008 SAP had encouraged pursuit of assays

24 for such potential mechanisms, and pursuit of the modification

25 of the uterotrophic assay to improve the ability to detect

26 antiestrogens. It is unclear at this time whether such efforts
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1 are really warranted and in general members of the current panel

2 did not express strong opinions on these issues.

3 For androgens? Three in vivo assays are able

4 to detect the effects on the androgen pathway. The Hershberger

5 assay measured changes in androgen-dependent tissues and is a

6 well-established and reliable assay. The male pubertal assay

7 evaluates sexual development, preputial separation, organ

8 weights and histology, and single point sacrifice serum

9 testosterone.

10 The fish reproduction assay contains five

11 required androgen-sensitive endpoints; testosterone is an

12 optional measurement. Several chemicals altered more than one

13 of these endpoints, although the interpretation of some effects

14 is made difficult by concurrent overt toxicity. 

15 As with the estrogen pathway in vivo assays,

16 the summary data presented indicate that multiple endpoints in

17 the male pubertal and FSTRA can be altered by a single chemical,

18 but what role these effects will play in the decision to move

19 forward is unclear until the magnitude and direction of the

20 effects are known and discussed as part of the weight of

21 evidence evaluation.

22 For the thyroid hormone, female and male

23 puberty assays include measurements of T4, TSH, and thyroid

24 gland weight and histopathology as endpoints to evaluate the HPT

25 axis. A variety of chemicals affected one or more of these

26 endpoints with no consistent pattern and with no endpoint
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1 clearly being the most affected, although T4 levels seemed to be

2 the most often altered parameter, followed by thyroid histology.

3 In some cases these effects were sex-specific. 

4 The AMA assay contains evaluations of

5 development and thyroid histology and EPA has provided a clear

6 decision logic for evaluating potential thyroid axis activity. 

7 For the seven chemicals reported to show effects in the AMA,

8 regardless of solvent interference issues, four showed effects

9 on both development and thyroid histology. So there was some

10 evidence of complimentarity there. And that's all I have to

11 say.

12 DR. DANIEL SCHLENK: Thank you. Dr.

13 Horseman.

14 DR. NELSON HORSEMAN: I agree. I have nothing

15 more to add at this point.

16 DR. DANIEL SCHLENK: Dr. Piersma.

17 DR. ALDERT PIERSMA: Just to add to what has

18 been said, I mean, I agree with all that has been said,

19 obviously. But in order to be able to give a little more

20 in-depth response to the questions given here, this both

21 pertains to complementarity as well as to redundancy and battery

22 performance.

23 Dr. Delclos already said that we would have

24 preferred to have a little bit more information. So perhaps

25 it's good to give more specifics about that. We talked about

26 directionality about potency and magnitude of responses. It
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1 would be good to have that kind of information.

2 In addition, talking about in vivo assays with

3 different endpoints, it would be interesting not to just have

4 the dose response of the most critical or most sensitive effect,

5 but all different effects so that the different effects can be

6 compared across the different assays in the battery to look at

7 their relative performances. 

8 Another thing, of course, is the situation

9 about 21 chemicals versus 52. I mean, the more the better, of

10 course.

11 And finally, if you want to evaluate assays

12 within a certain realm of activity, it would be good to have

13 compounds that also affect each of these endpoints within the

14 metrics. We know that within the 21, we don't have any

15 compounds that affect estrogen binding or uterotrophic assay. 

16 I understand the reasoning for the choice of

17 the chemical at this point, but for considering the questions

18 that we have at hand, it would be good to have a selection of

19 compounds that covers the entire endocrine activity space that

20 is within the metric. Just to have that more specific answer.

21 DR. DANIEL SCHLENK: Thank you. Dr. Roby.

22 DR. KATHERINE ROBY: I have nothing in

23 addition.

24 DR. DANIEL SCHLENK: And Dr. Scott.

25 DR. GEOFFREY SCOTT: Just one or two points

26 real quickly. In terms of complementarity on the androgen assay
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1 with the Hershberger, the male pubertal rat and the fish assay,

2 looking at the complementarity within each of those assays, I

3 was struck by the reported percent complementarity reported in

4 the report by EPA. 

5 I think it was 41 percent for the Hershberger,

6 35 percent for the male pubertal rat. Looking at the fish

7 assay, depending upon how many endpoints, it could be as low as

8 six percent agreement for two or six endpoints, where as it's as

9 high as 47 percent for four more endpoints.

10 To me as you look across those different

11 results, I think there is good complementarity exhibited in that

12 particular assay. I think one question that I did not sort out

13 in my analysis was how to deal with the overt toxicity. 

14 At this point I'm including it in there for

15 now, but I do think, looking back at that we'll have to sort

16 that out a little bit. That's all of my comment.

17 DR. DANIEL SCHLENK: Dr. Selvage.

18 DR. DANIEL SELVAGE: I just wanted to make a

19 couple of comments about the mammalian estrogen androgen assays,

20 in terms of the complementarity. 

21 DR. DANIEL SCHLENK: Can you get a little

22 closer to the mic there?

23 DR. DANIEL SELVAGE: So the pubertal assay,

24 you know, I've already made my comments about it regarding

25 potential or HPA axis and stress to alter that and make those

26 interpretations difficult to compare the results from that with
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1 the other assays because of the overlap between HPA and HPG and

2 HPT axes. Those things all work together, especially when

3 you're doing a pubertal assay where they are developing, it's

4 going to be really tough to compare the pubertal assays to the

5 other ones.

6 Also, I did want to say, speaking of estrogens

7 right now, the ER binding assay, where you are using rat cytosol

8 is, in my mind, essentially, an in vivo assay and it has a lot

9 of problems that are associated with that. It did provide

10 complementarity with uterotrophic assay pretty well, but it was

11 a bad assay in particular that is fraught with problems and has

12 a lot of variances with it. If you look at the protocol for the

13 ER binding assay, in terms of taking the cytosol from rats and

14 everything, leading the protocol is probably what curled my

15 hair. 

16 And it's been said already that a good idea

17 that they change that assay and there are maybe other ones that

18 will provide better complementarity. Same is pretty much true

19 with the androgen and the pubertal androgen assays in trying to

20 disentangle the different axes. And also, in my mind, the AR

21 binding assay is essentially an in vivo assay that is very

22 elaborate. If that can be changed to something more modern, I

23 think that would be a really good thing. That's it.

24 DR. DANIEL SCHLENK: Okay. Dr. Tillitt.

25 DR. DONALD TILLITT: I really don't have a

26 whole lot more to say about the complementarity of this other
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1 than agree with what Dr. Delclos said. I will say that upon

2 getting this additional information implied, if it wasn't

3 already stated, that I would also like to see the control

4 information. I guess I agree also with Dr. Piersma's statement

5 about looking at the other scientific relevant information, you

6 know, where we would see maybe positive controls and sort of

7 have a framework. I would also say that we don't have

8 information that indicates that these assays don't operate like

9 the original ought to either. That's about all I have to say.

10 DR. DANIEL SCHLENK: Okay. Thank you. 

11 Other panel members? Okay. Let me go to the Agency for 7a, any

12 questions or clarification?

13 DR. THOMAS SEEGER: No questions.

14 DR. DANIEL SCHLENK: All right. Thank

15 you. Let's go ahead and read 7b.

16 MS. CATHERINE AUBEE: Charge Question 7b:

17 Please comment on the battery performance with respect to the

18 anticipated redundancy across the 11 assays in the context of

19 the observed responses with the case studies. 

20 Please comment separately on the E-, A, and

21 T-related assays and endpoints.

22 DR. DANIEL SCHLENK: Dr. Delclos.

23 DR. BARRY DELCLOS: Okay. We'll start with

24 estrogens. In general, from the information presented, there

25 appeared to be generally a fairly poor redundancy in the battery

26 of tests used to assess this activity, due to a general lack of
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1 concordance among in vitro and in vivo assays as well as between

2 mammalian and fish in vivo assays. 

3 There were no clear ER binders and no test

4 chemicals active in the uterotrophic assay. On the other hand,

5 four of 21 test chemicals gave positive results in the ER

6 transcription assay. It might have been expected that one or

7 more of these compounds would have shown activity in one or both

8 of the other assays; that is, ER binding or uterotrophic. 

9 With regard to the in vitro assays, there

10 appears to be little agreement among the tests. Indeed the

11 transactive binding, transactivation and steroidogenesis assays

12 test multiple and diverse aspects of estrogenic properties. 

13 Thus, it is not surprising that these assays fail to exhibit

14 significant redundancy. 

15 As shown in the Agency presentations, there

16 were no compounds that showed positive only in vitro effects,

17 although in nine of the 10 cases in which both in vitro and in

18 vivo effects were seen, there was a positive in only one in vivo

19 assay, with the FSTRA of a positive assay in eight of those nine

20 cases. 

21 There were some potential issues of overt

22 toxicity in several of the positive in vivo studies, so it

23 appeared that there was only one compound, Compound Q, that

24 showed clear positives in two in vivo assays. 

25 In short, there is some redundancy across

26 assays for the estrogen pathway endpoints, but it remains to be
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1 determined how clear the interpretation of these redundancies

2 will be. The absence of positive uterotrophic assays in the

3 subset of chemicals on which summary results were reported and

4 performance problems related to toxicity and/or control

5 complications lead to significant uncertainties about E battery

6 redundancy. Also there seems to be a high proportion of

7 equivocals in the aromatase assay and it is not clear how

8 important that assay will be in the overall evaluation.

9 For the androgens, the data presented

10 indicates that there is redundancy across the in vitro and in

11 vivo assays designed to detect effects on the androgen pathway

12 in the sense that 13 of the 17 compounds presented as having

13 been tested in the six androgen pathway assays appear to show

14 effects in multiple assays. 

15 As with the estrogen pathways discussed above,

16 the value of these redundancies will be better determined after

17 the weight of evidence evaluation.

18 The thyroid hormone: As indicated in the 2008

19 SAP report and in the current White Paper and EPA presentations,

20 there is less redundancy in the assays for effects on the

21 thyroid axis. The 2008 SAP had suggested exploring additional

22 endpoints, such as thyroid hormone-sensitive gene expression and

23 transcriptional activation assays, but it is not clear if

24 additional endpoints to strengthen the evaluation of this

25 pathway are being considered. 

26 Three of the 16 chemicals tested in this
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1 battery showed some thyroid effect in all three thyroid assays,

2 three showed effects in both rat assays but not the frog assay,

3 and three showed effects in the frog but not the rat. Four

4 chemicals showed effects in only one sex in the rat assays. The

5 EPA conclusion that all three assays are necessary to evaluate

6 this axis seems appropriate. I think that's all I have.

7 DR. DANIEL SCHLENK: Okay. Dr. Horseman.

8 DR. NELSON HORSEMAN: I have nothing specific

9 to add here. I just want to reemphasize, I was aware that this

10 issue of some sort of additional battery controls that Dr.

11 Piersma brought up, and I think to reiterate the importance of

12 this. 

13 The inclusion of some sort of a ringer in this

14 battery or something that one has a good prediction by how it

15 ought to perform through this battery of tests, which I think is

16 what Dr. Piersma was alluding to, would be a huge advance,

17 something that you would expect to perform positively in some

18 and negatively in some of the other tests within the battery. 

19 I think that that would be a big advance

20 because the controls for the individual assays are not the same

21 as a control carried through the battery, which is what these

22 two questions are asking us about, as the performance of the

23 battery. Thank you. 

24 DR. DANIEL SCHLENK: And Dr. Piersma.

25 DR. ALDERT PIERSMA: Just to follow-up on

26 that, for your information, we just published a paper in
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1 reproductive toxicology where we actually tried that kind of an

2 approach, where we had a battery of different assays tested with

3 a series of compounds. It was like 20 assays with about 12

4 compounds for which we knew that each of the compounds would

5 have to be positive in one of those assays, but negative in the

6 others. 

7 You have a positive and a negative control

8 with the same compound and they are active compounds, not the

9 inert compounds like saccharin or penicillin, which is overall

10 negative and should be negative and is rather uninformative.

11 But with this kind of an approach, you can

12 look at specificity of assays within a battery and also look at

13 combined battery performance and identify those caveats that you

14 have in terms of mechanism within the battery. It's has just

15 come out in reproductive toxicology and I'm the first author, so

16 for your information that might be interesting to look at. 

17 Thank you.

18 DR. DANIEL SCHLENK: Dr. Roby.

19 DR. KATHERINE ROBY: I have nothing new to

20 add, but I would also like to reiterate this idea of having kind

21 of ringer in the battery. It certainly would provide another

22 level of confidence to the Agency that the system and the

23 battery actually are working the way you anticipate it to work.

24 DR. DANIEL SCHLENK: Dr. Scott.

25 DR. GEOFFREY SCOTT: Just a couple of comments

26 in terms of the redundancy analysis presented by EPA. For the
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1 androgen battery, just a couple of comments here. The results

2 indicated that at least one of the three bioassays, either the

3 Hershberger, the male rat or the fish test detected an effect. 

4 If you look at all three assays, 17 of the 17 compounds tested

5 with one of the tests. 

6 Now, when you start to look at the multiple

7 tests, when you see co-occurrence we only saw that all three

8 assays detected it, three out of 17, or about 17 percent at the

9 time. Two of three assays, we saw it, it ranged variably among

10 the combinations of the Hershberger or the male rat or the fish

11 test, and those percentages range from as low as five percent to

12 as high as 24 percent with those combination tests. 

13 So redundancy was observed in at least two or

14 more of the three bioassays or 12 of the 17 compounds. So about

15 70 percent of the chemicals tested with the male rat and fish

16 test accounting for about two-thirds of that redundancy, and I

17 thought that was sort of interesting to look at it that way. 

18 Again, the question of overt toxicity as a

19 confounder will have to be addressed in sorting through some of

20 that. In looking at the thyroid test -- and I'll just make a

21 couple of quick comments. 

22 In looking at the overall concordance among

23 the three assays, the AMA and the two female and male rat tests,

24 there was a positive response in all three pathways for 19

25 percent of the chemicals, I think tested. 

26 There was a negative response for all three
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1 for 19 percent of the chemicals. A positive response in the

2 male and female rat, but not the AMA in 19 percent of the

3 chemicals; positive response only in the AMA, 19 percent and a

4 positive response in the female rat and the AMA six percent; and

5 then a positive response in only one of the assays, 19 percent

6 of the chemicals.

7 So I think as you look through that, that kind

8 of gives you a little bit of a different look at what you all

9 presented, just slightly analyzing it a little differently

10 perhaps. 

11 I think the one thing that I was struck by,

12 that combination of fish assay and those two rat assays

13 specifically -- since we're commenting on the in vivo -- seem to

14 give you broad coverage for most of the endocrine endpoints

15 among all three pathways and it gives you very strong linkages

16 with both an ecotox species and a human health species. 

17 I know comments were made yesterday on some of

18 the presentations about only including one of the male rats, but

19 I happen to look at this. I think you're getting more complete

20 coverage with both of those. That's my opinion. You do have to

21 look at cost and other things in looking at redundancy. I'll

22 end with those comments.

23 DR. DANIEL SCHLENK: Thanks. Dr. Selvage.

24 DR. DANIEL SELVAGE: Yes. I concur with

25 what's been said already.

26 DR. DANIEL SCHLENK: Dr. Tillitt.
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1 DR. DONALD TILLITT: I agree with the comments

2 that have come before me here on this question. The only thing

3 I would say, maybe, is addressing this idea of a ringer or a

4 positive control. 

5 My idea is that we have a lot of that

6 information in the literature and if we could use that maybe to

7 frame these assays and the response to these assays, I don't

8 know and I don't understand how there would be a ringer sent

9 through all these assays to make comparisons with these

10 compounds already, other than what is already out there in the

11 literature, if that makes sense.

12 DR. DANIEL SCHLENK: Anybody want to

13 address that?

14 DR. KATHERINE ROBY: I'll chime in a little

15 bit. I'm not sure that it necessarily needs to be a compound

16 that is functional across the battery in total, but something

17 that will allow you to see that the battery is functioning. 

18 Like any kind of assay or any dataset that you get, if the

19 answer is nothing was positive, do you know that you could

20 determine anything that was positive?

21 So if everything is a "No," is your assay

22 system robust enough to identify a "Yes?" So I think that's one

23 of the things that having something in the system, and again, it

24 doesn't have to be every test, but something that is kind of --

25 that you have spiked the system a little bit and can see that

26 your test can identify a positive, especially with the example
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1 of the estrogen-binding or estrogenic type compounds.

2 In this case, there was nothing that was read

3 as a positive. We know that the system can protect the

4 positive, but in the context of how these were run, were we able

5 to detect the positive?

6 DR. DANIEL SCHLENK: Any other Panel

7 members want to -- Dr. Schultz.

8 DR. TERRY SCHULTZ: It would be nice to see if

9 the chemicals that we use to validate the assays, let's say, the

10 ones we've said have known activity, even though they wouldn't

11 be tested in all 11, would there be chemicals that were tested

12 in all of those that were related to a particular pathway. 

13 If that information could be provided, that

14 would allow a better way of looking at the redundancy and

15 overlap that you have with the 11 assays on a pathway-by-pathway

16 basis. 

17 So I wouldn't expect that there would be one

18 or more chemicals that was sent through all the pathways, such

19 as what was done in the Dutch study. But I would suspect that

20 you have information from earlier SAPs on this situation that

21 could be pulled out that would assist in making those types of

22 comparisons. And that would add a confidence to what we're

23 seeing. Thank you.

24 DR. DANIEL SCHLENK: Dr. Leonard.

25 DR. JACK LEONARD: I was taken by the lack of

26 concordance between the two estrogen in vitro assays. The ERTA
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1 is really a functional read out of ER binding. If you look

2 through the panel, there was very little concordance between

3 those two assays. I think there may be an answer to that. 

4 Yesterday, during the public comment period,

5 we heard from a laboratory who kind of gave us a historical

6 characterization of the full change in that particular assay,

7 which to my surprise, ranged from 80-fold to four-fold; that's

8 almost a 10-fold difference, in terms of amplification. My

9 laboratory has a lot of experience building these types of

10 Porter assays and if we ever see anything more than two-fold we

11 rebuild the assay.

12 So in part, it's somewhat -- there are some

13 quality control issues in the ERTA assay that seems to be

14 missing. I think that may need to be fixed so you can actually

15 get a much better sense about what happens in vitro. If it

16 binds to the ER, it should impact the ER response element.

17 DR. DANIEL SCHLENK: Any other comments? 

18 Dr. Scott.

19 DR. GEOFFREY SCOTT: Just to follow-up on what

20 Terry said just a second ago, I think it will be really

21 important to know, like looking at the thyroid assay -- just

22 giving you some summary analysis there -- where all three tests

23 found an effect in 19 percent of the chemicals. Are those all

24 in one class?

25 Because it may be highly redundant for one

26 class, but it may not be for another. I really think, as you go
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1 through and do your analysis, that will be very important to

2 evaluate the proficiency and whether it is redundant, there is a

3 balance between being complementarity and the redundancy in

4 complete coverage. 

5 So I really think having it analyzed by class,

6 since you do have that in the recordkeeping for the chemicals,

7 and I couldn't tell that from looking at it, that would be

8 important to me.

9 DR. DANIEL SCHLENK: Any other comments

10 for 7b? Okay. We'll go to the Agency. Any questions or

11 clarification?

12 DR. MARY MANIBUSAN: So the Agency would like

13 some clarification. This Ringer study, it sounds very

14 provocative. In comparison to what has already been done, in

15 terms of validating these individual Tier 1 assays, it was

16 brought before the SAP in 2008, a lot of the information sounds

17 like what Dr. Schultz had described, looking at chemicals by

18 pathway, presenting both strong positive, negative, weak agonist

19 and antagonist and so on, demonstrate the performance of each

20 individual assay along the pathways that were described, were

21 provided in the 2008 SAP. So we can bring that before the panel

22 and provide that information. Is that the type of control that

23 is being described for this Ringer study?

24 DR. DANIEL SCHLENK: Who wants to take it? 

25 Dr. Schultz.

26 DR. TERRY SCHULTZ: That type of information
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1 could be an excellent substitution for the Ringer study. I

2 believe the idea of the ringer study would be to take a compound

3 or a suite of compounds and run through all 11 assays, where you

4 would know it should be negative for a certain pathway. That

5 would be the recommended results. You understand?

6 I think there's a difference between -- I

7 should let those two people talk about the ringer assay. 

8 They're much more understanding about what they're talking

9 about. What I was talking about is, yes. 

10 I apologize; at my age I don't remember all

11 the information given in 2008, but it's difficult to answer

12 these questions in 2013 without having some of that information

13 on the tip of your tongue or at least brought back to you. 

14 The point is the Agency has the information

15 which would make it easier to make those judgments. Thank you.

16 DR. DANIEL SCHLENK: Dr. Piersma.

17 DR. ALDERT PIERSMA: I recognize that a lot of

18 the information that we're actually heading at has probably been

19 gathered at some point in the past. The point here is that

20 we're talking not just about the validity of individual assays

21 and the response to individual compounds; we're talking about

22 the battery approach. We're talking about complementarity and

23 redundancy. 

24 In that respect, it could be -- in my mind,

25 the basic requirement would be that the mechanistic space of the

26 whole battery is covered by the test compounds with which you
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1 actually judge the competence of the battery. 

2 So in this particular case, one conclusion

3 could be if these 21 compounds would cover chemical space, there

4 are no estrogens. So we don't need any estrogenic assay, just

5 to take it vary down to earth.

6 And of course, we want estrogenic potency

7 potential to be potentially covered within the battery, but then

8 you need at least a number of examples to show that a particular

9 battery is able to do that and also to judge whether you could

10 actually throw out one of these assays because of redundancy,

11 you would need some positive compounds to be able to make that

12 decision.

13 So it was one of my first surprises when I saw

14 the database that we actually didn't have any estrogen binders

15 or uterotrophic compounds within the group of compounds that

16 were tested, which makes it very difficult for me to make any

17 statement about the group of estrogens that we test in the

18 battery.

19 In other words, you would want to cover the

20 entire endocrine activity space that you would want to cover in

21 the battery, also in terms of the group of compounds that you're

22 testing.

23 DR. DANIEL SCHLENK: Does that provide

24 some insight?

25 DR. STEVEN BRADBURY: Yes, it does, both

26 taking a look at the historical record, understanding of AOPs,
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1 and maybe taking a look at empirical information.

2 As you write the report up, it might be

3 helpful in some of the considerations in looking at existing

4 literature, as well as opportunities because we know the battery

5 is designed to look at cross-species, cross-sex. So I don't

6 think anybody, you know, the 2008 SAP wasn't expecting

7 everything to be exactly the same across species and taxa, the

8 whole point being taxa metabolize chemicals differently, thing

9 like that. So that's part of the rationale for that.

10 So it would be if you were going to a ringer

11 experiment, how many variables are we trying to cover and how

12 many ringers would you need to be able to say you not only

13 covered activity space, structure space, species space, sex

14 space, metabolic capability space -- and that's also important

15 for researching the literature to see to what extent that

16 multi-variable hyperspace has been evaluated.

17 So as you guys think about that, that would

18 just be helpful to give us some thoughts to think about how we

19 can mine the literature or think about empirical approaches to

20 looking at that. 

21 And reflecting on the January SAP where we

22 know, getting back to the E thing, I think that it would be

23 helpful to get some feedback, is that useful to take a look at

24 what we determined there with only two or three percent of that

25 inventory space has the potential to bind to the estrogen

26 receptor.
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1 How can we use some of that information maybe

2 to help formulate a literature evaluation of those empirical --

3 not disagreeing at all, but just any additional detail you can

4 put in the write up would be helpful.

5 DR. DANIEL SCHLENK: Okay. So I will

6 leave that to Dr. Schultz and Dr. Piersma, and Dr. Roby, if you

7 guys can work on that that would be great. Dr. Horseman.

8 DR. NELSON HORSEMAN: I just want to come back

9 to this because I think Dr. Piersma's suggestion is very

10 important. It's complicated, but I think it needs to not be

11 over complicated. Well, it needs to not make it more

12 complicated than it might otherwise be. 

13 I think coming back to the battery performance

14 as it has been presented to us, each of these tests ends up

15 being basically scored as a binary. Something either scored

16 that did or didn't turn out positive or negative. And when that

17 happens, having something in there that you know ought to be one

18 or the other that goes through the battery. This is different

19 from the individual -- it could be the same compound -- but I

20 think it's really important for getting an answer to these last

21 two questions, which are not about the individual tests.

22 DR. DANIEL SCHLENK: All right. With that,

23 we're ready to move forward to Letter C.

24 DR. MARY MANIBUSAN: Charge Question 7c: The

25 EPA concluded that the battery has performed as anticipated by

26 the 2008 SAP. Please comment on this conclusion.
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1 DR. DANIEL SCHLENK: Dr. Delclos.

2 DR. BARRY DELCLOS: This is a relatively short

3 answer. The 2008 SAP concluded that the Tier 1 suite of assays

4 should be able to detect agents that alter E-, A-, and T5 pathways. 

6 There can be limited conclusions drawn at this

7 time given the amount of information that has been provided. 

8 The assays were able to be performed by several laboratories and

9 showed changes in the assay critical endpoints that will be used

10 to determine if a compound should be required to undergo further

11 testing to establish definitively the ability of the compound to

12 alter the E-, A-, or T- pathways. 

13 Again, without information on the direction,

14 magnitude and consistency of the observed effects, it is

15 difficult to fully assess how the assays performed as a group. 

16 In any case, the battery of assays did show responses in one or

17 more assays and/or endpoints for estrogen, androgen, and thyroid

18 hormones. 

19 As mentioned above and in previous charge

20 questions, overt toxicity overlapping potential hormonal effects

21 is a concern in several of the in vivo assays, particularly the

22 FSTRA and the pubertal assays. Thus, one might question whether

23 the degree of selectivity envisioned by the 2008 SAP was

24 achieved; the weight of evidence analysis will no doubt address

25 this.

26 DR. DANIEL SCHLENK: Okay. Dr. Horseman.
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1 DR. NELSON HORSEMAN: I have nothing to add.

2 DR. DANIEL SCHLENK: Dr. Piersma.

3 DR. ALDERT PIERSMA: Nothing to add.

4 DR. DANIEL SCHLENK: Dr. Roby.

5 DR. KATHERINE ROBY: Nothing to add.

6 DR. DANIEL SCHLENK: Dr. Scott.

7 DR. GEOFFREY SCOTT: Nothing to add.

8 DR. DANIEL SCHLENK: Dr. Selvage.

9 DR. DANIEL SELVAGE: I will add nothing.

10 DR. DANIEL SCHLENK: And Dr. Tillitt.

11 DR. DONALD TILLITT: I will add nothing.

12 DR. DANIEL SCHLENK: Okay. Other Panel

13 members for C? Okay. Let me go to the Agency. Things seem

14 pretty clear?

15 DR. MARY MANIBUSAN: No additional comments. 

16 Thank you.

17 DR. DANIEL SCHLENK: Great. All right. At

18 this point so that we can actually contact our administrative

19 staff, we'll go ahead and take an early lunch I think and get

20 back to Question 8, which should be fairly, relatively quick. 

21 Let's be back at 12:15. We'll do an early lunch and get back at

22 12:15.

23 (Whereas a lunch break was taken) 

24 DR. DANIEL SCHLENK: Everybody take a

25 seat, please. We'll get moving on to Charge Question 8.

26 DR. MARY MANIBUSAN: Charge Question Number 8:
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1 The EPA is committed to minimizing animal usage in the

2 screening battery while maintaining the effectiveness of the

3 battery to answer the question of whether a chemical has the

4 "potential" to interact with the endocrine system.

5 Question 8a: Based on the preliminary battery

6 performance evaluation, to what extent can the current Tier 1

7 battery of 11 assays be modified to reduce animal usage and/or

8 lower cost while adequately ensuring the Agency, the EPA's

9 ability to answer the question of whether a chemical has the

10 potential to interact with the endocrine system? 

11 Specifically, please comment on whether the

12 uterotrophic and Hershberger assays provide necessary

13 redundancies in the Tier 1 battery based on this preliminary

14 analysis. Please include in your comments what information may

15 be lost and what uncertainties may be introduced by absence of

16 either or both of these assays.

17 DR. DANIEL SCHLENK: Okay. Our lead

18 discussant for that is Dr. Delclos. 

19 DR. BARRY DELCLOS: Okay. Based on the data

20 presented, the majority of the panel indicated that it is not

21 clear that the uterotrophic or Hershberger assays should be

22 discarded from the assay battery at this time given that they

23 are well-established assays that appear to perform well. 

24 While the tests are limited in what they can

25 detect, agents with estrogen agonist or anti-androgenic

26 activities have been of particular concern up to this point and
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1 these assays generally give clear answers on these mechanisms. 

2 At the very least, a decision to remove these assays from the

3 battery should await the completion of the weight of evidence

4 exercise on these compounds to determine what role these assays

5 play in the decision process. 

6 Parenthetically I say that several members of

7 the panel felt strongly that the weight of evidence exercise

8 should include all of the compounds on which data have been

9 collected. In the case of the Hershberger assay, for example,

10 it can be seen from the data presented that a response in the

11 Hershberger assay was accompanied by a response in the male

12 pubertal or the FSTRA assay, but it was not indicated if these

13 effects were in the same direction. 

14 In the case of the uterotrophic assay, it is

15 not clear at this point if the negative response for the 21

16 chemicals evaluated in the set of chemicals under review could

17 be factored in to a decision on a compound that showed an effect

18 in the FSTRA assay only in the presence of overt toxicity. Even

19 if they are removed from the battery as absolute requirements,

20 they should be maintained as options if the other assays do not

21 provide clear results. 

22 While reduction of animal use and cost are

23 important, it is also important that the screening assays serve

24 their intended function to limit the number of compounds that go

25 to Tier 2 while minimizing false negatives. 

26 A potential problem indicated by the Tier 1
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1 results from the first set of 21 compounds is the potential

2 confounding of endocrine effects with overt toxicity in the

3 FSTRA and to a lesser extent, the pubertal assays. There was

4 plenty of discussion of the issue of dose selection in the

5 earlier questions. And that's all I have.

6 DR. DANIEL SCHLENK: Dr. Selvage.

7 DR. DANIEL SELVAGE: Yeah, I would like to add

8 a couple of things. I think I'm a little bit more enthusiastic

9 about keeping the Hershberger and uterotrophic assays than the

10 other discussants. These assays have the ability to very

11 specifically detect, in particular, uterotrophic estrogenic

12 activity and then Hershberger androgenic activity, and then if

13 they're expanded like Hershberger was, to measuring better

14 anti-androgenic or with the uterotrophic anti-estrogenic

15 activity, I think that they can do that fairly well.

16 What else? Also, since they use gavage,

17 metabolism is involved with those assays. You don't get that,

18 obviously, with the other assays. Let's see. Anyway, that's

19 what I'll say about those.

20 One other thing I wanted to say was that

21 obviously I worry a lot about stress and stress being activated;

22 something I haven't actually said at this meeting is "gavage." 

23 I'm sure the labs running these tests, the staff is well

24 training and does an excellent job. Gavage still can be a

25 stress, especially gavage of the compounds. Who knows what

26 exactly you're giving them. 
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1 So because of that stress, then you have less

2 of an idea of what's happening at the level of the testes or

3 ovaries in terms of production of testosterone or estrogen,

4 especially with the measures you guys have, the onetime

5 measures. I realize you don't measure estrogen, but

6 testosterone is just a onetime measure. 

7 So with these guys, you don't have that

8 parameter there and you don't need to worry so much about

9 whether your testosterone or estrogen levels have been altered,

10 you can really just see what's going on.

11 The other thing I wanted to add was I brought

12 this up with the last question was that the androgen receptor

13 binding and estrogen receptor binding are, well, technically

14 called ex vivo, but essentially, they have an in vivo component.

15 It's obviously been discussed a fair amount, replacing those

16 assays. 

17 Those assays, as the email that was read

18 yesterday, said they can use a lot of animals and they're

19 difficult to run. So I would say if you want to stop overly

20 using animals, that's where I would go, initially. I believe

21 that's it for me.

22 DR. DANIEL SCHLENK: Okay. Dr. Piersma.

23 DR. ALDERT PIERSMA: I agree with what was

24 said by Dr. Delclos, obviously. Perhaps to balance the comments

25 by Dr. Selvage, there is another school of thought which says

26 that uterotrophic and the Hershberger assays are basically in
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1 vivo test tubes which look at the single endpoint, and the

2 questions of that endpoint is whether it's adverse or not. It's

3 a change of weight of an organ. 

4 The question is what is the adverse health

5 effect connected to that. Adverse health effects are typically

6 or preferably studied in apical assays, and of course, we have a

7 number of apical assays in the battery.

8 On the other hand, looking at the specificity

9 of an estrogenic response, you could do that in estrogen binding

10 and more specifically, estrogen receptor transactivation assays.

11 And of course also for androgen and thyroid the same holds

12 true. 

13 So one could also come to plate, perhaps,

14 changing to a more robust in vitro assays for receptor binding

15 and receptor activation and then just skip the uterotrophic and

16 the Hershberger and go to in vivo immediately. Although, of

17 course, the apical in vivo assays have the issue of isn't that

18 first effect observed really mediated by the hormonal axis or it

19 may be mediated by something else. But that is a general

20 problem that we have with all in vivo assays, I believe.

21 I mean, it's that balance that we have to look

22 for. Actually, the current dataset doesn't give us enough

23 information to make up our minds around those points, but I

24 think these issues need to be considered.

25 DR. DANIEL SCHLENK: Okay. Other Panel

26 members. Dr. Green.
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1 DR. SHERRIL GREEN: From a different

2 perspective, as a laboratory animal veterinarian who has sat for

3 years on numerous IACUCs, I would like to commend the EPA and

4 the Panel and the public presenters on putting this issue as one

5 of the questions. 

6 Certainly, I think everyone here is committed

7 to reduced, replace, refine whenever possible, for a number of

8 reasons: to save money, to make the experiments as efficient as

9 possible and consider animal welfare and reduce the number of

10 animals wherever one can.

11 Speaking specifically to the AMA in Xenopus,

12 although this was not one of the charge questions, I did notice

13 that a couple of the slides presented on the total number of

14 animals used, showed numbers where more than half of the animals

15 used were Xenopus tadpoles. 

16 So to put that somewhat in perspective, one

17 breeding pair of Xenopus can produce an awful lot of adults. In

18 the wild, a healthy female at peak production might lay 10 to

19 20,000 eggs. If they are all fertilized -- and there are

20 various papers that predict the outcome of how many will survive

21 into sexually mature adults -- probably less than half of those

22 will survive through the tadpole stage, and probably only a

23 third of those will make it to the froglet stage. 

24 So the attrition, by default, in this animal

25 model is huge in the mild. And 300 tadpoles is not a lot of

26 animals from that perspective that we've used in the laboratory.
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1

2 So what would we lose if we turn to a

3 mammalian model to model the thyroid function if we move away

4 from Xenopus? Or for that matter, another aquatic species and

5 other axes such as in the fish studies. 

6 Xenopus is a well-established vertebrate model

7 of development. It's highly efficient and very hardy; meaning

8 that a couple of breeding pairs can produce many, many animals. 

9 If you move to study this system in a mammalian model, you're

10 going to use a lot more rats. It takes a lot more rats to

11 produce the numbers that you would get with Xenopus. So there

12 are some inherent advantages by using this species that would be

13 lost.

14 The second comment is regarding to the

15 attention paid to minimizing animal models. As I mentioned, I

16 think all groups involved in this are very focused on that for a

17 number of very good reasons. But there are checkpoints along

18 the way that probably should be acknowledged. 

19 Every institution has regulatory agencies,

20 animal use and care committees, and in order to do this work in

21 any animal, Xenopus, rats, fish, or mice, you would have to

22 submit an animal protocol to be approved by the institution's

23 animal use and care Committee. And in that you must report the

24 steps you've taken to reduce, replace, and refine. So you would

25 have to justify the number of animals used and you would have to

26 use the statistical test to support the numbers, and that would
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1 have to be approved by the IACUC before the work goes forward.

2 So there is one mechanism which is already

3 built into the system that ensures the minimum numbers of

4 animals that could and should be used. So all in all, I think,

5 again, kudos to all for putting this on one of the question

6 agendas and that due diligence is being done. 

7 As the weight of evidence progresses and the

8 EPA moves forward with Tier 1 and Tier 2, there will be

9 continual refinements as science advances and animal modeling

10 advances. But it's good to give attention to it now, and I

11 thank you all for doing that.

12 DR. DANIEL SCHLENK: Other comments? Dr.

13 Scott?

14 DR. GEOFFREY SCOTT: I would just reiterate

15 the one I made yesterday during the public comment period

16 regarding the cytosol fractions and basically looking at some

17 storage studies to look at each ending, the like use of those

18 products as opposed to having arbitrary time components. 

19 We are, according to the lab presenters, doing

20 proficiency as long as those assays are viable. If we can

21 extend the life on those, that means fewer animals and lower

22 cost. I think, to me that would worth pursuing a little

23 further. Thank you.

24 DR. DANIEL SCHLENK: Dr. Schultz.

25 DR. TERRY SCHULTZ: In the cases of looking at

26 these pathways for endocrine disruption, you're basically
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1 employing the adverse outcome pathway approach in that you

2 identified events at the cellular level, which would be the ER

3 binding and the transcription. And then you have an

4 intermediate effect, which would be the Hershberger or the

5 uterotrophic assay, and then you have an apical outcome that

6 you're looking at in the whole animal or population scenario.

7 The more comfortable you become with that

8 pathway, the more you're willing to spread the distance between

9 the different levels of biological organization. So I think

10 it's very logical that this question be asked because what

11 you're basically saying is we have a lot of experience in ER

12 binding and the transcriptional activation. So we're pretty

13 good at defining the structural space of the activity domain. 

14 We have some assays that allow us to do this

15 at the individual population level. In our Tier 1 we have these

16 ones that are intermediate. And you're asking, do we need

17 those?

18 Personally, I think that you need those in the

19 beginning in order to give the confidence to the public that

20 what you're doing is following the pathway. As you become

21 greater experienced with that, it would be more likely that

22 those things will be dropped out or that your ability to skip an

23 intermediate event in your pathway and still have that to be

24 scientifically justified.

25 DR. DANIEL SCHLENK: Any other comments

26 from the Panel? Okay. Let's go to the Agency. Any questions
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1 or clarification for the comments made?

2 DR. MARY MARIBUSAN: No additional comments. 

3 Thank you. 

4 DR. DANIEL SCHLENK: Okay. Let's go ahead

5 and read (b) into the record, please.

6 DR. MARY MARIBUSAN: Charge Question B: 

7 Please comment on the scientific criteria the Agency should

8 consider in evaluating necessary redundancies and eliminating

9 assays from the current battery.

10 DR. DANIEL SCHLENK: Dr. Delclos.

11 DR. BARRY DELCLOS: The decision to remove an

12 assay from the battery could potentially be made after a

13 thorough review of all data from the complete set of compounds

14 that have been run through the Tier 1 screening. If an assay

15 consistently produces results that are not clearly

16 interpretable, removal could be considered. 

17 Likewise, if assays produce data that always

18 only confirm the results of other assays and are not helpful in

19 resolving questionable data, they could be considered for

20 removal.

21 Several Panel members recommend a reanalysis

22 of the data using the entire dataset of 52 chemicals to provide

23 an assessment of complementary and redundancy relationships. 

24 This more robust dataset would also be amenable to analysis to

25 determine if a particular assay or assays could be eliminated

26 without increasing the probability of finding false negatives. 
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1 Further, after completion of this assessment

2 it would be useful to consider splitting the tier 1 assay

3 battery into two sub-tiers using the in vivo assays as a

4 prescreen followed by targeted in vitro tests to explore

5 mechanisms.

6 DR. DANIEL SCHLENK: Dr. Selvage.

7 DR. DANIEL SELVAGE: Yes. I concur with Dr.

8 Delclos. I would just like to also emphasize that this question

9 would be a lot easier to approach if we had all the data

10 available from the studies.

11 DR. DANIEL SCHLENK: Okay. Dr. Piersma.

12 DR. ALDERT PIERSMA: Just to follow-up on what

13 was said a little earlier, the scientific criteria should not

14 just depend on the chemicals that happen to be tested, but

15 should be dependent on mechanisms again. 

16 The Table 1 in the report is very good there. 

17 I mean, if those points really cover certain aspects of the

18 mechanisms of action, you could actually, already on the basis

19 of Table 1 say we need these assays and we can skip those

20 assays, given that those assays are validated and given that the

21 endpoints are valid and can be measured in a decent and robust

22 way.

23 I think that's more important and it gives you

24 a more general idea of how to select assays rather than test 20

25 compounds or whatever you have.

26 DR. DANIEL SCHLENK: Other Panel members? 
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1 Yes, Dr. Schultz.

2 DR. TERRY SCHULTZ: I would just like to refer

3 the Agency to Question 2 that we had yesterday afternoon. There

4 was some question about redundancy among the in vitro assays as

5 it related to the prioritization scheme. That would also fit

6 into the total answer from the Panel on Question Number 8b.

7 DR. DANIEL SCHLENK: Anyone else? Okay. 

8 Let me go back to the Agency. Any questions or clarification?

9 DR. MARY MANIBUSAN: No additional comments. 

10 Thank you.

11 DR. DANIEL SCHLENK: Okay. As is customary

12 for most panels, what we do, you'll have a chance, at this

13 point, to provide any further comments or inputs that you'd like

14 to provide for the record. We'll just go around the table

15 quickly here. We'll begin with Dr. Klaine. 

16 DR. STEPHEN KLAINE: Well, I just wanted to

17 thank both the public commenters and EPA for all the hard work

18 and great presentations. I realize that we probably just gave

19 you a bunch more hard work to do. But I think that it's pretty

20 critical that we need the answers and further work in order to

21 consider this in the entire set of issues that started in

22 January and are going to end up with the next two meetings in

23 June and July. 

24 So I think it's work well spent. In that

25 regard, I would like to say thanks for the great roadmap that

26 you guys put together for tying these things all together. So,
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1 thanks.

2 DR. JAMES MCMANAMAN: I too would like to

3 thank the EPA and the public commenters. They were great

4 presentations. I learned a lot and I hope that we've been able

5 to help you clarify some of the questions that you had to help

6 you move forward. I look forward to the June and July meetings.

7 DR. BARRY DELCLOS: I'll second the thanks to

8 the Agency and to the public commenters. They were very useful.

9 And to the SAP staff for a nice meeting. It's very nice to see

10 data coming out of this massive effort, finally. I was happy to

11 be able to participate. Thank you all.

12 DR. GEOFFREY SCOTT: I too would like to thank

13 the EPA and the public presenters for diverse presentations. I

14 think we heard on both sides of many issues, ranging from in

15 vivo test, in vitro test. I really appreciate all the foresight

16 and input that we got. It made our job a lot easier with the

17 clarity of the presentations. I really thank you all at EPA for

18 that in helping us understand the data.

19 A couple of things that I'm struck with as you

20 all move forward, we've heard from several of the other Panel

21 members in our concluding remarks, looking ahead to the weight

22 of evidence and next meetings on this. One of the comments I

23 made, and I'll just reiterate it, is knowing your controls. 

24 What I mean by that is knowing for each of

25 these assays what is normal. I think we saw a little bit of

26 evidence of that presented yesterday from some of the public
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1 presenters on one of the assays, just with the control data. 

2 I really think starting with some of that type

3 of approach and really getting that information is going to be

4 very critical because when you know what is normal, it's much

5 easier to understand what is not normal. And I really employ

6 you to look at that in particularly now that you actually have a

7 lot of different people performing the assay, which is also a

8 little different variable.

9 The other thing that I am struck with these

10 assays is the good balance in the combination of both screening

11 assays, in vitro assays and the live animal models. I think I'm

12 really pleased to see that fish are playing a key role in this,

13 being an aquatic toxicologist. I think they really are one of

14 your real cornerstones. And I think having those tied to

15 mammalian tests along that pathway and then with the frog test

16 is a good combination. 

17 As we talked today, the frog test is not

18 without problems. We're going to have to resolve this issue

19 that we talked about today. That's not the first time we've had

20 to do that with an assay. So I think we'll look forward to

21 continuing down that road of seeing where you go with this with

22 the full list of compounds. 

23 Finally, in terms of redundancy, I really do

24 think looking across those modes of action for consistency of

25 response across the assays, I'm really looking forward to seeing

26 that information. That's going to really tell us a lot. Again,
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1 thank you for having me here.

2 DR. DANIEL SELVAGE: I would just like to

3 reiterate the thanks to the EPA and public as well. I will

4 leave it at that.

5 DR. TERRY SCHULTZ: I think this SAP, looking

6 at the 21 chemicals is a wonderful intermediate step towards the

7 weight of evidence. In order for the weight of evidence SAP at

8 the end of July to really be meaningful, we need to look at the

9 information that you have on all 52 chemicals because it will

10 give us a greater confidence and it will eliminate the question

11 of transparency in how the 21 were picked from the list of 52.

12 DR. DONALD TILLITT: I guess I would also like

13 to thank the Agency and the public for the comments and

14 compliment them on their presentations. I thought it was very

15 good and very thorough. And reiterate many of things that

16 you've heard already. 

17 One of the things, I don't know if I did hear

18 it before was in the presentation of the data for the weight of

19 evidence, I think it might be useful to have some type of

20 statistical power analysis to understand what the expected

21 differences might be, based on the design of these studies and

22 based now on our track record of knowing what the variance is

23 going to be. To understand the power of the test and make those

24 comparisons in the weight of evidence portion of it.

25 DR. KATHERINE ROBY: I'd also like to thank

26 the Agency and the public presenters for really very clear and I
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1 think enlightening information that you presented for us. I'd

2 also like to say, though, that I look forward to some in-depth

3 data that we can really begin to understand the significance of

4 the entire battery and how that will move forward.

5 DR. ALDERT PIERSMA: Well, I'm not going to

6 repeat everything that was said. Thank you very much for a lot

7 of very clear presentations and a lot of information that was

8 very useful for the process and also for giving me insight in

9 how this process works in the U.S., being from a different

10 continent. 

11 I should also thank Dr. Jenkins and his staff

12 for getting me through the U.S. and EPA bureaucracy to even fill

13 out all these forms that were needed to get me here. I'm really

14 surprised that we go through it, but thank you very much for all

15 your help.

16 DR. REYNALDO PATINO: I have nothing much new

17 to say, other than thank you to Fred and Dan for the opportunity

18 to participate in this panel, and to the Agency and the audience

19 for their willingness to listen. I hope the comments were

20 useful. Thank you.

21 DR. DEBORAH MACLATCHY: I just would like to,

22 again, than the Agency and the public commentators on flushing

23 out what was in the White Paper and giving us more information

24 to go on as a panel. 

25 I would like to also say that as one of the

26 two international Panel members that certainly, I think that the
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1 work that EPA has done on this since the late '90s has been very

2 critical to the science and the regulation around the issue of

3 endocrine disruptors. I think that this is one step that I

4 think in 1998 we didn't think we'd be sitting here talking about

5 this in 2013, but I think it's clear that it's a very complex

6 issue. And what EPA has added to the weight of evidence and

7 volume of information has been critical in moving this field

8 forward. So, thank you.

9 DR. JACK LEONARD: I'll just share the

10 gratitude with all my former Panel members to the EPA and to the

11 public presenters. I realize that I was actually one of the

12 original EDSTAC members back in '98. And the assays that you

13 have chosen were actually thought processes at the time, and

14 it's amazing to me how they have morphed into what you've done. 

15 And I really appreciate the effort here and I look forward to

16 the future.

17 DR. NELSON HORSEMAN: Not to be redundant in

18 thanking the Agency and particularly the outside presenters. I

19 think from my experience on a few of these other panelists, this

20 panel in particular, the information the outside presenters were

21 able to provide really, really did help the Panel. 

22 The other thanks will go to the staff here at

23 the EPA for having put together a panel that I think was really

24 well-balanced and brought a lot to the table. 

25 The only other comment back to the report I'll

26 make is, this endocrine disruptor area I know is troublesome for
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1 the regulatory agency because you're dealing with a situation

2 which it really does depend on having healthy animals to see

3 effects that are conceptually not normal. 

4 So we've dealt over and over again, the

5 problem with this toxicity; and I encourage you to think really

6 hard and maybe come back with some notions about how to deal

7 with this problem of more isn't always better. I think that

8 will help move things forward. Thank you very much. 

9 DR. SHERRIL GREEN: I too would like to thank

10 everyone; especially, the public commenters provided some really

11 helpful information. I would like to say one last thing about

12 the bent tail. While it was really interesting and somewhat

13 unanticipated finding, I think it truly reflects some lack of

14 experience and husbandry management that those issues are

15 resolvable. And moving forward, I think, with time, the bent

16 tail will be less of an issue. Right now it doesn't seem like

17 that finding alone would be reason to remove the AMA from the

18 battery of tests. And again, thank you. 

19 DR. DANIEL SCHLENK: Well, thank you all. 

20 I would like to thank the Panel, each of you for your hard work,

21 for meeting together, getting everything put in a very logical

22 and articulate form for everyone to listen to. You've done a

23 great job in keeping us on time. You've made my job very easy,

24 which is wonderful. I'd like to thank, again, the Agency, for

25 the presentations, the materials and the steps forward. Again,

26 it's nice to see a path forward and a road map to move forward.
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1 I'd like to thank Fred and his staff for

2 getting us here and hopefully getting us away, and all of the

3 work that goes into that. Getting us coffee, and even cookies

4 this time, which is was awesome. 

5 I'd like to thank, again, the public

6 commenters, a very special thanks to you for the input that you

7 had for us. As you heard repeatedly, this was very, very

8 beneficial to us, in terms of putting this report together. 

9 So with that, any comments from EPA? Yes?

10 DR. MARY MANIBUSAN: Just a few closing

11 remarks, and perhaps as we close out this meeting I can spend a

12 few minutes just kind of setting that roadmap down again in

13 looking ahead. Thank you again to the Panel for all your

14 recommendations, suggestions and ideas. They are very helpful

15 to the Agency as we move forward. 

16 As we whet your appetite for the weight of

17 evidence analysis, just a brief reminder that the Agency was

18 very deliberate in the timing and sequencing of the July SAP. 

19 We want to take incremental steps forward, building upon the

20 experience and input that we're receiving here at this SAP, into

21 how we continue moving forward to formulating our

22 interpretations and our future approaches. 

23 With your input and insights on the assay,

24 performance in particular, the bent tail, we will be continuing

25 that conversation in the next month, SAP, as we embark on

26 completion and validation of the remaining Tier 2 assays. The
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1 LAGDA assay in particular, will have some insights to offer on

2 this bent tail observation and how we can provide some

3 additional advice in terms of test guidance to avoid those

4 particular observations. So we'll continue that conversation in

5 June. 

6 Thank you again for your comments on the

7 battery performance. I think it gives us a lot of assurances as

8 we move forward as an agency, and continue to issue test

9 guidance in how to do that more appropriately will be important

10 to receive your information, as well as employing all of your

11 considerations and adjustments to our evaluations in approaching

12 for reviewing and revising our evaluations for the remaining 52

13 compounds for List 1. And certainly with this peer review and

14 input from the panel, it will greatly achieve that scientific

15 soundness that we always talk about as an agency, as a whole.

16 As you've discussed, in terms of the July

17 meeting, I want to emphasize that all the input that we're

18 receiving on the Tier 1 battery and assay performance will be

19 integrated together with the other scientifically relevant

20 information in a weight of evidence assessment to help us build

21 a case and to make a determination of whether a chemical has the

22 potential to interact with the endocrine system. That is what

23 we plan to bring to you. We, again, appreciate that you're

24 allowing us to take this step-wise, slow pace progression in

25 moving forward.

26 It is with that incremental pace that we need
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1 to ensure an anchor, a scientific soundness before making those

2 regulatory decisions because of the impact that we've talked

3 about, in terms of resources, timing, and decision-making.

4 With that, I want to acknowledge the

5 excellence of our EPA team in putting together some complex

6 analyses for this meeting. We are looking at over 100s of assay

7 results, and within a short amount of time they've done an

8 excellent job. I want to acknowledge our team here at the table

9 as well as those in the office and across in our Office in

10 Research Development Program.

11 I want to acknowledge Dr. Fred Jenkins and the

12 SAP staff as a whole. Folks who attend SAP meetings really

13 enjoy it because of the logistics and all the things that the

14 SAP program does to ensure a very smooth transaction with the

15 program.

16 I too would like to reiterate the excellent

17 comments that were provided by the public at large. I think it

18 demonstrates that the endocrine program really reaches out to

19 all the experience within the contract laboratories, to our

20 regulated communities, and to the public to help us ensure that

21 we're moving forward in the right direction. So we definitely

22 appreciate their comments. 

23 And then last, but least I want to acknowledge

24 the Chair, Dr. Dan Schlenk, who keeps us on schedule, on time,

25 and on topic. And the rest of the panel, I have never been so

26 impressed with a panel that can speak to such a broad breadth of
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1 issues and across so many different taxa and across the Tier 1

2 assay and battery. 

3 The DFO, the SAP team absolutely did an

4 excellent job in picking each and every one of you. And if I

5 can opine a little bit, as we began this meeting we talked so

6 much about the EDSTAC and their conceptual framework and what

7 they thought about this program in putting together this Tier 1

8 and Tier 2 screening and testing approach. 

9 I like to believe that as we look back in

10 time, this 2013 year, this SAP meeting will be that reference

11 expert committee that we will call upon as bringing the program

12 up, in terms of the state of science, and really reflect how we

13 can do our jobs better, faster, more efficient. So thank you

14 very much.

15 DR. DANIEL SCHLENK: Thank you, Mary. So

16 with that, I will turn it over to Fred to close us out. 

17 DR. FRED JENKINS JR.: First, I want to give a

18 special thanks to Dr. Daniel Schlenk for his leadership. It's

19 always a pleasure working with you, Dan. This time was no

20 different. 

21 I want to thank the Panel. I really

22 appreciate you all's willingness to serve and your commitment

23 and working really hard throughout this process. I want to give

24 thanks to Dr. David Dix who is our newly appointed office

25 director. Dr. Stephen Bradbury, thank you so much. A special

26 thanks to Mary Manibusan. Mary served as a lead manager in
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1 leading her team to get prepared for this panel. It was a

2 pleasure working with you, Mary, in preparation for this

3 meeting. I thank you for everything.

4 A special thanks to Jess Rowland, another one

5 of the managers involved in preparing for this meeting. A

6 special thanks to Don Bergfelt. He was my lead point-of-contact

7 amongst the staff and he was extremely superb in getting me all

8 the information I needed and helping me and keeping me informed

9 with what was going on in preparation for the meeting.

10 Thank you to Gregory Akerman, John Liccione,

11 Amy Blankinship, Catherine Aubee -- I'm naming all the team

12 members -- Thomas Steeger, Vicki Dellarco, Patience Browne, and

13 Calvin Powell. Thank you to you all. It really was a pleasure

14 to work with you all.

15 Thank you to the public commenters. You all

16 did a great job in providing information and public input

17 through this process, to make this process work even better. 

18 Thanks to those who submitted written comments. Thank you very

19 much to the public for attending this meeting. 

20 I would be totally remiss if I did not thank

21 my FIFRA SAP staff, under the leadership of Laura Bailey. 

22 Thanks to Laura. I wanted to thank Sharlene Matten and Joe

23 Bailey, who are the other DFOs with the FIFRA SAP. Thank you

24 for your support.

25 Thank you to Donald Wood, our administrative

26 team lead and Joyce Coates and Shirley Percival who is also on
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1 our admin staff. And thank you to our contractor for their

2 support throughout this meeting. 

3 The last thing I need to say to you all is

4 that the meeting report will be ready no later than 90 days at

5 the close of this meeting. So I want everybody to have a great

6 holiday weekend and thank you all. This meeting is officially

7 adjourned. 

8 (Whereas the meeting was adjourned)

9 * * * * *

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A

AAALAC 285:10

AAM 431:21

abandon 318:4 320:13

aberrant 218:20

abiding 285:21

ability 18:23 21:13 22:12 44:17 51:23 57:1 62:1

74:10 77:14 96:16,19 128:1 153:24 168:22

179:15 202:5 204:10,17 259:24 260:7 266:17

297:2 307:9 310:16 332:6 357:1,11 358:7 404:14

417:6 441:14 444:25 449:26 450:2 460:18,25

481:11 483:9 485:10 491:22

abiotic 112:2,16

able 26:5,19 28:24,26 29:11,16,19 32:23 40:16,25

42:25 43:26 44:5,21 45:16,20 46:26 66:24 70:23

73:7 90:3 97:6,25 99:16 100:2 119:9 131:24

133:23 135:18 142:19 148:14 149:7 178:15

187:13 192:12 201:19 221:15 222:9 227:17 234:3

266:20 271:16 272:9 274:20 277:7 282:13 284:17

287:11 288:4 296:22 297:4 305:15 312:22 327:4

332:2,5,22 333:25 334:2 336:7 348:20 353:13

373:5 374:15 388:13,20 461:3 462:19 474:4

478:9,11 479:12 481:4,8 495:4,11 499:21

abnormal 103:3 104:13 431:25 432:1

abnormalities 213:9

abnormality 124:4,7 213:26 258:21 259:7,14

260:16,24 361:25 362:4

absence 88:12 128:5,11,12 249:1 404:25 411:19

447:13 450:22 451:3 468:2 483:15

absent 192:24 213:2

absolute 214:25 388:19 484:19

absolutely 86:10 112:24 135:8 241:26 409:13

417:11 420:17 456:24 504:3

absorb 13:15 113:16

absorbed 136:20

absorption 97:4 109:12 111:21 113:14 311:1

360:16

abstract 255:15 269:16

abstracts 223:2

academics 435:17

accelerated 47:20 52:25 268:4 398:25 401:26

402:4

acceleration 93:12

accept 197:9 392:9

acceptability 386:5

acceptable 60:2 64:5 187:12 247:3 284:8 344:25

356:20 358:24,26 359:22 371:4,8 373:9 375:20

386:14,17,26 387:10,11 388:23,26 393:2

acceptance 374:23 429:9 430:13 440:23

accepted 360:2

accepting 198:16 392:1

access 139:10

accessory 76:23 81:12 244:24

acclimation 99:21

accommodate 70:26 264:10

accompanied 161:20 167:20 172:8 484:11

accompanying 104:11

accomplishing 319:10

account 102:5 110:5 117:9 192:16 247:6 297:25

316:17 332:13 359:23 386:22 387:14 388:2

390:12 396:26 398:11

accounted 247:14 386:19

accounting 471:16

accredited 285:11

accuracy 219:6 247:23

accurate 120:4 219:8 361:6 362:7 441:14

accurately 351:15

Acetone 126:15

achieve 28:26 71:4 97:14,15 98:22 233:18 234:3

273:7 356:14 357:1 502:14

achieved 70:17 129:8 234:12 355:6 481:24

achievement 363:6

acidity 26:4

Ackerman 153:9 203:16

acknowledge 8:2 31:5 199:14 344:24 503:4,8,11

503:23

acknowledged 198:20 458:13 489:18

acknowledges 248:26 430:19

acknowledging 24:1 27:20

acronym 296:8

act 2:15 10:6,7,19 16:26 17:12 36:4 39:1 44:2,9,12

45:2,9,13 318:16

ACTH 82:18 294:22

acting 6:24 7:2,5 49:17 107:17,19 183:6,12 184:2

action 6:11 8:1 18:9 19:9,14,18 24:14 27:25 35:23

35:23,26 36:23 37:12,13 41:13 45:18 46:8,20

49:19 78:15 81:1 169:17 179:13 185:11,24

245:18 250:18 251:24 252:11 273:4,5 275:8

278:25 279:5,7 289:8,9 292:19 294:26 295:3

317:11,17 329:11 332:16,20 368:26 369:17

410:26 414:20 429:12 432:5 493:18 496:24

actions 16:21 42:4 369:24 427:20

activate 60:5 192:12 397:9 449:20 450:9 453:9

activated 485:21

activating 453:9

activation 19:15 42:8 43:25 46:24 56:17 60:4

177:20 230:25 232:23 329:7 335:8 365:4 372:4

372:17 377:7,19 380:16,20 382:19 387:17 397:6

468:23 487:15 491:12

active 17:10 34:23 35:2 37:16 45:22 53:5 153:25
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160:14 196:3 255:17 260:7 270:10 316:1 368:17

379:19,23 442:8 451:25 467:4 470:8

actively 101:18

actives 54:13

active-ingredient 34:3

activities 32:24 40:26 44:19 45:23 46:17 54:16

79:25 164:21 165:2,18 173:24 179:12 187:8

333:3 377:21 483:26

activity 26:7 28:12,16 40:20 43:22 44:5 45:16

46:21 47:14,18,22 49:22 51:23 52:17,22 60:14

65:23 66:7,8,15,16,18 67:7 91:22 150:24 154:21

154:25 155:9 159:3 169:21 170:4,15 173:12

177:3,6 192:11,23 195:2 197:17,18 233:5,21

240:1,18 241:3,3,6 245:18 246:15 250:16 251:8

251:22 255:4,5,7,11,19,24 256:9 257:20 258:1

261:24 262:6 268:20 289:5 290:22 292:8,8 311:4

311:8,20 315:8 342:6 349:20 383:9 387:20

395:20,25 427:11,11,14,16 448:9 449:5 453:4,12

456:5 459:12,15 462:6 463:12,19 466:26 467:7

474:10 478:20 479:13 485:12,12,15 491:13

Acts 10:5

actual 7:26 50:16,20,21,26 59:8 73:10 85:11 128:4

129:8 136:24 139:10 142:21 143:3,6 152:25

255:12 325:17 331:6 350:10 381:8 404:15,20

405:8 428:24 450:5

actuality 250:9

acuity 48:10

acute 102:22 273:16,19 274:1 276:22 309:11

419:11,16 422:13

adapt 139:1

adapted 52:16 336:16

adaptive 238:17

add 35:21 51:25,26 103:16 142:7 186:16 188:21,23

190:22 195:4 205:16 227:20 243:5 257:19,21

259:4 279:19 294:22 333:15 335:2,3 353:11,12

360:24 364:15 373:26 374:7 378:1,6 379:5,7,11

390:14,17 391:18 399:13 442:2 444:1,7 446:3

462:15,17 469:9 470:20 474:22 482:1,3,5,7,9,11

485:7 486:11

added 60:13 65:26 100:10 187:11 232:7 245:1

273:9 291:26 352:3 354:8,11 363:20 366:5

440:14 459:25 499:6

adding 67:26 101:15 280:3 354:9 357:8 377:16

392:19 420:11 454:20

addition 40:21 43:3 100:16 240:20 259:22 292:21

297:22 307:21 309:5 321:16 348:5,14 349:17

357:12,19 359:21,25 360:7 361:19 371:2 388:13

407:25 434:26 435:7 453:14 463:2,23

additional 8:12 14:11 18:20 23:25 27:22 33:23

39:9 48:11 55:22 66:4 67:15,26 68:2 72:16 98:10

101:16 102:5 106:16 110:5 125:1 130:4,5 169:6

197:9 202:4 212:7 223:20,21 225:3 245:18 248:6

254:7 258:12 275:15 278:12 279:11 280:24 304:5

310:15 311:7,23 313:7 314:9 324:1 327:12

348:16 350:2 351:4 357:8,10 358:2 360:22,24

362:24 363:19 366:3 378:14 395:26 399:13

405:24 420:3,11 430:25 435:5 444:14 446:13

451:21 454:2 466:2 468:21,24 469:10 480:3

482:15 492:2 494:9 502:3

additionally 98:7 104:22 106:23 165:23 167:12

168:9 178:19 180:14 254:24 376:2 423:21

address 30:11 39:13 40:2 117:15 149:17 240:21

260:14 287:24 305:15 307:3 322:10 332:18,21

333:2 344:23 362:16 363:26 364:6 368:13 381:14

410:7 416:15 473:13 481:24

addressed 118:13 281:20 316:10 366:2 369:22

381:15 409:10 410:5 433:11 471:19

addresses 250:23 309:10

addressing 159:20 373:25 447:23 473:3

adds 274:7 333:12 349:25 378:26

adequate 21:14 75:22 77:9 79:20 82:2 87:25 98:14

100:23 118:5 153:26 160:10 162:23 181:20 198:2

198:15 365:10 385:5 396:2

adequately 153:19 155:22 179:6 306:26 307:9

483:8

adhere 96:19

adhered 74:2 83:14

adjourn 420:23

adjourned 209:4 420:25 506:7,8

adjournment 207:20

adjunct 271:11

adjust 110:12 112:10 246:24 375:12 443:14

adjusting 110:11

adjustments 502:11

admin 506:1

administer 305:20,20,25 306:3

administration 5:2 83:22,24 210:21 350:10

administrative 2:12 305:24 385:23 482:18 505:25

administrator 17:6

admit 268:9

admits 319:20 392:18

adopted 310:23 454:10

adrenal 50:17 64:7 76:14 78:3,26 80:15 81:13 82:7

adult 74:16,20,21 75:11 259:22 351:7 435:14

adulthood 385:20

adults 325:22 415:20 488:17,21

advance 9:6 202:17 259:12 469:16,19

advanced 159:5 231:24

advancement 18:8

advances 231:26 490:9,10
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advancing 27:20 146:13

advantage 232:24 301:4 328:1

advantages 231:12,14 324:10 489:12

adverse 5:11 11:17,19 14:20 18:2 19:19 20:7 25:13

41:24 89:4 227:1 268:23 315:9 316:15,16,21

329:10 331:24 332:21 334:10,13,26 487:2,4,5

491:1

advice 2:19,21 10:11 11:1,2,25 12:14 13:6,18

30:11 33:2 37:22 38:2 124:6 125:20 196:8 198:6

213:25 215:21 363:13 502:3

advise 320:3,7

advised 163:25

advises 454:5

advisory 1:1 2:6,14,18 3:23 7:17 8:23 11:5,5 16:16

17:21,22 69:18 299:20 335:26

advocate 266:23

aeration 100:10,10

affect 61:19 65:19 82:14 94:16 117:18 120:25

130:21 141:26 146:6 157:24 158:4 159:8,16

161:14,26 213:12 227:1 250:3 266:7 273:24

275:16,19 291:15 297:24 315:9 316:22,23 329:6

359:6 362:4 386:8 387:7 389:19 398:26 399:4

404:14,24,26 405:9 428:12 434:13,14 437:23

441:14 463:13,15

affiliation 4:8 210:7

afflictions 433:21

African 115:23

afternoon 92:1,5,12,15 114:24 164:3 172:10

305:14 323:9 336:25 422:9 494:3

afternoon's 92:6

age 77:18,21,24 79:1 80:5,8 81:14 165:3 168:4

173:13 243:20 247:4 250:9 388:15,17,19 477:10

agencies 152:4 489:19

agency 1:12 2:13,20,23,23 7:16 8:21 9:11,13 10:7

10:21 11:1,21 12:5 17:1,9,19 23:9,10,24 27:13

28:5,16 36:20 40:24 55:8,13,18,20,21 68:10

69:16 70:4 92:17 100:22 102:8 106:4,23 107:3

114:14 122:13 125:20 128:22 134:4 152:11 156:8

156:11 158:3,17 163:21 165:12 178:23 182:8,20

183:3 184:7 194:13,15 198:20 212:7 215:7,11,20

218:1,8 219:13,15 248:4 249:22 250:1 282:3,4,14

283:1,24 286:12 287:1 301:1,4 313:7 318:5,8,10

319:22 320:16,19,23 348:5 352:5 358:7 359:8

363:10 365:19 368:8,23 369:3 370:21 371:14

372:11,22 373:4,7,17 374:2,5,14,19 375:2,11,15

376:2,15,20 377:14,18 380:13 381:13 382:15

383:18 387:15 393:6,25 403:4,19 408:26 409:15

413:2 423:7 428:17 429:1,3,12 433:6,8 436:17

437:14 440:19 445:26 450:11 453:16 454:5,24

466:11 467:15 470:22 476:10,12 477:14 482:13

483:8 491:26 492:7 494:3,8 495:8 497:13,26

498:18,22 499:18 500:1,24 501:15,17 502:8,15

Agency's 55:17 94:4 173:20 179:21 183:15 207:13

246:12 249:9 304:4 306:8 318:17 371:20 375:5

433:14

agenda 3:17 92:5 355:2

agendas 490:6

agents 243:11 443:4 481:4 483:25

ages 74:21 325:6

age-matched 348:24

ago 12:13 205:18 220:13 229:14 230:17 304:9

339:17 353:20 383:8 440:9 475:20

agonism 57:5 116:22 233:10

agonist 19:20 20:3 30:3 44:1 45:3 60:11,14 61:21

74:11 94:15 192:23 197:14 232:25 243:10 256:1

256:2 283:12,13 324:19 342:6,6 372:6 373:5

388:10 394:9 476:18 483:25

agonists 75:24

agree 69:7 246:12 264:13,16 322:14 357:24 378:15

384:4,9,13,17 413:13 417:11,12 419:7 445:12

457:1 462:14,18 466:1,4 473:1 486:23

agreeable 368:5

agreed 155:17 220:25 389:1

agreeing 2:16

agreement 133:11 375:4 464:8 467:10

agrees 334:5 370:24 372:16 373:7,11,17 374:9,19

376:15 386:9 408:25,25 426:23 428:16

AgroSciences 220:15

ahead 2:2 53:9 73:21 91:24 93:23 114:9 122:25

151:15,23 185:19 201:26 212:8 270:19 275:22

305:8 319:18 356:9 384:20 395:7 422:2 425:9

452:11 457:3,16,22 466:15 482:19 492:4 495:21

501:13

aid 359:20

aided 68:4

aimed 178:19

Akerman 31:7,18 53:14,16,17 68:15,20 71:24 72:4

72:11,26 172:9,10,11 194:2 203:24 380:14,14

381:1,7,19,24 382:7 505:10

Akt 400:13

al 122:12 212:23 214:2 255:14 261:15 263:17

290:16 291:6 427:22 434:3 437:8 458:8

albeit 29:22

albumin 401:3

Aldert 5:22,23 36:14 51:3 85:21 86:9,16 138:15

211:10,10 462:17 469:25 477:17 482:3 486:23

493:12 498:5

algal 457:9

algorithm 199:26

aligned 140:11
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alike 48:25

Allen 258:15,17,18 263:15 264:16,25 265:19 266:8

267:7,13 268:9 269:7 270:1,14 304:16

alleviate 263:3 375:21

Alliance 221:1,6,14,15,16

allometric 403:12,18,21

allotted 208:15

allow 42:25 53:1 66:16 71:12 94:26 98:15 106:19

110:11,14 236:8 254:19 284:25 320:19 336:16,20

338:18 360:1 393:5 405:3 430:17 445:3 451:1

473:17 474:14 491:14

allowable 99:20 166:25

allowances 110:22

allowed 17:14 118:26 233:14,17 237:1 424:17

allowing 18:17 230:22 231:15 355:6,7 393:21

502:24

allows 23:24 99:14 233:19 420:9

alluded 263:11 301:7

alluding 469:16

all's 504:22

alongside 20:12 24:22,23 313:25

alpha 57:3 60:9,14,26 61:4,7,21 75:4 245:17

341:25,26 342:7 365:4 372:2 377:4

alpha-ethinylestradiol 244:2

alpha-methyltestosterone 340:20 341:2

alpha-reductase 75:25

Alright 132:15

alter 251:26 360:18 374:11,11 400:11 464:25

481:4,12

alteration 165:6,8 170:17,26 406:6 434:10

alterations 77:15 78:19 81:5 95:24 103:11 104:1

104:17 167:9 168:9,12,13 169:6,8 170:2 174:1

175:12 276:20 442:4

altered 407:17 427:10,11,14 461:12,17 462:2

486:9

altering 169:5 302:7 376:15 428:17

alternate 225:19 344:10

alternative 128:21 257:4 291:3 293:1 301:11,14

322:16 344:5,12,14 403:21,24 422:21

alternatively 388:16

alternatives 256:25 289:21,23 290:2,12 293:5

322:2 330:5

altogether 187:21

AMA 114:26 115:2,22 116:20 117:13,15 118:3,16

120:12 124:14 126:5,13,25 127:5 128:11,14,22

129:14 144:13 146:10 150:24 157:26 158:12,13

158:15,20,24 162:24 200:1 213:24 214:17 215:12

216:7 241:23 253:19 254:11,21 255:3,5,7,19,22

256:4,10,15,26 257:18 258:20 267:19,19,23

269:9 271:26 284:8 294:8,13 300:5 318:13,16,18

318:21,22,25 319:1,5,9,14,16,17,21 320:3,8

321:6,19 322:23 376:26 422:21,23 425:23 426:6

427:2,4,9,12,14,16,20 428:9,21 429:8,11,20

430:13 433:6,7 434:19 446:18 448:1,15 462:4,7

471:23 472:2,3,4 488:11 500:17

amazing 499:14

ambient 218:23

ambiguities 71:1

amenable 128:10 369:17 492:24

amend 284:22

amended 10:6

amending 394:8

Amendment 17:12

amendments 17:19

America 305:13,15,19,20,22 306:6,6,7,17 310:22

313:5,23

American 439:6 458:1

ammonia 218:4 438:24 439:23

amount 57:18 214:21,25 215:5 254:20 258:9,11

264:8 272:12 291:25 297:5 304:12 352:4 354:8

417:14 439:24 459:5 481:7 486:15 503:7

amounts 85:7 344:18

amphibian 20:9,24 29:10 42:13 43:16 46:1 47:14

47:15,19 52:14 54:1 114:23 115:12 140:26 141:4

158:7 160:6 161:6 163:9,12,15,26 180:26 200:2

212:15 213:15 252:19,22 255:16 257:2,13 271:19

292:20 293:26 298:6 317:26 318:12,14 321:10

322:11 361:20 395:12 425:12 439:5 448:10

453:26

amphibians 6:3 128:13 157:17 211:16 254:12

265:9 293:22 321:8 401:16

ample 151:12 208:24 331:12

amplification 451:22 475:8

amplified 422:10

amplify 399:21 450:19

Amy 31:7,19,21 53:24 93:24 94:1 107:13 108:1,8

108:11,17 109:7 110:2,13,16,24 111:11,25

112:14,24 113:11,21 114:3,20 119:22 153:7

164:2,3 172:14 189:16,18 190:8 505:11

analyses 52:4 102:10 239:7 245:14 249:10,14

269:8 287:12 350:8 354:17 356:11 359:4 503:6

analysis 38:6 39:12 48:11 51:17 52:3 54:4,12 56:11

57:7 59:24 60:15 64:10 66:11 77:20,20 80:4 89:7

106:24 107:10 108:26 113:20,25 114:1 120:1,21

123:9 124:26 125:6 130:26 132:12 134:11 146:26

149:21 150:6 153:12,17 154:3 155:23,25 156:1,5

158:2 160:15 165:16 170:9 172:13 173:6 175:6

178:9 179:10 181:23 182:2,6 184:19 190:11

192:15 217:25 235:22 236:16 238:20,21 245:8,12

256:21,22 263:1 270:10 277:7 280:4 291:9
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294:15 297:20 299:1,8,15,18 300:9,10,14 301:1

303:5,11 344:16 349:23 354:11 357:15 366:20

394:26 396:5 404:1,3 414:15 448:16 451:10,17

452:15 454:9 464:13 470:26 475:22 476:1 481:24

483:14 492:24 497:20 501:17

analytical 128:4 272:5 360:15 363:21

analyze 91:2 249:11

analyzed 117:10 138:20 148:4 150:8 158:10 297:1

476:5

analyzing 32:11,13 245:23 417:16 472:9

anatomical 138:4 430:24

Anatomy 5:20

anchor 24:10 503:1

ANCOVA 77:20 80:4 249:11 350:8

Anderson 226:1

androgen 11:13 13:10 18:6,17 20:5 26:26 27:17

29:15 40:8 41:3,18 42:9 43:12 44:16,24,26 45:2,9

45:14 47:9 62:2,7,8 69:22 80:26 94:15 153:9

154:21 156:2 157:10 163:2 164:4,8,13,18,20

165:10,15,24 166:1,7,13,17 168:3,4,16,19 169:15

170:12,23,26 172:16 179:8 180:12,17 189:24

250:24 251:1,9,25 290:20 292:8 366:19 369:8,10

370:8 373:13 380:10 461:4 463:26 464:19 465:19

465:19 468:11,13 471:1 481:17 486:12 487:11

androgenic 47:1 75:24 76:17,25 79:25 83:12 93:14

154:20 245:16,25 246:1 311:3 485:12

androgens 43:12 65:24 164:21 461:3 468:9

androgen-dependent 76:9,18,20 81:17 461:5

androgen-mediated 329:6

androgen-sensitive 81:6 461:11

and/or 56:4 65:11 79:13 97:20 101:5 104:1 106:13

172:8 180:16 181:13 201:19 241:18,23 258:7

350:5 450:1 468:4 481:17 483:7

ANE 369:23

anecdotal 264:5 431:12,14

anecdotally 218:18

angst 224:16

ani 76:10

animal 19:25 29:19 60:19 90:21 186:9,9 191:1

243:23 244:15 247:4 248:12 259:21 260:26

262:25 266:14,21 269:13 274:12,24 307:8 320:13

322:20 327:22 328:14 330:15 331:15 339:6

350:16 351:2,7 355:4,5,5 401:1,8 414:22 419:1

419:13 436:4,19 437:1 439:19 451:4 483:1,7

484:22 488:2,9,24 489:15,20,21,22,23 490:9

491:6 496:11

animals 22:8 145:17 166:2 178:24 179:2 191:13

222:4 223:21,23 224:14,26 228:5 231:23 243:20

248:18,19,20 249:19 258:7,12 259:9,11 260:1,3

260:26 261:24 262:8,13 265:12 266:7,9,20,23

268:6,14 269:25 276:15 285:11 308:16 309:23

312:25 315:21 323:12,19,22,25 324:1,3,5,13,21

324:24 325:8,10,11,16,20,21 326:3,7,10,12,14,15

326:17,18,24 327:2,6,12,16 328:13 329:22

330:17,19,22 331:1,3 348:24 351:13 355:16

361:24 362:6 379:17,24 385:26 386:24 389:4

393:15 405:13,15 413:12 419:22,23 437:6 440:21

441:1,16,22 486:18,20 488:10,14,14,26 489:8,25

490:4,21 500:2

animal's 418:25

animal-intense 254:21

Ankley 311:9

Ankley/Gray 292:1,7

announcement 151:17

announcements 92:3

annual 443:13

anomaly 136:9,10 425:13

ANOVA 249:11

answer 18:14 24:16 30:26 37:16 82:8 131:13 142:2

183:14 184:24 194:13 197:10 200:12 202:14

219:1 225:6 283:23 296:14 298:25 300:8 303:13

307:9 316:24 317:2 321:2 344:3 363:24 365:18

384:18 449:10 455:2 459:9 463:20 473:19 475:3

477:11 480:20 481:3 483:3,9 494:6

answered 22:11 38:4 88:2 131:13 299:5 323:3

answering 30:19 196:22 199:6,7

answers 365:19 484:1 494:20

antagonism 50:8 57:5 233:10 236:3 237:2 342:9

antagonisms 116:26

antagonist 44:1 45:3 94:15 197:14 232:25 256:1

262:5 283:13 324:19 342:11 373:6 476:19

antagonists 75:24 328:2

anterior 137:15 213:4 259:18,20 266:24

antiandrogenic 93:15 154:20

antibody 239:1

anticipate 48:8 131:19 470:23

anticipated 96:14 97:2 107:1 133:17 348:10,18

458:21 466:18 480:25

anticipating 163:4

antiestrogenic 154:25

antiestrogens 460:26

antioxidants 270:12

antithyroid 77:14 79:25 427:10,14

antitoxin 457:12

anti-androgenic 47:2 76:19,26 79:25 245:16

276:19 351:19 483:25 485:14

anti-estrogenic 46:26 50:3 77:15 460:19 485:14

anti-thyroid 47:14 268:20 449:18 453:3 455:19

456:6,7

anybody 220:21 227:20 270:9,12 278:3 293:24
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323:6 364:25 422:18 473:12 479:6

anyway 205:16 319:18 320:14 394:24 485:18

AOP 334:18,24 335:16 336:2

AOPs 336:7 478:26

apart 26:23 130:22 287:11

apical 11:17 19:26 22:9 40:20 41:9 88:1 251:23

273:3 275:2 276:9,14 278:20 279:1 328:23 329:9

487:6,7,17 491:5

apologize 118:22 205:15 418:24 454:20 477:10

apoptosis 253:24

apparent 101:19 220:4 358:15 363:15 396:4

410:19 450:8 453:9

apparently 196:16 270:25 294:24 326:20 327:8,26

appear 61:6 101:4 109:1 137:24 139:10 191:11

215:5 232:17 291:20 299:2 433:12 441:9 468:13

483:23

appearance 102:13 105:2 135:12 137:17 266:2

452:14

appeared 160:11 215:24 216:1 325:4 458:18

466:25 467:23

appears 124:3 125:6 129:18 135:25 136:10,25

137:26 146:13 243:12 256:3 285:23 325:2 357:26

425:13,26 426:1 467:10

Appendix 234:22

appetite 416:2 501:16

applicability 331:17 453:10

applicable 52:11 245:21 260:4 309:17

application 225:18

applications 224:7

applied 38:13 98:25 192:21 458:7

apply 137:4 332:23 413:14

appointed 7:2 504:24

appointment 7:7 271:11

appreciate 7:1 51:7 205:25 363:22 394:2 495:15

499:15 502:23 503:22 504:22

appreciated 425:1

appreciates 305:15

approach 10:23 22:22 35:5 39:12 41:15 55:18

117:1 201:2 202:24 216:15,17 217:15,18 221:5

228:11,21 229:3 239:5 292:1,9 293:7,10,20

294:14 309:7 310:23 311:10 312:6 331:13 332:1

333:7 334:3,6 336:21 355:7 359:26 366:11

382:11 389:26 413:9 422:22 443:23 456:19 470:2

470:11 477:22 491:1 493:9 496:3 504:8

approached 220:13 422:6

approaches 9:13 216:4,8 231:12 293:2 301:14

331:21 384:15 479:19 501:22

approaching 109:11 502:11

appropriate 2:23,24 10:15,17 14:2,13 16:21 27:18

49:4 55:11 68:5 93:5 102:9 104:23,26 107:18

113:23 144:1 150:2 151:6 153:23 168:2 198:19

216:12,19 217:16 253:1 274:2 292:3 348:11,15

350:8 363:7 375:19 403:26 417:17 469:6

appropriately 14:18 128:17 378:20 450:10 502:9

approve 419:1

approved 489:22 490:1

approximately 27:6 234:3 250:15 307:19 308:11

337:8 348:13

approximation 148:10 330:26

aquaria 111:10 360:19,23

aquatic 4:13 5:4 6:2 56:10 110:18 112:3 115:24

119:23 126:5,19 153:1 210:23 252:17 264:23

272:2 273:14 276:6 277:17 321:8 358:19,22,25

363:7 411:7 432:26 439:7 489:4 496:13

aqueous 94:21 115:22 128:10,19,22

AR 44:25 55:2 56:17 61:24 62:6,7,9,16 63:3,20,25

64:4 68:8 69:24 164:26 165:13,16 167:1 169:19

223:15 230:24 231:11,21 232:10,13 245:26

324:18 327:6,11,15 335:13 338:3,8,26 355:7,12

355:23 365:3 373:3,3,8,11 378:16,17 465:20

arbitrary 238:11 261:22 490:18

area 6:16 16:4 26:22 30:16 45:22 69:19 220:3,18

221:25 231:3 430:25 442:15 499:26

areas 130:4,5 311:6 396:13

argue 269:2

argument 268:1

arises 315:19 351:18

Aristotle 30:21

Arkansas 210:19

ARLINGTON 1:16

arm 306:19

arms 90:17

aromatase 19:16 42:9 46:6,7 47:6,8 50:19 56:18

65:22,23 66:1,12,15 67:7,8 164:19 165:1,14,17

169:19,20 170:16 173:2,11,23 177:2,6 193:2

237:15 239:24,25,26 240:1,6,14,16,18 241:15,25

250:24 251:2 290:20 358:9 365:5 370:8 374:13

374:14,17 378:8 468:7

arrangements 3:8,12,13

array 234:19

arrive 208:7

art 435:22

article 299:7,11 300:17 303:16

articulate 500:22

articulated 32:18 314:3,24 332:24 333:4

articulating 315:3

artifacts 245:11 339:14

artifice 329:5

AR-related 69:17

ascribed 260:11,12,13 349:4
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ash 270:15

aside 72:20 120:11 202:16

asked 139:22 181:17 198:6,21 208:16,18 220:26

250:2 278:6,7 284:11 294:18 318:17,22 320:3,7

365:19 366:8 381:12 401:14 405:5,23 407:5,13

433:3 457:21 491:10

asking 22:22 24:15 131:19 140:16 189:11,14

196:25 197:21 201:21 267:8,10 269:23 295:13,13

363:25 364:9 372:23 415:10,10 429:3 444:12

469:22 491:16

asks 111:3

aspect 45:3 46:5 71:23 143:20 238:19 371:11

373:16

aspects 117:26 144:3 182:15 187:15 212:15 402:7

467:12 493:17

assay 1:5 4:2 20:9 23:14,18 28:25 29:10,22 30:6,10

31:20,21 37:25,25 42:8 43:25,26 44:10,25 45:1,4

45:8 48:19 50:4,6 51:21 52:11,14,18 53:19,26,26

54:2,3 55:5,6,15,24 56:4,16,17,18,18,18,21,25,26

57:1,4,7,7 58:3 59:23 60:2,4,8,14,17,18 61:20,23

61:24 62:1,1,4,4,5,7,10,11,11,17 63:1,3,20,24,25

64:4,6,7,11,12 65:3,10,14,15,21,22,22 66:3,5,13

66:19 67:3,8 69:9 70:24 72:7 73:8,15 74:1,4,10

74:10 75:4,5,8,8,13,16,20,21,23,24 76:1,20,25

77:3,8,11,12 78:10,10,14 79:5,18,21,22,22 80:18

80:24 81:2,25,26 83:15 84:6,7,17 85:22 86:21,23

87:2 89:20,22 94:2,7,9,12,18 95:6 101:10 102:11

106:10 109:10 113:20 114:23 115:12,21,26

129:18,24 130:12 136:24 152:4 153:22 155:19

156:16 157:19 158:7 160:6,17 163:26 164:9,26

165:24,25,25,26 166:5,16,20 167:1,4,19,23,25

168:3,4,18,21 169:18,19,20 170:16 171:3,20

172:7,19,20 173:14,15,19,21,26 174:6,24 175:1

175:11,15,18,23,26 176:10,18 177:1,1,4,6,20,25

179:26 180:8 181:12,13,14,15,23 182:17 184:11

187:12,26 188:9,10,17 189:1,5,11,11,22 190:4,9

190:9,15,25,26 191:2,10,12,20 192:1,5,15,23,24

193:15,19,26 194:4 195:10 196:18,19,19 197:7

200:7,12,12,16 201:1,2,7 202:13 203:2,17,17,21

203:22 204:2,14 210:3 212:16 215:8 230:25,26

231:9,13,16,16 232:4,15 233:23 234:24 235:1,5,6

235:9,10,11,14,15,15,17,17,20 236:1,3 237:3,4

237:16,21 238:5,25 239:24,26 240:2,6,7,14 241:5

241:25 242:1 243:9,14,16,25 244:5,19,21,22,23

244:26 245:3,9,15,15,24 246:17,21,26 247:8,9,19

248:9,15,20 249:1,20,25 250:3,24 251:5,14,18

252:4,10,19,22 253:7 254:25 255:16 256:5 257:2

257:5,7,13,15,17 258:7 260:7 266:7,19,22 277:13

288:26 290:20 291:7,11 292:9,12 293:22,23,25

293:26 296:20,25 297:25 298:1,7 301:5 308:3

310:9 318:1,12,14 319:11 320:5 324:9 325:17

327:23,24,26 328:4,10,11,16 329:2,22 337:14,15

338:9,15 340:18 341:17,23 342:10,18,18 346:10

346:24 348:7,14 350:14,15,26 351:5,8 355:13

356:13 361:9,20 362:14 365:2,24 366:1,11

369:18,19 370:18,24 371:11,15,17,19,22,23

372:2,3 373:3,11 374:14,17 375:7,22 376:10,13

377:5,15 378:8,8,20,20,21,22 379:1 380:16,24

382:12 383:25 385:17 388:22 390:20 391:8,14

393:3,7 394:8,15 395:12,13 396:6 399:4,11,19

401:14 402:15,19,25 404:11 405:8,16,22 407:14

408:1,4 409:3,9,16 410:3 412:12 415:13,15,19

417:11 420:1 422:21,23 425:13 426:18 427:2,15

427:25 428:13 429:16 431:19,20 442:4 444:22

448:1 451:12 453:15 459:13,14,16,21,22,25

460:2,8,25 461:5,6,6,10 462:4 463:15,26 464:1,7

464:12,23 465:3,7,8,10,11,13,17,21,21 467:4,6

467:19,19 468:7,8 469:2 472:12 473:18,21 475:6

475:11,13,21 476:20 477:7 478:4 481:9 483:22

484:9,11,12,14,18 491:5 492:12,14,25 493:2

496:7,20 501:23 502:1,18 503:6 504:2

assays 2:8 7:19,21 11:12,15,22,25,25 12:3,8,12,18

12:22,23 13:4,14 18:3,4 19:2,2,3,8,13,15,16,17

19:25 21:11,18,19,23,26 22:25 23:12,19,22 24:2

24:10,11,16 25:5 26:19,24 27:1,2,10,11,15 28:18

28:19,23 29:11,16,17,20,21,25 30:4,8,13,18,20

30:24 31:2,15,19,19 32:23 37:23 38:13,21 39:6

40:2,12,16 41:9,20 42:5,7,10,12,15,17,19,24 43:7

43:9,10,18 45:18,20,21 46:6,12,18,22,26 47:2,2,4

47:13 48:1,10,22,23,25,26 49:3,8,9,11,13,15

50:16,16,20,24 51:1,6,9,10,12,18 52:7 53:14,23

53:24 54:5,9,18,19,19,24 55:1,4,8,10,25 56:2,2,8

56:15,16,20 63:14 67:10 68:8,8,18,19 70:1,14,19

71:14 73:2,22,26 74:5,7,8 77:7 79:17 82:15,16

84:3,18,23 85:1,12,15,16 88:1,1 89:3,16 92:22

93:23 109:22 113:18,23 115:16,19 124:15 144:3

152:5,6,19 153:7,8,10,11,13,14,17,18,24,26

154:1,4,5,11,12,16,19,22,24 155:1,11 157:3,9,15

158:6,13,19 159:12,25 160:2,17 161:2 162:4,15

163:9,9,12,18,23,25 164:8,10,11,13,14,14,19,22

165:2,3,10,11,13,15,17,20,23 166:23 167:6,7,12

167:15,21 168:1,2 169:11,13,16,21,26 170:11,13

170:17,18,22 171:7,12,13,15,24,24,25 172:1,2,9

172:17,22,25 173:1,2,3,7,21,23 174:3,13 175:5,6

175:7,25 176:19,23,23,25 177:8,10,18,21,22,24

178:2,3,5,6,10,16,17,21,22 179:5,6,16,17,22

180:2,4,5,6,6,8,10,14,17,20,21,24,25,26 181:3,4

181:5,10,20,25,25,26 182:10 183:23 184:10,11

184:14 186:21,22,26 187:21,25 188:2,6,12,13,15

188:20,22 190:3 191:2,11,15,18,18,22 192:5
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193:12,16 194:21 197:12,20,22 198:11 199:5,19

200:7,15,21,22,25 201:8,8,8,23,25 203:2,7 204:7

204:19,20,22,25 205:2,5,13,21,23 206:1,5 223:13

223:15,16,19 225:19 226:7 227:23,24 230:24

231:5,18,21,21 232:2,10,20,22,23,26 234:8

235:12 236:1 237:20 238:2 239:1,9,23,25 240:8

241:25 243:5,5 245:21 246:9,10,13,14 248:10,12

248:24 249:6 250:13,14,16,17,18,21,26 251:4,13

251:17,21,24 252:8,11,13 255:18 261:13,26

277:26 278:1 286:8 289:11,12,18 290:19,22

291:8,13,17 293:7,11,20 294:21,22,24 295:8

297:10,16,24 300:13,15 301:18 302:13,15,25

307:8 308:12,14 310:8 311:6,8,25 312:3,4,9

313:25 315:7 320:19 324:15 327:16 329:13,15,19

335:16,25 337:4,4,6,16 338:4,6,20,22 339:4,10

341:14,14,22 343:6,13,15,21 344:4 345:3,5,23

346:4,12 347:24 348:2,4,15,20,25 349:8,11,13,15

349:19,25 350:23 351:17,20,22 352:9 353:4

355:7,18,26 358:4,9,12 365:5,23 366:3,12,13,14

366:17 367:2,9,17,25 368:13,15 369:6,9,13,14,16

369:22 370:2 371:3,4 372:17 373:9,18 374:4,20

374:26 375:6,12 376:9,24 377:7,19 378:16

379:17 380:3,9 382:19 383:19 384:23,24,25

385:18 386:6,6,11,16,19 390:2,23 391:4,10

392:18,26 393:5 395:10,16 396:2,23 397:5,16,17

404:15 406:1 410:11 418:14 419:23 427:13,17,18

427:18 431:12 442:10 443:18 453:14,18 454:10

456:20 458:13,18,22,24 459:12 460:1,2,16,23

461:3,15,23 463:2,6,11 464:2,19 465:1,4,19

466:8,18,21 467:1,2,8,9,11,13,24,26 468:2,11,13

468:14,20,23 469:1,2,4,5,20 470:2,3,5,12 471:4,8

471:9,23 472:5,12 473:7,7,9 474:9,15,26 475:3

475:10 476:15 477:3,20 478:10 481:3,8,15,16,17

481:21,22 483:7,12,16,21,23 484:1,2,4,20,23

485:3,9,10,17,18 486:16,17,26 487:6,7,10,14,17

487:20 490:20 491:14 492:9,17,18,25 493:3,19

493:20,20,24 494:4 495:25 496:1,10,11,11,25

499:12 501:26

assay-unique 42:24

assess 81:3 117:26 155:6 236:3 267:17 320:20

332:6,15 345:25 351:17 358:8 434:26 454:1

466:26 481:15

assessed 48:19 99:6 233:21 240:7,19 436:12

assessing 78:16 240:26 346:24 356:19 417:23

assessment 10:16 14:2,12,21 15:18 16:13,22 18:24

22:10,15,17,23 24:3,24 39:22 40:24 41:9 44:11

199:9 202:20 219:22 220:3 221:1,6 225:18

236:10 239:19 240:1 241:6 271:12 278:24 290:24

293:12 316:17 317:6,13 332:3 356:24 360:1

392:4 393:16 446:22 448:11 492:23 493:1 502:20

assessments 7:13 14:16 22:7,18 33:4 38:22 51:22

86:1

assessor 271:13

assign 158:17

assigned 116:3 141:6 446:19,22

assigns 392:2

assist 257:10 375:25 429:24 432:9 474:21

Assistant 296:6

associate 5:6 210:25 296:7 323:10 362:19 413:26

associated 14:20 30:4 54:8 78:13 94:19 104:16

117:19 122:1 131:16 138:5,10 152:5 153:7,8,10

178:25 266:3 291:25 365:26 366:26 367:1 368:13

369:24 401:22 402:3 425:20 433:5,22 439:12

447:9 465:9

associates 356:3 383:15

associations 122:16

assume 38:12 111:9 149:5 217:26 266:16 303:9

318:3

assumes 403:6

assuming 185:3 244:18 328:18

assumption 146:1 238:12

assumptions 127:15

assurance 129:23 204:26

assurances 502:7

assure 301:5 360:21 361:18 379:23

assuring 379:18

asymmetry 136:11

asynchronous 47:21 52:26

atlas 142:21

Atmospheric 5:2 210:21

Atrazine 333:23,24

atrophy 256:3 446:18

attaining 121:1

attainment 77:19

attempt 118:26 139:5 278:1

attempted 134:23 277:24

attend 298:24 503:12

attending 2:17 505:19

attention 49:24 68:13 135:25 207:23 229:12

248:11 324:2 355:21,26 408:18,19 439:10 440:5

489:15 490:10

attribute 390:26 400:6 436:7

attrition 488:24

atypical 340:16 449:17

Aubee 31:8,20 53:26 114:10,17,19 130:18,25

131:6,11,17 132:7,22,25 133:8,14,20,26 134:3,26

135:8,16 137:16,21 138:7,12,23 139:8,21 140:22

142:2,6 144:16 145:21 146:8 147:15 148:19

149:19 150:18 153:6 156:22 188:23 189:7,13,16

212:12,13 218:7 363:12,12 364:13 423:9 424:22
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446:2 454:26 455:11 456:9 466:16 505:11

audible 73:20

audience 226:2 229:1 322:17 498:18

audited 343:10

author 102:4 457:24,26 458:2,7 470:15

authority 2:22

authors 99:19

automatic 310:12

automatically 117:24 152:13

availability 121:22 453:11

available 3:18,19 13:8 40:12 43:5 71:22,23,25,26

72:3,3 73:26 74:15 79:2 85:11 91:19 92:4 94:8

106:21 108:23 133:4 167:6 180:24 208:12 217:19

218:1,8 221:7 229:8 244:16 245:24 287:18,23

292:23 296:21 307:15 310:11 311:14,25 326:9

327:16 328:8 343:19 344:1 349:17 355:18 371:25

376:19,20 377:16 382:22 450:6 454:11 493:10

average 97:25 99:2,6 307:17

averages 110:4 120:4

avian 20:23

avidly 401:2

avoid 198:2 206:12 225:4 228:6 276:23 453:12

454:12 456:1 502:3

avoiding 214:22 359:10

await 484:3

aware 91:20 134:18 138:5,12 156:8 170:5 182:8

183:17 268:17 321:9 322:2 390:10 397:11 439:6

469:9

awesome 501:4

awful 276:10 488:17

axes 40:9 43:14 47:14 49:15 465:2,20 489:5

axis 42:11 47:12 77:16 79:26 82:9 94:17,20 107:2

115:15,18 128:13 153:24 156:25 252:1 386:12

387:2 389:2 394:3,21 397:8,9,14 427:4,6,8 448:9

449:15,17,20 450:8,12,25 451:1,5 453:10 461:25

462:6 464:25 468:21 469:6 487:18

azols 28:14

B

b 160:7 167:17 174:21,25 213:2 328:26 446:17

452:11 454:21,25 492:5,6

back 13:2,23 14:7,23 15:23 24:13,19 25:10 26:24

27:4 28:4 33:26 36:5,8 37:7,26 38:19 40:4 47:6

48:14 53:11 71:10,25 72:15 86:18 87:14 90:1,14

90:16,18 91:1 114:14,16 122:22 137:7 142:16

143:3 144:19 145:13 149:23 151:15 186:10,20

195:13 198:9,13 200:6 203:15 207:14 221:26

224:5 226:18 228:4,16 230:1 233:2,3 236:23

238:17 239:10 262:15 270:26 277:10 278:10

283:1 288:11 298:21 300:3 305:8 318:25 322:25

324:11 340:25 341:12 343:22 356:6 372:25

380:20 393:25 395:5 404:22,24 416:19 419:15

420:18 436:14 439:25 441:15 442:20 444:16,18

445:20 446:6,9 457:6,15,17 464:15 477:13

479:22 480:8,13 482:20,21,21 494:8 499:12,25

500:6 504:9

backed 343:5 440:3

background 3:17 10:2 15:12 16:2 31:15 39:20

40:1 56:21 60:7 61:25 66:4,8 94:7 105:5 116:23

123:8 130:7 164:7 306:7

backgrounds 82:25

backup 305:24 402:2

backwards 194:9

bacteria 445:17

bacterial 275:17

bad 274:11 317:3 341:14 465:11

Bailey 505:21,23

balance 37:19 121:1 198:5,18 392:13,15 431:15

476:3 486:24 487:21 496:10

balancing 21:13 23:21 36:3,11 37:5

ballpark 419:5

bar 118:25 119:1

Barb 289:10

Barbara 242:24,25,26

bare 330:19

barely 68:21

Barry 4:23,23 50:2 84:15,22 85:13 210:18,18

380:7 390:16 398:20 459:2 466:23 481:2 483:19

492:11 495:7

bars 96:24 104:8,10,15 118:17,20,21

basal 238:1,17 239:16

basal-level 64:23

base 78:12 80:26 195:14

based 9:1 14:7 23:7,26 26:13 27:14 33:17 49:2

50:16 54:6 58:13 61:13 71:11 76:8 77:9 84:6

88:19 92:24 100:21 102:21 110:7 123:9 125:5

127:13 128:4 129:14 141:7,9,12,15,17,22 143:6

144:23 148:5,15 150:1 173:8,23 179:25 181:2,18

184:13 186:21 187:6,17 192:1,14 194:3 195:2,9

197:18,18 201:2,22 204:7 206:20,24 216:12

235:2,22 245:6,14 246:23 257:9 261:6 264:25

267:21 269:1 273:17 280:4 283:26 287:17,22

289:1 291:9 293:13,14 294:14 298:9 302:6

312:11 313:4 316:22,23 319:21 324:14,25 328:15

334:1 336:7,11 337:15 338:6 346:1 351:1 355:18

356:10,23 357:11,13 359:14 365:25 366:20

376:17,26 377:2 380:1,8 395:17 403:5 411:18

412:18 427:5 428:19 446:19,21 449:2,17 456:6

483:5,13,19 497:21,22

baseline 75:6,16 261:12,12,14,16 302:19,20,24

bases 301:10
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BASF 252:18

basic 26:5 191:17 375:4 398:9 399:22,24 477:25

basically 10:7 11:6,18 42:6 51:5 129:5 135:23

140:4 225:12 253:15 257:5 268:14 319:17 334:10

339:24 341:3 366:23 367:4,7,15,16 368:20,21

370:2,6,17 372:3 373:4 426:21 428:1 429:20

442:22 480:15 486:26 490:16,26 491:11

basics 64:11

basis 119:13 156:14 227:4 311:24 312:1 367:10

474:16 493:18

batch 72:8 150:6

batteries 27:16 32:4 205:4 369:22

battery 1:5 2:8 4:2 7:20,23,26 12:14 13:5 15:2,3

19:1,3 20:6 21:26 22:21 24:11 26:23,24 27:21,23

29:5,5,7,8,13 30:17 31:4,22 38:16,21 39:6 40:1

40:16 41:2 42:22 43:3 44:21 46:6,17 47:23 48:5

48:16,17,25 49:1,7,21,25 51:22 115:17 129:19

151:24 152:1 153:3,4,6,12 155:6,11,25 156:2,5

156:17,24 157:5,11 158:3 162:26 163:1,21 164:5

164:18 165:10,16 166:8 170:8 172:13,23 173:6

178:16,25 179:5,6,16,26 180:8 181:10,17,23,26

182:17 183:5 184:1,4 185:16 186:6,20 187:16,20

196:20,23,26 197:12 204:23 206:9 210:4 231:18

243:6 254:22 255:1 257:19,21 275:4,5 276:7

277:14,14 278:22,26 288:21 289:20,22,24,25

290:17 291:9 293:3 294:10 296:20 301:5,8,11,19

302:21 303:8 307:8 318:22 320:8 323:23 324:24

356:26 366:11,12,16 368:18 391:13 392:18

404:15 427:2,19 458:14,20 459:11 460:5 462:21

463:6 466:17,25 468:5 469:1,10,14,15,18,21,23

470:2,12,13,14,21,23 471:1 473:16,17 477:22,26

478:1,7,9,18,21 479:4 480:13,18,25 481:16 483:2

483:3,5,7,13,22 484:3,19 487:7 492:9,12 493:3

498:4 500:18 502:7,18 504:2

Bayer 258:18 288:16

bear 7:16 119:1 195:20 202:23 220:20

becoming 242:11

bedding 83:11

beg 297:3

began 504:5

beginning 25:21 28:6 80:3 101:12 138:18 171:4

207:14 253:16 290:6,18 410:13 445:24 491:19

begins 137:24 321:19 429:17

behalf 8:2 237:13 243:1 252:19 290:13 317:23

323:16

behave 111:7

behaving 266:15

behavior 56:10 237:26 335:11 405:10 407:17

behavioral 387:21 407:21

behooves 288:11

believe 69:23 92:8,16 110:12 115:11 126:14

130:12 144:19 184:13,26 186:17 192:20 194:11

198:22 199:13 212:7,24 214:11 215:11 217:8,16

224:9 229:23,23 263:3 272:18 295:10 303:7

317:1 321:21 322:5,7 326:24 363:16 367:11

369:3 377:12 390:13 405:15 413:6 417:20 420:15

440:16 477:2 486:20 487:20 504:9

believes 214:9 430:21 431:4

bending 434:12,18

beneficial 215:13 501:8

benefit 228:26 279:17

benefits 256:15 258:12 300:26 310:15 330:10

431:1

bent 121:13,25 130:19,22 131:3 133:24,25 134:17

134:21 140:5 212:17 218:3,18 263:9 264:1

265:18,26 266:3 267:2,5,12,26 285:18 319:25

425:11 426:17 428:12 429:8,13 430:18 431:3

433:4,15 434:26 435:4 436:6,14,21 437:9,20

438:5,22 439:4,21 440:2,10,15,20,23 441:1,4

444:16,18 445:24 500:12,15 501:24 502:2

Bergfelt 31:9 505:6

Berkeley 437:17

best 7:15 204:9,10 217:15 222:16 271:17 286:14

287:26 289:18 373:24 445:14

bet 216:20

beta 57:3 59:11 60:25 61:3 342:7

better 24:15 37:1,3,4,5 68:3 70:5 71:12 98:9 157:2

197:21 204:20 216:16 272:26 300:23,24 317:2

331:12 371:5 375:13,16 389:21 406:11,14,15

407:11,12 410:13 414:14 416:25 425:2 429:25

431:19 445:22 447:8 450:4 453:3 456:5,7 463:9

465:18 468:16 474:14 475:15 485:13 500:7

504:13 505:17

beyond 18:5 121:3,8 136:22 148:26 233:17 241:21

325:19 435:26 437:20 441:16 448:16

BG1 231:9,13 233:14 235:1,14,23 236:12,24 324:9

327:24 341:17

bias 392:8 454:12

biases 454:3

big 52:19 200:13 269:10 316:9 346:22 390:3 398:2

414:21 438:11 440:7 469:19

bigger 109:23 286:2 344:5

biggest 271:25

Bill 232:14

binary 21:17 480:15

bind 43:20 44:26 60:5 192:12 203:23 373:12

378:17,17 479:25

binder 62:25 63:21 171:8 203:22 340:12

binders 194:18 197:14 467:3 478:14

binding 19:14,23 42:8,9 43:20 44:25 46:23 49:14
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55:2 56:16,17,26 57:7,7,8,9,11,12,16,20 58:3,3

58:12,14,23,24,25 59:5,16,23 60:2 61:24 62:1,4,5

62:5,6,6,7,11,17,24,26 63:3,5,7,10,15,20,25 64:1

64:2,4 68:7,7,7,8 155:8 164:26 165:1,13,16 167:1

169:19 171:13 173:2,22 177:21 178:10 191:10,17

191:24,26 192:4 193:2,15 194:18,23,25 195:17

203:17,26 205:13 223:15 230:24,24 231:21

232:14 233:9 235:7,10,15,17 239:3 242:14

245:26 290:20 324:4,15,17,18 327:5,6,11,15,16

328:24 334:23 335:7 337:14,16 338:3 339:3,4,10

340:4 342:7,10 355:7 365:3,3 369:25 370:7,14,24

371:4,11,22 373:3,3,9,11 374:3 377:4,6,11,19

378:16 382:18,23 383:3,9 384:5 400:25 401:2

410:11 463:15 465:7,13,21 467:8,11 475:1

486:13,13 487:9,14 491:3,12

bindings 63:3

binds 57:13 60:11 401:2 475:16

bioactivation 182:10

bioactive 159:1

bioassay 111:21 351:10 361:5,25 405:18

bioassays 224:19 225:9,12 228:7 348:3 360:14

442:11 443:17 471:2,14

biochemical 95:21 138:9 257:14 434:10

biodegradable 253:14

biodegradation 360:16

bioinformatics 335:22

biologic 197:18

biological 28:12,16 32:24 155:3,7,13 164:24

179:12 222:17 246:22 247:6,14 290:10 300:23

393:4 409:1 422:16 446:5 457:10 491:9

biologically 23:21 234:10 391:9 393:9

biologist 114:19 403:23

biology 4:20 5:20 6:5 210:17 211:18 290:9 424:10

444:7 458:1

biomarkers 116:17 158:8

biomass 253:16

biometrician 403:22 404:6

Biomolecular 5:3 210:22

biomonitor 226:25

biomonitored 227:5

biomonitoring 226:25 227:25

biotoxins 446:4

bio-available 26:6

birds 42:2

birth 263:22 309:24

Bishop 323:7,9,10

bit 16:14 31:16 36:3 39:26 40:1 43:17 46:9 50:26

52:19 82:22 90:25 92:12 109:9 115:7 122:25

124:13 135:18 136:2 142:7 143:24 144:26 146:19

186:20 193:8 203:15 212:5 217:19 221:25 230:2

237:24,26 238:4,11 248:22 249:2 250:25 256:20

257:26 258:22 265:1 272:19,25 276:7,18 282:4

299:11 306:5 323:21 325:9 326:13 329:23 338:4

338:14,19 340:19 342:22 343:8 346:19 364:18

378:2 385:13 386:11,13 387:1,26 395:4 397:14

399:22 414:22 416:9 422:3 424:18 435:1 438:6

443:25 454:23 455:3,8 462:24 464:16 472:8

473:15,25 485:8 495:25 504:5

biting 194:1

black 118:17

blame 201:16 299:21

blank 111:12 160:3 166:21

Blankinship 31:7,20 53:25 93:24 94:1 107:13

108:1,8,11,17 109:7 110:2,13,16,24 111:11,25

112:14,24 113:11,21 114:3 153:7 164:2,3 189:18

190:8 505:11

block 98:16 222:20 411:17

blocks 222:20

blood 107:4 280:7 353:26

blotted 74:24 75:9

blue 219:24

Bmax 57:16 58:23

board 11:5 220:2 259:26 271:24

boarders 313:13

body 12:26 13:1 80:5 93:12 103:7,8,10,18 106:13

117:11 120:23 127:23 133:3 147:11 149:26

176:16 214:8 216:8,9,12,15,18 217:1,5 244:23

247:3,4,12,15 330:24 331:7 348:24 349:7 350:3

350:21,24 351:2 354:5 396:4 398:13,24 400:21

403:17 407:14,16,20,25,26 408:7,11,15,20

409:20 410:19,23 411:2 415:2 416:1,17 418:10

418:26 419:11 428:5 439:24

bodyweight 56:9 74:25 75:10,11,19 76:13 77:2,21

77:22 78:2 79:1,8 80:4,14 81:19 83:15,18

boiled 265:21,21

boilerplate 366:5

book 457:24 458:1

Borgert 224:9 290:16 291:6 296:3,4 302:3,9

303:12,21 304:8,11 305:1,5,6 308:20

Borghoff 220:14,15

born 234:19

bother 303:1

bottom 63:11 66:21 97:13 103:21 119:14,15

162:24 174:22 175:22 217:13

bounces 389:6

bound 57:19

boundaries 365:8 425:3

box 22:4 26:11 105:21 196:4 225:15

boxes 59:11

Boy 227:23
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BP2 216:23

Bradbury 8:13,15 15:16 16:1,12 33:15,23,25

35:13 37:4 38:8 138:25 190:21,23 195:3,5 200:5

202:26 203:12 478:25 504:25

Bradbury's 202:9

brain 278:7

Brausch 252:15,16,17 258:8,15 271:19 292:14

Brazil 313:14

breadth 503:26

break 26:23 53:10,12 91:24,26 92:7 149:10 151:10

151:14,15,16 207:19 270:20,22 305:8,10 356:5,7

395:3,6 422:7 457:3,17 482:23

breaking 203:1

breast 6:17

breath 395:4

breed 134:20

breeding 94:23,24,26 98:14,15 101:19 134:22

432:9,11 488:17 489:8

bridges 146:8

brief 4:8 16:7 74:1 94:7 115:1 164:7 233:11 296:5

297:21 422:10 457:18 501:17

briefly 67:14 212:17 214:1 243:3 344:17 345:22

366:6 385:17 387:5 422:19 424:24 427:22

bright 3:26 71:5

Brine 214:15

bring 7:15 20:20 24:19,22 39:9 114:16 148:2

195:20 220:19 221:22 222:8 224:16 227:17

260:13 277:8 289:6 337:11 339:11 349:9 432:15

435:21 476:21 502:23

bringing 23:13,14 24:13,20,26 38:20 69:25 196:1

197:16 199:2 202:12,21 285:3 432:14 504:11

brings 49:6

brining 199:3 202:22

broad 20:22 27:25 28:9,12 32:19,21,24 36:22 39:3

42:1 163:6 182:25 206:26 234:18 267:3,11 393:3

404:18 458:18 472:14 503:26

broader 10:11 54:15 70:18 71:11 299:18

broadly 159:14

broke 13:12,20

broken 190:11 459:3

brought 11:21 24:5 25:11,26 147:23 195:6 201:6

215:8 222:24 233:3 261:8 333:5 338:4 368:9

397:8 398:8 410:10 469:11 476:16 477:13 486:11

499:24

brown 441:5

Browne 31:8 505:12

BSF 316:26

bud 116:13

Budget 308:10

build 133:22 220:9 341:12 345:2 383:21 502:20

building 1:14 383:18 475:9 501:19

built 163:1,20 176:25 490:3

bulbocavernosus 76:11

bullet 248:11

bulleted 413:8

bullfrog 439:6

bump 239:16

bumping 381:13 382:13 411:7

BUN 354:3

bunch 229:10 323:1 494:19

bureaucracy 498:12

bus 207:10

busier 122:23

business 8:26 435:25

busy 122:22 304:18

bye 209:3

B7 234:22

C

c 167:1 174:24 213:4 439:4 480:23 482:13

CAAT 335:21

cadmium 402:9

cages 83:11

calcium 431:22 439:4,15

calculate 278:2 404:2

calculated 97:24 308:11

calendar 7:18

California 4:4 210:9

call 143:11 207:8 230:26 246:21 248:11 258:22

445:18 504:11

called 17:21 258:23 283:7 330:6 335:18,21 458:1

486:14

calling 296:23

calls 449:1

Calvin 31:9 505:13

Canada 6:6 211:19 226:17

cancelled 155:16

candid 303:13

candidates 366:21

capability 184:4 240:25 338:17 479:14

capable 27:16 154:20,22,24 191:24 204:24

capacity 142:19 183:5,16,26

capture 22:18 23:1 34:7 55:14 393:3

captured 295:7 363:2 370:11 391:17 412:15

captures 393:9

capturing 19:11 70:5

carbon 100:18 152:26 400:14

carcinogenesis 4:25

cardiomyocytes 241:18

care 343:9 419:2 424:11 455:2 489:20,23

careful 121:1 214:20 276:9 439:10
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carefully 73:16 117:26 145:1 147:7 315:2 358:8

387:9 436:12 439:18

careless 393:5

carried 151:3 469:21

carrier 95:4 98:8,24 100:13 362:15

carriers 362:11,13

carry 48:1 107:17 203:8 293:5

carrying 356:25

cart 266:12

cartoon 261:19 262:15

cascading 205:8

case 14:14,18 24:20 54:11,23 55:1 57:22 66:25

70:25 86:1 88:8 117:20 119:25 120:7,12 123:12

126:15,21 128:11,26 131:22,22,23 140:6 141:11

143:12 147:23 148:1 151:7 155:24,26 156:4,14

156:14 159:1,12 160:8,9,25 161:1,5,9,13,17,19

161:25 162:5,13,18,20 169:15 180:14 190:24

197:4 204:1 237:1 243:17 244:12 260:9 275:14

291:24 296:8 303:8 321:16 324:16 336:6 383:10

391:2 398:23 401:4 427:11,14,16 447:3 449:16

453:2 458:23 460:12 466:19 474:2 478:2 481:16

484:9,14 502:21

cases 61:8,18 63:16 65:6 67:11,18,19 71:1,24 72:6

73:3 84:9,9 85:21,25 86:4,6 88:11 91:2 93:13

100:10 101:2,22 103:5 108:26 110:3 119:12

120:2 125:7 126:16,17 127:1,26 128:3,17,20

137:22 139:11 141:9 145:5,7 147:20,21 148:7

150:10 156:15 161:3,7,8,24,25 162:17 166:22

167:15 171:7,11,23 174:17,19 177:17,18,21,24

178:1 180:18 213:10 238:2 239:13 243:21 244:1

244:11 245:20 272:17,17 325:26 326:2,14,15

359:2 368:12 373:21 375:22 381:15 385:3 398:21

409:22,23 411:4,4 462:3 467:17,20 490:25

case-by-case 311:24 312:6 409:16 410:2

case-study 55:3

cast 157:17 224:8

castrated 45:6 76:3,9

castration 351:14

catalogued 141:1

catch 273:11 280:25 455:6

catchall 162:13

categorical 236:12 453:24

categories 58:15 66:13 103:13,15,17 166:6 174:7

175:12,17

categorized 173:8

category 32:20 33:21

Catherine 31:7,20,21 53:26 114:10,17,19 130:18

130:25 131:6,11,17 132:7,22,25 133:8,14,20,26

134:3,26 135:8,16 136:4 137:16,21 138:7,12,23

139:8,21 140:22 142:2,6 144:16 145:21 146:8

147:15 148:19 149:19 150:18 153:6 156:22

188:20,23 189:7,13,16 212:12,13 218:7 226:2

330:1,2,3 363:12,12 364:13 423:9 424:22 446:2

454:26 455:11 456:9 457:21 466:16 505:11

caught 108:12 134:21 438:17

causation 431:9

cause 161:13 228:14 244:9 275:24 295:3 316:16

360:13 370:4 397:24 410:25 414:12 429:12

432:23 439:4 440:2,2,10,15

caused 60:13 431:23

causes 266:3 315:9 431:4 437:3

causing 126:12 149:3 253:25 342:9 407:9

caution 389:3,25 411:5

cautionary 387:1

caveat 415:16

caveats 470:13

cease 155:17

CeeTox 336:24 337:1,1

cell 5:20 60:8 64:7,8,24 125:4,8 139:6 141:20

155:8 160:3 173:10 177:6 231:18 233:15 235:26

236:7 237:4,25,26 241:15 341:19,21,23 342:1,12

355:8,18,26 399:24 400:15 402:10 446:25 449:12

450:21 451:11

cells 125:15 233:12 237:25 239:13 241:17 251:16

278:7 312:25 447:5 455:7

cellular 155:4 398:9 491:2

cell-based 240:8

cell-to-cell 226:9

Celsius 100:15

center 1:13 4:24 5:3,19 7:6 210:19,21 211:9 232:2

290:3 317:20,23

certain 13:25 17:4,24 137:19 138:8 141:5 163:4

193:20 257:6 267:20 269:15 277:18 312:5 314:7

333:9 402:7 408:20 409:23 425:19 432:22 439:14

440:9 442:23 447:12 463:12 477:4 493:17

certainly 17:17 30:24 183:8 197:3 198:15 199:9

203:24 207:3 236:4 259:26 262:10 284:4 300:8

321:25 328:13,20 339:6 382:15 390:25 418:16

440:15 444:19 453:12 470:21 488:6 498:26

502:13

certainty 14:15 19:6 24:17

Certified 220:2

cervix 75:1

cetera 28:15 385:24 387:21 389:20 397:3 460:21

CFR 144:19

chain 34:12

chair 3:23 194:8 288:17 316:26 503:24

chairing 3:23 4:4

challenge 35:24 39:6 53:5 89:25 153:1 199:4

342:21 389:13 391:26 392:20 394:11
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challenges 129:11 222:11 231:12 284:5 340:19

343:25 351:24

challenging 89:23 90:21 127:3 197:20

chamber 111:13

chambers 96:12 101:10 109:23

chance 120:25 149:23 212:5 281:10 284:1,15

399:12 494:12

chances 392:11

change 109:1,22 110:3 124:17 165:2,4 166:5

168:19,20,21 170:12,14 173:13,15 174:6 189:23

253:26 264:9 277:5 287:21 336:19 346:9 347:2

400:21 405:10 419:18 429:3 433:7 451:9 452:14

452:19 465:17 475:6 487:3

changed 86:5 191:5,5 264:9 286:9 465:22

changes 22:9 45:25 46:1,2 77:25 78:17,21 81:4,6

94:19 103:10,26 104:17 120:23 133:12 161:20

168:15 216:18 244:24 248:26 249:1,5,6,7 251:12

253:25 254:2 264:7 268:21,24 269:3 284:11

287:7 319:17 335:9 337:20,24 340:14 341:5

361:13 373:16 374:1 400:12 408:10 411:8 420:7

437:21 439:23 440:2 447:9 449:2 453:1,4 461:5

481:9

changing 261:24 276:21 287:17 428:24 487:14

chapter 114:26 205:9,10 458:1

characterised 115:24 170:12

characteristics 45:15 95:18 104:23 107:16,20

118:12 145:25 146:3 148:6 168:25 175:13 176:1

176:14 226:7 308:24 310:26 311:2 409:25

characterization 475:6

characterize 41:24 68:21 98:9 112:18 291:15

400:12

characterized 315:18 401:17 402:16

characterizing 339:1

characters 224:8

charge 13:25 30:9 48:16 86:14 115:10 133:3

201:21 208:10,25 212:6 258:19 261:10 284:3

307:3 329:21 355:3 356:10 365:1 384:22 391:22

392:26 395:9 425:10 436:4 446:16 457:20 458:12

466:16 480:24 481:19 482:25,26 488:12 492:6

Charleston 210:22

chase 21:21

cheaper 52:11

check 12:8 112:25 113:1 275:22 418:14

checking 387:12

checkpoints 489:17

Chem 35:21

chemical 7:11 11:12 13:9,19 14:5,20 15:5 16:11

18:15 20:14 21:16 22:2,12 23:1,6,25 24:24 25:11

25:24 26:4 28:13,13,17 30:26 33:4 34:13 37:8

38:22,23 40:6 51:22 54:12,16 56:3 57:2,21,25,25

58:8,10,13 60:5,11,16 61:22 62:1,13,14,20,22

64:17 65:18,23 66:1,6,10,14 67:22,24 72:9 74:11

75:18 78:6 81:21 83:2 95:2 96:8,9 97:3 98:3

101:6 105:14,22 106:8 107:2,20 109:12 113:20

131:20 148:5,6 155:22 159:13 162:21 167:1,3,5,8

168:15 169:7 170:1,3,10,14 174:25 175:2,23

176:11,26 177:5,23,26 179:1,11,13 181:16

183:11 184:1 186:10 188:1,12 189:8 190:15,17

192:3,6 195:24 197:10 198:25 202:15,17,25

203:26 206:20 207:1,4 216:23 223:13 224:21

225:15 226:25 227:5 234:24 242:9 246:7 270:10

271:15 291:18 294:6 297:23 307:10 308:12,24

312:12 320:20 324:17,18,21 325:5 327:8,12

328:26 330:17 332:6 333:2 349:20 357:26 360:19

361:15 362:11 366:18 368:25 369:7 370:22,22

378:16 380:19 391:4 395:24 411:8 461:17 463:17

478:3 483:3,9 502:21

chemically-relevant 331:19

chemicals 7:21,25,26 10:8,9,20,22 12:6,13 13:25

13:25 15:3 16:19,21 17:10,14,18 18:13,19 23:5,5

23:12 24:8,13 25:13,20,24 26:2,7,10,12 27:11

28:4,7,7,15 30:1 32:12,18,19 33:1,16 34:7 36:21

37:9 38:20 39:2,3,4,5 43:20 44:2,9,12 45:9,13

46:7 54:11,12,14,15,18,24 55:3,4,5,24 57:6 58:24

58:26 59:2,9,14,17,20,20 60:15,22,24 61:4,5,8,12

61:14,17,18,20 62:3 63:7,18,20,20 64:1,9,19,26

65:3,10,10,11,12,16 66:10,15,25 67:3,4,4,5,7,9

67:15,17,23 73:1 75:13,17,21 76:25 77:9,15 79:5

79:9,19,24 81:17 82:1 83:22 84:12 87:23 93:4

94:16 98:2 105:14 109:14 139:3 148:3,8 150:1,5

152:22,24 153:2 154:20,23,25 155:15,16,17,20

155:21,23,24 156:1,4 159:21 160:14,23,25 161:1

161:2,11 162:5,14 163:4 164:17 166:16,24 168:8

168:10,11,20 169:2,25,26 171:6,8,9,11,22 173:5

174:13 175:16,18,19,20,21 176:5,9 177:2,9,14

178:8 179:9,11,22 181:3,11 182:6,26 183:1,6

184:9,22 185:5,9,14 191:26 192:10,10,11 194:3

194:17,17,23 195:2 196:16,24 197:3,16,19,20

198:23 199:18,20 201:3,5,23 204:25 206:16,17

206:21,23,25 222:23,24,25 226:18 228:1 230:11

239:15 251:7 276:19 287:20 290:4 292:17,18

293:8 294:11,11 296:19 301:20 302:6 308:21,25

312:2 315:24 323:25 324:22,25 325:13,19 328:5

328:8 331:3,5 334:22 336:7,8 343:18 347:12

348:2,2,3,4 352:14 357:19,23 358:2 366:21

367:19,21 368:20,24 369:4 370:26 372:9 373:10

373:12 375:9,11 376:18,19 380:18,22 381:26

386:7 388:7 389:4 391:4 402:17 410:24,26 412:1

416:4 428:20,25,26 442:13 460:1,3,6 461:12,25

462:7 463:9 467:4,5 468:3,26 469:4 471:15,25
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472:1,3,6 474:9,11,18 475:23 476:6,17 479:8

484:16,16 492:22 493:14 497:6,9

chemical's 165:18,21 173:23

chemical-induced 157:18

chemical/physical 152:20

chemistries 354:1

chemistry 78:2 80:16 128:4 219:4 349:24,24

439:26

chewing 201:13

child 376:26

chime 185:1 447:21 473:14

chloride 455:20

chlorine 100:19 361:12

choice 32:11 238:23 463:16

cholesterol 342:23

choosing 32:17 348:14 350:25 351:1

chorionic 389:14

chose 36:21 206:19 291:14 307:11 344:11 415:1

chosen 196:24 197:4 306:13 350:18 499:13

Chris 296:3,4 302:3,9 303:12,21 304:8,11 305:1,6

308:20

chromatography 239:6

chronic 23:19 309:11 387:19 410:22,22,25,26

411:6

Cincinnati 6:13 211:26

circled 452:3

circles 105:18

circulation 401:6

circumstance 192:25

circumstances 215:13

circumvent 285:3

cite 126:24

cited 122:12,18,19 291:22 322:7

citing 424:21

CLA 313:5

clairvoyant 27:4

Clare 305:12,14,18 316:9 317:1 331:20

clarification 35:9 49:25 87:13 88:21 92:17 142:8

190:23 193:15 219:13 263:9 349:21 350:12

380:13 382:10,17 393:26 413:3 417:22 422:12

423:7,10 446:1 450:7 454:25 466:12 476:11,13

492:1 494:8

clarifications 93:22 135:22 282:17 350:7 363:11

clarified 122:13 351:1 390:20 422:21

clarify 88:23 89:5 141:14 189:7 200:6 363:14,18

495:5

clarifying 122:9,21 212:10,14 349:10 418:7 419:26

446:2

clarity 277:10 337:12 495:17

class 33:1 155:5 475:24,26 476:5

classes 28:13,14 37:8 54:16 179:11 446:8

classical 449:18

classification 58:14 61:11 62:25 66:13 167:2

classifications 58:12,13 59:16 62:24 63:19 194:5

classified 58:16,18 59:20,21,22 61:22 63:21,21,22

65:12 66:14,16 67:4,5,7 167:11 194:4

clean 218:24

cleaning 272:14

clear 21:9 28:16 35:11 40:20 86:10,11 106:5 113:8

123:16 160:16 199:3 202:18 243:8 314:4,16,22

349:26 358:10,11,12,23 360:1 390:24 409:17,25

418:5 419:26 424:25 455:13 459:21 462:5 467:3

467:24 468:1,7,23 482:14 483:21 484:1,15,21

497:26 498:7 499:5

clearance 245:1 252:2 373:1

clearly 18:7 20:7,10 32:21 72:3 88:13 156:16

195:23 196:7 198:17 243:18 260:20 266:14

321:12 332:24 362:12 365:2,17 367:23 368:12

398:22 419:18 451:10 462:1 492:15

Clemson 4:12 210:11

click 26:24

client 343:10

clients 343:11,24 352:21 354:16

climax 117:2 120:24 216:17 253:22,24 262:9,11

321:8

clinical 56:9 74:25 75:19 76:13 77:2 78:1,2 79:8

80:13,15 81:20 93:12 103:2,6 106:14 117:5

348:25 349:24,24 350:3 353:26 428:7

clinicals 93:18

clock 53:10

close 31:23 61:19 65:7,18 68:24 69:4 100:26

105:16 107:25 113:19 146:22,25 147:22,25 152:7

235:21 237:5 238:3 270:24 300:3 365:9 372:10

439:9 501:11 504:16 506:5

closed 208:9

closely 188:4 314:4 438:21 441:4

closer 82:22 406:24 464:22

closest 146:24

closing 182:3 231:19 236:15 501:10

cloud 459:26

clues 42:18

clutch 430:2,7,10,13 432:7,13,15,18 438:9 444:2

445:12

clutches 428:11

Coady 271:15

coalitions 220:9

Coastal 5:3 210:21

Coates 505:26

code 188:1 297:12,14

codes 188:6,11,12
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coefficience 109:8

coefficient 76:22 77:4 78:24 79:14 81:10 90:7

97:23 109:4,20,25 110:7 118:10 246:19 384:25

391:21 392:4,19

coefficients 392:14 406:4

coffee 501:3

cognizant 282:7

coherency 200:23

cohorts 134:24 382:26

coiling 431:26 432:1

coin 270:2

collaborated 455:16

collaboration 227:10 265:2 452:22

collaborations 226:23

collagen 434:8,11

colleague 194:1 289:3 424:19 458:6

colleagues 9:11,18,23 12:20,20,21 122:5 214:18

215:26 225:11 228:8 229:2 390:14

collect 247:15 296:22 297:4 303:17 327:7

collected 247:4 294:10 298:22 324:6 484:9

collecting 304:12 420:9

collection 22:15 183:22 248:21 273:10 298:14

collections 248:19

collectively 23:15 31:22

Colleen 336:24,25,26 345:12,15 346:1,14,25 347:6

347:9,15

College 5:10 6:13 211:3

Colleges 4:13

colloid 452:24 455:17

colonies 437:3

Colorado 4:21 210:17

colors 419:13

Columbia 5:16 211:7

column 59:6,8 95:11,12 98:4 216:10 259:24 266:16

columns 59:7

combination 21:23 26:2 117:13 178:26 361:3

404:26 406:7 418:5 471:12 472:12 496:10,16

combinations 44:20 160:20 163:7 370:4 471:10

combine 13:7 14:10 151:11 190:5 251:5 336:19

combined 11:4 151:12 310:26 430:14 470:13

Combining 20:12

come 12:2 14:6,23 36:5 52:26 72:15 91:1 151:13

222:22 235:4 237:3 261:22 268:10 279:24 283:20

284:1,24 308:7,8 322:18 324:2 339:9 391:24

403:1 416:22 424:9 431:8 436:6 446:9 459:9

470:15 473:2 480:8 487:13 500:6

comes 182:22 194:15 225:22 228:12 261:2 285:18

293:26 295:1 309:7 331:7 338:21 377:12 427:22

436:17 439:23,25

comfort 343:8

comfortable 72:23 89:16 301:17 376:11 491:7

coming 7:3 12:7 52:19 71:18 90:20 140:6 224:5

228:16 233:1 297:5 298:12 326:2 451:12 480:13

495:10

commend 315:2 488:3

commended 231:15

comment 3:7,9 30:10,16 33:26 34:6 69:12 92:13

111:8 135:11 139:18 152:2 198:5 207:25 208:9

208:12 243:5 264:21 284:25 295:7 305:12 312:15

316:3 321:1 330:3 345:10,23 347:10 356:12

362:21 364:23 365:12,13 372:22 377:3,23 378:9

378:14 379:14,15 380:7 385:7,8 395:22,26 396:3

398:11 399:18 402:3 405:5,24 407:5,13 414:1

415:8,16 418:23 420:4,6 426:4 430:20 441:5

445:8,12 447:7 453:21 454:21 458:20,24 464:16

466:17,20 475:4 480:26 483:11 489:14 490:15

492:7 499:25

commentaries 355:20

commentators 498:22

commented 322:8

commenter 92:3 134:4 208:15

commenters 151:21 207:24 208:2,6,23 219:17

368:11 449:6 494:17 495:3,8 500:10 501:6

505:15

commenting 92:10 207:16 219:16 271:2 305:9

317:23 330:11 356:5 472:13

comments 3:6,8,11,14,14 9:14 39:10 52:1 92:17

113:5 121:14 137:8 149:14 186:18 207:15,16,21

208:8,11 214:4 220:10,12 230:23,23 231:11,18

231:20 233:22 235:24 237:5 239:25 243:1,3

246:2 257:1 258:19 280:22 288:19 290:13 301:24

306:4 309:10 314:10 317:25 318:23,25 319:15

323:13,15 354:24 358:13 362:25 363:1,9,11,13

364:26 365:18 366:4 374:24 378:1,4 380:8,8

387:1 393:14 394:2,4 401:13 403:3 407:15

408:23 411:12 413:2,3 417:10,12 422:18 423:3,5

425:1 430:25 433:2 434:20,20 442:1 443:22

445:5,25 446:14 448:20 449:24 450:16 451:22

454:16,18 464:19,24 470:25 471:1,21 472:17,22

473:1 475:17 476:9 482:15 483:14 486:24 490:12

491:25 492:1,2 494:9,13 495:22 497:13 498:19

501:9 502:6 503:17,22 505:18

commercial 91:18

commissioned 255:14

commitment 504:22

committed 8:8 178:23 483:1 488:6

committee 2:14,16,18 10:26 17:21,22,23 21:8

55:23 137:5 221:19,20 231:9 299:20 305:26

306:3 323:17 489:23 504:11

committees 231:4,8 305:20 419:2 489:20
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committee's 38:14

committing 392:3,5

common 18:7 19:19 35:6 59:1 110:18,19 112:3

145:4 233:14 252:25 269:25

commonly 34:19 54:20 247:2,9 400:13 424:10

communicate 227:12

communication 52:4 226:10 227:10 314:19 390:11

communities 503:20

community 227:11 425:5

companies 238:26,26 303:15 304:2 306:12,18

308:2 309:16 330:7

company 133:2,10 246:7 271:9 322:4 337:7

comparable 10:19 442:18

comparative 5:9 6:6,19 155:10 211:3,19 212:2

355:25

comparators 302:20

compare 45:17 51:10 143:3 144:24 145:5 226:26

272:23 417:26 444:25 464:26 465:4

compared 20:8 102:16 103:7 104:1 109:14 123:2,5

125:9,23 126:10 141:25 142:22,25 158:19 163:2

216:25 262:4,7 292:17 369:19 425:23 426:3

434:17 446:26 447:13 448:25 463:6

compares 46:20

comparing 142:16,20,25 144:14 261:13 277:11

comparison 83:21,24 127:11 145:14 204:13 338:3

340:15 364:21 404:4 446:19,22 476:14

comparisons 204:8 216:21 254:2 302:24 306:15

328:9 473:9 474:22 497:24

compensate 412:18 448:14

compensatory 19:12

competence 374:7 428:12 429:10 430:4 478:1

competes 62:14

competitive 57:9,20 58:3,3 59:5 62:5,16 63:9,15

64:2 223:15 339:4

competitor 57:26

complement 21:24 311:25

complementariness 201:16 202:2

complementarity 48:24 153:13,26 154:5,8,10

155:1 157:9,12 159:25 163:1,20 164:9 167:24

175:10 180:7,24 181:4,21,25 184:10 204:16

250:13 329:17 409:3 458:14 462:21 463:26 464:2

464:3,11,20 465:10,18,26 476:3 477:22

complementary 19:4 48:26 167:23 168:22 171:26

172:18 175:5,6,18,24 176:12 178:4,21 180:4,13

180:15 200:15 309:7 427:12 458:21 459:18,22

460:5 492:23

complete 49:21 53:26 308:12,13 358:6 376:16

472:19 476:4 492:13

completed 343:20 428:18

completely 284:17

completing 25:4 33:3,3

completion 484:3 493:1 501:26

complex 40:19 49:16 66:1 245:3 332:19 336:4

366:16,23 458:22 499:5 503:5

complexity 394:3 431:11

compliance 361:18 372:15 418:26

complicate 127:25 215:15 252:5 396:22

complicated 146:20 371:12 402:6 456:1 480:10,11

480:12

complicates 452:15

complication 408:14 448:14,15

complications 254:10 468:5

compliment 46:19 497:14

complimentarity 48:21 49:9 462:10

complimentary 158:11 168:17 169:8 175:16

391:11 427:14,17

complimented 206:10

component 50:3,7 75:14 91:16 190:20 193:19

279:14 373:26 486:14

components 46:23 57:8 62:4 65:25 85:4 137:4,7

184:22 206:5 219:4 263:18 434:17 490:18

component-based 50:21

composed 58:4 62:17

composition 51:18 431:16

compound 32:25 34:9 84:2 86:11 89:14 183:24

188:1,5 190:7 203:19,21 241:19 242:21 255:12

279:15 307:20 315:9 327:2 341:11 345:25 383:25

390:1 392:12 408:4 409:8 455:20 467:23,23

470:8 473:15 477:2 480:19 481:10,11 484:17

compounded 61:15

compounding 266:18 270:5 443:1

compounds 7:24 11:3 17:11 27:7,17 30:15 33:7,12

34:1,14,16 35:16 36:16,18 38:7,13,15 44:15,26

45:2 47:3 53:5 131:14 185:4,26 187:16 191:15,17

195:23 197:13 232:18,21 234:19 235:9,18 236:18

237:19 239:21 240:26 244:8,9 245:19 246:1

251:14,25 253:12,14 254:12 255:17 257:20 258:1

260:7 268:20 272:4 291:10 306:21,23 307:1

309:8 310:11,16 312:17 313:20 316:4,7 317:17

341:10 343:14,21 344:6,12 355:23,25 364:21

367:11 368:9,16 373:19,20 374:17,26 378:19

385:22 392:11 396:19,24 398:8 399:5,23 400:9

401:26 402:4 406:23 408:4,6 416:19 443:5

444:19 460:13 463:13,15,19 467:7,16 468:12

470:3,4,4,8,9 471:4,14 473:10 474:1 477:3,21,26

478:3,11,15,15,21 484:4,8,24 485:1,25 492:13

493:25 496:22 502:13

comprehensive 41:1,9 43:15 44:11 45:12,23 46:9

46:14 157:14

comprehensively 49:12
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comprised 19:1 25:24 173:1

compromise 215:15 243:24

compromised 79:12 81:24 117:21 119:19 426:12

compromises 215:12 426:6

compromising 392:24

computational 7:6 16:4 24:7 25:15 26:12 39:12

195:26 225:23 320:18 377:8

computer 208:5

computer-assisted 454:9

compu-toxicology 226:6

ComTox 318:4,5,6 322:13 329:12

conceivably 70:26 144:9

conceived 331:25 333:6

concentrated 452:23

concentrates 455:17

concentration 57:17,22 58:1 62:12,12,15,23 67:6

96:11 97:1,16,19,20,22,23 98:16 101:3 102:15,18

102:20,24 103:20 104:6,13,14 109:11 111:2

113:9,15 126:18,22,23 127:2 136:5,25 142:18

147:4,11 167:3 169:1 186:12 232:4 234:4 240:23

241:5 242:21 253:1,6,9,10 254:14,15,15,16,18

273:22 340:2,15 360:26 361:5 381:12,17,21,21

411:9 412:2 414:7,15 449:23

concentrations 57:18,24,26 62:13 63:23 66:2,6,9

66:10 67:24,26 68:2,5 95:3 97:26 98:4,5 102:13

106:20,21 109:4,13,15 110:4,17,20 113:9,18

114:2 116:5 118:10 120:3 128:8,26 129:8,9

147:24 152:7 176:4 206:7 224:1 232:3,19 233:20

234:4 237:19 239:2,22 240:5 241:20 242:7,13

251:15 252:24 253:3,13,17 268:22,24 272:1,13

282:23 283:17 326:3 339:11,12,15,25 341:26

342:24 396:4 402:9 406:4 409:21 412:5,7 427:24

459:23

concept 11:18 153:15 154:8 159:25 164:9 167:22

168:1 169:10 291:6

conception 309:23

concepts 25:19

conceptual 18:13 31:16 198:22 199:11 333:5,14,15

334:11 457:25 458:4 504:6

conceptually 500:3

concern 86:8 131:26 187:6 278:25 315:5 360:13,17

361:9,24 386:13 387:25 389:23 391:6 402:21

448:23 449:5,7 453:13 454:13 481:21 483:26

concerned 40:24 70:7 213:11 310:12 352:11

concerning 143:9 144:12 321:6 372:19

concerns 71:1 82:14 131:8 140:19 187:8 205:26

242:12 266:18 269:10 297:10 323:20 357:2

361:19

concerted 452:4

concise 144:3

conclude 20:13 59:23 86:7 207:12,15 219:15

292:25 301:16 315:6 402:18 404:26

concluded 28:20 153:22 203:2 365:10 385:5 386:8

447:3 480:25 481:3

concludes 130:11 163:21 164:1 181:22 217:21

246:1 354:23 356:4

concluding 56:24 94:11 164:12 172:23 407:14

495:21

conclusion 37:9 48:23 61:16 63:25 65:14 70:25

74:4 75:20 77:7 79:17 81:25 118:1 163:24 171:26

178:4 183:12 251:20 261:23 365:12 378:19 385:7

447:8 449:11 469:5 478:2 480:26

conclusions 23:15 42:26 51:22 55:17,17 88:14

120:5 125:19 319:8 371:19 375:22 481:6

concomitant 372:12

concordance 48:21,22 153:14 154:10,11 167:24

169:12 170:24 175:10 176:19 178:20,22 180:5,7

180:9,19 181:24 184:17 187:25 190:3 192:20,26

200:24 235:7,14,16,21 236:23 251:5 291:11

292:19 299:15 328:4 408:9 467:1 471:22 474:26

475:2

concordances 192:22

concordant 145:16

concrete 269:15

concur 383:15 472:24 493:7

concurred 11:14 12:1

concurrent 60:21 116:5 125:10 126:9 141:26

401:21 446:23 461:14

concurrently 73:17 93:14

concurs 374:13

condition 131:15 258:21 259:11,15,21,22 260:6,21

263:13 265:14,16 266:14,24 420:7 439:13 441:21

448:13

conditioned 438:12

conditioning 420:5

conditions 56:4 96:8 99:1,24 109:22 116:2 118:5

128:4 152:21 218:22 261:1 265:6 285:26 302:15

329:5 338:22 357:11 433:15 435:10,12,14 436:11

437:9,11,12,18 438:22 439:11 440:12 454:3

conduct 22:23 71:14 73:7 79:13 81:24 85:6 87:4

94:10 96:14 130:1 167:14 175:2 178:17 179:16

227:22 248:14,15 277:19 311:5 347:23,26 348:14

348:19,20 349:25 351:25

conducted 23:19 55:22 64:12,14 66:7 70:24 79:17

81:25 94:21 106:11 185:13 306:20 311:18 348:6

349:9 351:11,22 402:12

conducting 179:2 248:9 277:26 308:4 344:4

348:17 351:26 352:2 446:22

conducts 23:10

conference 1:13 221:16 408:17
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conferring 215:25

confidence 96:16 117:17 127:26 128:3,12 148:13

156:12 203:25 238:9 239:4 345:2 406:21,25

412:25,26 470:22 474:22 491:19 497:10

confident 303:16 345:4

confidentiality 297:4,12 303:14

confined 313:13

confirm 30:3 403:19 492:18

confirmation 272:5 383:12

confirmed 291:21

Conflict 2:26

conformance 148:24 360:15

conformation 383:13

conformed 77:7 83:17

conforms 55:15

confound 401:11 409:20

confounded 244:22,25 409:18

confounder 471:19

confounding 118:8 263:1 300:7 312:21 396:26

397:14 406:26 409:13 443:9 450:19 485:2

confoundingness 445:2

confounds 238:19 241:12 452:3

confused 121:25 190:24 302:10

confusing 142:3

confusion 122:2 370:5

congratulate 368:7

congress 187:5 320:15

conjunction 25:2 55:11

connected 487:5

connection 42:3

connective 138:11 433:24

connotation 405:16 412:26

consensus 447:20

conservative 427:5

conserved 169:25 377:12

consider 41:20 46:16 48:12 49:4 50:11 85:23,25

86:2 93:11 143:19 146:15 150:25 163:22 220:26

228:7 245:25 248:4 262:2 274:25 277:4 288:25

289:22 292:4,5,26 301:24 306:26 321:15 331:4

339:7 351:25 355:16 360:7 381:20 393:18,21

404:20 405:7,20 413:20 424:13 453:17 454:6

456:2 457:11 488:9 492:8 493:2 494:21

considerable 401:25

considerably 68:23 128:9

consideration 44:23 70:22 108:3 187:15 199:3

236:25 244:13,16 262:22 270:15 289:1 308:14

350:21,25 351:4 379:20 391:18 392:21 393:16,22

450:5

considerations 40:18 42:21 53:3 111:18 121:10

143:19 187:3 260:2,4,25 262:26 288:20 349:10

351:11 479:3 502:11

considered 29:2 40:11 46:6 48:19 52:16,21 58:20

60:16 61:13 63:22 75:22 77:9 78:7,18 79:15,20

80:20 82:1 84:12 85:10 87:23 143:11,20 153:25

154:19 155:21 157:24 159:6,7 166:7,12 172:4

174:10 176:7 177:26 178:7 179:5,19 186:26

187:2,12,20,21 188:16 206:2,11 213:1,3 214:26

216:4,7 218:8 234:23 236:21 240:2 243:24

313:20 359:3,26 360:11 372:18 373:5 375:2,15

381:21 385:4 386:7 387:10 391:14 412:16,16

413:18 422:16 424:11 428:9 433:21 436:13

437:10 438:14 450:11 468:25 487:24 492:16,19

considering 48:3 128:21 153:16 159:14,19 214:24

226:12 403:15 416:16 463:17

considers 152:11 158:3 450:11

consist 41:26 326:21 366:16

consistency 23:12 55:23 118:9 152:22 154:5 182:9

410:2 481:14 496:24

consistent 12:4,15 39:2 65:16 67:9 68:18 75:21

77:8 79:19 82:1,26 118:5 120:13 126:23 127:12

130:9 131:25 159:17 161:17 163:24 179:17 181:5

188:12 232:15 234:26 259:1 262:12 314:17

370:25,26 373:18 374:17 377:22 426:1 447:4

449:12,15 451:6 461:26

consistently 29:16 30:1,7 67:17 72:22 149:1

160:18 178:15 179:16 264:14 269:9 365:25

492:15

consisting 155:8,21

consists 366:12

consortia 282:12

consortium 221:2 230:4 330:7

constant 57:14,17,23 62:11,12 452:25 455:24,26

constitutes 165:9

constraints 223:15

construct 60:9,10 334:11 340:1

construction 86:20

consult 282:3 404:7

consultant 271:8 285:9 296:6 356:3

consultation 3:2

consulting 271:8

consumption 74:25 79:8 81:20 93:13

consumptions 77:2

contact 135:6 382:26 482:18

contain 370:19

contained 101:26

containing 60:9 62:18 66:1 116:9 360:23

contains 94:18 459:11 461:10 462:4

contaminant 5:16

contaminates 444:7

content 83:16,18 86:26 91:15,18 122:17 191:7
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438:24

contention 191:14

contents 84:23

context 7:26 9:26 41:10,20 48:7 50:23 52:13,15,17

52:23 70:13,16,18,19 71:11 92:21 125:4 136:4,15

136:23 142:8,19,23 143:17,25 144:1,4 151:6

155:2 186:23 187:1 206:2,12 231:17 251:11

269:18 307:15 333:8 365:20 377:5 413:19 416:7

447:25 449:14 450:20 458:23 466:18 474:4

continent 498:10

continual 490:9

continually 285:18

continue 7:15 49:5 126:4 130:2 151:23 247:20

257:5 271:1 287:18 288:4 305:9,11 315:4 353:12

393:13 501:21 502:4,8

continued 309:25

continues 399:9

continuing 196:15 299:10 496:21 501:24

continuous 120:22 143:12,15,25 453:25

continuously 436:12 438:26

contract 55:20 133:10 217:26 245:4 283:22 285:10

286:21,22 314:2 337:1 341:18 347:22 435:1

440:19 503:19

contractions 339:19

contractor 506:1

contractors 283:24

contrast 366:22

contribute 108:25 355:3 387:22 412:4

contributing 250:24 356:12 365:6 407:18 439:21

contribution 459:20

contributors 31:9 246:20

control 28:1 58:5,6,7,8 59:11 60:18,26 61:1,2,2

62:19,19 63:17,17 64:15,26 66:8,8,9 73:17 75:3,7

75:14,16 95:1,4,5 96:4 97:8,16,19 98:11,21,22,25

98:26 99:2,11,14,18,21 100:25 102:17 103:7

104:1,20,22,24 105:15,20,24 115:4 116:5,5

117:11 120:18 121:8 124:5,24 126:11 127:8,20

133:10 141:12,15 142:14,22,25 143:5 144:14,15

144:24 145:5 165:22 166:3,11 174:9 203:18

204:8,9,9,11 213:18 234:23 240:9 243:26 246:16

248:16 249:25 256:18 261:13 269:25 272:24

277:12 296:23 297:23 299:4,12 303:6,17,22

304:3,25 340:10,16 342:14 346:2,2,16,20 351:6

357:12 364:22,22 370:18,23 372:12 378:19 380:2

380:19 384:23 386:1 391:15 427:25 443:16,23

444:18,22,26 445:4,12,14 446:23 447:1 448:6,12

448:19,22 466:3 468:4 469:21 470:7 473:4

475:13 476:22 496:1

controlled 56:3 66:22 99:4,12 203:26 254:5 309:3

349:14,16 359:24 378:26

controls 46:4 63:12 99:7 119:2 120:16 121:2

123:23 124:20,20 125:10,23 126:10 140:21

141:17,21,25,26 142:8,9 144:12 145:15 162:2

193:21 205:22,22 216:26 253:20 254:2,7 261:16

261:17 262:4,7,14,17,18 269:25 272:20,21,21,23

277:11 299:7 303:9 305:1,2 320:1 339:23 341:13

356:21 360:4 361:23 382:2 391:13 417:26 419:25

423:16 426:6 447:14 448:3 466:6 469:10,20

495:23

convene 27:5

conversation 383:21 422:10 501:25 502:4

conversely 147:23

converts 65:24

convey 48:23

convince 303:15 319:5

convinced 434:14

convincing 255:16

cookies 501:3

Cooperation 11:23 12:25

Cooperative 6:1 211:14

coordinate 7:12 330:6

coordination 6:25 7:9,9 15:19,19 25:1 39:22,23

227:10

Coordinator 271:9 296:7

copper 432:23 434:7

copy 311:12

corner 284:24

cornerstones 496:14

corning 281:17,18,25

Corporation 252:18 321:22

correct 59:19 72:4 88:4,18 110:13 113:1,21 143:1

148:19 185:3 189:2 236:13 300:5 303:11,12

312:19 318:17 345:14 353:2 392:22 403:25

412:23 413:17 415:11

corrected 282:22 361:17

correction 236:9 237:1 318:15,17,20 319:16

403:12 406:13

corrections 135:21 282:16

correctly 57:13 90:10 108:16 148:17 189:19 370:4

370:15

correlation 136:21

correlations 214:5 298:17

correspond 165:4

corresponding 162:17,19 177:25 204:2 249:1

314:8

corresponds 145:19 173:16 174:22

corroborate 41:23 276:8

corroborated 159:8 161:7 162:1,10

corroborating 451:3

corroboration 27:26 49:20 278:26
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corticocarcinoma 64:7

corticosteroid 423:2

corticosterone 61:2 82:9,17 381:5

Cosmetic 10:6 16:26

cost 21:11 198:21 222:2 274:25 279:16,19 288:8

291:25 307:9 308:10 353:12 360:24 367:3 379:16

444:14 472:21 483:8 484:22 490:22

costing 254:24

costly 198:24 222:1,4

costs 178:24 179:2 308:15 349:25 379:23 400:18

cost-effective 238:7

cost-effectively 320:20

cost-savings 349:23

couch 367:7

Counsel 3:3

count 61:2 330:18,24 331:7

counterintuitive 125:24

counting 279:20

countries 12:26 313:14

country 309:18,19

couple 2:11 9:25 37:24 42:10 69:18 92:2 120:15

122:9 132:17 134:15 141:3 186:19 198:4 203:1

207:21 208:21 212:14 221:24 224:15 226:18

229:14 256:12 286:19 290:2,11 295:17 297:21

304:15 336:6 337:10 339:7,12 341:18 352:8

353:20 361:19 372:19 382:9 391:18 398:4 403:1

405:6 406:22 422:5,6 437:13 442:1 464:19

470:25 471:1,21 485:8 488:13 489:8 495:19

coupled 120:8

course 19:8 24:18 27:9 31:11 68:22 84:13 89:20

128:9 136:15 145:4 198:9 222:11,24 225:5

227:14 238:15 273:2 299:6 301:1 304:4 335:1

389:6 391:7 427:21 463:8,10 478:6 487:6,11,17

covariance 77:20

covariants 82:15

covariate 77:21 80:5 113:25 114:1 299:1 350:8

cover 19:9 22:16,17 42:1,25 139:15 187:8 231:11

308:18 310:8 478:3,19,20 479:11 493:17

coverage 43:9 460:17 472:14,20 476:4

covered 186:13 256:11 292:9 329:7 427:20 477:26

478:7 479:13

covering 242:8

covers 309:23 378:8 463:19

Cowper's 76:12

co-activators 342:2

co-efficient 153:1

co-factor 434:7

co-formulated 214:15

co-occur 156:12

co-occurrence 471:7

co-repressors 342:2

crash 232:21

CRE 318:15,15 319:19

create 17:5 21:24 332:2,7

created 140:23

creatine 354:3

creating 336:10

CRE's 318:20

crinkled 445:17

crippled 323:1,2,3

crippling 452:16

criteria 29:1,2 54:6 56:1,3,5,22 58:21,22,25 59:4,7

59:10,25,26 61:4,7,9,17,21 63:2,5,8,9,15,16,18

63:26 64:3,3,23,25 65:5,6,17 66:19,20,21,23,25

66:26 67:10,13 68:22 70:14,22,25 71:2,8,12 72:6

72:16,21,24 74:3 75:8 76:21 77:4 78:23 79:2,15

81:9,10 88:19 93:11 94:9 96:3,4,7,20,23 97:10,11

97:14,18,21 98:17,25 99:22,23 101:1,1,4,4

106:12 111:19 115:6 117:12,13 118:3,9,13,17,19

118:24 119:2,10 120:13,16 125:1 129:16 135:24

141:4 142:12 147:6,14,18,19 148:11 152:12,13

165:9 173:18 187:17 200:3 218:10 223:17,22

237:23 238:3,6,20 243:18,26 246:12,13,19,25

247:1,5,8,10,11,18,21,23 248:3,5,7 252:22 253:3

253:18 271:20,22 272:2,9,10,13,16 273:2,12,17

279:22 284:4,5,6,12 300:11,13 332:24 338:18

340:21 341:3,6,13 344:13,18 345:1 349:12 351:8

351:20,23 356:14,19,20 357:1,15 358:15,23

359:2,2,5,7,10,19,22 360:1,11 361:16,19 362:8

362:12,16 363:19 364:10 365:2,8,9,14,26 370:20

370:21 371:3 373:19,22,25 374:20 375:1,12

376:1,4 378:21 379:18,20,22 380:2,15,17,25

381:8,18,25 382:1 389:25 391:20 393:7,9,18

418:20 429:9 492:7 493:13

criterion 59:12,15 96:13,23 97:1,8 98:11,20 99:4

99:11 100:15 119:7,21 135:13 147:20 148:8

233:24 234:15 235:8 252:23 350:24 392:2,19,22

critical 10:25 17:23 18:10 25:23 27:2 43:21 83:19

147:11 163:26 202:20 224:13 226:26 227:1,22

306:8 362:8 369:20 399:2 411:24 451:12 463:4

481:9 494:20 496:4 499:2,7

critically 331:9,23

criticisms 242:6 319:13

CRO 347:26 355:12

crooked 121:13 441:15

Crop 237:12

CropLife 305:13,14,19,20,22 306:5,6,7,17 310:22

313:5,23

CropScience 258:18 288:16

CROs 348:19 393:22
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cross-comparison 239:9

cross-reactivity 239:3

cross-section 12:12 34:2,3 35:20,22 36:7 37:21

451:23

cross-sex 479:5

cross-species 479:5

CRYSTAL 1:15

cues 45:11 52:24

cull 299:10

culled 326:12,23

culling 324:3 326:14,18

culminated 367:16

culmination 230:14

culture 312:26

cumbersome 231:22

cumulative 361:10

curious 107:8 204:18

curled 465:14

current 27:14 31:15 49:3 122:20 157:11 225:4

226:23 227:14,16 307:7 309:9 320:13 322:17

327:26 331:18 332:4 335:1 336:18 355:8,23

369:22 422:23 453:20,25 456:8 459:11 461:1

468:19 483:6 487:22 492:9

currently 6:16 20:19 122:20 128:22 143:16 157:3

158:17 231:21,24 256:15 257:18,22 258:10

309:21 330:4 332:17 334:8 350:14 379:22

curvature 121:11,12,21,24 122:6 123:18 124:4,8

130:8 136:9 137:10 212:17,20 213:3,13,18,23

214:6,19 215:4,14,20 218:14 256:24 259:18

425:11,16,20,21,25 426:2,5,7,10,17 429:17,18

432:22,25

curvatures 361:22

curve 14:12,21 58:1,3 59:6 62:15,17,24 68:3

137:14 194:3 341:1 372:8

curved 63:10 436:25

curves 66:22 234:2 339:19 340:15 345:3 346:17

customarily 21:17

customary 210:5 494:11

cut 226:15

cut-offs 332:25

CV 97:23,23 113:8 119:20 147:4 385:2,8 387:23

389:25 390:19 391:3,5

CVs 98:1 109:16 113:14 119:24 120:8 129:2

147:19 152:6 246:19 247:3,10,12,26 359:21,22

386:5,13,14,17 387:8,22 388:26 391:7 392:22

393:14,17,17,20,23

cycle 34:25 78:14 115:25 249:18,18 295:21 353:8

353:19,22 387:13 388:5,14 459:19

cycled 388:9

cycles 249:24 353:7 388:4

cyclicity 77:25 78:20 252:5 386:18,20 394:7

cycling 295:20 386:14,24 387:12 388:1 394:24

CYP19 240:15,16

cytosol 57:15 59:3 62:8 63:6 324:5,6 327:7,15

338:7,24 339:2,5 342:8 355:9,13 371:13 379:16

380:8 465:7,13 490:16

cytosolic 231:23 371:23

cytosols 57:2 64:2

cytotox 235:25 236:12,14,26

cytotoxic 239:17 381:22

cytotoxicity 30:14 61:14 67:22,23 231:19 233:21

233:22 235:24 236:2,6,9,10 237:2,18 239:10,14

239:14 241:12,14 242:5,11 343:1 360:8 372:19

372:25 381:6,8,11,17,20 382:4,6

cytotoxity 380:22 381:13 382:13

cyto-specific 232:18

D

d 174:26

daily 77:18 80:2 95:15 99:6 348:24 361:1 428:6

438:23

Daland 220:14

Dale 434:5

damage 361:14

dams 326:21,26

Dan 4:3 5:5 82:6 210:1,7,24 230:22 390:22 399:22

498:17 503:24 504:19

Daniel 3:22,25 5:5 6:22 8:12 15:16 31:26 32:6 33:5

35:7 36:13,24 38:3,10 39:19 49:26 50:13 51:2,25

52:5 53:7,13 68:14 69:14 70:2,11 71:15 72:17

73:12,19,21 82:3,6,12,18,21,23 83:20 84:14

85:20 86:17 87:9 88:6,20 89:11 90:4 91:11,21

92:1 93:21 107:6,21 108:4 109:2,18 113:5 114:8

130:14 132:16 134:7,16 135:6 137:1 138:14

139:4,13 140:17 145:12 147:1 148:21 149:9

151:9 156:20 182:19 184:20,25 185:20 186:17

187:22 190:21 192:8,18 193:3,7,11 194:6,10

195:3 196:12 197:23 199:15 202:7 203:12 204:4

206:14 207:5 210:24 212:4 217:22 219:11 229:16

229:19 230:19 237:7 242:2,22 246:3 252:14

258:3,14 263:6 265:24 266:26 267:24 269:21

270:6,18,23 277:21 278:15 280:17 285:5 287:15

288:13 293:16 294:16 296:1 301:26 303:4,24

304:22 305:4,7,11 315:15 317:18 321:3 323:6

329:25 336:22 345:7,21 347:7,13,16 352:6,8,16

352:18,25 353:3,23,24 354:20,23 356:8,15

362:18,22,26 363:3,9,23 364:14,17,24 365:15

378:5,12 379:3,6,7,9,12,25 380:5,12 382:8

383:14,23 384:2,8,10,11,12,16,20 385:10,12

390:15 391:16 393:12,24 394:17,18 395:2,7

396:7,9 398:19 399:20 401:12 404:12 408:21
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410:15 411:11 412:9,21 413:1,22,24,25 415:5,9

416:11 417:9,18 418:12,22 419:9,20 420:2,13,18

420:22 422:1,17,25,26 423:4 424:23 425:8

426:13 434:22 441:25 445:6,9,25 446:11,15

447:16 450:14,17 451:7,19 452:10 454:14,17,19

454:22 456:21,23,25 457:2,7,16,19 458:10,26

462:12,16 463:21,24 464:17,18,21,23 465:24

466:10,14,22 469:7,24 470:18,24 472:23,24,26

473:12 474:6,24 475:17 476:9,24 477:16 478:23

480:5,22 481:1,26 482:2,4,6,8,9,10,12,17,24

483:17 485:6,7 486:22 487:25 490:12,24 491:25

492:4,10 493:6,7,11,26 494:7,11 497:2 500:19

504:15,18

dark 59:11

data 12:2,7 20:12 23:9,15,16 24:23 26:17 27:6 28:5

32:14 36:16,17 39:9 51:4,5 54:22 55:7,13,14,19

67:18 68:7,7,8,11 70:10,21 71:25,26 72:9,15,16

73:6,10 86:23 95:8,10 106:4,5,7,24 119:9 123:9

124:1 125:2,5,17 127:15,26 129:19 130:26 131:2

131:25 132:7,9 138:17 139:7,11,19 144:18

148:24 149:7 150:7 155:19 156:19 158:26 167:6

175:3 179:4,9,20 185:14 187:4 191:26 196:9,10

196:10 201:22 204:13 213:9 216:22 217:18 218:7

218:9 222:16,22,23 224:19 225:19,20 230:6,7

234:1 238:9 241:13 243:26 244:16 245:10,24

246:23 247:25 248:19,21 249:11,18,22 251:5

252:5 254:11 255:10,13,21 260:17 277:6 283:7

285:16 287:11 288:1,11 290:5,26 291:16 292:15

292:21,23,24 293:13,14 294:8,9,15 296:12,18,21

296:22,23 297:7,11,19,22,22 298:12,14,23 299:4

299:6,7,10,11,12,12 300:4,9 301:9,12,19 303:1

303:16,17,22,22 304:1,3,12,13,14,25 306:22

307:15,20,23 308:17,20 309:15 310:6,11 311:2,2

311:7,19,26 312:11 313:16,26 315:5 317:6

319:21 320:3,6 326:8 328:7 331:16,16 340:5,25

341:8,9,11,15 342:5 343:17 345:1,5 349:14,16

350:2 356:11 357:17 358:6,16,19 360:6,8,12

362:14,15 365:10 369:11,12,12,15 373:9 375:3,7

376:17,18,20 377:20 380:1 382:20,22 384:1,5,6

385:5 386:20,22 387:8 394:20 396:22 400:7

403:9 404:3 408:12 412:6 414:19 417:16,17

418:19 424:15 426:1,25 434:10 444:3,21 451:3

454:6 456:14 461:16 468:9 483:19 484:8,10

492:13,17,19,22 493:9 495:10,18 496:1 497:18

498:3

database 355:22 478:14

datapoint 414:15

dataset 39:16 108:9 217:3 297:2 298:15 299:18

306:14 333:24 414:5 473:18 487:22 492:22,24

datasets 130:3 216:22 247:26 296:24,25 358:7

data's 71:22,23

data-rich 306:23

date 17:24 267:20 350:9 380:4 434:10

dates 243:20

David 6:24,26 12:18 39:7 51:26 225:26 504:24

David's 9:26

Davies 356:2

day 74:17 76:2 80:2,3 91:23 98:23 99:3 112:25

115:22 116:21,21,21,25 117:2 118:10 120:8,18

124:19 127:7,19,24 129:1,4 138:2 147:19 151:1,1

178:13 207:8 209:1,5 210:2 216:24 217:8,12

273:8 327:3 340:13,13 350:9,10 353:6,6,8,14,22

360:19 386:23 387:12 388:5,14,18 420:23,26

422:2 430:9 436:12 448:6

days 9:5 10:1 11:17 74:18,22 76:3 77:18 94:24

95:2 98:10,23 110:8 116:2 147:4 208:21 229:14

250:11 257:7 269:19 274:6 326:24 338:25 348:13

350:12 417:26 425:14 427:25 428:5 430:8 437:20

437:22 448:3 506:4

deadlines 348:21

deadly 406:7

deal 47:5,12 145:9 194:18 315:25 334:8 464:13

500:6

dealing 268:10 269:10 406:25 410:21 435:15 438:5

442:9,11 500:1

deals 202:10

dealt 50:8 258:20 263:17 500:4

Dear 355:1

death 236:7 241:15

Deborah 6:4,4 71:16 72:2,5 211:17,17 362:24

378:13 381:4 384:4 498:21

debris 445:17

decade 25:4 231:3 299:26

decades 263:25 337:16

decent 493:21

decide 22:1,3 23:24 24:24 405:2 414:12

decided 37:19 144:1 149:26 354:16 396:11

deciding 375:26 399:5

decision 2:22 8:22 10:17 14:3,13 146:10 158:25

182:14,15 202:24 244:7 246:13 255:25 312:9

333:8,9,10 359:26 375:25 430:9 461:18 462:6

478:12 484:2,5,17 492:11

decisions 7:11 38:23 145:1 307:5,6 334:1 377:24

503:2

decision-maker 291:23

decision-making 288:23 503:3

decline 128:9 321:10

decrease 64:21 76:19 93:11,13 96:1 100:9 103:18

127:21 166:3 238:13,16 241:4 275:12 455:24

decreased 75:19 77:2 79:7 103:3 106:13 125:7
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176:16

decreases 81:19 100:12 125:23 238:5,8 244:23

275:9 339:10,18

decreasing 167:10

dedicating 33:3

deem 71:8

deemed 100:22 167:13 174:25

deep 299:10 302:24

deeper 297:8,10 299:17,23

default 488:24

defects 138:11

defer 223:6 295:10

deficiencies 81:23 264:21 360:14 431:5 432:21

439:3

deficiency 261:6 431:17

define 68:3 166:26 332:25 356:18 359:13 366:5

406:11,15 407:11 413:5 425:2

defined 54:6,21 75:2 76:17 103:1 110:25 154:9

155:7 166:19 167:2,23 168:3,9,19,23 169:5,11

170:24 171:2 174:16 175:10 177:12 185:7,26

193:26 350:16,20 365:25 366:13 369:26 402:18

412:1 413:12

defines 166:1

defining 69:6 313:11 317:11 356:20 359:12,22,25

406:14 491:13

definitely 105:26 135:25 159:19 258:8 503:21

definition 193:25 259:17 315:9 358:23 359:9

410:18

definitions 258:24 348:7

definitive 22:6 143:23 198:24 313:11 350:5 366:25

430:17

definitively 481:11

deformed 445:22

deformity 121:15

degenerative 427:7

degradation 439:23

degree 48:22 49:8 51:16 78:8 80:21 96:16 100:15

156:11 260:24 335:15 411:13 454:11 481:23

degrees 48:21 334:14

delay 125:15,21 130:10 146:20,23 159:7 262:6,12

262:20 263:2 268:14 447:5

delayed 269:19 295:1 447:15

delays 268:21

Delclos 4:23,23 50:1,2 84:14,15,22 85:13 210:18

210:18 380:6,7 390:15,16 398:19,20 413:26

459:1,2 462:23 466:1,22,23 481:1,2 483:18,19

486:24 492:10,11 493:8 495:7

deliberate 69:12 208:25 501:18

deliberately 313:24

deliberation 376:22

deliberations 208:10 227:18

delivered 111:14 360:19,22 361:5,6

delivering 111:19 112:7,8

delivery 112:10,11,25 126:4

Dellarco 31:8 505:12

demand 373:18

demonstrate 32:22 89:21 134:12 180:7 199:5

362:12 365:7 373:11 374:10 378:15 476:19

demonstrated 18:3 26:3 28:20,23 29:20 180:9

181:4 239:13 255:4,5 356:25

demonstrates 128:7 378:22 434:10 503:18

denaturation 240:24

Denoise 342:17

denoted 174:20

denotes 166:19

density 428:1 437:24

Department 5:9,15,19 211:3,6

departure 22:14 226:26 227:2,7,8

depend 89:16 403:13 417:3,22 493:14 500:2

depended 353:11 385:19

dependent 129:21 166:2 168:4,16,19 170:12

175:13,14 189:23,24 190:10 241:4 335:4,5 372:4

426:26 493:15

depending 116:1 165:11 219:7 275:14 333:8

334:15 352:20 353:13 464:7

depends 90:9 424:2,3

depicted 446:20

depth 51:4,8

Deputy 3:2

DER 95:10 283:18

DERs 55:14,14,21 283:8

descending 58:1 62:14,23

describe 73:7 123:20 172:16,18 296:12 334:12

385:17 447:25

described 121:26 124:2 141:5 163:17 175:9 200:8

259:3 294:23 476:17,20,23

describes 154:10,11

describing 258:26 268:13 315:24 316:6

description 4:8 74:1 249:21 357:19,20

descriptions 125:4

design 41:22 74:2 77:17 80:1 87:15 98:16 117:20

181:5 257:5 258:6 261:25 263:3 332:26 336:7,18

367:2 427:21 428:24 433:7 497:21

designate 17:6

Designated 2:10 9:18

designation 203:20,21

designed 28:15 41:2,16 42:15 44:2,8,12,26 45:9,25

172:26 176:24 180:24 201:23 222:19 223:10

246:14 257:18 262:8 333:2 366:17 367:4 369:7

459:12 468:11 479:5
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designing 336:5

designs 115:3 355:24

desire 228:17 232:9

desired 99:13

despite 120:9 408:14

DESTs 343:19,23,23 345:16 350:12

detail 36:15 50:26 51:17 97:22 109:8 115:7 129:12

134:11 139:20 157:15 196:4 251:10 312:13

323:21 330:16 334:14 336:10 366:7 437:24 480:3

detailed 129:16 159:15 223:3,6 270:10 422:22

446:9 457:14

details 35:12 85:11 138:16 237:22 435:10

detect 27:19 41:16 44:2,8,12,17,26 45:9,25 46:15

47:1 77:14 94:15 128:1 157:17 217:12 240:8

246:14 249:24 250:17 251:2,21,25 252:3,11

255:17 260:7 366:18 392:6 397:18 453:3 459:12

460:18,25 461:4 468:11 474:5 481:4 483:25

485:11

detectable 402:5

detected 34:10 471:3,8

Detectible 161:19

detecting 41:11 45:13 46:7 120:25 154:20,22,24

157:10,12 184:1 191:24 204:24 276:11 328:1

341:24 369:17

detection 41:3 147:25 156:24 276:19 372:5 391:9

459:15 460:17

detects 43:20,25 45:1 46:1 62:1 75:24 79:24

243:11

determination 10:8 13:26 14:24 15:5 23:23 143:26

156:13 183:16 291:15 294:12 339:2 502:21

determinations 70:23 111:13 160:16 183:18 361:1

405:4 407:16 408:5

determine 10:13 11:12 13:9,19 14:10 17:3 18:21

44:21 55:14 57:12,21 65:22 71:11 96:15 118:23

119:10 126:11 127:3 130:22 131:5 142:17 145:15

199:23 216:11,15 242:14,20 243:17 251:23

255:11 282:14 296:20 311:11 325:15,25 339:24

357:18 366:20 390:1 410:3,4 429:12 473:20

481:10 484:4 492:25

determined 44:5 53:6 119:18 149:15 183:4 219:5

311:24 360:16 410:12 424:2,9 460:12 468:1,16

479:24

determining 52:13 70:24 116:16 117:17 144:5

173:18 182:12 294:26 302:17 426:11

develop 40:12,16 49:5 53:4 132:5 186:8,22 199:25

245:10 266:10 307:18 326:10 382:19 441:17

444:24

developed 17:1 40:13 50:11 55:9,13 85:12 150:23

158:24 256:6 259:7 321:26 322:2,11 333:14

436:21 441:4

developing 245:6,23 256:5 383:8 386:14 441:22

444:22 465:3

development 5:17 7:3 8:7 11:23 12:18,24,25 17:20

18:11 20:24 25:2 40:23 47:20,20,21 52:25,26

77:13 78:11 79:23 80:24 85:1 116:24 122:5

124:25 125:16 131:16 135:2 150:14 159:5,6

161:6 175:13 183:4 195:11 215:25 218:5 224:12

260:6,15 261:4 262:25 266:4 268:21,25 269:12

306:9 307:24 309:1 310:4,5 311:10 320:1 326:5

334:19 336:13 367:13 377:8 401:20,23 402:7,14

402:16 408:18 416:7 425:19 432:21 433:9 447:6

450:6 452:21,26 453:11 455:24 461:7 462:5,9

489:7 503:10

developmental 5:24 23:17 45:11 46:2 52:15,24

125:15,21 130:10 132:6,8 145:24 146:13,14,20

146:23 155:10 158:8 159:7 161:26 214:8 215:24

262:6,12,20 263:2 268:13 298:26 362:3 428:5

448:22,24

developments 336:2

develops 260:20

deviated 391:5

deviation 68:19 90:16 120:11 129:1 148:9 282:5

282:10 286:2 358:26 359:5,14,15 360:10 365:13

372:17 374:21 375:19 385:8 393:18 403:13

423:18 424:1,4,6

deviations 29:1 30:12 65:19 79:14 91:7 96:14

119:24 179:19 359:18 360:12 365:9 373:23 375:2

376:6,7 386:26 391:2

dexamethasone 62:19

DFO 2:12 504:3

DFOs 505:23

diagnosed 141:6,15,20

diagnoses 125:3 132:1 146:17 447:12

diagnosis 125:8 141:19,22

diagnostic 11:16 47:26 95:25 125:1 130:10 159:6

451:6

diagnostically 437:1

diagram 64:11 90:6

dictated 110:25

die 415:22

diestrous 353:14 388:18

diet 83:17 85:5 91:18 214:8,17,21 215:7 250:2

260:1 263:19 361:26 390:25 446:4

dietary 91:15 121:10 128:21,23 130:7 250:3

diets 83:18 85:3,8,9 214:14 250:6,9 263:18 265:5

265:21 270:16

differ 54:19 108:15 358:26 393:21 411:12 439:7

differed 161:18

difference 108:20 140:13 146:3 163:8 166:11

174:9 216:13 217:10 219:6 232:25 269:16 293:25
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316:13 346:19,22 392:6 414:9 415:25 417:1,14

419:25 426:23 448:4 475:8 477:6

differences 22:20 29:2,18 109:14 149:6 161:19

163:11 180:21 188:19 189:3 204:17 214:13

216:25 217:4 218:13,14 224:17 250:19,20 252:9

293:19 325:6,6 338:5 346:7 353:21 376:4 408:7

417:7 420:10 426:1 497:21

different 17:11,17 26:3 27:25 29:11,19 30:14

48:20 51:9,13,18 59:1 68:17 69:24 105:13,20,23

120:3 122:16,26 123:1 125:18,19,19 140:9 141:3

143:5 147:2 148:1 149:25 152:19,22 155:2

159:16 160:20 161:18 163:9,13 164:11,24 165:22

166:2 169:17,19,22,25,26 171:17 172:2 182:26

189:26 190:11 193:1 213:15 215:20 216:4,8

238:12,25,26 249:11 252:11 256:7,25 258:22,22

258:23,24 259:5,17 261:26 262:1,17,21 263:17

263:18,18 265:5 270:4 281:11 289:25 302:15

316:21 332:7,11 333:20 334:1,14,25 335:14

350:12,19 351:2 355:6 360:19 368:2 371:13

385:1 396:18 402:26 403:10 416:1,8,9 419:23,26

422:22 431:20 432:3 433:25,26 439:19 442:21

446:8 463:3,5,5,6 464:10 465:20 470:2 472:8

480:18 488:1 491:9 496:7,8 498:9 504:1,20

differentiate 44:1 402:14 419:22

differentiating 416:26

differently 138:21 139:1 261:20 422:14 472:9

479:8

differs 341:20

difficult 59:13 65:3 85:3 97:3 98:4 128:2,16 129:9

130:6 132:4 133:12 152:25 243:17 246:15 250:21

251:23 252:10,23 253:17,18 254:3,8,10 256:17

256:22 272:3,3 295:24 346:11 347:10 348:19

349:6 357:18,25 358:17,18 367:26 395:20 399:4

400:3 414:12 417:13 431:10 433:17 448:12

460:11 461:14 464:26 477:11 478:16 481:15

486:19

difficulties 30:14 223:12 243:14,24 270:26 271:20

273:1 275:1 382:11

difficulty 61:6,20 184:6 223:16,18 253:12,19

dihydrogen 228:12

diligence 490:6

dilution 100:23 126:11

Dimethylformamide 126:16

diminish 102:11

diminishes 415:23

diodinations 163:16

dione 65:26

dip 219:5

direct 4:12 182:20 314:12 334:12 450:21

directed 47:15

direction 51:20 78:17 81:4 159:15 170:25 182:9

188:14 200:4 206:6 346:5 460:10 461:19 481:13

484:13 503:21

directionality 51:9,17 459:8 462:26

directly 110:16 126:6 137:4 361:7 407:17,20 433:3

436:17 450:26

director 5:2 6:24 7:2,5 8:13 15:11,18 133:15

305:19 336:26 504:25

disagree 322:17

disagreeing 480:3

disappointed 248:22 307:1

discard 338:26

discarded 380:1 483:22

discarding 326:24 338:11

discern 96:16 204:17 300:24 362:13 364:1 404:24

409:23 417:6 444:25 445:3

disclosure 3:1 402:11

disconnect 283:14

discontinue 430:9

discount 117:25

discounted 53:2

discounts 152:14

discovered 108:7 316:4 411:20

discrepancies 342:5

discrepancy 147:13 357:21 428:26

discriminate 204:10 404:14 445:1 458:19

discuss 40:1 54:17 56:21 65:21 67:14 73:25 94:8

97:22 101:7 102:12 115:5,7,10 139:11 164:9

167:22 169:10 207:20 222:16 248:23 289:10

307:13 347:25

discussant 356:16 362:19 363:2 365:15 385:10

396:7 426:14 434:23 447:16 459:1 483:18

discussants 396:14 397:23 398:6,18 413:26 447:19

459:4 485:10

discussed 13:24 55:26 115:16 119:22 120:15 122:4

129:12,12 135:21 145:6 153:4,21 154:14 157:15

166:8 179:14 180:11 182:13,16 195:22 237:23

242:16 244:23 257:2,24 319:26 342:22 349:4,5

349:12 361:20 397:6 409:6 459:20 461:20 468:15

486:15 502:16

discusses 102:25

discussing 55:25 94:2 121:24 141:19 153:6 164:4

172:11 241:22 258:22 363:17

discussion 50:25 56:14 145:14 150:22 192:21

205:17 249:9,16 251:3 272:19 281:9 285:18

321:12 347:19 364:19 370:1 391:25 413:5 416:23

423:11 485:4

discussions 252:24 302:8 377:1 390:17 399:8

disease 6:21 295:3 443:12,18

diseases 437:4
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disease-free 443:21

disentangle 465:20

dish 445:15

dismiss 250:21 252:10

dispense 302:26

displaced 58:17,20

displacement 204:8

displayed 79:5

disqualify 405:22

disqualifying 400:6

disregard 411:3

disrupt 154:21,23,26 398:9

disrupter 185:24 186:4 315:8,19

disrupters 153:23 315:7 316:4 392:13

disrupting 385:21 396:18,24

disruption 5:12 6:3 96:1 211:16 367:20 369:16

404:19 405:1,2 411:19,24 412:3,6 449:15,17

456:5 490:26

disruptions 353:19

disruptive 7:14 306:9,19 319:11 366:10

disruptor 1:4 2:7 4:1 10:26 16:7 17:22 210:3 397:5

411:15,16 414:4,21 499:26

disruptors 27:19 400:11 414:10 499:3

dissect 88:13

dissecting 445:16

disseminated 318:18

dissociation 57:13

dissolved 96:10 97:17,20 99:25 100:9 101:2,5

361:11 432:24

distance 491:8

distinction 21:9

distinguish 45:3 57:4 59:13 244:8 373:5,12 374:10

374:15 378:16 447:8

distress 350:17

distributed 94:22 101:10 109:5 163:7

distributing 94:26

distribution 42:21 98:15 101:15 113:17 118:11

119:2,9 120:24 155:17 267:2,4,5 311:1

disturbing 319:23 322:23

dive 297:8,10 299:11,17,23 302:24

diverse 467:12 495:13

diversity 41:6

divide 420:8

dividing 403:6

division 15:19 39:22 53:15,17 93:25 114:11,20,21

144:17,18 323:11

Dix 6:24,26 16:1,1 24:12 39:7 51:25,26 225:21,26

504:24

DMF 126:17

DMSO 233:16,18 344:12

docket 1:7,9 3:16,17,18,20 306:26

dockets 3:18

document 128:15 133:4 134:10 135:22 142:9,12

150:15 154:9 248:1,26 282:17 325:4 364:1 368:1

377:13 391:1 430:5 438:6

documentation 393:8

documented 52:10 124:5 183:2 199:21 343:15

357:15

documents 55:15 133:4,21 143:7 283:10 284:23

320:16 370:13

doing 12:21 134:9 143:14 149:17,21 150:3 198:9

220:17 223:20 224:4,4 226:21 227:15 268:26

273:19 274:7,24 279:3 281:2 283:12,22 286:22

288:1 299:15 304:3,19 324:19 332:9 337:5 343:9

344:15 352:13 354:4 377:24 387:16 388:13

389:20 396:11 398:3 404:16 422:26 423:14,20

429:25 438:20 444:11 455:14 465:3 490:11,19

491:20

dollars 222:3 224:22 230:15 308:12

domain 183:11 192:2 194:19 195:17 207:1 377:11

377:21 491:13

domestic 226:17

Don 5:14 31:8 72:20 211:5 278:15 420:5,14 505:6

Donald 5:14 113:7,19,24 114:7 211:5 278:17

279:16 280:2,16 379:10 384:13 408:22 417:10

420:15 445:8 465:25 473:1 482:11 497:12 505:25

dorsal 80:9 433:23

dorsal/ventral 137:11

dosage 414:13

dose 14:12,21 22:8,14 35:24 75:7,18 76:4,5,6 78:4

78:4,5,6,7,8 80:17,18,19,19,20,21,22 87:13,15,18

87:19,24 88:1,9,10,24 89:2,3,6,7,10,15,17,18,23

89:24,26 90:1,7,12 91:8 92:21,23,25,25 93:5,9,20

93:20 102:24 106:17 109:20,21 111:14,15,19,22

112:1,7,8 180:2 186:2,8,10 199:8 223:10 232:20

243:15 245:7,21 254:10,10 272:6 273:13 274:8

276:22 312:16,19,20 316:15 325:2,15,20,25

332:21 346:18 348:7,8,12,15 349:1,3,3 350:4,10

350:15,16,18,25 351:6 352:2 353:12 357:7,8,8

360:22 361:7 367:6 396:2 397:1 398:12 399:3,6,9

399:10,12,13,16 400:7 404:26 405:26 406:6,12

406:16,22 407:1 409:8 413:6,10,14,20 417:1

424:26 425:6 463:4 485:4

dosed 74:22 76:3 309:26

doses 21:19,19 22:7 86:7 87:25 88:15,25 93:13,15

93:16,17 109:21 148:25,26 186:2,12 206:7 244:1

244:2,14 245:12 274:1,4 309:12 326:10 363:7

389:24,25 396:17,20,20,25 397:24 399:8,11

404:22 405:2 406:14,23 408:7,12 411:26 413:19

416:4
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dose-response 18:23 41:25 341:1,4

dose/concentration 395:13,18

dosing 23:20 74:18 75:22 77:9 79:19 82:1 89:1

98:8 111:1 112:17,18,22,26 148:24 249:8 357:9

360:14 361:8 409:10 418:16

double 418:13

double-log 403:9

doubt 481:24

Dourson 92:8 207:8 219:21,25 220:1 229:18

230:16 291:4 314:13

Dourson's 219:23

Dow 220:14 246:7 271:15

downing 346:3

downstream 115:15 156:25 158:8 400:16,26

down-stream 264:12

Dr 2:1 3:22,25 4:10,11,15,19,23 5:1,5,8,14,18,22

5:26 6:4,8,12,18,22,24,26,26 8:12,13,15 15:16,16

15:21 16:1,1,1,12 24:12 31:14,18,19,23,26 32:2,3

32:6,8,9,15 33:5,5,6,7,14,15,23,25 34:7 35:7,7,8

35:13,14 36:3,13,13,14,19,24,24,25 37:4 38:3,3,4

38:8,10,10,11,18 39:7,19,23,24,24 49:26,26 50:1

50:2,6,13,14,15,18 51:2,2,3,16,25,25,26 52:1,5,5

52:6,13 53:7,8,13,16 57:1 68:14,14,16,17,20,26

69:14,14,15,20 70:2,2,3,7,11,11,13 71:15,15,16

71:24 72:2,4,5,11,17,19,20,26 73:12,14,19,21,22

73:24 82:3,4,5,6,10,12,13,18,20,21,21,23,23,24

83:1,4,6,8,13,20,20,21,25 84:1,4,14,14,15,20,22

84:25 85:13,20,20,21 86:2,9,13,16,17,17,18,18

87:1,3,5,6,7,9,11,12,17,21,22 88:2,4,5,6,6,7,11

88:16,18,20,22,23,26 89:5,8,11,11,12,19 90:4,4,5

90:9,11,14,22,23 91:1,5,10,11,13,14,19,21 92:1,8

93:21 107:6,7,8,21,21,23 108:4,4,5,8,9,11,14,17

109:2,2,3,7,18,18,19 110:10,14,21 111:8,12

112:5,19 113:2,5,6,7,19,24 114:7,8,17,18,20

115:19 130:14,15,16,19 131:4,7,12 132:4,15,16

132:16,17,23 133:7,9,19,23 134:2,5,7,7,8,14,16

134:17 135:6,10,17 136:1,3,26 137:1,1,3,13,14

137:17 138:4,9,13,14,15,25 139:4,4,5,13,13,14

139:21,22 140:15,17,17,18 142:1,4,7,24 143:1,8

143:17 144:7,9,11 145:11,12,12,13 146:5 147:1,1

147:2 148:16,20,21,21,22 149:9,10,11 150:13,20

150:21,22 151:9,10,24,25 153:21 156:20 157:8

159:26 172:10 178:11 182:4,19,23,24,24 183:3

183:20,26 184:20,20,21,24,25,25,26 185:1,8,18

185:20,22 186:6,16,17,19 187:22,23,24 188:11

188:25 189:2,4,10,15 190:2,18,21,21,23 191:9,22

192:8,8,9,14,18,18,19 193:3,5,7,9,11,13,14,25

194:2,6,6,7,10,14 195:1,3,3,5,5,8 196:12,14,15

196:21 197:23,23,25 198:8 199:15,15,16 200:5

202:7,8,9,9,26 203:12,12,13,14,16,24 204:4,4,5

204:22 205:7,25,25 206:14,14,15,18,19,22 207:5

207:8,22 210:1,10,13,16,18,20,24 211:2,5,8,10

211:13,17,21,25 212:1,4,12,21 214:3 215:23

216:6 217:22,23,24 218:7,17 219:11,23,25,26

220:14 223:10 224:8,9,9 225:10,21 226:14 228:8

228:21 229:16,18,19,25 230:19,21 237:7,8,11

242:2,2,4,12,22 246:3,6 252:14,16 258:3,4,5,8,14

258:17 263:6,6,8,15,24 264:16,19,25 265:17,19

265:23,24,24,25 266:8,26,26 267:1,7,10,13,24,24

267:25 268:9 269:5,7,21,21,22 270:1,6,6,7,14,18

270:20,23 271:4,15 277:21,21,23 278:4,5,8,11,14

278:15,17,21 279:16,18 280:2,5,16,17,19 285:5,6

285:7,13,17 286:4 287:15,15,16,25 288:13,15

293:16,16,18 294:1,16,16,17 295:9,12,15,17,26

296:1,4 301:26 302:3,3,5,9 303:4,5,12,20,21,24

303:24,25 304:8,10,11,21,22,23 305:1,3,4,5,6,7

305:11,14 315:15,16,17 316:9 317:18,22 321:3,4

321:5,17,20,21 322:4 323:6 329:25 330:2 336:22

336:25 345:7,8,9,12,13,15,19,21,21,22 346:1,8

346:14,21,25 347:4,6,7,9,13,15,16,18 352:6,6,8

352:14,16,17,18,19,25 353:1,3,10,23,24,24,25

354:2,10,14,19,20,22,23 355:1 356:8,10,15,16,17

362:18,19,20,22,22,24,26,26 363:1,3,4,5,9,12,14

363:23,23,24 364:13,14,14,16,17,18,24 365:1,15

365:16,17 378:5,5,6,7,11,12,12,13 379:3,3,4,6,6

379:7,9,9,10,12,13,14,25,25,26 380:3,5,6,7,12,14

380:21 381:1,4,7,10,19,23,24 382:5,7,8,9,10,25

383:14,17,23,23,24 384:2,3,4,8,8,9,10,10,11,12

384:12,13,16,19,20,22 385:10,11,12 390:15,15

390:16 391:16,16,17 393:12,12,13,24 394:1,2,17

394:17,18 395:1,2,7,9 396:7,8,9 398:11,19,19,20

399:20,20,21 401:12,12,13 404:12,12,13 408:21

408:21,22 410:15,16,17 411:11,11,12 412:9,9,10

412:11,21,21,23 413:1,4,22,22,24,25,26 414:1,2

414:8,16,18 415:5,5,7,9,12 416:11,12,13,14

417:9,9,10,12,18,18,19 418:12,12,13,22,22,23

419:9,9,10,20,20,21 420:2,2,3,13,15,18,19,21,22

422:1,8,9,9,17,18,19,25,25,26 423:4,11,26

424:23,23,24,24 425:8,10 426:13,14,15 430:19

434:22,22,23,24 438:7 441:13,25,25,26 442:1,2

443:15,26 444:3 445:6,6,7,8,9,10,11,11,25 446:3

446:6,11,13,15,16 447:16,17,18 450:14,14,15,17

450:17,18 451:7,7,8,19,20,21,22 452:10,11,13

454:14,14,15,17,17,18,19,19,20,22 455:1,9,12

456:21,21,22,23,23,24,25,26 457:1,2,4,5,7,9,16

457:19,20,23 458:10,12,26 459:1,2 462:12,12,14

462:16,16,17,23 463:21,21,22,24,24,25 464:17

464:17,18,21,23 465:24,24,25 466:1,4,10,13,14

466:22,22,23 469:7,7,8,10,16,24,24,25 470:18,18

470:19,24,24,25 472:23,23,24,26,26 473:1,12,14
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474:6,7,8,24,24,25 475:17,18,19 476:9,12,17,24

476:25,26 477:16,16,17 478:23,25 480:5,6,6,6,7

480:8,9,22,24 481:1,1,2,26,26 482:1,2,2,3,4,4,5,6

482:6,7,8,8,9,10,10,11,12,15,17,24,26 483:17,18

483:19 485:6,6,7 486:22,22,23,24,25 487:25,26

488:1 490:12,12,14,24,24,25 491:25 492:2,4,6,10

492:10,11 493:6,6,7,7,11,11,12,26 494:1,2,7,9,11

494:15,16 495:2,7,12 497:2,5,12,25 498:5,11,16

498:21 499:9,17 500:9,19 501:10 503:11,24

504:15,17,18,24,25

dramatic 400:22

dramatically 240:12 253:26 443:7

drastic 148:2

draw 51:22 194:26 326:7

drawing 353:26 355:20

drawn 481:6

draws 305:26

drinking 10:19,21 13:26 17:12,15 39:1

drive 1:15 389:11

driving 86:3 317:12

drop 129:7 381:16 416:16

dropped 414:13 491:22

dropping 163:22 413:20

drops 129:4

Drouin 121:26

drove 51:12

Drug 10:5 16:26

dry-run 301:10,13

due 29:18 96:2 97:3 103:9 105:21 106:6 163:14

167:13 174:25 236:7 252:3 255:20 306:24 323:24

325:5 329:5 357:4 360:15 386:18 400:23 437:10

437:21 466:26 490:6

Duke 271:11

Duluth 265:3 408:16

dump 451:17

Dunnet's 127:15

duplicative 19:5

DuPont 304:18

duration 99:17 100:7 137:26 243:16 309:12

Dutch 474:19

dwell 212:5

dying 411:22

dynamics 400:12

E

E 28:21 48:10 49:12 153:19,23 154:13 155:5

168:15 170:1 172:20,23 173:6 174:18 179:24

181:6 184:2 189:12,21,25 190:4,16,26 191:4,6,6

197:11 200:20,21,26 201:8,26 202:5,25 375:9

395:14 458:15,24 460:5 466:20 468:5 479:22

481:4,12

Earl 225:11 228:8 291:5,22

earlier 115:16 119:22 128:26 138:25 139:22 145:6

150:19 159:26 163:18 171:2 177:14 195:6 199:17

235:22 242:6 256:23 267:9 268:1 271:6 278:6

284:10 289:4 292:14 308:19 309:10 311:20

314:13 334:20,21 338:5 342:22 360:25 371:7

385:25 388:18 412:11,24 474:20 485:5 493:13

early 5:17 19:22 53:3 187:10 209:1 335:3,4 342:23

385:20 430:7 482:19,21

earth 478:5

ease 297:20 349:9

easier 322:22 353:15 374:22 388:7 392:14 477:15

493:9 495:16 496:5

easily 52:17 279:4 302:10 351:9,10,20,22 361:16

417:14 418:17

easy 131:5 199:1,13 243:10 407:16 500:23

EAT 48:20 49:22 91:22 183:2 400:26

eco 242:18

Ecoducts 4:13

ecological 14:19 20:21 22:18,19 25:5 185:13

187:13 226:12,24 256:6 269:18 278:19 315:13

Economic 11:23 12:25

ecotox 296:15 310:18 472:16

ecotoxicologist 6:7 211:20 252:18 258:18 288:16

ecotoxicologists 306:2

ecotoxicology 125:26 210:11 231:2 290:3

eco-exposures 227:26

eco-habitat 439:7

eco-toxicity 144:18

EC50 340:24

edema 431:25 432:1

edge 90:24,25 344:20,23

edification 147:8

EDMVAC 231:5

EDSP 1:5 7:13,19,21 15:17 16:17 25:20 26:14,23

28:4 38:12,17 53:19 54:14 55:11 115:17 153:22

164:5 172:12 178:15 179:4 181:2 183:5 186:6

246:9 296:11 318:1,4,10,22 319:14,21 320:8,13

320:17 322:17 336:14 337:3,8 347:23 409:5

EDSP-related 7:18

EDSTAC 10:26 11:14 18:12 20:10 21:8 41:4,16

198:10,22 199:12 281:3 286:6,9 299:19,22

306:10 499:12 504:6

EE 244:2

effect 17:4 21:21 65:11 79:7 83:12 84:19 85:22,24

85:24 86:4,12 90:2 103:12,15 104:4,5,8,10,12,16

109:1,22 125:12 126:12 127:3 128:1,5,6,11

135:26 142:21 144:25 145:9 146:4,6,13,18

150:10 160:2,3 161:13 162:9 170:26 172:5 182:9

188:1,2,3,4,9,9,14,17 192:24 204:18 227:2
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238:15 239:11,11 242:14 248:17 286:24 291:13

316:14,15,16,16,21 317:15 321:26 335:12 355:17

360:20 362:15 364:20 367:22 388:8 397:16

405:19 407:8 410:4 412:13,17 413:11,17,17,21

414:12 416:3 429:23 433:22 441:20 444:25 445:3

460:14 463:4 469:1 471:3 475:23 484:17 487:5

487:18 491:4

effective 25:16 53:6 75:7 151:8 195:14 312:16

314:1 351:6

effectively 163:26 312:2 366:17

effectiveness 317:21,24 483:2

effects 5:17 11:17 14:20 16:23 17:4,5 18:2 21:21

22:9 23:26 24:1 29:17,17 41:12,24 47:26 53:15

53:17 56:12 77:1 79:23 88:9,12,15 89:4 93:25

103:16 114:11,20 120:5 124:10,24 126:12,21,24

127:7,24,25 128:24 154:6 157:13 160:4 161:7,8

162:17,19,20,22 163:14,16 176:14 181:1 182:11

183:1 185:14 213:21 224:19,22 226:26 252:3

291:2,17 302:14,14 315:19 316:2 329:10 344:20

344:23 351:19 352:23 363:15 364:21 387:7 391:6

391:9 395:14,17,18 396:5 398:22 400:16 401:25

402:9,14,14,18,21 406:5 407:20 408:8 409:23,26

410:20,25 411:1 412:12 413:12 414:23 416:1

417:1 431:25 432:10 434:4 437:10 447:11,14

448:11,25 450:6 452:16 454:1 459:26 460:2,3,11

460:11,19,19 461:4,13,18,20 462:3,7,8 463:5,5

467:16,18 468:11,14,20 469:2,3,4 481:14,20

484:13 485:2 487:5 500:3

efficacy 84:2 234:22 379:1

efficiency 282:4 288:22 383:19

efficient 25:16 301:15 314:1 331:22 488:8 489:7

504:13

efficiently 26:21 36:12 49:16 320:18 366:17

effort 10:5 12:23 15:15 20:20 25:4 32:13 36:2 50:7

53:3 98:4,9 145:15 178:14,19 205:3,5 220:17

397:23 452:4 495:10 499:15

efforts 9:7 12:4 15:24 335:13 336:4 460:26

effort-related 37:1

eggs 98:22 99:3,7 273:7,11,11 412:14 432:17

445:14 488:19

Ehrich 4:15,15 32:2,3 206:14,15,19 210:13,13

362:19,20 378:6,7 379:25,26 383:23,24

EIA 239:5

EIAs 238:23

eight 66:9,9 74:21 99:13 138:2 147:18 169:4

220:13 325:22 467:19

Eighteen 64:9 66:10 173:5

eight-to 325:10

either 46:18 47:20 48:9,12 51:5,12 55:19 61:23

65:11 74:18 97:15 98:22 118:10 139:25 151:19

167:20 171:21 189:6 203:21 208:3 216:15 243:6

244:12 251:8 258:6 291:21 293:21 296:8 321:18

350:9 379:8 387:16 388:3 390:2 392:3 399:15

403:10 404:25 433:16 466:9 471:2 480:15 483:16

either/or 302:11,22

elaborate 194:12 312:22 434:24 465:22

elaborated 311:15 430:5

elaboration 52:12

elastin 434:8,11

elected 84:11

electron 433:17

element 243:6 475:16

elements 40:2 43:11 51:1 52:9 115:5 117:12,15,26

143:26 214:15 243:7 400:14 434:25

elevated 361:11,12

eleven 30:24 67:6 79:9,11 200:25

elicit 74:11

elicited 161:12 165:17 168:8 169:2 181:11,12

397:1

elicits 312:21

eliminate 257:24 289:18 293:21,21 334:2 397:24

415:1 429:14 438:15 440:4 445:24 451:10 497:10

eliminated 328:15 434:18 492:25

eliminates 451:13

eliminating 27:8 197:26 276:24 293:2 413:15

492:8

elimination 288:26 289:24 293:1 311:1 324:10

328:10,20

ELISAs 238:23

Ellen 229:22,25,26 271:3,4 277:23 278:4,8,14,17

278:21 279:18 280:5,19 285:13 286:4 287:25

296:6 308:19

emaciated 416:2

email 354:24 371:16 486:17

emailed 447:19

embark 501:25

embed 24:3

embryo 431:19 432:12

embryos 429:24 432:8,25

emergence 116:18

emerging 7:13

emeritus 5:9 211:2

emides 28:14

emphasis 226:21 332:12

emphasize 39:15 41:8 180:20 182:3 214:23 296:11

296:21 331:9 451:9 493:8 502:17

emphasized 59:14

emphasizes 107:3 180:25

empirical 235:2 479:1,19 480:2

empirically 424:2,9 427:3
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employ 496:5

employed 120:4 396:23

employing 491:1 502:10

empty 111:13

encompass 164:23

encompassing 366:4

encountered 30:12 67:25 243:21

encourage 68:5 283:1 294:12 301:23 310:22 315:3

315:11 347:11 414:14 500:5

encouraged 68:11 460:23

encouraging 460:15

ended 26:15 277:1 324:26

endocrine 1:4 2:7 4:1 5:12 6:3 7:14 8:1 10:4,12,15

10:26 14:1 15:11 16:3,7,23 17:5,13,21 18:16,22

18:26 19:7 21:5,16 22:4,13 24:14 26:6,8 27:19

31:1 40:4,9,10,14,19 41:17 42:23 43:1 44:19,22

51:23 73:26 94:14 95:26 96:17,19 103:9 105:11

106:19,26 126:24 153:5,15,24 154:6 156:10,12

159:2 160:23 171:16 172:4 178:7 183:6,13

185:24 186:3,23 187:5,8,10 198:26 202:16,18

204:14 210:3,26 211:15 224:22 229:21 230:3,3

243:1 246:15 250:16,17 251:8,21,24 252:12,12

252:20 271:2,9 273:4,5 275:8 278:23,25 279:7

280:1 281:3 282:11,16 288:17 289:8 290:13

291:19 294:26 295:2,3 296:2 305:21 306:9,19

307:11 310:17,21 311:8,20 312:1,14,18,23

314:11,23 315:7,8,8,18 316:2,4,11,13,21,22,26

317:16 319:11 321:25 337:5,6 366:9 367:19,22

369:16 385:5,20 392:13 395:20,24 396:5,18,24

397:5,7,19 398:10 400:4,16,20 404:14,19 405:1,2

407:8 409:14,17,26 410:4,25 411:3,7,15,16,19,23

412:3,6 414:4,10,21 448:20 463:19 472:14

478:20 483:4,10 485:2 490:26 499:3,26 502:22

503:18

endocrine-active 27:17

endocrine-mediated 41:12

endocrine-related 56:12 76:7 77:24 80:8 86:3,5

88:12 90:2 317:8 398:23

endocrine-specific 44:19 311:11,21

endocrinologist 5:21 6:6,14 211:19,22

endocrinology 5:7 125:26 211:9 294:19

endorsed 322:1

endpoint 51:11,17 66:2 79:6 81:18 85:22,26 86:12

95:12 102:7,10 105:1,7 106:26 107:10 108:20

111:17 114:5 124:14 159:16 160:18 161:1,16

162:10 165:14 166:6 167:9,26 168:18 169:1

173:19,22 174:7 175:12,17,19 176:3 217:11

235:1 241:8 249:17,21,26 255:24 269:1 273:2

275:1,10 278:19,20 279:2,2 291:21 294:4 300:15

302:13,13 316:18 317:10,12 408:1,15 410:22,22

449:4 459:13,20,23 460:14 461:26 487:1,2

endpoints 19:26 21:6 22:10 27:25 41:6 42:24

44:10,20 45:14 47:9,26 48:1 49:19 64:13 74:23

74:26 76:7,12,14 77:23,24 78:1,12,18,25 79:10

80:7,8,13,26 81:5,11,12,22 86:5 94:18 95:6,8,13

95:14,16,22,24 96:1,5 103:11,24,26 104:1,5,5

106:26 108:13,15 116:20,21,25 117:2,4,7 120:22

127:4 130:21,24 144:13 150:9 152:20 154:4,10

154:12 157:19 158:10,18 159:24 160:2 161:18

162:14 164:23 166:12 167:10,24 168:3,9,10,12

168:14,24 169:3,5,7,13,17 170:25 172:17 173:7

173:13 174:10 175:5,10,26 176:6,10,19 180:8,10

180:13 183:17 187:7,11 189:20,26 190:10,14

191:6 200:16,23 249:2,7 250:3 251:6,23 252:4

257:14 262:25 263:2 266:6,21 269:8 273:3 275:2

276:1,9,15,25 277:4,8 279:19 287:12 289:2,4,7

291:26 293:26 300:22,24 301:13 302:16,25 310:9

314:5 317:7 349:22 356:22 369:10,23 384:26

385:3 391:3,12 397:19 398:14 409:1 410:3 428:3

433:5 458:18,25 459:18,23 460:7 461:11,13,16

461:24,26 463:3,13 464:7,8,9 466:21 467:26

468:22,24 472:14 481:9,17 493:21

ends 480:14

end-life 121:7 132:11

energy 399:24 400:11,12

engage 222:17

England 5:6 210:26

enhance 84:7 252:2 288:21

enhanced 153:25

enhances 409:2

enjoy 503:13

enjoyable 8:9

enlightening 498:1

ensure 2:24 3:4 41:2 55:23 60:22 71:4 98:14 118:6

179:7 350:16 351:13 386:1 503:1,14,20

ensures 490:3

ensuring 2:14 7:10 130:8 181:20 198:24 307:9

396:1 483:8

entails 35:19

enter 136:17 304:3

entered 267:16

entertaining 228:21

enthusiastic 485:8

entire 147:11 196:13 253:5,7 324:23 382:15

422:15 436:20,25 440:13 463:19 478:20 492:22

494:21 498:4

entirely 436:2

entitled 210:2

envelop 273:22

environment 310:16 363:7 385:22 436:5 439:10
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environmental 1:11 4:3,13 5:3 93:25 96:8 99:24

114:11,19 116:1 118:4 135:4 152:20 210:8,22

211:6 229:2 234:19 271:12 368:8 396:18

environmentally 402:8

environments 263:26

envisioned 481:23

enzyme 60:12 65:23,24 155:9 164:21 165:2 169:21

170:3,15 173:11 177:3,6 240:9,23 241:3,7 244:25

245:2 349:20 351:17

enzymes 46:10 64:8 241:4 252:2 354:1

EPA 2:21 3:19 7:4 8:7 12:19,20 49:4 67:26 71:20

92:6 99:20 122:19 155:15 220:17,22 222:13,21

222:23 224:20 226:15,21 228:24 230:6 231:15

237:23 241:22 243:23 252:25 257:15 268:1

284:21,25 285:16 286:7 288:2,20 296:21 297:1

299:6,12 303:23 304:13 306:19 307:22 311:10

313:6,24 314:8 315:2,11 318:3,17,22 319:20

320:3,7,13,15,16 321:22 322:8 327:18,20 334:17

335:14 336:6 343:11 344:2,9,25 349:17 355:19

360:25 361:26 362:15 365:10 385:5 386:7 390:18

394:1 396:23,25 423:1 425:19 442:18 459:24

462:5 464:4 468:19 469:5 470:26 480:25 483:1

488:3 490:8 494:17 495:3,13,17 497:3 498:12

499:1,6,10,23 501:9 503:5

EPA's 7:16 233:2 288:9,24 307:9 319:16 328:17

365:12 385:7 411:25 483:8

EPA-HQ-OPP-2013-0075 1:7

EPF 237:13 243:1 296:12 306:4

epidemiologists 306:1

epididymides 80:10,11 81:12

epigenetic 437:10

epithelial 402:10 450:21,25

equal 119:4 126:18 270:17 369:12

equation 283:24

equations 282:23

equipment 282:19

equivalent 62:26 231:13 232:24 235:12 250:5

313:26

equivocal 58:15,20 59:21 63:22 65:13 66:17 67:5

166:22,23 171:10,25 174:17 177:1,14 193:16,16

193:17,17,18,25 194:3 203:22

equivocality 193:23

equivocals 468:7

ER 19:22 43:19 44:1,2 46:23 56:16,17,26 57:2,3,3

57:5,5,7,7,12,14,22 58:22 59:21,23 60:2,3,5,9,11

60:14 61:26 62:4,6,11,26 68:8 69:24 173:1,22

177:21 178:10 193:2,15 194:17,18,23,25 223:15

230:24 231:11,20 232:10,14,15 235:7,10,14

324:17 327:6,7,15,15 334:17,21 335:13,15

337:14 338:1,3,7,24 341:25,26 342:7,7,7 355:7

355:23 365:3,3 370:14,24 371:4,11,22 372:2

374:3 377:4,4,6,11,15,19,19 382:18,23 383:3,9

384:5 465:7,13 467:3,5,8 475:1,16,16 491:2,11

erode 202:5

erratic 56:10

error 106:6 249:13 392:5,9,10 435:24

errors 117:22 206:13 282:21 392:3,17,20 403:13

424:12,14

ERT 327:23

ERTA 56:17 61:12,16 173:2,10,22 177:26 192:23

230:26 231:9,12,16,16 233:7,11 235:1,7,9,15,17

235:20 236:1 324:9 327:24,26 328:10 338:1

340:19 344:20 345:23 346:12 380:20 474:26

475:13

ER-related 69:16

especially 108:20 110:5 113:14 195:15 214:9

224:14 226:12 239:1,14 241:17 262:3 269:10

275:2 340:20 343:26 358:25 367:6 382:1,11

412:12 418:15 435:10,11 437:19 456:16,17 465:2

473:26 485:25 486:4 500:10

ESR1 460:18

ESR2 460:20

essential 442:24

essentially 10:18 11:7 12:10 116:23 213:2 244:4

291:18 306:17 328:11 424:19 465:8,21 486:14

establish 17:18 22:10 28:11 187:5 307:7 359:9

370:21 481:11

established 56:3 71:5 165:18 166:24 173:25 184:2

184:5 258:10 306:11 356:18 358:15 359:2,5,19

401:18,19 419:11

establishing 244:5

esteemed 220:1

estimate 325:1 361:6 416:5 419:4

estimated 361:4

estimates 324:15

estimation 205:11

estradiol 50:23 57:13,18,23 58:5,17 59:5,11 60:19

60:25,26 61:3,5 64:9,13,18,21,24 65:1,11 75:4

95:24 169:22 174:1 239:2,5 240:4 241:16,16

295:20 337:17 351:6 354:13 373:15

estrogen 10:10 11:13 13:10 17:5 18:5,16 19:20

20:3,4,7 25:13 29:24 30:3,4 40:8 41:3,18 42:7

43:13,19,20,24 44:6,9,13,16 57:3,14,23,25 58:11

60:10 65:26 74:11 78:12 83:16 94:15 114:2,4

153:10 154:26 155:26 157:10 163:2 172:12,18,26

173:22 174:8,11,14 175:13,14,26 176:6,9,12,23

177:10,19 179:8 180:12,17 189:23,23 190:10

191:10 192:12 230:25 238:20 242:14 244:8 251:9

251:15,25 275:11 277:16 283:12 292:8 335:19,20

335:23 366:19 369:8,10 370:6 372:4,5 374:12
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459:10 461:15 463:15 464:19 467:26 468:15

474:26 478:14 479:25 481:17 483:25 486:3,5,9

486:13 487:9,10

estrogenic 17:7 20:1 26:26 27:17 29:26 46:21

69:21 75:2,13 77:15 83:12 154:25 192:11 196:17

233:4 243:11 279:14 311:3 459:12,15 460:17

467:12 474:1 478:4,6 485:11 487:9

estrogenicity 251:16 460:13

estrogens 43:11 47:5 65:24 175:7 239:12 465:6

466:24 478:4,17

estrogen-binding 474:1

estrogen-dependent 79:6

estrogen/androgen 46:14 372:8

estrone 240:4 241:8

estrous 249:18,18 252:5 353:7,7,19 386:23 388:1,4

459:19

estrus 77:25,25 78:19,20,20 459:17

et 28:15 122:11 212:23 214:2 255:14 261:15

263:17 290:16 291:6 385:24 387:21 389:20 397:3

427:22 434:3 437:8 458:8 460:21

Ethical 323:11

ethics 2:24 3:2,4 9:20

ethinyl 351:5

ethoxyquin 442:21

ethynyl 75:4

Europe 12:20 313:14

European 290:3

euthanizing 248:18,20

evaluate 7:25 12:3 28:21 54:5 59:2 94:25 128:2

135:13 141:4 153:12 154:12 156:11 164:17

166:17 168:22 169:14,21 172:17 176:20 178:14

178:19,21 181:16 186:23 197:4 214:16 284:11

292:2 297:16 302:20 313:24 314:5,21 347:11

348:11 353:19,26 375:8 406:20 409:15 415:2

419:16 461:24 463:11 469:5 476:2

evaluated 18:19 54:18,24 55:5 57:6 58:6,24,26

59:9,17,24 60:15 61:12 62:3 63:20 64:9 65:16

66:11,11 67:3,9 95:9 96:15,19,22 98:2 100:26

103:4,24 104:2 106:11,15 108:14 116:20 117:2,6

126:13 132:21 154:15,16 155:2 167:26 173:5

174:13 176:5 178:8 189:22 194:23 204:23 218:12

235:10 273:23 284:7 307:16 313:8,22 325:13

348:26 351:15 371:2 374:26 385:3 428:25 429:1

479:16 484:16

evaluates 60:4 74:10 158:7,14 461:7

evaluating 7:19 29:26 55:8 72:24 95:26 99:1 101:8

105:10 106:8 115:13 143:24 144:18 153:11 154:4

158:21,26 170:8 172:2,4 178:7 179:23 181:6

182:6 183:10 187:12 216:5,24 302:6 303:1

305:16 310:20 313:16 331:4 350:22 367:24

413:20 458:15 462:6 492:8

evaluation 11:3 28:23 29:4 31:6 34:23 55:9,13

56:22 94:8 95:10 101:20 103:1 106:4,19,21

107:18 126:25 159:21 164:4,20,22 165:10 166:8

168:1 172:11 202:13 224:13 235:16 254:8 283:6

283:8,11,15 290:5 291:17 292:10,22 294:11

298:15 304:17 306:23 311:17 348:5 365:24

381:11 386:10 414:6 428:8 456:18,19 460:15

461:21 468:8,17,24 480:2 483:6

evaluations 31:10 108:17 143:22 166:15 236:17

354:3 462:4 502:11,12

evenly 163:7

event 42:19 224:11 334:13 340:4 342:7,10 349:20

391:10 400:20 414:4 419:12,18 491:23

events 19:22 315:22 400:15 401:10,22 414:3,10,10

414:26,26 491:2

eventually 232:22 300:20 336:14,20

everybody 15:22 111:23 114:18 124:12 156:21,22

208:26 209:2 221:7,9 230:21 267:19 287:3 334:5

396:14,16 482:24 506:5

everybody's 207:22

evidence 18:20 23:21,24 24:19,22 39:9 43:2,5

47:25 48:4,7 49:22 89:4 107:1 131:20 154:17

156:13 159:21 160:15 182:5 183:9 184:18,19

187:3,20 190:14,19 194:8 195:16 202:11,12,24

203:9 206:12 228:10 236:17 244:6 245:23 255:16

256:1 260:8 266:1,5 269:2 300:21 331:12 332:24

336:20 367:26 368:3 371:20 373:1 376:21,24

389:26 391:25 392:1 410:6 416:23 428:9,21

430:16 432:25 461:21 462:10 468:17 481:24

484:3,7 490:7 495:22,26 497:7,7,19,24 499:6

501:17 502:20

evident 451:26

evolves 14:6

ex 427:6 486:14

exact 147:17 287:21 294:4 417:5

exactly 35:11 83:13 86:13,13 113:7 147:16 267:10

303:17 304:14 330:24 363:18 399:17 418:6 479:7

485:26

examination 426:25

examinations 430:22

examine 310:3 362:1

examined 48:21,22 80:3 271:21 273:6 310:1

examines 95:6 153:17 256:5,6

examining 434:3

example 23:25 25:14 34:18 55:1 58:2,10 62:16,22

64:19 73:15 104:12 118:25 119:11 125:17 127:6

130:6 131:18 138:1 141:10,18 146:23 149:7

154:19 160:26 161:11,22 162:3,12 164:26 168:15

169:7 171:6,11,22 173:10 176:11 177:16,23
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244:10 251:13 257:6 275:12 285:19 296:13,15

298:2,4,5 300:4 308:2 309:22 325:8,16 326:16

328:26 333:24 353:15 358:9 359:12 360:4 371:9

398:24 400:10 401:23 402:6 405:14 409:22,24

412:13 414:17,19 432:23 439:9 473:26 484:9

examples 30:13 56:8 105:12 106:1 132:19 152:26

160:20 164:11 170:21 171:19 172:22 204:1

290:11 478:8

exceed 76:5 78:5 80:19 87:18 89:2,24 152:13 349:3

exceeded 67:12 254:5 347:14 373:22 386:6

exceeding 253:20 254:7

excellence 219:21 503:5

excellent 320:22 477:1 485:24 503:8,16 504:4

exception 62:7 126:6 129:13 263:2 294:6,7 441:10

446:17

exceptions 403:18

excess 77:4 327:2 432:22 440:1

excessive 244:2 310:14 439:20

exchanged 447:19

excited 16:2

exclude 107:9

excluded 101:20 102:7 108:19 120:21

excluding 106:23 107:12

exclusion 195:25 408:12

excrement 264:8

Excuse 185:18

execute 26:20 28:24 178:15 356:13

executed 12:12 15:2 37:23 54:5 56:25 365:25

execution 94:9 393:5

exemplified 20:2 27:11

exemplify 22:3

exemplifying 24:21

exercise 54:7 200:9 201:4 377:17 460:9 484:4,7

exercises 268:19

exhibit 29:16 467:13

exhibited 180:15 464:11

exhibiting 107:19

exist 43:4 206:11 270:5 286:8 424:15

existent 23:18

existing 54:21 92:26 182:11 183:17 187:3,6,17

227:26 290:5 309:6 310:25 311:19,26 332:13

333:12 359:24 479:3

exists 52:9 309:21 334:16

exogenous 244:26

expand 18:5 39:11 377:21

expanded 10:11 485:13

expansion 17:26

expect 19:26 38:6 145:20 184:10 200:24 201:12

224:20 235:18 249:7 251:13 262:6 383:11 469:17

474:17

expectations 129:14 375:6

expected 59:7 60:1,24 61:10 63:12 65:7,18 67:1,12

68:25 73:5 75:15,22 105:6 106:11 110:2 116:21

116:26 124:17 155:6 163:8 184:15 203:23 204:19

205:1,6 327:19 373:23 375:7 383:6 419:5 431:16

467:6 497:20

expecting 409:9 479:6

expects 49:5

expense 306:21 440:14

expenses 274:7

expensive 144:2 224:14 254:24 274:22 276:17

322:21 383:13

experience 69:16,23,24,25 102:10 130:2 220:20

222:7 230:16 234:9 268:10 289:14 337:3 348:1

350:2 351:12 368:14 375:13 392:1 420:6 431:2

431:18 432:6 433:19 435:3,24 437:16 442:9

451:13 475:9 491:11 499:19 500:14 501:20

503:19

experienced 439:16 491:21

experiences 220:21 222:23 314:2

experiment 57:8,9,12,16,20 59:5 60:22 62:6 72:14

399:6 479:11

experimental 60:20 65:18 66:20 115:2 122:4

308:16 329:5 425:18 427:21 428:24 433:7

experiments 35:18 63:10 239:8 339:20,21 372:20

488:8

expert 6:10 26:11 195:11,26 220:2,3 231:7 300:7

382:19 431:2 504:11

expertise 4:8 5:4,11,16 6:2,20 8:3,4 16:2 17:20

445:13

experts 27:5

explain 72:14 147:12 193:18 194:24 320:2 323:2

329:16 426:24 430:26 435:9 449:7

explained 319:3 320:9

explanation 35:12 147:8 188:26 189:5 262:13

319:23 322:25

explicit 382:14 393:7 411:18

explicitly 17:13 118:13 315:11

explore 222:16 394:9 493:4

exploring 468:21

ExPonent 242:26

exposed 95:2 242:18 253:8,11 256:19 272:5 275:11

321:16 419:6 427:23 449:23

exposure 15:18 17:14,17 23:6,7 26:10 33:17,18

34:8,14,15 35:5 39:22 90:6 94:21,25 96:8,9 98:9

98:12,14,20 99:26 101:6 107:20 111:10 115:22

118:4,10 119:23 120:5 128:4,10,12,19,21,22,23

129:2 130:7 160:10 162:23 163:13 182:10 185:14

187:17 195:2,9 196:1 197:18 206:21,24 224:3,6
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226:12,13,15,19,20,22 227:4 228:2,3,11,11,13,16

242:17,19 243:16 256:7 276:5 277:17 308:25

309:11 311:2 312:12 317:15 332:14 361:11,23

399:16,18 406:4 409:21 449:19 453:2 455:19

exposures 224:2 228:4 411:20 432:3 448:17

exposure-based 227:25 399:16

express 105:4 461:2

expressed 57:4 364:19 409:21 449:5

expresses 64:8

expressing 107:15 428:11

expression 60:9 343:3 370:7 434:6 442:12,16,17

443:7 468:22

extend 2:15 3:21 490:21

extended 20:17 348:12

extending 379:21

extension 69:22

extensions 287:2

extensive 125:25 157:9 307:21 355:22 431:2,18

457:4

extensively 289:12 308:25 319:26 321:23 331:20

extent 85:8 127:4 153:12,14,16 181:24 182:2,12

281:8 331:10 389:12 390:4 391:7 416:17 425:3

427:5 479:15 483:6 485:3

external 8:24 27:13 142:26 243:19 261:14

extra 274:18

extract 446:7

extraction 371:12

extremely 39:17 120:8,9 242:13 256:22 307:21

331:14 505:7

ex-vivo 335:9 355:17,23

eye 27:7 135:23 314:5

eyes 250:1 313:12

E-value 193:24

E2 234:2,4 239:17 339:26 340:26 346:17

F

f 19:18

Faber 427:23 428:4

fabric 427:17

FACA 11:1

face 339:22 344:15

faced 184:7,8 435:26

faces 8:17

facilitate 98:8 155:20 289:26

facilities 61:6 232:10 306:18

facility 349:16

fact 27:23 36:19 132:13 188:16 192:2 194:15

205:20 225:21 228:18 232:5 234:15 235:19 242:6

242:10 247:20 248:2 253:8 255:18 259:6 272:22

275:19 280:26 289:1 295:7 299:21 306:14 315:7

316:3 321:23 322:9 324:4 377:11 391:22 400:26

408:24 436:9 441:2 450:9 460:13

factor 109:13 113:13 124:2 263:1 266:19 268:7

270:5

factored 484:17

factors 111:20 118:8 121:19,23 135:4 136:17

144:6 214:6,7,12 262:23 268:17 303:18 390:25

397:14 409:14 431:8

fail 467:13

failed 72:13,14 193:19 249:24

failing 392:6,12

fails 319:10

failure 449:19 450:8 453:9

fair 195:15 304:15 388:25 486:15

fairly 35:22 46:22 52:17 120:12 125:6 133:11

157:14 161:17 254:25 299:2 396:10 440:19

451:16 452:25,26 455:23 457:4 466:25 482:20

485:15

fall 70:21 71:7 339:19 340:10 341:5 392:1

fallen 273:12

false 21:12,13 28:1,1 29:14 41:7 42:17 154:1,2

198:16,16,18 225:4,5,6 232:18 234:18 235:4

236:22 240:24 245:13 339:25 340:6,17 341:24

367:4 390:3 392:7 484:25 492:26

familiar 4:9 43:8 85:15 303:9 415:17 456:14

family 134:25 434:6

far 15:25 71:20 115:6 129:23 138:20 255:23

273:22,26 295:22 296:22 297:1 322:20,21 331:2

343:7 355:20 383:5 424:1 425:13 440:19 450:13

fashion 42:16 202:24

fast 152:12 158:25 419:8

faster 52:12 289:26 293:9 504:13

fat 263:21

Fate 93:25 114:11,19

fathead 94:5,22 100:24 274:12

favor 268:11

favorite 89:13

FDA 4:24 210:19

feature 452:18,20

features 19:12 453:4 455:25

feces 439:24

fecundity 95:15 96:1,6 97:16,20 98:11,12,13,18,21

99:1,2,5 102:4 103:11 104:12,18 165:6 168:24

173:14 175:26 176:14 244:10 273:2,3 275:7,9

276:24 277:3 278:18 279:19 291:21,23 292:4

335:12

Federal 2:10,14,26 9:18 10:5 16:26 17:21

feed 121:21 122:8,16,26 123:2 215:5 259:24

263:18,25 266:17 336:15 425:23

feedback 8:10 11:24 12:11 13:4,12,13,16 36:2,7,12

37:6,22 39:15 40:7 49:6 86:20 138:24,26 155:20
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feeding 103:3 121:7 123:4 214:25 215:3 264:6

265:17 407:18,24 425:20 426:3 442:4,5,16

feeds 13:23 342:23

feel 17:24 39:5 137:5,9 186:12 191:19 194:18

261:5 279:6 283:18 306:24 313:17 345:4 394:23

426:18 431:3 437:20

feeling 398:1 459:4

feels 371:18 374:5 376:5,7,23 428:8,20 429:8,23

433:3

fell 59:26 61:9 66:26 67:12 68:21,23 70:16 93:2

344:17 385:1 391:23

Fellow 231:2

felt 28:17 36:6 52:18 93:4 217:14 293:3 303:16,18

365:22 371:10 398:7 416:18 484:7

female 20:2 29:25 42:13 44:8 45:24 46:13,24 47:14

55:2 74:8,16,16 77:11,12,13,17 80:17 81:2 82:26

83:2,4,15,19 87:19 93:8 98:22 99:3 101:23

104:18,20,24 107:15 115:19 124:15 155:9 157:16

157:22 158:5,19 161:3,6,12,17 162:16,18,21

163:11 165:3,5,6,8 173:3,14,16 174:6 175:15,17

177:1,3,4,7,7 180:16 188:1,2 189:4,10,11,19

190:15,25,26 191:4 246:10,26 248:1,9,15 249:20

250:3,14 251:17,20 255:3 273:7 275:12 290:21

295:18 348:3 353:4 354:14 384:24 385:18 386:15

386:16 387:5 388:22 394:4 395:11 408:7 445:14

456:11 459:16,20 460:2,7 461:22 471:23 472:2,4

488:18

females 74:21 94:23 99:13 101:11,13 114:2 161:9

249:25 250:10 276:3 295:20 350:11 394:21,23

408:11 412:12,14

feminization 411:21

fertility 96:5 97:17 99:9 176:13 276:25 277:3

279:19 309:13

fertilization 95:15 99:5,5 116:3 168:24 176:1

445:20

fertilized 99:8 488:19

FETAX 52:9,21,21 53:1,2 431:20

fewer 30:19 197:22 258:7 490:21

FFDCA 14:14 17:9,11,17

fiber 270:15

fibers 434:8

field 261:9 265:16 424:10,10 499:7

FIFRA 1:1,8 2:6,18 3:22 8:19 154:3 157:23 185:12

367:21 377:2 443:11 505:21,23

Fifteen 100:2 155:15

fifty-some 36:4 37:9,22

figure 90:24 96:21 97:9,10 101:25 103:12,21,26

104:7 105:14,22 118:15 128:25 131:1 148:1,22

158:23 260:14 268:8 301:12 344:9 453:9

figured 36:1 201:18

figures 205:21

file 133:1

filed 3:1 318:15

fill 311:26 498:12

filled 283:4

filling 283:25

filter 26:7,9,10,10

filters 26:2,3 439:14

final 25:21 50:9 75:9,10 78:26 81:15 83:16 139:3

171:22 172:12 207:14 212:10 219:12 227:19

229:9 247:12 314:19 347:16

finalize 138:24

finalized 330:26

finalizing 139:2

finally 55:21 56:24 65:21 67:14 74:4 94:11 104:15

105:7 106:25 164:12 166:10 172:6 174:8 178:8

221:25 225:18 228:19 233:2 326:25 328:22

332:20 350:6 398:16 400:21 444:15 463:11

495:10 496:23

financial 3:1 308:15

find 88:8 90:12 138:19 221:16 224:24 227:6

258:23,25 267:3 275:14 279:1 280:25 282:16

288:4 290:14 424:18 457:25

finders 274:13 278:3

finding 17:15 49:22 92:22,26 146:16 157:25 218:6

250:16 291:11 355:12 400:5 447:4 449:14,21,22

450:5 492:26 500:13,17

findings 27:14 87:25 156:7 159:9 186:24 227:14

227:16 244:9 250:21 251:24 264:15 276:8 349:6

373:8 375:5 395:19 446:18,20,23 449:25

fine 113:13 220:23 364:1 389:7 407:9,10 441:11,17

fine-tuned 437:19

finish 231:19 261:22 343:22 394:10

finished 343:23 345:18

finishing 261:20

firm 341:15

firmly 303:7

first 6:24 11:11 12:10 13:13 14:5 17:25 34:1 39:16

43:19 53:10,18 54:13 56:20 74:13 75:26 77:25

78:19 94:7 97:21 115:1,8 146:12 150:5 155:14

160:26 164:7 172:16 185:21 195:8 206:23 207:25

208:3 209:1 219:20 220:16 222:20 223:10 226:22

228:2,23 229:14,22 230:20 246:11 255:23 280:23

291:13 299:14 300:14 301:12 302:6,12,23 317:26

323:22 328:8 331:25 332:9 337:7 350:10,18

353:7,14 361:22 362:18 365:23 369:2 376:16

385:16 388:18 424:3 425:14 428:17 436:26 437:7

437:19,22 442:5 447:17 455:2 457:26 458:7

459:9,17 470:15 478:13 485:1 487:18 496:19
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504:17

first-pass 163:14

fiscal 320:16

fish 5:17 6:1,3,7,15 20:2,24 29:22,25 31:20 42:2,14

43:16 44:12,13,14,14 45:12,14 46:13,24 47:13

53:25 55:4 56:9 93:23 94:2,20,22 95:1 99:12,14

101:9,10,14,17,19,22,26 102:4,9,11 103:5 104:20

104:22,24,25 105:3,3,5 106:9,24 107:12,14,17

108:6,10,11,15,22,25 111:6,10 112:4 115:21

165:7,7,25 170:14,16 172:8 173:3,15 174:9,24,26

175:25 177:1,4,8,22 178:3 181:13 211:14,16

225:13 244:10 265:20 274:16,19 275:14,21

277:25 280:8 291:10 324:20 328:16 329:2 335:11

357:12 360:13,23 361:9,13,14 383:25 395:11

396:6 398:17 399:19 406:2 407:14 408:1,3

410:21 411:21,21 412:12 415:19,20,21 416:6

417:1,4,11,13 420:4 424:9,10 427:13 432:20,25

442:10,11,15,17 444:19 455:16,18 458:1 461:10

464:1,6 467:2 471:3,10,15 472:12 489:5,21

496:12

Fishery 458:2

fishmeal 263:23

fit 66:17 139:26 251:12 367:21 407:6 494:5

fitness 94:13 95:16

fits 49:13 91:9 190:16 410:24

five 3:15 19:1 46:22 47:4 56:15,20 67:4 76:8 93:7

111:3 119:25 126:16 137:3 148:8 166:1 169:6,8

231:11 235:9 236:20,21 245:17 255:18 337:4

343:21 366:3,13,14 372:9 425:22 440:9 448:17

459:22 461:10 471:11

fix 257:7

fixed 257:4 258:6 261:25 475:14

flag 236:7

flagged 170:7

flagging 289:2

flakes 442:17

flattened 450:24

flavor 298:20

flaw 319:2

flawed 320:5

flex 121:14

flexibility 282:19 336:16

flexure 137:11,12

flexures 431:24 432:2

flow 110:12 214:24 361:1

flows 10:5

flow-rate 215:1

flow-thru 218:23

fluctuate 128:9

flushing 498:22

fly 298:19

focus 7:9 16:13 17:6 25:12 26:16,18,22 29:5 38:21

40:10 48:5 69:21 163:9 175:7 182:16 187:14

193:11 221:26 237:18 258:19 288:19 292:7 301:7

369:10 372:14 435:1

focused 7:18 21:8 41:18 69:19,20 182:5 226:23

263:16 396:13 489:16

focuses 257:14

focusing 15:1 16:8 17:7 25:15 39:4 265:26 266:1

fold 241:26 346:25

folks 92:15 200:8 222:21 227:15,17 228:12 277:24

278:7 422:7 503:12

follicle 76:15 452:24

follicles 452:1,2,2

follicular 125:8,15 141:20 143:4 446:25 447:5

449:12 450:21 451:11 455:7

follow 35:14 36:14 38:5 39:7 53:25 70:2 88:7 90:5

91:5 107:22,23 108:6 110:10 113:25 134:8 136:3

142:5 143:8 144:11 146:5 150:13 183:20 185:18

185:21,22 197:25 202:8 264:10 281:22,24,24

282:9 338:18 352:23 382:11

followed 55:8 67:25 97:16 174:21 281:4 462:2

493:4

following 9:26 31:14 35:11 78:18 153:4 154:14

159:4 172:14,15 212:13 213:5 231:3 282:8,8

310:23 323:23 337:8 368:21 445:11 453:2 491:20

follows 114:25 121:19

followthrough 357:5

follow-up 33:6 70:3 265:25 345:10 351:18 364:16

412:10 413:16 423:10 469:25 475:19 493:12

food 10:5,6 16:26 33:20 34:12 74:25 77:2 79:8

81:19 83:11 93:13 123:6 265:15,20 270:11,12,15

272:20 277:11 426:23 439:24 440:1,1,13 442:6,9

442:21,21 443:3 444:4,5,9

foods 270:9

food-use 34:11

food-used 14:15

foot 296:10

footnote 160:7 162:5 174:22,26

footnoted 160:3

footnotes 171:4 247:16

fore 259:4

forecasting 202:10

forelimb 116:18

foresee 346:23 401:11

foresight 495:15

forever 286:15

forgive 385:14

form 70:25 176:15 213:2 333:11 375:25 386:2

500:22
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formal 363:21

formally 282:26 362:16

format 172:15 231:26 240:13 367:5

formation 434:7,11

formed 17:24 367:9

former 499:10

forms 19:3 498:13

formulate 480:2

formulating 501:21

formulation 214:21 348:12 351:26 352:1

Fort 427:22

forth 8:4 206:20 312:13 363:21 368:9

forthcoming 48:4

forum 229:21 230:3,4 243:2 252:20 271:2,10

282:11 287:16 288:17 290:14 296:3 305:21

312:14 314:11 316:11 317:1 448:21

forward 8:10,21 9:13,15 11:6,26 12:9 14:21 16:3

16:22 18:4,24 19:4 20:14,23 22:2,5 23:8,13,25,26

24:20,22,25,26 25:17,23 26:13 27:2 34:21 36:10

37:6 38:2 39:8,11 144:10 194:8 196:6 197:16

198:23 202:13,19,21 222:5 282:25 283:3 286:6

288:4 292:3,6 307:26 310:23 313:1 314:1,21,26

336:13,17 343:26 351:1 375:14 378:24 383:1

384:14 390:1 392:11,12 425:9 436:3 446:12

461:19 480:23 490:1,8 495:6,6,20 496:20,25

498:2,4 499:8,15 500:8,15,25,26,26 501:15,19,21

502:8,25 503:21

forwards 28:2

found 16:23 17:15 29:10,15,24 87:25 164:15

166:11 224:23 265:6 291:10 292:19 385:22 387:9

431:22 432:1 433:17 442:14,18,21 450:24 457:26

458:5 475:23

foundation 198:10

founded 337:7

founding 18:12 198:10

four 20:21 22:17 33:17 34:14,15 47:2,2,7 55:4

60:24 74:7 76:26 79:6,10 92:23 94:23 95:5 97:11

98:1,21,23 99:9 101:1,11 103:17,25 116:9 119:4

162:3,13 166:6 168:3,8,9,12,12,13 169:6 170:18

173:1,20 217:10 222:19 254:6 271:21 298:16

326:23 341:18 351:22 381:2 395:5 448:5 460:3

462:8 464:9 467:5 469:3

fourth 99:11 118:25 119:1

four-fold 342:13 347:2,2 475:7

four-page 229:13

fraction 379:19

fractions 379:16,23 490:16

frame 473:7

framework 11:6,6,20 12:2 18:13 31:17 158:20,25

198:23 199:11 200:17 290:4 333:5,7,14,15

363:21 466:7 504:6

frameworks 290:4,8

frank 11:16

fraught 465:11

Fred 2:1,9 3:25 9:17,22 151:19,21 207:21,22 423:6

498:17 501:1 503:11 504:16,17

free 126:11 338:14 454:11

freedom 338:19 344:21

frequency 100:6 264:1 405:12 413:12

frequently 126:5 386:5

freshwater 457:12

friends 278:9

frog 31:21 115:13,23 128:24 129:20 161:13,23

162:18 215:8 256:7 259:22 274:11 277:24 294:6

431:19 434:6 439:6 469:2,3 496:15,17

froglet 488:23

frogs 6:15 42:2 134:21 158:22 260:18 285:19

292:16 321:9 362:1,8 416:6 438:12,14,18 439:25

445:14

front 318:24

frontend 432:16

FSH 86:22,23 87:2,7 295:23 388:12

FSTRA 94:3,5,12,21 95:9 96:4,18 101:8,10 102:13

102:26 103:25 106:10 114:22 126:25 166:10

167:8,12,21 168:23 171:21 180:14,16 198:1

241:23 271:15 275:26 277:2 279:12 291:7,12,25

408:26 459:22 460:1,7 461:17 467:19 481:22

484:12,18 485:3

fulfill 311:6

fulfilled 312:5

fulfills 41:4

full 66:7 92:13 108:12 185:13 190:6,6 204:23

206:7 256:8 297:2 333:24 349:24 354:7 357:8

372:24 402:11 430:8 448:19 475:6 496:22

fully 115:24 264:23 290:12 326:7 332:18 378:21

439:7 455:23 481:15

fun 276:17

function 21:14 47:11 49:10 77:13 79:23 111:15

231:2 309:2 385:20 398:10 411:4 427:4,10,12

484:24 489:3

functional 51:1 473:16 475:1

functionally 231:13 232:24 235:12 313:25 434:12

451:24

functioning 205:1 473:17

functions 427:6

fundamental 8:26 23:3 30:9

fungicides 32:25 37:14

funny 281:26

further 104:25 105:6 112:14 144:10 236:25 257:3

259:14 290:23 291:19 306:22 307:22 308:8
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311:11 314:24 319:8 333:1 357:26 358:15 359:8

359:25 362:21 375:24 427:9 428:14 429:19 430:1

430:12 431:7 433:8 445:8 450:15 454:6,15,18

481:10 490:23 493:1 494:13,20

Furthermore 98:8 369:11

future 16:15 38:6 39:14 133:18 156:13 159:22

215:10 225:25 232:1 236:4 310:23 333:17 453:18

499:16 501:22

fuzzy 441:5

G

G 170:1,10 175:2 177:23 190:5

gain 102:10 130:2 388:13 419:14

gained 222:7 353:5 375:13

Gary 291:22

gatekeeper 225:9,12 228:7,20 229:3 293:7 294:14

311:8 321:12

gather 10:15 290:26

gathered 477:19

gauged 168:21

gavage 83:23 351:5 352:12 385:23 485:16,22,24,25

gavaging 418:16

geared 341:24

gears 124:13

gem 250:11

gender 102:6 103:25 250:20 252:9 325:6

gene 50:16 60:6 240:15,16 335:8 343:2 370:7

372:4 382:18,23 443:7 468:22

general 3:3 4:17 32:1 41:10,26 46:4 51:3 52:6

56:22 59:25 60:7 82:16 94:10 101:7 104:19

114:25 115:2 117:24 125:20 129:15 136:19,23

138:15 142:20 152:18 163:20 175:5 179:21

182:25 186:15 231:20 239:7,25 285:20 296:17

322:21 356:22 364:2,4 370:26 372:22 376:5

386:9 389:23 398:1 427:4,21 428:7,11 431:24

432:2,10,19 434:20 447:24 461:1 466:24,26

487:19 493:24

generalized 159:8

generally 37:23 46:21 60:2 64:4,12 65:16 67:1,9

79:7 82:15 100:26 101:12 103:19 106:18 108:21

110:6,25 112:25 117:19 120:21 129:14,26 137:25

140:7 213:17 214:21 216:11 218:9 232:17 243:15

245:8 286:1 294:23 309:16 315:22 318:21 372:16

373:9,20 374:20 376:7 403:8 446:7,19 466:25

484:1

generate 416:20

generated 216:23 230:6 238:9 290:7 317:7

generation 310:1 320:5

generations 310:5 438:18

genetic 82:25 121:23 135:4 295:4 431:5

genetically 438:19

genetics 431:10

gene-based 50:20

genially 106:10

genistein 250:5

genome 399:25

gentleman 423:1

Geoff 5:1 87:12

GEOFFREY 5:1 33:6 35:8 68:17,26 87:12,21 88:2

88:5 90:5,11,23 91:5 109:19 110:10,14,21 111:8

111:12 112:5 113:2 139:14 140:15 148:22 204:5

205:7 210:20 270:7 277:23 278:5,11 303:5,20

345:9,13,19 363:1,24 379:14 404:13 412:23

416:14 420:3 442:1 446:6 457:5,9 463:25 470:25

475:19 482:7 490:14 495:12

Geological 5:15 211:7

germane 200:2

getting 9:4,21 12:11 13:12 36:11 39:15 71:20

72:23 90:14 111:16 148:23 190:24 200:19 203:5

203:10 261:26 276:16 284:15 300:20 327:21

338:23 381:15 383:3 387:19,24 409:7 411:25

412:6 416:12 432:17 440:20 443:13 444:2 456:13

466:2 472:19 479:22 480:20 496:3 498:12 500:21

501:2,2,3 505:7

get-go 445:15

gill 361:14

give 2:3 13:6,13,17 15:11,17 16:15 19:13 25:7

26:25 27:12 29:11 73:15 89:14,22 92:21 115:1

116:15 196:8 200:5 205:11 213:6 220:21 222:13

248:6 278:12 296:13,15 298:5,20 301:22 306:5

328:11 343:8 353:9 373:1 389:13,13 393:22

410:13 413:15 414:16,22 418:18 423:22 462:19

462:25 472:14 479:18 484:1 487:22 490:10

491:19 497:10 504:17,23

given 40:18 89:15 100:6 119:18 123:10,11,15

124:4 143:20 163:19 184:10 185:23 193:24

196:16,17 203:25 213:18 214:25 223:1,2 236:25

267:4,15 305:16 329:23 352:4 357:20 360:2

364:10 391:1,4 393:16,20 402:15 416:3 417:2

425:18 429:13 430:2 446:23 447:8 459:5,9

462:20 477:11 481:7 483:22 493:20,20

gives 51:5 118:15 204:9 246:16 276:6 316:14

326:19 405:21 428:2 451:5 472:8,15 493:23

502:7

giving 94:6 164:6 190:26 191:3,8 206:3 221:26

271:5 297:21 298:3 300:24 328:18 343:18 390:3

406:10 442:26 475:22 485:26 498:8,23

glad 195:5,7 227:17

gland 52:23 268:24 414:24 446:18 451:24,25,26

461:24

glands 76:12
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glans 76:12

gleaned 45:17

global 309:15 310:19 314:20

globally 335:20

globulin 401:2

gloss 256:12

GLP 285:11,14,15 343:10

glycogen 400:23

GnRH 388:9,11 394:8

go 2:2,11 4:7 9:20 11:3,26 13:1,3 14:21 31:22

34:22 35:2,25 36:5,8,10 37:6,26 51:7 53:9,10

71:25 73:21 74:6 87:14 89:3,26 90:18 91:7,24

114:9 136:20 141:18 143:3 151:14,23 163:3

172:9 183:24 185:19,21 186:20 196:1,4 199:25

208:13 210:5 212:8 217:15 221:5 225:16 233:7

233:14,17 237:15,22 239:10 241:21 250:22

253:22 261:25 270:19 274:18 277:10 279:1,6

283:3 290:16,22 292:3 297:26 298:26 304:2

305:7 307:14 319:18 321:8 323:20 330:13 333:26

334:6,15 336:9 339:21,21 341:12 343:22 356:9

363:10 364:24 380:13 383:1,5,14 384:14,20

386:16 389:5 390:9 393:25 395:7 398:1,17 399:5

404:24 410:2,10 413:2 416:19 419:15 420:18

425:5,8 437:24 441:1,2,17 445:15,19,20,26

452:11 454:24 457:3,16,22 466:11,15 475:26

476:10 482:13,19 484:24 486:20 487:16 491:26

492:4 494:8,14 496:21 498:14,24 499:22

goal 7:12 54:4,7 110:17 153:12 297:8 319:11 335:1

goals 54:3

goes 134:11 137:11 186:8 198:17 201:1,1 202:2

272:12 291:18 335:6 343:7 356:1 480:18 490:1

501:3

going 2:2 11:6 23:8 24:19 31:16,18,20 34:22 35:1

36:20 37:15,19 38:1,18 40:4,26 42:18,25 43:15

44:17 46:25 47:9 53:10 56:14 61:25 70:5,9,11

71:10 72:8 73:25 90:16 92:8,14 111:15 112:3

122:25 135:25 137:6,8 140:10 142:16 148:6

151:10 159:10 160:19 170:21 178:12 190:7 193:3

195:24,25 196:21 203:4,4,15 207:7,8 212:13

220:10,25 221:9,13 224:25 225:16,24 229:22

230:13,13,17,23 231:10 233:7 242:13 248:7

250:22 252:9 256:11 258:19 262:1,15 267:25

270:2 271:14 275:24 278:8 279:6,23 280:5

281:26 284:26 288:2,19 289:23 292:2,6 293:12

294:18 295:9 296:15 298:19,21 299:14 300:9

303:7 312:24 313:12 316:20,22,23 317:2 323:15

330:10 331:5,6 332:11 334:5,5,15 336:5,9,14

337:26 338:7,8,12 339:4,14,20,24,26 340:1,12,13

341:5,21 342:3,8,9,10,19 344:5,15 346:10,22

370:1 374:7,22 378:10 381:14,14 385:13,24

386:26 387:3 388:21 389:11 391:24 392:15

394:24 396:10,13 397:19,22,24 398:8,14,16,21

399:1,18 400:3 401:4,6,7 404:22 407:8,14,26

408:2,24 409:13,18 410:2,5,23 411:8,9 412:4

413:19 424:18 428:2 447:20 449:9 457:19 459:3

465:4 479:10 486:10 489:10 494:22 496:3,18,26

497:23 498:5 505:9

gold 238:21 244:4 245:15 313:22 337:15

golden 263:14

gonad 412:19

gonadal 77:16 95:19,19 104:18 168:26 176:2

335:10 394:11

gonadotrophic 295:4

gonadotropin 389:14

gonado-somatic 176:2

good 2:5 3:25 4:19 5:8,22 6:12,18 8:15 9:14 12:7

15:21 30:25 32:15 34:2 35:9,20 36:7 37:19,26

39:25 46:3 47:8 49:14,15 52:22 53:16 92:1 94:1

108:1,3 112:19 113:1 133:19 149:7 152:22 164:3

172:10 203:7 209:2 210:1,10 212:12 229:5,18,26

230:18,21 237:11 242:25 246:6 252:16 258:17

275:22 276:1,3,4,6 277:15,15,17 278:26,26 282:6

282:18 283:2 284:18 287:10 292:19 295:25

305:14 317:1 318:7 323:9 333:13 334:22 336:25

343:5 345:5 355:18 362:8 380:2 383:11 385:24

386:3 390:12 394:23 400:10 404:23 410:9 415:11

420:5,8 422:1 423:9 436:22 444:4,9 445:14 452:5

460:17 462:25 463:1,12,18 464:11 465:16,23

469:14 489:17 490:10 491:13 493:16 496:10,16

497:15

GOP 352:1

gosh 436:24

gotten 451:15

government 220:8 221:11,22

grab 227:20

grade 141:20 142:13 449:1

graded 142:17

grades 142:11 446:19

grading 132:20 133:6 141:7 143:6 454:1,2

gradings 143:3

gradual 419:17 422:15

Grainger 437:15 445:23

Grainger's 440:17

gram 250:5 417:13

grams 417:14

grand 50:3

graph 34:21 59:10 63:14 64:18 65:2 104:17

gratitude 499:10

gray 59:11,13 65:4 224:8 225:11 228:8,21 291:5

311:9
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great 12:2 91:12 92:7,15 113:6 124:8 134:14

182:22 194:18 196:4 226:4 228:7 248:6 249:13

249:21,26 271:22 275:10 276:2 284:12 288:2

330:16 343:9 350:13 368:15 383:4 396:19 416:11

422:17 423:4 480:7 482:17 494:18,25 495:3

500:23 505:16 506:5

greater 30:23 97:22 99:3,12,17,26 100:3 105:17,25

111:5 120:21 139:19 166:25 235:15,25 236:18,26

254:6 257:11,26 349:6 350:20,24 359:16 371:8

376:10 406:15 417:15 430:14 431:23 448:4

491:21 497:10

greatest 244:12

greatly 9:11 286:9 502:14

green 6:18,19 52:5,6 82:23,24 83:4,8 96:24 104:15

127:9 132:16,17,23 133:7,9,19,23 134:2,14,17

135:10 136:26 139:22 147:1,2 148:16,20 182:23

182:24 183:20 199:15,16 212:1,1,21 217:23,24

218:17 258:4,5 263:7,8,24 264:19 265:17,23

285:6,7,17 303:24,25 304:10,18,21 321:4,5,20

322:4 353:24,25 354:10,19 363:4,5 417:18,19

418:22,23 422:18,19 434:23,24 441:25 442:2

443:15,26 444:3 445:10,11 450:14,15 454:14,15

456:26 457:1 487:26 488:1 500:9

Green's 202:9

Greg 31:7,18 53:17 153:9 172:9,11 380:14

Gregory 53:14,16 68:20 71:24 72:4,11,26 172:10

194:2 203:24 380:14 381:1,7,19,24 382:7 505:10

grew 83:15 265:15

grey 118:21,21

grief 286:12

Grim 261:15

gross 158:14 330:26 430:24 446:17

group 18:12 35:10,24 36:1 37:12,13 42:17 54:13

96:10 98:14,15 101:19 103:3,23 105:16,19

106:19 109:9,15,17 113:13 134:22 218:2,19

220:6 221:8,10,12,20 222:9 225:23,23 230:8

244:11 245:9,9 288:17 290:7 303:23 330:7

335:24,26 336:1 337:11 363:6 381:2 399:3

425:18 438:8 447:21 478:15,17,21 481:15

grouped 158:12

groupie 281:4

groups 11:24 41:5 94:23,24 95:1,1 105:17,23

152:16 231:7 274:19 285:9 361:23 387:9 427:24

489:16

group's 222:12

grow 136:12 253:14 259:25 265:4 266:9 415:22

436:24

growing 136:14 419:23 439:25 441:5

grows 260:22

growth 20:24 40:23 95:14 100:24 110:9 117:9

120:22 126:7 127:25 130:23 217:16 257:24

260:15,25 261:3 266:4,11 309:1 362:4 395:17

401:20,25,26 402:4,7,14,16,19,21,24,25 404:10

404:10 405:10 408:18,19 410:23 412:12,17,18

413:11 415:14,21,23 416:7 417:19,23,25 418:4,8

419:7 433:26 439:21

GSI 95:19 102:7 104:20 168:25 275:9 409:22

412:15

GSIs 412:19

guess 33:11 72:5 131:12 149:5 183:21 189:6

194:11 219:14 293:23 303:6 327:18 376:25

381:24 418:20 466:4 497:12

guidance 67:26 88:24 89:14,17 106:16 110:23,25

128:15 129:25 130:4,6 133:4,21 134:9,10 142:9

142:11 143:7 144:6 146:22 165:11 232:16 248:6

254:13 261:15 272:19 282:13 357:5 358:10 359:6

360:7 364:3,4 373:1 375:18,21,21,24 391:1

395:13 396:1 405:24,25 413:8,15 426:9 429:13

430:4 433:18 446:20 447:8 502:3,9

guide 60:21 140:20,23 141:2,5,10,16 154:4 311:21

guideline 50:4,10 54:22 55:15 61:7,19 96:4 97:7

98:18 99:6,20 100:1,14,17,26 101:13 114:23,23

115:5 116:7,12 117:14 119:3 120:17 123:3,5,13

124:26 126:2 127:12 128:14 129:25 132:9 136:2

143:16 146:10,11 148:11 149:22 150:12,15

158:23 162:4,9 165:26 214:17,26 215:10 232:7

232:11,13,15 233:12 236:1 245:3,6,13 250:4

268:10 272:24 273:15 276:21,26 277:3 281:15,19

281:23,23,24,25 282:9 283:14,15,18 298:7,9

300:5 327:19,25 342:23,24 356:18 363:20 365:25

370:19 371:6 372:10,12,14 390:19,21 425:24

426:3 449:1 453:18 459:18

guidelines 28:26 54:7 55:11 68:1 70:4 72:21 77:8

79:18 81:26 83:14,26 84:12 87:17 88:3,19,26

91:14,17,20 99:24 102:15,17 104:21 110:11,14

117:11 120:14 124:17 126:19,26 128:20 129:17

135:17,23 146:14 152:12 158:20 165:12,19 167:2

173:20,25 174:16 203:20 227:22 236:8 246:25

257:22 267:19,19 277:2 281:2,6,9,14 282:2,8,20

282:22,26 283:3,9 284:2,13,13,22,26 285:1,22

286:1 301:6 314:8 319:18 322:1 333:12 337:8

338:5,13,23,26 339:8 343:16 344:7 345:16

349:17 350:7,11,15,26 352:10 354:7 356:13,18

365:3 372:15 378:25 384:26 391:23 395:10

404:22 405:3 418:6 448:1,15,17 453:26

guideline-recommended 385:2

guides 307:25

gun 193:7 269:5 454:22

gut 352:11 431:25 432:1

guy 386:12 387:2 389:2 397:8
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guys 82:8 201:16 212:9 229:14 276:21 287:5 364:7

390:10 479:17 480:7 486:4,7 494:26

H

haemorrhaging 122:2

hair 465:15

half 29:4 66:20,21 87:20 102:1,21 202:9 239:26

357:7 404:23 488:14,21

halfway 89:3

half-log 67:26 68:2,5 406:14,23 410:11

Hamner 335:18

hand 28:6 95:11,12 137:2 140:24,24 163:15 401:26

406:17 439:1 463:18 467:4 487:8

handcuffed 375:15

handful 160:20

handle 260:11 295:25 300:23 344:10 440:20

447:17

handouts 219:22

handpicked 201:25

hands 203:3

hanging 15:4

happen 69:7 72:10 117:23 208:22 285:25 294:12

328:15 385:24 405:15 436:21 439:9 472:19

493:14

happened 192:10 221:16 253:23 450:9

happening 264:6 292:16 310:3 315:20 389:17,21

440:6,20 486:2

happens 240:19,26 248:18 253:26 308:6 338:9

341:1 437:5 475:15 480:17

happenstance 193:1

happy 130:12 293:14 444:17 495:10

hard 8:4 9:3,4,5,14 12:19 15:9 105:26 140:9

152:12 158:25 170:19 243:18 255:10 264:3

318:13 341:2 372:23,26 494:17,19 500:6,20

504:23

hardness 438:25

hardy 489:7

Harland 437:17

harm 14:15

harmonize 232:10

harmonized 13:1

hatch 445:22

hazard 16:19,20 223:11,26 224:4 226:20 315:13

hazards 307:25 330:10

hazard-based 228:17,18

HCG 389:13 394:11

head 12:16 38:8 113:12,17 264:3

headed 16:15 25:8

heading 477:18

health 5:3,24 6:20 16:18,24 18:1 22:24 53:14,17

118:6 185:13 210:22 211:12 220:7 226:17 305:19

305:25 310:15 361:24 362:1,5 472:16 487:4,5

healthy 89:16 260:3,26 488:18 500:2

hear 52:7 121:14 139:16 219:19 256:12 296:17

497:17

heard 220:17 227:21 231:6 233:2,24 242:5 252:21

253:19 254:9 255:8 256:20,23 268:1 271:19

273:13 277:23 278:13 281:17 289:3,10 291:4

292:14 296:10,16 297:26 298:3 300:6,12 301:24

303:2 304:16 329:12 331:20 332:10 348:16

364:11 379:16 405:26 416:16 475:5 495:14,20

497:16 501:7

hearing 270:26 325:26 328:23 331:14

heart 183:21

heavily 290:5

HED 73:14

height 125:16 139:6 143:13 144:8 402:11 447:6

450:20,22 451:11 453:5

HeLa 235:22 236:24 341:20,23

HeLa-assay 231:12,14

HeLa-derived 60:8

HeLa-test 233:12

held 57:23 190:19

Hello 156:22 164:3 316:25

help 24:24 51:7 52:12 69:5,6 86:25 98:8 110:4,17

110:19 112:17 134:12 187:26 195:14,26 196:6,10

205:15 215:9 244:7 276:22 277:18 287:1 300:14

300:16,19 301:4,6,7,10 329:16 331:10 332:8

334:7 336:7 341:14 349:21 364:11 368:15 380:23

383:16 394:26 406:15,17,18 408:2 444:13 480:2

495:5,5 498:15 499:21 500:8 502:20 503:20

helped 68:3

helpful 69:13 91:8 118:12 150:19 195:19 215:18

249:8 258:26 276:25 277:9,12 282:17 343:26

344:4 349:10,19 383:18,21 393:6 413:6 420:12

423:24 425:4 430:23 435:6 460:14 479:3,18,23

480:4 492:18 500:11 501:14

helping 69:6 205:9 495:18 505:8

helps 13:1 111:17 334:6 411:10

hepatic 244:25 252:1 351:17

herbicidal 35:23,26 37:13

herbicides 32:24 34:18 440:10,13

Hershberger 29:21 42:12 45:1 55:2 74:8 75:23,24

84:8,9 86:23 93:7,14 165:24,26 167:19 168:18

170:13 171:20 172:7 181:12 243:5 244:21 251:1

283:16 288:26 289:3,11 290:20 292:10,12 293:2

324:10,19 328:13,20 329:1 348:3 351:10,17

384:23 391:11 395:10 397:17 461:4 464:1,5

471:3,10 483:12,21 484:9,11 485:9,12,13 486:26

487:16 491:4

Hershbergers 87:26 289:6
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Hershberger/Uterotrophic 325:9

heterogeneous 451:24

hey 221:13 222:10 227:19 228:8

Hi 4:19 5:18 82:6 178:11 210:16 288:15

high 35:25 76:4 78:4,8 80:18,22 87:18 89:21 92:25

93:5,9,20 102:24 103:20 104:12 113:8,8,12,14,18

113:26 120:8 123:17,25 129:2,7,22 138:3 147:21

152:6,26 156:23 159:11 179:1 186:14 206:4,16

206:21 207:3 215:14 224:1 227:8 232:3,19 234:4

235:16 236:2 237:19 240:5,16,22 241:19 242:7

242:13 245:12,21 251:22 264:1 265:13 274:12,24

274:24,25 291:11 293:5 306:15 312:20 339:10,11

339:18 340:15 341:26 346:26 348:6 349:2 356:24

361:23 386:17 387:8 389:23,24 393:10 396:17,20

396:25,26 397:18 399:10,16 406:4 411:26 414:14

416:4 426:16 432:9 449:23 451:13 464:9 468:6

471:12

higher 33:21 34:8 88:9,24 89:6 182:7 183:10

198:23 233:18,19 234:12 251:14 263:21 267:9

290:10 326:10 333:18 338:16 339:15 345:5 351:7

367:6 387:22,23,23 392:4 403:10 404:26 408:12

417:7

higher-level 41:22

highest 67:6,24 75:18 102:15,18 103:23 104:14

105:19 106:18 111:2 127:21 186:8 214:11 218:15

234:4,7 242:20 254:15,16,18 332:20 381:16

395:18

highlight 170:21 178:16 365:26

highlighted 17:25 223:14 378:14

highlighting 152:4

highlights 450:19

highly 128:18 198:24 206:21 225:19 228:23 238:26

249:3,17,18 251:21 253:14 355:17 371:11 377:11

385:19 387:6 397:23 425:26 431:7 443:2 475:25

489:7

high-production 34:4

high-throughput 24:7 26:11 225:20 226:6 231:26

288:3 318:4,6 320:19 329:12 335:16 377:20

382:20

hikes 143:4

hill 63:11 337:23 340:24

hind 116:12,17 117:8 149:12,20 150:2,8,10,26

158:9 212:18 213:3,4 216:5,24 217:8 256:20

259:5,19,20 266:25 298:8,15 304:24 402:7,26

403:5 404:1 423:12 428:3 450:7

hip 416:8

histochemical 452:20

histological 124:16 125:21 159:9 447:9 448:16

451:23 452:14,18 453:4 454:6 455:25 456:8

histologist 125:12,13

histology 45:26 46:2 74:26 77:26 78:20,21 80:10

80:15 81:7 125:6 140:19 145:14,16,19 158:4

161:7,8 162:1,17,20 257:26 261:11 268:2,6 269:1

428:6 433:4 456:10 459:18 461:8 462:2,5,9

histopathologies 433:26

histopathologists 125:19 139:10

histopathology 47:16 52:23 95:20 104:18 117:4

124:13,19,22,26 125:1 126:1 130:9 131:9 132:12

132:19 133:16 134:10,10 141:4 142:9 143:9,15

143:18 144:2 146:7 158:14 168:5,25 175:14

176:2 189:21 255:23,26 256:2,14 257:11 258:13

275:15 335:10 402:10 428:6 430:25 433:14 435:7

448:12 461:24

historical 10:3 100:21 123:9 135:16 254:11 341:8

341:15 349:14,16 393:23 475:5 478:26

historically 393:17

history 31:16 196:17 315:18 318:14 434:4

hit 146:15 236:20 443:26

Hitch 434:5

hits 308:3

HLLS 216:10

HLL-W 216:9

hobbyist 436:22 441:3

hold 22:4 51:20 156:20 212:9 225:15,16 291:18

363:8

holders 230:5 282:12

holding 91:23

holds 487:11

holiday 506:6

Holmes 316:25,25

home 119:1

homeostasis 40:6

homogeneity 127:16

hone 195:26

hope 8:9 37:23 318:24 356:1 416:12 459:2 495:4

498:19

hopefully 206:9 320:2,9 322:22 326:6 328:10

329:23 390:13 501:2

hoping 13:3 248:23

hormonal 41:3 43:10 45:25 115:15 153:20 154:21

154:26 155:5,5,26 156:2,6,25 157:10 158:6 179:8

245:8 366:19 481:20 487:18

hormone 6:11 18:17 43:1 47:17 49:12 64:16,26

76:15,16 85:17 116:23 117:1 154:15,23 157:20

238:1 239:12 245:10 248:25,26 249:3,6 252:2,5

280:12 294:21 295:10,13,25 353:15,21 354:10,15

354:17 369:8 377:11 401:5,7 450:22 455:14

461:22 468:18

hormones 40:11 44:17 50:22 82:11 84:19 157:21

238:13 295:4,14,22 309:3 373:16 401:19 481:18
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hormone-mediated 300:18

hormone-sensitive 468:22

horse 266:11 429:4

horseman 6:12,13 38:11 86:17,18 87:3,6 89:11,12

109:2,3 145:12,13 146:5 184:25,26 185:18,22

192:18,19 193:5,9 197:24,25 211:25,25 267:24

267:25 269:5 294:16,17 315:16,17 391:16,17

411:11,12 462:13,14 469:7,8 480:7,8 481:26

482:1 499:17

Horseman's 90:15

host 229:23

hot 281:17,18,25

hour 102:19 208:18,18 230:18

hours 233:13,16,22 238:16 299:25 355:15 409:8

435:26

house 390:6

housing 83:9,18 218:23

Hoverman 458:8

HPA 82:9 386:12 387:2,17 389:2 397:8 464:25

465:1

HPB 331:5

HPG 42:11 43:14 44:3 47:12 49:15 79:26 94:17,19

107:2 155:26 156:2 394:3,21 465:1

hpt 20:5 43:14 47:14 49:15 115:14,18 128:13

129:21 156:5,25 427:4,6,8 447:14 448:9 449:15

449:17 450:8,25 451:5 453:9 461:24 465:2

HTS 318:7 322:13

http 1:8

huge 272:12 276:16 342:26 469:16 488:25

hugely 123:9

human 17:16 18:1,2 22:17,24 34:12 60:9 66:12

117:22 185:13 226:12,24 227:5,26 242:18 305:19

305:25 310:15 327:15 330:6 365:5 389:14 472:16

Humane 323:16 330:1,4

humans 17:4,7 315:13 436:11

hundred 338:10 346:15

Hunting 355:12

husbandry 260:1 265:8 285:20,26 286:15 431:2,4

432:26 435:2,10 436:1,11 437:9,11,23 439:26

443:26 500:14

hydroxide 245:17

hydroxy 342:23

hydroxycholesterol 239:16

hyperspace 479:16

hyperthyroid 401:8

hypertrophy 125:9 141:21 262:4,7,12 446:18,26

449:12

hypothalamic 47:11 77:16 427:10,12 450:3

hypothalamic-pituitary-gonadal 252:1

hypothalamic-pituitary-thyroid 115:14

hypothalamus 44:6 45:5

hypotheses 302:17

hypothesis 201:26 263:12

hypothetical 268:4

hypotrophy 400:15

H-O-V-E-R-M-A-N 458:8

H295R 64:7 237:3,25 251:15 365:4

I

IACUC 419:11 490:1

IACUCs 418:26 488:3

ICCVAM 234:21,26 235:24

IC50 337:19

idea 196:16 206:10 220:16 223:23 224:1,6,25

226:12,13,23 227:9,10,21 228:10,15 262:3

277:24 283:2 285:20 292:15 311:7 312:8 318:7

331:24 371:8 389:21 409:6,7 410:1,11 465:16

470:20 473:3,5 477:2 486:2 493:24

ideal 436:11 437:10

ideally 145:23 441:19

ideas 196:1 225:1 410:10 501:14

identification 16:20 179:7 223:26 224:5 234:18

259:1 359:20

identified 28:22 52:25 74:4 76:19 77:3 96:3,13,20

96:24 107:14 115:9 117:12 119:13 125:11 142:14

156:16 167:17 173:9 175:25 178:17 180:1,8

185:10 200:4 217:10 223:13 243:24 257:19

271:22 299:14 317:9,17 446:25 491:2

identifiers 132:11

identifies 97:13 124:26 307:25

identify 22:9 43:9 45:20 48:9 54:8 77:12 78:14

79:23 81:1 127:10 153:19,24 172:26 183:6

186:24 190:3 214:13 215:13,18 217:9 222:15

255:2 311:20 314:1 315:10 367:18 369:7 427:3

430:7 470:13 473:22,26

identifying 21:5 27:16 49:17 157:4 245:16 311:8

433:1

ignoring 364:7

III 356:11,24 357:18

illuminated 184:12

illustrate 105:6

illustrated 96:26 103:20 260:20

illustrates 169:24

illustrative 148:23

image 116:12 454:9

images 132:19,26 133:24 134:11 212:19,22 217:24

452:1

imagine 15:9 140:11 218:19 398:24

imbalance 431:6

immature 44:3 74:16,17 75:10 243:12,22 244:19

351:7,9
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immediate 334:17

immediately 333:20 487:16

immunoassay 239:5

immunocytochemistry 455:14

immunoreactivity 452:23 455:4,16

impact 65:8 67:1 77:6 79:16 84:2 178:18 179:20

218:25 250:12 269:11,12,12 312:26 330:24

360:11 373:23 375:3 376:21 380:24 399:24 400:1

400:11,16 415:14 424:6 429:9 434:18 475:16

503:2

impacted 266:22 392:12

impactful 218:5

impacting 107:2 426:18

impacts 231:18 262:25 404:10

impair 259:24 260:6 266:17

impairment 266:15 334:18

impending 315:13

impetus 27:3

implementation 2:22

implemented 16:10

implications 213:14 314:20

implied 34:6 466:2

implies 115:13

imply 298:4

importance 8:19 29:12 107:3 180:20,25 214:23

385:4 396:19 434:6 469:11

important 2:20 3:12 8:11 9:2 18:24 21:8 26:9

29:25 30:6 31:2,9 38:25 59:13 74:12 81:4,12

83:16 152:8 157:2 160:12 163:18 196:7 200:17

200:17 207:26 208:6,7,14 214:9 218:3 223:8

225:24 226:16 227:24 229:6 235:26 241:20

246:20 251:18 253:2,5,21 255:23 256:8,16

258:25 259:19 260:2,5 261:11 262:2,18,20,26

279:13 283:21 287:7 288:6 297:15 307:12 313:10

313:17 330:21 331:9 349:15 361:8 374:5 396:25

396:25 398:7 400:8,18 402:13 405:11 407:20,23

407:25 408:15 416:18 417:23 427:6 428:10

431:13 432:26 435:9 436:3 437:25 438:3,9 439:2

441:20 442:3,8,23 468:8 475:21 476:1,8 479:14

480:10,20 484:23,23 493:23 502:9

importantly 22:13 27:18 238:8

importer 155:17

imposed 70:15

impossible 9:23 398:22

impressed 503:26

improve 124:25 260:3 277:19 301:6 331:10 432:13

443:26 460:25

improved 231:15 336:18

improvement 234:11

improvements 260:1

improves 432:18

improving 226:24 314:6

inability 78:14 81:1 233:15 259:23

inactive 368:17

inaudible 136:16 187:10 195:18 352:12 366:20

374:11 397:3 399:25 401:5,24 414:23 431:11

433:21 449:24 453:2

inception 323:24

incidence 121:11,20 123:17 125:2,7,23 141:6,19

262:11 267:5 416:17 425:25 426:2 431:23 446:25

446:26 449:11

incidences 267:9

incidents 213:23 262:4 266:2

include 27:24 42:7,20 43:16 58:23 68:11 73:10

74:24,26 76:10,13,14 77:20,23 78:1 80:4,13 81:5

95:13,17,22 102:18 116:18 117:8,11 119:4 130:5

135:14 170:3 171:9 177:14 246:13 292:24 319:25

356:19,20 359:14 380:4 394:8 428:3 441:19

449:26 451:9 461:23 483:14 484:8

included 41:5 42:5 50:9 54:13 58:8 60:22,26 61:1

67:18 72:1 73:16 75:4,18 77:1 82:15 87:8 89:7

95:22 106:4,5 116:6 118:3,16 176:13 180:2

199:24 214:8,14 247:9 263:20 304:26 319:14

356:21 357:22 360:4,9 375:20 378:11 386:21

441:18 448:23 458:13

includes 28:13 32:25 41:5,19 42:23 58:4,8 64:15

66:8 76:20 78:24 80:25 81:10 116:9 117:14

121:21 177:11 292:9 354:12 459:13,16,19

including 42:2,3 56:8,22 74:2 78:25 80:7 95:18,23

96:8 152:20 155:3 156:17 157:20 164:22 168:25

179:13 208:23 220:22 225:26 229:10 263:26

304:25 309:2,12 312:12 332:14 361:10 396:4,20

404:19 409:19 426:5 464:14 472:18

inclusion 105:9 164:19 391:13 469:13

inclusive 17:10,26 18:6,9 23:10,16 24:6 28:9,12

32:24 41:1 51:17

incoming 221:8

incomplete 243:21 358:7

inconclusive 61:13 167:11 169:4 176:7 177:26

inconsistencies 283:9 319:4

inconsistency 448:24

inconsistent 283:16 454:4

incorporate 125:3 139:2 163:14 244:17 256:7

394:14 453:18

incorporated 187:19 188:20

incorporates 44:4 45:4 74:13 75:26

incorrect 102:2,3

incorrectly 59:18 101:9,22,26 106:24 107:10 315:6

447:22

increase 64:21 74:12 75:3,15 76:18 86:25 101:5
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110:19,22 113:15 125:16 137:25 166:3 174:4

238:13 239:4 241:15 251:15 253:16 257:23,25

272:14,14 275:10 276:1 392:5,10 399:12 418:11

447:6 455:24,25

increased 98:5 100:10 102:16 245:1,13 262:7

263:22 361:14,15 401:5 443:7

increases 109:25 110:22 137:26 249:12 343:4

425:15 448:10

increasing 57:17,24,26 62:13 66:6 126:7 339:25

444:14 492:26

increment 357:7

incremental 501:19 502:26

incubated 57:24

independent 2:19 27:5,13 44:6 45:5 58:4,14 62:17

64:14 66:7 222:9 305:22 314:14 452:21,26

453:12

indeterminate 415:21

index 95:19 176:2

indicate 27:15 44:5 56:12 59:12 104:15,25 118:20

158:5 166:21 174:1,7,15,22 181:11 183:24 253:4

279:14 311:2 315:12 356:24 461:16

indicated 9:9 12:19 16:12 24:12 33:25 57:13,15

64:20 105:12 152:15 174:3,17 179:22 185:8

186:7 206:22,26 214:18 215:26 216:17 254:13

257:20 291:11 359:4 360:5 390:18 399:7 425:1

448:21 460:16 468:18 471:2 483:20 484:12,26

indicates 18:20 160:1,3,4,7 162:6 174:25 175:2

255:26 466:8 468:10

indicating 358:5 395:19

indication 45:16 70:21 123:24 201:19 202:3,18

250:16 251:8,16 262:11 343:16 416:25

indications 40:22 45:11 151:7 159:4 245:19

indicative 95:16 166:7,13 174:11 447:14

indicator 46:3,4 118:4 151:4 234:17 412:18

indicators 47:21 125:21,22 130:9 154:6

indices 253:25

indirect 34:11 112:22 412:13

indirectly 126:7 142:25 145:22

individual 26:19 38:16 43:18 45:7 55:25 74:1

95:10 105:5 110:9 117:5 119:8,13 130:12 131:2,3

131:16,20 132:10,10 136:22 152:3 153:13 156:19

163:17 180:4,6 184:14 200:7 204:22 227:23

240:21 260:18 344:2 348:7 357:11,14,20 371:1

375:12,20,26 393:22 410:22 411:6 469:20 476:15

476:20 477:20,21 480:19,21 491:15

individually 57:10 199:19

individuals 44:4 117:16 120:20 130:22 141:20

145:23 357:14 448:16

induce 241:15 252:1 407:7 444:18 445:1

induced 175:23 407:8

inducer 64:15

inducers 244:26

induces 349:20

inducing 242:11 263:13

inducive 60:17

induction 64:26 234:7,7,12 238:15 240:2,8 245:2

342:13,15 346:26 347:2 351:18

industry 124:1 133:5 213:8 215:2 220:9,19 221:21

222:22,24,25 284:19 285:15 286:7 290:7,26

306:2 314:15 425:22

industry's 288:9

inert 17:11 32:25 34:4 38:26 179:13 185:11 195:23

207:2,3 368:17 470:9

inexpensive 451:16

infections 275:18

infectious 437:4

infer 438:1

influence 130:23 164:21 387:4,18 453:20

influenced 394:16

influencing 36:18

inform 19:6 88:1 164:8,13 165:15 173:22 187:4

195:14 196:11 215:9 289:6 290:8

information 7:24 10:15 13:7,8,9,18,19,23 14:9,10

14:25 15:5 20:13 22:1 23:17 24:23 26:25 28:21

29:12 31:3 33:24 36:26 37:3 38:16 41:25 43:3,4,4

44:20 45:17 47:8 48:6 52:22 54:20 55:16 56:21

60:7 61:26 66:4 69:12 70:10 71:18,20 72:24 73:3

86:21,23,25,25 94:13 95:11 100:22 118:23

129:26 131:5 135:7 137:6,25 138:10 148:15

149:5 155:12 159:15 162:7 164:15 178:12,20,22

179:23,25 180:5 181:18,24 182:12 184:13,17

186:25 187:18 188:13 190:26 191:3,3,4,4,7,8

192:16 195:19 196:6,9 197:7,8 202:14,23 205:16

206:3,11 212:7 215:9 217:20 219:15 220:6,20,21

221:18 222:12,24 223:3 228:1 229:5 233:6 235:2

245:18 250:25 251:11,17 264:4 276:4,7 277:8,15

279:11 280:24 284:16,19 287:18,22,23 288:5

289:15,19 290:6,14,25 291:1 292:11 293:19

294:1,2 295:1,7 297:13,22,24 300:25 302:19

305:16 308:21,23 309:20 311:26 313:26 314:26

318:16 328:12 331:6,10 332:6,13 333:2,8,10,26

334:1,8,11 335:2 345:17 350:3 352:20 356:23

357:24 363:22 364:1 369:2 371:24 375:8 377:16

377:24 378:26 382:22 383:2 399:17 406:3 407:4

412:3,15 420:19 426:22 430:22 431:12,14 434:25

435:5 436:17 444:2,12 446:9,10 447:20,24

453:16 457:14 459:6 462:24 463:1 466:2,4,5,8,24

469:26 470:16 473:6 474:13,20 476:16,22,26

477:11,12,14,18 479:1 480:1 481:7,13 483:14

487:23 496:3,26 497:9 498:1,7,23 499:7,20
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500:11 502:10,20 505:8,16

informative 23:20 27:24 29:9 30:24 129:19 145:18

301:13 369:13

informed 7:22 10:16 38:17 115:6 174:14 270:23

423:6 505:8

informs 335:23

infrequent 436:7

ingredient 38:26

ingredients 17:10 34:4,24 35:2 37:16 195:24 196:3

270:9,11 368:17 442:8,26

inherent 70:6 86:21 163:8 185:23 204:9,10 234:9

246:22 247:14 489:12

Inherently 37:16

inhibit 46:7,10 64:19 65:23

inhibited 401:24 402:8

inhibition 64:18,26 66:17 67:6 177:2 236:6 238:16

240:1,6 277:16

inhibitor 64:16 66:14

inhibitors 67:4 75:25 251:2 276:12 374:16 449:19

inhouse 390:8 393:17,17,23

initial 7:21,25 23:5,7 28:3,19 33:16,21 36:7 39:3

49:7 52:17 67:15 69:26,26 124:1 125:5 187:16

201:14 213:8 291:8 313:9 322:12 332:13 360:21

376:14 397:25 409:8 428:16,20

initially 129:24 206:22 208:16 257:15 345:14

348:18 370:23 486:20

initiate 51:23

initiated 101:13 115:26

initiating 19:22 42:19 43:21 334:13

initiation 112:1 137:23 249:19 348:14 425:14

inordinate 444:13

input 8:11 9:12 10:25 25:18 37:20 181:17 182:1

201:14 205:26 227:24 306:8 368:12 380:5,12

454:23 495:16 501:6,20,23 502:14,17 505:16

inputs 494:13

insecticidal 35:22,25 37:12

insecticides 34:18

insensitive 191:18,20

insight 171:15 186:3 229:4 278:12 329:23 451:5

478:24 498:8

insightful 418:20

insights 8:9 27:13 203:9 222:16 501:23 502:1

insignificant 376:6

insolubility 240:23

inspected 285:12

instance 142:10 190:4 246:26 248:25 255:6 298:6

300:13 353:6 360:6 387:19 388:3 389:13 397:2

426:16 451:1

instances 68:23 69:7 72:12 143:4 358:8

Institute 4:12 5:24 211:12 335:18

institution 419:10 489:19

institutions 439:14

institution's 489:22

instruction 459:8

instrument 106:6

insufficient 331:19

insufficiently 247:13

insult 241:14

insure 134:12

intact 49:15 77:13 79:24

integral 129:18 409:4

integrate 13:8 15:5 199:8 228:17 378:4

integrated 24:24 39:12 40:5 44:11 47:12 150:23

202:23 220:15 309:7 311:17 334:19,19 347:17,21

407:19 502:19

integrates 205:11

integrating 13:18 155:12 202:13

integration 23:15

integrative 42:16,20

integrity 155:10,11 165:5,8 170:4 173:16 177:4,7

388:10 394:21

intend 249:22

intended 19:3 21:10,11,20,24,25 22:18,21,21,26

24:11,12 26:25 27:10,12 29:6,8 30:25 94:12

115:2 118:5 131:18 141:12 154:12,16 158:11

169:13 176:20 181:5 199:4 223:23 264:14 287:19

314:22,23 330:12 369:15 420:14 427:2 484:24

intends 134:5 318:4 332:17

intensity 452:25 453:1 455:22

intensive 240:11 331:15

intent 35:10 90:3 178:14 186:7 199:12 221:22

353:26 364:6

intention 198:11,22 199:6

intentionally 122:14

inter 133:11 297:17

interact 10:9,14 11:13 13:10,20 14:1,5 15:6 18:16

19:7 21:16 22:3,12 31:1 41:17 57:2,25 58:11 62:2

94:14 128:13 197:11 198:25 199:8 202:16 244:8

251:25 278:23 307:10,11 310:17,21 366:18 367:5

369:8 483:4,10 502:22

interacted 59:20

interacter 203:17

interacting 312:18,23

interaction 18:22 30:19 43:26 44:7 45:6,12 95:26

96:17 106:20 115:18 154:13 157:4 158:5 159:7

159:18 163:5 164:1 169:14 171:16 174:11 190:17

202:18,25 245:26 314:23 396:5 433:5

interactions 28:21 41:4,23 42:26 43:15 46:9 47:8

94:17,19 103:10 105:11 153:19 155:4 156:25

169:21 172:26 176:21 178:8 179:7,24 181:6
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226:10 375:8 458:15

interactive 40:19 58:15,15,16,18,21 59:22 63:1,1

interacts 57:21 316:13

interchangeably 157:1

interconnect 19:5

interdisciplinary 55:22

interest 2:26 121:11 203:21 213:22 289:19 377:20

411:26

interested 85:18 124:11 138:26 185:24 230:9

258:5 292:11 330:8 430:26

interesting 91:6 225:1 241:23 328:3 402:6 419:15

463:3 470:16 471:17 500:12

interestingly 162:8 327:1

interfere 126:6 398:26 427:3

interference 144:21 160:8 161:24 174:26 239:3

329:2 363:16 462:8

interferences 46:15 167:13

Interior 5:16 211:6

interject 264:19

intermediate 39:15 127:20 129:5 368:19 491:4,16

491:23 497:6

internal 122:1 140:21 204:26 261:16

internally 145:16

international 11:24 12:23,26 158:24 498:26

interpret 42:25 47:24 132:5 243:10 329:4 349:6

351:19 395:20

interpretable 492:16

interpretation 10:11 54:8 55:23 61:15,20 62:10

65:8,19 67:2 71:13 77:6 79:13,16 81:24 127:26

130:9 131:9 132:2 139:18 163:19 178:18 179:20

213:24 215:16 241:13 243:25 252:6 261:17 262:2

264:18 275:2,19 277:19 300:20 324:12 334:7,8

349:21 350:1 357:5,24 358:17,18 359:6 360:12

363:15 366:1 373:24 375:3 379:1 380:24 386:9

389:24 396:22 398:26 409:14 411:9 434:19

452:15 459:26 461:13 468:1

interpretations 261:2 350:5 388:24 449:25 464:26

501:22

interpreted 43:2 141:16 160:13 387:9 388:26

interpreting 68:4 105:10 125:20 141:25 222:16

245:5

interpretive 159:20

interrupted 42:11 44:3

intersect 340:5,7

intervening 331:26 334:14

inter-laboratory 70:6 247:19 319:4,9 367:13

intimately 303:8

intra 133:11

intra-lab 297:17

intrinsic 111:20

introduce 3:21 15:17 26:6 115:4 134:21 146:26

197:9 210:6 213:23 360:20

introduced 114:18 159:26 341:25 352:12 357:10

483:15

introducing 15:12 424:14

introductions 422:3

invalidate 402:22

invalidated 144:22

inventory 479:25

invertebrate 20:25

invertebrates 42:3

investigating 396:17

investigator 122:11

invitation 219:26

invited 220:26 386:12

invites 89:15

involve 352:2

involved 7:4 150:16 171:18 178:24 241:16 262:23

268:17 281:16 304:17 308:16 336:2 348:1,17

351:24,26 371:21 379:17 397:7,15 455:14 485:17

489:16 505:5

involves 23:14 78:10 80:24 307:19 366:23

involving 452:22

in-depth 300:9 301:9 450:11 462:20 498:2

iodide 121:22 123:6,8,11 453:15

iodine 91:15,17 426:23

ionic 431:6,15

ions 439:14

irrelevant 253:11

irritant 352:22,22

isolated 106:18 342:8

isolation 371:23 451:5

isometric 403:7,8,12,17 423:13,19

issue 38:19 135:19 197:26 198:1 200:14 213:20

233:23 237:18 239:10 240:9 242:5,8,9,11 264:17

272:1,20 274:14 277:11 280:7 282:4 287:24

297:2 313:6 315:25 316:20 318:9 326:12 327:10

343:1 344:6 355:19 357:6 362:5,10 372:13,19

373:1 378:24 381:7,10 387:13,22 391:21 401:11

402:24 410:19 411:24 426:17 429:8 431:4 432:26

433:4,20 440:7 443:24 450:19 451:12 452:3

469:10 485:4 487:17 488:4 496:18 499:2,6

500:16 502:8

issued 12:6 155:15 299:26 376:14 428:16

issues 2:20 14:19 30:12 39:13 54:8 67:25 74:3

75:16 77:3 79:12 115:9 148:7 152:4 159:20

178:16 180:1 215:21 230:14 232:2,3 233:12

237:18,22 239:18 240:22,23 241:11,12 243:19

248:24 257:24,25 262:24 263:4 269:7 270:3

276:16 277:10 280:7,10 295:23 297:4 300:7,16
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300:20 303:14 312:4 322:10 327:4 328:15,19,20

339:22 340:11,14 343:4 361:25 364:12 365:26

368:13 369:4 370:26 381:6,16 397:21,22 436:1

437:21 442:2 447:25 453:13 456:1 459:26 461:2

462:8 467:21 475:13 487:24 494:21 495:14

500:14 504:1

issuing 12:13 284:23 314:9

items 2:12

iterations 70:17

iterative 71:19

it'd 393:6

it'll 370:4 419:15

i.e 388:17

J

J 169:7 170:14

Jack 6:8,8 83:21 84:1 91:14 108:5,9,14 139:5

211:21,21 267:1,10 399:21 414:2,18 419:10

422:9 450:18 451:21 454:18 456:22 474:25 499:9

JAMES 4:19 50:15 187:24 188:25 189:4,10,15

190:2,18 191:9 192:9 193:14 210:16 345:22

346:8,21 347:4 495:2

January 8:17 10:3 24:4 25:10,26 26:15 195:22

233:3 288:3 372:1 377:2 383:20,26 479:21

494:22

Japan 12:22 313:14

jargon 11:18

Jeff 112:19 210:20

Jenkins 2:1,9 151:22 207:22 219:26 230:22 355:1

498:11 503:11 504:17

Jess 31:6 92:20,20 505:4

Jim 4:20 210:16

job 16:6 27:1 30:18 31:2 70:5 305:23 347:10

429:25 485:24 495:16 500:23,23 503:8 504:4

505:16

jobs 504:13

Joe 505:22

John 31:7 53:23 73:14,14,24 82:10,13,20 83:1,6,13

83:25 84:4,20 86:2,13 87:1,5,7,17,22 88:4,11,18

88:26 89:8,19 90:9,22 91:1,10,19 252:15,16,17

258:8 271:19 272:4 284:10 292:14 304:18 394:1

394:1 395:1 505:10

joined 416:8

Jonckheere-Terpstra 127:14

journal 122:21 319:6 457:24 458:2

Joyce 505:26

JR 504:17

Juberg 220:14,14

judge 478:1,9

judged 236:18 358:21

judgment 47:25 69:9 221:13

judgments 477:15

July 13:17 15:4 24:20 51:19,24 193:4 196:7,13

199:25 202:10,21 494:23 495:6 497:8 501:18

502:16

jump 193:7

jumped 454:22

June 20:21 24:26 494:23 495:6 502:5

jurisdiction 309:17

jurisdictions 309:18

justified 491:24

justify 489:25

juvenile 77:14 79:24

K

K 177:5 380:7 390:16 398:20

Kalamazoo 337:2

Kansas 5:19 211:9

KATHERINE 5:18 88:23 89:5 196:15 211:8

356:17 379:4 384:9 393:13 418:13 463:22 470:19

473:14 482:5 497:25

Kathy 5:18 211:8

Katie 271:15 316:24,25,25

Kd 57:14 58:23 63:3,5

keep 14:23 54:26 87:22 103:14 132:26 135:23

144:1,3 152:8 206:1 275:3,13 279:10 341:8

375:15 432:16 438:9

keeping 140:4 143:21 432:7 444:12 485:9 500:23

505:8

keeps 228:16 503:24

Kelsis 232:14

kept 224:5 438:23

key 17:25 40:23 44:10,14 47:5 91:16 111:23

116:17 117:7 233:23 241:11 369:23 400:14 429:7

434:16 496:12

Ki 339:20,21

kick 35:26 90:24

kicked 400:15

kicking 90:25

kicks 316:21

kid 411:17

kidney 76:14 78:3 80:15 348:26 354:1

kidneys 78:26 80:14 81:13

killed 324:3

kind 13:15,20 14:16,25 34:26 36:11 38:5 45:12

46:20 47:19 48:6 49:11 51:13 53:2 69:7,19 71:6

83:11 85:7,9 89:22 93:6 134:12 136:8,23 137:6,6

142:16 143:6,9 149:5,7 188:8 193:11 195:19

196:15 200:11 203:5,7 206:6 207:9 218:5 225:9

251:17 280:10 294:11 300:10,14 301:1 323:13

326:6 327:10 332:16 333:14 334:2 346:6 347:13

353:4 366:4 370:11 371:15 411:13 413:15 422:3
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470:20 472:7 473:18,24 475:5 501:12

kinds 14:11 34:12 40:21,26 46:17 47:9 123:1 134:9

142:15 149:6 196:5,8 299:9 300:7 334:24 390:9

kink 441:13

kinking 137:11 433:23

kits 238:26

Klaine 4:10,11,11 32:8,9 36:3 38:3,4 88:6,7,16

210:10,11 494:15,16

Klaine's 33:7 35:14

knew 35:20 148:5 201:22 220:17 230:13 470:4

know 3:9,11 7:20 10:2 32:13 34:26 35:15,18 38:1

39:5 40:14,23 43:6,12 45:3,13 46:20 47:7,24 48:3

48:9 49:13 50:10 52:8,25 53:2 69:1,1,7 70:19,20

70:20,23,26 71:4,6 72:20 82:7,15,24 84:16 85:8,9

86:7 88:3 90:1,25 91:15 100:7 107:8 109:8

111:14,19,22 112:7,9,21,21 113:10,11,16 132:2

134:5,18 135:2,3,5 136:1,5 137:7 138:7,8 139:24

141:6 142:13,21 146:18 150:17,17,20 162:26

186:8 191:23 193:1,20 194:18 195:17 197:15

203:1,9 204:24 205:17 214:3 218:17,25 221:2

222:2,4 224:5,10,22,23 225:22 226:1,7,14,22,24

227:13,26 230:1 234:11 239:12 241:13 250:26

255:23 261:20 264:5 265:13 266:1,10,10 269:17

272:4 274:24 275:5,23 276:10 278:5,19,20 280:6

280:8 281:18 282:1,24 283:23,23,25 286:12,13

286:15 287:13 293:18,23 294:3 295:2 296:5

297:17 299:19 300:21 301:9 302:10,18 303:13,22

304:5 316:8 321:12,25 322:20 323:3 329:10,12

330:24 331:24 340:12 341:17,23 344:14,21,22,22

345:2 352:21 353:14,25 354:13 364:7 370:16

375:16 381:2,14,18 383:12 386:23 389:8 390:8

394:15 397:2,11 399:19,23 409:2 410:7,10

411:10 415:15,17,19 416:2,22 417:4 419:24

423:15 424:20 435:23 438:10 439:6 440:15 442:8

443:6,11 444:18 456:6 463:14 464:24 466:6

472:17 473:8,19 474:3 475:21 477:4 479:4,6,22

480:17 496:4 497:17 499:26

knowing 35:25 85:18 88:14 206:9 495:23,24

497:22

knowledge 36:10 78:12 80:25 92:26 139:8 222:15

260:8 310:16 321:6

known 18:7 19:10 40:17 76:8 159:1 183:1 184:3

191:23 199:18,20 253:6,8 318:13 323:12 367:19

369:15 401:17,19 431:20 440:10 461:20 474:10

knows 318:3 404:7 485:25

Kow 28:10 37:14 195:18

Kows 35:16

kudos 490:5

L

L 105:14

lab 71:19 72:10 123:10 140:12 216:22 220:15

248:3 249:24 263:12 264:22 265:10,11,12,18

266:1 282:19 286:22 338:19 339:22 340:13 341:7

341:18,20 343:8,22 344:14,20 354:12,12 358:1,1

358:20 393:20 402:11 404:6 417:22 437:15,17

442:16 445:23 490:19

LABC 76:12 80:10

labeled 59:18

labeling 373:4

labor 240:11 291:25 331:15

laboratories 12:3 26:19 28:24 54:6 59:1,2 63:7

66:24 67:17,25 68:11 98:5,7,9 100:10,22 101:4

102:7,10 106:3,4 108:18 111:25 112:15 120:26

121:5,6 129:24 130:5 178:15 179:15,18 252:25

264:2 283:22 300:22,22 302:14 349:14 356:12,25

357:1,4,6 358:6,13,14,20,21 360:17 365:6 371:13

374:15 375:26 385:1 426:10 429:13,22 435:1

454:5 481:8 503:19

laboratory 6:21 30:11 60:1 64:4 65:15 67:8 70:20

71:14 123:4 129:13,26 139:25 144:20 223:14

247:15,25 263:25 282:7 306:18 337:5 343:7,12

344:4,15 345:11,18 347:17,21,22 350:19 357:11

357:13,18 358:18 371:4 372:13,15,20 373:8

374:4,25 375:5 380:23 391:14 414:20 426:26

429:25 435:14,17 436:4,5,19 437:1 439:9,19

449:22 475:5,9 488:2,26

laboratory's 351:12 373:17

laboratory-reared 438:12 439:5

labor-intensive 274:23 371:12

labs 71:20,21 72:7,26 87:24 110:11 133:10 134:20

140:8,13 147:13 212:24 214:26 217:26 229:5

234:9 245:4 247:20,21 250:8 265:4 272:11 273:8

285:10,11,15,21 286:3,19,21 324:16 337:9

341:10 345:1 357:22 361:17 364:12 381:5,6

390:6,10,11,26 393:22 417:24 418:7 435:17

437:14 440:3,19,26 485:23

lack 157:3 163:20 171:14 240:25 254:11 256:18

272:19 449:4 466:26 474:25 500:13

lacked 251:10

lactation 6:16

laevis 6:21 115:23 121:12 122:7 263:10 321:20

363:8 425:12 436:5,9 438:2,11 439:17 440:11

446:20

LAGDA 257:9 502:1

laid 11:7,8,10 19:21 20:7 34:1 368:1

large 59:14 85:14 186:11 228:4 231:21,23 260:26

323:22 324:13 335:15 370:25 373:19 387:11

389:12 391:7 393:4,15 400:2 435:14,18 437:3,6

444:21 452:1 503:17
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largely 437:21

larger 171:3 177:13 185:5 259:11 260:22 266:9

337:25 399:16

largest 118:18

larvae 428:2 429:17,18

larval 20:23

Lastly 432:24 434:9

late 234:21 499:1

lateral 137:12,13 431:23 432:2 433:21

latest 7:7

launch 225:6

Laura 505:21,22

Laurier 6:5 211:18

laws 2:26 339:17

lawyers 10:17

lay 445:14 488:18

LC/MS/MS 238:22

LC50 102:19,23 254:14,18 273:16,19,19 274:1

276:22 277:24 278:1 406:2 409:8 413:7 414:8

lead 52:2 70:9 125:18 231:2 234:17 241:15 244:3

244:24 245:13 259:23 273:18 310:13 316:16

330:23 346:23,23 356:15 363:2 365:15 385:10

394:12 396:7 403:13 408:11 413:23 426:13

432:21 447:16 459:1 468:5 483:17 504:26 505:6

505:26

leadership 7:10 504:18 505:21

leading 9:4 194:22 225:22 431:15 465:14 505:1

leads 71:1 240:24 371:15 432:24 433:6

Leah 347:17,18,20 352:14,17,19 353:1,10 354:2,14

354:22

lean 111:23

leaning 285:19

learn 332:8 435:24

learned 221:26 222:11 313:8 314:16 331:26

383:20 424:12 495:4

learning 287:4 299:13

leave 30:21 31:12 194:13 433:12 480:6 497:4

leaving 120:1

led 18:13 235:3 400:12

left 3:22 4:10 23:4 59:5 64:18 95:11 105:14 122:12

208:21 247:10 337:17

legislative 16:25 367:21

length 117:8,9,10 120:23 127:7,19 131:2 149:12,13

149:20,21 150:2,4,8,10 151:1,4 158:9 212:18

216:5,9,10,15,16,18,20,24 217:1,2,5,8,14 256:21

258:11 298:8,8,16 304:24,25 402:8,26 403:5,5

404:1,1 417:21 420:4,7,8,16 423:12 428:4,4

429:7 434:15 441:15 450:7

lengths 259:19,20 408:5 420:16

Leonard 6:8,8 83:20,21 84:1 91:13,14 107:21

108:4,5,9,14 139:4,5 211:21,21 266:26 267:1,10

398:11 399:20,21 414:1,2,18 419:9,10 422:8,9

450:17,18 451:20,21 454:17,18 456:21,22 474:24

474:25 499:9

leopard 439:6

Les 31:14 84:25

lesion 138:6 143:11 430:25 432:23 433:1,15

434:14 449:2 450:1,3

lesions 142:10 447:12

Leslie 39:21,24 50:6,18 52:13 70:13 84:25 134:8

136:3 137:13 142:7 143:1,17 144:9 150:22

186:19

lessened 447:13

lesser 78:8 80:21 485:3

lessons 221:26 222:11 299:13 313:8 314:15

lethal 122:1 134:19,20

lethality 404:15,20

lethargy 103:3

Letter 480:23

letters 370:3

lettuce 265:21

let's 6:23 37:20 53:9,11 73:21 91:24,24 93:23

113:5 114:9 151:14,23 193:11 212:8 270:19

271:1 279:10 294:17 305:7,8 315:20 317:19

329:26 330:13,15 336:19 356:5 379:19 384:20

395:5,7 420:23 425:8 447:17 457:3,16 458:11

466:15 474:9 482:21 485:18 491:26 492:4

levator 76:10

level 1:13 51:4,13,14 52:20 55:22 64:25 78:7 80:21

109:1 116:8 118:11 119:8 120:1 127:20,22 129:3

129:6,7 132:10 139:25 147:21 148:11 156:23

159:11 165:4 173:16 185:25 198:2,3 199:10

206:4 216:26 217:6,12 234:7,7 238:1,17 250:6,7

259:14 275:23 296:10 299:1 306:15 312:16,19,20

314:25 316:15 332:26 333:9,9,18 334:7 337:22

349:3 350:18 351:5,6 352:2 356:24 392:23 393:2

407:7 414:25 451:13 459:6 470:22 486:2 491:2

491:15

levels 63:11 77:26 78:22,26 80:12 81:7,8,14,14

84:17 85:17 92:23 105:5 107:25 114:4 116:4,23

117:1 120:3,5 123:8 124:22 127:8 129:4,10

141:24 148:12,17 155:3,7,12 157:20 160:10

162:23 164:24 168:6,16 173:8 174:1 185:7,26

216:25 217:1,4,10 233:16,18 234:9,12 263:18

265:13 290:9,10 295:25 333:13 334:1,25 339:15

342:2 348:8 349:1 350:18 353:15,21 361:11,12

374:12 397:1,18 415:2 442:17 462:1 486:9 491:9

Levine 230:20,21 231:1 237:8

LH 86:22,23 87:2,7 295:23 388:12 389:18 394:13

LH/FSH/TSH 294:22
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liaison 2:12

Liccione 31:7,19 53:23 73:14,14,23,24 82:4,10,13

82:20,21 83:1,6,13,25 84:4,20 86:2,13 87:1,5,7

87:17,22 88:4,11,18,26 89:8,19 90:9,22 91:1,10

91:19 115:20 394:1,1 395:1 505:10

lies 366:24

lieu 244:19

life 5:17 115:25 355:12 362:5,7 415:22 490:21

lifecycle 42:1 44:15

life-threatening 315:22

ligand 57:23 58:19 62:8,12,14 233:9 370:18

light 184:18 186:26 377:10 383:2

light-emitting 60:13

liked 249:16

likelihood 121:8 206:24 390:3 392:5

likewise 45:13 492:17

limb 116:13,17 117:8 139:7 149:12,20 150:2,8,10

151:1 158:9 212:18 216:5,24 217:8 256:21

259:19,20 298:8,15 304:24 402:7,26 403:5 404:1

423:12 428:3 450:7

limbs 213:3,4 259:4,5 266:25

limit 3:14 58:10 62:21 76:6 78:6 80:19 85:7 89:1

102:19 109:11 113:20 126:18 147:22,25 166:26

218:12 241:21,24 242:20 358:7 406:21,25 412:25

484:24

limitation 133:15 243:22 256:15 342:19 454:2

limitations 78:9 80:23 256:10 276:13 342:18

360:15

limited 21:19 45:19,22 47:18 67:6 102:23 168:22

169:18 176:25 232:4 235:3 249:19 276:18 352:4

358:16 367:6 386:2 424:7 459:5 481:6 483:24

limits 79:14 100:17 218:11 345:2 356:20 358:24

382:12 384:25 385:2,9 390:19 391:3,5 443:15,16

line 46:25 60:9 64:8 107:1 133:9 135:2 141:24

147:2 217:13 227:18 228:4 231:18 233:16 235:26

237:4 294:13 340:4 341:5,19,21,24 342:12,13

355:18,26 407:9,11

linear 249:12 340:6

lines 47:1 71:6,17 109:19 134:25 183:9 184:18

221:14 237:25 280:2 355:8

lineup 299:16

Lineweaver-Burke 340:1

link 331:23 398:22

linkages 335:3 472:15

linked 121:20 122:8 365:18 432:4

links 319:15

lipophilic 128:18 253:13

lips 194:1

Lisa 280:5 288:14,15,15 294:1 295:9,15,26 308:19

list 7:21,25 15:3 23:5,6,7 26:14 28:3 33:18,21

34:13,17 35:6,17 38:12 39:4,5 69:26,26 137:8

171:9 219:20 226:18 229:21 230:10 247:9 296:12

298:1 313:7,9 314:9 318:24 324:22 328:8 337:9

344:1,6 347:23 366:14 496:22 497:11 502:13

listed 3:17 31:6 54:3 58:12 60:23 95:11,12 142:12

164:23 166:6,12 169:8 171:9 174:10 246:25

297:14 298:1 299:3 310:7 331:13 344:7 371:9

391:6 459:4

listen 280:19 498:19 500:22

listened 147:7

listing 297:21

lists 33:16 59:6

liter 123:14

literally 436:22 441:3 448:13

literature 43:4 183:9 186:25 187:19 243:8 244:11

258:24 290:7,26 326:9 404:8 424:9 473:6,11

479:4,15,19 480:2

liters 338:7

litter 248:17

litters 248:14,16 326:20,23

little 15:12 16:14 31:16 39:26 40:1 43:17 46:9,19

50:26 51:8 52:19 82:22 87:13 90:25 92:12 108:6

115:7 122:25 124:13 135:18 136:2 142:7 143:24

144:26 146:19 170:19 186:20 190:24 193:8

203:15 204:6 217:19 221:25 230:2 248:22 249:2

250:25 251:3 255:15 257:26 261:19 264:13,26

272:25 274:15 276:6,11,18 281:26 299:11 304:9

306:5 308:1 323:21 326:13 329:23 331:5 338:4

338:14,19 340:18 342:22 343:8 346:19 352:11

364:5,18 378:2 385:13,14 386:11,13 387:1 395:4

397:13 399:21 407:22 415:16 416:6,8 419:8

424:18 435:1 438:6 442:15 443:25 454:23 455:3

455:8 462:19,24 464:16,21 467:10 472:8,9

473:14,25 475:2 485:8 490:22 493:13 495:25

496:8 504:5

live 312:25 433:16 496:11

liver 76:14 78:2 80:14 81:13 335:9 348:26 349:18

349:19 351:17 354:1 400:24,24 401:1 414:26

liver/gene 377:6

lives 320:13 322:20

load 208:5 436:9

LOAEL 93:2

LOBBY 1:13

located 337:2

LOD 113:22

LOEC 278:2

log 28:10 35:16 37:14 195:18 357:7

logic 11:19 146:10 159:4 192:21 267:26 268:3,8

280:4 303:10 462:6

logical 333:7 491:10 500:21
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logistics 9:20 248:8 503:13

logPC50 340:23

logs 404:23

long 85:9 120:2 205:17 254:25 256:26 266:20

276:17 278:26 281:3 286:12 296:5 303:26 304:1

304:6 309:12 331:26 346:25 353:11 377:26 378:2

383:8 412:7 418:8 422:2 490:20

longer 22:7 119:23 122:19 238:7 318:10 353:18

longevity 241:2 401:5

long-chain 26:5

long-term 198:24

look 8:8,10 9:13,14,14 10:7,13,20 11:14,15 13:5,25

14:17 17:24 18:5,8 19:17 20:4,5 21:1 27:10 29:4

32:5 35:15,16,21 37:20,25 40:12 50:21 51:21

81:3 90:6 91:6 109:7 111:13 139:1,17 144:9,26

145:25 148:24 149:7,23,24 176:22 187:24 190:14

195:16,20,23 200:15,21,21,22 201:7 202:5,23

204:6,12,21 205:20,21 218:3 228:22 231:26

233:9 234:1 242:19 246:16 247:7 251:1 255:1

257:12 259:4 268:19,19 272:20,26 278:1,22

280:10 284:15 287:5,7,11,17 288:11 299:15

300:3 302:7,12,14 307:4 308:6,10 312:7 314:23

314:25,25 324:14 331:8 335:7,8,9,10 339:18

340:11,19 341:12 343:2 352:13,23 354:1 365:19

371:14,16,26 372:11 375:11 377:14,18 378:10

379:21 382:21 383:12 387:17 390:11 394:12,14

394:19,25 400:19 401:6 405:17,21 406:9,21

407:3 415:4 416:19 433:6,9 437:4,7 441:4 443:3

443:3 444:17 445:20,21 451:22,26 456:10 457:11

463:6 464:10 465:12 470:12,12,16 471:4,6,17

472:7,8,19,21 475:1 478:26 479:1,5,23 487:1,21

490:17 495:6 496:6,20 497:8 498:2 499:15 504:9

looked 17:18,20 26:1 28:11 29:7 34:8 70:4 73:16

85:1 108:18,26 109:24 119:6 122:26 123:5 135:3

139:9,19 144:16 176:17 177:9 204:13 213:9

231:9 236:24 252:12 261:7 265:22 278:3 285:26

293:24 299:6 313:12 371:23,24 380:11 400:19

414:18,20 442:20

looking 7:20 10:4 12:12 19:20,24 38:24 40:25

46:22 47:23 48:14 73:18 85:2 115:1 131:19,20,24

134:13 136:24 139:18 140:8,25 141:26 145:23

147:3 149:6 187:7 188:5,16 189:21,25,26 190:9

192:7 195:25 198:9 199:10 200:26 201:1,8,8,8,25

203:2 204:15 205:8,9 233:8 234:24 237:2 239:26

246:18 251:5,18 261:5 269:9,14 270:8 278:12

279:12 284:18 286:6 293:6 298:21 301:9 303:6

309:6 313:14 315:26 317:7,10,11 321:24 327:20

334:22 335:17,19,22,24 337:14 339:8,13 340:9

340:23 341:16,19,22 342:6,12 343:26 346:8,12

347:1,3,11 352:11 364:7 367:18 373:15 380:17

383:18 397:11,26 400:8,14 407:14 410:2 414:7

417:16 418:1,11,18,19 424:26 428:3 429:5

433:16 442:8,11 451:11 455:4 457:13 464:2,6,15

466:5 471:20,22 472:21 474:14 475:21 476:7,17

479:3,20 487:8 490:16,25 491:6 495:21 496:24

496:25 497:5 501:13 503:6

looks 19:1 72:6 115:17 116:13 133:25 147:5

191:10 212:20 213:6 219:24 221:8 226:20 309:10

319:19 324:16 340:4 345:23 346:21 372:3 455:17

loops 86:20

loosen 71:12

LOQs 113:22

lose 254:1 275:21 416:2 489:2

losing 21:4

loss 56:9 349:7 350:3,21,24 351:2 354:5 400:2,23

400:23 409:20 415:3 418:17,26 419:11,14,16,24

422:11,14,15

lost 343:1 346:26 378:23 379:2 483:15 489:13

lot 12:19 15:9 19:13 21:18 32:10 61:25 86:20

111:20 112:5 134:11,11 140:26 151:2 199:10

205:17 222:7,20 223:2 227:23 228:14,22 229:5

230:15 234:18 246:4 263:4 272:3,11 273:9,13

274:5,5 276:10 277:1 279:20,20,24 281:5 282:13

282:13 284:16 286:7,12,13,14,16,26 288:1,10

289:14 290:26 294:9 297:24 298:3 299:11,17

301:1,2 310:6,19 312:13 315:5 330:11,22 331:15

331:26 336:12 338:12 339:1 341:11,20 345:18

353:15 354:9 374:22 375:16 378:3 379:16 391:24

397:10 405:16,26 406:3 407:19 415:23,25,26

435:24 443:12 452:1 465:8,12,26 473:5 475:9

476:16 477:17 485:21 486:18 488:17,25 489:10

489:10 491:11 493:9 495:4,16 496:7,26 498:6,7

499:24 502:7

lots 12:26 220:25 224:18 276:15 286:21 295:5

337:3 341:9

loud 205:15 406:9

love 157:7 303:21

lovely 122:22

low 48:12 49:22 97:4 113:9,17 127:1 129:6 147:24

186:14 224:2,2 239:1,21 250:8 347:2 361:11

387:19 407:5 464:7 471:11

lower 21:11 68:3 86:7 88:10,15,25 89:3,6,18,26

92:25 93:20 113:15 126:23 148:25 199:10 215:4

262:3,11 268:22 307:9 357:9 367:3 388:6 399:6

403:11 404:22 405:2 406:24 412:7,25,26 413:19

414:7 436:10 446:25 449:11 483:8 490:21

lowering 178:24

lower-dose 21:21

lowest 105:15

LSR 356:3
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Luc 324:9

luciferase 60:6,10,12

luciferin 60:12

luck 194:25 230:18

LUMI-CELL 327:24

lunch 91:24,26 305:8,10 482:19,21,23

luteinizing 76:15

luxury 286:16

lysyl 434:5,7

L1 105:16

L2 105:24

L3 105:19

M

M 177:2

machinery 35:1 342:1 398:9 399:22,24

MacLATCHY 6:4,4 71:15,16 72:2,5 211:17,17

362:23,24 378:12,13 381:4 384:3,4 498:21

magnesium 439:15

magnitude 78:17 81:4 105:25 159:15 182:8 188:14

337:25 405:12 460:10 461:19 462:26 481:14

main 40:22 103:13,24 244:15 323:20

maintain 40:6 97:6,25 98:5 99:16 100:2 110:17,20

156:14 252:23 253:4 259:23 345:11 357:12 407:9

maintained 99:25 252:26 484:20

maintaining 98:3 253:13,17 289:19 362:7,8 393:10

406:26 435:13 483:2

major 28:22 33:10 75:15 77:3,5 79:12,16 81:23

83:17 120:11 129:1,7 239:11,11 253:24 319:2

344:19 351:24 359:12,16 360:14

majority 59:14 61:18 106:11 176:8 230:10 360:10

365:8 372:10,16 373:21 460:1 483:20

making 2:22 8:22 14:17,20 88:13 112:20 147:26

160:15 182:14,15 183:16,18 184:8 198:1,6

206:12 254:2,7 277:5 284:21 287:6 301:17 338:9

411:26 474:21 503:1

male 29:21 42:13 45:8,25 46:12 47:7,14 74:8 76:2

76:9 79:21,22,24 80:1 82:25 83:2,4 87:19 93:8

94:20 101:22 102:5,8 104:20,22 105:3,3,5,13

107:16,17 108:16,22 115:18 124:15 155:9 157:16

157:22 158:4,19 161:3,12,17 162:19 163:11

165:3,5,7,8,24 166:5 167:20 168:2 170:4,13,15

171:21 172:8 180:15 181:13 191:3 225:13 246:10

247:8,19 248:1,9,15 250:14 251:2,20 255:2

275:10 276:2,18 279:13 284:6 291:7,12 292:9,12

293:24,25 295:18 325:12 328:16 329:3 348:4

355:13 384:24 385:18 394:5 395:11 408:7 456:11

461:6,17,22 464:1,6 471:3,10,15,23 472:2,18

484:11

males 94:23 99:13 101:11,13 107:10 113:26

295:19 350:11 354:17 388:25 394:22

malformation 428:12 433:23

mammal 292:19 294:5

mammalian 20:16 21:1 31:19 43:16 44:9 45:9

53:24 73:22 74:7 187:9 246:9 250:26 255:18

308:26 309:14 347:23 351:22 397:15 415:17

456:20 464:19 467:2 489:3,9 496:15

mammals 42:2 292:15

mammal-based 292:24

management 308:10 335:24 336:26 500:14

manager 504:26

managers 505:5

mandate 367:21

mandated 187:5 310:10

mandates 16:26

Manibusan 15:11,18,21 32:15 33:14 36:19 38:18

51:16 69:20 133:16 182:4 196:21 198:8 202:8

223:11 356:10 365:1 382:9 383:17 384:19,22

395:9 422:10 425:10 446:13,16 458:12 476:12

480:24 482:15,26 494:9 501:10 504:26

Manibusan's 226:14

manifesting 425:11

manner 217:17

Mann-Whitney 127:17

manufacturer 123:7 155:16

manufacturers 444:11

manuscript 122:12 212:23 213:4 214:2 263:17

man-hours 308:15

map 500:26

margin 224:3

marginal 127:23

margins 228:2,3,4

MARIBUSAN 492:2,6

marine 417:4

Marion 4:15,15 32:3 206:15,19 210:13,13 362:20

378:7 379:26 383:24

Marked 244:23

marker 95:21 453:6,10,12 455:19 456:5,8

markers 116:16 456:8

market 155:18

Marty 224:9 246:5,6,7 280:11 284:5 289:3 295:10

295:17 326:13

Mary 15:10,18,21 31:26 32:15 33:14,25 34:9,21

35:23 36:19 38:18 51:16 69:20 196:21 198:5,8

199:16 202:7,8 356:8,10 365:1 382:8,9 383:16,17

384:19,22 395:9 425:10 446:13,16 458:12 476:12

480:24 482:15,26 492:2,6 494:9 501:10 504:15

504:26,26 505:2

Mary's 35:14 51:26

masking 145:9

mass 412:18 416:1 418:11
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Massachusetts 6:9 211:24

massive 284:26 323:24 495:10

match 19:26 146:21 227:3 269:26 270:2 272:23

273:8 342:9 448:16

matched 124:19 142:11 145:23 256:16,17 268:5

328:6 432:16 448:18

matching 257:10 268:1,16 269:23,24,25

mated 309:25

material 276:16 306:26 319:3 339:26 340:2

materials 3:17 10:2 55:16 92:5,11 129:25 229:7,23

337:9 339:16 500:25

mating 438:10

matings 436:15 438:15

matrix 239:3

Matten 505:22

matter 69:25 143:2 145:8 185:4 219:18 261:21

309:19 375:2 436:1 489:4

mature 101:13 351:14 488:21

maximal 234:6 238:15

maximize 41:6 42:16 198:15

maximizes 225:5

maximizing 223:24

maximum 60:17 76:5,22 78:5 80:19 87:19 89:2,17

89:24,24 90:6,12,14 96:9 100:17 102:20,24

126:18,23 166:25 186:1 223:21 232:4 254:14

273:22 274:8 325:2,15,20,25 331:10 345:24

346:10,13,14 348:8,15,26 349:3 350:4,15,17,25

405:26 406:12,16 413:5,14 424:26 425:6

maximum-tolerated 89:14

McMANAMAN 4:19,20 50:14,15 187:23,24

188:25 189:4,10,15 190:2,18 191:9 192:8,9

193:13,14 210:16,16 345:21,22 346:8,21 347:4

495:2

mean 36:26 37:2 55:3 68:26 72:11 78:24 81:10

84:20 85:14,26 97:24 99:2,8 108:25 130:20

138:19,19 143:18 186:20 190:3 205:22 222:3,14

225:22 227:15 266:5,20 272:1,2 273:18 275:4,10

279:5 280:12 287:3,8,26 298:4 332:16 352:11

354:8 381:19 390:5 400:10 403:18 412:15 415:21

416:20 462:18 463:9 487:21 493:17 495:24

meaning 218:1 489:7

meaningful 249:2 250:19,20 252:9 269:20 391:23

393:9 433:18 497:8

means 123:3 131:10 138:23 155:12 184:1 247:11

250:9 260:18 287:4 311:23 390:26 415:21 448:5

490:21

meant 19:5,6 24:10 135:14 152:12 196:5 260:14

280:23 412:25 454:21

mean-measured 253:3

measurable 407:20

measure 34:11 82:8 112:15,17,23 113:4 133:13

240:3 261:21 272:1 295:19,23 359:17 360:22

361:7 389:8,11 402:13 407:16,19 408:20 419:26

456:13 486:5,6

measured 50:23 57:19 60:13 64:13 75:14 76:8

77:23 80:7 82:16 95:16 97:22 98:13 100:1,9

104:3 105:8 109:4,14 110:3 120:2 128:8,26 143:4

148:17 151:1 152:6 157:22 166:1 188:15 219:9

219:10 241:8 266:7,22 269:8 295:19 387:6 461:5

493:21

measurement 66:2 95:17,23 100:4,6 105:4,13

106:25 152:19 180:10 217:9 248:25 269:15 361:1

361:3 429:8 434:15,16 452:9 461:12

measurements 50:17 80:16 90:8 95:15 98:10

100:21 105:9 110:6,8 112:21 113:22 132:12

216:5 218:15 248:26 249:3 252:5 266:19 268:6

292:5 295:11 335:7 387:13 402:25 420:4,11

454:10 461:23

measures 45:26 47:17 57:1 60:14 66:5 101:5 143:9

143:25 151:2 373:16 389:5,12 486:4,5

measuring 107:4 111:9 112:22 180:6,7,26 280:12

295:3 353:15 397:19 398:15 417:21,23 418:3,9

485:13

meat 10:1

mechanism 41:13 44:22 85:24 88:14 96:2 279:26

329:11 331:24 398:23 470:14 490:2

mechanisms 19:13 40:15,17 49:14,17 78:15 81:1

169:17,23 222:17 245:17 279:5 317:8 332:16

367:19 369:17 411:1 433:25 449:18 450:8,12

455:23 456:7 460:20,24 484:1 493:5,15,18

mechanism-based 330:9

mechanistic 42:18 74:14 76:1 157:3 176:24 278:20

279:4,10 289:19 324:11 369:15 427:19 477:25

mechanistically 86:7 235:12

median 119:8 120:18 121:2 124:19,20 448:2,19,21

mediated 156:9 171:16 183:13 239:14 329:6

460:18,19 487:18,19

mediating 85:26

medical 4:17 5:19 6:9 210:15 211:9,23 294:19

306:1

medicine 4:21 5:9,10 6:14,19,20 211:3,4 212:2,3

323:18

meet 17:14 33:17 58:21 59:25 61:9 66:24 97:18

118:21 119:7 147:10,19 148:7 193:20,21 207:19

207:20 238:6 243:18 272:8,9,11,15 273:9,12

274:3 279:22 307:20 326:22 371:3 378:21 392:15

392:18 440:25

meeting 1:2 2:7,7,10,17,25 3:5,9,16,23 7:16,18 8:9

8:16 9:24 15:25 51:24 61:6,21 97:12 101:1,2

106:12 114:15 121:6 151:19,22 159:22 196:13
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205:8 208:3 209:2,4 223:13,17 224:16 272:13

273:1 274:14 298:25 349:4 351:24 355:2 356:1

381:26 385:2 391:23 392:1 397:6 408:16 420:25

448:9 485:22 495:9 500:21 501:11 502:17 503:6

504:5,10 505:3,5,9,19 506:2,4,5,6,8

meetings 8:18 9:19,24 494:22 495:6,22 503:12

meets 41:11

Mel 226:1

member 4:14,18,21 12:26 210:15 306:11,17 308:2

371:10 408:25 431:18,22 439:16 449:22 452:23

members 2:4,25 8:5 15:8,12 18:12 93:22 194:16

198:10 199:12 218:1 297:3 306:7,20 363:4

372:18 377:4 378:3 379:12 393:25 410:16 412:21

422:6 431:1,3 432:6 433:19 434:21 445:9 451:20

460:10 461:1 466:11 474:7 482:13 484:6 487:26

492:21 493:26 495:21 498:26 499:10,12 505:12

membrane 460:21

mention 50:24 55:26 139:16,16 260:13 280:22,23

322:24 327:24 355:19 382:10,17

mentioned 41:14 53:21 54:10,25 56:16 57:1 67:20

84:15 115:5 117:8 126:2 129:1 138:25 146:20

149:12 157:8 212:5 223:18 259:8 272:4 280:11

283:5 284:10 324:8 327:6 334:20,21 335:15

339:12 360:26 371:7,16 390:22 444:6 452:13

453:22 454:8 456:14 481:19 489:15

mentioning 133:16 382:20 412:24

mentions 380:21

merely 171:1

mesocosms 265:3,3,14

message 10:19 65:4

messages 314:16

messengers 40:6

met 2:15 3:4 58:25 59:12,15,26 61:4,5,17 63:5,7,14

63:16,18,26 64:3 65:5,7,17 66:26 67:11,11 72:7

72:16 96:22,23 97:1,7,10,11,12 98:19,26 99:8,22

117:26 118:19,24 119:10 129:14 135:24 147:5,13

147:18 152:15 179:18 218:10 243:26 271:21

274:16 284:2 349:13 351:9,20,23 359:2,3 360:2

372:8 373:20,22 374:20,22 375:1,6 376:1 392:13

392:24 447:19

metabolic 192:25 329:7 479:14

metabolism 19:12 27:26 29:18 42:20 44:4 45:4

46:10,15 49:20 74:13 75:26 80:25 163:14 171:17

182:11 226:9 235:13 311:1 385:24 485:17

metabolize 479:8

metabolized 241:1

metabolizing 240:25

metamorphic 117:1 120:24 121:16 145:17,20

216:17 252:19,22 253:22,23 257:2,15 262:9,10

285:19 321:8 417:20 451:4

metamorphose 429:19

metamorphosing 157:16

metamorphosis 20:9 29:10 42:13 46:1,3 47:16,19

52:7,14 53:4 54:2 114:23 115:12,13 124:17

125:22 128:24 129:21 136:13,21 140:26 158:7,22

160:6 163:26 200:2 212:16 213:5,14 259:21

261:25 265:10 267:9 268:5 271:19 293:22 298:7

317:26 318:12,14 361:20 362:2 395:12 401:14,16

402:25 404:10 415:13 425:12 441:2,11 447:5,10

447:15 448:10 449:3,13 451:2 452:15,19,26

453:26 456:15

method 13:6 24:5 26:20 94:26 115:22 128:10

129:23 238:20,22,22 314:5 319:10 326:6 327:9

327:12 402:25 403:6,11,12,22,24 404:7

methodical 158:20

methodologies 187:9,13

methodology 24:21 25:11 26:1 38:24 51:20 203:9

203:11 231:14,16 232:11 233:11,12 357:5

methods 12:21 13:1 17:3 20:22 24:7 27:8,22 55:16

163:13 219:3 280:9 314:7 320:18 327:15 333:14

367:12 371:21 458:1

methoxychlor 294:7

methyltestosterone 61:1,7,22 381:5

metric 74:12 195:9 420:8 463:20

metrics 409:1 463:14

mic 82:22 270:25 464:22

mice 489:21

Michael 219:21,25 229:18

Michigan 337:2

micro 338:7

microbial 126:7 457:15

microbiology 6:10 211:22

microcystin 457:12

micrograms 123:14 250:5

micrograph 141:23

micrographs 133:6 141:10,15

micromolar 232:5,5,6,7 239:22 342:25,26 343:2,3

343:5

micron 214:14,16 215:3 265:19

micronutrients 432:21

microphone 219:19 227:20

microscope 433:16,17

microsomal 65:26

microsomals 66:12

microsporidia 275:18

micro-liters 338:8,10,10,12

middle 33:10 59:7 113:17 119:21 259:13 260:12

429:4

midway 39:16

mid-test 104:13
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mid-treatment 105:24

might've 273:11

Mihaich 229:22,25,26 270:20 271:3,4 278:4,8,14

278:21 279:18 280:5,19 285:13 286:4 287:25

296:6 308:19

Mike 220:1 230:16 291:4 314:13

mild 125:21 130:10 213:2 488:25

milk 6:17

million 85:14 102:20 222:3 224:21 230:15 288:7

299:24 308:12

mimic 244:19

mind 54:26 87:22 103:14 152:9 163:3 206:1 221:9

446:5 465:8,20 477:24

minds 487:23

mine 458:6 479:19

miniaturization 345:3

miniaturize 338:15 345:4

miniaturized 240:12

minimal 307:4

minimally 438:24

minimize 21:12 32:12 41:7 42:16 118:8 215:19

254:20 329:22 355:4 367:4 392:11 396:1 405:24

443:23 444:16

minimizing 130:7 372:14 483:1 484:25 489:15

minimum 64:25 96:10 111:3 117:16 120:17 121:1

121:6 167:3 330:19 427:24 490:3

minimums 100:4

Minnesota 265:2

minnow 94:5,22

minnows 100:24 274:12

minor 29:1 79:14 119:25 148:9 248:2 319:17

359:11,12,15 372:18 374:21,22 378:9 380:7

385:4 386:7,26

minus 51:13 337:23 338:24 359:17

minute 151:15 280:6

minutes 2:11 3:15 9:25 194:10 208:17,17,18

230:18 231:11 347:14 385:17 501:12

mischaracterized 122:15

misidentified 101:19

misidentifying 101:14

missed 101:3 102:3,8,9,11 108:7,15 119:22 148:8

193:18 239:20 247:2,9,18 390:14 447:22

misshapen 445:17

missing 27:1 31:3 36:25 71:18 197:8,8 247:5,12

475:14

mission 7:16 16:9,9,17 220:6

Missouri 5:16 211:7

mistaken 101:22

misunderstanding 302:10,11

misunderstood 381:3 382:3

misused 315:5

mitigation 16:22

mitochondria 400:11

mix 188:6 265:21 410:24

mixed 188:11

ml/l 126:19,22

MoAs 48:17

modalities 48:20 153:5 155:2 164:25 165:5 169:11

169:25 170:1,3 172:2 173:8 175:19 176:17 177:3

177:6 185:7 186:5,23

modality 154:17 169:13 170:24 173:11,12,16

174:5 175:20 176:20 177:12 186:4

mode 19:14,18 35:22,23,26 36:23 37:12,13 49:19

185:10,24 243:12 245:18 251:24 273:4,5 275:8

278:25 279:7 289:8,9 294:26 295:3 317:11,16

329:11 369:24 427:19 429:12 432:5

model 25:14 52:15 66:17 74:17,20,21 75:10,11

115:13,24 233:3,7 235:13 243:12,22 244:20

291:9 312:25 334:22 351:8 427:4,7 488:25 489:3

489:3,6,9

modeling 490:9

models 74:15,16,23 233:2 351:9 489:15 496:11

moderate 213:3

modern 232:15 316:6 465:22

modes 8:1 18:9 19:9 24:14 27:25 179:13 250:18

252:11 279:5 292:18 332:20 368:26 410:25

496:24

modification 460:24

modifications 257:22 277:18 453:17

modified 70:26 179:6 215:7 307:8 483:7

modify 71:12 292:10 332:26

modulate 47:3

molar 58:9 62:20 63:24

mold 441:7

molecular 19:22 40:21 139:6 155:4 211:22 334:12

336:1

molluscan 443:10

moment 137:22 234:20 435:9

moments 449:7 453:22

money 224:15,25 248:10 288:10 320:12 354:8

488:8

monitor 112:25 358:3

monitored 94:24 98:12 253:6 356:22 436:12

monitoring 249:20 430:1,7 459:19

monitors 438:26

monotonic 127:12

monoxide 228:13

Monsanto 231:2

month 20:21 24:26 25:21 215:9 230:17 285:1

501:25
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months 220:13 339:1

morbidities 437:2

morbidity 429:14 430:14 437:4 440:22

morning 2:5 3:26 4:19 5:8,14,22 6:12,18,23 8:15

15:21 16:6 39:25 53:16 92:10 94:1 151:20,20,22

207:13,24 208:4 209:2 210:1,10 212:12 219:25

229:26 230:21,23 237:11 242:25 246:6 252:17

258:17 296:5,11,17 301:24 302:8 420:24 422:1,3

423:10 424:25

morphed 499:14

morphological 116:16

morphology 116:17

morphometric 120:23 216:18 451:10 452:9 456:18

morphometrician 424:20

morphometricians 424:11

mortalities 99:14 119:12,16 437:2

mortality 74:26 76:13 78:1 80:14 99:19,21 117:21

119:11 395:18 404:15 413:10 428:6 430:14

440:22 443:13,16

mortality/lethality 405:8

mouth 433:20

move 8:21 9:15 12:8 18:4,24 22:5 23:24,26 24:2

25:17,23 26:13 27:2 28:2 38:2 39:8,20 56:14 60:3

61:24 64:6 73:22 93:23 114:9 143:21 152:1

156:18 159:20 183:10 194:8,8 196:6 198:23

202:19 219:15 239:24 264:20 266:15,21 288:4

311:16 314:21 317:19 329:26 333:18 364:26

374:24 375:14 378:24 384:14,21 392:12 395:3

425:9 438:6 443:21 444:14 446:11 457:19 461:18

480:23 489:3,9 495:6,20 498:4 500:8,26 501:15

502:8

moved 22:2 33:18 53:3 263:3

movement 20:14

moves 16:11 23:1 24:25 26:14 143:22 336:17

390:1 490:8

moving 16:3 20:23 21:3 34:20 39:11 51:19 75:23

77:11 79:21 127:18 153:3 198:2 206:6 237:9

282:25 310:23 313:1 329:10,12 330:8 332:1

336:13 351:1 373:3 374:13 377:25 392:11 399:15

436:3 448:13 456:1 482:25 499:7 500:15 501:21

502:25 503:21

MTD 243:17 351:1 399:10,15

muddies 279:9

mull 301:23

multi 221:20 250:11

multifactorial 431:9

multigenerational 309:12

multigenerational-level 42:1

multimodal 246:14

multiparameter 180:21 460:5

multiple 22:16 27:24 40:15,15 48:1,1 49:11,19

59:2 73:1 125:13 155:3,12,13 157:8,19 159:24

171:7,12 177:10,18,24 180:6 200:16,23 224:21

229:6 308:26 332:19 341:9,10 358:14 369:17

384:6 392:18 409:1 461:16 467:12 468:14 471:6

multiple-endpoint 51:12

multiplying 325:18

multivariate 245:12

multi-drug 442:12

multi-generation 20:24,25 21:3 24:2 25:6

multi-national 309:16

multi-parameter 19:9 458:22

multi-reproduction 23:18

multi-stakeholder 221:5

multi-tiered 55:18

multi-variable 479:16

multi-well 240:12

multi-year 320:17

murky 264:13

muscles 76:11 136:11

muscular 136:9 433:22

musculoskeletal 121:15

mutagenesis 134:23

mutation 121:26 122:1 134:18 436:9 438:19

mutations 121:25 134:19 136:17 139:23 436:7

441:13

N

naive 302:23

name 2:9 4:3,7 5:14,22 53:16 73:13 92:18 114:18

115:12 140:5 182:22 210:6,7 220:1 237:11

242:25 246:6 252:17 258:17 271:5 305:18 317:22

336:25 347:19

names 258:23 457:24

naming 505:11

nanomolar 234:5 346:17

nanomole 235:13

narrative 125:4

narrow 438:3

narrowly 369:25

nation 435:19

national 4:24 5:2,23 7:6 11:24 210:19,20 211:11

306:6

native 144:14 265:9 364:22

natural 265:15

naturally 121:16 134:19

nature 40:19 138:18 176:24 331:7 369:9 395:22

410:18 458:21

NC 105:15

NDR 442:22

Neal 242:24,25,26 289:10
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near 38:5 89:14 105:21 109:11 133:17 171:4

236:14 242:11 399:9

nearly 63:15 64:3

necessarily 55:3 73:6 107:19 112:17 123:10 145:25

150:3 152:10 160:24 189:24 201:3 216:13 299:5

316:14 317:9 321:25 401:22 402:22 415:14

473:15

necessary 102:24 182:2 199:24 241:26 245:8

282:15 291:1,19 292:4 302:23 307:5,20 393:10

398:9 399:9 400:7,25 458:18 469:5 483:12 492:8

necessity 241:25 311:11

necropsied 74:22 353:14

necropsies 244:18 327:3,4

necropsy 74:20 295:21 352:10,13,23 353:22

355:15 394:7

need 14:21 22:23 23:24 41:8,21 71:9,11 85:4

112:10,12 137:6 138:21 143:24 160:14 198:15,18

201:15,20 202:17 206:1 207:19,20 220:18 226:21

227:14,15 228:1,12,14 248:19 253:4 263:8

270:24 273:26 274:23 280:15 283:23,25 290:8,22

291:14,16,22,26 293:13 296:11 299:17 300:5,10

309:19 311:23 312:7,18 314:4,7 323:25 326:20

327:6 330:16 333:9 341:15 344:25 351:18 359:23

372:14 384:6 390:11 392:23 394:26 396:19 404:6

408:17,19 412:16 417:7 419:22,26 432:12 434:25

435:11 438:9 440:5 475:14 478:4,8,11 479:12

486:8 487:24 491:16,18 493:19 494:20 497:8

502:26 506:3

needed 14:11 27:26 49:20,21 64:8 117:17 220:21

236:16 248:10 254:8,23 282:22 307:6 327:9,11

336:12 358:24 396:1 405:24 420:19 498:13 505:8

needs 172:4 178:7 188:16 202:19 208:22 214:26

222:13 224:26 236:13,25 260:2 294:15 297:25

299:8 308:14 311:2,7,26 326:23 329:13 333:2

336:18 350:26 358:10 360:16 389:26 390:20

393:3 398:10 409:10 412:16 424:1,2,8 430:5

473:15 480:10,11

negative 28:1 30:1,7,8 40:7 58:8 61:2,23 65:10

75:13 95:4 105:15 126:10 144:24 166:26 171:10

189:12 191:6 197:3,5,14 203:22 225:14 272:21

272:23 318:21 329:3 332:25 339:22 340:10,16,22

356:21 369:18 370:17 387:24 392:7 427:25

458:19 470:5,7,10,10 471:26 476:18 477:4

480:16 484:15

negatively 400:16 469:18

negatives 21:12 29:14 41:7 42:17 43:6 61:13 154:1

198:17 205:22 225:4 360:4 367:4 383:11 484:25

492:26

negligible 116:23

neither 76:3 433:13

nelson 6:12,12 38:10,11 86:18 87:3,6 89:12 90:18

109:3 145:13 146:5 184:26 185:18,22 192:19

193:5,9 197:25 211:25,25 267:25 269:5 294:17

315:17 391:17 411:12 462:14 469:8 480:8 482:1

499:17

net 157:17

Netherlands 5:23 211:12

neurethrone 370:15

neuroendocrine 211:1

neuroendocrinology 5:7

neurons 241:17

neurotoxin 228:13,14

neutral 220:5

never 30:7 197:4 251:16 332:5 503:25

new 5:6 7:12 15:23 145:3 187:13 210:26 231:13

284:23 287:3 293:14 307:17 313:6 338:11 411:17

412:4 435:25 470:19 498:16

newer 456:13

newly 7:2 101:13 504:24

NF 116:1,16 117:8 118:11 119:2 120:16,18,20

121:3,8 124:19 137:23 215:26 216:1 267:11

448:2,2

NF56 269:17

NF57 269:17

NGO 220:5 229:2 429:10

NGOs 229:10

nice 86:19 225:9 269:14 299:16 332:12 336:14

340:6,26 416:21 474:8 495:9,9 500:26

nicely 222:22

Nieuwkoop 427:23 428:4

Nieuwkoop-Faber 116:1

Nigerian 263:13

night 114:15

NIH 12:20 231:8

nine 81:18 147:3 164:25 170:1 173:8 229:10 230:3

272:8 420:24 460:1 467:17,19

Nineteen 60:14

NOAEL 93:1

nod 370:16

nodding 38:8

NOEC 278:2

noise 201:11 346:3,26

nominal 109:15 129:9 152:7 253:9,10 360:22

361:4,7 445:13

nominals 149:2

non 195:23 252:12

nonconcordance 329:18

nonconformities 359:13,26

nonendocrine 96:2 407:10

nonexistent 48:13 116:24
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nonlinear 340:7

nonparametric 127:16

nonprofits 221:2,21

nonselective 44:18

nonspecific 58:24 63:3,7 96:2 240:4 252:3 255:20

non-androgen 329:6

Non-Animal 335:24

non-binder 62:25 63:21

non-conformities 361:10,16

non-descript 359:11

non-dose 244:3

non-ED 185:7

non-effects 406:6

non-endocrine 250:17 289:5,8

non-FIFRA 38:15

non-inhibitors 66:17 67:7 374:16

non-interactive 58:21

non-monotonic 339:19

non-normalized 150:7,11 217:4,11

non-optional 280:3

non-pesticides 34:23

non-response 369:19

non-specific 239:3 244:9 300:18 433:22

Noramly 437:8,15 440:16

norethindrone 58:6,7 337:18

norm 68:19 69:8 359:14 375:19

normal 104:25 105:3 107:16,17,19 109:6 141:9,22

141:23 186:4 238:18 309:1 385:19 398:9 427:3

446:20 447:10 449:2 495:25 496:4,5 500:3

normality 127:15

normalization 212:18 216:4,8 217:15,18 298:8,17

300:6 423:12,14,20

normalizations 149:25

normalize 127:7 151:5 217:16 256:20 420:10

normalized 117:9 149:12 150:10 158:9 216:24

normalizing 149:26 150:2 216:8,11,19 217:1,2,5

402:26 403:5 420:6

normally 107:19 109:5 111:6 125:15 265:3 266:15

342:16 441:11,23 445:18 447:5 448:9 449:20

452:19

notable 402:9

note 2:20 3:13 20:5 30:1,6 54:17 56:11 59:17 115:9

127:10,12 129:6 136:8 157:3 159:13 160:12,22

167:18 171:24 175:22 177:18,26 207:25 208:6

247:16 253:2 366:7 378:18

noted 20:10 29:22 30:13 75:17 77:5 86:4 95:24

103:8 105:18 125:13 126:13 155:14 156:17

161:15,25,26 247:1,25 308:9 366:19 369:21

370:14 371:5 372:3,7 373:4 374:21,24 378:22

385:25,25 386:20 390:4 395:17 397:22 448:8

458:17

notes 207:23 375:10,24 432:20

noteworthy 217:7

notice 171:19 352:10 488:12

noticed 83:23 86:21 91:2 138:16 247:2 298:12

441:5

noting 29:1 434:3 448:26

notion 315:18 411:18

notions 500:6

not-for-profit 306:6

novel 436:2

number 1:7 3:17 12:22 33:9 46:18 54:18,23 55:1

59:24 70:17 71:17 74:5 78:25 96:21,23 103:5,12

117:16 130:16 141:7 147:3,17 153:18 160:12,13

162:3 175:11 178:23 179:1,6 181:10,20 191:13

194:15,16 208:1 218:20 227:21 228:5 231:23

235:3 245:13 247:17 252:21 253:23 254:9 255:22

256:10,25 257:22 277:24 283:8 284:3 289:4

292:17,26 296:24,25 297:9,18 307:4 308:15

312:3 329:21 330:19 334:18 335:14 355:16

357:21 365:1 370:4 371:2 375:10 379:17,24

385:26 386:17 388:25 392:10 395:9 401:24

405:13 406:24 415:11 416:22,26 417:15,25

425:10 432:8,25 433:11 435:18 445:18 446:16

448:8 454:9 457:6 478:8 482:26 484:24 487:7

488:7,9,13 489:17,25 494:6

numbers 57:15 123:20 193:20 255:17 287:21

296:26 323:22,24 324:13 330:25 355:5 357:22

388:20 437:6 488:14 489:11,26 490:3

numerous 128:15 443:26 488:3

nuptial 95:18 104:23 107:11,24 165:7 168:25

nutrient 431:5 432:20

nutrients 432:22 442:14,25

nutrition 431:10

nutritional 261:6 264:21 295:4 431:17 439:3,8

O

OACD 84:16

objective 41:11 57:21 62:10 347:25

objectives 222:14,18

obliged 419:12

observance 428:6

observation 67:21 68:6 102:25 106:17 117:5

127:13 136:19 172:3 178:6 247:22 262:3 263:9

387:21,21 432:19 502:2

observations 56:23 67:15 94:10 101:8 104:19,25

106:13 115:8 125:18 141:7 172:21 186:20 246:8

263:19 266:13 315:19 348:25 350:4 407:21

438:23 502:4

observe 255:19

observed 29:17,22 56:7 59:8 60:19 61:14 67:23
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100:12 103:19 104:8,11,12,19 105:2 106:2,14

120:5 126:22 127:7,19,21,24 156:7,15 161:9,14

167:10,16,19 170:3,6,25 171:15 172:1,6 174:16

174:19,23 175:25 177:15 178:2,5,5 180:19

214:11 254:19 255:7 256:2 257:16 266:20 291:17

350:3 357:3 361:23 362:15 372:19 380:22 398:22

399:2 404:21 425:14 428:3 429:18 447:1 449:18

458:23 459:26 466:19 471:13 481:14 487:18

observer 133:11

obtain 25:18

obtained 165:21 166:23 310:9 348:24

obvious 68:24 117:22 221:19,24 326:14 361:25

370:9

obviously 82:13 83:15 89:12 314:10 319:4 325:25

367:4 370:11 372:2,5 381:15 386:3 389:11 397:4

410:23 451:12 453:6 462:19 485:18,21 486:15,24

occasional 436:6

Occasionally 89:9

occupational 33:19

occur 41:24 46:2 96:14 123:25 152:9,10 238:16

258:9 357:13 391:3 416:4 419:16 436:11 449:3

occurred 119:17,26 123:21 160:4 171:16 255:11

335:11 435:12

occurrence 101:9 102:11 124:6 130:8 136:6

212:16 213:26 215:19 218:2 285:23 406:11

426:10 435:4

occurrences 437:9

occurring 19:22,23 134:19 159:18 286:19 319:24

322:26

occurs 18:22 101:11 259:18,20 405:13,14 408:8

Oceanic 5:2 210:21

OCSPP 8:2 31:5

odd 191:16 329:17

oddball 192:24

OEC 128:14

OECD 11:23 200:10 216:7 217:13 231:7 234:13,15

255:14 272:21,22 276:26 277:1 281:5 290:2

298:11,22 327:25 333:4,5 335:23,26 336:5

Oecologia 458:8

OEC-sponsored 327:19

offer 250:19 310:14 395:13 453:23 502:1

offered 302:8

offering 287:1 288:19

office 3:3 6:25 7:3,8,11 8:5,5,6,14 9:18,23 15:19

25:1,2,2 39:22 53:15,17 93:25 114:11 122:5

135:1 308:9 311:9 425:19 503:9,9 504:24

Officer 3:2

Official 2:10

officially 506:6

officials 2:24 9:18

off-spring 309:24,24 310:1,5

oftentimes 277:26 279:21

Oh 82:10 84:4,20 87:5,21 107:21 346:14 415:12

436:24 457:4

okay 32:7 37:26 38:7 39:19,21 53:9 69:14 71:15

72:2,5,8 73:21 82:3,18 84:4,11 85:18 86:9 87:9

87:17 88:5,16 89:11 91:10,11,12,21,24 92:1,15

93:21,22 107:21 114:9 130:18 132:25 133:7,19

134:2,14 136:26 137:1 138:13 140:22 142:1,6,17

143:3 145:11,12,25 148:20 149:9,11 151:9,9,11

151:17,23 156:20,21 189:14,16 190:20 206:14

207:5,7 219:14,20 220:23 221:18 229:4,17,19

230:20 237:9 242:22,23 246:4 252:14 258:15

264:19,21 266:5 270:18,20 271:1 277:21 280:18

280:21 282:10 286:4 287:15 288:13,14 295:9,15

296:2,18 301:26 303:20 304:10 305:4,11 310:9

317:19 323:6,9 329:26 330:2 332:9 333:19 334:4

336:9,22,23 345:8 347:7,8 352:18,25 353:23

354:19,21,23 356:4,8,17 363:10 364:17,26

370:10,16 378:12 380:4,5,12,13 382:7,7,8 383:23

384:3,17,20 385:12 386:4 388:21 390:15 393:12

393:24,25 395:2 396:9 397:4 412:9 413:1,2 415:5

415:9,12 416:11,12 417:18 418:12 419:9 420:2

420:13,16,18,22 445:26 446:11,15 447:17 451:19

454:24 455:12 456:9 457:2,7,16 458:11,26 459:2

465:24 466:10,11,23 469:7 476:10 480:5 481:26

482:12,13 483:17,19 486:22 487:25 491:26 492:4

493:11 494:7,11

old 325:10,11,22 411:14

older 259:9 317:4 432:17

Olmstead 122:11 212:22 214:2,3 258:16,17,18

263:15,17 264:16,25 265:19 266:8 267:7,13

268:9 269:7 270:1,14 304:16 430:19

omissions 247:26 248:2

omitted 374:4

onboard 259:1

once 34:26 81:19 100:8 101:17 104:4 136:20,20

142:16 143:2 161:17 208:9 217:18 227:9 269:6

304:1,14 322:9,23 328:20 388:22 389:2,23 397:8

430:6

ones 97:19 129:5 185:4 196:1 223:14 230:5,6

235:19 266:9 274:20 295:14 300:24 310:19

314:24 325:10 369:2 386:24 409:18 440:15

444:24 445:16,21 465:5,17 474:10 491:16

onetime 486:4,6

one-generation 20:18

one-third 220:9 409:8 412:26 413:6 414:8

one-time 280:11

one/one 423:18

online 436:22
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onset 250:10 367:11 418:16

Ontario 6:6 211:19

onus 416:21

on-hand 368:14

open 1:2 27:12 182:18 183:8 220:24 221:5,6,9

377:23

opening 6:23 8:13 15:26 77:19,22,25 78:19 79:1

173:14 182:4 250:10 398:25 459:17

operate 16:25 21:13 111:6 146:1 466:8

operating 24:12 149:21 221:4

operation 451:18

operational 22:26

operationally 415:3

operator 451:14

opine 504:5

opinion 37:2 200:17 224:17 232:4 235:6 236:13,26

258:6 269:23 285:10,24 289:17 353:4 364:19

402:17,22 420:5 422:13 439:22 444:10 446:21

447:21 449:10,13 472:20

opinions 461:2

OPP 1:9 8:20

opportune 355:24

opportunistic 441:8

opportunities 21:2 29:6 479:4

opportunity 3:5 181:19 208:8,11 231:4,7 237:14

237:14 246:24 248:4 284:18,22 287:10 310:3

321:1 330:3 331:19 347:19 383:19 400:9 498:17

opposed 144:15 186:1,4 241:6 267:14 268:12

404:2 407:21 490:18

opposite 147:24 268:18 272:24 417:5

oppressed 452:7

optic 453:15

optimal 106:22

optimize 24:16 327:9

option 36:4,6,6 236:11 243:11 245:9 285:15 292:6

293:21 444:17

optional 74:26 76:14 86:22,24 95:22 105:7 106:26

114:5 168:26 176:3 291:26 292:5 294:23,23

354:16,16 459:23 461:12

options 16:22 257:12 302:8 484:20

oral 3:14 74:14,18 75:26 76:3 83:23 84:8,9,10,11

84:13 351:5 354:24 430:17 433:14

orally 77:18 80:2

orange 96:25 104:10

ORD 212:24 214:9,18 215:25

order 22:1,23 24:7 31:13 33:18 51:6 53:22 54:14

162:6 197:10 198:18 223:21 228:13 229:23,25

230:5,8 242:13,20 272:15 274:20 299:9 314:3

352:4 359:23 376:14,18,25 412:17 428:16,20

443:17 462:19 489:20 491:19 494:20 497:7

orders 12:6,13 105:25 155:15 195:9 206:24 281:21

284:23 297:3 299:26 306:12 313:6 314:9 318:10

320:26

ordinarily 294:24

ordinary 185:6

organ 19:24 76:21 77:22 78:13 80:6 81:6 155:4,9

168:16 170:2,4,10 174:5 176:26 244:24 459:17

461:7 487:3

organic 100:18 152:26

organics 111:21

organism 40:7 132:8 165:4 173:15 205:11 253:11

400:2

organisms 109:23 118:6 140:2 253:7 256:19

361:26 403:17 404:17

organization 11:23 12:24 155:13 173:9 290:10

306:7 334:26 337:1 347:22 491:9

organizations 155:3,8 164:24 283:22 314:2 335:14

409:2

organized 9:21 290:9

organizes 333:8 334:11

organizing 221:19,20 290:17 333:21

organophosphates 28:14

organs 40:5 155:4 175:14 309:2,3

original 199:12 348:18 353:26 458:7 466:9 499:12

originally 70:15 287:18 348:10 354:2

Ortego 288:14,15,15 294:1 295:9,15,26 308:19

OSCP 7:2,8 8:2 15:9 225:21

OSRI 54:21,21,26 182:12 233:6 244:11 311:19

312:11 324:26

osteolathyrogenic 434:4

ought 192:23,26 466:9 469:15 480:17

outcome 5:11 11:17,19 19:19 20:7 25:13 43:22

48:12 268:23 329:10 331:24 334:10,13,26 488:20

491:1,5

outcomes 11:17 48:8 49:7,17 136:24 332:21

358:17 359:6 428:24

outer 372:9

outlier 219:2 344:16

outliers 105:2 106:1,3 344:19,22 372:11

outlined 311:9

outlines 55:7 337:26

output 94:25

outside 60:1 61:9 66:26 67:12 68:21,23 69:8 79:14

147:23,25 247:10,11 341:5 344:18 499:18,20

ovarian 78:14 388:3

ovariectomies 243:21

ovariectomize 325:22

ovariectomized 44:3 74:16,20 75:11 243:12

ovariectomy 243:20

ovaries 78:20,25 388:12 486:3
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ovary 77:26

overall 13:23 30:10 31:23 41:11 45:18 46:17 47:12

47:23 49:2 60:1 64:3 65:4 67:1 93:18 100:25

106:10 108:20 109:1 120:12 131:15 145:1 159:11

167:14 181:2,23 200:11 235:6 248:3 258:7

277:13 299:9 300:15,21 349:8,13 351:8,19,23

356:24 371:3 373:8 374:9,14 375:5 385:4 386:5,8

386:25 389:1,22 391:26 425:16,25 430:13 432:15

460:4 468:8 470:9 471:22

overarching 230:14

overfed 122:14

overfeeding 214:22 264:6,13,14,17 439:18,20,22

442:4,26

overlaid 16:14 23:2

overlap 120:3 129:5 189:24 427:18 465:1 474:15

overlapping 481:20

overlaps 124:14 158:13 381:17

overly 486:19

overnight 233:14,17 436:22 441:4

overrides 397:9

overshadow 414:5

overt 29:23 30:13 56:6,7,8,11,13 75:17 77:1 79:7

79:11 81:18,23 82:7 85:23,25 86:3,6,10,14 88:8

88:12,14 91:3 92:18 93:6,10 98:6 102:14,16,26

102:26 103:13,19,22 104:7,9,11,16 106:18

117:22 156:8,15 160:5 161:10,15 162:22 167:16

170:6 172:3 174:20,23 176:15 178:6 180:2

244:22 254:16,20 255:20 269:11 273:15,18,24

274:5,10 276:23 312:21 316:5 325:26 326:4

328:21 329:1 383:9 387:20 395:16,21,23 396:1

396:21 397:2,24 398:2,21 399:3,12 400:3,5,19

402:18 404:13,21,25 405:1,3,24 406:13 407:18

409:7,9,12,18,19,24,26 410:8,18 411:3,19,21

413:17 414:3,4,23 419:12 459:25 461:14 464:13

467:21 471:18 481:20 484:18 485:2

overtly 244:14 315:21

overview 15:17 16:7 32:1 53:19 56:19 94:6 115:1

118:15 153:8,9 156:23 159:11 164:6 171:3

172:14,16 174:12 177:13 213:6 231:10 237:17

overweigh 414:5

overwhelmed 207:11

over-feeding 261:2 263:11,13,16

ovotestes 409:24

OVX 351:7,9

oxidase 434:5,7

oxidative 239:14

oxygen 96:10 97:17,20 99:25 100:9 101:3,5 361:11

432:24

oyster 443:12,21

oysters 443:11,20

o'clock 395:5 420:24

O-S-R-I 54:21

P

P 35:21

pace 502:24,26

package 434:17

page 59:18 157:26 174:15 281:10

pages 257:1 280:22 318:23 319:15 380:20

paid 489:15

pair 488:17

pairs 432:9,11,14,16 438:10 489:8

panel 1:1 2:3,6,13,15,18,21,25 3:23,26 4:5,7,14,18

4:22,26 7:1,17 8:3,19,23 11:5 13:17 15:23,23

16:16 24:5,13,19 25:22,26 26:18 27:4,5 30:9

37:20 38:17,20 39:25 48:19 49:23 52:2 64:18

69:11,25 72:18 93:22 107:7 114:9,13 115:10

130:15 132:18 137:5 164:20,25 175:11 177:16

182:20,20 184:6 188:13 194:16,22 196:2,23

197:6,16 199:1,22 207:18 208:9,24 210:2,6,15

212:15 218:1 219:22 220:1 229:17 234:13 246:4

258:4 259:13 260:11,15 285:6 288:20,25 301:17

305:15 315:16 321:4,14 345:8 349:24,25 354:7

358:8 363:3,11 365:22 366:2,4,7,9,15 367:7,10

367:16,22,24 368:7,11,16 369:6,11 370:21,24

371:10,18 372:11,16,18 373:7,10,17 374:5,9,13

374:19 375:4,10,18,24 376:2,5,7,11,12,14,19,23

377:3 378:15 379:12 382:15 384:17 386:9 389:1

389:22 390:14 393:24 396:16 398:1,5 408:25,25

409:5 410:6,15 412:21 413:13,18 422:6 426:15

426:22 427:1,9 428:8,14,16,20,23,26 429:8,11,16

429:23 430:1,3,12,19,21 431:1,3,4,7,18,21 432:6

432:20 433:3,17,19 434:9,11,13,21 435:6 438:8

439:16 440:22 445:9 449:8,10,22 451:19 452:17

452:23 453:23 454:5,23 458:17 460:10 461:1

466:11 474:6 475:2 476:21 482:12 483:20 484:7

487:25 488:4 491:26 492:21 493:26 494:6 495:20

498:18,24,26 499:10,20,21,23 500:20 501:13

502:14 503:25,26 504:21 505:1

panelists 411:13 499:19

panels 69:18 494:12

panel's 33:2 329:21 425:1

paper 9:10 30:13 32:16 50:5 59:18 71:17 114:26

122:4,10,12,15,18,23 129:13 154:9 164:16

166:18 174:15 181:18 197:2 213:20 231:17

234:20 247:24 248:23 252:25 254:5 272:8 273:7

273:23 284:7 288:24 290:15 291:6,6,20,23 292:7

292:14 294:3 311:9,12 322:7 324:3 328:17

337:12,21 339:17 349:5 350:20 353:20 358:5,16

359:5 366:10,12 369:21 370:3,14 372:3,7 374:2

374:25 378:23 380:21 386:21 399:7 403:4 430:16
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433:13 436:16,18 437:15 438:1,4 440:17 458:3,5

468:19 469:26 498:23

papers 424:21 426:25 440:8 488:20

paradigm 18:25 145:3 330:9 332:2 336:11 385:23

paragraphs 370:12

parallel 26:12 39:1

parameter 60:25 90:10 135:12 219:2 371:15 387:3

387:11 388:19 410:24 462:2 486:8

parameters 34:12 56:4 58:23 59:6 61:5 63:10 65:6

66:21 82:9 95:9,10,13 100:18,21 127:25 180:6

247:10,17,17 297:13,16,18 299:3 356:19 360:21

372:8 375:23 386:17 387:5,18 389:19 390:9

393:18,23 397:7,10,20,26

Parenthetically 484:6

part 9:2 35:24 37:22 40:9 50:7,7 68:24 70:15 84:26

102:22 109:25 129:11,19 171:17 184:19 185:9

187:2,16,20 195:10,11 200:3,13 202:26 204:14

205:4 206:25,26 221:26 225:20,21 227:17 228:21

286:20 305:21 319:11,14 320:8 322:24 325:14

326:8 354:11 368:3,9,18 381:11 387:2 388:22

391:4 398:11 409:4 435:23 444:10 453:25 461:20

475:12 479:9

partially 453:22

participant 3:1

participate 221:23 231:4,7 347:19 495:11 498:18

participated 231:5 306:10 347:22 358:13

participating 9:9 327:18 361:17

participation 10:25

particular 4:6 25:15 26:13 33:18 41:12 43:23,26

44:10 45:4 47:22 50:22 51:11,23 72:14 110:24

125:8 141:18 151:7 154:13,17 158:18 169:14

170:26 173:19 180:9 187:8,11 190:7 192:4,5,26

193:12 198:6 208:19 212:8 215:23 219:2,12

236:14 242:9,21 243:11 263:19 264:9 267:18

273:9 294:22 321:15 322:8,8 328:9,18 332:12

346:10 352:14 369:18 370:20 371:7,21,26 378:3

380:22 386:15 389:5 397:16 401:1 414:5 431:14

432:4 433:1 449:16 453:2 464:12 465:11 474:12

475:6 478:2,8 483:26 485:11 492:25 499:20

501:24 502:1,4

particularly 6:7 30:5 64:1 78:18 116:22 125:22

163:25 178:17 204:19 218:2 236:2 237:2 245:20

270:11 351:18 352:9 399:2 401:17 402:10 404:23

406:1 431:13 434:25 435:25 437:26 456:6 481:21

496:6 499:18

parties 221:23

partition 152:26

partly 289:1 452:2

parts 30:23 85:14 102:20 403:17

partway 36:2

pass 33:15,23 74:13 75:26 262:10 344:13 413:25

passed 318:18

passes 186:13

pass/fail 71:6

Pat 330:15 331:2

path 275:13 286:11 314:1 500:26

pathogen 441:8

pathogens 123:26

pathological 430:21

pathologies 434:1

pathologist 142:15 446:21 447:3 452:4

pathologists 134:13 450:24

pathologist's 446:24

pathology 138:5 158:15 430:24 446:17

pathology's 449:11 450:4

pathway 11:19 13:11 14:18 17:8 18:6 19:19,20

20:1,7 25:13 26:26 29:15,24,26 30:4 43:11,19,23

44:24 45:18,19 46:23 69:21 115:15 153:7 154:13

154:22,24,26 155:26 156:2,6,10,26 158:6 163:2

163:21 164:4,14,20 165:15,24 166:8,14 169:14

169:15 170:23,26 171:17 172:12,12,16,18,20,23

173:1,7,22 174:8,11,14 176:6,12 177:11 180:9,23

190:12 205:9 251:1 268:23 329:11 334:26 335:13

335:17,19 336:11 342:24 369:16 373:15 375:9

377:15 400:13 410:25 461:4,15 467:26 468:11,13

468:25 474:12 476:18 477:4 491:1,8,20,23

496:15

pathways 5:11 11:18 18:6,7 20:4 23:6 25:14 28:22

29:8 33:18 34:14,15 40:2 41:17 43:10,18 45:10

46:14 48:20 153:9,10,15,20 154:7,15 157:11

163:3,10 164:8,18 166:17 175:8 176:21,23 179:8

179:24 180:12,17,22 181:1,7 183:2 184:2 189:13

190:1,16 191:8 250:25 251:9 334:10 335:21,23

336:3 366:19 367:22 395:15 400:13 458:16

468:15 471:24 472:15 474:18 476:20 481:5,12

490:26

pathway-based 334:6 336:21

pathway-by-pathway 474:15

Patience 31:8 505:12

patient 295:1

Patino 5:26,26 36:24,25 107:7,8,23 130:15,16,19

131:4,7,12 132:4,15 140:17,18 142:1,4,24 143:8

144:7,11 145:11 149:10,11 150:13,21 203:13,14

211:13,13 265:24,25 269:21,22 293:17,18 295:12

304:22,23 305:3 364:14,16,18 401:12,13 412:9

412:10 415:6,7,12 419:20,21 423:11,26 447:17

447:18 452:11,13 455:1,9,12 457:20,23 498:16

Patino's 216:6

Patricia 323:7,9,10

pattern 123:16 160:16,24 163:4 202:1 251:12
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461:26

patterns 184:9,12 201:9 329:17

pause 227:14 301:22

pausing 227:16

pay 135:25 343:24 408:17 440:5

paying 408:19

PC10 233:24,25 234:6,14,16 235:8,20 236:16,18

340:24

PC20 235:1,21 236:17,19

PC25 234:25

peak 35:15 52:19 488:18

peaking 117:1

peer 2:19 7:10 8:20,24 9:1,10 10:10,24 11:5 27:14

150:24 200:10 290:7,25 318:18,21,24 319:1,2,13

319:19 502:13

peer-review 8:18,25 37:20

penicillin 470:9

penis 76:12

people 3:6 6:16 135:19 198:4 207:10 220:25

221:11 222:3,5,8,25 223:4,5,18,19 224:17 226:1

226:3 227:13 258:22,24 259:1 265:9 271:11

286:7 301:5 302:25 304:15,16 322:14,17 323:11

330:22 331:20 343:8 355:11 368:14 418:2 432:13

432:13 454:13 477:7 496:7

perceived 70:16 106:3

percent 58:17,19 60:17 66:15,16,18 69:1,1 75:9,10

76:22 77:3 79:14 87:21 90:7,13 93:12 96:5,5,10

96:26 97:26 98:1 99:6,9,12,17,18,25 100:3,4,5,8

100:9 101:21 102:2 103:21 110:22 119:26,26

120:8 123:4,22,23 124:5 148:9 149:1,4 173:24

204:7 214:20 215:3 218:2 230:4 233:17,18 234:6

234:22 235:15,21,26 236:2,6,6,14,23,26 250:15

251:7 253:4 254:6 264:1 265:11 272:9 273:8

301:20 328:4 343:1 345:2,24,25 346:9,10,13,15

347:3 349:7 350:21,24 351:2 359:16,16 371:8,9

376:4,6,10,10 381:20 400:23 405:9,9,10,10,15,15

406:21,24 412:25,26 413:11 415:11,15,20 416:5

416:16 417:1,12,15 418:15,17,21,25 419:3,7,7,11

422:14,15 425:17,22,23 426:16 428:11 429:15

430:15 440:4,23,24 441:21 443:14,16 453:8

464:3,5,6,8,9 471:8,11,12,15,25 472:1,2,3,4,5

475:23 479:24

percentage 33:12 90:26 91:7 407:24 422:11

percentages 471:11

percentile 68:26 359:15 448:5

percentiles 119:5

perception 70:9

perchlorate 414:21,22 415:2

Percival 505:26

perfect 346:5 389:7 452:10

perfectly 246:12

perform 13:4 32:23 38:22 50:9,11 130:7 199:5

203:19 204:19 339:20 443:17 469:15,17 483:23

performance 1:5 2:8 4:2 7:19,20,25 26:18,23

28:26 29:2 30:11,17 31:22 38:16,21 49:7 51:21

53:19 54:1,3,6 56:1,3,4,5,22 58:22,25 59:4,10,12

59:15,25,26 60:2 61:3,7,16,21 63:2,5,9,15,16,18

63:26 64:3,4,23 65:5,6,15,17 66:19,21,23 67:8,10

67:13 70:4,14,16,25 71:2 72:21,21,24 74:3 75:5,8

75:12,20 76:21,24 77:4,8 78:23 79:2,4,15,18 81:9

81:16,26 88:19 94:9 96:3,5,20,22 98:17 99:22

100:25 106:12 117:13 118:3,7,17 119:2,21

120:12,13,16 129:14,16 135:24 147:5,10 151:24

152:1,5,11,15 153:3,4,11 155:25 156:2,5,23

158:3 159:14 162:26 164:5,18 170:8 172:24

173:6 179:16,18 180:1,1 181:17,23 182:17

196:23,23 204:15 210:4 213:21 215:15 218:10

222:20 223:14,17 231:17 235:6 237:23 238:3,20

243:4 246:11,13,17,19,21,25 247:1,5,8,10,11,18

247:21,23 248:3 251:19 252:22 253:3 266:4

271:20 272:2,8,9 273:2,12 284:4,4,6,12 296:20

297:16,25 300:11,12 301:5,5,18 302:13,16

308:24 337:13,13,21 338:1,18 340:21 344:13,18

345:1 346:11 347:26 349:12,15 351:8,20,23

356:14 357:15 358:22 359:7,10,22 361:16 362:8

362:12,16 365:2,8,9,14,26 366:11 367:25 370:20

370:21,25 371:3,4,6 372:13 373:8,18,19,21,25

374:16,20,25 375:1,5,12 376:4,8 379:18,19,22

380:2,15,17,25 381:8,11,18,25 382:1 391:20

392:2,19 429:9 430:10 432:7 438:9 444:2 458:21

460:5 462:22 466:17 468:4 469:22 470:13 476:19

480:13 483:6 501:24 502:7,18

performances 152:4 463:7

performed 29:5 55:19,21 57:16 64:12 71:5 73:9

75:21 106:2 130:25 217:26 291:13 321:7 325:17

348:24 350:1 373:12 374:10,14 378:16 397:5

480:25 481:8,15

performing 12:14 23:11 24:11 26:25 66:24 120:26

179:17 205:6 252:24 348:12 357:14 431:19 496:7

performs 48:5 65:3 69:26

period 3:7,9 78:10,11 80:24 92:10,14 99:21 116:18

203:6 207:17,26 208:9 219:16 238:10 249:8,20

271:2 305:9,12 356:5 385:19 415:24 417:20

419:5,17 422:15 430:20 475:4 490:15

periodic 123:9 391:14

peripheral 163:16

periphery 452:24 455:17

permanent 4:14,18,22,26 15:22

permits 236:1

person 182:21,21 396:11
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personal 247:22 435:3

personally 261:5 264:16 389:10 399:14 491:18

personnel 327:4

perspective 10:4 132:21 135:17 223:8 228:20,26

249:3 252:7 279:10 294:18 295:6 303:26 321:14

323:20 328:14 347:26 400:5 439:19 488:2,16,26

perspectives 222:17 223:9,9 224:12 225:26 226:5

pertains 462:21

pertinent 74:23 279:26

perturbances 46:10

perturbations 43:11 79:26 157:18

perturbed 40:15 279:4

pesticidal 36:23 179:12 195:23

pesticide 2:20 8:5,14 10:8 13:25 14:15,17 15:8

17:10 23:6,17 25:3 28:7,13,15 32:19 33:8,10,16

34:11,22,23 39:4 53:15,18 54:13,16 93:25 114:11

114:22 155:18 185:10,11 197:20 307:17 309:8

310:11,20 325:14 383:9

pesticides 10:13 34:3,25 159:2 185:15,17 207:1

292:21,23 307:14 308:7 310:13 331:4 368:26

440:10,13

PETA 323:8,12,16

Petri 445:15

PGP 442:12,15,22

pH 218:4 438:2,3,24 439:3,12,23

pharmacologist 296:15

pharmacology 4:16 210:14,25

phase 93:14 98:13,13,19,20 99:20 101:12,16,18

159:13 216:7 217:13 244:26 245:25 333:16

phased 243:7

phases 149:24

phenomenon 212:25 215:12 411:20 426:12 433:22

433:25 448:10

phonetic 121:26 232:14 356:2 370:15

photo 94:4

photographs 435:7

photomicrographs 433:15

physical 97:3 98:3 148:5 195:24 308:24 312:12

368:25

physical-chemical 54:15 310:26

Physicians 323:17

physicochemical 24:6 26:4 28:9 32:22 36:22

physiochemical 332:14

physiological 6:10 211:23 315:20

Physiology 211:26

phytoestrogen 84:16,17,23 250:4,9

phytoestrogens 85:2

phytoestrogen-free 85:3

pick 40:17 44:19 90:3 274:20 342:10 438:18

445:16

picked 191:19,20 255:18 316:7 340:26 342:7 398:4

497:11

Pickford 255:14 292:13 294:3

picking 137:5 387:20,25 504:4

picture 93:18 256:8

pictures 258:25 264:5

piece 19:4 89:17 198:6 396:15

Piersma 5:22,23 36:13,14 51:2,3 52:1 85:20,21

86:9,16 138:14,15 211:10,10 462:16,17 469:11

469:16,24,25 477:16,17 480:6 482:2,3 486:22,23

493:11,12 498:5

Piersma's 466:4 480:9

pigeons 6:15

piling 392:17

pilot 223:20 228:6

pipette 445:21

Pipiens 261:8 264:23 265:1

pituitary 44:7 45:6 47:11 77:16 78:3,26 80:14

81:13 388:11 427:10,12 450:2,3

place 89:18 218:19 274:2 316:17 351:14 355:8

370:23 372:23 379:22 389:6 400:17 411:6 414:3

419:19

placed 198:10 306:26 351:14

placeholder 196:5

places 71:17 357:21,23

plagued 239:2

plan 20:20 156:14 366:16 502:23

plans 320:13 409:15

plasma 94:20 95:21 105:13 106:25 168:5,26 169:1

176:2,3 341:26 459:23

plate 73:17 265:20 281:17,18,25 346:7,18,19

487:13

plateau 63:11 337:22

plates 64:13 344:23

plating 233:13,16

plausible 23:21

play 121:23 201:10,11 205:18 397:16 431:8 443:5

444:8 461:18 484:5

played 303:19 306:8

player 285:20

playing 201:12,18 202:2 496:12

plays 163:18 316:5

pleasant 411:10

please 2:2 3:9,14 54:17,26 59:17 73:13 103:14

151:18,21 167:18 170:5 171:19,24 177:18 189:7

208:3,7,11,15,17,19 219:18 237:6 281:10 356:11

365:12,13 385:7,8 395:22,26 405:23 426:4 447:7

457:6 458:20,23 466:17,20 480:26 482:25 483:11

483:14 492:5,7

pleased 8:16 242:26 496:12
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pleasure 114:26 504:19 505:2,13

plenty 35:17 441:22 485:4

plot 105:21 304:24 403:9

plots 340:1

plotted 340:26

plotting 106:7 298:23

plus 33:12 51:13 66:1 117:4 230:15 277:18 298:22

299:25 326:25 359:17 399:23 414:20

PND 350:10

pockets 195:18

point 3:21 4:6 22:14 27:19 34:20 49:4 58:26 59:13

108:2,3 109:3 112:15 124:7 137:24 147:26 183:4

183:15 186:8 195:13,15 197:1 201:2 207:11,16

208:2 212:11 217:9,20 218:8 219:12,14 226:26

227:2,7,7 228:25 238:11 244:7 248:12 249:4,9

259:16 261:22 262:17 270:19 280:11 283:19

296:18 297:5 298:26 300:2 303:9,18 304:4 305:7

314:19 320:6,11 336:12 340:5,7 343:20 346:3

347:1 349:18 355:24 359:1 369:20 378:23 392:15

407:23 410:8,12,13,17 415:1 419:13,18 426:11

436:4 440:9 443:8 445:26 448:26 451:15 454:24

457:8 461:8 462:15 463:17 464:14 477:14,19,19

479:8 482:18 483:26 484:15 494:13

pointed 27:23 35:23 90:19 139:25 147:15 318:20

438:7 441:14

pointer 118:26

pointing 451:4

points 9:26 116:21 117:7 128:14 249:11 260:5,17

282:13 296:17 304:3 317:24 341:16 398:4 408:12

414:6 437:25 444:1 463:25 487:23 493:17

point-of-contact 505:6

policies 183:15

policy 6:25 7:9 15:19,20 25:1 39:22,23 144:17

183:3 229:21 230:3,4 243:2 252:20 271:2,10

282:11 288:17 290:13 296:2 305:19,21 312:14

313:2 314:11 316:11,26,26 317:6 448:21

pollute 439:22

Pollution 7:12 8:6

polymer 26:5

pondering 114:15

poor 120:9 128:8 235:7 298:17 430:10 460:6

466:25

poorly 128:18

population 117:3 218:21 227:6 240:21 242:18,18

267:2 386:1 403:15 404:18 405:16,18 417:2

429:15 491:6,15

populations 17:16 122:7 139:23 214:20 215:20

262:22 270:4 359:24 425:21 435:14

population-relevant 279:2

Porter 475:10

portion 33:11 44:15 68:3 121:7 283:13 430:2

497:24

portions 163:10 451:25,25

pose 301:17 309:4

posed 302:17

posing 196:22

positive 21:13 28:1 30:7 41:20 43:6 58:5,5,7 59:11

60:16,18,19,25,26 61:1,23 62:18,19,23 63:12,12

63:17,17 64:15,20,26 66:9,22 75:2,3,6,14 76:17

76:19 78:17 81:3 146:15 156:7 157:25,26 165:17

165:20 166:25 171:8,13 173:18,23 174:1,8

177:19 178:9 189:12 191:7,11,15,16,21 192:4,24

193:2 197:5 198:16 205:21 233:24 234:15,17,23

234:23,25 235:2,8,20 236:8,18,22 243:26 246:16

250:21 252:10 291:11 294:5,5,7 308:3 315:6

329:1,1,3 332:25 337:18 340:6,17,22,22,23

341:24 342:13 346:2,2,9,16,20 351:6 356:21

360:5 369:19 370:17,20,23 380:2,19 382:1 383:7

384:23 390:3 391:6,13,15 395:19 399:4 444:18

444:22,26 458:19 466:6 467:5,16,18,19,22 468:2

470:5,7 471:24 472:1,3,4,5 473:4,19,20,26 474:3

474:4,5 476:18 478:11 480:16

positively 88:17 469:17

positives 61:12 154:2 198:18 225:5,6 232:18

234:18 235:4,9 240:24 245:13 251:22 339:25

467:24

possibility 83:12 191:19 270:5 394:8 448:23

453:17

possible 29:20 48:8 91:12 100:7 116:8 124:23

126:4 132:11,18 135:6 136:17 141:1 146:22

153:18 163:7 186:3 190:2 207:13 221:23 248:16

302:14 331:10 362:13 376:21 399:15 409:22

423:22,23 428:22 429:12 448:19 449:25 488:7,9

possibly 33:13 56:9 121:20 133:24 213:5 276:11

293:8 310:24 317:8 358:1 404:16 406:10 430:3

444:8

post 116:2 196:10 362:1

posted 148:25

poster 224:20 376:26

posterior 213:2 259:19 266:24

posters 224:15,18

postnatal 74:17 76:2 77:18 80:2,3 350:9

potassium 431:23 439:15

potency 51:9 224:2,6 228:11,11,15 316:15 462:26

478:6

potent 227:7

potential 10:9,14 11:12 13:10,20 14:5,22 18:15

19:15 21:16 22:3 24:1 26:10,13 27:16,19 28:21

29:14 30:19 31:1 34:8 35:5 41:11,17 42:26 48:9

48:12 49:22 56:12 60:5 94:14,15 95:26 96:16,19
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102:14,26,26 103:9,22 104:7,9 105:2,11 106:19

115:14,17 116:22 118:8 125:12,14 126:24 146:4

153:19 154:6 156:24 157:4,12 158:5,21 159:2,6

160:7,23 161:24 163:5 165:8 166:13 167:13

170:6 172:4,26 174:26 176:15,20 178:7 179:7,23

180:21 181:1,6 197:11 198:25 201:26 202:5,15

202:18 213:25 233:4 242:11 249:12 251:21 256:9

278:23 293:6 299:14 307:10,25 308:26 310:20,21

311:3,8,20 312:2 317:15 320:20 332:15,19

351:19 352:22,23 353:19 362:10 366:18 367:5

369:7 375:8 392:16 395:19 396:5,17,26 406:25

409:13 413:21 419:6 426:18 429:9 431:4 448:15

449:15 460:20,24 462:6 464:25 467:21 478:7

479:25 481:20 483:4,10 484:26 485:1 502:22

potentially 46:19 105:9 107:2 140:13 152:6 154:17

218:13 235:4 275:12 323:24 325:4 329:2,21

380:16 397:14 414:7 440:2 447:4 455:4,26 456:4

456:7 459:24 478:7 492:12

potentials 29:18 101:14

POTOMAC 1:14

Powell 31:9 505:13

power 21:24 22:9 117:20 208:2 257:25 258:13

391:8 497:20,23

PPS 165:3 168:4

practical 144:4 224:7

practice 110:18 144:18,23 145:4 233:14 272:22

273:17

practiced 286:1

practices 144:20 222:16 282:7 310:14 439:26

practicing 403:23

practitioner 246:8

practitioners 229:5

preceded 299:26

preceding 179:14,26 180:11 181:11,19 453:22

precipitate 239:21

precise 33:9 406:23

precisely 306:13

precision 335:2 406:15

preclude 26:7

precursors 399:25

predict 183:23,24 235:22 361:4 488:20

predicted 124:3 192:23

predicting 160:24 205:13

prediction 469:14

predictions 335:7

predictive 233:4 292:15,20 330:8 332:2,4,8 336:11

predominantly 66:26

preexposure 274:18

prefer 287:17,25 316:10,24

preferable 322:16

preferably 487:6

preferred 74:21 249:15 462:24

pregnancy 309:24,26 310:4

preliminary 57:7 118:16 138:18 155:25 156:1,5

158:2 173:6 192:15 483:5,13

premature 163:22 244:5 245:22

prep 59:3 122:18 338:8 339:5

preparation 15:25 57:15 232:8 241:3 451:23 505:2

505:9

preparations 63:6 231:23 378:20,25

prepare 304:6

prepared 505:1

preparing 205:4 505:5

preps 342:8

preputial 80:3,5,8 81:5,14 461:7

prescreen 366:24 493:4

prescreening 69:19 377:9,21 382:18

prescriptive 232:16 281:15 364:2,5

preselected 197:17,18

presence 56:13 77:1 79:7,11 81:18,23 85:23 86:6

104:6,9 127:2 156:8 159:5 160:5 161:9,14 162:22

167:16 170:6 174:20 176:15 276:2 339:26 360:8

395:20 426:4 450:23 484:18

present 51:5 53:24 56:20 57:14 83:16 86:11

100:11 125:22 134:5 162:2 164:10 196:13 220:1

243:1 246:8 289:23 352:24 460:9

presentation 32:10 39:20 51:4 52:3 53:13,25 54:1

73:25 92:4 93:24 94:6 114:10,25 130:11,12 146:9

151:13,18,21 153:21 157:2 158:12 164:2,6 175:9

182:4 185:9 207:9 208:2 212:9,10 219:23 225:10

225:11 226:1,14 229:20 233:25 241:11 242:5,23

252:15 280:18 281:1 285:8 314:4 326:13 379:15

430:19 433:14 497:18

presentations 3:19 31:13 50:25 53:20,22 124:16

151:11,23 152:3,15 153:4 154:15 176:18 179:15

179:26 180:12 181:11,19,22 206:4 207:13 208:5

223:1 229:7,20 230:2 256:12 308:18 312:4 313:4

354:24 430:17 467:15 468:19 472:18 494:18

495:4,13,17 497:14 498:7 500:25

presented 86:19 103:26 115:19 130:26 164:25

166:17 168:7 171:4 172:15 174:14 177:13 181:18

184:14 219:15 255:25 259:3 291:5 294:2 303:6

356:11,23 358:20 362:12 367:8 407:4 426:22,25

430:16 460:4 461:16 466:24 468:9,12 470:26

472:9 480:14 483:20 484:10 488:13 495:26 498:1

presenter 39:21 224:20 237:10 246:4 258:15

288:14 296:2 305:12 317:19 323:7 329:26 336:23

347:17

presenters 6:24 54:17 151:18 186:7 488:4 490:19

495:13 496:1 497:26 499:11,18,20
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presenting 51:14 53:19,22 92:9 114:24 156:23

159:15 172:22 252:19 271:14 278:6 476:18

presents 433:15 449:16

presumably 136:16 185:25 188:25 298:9 386:18

presumed 414:3

pretend 333:25

pretty 27:4 35:6,20 36:1 69:4 140:11 148:2,26

150:9 207:11 219:7 274:12 284:24 323:23 342:15

345:4 347:2,3 348:21 380:9,11 396:12,15 397:10

401:19 438:25 452:5 465:10,18 482:14 491:12

494:19

prevalence 122:6 123:17 124:3,8 138:3 213:17

214:6,19 215:4,14 425:15,16 426:5,7 428:10

441:20

prevalent 45:16 136:6 218:15 331:25 425:22

prevent 407:12 442:13

preventing 406:18

Prevention 7:12 8:6

prevents 442:23

previous 10:4 48:14 131:13 166:20 175:9 176:18

186:7 195:12 311:16 312:3 313:4 314:3 397:13

399:17 481:19

previously 53:21 54:10,25 56:16 155:14 185:6

333:6 367:10

pre-exposure 98:12,13,19 99:20 101:12,16,18

107:14 111:26 275:20

pre-market 307:24

pre-metamorphosis 401:20

pre-pregnancy 310:4

pre-pubertal 243:23

pre-screen 194:23

pre-screened 308:4

pre-screening 184:22 194:9 196:10

pre-validation 249:23,24 367:13

price 198:21

pride 343:9

primarily 6:3,16 22:15 25:6 38:24 40:7 44:21

47:16 105:3 137:13,14 198:5 257:14 321:24

416:9

primary 23:11 26:22 40:11 54:4 55:19 69:21 76:7

85:24,24 114:21 243:6,22 244:1 401:18

principal 122:11

principle 61:26

principles 25:19 186:21

printouts 297:23

prior 3:8,13 7:6 94:25 98:14 107:14,20 149:21

183:4 184:3 244:18 281:13 290:5 308:4 309:23

322:1 348:13 351:14 352:21 388:4 423:13

priori 302:26 402:17

prioritization 11:8 16:11 18:14 23:1,5 24:5 25:11

26:14 227:26 366:25 371:20 372:1 377:3,9 494:5

prioritize 23:6 24:8 25:12,20 26:1 228:3

prioritized 33:17,21

prioritizing 25:23 226:13

pro 429:19

probability 492:26

probable 431:8

probably 25:3 69:11 72:21 107:15 112:20 113:22

113:22 139:17 149:17 185:1 193:5 216:19 217:15

217:17 220:4 222:15 223:6 240:12 248:2 272:26

276:18 280:9,13 281:8,16 282:5 289:18 296:8,16

296:26 305:22 313:2 319:22 321:9 331:5 342:16

380:20 400:6 407:12 411:14 412:15 415:25 416:4

418:15,17 419:22 435:18 438:2 440:24 441:7

444:11 445:23 450:2 465:14 477:18 488:21,22

489:18 494:18

problem 37:1 69:2 109:26 148:23 215:6 266:6

275:24 281:21 282:1,1 283:24 388:1 392:16

398:14 403:26 406:18 407:3 413:16 415:14

422:16 424:3,14 437:2 438:15,16,20 441:9

444:16 484:26 487:20 500:5,7

problematic 249:2 266:8 347:5 371:11 399:1

408:11 413:7

problems 28:22 83:17 121:5 245:5 257:16 266:3

271:25 276:23 281:7 285:3 287:6 299:14 318:1

319:21,24,25 320:2,5,9 322:22,24 346:24 397:19

406:18,26 443:9,18,19 465:9,11 468:4 496:18

procedure 149:14 283:11,15 298:13 333:21 454:12

455:9,13 457:26

procedures 55:9 126:25 283:6 300:6 314:5 454:9

458:6

proceed 221:14 430:8 451:2

proceeding 387:2

proceeds 314:8

process 8:11,25,25 9:2,9 11:2,7,8 13:2 14:4 27:8,12

32:17 34:1,5,22 35:1 53:4 55:7 72:10 101:15

129:21 150:6 160:15 182:16 196:8 268:16 293:9

320:25 324:3 333:26 336:16,17 362:3 367:18

401:17 429:2 484:5 498:8,9 504:23 505:17,17

processes 8:22 11:22 25:20 39:8 49:16 183:7,13

200:11 427:7 499:13

produce 17:5 78:7 80:21 264:15 316:14 439:25

450:2 488:17 489:8,11 492:17

produced 18:13 60:12 62:15 66:5 99:7 254:23

264:10

produces 492:15

producing 62:23 412:14

product 307:17 308:23

production 64:14,16,20,22,24 65:12 66:3 206:16

206:21 207:4 238:1 239:12,17 240:4 241:16
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251:15 266:10 310:3 373:15 389:17 435:16 486:3

488:18

productions 50:22

products 50:17,21 306:13 310:21,25 490:18

professional 47:25 446:21

professor 4:3,12,20 5:6,9,25 6:5,9,14,19 210:8,11

210:13,17,25 211:3,10,18,22,26 212:2

proficiencies 358:18

proficiency 67:16,16,18,19 72:22,25 73:3,5,9,16

73:18 246:21 247:15 343:7,14,17 345:10 356:12

356:19,22,25 357:1,13 358:3,6,8,14 360:11 374:3

374:6 391:14 476:2 490:20

proficient 73:2 343:12,14

profile 78:6 80:20 84:12 87:23 283:7,26

program 1:4 2:8 4:2 7:14,14 9:16 10:4 13:23 14:17

15:8,11,17 16:3,8,9,10,17 17:1,13,24 18:11,26

22:26 25:3,16 34:24 39:11 40:10 70:15 135:1

184:23 187:6 202:22 210:3 285:16 286:5 287:14

288:2,10 298:11 306:9,20 311:17 313:10,11,11

313:15,18,21 314:6,20 319:12 323:23 329:13

331:2,7,11,14,18,25 366:10 377:7 397:5 503:10

503:14,15,18 504:7,11

programs 8:5,14 16:13 53:15,18 84:26 93:26

114:12 293:12

progress 49:5 133:21 195:12 259:11,15 290:9

332:5 417:24 418:6

progressed 121:3 441:11

progresses 490:7

progressing 121:8

progression 121:17 336:20 447:10 502:24

progressively 335:4

project 296:23 298:14 307:26 330:6 335:18,21

336:26

projected 23:2

projects 221:8

prolonged 274:5

prominent 285:22

promote 118:6

promotion 127:19

pronounce 321:18

pronouncement 301:18

pronouncing 318:13

proper 14:2 112:18 364:21 386:1

properly 10:22 60:23 111:7 203:26 219:19 262:13

262:16 313:20 318:7,19 322:14 409:17 450:9

453:19

properties 24:6 26:4 28:9 32:22 35:21 36:22 54:16

97:4,5 98:3 109:12 195:25 206:20 312:12 368:25

467:12

property 113:14

propionate 283:17

proportion 33:8,10 251:22 405:18 468:6

proportional 149:16

proportions 123:26

proposal 291:4 328:17 367:17

proposals 292:26

propose 293:2 328:7

proposed 13:6 18:4 20:15 49:3 121:20 215:8 257:9

257:13 276:20 288:25 291:7 293:21 355:2

proposing 215:7 293:24 383:26

prostate 62:8 76:10 80:9,9 324:6 355:10,15

protect 16:18 220:7 239:13 321:15 474:3

protection 1:12 10:7 17:26 113:20 237:12 241:17

310:15 323:19 368:8

protective 16:23 22:24

protein 122:17 241:5 263:21 264:7 270:15 339:2

proteins 335:9 400:25 442:12

protocol 21:3 128:21 237:22 245:5,6 281:15 282:5

282:9,10,20 319:10 326:17 330:20 360:21 363:26

364:2 371:12 372:5 376:15 408:26 428:15,17,21

431:20 433:10 451:9 465:12,14 489:22

protocols 5:12 26:20 28:25 128:23 287:17,22

302:7,7 321:24 324:14,21 325:19 330:25 376:12

376:25

proud 271:17

prove 322:15

proven 413:7

provide 3:11,13 8:8 16:7,10 27:25 30:25 41:9,24

42:17,20 46:19 47:7 48:24,26 49:20 52:22 56:20

56:24 69:13 87:24 94:7 95:25 102:17 107:1

117:17 118:22 129:26 137:6 150:14 153:9 154:1

154:17 157:9 164:7 171:14 172:16 181:17 208:8

208:11,12 212:7 220:6 222:15 230:23,23 243:3

243:20 278:19 279:11 306:15 349:21 350:2 358:2

358:6 361:6 369:15 393:7 416:25 433:17 447:24

447:24 449:24 453:15 457:20 458:14 460:9 465:9

465:18 470:21 476:22 478:23 483:12 484:21

492:22 494:13,14 499:21 502:2

provided 2:25 11:1,2 16:1 17:23 31:23 60:8 91:15

123:13,20 134:9 143:7 165:12 176:11 178:13

179:25 180:4 200:18 219:23 255:21 305:17

308:20 319:3 350:7 357:17 358:16 360:6 365:20

365:21 368:14 375:7 448:20 449:6 459:6 462:5

474:13 476:21 481:7 500:10 503:17

provides 2:19,21 3:5 94:13 128:15 129:19,23

155:11 157:11 158:20 198:2 245:18 331:15,18

333:7

providing 7:9 31:14 48:6 49:6 128:12 151:6 153:8

172:23 179:23 208:2 232:16 285:16 305:23 306:8

368:8 402:1 505:16
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proving 158:26

provisions 2:14,26

provocative 476:14

pro-metamorphosis 253:22 401:21

prudent 36:6

prudentially 430:3

PSH 450:23

pubertal 20:2 29:11,21,25 42:13,13 44:8 45:8,24

45:24 46:12,24 47:7,13,17 55:2 74:8,8 77:11,12

77:13 78:11 79:22,22,23 80:18,24 81:2 83:15,19

84:18,23 89:3 93:8,8,17 115:19 124:15 157:16,22

158:5,13,19 161:3,12 165:3,25 166:5 167:20

168:2 170:13,15 171:21 172:8 173:3,14 174:6

175:15,18 177:1,4,8 180:14,16,26 181:13 188:1,2

189:1,5,11,11,20 190:15,25,26 191:2,2 225:13

244:23 246:10,13,26 247:8,19 248:1,9,15,24

249:20,25 250:3,14,17,21 251:2,17,20 252:10

255:3,8,19 257:19 276:18 284:6 290:21 291:7,12

292:9 293:23,25 294:21 295:18 300:13 310:9

325:12,12,16,21 328:16 329:3 348:3,4,25 349:8

349:13,19,25 350:7 352:9 353:4 354:15 384:24

385:18 390:23 394:4,5 395:11,11 397:15 427:13

427:15 456:11 459:16,21 460:2,8 461:6,17 464:1

464:6,23 465:3,4,19 481:22 484:12 485:3

pubertals 20:9 47:15 83:2 84:13 87:19 276:11

289:2,4 295:11 324:20 325:8 326:16

puberty 244:18 250:10 295:2 385:19 461:23

public 2:16 3:6,7,8,9,11 5:24 8:24 9:8,12 10:25

16:18,24 27:12 33:26 34:5,17 92:3,10 134:3

139:3 149:13 151:18,20 207:16,23,25 208:1,6,8,9

208:11,15,23 214:4 219:16,17 220:7 257:1 271:2

305:9,12 354:24 356:4 368:11 377:3 379:15

380:8 403:3 407:4 430:20 449:6 475:4 488:4

490:15 491:19 494:17 495:3,8,13,26 497:3,13,26

498:22 499:11 500:10 501:5 503:17,20 505:15,16

505:19

publically 454:11

publication 234:21 281:13 333:23

publications 238:12

publicly 318:17

published 24:23 135:22 140:20,22 141:2,16 244:10

264:4 290:15 333:23 353:20 404:8 435:3 436:16

437:8,15 438:3 440:9 452:1 469:26

pull 9:24 150:18 202:4 235:19 267:17 304:2

309:14

pulled 230:17 370:12 474:21

pulling 15:10 280:21

pulsatile 295:24

pun 264:14 420:14

punctuates 30:22

pups 326:21

purchase 341:19,21

purchasing 432:13

purely 195:9 236:11 313:13

purpose 39:26 56:11 57:11 74:2 77:12 79:22 115:2

187:4 198:11 315:3 320:4 322:16 365:23 395:24

402:23

purposefully 263:12

purposely 51:8,19 269:3

purposes 62:25 68:12 124:9 166:8 170:8 307:19

308:1 323:5 361:8 402:19

pursuing 292:11 490:22

pursuit 460:23,24

purview 38:14

push 21:20 261:3 286:25 329:14 339:14

pushing 236:14 273:21 417:6

put 8:4 9:5,11,19,22 16:22 19:4 22:4,25 23:7 24:23

32:3 33:26 34:5,21 108:2 149:22 202:16 221:2

225:14,16 251:11 261:12 269:18 271:10,16 274:2

274:13 278:2 284:13 287:14 307:26 314:26

338:15 339:17 343:15 367:15 378:1 385:16

397:11 416:21 422:8 423:8 425:3 445:15 480:4

488:16 494:26 499:23 500:21

puts 333:11

putting 113:3 203:10 220:16 266:11 338:11 372:22

414:11 431:24 488:4 490:5 501:8 503:5 504:7

puzzle 19:4

puzzled 32:11

pyrethroids 28:15 35:17,18

p.m 91:25

Q

QAQC 112:20

QA/QC 443:26

quad 115:23

qualified 296:14

qualitative 35:5 139:12 143:10,21 425:26

qualitatively 34:7 103:4 140:10 143:24

quality 10:6 71:4 73:17 100:18,22 115:4 117:11

118:12 129:22 217:25 218:4,10,23 264:7 281:12

318:16 322:15 432:8,9,13,15,18 435:11,11 437:2

437:6,21 438:22,25 440:7 441:9 442:5 443:3

444:3,4,9 445:12,22 475:13

quantify 18:23 369:23 374:22

quantitative 34:6 68:19 69:3 143:10 236:10,13

359:9 373:26 407:26 408:1 449:4 453:16 454:6

454:10 456:17

quantitatively 359:13

quantity 121:21 122:8 123:5

quasi 315:20

query 334:25
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question 14:7 18:15 21:15 22:11 24:15,16 30:19,26

30:26 32:6,16 33:6,7 34:7 35:11,14 38:12 48:16

50:2 51:3 52:7 83:9 89:13 90:15 113:6,24 131:7

131:13,18 133:3 135:10 137:3 138:15 139:22

140:14,18 142:3 144:11 145:18 147:3 149:3,11

149:14,15 160:9 162:23 180:3 181:8 182:21,22

182:24,25 183:21,21 187:26 189:8,19 190:19

191:10,12,25 192:20 193:15 194:7,12,14,21

196:22,22,25 197:6,10,15,21 199:6,7,17 202:9,10

202:15 203:15,16 204:5 205:17 206:15 215:22

216:6 217:25 219:1 241:2 244:1 261:10 265:26

267:8 268:26 269:22 270:7 284:1 285:8 294:18

299:6 301:17,23 304:23 307:4,4,10 309:4 316:9

317:1 321:5 329:4,21 344:3 352:7 353:3,5 355:3

356:9,10,16 362:14 363:4,10 364:26,26 365:1,16

365:18,19,20,21,22 366:8 367:8 377:4 378:3,7

384:21,22 385:11 392:25,26 393:25 395:3,5,8,9

396:8 416:15 418:24 419:24 423:7,11 425:9,10

433:3,4,5,11 446:1,3,16 447:19,26 449:9,10

450:13 453:8,13,23 454:23,25 456:10,18 457:3,4

457:6,10,20 458:12 459:4,5 464:12 466:16

471:18 473:2 480:24 481:22 482:20,25,26 483:3

483:5,9 486:12 487:4 490:5 491:10 492:6 493:8

494:3,4,6 497:10

questionable 167:13 174:25 175:3 320:6 329:14

492:19

questioning 196:19

questions 31:25 32:1 36:15 39:10 49:24,26 50:13

53:7 68:14 71:19 72:17 73:19 82:3 86:14 87:10

88:20 91:22 92:18 93:21 107:5,6 114:8,14 115:10

130:13,14,17 131:8,21 132:18 137:8,8 145:7

149:17 151:13 156:21 182:18,19 184:20 187:23

193:12 194:20 195:6 197:23 201:21 204:12 207:5

208:10,25 212:6,6,9,10,14 217:23 219:11,12

220:4 223:6 226:4 229:12,16 237:6,7 242:2,23

246:3 252:14 258:3,14,20 260:14 263:6 270:19

277:21,25 278:15 280:17 282:14 284:3 285:5

288:14 289:26 293:15,16 296:1,14 298:5,13,18

298:25 299:9 300:8 301:26 302:1 315:15 317:19

321:2,3 323:3 329:26 336:22 344:2 345:6,7 347:8

352:9 354:20 360:25 363:10 368:5 375:22 379:26

380:13 382:10 390:7 391:22 393:26 403:2,2

407:5 413:2 422:4 423:7 445:26 447:23 450:10

453:20 454:25,26 462:20 463:17 466:12,13

469:22 476:10 477:12 480:21 481:20 485:5 487:2

488:5,12 491:26 494:8 495:5

quick 21:7 25:10,25 207:19,23,23 210:6 213:6

229:26 231:10 237:17 238:17 248:8 304:23

326:19 336:22 354:26 396:12,15 471:21 482:20

quickly 13:22 25:7 27:21 74:6 256:13 298:2

320:20 330:13 333:20 334:15 347:25 354:8

414:24 463:26 494:15

quite 85:14 105:10 106:7 109:9,16 119:15 145:4

222:9 225:10 237:23,26 238:4,24 239:20 240:11

258:22 274:11,23 325:9 414:22 441:2,11 451:25

455:6

quote 30:21

quotes 431:25

quoting 318:26 320:15

Q)SAR 24:6 382:19

R

rabbit 261:20

race 261:20

radioactive 337:15 373:4

radioactivity 57:19

radiolabeled 58:17,19 62:12 65:26

radiolabled 57:18,22

rainbow 371:22 377:6,6 382:18

raise 145:7 265:4,9 361:24 372:18 381:12 414:25

435:18

raised 205:26 265:12 361:19 390:5,8 437:26

447:26

raises 298:18 300:6

raising 357:2 435:22,25

Raleigh 408:16

ran 92:22 333:22

Rana 261:8,8 264:23,26 265:1,18

random 340:26

randomized 98:16

randomizing 83:14

randomly 386:14

range 18:3 20:22 27:26 28:8,10,12 32:19,21,24

36:22 37:14 39:3 41:5 42:2 49:20 54:15 58:9

62:20 67:12 78:24 81:11 83:18 85:14 87:25 92:22

92:26 96:11 97:7 99:9 104:21 105:15 109:16,21

113:26 179:11,12 186:12 206:7,26 232:20 236:21

247:3 274:13 278:3 284:8,9 286:1 309:11 340:24

372:9,24 388:26 393:4 395:14 396:19 403:14

412:2 424:4,5 425:17,22 438:3 458:18 471:11

ranged 471:9 475:7

ranges 59:8 60:1,24 61:10,19 67:1 68:25 345:24

349:16 359:14 372:10,12

range-finder 354:4 406:15,21,24

range-finders 274:6 406:12

range-finding 243:15 245:7 277:26 324:2 325:1,5

325:13 326:1 330:18 348:6,17,23 350:1 400:8

406:1 410:12

range-finger 352:3

range-of-finding 387:16

ranging 98:1 495:14
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rank 453:24

ranking 131:15 143:10,16

rankings 140:20

rapid 74:13 75:25 238:7 249:5 319:26

rapport 344:8,9

rare 246:1 436:13,14

rare-ability 70:6

rat 20:9 22:17 29:10 47:17 57:2 62:8 74:16 77:11

77:12 79:3,21,24 80:18 81:2 83:1,3 115:19

124:15 158:5,13,19 161:6,8,23 162:21 163:9,12

165:3,24,24 180:26 187:10 189:1 190:25,26

191:8 225:13 255:8 257:19 291:10 294:8 324:5

355:9,9 386:16 389:7 397:2 423:2 427:13,15

464:1,6 465:7 469:2,3,4 471:3,10,15,23 472:2,4

472:12

rate 66:5 96:6 98:6,18 99:2,3 110:12 214:24

249:13 268:4 360:18 415:23 417:23,25 418:4

419:7 430:14 440:23 441:10,20 444:16

rates 110:19,23,25 263:21,22 272:14 361:23

407:18 418:8 443:13

rating 236:12

ratings 236:11

ratio 102:2,3

rationale 87:24 107:9,11,24 140:20 144:12,13

150:14 186:15 196:19 201:6 393:7 479:9

ratios 216:18 404:3

rats 56:9 74:17,19,22 76:2,2,9 77:14,17 80:1,2

82:26 85:4 157:16,22 161:4,12,14,17 162:16,18

163:11,13 191:2 324:16,18 327:7,9 353:6 355:14

384:25 385:18 386:15 387:12 388:4,9,10,14,17

389:7,13 390:5,5,8,23,24 399:23 401:1 456:11

465:13 472:18 489:10,10,21

raw 404:3

reabsorption 434:13

reach 156:19 166:24 233:19 257:6 267:9 341:2

reached 116:2 120:20 262:8

reaches 503:18

reaching 118:1 234:12

reacting 43:10

reaction 60:13 65:25

read 65:4 142:9 143:2 170:20 196:4 282:20 354:25

356:9 360:5 366:6 385:14 395:7 408:3 423:23

424:19 426:20 432:17 434:2 439:1 441:14 450:21

457:23 458:11 466:15 474:2 475:1 486:17 492:5

readdressed 371:6

readers 438:4

readily 336:17

reading 69:2 324:3 325:3 370:1 438:1 455:13

ready 11:26 39:19 92:2,11 149:10 156:18 205:23

364:26 446:11 480:23 506:4

read-across 35:3 37:8

real 10:1 21:15 46:16 111:23 149:3,5 197:16 210:6

245:5 283:23 300:24,26 354:25 405:25 409:3

430:20 463:26 496:14

realize 223:26 262:20 271:4 390:5 433:20 486:5

494:18 499:11

realized 101:23 305:23 368:2

realizes 368:16 369:6 426:16

realizing 14:6 201:11

really 8:25 12:1 16:7 17:23,24 18:21,24 20:8 21:8

21:13 24:10,14 28:8 30:10 32:26 35:10 36:5 40:9

43:13,15 47:6,7 48:8 52:22 68:22 72:7,21 88:3

89:2 118:21 119:10 123:19 131:25 136:9 138:3

139:26 140:25 144:21 147:22 148:23 153:1

195:12,13 204:10 205:12 207:23 217:8 218:3

223:11 225:6 226:15 228:20 232:14 234:24

236:13 238:12 241:11,20 253:17 257:23 258:25

260:26 264:3 265:10 266:14 267:20 270:10

271:22 273:21,25 274:25 275:5,8,23,24 276:25

277:18 281:3,11,14,19 282:9,20 283:2 284:18

286:7,15,17 287:7,10,13 288:6,11 291:1 295:15

295:24 296:7,14 300:2 301:4,13 302:16 309:4

317:1 322:25 325:3 329:13 330:23 331:24 334:22

341:14 342:3 344:21 347:25 348:19 349:15,26

350:26 351:24 352:3,19 353:21 354:6 364:6,9

378:21 386:8,21 387:9 388:21 389:3 390:1

391:21 394:23 396:22 398:1,2,7,10 400:12,14

401:7,8 405:17,21 407:5,6,22,26 409:2,4 411:24

412:17 413:6 414:23 416:9,18 417:3 419:15

435:23 439:2,22 442:3 443:1,6 444:1 450:21

451:18 456:11 461:1 465:4,23,25 475:1,20,26

476:5 480:20 486:10 487:18 493:17 495:15,17

496:2,3,5,12,13,23,25,26 497:8,26 498:3,13

499:15,21,21,23 500:2,5,10,12 503:12,18 504:12

504:21,23 505:13

realm 143:23 463:12

real-world 24:13 27:11

reanalysis 492:21

reared 435:15

rearing 437:18,26

reason 22:16 38:25 72:1 193:24 202:26 203:4

222:1 227:11 236:5 238:25 294:25 330:21 341:24

375:14 378:2 402:18 428:25 435:21 442:23

500:17

reasonable 14:15 37:21,21 57:15 200:20,22 413:13

413:20 415:20 416:5 440:24

reasonably 37:11,12,24

reasoning 463:16

reasons 14:6 28:8 32:18 145:6 188:19 199:24

297:12 488:8 489:17
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rebuild 475:11

recall 148:16

receive 30:10,16 33:2 54:14 132:26 324:25 502:10

received 25:21 26:17 28:6 318:21 354:25

receiving 501:20 502:18

receptor 25:13 42:8,9 43:21,24,25 44:26 45:2

49:14 57:3,17,19,26 58:11,18 62:2,13 155:8

165:1 177:19 191:25,26 192:12 195:17 230:25

233:9 235:17 244:8 251:26 256:6 290:20 335:19

335:20,23 369:25 373:13 479:26 486:12,13

487:10,14,15

receptors 57:14,23 380:10 460:21

recess 457:18

recessive 121:25 436:7 438:19

recipients 230:8 306:12 314:3

recognize 133:14 136:13 145:4 198:13 205:20

408:10 477:17

recognized 135:19 175:4 187:10 217:17 441:1

recognizes 101:14

recognizing 40:5 178:25

recombinant 231:24 240:14,15 327:15 355:8 365:5

371:17 380:10

recombitant 66:12

recommend 108:2 132:9 150:3 278:17 280:3

284:25 289:23 302:6 318:8 319:6 320:23,24

321:14 379:20 419:2 423:20 437:24 443:3 492:21

recommendation 33:2 84:5 98:20 107:9 234:26

235:25 274:1 278:13 284:21 289:17 363:14

368:19,22 377:18 382:21,26 423:17 433:6 438:13

438:21 449:13

recommendations 2:21 12:15 13:21 17:23,25

18:10 21:2 25:19,22 41:4 49:2 71:3 85:7 123:3

144:10 149:22 298:9 299:23 363:6 377:22 382:12

382:14 390:19 391:5 423:13 425:5,24 435:2

438:7 440:18 446:3 455:10 456:16 501:14

recommended 41:16 45:21 68:1 76:22 77:20 97:26

98:18 119:24 121:2,6 126:19 150:9,16 157:23

214:17 245:12 257:15 296:19 297:9 299:23 357:7

360:23 365:13 370:23 371:14 376:7 377:10 385:8

390:19 391:3 433:8 440:16 452:9 477:5

recommending 145:22 326:5 363:19,19,20 380:9

394:14 423:15 455:3

recommends 60:21 101:15 106:23 116:7 119:3

120:17 123:13 124:18 126:3 146:22 313:5,24

370:21 372:11 375:18 376:2,20 403:4 428:23

429:11 430:1,12 448:15

reconvene 91:24 92:2 270:21 420:23

record 95:10 108:3 139:3 354:25 356:9 368:6

383:22 385:16 395:8 422:8,13 423:8,23 424:19

424:21 426:20 434:2 457:21 459:3 478:26 492:5

494:14 497:22

recorded 77:19 95:9,12 100:4,20 102:3 103:12

137:20 357:16

recording 438:23

recordkeeping 362:9 444:10 476:6

records 55:14 283:8 343:16 345:11 362:7 444:4,12

recover 233:18

recovery 120:9 128:8

rectify 242:8

recurring 453:3

red 104:8 105:18 127:9 342:13

redesign 228:6

redo 72:7 200:9 375:26

reduce 29:6,13 31:3 66:18 120:24 121:8 124:6

127:26 153:18 181:20 191:13 213:26 258:8

260:24 288:22 291:24 307:8 329:22 379:23,24

393:17,21 406:11 429:14 432:18 483:7 488:9

489:24

reduced 121:7 123:2 131:15,16 181:9 214:19

215:3 312:2 398:25 407:17 408:5 409:19 425:23

426:3 449:26 450:1 488:7

reducing 101:5 123:4 178:23 215:5 293:6 307:6

410:8 413:16 426:10 432:9 442:4,24

reductase 245:17

reduction 66:15 103:1,7 228:5 290:1 355:5 484:22

reductions 103:8,9

redundancies 29:20 30:18 42:23 48:17 49:9 170:2

182:1 196:26 198:1 204:16 468:1,16 483:13

492:8

redundancy 20:6 46:19 48:22,24,26 153:15,17,26

154:8,11,16 155:2 157:9,12 159:26 163:1,20

164:10 169:11,12,18,26 170:10 172:1,19 176:17

176:19,22,25 177:12 178:5,22 180:10,25 181:4,9

181:21,25 182:2 184:11 188:20 194:21 200:14,21

201:15,20 202:1,4 251:4 278:26 329:17 377:4

383:4 458:14 462:21 466:18,25 467:14,25 468:6

468:10,20 470:26 471:13,16 472:21 474:14 476:3

477:23 478:10 492:23 494:4 496:23

redundant 170:24 328:18 329:19 382:22 391:12

408:23 475:25 476:2 499:17

reemphasis 213:8

reemphasize 469:9

reevaluate 217:18 414:15

reevaluated 428:22

reevaluating 34:25

reevaluation 376:25

refer 69:17 76:11 247:24 257:1 268:13 366:6

400:22 426:21 437:14,17 438:4 494:2

reference 7:24 48:20 60:22,24 61:14 62:8 141:11

150:1 203:18 223:13 261:12 262:17 292:14 328:5
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344:12 346:16 380:18 381:2,2,26 442:15 449:21

449:24 504:10

referenced 61:8,17 133:3 197:13 290:12 367:11

380:18

references 239:7 311:19 402:1,3 423:22 424:17

457:21

referencing 444:22

referred 54:21 94:3 96:4 99:23 121:13 230:26

311:20

referring 87:1,3 380:15,17 382:2 430:23 455:21

refers 419:24

refine 49:5 160:14 488:7 489:24

refined 332:10 336:19

refinement 301:7

refinements 490:9

refining 349:10

reflect 12:23 128:11 163:15 187:19 227:14 313:3

369:4 375:13 504:12

reflected 11:19 201:21

reflecting 9:10 121:11 183:15 227:16 306:4 479:21

reflection 240:19 372:21

reflective 94:19 96:7

reflects 8:21 232:14 500:13

reformatted 344:23

refrained 51:19

regard 75:12 81:9 91:2 192:19 261:10 305:23

307:13 319:1 320:11 350:14 357:21 358:19

359:21 360:13 365:10 409:12 430:23 467:9

494:25

regarding 32:1 93:22 122:9 124:7 129:20 170:25

179:26 212:15,16 318:16 319:8 363:11,14 379:15

387:1 393:8 413:3 423:2,11 435:10 446:1 464:24

489:14 490:16

regardless 41:12 170:23 177:11 204:14 257:8

432:12 462:8

regards 64:1 78:9 92:17 130:19 134:17 164:18

350:8 368:25 371:24 393:15 402:24 418:25

447:26

regime 215:3

regimen 399:18

regimens 94:20

regimes 367:6 425:20 426:3

region 137:15

regions 141:11

register 294:8

registered 310:25 315:24

registrants 274:5 281:22

registration 34:24 185:15,17 307:19,21,22 308:1,5

308:8,22

registrations 179:1 185:12

regression 403:26

regroup 395:4

regular 249:24 432:12

regularly 285:12 394:24 432:17

regulate 316:22,23

regulated 412:2 416:8,9 425:5 503:20

regulating 10:22

regulation 214:20 412:5 499:2

regulations 2:24 9:1 144:19

regulator 401:18

regulatory 2:22 10:17 14:3,13 112:3 227:11

286:20 289:13 307:18 317:20,24 322:16 323:5,11

330:4 331:6,16 489:19 500:1 503:2

reinforce 29:12 377:20

reiterate 147:8 364:20 435:11 469:11 470:20

490:14 495:23 497:3,15 503:16

rejected 279:23 360:3,7

rejection 319:6

relate 43:18 118:9 192:20

related 3:18 11:10 35:14 56:5 83:9 86:14 117:18

120:16,22,25 123:24 128:1 135:10,26 136:5

137:18 141:26 142:18 157:18 158:3,18 160:2

183:6 191:6 195:11 203:14 213:25 218:14,22

244:3 246:9 248:9,24 251:24 264:7 284:23

300:16 361:22 362:10 365:23 391:20 392:22

394:10 397:14 407:10,10,17 409:14,17,26 410:4

411:20 417:11 427:10 431:15 433:24 438:19

441:8,12 468:4 474:12 494:5

relates 89:13 94:9 159:25 191:12 203:16 240:10

312:15 361:9 392:2

relating 81:12 120:5 355:3

relation 260:15 362:11 377:15 382:6 433:2

relationship 18:23 139:6 149:15,20 261:3 267:5

299:2 403:7,8,11,17,20 423:13,19

relationships 150:4 492:23

relative 78:8 80:22 109:21 158:18 186:10 346:2

377:23 463:7

relatively 38:5 123:25 127:1 140:7 148:9 152:22

245:16 263:26 333:20 367:3 386:17 387:11

411:17 448:12 460:6,18 481:2 482:20

release 295:24 303:15 389:15

released 315:4

relevance 252:8

relevant 17:3 20:13 23:16 43:3 54:20 125:3 150:19

159:12 160:4 162:7 180:16 182:11 186:3 187:11

192:16 199:13 202:14 206:3 225:19 233:6 278:19

290:25 295:14 312:11 313:26 339:13 385:23

392:25 402:8,24 453:22 466:5 502:19

reliability 179:20 234:14 243:4,25 346:24 373:24

reliable 175:3 205:11 232:23 235:3 238:24 243:13
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244:21 326:8 373:10 459:14 461:6

reliably 243:11 244:7 245:10 251:2

reliance 251:23

relied 126:5

relies 42:22 294:26 453:26

rely 205:3 324:5

remain 455:26

remaining 20:21 25:5 33:1,4 36:10 38:1 99:18,23

106:20 117:6 126:16 148:15 162:13 168:11

343:25 376:19 501:26 502:12

remains 2:23 425:21 452:25 460:21 467:26

remark 355:3

remarkable 141:8 225:21

remarkably 163:7

remarked 411:14

remarks 6:23 8:13 15:26 94:11 122:9,21 139:15

164:12 212:14 217:21 231:19 236:15 495:21

501:11

remember 8:17 40:4 85:16 147:17 150:26 205:7

246:20 278:22 299:24 411:14 477:10

remembered 205:10

remind 114:13 182:20 219:17,22 329:20 330:18

342:5 366:6 416:12

reminder 25:10,26 359:4 501:17

remiss 505:20

remote 119:1

remotely 135:3

removal 355:14 492:16,20

remove 27:2 102:9 197:7 244:6 245:24 250:14

344:22 355:15 414:14 484:2 492:11 500:17

removed 106:3 108:19 391:12 414:6 484:19

removing 245:25 278:18 338:10 344:19 404:17

RENALDO 305:3

render 124:8 365:14 376:8 385:9 395:23 402:19

403:11 405:6 426:8

reorganize 288:21

reorganizing 289:22

rep 116:10

repeat 23:19 72:13 88:25 274:3 364:23 399:6

455:2,8 498:6

repeated 238:3,6,8 278:13 337:25

repeatedly 501:7

repeating 71:9

repeats 330:18

rephrase 267:7 376:3

replace 231:25 488:7 489:24

replaced 225:19 232:22

replacement 324:9 360:18 370:17 453:19

replacing 371:16 486:15

replica 425:17

replicate 100:1 101:21 102:1,5 119:11,17,18

448:18

replicates 95:5 98:21,25,26 101:26 116:9 117:16

119:13,17 152:16 427:25 428:2

replot 340:2,5

report 3:1 9:6,22 20:10,11 25:21 36:15 67:20 68:9

70:10 71:22 72:1,12,22 73:7,7,11 91:12 118:14

119:8,9 138:1,16 139:14 147:4 149:12 150:12,23

154:3 158:1 198:14 205:21 216:7 217:14 250:23

251:10 318:21 319:2 343:17 367:5 371:26 376:3

377:2 408:3 417:24 418:3 423:25 426:21 430:3

436:24 437:19 446:24 464:4 468:19 479:2 489:23

493:16 499:25 501:8 506:4

reported 67:17 72:23 73:4 75:17 88:9 100:6,17

102:14 103:5,15,16,20,22,22 104:4,6,7,14,16,18

104:21,22 106:5,5 119:6,8,12 121:5 123:4,6,6,18

125:9 132:10 136:6 137:23 140:2 158:16 166:20

166:22,23 167:9 168:10,11,13,20 169:3,5,6,7,26

170:11,15,22 171:2,8,10,20,21,25 172:7 174:17

175:12,18 196:2 218:18 247:24 294:5 359:21

361:4 364:8 367:11 374:2,7,18 390:24 396:21

425:12 434:12,26 436:20 437:13 439:4 448:22

449:22 452:23 462:7 464:3,3 468:3

reporter 60:6 372:4

reporting 103:2,18 113:3,4 418:8

reports 3:3 67:19 68:12 85:12 149:23 150:19

167:24 169:12 263:26 269:9 285:2,23 343:17

348:22 374:4 434:6 437:5

represent 28:8 32:18 33:9,13 34:18 54:12 104:10

118:18,22 165:2,4,7 171:6,11,23 173:11,15

177:23 181:26 188:18 206:26 249:4 260:17,18

285:9 301:20 342:3 447:20 450:3

representations 314:17

representative 28:14 30:2 32:26 35:10,22 37:11,13

37:15 38:19 133:5 145:26 155:22 197:19 199:3

212:26 221:11 368:25 422:20

represented 36:22 54:15 173:21 306:3 322:6

428:15

representing 174:5 323:8 336:24 347:21

represents 58:26 101:25 104:8 119:1 128:25

164:26 170:17 173:10 179:11 234:6 305:13

368:17 376:13

reproduce 101:18

reproducible 129:23 319:1

reproducing 274:19,21 275:21

reproduction 20:16,18,25 21:3,4 24:3 25:6 40:22

42:14 44:18 45:15 53:26 55:5 94:2 100:24 115:21

165:25 173:4,15 181:13 225:13 275:25 279:6

292:4 309:1,13,23 408:18 461:10

reproductive 4:20,25 5:20,24 7:4 16:4 20:2 44:15
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44:16 94:13,25 95:14,16 98:23 168:5 175:14,15

210:17 211:8,11 244:16 273:7 309:2 310:11

334:18 360:14 387:5 388:16 395:12 396:6 397:20

427:13 459:17 470:1,15

reputable 319:5

request 68:10 133:1 318:15,16,20 319:16 423:21

requested 208:16 212:19 306:21 345:14,15 354:7,7

requesting 382:23

require 231:22,23 304:4 307:22 308:8 311:18

450:5

required 17:9 49:12 126:10 132:9 162:10 178:26

179:9 181:10 185:12,15,17 186:22 198:21 223:22

232:20 243:15 248:12,14 249:10 250:5 275:13

280:3,7 295:11,21 307:22,23 308:5,10 309:17

310:25 311:12 312:10 321:7 348:10 352:1 354:15

354:18 384:24 399:10 409:16 435:6,16 459:13

461:11 481:10

requirement 112:20 250:4 281:22 286:26 290:18

292:6 293:11 295:18 311:5,18 355:9 459:25

477:25

requirements 3:4 54:22,25 162:6 163:17 274:15

274:16 307:20 308:18 309:15,15 310:18 312:5

361:1 379:21 439:8 484:19

requires 16:26 297:10 312:6 452:3

requiring 285:2

rerun 360:3

research 4:24 5:3 6:1 7:3 8:6 12:21 25:2 45:22

122:5 135:1 210:19,22 211:14 260:4,10,23

283:22 306:19 311:10 314:2 323:10 337:1 341:18

396:12 425:19 440:26 458:7 503:10

researchers 435:15

researching 479:15

reservations 234:14

reside 306:2

residential 33:19 34:10

residual 100:18

residuals 404:2,2

resistant 442:12

resolution 289:26

resolvable 500:15

resolve 121:16 215:5 299:9 496:18

resolved 296:7 298:4 328:16,21

resolves 213:13

resolving 492:19

resorb 213:15

resorbed 136:14 362:2

resorption 52:18 116:19 253:24

resource 37:19 331:15

resources 33:3 36:12 248:10 288:8,9,9,22 290:1

293:6 299:24 304:5 320:25,25 348:10,16 503:3

resource-intensive 279:21

respect 80:23 127:23 153:5 156:24 162:25 179:15

180:3,23 181:8 213:24 214:21,26 238:4 311:3

383:20 400:21 448:24 455:4 458:21 466:17

477:24

respected 28:25

respectfully 165:5,9

respects 331:8

respond 44:10 49:24 70:12 203:26 267:22 361:13

427:7 449:26 457:5

responding 84:26 378:3 388:11,12

response 14:12,21 21:17 35:15 51:6 52:1 58:1 59:6

60:10,17,18,19 62:23,23 66:22 68:3 73:20 74:11

75:2 76:17,19,25,26 78:17 79:5,10 81:3,17,21

84:7 136:5 142:18 159:24 161:2,12,23 165:9,17

165:21,21 166:1,3,7,13,19,21,24,26 167:16,19,20

168:8,20 169:2,3 170:2,4,10,15 171:2,14,15,20

171:20 172:7,8 173:19,23 174:2,3,5,8,10,16,17

174:19 175:20,21,23,26 176:6,9,26 177:2,3,5,7

177:22 178:2 181:12,12,16 189:25 191:5,21,24

192:13 194:3 202:9 204:2 205:19 206:6 212:14

216:6 223:10 234:11,17,23,25 235:2,3 236:7

238:18 283:1 294:14 295:16 300:17,18 319:16

332:21 340:16,17 341:3,3 342:3 345:26 346:9,13

346:14,17,18 372:7 407:24 417:1 426:26 429:14

429:26 431:5 432:4 442:25 462:20 463:4 471:24

471:26 472:1,3,4,5 473:7 475:16 477:21 484:10

484:11,15 487:9 496:25

responses 19:24 42:20 43:7 54:9 64:20 153:13,14

154:5 155:5,9,9 156:9,12 158:21 159:11,16,17,24

160:17,20,24 161:16 162:14 163:4,6,13,15,19

164:9 167:23 168:17,22 169:8 170:6,23 171:12

171:13,23,26 172:19,19 174:20 175:6,17,19,24

176:12,13,22 177:7,10,17,19,20,24,25 178:3,4,9

180:13,15 182:9 184:9 192:1 195:20,21 200:15

206:4 244:3 251:12 292:20,20 297:23 328:24

329:3,24 366:1 367:7 402:15 432:2 458:23

460:17 462:26 466:19 481:16

responsibility 38:15 114:21

responsible 2:13 230:10 323:17 450:8

responsive 45:11 101:4 168:3 176:10 268:7

responsiveness 389:18 394:13

rest 38:6,17 289:13 291:14 346:18 382:26 398:5

438:8 503:25

restate 189:8

restrictive 415:16 416:7 418:15,21

result 12:19 58:1,13 59:19 96:15 102:16 109:13

124:9 166:23,25 189:12 191:16 232:18 240:18

258:6 264:8 285:21 361:15 374:8 378:19 413:10

439:21
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resulted 64:17 141:2 168:15 408:4

resulting 320:5 397:1

results 28:1 51:7 53:23,24 54:18 55:16,24,25 56:23

59:1 63:4 65:9 66:24 67:11 68:4 73:5 78:16 88:1

92:24 94:10 102:14 105:10 117:25 127:13 138:21

164:11 166:16,22 167:5,12 170:22 171:3,10,25

172:22 174:12,25 177:11,12,15 180:4 181:3

189:12 192:15 193:17 203:25 204:6 205:12

215:16 216:12 227:12 235:8 310:8,26 312:26

313:25 315:4,6,12 321:26 324:11 328:18 329:15

330:22 332:26 353:9 365:7 368:9 369:13 371:18

373:11 374:9,23 378:15 385:4 387:18 395:23

402:19,22 405:6 408:6 409:14,15,20 429:10

430:3,13 434:19 454:4 458:19 460:4 464:11,26

467:5 468:3 471:1 477:5 484:21 485:1 492:15,18

503:7

retain 349:19 351:16

retained 170:9 213:5

rethink 36:9

retrospective 236:15

retrospectively 199:18

returned 117:3

returns 238:18

revalidation 27:9

revamping 202:21

reveal 430:22

review 1:4 2:19 4:1 7:10,22,23 8:20,24 9:1 10:11

10:25 11:5 12:5 15:15 23:10 27:6,14 28:19 30:22

34:24 38:20,24,24 47:25 48:4,10,15 49:6 55:7,12

55:19,22 118:16 125:2 150:24 156:17 159:13

163:3,18 179:4,21 181:2 196:17 200:10 210:2

218:9 227:22 231:4,8,9 234:13,16 243:23 281:13

290:25 296:11 308:7,8 318:18,21,24 319:2,13

365:23 425:26 438:22 439:20 458:2 484:16

492:13 502:13

reviewed 3:3 115:6 122:20 129:22 213:19 254:4

255:4 296:20 307:2 319:22 367:16 425:13

reviewer 117:24 118:13,23 319:2,5,19

reviewers 68:4 125:24 130:5 133:5,10 158:26

283:25

reviewing 68:12 114:22 156:16 179:2 372:13

502:12

reviews 9:10 16:16 55:19,20 138:17,25

revise 52:3 344:26 372:14 373:25 390:21

revised 314:8 385:12

revising 27:7 70:4 372:12 502:12

revision 300:5

revisit 14:7,24 91:3 277:10

revisited 359:7,23 392:23

revisiting 71:10 106:16 380:16 382:2

revs 442:22

Reynaldo 5:26,26 36:25 107:8,23 130:16,19 131:4

131:7,12 132:4,15 140:18 142:1,4,24 143:8 144:7

144:11 145:11 149:11 150:13,21 203:14 211:13

211:13 265:25 269:22 293:18 295:12 304:23

364:16,18 401:13 412:10 415:7,12 419:21 423:26

447:18 452:13 455:9,12 457:23 498:16

re-amplify 87:14

re-characterize 338:25

re-examine 248:5

rhyme 203:3

RIAs 238:22

rich 306:13

rid 297:15 326:25 327:21 329:14 353:17 438:20

right 3:11 26:3 43:14 59:8 64:19 83:6,9,13,13

88:18 90:19,22 92:8 93:19 95:12 105:22 108:14

111:11 112:24 114:4 119:20 131:6,11 132:7

133:14,23 135:3 137:22 149:9 151:25 183:21

186:18 189:1,15 200:12,26 203:18 205:2 208:3

229:18 236:8 242:12 247:11 263:24 264:20

270:19 271:1,14 278:14 279:8 282:2 285:4,24

288:2 303:17 305:6 319:20 334:25 335:6 337:18

343:3 347:4,15 353:10 364:24 381:23 388:23

398:18 411:15,21,25 416:26 420:22 422:20 423:8

425:8 433:12 458:10 465:7 466:14 480:22 482:17

500:16 503:21

ring 184:4 204:23 452:25 453:2 455:18,18,22,22

ringer 469:13 470:21 473:3,8 476:13,23 477:1,2,7

479:10

ringers 479:12

risk 10:16 14:2,12,16,21 16:13,21,22 18:24 22:7,10

22:14,17,23 24:3 38:22 40:24 199:9 219:21 220:2

221:1,6 224:3 227:1 228:25 271:12,13 278:23

315:13 316:17 317:6

risk-assesses 306:1

risk-assessment 279:3 291:16 305:26 314:26

317:14 330:5 332:22 336:8 366:26

river 429:6

Riverside 4:4 210:9

road 387:18 496:21 500:26

roadmap 16:11,14 494:25 501:12

Robert 440:16

robust 36:1 39:5 199:4 228:10 313:18 341:13

342:18 346:3 408:26 473:22 487:14 492:24

493:21

robustly 28:18

robustness 378:22

Roby 5:18,18 88:22,23 89:5 196:14,15 211:8,8

356:16,17 363:14 379:3,4 384:8,9 393:12,13

418:12,13 463:21,22 470:18,19 473:14 480:6
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482:4,5 497:25

rock 372:23

rodent 73:26 250:6 418:14,24 419:19

rodenticides 32:25

rodents 400:22

role 8:23 121:23 163:19 182:12 306:8 443:5 444:8

461:18 484:4 496:12

roll 203:5

rolling 28:5

room 71:10 207:19 210:5 218:24 224:24 436:20

rote 311:18

rough 148:10 222:2 385:14

roughly 119:25 304:6 330:17

round 39:16 115:8 280:18 318:9 328:8 330:11

376:16 428:17

roundabout 183:14

route 74:14 75:26 76:4 83:22 182:10 276:6

routes 74:19 83:24 256:7

routine 70:20 456:12

routinely 132:25 223:25 374:6

routines 360:26

Rowland 31:7 92:20,20 505:4

rows 119:15

rule 83:12 129:13 158:25 376:9

rules 152:13

run 11:9 20:22 58:4,16,18,20 62:18 64:15 66:7,8

73:17 92:25 93:13 150:7 184:22 222:2 248:20

270:3 291:2 327:2,7,8,11 337:7,8 342:11 343:6

343:13 344:11 346:16 360:5,6 374:6 375:20,26

383:26 406:22 412:7 474:4 477:3 486:19 492:14

rundown 326:19

running 60:23 204:25 205:3 239:6 268:11 327:13

339:2,3,4 341:10,20 343:14 344:10 346:15 348:1

352:9 382:12 394:18 404:22 431:11 485:23

runs 58:4,14 60:20 62:17 64:14 66:7 72:13,14

327:3 337:26 339:3 340:26 359:20 371:1 427:25

R.E 457:26 458:3

R1881 62:8,18 63:15

S

S 170:3 176:26

SAB/SAP 12:5 27:4

saccharin 470:9

sacrifice 29:7 386:23 388:14 461:8

sacrificed 231:24

sacrificing 24:17 388:4,17

safe 10:19 17:12 39:1 69:15 185:7 216:20

safeguards 154:1

safety 7:11 185:26

sales 155:17

salts 417:5

sample 107:4 120:7 134:24 140:8

samples 133:25 193:20

sampling 112:15 117:7 269:3

SAP 1:1,8 3:10 7:23 8:19,19 9:18,23 10:3 11:10,22

12:1,10,15 13:3,6,21 15:1 20:11,20 23:3 24:4

25:12 26:15,16,18 27:9 37:18 39:8,9 48:4,15

51:19 153:22,26 154:3,10 155:7 157:23 163:25

164:20,25 167:24 169:12 173:9 175:4 181:17

182:1,5,13,17 195:22 196:7 200:8,13,18,19,25

201:6,7,9,15,18 202:11,21 215:9 233:3 281:16

287:8 288:3,24 296:19 297:9 299:23 306:25

320:3,7 321:23,23 322:1,8 355:2 367:25 368:3,9

368:22 372:1 376:17 377:3 380:9 383:20 428:19

430:17 458:13 460:16,23 468:19,21 476:16,21

479:6,21 480:26 481:3,23 495:9 497:5,7 501:18

501:20,25 503:12,12,14 504:3,10 505:21,23

saprolegnia 441:8

SAPs 10:4 27:11 182:16 184:3 195:12 203:1 242:6

474:20

SAP's 21:2 25:18

SAR 37:7 233:2,3 334:21,22

sat 488:2

satisfactorily 70:24 323:4

satisfied 2:25 54:20,26 118:17 162:6

satisfies 127:15

satisfy 39:10 54:22 167:7 335:17

saturation 57:8,11,12,16 58:23,25 62:5,6 63:4 64:1

68:6,7 99:26 339:3

save 92:12 488:8

saving 324:26

savings 197:26

saw 88:9 123:19 150:10 159:24 160:9 203:25 204:2

251:16 255:18,25 265:7 271:26 294:14 471:7,9

478:13 495:25

saying 33:11 72:7 73:4 131:25 257:5 266:23

302:22 351:21 380:4 407:11 417:12 455:6 456:4

491:11

says 123:3 273:15 281:18 282:8 431:13 486:25

scale 141:7 267:11 435:16

scales 133:6

scaling 286:20 403:8

scanning 355:1

scarce 320:25

scenario 491:6

schedule 23:3 39:18 432:12 443:14 503:24

scheme 69:19 225:4 377:3 409:10 431:9 449:1

454:1 494:5

schemes 411:9 454:3

Schlenk 3:22,25 4:3 6:22,26 8:12 15:16 31:26 32:6

33:5 35:7 36:13,24 38:3,10 39:19,24 49:26 50:13
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72:17 73:12,19,21 82:3,21,23 83:20 84:14 85:20

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93:21 107:6,21 108:4 109:2,18 113:5 114:8,17,18

130:14 132:16 134:7,16 135:6 137:1 138:14

139:4,13 140:17 145:12 147:1 148:21 149:9

151:9 156:20 182:19 184:20,25 185:20 186:17

187:22 190:21 192:8,18 193:3,7,11 194:6,10

195:3 196:12 197:23 199:15 202:7 203:12 204:4

206:14 207:5 210:1,7 212:4 217:22 219:11,26

229:16,19 230:19 237:7 242:2,22 246:3 252:14

258:3,14 263:6 265:24 266:26 267:24 269:21

270:6,18,23 277:21 278:15 280:17 285:5 287:15

288:13 293:16 294:16 296:1 301:26 303:4,24

304:22 305:4,7,11 315:15 317:18 321:3 323:6

329:25 336:22 345:7,21 347:7,13,16 352:6

353:24 354:20,23 356:8,15 362:18,22,26 363:3,9

363:23 364:14,17,24 365:15 378:5,12 379:3,6,9

379:12,25 380:5,12 382:8 383:14,23 384:2,8,10

384:12,16,20 385:10 390:15 391:16 393:12,24

394:17 395:2,7 396:7 398:19 399:20 401:12

404:12 408:21 410:15 411:11 412:9,21 413:1,22

413:25 415:5,9 416:11 417:9,18 418:12,22 419:9

419:20 420:2,13,18,22 422:1,17,25 423:4 424:23

425:8 426:13 434:22 441:25 445:6,9,25 446:11

446:15 447:16 450:14,17 451:7,19 452:10 454:14

454:17,19,22 456:21,23,25 457:2,7,16,19 458:10

458:26 462:12,16 463:21,24 464:17,21 465:24

466:10,14,22 469:7,24 470:18,24 472:23,26

473:12 474:6,24 475:17 476:9,24 477:16 478:23

480:5,22 481:1,26 482:2,4,6,8,10,12,17,24

483:17 485:6 486:22 487:25 490:12,24 491:25

492:4,10 493:6,11,26 494:7,11 500:19 503:24

504:15,18

school 4:21 6:9,20 211:23 212:2 486:25

Schultz 5:8,9 69:14,15 70:2,3 137:1,3,14,17 138:4

138:9,13 184:20,21 194:7,14 195:5 211:2,2

215:23 242:3,4 287:15,16 302:3,5 365:16,17

378:5,11 380:21 381:10,23 382:5,10,25 410:16

410:17 426:14,15 434:3,23 438:7 441:14 451:7

454:19,20 456:24 474:7,8 476:17,25,26 480:6

490:24,25 494:1,2 497:5

Schultz's 380:3 451:22

science 6:25 7:8,15,17 8:23 9:1 11:5 14:6,8,23

15:19 18:8 25:1 27:15,20 39:23 40:12 49:3 220:5

231:1 335:1 338:14,17 343:6 376:13 428:15

435:23 490:9 499:2 504:12

sciences 4:3 355:12 375:14

science-based 313:18

scientific 1:1,4 2:6,18,19 3:23 4:1 7:10,13 8:3,4,22

9:12 10:22 11:5 16:16 17:20 20:13 25:19 27:13

31:10 37:7 69:18 70:8 129:15 167:6 210:2 239:20

271:9 296:7 312:11 313:26 319:6 354:9 367:10

429:10 466:5 492:7 493:13 502:14 503:1

scientifically 17:3 23:16 54:20 162:7 182:11

192:16 202:14 206:2 233:6 243:10 248:17 491:24

502:19

scientist 301:22 303:21

scientists 9:10 55:20,21 222:7 306:2

scientist-oriented 313:3

scoliosis 121:14 215:25 263:21

scope 445:16

score 260:11,12,13

scored 88:17 480:15,15

Scott 5:1,1 33:5,6 34:7 35:7,8 68:16,17,26 70:7

87:11,12,12,21 88:2,5 90:4,5,11,23 91:5 109:18

109:19 110:10,14,21 111:8,12 112:5,19 113:2

139:13,14,21 140:15 148:21,22 204:4,5 205:7,25

210:20,20 270:6,7 277:22,23 278:5,11 303:4,5,20

317:20,22,22 321:17,21 322:5 345:8,9,13,19

362:26 363:1,23,24 379:13,14 404:12,13 412:22

412:23 414:8 416:13,14 420:2,3 441:26 442:1

445:6 446:3,6 457:4,5,9 463:24,25 470:24,25

475:18,19 482:6,7 490:13,14 495:12

Scott's 417:12 445:12

screen 17:10,14 18:14 21:12,14,15,21,25 22:20

26:14,17 35:4 40:17 46:13 49:12 52:17 74:13

75:25 143:20 146:16 153:23 157:20 163:23 164:1

176:11 186:13 198:12,15,16,19 199:11 226:22

227:8 233:5 274:26 277:19 312:16 314:22 354:12

428:10 440:12,14 452:4

screened 33:8 40:20 226:19,19 316:5 368:18,20

screening 1:4,5 2:8,8 4:1,2 5:12 7:14 10:26 16:8,12

16:18 17:1,22 18:11 19:1 21:9,26 23:1 25:20 27:6

28:4 29:13 31:15 33:22 40:10,25 41:10 48:16

49:16 73:26 84:7 87:4 89:20,22 94:12 124:10

134:23 135:5 139:23 155:26 156:3,6 187:5 198:2

202:22 210:3 213:24 225:2,20 235:5 254:25

255:16 291:19 299:20 306:9,16,19 311:25 312:1

313:6,25 315:12 318:5,6 319:12 336:1 337:6

366:10,10,16,24 367:20 377:17,20 382:11 384:15

385:6 392:24 393:3 395:24 396:2 398:3 399:3,11

422:12 427:2,13 444:6,7 446:4,4 483:2 484:23

492:14 496:10 504:8

screenings 226:6 322:12

screens 21:10 146:15 185:6 230:13 232:17 307:5,7

315:10 452:8

scrolling 370:10

scrupulous 439:26

SDWA 39:3
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se 294:4 426:24

seat 482:25

seated 2:2,4 3:10 184:8

second 2:3 7:17 11:9 23:8 24:9,9 26:22 29:4 30:16

54:7 56:5 67:21 78:7 80:21 83:8 87:13 91:8 94:7

98:11 102:12 131:13 161:11 164:8 191:9 198:3

238:22 242:4 248:11 264:20 294:13 310:1 318:3

320:11 332:15 344:6 351:16 361:9 362:10 363:5

367:20 443:8 475:20 489:14 495:7

secondary 23:11 45:15 55:20 95:18 104:23 107:16

107:20 168:24 176:1,13 243:7 409:25

section 17:12 69:3 152:2 156:16,17 250:23 356:11

356:23 357:17 379:15

sections 150:19 248:1

secure 49:18

see 3:19 8:16 12:8 13:16,17 18:2 19:19,26 20:1,22

21:18 34:17 35:6 37:18 39:14 49:9 52:24 62:16

63:4 83:17 91:6 93:1 105:26 107:24 117:7 119:14

121:18 122:15,24,25 123:16,17,22 127:8,12

128:25 129:2 132:19 133:24 134:24 136:4 137:19

138:2 140:5,12 141:10,12,17,19,24 144:24

145:25,26 146:12,20 147:24 149:19 150:15 160:3

160:7,26 162:19,24 184:11 188:4 191:4 200:18

206:3 208:3 209:1 216:9,13,21,23 217:3,5 218:13

219:3 221:10 223:3 224:8,23 229:14 234:2,10

247:1,7,12 249:7,8 250:11,15,25 251:3,13,22

259:8,9,21 260:20 261:3 262:6 263:21,22 264:5

266:21 267:23 268:20,20,21,24 269:2 271:26

275:23 276:5 277:8 281:11,13 285:1 286:23

290:21 291:2 294:17 301:14 302:11,19 311:13,16

313:15 317:8 324:14 325:19 328:14 334:1 335:11

337:17,19,22,23 340:4,6,14,26 342:4,26 343:2,3

343:4 344:21,24 346:4,11 353:21 354:4 372:22

383:15 386:21 387:8 389:8 399:14 400:2,22

404:20,24 405:1 408:10 409:9,21 414:26 415:2

416:20 432:2 436:23 437:5,20 443:4,6 444:20

452:1,2 466:3,6 471:7 473:17,25 474:8 475:10

479:15 485:18 486:10 495:9 496:12 500:2,26

SEEGER 466:13

seeing 21:20 23:22 24:1 70:21 90:8 91:8 131:26

137:23 152:18 185:25 186:2 201:9,10 202:1

203:6 212:25 213:13 251:7 265:11,15 268:25

275:16 286:24 322:23 329:15 387:23 388:3

394:20 398:2,25 474:23 496:21,25

seek 124:5 213:25

seeks 125:20 215:21

seen 41:23 42:4,6 47:21 48:1 56:12,13 61:26 71:18

74:5 125:13 157:6 160:21 161:22,24 162:4,9,22

176:14 177:17 249:16 250:18 276:13 285:23

329:18 355:19,20,24 417:14 436:10 440:3 443:9

467:18 484:10

segue 452:11

select 146:24 192:10 246:16 348:6,10 429:22

443:22 493:24

selected 28:7,17 36:17 52:14 54:11 56:23 75:7 76:5

78:4 80:18 92:25 93:5 124:18,22 145:22 155:23

179:10 182:26 185:9 186:21 187:17 206:23 349:3

349:22 351:26 355:25 368:24 369:1 376:24 383:8

432:6,11

selecting 93:20 268:6 311:24 312:15 429:25

selection 32:17 36:18 78:4 80:17,20 87:13,24,24

92:21 102:13 106:17 146:7 180:2 195:1 243:15

311:21 350:4 396:2 405:26 410:9 463:18 485:4

selections 352:2 410:10

selective 7:23

selectivity 481:23

selenium 432:23

sell 303:15

Selvage 5:5,5 82:5,6,6,12,18 210:24,24 352:6,8,16

352:18,25 353:3,23 379:6,7 384:10,11 385:11,12

394:2,17,18 396:8,9 399:22 413:22,24 416:12

422:25,26 424:25 464:17,18,23 472:23,24 482:8

482:9 485:6,7 486:25 493:6,7 497:2

semantics 90:16

semi 143:10

seminal 76:10 80:10

semi-quantitative 454:1

semi-terrestrial 264:24

send 72:9

sending 320:26

Senior 231:1 237:12 305:18

sense 16:15 25:7 37:8 116:15 139:12 143:5 184:8

220:24 228:22 229:2 242:19 281:8 354:3 408:26

440:11 468:12 473:11 475:15

sensitive 18:1 21:5 22:18 78:10 80:24 116:22,26

160:18 215:24 216:2 249:6 251:21 255:24 269:1

275:7 291:8 303:16 316:18,19 317:7,12 404:17

404:18 410:22 455:19 463:4

sensitivity 21:5 29:7 41:6 42:16,18 48:24 74:14

75:5 76:1 124:25 196:18 198:15 223:22,24

257:26 277:7,15 287:12 293:19,25 294:3 453:3

sent 71:26 473:8 474:18

sentence 214:3

sentinel 227:1

SEP 283:18

separate 119:17 191:1 367:26

separated 239:6

separately 117:10 132:13 158:10 366:3 458:24

466:20

separation 80:3,6,9 81:6,14 274:8 461:7
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sequencing 501:18

sera 214:14,16 215:3 265:19

series 137:8 179:26 229:20 281:9 298:20 321:26,26

357:9 367:20 470:3

serious 319:20 330:24

serum 157:24,25 400:25 401:3,6 461:8

serve 2:12,16 184:1 198:11,14,18 296:6 484:23

504:22

served 153:23 299:19 371:6 504:26

serves 61:2

service 21:12 343:24

session 54:1 133:17 199:25 224:7 225:25 227:19

377:23

set 11:11 22:14 28:6 38:13 39:17 44:24 49:3 55:9

67:15 71:2 150:5 155:14 182:26 185:5,7 186:22

193:21 230:12 253:10 274:1,17,17 315:26 348:26

363:21 390:20 392:22 393:8 399:10 406:1 414:5

430:6 436:23 443:16 484:16 485:1 492:13 494:21

sets 55:24 139:7

setting 33:19 34:10 254:10,10 273:14 276:22

395:13 399:16 406:2 413:14 418:20 501:12

settled 235:1

setup 271:18 274:20 275:4

seven 76:26,26 81:21,22 103:24 104:5 138:2 151:1

151:1 154:24 166:12 168:10,20,23 169:3,5,7

172:25 174:10,13 175:25 176:6 177:10,17,21

252:26 255:4 257:20 284:8 437:19,22 460:3

462:7

seventeen 62:3 79:9 99:8 155:26 164:17

severe 141:8 213:5 259:6 266:14

severities 259:17 265:6

severity 125:2,8,23 133:6 141:7 142:10,13 143:10

259:10,22 262:4,11 395:22 405:9,11,12 410:18

413:10 446:19,25,26 447:13 449:2,11 455:26

sex 45:15 76:23 95:17,18,23 101:14 102:2 104:3,23

105:8 106:25 107:4,10,16,20 108:7,15 163:10

168:24 169:1 170:17 176:1,13 189:3,5 244:24

275:9 409:25 459:23 469:4 479:13

sexed 101:9,22,26 102:4,8,9,11 106:24 107:10

108:16

sexes 161:19

sexual 175:12 461:7

sexually 351:14 488:21

sex-specific 462:3

shades 59:13 65:4

shallow 429:5

shame 301:3

shape 125:4 386:2

share 197:1 220:20 222:8 499:9

shared 297:9 449:7

Sharlene 505:22

sharp 339:10,18

SHERILL 83:4

Sherril 6:18,18 52:6 82:24 83:8 132:17,23 133:7,9

133:19,23 134:2,14,17 135:10 136:26 147:2

148:16,20 182:24 183:20 199:16 212:1,1 217:24

218:17 258:5 263:8,24 264:19 265:17,23 285:7

285:17 303:25 304:10,21 321:5,20 322:4 353:25

354:10,19 363:5 417:19 418:23 422:19 434:24

445:11 450:15 454:15 457:1 488:1 500:9

shift 125:7 337:19

shifting 357:7,8

Shirley 505:26

short 70:22 71:8 93:6 115:21 181:13 243:16

306:25 385:1 391:23 392:1 396:10 419:17 424:5

467:25 481:2 503:7

shortcomings 385:3

shorter 21:10 258:7

shorter-term 367:3

shortly 3:20 17:19

short-term 42:14 53:25 55:5 94:2 225:13 360:13

395:12 396:6

shot 193:6,10 200:5

should've 278:6 383:7

show 58:2 59:7,8 66:20,21 76:26 128:3 146:9

159:10 160:19 174:12 175:16 177:2 178:1 181:15

191:6 214:5 215:4 245:19 266:2 304:24 308:17

334:20 340:3 342:14 343:13 424:13 426:17

429:17 433:26,26 440:9 462:7 468:13 478:8

481:16

showed 34:21 62:22 65:11 73:6 76:25 79:9 81:17

81:21 93:9 113:26 131:3,17,23 148:1 161:1

175:19,21 176:6,9,12 177:5,9,21,25 224:15,18

233:8 254:16 255:4 267:2 273:24 298:16 304:7

328:4 460:2,14 462:8 467:16,24 469:1,2,3,4

481:9 484:17 488:14

showing 67:5 86:19 146:9 159:23 192:22 251:8

297:1 299:16 391:6 438:19 460:3

shown 58:22 62:15,24 63:10,13 64:25 65:25 66:14

66:19 73:1 94:4 95:8 97:9 106:7 129:24 166:15

170:1 173:7 174:7 175:16 176:8 213:19 238:11

296:25 432:7 455:15,19 467:7,15

shows 53:21 54:23 56:19 59:4,16,19 61:11 63:2,14

63:19 64:11,17,19 65:2,9 66:23 103:12 105:12

116:12 121:19 125:18 127:9 166:16 171:1 172:25

175:11,24 176:26 187:26 189:12 216:3 233:15

399:4 455:22

shrimp 214:15 443:18

Shultz 194:6 451:8 456:23
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shut 342:1

sic 445:2

sick 401:1,9

side 123:22 127:11 143:13 144:8 187:9,13 268:18

270:2 392:21 429:5 443:26

sides 186:13 495:14

sign 411:2 415:14

signal 44:6 342:17 346:3,22 400:13

signaling 28:22 40:8 154:21,23,26 173:10 179:24

181:6 375:9 395:15 458:15

significance 158:21 216:14 498:3

significant 29:16 64:21 75:3 103:6 109:13 127:6

127:21 150:11 157:23 159:4 160:1 162:9 166:11

167:9 174:1,4,9 179:19 214:13 216:13,25 217:3,9

234:10 255:9,17 285:20 317:10 350:17 353:21

354:8 355:5 362:6 385:26 391:9 401:25 404:20

405:19 408:7 417:3 467:14 468:5

significantly 108:16,25 257:19,21 398:24 408:5

438:20

signs 56:10 74:25 75:19 76:13 77:2 78:1 79:8 80:13

81:20 82:7 93:12 103:2,6 106:14 117:5 259:8

409:12,24 428:7 429:24

silence 451:1

silencing 450:25

silent 451:26

silico 226:6 371:21 384:15

similar 21:1 22:6 38:12 39:2 42:6 44:24 62:6,11

80:17 83:11 125:18 146:2 172:14,15 212:25

213:17 226:21 257:8,15 264:25 271:18 280:11

291:5 296:26 315:23 335:22 358:23 433:26

443:18 447:1 449:21,21

similarities 42:22

similarly 71:16 81:2 165:6

simple 144:2,4 206:15 297:22

simply 149:21 159:23 215:4 302:26 320:4 366:7

429:22

single 35:4,4 89:6 119:17 124:2 160:16 173:21

175:20 190:8,9,15,17 214:12 240:15,20 249:4

334:7 369:15 461:8,17 487:1

single-generation 21:4

sit 270:24 438:26

site 1:8 434:14

sites 40:15 442:18

sitting 499:4

situation 71:6 151:6 368:21 380:25 392:17 411:5

443:1 460:21 463:8 474:20 500:1

situations 71:7 180:20 369:3 375:1 435:16 453:8

six 19:2 59:1 65:10 74:21 79:6 81:21 96:3,20,22

97:10,11,14,18,21 100:3 101:1 102:1,1 126:13,16

137:4 147:13,18 154:19 164:13 165:10 170:22

245:9 257:20 273:6 339:1 378:2 395:10 464:8,8

468:13 472:4

Sixteen 156:4

size 49:13 109:23 151:5,5 214:8 245:9 265:15

269:13 346:19 403:1,14,16,25 405:12 415:25

420:10

sizes 244:11 424:5,6

skeletal 136:10 433:24

skew 120:24

skewed 109:5,6

skip 122:25 370:1 422:3 487:15 491:22 493:19

Slaughter 317:20,22,23 321:17,21 322:5

slices 335:9

slide 22:26 31:12 32:4 43:8 50:3 53:21 54:4 55:7

56:15,19 58:2,13 59:4,16,19 60:8 62:15,24 63:2

64:17 65:2,25 66:23 86:19 97:13 101:25 102:21

102:25 103:26 105:6,12 118:22 121:19 125:17

127:18 135:11 139:26 140:1 147:3 157:6 164:15

166:20 168:7 169:9,24 172:25 174:12,22 175:11

175:22 177:17 180:11 196:13 216:3 229:9 230:1

233:11 248:8 296:25 298:19 331:14 452:5

slides 3:18 42:6 74:6 142:17,19 160:19 174:7 233:1

271:16 297:21 298:20 304:7 488:13

slight 68:18 70:7 90:16 96:14 127:19 133:12 357:4

359:3,5,11 371:5 373:23 374:1 376:3

slightly 59:26 61:9 66:26 67:12 68:25 69:8 147:22

147:25 169:22 250:7 253:9 258:9 262:21 373:22

422:10 436:10 472:9

slop 340:6,7,24

slope 63:11 337:23 403:9,10 424:1,4,6

slops 403:14

slow 204:6 502:24

slowly 455:3,8

small 140:7 228:1,3 244:11 245:10 280:8 337:1,20

337:24 341:5 343:21 344:18 347:3 375:11 376:5

450:22 452:2

smaller 33:11 265:13 400:7

smart 275:5

smooth 503:14

snapshot 21:7 25:25 261:26

snapshots 262:1

snout 417:21 418:3,9,9 428:4 434:15 441:15

snout-vent 117:9,10 120:22 127:19 131:2 149:13

149:20 150:4 151:4 216:10,16,20 217:2,14 298:8

298:15 304:24 403:5 404:1

snout/tail 139:7

Society 323:17 330:1,4 458:2

softer 431:22 439:15

softness 439:13

software 133:13 282:19 451:9
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solid 304:19 384:6

solubilities 372:24

solubility 28:10 30:14 58:10 62:21 67:22,22,24

97:4 102:19,23 109:12 111:2 147:22 152:26

232:3 237:18 239:15,17,19 240:10 241:19,21,24

242:9,17,20 273:17 340:11,14 357:6 362:11

382:13

soluble 35:16 63:23 128:18 167:3 344:7

solution 98:6 111:1,3 112:17 340:10

solutions 112:26 410:7

solve 257:16

solved 320:10

solvent 95:4,5 98:8,24,25 100:13 110:26 116:5,6,8

120:10 126:3,9,9,11,12,12,14,15,17,21,22 127:2

127:2,8,20,23,24 144:14,15,21,25 145:5,9 160:8

161:24,25 162:2 167:13 174:26 240:9 241:21

255:10,12 272:20,22,24 277:11 329:2 363:15

364:20,22 446:8 462:8

solvents 126:4,6 272:18 344:7

solves 320:2

solving 407:3

somatic 95:19

somebody 37:14 185:1 292:11 320:1 327:23

370:15 404:7 435:24 436:20 445:15

something's 69:8

somewhat 44:18 136:6 142:4 143:21 186:25 252:8

266:8 278:18 303:15 426:26 475:12 488:16

500:12

soon 284:24 338:2 376:19,20 428:22 429:4

sooner 318:8 320:23

SOP 232:13,14

sophisticated 245:4 451:16

sorry 91:23 170:19 193:14 215:26 347:9,13 390:6

415:12 422:26

sort 9:21 10:19 12:16,25 13:1,12 34:20,21 38:11

39:15 92:12 109:1 110:4,7,25 111:6,26 112:1

113:13,17 131:24 132:1 133:21 140:24 142:19

146:8,18 149:8 150:3,6 186:2 189:23 192:21

200:4 201:6,10,12 204:26 205:14,23 212:5

216:20 240:10 258:24 259:17 261:2,6 262:16

265:2 266:11 268:17,23,26 270:14,15 273:24

277:5,7 287:12 296:9 297:3 301:10,16 312:6

315:23 317:11 328:23 330:26 332:17 333:16,25

337:11,26 344:1 363:21 367:13 368:21 373:16

379:21 386:1 400:3 407:2 411:17 414:9 416:3

422:2 423:14 431:8 459:8 464:12,15 466:6

469:10,13 471:17

sorting 101:11 415:1 471:19

sorts 225:26 272:15 315:22

sought 25:18 182:1

sound 7:10 10:22 70:8 183:14 243:10 307:5 330:22

459:3

soundness 129:15 502:15 503:1

sounds 196:12,13 452:10 476:13,16

source 57:3 62:9 66:12 109:26 111:16 112:9

360:20 361:26 362:7 393:15,16 400:24 435:8

sources 17:15 383:3 400:24

SOUTH 1:14,15

so-call 327:22

space 54:12 155:22 192:3,6 463:19 477:25 478:3

478:20 479:13,13,13,14,14,25 491:13

spaced 78:7 80:21

spaces 166:21

space-wise 140:1

span 155:3

spawn 98:23 432:9

spawning 98:14 273:10 412:13,14

spawns 214:13

speak 43:19 46:18 82:22 136:1 137:21 142:26

150:20 189:17 192:5 194:1 208:17,19 219:18

234:20 237:14 322:6 503:26

speaker 219:18,20 228:22 229:22 230:20 271:3

speakers 224:11 229:24 270:24 311:16 324:8

speaking 3:7 31:10 214:4 224:10 237:13 309:16

403:8 446:7 465:6 488:11

speaks 308:18

special 133:1 501:6 504:18,25 505:4,6

specialize 5:7 210:26 211:15

Specially 375:18

species 22:19 94:5,22 115:22,24 212:24,25 213:15

240:17 250:19 252:9 263:11,25 276:10 277:17

308:26 315:13 316:19 321:15 377:12 411:7 423:2

426:5 431:21 432:26 435:4 436:8 439:5,7 442:19

443:9,20 472:16,16 479:7,13 489:4,12

specific 11:10 14:18 24:20 25:14 28:26 29:8 30:12

32:17 46:7 47:10,26 49:24 56:4 78:15 81:1 85:22

85:23 86:1,12,12 88:3 102:6 103:25 106:26 107:9

115:8 130:3 134:1 136:24 137:4,18 139:23

141:11 154:6 160:24 163:10 169:16 173:19

176:21 182:21 187:25 189:8,20 190:4 199:20

208:19 212:14 218:15 219:1 241:3,6 245:16,19

247:4 255:24 273:3,4 275:8 279:11 289:5,9,12

291:8 295:21 296:13 298:25 311:5 312:9 317:11

338:6 343:15 352:20 359:10,19 364:10 368:13,26

370:2 376:9 380:18 381:25 395:14 400:6 401:14

410:19 414:17 419:8 420:1 425:6 432:4 446:5

447:23 458:6 463:20 469:8

specifically 18:16 27:7 36:21 45:14 76:21 77:24

139:9 149:24 173:26 174:4 203:16 212:16 213:12

231:18 246:9 258:20 270:8 295:12 306:11 307:3
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specificity 19:14 48:23 74:15 76:1 252:7 289:15,19

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specifics 43:17 386:10 462:25

specified 173:24 365:7 384:26

specify 87:18

specimen 124:18 141:13 145:22

specimens 124:21 146:7,21 212:20 214:7 435:8

447:1

spectrophotometric 219:6

spectrum 190:6 369:4 392:26 393:9 404:18

spend 9:25 61:25 320:24 501:11

spending 299:24

spent 224:26 288:10 494:24

spiked 473:25

spinach 265:21

spinal 121:11,12,21,24 122:6 123:18 124:3,3,8

130:8 212:17,19 213:18,23 214:6,19 215:4,14,19

218:14 256:23 425:11,16,20,21,25 426:2,5,7,10

426:17 432:24

spine 137:10 432:22 441:13

splitting 493:2

spoke 223:4 422:19 423:1 424:24

spoken 314:13 330:12

sponsors 229:9,10

sporadic 244:9 245:19 437:2

spot 220:20

Sprague-Dawley 79:2 83:1,5,6 390:23

spread 109:10 119:16 267:22 491:8

spreadsheets 304:13

spring/summer 12:11

squared 214:11

stab 196:21

stability 342:12 348:12 352:1 399:25

stabilize 270:12 394:20

stabilizer 442:22

stable 342:15 353:9

staff 3:10 92:6 270:24 343:12 482:19 485:23 495:9

498:11 499:22 501:1 503:12 505:7,21 506:1

stag 124:19,20

stage 116:1,2,13,16,24 117:8 118:11 119:2,8,9

120:16,18,18,21 121:2,3,6,9 124:19,22 132:6,8

137:18,19,23 145:17,23,24 146:1,2,6,13,14,21,24

203:8 214:8 215:26 216:1 230:12 253:21,21

254:1,2,3,5,7 256:16,17,21 257:4,6,6,9,10 258:9

259:9,10,13,14 262:25,26 267:17,20 268:1,5,12

268:16 269:15,16,19,23 270:2 289:25 293:20

295:21 298:26 310:18 392:3 417:25 418:9 427:23

428:5 429:17,19,20,24 448:2,6,7,16,19,22,25

450:7 488:22,23

staged 258:6 293:10

stages 5:17 119:4 216:2 259:6,12 269:26 418:6

448:5

stage-dependent 433:9

stage-fixed 263:3

stage-matched 262:14,16 269:6

stage-matching 262:23,24

staging 95:19 145:20 267:3 301:11 312:8

stakeholder 281:12 284:25

stakeholders 228:26 229:6 230:9 314:15 330:7

stance 409:16

stand 206:1 291:8 313:21

standard 55:9,9 85:10 93:1 126:19,25 142:26

143:7 144:17 205:3 238:21 244:4 245:15 250:6

272:22 283:6,11 313:22 337:15 352:26 353:1

359:18 413:14 438:25 449:1 452:18 453:4,7

standardization 83:10

standardize 327:10 445:3 454:7

standardized 51:20 52:10 85:8 322:15 326:6

standardizing 304:12 371:14

standards 152:16 179:18 184:3,15 191:23 193:20

193:21 204:26 337:13,14,21 338:1 346:12 393:10

standpoint 37:7 232:11,12 287:1

Stanford 6:19 212:2

start 12:2,6 15:26 33:14 35:26 39:4 74:1,9 89:18

93:23 94:6 130:17 137:19 148:13,25 164:6

194:20 198:8 200:13 218:19,20 227:3 254:1

259:8 261:21 263:10 268:25 269:10 275:24

279:22 281:5 283:19 286:7,11 287:11,21 290:19

302:20 325:18 327:20 330:23 335:3,8 339:14

340:9 408:24 414:8 429:5 466:23 471:6

started 2:2,11 12:4 28:5 92:16 200:26 286:5

298:12,21 303:6 422:5 494:21

starter 265:20

starting 20:15,23 23:4 27:19 28:3 29:9 49:4 56:26

57:11 108:22 135:20 138:1 184:12 186:9 274:6

302:7 303:9,10,18 409:7 410:8,12,13 496:2

starts 26:16 101:17 208:10 209:2 267:20 331:7

starve 416:2,2

state 4:7 27:15,20 49:3 73:12 92:18 135:1 138:17

182:22 198:17 207:18 210:6 211:15 315:11 335:1

376:13 428:15 451:4 504:12

stated 28:8 86:10 99:19 105:8 164:20 176:18 182:4

234:16,22 257:22 319:2,8,10 359:4 365:3 391:2

440:22 447:22 459:24 466:3

statement 144:20 172:23 318:18 367:7 397:13

402:3 466:4 478:17
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statements 56:24 73:4 122:10 358:4 402:2 449:6

457:25

states 1:11 17:13 99:6,24 313:13 381:18 448:1,17

state-of-the-art 375:16

statistical 22:8 77:19 80:4 106:2 108:26 117:20

127:10 143:25 249:10,14 254:7 256:21,22 257:25

263:1 269:8 277:10 299:3 359:17 391:8,21 404:3

452:8 453:25 489:26 497:20

statistically 64:21 103:6 105:20,23 127:6 165:22

166:10 167:9 174:9 216:14

statistician 298:23,24 304:8,17

statisticians 297:20

statistics 150:7

status 291:18

statute 10:10 13:24 69:20

stay 208:15

Steady 202:2

Steeger 31:8,21,23 114:20 151:10,24,25 157:8

159:26 178:11,11 182:25 183:3,26 184:24 185:1

185:8 186:6 188:11 189:2 191:22 192:14 193:25

195:1,8 204:22 205:25 206:18,22 413:4,4 414:16

420:19,21 424:23,24 505:12

stemmed 244:1

step 13:13 23:4,8,14 24:9,9,18 65:23 196:7 202:6

222:5 288:11 300:3 332:9 366:25 367:20 368:19

417:17 497:6 499:3

Stephen 4:11 32:9 38:4 88:7,16 210:10 494:16

504:25

steps 13:13,20 23:3 138:20 215:19 334:14 335:3,3

335:4,5 347:10 361:17,18 368:2 489:24 500:25

501:19

step-wise 502:24

sterile 85:4

steroid 6:11 43:12,13,13 46:10,11,15 169:1 170:17

176:3 252:2 280:4 292:5

steroidogenesis 19:15 42:7 46:5,8 47:3 50:15,19

56:18 64:6 65:14,15 73:15 94:16 164:19 165:2,14

165:20 169:20,20 170:16 173:2,11,26 177:22

178:3 223:17 237:3,15,21 242:1,15 251:14,26

277:16 290:21 328:24 342:21 358:9 365:4 369:25

370:8 373:14,14 374:3,10 378:8 467:11

steroidogenic 240:7

steroids 95:23 104:3 105:8 106:25 107:4 275:9

276:12 459:24

Steve 4:11 8:13 190:23 195:5 200:5 202:26 210:10

230:20,21 231:1 338:4

STEVEN 8:15 33:25 35:13 37:4 38:8 478:25

Steve's 347:1

stick 269:5 302:1

sticks 219:5

stimulate 389:14 394:12

stimulating 76:16 157:21 389:3

stimulation 345:24 450:1

stock 111:3 112:22,26 360:26 361:8 435:8 436:15

437:24

stocks 112:15

stomach 352:13,22

stop 16:19 225:15 287:13 486:19

stopped 275:21 317:16 429:7

storage 338:22 379:20 490:17

store 338:24,26

story 159:17

straight 199:17 330:13 422:4

straightforward 125:6

strain 214:9 260:20,21,21,22

strains 82:25 260:19 419:8 443:22

strange 203:7

strategically 24:8 26:1

strategies 333:1

strategy 32:12 225:2 244:6 245:23 333:1 334:20

Strauss 457:26 458:3

stream 429:4

streamline 199:23 302:21 422:2 451:18

strength 46:16 78:9 257:11 409:4

strengthen 460:12 468:24

strengths 80:23 255:22 256:4 275:26

stress 8:19 82:10,13 186:9 239:14 252:4 312:24

387:3,20 389:3,3 394:16 397:7,9,14,18,21 405:1

405:3 414:9,26 464:25 485:21,21,25 486:1

stressing 275:25

stress-related 249:5 387:7

strict 348:21

strike 121:1

string 214:14

strive 328:10

strives 225:4

strong 55:16 58:4 60:25 62:18 63:12,16 197:13,14

267:26 340:22 461:2 472:15 476:18

strongly 122:8 313:5,18,23 433:3 484:7

struck 147:9 464:3 472:11 495:19 496:9

structural 377:21 491:13

structure 114:25 309:2 331:18 332:4,7,12 479:13

structured 313:15 336:15

structures 332:11 427:5

struggle 228:24

STTA 327:26

stuck 445:17

students 4:17 210:15 294:19

studied 214:7 438:8 439:18 487:6

studies 21:1 22:6 23:18 24:3,21 25:6 42:1 54:11
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55:1,12 56:9,10,23 58:6 59:24 66:11 71:5 75:16

77:5,10 81:24 82:26 84:5,16 92:22,23,24 93:1,2,2

93:9,19,20 94:8,11 96:18,21,23,24 97:1,6,11,12

97:12,15,25 98:19,26 99:2,8,9 100:2,3,12,13,14

100:20,23,26 101:1,20,21 102:1,1,3,22,23 103:12

103:16,17,21 104:2,3,19 105:3,9 106:2,12,15

111:9,9 114:5,22 115:6,8 118:16,18,20,22 119:6

119:7,14,16 121:12 122:14 123:18,19,25 125:13

125:18 126:13,24 128:1,2 129:15,22 130:3 132:1

132:26 134:1 135:20 138:5 147:5,9,16,18 152:14

162:4 168:13 169:4,6 179:3,21 180:15 182:13

183:9,10,17,18 185:16 187:6 198:21,24 218:9,13

222:1,8 223:20 224:4,22 225:14 228:6,6,23

236:16 243:15 244:17 245:14 250:11,14 252:26

254:4,6,16,17 255:2,3,9,19 266:1 267:13,18

268:2 270:8 271:21,23 272:7 273:6,14,23 274:4

275:6 276:14 278:24 282:6 284:7,16 285:14

286:6,21 287:8,9 292:22 293:3 298:10,21,22

304:1,6 314:24 324:2,4 325:1,14,15 326:1,8,9,11

327:5,22 336:6 337:8 343:10,20 345:18 348:6,12

348:13,17 349:2 350:1 352:1,3,5 355:11 359:22

387:4,16,16,17 394:19 397:15 419:12 425:13,17

425:23 426:2,17 447:2 448:23,24 454:4 458:23

466:19 467:22 489:5 490:17 493:10 497:21

study 20:16,17,18,25,25 21:3,4 22:22 23:20 26:20

54:23,25 55:16 56:22 61:13,15 65:8,20 67:2,18

67:20 68:9,11 72:1,12 73:10 74:2 75:6 77:6,17

79:13,16 80:1 83:10 92:26 95:6 96:15 97:7,10,14

97:17 98:24 99:16,18,19,19,22 101:8,12 102:2,4

102:16 103:14 104:26 105:25 106:9 108:7,10

109:16 111:3 116:6 117:25 118:1,7,14 120:6,12

121:7 123:10,11 124:9 127:18 128:10 129:2

137:24,26 138:1 139:11 140:21 141:17 144:21

145:2,10 148:15 152:14 155:24 156:1,4 159:12

160:8,10,25 161:1,21 162:5,14,24 163:17,19

167:11,14 169:3,4 176:7 178:18 213:19,20

214:16 215:15 218:16 225:13 244:10 245:7

249:23 253:5,6,15 254:22,24,24,25 255:13 257:9

271:15,19 273:20 274:6,11,17,22 275:19,20

276:3,26 277:2 279:12,19,20,22,23 280:15 282:6

283:6,10,11,12,16,26,26 310:2 319:5,7 325:5

327:19 328:4 344:19 348:18,23 349:26 350:5,22

351:11,15,18,25 353:11,12 359:6 363:16 365:14

374:21 376:8 379:20 381:3 382:24 385:9 386:1

395:23 399:2 402:11 409:1,20 419:1 425:14

426:8,11,11 428:24 430:2 433:7 434:3,4 435:9

441:19 446:23,24 452:22 453:6 455:15,21 474:19

476:13,23 477:1,2 489:9

studying 102:18 265:18

study's 175:2

study-specific 214:24

stuff 9:20 136:16 142:17 187:7 281:3 334:16

370:11 441:5

subcutaneous 74:19 76:4 84:6,8,10

subject 38:23 51:24 112:9 159:22 245:11 249:5

326:7 454:3

subjected 74:19 326:3 387:6 407:21

subjecting 326:10

subjective 133:12 407:22

subjectivity 451:11

subjects 337:10

sublethal 273:24

submission 23:17 308:9 348:21 351:25 352:2

426:7

submissions 125:5 162:8 215:2 385:1

submit 72:13,15 73:6 352:5 489:22

submittal 324:25

submitted 23:9 28:4,5 36:16,20 68:9,10 72:15

77:10 114:22 155:19 246:24 290:13 297:13

308:21 323:14,16 348:5 375:7 505:18

subpart 40:8,9

subsample 28:11 30:3 32:26 38:19,25,26 117:2

155:21 192:3 196:24 199:3 206:26 269:24

subsamples 206:25

subsequent 19:23 182:16 332:18 416:23

subsequently 10:10 68:10

subset 54:14 148:4 150:1 179:10,11,22 181:3,16

183:11 185:10 247:25 298:16 299:4 301:19 358:2

400:7 468:3

substance 116:3 120:9 126:3,8 127:4 128:7,13,18

128:24 129:20 148:17 157:24 160:11 348:11

350:10 352:20 378:26 419:6

substances 17:4 97:3 128:16 130:6 153:25 159:1

226:17 313:7,9 314:10 315:6,22 348:9 397:1

427:3

substantial 17:16 29:3 297:5

substitution 477:1

substrain 390:25

substrate 60:12 65:26

subtle 22:9

subtract 227:20

sub-chronic 309:11

sub-legal 112:6

sub-lethal 395:17 408:8

sub-q 83:23

sub-sampling 262:21 268:14,26 270:4

sub-tiers 493:3

success 95:15 99:5,5 168:24 176:1 371:14

successes 71:13 222:10

successful 8:9 118:7 205:5 209:1
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successfully 128:16 130:1 385:21

suck 445:21

sudden 443:11

suddenly 436:21 442:17 443:12

Sue 237:10,11,11 242:12 246:5,6,6 280:11 284:5

289:3 295:10,16,17,26 326:12

suffering 315:21

sufficient 41:6 48:9 85:11 107:5 129:26 251:10

322:15 376:24 428:21

sufficiently 104:26 247:6 393:3

suggest 102:15 124:2 125:14 128:20 138:10 204:3

266:2 294:9 341:9 374:6 376:24 387:15 388:1

392:9 406:19 415:3 420:11 440:6 460:4

suggested 83:23 102:4 126:23 131:14 160:17

192:17 228:8 255:7 286:9 288:25 331:21 332:11

359:8 397:23 426:25 443:15 445:23 468:21

suggesting 27:5 394:7

suggestion 303:2 327:14 394:6,11 403:18,22

416:18 444:15 456:2 480:9

suggestions 36:8 52:2 102:17 245:21 287:6 290:16

293:20 301:11 313:1 404:9 405:6 406:10 407:2

446:7 501:14

suggests 122:6 162:25 192:1 213:9 247:5,13,20

350:20 425:19 450:25

suitability 237:19

suitable 40:11 186:11

suite 108:12 185:14 477:3 481:3

sum 30:23 43:6

summarize 49:11 172:21 214:1 351:21

summarized 125:2 222:22,23

summarizes 59:10 96:21 97:10 377:26

summarizing 178:12

summary 31:23 50:3 75:12 76:24 79:4 81:16 97:9

150:23 229:4,13 323:13 360:6 368:8 375:4

396:12 461:16 468:3 475:22

summation 430:4

summer 13:5 48:4

superb 505:7

superfluous 452:8

superfund 442:18

superior 276:11

superscript 167:17 174:21,24

supervision 321:22,22

supplement 27:21 72:6 290:25

supplementation 123:11,13,15

supplier 140:2,6,11 390:5,25

suppliers 139:16,19 140:9,13 243:19

supply 436:16

support 49:21 100:24 178:26 183:12 185:11,15,17

305:24 311:7 312:8 314:10 328:20 344:1 380:11

489:26 505:24 506:2

supported 255:13 283:13 371:19 378:19

supporting 220:10 328:24

supports 292:14

supposed 137:7 329:18

sure 10:21 14:17 32:10 34:13 40:25 49:16 89:20

108:5 110:2 111:25,26 112:7 132:22 134:16

135:24 137:21 139:2,17 147:15 150:18 189:9

198:2 208:4,12,22,24 247:23 258:1 274:16,18,20

275:3 278:24 280:10,15,25 282:18 283:19 284:13

313:19 321:25 322:9,10,18 325:2 326:22 345:20

363:25 368:4 370:3,11 378:14 380:9 390:10

391:24 394:3 402:1 411:26 417:8 422:13 423:17

428:26 444:4,8 447:21 457:7 473:15 485:23

surface 445:18

surfaces 113:16

surgical 355:14

surpasses 165:18

surpassing 173:24

surprise 308:1 475:7

surprised 194:16 205:12 278:18 443:6 498:14

surprises 478:13

surprising 30:2 119:22 467:13

Survey 5:15 211:7

survivability 136:22 260:25

survival 95:14 96:6 97:8,17 99:11,12,17,18 100:24

103:1 106:13 350:16 351:2 362:5 409:19

survive 488:20,22

Susan 220:14,15 339:17

susceptibility 429:20

susceptible 289:5

suspect 111:4 186:25 289:7 415:26 416:3 474:19

suspecting 286:2

sustained 238:14 249:6

swimming 56:10 103:3 104:13 323:1

switch 124:13

switching 327:21

Sylvatica 261:8 264:26

symptoms 400:3

synchronizing 353:7 388:3 394:7

synchronous 159:5

synchrony 253:19 256:18

syndrome 426:24 429:13 430:18 431:3,15 444:24

444:26

Syngenic 422:20

Syngenta 237:12 321:21 322:5 422:20

synonyms 456:25

syntheses 43:13

synthesis 43:12 46:11 276:12 449:19

synthesize 64:8
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synthesized 10:18

synthetic 62:7

system 11:13 14:1 18:16,22 19:7,10 21:17,20 22:4

22:13 26:11 30:8 31:1 40:5,5,7,14,19 41:3,17

43:1 44:9,13 45:10,14 46:8 60:4,6,23 63:24 64:24

89:16 94:14 98:6 110:19 111:1,22 112:2,25

132:20 133:13 155:10,11 165:5,8 170:4 173:17

177:3,7 195:11 198:26 202:16,19 204:14 211:1

228:17 241:14 264:9 276:9 278:23 286:25 307:11

310:17,22 312:18,23,25 314:23 316:14 332:10,23

336:19 339:14,15 341:23,23 342:19 346:5 369:9

370:7,8 382:20 384:5 409:3 432:18 470:22

473:22,23,25 474:3 483:4,10 489:9 490:3 502:22

systematic 94:26 224:13,26 409:16 458:3

systematically 35:2

systemic 23:20 156:13 250:15 251:6 252:3

systemically 26:6

systems 6:10 10:12,15 17:3,7 24:15 29:19 42:23

49:12 155:6 195:26 211:23 220:15 241:17 332:19

342:20 347:17,22 369:10,23 384:7 397:8 398:13

415:17 416:10 439:1

T

t 28:21 48:10 49:12 153:19,23 154:13 155:5 167:5

167:8 179:24 181:6 184:2 189:12,25 190:16

191:1,3,4,5,7 197:11 200:21,22,26 201:9,26

202:6,25 395:14 424:8 458:15 481:4,12

TA 328:24

table 46:20 54:23 59:17,18,19 61:11 63:19 65:9

66:20 97:13 100:16 102:21 107:26 121:18 122:22

124:2,12 139:15 157:7 159:11 160:1,13 164:15

164:23 166:6,12,16,17 167:17,18 168:7 169:24

170:7,19 171:1,2,3,5 174:14,15 175:22,24 176:8

177:13 200:18 206:16,18 213:19 221:23 247:17

277:9 285:4 319:4 333:11 369:21 386:12 397:9

426:21 493:16,19 494:14 499:24 503:8

tables 127:11 156:17 304:3

tadpole 116:13 125:16 214:10 253:25 259:23 267:4

267:16 401:23 402:6 403:25 436:6 437:18,26

441:10 447:6 488:22

tadpoles 115:26 116:3,10,17 117:1,3,6 120:19

121:3,8,12 122:13 123:22 124:5 130:8 136:14,19

138:2 148:18 213:18 216:17 253:14,19,20,22

254:1,3,5,22,23 256:22 257:6 259:5,7 267:8

285:19 304:26 323:1 324:20 363:8 402:26 403:14

417:21 418:1,10 425:12 427:23 428:1 430:22

435:15,18,22,26 436:15,21,24,25 437:3 438:23

440:11 441:3 448:2,17,19,22 449:23 488:15,25

tail 52:18 116:18 121:13,13,14,25 131:3 133:24,25

134:18,21 135:11 136:8,10,11,14,15 137:15

140:5 212:17 218:3,18 253:24 263:9 264:1

265:18,26 267:2,6,12,26 285:18 361:22 362:2

418:4,10 425:11 426:17 428:12,12 429:8,13,17

430:18 431:3,23 432:2 433:4,15,20,21,23 434:12

434:13,18,26 435:4 436:6,15,21 437:9,20 438:5

438:22 439:4,21 440:2,10,15,20,23 441:2,4,16

445:24 500:12,16 501:24 502:2

tailored 311:6

tailoring 331:19

tails 130:20,22 135:11 136:20 213:16 266:3 319:25

436:25

take 2:11 10:7,13 11:14,15 13:4,7,25 14:9 16:20

32:4 35:15 36:7 37:20 38:1 39:11 53:9 91:22

107:5 130:13 151:10,15 195:20 196:21 201:7

229:12 237:13 241:11 242:19 251:6 270:20

272:26 279:22 284:22 286:15 293:14 295:21

296:9 299:21 300:3,9 301:4 304:1,5,6 305:8

343:9 345:6 356:5 375:25 387:14 390:1 395:4

396:26 400:17 420:15,16 440:19 444:21 457:3

476:24 477:2 478:5 479:23 482:19,24 501:19

502:24

taken 53:12 91:26 94:4 110:8 116:12 151:16

159:17 177:13 215:19 225:10 228:20 270:22

297:25 305:10 308:14 350:21 356:7 361:18

386:22 395:6 398:10 474:25 482:23 489:24

takes 9:5,19,22 286:13 307:17 308:13 316:17,17

417:24 418:8 435:23 445:13 489:10

talk 15:4 39:26 57:9 86:19 124:13 135:18 137:4

139:17 171:4 231:16 232:26 241:24 246:11

256:26 280:6 289:3 291:4 307:14 323:15 330:12

330:14,15 331:20 337:20 340:18 442:5 443:25

456:11 477:7 502:15

talked 109:20 201:9 230:16 250:23 276:14 284:4

341:17 368:1 380:25 383:25 397:13 422:7 442:3

444:3 462:25 496:17,19 503:2 504:5

talking 84:3 90:10 91:16 93:23 117:21 152:3

184:15,16 205:10 222:21 223:26 258:26 280:8,24

281:17 293:7 316:12 317:4 321:18 322:12 324:23

333:12 337:24 339:21 370:6,7,16 381:25 382:3,5

400:4 414:8 417:13 418:14,24 463:2 477:8,9,20

477:21,22 499:4

talks 56:1 283:12 298:7 434:5

tall 450:23

tank 111:16 112:2 114:2 214:24 267:4,11 427:26

428:2 436:20,25 441:3

tanks 101:16 152:21 215:1 260:18 262:21 267:2

272:15 274:18 363:8 438:26

tank-side 219:5

target 93:19 165:1 293:13 360:21 388:17 440:24

448:13 456:1

targeted 22:21 40:13 130:4 155:8 293:12 493:4
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targets 71:3 400:26

task 199:1,13,22 220:25,26

taught 424:20

taxa 11:15 20:22 22:16 27:25 29:12,13,26 42:2,23

49:19 155:13 180:22 181:7 188:19,26 479:7,8

504:1

taxonomic 41:5

teach 4:16 210:14

teaching 271:12

team 15:8,10,12 31:6 503:5,8 504:3 505:1,11,26

tease 300:16 409:17 431:10

teasing 287:11

Tech 4:16 6:2 210:14

technical 30:12 74:3 75:16 117:22 193:24 221:18

223:12,16,18 243:4 270:24 288:17 344:1 426:9

445:13

technically 486:13

technician 106:6 107:15

techniques 35:3 128:15 446:8

technologies 195:11

technology 231:25

Telephone 1:9

tell 72:12 140:10 147:16 159:17 228:12 251:12

259:5 271:5 304:13 312:3 322:18 325:3 388:7

414:19 445:1 476:7 496:26

telling 35:9 232:24 289:16 413:18

tells 192:6

temperature 96:11 97:7 100:14,16 218:4,24 241:5

340:13 357:12 438:24 439:3,12

temperatures 361:11

templates 283:7,26

ten 63:21 65:10 93:11 110:22 126:14 141:20

147:17 178:1 208:18 224:3 347:14 417:12

tend 137:17 157:1 254:1 261:3 263:20 266:9,9

392:5 417:11 433:24

tended 113:15,16 152:21 263:21,22 267:8

tends 259:11

Tennessee 5:11 211:4

tens 299:25

tension 224:24

tenth 119:4

ten-week 325:10,22

TERA 220:5,9,11 227:11 291:4 314:12

teratogenesis 431:19

teratogenic 432:4

term 21:10 22:7 62:25 115:21 119:23 181:13 224:3

227:1 367:3

termed 128:17

terminal 247:2

terminate 257:7 419:13

termination 95:7,17 101:24 108:12 116:25 117:6

118:11 119:3 120:17 121:2 138:3 425:18 448:3

terminology 122:2 359:11

terms 8:24 9:12 10:20 11:16 12:23 13:24 16:11,15

16:19,21 19:6,14,14,18,21,22,24 20:19 21:5,25

23:17,21 24:4 29:8,21 32:10,13 37:6 39:3 40:22

40:24 42:4,18,24 43:6,8,10,15 44:18 45:18 46:9

46:14,17,19 47:8,8,11,23 48:5,10,15 49:6,8,9

50:8,21,23,26 51:16 52:16,22,25 62:26 69:23

70:14,19,23 71:3,6,13 78:16 80:1 81:3 83:14,25

84:1,20 85:2 87:15,25 89:19 109:21 110:6,18,26

111:2 113:8,25 130:20,23 132:2 134:8 135:16

136:12,20 142:12 143:6,19,26 144:6 154:4 157:1

179:1,12 181:9 184:10 187:3,3 188:6 189:20,25

192:12 193:19 198:10 200:20,22,26 201:7,14,21

204:15,16 222:4 224:14 237:21 240:22 259:18

260:15 262:2,3 265:8 266:6 267:3 278:2 308:15

332:1 335:13 349:23 353:3 376:3 388:11,12

389:22 398:14 402:16 413:14 417:19,23 418:25

424:26 456:17 459:8 463:26 464:20 465:13

470:14,26 475:8 476:15 478:21 486:3 496:23

501:8 502:3,16 503:3 504:12

Terry 5:8,8 69:15 70:3 137:3,14,17 138:4,9,13

184:21 194:7,14 211:2,2 242:4 287:16 302:5

365:17 380:21 381:10,23 382:5,25 410:17 426:15

442:2 443:21 444:1 451:8 454:20 456:24 474:8

475:20 476:26 490:25 494:2 497:5

tertiary 23:11

test 7:26 12:6,13 17:2,7 20:21 21:22 27:22 28:17

28:25 35:4 52:9,10 54:7,14 55:11 57:2,21,24,25

58:8,10 59:2 60:11,21,23 62:13,14,19,22 64:17

64:19 65:23 66:1,6,10 67:6,9,22,24,24 68:1 70:6

70:19 71:9 72:8 73:16 75:17,21 83:22 84:17 94:5

94:21 95:3,6,17 96:8,11 97:1,3,16,19,22,23,26

98:3,5,6,6,15 99:17,24 100:1 101:3,10,23 102:13

102:15,18 104:14 106:2,20 108:12 109:4,23

110:17,20 111:1,2,26 112:2 115:5,22 116:3,12,14

116:25 117:3,6,11,14,18,20 118:6,11 119:3,23

120:9,17 121:2 122:7 123:26 124:17,18,26 126:3

126:6,8,26 127:4,11,14,15,17 128:5,7,14,16

129:16,20,25 130:1,6,7 135:13,17,22 137:23

138:3,20 140:2,23 144:24 146:10,10,14 147:3,10

148:17,26 149:19 151:2 152:9,10,25 155:15

157:24 158:20,23 159:1,21 160:10 162:6 167:3

173:20,25 174:16 181:3 186:9 194:5 195:9

196:16 201:6 206:24 214:17,19 215:10,20 219:7

228:1 230:5,8 232:7,11,15 233:19 234:24 236:1

237:19 239:15,20 241:19 243:6 246:25 249:12

250:4 252:23 253:1,13 262:8 264:2 268:11

271:22 272:1,3,3 281:21,23 282:12 283:17
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284:23 290:8,18 297:2,23 298:7,9 299:6,7,11,25

300:4,16 301:6,12 303:8,8,16,22 306:12,16,21

307:22 308:4 309:18,23 310:7,8,10,25 311:18,22

314:3,5,8,9 318:2,9,22 319:10,17,21 320:3,8,14

320:26 321:6,11,13,24,26 322:1,8,17 323:2,4

324:14,17,17,21 325:19 326:15,17 327:7,19,25

328:18 330:23 333:12 337:9 339:15,26 340:2,24

343:18 347:12 348:9,11 349:20 350:18 352:19

355:16 356:13,17,18 357:12,14,20,20,22 358:1

358:13,15,22 359:20 360:1,2,5 361:26 365:3,24

368:16,24 369:22 372:10 373:19 375:8,10 376:25

381:12 383:5,7 384:26 385:2,21 388:10 390:18

393:23 394:21 395:10 396:19 397:1 405:13

408:26 409:4 410:13 423:2 425:18,21 426:5

429:10,23 430:9,9,13 431:21 443:14 448:1,3,15

448:26 453:26 454:7 460:6 467:3,5,12 471:3,11

471:16,20 473:24,26 477:26 478:17 487:1 489:26

493:24 495:15,15 496:15,17 497:23 502:3,8

tested 58:9 62:20 73:1 75:18 76:4 77:9 78:8 79:19

80:22 85:16 93:15 152:23 167:4 185:5 186:11,13

186:14 206:8 214:14 224:21 238:14 242:21

263:12 301:21 308:26 321:19,23 323:26 324:23

325:16 355:23 357:19,23 358:1 361:15 370:26

373:10 376:18 378:20 383:25 395:19 408:4

416:20 428:20 429:1 468:13,26 470:2 471:4,15

471:25 474:11,11 478:16 493:14

testes 80:10,11 81:13 355:15 389:18,20 394:13

486:2

testimony 379:16

testing 10:26 16:12,19 17:22 18:11,20 20:14 21:10

22:5,22 23:2,25 24:14 26:8 39:17 48:11 54:6 59:1

61:6 67:16,16,19 70:20 72:22,25 73:2,3,5,9 84:1

89:21 110:18 112:4 128:15,23 143:24 153:1

178:24,25,26 182:7 183:5,10,25 184:4 185:12

196:1 202:19,22 204:24 206:23 224:1,19 225:15

228:5 230:10 232:9 234:18 244:13 268:7 285:2

288:7 289:13 290:6,17 299:20 306:15 307:14,18

307:23,25 308:1,9 309:7,9,13,20,25 310:13

311:12,17,24 315:23 317:4,5 323:11 326:7 330:5

330:9,23 333:1,6,18,21 334:2,19 345:10 356:19

356:25 357:4,5,22 358:6 359:1 366:21,22 372:24

374:3,15 376:14,16 377:25 388:8 389:4 393:11

393:22 395:14 396:22,23 397:25 406:5 422:23

423:13 425:6 428:16,18 433:20 435:17 440:12

443:10,11,14 444:14 453:7 478:22 481:11 504:8

testosterone 50:22 64:9,14,20,22,24 65:1,12 76:15

80:12 81:7,14 95:23 168:6,16 169:22 238:21

239:2,5,17 245:1 283:17 295:19 354:18 373:15

374:11 389:6,11,14,17,21 394:12,15 461:9,11

486:3,6,9

tests 14:11 18:21 20:15 22:15 38:17 41:26 51:13

69:16,17 126:5,20 138:21,22 150:3 178:26 179:9

185:13,25 186:1,6 199:23 200:2 206:25 223:20

225:3 290:9 291:1 307:5,16,19 309:5,6 310:24

315:3 316:6 320:2 321:7 322:11 325:7 328:14

331:1 334:25 356:21,26 358:19,22,23,24,25,25

366:23 374:6 383:4 385:23,26 422:12 430:6

438:25 460:6 466:26 467:10 469:15,18 471:5,7

471:12,23 475:22 480:14,21 483:24 485:23 493:4

496:15 500:18

test-tube 337:15 338:6,11

test-tubes 231:22 338:15

test-tube-based 240:11

tetramin 442:16

Texas 6:1,2 211:14,15

text 138:19 357:23

thank 3:24 5:13 6:22,26 8:3,7,11,12 9:3,6,8,17

15:7,8,10,14,16,24 25:22 31:24,26 32:5 35:8,12

39:24,25 49:23 53:9 68:13 73:24 86:16 88:5

92:20 93:22 114:7,17,18 132:15 139:21 140:16

147:14 148:20 150:21 151:25 182:17 187:22

188:24 203:12 205:18 208:26 209:2 212:4 217:22

217:24 219:26 229:11,15,16 230:22 237:6,14

242:1,23 246:3 252:13,16 258:2 263:4 264:20

265:23 271:4 278:14 280:16 285:7 296:4 301:24

303:20 304:21 305:3,5,6 315:13,15 317:18,19

321:1,3 323:7 329:24,25 330:2 336:23 347:8,15

347:18 352:5,6 354:21 356:15 362:18,22 363:13

363:22 364:13,23 365:17 378:4,5 379:5,11

384:19 391:16 394:2 395:1 401:12 404:11 408:20

412:20 420:21 422:16 423:9,25 424:22 425:7

426:13,15 434:21,22 441:25 445:6 447:18 451:6

454:13 456:9 458:9,26 462:12 463:21 466:10,14

469:23 470:17 474:23 477:15 482:16 490:11,23

492:3 494:10,17 495:3,11,12,17 497:1,13,25

498:6,11,14,17,20 499:8 500:8,9,18,19,20,24

501:1,5,13 502:6 504:13,15,21,25 505:3,10,13,15

505:18,20,22,23,25 506:1,6

thanking 15:26 499:18

thanks 2:15 3:22,25 32:9 38:10 39:19 52:5 82:22

91:25 92:19 114:9 133:7 182:23 230:19 237:7,9

242:22 246:4 252:15 258:15 270:19 295:26 296:2

304:10 345:20 347:16 353:23 354:19,22 355:26

363:3,8 364:14 368:11 379:25 382:7 410:15

415:5 422:17,25 423:4 446:14 458:10 459:2

472:23 494:25 495:1,7 497:3 499:22 501:6

504:18,24,26 505:4,6,18,22

theme 223:25

theory 185:23 353:13 411:16,23

thermometric 427:11,16
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thin 450:23

thing 111:23 129:6 136:8 146:12 150:26 152:8

157:3 191:11 208:3 217:7 221:4 222:6 225:8

229:7 247:7 253:26 254:17 261:11 262:2,19

266:12 268:8,24 269:14 279:18 283:4 286:18

287:12,26 297:19 320:22 329:7 333:13 335:22

338:21 343:26 344:16 383:11 386:1,3 394:16

398:7 404:20 405:19,23 406:10,20 411:7,15

412:24 416:1,3 436:26 442:20 459:8 463:8

465:23 472:11 473:2 479:8,22 485:20 486:11

496:9 500:11 506:3

things 5:13 12:16 36:9 37:24 40:22,23 45:26 46:25

47:20 51:9 52:26 69:5 70:8 71:2,21 74:24 122:16

133:15 135:12 141:5 142:15 143:19 144:26

170:25 194:21 195:19 198:3 203:6 205:24 218:5

218:24,25 221:2,19,24 226:11 227:21 242:16

247:12 253:23 258:23 260:2 263:20,23,23 265:8

272:15 275:2,4,7,18 276:1,17 279:10,13,24 282:2

282:15,18,26 283:10,20 285:14 286:15 288:1,3

295:5,17 297:26 299:16 303:7 304:19 308:11

315:26 316:1 327:10 328:21 336:5,12,13 337:12

339:7,9 340:9 342:1 344:8,14 351:3 354:13 374:1

378:9 381:18 383:6,7,10 394:13,14 404:16,19,23

405:20 407:19 408:17 411:20 416:18 417:5 422:3

422:5 424:11,13 430:7 432:18 437:23 442:7,13

442:24 443:2,20 451:17 452:7 465:2 472:21

473:23 482:13 485:8 491:22 494:26 495:19

497:15,17 500:8 503:13

think 7:22 11:1 12:16 21:7 33:8,26 37:24 38:4 39:7

39:9,12,15 50:5 52:1 69:2,5,11 70:18 71:9 73:1

84:8,10 85:6,10,15 111:6,22,23 112:12,19 121:14

122:20,22 123:13 131:22,23 132:20 133:3,14

134:3 141:16 143:17,22 144:5 145:21 146:26

147:16,17 148:22 149:1,4,6,13 152:8,18 172:9

183:23,26 184:6 186:15 187:4 188:15 189:16

191:25 192:10,14 193:3 195:15,19 198:8 199:16

200:14,20,20,22 203:15 204:1,18 205:26,26

207:7,8,10 212:20,25 215:22 217:7 223:8 225:23

226:10 228:19 229:6 243:8 244:1,5,7,12,15,22

245:3,9,22,25 246:19,24 248:3,5 258:10,25

259:19,26 260:5 262:5,16 268:12,18 269:18,19

269:24 270:1,2 271:26 272:25 273:13,18,25,26

276:7,24 277:6,9,19 278:9 279:5,6,9,12 280:5,12

280:14,14,18,22 281:8 282:25 283:5 285:22

286:5,22,23 287:4,4,7,13,19,25,26 288:6,10

289:5 292:2,18 293:5,10 294:14,15 295:23 296:8

296:10 297:8,10,25 298:16 299:5,8,13,16 300:10

300:19,26 302:19,26 303:1,2 304:13,20 315:25

316:10,12 318:6 319:22 320:22 321:13 322:7,13

322:13,21 323:4 324:7 325:10 326:12 328:15

329:11,13,16 331:9 334:4,5 341:13,15 346:11

353:5,8,10,16,18 354:3 355:20 362:17,20 363:25

364:5,9,10,11,21 367:8 372:21 378:11,18,23,24

383:2,17,21 384:13,17 386:5,9,25 387:2 388:12

388:22,24 389:1,19,23 390:20 391:18,26 393:14

394:26 396:16 397:21,26 398:13,17,20 399:18

400:5,18 404:21 405:3,11,20,25 407:4,22,26

408:14,14,19,22 409:4,6 410:1,5,8,11 411:24

412:11,19 413:5 414:2,11,13 415:9,13 416:6,16

416:21,24 417:16 418:15 419:7,21 420:23 422:8

424:1,8 433:10 435:18 436:3,26 440:18 441:24

442:3,7 443:15,21 444:1,19,26 445:4 446:8

450:19 452:5 456:19 457:11 464:5,11,12,15

465:23 469:6,11,15,19 471:25 472:7,11,19

473:22 475:3,14,20,26 476:5 477:6 479:6,17,18

479:19,22 480:9,10,13,20 482:19 485:8,15

487:24 488:6 489:16 490:4,22 491:9,18 493:23

494:19,24 495:14,25 496:2,11,13,14,20,24 497:5

497:19 498:1,26 499:3,4,4,5,19,23 500:5,7,13,15

502:7 503:17

thinking 13:14 131:21 139:22 141:24 195:6 205:15

249:9 263:22 270:11 273:26 276:21 325:24 347:1

406:9

thinks 102:8 431:7

third 24:18 99:4 161:22 164:10 254:13 273:16

406:2 488:23

Thirteen 103:16

thirty-something 36:11

Thomas 413:4,4 414:16 420:21 424:24 466:13

505:12

thorough 23:10 296:11 298:14 299:18 300:3

303:13 492:13 497:15

thoroughly 313:22

Thorp 305:12,14,18 316:9

thought 19:2 51:14 101:17 107:13 108:24 205:16

220:18,23 294:19 303:14 317:2 345:19 381:1,24

382:2 386:25 391:10 393:14 423:1 471:17 486:25

497:14 499:13 504:7

thoughts 9:15 143:14 228:23 391:19 479:18

thousand 224:3 296:9 436:5

thousands 299:25

three 9:5 10:1 12:13 20:4 23:14 34:14,15 45:19

46:26 47:4 58:4,14,14 60:20 62:17 63:20,22

64:14 66:6 74:18,22 92:23 93:8,15,18 95:3 96:7

103:13,15,17 104:2 105:8 106:21 109:17 114:5

115:16 116:4 119:7 132:20 148:13 149:24 153:15

154:22 157:14 159:12 160:26 161:2,2,3,6,11,20

161:24,25 162:5 163:22,25 164:14,14 165:13,23

168:1,12,12 169:5 170:2,11 171:8 173:2 174:7

175:7,12,17,18,19 176:9,12 177:5,18 183:23
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339:3 341:10 350:23 361:16 366:19 380:20 399:8

399:11 405:20 408:17 415:24 427:24 431:8,13

435:19 438:18 459:11 461:3 468:26 469:1,2,3,5

471:2,4,7,8,9,14,23,24,26 472:15 475:22 479:24

three-week 325:11,21

threshold 166:24 173:24 406:5

thresholds 165:18

thriving 260:3

throughput 345:5

throw 207:10 478:10

throwing 203:3

thrown 144:22

thru-put 338:17

thumb 376:9

thyrocyte 453:5

thyrocytes 449:26

thyroid 6:11 11:13 13:10 18:6,17 20:5,8,8 26:26

27:17 29:9 40:8 41:3,18 45:19,21,26 46:1,4 52:17

52:20,22 53:5 69:22 77:13,26 78:21,26 79:23

80:10,11 81:7,13 84:19 85:17 91:15 115:15

116:22,23,26,26 117:4 124:13,18,22,25 125:1,6

125:15 130:9 131:9,15 132:19 140:19 141:4

143:9,12,15 144:8 145:13,16,19,26 146:2,17

153:7 155:10 156:5,25 157:4,12,18,20,21 158:4,6

158:14,15,18 159:6,8,12 160:2,4,14 161:7,8,20

162:1,17,20,21 163:2,10,21 164:1 166:15 179:8

180:23 181:1 187:26 189:19,20 190:10 213:25

251:9 252:1 254:19 255:2,4,5,7,11,17,19,24,26

256:1,2,3,5,6,9,14 257:20 258:1 260:7 261:11,24

262:5 268:2,21,24 269:1,3 292:8,10,15,18,22,23

292:24 293:26 295:10,13 321:25 322:10 336:3

366:20 369:8 397:20 401:2,5,7,18 402:10,10

414:20,24 427:7 428:5 433:4 446:17,18,20 447:5

447:9 448:11 449:2,4,19 450:1 451:23 452:14,18

452:20,24 453:12 454:1 455:5,14 456:5,10,14,20

461:22,23 462:2,5,6,9 468:18,21,22 469:1,1

471:20 475:21 481:17 487:11 489:3

thyroidal 154:23 188:21

thyroidogenic 311:4

thyroids 47:5 140:26 256:16,17 257:11

thyroid-dependent 78:12 80:26

thyroid-related 79:10 81:22 124:10 213:12

thyromimetic 47:18 450:26,26

thyrotropin 157:21

thyroxin 252:2 401:2,5

thyroxine 157:21 455:15

tied 361:26 496:14

tier 1:5 2:8 4:2 7:19,23,26 11:11 13:7 14:22 15:2

18:3,4,14,21,21,26 19:17 20:12,14,15,21 21:9,9

21:10,18,22,26 22:2,5,15,20,22,25,25 23:14,22

23:25 24:2,10,11,22 25:5 26:14,19,23,24 27:10

27:10,15,23 28:4,17,19,23 30:4,17,24 31:15

32:23 36:16,17 39:6,16 40:1 41:16,19,21,22,23

41:26 42:5 48:11,15 49:3 52:11 53:23 54:5,14,24

55:8,10,19 56:2 69:26 87:4 115:17 129:19 143:23

144:8 153:22 154:19,22,24 155:14,19 156:7,24

157:4 158:2 163:18,23 164:5,18 165:10,16 167:5

172:12 178:6,15,25 179:5,9,16 181:2,10,26 182:7

183:10,22,24 185:25 195:10 196:9,10 197:12,20

198:11 199:4,11 200:3 202:13,19 206:23 210:3

215:8,10 222:1,12 223:10 224:13,13,14,25 225:2

225:2,3,6,7,9,19 230:4 233:5 243:6 244:6 246:9

246:23 250:18 252:10,12 257:9 268:11 278:22,22

284:26 288:21 289:22 290:17 291:16 293:20

294:10 306:12 307:16 309:5 310:8,9,13,24,24

311:5,25 313:6,24 314:9,21 315:3,7,10,12 318:22

319:21 320:8,13,26 322:17 328:1 329:7,22

330:23 331:1 332:13,15,17,25,26 333:24 334:9

335:18 347:23 358:21 365:23,24 366:10,15,21,22

366:23,25 367:2,9 368:18,24 369:14,16,22

374:25 375:6,7,21 376:13,14 377:5,15,25,25

382:23 383:4,7,19,26 392:4,8,24 395:19,24 399:5

402:20,23 409:5 422:23 427:2,17,19 428:10,16

450:12 458:14,17 459:11 460:16 476:15 481:3

483:6,13 484:25,26 490:8,8 491:15 492:14 493:2

501:26 502:18 504:1,7,8

tiered 14:4 202:22 332:10,23

tiering 11:2 245:25 331:21 336:19

tiers 11:11 332:18,20

ties 144:19

tight 267:1,21 299:2

tighten 388:20

tighter 392:14

till 287:23 356:6

Tillit 445:7,8

Tillitt 5:14,15 72:19,20 113:6,7,19,24 114:7 211:5

211:5 278:16,17 279:16 280:2,16 379:9,10

384:12,13 408:21,22 412:11 417:9,10 420:15

465:24,25 472:26 473:1 482:10,11 497:12

time 4:7 8:7 14:16 15:15 18:8 19:19 24:13 25:12

27:15 37:2,5,6 40:12,17 48:16 50:12 61:25 74:6

89:9,23 90:13,25,26 91:7 92:4,12 97:24 109:22

109:24,24,25 110:3,6 113:1 116:21 117:23,23

120:4 122:25 124:1 132:14 136:10,15 147:9,12

151:12,14 163:23 167:11 194:11 195:13 199:12

201:26 205:17 207:26 208:5,7,13,15,19,23,24

212:8 217:9 229:12 238:11,15 241:4 247:20

248:10 249:4 256:26 257:7,10 258:7,9 261:25
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307:6 313:19 315:14 318:13 320:12,24 331:26

333:24 342:15 343:22,22 348:9,16 351:26 352:4

352:4 357:16 364:25 372:23,24 379:21 383:8

386:23 407:8 408:13 415:24 417:24 419:5,17,17

423:25 425:15 437:5 459:17 460:26 471:9 481:7

483:22 490:18 496:19 499:13 500:15,23 501:4

503:7,24 504:10,19

timeframe 24:4 286:26 306:25 348:13 353:18

timeline 127:7 348:18

timelines 351:25

timely 155:20 344:9 368:12

times 73:4 157:7 161:21 227:13 238:6 286:16

339:12 341:11,18 357:18 403:1 432:14 448:8

452:7

time-constraints 306:24

time-dependent 433:10

time-weighted 110:4

timing 37:20 501:18 503:3

tiny 265:10

tip 418:4 477:13

tired 124:12

tissue 57:4 138:11 148:17 163:13,15,16 166:4

168:5,19,21 170:12 226:10 240:17 433:24

tissues 40:6 45:10 76:9,23 81:12 168:5,26 175:15

351:15 461:5

titled 4:1

today 7:24 9:4 23:9,13 25:8 26:16 28:3 30:9 31:11

31:13 56:1 92:4,9 94:6 151:19,22 157:7,15 164:6

197:16 198:9 201:13 204:25 207:8,13 210:2

214:4 223:5 224:10 226:3 252:18 256:23 258:19

289:23 298:25 304:7 317:23 319:26 324:23 333:6

334:21 339:12 342:22 354:25 361:21 364:12,20

379:14 382:16 385:25 388:18 403:2 407:4 428:9

436:25 446:10 496:17,19

today's 7:17 14:7 15:25

told 203:4 224:20,23 320:15 322:24 327:9

tolerance 90:14

tolerate 233:16

tolerated 18:17 76:5 78:5 80:19 87:19 89:2,17,24

90:7,12 186:2 254:14 273:22 325:2,15,20,25

348:8,15 349:1,3 350:4,15,18,25 405:26 406:12

406:16 413:5,14 424:26 425:6

Tom 31:8,21,23 151:25 178:11,11 183:3,26 184:24

185:8 186:6 188:11 189:2 191:22 192:14 193:25

195:1 204:22 205:25 206:18,22 416:14

tomorrow 91:12 92:9,10,14 114:14,16 115:11

121:15 132:23 149:13 151:19,20 207:9,24 209:1

420:24

tongue 477:13

tonight 207:21

tool 30:25

toolbox 195:14

Toole 336:24,25,26 345:12,15 346:1,14,25 347:6,9

347:15

tools 25:16 26:12 289:24 377:8

top 16:14 20:16 23:2 29:9 63:11 66:20 87:15

103:11 105:21 113:12,16 168:7 177:16 213:1

217:4,6,12 219:25 232:20 260:11 337:22 392:17

392:20

topic 2:7 56:6 102:12 125:20 404:9 503:25

topics 55:26 285:17

tortoise 261:21

tossed 337:11

total 33:8,12 54:10 57:6 63:6 99:14 100:18 155:20

168:17 324:20 360:18 369:7 377:19 434:17

448:18 473:16 488:13 494:6

totality 190:14

totally 457:1 505:20

Touart 31:14 39:21,23,24 50:1,6,18 52:13 53:8

57:1 70:11,13 84:25,25 134:5,7,8 135:17 136:1,3

137:13 142:7 143:1,17 144:9 150:20,22 153:22

186:16,19

Touart's 86:19

touch 37:26 324:7 337:10 344:17

touched 241:7,22 326:13 344:26

touches 38:11

touching 212:17

tough 465:4

tougher 303:15

Toxci 333:23

toxic 244:14 316:16 368:26 398:8 399:23 400:9,19

411:21 412:1 414:26 416:3 419:6,12 432:19

toxicants 432:3

toxicity 19:21 29:23 30:13 56:6,7,8,11,13 75:17

77:1 78:6,8 79:7,11 80:20,22 81:19,23 82:7 85:23

85:25 86:3,6,10,15 87:23 88:8,13,14,24 89:1,6,13

89:15 91:3 92:18 93:6,10,10 102:14,16,26,26

103:2,6,13,19,22 104:7,9,11,16 106:14,18 110:18

112:4 117:5,22 126:5,20 156:8,13,15 159:8 160:5

161:10,15 162:22 167:16 170:7 172:3 174:20,23

176:15 178:6 180:2 185:5 244:17,22 250:15

251:7 252:3 254:16,20 255:20 269:11 273:15,18

273:24 274:5,10 275:13 276:23 312:21 316:5

320:21 325:26 326:4,8,11 328:21 329:1 330:9

335:22 350:17 354:5 360:8 380:24 395:16,21,23

396:1,21 397:2,2,25 398:2,21 399:3,12 400:1,4,5

400:19 402:18 404:13,21 405:25 406:14 407:10
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411:7,19 414:3,4 419:1 431:24,24 459:25 461:14

464:13 467:22 468:4 471:18 481:20 484:18 485:2

500:5

toxicodynamics 19:11

toxicogenomics 7:5 336:1

toxicokinetic 19:12

toxicologic 43:4

toxicological 4:24 14:18 24:7 186:1,4 187:7,18

210:19 252:8 309:9 321:7 332:2 377:8 440:14

toxicologically 315:21 316:1

toxicologist 5:25 210:23 211:6 220:2 237:12

242:26 246:7 271:13 305:26 496:13

toxicologists 306:1

toxicology 4:13,16,17,25 5:4 6:2 7:4,4,6 16:4,5

25:15 26:12 93:1 125:25 185:6 195:26 210:8,14

210:25 211:11 219:21 225:23 290:3 309:14

320:18 330:6 470:1,15

Toxics 8:6

toxin 457:6,12

toxins 444:7 446:5 457:10,10,12,15

trace 436:14

track 14:23 132:11 140:4 275:3 341:8,8 432:7,16

438:9 497:22

trackable 304:20

tracking 122:24 140:1 408:15

trade 306:6

tradeoff 242:10

trade-offs 367:1

traditional 125:25

tragedy 331:8

training 125:25 133:17 343:16 485:24

trait 447:1

transaction 503:14

transactivation 19:23 346:4 467:11 487:10

transactive 467:11

transcription 343:4 372:4 399:26 467:6 491:3

transcriptional 42:8 43:25 46:23 56:17 60:4

177:20 230:25 232:23 365:4 372:2,17 377:5,19

380:16,19 468:23 491:12

transduction 400:13

transformation 235:13 424:16

transforms 293:11

transient 418:17

transition 182:7 320:17 333:16 385:20

transparency 156:15 497:11

transparent 8:25 27:12 221:5,7,9 284:17

transporter 442:12

trans-activation 382:23

travel 221:11

travels 285:8

trays 273:10

treated 74:18 77:17 80:2 166:2 233:13 386:24

treatment 95:1,5 96:9 97:24 103:2,23 105:16,19

105:23 106:18 109:9,15,17 110:9 116:4,4,8

117:18 118:11 119:12,26 120:3,25 123:24 124:21

127:8,20,21 128:1 129:3,6 135:26 141:24,25

142:15 146:6 147:21 148:10,12 152:16 158:3

213:18,21 216:25,26 217:1,4,6,10,12 233:15

238:10 268:4 269:17,24,26 323:11 387:9 396:4

405:19 425:18 427:24 448:11,17,25 449:12

treatments 117:16 120:1 123:23 125:9 142:10

143:5 152:10 261:18 262:18 274:9 304:26 419:25

423:16

treatment-induced 125:14

treatment-related 125:12 213:10,11 447:4,10,14

tree 192:21 255:25 359:26 375:25

trend 127:13 132:1 249:12 340:4 431:12

trends 167:10 447:1

triage 344:2

trial 435:24

triclosan 401:26 402:5

tried 149:26 265:4,5,19,20,20 298:14 433:14 470:1

triggered 20:1

trip 220:10

tritiated 66:3,5 240:3 241:7 339:26

trivial 339:5

tropic 294:21 295:13,22 450:21

tropicalis 122:8 212:23 214:7 260:19 263:10 438:1

439:13

trouble 274:14 381:26 450:20

troubles 275:17

troublesome 499:26

trout 265:20 371:22 377:6,6 382:18

trouts 263:14

trout-starter 263:23

true 193:23 194:2 222:15 240:19 255:9 300:23

340:3 363:8 392:6 397:4 465:18 487:12

truly 106:8 157:3 193:16 305:22 312:22 313:21

342:3 500:13

try 34:2 85:8 89:23 108:18 110:17 130:21 142:17

144:1 146:25 186:7,24 190:2 203:5 248:15 256:8

260:10 272:11,13,15 273:9,11,12 275:15 282:18

283:19 284:18 286:14 296:5 321:2 326:9 385:13

394:19 408:23 416:14 440:24 445:4 447:20

trying 46:16 47:24 71:4 90:11,15,17,18,24 91:3

109:26 111:23 205:14,15,23 267:7 268:7 274:2,3

284:17 286:25 299:10 329:9 367:7 406:19 443:22

465:19 479:11

TSH 45:26 78:1,22 80:11 81:8,15 157:21,25 158:4
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tubercle 206:20

tubercles 95:18 104:24 107:11,24 165:7 168:25

276:2

tubes 273:10 338:16 487:1

Tuesday 3:26

turn 278:9 298:2 396:13 398:5 480:16 489:2

504:16

turning 418:1

turnover 110:19 264:9

turnovers 111:4,4

turn-over 272:14

Twelve 97:25

twenty 36:9 356:6

Twenty-one 96:18

twice 112:25

two 10:5 11:11 13:13,20 16:25 21:19 47:4 48:8

55:26 57:7 59:21 60:20 62:4 65:12 67:4 74:15

75:17 76:18,20 81:18,22 83:24 88:25 89:9 93:15

93:17 94:23 97:18 99:1,14,23 101:3,11 105:12,13

105:21 106:20 115:17,18 116:20 117:13 119:15

120:1 130:17 137:4 148:12,15,25 149:16 158:10

161:8,16,20,25 162:17 164:22 166:1 167:12

168:11,13,19 169:6 175:19 177:3,23 180:13

189:26 190:5 191:8 195:18 198:5 200:4 216:25

217:4,11 223:4 224:7 225:8,12,14 226:3 227:3

228:22 229:12 245:5 246:20 250:11,26 255:9

257:12 258:19 259:17 260:13,19 269:19 270:4

274:9 275:20 277:1 280:18 284:8 291:17 293:7

308:13 310:5 317:24 327:3 328:19 351:11 359:13

359:17 373:16 380:22 383:4 392:25 399:8 403:7

403:20 414:3,11 416:18 417:14 432:14 433:25

435:19 438:17 454:26 457:14 460:11 463:25

464:8 467:24 469:22 471:9,13,23 472:12 474:26

475:3 477:7 479:24 480:21 493:3 494:22 498:26

two-assay 228:20

two-fold 475:10

two-generation 20:16 309:22

two-stage 267:14

two-step 367:18

two-thirds 220:8 471:16

two-tiered 11:7 18:11 41:15

two-year 286:25

tying 324:11 328:23 494:26

type 47:22 85:3 95:11 121:21 122:8 131:18 135:5

136:4 142:18 143:23 153:1 159:18 161:23 165:11

170:22 171:1 174:23 197:8 201:16 206:12 214:8

218:23 241:14 249:12 265:15 266:12 293:7 299:8

375:21 383:13 392:3,3,5,9,10,17,19 399:16

403:19 411:5 442:25 474:1 476:22,26 496:2

497:19

types 29:11 122:16,26 131:21 149:25 163:6 164:11

212:25 265:5 328:21 337:24 369:2 392:25 431:11

442:26 443:4,4,6 474:21 475:9

typical 22:6 265:3 354:12 440:23

typically 65:7 95:3 97:18 101:23 108:8 109:5

127:14 179:17 234:8 262:8 270:16 273:19 410:23

417:21 418:3,26 429:16,18 433:21 453:24 487:5

T-related 458:24 466:21

t-test 144:25

T0 366:24

T1B 288:21 289:13,18 291:14

T3 162:9 354:13

T4 45:26 77:26 78:22,26 80:11 81:7,15 157:22,24

158:4 161:2 162:11,16 189:21 191:5 295:18

354:13,15,17 452:23 455:7 461:23 462:1

U

UC 437:17

ultimate 12:24 46:11

ultimately 43:22 127:24 184:17 215:11 294:26

307:26 393:2

UMASS 211:23

unable 146:21 298:24

unacceptable 371:9

unaffected 266:21

unanticipated 500:13

unaware 128:23 452:17

uncertain 453:10

uncertainties 468:5 483:15

uncertainty 18:18 145:8 146:16,26 156:9 197:9

199:10 213:23

uncharacteristic 148:3

unclear 119:14,16 249:17 252:8 369:1 460:26

461:19

uncompromised 148:12

undergo 355:14 481:10

undergoing 385:18 456:15

undergone 289:12

underlie 8:22

underlying 204:18 206:5 362:1

underneath 273:10 380:3

underscoring 17:2

understand 31:2 36:15 69:4 85:19 90:10,15,19

108:6 142:24 145:14 189:18 190:13 199:9,18

246:12 249:26 251:18 271:23 275:16 284:17

286:14 289:14,15 294:13 300:14 301:2 302:16

312:17 342:20 368:23 394:6 411:25 423:17 435:7

455:23 463:16 473:8 477:5 495:18 496:5 497:20

497:23 498:3
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understanding 19:21 20:6 38:14 110:7 186:11

188:21 196:18 197:11 203:18 219:3,9 249:14

250:24 272:25 279:25 291:2 336:11 366:5 413:17

430:18 450:4 477:8 478:26

understands 366:8,9,15 367:23,24 369:11 376:12

396:16 427:1,9 428:14 429:16 430:4 434:9,11

understood 36:20 49:14 52:16 87:15 189:9 221:25

345:11 358:25 360:24 394:4

undertake 12:4

undertaking 296:13

undesirable 355:17

undetected 389:5

uneaten 439:24 440:1

unexpected 113:26 436:2

unexplained 318:1 320:4,9

unfamiliar 116:11

unfortunate 306:24

unfortunately 135:2 286:16 452:17

uniform 85:10 297:20

uniformed 389:16

uniformity 134:12

uninformative 128:19 470:10

unintelligent 222:5

unique 16:17 28:7

Unit 6:1 211:14

United 1:11 313:13

units 117:21

universally 370:22

universe 17:18 25:12,24 26:1 37:9 192:9

University 4:4,12,21 5:6,10,19,25 6:2,5,9,13,20

210:8,12,17,26 211:4,9,11,15,18,26 212:2 265:2

271:12 437:16

unknown 8:1 24:14 96:18 159:2 160:23 163:5

184:9 367:22

unknowns 184:16

unlabeled 58:5 62:18

unmet 97:18

unnecessarily 198:3

unnecessary 30:17 48:17 182:1 196:25 197:26

310:14

unpublished 212:22

unrealistic 128:19

unreasonable 14:19 409:10

unreliable 124:9 359:20 365:14 376:8 385:9 391:8

395:23 402:19 405:6 426:8

unsatisfactory 71:9

unstable 160:11

unsure 255:21

untestable 63:23 167:2

unusual 436:13

unusually 381:20

upcoming 39:8

update 227:22 283:2

updated 282:26 284:14

updates 10:20

upgrade 338:19

upgraded 314:7

upper 175:11

upset 10:17

upside 418:1

uptake 111:21 126:7 442:13,23,24

upwards 254:24

up-front 272:12

up-regulated 239:13

up-take 361:15

urge 327:20

URL 438:4

usage 307:8 355:4 483:1,7

use 11:11 12:2 22:6 25:15 26:3 32:13 33:8,10,12

35:1,3 36:9 68:5,20,22 75:7 84:6,11 85:8 89:17

89:18 102:19 104:26 110:26 116:8 118:26,26

120:10 126:3 127:14,14,16 133:13 134:20 157:1

157:19 163:12 184:3 196:5,8 219:7 226:11 228:1

231:15 248:12 249:22 250:8 257:4 258:7 272:18

274:8,12,24 275:5 281:25 288:7,22 289:13

290:19 291:20,26 293:11 298:14 301:12 303:7

317:26 318:4,6,11,22 319:18 320:3,8,18 322:20

324:4,18,24 325:10 326:9,15 327:1,6,12,16

329:22 330:15 331:9 332:10,26 338:7,8 339:7

350:23 355:8 361:8 362:14,15 365:11 367:5

371:5,11 377:23 379:19 384:1 385:26 388:16,18

396:17,25 397:24 399:9 403:26 404:2 406:13

407:26 408:1 412:24 419:1 420:5 429:24 439:1

444:21 449:1 453:19 454:6 473:6 474:9 480:1

484:22 485:16 486:18 489:10,20,23,26 490:17

useful 28:20 38:15 52:2 105:10 106:7 107:1 130:4

131:4 133:8 149:5 158:26 179:23 183:18 217:9

217:18 218:16 219:3,10 245:20 276:7 288:4

301:15 321:11 350:6 375:7 379:19 386:22 411:18

412:4,8 420:17 426:9 430:3,11 453:15 456:17,19

459:7,24 479:23 493:2 495:8 497:19 498:8,20

usefulness 118:2 238:5

uses 47:25 60:8 62:7,11 64:7 115:13,22 240:14

325:9

USGS 6:1 211:14

usually 52:19 93:11 116:2 119:25 123:20 131:2

132:8,12 140:3 158:15 258:9 274:13 326:21

327:2 341:11 400:23 417:2 425:11 436:14 437:4

437:10 438:17 441:9 452:5

uterine 57:2 74:12,24 75:3,9,15 174:4 244:17
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uterotrophic 19:25 29:26 30:6 42:11 44:1 45:5

46:24 50:4,6 55:2 74:7,10 84:5,11,17 85:15 87:26

93:6 173:3,21 174:3 175:23 177:21 178:9 181:15

191:12,17 192:13,24 196:18 197:2,5,7 204:2

235:11,18 243:4,9 244:19 283:10,11 288:26

289:2,6,11 293:1 308:3 324:11,18 325:23 328:5

328:11 348:2 350:14,15,26 351:5 384:23 391:11

395:11 397:17 459:13 460:25 463:15 465:10

467:4,8 468:2 478:15 483:12,21 484:14 485:9,11

485:14 486:26 487:15 491:5

uterus 74:26 77:26 78:21,25 355:9

utility 95:25 152:14 192:5 215:12 244:12 249:17

250:1 257:23 392:24 404:11 426:6 428:13 429:10

utilization 399:25 400:14

utilize 98:7 349:16

utilized 40:3 48:6 389:26

utilizing 41:15

Utrecht 5:25 211:11

U.K 356:3

U.S 5:15 211:6 323:17 368:7 498:9,12

V

V 176:12

vagina 75:1

vaginal 77:19,22,24 78:19,19 79:1 173:14 250:10

398:25 459:16

valid 205:26 248:17 280:15 372:5 411:4,5 449:13

493:21

validate 183:22 201:23 318:5 320:17 453:19 474:9

validated 11:11 12:3,12 17:2,7 20:17 30:5 31:15

84:22 128:23 186:22,26 191:23 197:13 199:19

206:10 232:23 243:9 289:12 318:7,19 322:14

327:25 333:15 367:10 453:7 456:3 493:20

validating 84:21 226:11 286:8 476:15

validation 11:22 12:24 13:2 17:2 20:20 25:4 50:7

53:4 70:15 84:5,6,16,26 85:12 87:2 129:24

140:23 149:23 150:24 158:24 186:21 199:17

200:7,9 201:4 205:4 216:7 217:14 231:8 234:1,2

234:13,15 235:23,23 245:14 247:19 268:19 269:2

285:2 286:11,18,24 298:10 319:5,6 327:19 328:4

335:24,25 344:19 355:25 367:14 501:26

validations 232:6 236:24 239:8 335:25

validity 117:12,15,25 118:1 120:13 129:15 130:20

145:1 149:17 271:22 319:9 375:3 396:3 426:6

429:11 447:7 477:20

valuable 39:17 197:7 226:13 228:24 229:3 277:13

320:25

value 9:12 57:14 69:4,8 102:4 105:16,24 106:6

108:24,25 194:5 233:24,25 234:14,16 236:16,17

236:18 249:4 332:1 349:26 353:11 354:9 369:12

377:14 378:7,25 430:20 468:16

valued 453:7

values 28:10 59:7,8,26 60:24 61:8,18 63:5,12 65:7

65:18,19 66:26 68:21,23 78:24 81:11 104:20

105:15,17,21,25 119:8 198:5 236:20 371:7 372:9

373:22,23 381:9 388:5

variability 78:13 96:9 123:17 146:17 234:9 237:26

238:4 240:16 246:22 247:6,14 252:4 267:21,22

297:17,18 300:23 345:23,26 346:5 353:17 387:8

387:24 390:26 391:7 393:4 426:2 428:11 448:21

variable 109:24 143:12,15 239:1 249:4,18,26

394:15 425:26 496:8

variables 86:22 122:24 149:16 204:11 403:7,20

443:10 453:24,25 479:11

variably 471:9

variance 90:7,13 97:2,16,23,26 109:8,20,21,24,25

110:1,7 111:14,15,16 112:9,10 127:16 149:8

246:21 300:15,21 302:13 393:2,9 417:2 497:22

variances 109:5 240:21 390:26 393:8 465:12

variant 240:15,15,20

variation 77:4 78:24 81:11 97:19 101:3,6 105:5

108:21 109:4 118:10 246:19 253:5 340:12 357:3

357:4,10,13 358:15,20,22 359:1,4,9 360:20

384:25 387:11 388:2,6 391:21 392:4,14,19

393:15 406:4 417:16

variations 76:22 200:18 356:26 359:23 386:4

390:10

varied 70:10 84:23 85:18 162:14

varies 165:11 253:9,9

variety 11:14 43:18 45:10 142:17 246:15 314:14

335:14 402:15,17 432:3 461:25

various 11:15 26:2 28:13 43:9 46:17,22 51:6 70:14

81:11 125:3 217:26 219:4 227:24 293:19 302:15

302:17 304:2 314:17 325:6 356:20 385:22 389:20

448:22 488:20

variously 121:13

vary 109:9 354:12 376:9 478:5

varying 371:13

vast 437:16

vehicle 344:11,13,14

vehicles 344:5 348:11

vehicle-control 78:23 81:10

vendor 438:11

vendors 435:19 438:10

vent 417:21 418:2,9 428:4 429:7 434:15 441:15

ventilation 361:14

ventral 76:10 80:9 433:23

verify 75:4

version 122:23 157:6

versus 140:20 142:21 204:8 233:10 248:19 256:5
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veterinarian 436:4,19 437:1 488:2

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viability 343:1,4

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visually 106:1 140:12

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warranted 106:17 319:9 331:13 461:1

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166:16 168:8 169:2,4 244:2 340:20 341:1 373:10

386:6 460:1 468:12 471:4,4,8,8,14

18 57:6 58:24,26 59:9,17,19,20 65:2,9,16 66:11

67:3 74:17 118:16 123:12 126:13 147:5 155:24

174:13 175:20 176:5 252:26 254:4,16 257:1

284:8 298:21 303:26 304:6 324:19 348:1 370:25

373:18 374:17 425:12

19 58:6 61:4,5,11 65:23 192:10 235:9 370:14 372:9

471:24 472:1,2,3,5 475:23

1982 458:3

1985 434:3

1992 121:26

1996 10:6,20,24 16:26 17:12,19 306:11

1998 11:2 19:18 20:10 198:14 310:10 368:1 499:4

1999 11:4 12:5 27:4

2

2 18:4,21,21 20:14,15,21 21:9,22 22:2,5,15,22,25

23:25 25:5 41:21,22,26 48:11 103:2 141:8 142:13

143:23 144:8 168:17 171:11 177:23 183:24

185:25 202:19 206:13 210:2 215:8 224:13,13,25

225:3,6 244:26 257:9 268:11 284:26 285:11

291:16 309:5 310:8,24 313:7 314:9 318:23

319:15 330:23 331:1 344:1 364:26 365:1,16,18

365:20,22 366:21,22,25 368:17 377:25 392:3,5

392:10,17,20 399:5 423:18 431:5 450:12 460:7

484:25 490:8 494:3 501:26 504:8

2X 185:26

2-fold 234:11

2-ish 196:10

2/3 111:5

2:45 151:15

20 53:11 90:12 97:26 98:19 116:9 119:26 201:5

223:2 236:2,6,6,14,22 249:7 253:4 254:6 273:23

292:18 294:19 342:16,25 343:2 348:13 376:10

381:20 405:9 428:1 430:15 440:23 448:18 470:3

493:24

20% 147:4,20

20,000 488:19
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200 326:18 338:12 345:25

2000s 13:2 317:5

2004 231:6

2005 434:5 436:16 437:15 458:8

2005/2006 195:13

2006 231:6

2007 318:25 427:22

2007'ish 33:26

2008 7:23 11:21 12:15 13:21 20:11 27:14 28:20

30:5 48:15 153:22 154:3,10 155:7 157:23 163:25

164:20,25 167:23 169:12 173:9 175:4 200:8,13

200:18,19,19 201:9,10,15,18,22,22 202:2 287:8

287:19,19 296:19 297:9 299:22 318:15,25 322:25

367:16 368:19,22 375:13,16 376:13 380:9 428:15

458:13 460:16,23 468:18,21 476:16,21 477:11

479:6 480:26 481:3,23

2009 28:5 155:15 233:4 318:25 367:12 372:1

2010 255:14 257:13

2011 28:6 290:15 291:6

2012 320:16

2013 1:17 28:6 375:17 377:2 477:12 499:5 504:10

2014 327:20

21 1:17 28:7 32:11,17 36:17,18,21 37:9,21 38:1,19

38:26 54:11 55:5 95:2 96:22 97:11,25 98:2,10,19

98:26 99:2,8 100:2,14,20,25 101:8,20 102:22

103:16 104:2,3,19 105:8 106:2 110:8 113:18

114:5 115:22 116:21,25 118:10 120:7,18 124:19

127:7,24 129:1,4 147:4,19 155:21 179:22 182:25

185:3 192:11 194:17 201:25 206:19 216:24

217:12 222:23 257:7 271:21 272:7 273:6,24

284:16 287:18,22 348:2 350:9 368:9,16 369:1,4

374:26 375:11 376:17 408:4,6 428:5,19 429:1,17

430:8 448:3,6 463:9,14 467:5 478:3 484:15 485:1

497:6,11

21-day 97:24 99:13,26 119:20 253:7,15 257:17

258:10 273:20 409:9

22 77:18 222:24 350:11

22R 342:23

22202 1:16

229 277:2

23 1:17 80:2 350:11

23,000 226:18

230 276:26 277:4

24 64:12 99:14 233:22 355:15 471:12

24-hour 238:10

25 58:19 66:18 100:15 102:2 234:22

27 59:18

2777 1:15

28 427:25

28-day 93:2

29 273:8

3

3 58:9 62:20 93:6 103:6 141:8 178:1 200:3 206:16

206:18 216:7 217:13 384:21,22 385:11 393:25

431:6

30 38:1 80:3 98:1 101:21 149:1 292:18 337:9 348:6

399:23 414:20

30,000 331:3

30-day 249:7

300 250:4 338:8,9 488:25

305-5805 1:9

31 157:26 376:19 458:3

31,000 324:24,24,26

32 139:15 247:8 284:6 426:21

320 254:23 324:20

3300 324:26

35 464:6

350 85:14

38 164:15

39 166:18

4

4 59:19 63:5 318:23 319:15 395:8,9 396:8 418:9

423:7

4X 234:3

40 144:19 301:20 342:25,26

400 274:12

41 464:5

42 76:2 77:18 353:6

43 272:9 353:6

45 100:4 324:19 326:17,25

47 464:9

48 76:2 238:16 324:19

5

5 61:5,12 69:1 75:25 123:14 148:9 405:9 408:6

425:9,10 433:3,4 446:1

50 12:6 27:6 34:1 36:16 37:18 58:17 66:15,16

87:21 123:4 215:3 230:15 250:15 251:7 287:8,20

294:11 296:9,19 299:24 368:17,20 405:10,14,15

425:23 440:3 443:13

50s 317:5

500 274:12

501(c)(3) 220:5

51 116:1,13 137:24 194:17,22 216:1 259:9 267:20

302:6 427:23 429:20,24 448:2

52 28:7 32:12,14 33:1,4 36:20,26 37:2,15 54:12

155:20,22 179:9 182:26 184:21 185:4,10 187:16

195:1 196:25 197:19 271:23 287:8,9,19,23,26

306:21 307:1 313:20 324:22 331:3 368:24 369:1
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369:5 376:18 428:19,25,26 463:9 492:22 497:9

497:11 502:12

52-chemical 32:19

53 80:2

54 259:9

55 216:1 429:21

57 120:18 418:9 448:4

58 100:5 124:21,22 257:6 258:9 267:17 269:19

595 324:21

6

6 63:24 79:5 101:20 433:5,11 446:17 453:8,13,23

454:23,25 457:6

6a 449:10 450:13 451:20

6b 452:12

60 99:25 100:3,4,8,9 120:8 121:4,9 253:21,21

254:1,3,3,5 256:21 326:25 343:1 355:13 448:7

600 330:17,21

61 34:13 120:21

62 267:17

64 280:22

67 34:17 35:17 37:12,14,14 93:19 155:15 195:8

287:20

7

7 116:21,21 117:2 127:19 217:8 274:6 430:9 457:3

457:20 458:12

7a 458:11,20 466:11

7b 466:15,16 476:10

7c 480:24

70 75:7 103:21 149:4 471:15

703 1:9

72 238:16

75 66:16 96:26 345:24 346:10

8

8 63:6 108:22,22 135:11 307:4 329:21 355:4

482:20,25,26

8a 483:5

8b 494:6

8-day 257:12

8:30 151:19,22 208:4

80 75:7 251:7 338:25 342:15 428:2

80s 317:5 443:11

80-fold 475:7

83 99:18 426:16

87 123:22 124:5

890 277:3 281:9

9

9 81:16 209:2

90 99:9,12,17 248:19 337:7 338:25 506:4

90s 234:22 317:5 499:1

90th 119:5 448:4

92 123:23 218:2 264:1 328:4 425:17,22

94 99:9

95 99:6 230:4 235:15,21 236:23 345:2 406:21,24

412:25,26

96 102:19 240:13 324:20 338:16 409:8 435:26

96-hour 273:19

97 205:8

98 499:12