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FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

MEETING ON SCIENTIFIC ISSUES ASSOCIATED WITH 

INTEGRATED ENDOCRINE BIOACTIVITY AND EXPOSUREBASED PRIORITIZATION AND SCREENING

DOCKET NUMBER: EPA-HQ-OPP-2014-0614

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

DECEMBER 2-4, 2014
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 

TABLE OF CONTENTS

OPENING OF MEETING AND ADMINISTRATIVE PROCEDURES ..................................... 3

INTRODUCTIONS AND IDENTIFICATION OF PANEL MEMBERS................................... 5

WELCOME AND OPENING REMARKS ....................................................................................... 8

INTRODUCTION......................................................................................................................... 14

CURATED REVIEW OF UTEROTROPIC LITERATURE AND COMPARISON TO 

TOXCAST ESTROGEN RECEPTOR (ER) AGAINST AREA UNDER THE CURVE (AUC) 

DATA .......................................................................................................................................... 143

COMPARISON OF TOXCAST DATA AND LIST 1/TIER 1 ER ASSAYS AND ER 

AGONIST BIOACTIVITY OF ENDOCRINE DISRUPTOR SCREENING PROGRAM 

(EDSP) UNIVERSE CHEMICALS ........................................................................................ 174

ANDROGEN PATHWAY DATA AND MODELS ....................................................................... 195

ANDROGEN RECEPTOR (AR) INTERPRETATION AND APPLICATION ..................... 248

HIGH THROUGHPUT EXPOSURE; TOXICOKINETIC/DOSIMETRY............................... 266

INTEGRATED (RTK) BIOACTIVITY EXPOSURE RANKING (IBER, MODELING 

UNCERTAINTY ......................................................................................................................... 339

IBER INTERPRETATION AND APPLICATION ................................................................ 387

FUTURE DIRECTIONS ........................................................................................................... 418

PUBLIC COMMENTS PERIOD ............................................................................................... 433

CHARGE QUESTION: 1.A.................................................................................................. 532

CHARGE QUESTION: 1.B.................................................................................................. 552

CHARGE QUESTION: 1.C.................................................................................................. 560

CHARGE QUESTION: 1.D.................................................................................................. 578

CHARGE QUESTION: 1.E.................................................................................................. 591

CHARGE QUESTION: 1.F.................................................................................................. 607

CHARGE QUESTION: 2.A.................................................................................................. 617

CHARGE QUESTION: 2.B.................................................................................................. 638

CHARGE QUESTION: 2.C.................................................................................................. 645

CHARGE QUESTION: 2.D.................................................................................................. 649

CHARGE QUESTION: 3.A.................................................................................................. 660

CHARGE QUESTION: 3.B.................................................................................................. 674

CHARGE QUESTION: 3.C.................................................................................................. 704

SUMMARY AND PANEL COMMENTS...................................................................................... 713
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1 OPENING OF MEETING AND ADMINISTRATIVE PROCEDURES

2 

3 DR. FRED JENKINS: Good morning again. I want

4 to welcome everyone to this FIFRA Science Advisory Panel

5 Meeting. The title of this particular meeting is Integrated and

6 Endocrine Bioactivity and Exposure-Based Prioritization and

7 Screening.

8 My name is Fred Jenkins and I am the

9 Designated Federal Official for this meeting. Before we get

10 started, I would like to take just a couple of minutes to go

11 over a few administrative items. 

12 As the DFO, I serve as the liaison between the

13 panel and the Agency. I'm responsible for ensuring that the

14 provisions of the Federal Advisory Committee are met. I wish to

15 extend my thanks to this entire panel for attending this meeting

16 and participating in this meeting. I would like to also extend

17 my thanks to the public for attending this meeting.

18 The FIFRA SAP is a Federal Advisory Committee

19 that provides independent and scientific peer review and advice

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

21 the panel only provides and recommendations to the EPA. All

22 decision-making and implementation and authority remains with

23 the Agency. 

24 We will work with the appropriate Agency

25 officials to ensure that all appropriate ethics and regulations

26 are satisfied for this meeting. Panel members have been

27 provided the provisions of the Federal Conflict of Interest
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1 laws, and each participant has signed a Disclosure Report. I,

2 along with our Deputy Ethics Officer and the Office of General

3 Counsel have reviewed these reports to ensure all ethics

4 requirements are met. 

5 This meeting provides an opportunity for

6 public comment. We have several people who will be speaking

7 during the public comment period. If you have not made prior

8 arrangements and you wish to make public comments today, please

9 let me know or someone else or my SAP staff know. 

10 The SAP staff members are seated right to my

11 right, behind me. Without having made prior arrangements, we

12 ask that if you haven't made prior arrangements to provide

13 public comments today, we ask that you limit your comments to

14 five minutes. 

15 There is a public docket for this meeting. 

16 The docket number is listed on the agenda. All background

17 materials and other related documents are available in the

18 docket. I think you will see the EPA presentations will be

19 available as soon as possible on the docket. 

20 At this point, I want to introduce and extend

21 my thanks to Dr. James McManaman, to my left, who is serving as

22 the session FIFRA SAP session Chair for this meeting. Thank

23 you.

24 

25 INTRODUCTIONS AND IDENTIFICATION OF PANEL MEMBERS

26 

27 DR. JAMES MCMANAMAN: Good morning. Thanks,
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1 everyone for coming out to this gloomy Monday morning -- or

2 Tuesday morning, I guess it is. I'm a day behind. 

3 So this looks to be a really exciting and very

4 interesting session. Some very complicated and very integrative

5 approaches are being discussed, and as the Chair of this

6 session, I am very pleased to be a part of this. 

7 In this session, the panel is made up of

8 permanent panel members and ad hoc members who are brought in

9 for their specific expertise in the areas that are being

10 discussed today. The session will involve two parts, where we

11 have a presentation by the EPA and public presenters, and then

12 the panel will discuss various charge questions that were

13 assigned to them.

14 So with that as a preliminary overview of

15 what's going to happen, I will now ask other panel members to

16 introduce themselves. 

17 DR. BARRY DELCLOS: Barry Delclos from the

18 FDA's National Center of Toxicological Research in Arkansas. 

19 And I'm a toxicologist.

20 DR. DANA BARR: I'm Dana Barr. I'm a

21 professor of environmental health and exposure science at Emory

22 University in Atlanta.

23 DR. DAVID JETT: Dave Jett. I'm from the

24 National Institutes of Health, and I'm the director of the

25 Chemical Defense Program. 

26 DR. VERONICA BERROCAL: I'm Veronica Berrocal

27 and I'm an assistant professor of Biostatistics at the
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1 University of Michigan.

2 DR. TERRY BROWN: I'm Terry Brown, professor

3 of biochemistry and molecular biology at Johns Hopkins

4 University. 

5 DR. ROBERT DENVER: Good morning. I'm Bob

6 Denver and I'm a professor and Chair of molecular cellular

7 developmental biology at the University of Michigan.

8 DR. CATHERINE PROPPER: I'm Cathy Propper. I'm

9 a professor of environmental endocrinology at Northern Arizona

10 University.

11 DR. DANIEL DOERGE: Dan Doerge, FDA's National

12 Center for Toxicological Research. I'm a research chemist.

13 DR. EDWARD PERKINS: Ed Perkins, Senior

14 Research Scientist, Army Corps of Engineers.

15 DR. THOMAS POTTER: Tom Potter. I'm a

16 research chemist for the USDA Agricultural Research Service.

17 DR. GRANT WELLER: I'm Grant Weller. I'm a

18 research scientist and statistician at Savvy Sherpa in

19 Minneapolis. 

20 DR. DANIEL SCHLENK: Good morning. I'm Dan

21 Schlenk. I'm a professor of aquatic ecotoxicology and

22 environmental toxicology at the University of California,

23 Riverside.

24 DR. JIM MCMANANMAN: Okay. So a couple of

25 other points I need to make is please speak into microphone. 

26 The switches for the microphone are on the top, so make sure

27 that they're on. If you don't want your side conversations
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1 recorded, make sure they're turned off when you're finished. 

2 Please, always whenever you have a comment or

3 your first statement, please introduce yourself because this

4 meeting is being record and it'll help us keep track of who made

5 what comment so we can have that and record it in the minutes of

6 the meeting.

7 Okay. With that then I think we'll turn it

8 over to Dr. Dix for preliminary comments.

9 

10 WELCOME AND OPENING REMARKS

11 

12 DR. DAVID DIX: Good morning, this is David

13 Dix. I'm the Director of the Office of Science Coordination and

14 Policy in the Office of Chemical Safety and Pollution Prevention

15 at the U.S. EPA. And I welcome everyone to EPA for this FIFRA

16 SAP. 

17 I appreciate all the hard work that's gone

18 into the preparation for it, both by the presenters, as well as

19 the panel. And I greatly appreciate, on behalf of the Agency

20 and on behalf of the Office of Chemical Safety and Pollution, I

21 extend our gratitude in advance for the work and the

22 contributions you'll make to review our scientific basis for

23 moving forward with the Endocrine Disruptor Screening Program. 

24 The Endocrine Disruptor Screening Program is

25 managed out of the Office of Science Coordination and Policy. 

26 It's a partnership across the Office of Chemical Safety with the

27 Office of Pollution Prevention and toxics and the Office of
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1 Pesticide Programs, as well as with our sister offices of the

2 Office of Water and the Office of Research and Development.

3 We also have external partnerships, and those

4 will be represented in some of the presentations. At this SAP,

5 our partnership with the National Institutes of Environmental

6 Health Sciences and several members of that organization will be

7 actually making some of the presentations, as they become

8 important partners in this program, the Endocrine Disruptor

9 Screening Program.

10 So this is a roadmap slide that you'll be

11 seeing throughout the course of the presentations and it

12 represents the seven sections of the White Paper that was

13 presented on November 3, both to the panel and publically,

14 through the docket. And it'll serve as a representation of

15 where we're at in the series of presentations relevant to these

16 seven sections. I just wanted to show you the image of that and

17 make a few introductory comments about the program before I turn

18 it over to Steve Knott.

19 So the endocrine program was established in

20 1998 in response to legislation passed in 1996. It's focused on

21 protecting both human health and wildlife from potential impacts

22 on estrogen and androgen and thyroid pathways. And it's a

23 three-part approach to the screening; the first step being

24 prioritization based on exposure and bioactivity. The second

25 being screening for bioactivity, and the third, testing for dose

26 response and adverse effects.

27 As the program has developed and matured over
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1 the last 15 years or so, a variety of different data streams

2 have come online and incorporated into these three steps. And

3 at this point in time, for prioritization, we're looking at high

4 throughput screening data, particularly from the ToxCast program

5 at EPA and the Tox 21 Federal collaborative.

6 And also from exposure data from the ExpoCast

7 project within EPA. Also, bioactivity data has continued to be

8 developed from quantitative structural activity relationship

9 models and other data sources being utilized in our developing

10 approach to prioritization in looking at both bioactivity and

11 exposure.

12 Screening incorporates all those data streams,

13 plus, as appropriate, additional data, particularly from the

14 EDSP Tier 1 battery of screening assays. And then at the

15 testing level, where we look to demonstrate or establish dose

16 response and adversity, additional multi-generational tests

17 across multiple tracts are incorporated. 

18 And so over the course of the program's life,

19 there's been consistency in at least the establishment of these

20 three goals. The prioritization are steps in the program. The

21 prioritization step, the screening step, and the testing step. 

22 I'll just point out a couple of references

23 that might be useful, for those who are looking for more

24 information on the program. One publication that came out in

25 2011 is especially significant for where we are today in 2014,

26 and this was the EDSP 21 work plan which laid out a series of

27 goals for bringing in high throughput and computational tools
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1 into the program.

2 And this publication, a short summary of the

3 workplan is published and available on the EDSP website. And

4 then the other document that I would refer people to is our

5 comprehensive management plan for the EDSP. It's something

6 first published in 2012, updated in 2014 and the plan is to now

7 make it an annually updated document that is primarily used for

8 internal management practices of the program, but is made

9 publically available and is a good resource for both where the

10 program is and where the program is going. And so I'd refer

11 interested parties to that comprehensive management plan.

12 A quick update on where the program is more

13 broadly at the present time. The first bullet here identifies

14 the List 1 chemicals. At this point, there's 52 List 1

15 chemicals that are undergoing review. There are Tier 1

16 screening datasets and we're finalizing weight of evidence

17 determinations of endocrine bioactivity and considering possible

18 Tier 2 testing for those compounds. 

19 There's 107 List 2 chemicals that have been

20 published in the past years and these are being prioritized for

21 further screening consideration, using the high throughput and

22 computational methods being reviewed today, as they continue to

23 be developed and refined. The larger universe of EDSP chemicals

24 are also being prioritized, using the high throughput and

25 computational methods. 

26 And then I would finally point out the

27 scientific steps that have gotten to where we are today. At
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1 this point, I would take you back to the January 2013 SAP, where

2 we first presented tools for prioritizing the universe of EDSP

3 chemicals, using computational toxicology tools. And then a

4 series of SAPs in the rest of 2013, May, June, July and

5 December, where we looked at the Tier 2 battery of tests that

6 performance of the Tier 1 screening battery and then the weight

7 of evidence approach for analyzing those Tier 1 results, and the

8 new high throughput methods to estimate chemical exposure in

9 July of this year. And finally, that brings us to where we are

10 today, at the endocrine activity and exposure-based

11 prioritization and screening SAP.

12 So with that, I'll turn it back to the Chair

13 and our next speaker.

14 DR. JAMES MCMANAMAN: Any questions for Dr.

15 Dix before we move on to Mr. Knott?

16 Okay. 

17 

18 INTRODUCTION

19 

20 MR. STEVEN KNOTT: Okay. Thank you, Dr.

21 McManaman. Good morning, everyone. Again, I'd like to thank

22 the members of the panel and the public for participating in

23 today's session of the FIFRA SAP. My name is Steve Knott and

24 I'm the Director of the Exposure Assessment Coordination and

25 Policy Division and also the Science Coordination of Policy in

26 the Office of Chemical Safety and Pollution Prevention.

27 As David mentioned, we are the division that
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1 has the lead in coordinating the implementation of the Agency's

2 Endocrine Disruptor Screening Program. 

3 In the introduction today, I'm going to

4 briefly go over some logistics of the roadmap that David

5 presented, talk a little bit about the historical perspective on

6 what we're calling the "Evolution of the EDSP" to make greater

7 use of high throughput tools and also highlight in several

8 places the focus for this particular FIFRA SAP meeting. I have

9 the charge for this meeting.

10 As David already mentioned, a couple of the

11 key documents, the one document I wanted to highlight is

12 obviously the White Paper for today's meeting. The

13 presentations, my presentations, the presentations that follow

14 are going to track very well through the different sections of

15 the White Paper and also the particular charge questions. This

16 paper is available in the public docket. I think the docket

17 number is on all the materials that have been available to the

18 panel and the public.

19 As I mentioned, throughout the presentations

20 today, we're going to come back to this roadmap that ties to the

21 various sections of the White Paper; the first section being the

22 introduction. Section 2 addresses the estrogen pathway,

23 provides information that is relevant to Charge Question 1.

24 Section 3 addresses the androgen pathway and

25 the questions related under Charge 2. Sections 4 and 5 relate

26 to exposure and the toxicokinetics and reverse toxicokinetics. 

27 And these are actually the subject of the FIFRA SAP review in
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1 July 2014. And then Section 6 addresses the integrative

2 bioactivity and exposure ranking approach, which is the major

3 area of three. 

4 And then at the end, we'll provide some brief

5 information on future directions, including some perspective

6 future SAP topics as we continue to make progress.

7 So just to start off with the major focus for

8 this week's SAP review, we are really looking, at this point, at

9 the estrogen and androgen bioactivity, using them both alone and

10 in an integrated bioactivity exposure ranking to set priorities

11 for Tier 1 screening. 

12 In addition, we're looking for the estrogen

13 receptor model to begin using the model outputs for weight of

14 evidence -- to contribute to weight of evidence determinations. 

15 And also to begin to substitute for specific endpoints in the

16 current Tier 1 battery. And again, that's the subject area for

17 Charge Question 1. 

18 For the androgen pathway for the AR model,

19 again, we're looking at the perspective application and the

20 integrative bioactivity exposure approach the ranking, and also

21 using the bioactivity alone for ranking. And we have some

22 initial comparisons to support or would be further developed

23 into using the AR model for weight of evidence determinations

24 and also for substitution, again, in the Tier 1 battery.

25 And then the third major charge area, again,

26 is looking at the overall approach, the integrative bioactivity

27 exposure ranking and looking at that as an overall approach to
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1 setting priorities.

2 So to turn back to a slide that David

3 presented and provided a little historical perspective on the

4 evolution of the program, those who are familiar with the EDSP's

5 implementation over the years will be very familiar with the

6 emphasis that's been placed on the Tier 1 screening, Tier 2

7 testing phases of the program. 

8 However, as originally envisioned, the program

9 did include prioritization and the Endocrine Disruptor Screening

10 and Testing Advisory Committee had advised the prioritization

11 step where the data resulting from high throughput screening

12 would be combined with exposure-related information and any

13 other effects-related information to set priorities for Tier 1.

14 The overall conceptual model that they

15 presented, again, includes this prioritization step, the

16 screening, more detailed screening for bioactivity, and then,

17 ultimately, testing phase which would provide the dose response

18 information that's more typically or traditionally used in a

19 quantitative risk assessment. And as you move through the

20 various phases of the program, fewer chemicals are anticipated

21 to pass along into each particular phase. 

22 So in 1999, the Agency brought some of the

23 high throughput tools available at that time to a joint meeting

24 of the SAP and the Agency's Science Advisory Board. And the

25 panel at that time advised that EPA had failed to demonstrate

26 the utility of the high throughput tools for setting priorities.

27 Therefore, the program subsequently focused on implementing the
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1 program by setting priorities based more on qualitative exposure

2 considerations and also some regulatory considerations. This

3 led to the first list that underwent screening in the program. 

4 This list was all pesticide chemicals with active ingredients

5 and also high production volume chemicals that are used as inert

6 ingredients. 

7 And the considerations in setting priorities

8 and identifying those chemicals included this qualitative look

9 at various exposure pathways, including the consumption of food

10 and water containing pesticide residue, the residential use of

11 pesticide products and also occupational contact with treated

12 surfaces.

13 For the high production volume inert

14 chemicals, a little bit of a different approach was taken. They

15 looked at human biological monitoring, some animal tissues that

16 have human food uses, such as fish tissues, also drinking water,

17 and indoor air. And this approach yielded a list of 67

18 pesticide chemicals that received Tier 1 test orders around

19 2009/2010.

20 For List 2, the Agency continued to address

21 pesticide chemicals by looking at registration review schedules;

22 in particular, the review schedules for fiscal years 2007 and

23 2008. And then also began to bring in water contaminates into

24 the program by considering contaminates currently regulated with

25 the National Primary Drinking Water Regulation or unregulated

26 contaminates that were on the third contaminate candidate list

27 or CCL 3. 
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1 So this approach to setting priorities has

2 generated two lists total of 174 chemicals out of a potential

3 universe in 10,000 chemicals. So the program during this time

4 is also very focused on the development of test systems, in

5 particular the Tier 1 screening battery and the Tier 2 test. 

6 And these have been the subject of previous FIFRA SAP reviews.

7 The Tier 1 battery, which was released as

8 harmonized test guidelines of 2009 consists of five in vitro and

9 six in vivo screenings, covering endpoints in the estrogen,

10 androgen, and thyroid pathways. And it should be noted that

11 these are low throughput test systems and our experience, to

12 date, with these is one of the drivers beyond evolving the

13 program to make greater use of high throughput tools. In

14 particular, taking advantages of the advances that have taken

15 place since 1999.

16 Just to expand on that last point a little

17 bit, as described in Section 2 of the White Paper, the Agency

18 has completed comparisons that support the substitution of some

19 of the high throughput and computational tools for specific

20 estrogen endpoints represented in the Tier 1 battery; and in

21 particular, for estrogen agonists, we're looking at the ER

22 binding assay, the ER transcriptional activation and

23 uterotrophic study. Again, this is the subject of Charge

24 Question 1. If I say that too much, let me know.

25 And similarly, as described in Section 3 of

26 the White Paper, we have initiated some comparisons and have a

27 path forward for looking at the androgen pathway. And again,
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1 supporting the substitution of high throughput and computational

2 tools for some of the specific androgen endpoints. And I've

3 highlighted here the androgen agonist, in particular, with the

4 AR binding and the Hershberger studies in the Tier 1 battery. 

5 And this is the subject of Charge Question 2.

6 So to go back for a minute to the evolution of

7 the program, as I noted, we had a fairly low throughput approach

8 to setting priorities with a set of low throughput test systems

9 in the Tier 1 screening battery. So it was readily apparent

10 that to move through this process was going to take decades. 

11 And that may have been optimistic to move through the 10,000

12 chemicals that are a part of the EDSP Universe. So a new

13 approach, a new strategy was needed to be implemented to begin

14 to move through in a more strategic way, a more targeted testing

15 and to address a larger number of chemicals. And the recent

16 advances in computational toxicology really presented an

17 opportunity or an evolutionary turning point for us to

18 accelerate this pace through the screening and testing for the

19 EDSP.

20 Some of the advantages of implementing the

21 high throughput test systems and computational tools are the

22 increased speed, being able to rapidly screen chemicals and use

23 predictive models. The increased capacity associated with that,

24 overcoming the throughput limitations of the existing test

25 systems that requires such a longer time and more resources.

26 Other benefits include eventually being able

27 to replace some of the existing animal test systems with
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1 non-animal alternatives and also the computational aspects of

2 putting information in a computable format facilitates open

3 access to data and it also will ducktail nicely with a larger

4 adverse outcome framework that we're putting together. And as

5 we work through this, it will be important for the EPA to

6 continue to partner with other federal and state agencies, the

7 industry non-governmental organizations to continue validating

8 and tools basically -- applying tools to basically increase the

9 tool chest that we have available to us.

10 As David mentioned, in 2011, the Agency issued

11 an EDSP21 Workplan. This is a somewhat modified diagram for the

12 Workplan, which basically lays out the model that is being

13 followed for making greater use of high throughput and

14 computational tools in the program. It basically highlights

15 three phases, the first phase being, really, the application of

16 these tools, just to priority setting. 

17 And the second phase, again, expanding the

18 priority setting, but also beginning to look at substituting

19 particular high throughput and computational tools for some of

20 the endpoints in the existing Tier 1 screening battery. And

21 then longer term, the goals is ultimately, perhaps, replacing

22 the full Tier 1 screening battery or substituting the

23 computational tools for that battery.

24 Moving through each of these phases requires

25 building confidence in the available tools. And we are really

26 now, we believe, well into Phase 2, where we're poised to begin

27 substituting the results of high throughput and computational
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1 tools or specific endpoints in the battery. And then later

2 today or tomorrow in a sort of future directions presentation,

3 I'll talk a little bit more about how we get more into Phase 3

4 and the possibility for some future SMP topics.

5 So working from the 2011 Workplan, I would say

6 transition began in earnest in 2012 to validate and incorporate

7 these tools into the program. Basically, they're looking to

8 build confidence at these three areas. They have to demonstrate

9 the methods are scientifically sound, that they can effectively

10 detect endocrine bioactivity via receptor-mediated interactions.

11 And I think it also effectively detects endocrine bioactivity

12 via broader endocrine pathway effects.

13 And an important component of building

14 confidence has been the series of FIFRA SAP reviews, the peer

15 reviews of the science that we've developed and brought before

16 the panel. And David went through a listing of these earlier,

17 but this just sort of reiterates some of the key meetings that

18 have taken place, including in January 2013, the Agency brought,

19 I believe at that time, eight estrogen receptor high throughput

20 assays, also an approach based on physical chemical properties

21 and QSAR. 

22 In July of this year, we brought exposure

23 tools, the ExpoCast model, the high throughput toxicokinetics

24 and reverse toxicokinetics. The meeting this week is focused on

25 additional estrogen receptor assays, a total of 18 assays, nine

26 androgen receptor assays, additional QSAR and then this

27 integrated approach for bringing bioactivity and exposure
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1 information together. And then we'll talk a little later about

2 the possibility of future topics, really, as we begin to bring

3 this into an adverse outcome framework. 

4 So again, to go back to the conceptual model

5 that David presented, he mentioned that we're looking now at

6 more data streams in each phase for prioritization, bioactivity

7 and exposure using QSAR models, ToxCast, ExpoCast, monitoring

8 data. And then what we call other scientifically relevant

9 information which is really other exposure and effects

10 information that are available for use, for setting priorities. 

11 Similarly, in screening for -- which is more

12 focused on bioactivity, we're making use of QSAR models, ToxCast

13 and EDSP Tier 1 screening battery. And then testing is, again,

14 more risk assessment focus, Tier 2 type data for the generating

15 dose response information. And again, the focus of our meeting

16 this week is really on the prioritization in the screening

17 boxes. 

18 So I'm getting a little ahead. The current

19 meeting of the SAP, the review this week is again, focused on

20 using high throughput bioactivity tools, both alone and in this

21 integrated bioactivity exposure ranking approach to set

22 priorities. This is one of the focuses of Charge areas 1 and 2.

23 We also are focused on beginning to use these

24 tools for weight of evidence evaluations, determinations and

25 also to begin substituting for endpoints in the Tier 1 battery. 

26 And then again, the other major area is looking at the overall

27 bioactivity exposure ranking approach.
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1 So without being too redundant, I'll just sort

2 of sum up on this slide. Again, putting this in the broader

3 context, in July of this year we brought the ExpoCast model and

4 high throughput toxicokinetic and reverse toxicokinetics. Those

5 are the grayed out areas on this diagram. The focus for this

6 week is on the expansion of the ToxCast for the areas I've

7 already mentioned, prioritization, weight of evidence,

8 substitution and the overall integrated bioactivity exposure

9 approach.

10 The one thing that we're emphasized in each of

11 these meetings is the fit-for-purpose aspect of the models we're

12 developing and the charge that we bring to the panel. These

13 tools are not intended to be precise assessments like we have

14 traditionally performed to support quantitative risk assessment.

15 These tools are, I guess, as advertised. It's prioritization

16 and screening at this phase.

17 And with that, I just have some links for

18 additional information, and I'll be happy to address any

19 questions from the panel. Thank you.

20 DR. JAMES MCMANAMAN: Thank you Mr. Knott. 

21 Any questions from the panel for these general comments? Dr.

22 Barr.

23 DR. DANA BARR: I just have one clarification

24 question on Slide 16. If you could go back to that. The Y-axis

25 represents here exposure, and bioactivity is just represented by

26 the area under the curve that's plotted there. It's just a

27 little bit confusing and I want to make sure I understand it
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1 correctly.

2 MR. STEVEN KNOTT: So I think this diagram is

3 really kind of a combination of exposure information and

4 bioactivity information and it's intended to demonstrate how we

5 would look at, really, the distance between exposure and

6 bioactivity in trying to set priorities. It's really more

7 qualitative, I think, than quantitative. It's meant to just

8 sort of depict the approach, the overall approach.

9 DR. DANA BARR: I guess what's confusing me --

10 not really confusing, I just want to clarify that the exposure,

11 the green curve does tend to move up as it gets to higher

12 priority, but the bioactivity one just stays the same. So I

13 wondered if the Y-axis represented that increase in exposure and

14 that's why it's moving.

15 DR. DAVID DIX: So one follow-up, general

16 comment about this figure, it's trying to represent a simulated

17 distribution, incorporating some of the aspects of variability

18 and uncertainty and that's the aspect of the two curves, if you

19 will. And then it's really a general representation. But you

20 could have bioactivity coming down or exposure coming up, but

21 whatever situation, they become closer, if you will. It would

22 be an area where we'd be more concerned. And in the actual

23 presentation in the IBER section, you'll see that we represent

24 it as a ratio, as a proposed approach for making that comparison

25 between bioactivity and exposure.

26 MR. STEVEN KNOTT: Let me add too. I realize

27 what you're saying. It probably would be better to think of
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1 this flipped on its side because things are increasing as you

2 move down the X-axis. So it turned on its side, the idea here,

3 though, is that we're looking at, for instance the -- well, why

4 don't I leave that for a later presentation. 

5 But I think you're right. It probably be

6 easier to envision it flipped on its side. So that might be a

7 good thought for future use in this diagram.

8 DR. DANA BARR: Thank you. I think I have it

9 now. 

10 DR. JAMES MCMANAMAN: Other questions? While

11 we're on this, I have a question about it too. So what is your

12 thinking about, in terms of low and medium priority? The

13 exposure graphs, in both cases, low and medium, don't overlap

14 with the bioactivity, presumably. So what separates the low

15 priority from the medium priority, assuming that, you know, that

16 there is no overlap in the medium priority with exposure to

17 bioactivity. So how do you rationalize those, that separation?

18 MR. STEVEN KNOTT: All right. Well, I'll take

19 a shot at this. I think some of that depends, too, on what we

20 hear during the peer review this week. You know, again, this is

21 meant sort of just to show that when looking at those distance

22 in order to bend things by priority, I don't think that we have

23 defined, at this point, what those absolute distances would be

24 to put things in different bins. And I think that depends

25 somewhat on the uncertainty in the analysis. 

26 Again, I think what we hear during the review

27 this week will certainly inform what direction to go with some
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1 of that. David.

2 DR. DAVID DIX: Sure. I would suggest we

3 probably reconsider, or if you want, we could address that issue

4 after that portion of the presentation on the integrative

5 bioactivity and exposure ranking. But I think one of the

6 reasons we took the ratio-based approach is to have a more

7 continuous distribution. This conceptual diagram is, perhaps,

8 implying bins that we don't necessarily have in mind, unless you

9 all think that type of approach was better. 

10 So I wouldn't make a qualitative distinction

11 between low, medium and high. It's more of a continuous

12 distribution, in this case, of the way we presented at ratios,

13 where you have the highest to the lowest. And where one draws

14 the line would depend on a variety of issues relating to program

15 practice and policy. 

16 DR. JAMES MCMANAMAN: So the artist didn't

17 quite capture what you had in mind.

18 DR. DAVID DIX: Yeah. You can blame the

19 person who came up with this. I've hear this referred to as the

20 Pinocchio slide and the Dix slide. So I don't know what that

21 means, exactly what they were trying to capture in those

22 characterizations.

23 DR. JAMES MCMANAMAN: All right. Thank you. 

24 Dr. Denver.

25 DR. ROBERT DENVER: So I have a question about

26 the Agency's focus. High throughput screening computational

27 approaches are obviously essential for dealing with this massive
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1 number of chemicals and potential for endocrine disruption, but

2 what it necessarily does -- and this was raised in the initial

3 review of the Tier 1 assays -- is it necessarily focuses on a

4 particular mode of action, which is receptor-mediated. And it

5 really doesn't consider, as we know, many other potential modes

6 of actions, such as impacts on hormone biosynthesis transport,

7 cellular uptake metabolism, and those kinds of modes of actions.

8

9 So I'd just like to know if the Agency is

10 considering high throughput assays for these other potential

11 targets. You mentioned that, I think in one of your slides that

12 you had hoped to capture some of the broader impacts on

13 endocrine systems. I think that was Point No. 3. And clearly,

14 you can capture some of these in these high throughput assays,

15 but probably only a small fraction of those other potential

16 targets or endpoints. 

17 DR. DAVID DIX: The response to that would be

18 yes. That's one of the reasons, I think, that the Agency in its

19 research program developed the ToxCast project, looking at not

20 just receptor-based or receptor targeting assays, but a wide

21 range of 700 plus assays. And there's a continued development,

22 I think, within the research program to expand on that. As we

23 translate that into practice in the Endocrine Disruptor

24 Screening Program, we will be looking to incorporate steroid

25 agenesis endpoints, other endpoints significant to metabolism of

26 steroids and other hormone systems. And particularly, as we

27 look to address not just the estrogen and androgen, but the
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1 thyroid pathway, those non-receptor-mediated approaches will

2 become critical. And what we're doing is kind to starting off

3 on the low-hanging fruit on the more solid scientific ground,

4 where we have more data and more confidence. It's a good place

5 to develop some of the methodologies and to bring into practice

6 some of the tools that we think are ready now and that will

7 inform iterative approaches to bringing online these more

8 complex suites of assays and assessing more complex biology.

9 DR. JAMES MCMANAMAN: Dr. Brown.

10 DR. TERRY BROWN: I think I'm getting ahead of

11 the process, in terms of your presentations, but I guess I had a

12 question related to the redundancy of some of the assays and the

13 high throughput system. For instance, why are there three

14 different estrogen receptor-binding assays, et cetera? 

15 I mean, is there some decision-making process

16 that's going to take place over time that will eliminate some of

17 this redundancy and streamline the process?

18 DR. DAVID DIX: Yes, there is. And there has

19 been a process. There has already been assays that have come on

20 and off line, so to speak, for the research project. And in the

21 course of the analysis of this dataset for our application to

22 the EDSP, there's been some changes, in particular, to, I think,

23 it was androgen receptor set. It's with additions and

24 subtractions based on performance of the assays when looked at

25 in toto, in the integrative model, where you can sort of sense

26 that there was contributions or no contributions from specific

27 assays. So and/or poor performance relative to referenced
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1 chemicals. I think the later presentations will address that in

2 part, but that is an ongoing goal of the program, both the

3 research program and the regulatory practice to make this as

4 efficient as possible. We see some real strengths in the

5 redundancy and in time, with what to increase those strengths by

6 improving efficiency, by getting rid of things that aren't

7 really value additive.

8 But until the data is all brought together and

9 compared across larger datasets for other assays, it's hard to

10 make that determination of priority. I think it's imprudent to

11 say a single assay, or even a small suite of assays are truth. 

12 I think it's better to take in all the information at hand and

13 analyze it as completely as possible and then start to winnow

14 out what approximates or gives the best predictions or

15 probabilities for predicting the outcomes we choose. In this

16 case, are complex -- both primarily complex results and

17 animal-based systems. 

18 DR. JAMES MCMANAMAN: Any other questions? 

19 All right. We'll move on then. I have Dr.

20 Judson as being the next presenter. 

21

22

23

24

25

26

27
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1 

2 ESTROGEN PATHWAY DATA AND MODELS

3 

4 DR. RICHARD JUDSON: Good morning. This is

5 Richard Judson. I actually have a question before I start for

6 the DFO. So I got a number of several distinct, complicated

7 topics. I would hate to get to the point of all the way, you

8 know, an hour or so into this and having lost somebody on the

9 panel early.

10 Is it okay if somebody is totally confused

11 they raise their hand and we cannot get lost?

12 DR. FRED JENKINS: After each of your

13 presentations, they'll be an opportunity for the panel to ask

14 clarification questions, as directed by the Chair. 

15 DR. RICHARD JUDSON: This one presentation,

16 this next section is actually effectively, three presentations. 

17 If this an issue, we'll just move on. 

18 DR. FRED JENKINS: Yes. Let's just go as

19 scheduled. Do the whole presentation and then there will be an

20 opportunity for clarification.

21 DR. RICHARD JUDSON: If people look lost, I'll

22 try to --

23 DR. JAMES MCMANAMAN: If the panel has the

24 deer in the headlights look, go ahead and stop and see things

25 are clear.

26 DR. RICHARD JUDSON: Right. Great, great,

27 great. Having made this presentation many times, I see a lot of
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1 deer in the headlights. So this is something a little new for a

2 lot of folks. So there are several big themes I'm going to

3 throw out. One is that we have this idea of using multiple

4 assays. And I'll actually comment on Dr. Brown's question, as

5 appropriate. 

6 We're looking at different technologies,

7 different points in a pathway. So this is really a generic

8 pathway-based approach to testing. And what we've learned, not

9 only from these particular 18 assays or the nine assays, we're

10 looking in the androgen. We have a suite of 821 assays that

11 we've tested most of these chemicals in and looking at how

12 assays light up, correspondingly, both in a receptor-mediated or

13 non-receptor-mediated, we're learning much more about the

14 biology.

15 So this idea that no assay is perfect, again,

16 I don't even know what perfect would mean, but there is lots of

17 assay interference that comes from assays being developed for

18 pharmaceuticals, essentially. You know, pharmaceutical class

19 compounds. And now we throw metals in there. We throw

20 surfactants and all sorts of crazy kinds of chemistry that the

21 assays weren't designed for. So these assays which are, you

22 know, complicated biological machines, do all sorts of things we

23 didn't expect. And so by looking at a broad suite of pathways

24 -- a broad suite of assays, we can understand not only sort of

25 receptor-mediated effects we're looking for, but generic,

26 cell-stress kind of activity that shows up differently for

27 different chemicals. And it's probably true activity, it's not
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1 noise. So I would say assay interference is something I run the

2 assay today and I run it tomorrow and I get the same answer. 

3 It's an answer I didn't expect, but it's reproducible, whereas,

4 noise, as I run it today and tomorrow, I get totally different

5 answers.

6 So a lot of what I'm going to show you is

7 really assay interference or real biology, which is not

8 necessarily estrogen-based. Okay. We have all of these assays

9 for the estrogen pathway we're going to use a model to

10 integrate. And the model is -- it looks complicated if haven't

11 thought about it a little bit, but it's really about as simple a

12 model as you can come up with. So I'll go into that a little

13 bit more. And we're going to take the model, we're going to

14 valuate it against referenced chemicals. Some people have

15 looked at that think that what we're doing is we are training,

16 you know, it's a typical machine learning method where we train

17 a model and set parameters based on reference chemicals and then

18 be run on others. That's not true at all. 

19 This is kind of a parameter-free model and

20 we're simply evaluating the model against the referenced

21 chemicals to get some confidence and then moving forward. And

22 the methodology is general enough for a pathway-based approach

23 that we're able, you know, you'll see the androgen version and

24 we have a number of other pathways we've applied this to. 

25 One other introductory comment is that we have

26 taken this -- I mean, one reason we throw all of the assays, 18

27 assays at this one pathway is to have a test set to really
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1 understand modeling. It's not so much -- we didn't say, okay,

2 we really need to understand the estrogen receptor pathway. 

3 This was a place where there were lots of assays available and

4 so we could actually understand where assays and different cell

5 types worked better than others and do you integrate data in all

6 of these crazy ways. 

7 So this is a good test bed for modeling. And

8 we built, probably, half-a-dozen models along this dataset to

9 already to predict is this chemical interacting with the

10 estrogen receptor or not. And all the models give about the

11 same answer, except at the margins. And the margins are the

12 ones that are really -- it's really hard to say, okay, is this

13 really a weekly estrogenic or is it some sort of assay

14 interference? 

15 Those are the ones where we would probably

16 say, you know, we have to do some sort of a follow-up. I think

17 I'll talk about that a little bit more. So in all of the

18 material, you've got the slides we're showing today and you got

19 the White Paper. There are also these 100 -- multi-hundred-page

20 supplementary documents and they're simply versions of slides

21 for all 1,812 chemicals that I will, you know, I'm not going to

22 show you all 1,812 chemicals. That would be very painful. But

23 should you want to go see them, all the data is there. And at

24 the end of the presentation, if we still have a little time, we

25 actually have a website, a dashboard that just came live a week

26 or so ago, where all of the actual dose response data is. So

27 you can go in and -- you folks or the public can go in and
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1 actually look at all of the data and make your own conclusions.

2 So one of the major themes here is that all

3 assays have these false positives and negatives, which are not

4 noise. They're a consequence of the complicated biology going

5 on when you throw solvents and surfactants and anti-cancer drugs

6 which are supposed to kill cells and metals and pesticides and

7 drugs in there.

8 This is not an in vitro problem. You know,

9 there have been people who've looked at our assays and say oh,

10 your assay didn't get my chemical rights, so therefore, the

11 assay is wrong. As far as I can tell, every assay -- so we've

12 looked at 821 assays and they all have these issues that I'm

13 going to talk about. 

14 And then Warren Casey, later this morning is

15 actually going to talk about a review of the uterotrophic assay,

16 the guideline assay, the Gold Standard, and show how variable

17 that is. So no assay is perfect. And it's partly not perfect

18 because we haven't designed it to deal with the kinds of

19 chemistry that we're throwing at it. There are other reasons,

20 but that's one reason. So you'll see this idea of false

21 positives, false negatives and trying to understand what is

22 driving those. Because we don't want to take some data that we

23 say oh, you know what? Some of the estrogen assays are lighting

24 up. Maybe that really is estrogenic. But if we can actually

25 make a good case, no, that is not estrogenic. Those assays are

26 lighting up because of some particular cytotoxicity pathway that

27 we are more confident to set that chemical aside and say it's
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1 not really estrogenic, instead of being super cautious and say

2 if any assay lights up, we're going to call it estrogenic and

3 then follow that to its logical end, which is more testing.

4 Okay. So this is a really critical slide. 

5 You'll see this twice and a little cartoon of it multiple times.

6 So if the panel members have their own versions of the slides,

7 you might think about clicking back to this periodically. So

8 really is the model. So I'm going to spend a fair amount of

9 time here. And unfortunately, this -- I need a telescope to

10 read these slides up here. So let's focus for the moment on the

11 blue -- that blue series there. So this is the real biology. 

12 And I know some of you are nuclear receptor experts and some of

13 you aren't. So bear with me a little bit. 

14 What happens when a chemical binds to a

15 nuclear receptor like the estrogen receptor and the androgen

16 receptor is the first step is that you get two copies of the

17 receptor, which are sitting in the cytoplasm. They dimerize

18 then they translocate to the nucleus. They recruit cofactors

19 and form a mature transcription factor. That transcription

20 factor sits down on the DNA it creates. RNA, the RNA gets

21 turned into protein and with the estrogen receptor, at least,

22 one of the consequences that you have is self-proliferation. So

23 we actually have assays, with the exception of the cofactor

24 recruitment, we have assays for all of those technologies. 

25 So again, looking at the blue nodes -- I

26 named, essentially, the function that corresponds to each of the

27 blue nodes. And that's what happens if you have an agonist. If
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1 you have an antagonist, you get receptor binding, dimerization

2 happens, cofactor recruitment happens and the transcription

3 factor actually binds to the DNA, but for reasons even the

4 experts aren't entirely clear on. You don't have RNA

5 transcription. It probably has something to do with on and off

6 rates of the transcription factor on the DNA. You know, at

7 least that's one hypothesis.

8 So if we could shrink ourselves down and do

9 incredible journey and jump into the cells, we could actually

10 watch all those things happen and we would know the truth about

11 this chemical of what's actually happening. But we can't do

12 that. And that's why we had this idea of an assay.

13 And so all of the white stars all over this

14 correspond to assays. So again, looking at the top, we have

15 three mining assays. So they're directly attached to the

16 binding -- to the receptor. We have six dimerization assays and

17 those are actually -- we actually have, in humans there's an

18 estrogen receptor alpha and a beta. And so we have alpha-alpha

19 dimers, beta-beta, and alpha-beta dimers. And those assays are

20 actually run at two different times, eight hours and 24 hours

21 because the dimerization process actually happens relatively

22 quickly so you can see activity at eight hours.

23 Then we have two assays for DNA binding. 

24 Again, an eight and a 24-hour assay. We have two assays that

25 look at -- actually measuring RNA products; two assays that are

26 measuring protein products and one assay that's measuring cell

27 proliferation. And then on the antagonist side, we have two
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1 assays that are measuring an antagonist mode for the protein

2 proliferation.

3 So now imagine what happens if you have a true

4 estrogen agonist. It's going to bind. It dimerizes. So in the

5 cells, it binds, dimerizes, recruits cofactors and bind to the

6 DNA. RNA makes protein and causes proliferation. And so

7 therefore, if our assays were good, all of the assays, with the

8 exception of those two at the far left, would light up. And it

9 would light up in a dose response, concentration response mode

10 and they should parallel one another.

11 Again, what we have learned from looking at

12 this 821 assays is we have lots of chemicals that light up one

13 particular kind of assay as a consequence of some kind of cell

14 stress and that tends to be cell type specific or maybe even

15 technology -- read-out technology specific. So if you imagine a

16 fluorescent compound that you throw into a fluoresce read-out

17 assay, the fluoresce assays are going to light up but that has

18 nothing to do with estrogen binding. It's really because the

19 chemical is mimicking the detection technology or with the

20 cell-free binding assay. So these are radioligand-binding

21 assays.

22 So you have a radioactive, radiolabeled

23 estradiol in the protein. And the way the assay is supposed to

24 work is you have another estrogen mimic, which competes and then

25 it knocks out or displaces some of the radiolabeled estrogen. 

26 And so within the 24 hours, you just measure the radioactivity

27 and the solution and that tells you something about how much has
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1 been displaced in the relative binding affinity.

2 Now imagine taking a surfactant, throwing it

3 in there that causes the receptor protein to just denature. 

4 What it's going to do is just falls apart and gives up the

5 radioligand, and it will do that in a nice, concentration

6 response mode. And so you actually see something that looks

7 like receptor binding or competitive binding, but it only

8 happens in those assays because you are interfering with the

9 technology of that assay.

10 So that's what all of these gray nodes are,

11 what I'm calling pseudo receptors. So there's some process, and

12 who knows what that is. And occasionally, maybe we can figure

13 that out. But it's a process which causes just those assays to

14 light up because of interference, either with the particular

15 cell type or with the particular technology.

16 So now imagine a chemical, which one of these

17 denaturing chemicals, it's going to only light up assays A1

18 through A3, those self-rebinding assays. And so you would

19 conclude -- so imagine those two patterns that you see in the

20 assays. One lights up everything except the ones on the far

21 left, and one only lights up A1, A2, and A3. You could

22 automatically just look at it and say oh, the first one is

23 almost certainly a real agonist and the second one is probably

24 interfering with those assays with that technology.

25 So the only thing the model is doing, and

26 there are a few equations here, but really all its doing is

27 looking at the pattern concentration by concentration of which
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1 assays are lighting up and saying what is the most likely thing

2 going on. That I have agonist activity, antagonist activity, or

3 activity in one of these so-called pseudo receptors. So that's

4 really the model. 

5 Specifically asked, you know, pulling back and

6 making that Dr. Brown's question was answered, why we have all

7 these assays, again, is because we want to just see, let's throw

8 everything at one problem and see what we can learn from that. 

9 But as we go forward, you know, we do need to go and say what is

10 the minimal set of assays that would really help us sort the

11 estrogenic compounds from the non-estrogenic compounds. And the

12 big dataset that I'll walk through, I think these are the tools

13 to do that, but we haven't done that yet. Okay.

14 All right. So we'll come back to this in a

15 moment. I'm very quickly going to walk through the assays. 

16 This is not really a presentation about the assays, but feel

17 free to come back at the end if you have specific questions. So

18 we have these three radioligand binding assays, one in human

19 cells, one is bovine and one is mouse. I think "yes" in mouse. 

20 So I've already talked about that a little bit. The protein

21 complementation assays --

22 Oh, important point; these assays were run,

23 for the most part, small companies. So we went and put out a

24 request for proposals, saying we want assays with different

25 parameters and companies bid on that. So almost all of the

26 assays are run at small companies of various sorts and so I'll

27 talk about the names. So this is Odyssey Thera, the company
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1 that ran this Novascreen, Caliper. They've been bought so many

2 times, I'm not sure exactly who they're owned by today. But

3 anyway, we call those the Novascreen assays. 

4 The Odyssey Thera assays are protein

5 complementation assays, and I guess the technology is FRET. So

6 where you actually have two half -- the way I think about it,

7 two half fluorophores, one on one version, one copular protein

8 than the other. And when those two proteins come together to

9 dimerize, you get the completed fluorophore and it glows.

10 The RNA transcription assays, this is by

11 Attagene, and it's a sequencing technology, a very highly

12 multiplex assay and they run in a cis mode and a trans mode, and

13 between those two, you get about 80 or 82 readouts in two wells.

14 And so it's a really nice survey assay. It has some

15 interesting assay interference that I'll mention later, because

16 of this very high multiplexing. But the readout is actually

17 sequencing. So you're actually sequencing complimentary -- or

18 RNA, complimentary DNA, I guess.

19 Then our partners at NIH, as part of the Tox21

20 collaboration up in Rockville, the National NCGC, they have a

21 very ultra-high throughput system where they've actually,

22 instead of running 1,800 chemicals, they've run about 8,200

23 chemicals through their suite of assays. But these are protein

24 read-out reporter gene assays running in two different

25 reporters. There is a beta lactamase and a luciferase and they

26 run both agonist and antagonist mode.

27 And then a company called ACEA runs a
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1 real-time self-proliferation assay. It's actually looking at --

2 it's an independence measurement, whereas cells proliferate,

3 they cover the bottom of a well and increase the impendence

4 across this. And you can actually see impendence changing its

5 time, as a function of time. Okay. So those are the 18 assays.

6 So now I want to talk a little bit about the

7 rationale for this model and the idea of pseudo receptors. What

8 this graph is showing, and in case folks have not seen the heat

9 map, I'll step through this a little bit, but we take,

10 essentially, an Excel spreadsheet with 18 assays. Those are the

11 columns, and 1,800 chemicals, which are the rows. And you do

12 hierarchical clustering, which essentially means you're soring

13 the rows in the columns. So columns that look the most alike

14 tend to be close to one another and rows that tend to look

15 alike, cluster next to one another. And there's no optimal --

16 there's no global solution to that, but you could do a pretty

17 good job.

18 So the 18 columns of this -- and then the

19 spreadsheet is colored, so if a chemical is positive in an

20 assay, it's red, and the more potent it is, the redder or dark

21 red it is. And if it's inactive, it's white. And I'm only

22 showing the rows that have at least one active assay. So this

23 is just looking at the assay. And the assays are quantified by

24 what we call the AC-50, the concentration where you're 50

25 percent towards the maximum response for that chemical in that

26 assay.

27 And so this is totally model-free. And what
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1 you see is that if we look at the bottom, and you may have to

2 see it on your screens in front of you, the assays tend to

3 cluster by technology. And so we see that band up at the top,

4 where I labeled "many assays are active," those are probably the

5 true estrogen. So we have lots of assays or almost all of the

6 assays are active. And notice in that top big band, there's an

7 even more constant band at the bottom of that, where almost all

8 the assays are positive, except for the left two, and those are

9 the antagonist assays.

10 So those chemicals that are red all the way

11 across are probably, almost certainly agonists. Anybody could

12 look those results for those chemicals and say yeah, those

13 agonists. But then below in that bigger band below, you can see

14 certain assays are essentially lighting up all by themselves. 

15 So there's some sort of technology interference which is only

16 causing those assays to light up. And the clustering tends to

17 be by technology. So we can say, for instance, the big set of

18 two assays labeled R6 at the bottom, those are the Attagene

19 assays. There's some process which causes Attagene to light up

20 both of those assays. They're both run in the same cell type. 

21 They're both the same read-put, and so there

22 must be some specific kind of interference that those assays are

23 prone to, but also notice that they appropriately light up for

24 the true agonists. So they're giving you good positive

25 information. They're sensitive, but they're not very specific,

26 so we need to understand that. 

27 So now we're back to this picture. One more
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1 point I want to make, which is part of the equation on the next

2 slide, is just trace the lines. So what the model essentially

3 says is if a chemical interacts with a receptor, it should light

4 up every line, every assay that you can trace through. And the

5 lines could be the lines going down through the pathway or the

6 straight lines or the wiggly lines that go out to the assay. 

7 So for instance, if you bind to the receptor,

8 R1, the blue receptor, the bottom assay, A-16, should also light

9 up because there is a set of arrows that get all the way down

10 there. So whether a line is solid or dotted doesn't make any

11 difference, whereas, if you interact with pseudo-receptor R8,

12 which is the gray node at the bottom-right, the only assay that

13 should light up is A16 because that's the only place that arrow

14 goes to. 

15 So here's the model. It's actually quite

16 simple, but let me walk through this and make sure folks kind of

17 understand this. So this is a typical what's called an inverse

18 problem. So what we're going to assume, given the top equation,

19 is that an assay base of "i" -- some assay should light up if

20 there's a line connecting it to Receptor Rj if Receptor Rj

21 lights up.

22 So R1 is the true agonist receptor and Fij is

23 simply one, if one of those black lines with the arrows connects

24 the receptor to the assay and it's zero otherwise. So it's the

25 simplest possible "yes" "no" model. So if we knew what the

26 receptor was, we could tell you what's going to happen with the

27 assay, but instead, what we know if what's going on with the
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1 assays and we want to know what the receptor is. So that's why

2 it's an inverse. And we do a simple least-square minimization. 

3 And that's the second equation where we take the -- let the

4 computer guess what receptor is true or not. You know, there's

5 lots of ways to do this. 

6 You say okay, I think for this chemical, this

7 receptor turns on and then that gives you a prediction of what

8 assays you turn on, so that's A-predicted in the second equation

9 and you subtract the truth or what you actually measure --

10 certainly not the truth -- and you iteratively have the computer

11 guess solutions to what the receptors are doing until you

12 minimize the difference between the predicated assay behavior

13 and what you actually measure. And there's actually a genetic

14 algorithm plus a gradient minimization step going on there.

15 So it's just like doing any kind of curve

16 fitting. That's really all we're doing is curve fitting. It's

17 a constrained minimization problem because we want the assay

18 values to go from zero to one. And they actually do this step

19 for every chemical for every concentration.

20 And so you see there's no free parameters

21 here, with one exception, which I'll come to next. And the

22 answer for any chemical doesn't depend on any other chemicals. 

23 Every chemical is sort of a new thing for the model.

24 What I've done is I've added this penalty term

25 which is what it's really doing is trying to enforce a

26 biological constraint on the problem. So for those of you who

27 are a little bit mathematically sophisticated, will understand
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1 that this equation, number one, doesn't have a unique solution. 

2 So there's whole family of solutions that would actually give

3 you a zero error. 

4 It makes sense to me that a chemical is

5 probably not doing 18 different things or 10 different things,

6 or 100 different things; it's probably doing a small number of

7 things. And so what that penalty does is it drives you towards

8 one particular solution which has a minimal set of these

9 receptors turned on, one or two or three, rather than six, or

10 eight, or nine. 

11 So we're putting a little bit of biology in

12 there. And the parameters, alpha and SR-zero, I tweaked those a

13 little bit long ago, two years ago to get so that I don't have

14 just one solution and I don't have 18 solutions, I've got a

15 smallish number of solutions.

16 So that's how you come up with the

17 concentration response curves and momentarily, we'll actually

18 look at one of those. And then finally, we're going to

19 calculate the area under the curve. And I'm actually going to

20 come back, I'm going to flip forward a couple of slides and come

21 back to this because I think it's easier to think about once

22 we've actually looked at an example.

23 So here, I'm now on Slide 16. I guess it's

24 Slide 16. So this is an example, this phenol language is a

25 well-known estrogen mimic, and on the left-hand panel, you're

26 going to see many of the pictures. On the left-hand panel shows

27 the concentration response profiles for the 18 assays. And the
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1 right is the model output, which is essentially the receptors

2 and the pseudo receptors. The blue curve that you see on the

3 right is the agonist and the red, if there was a red one, that

4 would be antagonist mode. 

5 And so really, you can think about all this

6 has done has taken all those assays over on the left and it's

7 kind of taken an average of them to get the agonist or the

8 antagonist mode. But it's done in an objective way, using those

9 equations. The area under the curve is -- you can think about

10 that blue curve, and let's just calculate the area under the

11 curve on a log scale. And that's what all the AUC is, it's just

12 integrating across there. Now, notice, there are those two

13 other -- two pseudo receptors, there's a pink one and a black

14 one, which are turned on at relatively low concentrations. They

15 go up and they come down.

16 Let's look at the pink one for the moment. On

17 the left-hand panel, the actual assays, the pink assays, which

18 are the Attagene assays, turn on earlier. They're a little more

19 sensitive for this chemical than the other assays. And so

20 again, I'm running the model for every concentration. At this

21 first concentration, you're seeing a lot of pink activity and so

22 the model say, huh, the most obvious solution is that the

23 pseudo-Attagene, pseudo-receptor is turning. But then as we get

24 further into concentration, all of the assays start to turn on,

25 and so the most likely solution is that it's really an agonist. 

26

27 And so the likelihood -- you can think these
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1 curves on the right are really kind of probabilities of which

2 pseudo-receptor is turning on. So the pink one comes up, but

3 then it turns off because the most likely solution is true

4 agonist activity.

5 As I calculate the area under the curve, that

6 pink one actually has a lot of area under it, but the model has

7 kind of decided, as we move along to the right, that it's not

8 right. And so what I do is I discount that. And I'm going to

9 go back now two slides to the area under the curve calculation. 

10 And notice that I have -- all it is doing, you know, that bottom

11 equation is just an integral. There's no magic about that,

12 except I have the sign of the slope.

13 So while a curve is going up, you add and as

14 it turns down, you subtract. And so those bunks you see at low

15 concentration for many assays may end up not contributing. So

16 it's a heuristic, but it ends up being, I think, being useful. 

17 One final introduction to the model is okay,

18 what does a number mean here? We're going to see these AUC

19 numbers a lot. You can simply say let's imagine all 18 assays

20 turn on and they all turn on at the same concentration and now

21 let's just slide the potency back and forth. Let's start really

22 potent to really weak. And that gives us this calibration

23 curve. Okay, what would the area under the curve be? And it

24 turns out an AUC of 0.1 is about 100 micromolars. We only test

25 our assays up to 100 micromolar. We don't go beyond that for

26 the assays. And so an AUC of .1 or less is in the range where

27 it's weaker than we can test. So that's why for a lot of what
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1 I'm going to show you, I'm using a cutoff of .1 just because

2 that's really the limit of activity that we're measuring.

3 All right. So we've looked at BPA. Now

4 here's four more typical curves. So BPA is on the top-left. 

5 We've already looked at that. Raloxifene is at the top-right. 

6 It's a drug. It's a true antagonist and notice that what we get

7 is a red curve. That's the only activity, so that's really an

8 antagonist. 

9 Then the bottom right is alpha-Cyclodextrin,

10 which is almost certainly -- it's either -- it's a chelater. 

11 It's just grabbing the estradiol out of the protein where it's

12 competing for it and then making it, you know, soluble. But

13 anyway, the only assay known for alpha-Cyclodextrin are the

14 three protein-binding assays. The left-hand one is the dimethyl

15 hydrogen phosphate, which is turning on a number of assays and

16 it's actually given a little bit of a mixed signal. There's a

17 little bit of red and a little bit of blue, and a little bit of

18 green. So this is a case where the model is not entirely clear.

19 Okay, is this really an agonist, an antagonist or is it assay

20 interference.

21 Now, I happen to know, just because I've

22 stared at these long enough that all of assays it's turning on

23 are actually two different technologies, but they're both the

24 Odyssey Thera assays, the protein complementation and the DNA

25 binding. So they're both FRET assays and they're both run in

26 the same -- all in the run cell type. So probably, it's some

27 sort of an interference either with the FRET assay technology or
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1 that cell type. So we could think about those things

2 post-factor.

3 All right. So we have a model, and then

4 model, again, was not trained against the reference chemicals. 

5 So can actually now just go test against the reference

6 chemicals. And we started with, you know, there's been a lot of

7 work over the last 15 or 20 years, coming up with reference

8 chemicals for estrogen receptor binding in vitro or in vivo. We

9 started with the list from the OECD test guidance 455 and a lot

10 of consultation with folks at NICEATM and elsewhere to come up

11 with a list of -- I think we came up with 45 reference

12 chemicals. 

13 And this, I think, is the biggest set of

14 reference chemicals anybody ever uses for validating any kind of

15 certainly, EDSP test. So it's a big list with positives and

16 negatives and here's an "I" chart, which I certainly can't read,

17 but you can see this. We have convents, which are very strong,

18 moderate, weak, very weak, and negative.

19 Most of the positives are what people would

20 characterize as kind of easy compounds. I think what we're

21 learning from testing 1,800 compounds is there are a lot of

22 other harder compounds to distinguish truly estrogen activity

23 from other kinds of activity, but I'll talk about that a little

24 later.

25 So this is the results from looking at the

26 reference chemicals. And let's focus on the one on the left,

27 and hopefully you can see this on your little screens. A
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1 chemical, which is labeled as green, the experts say should be

2 positive and so it should not be on that left-hand wall. A

3 chemical, which is labeled red, is supposed to be negative and

4 so it should be on the left-hand wall. 

5 What am I showing is the AUC value? And

6 notice, AUC is really proportional to the log of the potency. 

7 So people sometimes say oh, your AUC goes from .1 to one,

8 whereas potency goes from 10 to the minus 8th to 10 to the minus

9 3rd. Well, you should really be comparing the AUC value with

10 the log of the potency and micromolar. But anyway, it turns out

11 that almost all the positives, with the two exceptions down at

12 the bottom, which I should have memorized because I can't read

13 them here. I should've had my reading glasses. 

14 Diethylhexyl phthalate and Dicofol are

15 inactive in the model, but notice the bottom one, Dicofol, has

16 an R4 activity. So it's lighting up some of the assays, and we

17 can wander back and figure out which assays that is R, but it's

18 not totally week. The model says it's probably just assay

19 interference.

20 Then I boxed this one interesting chemical,

21 which is Di-n-butyl phthalate, which is very weak. And if we

22 look at the little inset, it actually has a little bit of blue

23 and it has a little bit of red. So the model can't quite decide

24 whether it's an agonist or an antagonist. So it shows up with

25 activity in both the agonist and antagonist, whereas, again, the

26 experts say it should only have agonist activity and not

27 antagonist activity.
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1 The panel at the right, the box-and-whisker

2 plots shows the AUC, again, roughly proportional to the log

3 potency of chemicals in these different bins, you know, the

4 expert bins and you can see that the AUC has a pretty good

5 correlation with the potency ranges that the experts have come

6 up with, based on lots of prior experience running these

7 chemicals in lots of other in vitro and in vivo assays.

8 Having done that, now we've run this total of

9 1,812 chemicals through all of this and we see a combination of

10 chemicals where nothing happens; it's totally inactive in all

11 assays. Chemicals where it's clearly an estrogen agonist or

12 clearly an estrogen antagonist in a set of chemicals where the

13 model says it's somehow pseudo-receptor activity. It's lighting

14 up only one technology or maybe only one assay. 

15 And like I say, this is really a work in

16 progress to try to understand all of the pseudo-receptor

17 activity based on data from the other 800 and some-odd assays

18 that we have. So we cytotoxicity, cell stress, and so on. And

19 so there are a number of other factors that I -- I'm not going

20 to spend a lot of time on -- this is in the White Paper -- but

21 it's really just the point that for many of these what we call

22 false positives because of assay spoofing, we understand the

23 basics of what's going on. Not all of the chemicals, though.

24 So this is another kind of heat map. So the

25 previous heat map was all the chemicals and all the assays. 

26 This is the heat map, where on the X-axis are all of the

27 receptors and pseudo-receptors. And so you can see that box of
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1 chemicals at the top-left, those are the ones where the models

2 said you know, what, those are true agonists. And you can

3 believe that. There are a few antagonists, there aren't many

4 antagonists, but then we have groups of chemicals which light up

5 each of the -- pretty much, one and only one of the

6 pseudo-receptors. 

7 And again, this is a consequence of that

8 choice made earlier on to favor solutions that tend to light up

9 one or two receptors and not lots of them weekly. And then the

10 table just shows you how many chemicals are in each of those

11 bins. So essentially, out of 1,800, there's only 97 chemicals

12 which are agonists. The model says they're agonists at .1 or

13 above, AUC of .1 or above. There are a number of other very

14 weak ones, but there are other reasons why we might discount

15 those.

16 DR. JAMES MCMANAMAN: We were just discussing

17 and we thought we'll just go to the break and then we'll stop

18 there and I'll allow questions to be answered after the break

19 and then you can go on. Does that make sense?

20 DR. RICHARD JUDSON: Okay. I'm actually at a

21 kind of a theme stop here. 

22 DR. JAMES MCMANAMAN: Okay. 

23 DR. RICHARD JUDSON: If it would make sense to

24 have some questions and take a break --

25 DR. JAMES MCMANAMAN: That's fine with me,

26 yes. Why don't we do that then.

27 DR. RICHARD JUDSON: Great.
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1 DR. JAMES MCMANAMAN: Dr. Schlenk.

2 DR. DANIEL SCHLENK: Thanks for that

3 presentation. That cleared up quite a bit, actually, at least

4 in some of the documents that I had. I had a couple of

5 questions regarding mostly the January 2013 meeting that we had

6 spent a fair amount of time with the Rainbow trout estrogen

7 expert sort of approach and the transactivation assays. I

8 noticed that all the assays in this particular panel had

9 eliminated any of that information that was present before. And

10 I'm just curious, is that something that has made a decision? I

11 think Dr. Dix has mentioned that some things were cut out and

12 some things were more streamlined. So I'm just kind of curious,

13 are we just going straight for the high throughput stuff or are

14 those transactivation assays still going to be a part of future

15 decisions?

16 DR. DAVID DIX: As far as the EDSP is

17 concerned, we are interested in continuing to evolve our science

18 and we're willing to take in new data and exclude earlier data. 

19 Primarily, in this case, under the advisement of our Office of

20 Research and Development. I will point out that as the science

21 supporting the EDSP has evolved and some of the expectations and

22 requirements for a government-based science and its use of

23 science has evolved, we're firmly committed to transparency and

24 public accessibility to the data and the methods. And so that's

25 a consideration as we move forward.

26 We're not, at this point, excluding the

27 possibility of continuing to use those data and models. It's
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1 just a question of the continued evolution of those and the

2 availability, accessibility and transparency from the Office of

3 Chemical Safety perspective and the EDSP perspective. That's

4 something that is beyond our control and we depend on the Office

5 of Research and Development. We could ask colleagues from the

6 Office of Research and Development to comment a little further. 

7 I'd ask if you'd like that, if you would give us some time to

8 confer and develop that response and provide it at a later point

9 in the panel. I'd appreciate that.

10 DR. DANIEL SCHLENK: Personally, I would like

11 to see that just because we spent so much time on it a couple of

12 years ago. One of the things that came out of that meeting was

13 the fact that it was so focused on estrogen receptor binding and

14 not necessarily activation. I remember that going over, and

15 over, and over again. And it seems -- and I just want to

16 clarify that we're moving beyond just the binding now. We're

17 actually moving onto estrogen activity. Would that be sort of

18 accurate?

19 DR. DAVID DIX: That's absolutely accurate. 

20 I'll let Dr. Judson respond to that further. I think that's

21 very explicitly defined in the USEtox model. And it does get

22 beyond the transparency issues. It gets down to the redundancy

23 issues. So we're looking at three binding assays here that have

24 a consistent dataset across the 1,800 plus chemicals, and that

25 number continues to grow.

26 So I think -- and I'll let Dr. Judson refer to

27 this -- but I think from kind of a gross perspective or a
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1 program perspective, it's difficult to bring in small datasets

2 and incorporate them into this approach in an efficient manner,

3 something on the order of hundreds of chemicals versus having a

4 uniformed dataset. There are some potential redundancy issues

5 and we'll develop a better answer for you later on that.

6 DR. JAMES MCMANAMAN: Great. Richard.

7 DR. DANIEL SCHLENK: Just a couple more

8 questions. I just wanted to clarify, too, on slide I think

9 it's, seven, those are cell-free assays, correct? Those are

10 actually recombinant-based assays?

11 I say cell-free, but they're actually

12 recombinant assays and not tissue-based?

13 DR. RICHARD JUDSON: No. Actually --

14 DR. DANIEL SCHLENK: Except for the bovine

15 one, maybe?

16 DR. RICHARD JUDSON: Yes, that is right. The

17 bovine one is a cell-based. The other two are recombinant.

18 DR. DANIEL SCHLENK: Last question. So I just

19 realized, in terms of your AUC calculations, how are you dealing

20 with non-monotonicity?

21 It seems that, particularly with Bisphenol A,

22 you're getting that sort of up and down, non-monotonic dose

23 response curve. As we know in a lot of the endocrine-based

24 assays, even in the in vitro assays, you still sort of struggle

25 with a lot of that. And I'm curious, it seems that, if I'm

26 understanding correctly, you're actually subtracting away from

27 AUC if you do see a downward sort of slope, as opposed to
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1 leaving it alone, I guess. I'm not sure how you would do it. 

2 I'm just curious of how you actually deal with non-monotonicity

3 and if that's something that goes into the model.

4 DR. RICHARD JUDSON: So we're back here to the

5 slide. The non-monotonicity you see here is purely an artifact

6 of the model. It has nothing to do with the biology. True

7 non-monotonicity would be if on the left-hand side one of the

8 assays goes up and then it comes down again. We do see that,

9 but when you see it coming down it is always a consequence

10 cytotoxicity. 

11 So in the actual assay -- assay-by-assay

12 analysis, which one can see from the dashboard, where you're

13 actually seeing the raw data, we see that. But for this model,

14 I just ignore that. If it goes up, I let it say up. It would

15 be possible to include that cytotoxicity, but we're already

16 taking it out otherwise. 

17 DR. DANIEL SCHLENK: So you're saying every

18 assay that you see, that's due to cytotoxicity? You've never

19 seen a non-monotonic response in any other bioassays?

20 DR. RICHARD JUDSON: We have stared at lots,

21 and lots, and lots of data. I can't guarantee out of the 2

22 million dose response curves we have that there isn't one that

23 does something else, but that's the vast majority of anything

24 that does go up and come back down.

25 DR. DANIEL SCHLENK: Interesting. Thanks.

26 DR. JAMES MCMANAMAN: In continuing on with

27 Dan's point, about 2013, one of the questions that we had back
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1 then was the issue of non-receptor estrogen activation. And I'm

2 just wondering, everything that you presented was very clear

3 about individual assays; but when you put things together into

4 getting a total totality of activities, how would something who

5 doesn't bind estrogen receptor, is so it would not be active in

6 the first three assays that you use, but nevertheless has

7 estrogenic activity in terms of transcriptional activity. How

8 would that fit into your area under the curve and how would you

9 prioritize something in a compound like that?

10 DR. RICHARD JUDSON: I'm not sure that we have

11 an example of that, but should we have a chemical that lit up

12 all of the agonist assays except the mining assays, it would be

13 called an agonist.

14 In fact, we have some examples. Just because

15 we've shifted far enough to the right that those binding assays

16 just don't turn in the concentration range where we look at. 

17 And those are classified as agonists. But we've looked around

18 for examples where somebody can say, you know what, this

19 chemical actually causes estrogen activity in a

20 non-receptor-mediated fashion. And we just don't have good

21 reference chemicals to play with there.

22 DR. JAMES MCMANAMAN: Okay. Follow-ups?

23 DR. DAVID JETT: I think this goes to what

24 I've been thinking about the past five minutes about this term

25 pseudo-receptor pathway. You can really think of this of sort

26 of potentially an alternate pathway when it light up, an

27 alternate. Because I know there are -- and it's concentration
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1 dependent because I know there are examples. Just because

2 something doesn't light up at the receptor end, it could still

3 add a different, at an even lower concentration, light up

4 something at the protein production and proliferation. So I

5 don't know if that maybe was throwing me a little bit about

6 calling it sort of a pseudo-receptor pathway, but I think that

7 as you incorporate more non-receptor-mediated assays into this,

8 these may go down. You know, they may light up last.

9 DR. RICHARD JUDSON: So for instance, if

10 somebody had specific hypothesis about what would happen if you

11 had non-receptor-mediated activity, it would turn on these

12 specific pieces of the pathway. We could add another

13 pseudo-receptor into the model and chemicals that were clearly

14 giving that pattern would point that direction. So it would be

15 easy to extend that.

16 One other piece of the pathway, which is not

17 here in that we have some data for it, we just haven't gotten

18 around to using it yet, is there's actually further downstream

19 activity where there's the whole cascade of genes which are

20 regulated by the estrogen receptor pathway. There may be 1,000

21 genes. So we have some microarray data for some of these

22 chemicals and you would expect to see, again, that whole cascade

23 of genes downstream that would turn on, which would be more

24 confirmatory evidence that either you have true estrogen pathway

25 activity or something else. 

26 DR. JAMES MCMANAMAN: Dr. Denver.

27 DR. ROBERT DENVER: So most of these processes
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1 that you're assaying occur within seconds and minutes in cells. 

2 I'm curious why the selection of the later time points for the

3 high throughput assay is eight and 24 hours because it seems to

4 me that that could lead to the detection of indirect effects in

5 confounding of the actual effects. Or even missing some of the

6 direct effects that might be extinguished by that point. 

7 DR. RICHARD JUDSON: That's a great question

8 and I'm unfortunately not the expert on the assays, but we've

9 gone around and around and around trying to decide what time

10 points to use. And what we would really like to do is a time

11 series to be able to answer these sort of questions, but I'm

12 sure that some of the problems we see with assays have to do

13 with the fact we are not looking at the right time.

14 DR. ROBERT DENVER: Just to follow up,

15 wouldn't it be prudent, at this point, to do some time series

16 for some of the model chemicals.

17 DR. RICHARD JUDSON: In fact, we're setting up

18 a lab and that would be one of their -- okay, I am pointed out

19 that the ACEA assay is actually a real-time assay. So we

20 actually see proliferation every two minutes for over 48 hours.

21 DR. ROBERT DENVER: I see. That's a cell

22 proliferation assay.

23 DR. RICHARD JUDSON: It's a cell proliferation

24 assay, yes.

25 DR. ROBERT DENVER: Just one quick last

26 question. I was trying to understand the Attagene assays, and I

27 looked on their website. I could understand how the cis mode
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1 multiplexed assay worked, but I couldn't quite understand how

2 the trans mode multiplexed assay would work.

3 MR. RICHARD JUDSON: I'm going to throw out a

4 life line.

5 DR. RUSSELL THOMAS: I'm Rusty Thomas. I'm

6 the Director of NCCT. In the Attagene assay to trans assay,

7 essentially they're overexpressing the receptor of interest, so

8 it's not in trans format. 

9 In its cis format, they're relying on the

10 endogenous receptor and in trans format, they're overexpressing

11 --

12 DR. JAMES MCMANAMAN: I don't think you're

13 close enough to the mic to pick up everything.

14 DR. ROBERT DENVER: I understand the principle

15 of the assay. I just didn't understand how the trans assay was

16 multiplexed. I could understand how the cis assay was.

17 DR. RUSSELL THOMAS: Oh. It's multiplexed in

18 the same way as the cis. So it's based on the reporter,

19 essentially, and the reporter --

20 DR. ROBERT DENVER: But the reporter is a

21 fusion with GAL4, and I don't understand how that would

22 distinguish -- sorry. The nuclear receptor is a fusion with

23 GAL4, which would bind to a GAL4 binding site. But then I don't

24 understand how that would be multiplexed in a single cell.

25 DR. JAMES MCMANAMAN: Dr. Denver, could you

26 put the microphone in front?

27 DR. ROBERT DENVER: I'm so sorry. So I didn't
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1 understand how the nuclear receptor GAL4 fusion for each

2 individual nuclear receptor could be multiplexed in the cell

3 with distinct reporters for each nuclear receptor GAL4 fusion.

4 DR. RUSSELL THOMAS: If I understand your

5 question, the multiplexing is not due to the GAL4 recombinant

6 receptor. That's not multiplexed, it's the reporter itself

7 that's multiplexed. Did I understand you correct?

8 DR. ROBERT DENVER: No. I understand that

9 from the cis assay that the reporter allows for multiplexing. 

10 What I didn't understand is given that this is a nuclear

11 receptor GAL4 fusion.

12 DR. RUSSELL THOMAS: Yes. Right. And that's

13 not multiplexed, right? That's a single --

14 DR. ROBERT DENVER: So then each cell has a

15 distinct nuclear receptor GAL4 fusion.

16 DR. RUSSELL THOMAS: Well, exactly. It's

17 trans assay. 

18 DR. ROBERT DENVER: Trans assay. So the trans

19 assays are not actually multiplexed?

20 DR. RUSSELL THOMAS: Correct. Yes. 

21 DR. ROBERT DENVER: Okay.

22 DR. RUSSELL THOMAS: I thought I understood

23 you correct.

24 DR. JAMES MCMANAMAN: Dr. Potter.

25 DR. THOMAS POTTER: I want to go back to a

26 comment I heard from Dr. Dix about assay redundancy. This is

27 sort of general question. Where are you headed with the
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1 informit that you have about assay performance, and is it your

2 intent, ultimately, to parse the set of assays into things that

3 perform better, in terms of signal to noise and that sort of

4 thing, as you move forward?

5 DR. DAVID DIX: From our perspective, we think

6 this model performs pretty well and I think you'll see that over

7 the course of these presentations. I can't predict what the

8 future comparisons will tell, but at this point in time, this

9 model is performing better across a larger set of reference

10 chemicals than anything else in the literature and in any other

11 regulatory practice.

12 As you'll see later today, it performs very

13 well, relative to a variety of other in vitro and perhaps, more

14 importantly, the in vivo uterotrophic assay. So this is an

15 enormous amount of work to get it to this point and we see some

16 value in the model and the assay dataset as it stand now. So

17 certainly, longer term there might be some potential to trim it

18 down, but how many years? How many dollars? And what's the

19 value added. I think at this point, from a programmatic

20 perspective, we're extremely happy with this dataset and the

21 model performance. 

22 I'll let Dr. Judson respond, in terms of model

23 development. But from a programmatic perspective, here at the

24 tail end of 2014, you know, after the Agency has invested a

25 decade and many millions of dollars in the ToxCast research

26 project, as well as the Tox21 federal collaborative, as well as

27 all of the money and time invested in the Endocrine Disruptor
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1 Screening Program, we feel like now we have a tool that's given

2 us answers on close to 2,000 chemicals that appear to be very

3 reliable, vis-à-vis, smaller datasets, based on a smaller number

4 of reference chemicals. So it gets back to this question of

5 truth and the return on an investment. We have a commitment to

6 screen compounds for the potential to disrupt endocrine systems

7 in humans and wildlife and we're very anxious to deliver on that

8 commitment.

9 DR. THOMAS POTTER: Again, I certainly

10 appreciate your thoughts here about the model performance. And

11 certainly, what we've seen so far, it's certainly impressive. I

12 want to get that out on the table. What I was asking

13 specifically was that you have multiple binding assays, just use

14 this as an example. So you feel it's appropriate at this stage

15 to retain multiple binding assays as you move forward in this

16 exercise in order to generate the appropriate level of

17 information to make your assessments.

18 DR. RICHARD JUDSON: This meeting is actually

19 kind of a critical waypoint -- I think we want to hear what you

20 folks are going to say. So we have about 1,000 other chemicals,

21 where we've run many of these assays. We have the data, we just

22 haven't gone through the whole modeling effort. But we're

23 really going to stop and think, what do we do next? 

24 In an exercise that I know how to do, it's

25 just work, is to say, okay, what is the minimum set of these

26 assays that still gives us good enough performance. It

27 certainly would not have three binding assays in it, for
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1 instance.

2 DR. JAMES MCMANAMAN: Yes. Dr. Propper.

3 DR. CATHERINE PROPPER: I'd like to come back

4 to this figure right here just to make sure that I understand

5 everything because I've spent a lot of time over the last week. 

6 So the blue line on the right is not representative of the area

7 under the curve of all of the assays, just those three assays

8 that are represented in R1 or is it for all of the assays?

9 DR. RICHARD JUDSON: So the process is we take

10 all the assays on the left and we do one concentration at a

11 time. Essentially, the model says the most likely receptor to

12 light up at that concentration is, in this case, it is for most

13 of the concentrations, it's the blue curve, the bottom line. 

14 It's true. So that's where the blue curve comes from or the

15 pink curve or so on.

16 DR. CATHERINE PROPPER: It's R1. So what it's

17 saying is R1 -- so the blue line is not representative of all of

18 them, it is really representative of those three binding assays.

19 DR. RICHARD JUDSON: No, no, no. So let's go

20 back to the big picture.

21 DR. CATHERINE PROPPER: Right.

22 DR. RICHARD JUDSON: Okay. So R1 is, again,

23 the little arrow at the top-right, the blue arrow.

24 DR. CATHERINE PROPPER: Right.

25 DR. RICHARD JUDSON: It points directly to A1,

26 A2, and A3, but it indirectly -- you can see arrows go down to

27 Node N1, Node N2, and so on. So R1 is integrating all of the
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1 assays that you can get to by an arrow from that top blue. 

2 So it's everyone on the middle and everything

3 on the right, everything except those two at the bottom left.

4 DR. CATHERINE PROPPER: That helps a lot. All

5 right. Thank you. Can we go back to the line again and just

6 the green vertical line in all of these assays, I wasn't able to

7 find what that was defining.

8 DR. RICHARD JUDSON: Okay. Right. Sorry. 

9 Because I probably didn't tell you. That is a simple -- it's

10 essentially the average -- on the left-hand side, every curve

11 has an AC50, the concentration where it's halfway up to the top.

12 What I do is I simply take the log of that value and take the

13 median and that's what gives the green line.

14 DR. CATHERINE PROPPER: Okay. So it's kind of

15 an average AC50 for across all the assays.

16 DR. RICHARD JUDSON: Right. Yeah. It's just

17 kind of a handle to hold onto.

18 DR. CATHERINE PROPPER: Okay.

19 DR. RICHARD PROPPER: And then another point I

20 didn't make was that that gray region in the red, the gray

21 region is where cytotoxicity is happening for the chemical. And

22 we have many cytotoxicity assays in the red, is the equivalent

23 kind of median AC50 for the cytotoxicity assays.

24 DR. CATHERINE PROPPER: Okay. Thank you. 

25 That helps very much.

26 DR. JAMES MCMANAMAN: Your question is, Dr.

27 Jett?
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1 DR. DAVID JETT: I think I may have lost my

2 chance to follow up on the redundancy issue, but I think we need

3 to remember that any good screening program is going to have

4 some redundancy built into it in the form of orthogonal assays

5 as well as counter screens and so forth. So the question is

6 whether or not those three estrogen receptor assays are exactly

7 the same, the same components and read outs and so forth or are

8 they slightly different?

9 DR. RICHARD JUDSON: I mean, they're certainly

10 different because they come from different species. One's a

11 recombinant or two are recombinant and one is a cell base. So

12 every one of these assays is slightly different.

13 DR. DAVID JETT: So I think that it's just

14 before we start thinking about trimming, we should try to make

15 sure that we have the good redundancy built into the programs.

16 DR. RICHARD JUDSON: There's a cost component.

17 So some of these assays are part of multiplexes. And you pay

18 for the multiplex and you get all the assays and it doesn't help

19 you any, cost-wise, to take something out. That's not always

20 true sometimes.

21 DR. JAMES MCMANAMAN: Yes?

22 DR. GRANT WELLER: I'd like to go back to

23 Slide 12. This relates to the redundancy part. In your

24 unsupervised clustering --

25 DR. RICHARD JUDSON: Okay. Which one is Slide

26 12?

27 DR. GRANT WELLER: The previous. The heat
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1 map.

2 DR. RICHARD JUDSON: Oh, the heat map. This

3 one?

4 DR. GRANT WELLER: Yes.

5 DR. RICHARD JUDSON: Okay. Great.

6 DR. GRANT WELLER: I would've expected that

7 the receptor R4 assays were all clustered together, and yet

8 there's a group of two that A4 and A5 are pretty far from six

9 through nine. Is there an explanation for that?

10 DR. RICHARD JUDSON: So let me -- four and

11 five. I can only speculate. So four and five are the

12 alpha-alpha. So estrogen receptor alpha versus alpha, where the

13 others are alpha beta or beta-beta. 

14 Although there isn't a lot of difference

15 between alpha and beta activity, it appears -- we've actually

16 looked a lot at that -- there is some. So it may be it's just

17 chemicals. There are a handful of chemicals which are

18 beta-specific, which are dragging those two pieces apart.

19 DR. JAMES MCMANAMAN: Richard Judson. Yes,

20 Grant.

21 DR. GRANT WELLER: One more question on the

22 curve fitting exercise on Slide 14. 

23 DR. RICHARD JUDSON: This one?

24 DR. GRANT WELLER: So at first it looks just

25 like a linear regression, but am I correct in saying that in

26 your minimization, you can actually make that epsilon square,

27 you can make that zero for each chemical?
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1 DR. RICHARD JUDSON: No. Probably if I worked

2 at it I could, but the epsilons, for the most part, get down to

3 the 10 to minus 4, 10 to the minus 5. They get very small, but

4 there are certain cases where it just doesn't work very well. 

5 None of the receptors or the pseudo-receptor models work well

6 enough to be able to get you down to a zero, but almost always

7 it gets very small.

8 DR. JAMES MCMANAMAN: Even for comparing

9 estrogen with estrogen? What would you get there? Zero, I

10 would assume.

11 DR. RICHARD JUDSON: The true estrogens, those

12 numbers absolutely gets really small.

13 DR. GRANT WELLER: I'm just trying to get my

14 head around this exercise here. Have you done analyses on a

15 distributional assumption on that error term or an analysis of

16 model fit?

17 DR. RICHARD JUDSON: No, I haven't done that. 

18 But I do look at when it doesn't converge. So there's just

19 whatever routine I'm using has a convergence criteria and

20 typically, when it doesn't converge, one assay at the top of the

21 pathway and one at the bottom wide up, which is just not one of

22 the options I'd give in the model.

23 DR. GRANT WELLER: Thank you.

24 DR. JAMES MCMANAMAN: Okay. Dr. Delclos?

25 DR. BARRY DELCLOS: I just had one question

26 about what you said about alpha and beta not being very

27 different. Are the protein interaction assays the only ones
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1 where you specifically interrogate the R beta? Because the

2 others all seem toward alpha.

3 DR. RICHARD JUDSON: The protein

4 complementation is the only one with the R beta in it. 

5 DR. JAMES MCMANAMAN: Dr. Propper.

6 DR. CATHERINE PROPPER: Just one follow up on

7 that. Is there any plans in the future to incorporate the

8 membrane ER into an assay?

9 DR. RICHARD JUDSON: We would love to do that.

10 We've looked around for a high throughput assay available for

11 that and we just haven't been able to get a hold of one.

12 DR. CATHERINE PROPPER: Thank you.

13 DR. JAMES MCMANAMAN: Okay. If there are no

14 other questions, then let's take 15-minute break and be here at

15 13 after.

16 (Brief recess.)

17 DR. JAMES MCMANAMAN: All right. For the

18 record, Dr. Weller and Dr. Berrocal had conversations with Dr.

19 Judson during the break and they are not statistics. And they

20 were the details, I guess, of the statistical methods that were

21 being used. 

22 DR. RICHARD JUDSON: I can summarize very

23 quickly --

24 DR. JAMES MCMANAMAN: Okay.

25 DR. RICHARD JUDSON: -- without getting too

26 bogged down. So it goes back to the model and the equations. 

27 And at least the conversation I remember had to do with the
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1 penalty term.

2 DR. VERONICA BERROCAL: Yes.

3 DR. RICHARD JUDSON: So Dr. Berrocal made a

4 comment that quite often when one does penalize least-squares,

5 you actually do a parameter sweep to find out how to best fit

6 that. My response to that is yes, I could've, and then you

7 would actually look at the performance as a function of those

8 parameters. 

9 I really chose those very quickly, in kind of

10 round numbers to give something that was biologically

11 meaningful, but it would be possible to go back and do such a

12 parameter sweep.

13 DR. VERONICA BERROCAL: So actually, the

14 question was how sensitive the estimates of the Rs are on this

15 penalty parameters. And with Dr. Weller, we asked whether --

16 what this was after. We wondering whether this penalty

17 parameter change by concentration or they are constant.

18 DR. RICHARD JUDSON: They are constant. 

19 DR. JAMES MCMANAMAN: If these are questions

20 that will be addressed during the charge questions, then we'll

21 just go ahead and delay the answer to those until then. I just

22 wanted to have it on the record so that we wouldn't be

23 penalized.

24 DR. RICHARD JUDSON: Okay. All right. Get

25 our hands slapped. Getting back to my other slides, I want to

26 make one more comment about redundancy. Another way to think

27 about redundancy is there are many things that an estrogenic
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1 chemical might do, in vivo. We just don't care does it bind in

2 vitro. We're aiming towards protecting people and fish and

3 frogs and so on. 

4 Another assay that we have access to that

5 we're hoping to be able to run is a cofactor panel. So the

6 estrogen receptor activity can be very different, depending on

7 what tissue type you're in and that's really driven by different

8 cofactors. And this cofactor panel assay is very expensive, so

9 you couldn't run in high throughput mode, but we could take the

10 97 actives and we could actually see what the difference in

11 cofactor recruitment is and that would tell us something about

12 what target organs might be for that chemical. So we can

13 imagine doing much more redundancy or dig deeper for selected

14 chemicals.

15 Given that, let's get back to where we were. 

16 DR. JAMES MCMANAMAN: Dr. Perkins.

17 DR. EDWARD PERKINS: If we could back up to

18 the AUC, area under the curve summarization. 

19 Before that. The math. The math.

20 DR. RICHARD JUDSON: The math. Okay.

21 DR. EDWARD PERKINS: Since some of us are

22 math-challenged, we're kind of curious, if you could explain a

23 little bit or once more on how the area of curve summarizes of

24 these results and if there is any bias introduced by the number

25 of assays for a particular event or does it integrate things in

26 an unweighted manner, just taking into account, essentially,

27 potency and the total area under the curve.
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1 DR. RICHARD JUDSON: That's a good point. If

2 we think about the model, when you see the cartoon at the

3 bottom-right here, we have three biding assays. So you could

4 think well, I'm only going to weigh those each by a third, and

5 we have six of the cofactor recruitments so I'm going to weight

6 them by a sixth. I spent a month or two playing with all sorts

7 of weighting schemes and it made no difference. That the 97

8 actives were always active and the ones that were inactive were

9 always inactive. 

10 Again, there were some things kind of in the

11 middle that maybe pushed above .1 or below .1, but I always opt

12 for the simplest model, and since there were many ways to weight

13 things, let's just do it unweighted and that does a good enough

14 job. So that answers that question. Does that answer the whole

15 question?

16 DR. EDWARD PERKINS: I guess the other one was

17 if you have more assays for one event versus another event, does

18 that impact?

19 DR. RICHARD JUDSON: And I guess, empirically,

20 I would say no because if I downweighted -- so I have six assays

21 there in the middle, if I downweight those as a six, that's kind

22 of testing the hypothesis than having six and it doesn't make

23 any difference. So I think the answer is that's not an issue. 

24 Okay.

25 DR. JAMES MCMANAMAN: Okay. With that final

26 question, let's move on.

27 DR. RICHARD JUDSON: Okay. We got to this
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1 slide, which summarized where we were with classifying chemicals

2 as being an agonist and antagonist, totally negative. And I

3 don't even show those on this slide, or active in one of the

4 pseudo receptors. 

5 So the model, let's think about the

6 pseudo-receptor actives. We have one or two assays turned on,

7 but the model says those are not estrogenic. So we could

8 believe the model and move on, but what we really want to do is

9 say okay, they're not estrogenic, but they're positive. If we

10 understand the biology or the chemistry of why they're positive,

11 we feel more comfortable saying they really aren't estrogenic. 

12 And so that's really just the next theme here.

13 And I've got a lot of slides and there's a lot

14 of text in the White Paper. I'm not going to go into a lot of

15 detail because this is really a work in progress, but I'm going

16 to give you a flavor of how we're trying to understand the

17 pseudo-receptor activity.

18 The biggest piece of this, and we see this not

19 only in estrogen receptor assays, but every other assay we see

20 is really driven by non-receptor-mediated activity in the assay,

21 which is driven by cell-stress or cytotoxicity.

22 So this is a cartoon, and I'll show you some

23 real data that on the left-hand side are three chemicals. And

24 again, each chemical we've run up to 821 assays, so lots of

25 assays. They're cell-free, they're cell-based, they're nuclear

26 receptors, they're primary cells, they're cell lines, fluoresce

27 readouts, any kind of readout you can imagine.
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1 If we now take all of those assays and we fit

2 them -- we see an activity and we fit them to a Hill curve or

3 one of these gain/loss curves and we say, okay, what's the AC50,

4 the concentration where you're halfway to max. And we simply,

5 for the actives, for a given chemical, we just plot a histogram.

6 So the left, the top is one example chemical where we see --

7 and at the left is very low concentrations; at the right it's

8 very high concentrations. 

9 Pretty low concentration, maybe one assay

10 lights up, like an estrogen receptor assay or something and then

11 another assay lights up. But then suddenly we see this big

12 burst of activity and one chemical is at 10 micromolar. There's

13 another chemical where that's at 100 micromolar, and there's

14 another chemical where we don't see that at all. If you go and

15 say, okay, so what's going on? What's causing this burst? Now

16 you take all our cytotoxicity assays -- and I think there are

17 35, 37 cytotox assays, which are again, different readouts,

18 different cell types, primaries versus cell lines. The burst is

19 always happening, always happening where cytotoxicity is going

20 on.

21 So a hypothesis is that the cells are really

22 sick in that region and things that we don't necessarily

23 understand is going on. Now, it turns out that every cell

24 doesn't die in the same way. I mean, every chemical doesn't

25 cause cells to die in the same way. There are certain chemicals

26 which only kill certain primary kidney cells. Primary kidney

27 cells. Other get up into the 100 micromolar. Others only cause
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1 decreased proliferation in HepG2 cells. Something like that.

2 But regardless, where you start seeing this

3 sort of gross cell pathology happening, you see this burst. So

4 on the two top chemicals on the left, we actually see the burst.

5 The bottom one, the hypothesis is we don't see it because we've

6 only tested up to 100 micromolar. And that chemical, you would

7 have to have tested further.

8 So in order to compare chemicals, we've done

9 just a little coordinate transformation and what we've done is

10 called the center of the cytotoxicity bursts. And all that is,

11 is we take all the cytotox assays, which are positives, take the

12 log rhythm of their AC50 and take the median of that and that's

13 what the red line is. 

14 And then we take, essentially, a standard

15 deviation around that so it's actually a median absolute

16 deviation, which is the equivalent of a standard deviation if

17 you're talking about median instead of mean, but it's the

18 standard deviation. You take the three standard deviation and

19 you take the average of that standard deviation across all the

20 chemicals. That defines that gray region. So it's not perfect,

21 but it's kind of a useful metric to compare chemicals.

22 Then we call the red line for each chemical

23 zero, in what we call Z-space. And then the Z-score is simply

24 how many standard deviations to the left is an assay being

25 active? And if it's within that three standard deviations, it's

26 kind of in the burst. You can hypothesize that it's really

27 coming from cytotoxicity. And almost all of this bursting
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1 activity is in the gray region. So it's a little bit of a

2 simplification, but it gets us started. 

3 So what we've done is taken each assay hit,

4 which is an AC-50 and micromolar space and converted it -- it's

5 really just multiplied by a scaling factor and subtract and

6 offset and we get its Z-coordinate. At the right, all we've

7 done is we've taken and lined up the red lines for all the

8 chemicals. And the reason that's useful is because every

9 chemical has its characteristic concentration where it turns

10 things on, but if we use the cytotoxicity is kind of an origin,

11 to think about it, we can put everybody on the same coordinate

12 system.

13 What you see -- I'll get to that. And here's

14 an example of a real chemical. So the one on the previous one

15 was a cartoon, but this is our favorite chemical, Bisphenol A. 

16 So you can see there are lots of hits outside of the gray region

17 and that's mostly the estrogen receptor assays. And then we see

18 this big bolus of activity in the gray region. And those red

19 are actually the AC50s for the cytotoxicity assays. So that's

20 one way to look at it.

21 Another way -- and these other graphs may or

22 may not be useful -- is to actually take all of the, instead of

23 just plotting a histogram, take each assay and plot its AC50

24 value, which is the X-axis and plot the top. So the assays

25 start at zero and they come up to some top value and then color

26 them with different colors. And the only color to really worry

27 about here are blue, which are all the nuclear receptor assays,
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1 which is estrogen and androgen glucocorticoid, PPAR, and

2 progesterone, for the most part. 

3 And then those pink "up" arrows which are

4 actually circled, that's one -- pink and red are cytotoxicity

5 assays. And so you can see I've circled the cytotoxicity assays

6 in the right-hand panel. But you can see that most of the

7 nuclear receptor assays are to the left of the line there, which

8 is the equivalent of left of the gray box, and they have the top

9 as close to 100 percent. So that is what you would expect for

10 an estrogen receptor assay. Anyway, just an introduction to

11 what we're going to see a little later.

12 Now, let's go back to Z-space. What we've

13 done here, I've taken three different technologies which each

14 have several assays and I simply plotted the histogram of all

15 hits for all chemicals in that technology. So up at the top

16 left, this is the Attagene cis, which are run in HepG2 cells and

17 it's 50-odd endpoints across 1,800 chemicals. And you see this

18 interesting bimodal distribution. 

19 There's a peak at zero, which is where

20 cytotoxicity is happening and then there's a second peak about

21 five or six. And so the assumption -- and we see this for every

22 technology, so it's not one lab is being sloppy and done with

23 what they're doing, it's every technology, every lab you see

24 this. 

25 So this is probably a real phenomenon, which

26 you see complicated biology happening when you get into this

27 cell-stress region. And so the peak around zero is probably
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1 where it's not receptor-mediated activity. That would be the

2 first assumption. So I show that in a HepG2 cell right at the

3 top left. The bottom left is a particular assay battery run by

4 a company called BioSeek, which are human primary cells, and you

5 can see this big peak at zero and then a broader distribution

6 out above three.

7 And then most interestingly, the no-screen

8 nuclear receptor assays, which is this whole collection of

9 radioligand-binding assays. So you're clearly not killing

10 anything because there aren't any cells there to kill, but the

11 kind of chemicals that kill cells, think about surfactants, are

12 also going to go in and disrupt and do crazy things to these

13 bare proteins. So again, you see this bimodal distribution.

14 So if we can account for a lot of the

15 pseudo-receptor as being really in this low Z region, that's at

16 least a plausible explanation for why we would believe it's not

17 estrogenic. This may be just a just-so story, but thinking

18 about specific versus non-specific, and there are a few amounts

19 here, so imagine a cell and you've engineered it as a reporter

20 gene assay and so there's this red point on the chromosome,

21 which is if you bind a transcription factor to it, it will light

22 up.

23 For a specific activity, a chemical comes in,

24 it binds to the receptor that causes activation of that

25 particular element and you get some sort of a fluoresce. So

26 that's how these cells -- all of these assays are engineered in

27 some fashion like that. But now you're at a very high
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1 concentration and you throw lots of the chemical. It gets into

2 the cell and you have oxidated stress. The ER stress. There's

3 a whole bunch of mechanisms by which you could make the cell

4 sick and there's all sorts of activity -- you know, the cell is

5 going through apoptosis or necrosis, which causes some kind of

6 nonspecific activation of many genes and it happens to be the

7 gene that you've engineered as your reporter, also gets turned

8 on.

9 So that turns on, but it has nothing to do

10 with mining of the ligand to the receptor you're probing. So

11 some of the things that we're looking at and we have assays for

12 oxidative stress, ER stress, reaction with DNA and so on. We

13 haven't sorted all this out. This is going to be the next two

14 or three-year, some number of years to get all that worked out.

15 So now that I've kind of explained it, I have

16 lots of examples. I have days' worth of examples that I was

17 asked to put in here, and I'm not going to go through those. As

18 you think about this and are writing this up and want to ask

19 questions, I have examples of different kinds of chemical. 

20 So 17-beta Estradiol. This is the natural

21 ligand. So if we look at the top-left, you've seen these kind

22 of graphs before. You don't see all 18 assays, but that's

23 because most of the assays were actually up at a 100 percent

24 below the lowest concentration tested. So they're sitting up at

25 the top of the graph. And then at the right you see the blue

26 curve, is actually up at the top far left. So the activity is

27 actually happening somewhere below where we test. And this is
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1 where you get one for the AUC. So if you get a curve like that,

2 we've just normalize it to one.

3 At the bottom left we see lots of those blue

4 dots. These are the nuclear receptors which are estrogen. This

5 also lights up androgen assays. It probably weakly hit the

6 progesterone and glucocorticoid assays too. There isn't much

7 happening in the cytotox region, estradiol makes some of the

8 proliferation assays, you get decreased proliferation but you

9 don't actually kill anything.

10 So there's that scatter plot and then the

11 burst plot you see on the right. And you can see lots of

12 activity spread over the non-cytotox region. Here's just

13 another real estrogen. You can see exactly the same kind of

14 pattern, except that it's turning on at concentrations within

15 our tested range. And so you see all of the assays turning on,

16 at the top-left you see a blue curve. With some of these

17 pseudo-receptor curves coming up and down, and that's because

18 there's not -- if you had a perfect system, all of the assays

19 would all turn at the same concentration, instead of being

20 spread out like this. This is an assay technology issue. And

21 this is actually the biggest source -- and we'll hear about this

22 later in the day -- of uncertainty. 

23 What is the true potency of this chemical? 

24 You know, when you want to finally compare that to exposure, is

25 it way at the left? Is it .1 micromolar? Or is it 10

26 micromolar? You know, it's somewhere in that range. And so we

27 have to build that uncertainty into our later models.
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1 Raloxifene, it's the antagonist in the same

2 sort of behavior, except that it's only the antagonist assays

3 that light up. This is an interesting pair of chemicals, which

4 are the non-phenols, which are both reference chemicals. There

5 is a non-phenol, for those of you who aren't chemists, it's a

6 benzene ring with a hydroxyl group, an OH group and then a long

7 tail. And that tail can either be linear or it can be branchy. 

8 And it's known that the linear form is very, very weak. The

9 branched form is somewhat more potent. But they really do get

10 into the receptor-binding side of the estrogen receptor. 

11 So this is the branch and this is the one

12 that's more potent, and you can see all of the assays turn on;

13 we get a nice blue curve. At the bottom-right, you can see

14 there's some activity outside the gray region. At the bottom

15 left, you can see all those blue dots in the upper-left

16 quadrant. So this is a signature of something which is really

17 estrogenic.

18 That linear one, we see most of the assays

19 turn on, but if you look at the bottom-left, bottom right, 100

20 percent of that activity is happening about the same time

21 cytotoxicity is going on. So these are really tough chemicals

22 to deal with, more from the regulatory side or the risk

23 management than anything. The model says it really is

24 estrogenic, but you could get fooled into saying oh, that's just

25 all cytotox. 

26 So the model actually says this is estrogenic,

27 so you probably trust the model, but you can say okay, well, if
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1 estrogenicity is only happening when you're killing cells, you

2 may have other issues to worry about before you worry about

3 estrogen activity. But the important thing is that the model is

4 able to pick that up. I think that's important. Kepone is

5 another one of these weak estrogens. Butyl benzyl -- so I have

6 several examples of these very weak compounds that the experts

7 agree are probably truly estrogenic, but the activity is all

8 happening in the cytotox region, but the model is able to pick

9 those up.

10 DEHP, a phthalate, there's a lot of argument

11 among the experts, but the consensus is this should be

12 estrogenic, but our model says no. And in fact, only a single

13 one of the assays lights up. My hypothesis could be that the

14 activity is simply up above 100 micromolars. So we're just not

15 seeing any activity. And I've actually looked at the raw data

16 to see do we see the hints of things turning on at a 100

17 micromolar. Not really.

18 So there are going to be chemicals like this

19 where they're probably weak estrogens, but we just don't capture

20 them because we haven't gone high enough. Tributyltin, this is

21 kind of a notorious chemical that it turns out it lights up all

22 of the nuclear receptor assays and there's been some hypotheses

23 that this is why Americans are all fat because it actually

24 lights up the PPAR gamma, it's a PPAR gamma agonist. And here,

25 the model says it's a weak antagonist, but if we look at the

26 plots on the bottom, this chemical is just doing all sorts of

27 crazy things in every -- it lights up 50 percent of every assay
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1 we look at over wide ranges. So if it is doing something to

2 PPAR, to estrogen or to androgen, it's doing it at a very

3 non-specific way. At the same time, it's killing cells and

4 doing all sorts of other things.

5 So this would be a chemical that I wouldn't

6 say was estrogenic, but we'd have the risk manager somewhere

7 downstream would have to look at this data. You don't need to

8 look at all the data. You just don't look at the estrogen data,

9 you look at this whole smattering of other things going on to

10 decide what to do with interesting cases.

11 So anyway, lots of examples. What I've shown

12 you are kind of the hard ones, the ones where the model say yes,

13 but there's something else say, probably not or the model says

14 no, but there's some other information that it maybe is yes. So

15 you can't just take the model results and say okay, I'm done,

16 I'm going to go tell people what to do next. You have to look

17 at the data.

18 The antagonists, so there are 97 agonists and

19 I believe those antagonists, this is a critical point and we'll

20 come back to it with the androgen receptor model, where androgen

21 receptor -- antagonists are more important or people worry about

22 them more than agonists. 

23 In order to be called an antagonist in the

24 model, you really need to have those two antagonists assays to

25 turn on. Antagonist assays, how we run them are loss of signal

26 assays, and those are also confounded by cytotoxicity. So you

27 can see loss of signal. All the agonist assay start off as zero
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1 and you get a gain of signal, whereas antagonist, you start off

2 at a 100 percent and you see a loss. You see exactly that same

3 loss if you have cytotoxicity. So it's quite often hard to pull

4 those apart, which is why you have to look at all of this other

5 effect.

6 I just listed the chemicals and the ones I

7 listed in red are the Tributyltin, Dibutyltin, the mercuric

8 acetate, which are chemicals that are doing all sorts of things

9 and almost certainly not estrogen agonists. You could probably

10 explain them as other phenomenon. 

11 So I've talked about cytotoxicity is a good

12 reason for this non-receptor-mediated activity. We've gone

13 through a number of other things and we're running behind and if

14 people really want to dig into this, we can do that, but I think

15 it's the point that we're moving towards understanding a lot of

16 this and we're trying all of the diagnostics -- any diagnostics

17 somebody suggests to us, we're trying to see whether there's

18 some signal there. So we would welcome diagnostics to look at.

19 One particular thing, I'll just mention

20 this, I have PAINS, Pan Assay Interference Compounds. So this

21 idea of assay interference is not something we suddenly

22 discovered and you get patted on the head about them. The

23 pharmaceutical industry has been facing this for years because

24 they go screen a million compounds for some receptor and then

25 each hit, they have to go spend a lot of money following up on. 

26 And they know that there are classes of chemicals that light up

27 assays through some non-receptor-mediated activity. So there's
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1 some QSAR tools and other rules of thumb for detecting chemicals

2 that are probably painful. 

3 So one diagnostic we did was to run the

4 standard QSAR model that's out on the web to detect painfulness

5 and it's a complicated table to look at, but basically what we

6 see is that most of the chemicals, we do see a difference, more

7 activity in the estrogen receptor actives for the chemicals this

8 model calls painful, but it's mostly activity that's happening

9 already in the cytotoxicity region. So it looks like what this

10 model, the QSAR, the PAINS the QSAR is picking up is really the

11 ability for a chemical to be very cytotoxic or cause oxidated

12 stress.

13 We looked at chemical class. So there are

14 specific chemical classes that cause activity in one

15 pseudo-receptor or the other. And there was no statistically

16 significant finding there except for chemical classes that are

17 most likely the agonist and they were the usual suspects. So

18 steroids and phenols and so on. A bottom line, if we can go

19 back and look at the 97 chemicals, we say these are really -- we

20 believe those are agonists, there were almost no surprises

21 there. So it really is the typical chemical classes across

22 these 1,800 chemicals that are ones that are being estrogenic in

23 vivo, in vitro.

24 There's some phys-chem properties that tend to

25 show up more in certain pseudo-receptor chemicals than others,

26 but the signal is weak enough that you can't really make a rule

27 yet. One other validation effort that we've gone through -- so
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1 the data is kind of -- I'm at the end of a theme there. So

2 that's thinking about diagnostics for understanding the

3 pseudo-receptor actives. We're making progress there, but we

4 don't have all the answers yet.

5 So now that our data seems to be internally

6 consistent, it worked well with the experts' reference

7 chemicals, but we know there's a lot of estrogen receptor data

8 in the literature, so we worked with a big group of people to

9 build a literature database, a lot of it coming from the FDA's

10 endocrine disruptor knowledge base that's already there to say

11 okay, can we forward validate the model results. 

12 So we were able to come up with about 7,000

13 chemicals that had at least one literature report. You

14 shouldn't be surprised at this point, from the literature, for

15 each given chemical, you can see a wide range of positives and

16 negatives. So you have a chemical where it's been tested 10,

17 half are negative and half are positive. We see chemicals where

18 every time you've tested it, it's positive, every time it's

19 negative, but you have the same discrepancy because again, you

20 know, I test this one assay for this one chemical in my lab and

21 I report it, but I don't have any context around it. So by

22 doing this compilation from the literature, we're able to put a

23 little more context there.

24 And then it turned out there was a relatively

25 small overlap with our agonist and antagonist, but if we look at

26 the bottom two rows here in what we call initial and final is --

27 gosh, I shouldn't have even said that because I don't remember
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1 that. But the basic finding is we do a pretty good job or

2 agonists are ones that if we look in the literature and they

3 have been looked at four times, five times, six times, all of

4 those times report them as being agonists. We have very good

5 specificity once we've sort of cleaned up the literature data. 

6 So throwing out all of those chemicals where it's only been

7 looked at in one lab or it's been looked at in four labs and

8 it's half positive, half negative. 

9 There are a number of chemicals where we don't

10 see any activity. There are actually 11 of them, where they've

11 been reported in the literature four times and they were all

12 positive. So those are ones that are worth looking at. And

13 most of those chemicals seem to be ones that are consistently

14 active, but they're active in the cytotoxicity region. So each

15 lab is independently sort of seeing the pseudo-receptor

16 activity, but didn't have the ability to put context around

17 that. We're digging into that, but the point is, our model is

18 clearly not perfect, we're going to miss some chemicals.

19 So a little bit of forward validation, again,

20 in the interest of time, all of the data is now public on this

21 EDSP21-4. The URL is here. The slides are public, so anybody

22 in the public can get to this and play with this. Later in the

23 day, if it's important to dig in and actually look at some data,

24 we can use this to actually look at the data, but I think people

25 are getting tired, I've been talking a long time.

26 Next is the QSAR modeling. So this was a big

27 topic of January 2013. As much as we would like to be able to
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1 run every chemical, all 10,000 EDSP Universe chemicals in all of

2 these assays, we can't do that for a variety of technical and

3 cost reasons. So what you would ideally do is use all of this

4 data to build a QSAR model and the EPA has had reviews of the ER

5 expert system a number of times. Here, we actually did

6 something a little different and used the same idea that no

7 assay is perfect and no model is perfect. And we knew lots of

8 people that published estrogen receptor QSAR and docking models,

9 so we went out to the whole world community and said would you

10 folks like to play with us and let's build a common set of

11 chemicals, a common trading set and throw lots and lots of

12 models at that. Again, that's not the focus of today, but I

13 just to summarize where we are with that.

14 So this is a project called CERAPP, the

15 Collective Estrogen Receptor Activity Prediction Project and the

16 goal was to take the ER AUC scores, the training data, it's

17 truth, if you'd like, to train QSAR models and then forward

18 validate with this big literature dataset. And we actually

19 built a high-quality QSAR-ready chemical structures for about

20 32,000 chemicals because it's cheap and we might as well run all

21 chemicals you're interested in. And so what we did was we

22 collected -- I'll actually talk about that, but anyway, the

23 process was we got together a group of experts who were experts

24 at getting QSAR-ready structures together. We built the curated

25 structures. We curated the literature dataset. Everybody went

26 off and built their models, sent them in. We built a consensus

27 of model of models.
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1 You can see the participants. Lots of folks

2 here, U.S.A. and Europe; some of our FDA and NIH colleagues

3 included. The process for curating the structure is one Alex

4 Tropsha at UNC. He's really the driver of this. At least

5 there's a group of people who think he's the best and he's the

6 most rigorous about this. So he was happy with what we did to

7 get the structures all ready.

8 The chemical universe we looked at is the EDSP

9 Universe, plus we have a big database of chemical use of all the

10 40,000 chemicals where we know at least one use that's put in

11 there, the Canadian domestic substances list which they compiled

12 five, ten years ago of things that they think Canadians are

13 exposed to and some other lists. And then once we create a

14 unique structure, that's about 32,000 structures, each of which

15 is mapped one or more cash registry numbers. And then we gave

16 all of the groups a subset of the ToxCast data, the ones that

17 have chemical structures. We actually have oils and things,

18 which are not many chemicals and had them go at it.

19 And then we got back 22, 33, 42 different

20 models, and these are binding model, agonist models, antagonist

21 models, QSAR models and docking models, and so on. Some of them

22 were yes/no; some of them were actually quantitative. The

23 result, once you combine all those models together, most models

24 say most chemicals are inactive, which is what you would like. 

25 There's a thin slice of chemicals where everybody says they're

26 active. So those would be obvious ones to go throw into the in

27 vitro assays next and really confirm that and then work our way
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1 down into ones where fewer and fewer models were positive.

2 On this slide, what is an evaluation, we took

3 again these chemicals from the literature that had not been part

4 of the training set and we took higher and higher quality

5 slices. So we started with any chemical and we would say, okay,

6 if you've been tested once and it was positive, we would call it

7 positive. If you had been tested three times and it was two

8 positive and one negative, we would call it positive.

9 And then we said, okay, how well does the

10 model of models do against that? And then we finally said we

11 want chemicals where it's been tested at least eight times and

12 all eight labs got the same answer. And that's really what this

13 plot on the right is showing and green is sensitivity and red is

14 specificity and blue is balanced accuracy, which is the average

15 of sensitivity and specificity.

16 And we see that the models against this high

17 quality consistent dataset are getting about 90 percent

18 sensitivity and specificity, which is really good for a model,

19 but you have to have -- but any individual model may do much

20 worse and have a much better -- individual models tended to be

21 trained to be very sensitive and not very specific or very

22 specific and not very sensitive. 

23 So by putting all those together, we actually

24 get a nicer balance. And we actually have it in the slide, now

25 that the left is actually showing, we can actually -- with the

26 quantitative models, where we actually predict a chemical should

27 be weak, moderate, strong, negative, we get kind of a nice trend
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1 with the activity. And the slide on the right is simply

2 pointing out that if we say, okay, what chemicals are we going

3 to call positive in order to do follows-up?

4 We're not going to go bring in a chemical

5 because these QSAR models have their plots, so we're going to go

6 do high throughput screening next. But it turns out it's

7 relatively insensitive to say -- to choosing chemicals that 5

8 out of 20 models were positive or 10 out of 20. You get about

9 the same accuracy for moving forward with that. So that's kind

10 of a nice result.

11 And in summary -- again, I don't think there's

12 a charge question about this, but we wanted to talk about this

13 because we had multiple SAPs about the expert system before. We

14 through lots of models at this. All of the modeling experts and

15 if you use high quality data to validate, post-validate the

16 model, you do pretty well. And the consensus seems to be better

17 than any individual model. And we would propose taking the

18 chemicals that are positive in many assays and doing follow-up

19 in vitro testing.

20 And finally, the conclusion of this whole

21 section, and think you'll finally be done with me, we've built

22 this in vitro model from lots of different assays. We've

23 actually run 1,800 chemicals and about 1,400 of those are in the

24 ESDP Universe, the 10,000. So we've actually covered a big

25 chunk of that universe already. We can distinguish agonist from

26 antagonist. We do a good against reference chemicals and

27 there's a relatively small number of chemicals out of that 1,800
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1 which consistently show to be positive for estrogen receptor,

2 which is what we would kind of expect. So that was a nice

3 result. 

4 So I am done. Thank you very much. I'll take

5 questions.

6 DR. JAMES MCMANAMAN: Okay. Dr. Barr.

7 DR. DANA BARR: Thank you for a nice

8 presentation. That helped explain a lot from the information we

9 have. I was curious, the chemicals that you tested, did you

10 consider any of the biotransformation products or metabolites or

11 just the parent chemical?

12 DR. RICHARD JUDSON: That's an embarrassing

13 question. Thank you for asking. We tested parent chemicals. 

14 Among the pesticides, we have a handful of metabolites that we

15 tested for other reasons, but with the possible exception of the

16 Attagene assays, which are run in a HepG2 cell line variant,

17 which had some of the CYPs activity boosted, none of these

18 assays are metabolically competent.

19 One thing that as you write your report or

20 think about this, if you folks are experts, we've been looking

21 for good reference chemicals where it's known, this is a

22 chemical that has to be bioactivated to be estrogenic or

23 androgenic or hit the steroidogenesis pathway. If we had those,

24 then we would actually know whether we're doing a good job or

25 not. But it's been really hard to come away with good reference

26 chemicals to test those ideas.

27 DR. JAMES MCMANAMAN: Questions? Dr. Schlenk.
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1 DR. DANIEL SCHLENK: Just a quick question. I

2 should've asked this before, but on Slide 63, just so that I'm

3 clear, you now are actually defining what very weak, weak,

4 moderate and strong is based on that ratio value. I'm sorry,

5 not ratio value, the AUC value. That potency level. The .6 to

6 .75, the .75 to .9, those values.

7 DR. RICHARD JUDSON: This was part of the QSAR

8 effort, which was -- the axis here is totally different so I

9 didn't explain this. What this was kind of an odd finding. If

10 you say okay, I have a chemical I tested in 20 models, what

11 we're plotting there is the number of models it is positive in. 

12 So it turns out that the number of models that a chemical was

13 positive in is correlated with the potency from the literature. 

14 And then it probably has to do with this kind of censoring issue

15 that a lot of labs -- no labs test up to infinite concentration.

16 So if a chemical is really weak, some labs will have seen the

17 activity in their tested range and some won't. 

18 DR. DANIEL SCHLENK: So if we go back to the

19 AUC listing, where you have a similar figure of very weak, weak,

20 moderate and strong, I forget which slide that is, but are those

21 values known yet?

22 DR. RICHARD JUDSON: So those -- and Warren

23 Casey maybe can answer that, again, we had the NICEATM folks

24 give us the chemicals and put them into bins. And the reference

25 chemicals comes from the literature. And so they've compiled

26 what are the AC50, IC50s for this chemical across the

27 literature. And they made kind of bright lines. If you were
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1 below 10 to the minus 8, we're going to call it strong. So

2 there are defined boundaries there. I think they're actually

3 someplace in the White Paper.

4 DR. DANIEL SCHLENK: You didn't do that on the

5 AUC; that was done in this expert panel that had put that

6 together.

7 DR. RICHARD JUDSON: Correct. And only for

8 the reference chemicals.

9 DR. DANIEL SCHLENK: I see. Thanks.

10 DR. JAMES MCMANAMAN: Any questions? Yes, Dr.

11 Doerge.

12 DR. DANIEL DOERGE: I have a question, sort of

13 a philosophical start and then a technical end. And that is the

14 selection of AUC for your quantitative criterion name. I mean,

15 obviously that has a different and a very specific meaning in

16 other contexts. So I'm wondering, first of all, how it is that

17 you came upon that name for that. And secondly, I'm concerned

18 how that translates into what we're using here for reference

19 values. The way you explain the assay performance, say for

20 estradiol, beta-estradiol is that it was tested at

21 concentrations that were basically maxing out the responses. 

22 Typically, when one thinks about areas under

23 the curve, you think about going all the way to the origin and

24 then adding up to where their maximal site. My question is then

25 how then when we set that AUC for 17-beta estradiol to 1, and

26 then references everything else across that, how, technically,

27 do you do that to avoid truncating information for the potent
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1 ones versus overestimating activity for weaker ones, just

2 because of where they fall into that concentration range.

3 DR. RICHARD JUDSON: Right. Let me see if I

4 can find a couple of slides to address that. Okay. First of

5 all, the main area under the curve, I mean, it really is the

6 area under the curve. So that's where it comes from. But

7 there's this truncation effect. Let's go to Raloxifene; this

8 will serve the purpose. 

9 We haven't actually extrapolated out to zero

10 concentration and seen where that comes down. And so what that

11 effectively does is it makes the -- so if we had done that, then

12 there would be a linear relationship between AUC and potency. 

13 So take this blue curve, potency is just taking that blue curve

14 and shifting it to the left or to the right. So you shift it to

15 the left by a certain amount. You add a linear amount as you

16 shift it to the left or to the right. But as we shift to the

17 right, for instance, so we haven't actually test it all the way

18 up, you actually start truncating the right part of the curve,

19 as we shift to the left like with Raloxifene, we've lost the

20 left part of the curve. So what that does is instead of being a

21 linear relationship between AUC and true potency, whatever that

22 is, we actually did a curve. Let's go back further. It's not

23 too much further. We get that curve.

24 So it's still a monotonic relationship, but if

25 we had perfect concentration coverage, that would be linear.

26 DR. DANIEL DOERGE: So that's asymptotic to

27 one. 
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1 DR. RICHARD JUDSON: It's asymptotic to one. 

2 That's right. Yes.

3 DR. JAMES MCMANAMAN: Other questions? Yes,

4 Dr. Weller.

5 DR. GRANT WELLER: Just a clarification

6 question on Slide 60, on the QSAR program. So when you get

7 these models, I'm wondering what it is you get from them. In

8 particular, the qualitative versus quantitative. Is qualitative

9 just active versus inactive versus quantitative being some

10 score?

11 DR. RICHARD JUDSON: That's exactly right. 

12 Qualitative is yes/no and quantitative is a score from zero to

13 one.

14 DR. JAMES MCMANAMAN: Other questions? 

15 I have two brief questions. I know that you

16 have to start somewhere, but there is a question of

17 potentiation. All these compounds are not found by themselves. 

18 So one compound could potentiate the actions of another

19 compound, whether it be by cytotoxic methods or not. Is there a

20 plan to use the approach that you've described to try to sort

21 out those kinds of interactive problems?

22 DR. RICHARD JUDSON: Yes. As part of the

23 Tox21 screening effort in there with the four protein assays,

24 two agonists, two antagonists, within the 8,200 samples they

25 ran, they actually ran a number of estrogen mixtures and

26 cytotoxicity mixtures with up to maybe 32 compounds. So they

27 could address this whole mixture issue, but if you have two
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1 estrogens or four estrogens or 10 estrogens in different

2 concentrations, I have been told, I haven't seen the manuscript

3 yet, but that for those estrogenic mixtures, an additive model

4 -- dose additive model actually works great. So there is no

5 surprising synergism going on, but we have the ability to do

6 those kinds of experiments.

7 DR. JAMES MCMANAMAN: I have one other

8 question. The assays that you've chosen to look at gene

9 expression, the cell-based assays, all of those, presumably, are

10 performed on plastic dishes and it's known that the

11 three-dimensional cells, when they're in a three-dimensional

12 matrix can be quite different than they are in taxa, tax tissue

13 could be quite different than what they are. Does the fact that

14 these differences can exist, does that affect your ability to

15 perform, say, for instance, cytotoxicity? They may be much

16 lower cytotoxicity if they were in a three-dimensional

17 environment rather than a two-dimensional environment.

18 DR. RICHARD JUDSON: That's an interesting

19 question. I don't know the answer to that. Most of the assays

20 we've run are the model layer assays, but we are working with

21 some people to do complex 3D co-culture systems. And in fact,

22 we got in some data just recently that is stem cell-based that

23 cited that stem cells in a three-dimensional format of some

24 kind. And we actually chose the concentration to test based on

25 the cytotoxicity range from these other assays. And at least

26 the cytotoxicity was happening about where we would've predicted

27 it. It was certainly not way off, but that's just an anecdote.
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1 DR. JAMES MCMANAMAN: Okay. Thank you. Yes?

2 DR. EDWARD PERKINS: When you were talking

3 about DEHP, you mentioned that one of the reasons for not

4 detecting it as an agonist was that the concentrations were

5 probably needed to activate that was in the cytotoxic region in

6 your assays or you just weren't assaying at a high enough level.

7 What is the strategy to capture those things that might be --

8 that are beyond your testing concentrations that still might be

9 relevant?

10 DR. RICHARD JUDSON: So I'm going to

11 editorialize and I'm going to let other people -- so what we're

12 mainly testing for, with the exception of -- you know, ECOTOX is

13 actually a whole different ball of wax, right? But in human

14 accidental exposures, it seems unlikely -- and we'll actually

15 talk about some of our exposure modeling, John Wambaugh will

16 talk about that later. 

17 To be able to get up to 100 micromolar, that's

18 a lot of chemical. So if something only turns on an interesting

19 receptor at a millimolar, well, probably you'll never be

20 exposure at that level. And so that's kind of the rationale for

21 only going there and not worrying about other things. Now, if

22 you have fish in the downstream of something where you're

23 spitting out horrible high concentrations, that's a different

24 issue. David, do you want to --

25 DR. DAVID DIX: This is an interesting topic

26 and Richard has referred to some of the ranges of screening

27 assays. I think most of them max out around 100 micromolar. 
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1 There might be a few of those ToxCast assays that go up around

2 200 micromolar. And there is the issue of how that relates to

3 environmental exposures to humans and to wildlife and that's

4 something that the July SAP and high throughput exposure

5 modeling gave us some feedback on and that we are in the process

6 of taking to heart. 

7 They recommended that we consider some of the

8 exposure modeling outputs that we'll have some presentations on

9 by John Wambaugh later today to think about both in vitro and

10 potentially, in vivo testing and screening and testing ranges to

11 make the results more relevant. So this would be an area of

12 continued understanding and then continued application within

13 the program, in terms of screening and testing ranges that are

14 relevant to protecting human health and wildlife. 

15 DR. JAMES MCMANAMAN: Dr. Brown has a

16 question.

17 DR. TERRY BROWN: So your QSAR modeling data

18 suggests, I think, if I'm understanding the presentation

19 appropriately, thousands of compounds that potentially combined

20 thousands of compounds that could agonists, antagonists and yet

21 your data, to date, so far, suggests that primarily it's

22 phenolic and steroidal compounds that are active in the system.

23 Is there a dichotomy or a congruence between

24 your QSAR data and the actual data that you've produced so far,

25 in terms of the types of compounds that you anticipate binding? 

26 And if not, is there a plan to go further with the QSAR data

27 into other structural realms to decide whether in fact there are
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1 other types of compounds with other structural permutations that

2 may be active in the system.

3 DR. RICHARD JUDSON: No. Actually, it's

4 really a point of numbers. So with the in vitro, we've tested

5 1,800 compounds and we got 97, which is 5 percent.

6 DR. TERRY BROWN: Right.

7 DR. RICHARD JUDSON: With the QSAR, we tested

8 -- or had people on 32,000 in their thousands. It's about 5 to

9 10 percent. So it's still about the same fraction. And if you

10 look at the QSAR positives, they're the typical phenolic

11 estrogens. Turns out that the world -- the chemical industry

12 likes to use phenolic compounds.

13 DR. TERRY BROWN: Sure.

14 DR. WARREN CASEY: And so there are lots of

15 those out there that pop up. Did that answer your question?

16 DR. TERRY BROWN: Yes. That helps. 

17 DR. WARREN CASEY: Oh, yes. And there are

18 actually also lots of pharmaceuticals in the --

19 DR. TERRY BROWN: Right.

20 DR. RICHARD JUDSON: So we put every known

21 pharmaceutical into that QSAR dataset also.

22 DR. TERRY BROWN: Okay. And then another

23 question. It's kind of a follow-up on what Dr. Barr asked

24 previously about the transformation potential. We understand

25 that your system doesn't test for transformation metabolic

26 conversion of the compounds. What does your data say about

27 parent compounds versus their known metabolites, in terms of
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1 what you might expect for the various active compounds being

2 potentially metabolites of a parent compound?

3 The dataset is really, really small. Let me

4 give you one little piece of data. There's one other set of

5 assays that we ran that we didn't report here. They were the

6 dimerization assays, but they were run plus or minus S9, and

7 this was kind of a prototype of how we could actually do this. 

8 And the plus S9, we actually incubate the chemical with the S9

9 fraction and generate, potentially, a soup of metabolites. And

10 then the minus S9 had some kind of protein in there, so you get

11 the same kind of loss of chemical through protein binding. And

12 I don't understand the issues with all of that.

13 So anyway, we then ran a thousand chemicals or

14 so through the plus and minus S9 for one estrogen receptor assay

15 and what we saw -- there are a handful of chemicals -- there is

16 one, benzanthracene, which is a four benzene rings all put

17 together. Okay, that is not estrogenic, but it gets oxidized

18 and has a hydroxyl group put on, and that is estrogenic. So we

19 could clearly see that going in the minus S9 being in active,

20 the plus S9 being active. So we had kind of one reference

21 chemical saying the system worked and shifted there. 

22 But the technical problem is because we allow

23 so much chemical in the PutA protein binding that the whole dose

24 response curves got shifted two orders of magnitude to the

25 right. So the only compounds we could actually see activity in

26 were ones that were really, really potent. So the ones that

27 went from inactive to weak, we just couldn't see. That doesn't
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1 really answer your question, but we just don't have enough

2 parent metabolite pairs to -- and certainly not ones that where

3 one or another is known to be estrogenic to be able to say

4 anything, in general.

5 DR. TERRY BROWN: I mean, it comes to mind, to

6 me at least, that the phthalates are one of those classes where

7 there's a significant number of different metabolites that have

8 been proposed to potentially have endocrine disruptor activity.

9 DR. RICHARD JUDSON: Right. Right.

10 DR. JAMES MCMANAMAN: I think Dr. Dix had a

11 comment to Dr. Brown's question.

12 DR. DAVID DIX: I just wanted to add to that

13 answer. In the course of reviewing the results from the ToxCast

14 dataset, as well as Tox21, in general, the observation is that

15 compounds that are either weakly estrogenic and/or metabolized

16 to become estrogenic, the parents are showing up as positives in

17 this system of ToxCast and the Tox21 assays. So for whatever

18 reasons, at least things that are kind of -- from the

19 literature, unambiguously positive estrogen agonists, we don't

20 seem to be missing things, from a programmatic perspective. 

21 The example that might be worth looking at is

22 the methoxychlor HPTE, which also could be some sense of a

23 quantitative difference between the parent and metabolite. And

24 then I think the phthalates, from a programmatic perspective,

25 fall into line pretty well. If you look at say, for example,

26 the butyl benzyl phthalate result, vis-à-vis, the DHEP, the

27 diethylhexyl phthalate, I would characterize the literature
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1 around the estrogenicity of diethylhexyl phthalate as unsettled.

2

3 DR. JAMES MCMANAMAN: Other questions. Dr.

4 Jett.

5 DR. DAVID JETT: I was looking at some of the

6 earlier slides. The QSAR, is that going to be a starting point

7 or is it just going to be one of the several tools you'll use to

8 start your prioritization process. 

9 DR. RICHARD JUDSON: We have not decided. So

10 again, I think we'd be happy to take suggestions from the panel.

11 DR. DAVID JETT: And I guess the other

12 question, unrelated is this issue of mixtures. I know that this

13 is probably something you should be thinking about down the road

14 and starting here -- but looking at mixtures, there's potential

15 for potentiation with non-estrogenic chemicals that -- but then

16 the universe doubles in size or triples in size, but that's

17 something to take into consideration for the long run.

18 DR. RICHARD JUDSON: One other experiment we

19 are doing, which actually addresses the ECOTOX issue, so we're

20 working with our colleagues at the Duluth Lab and taking water

21 samples. These may actually be involved in that project where

22 these are water samples, upstream and downstream. So they are

23 some very complex mixture. And the question is what kind of

24 bioactivity do they have? Are they estrogenic, androgenic? Do

25 they have PPAR activity? Are they cytotoxic?

26 So we can actually take those complex samples

27 and run them through some of these multiplexed assays and get a
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1 starting point to say, okay, there's something estrogenic there.

2 We don't necessarily know what it is. Again, it's a starting

3 point for doing some further analysis. At least the preliminary

4 work there looks pretty good. It's giving us a lot more

5 information than the prior lower throughput assays that were

6 used to do some of that activity detection.

7 DR. JAMES MCMANAMAN: Other questions? If

8 not, then I think we'll break for lunch now. Since we're

9 running a little bit behind time, let's just take an hour for

10 lunch. So be back at 1:20.

11 (Whereupon, a luncheon recess was taken.)

12 * * * * *

13 DR. JAMES MCMANAMAN: I think we'll start this

14 afternoon session. As you can see, we have some new

15 audio-visual equipment. Whoever donated their suitcase, let us

16 know, we'll give it back.

17 Warren Casey, I think you're up next.

18 DR. WARREN CASEY: Yes, I am.

19 DR. JAMES MCMANAMAN: And we can get started

20 with you then. 

21 

22 CURATED REVIEW OF UTEROTROPIC LITERATURE AND COMPARISON TO

23 TOXCAST ESTROGEN RECEPTOR (ER) AGAINST AREA UNDER THE CURVE

24 (AUC) DATA

25 

26 DR. WARREN CASEY: I want to thank Richard for

27 doing a great job of explaining the ER AUC model and for running
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1 over so I have the coveted after lunchtime slot. 

2 Most of you probably never heard of NICEATM. 

3 We are an office of a national toxicology program and we

4 specialize in helping agencies validate and adopt new methods

5 which replace, refine, or reduce animal testing. We work with

6 U.S. agencies, and we also have quite a bit of international

7 outreach. We work with OECD and we work with our counterparts

8 in Canada, Japan, Korea and Europe. So we have quite a bit of

9 experience, about 15 years of experience validating methods,

10 both for U.S. agencies and internationally.

11 One of the projects we headed up a few years

12 ago is the estrogen receptor transactivation assay, based in the

13 BG1 cell line. So that was eventually adopted by EPA and

14 accepted in their EDSP Tier 1 screen. We got it into the test

15 guideline program at OECD, and then we nominated it to Tox21,

16 where it was adopted to high throughput screening and that's

17 actually one of the assays that's in the ER model. 

18 That is really the reason that we're here

19 today, it was born out of that project. The issue that we're

20 trying to address is whether or not the in vitro assays in

21 ToxCast can provide essentially the same information at the

22 three ER-based assays that are currently in the Tier 1 screen. 

23 And since two of those, the binding and the transactivation are

24 part of the ER model, the real question is does the new approach

25 provide, essentially equivalent information as the uterotrophic.

26

27 I guess another way to look at it is does the
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1 uterotrophic add value in the context of all of the in vitro

2 data. So if you compare the two results directly, other

3 compounds that are missed because you don't use the

4 uterotrophic, what are the limitations of removing the in vitro

5 bioactivity screen.

6 To give you an overview, what we wanted to do

7 was to look at results that were produced in uterotrophic

8 studies and then compare those results directly with the ToxCast

9 model. The problem is there was really no good dataset to do

10 that. So we had to set out to develop a dataset of reference

11 chemicals based on the literature and then we could compare

12 those directly with the ToxCast assays.

13 So I'm going to talk for the next few slides

14 about that effort to develop a database of uterotrophic studies,

15 but it's helpful to understand the context of the uterotrophic

16 assay itself, why it was developed and how it was being used. 

17 Probably, the most important aspect is to understand that it's

18 not a toxicity test, it's a screen for bioactivity. We're not

19 measuring endocrine disrupting. 

20 The only thing were trying to assess is

21 whether or not a chemical has the potential to be bioactive. 

22 And you get two outcomes; you can get a yes or no. It's

23 bioactive or it's not and then you also get an estimation of

24 potency. What's the lowest dose that causes a statistically

25 significant effect? And the principal of the assay is fairly

26 straightforward and that's the uterus response to estrogens. 

27 They stimulate growth of the uterus. And if you can prevent
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1 endogenous estrogen production either by using an immature

2 female or a female that's been ovariectomized, then the uterus

3 becomes very sensitive to any exogenous estrogens. And the

4 figure up there just shows a good example of the profound effect

5 that the estrogen can have on the uterus in a very short period

6 of time. So this is the results of a 3-day assay. The one on

7 the left is a picture of a control uterus on the right as with

8 estradiol, and you can visually, easily see difference in those

9 two.

10 And the increase in weight, which is the

11 ultimate endpoint that's being measured in this assay is due to

12 both cellular proliferation and water retention. So it's a

13 combination of those things that really increase the size, and

14 more specifically, the weight of the uterus.

15 The assay was validated through the

16 organization of economic cooperation and development. OECD, in

17 a program that started in 1999. So it took four years and about

18 20 labs participated in the validation of this study. At the

19 time, it was state of the art. This was the best way, the most

20 sensitive way to determine whether or not a chemical had the

21 potential to be estrogenic and the validation resulted in two

22 guidelines, one at OECD and one at EPA. The outcomes was that

23 there were three rodent models that were accepted, the

24 ovariectomized rat, immature rat, and the ovariectomized mouse. 

25 The immature mouse was deemed too small. The mice are small to

26 start with and immature mice are very small. So trying to weigh

27 the uterus of an immature mouse leads to a lot of error.
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1 The dosing can be administered either orally

2 or by injection, subq or IP. Ninety-nine percent of all the

3 injection studies we saw were from subq. So anytime you see

4 injection, it refers to a subq. There are different

5 philosophies of which route of dosing you should use. The OECD

6 recommends that you should use the dosing route that most covers

7 the expected route of exposure, by ingestion or you would use

8 subcutaneous if you were expecting a dermal exposure or

9 inhalation exposure.

10 The EPA, on the other hand, recommends to be

11 safe-sided, to use subcutaneous injection which will result in

12 higher bioavailability and dosing in required for a minimum of

13 three days. So these are some data that came out of the OECD

14 validation study. A couple of things to note. So the X-axis is

15 the dose of the chemical being tested and that's in milligrams

16 of chemical per kilogram body weight of the rat per day. And

17 then the Y-axis is the increase in uterine weight gain.

18 There were only seven total chemicals tested. 

19 So there were five weak positives on the right. So DDT,

20 methoxychlor, Genistein, BPA and nonylphenol, and then there was

21 one strong positive control. There was also a negative control

22 that's not shown. 

23 So as couple of things that are noteworthy

24 that would be important for later discussions is the concept of

25 lowest effect level. So you want to make note of the lowest

26 dose that causes a statistically significant increase and it

27 should be done in a dose response, if you need at least two
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1 doses. That does not have to be at the bottom of the curve. In

2 fact, depending on how you design your experiment, you could end

3 up with a situation like we have here which really, halfway up

4 the dose response curve that you get your LEL. So that would be

5 the reported LEL for that particular study.

6 If you notice, I put an extra set of data on

7 here from the oxychlor. If it was tested at a lower

8 concentration, it may not produce a response. So in this case,

9 you would have no effect, but if you report no effect, it's also

10 important to note what dose you've got, what was the maximum

11 dose tested.

12 So it's possible to have two seemingly

13 conflicting results, a positive and a negative, although if you

14 look at the doses, it actually makes sense because the maximum

15 dose tested for the inactive report was lower than the lowest

16 effect level. As I said, this was state of the art in 1999. If

17 you think about how much things have evolved in the past 25

18 years, our understanding of mammalian physiology, the advances

19 in molecular biology, the commercialization of a lot of assays

20 and the founding of companies around IP that focuses on these

21 new molecular biological techniques. All of that has really

22 resulted in assays that are now much more robust, especially in

23 vitro assays that are much more robust and much more reliable

24 than those available in 1999.

25 So the ToxCast assays and the way they've been

26 put together by the model that Richard described is really

27 state-of-the art for right now. And the question is how do
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1 those results compare to the state-of-the-art in 1999 in the in

2 vivo system?

3 So what we're really talking about is

4 validation, the fit-for-purpose use of the in vitro tools to

5 identify chemicals that have the potential to interact with the

6 human estrogen receptor. The definition of validation is the

7 process by which the reliability and relevance of a test method

8 are established for a specific purpose. And this comes from the

9 guidebook on validation, which is the OECD Test Guideline 34.

10 And you should establish the relevance and

11 reliability using this set of reference chemicals that have

12 well-characterized performance in the Gold Standard method,

13 which in this case would be uterotrophic.

14 So what do we mean by reliability and

15 relevance? The relevance is what most of the rest of the

16 presentation will be spent on. That is, concordance. If you

17 get a certain outcome in the uterotrophic, what outcome do you

18 get in the in vitro? The reliability has historically contained

19 two components. Is the assay repeatable within a lab and can

20 you transfer the method to another lab and get the same results?

21 In the new concept of validation, or at least

22 for high throughput methods, the transferability issue is

23 something that needs to be addressed because there is no

24 transferability. There's no intent to transfer these methods to

25 another lab. But the way the model has been put together, as

26 described by Richard earlier, the reliability is built into that

27 model. 
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1 So you have 16 different assays that are

2 providing information on one endpoint. And the concordance of

3 those assays really is a measure of the reliability of the

4 system. So instead of the older model of one method in three

5 labs, now you have 16 methods and five labs and if they all give

6 very consistent results, that helps establish the reliability of

7 the method.

8 So we needed to develop -- we needed some

9 reference chemicals and the OECD validation, we have seven. And

10 there were really no other lists of chemicals that could be used

11 to determine what uterotrophic outcomes should be for a larger

12 list than seven, because seven, the chemicals really wasn't

13 sufficient to validate the new ToxCast assays. So we put

14 together a strategy to use the literature to identify chemicals

15 that had a reliable outcome in the uterotrophic assay.

16 We started with a very broad literature search

17 of 1,500 chemicals, using broad search terms, uterotrophic,

18 various spellings of uterotrophic, uterine weight. As you can

19 imagine, we got thousands of hits. We then narrowed those hits

20 down to things that were going to be more relevant. So for

21 example, limiting the hits to rodents, mice or rats. 

22 We then looked at 700 papers and extracted 42

23 descriptors from each of those papers. We did that twice. It

24 was done separately by two different analysts in our group. And

25 we developed a set of minimum criteria, which we thought would

26 further refine the results to those studies that were most like

27 guideline studies, either OECD or EPA, and that's the database
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1 that was made available to the panel.

2 This is a list of some of the descriptors that

3 were captured. This is a list of the guideline-like study

4 criteria that we developed. Notice that it's guideline-like. 

5 There are certain aspects of these criteria that aren't quite

6 guidelines, to the no of 5; both guidelines requires an n of 6. 

7 There's always a desire to balance multi-develop, to have as

8 many reference chemicals as possible. So as we increase the end

9 value, we reduce the number of chemicals available for the

10 reference database. And after discussions with people who ran

11 the assay and pathologists, the conclusion was made that an n of

12 5 is sufficient if the statistics were there to support that

13 there was a statistically significant change or no change.

14 So this is the overview of the different study

15 designs that are in the database. It's difficult to assign very

16 much quantitative value to these, just because there's so many

17 variables; it's hard to do a direct comparison. But you can

18 easily see that the immature rat model is by far, the most used

19 model. And about 70 percent of all the assays were done in the

20 immature rat and about 70 percent of those were done using the

21 injection as the route of exposure.

22 As we were putting the database together, it

23 was obvious that there were chemicals that had discordant

24 outcomes. So this shows every chemical that was tested more

25 than once, whether or not it was reported, if the results were

26 consistent or if they were inconsistent with each other. So the

27 X-axis is the rank order, just by the total number of studies
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1 that a chemical had and the Y-axis is the number of studies. 

2 The blue represents the number of active assays and the red

3 indicates the number of inactive assays. 

4 One of the things that becomes obvious when

5 you look at this is there are a couple of factors that drive

6 discordance. No. 1 is the study design, obviously, and the

7 other thing is the number of times the chemical is tested. 

8 Certain chemicals become popular for testing for whatever

9 reason. BPA is a good example. And the more you test the

10 chemical, especially using various study designs, the more

11 likely you are to get discordant results.

12 So about half of the discordant results we saw

13 may be attributable to study design. This is butlyparaben. 

14 This is a radar plot that shows the different study types. Each

15 corner of the plot represents a different study design. The

16 numbers in parentheses represent the lowest effect level or a

17 maximum dose tested if it was negative. The red line is

18 negative studies and the blue line is the number of active

19 studies. And the individual concentric lines are the number of

20 studies themselves.

21 So for example, the immature rat, by

22 injection, there were five studies that produced positive

23 results and the lowest effect level in those five studies was 70

24 mgs per kg per day. And you can see that every study that used

25 injection had a positive result and both studies that used oral

26 dosing had a negative result. So you may reach the conclusion

27 here that study design heavily influenced the outcome of
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1 uterotrophic tests. 

2 About half the chemicals had the exact same

3 study design and discordant outcomes. So this is data from EPA

4 tested in immature rat model, injection and oral. So both study

5 designs are represented here. Each dot represents a single

6 study and in this slide, they're all active. So these are all

7 positive outcomes. The activity ranged from 2 mgs per kg per

8 day, all the way up to almost 1,000 mgs per kg per day.

9 So that means that those experiments, those

10 assays that already been done has shown the lowest effect level

11 of 1,000. Those are very close to being negative. That's done

12 in a dose response for the highest dose, which is usually 1,000,

13 produced a positive result. So there's quite a bit of

14 discrepancy in the range of reported activities for BPA. One

15 thing that was consistent is that the injection seemed to

16 produce a more sensitive response than the oral, which is

17 consistent with what we know about metabolism BPA being cleared

18 in the gut.

19 The red dots now indicate studies that

20 reported BPA as inactive and the associated dose levels. So we

21 had three in the injection model. There were three that used

22 the dose of 10 mg per kg per day that found it to be negative

23 and three that used the same dose found it to be positive. And

24 there was one study tested at 1,000 mgs per kg per day by

25 injection that saw no statistically significant increase in

26 uterine weight.

27 So we knew there were a lot of variability
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1 associated with the uterotrophic assay and the study design. So

2 when it came to choosing reference chemicals, we needed to take

3 that into account. The choice of reference chemicals is the

4 cornerstone of any validation study. Everything is based around

5 the performance with those chemicals, so we needed to be sure

6 about the activity of each given chemical and make sure it was

7 well characterized in the uterotrophic assay.

8 So we came up with the following criteria for

9 selecting reference chemicals, using the database that we

10 previously developed. And now it's to say that we needed

11 independent confirmation that a chemical was either active or

12 inactive. For the active, it was actually fairly

13 straightforward. If two labs reported an active result, we

14 thought that was sufficient information to say that that

15 chemical has the potential to interact with the estrogen

16 receptor.

17 Inactive is a little bit more difficult. 

18 Negatives are sometimes hard to prove. So we knew we needed at

19 least two independent assays. So we needed the two and so the

20 question was how many do you need? And we thought two was

21 sufficient, as long as there was no conflicting information. If

22 a third assay, if you had two negatives and then a third said it

23 was positive, we really didn't know how to classify that, so we

24 just did not use those chemicals. 

25 And based on that, we ended up with 43

26 reference chemicals; 29 active and 13 inactive. One of the

27 things you want to make sure when using reference chemicals is
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1 that they do cover the range of responses that you're

2 anticipating. And so this graph represents the lowest effect

3 levels for the reference chemicals. These are in rank order,

4 starting with the most potent lowest effect level, going left to

5 right. So the most potent are bound in the lower left-hand

6 corner and potency decreases as you go up and to the right. The

7 bars represent -- the dots are the average, the geometric mean

8 of all lowest effect levels that were reported for that chemical

9 and the bars represent the lowest of the lowest effect levels. 

10 So the bottom of the range.

11 So we now wanted to compare those results with

12 what we saw in vitro. So this is the same slide Richard showed

13 previously. And I want to point out a couple of things. On

14 this curve, the most active is up here at a value of one. Once

15 you reach a level of .1, you're really at the 100-micromolar

16 limit and that was the upper limit of testing for most of the

17 ToxCast assays. So we were considering things active if they

18 had a score of .1 or higher. You'll also notice that something

19 that was mentioned earlier that the upper end of the range is

20 fairly compressed. So the change in X, the change in the AUC

21 value, as you go across the highest potencies, the two logs of

22 highest potency, it really doesn't change that much. A value of

23 .9 and a value of .1 could actually be two logs difference in in

24 vivo potencies. 

25 So this model is actually doing a very good

26 job of differentiating lower potency chemicals while still

27 flagging the other chemicals as being highly potent. I'll show
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1 you some data in just a minute that demonstrates that the rodent

2 bioassay is actually extremely sensitive. The dynamic range is

3 very good. It's very good at picking up strong

4 pharmacologically active estrogens. 

5 And the AUC model is also very good at picking

6 them up. All the very strong chemicals get a score close to

7 one, whereas, the weaker chemicals, we get a better

8 differentiation of.

9 So this represents the AUC scores for all of

10 the reference chemicals. Again, this is rank order, the highest

11 score on the left representing the most active. The lowest

12 score on the right being the inactive and the line here

13 represents the .1 level that anything above that we're

14 considering active. Anything below that we're considering

15 inactive.

16 So when you overlay the uterotrophic outcomes

17 on this graph, which is what's shown here, the blue dots are the

18 uterotrophic actives, the red dots the uterotrophic inactives.

19 You can see there's actually a very good

20 division at that .1 line. There are really only two outliers,

21 kaempferol and D4. So kaempferol is predicted to be positive in

22 ToxCast. It's moderately -- it produces a moderate to weak

23 agonist signal in the ToxCast assays, but it was shown to be

24 inactive in the uterotrophic. 

25 So there are a couple of reasons that could

26 be, 1) are we sure we tested kaempferol and wasn't there at the

27 concentrations we thought in the ToxCast assay? Are we testing
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1 the right chemical and the answer is yes, QC has confirmed that

2 kaempferol was tested and it was at the concentrations we

3 expected. The other most likely reason here is that there was a

4 lack of bioavailability in uterotrophic. Kaempferol is known to

5 be glucuronidated, it's rapidly cleared. So there's a good

6 possibility that it was just never a sufficient quantity of

7 kaempferol -- a systemic exposure just wasn't enough to cause an

8 uterotrophic response.

9 D4 is the opposite. It had low or no activity

10 in the ToxCast assays, but did produce uterotrophic responses. 

11 And again, if we start with the basics, was D4 in the well? Do

12 we know that was there and it was at the concentration. There's

13 supposed to be an answer to that is we don't know. 

14 The structure is fairly unique. It makes it

15 an outlier in the GC mass spec analysis, so they're going to

16 have to develop a separate method to look at that. It is highly

17 volatile. It's got a paper pressure of 125 kilopascals at 25

18 degrees C. So there's a possibility that 1) it is already

19 evaporated and it was never actually tested or, 2) it could be

20 that it's metabolized to a more active form, so we really can't

21 differentiate between those two.

22 So if you look at just the qualitative yes/no,

23 did they agree performance, you end up with performance

24 statistics that are very good. For any in vitro assay, these

25 numbers are amazing. And really, it doesn't matter how else you

26 do it, whether you use the method Richard described or any other

27 method. You can't get any better than this because the false
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1 positive, there was an AUC score, so it doesn't matter what

2 decision criteria you used to call it active or inactive. It's

3 positive across the board so it's going to come out as a

4 positive and it came out negative in uterotrophic. That's not

5 going to change and the opposite is true with D4; it produced

6 zero response. So you're not going to come up with the model

7 that now calls D4 active. So this is as good as a performance

8 as you could possibly get with any decision criteria on calling

9 actives an inactive.

10 And the next thing we want to do is to look at

11 the concordance of the correlation between the potency in the

12 uterotrophic versus the activity in the ToxCast models. And

13 that's what this represents. So on the bottom is the ER agonist

14 score, the AUC. I've reversed the scales so that it's a little

15 more intuitive. So the most active compounds are going to be

16 down here and the least active are up on the top right. The

17 Y-axis is the average, the geometric mean of the lowest effect

18 levels. 

19 So this demonstrates what I was talking about

20 earlier. If you look over here on the left, the rodent model

21 does a very good job of differentiating all of those stronger

22 compounds, whereas they have essentially the same AUC score. 

23 But the AUC score spreads out more at the lower potency ranges. 

24 Now, if you get back to -- but it's important

25 to note that the AUC that's flagging is strong ER agonist. It's

26 not doing as good a job as separating them out. And if you get

27 back to the fit-for-purpose concept, if you look at these, these
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1 are all pharmacological compounds or naturally occurring

2 estrogen. So if the goal is to differentiate the potencies of

3 pharmacologically active estrogens, then the rodent bioassay is

4 probably the way to go. If you want to have a high throughput

5 method to identify those -- so these were flagged -- but then

6 differentiate the weaker agonists. The ToxCast assays actually

7 do a really good job of that.

8 So based on all that, we've come to the

9 following conclusions:

10 The performance of the model is excellent,

11 with 95 percent accuracy. In this particular case, the positive

12 predictive value is exactly the same as the sensitivity and the

13 negative predictive value is also the same as the specificity

14 and all of those performance characteristics are excellent. You

15 won't find any in vitro assay that's ever been validated with

16 performance better than this. 

17 There's also a quantitative relationship. So

18 if you go back to this slide, notice the complete absence of

19 data down here. So what that says is that if you have a model

20 score anywhere between .8 and .3, you can be pretty sure that

21 the lowest effect level in a rodent bioassay will be probably

22 above 1 mg per kg per day, which is actually a really valuable

23 piece of information. We don't see anything with a low AUC

24 score that is also highly potent in the rodent model. 

25 Everything with high potency is flagged as a 1.

26 The other thing it says is that we don't see

27 chemicals over here that drop down. So that would be indicative
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1 of chemicals that are metabolized to a more bioactive form. So

2 they have a low in vitro score and when they're put into an

3 animal, they become highly active and we just don't see any

4 evidence of that happening with these reference chemicals. 

5 So finally, if you look at the goal of the

6 assay to identify things that have the potential to interact

7 with the estrogen receptor, I think the fact that we used 16

8 different assays, it adds a lot of confidence. If you think of

9 it as a triangulation, if you have one assay that provides one

10 result, that can be informative. If you have two assays that

11 are saying the same thing, that produces a higher level of

12 confidence. In this case we have 16 assays performed at five

13 different labs, using five different technologies and tissues

14 derived from five different human tissue types.

15 So when all of those are saying the same

16 thing, it's pretty convincing that that chemical has the

17 potential to interact with the receptor. And we really don't

18 see any downside to not having the uterotrophic data. In fact,

19 metabolism keeps coming up and that is an important

20 consideration.

21 I would argue that it's more likely that the

22 uterotrophic would inactivate the chemical. That's much more

23 likely than activating a chemical to become uterotrophic. And

24 those are my conclusions and I'll be happy to take any

25 questions.

26 DR. JAMES MCMANAMAN: Okay. Any questions for

27 Dr. Casey?
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1 Dr. Schlenk.

2 DR. DANIEL SCHLENK: In terms of evaluating

3 the studies, did chemical period come up as far as a particular

4 benchmark, in terms of classifying them as something you wanted

5 and something you didn't want?

6 DR. WARREN CASEY: We made note, but we did

7 not make it a criteria. A lot of papers don't report purity. 

8 So that was one of those things, for a true guideline study, you

9 would have to have a period. You would have to have a C of A to

10 show the chemical was there and it was at the concentration. 

11 But most of the literature did not get into that level of

12 detail.

13 DR. DANIEL SCHLENK: When you said group size

14 had to be in a five or less --

15 DR. WARREN CASEY: Five or more.

16 DR. DANIEL SCHLENK: Five or more. So that

17 was for each study that you evaluated to keep that study as a GL

18 study, correct?

19 DR. RICHARD JUDSON: Correct.

20 DR. DANIEL SCHLENK: Okay. And then one last

21 one. You said they had to be active -- for something to be

22 active to be included, it had to be positive in two different

23 studies.

24 DR. WARREN CASEY: Independent studies. 

25 Right.

26 DR. DANIEL SCHLENK: Now, did those studies

27 have to be the same design or could they be different designs?
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1 DR. WARREN CASEY: No. We didn't

2 differentiate, so it had to be the same. Just active in any two

3 independent studies.

4 DR. DANIEL SCHLENK: So whether it was oral or

5 injection or if it was --

6 DR. WARREN CASEY: It was an oral and one

7 injection. That was good enough.

8 DR. DANIEL SCHLENK: And it was basically just

9 a yes/no for that?

10 DR. WARREN CASEY: Completely yes.

11 DR. DANIEL SCHLENK: Right. Okay. Thanks.

12 DR. JAMES MCMANAMAN: Other questions? Dr.

13 Propper.

14 DR. CATHERINE PROPPER: And this could be

15 pretty irrelevant since I didn't serve on the July panel, I'm

16 not as familiar with the recommendations, but why were the

17 puberty assay and the fish assay left out of the literature

18 curation or not considered as part of that curation for looking

19 for reference chemicals? 

20 DR. WARREN CASEY: Well, I might let David

21 address that. I can address the one part of it. This actually

22 arose as an independent activity. We started doing this based

23 on our work with the BG1. We wanted to put together a database

24 of outcomes to try to connect just the transactivation assay to

25 the uterotrophic to reproductive developmental toxicity. So we

26 had started this and focused on uterotrophic and that's when we

27 started talking to the folks at the EPA and we realized that we
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1 had a tool that they may be able to use. So part of the answer

2 is that we had already started this independently, but there may

3 be more to it.

4 MR. STEVEN KNOTT: Just to build on that a

5 little bit, in the intro we talked a little bit about how this

6 has been an iterative building process. And so, you know, this

7 is the step we've arrived at for the December review here, but

8 we'll be talking about some future work that will be conducted,

9 and again, progressing further into a more AOP framework. That

10 will bring in some of the other test systems, as you will see it

11 in the future work.

12 DR. JAMES MCMANAMAN: Other questions? Yes?

13 DR. THOMAS POTTER: I had a question; it

14 relates back to the White Paper. I think you had a brief

15 mention of it on the slides that had to do with phytoestrogens

16 in the diet. And I recognize that's probably a complex issue

17 and I was kind of left in the White Paper, kind of wondering

18 where you went with that. What were your conclusions in how you

19 chose to move forward as you did?

20 DR. WARREN CASEY: So that was a seventh

21 criteria that we didn't filter on. We made note of it. Again,

22 it was one of those things that a lot of times wasn't reported,

23 whether or not the phytoestrogen-free diet was being used, and

24 if so, what it was. It came down to a decision. It's a balance

25 between having enough data and enough chemicals, versus if we

26 limited everything to a strict guideline. So if we limited the

27 phytoestrogen, if we put that as a seventh criteria and we
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1 limited it to six animals and we required purity, we'd have

2 about four studies to look at.

3 So it was really a balance of trying to be as

4 inclusive as possible and not exclude potentially good studies,

5 even though, ideally, you should have an estrogen-free diet and

6 have it documented on what diet you're using, but it wasn't

7 always reported for a lot of the papers.

8 DR. JAMES MCMANAMAN: Other questions? Dr.

9 Propper.

10 DR. CATHERINE PROPPER: Could we look at Slide

11 21?

12 DR. WARREN CASEY: Which one is that?

13 DR. CATHERINE PROPPER:

14 DR. WARREN CASEY: Yes. 

15 DR. CATHERINE PROPPER: And it is quite

16 compelling because of that large white field there with nothing

17 and I just want to make sure that in each of those studies where

18 the LEL is looking like it's certainly not lower than one, was

19 there always a non-zero no effect level in each of those

20 studies?

21 DR. WARREN CASEY: Yes. Non-zero, no effect. 

22 Yes. In the White Paper, instead of showing the geometric mean,

23 I've actually shown every LEL that was reported. And you can

24 see the range. Even looking at the range, you just don't see

25 outliers down in here. It was surprising to me how, even though

26 there was a large range, it remained fairly consistent. It was

27 within a couple of logs. 
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1 So you didn't see something six logs away from

2 everything else. I was also pretty amazed at how sensitive the

3 rat assay is, as far as the dynamic response. Again, if the

4 goal is to differentiate between estradiol and

5 diethylstilbestrol as the right model. But the ToxCast assays

6 did a great job of flagging all those as very strong, which is

7 really the goal.

8 DR. JAMES MCMANAMAN: Other questions? 

9 There's a conference going on. You guys are okay?

10 DR. WARREN CASEY: Yeah. David just wanted me

11 to mention the figure in the White Paper that showed all of the

12 LELs.

13 DR. JAMES MCMANAMAN: Okay. Dr. Propper, was

14 that --

15 DR. CATHERINE PROPPER: Figure 2.14, right?

16 DR. JAMES MCMANAMAN: Yes.

17 DR. CATHERINE PROPPER: So I'm still -- let me

18 chew on this a bit.

19 DR. JAMES MCMANAMAN: Okay. Other questions. 

20 All right. If not, thank you very much, Dr. Casey. We'll move

21 on.

22 The next presenter I have on my list is Dr.

23 Browne.

24 

25 COMPARISON OF TOXCAST DATA AND LIST 1/TIER 1 ER ASSAYS AND ER

26 AGONIST BIOACTIVITY OF ENDOCRINE DISRUPTOR SCREENING PROGRAM

27 (EDSP) UNIVERSE CHEMICALS
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1 

2 DR. PATIENCE BROWNE: Good afternoon to

3 everyone. My name is Patience Browne and I'm in the Exposure

4 Assessment Coordination and Policy Division of the Office of

5 Science Coordination and Policy. This talk will be a little bit

6 of a directional shift, I suppose. You've heard from our

7 research partners at EPA and at NTP, talking about some of the

8 --

9 DR. JAMES MCMANAMAN: Excuse me, Dr. Browne, I

10 think you're going to have to move the microphone a little

11 closer.

12 DR. PATIENCE BROWNE: Okay. Sorry about that.

13 So we've been listening to a lot the innovative research that

14 has gone into some of these model building exercises and my talk

15 will shift a little bit and discuss how we're implementing some

16 of these tools in the programmatic offices. 

17 This goes back to our roadmap presentation. 

18 This is really talking about application of some of the

19 estrogen-receptor bioactivity models you've heard about this

20 morning and right after lunch. Now, I want to touch on some

21 major points of this section. First of all, this ER model,

22 computational model is most mature and it's used as an example

23 of where we plan on going with our androgen receptor model as

24 well, which we'll discuss later this afternoon.

25 We should recognize that the EDSP was

26 originally established to evaluate environmental chemicals with

27 estrogen agonists activity specifically, and the additional
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1 activities and pathways were added at the EPA administrators

2 discussion and following recommendations of the initial EDSTAC

3 report. But consistent with this, the Tier 1 battery has much

4 more extensive coverage of estrogen agonist activities than

5 other bioactivities or endocrine pathways. And this is somewhat

6 reflected in the assays that are included in the ToxCast suite

7 as well. As you've heard a lot of discussion this morning,

8 we're really interested in evaluating the application of these

9 tools in a fit-for-purpose manner. And that's outlined, I

10 believe, in the charge questions, quite explicitly.

11 So to provide a little bit of background, as

12 you've heard, there was a January 2013 SAP meeting on

13 prioritization of chemicals in the EDSP Universe, using

14 computational tools and just some comments from that report

15 included that the approach presented in that meeting was

16 appropriate for prioritization and to identify chemicals that

17 may initiate ER-mediated events.

18 In addition, screening techniques can be

19 conducted relatively quickly and efficiently and reduce the

20 necessary animal testing. And these are in line with the

21 strategic goals of EPA. This discussion you've heard today and

22 the subsequent discussions this afternoon are really a

23 refinement of the original approaches that were presented during

24 that meeting and an expansion of the original model. We're

25 presenting 18 versus eight assays, and these have been

26 integrated into a network model that you heard a great deal of

27 detail about. And also, as you've heard as well, we have an
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1 extensive set of reference chemicals, and I'll touch on that as

2 well.

3 One thing I should also note about that 2013

4 meeting, given the task at hand and the 10,000 chemicals in the

5 ESDP Universe, we really shifted towards high throughput methods

6 that could accommodate a huge number of chemicals in a

7 relatively short period of time and transition towards things

8 like the SQAR consensus model that you heard Richard report on,

9 which evaluated the estrogen activity of some 30,000 reference

10 chemicals. And you saw from that model, much like the

11 uterotrophic assays that the more consistent the model outcomes,

12 the greater predictive power it has. So the more models that

13 agreed, the higher you assume the estrogenicity to be, and that,

14 in fact, was true in the demonstrations.

15 I'd like to open the talks that talk about how

16 we implement these tools with defining some clear objectives. 

17 And these are very consistent with the White Paper as well. 

18 Before using the model to evaluate ER bioactivity in the

19 existing EDSP framework, the EPA really needs to evaluate the

20 scientific rigor behind these methods. And we have three

21 defined goals that are outlined in the White Paper and those are

22 first, to prioritize chemicals for further EDSP screening and

23 testing, based on the estimated ER bioactivity.

24 The second is to contribute to the weight of

25 evidence evaluation of a chemical's potential bioactivity, and

26 this would be with Tier 1 battery results and any other

27 scientifically relevant information. And last, to substitute
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1 for specific endpoints in the EDSP Tier 1 battery. And we will

2 define these very explicitly which endpoints we're talking about

3 here. 

4 So this is just kind of a brief review of the

5 things that Richard covered earlier and there are a few things

6 I'd like to point out. We're using the ER agonist bioactivity

7 as a demonstration of the proposed approach to integrating these

8 computational tools into the EDSP framework. So we're looking

9 at primarily 16 ToxCast assays that are relevant to the estrogen

10 agonist pathway. And you see on the right side graph there, the

11 output of those 16 assays, as Richard told you this morning, we

12 do have both the ER agonist and antagonist outputs from the

13 model.

14 The blue line here shows the estrogen agonist

15 activity. The red line is the antagonist activity, and you can

16 see, there's no question that the chemical displayed here has

17 clear estrogen agonist activity and this would be a fairly high

18 priority chemical, based on the AUC score. 

19 Whenever we evaluate the potential of using

20 any new tool or assay in the program, or any other program for

21 that matter, we need to rigorously evaluate the data behind that

22 new tool, and we did this largely by using reference chemicals. 

23 You heard about the 45 in vitro reference chemicals; Richard

24 spoke about the outcome of that today. 

25 I'd like to just emphasize -- I think this was

26 mentioned, but I'd like to emphasize that the selection of

27 reference chemicals is incredibly complex and people tend to
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1 nominate the chemicals they study or they're interested in. 

2 This set of in vitro reference chemicals was peer-reviewed. 

3 This was an OECD and ICCVAM effort. These were chemicals that

4 had consistent outcomes in ER assays run in different labs, and

5 oftentimes in different types of ER assays. For example, a

6 binding assay, as well as a transactivation assay. And the AC50

7 is resulting from that chemical run were in a similar range. 

8 So there was really a lot of scrutiny behind

9 this set of in vitro reference chemicals and there was a lot of

10 data to support their nomination and their potencies.

11 Warren just told you a lot about the selection

12 of in vivo reference chemicals. Again, these were chemicals

13 that had consistent responses and sort of the Gold Standard

14 bioassay.

15 So those two steps have been addressed and

16 I'll just circle back around to them today. The third step in

17 this for our program, anyway, is to compare how well the model

18 performs against the current method; in this case, those are the

19 Tier 1 assay results.

20 So touching base on the first two aspects, and

21 I'm sorry this is a little small, but you see the balance

22 accuracy, which you've heard earlier today, is about 94 percent,

23 almost 95 percent for the in vitro reference chemicals. It's

24 actually 95 percent for the in vivo reference chemicals as well.

25 And this, for anybody who works in this arena, is really fairly

26 remarkable. This is incredibly strong.

27 The thing that you haven't yet heard is how
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1 well these methods compare with what's currently in use, the

2 Tier 1 assays. So there are three Tier 1 assay endpoints that

3 capture information about estrogen receptor interactions. Those

4 are low throughput binding assay, a low throughput, ER

5 transcriptional activation assay, and the uterotrophic assay. 

6 Just to touch on Dr. Propper's question

7 earlier of why there weren't additional assay endpoints

8 included, we were really, at this point, interested in confining

9 the comparison to receptor-relevant interactions,

10 receptor-mediated interactions. And for both the pubertal and

11 the fish assay, these are assays with an intact neuroendocrine

12 system; whereas, the uterotrophic is an interrupted

13 neuroendocrine system. So there are a lot of confounding

14 effects. We wanted to start simple and move on. We do have in

15 our plans for the future, those additional comparisons and Steve

16 Knott will touch on that later.

17 So this is a slide that's difficult to read,

18 but this is the output of the Tier 1 assay data for the List 1

19 Tier 1 chemicals and the agonist scores for the same chemicals. 

20 First of all, I should note that we have 52 chemicals in the

21 final List 1. We have ToxCast data for 49 of those 52. This is

22 not a full list. In the interest of readability, which is sort

23 marginal, at best, on this one, any chemical with an ER agonist

24 and antagonist ToxCast score of zero and Tier 1 ER-binding, ERTA

25 and uterotrophic negative results were removed. So there are

26 some additional data as well. Those are included in the

27 appendices of the White Paper, for those that are interested.
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1 The point here that I'd like to make is for

2 the highest-ranking chemical in List 1, we see an ER agonist

3 score of .04. So that's well below the cutoff of .1 we're using

4 that comports to the 100 micromolar AC50 score. So we really

5 don't see anything that's particularly agonist active, nor do we

6 see anything that's antagonist active. In addition, there are

7 no chemicals on this list that according to the outcome of the

8 Tier 1 ER assay shown here would be considered estrogenic.

9 The second point that I'd like to make is on

10 these chemicals were so modest, and "modest" is perhaps, a

11 generous term when we see up to a 25 percent gene induction,

12 which is quite small for an ERTA assay. We do have, often, some

13 associated ER agonist score. It's weak, but it is detected.

14 So what we see is a very, very strong

15 agreement. And if we were to evaluate this, it's really as good

16 as it gets, right. This is about 100 concordance. None of the

17 chemicals would be considered ER bioactive based on the high

18 throughput assays and none of them would be considered ER

19 bioactive on the low throughput assays.

20 One thing I should note when we talk about the

21 comparisons with these different chemical populations, they

22 don't always overlap 100 percent. And rather than get into the

23 messy details of this figure, what I'd like to point out is that

24 the EDSP Universe, as you've heard, is about 10,000 chemicals. 

25 The ToxCast Universe is about 1,800. About 1,450 of those

26 actually overlap.

27 Forty of the 45 reference chemicals or so are
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1 in the EDSP Universe. As you've heard, many of those are

2 pharmaceutical chemicals, so they don't necessarily fall into

3 the current universe. Forty-nine of the List 1, about half of

4 the List 2; that's 56 out of 107, and then 98 uterotrophic. So

5 we're dealing a fairly complicated then diagram, but the only

6 take home point here, really, is that not all chemicals have all

7 types of data.

8 When we look at how that ER agonist

9 bioactivity falls out, I want to point out a couple of things

10 about this figure before I discuss it much. You've seen the ER

11 agonist scores as logs, typically. So this is a linear scale. 

12 It's a little bit different, but you've seen much of this data

13 before. That .1 value, which is 100 micromolar, is outlined

14 right there. But what I'd like to show you is just that when we

15 look at this, we have the List 1 chemicals which are shown in

16 orange here and the List 2 chemicals. Whoops, the other way

17 around. Sorry.

18 The List 1 chemicals in blue, List 2 in

19 orange. None of these approach that .1 line, and many of them

20 actually co-localized with the ER reference chemicals that are

21 considered inactive, those black dots right there. And in fact,

22 most of them are true zeros. I'll report the figures on that in

23 a minute. Perhaps more importantly is when we look at the

24 universe chemicals -- again, this is a bit hard to see, but

25 those are shown in gray right here -- many of those are in the

26 range that have AC/AC50 -- sorry. AUC scores that are in the

27 same ballpark as the positive reference chemicals. And we see
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1 this cluster over here, we're really talking about the most

2 potent reference chemicals in the EDSP chemical universe. 

3 There are 97 of those 1,450 that are above

4 that .1 line. And what we're really looking at as potentially

5 the highest priority chemicals for additional screening and

6 testing in the program.

7 When we call some of those out, just to give

8 you a point of reference for different specifics here, we have

9 estradiol, which is, of course, our native ligands. We have

10 equilin, which is a pharmaceutical steroid; BPA, a reference

11 chemical is about halfway down that scale. Here were have our

12 nonylphenol (branched), which we heard earlier today is more

13 estrogenic than the linear form, and we see those separate

14 nicely. And various other reference chemicals that were

15 discussed, some in the universe, this paraben; that would be a

16 reasonably high priority. DHT, which is a reference chemical in

17 the same ballpark here. 

18 I just wanted to point out the highest

19 ranking, both List 1 and List 2 chemicals, diazinon being the

20 highest-ranking List 1 and terbufos being the highest-ranking

21 List 2 chemical. The number got missed on the terbufos; it's

22 .06. So these are, again, quite a bit below that 100-micromolar

23 cutoff. 

24 For those of you who might be interested in

25 more details, all of this information and a great deal more is

26 available in the Appendix 3 in the supplementary data. So if

27 you're interested in kind of hunting around and sorting things,
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1 it's there for your viewing.

2 So when we take a step back and we evaluate

3 the ER model performance, we recognize that any new method has

4 to be fit-for-purpose, and ideally perform as well or better

5 than the existing models. 

6 We've talked a bit about redundancy today and

7 I think we should recognize that there's a real possibility that

8 the ER high throughput model actually has greater sensitivity

9 than the existing methods and use, due to the redundancy in the

10 assays. We have a single ER-binding assay and a single ERTA

11 assay, both of which are low throughput in the existing Tier 1.

12 You've heard today that the 18 assays in

13 ToxCast cover that ER signaling pathway through multiple

14 different endpoints and using different technologies. I think

15 the redundancy is certainly increasing the sensitivity. The

16 ToxCast model performance was evaluated with a relatively large

17 set of reference chemicals. There were 45 reference chemicals

18 total when we evaluate the similar comparisons for the Tier 1

19 assays. The ER binding assay was initially evaluated with 23

20 reference chemicals. That number is actually a bit misleading

21 because about a third of those reference chemicals didn't

22 produce the expected results in the interlab validation studies.

23 The Tier 1 ERTA binding assay was evaluated

24 with 12 reference chemicals, and Warren told you earlier that

25 the Tier 1 uterotrophic was evaluated with seven. I should also

26 note that almost all of those reference chemicals for the Tier 1

27 validation exercise were included in the rest of the ToxCast set
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1 of 45 reference chemicals as well.

2 I think you've heard some real caveats about

3 using the uterotrophic results as the truth, as the Gold

4 Standard. Warren showed you that about a quarter of the

5 chemicals tested with more than one guideline study had

6 contradictory results, potentially. I mean, he showed that in

7 one single radar plot, but there's an appendix of all of the

8 radar plots that are associated with the graph he put up.

9 There's also the caveat of using the Tier 1 ER

10 endpoints as true. I should note that we've taken some of the

11 data out of context to serve our purpose. That we really are

12 only using ER relevant endpoints in the Tier 1 battery and it

13 was designed to be used as a battery. So we're deliberately

14 neglecting some of the endpoints at this point. But the in

15 vitro Tier 1 ER interactions determine from the 1 ER binding and

16 one ERTA, again, don't have that degree of redundancy and the

17 use of multiple different technologies.

18 In conclusion, circling back around to the

19 initial objectives that were defined, in terms of using these

20 new methods for prioritizing chemicals for further screening and

21 testing, we see that List 1 had basically zero agonists and zero

22 antagonists. And about 73 percent of those chemicals on List 1

23 had no activity whatsoever; those are zero AUC scores.

24 Similarly, for List 2, we have zero agonists,

25 zero antagonist chemicals and 86 percent of those had true

26 zeros. When we look at the universe of about 1,450 chemicals,

27 for which there are ToxCast data, we have a similar number,
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1 about 80 percent with true zeros there. So that's all

2 consistent. But what we really want to go after is that 4.4

3 percent or so, those 97 chemicals that do have bioactivities

4 that are above that .1 line. We like to focus our attention and

5 additional screening and testing efforts on those chemicals. 

6 I think we've shown, given the model

7 performance, with 95 percent or better, based on both in vitro

8 and in vivo performance against reference chemicals and 100

9 percent concordance with the Tier 1 data that this method can

10 actually contribute to the weight of evidence. It can be used

11 to evaluate ER interactions. And it's not clear from this

12 analysis what would be added running a Tier 1 ER binding assay,

13 a Tier 1 ERTA or an uterotrophic assay if the ToxCast data were

14 available. And that's really what we consider when we think

15 about substituting.

16 So that was a little bit of a recap and a

17 little bit of new data, but I'd be delighted to take any

18 questions you might have.

19 DR. JAMES MCMANAMAN: Thank you very much. 

20 Questions for Dr. Browne? Dr. Jett.

21 DR. DAVID JETT: That last line again. The

22 estimated bioactivity -- sorry if I missed this-- what is that? 

23 You said that List 1 and List 2 did not basically predict at all

24 the estimated bioactivity.

25 DR. PATIENCE BROWNE: Right. Sorry, I should

26 explain that better. The List 1 and List 2 AUC scores for

27 agonists being the R1 and antagonist, none of those were above
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1 that .1 cutoff that we're using for 100 micromolar. So AC50. 

2 So there may be some very, very modest activity, but none of

3 those would be considered potent agonists or antagonist and

4 about three-quarters of those have two zeros for both the R1 and

5 R2 agonist and antagonist AUC scores.

6 DR. JAMES MCMANAMAN: That was Dr. Browne. 

7 Now, another Dr. Brown.

8 DR. TERRY BROWN: The other Dr. Brown. I was

9 intrigued by the fact that dihydrotestosterone falls in the

10 midrange of your compounds. 

11 DR. PATIENCE BROWNE: For ER.

12 DR. TERRY BROWN: For ER.

13 DR. PATIENCE BROWNE: Right. It's incredibly

14 potent for AR. Right.

15 DR. TERRY BROWN: Well, obviously, yeah. I'm

16 just kind of perplexed to the fact that it's as active as it is

17 in an estrogen receptor specific panel of assays.

18 DR. PATIENCE BROWNE: What I can tell you is

19 that if you throw enough steroid on basically, the nuclear

20 receptor, they tend to be positive. So those are run at

21 reasonably high concentrations, but there is a possibility, I

22 suppose. 

23 No, that's not true, I'm not going to say

24 that. Sorry. Trenbolone can certainly be aromatized but DHT

25 should not be. So that is consistent with the published reports

26 that we've seen.

27 DR. JAMES MCMANAMAN: Dr. Schlenk.
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1 DR. DANIEL SCHLENK: You know what, the data

2 looks very intriguing. I just have one question. It just

3 bothers me a little bit and that the para nonylphenol data. 

4 That's just above the threshold and yet, I mean, that's a

5 compound that we all know, I think it's got to be positive of an

6 even an uterotrophic assay. 

7 So what's your thoughts on compound that are

8 -- I know that was dealt with, with Dr. Judson, in terms of

9 maybe being a little bit too toxic for the in vitro assays,

10 perhaps, but what are your thoughts on how to get around that or

11 to pick up compounds that display those same types of

12 chemistries, I guess. 

13 DR. PATIENCE BROWNE: I think you ask a very

14 good question and we've been talking about running some

15 animal-based experiments to refine some of those very, very low

16 bioactivities, based on the model scores and this sort of how

17 low is no question. I think that's in the future for us, but

18 where we really are now is prioritizing the chemicals that have

19 clear demonstrated bioactivity, based on the assay outputs.

20 So the closer we get to that line, I think we

21 move down in priority. What we're really looking for is the

22 most bioactive compounds first for a prioritization exercise.

23 DR. DANIEL SCHLENK: Okay. I understand that.

24 I would guess I would put that as a bioactive compound, though,

25 based upon the in vivo data that's present. And if, I guess if

26 you throw out the uterotrophic data that's present on that

27 compound, then you lose that. That's what I'm trying to say, I
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1 guess.

2 DR. PATIENCE BROWNE: Okay. A couple of

3 things. First of all, I think we would still consider it

4 bioactive, based on the output as well, less so than the

5 branched form, of course. And the data wouldn't be thrown out,

6 necessarily, it would just be a matter of when we put it up for

7 additional screening and testing and it's very likely that at

8 this point, if we were to -- we're talking about substitution

9 for only the ER binding, ERTA and uterotrophic, so we would

10 likely have a positive result in the fish assay and likely have

11 a positive pubertal result. So hopefully, we wouldn't be

12 missing anything.

13 DR. JAMES MCMANAMAN: Other questions? Yes,

14 Dr. Propper.

15 DR. CATHERINE PROPPER: I'm not sure this is

16 appropriate for your or the previous speaker, but I'm looking at

17 Figure 2-15, which were those studies that were included in the

18 non-reference chemical study where the concordance within the

19 uterotrophic assays weren't that great. And in here, the ER

20 agonist area under the curve values definitely came in if you're

21 given the iffy uterotrophic results. So that kind of

22 intermediate range, even below .1 that we're seeing some

23 positive hits.

24 DR. PATIENCE BROWNE: Right.

25 DR. CATHERINE PROPPER: So how do you deal

26 with that kind of intermediate when they aren't a lot of data

27 available?
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1 DR. PATIENCE BROWNE: You ask a good question

2 and I think we're trying to resolve that ourselves. I think

3 that at this point, we have a fairly strong amount of confidence

4 in things that are above that .1 level and that are true zeros,

5 that midrange is something we're working hard and have some

6 research collaborations planned in the future to help resolve.

7 DR. JAMES MCMANAMAN: Questions? All right. 

8 We can take a break now. So we'll take a 15-mintue break and

9 then we'll begin with the next presentation, which is Dr.

10 Kleinstreuer. 

11 (Brief recess.)

12 DR. JAMES MCMANAMAN: So the next presenter is

13 Dr. Kleinstreuer on androgen pathway and data models. The floor

14 is yours.

15 

16 ANDROGEN PATHWAY DATA AND MODELS

17

18 DR. NICOLE KLEINSTREUER: I will be taking us

19 through -- so first of all, my name is Nicole Kleinstreuer; I am

20 a contractor for integrated laboratory systems, contracting for

21 the National Toxicology Program, Interagency Center for the

22 Evaluation of Alternative Toxicological Methods, or NICEATM. 

23 And you just heard, two talks ago, from Warren Casey, our

24 director. So I will be going through the androgen receptor

25 model and I hope that this presentation will serve to reinforce

26 and strengthen a lot of the information that we heard earlier

27 this morning from Richard on the construction of the model and
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1 how, in this case, it can be applied to a different pathway. So

2 still an endocrine-related pathway, but the androgen receptor

3 pathway. 

4 So the androgen receptor model following the

5 example of ER, also combines results from multiple in vitro

6 assays. In the case of ER, we had 18 assays. Here we have only

7 nine assays, but we still are covering different points along

8 the biological pathway with a variety of different technologies

9 and because we are integrating multiple different in vitro

10 assays that each have their own little quirks and personalities,

11 we still have to deal with this concept of potential assay

12 interference and weeding out sort of the false positives and

13 false negatives and responses that are driven by noise and

14 cytotoxicity from the real biological responses. 

15 So our real agonist or in the case of AR,

16 antagonist being the more biologically relevant response that

17 we're concerned about for environmental chemicals. And so we've

18 used a similar sort of mathematical model to integrate these

19 assays along the AR pathway and we're evaluated this model

20 against the current set of reference chemicals that we have. 

21 And again, you'll hear me emphasize this many

22 times throughout the presentation, but this model is earlier

23 stages than the ER model. And that's just a function of how far

24 along the research is as well, and the availability of good

25 vetted reference chemical sets. So this is sort of snapshot in

26 time of where we are with the AR pathway model.

27 So I'll go through each of the assays quickly
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1 because these are the same technologies that you've already seen

2 for the ER pathway model, but these are for AR. One thing that

3 is important to note on this particular slide, these are the

4 radioligand biochemical binding assays. So we have two AR

5 binding assays. We have a human AR binding assay and this is

6 the important note, we have a chimp AR binding assay.

7 This is a correction to the White Paper. The

8 table in the White Paper lists a rat AR binding assay. I take

9 full responsibility for not changing the "R" to a "C" where I

10 should've in that table. So the actual assay that was used in

11 the data and in the pathway model is a chimp AR binding assay.

12 Next, we have approaching complementation

13 assays that are run by Odyssey Thera. This looks at approaching

14 complementation between -- this is actually a cofactor

15 recruitment between AR and SRC1. We have two different time

16 points here, the 8 and the 16 hours. And then we have the

17 Attagene AR trans assay. Again, this is a transcription

18 activation assay. And then we have the four Tox21 assays that

19 are run in the ultra-high throughput format. We again have the

20 beta lactamase and the luciferase-based assays, run both in

21 agonist mode and in antagonist mode. 

22 So this shows a similar heat map. So you'll

23 notice that almost all of the content in this presentation has

24 been content matched, wherever possible, to the ER pathway

25 model. So this should look pretty familiar at this point. So

26 this shows a heat map with hierarchical clustering applied

27 across the AR assays. So all of the chemicals, which are the
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1 rows in this heat map, that were negative across all the assays

2 have been removed. So we're only looking at around the 400 or

3 so chemicals that had activity in at least one of the AR assays.

4

5 Again, the takeaway here is that the assays,

6 by and large, cluster by technology. So you have groups of

7 chemicals that are active across all of the assays or a majority

8 of the assays along the pathway, but then you have groups of

9 chemicals that are only active in one assay or they're only

10 active in two assays that belong to the same technology type. 

11 And so again, we have these pseudo receptors that are divided by

12 assay technology type to try and help us differentiate between

13 true bioactivity and technology specific assay interference.

14 So here is a close-up of the AR model. Again,

15 there white stars are the assays here. So we have our nine

16 assays that map to various points along the biological pathway. 

17 And so at the risk of being a little bit repetitive, I'm going

18 to do the same thing that Richard did and walk through an

19 example, two examples. We have the agonist pathway here. So

20 this is R1. 

21 You have a ligand binding to the androgen

22 receptor, acting in an agonist form, causes dimerization and

23 translocation to the nucleus and cofactor recruitment, which

24 causes the activated transcription factor complex, which binds

25 to the DNA, transcribes RNA and causes protein production. So

26 if you had a true AR agonist, you would find A1 all the way

27 through A7 being activated. And these two antagonist assays
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1 over here should be inactive. 

2 So from the standpoint of actual concern, as

3 far as the biology of this pathway, we know that more compounds

4 are much more likely to act as AR antagonists. So most of this

5 presentation is actually going to be focused on looking for the

6 antagonist response. So the vast majority of compounds that we

7 see that are true agonists are your steroid pharmaceuticals,

8 your testosterones, whereas we have compounds that are acting

9 through this R2 pathway. 

10 So they are binding to the AR receptor, but

11 they're acting in an antagonist fashion. So they would activate

12 both of these binding assays. They would activate the cofactor

13 recruitment assays, but then they would light up the antagonist

14 transcription suppression assays and none of these three assays

15 over here, which are specific to the agonist pathway.

16 So this is a revisitation of the computational

17 model. Again, as Richard mentioned, this is simply a linear

18 additive model. And this may be naïve, but the way that I like

19 to think about this is simply the probability that a chemical is

20 actually acting through the receptor. So if you get an R2 value

21 of one, you're basically 100 percent certain that that's a true

22 AR antagonist, whereas, if you have your pseudo receptors being

23 activated, then you're pretty certain that that's acting through

24 an assay-specific, technology-specific interference pathway.

25 So again, it's just solving this constrained,

26 at least whereas minimization problem to look at the difference

27 between what you would predict to be the case if a compound, at
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1 a certain concentration, was acting through a specific receptor

2 versus what is actually measured and what you observe in the

3 assays.

4 So this shows the calibration curves for the

5 AR pathway. So again, following the example of the ER pathway,

6 the calibration curve is normalized for a true agonist ligand. 

7 So for the agonist AUC values, again, you get a value, an AUC

8 value of .1, being equivalent to an activity in the assays

9 around 100 micromolar. Because we normalized to a true agonist

10 along the pathway, the antagonist values are, by in large,

11 lower, but you get the same calibration curve here where you see

12 an AUC value of .05, is equivalent to activity at around 100

13 micromolar in your in vitro assays.

14 And it's important to note here that these are

15 not hard cutoff values or thresholds where we say, okay,

16 everything above this is important and everything below this we

17 throw away. That's certainly not the case at all. These are

18 just calibrations to help us understand where we think that

19 bioactivity is actually taking place. 

20 So we know for the AR antagonist model, an AUC

21 score of .05 is about equivalent to 100 micromolar, where we're

22 actually seeing that activity against that antagonist pathway. 

23 Certainly, everything with a non-zero AUC score is still

24 considered and examined very closely because that is showing

25 activity in one or more of those assays. 

26 So selecting the AR reference chemicals was

27 challenging because unlike ER, there doesn't seem to be a widely
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1 accepted list that's been vetted by experts where these AR

2 reference chemicals have been assigned potencies going from very

3 weak up to strong. Instead, we have several reports that have

4 emerged around the validation of in vitro test methods that

5 target the AR pathway that recommend lists of reference

6 chemicals. It's important to note that these two reports

7 actually are not 100 percent consistent in their recommendations

8 across the chemicals. 

9 So what we did is we took those reference

10 chemicals that actually had consistent results in both of these

11 reports. So this is the Interagency Coordinating Committee on

12 the Validation of Alternative Methods that has representatives

13 from all the federal agencies on it, so ICCVAM. And then, of

14 course, the OECD report on the AR-STTA assay. 

15 So there were 23 chemicals that were

16 consistent between those two reports and also had ToxCast

17 results. So we took those as our AR reference chemicals. And

18 we would certainly welcome recommendations from the panel on

19 further AR-reference chemicals that could be used to validate

20 this particular model.

21 At the moment, these are the reference

22 compounds that we're working with. As I mentioned, there's 23

23 compounds and they can be divided into pure agonists, pure

24 antagonist, purely inactive compounds and then these yellow

25 compounds here at the bottom are selective androgen receptor

26 modulators. So these are compounds that are expected to have

27 both agonist and antagonist activity against the AR pathway.
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1 And so this shows you the performance of those

2 reference chemicals. Again, just to orient you, this a lane

3 plot. On the X-axis, you have your AUC, which goes from,

4 essentially zero, all the way up to one. And then the top panel

5 are the expected results -- sorry; the agonist reference

6 chemicals. And then the bottom panel are the antagonist

7 reference chemicals. 

8 A lot of these chemicals overlap because there

9 are certain pure agonists that are expected to be negative in

10 the antagonist pathway and vice versa. So those compounds that

11 are green are supposed to be active in this pathway, and the

12 ones that are red are supposed to be inactive and follow along

13 this line, and the same for the bottom.

14 So the takeaway here is that all of the pure

15 agonist and pure antagonist AR reference chemicals were

16 correctly identified by the respective models. All of the

17 inactive chemicals that were supposed to be inactive in both

18 agonism and antagonism were also correctly identified by the

19 agonist and antagonist models as being inactive.

20 Interestingly enough, all of the SARMs were

21 also correctly identified by the model -- I should say the

22 majority of the SARMs were also correctly identified by the

23 model as being both agonist and antagonist. The exceptions here

24 were linurons. Linuron is up here. And this was a SARM that

25 was not correctly identified for its agonist activity, but it

26 was correctly identified as having antagonist activity and

27 fluroanthene, which is a SARM that should have been identified
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1 as being both agonist and antagonist and was called inactive by

2 the model.

3 So this is just to orient you through the next

4 few slides. I'm going to take you through several example

5 curves, through example compounds, mostly reference compound and

6 then we'll get into some more "interesting" environmental

7 compounds. So this is just a zoom-in on the legend so you can

8 remember that these black lines are the binding assays. The

9 green lines are the cofactor assays. You've got your

10 transcription assay, the Tox21 agonist and then the Tox21

11 antagonist. 

12 Then, just to remind you, the blue line is the

13 agonist model performance and then the red line is the

14 antagonist model performance. And then the rest of these model

15 legends are the different pseudo receptors or the assay-specific

16 interference pathways.

17 This is our pure ligand, pure agonist ligand. 

18 This is the testosterone propionate as a positive control in the

19 Hershberger assay. You can see here that it hits all of the

20 agonist assays at concentrations well below the cytotoxicity

21 burst. Again, these are the assay plots on the left-hand side

22 and these are the model predictions on the right-hand side. So

23 this blue line is the R1 agonist model prediction and it has a

24 score of one. And on the bottom here, you can see similar plots

25 to what you've seen already earlier today. 

26 So this vertical line here marks the

27 cytotoxicity, median AC50. So essentially, anything to the
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1 right of that vertical AC50 line is happening in the

2 cytotoxicity region. Anything to the left of that vertical line

3 is happening before you see cytotoxicity. And then this

4 horizontal line right here marks a top value of 50 percent. So

5 you would expect that anything that's really truly activating

6 that assay would fall around the 100 percent areas, as far as

7 its top response.

8 So you see all of the nuclear receptor assays,

9 largely AR, in this region of true activity above any kind of

10 noise threshold and below cytotoxicity. So I'll go through the

11 rest a little more quickly.

12 This is our antagonist reference compound,

13 hydroxyflutamide. Again, you see this interesting phenomenon

14 where the model starts to predict a pseudo-receptor pathway

15 because these binding assays get turned on at concentrations

16 below where you start to see activity in the other assays. And

17 so the model thinks, oh, well, you know, when it's looking at

18 the concentration response, at this low, low concentration, you

19 only see the binding assays being activated. So maybe it's a

20 pseudo receptor. 

21 Maybe it's assay-specific interference. But

22 then as you increase in concentration, all of these other

23 antagonist assays are hit. And so the model then recognizes no,

24 this is a true antagonist compound and predicts that it would

25 act through the R2 or the antagonist AR pathway. This is a

26 compound that did not show any cytotoxicity, so you see here,

27 you don't have this gray region where you see the cytotox burst
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1 and you see all the nuclear receptor assays being hit here above

2 this threshold, this 50 percent threshold.

3 Vinclozolin is another antagonist reference

4 compound example. Again, you see the binding assays being

5 activated at low concentration and then all the other assays

6 start to turn on as you increase in concentration and you get a

7 nice curve for your antagonist model prediction. And again, you

8 see no cytotoxicity region; same thing with procymidone, you see

9 that exact same kind of response. So I'm just going to keep

10 going through these.

11 Here is a compound that is an antagonist

12 reference compound, but it is actually turning on a number of

13 assays. So it's actually predicted to have potential agonist

14 and antagonist effects and it's confounded by cytotoxicity. So

15 this is something where pretty much all of the activity is

16 happening with that cytotoxicity burst region right here, but

17 this isn't something that we would discount, necessarily,

18 because of cytotoxicity. This is an example where going back

19 and retesting this concentration at a tighter dose range would

20 be potentially really useful, but this certainly has non-zero

21 model scores and would be examined as a potentially active

22 compound against the AR pathway.

23 Here is another example of a SARM. So this

24 compound is a selective androgen receptor modulator. It is

25 known to have both agonist and antagonist activity against the

26 pathway, and the model picks this up very nicely. It's

27 interesting that even through that penalty terms is included in
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1 the mathematical model to try and drive the solution towards a

2 unique receptor, the bioactivity against the pathway is so

3 clearly both agonist and antagonist for these SARM reference

4 chemicals, you still get that as the result of the model. This

5 is actually predicted to be both an agonist and an antagonist

6 because it's hitting all of the relevant assays.

7 So here is the example of the SARM that was

8 missed. So this is linuron and this was only active in the

9 binding assays and in the Tox21 antagonist assays. So the

10 antagonist activity was picked up by the model, but the agonist

11 activity was not. And this is fluroanthene, this is the one

12 reference compound that was missed by the model, completely

13 missed, essentially, where there was -- it's a SARM and neither

14 agonist nor antagonist activity was picked up. Interestingly

15 enough, though, this compound was active in both of the Odyssey

16 Thera cofactor recruitment assays. It's hard to tell them

17 apart, they have very similar dose response patterns here.

18 So this compound was actually predicted to act

19 through that technology-specific assay interference pathway that

20 maps to that Odyssey Thera cofactor recruitment assay

21 technology. So this is also, I would argue, this is a compound

22 that wouldn't necessarily be missed by a prioritization scheme

23 because any compound that has a significant score in any of the

24 receptors or the pseudo receptors is still going to be looked at

25 more closely.

26 So this shows the bioactivity across the 1,855

27 compounds. Again, we've left out -- and this divides it by R1
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1 and R2. So agonist and antagonist model predictions. And then

2 these are all the pseudo receptor pathways. So these are

3 technology-specific, R3 through R7 and then A1 through A9 are

4 individual assay-specific. So you're going to get some overlap

5 there between assays that belong to those pseudo-receptor

6 pathways. In this case, R7, for example, is going to cover both

7 A6 and A7.

8 So here, all of the inactive compounds have

9 been excluded from this heat map. So this is only about 400 or

10 so chemicals that showed activity, predicted activity in any of

11 these pathways. Here, as expected, the AR agonist pathway is

12 the most biologically relevant as opposed to ER, where we're

13 looking at agonism. For AR agonism, we only get about 25

14 chemicals that score above .05 or in this case, you can actually

15 apply the same .1 criteria and you get the same number. And

16 those are pretty much exclusively steroid pharmaceuticals that

17 you would expect to be agonist in this pathway.

18 The antagonist, on the other hand, there's

19 more bioactivity across a wider range of chemicals, so there are

20 steroid pharmaceuticals in this group, but there are also

21 environmental chemicals that are scoring against this AR

22 antagonist pathway.

23 So here's an example of a group of

24 environmental chemicals. And I think that we'll be hearing a

25 lot more about this particular chemical class later in the SAP,

26 but the conazoles, there were 30 conazoles in the ToxCast

27 library. Fourteen out of 30 of them had relatively high
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1 antagonist R2 AUC values. So that, essentially, when I say

2 relatively high, that just means that there was activity -- they

3 had AUC scores above .05, meaning, from the antagonist context

4 that they potentially have antagonist activity at concentrations

5 at or below 100 micromolar. 

6 So they can be loosely divided into two

7 different types of responses. You have conazoles here that are

8 showing activity that's very much confounded by cytotoxicity. 

9 So again, you have that gray region of cytotoxicity, and most of

10 the assays are lighting up right around the same time where you

11 start to see those cell stress or cytotox-driven responses, but

12 then you also have several conazoles that seem to be acting

13 without the confounding influence of cytotoxicity at all. So

14 they're still at relatively high concentrations, but they are

15 turning on assays without the added influence of cytotox.

16 Another example in the environmental chemical

17 set are antimicrobials. So triclosan was predicted to be an AR

18 antagonist. And this is consistent with some reports from the

19 literature that we've seen. And while, again, a lot of this

20 activity is confounded by the cytotoxicity region or

21 cytotoxicity burst, the activity, especially in the binding

22 assays, really is seen at concentrations prior to where you

23 start to observe any cytotoxic response. You contrast that with

24 triclocarban, which is another anti-microbial that's often used

25 in place of triclosan, you could see that all of the activity

26 against the AR assays is occurring well into that cytotox burst,

27 and in most cases, past the median concentration for
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1 cytotoxicity.

2 So this slide is a repeat of one of Richard

3 slides, but it's just to remind you what we mean by that

4 cytotoxicity burst. Essentially, that you have your range of

5 cytotoxicity and then we've taken three median absolute

6 deviations and then centered that to produce a Z score. And so

7 your Z-score, anything above three is considered concentrations

8 well below, where you start to observe cytotoxicity. And so

9 this just gives you a closer view of these hits. 

10 Sorry. Actually, the axes are actually

11 different on these two, so just pay attention to that. So there

12 are actually nine hits that had a Z-score above three for

13 triclosan, and the majority of those are those nuclear receptor

14 assays. So that's in this quadrant right here. Whereas, for

15 triclocarban, there are only seven hits that had a Z-score above

16 three that were not in the cytotoxicity region, and those are in

17 other cell-based assays that are not relevant to AR. 

18 So essentially, here, we would believe that

19 triclosan is potentially acting as an AR antagonist, whereas

20 triclocarban may not be. It certainly deserves further

21 examination, but all of that activity seems to be happening in

22 that cytotox region. 

23 Now, for the next few slides, I'm going to go

24 through the same type of analysis that Richard touched upon

25 earlier, understanding these assay interference pathways and

26 whether or not we should believe that a compound that, for

27 example, is only lighting up the binding assays or it's only
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1 lighting up the cofactor recruitment assays, is potentially

2 having true bioactivity against the pathway or is it being

3 driven by other factors? 

4 Is it being driven by cytotoxicity? 

5 Is it being driven by noise? 

6 Is it being driven by some sort of strange

7 chemistry like the surfactants, the D nature of the proteins in

8 the radioligand binding assays?

9 So we've done a number of diagnostic tests to

10 try and separate out that real activity from that sort of pseudo

11 activity, and they are listed here. They include looking at

12 noise, looking at cytotox, looking at crosstalk or

13 cross-reactivity, the pains, compounds and then looking for

14 trends. And this is just more associative, looking for trends

15 and structure of physico-chemical properties. And again, this

16 is all a work in progress.

17 So these are the initial results for the AR

18 pathway, looking at the drivers or pseudo receptor bioactivity,

19 so these could be potentially low T-scores, so low top values. 

20 And a lot of those could be due to seeing activity at say, the

21 highest tested concentrations. So those curves may have upper

22 asymptotes that are nonresolved. 

23 So you can see that for a number of the pseudo

24 receptors, this case it's R4, A1, A4, A8, you have a significant

25 difference between the top values for the compounds that are

26 active only in that pseudo receptor or that assay-specific

27 pathway, versus your comparator. And your comparator is set for
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1 all of these, except for the last one, are your true antagonist.

2 So your antagonist, the compounds that were predicted by the

3 model to be strong antagonists.

4 And then we've done the same thing for the

5 Z-scores. So a low Z score means that it's in that cytotoxicity

6 burst region and then you have your comparator. So your true

7 antagonist are at the bottom here, your true agonist. And you

8 can see that for several of those pathways, you get a

9 significant difference. 

10 So here, for example, on your R7, your average

11 Z-score is .4, which means that all of that activity is

12 happening right around that cytotoxicity median concentration

13 value and then, of course, for your antagonist, they're higher. 

14 They're not going to be too much better because they're also

15 confounded by cytotoxicity, but you do have a significant

16 difference between those two groups.

17 So these show you the distributions of the top

18 values for those assays, where it was significant. And I think

19 one of the important messages here is that some of these may be

20 due to that technology. So in this Odyssey Thera assay here,

21 you can see that this distribution, this T distribution is quite

22 skewed to the left. And so this assay potentially has issues

23 with non-normal noise. 

24 And then here, the Z distributions for the

25 assays where there was a significant difference between the

26 compounds that seem to be acting through the assay-specific

27 technology interference or pseudo-receptor pathways versus those
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1 true agonists or true antagonist. Again, here, you don't see

2 that bimodal distribution that you normally see, where you see a

3 lot of hits over here and this high Z-score region. So a lot of

4 those hits are occurring right around the cytotoxicity region

5 here.

6 Then we looked at crosstalk and

7 cross-reactivity. So the question of -- so Attagene, for

8 example, being a multiplex assay, you know, having one target

9 fire is not going to cause other readouts to fire or

10 potentially, do we see promiscuity among all of the nuclear

11 receptors, for example. If you have a compound that's potent

12 against one receptor, is that going to be potent across all the

13 other receptors in that battery?

14 So we ask those questions, essentially, are

15 the pseudo-receptor actives more likely to be active in many

16 readouts than those that were inactive. So we tested for

17 cross-reactivity in these different technologies. So we tested

18 for cross-reactivity just within the Attagene assays or just

19 within the nuclear receptor battery of Nova Screen, or just

20 within the Tox21 BLA, or luciferase technologies.

21 So this gives you the results for Attagene and

22 Nova Screen. Just one example, essentially, is that chemicals

23 that were only active in the chimp AR binding assay and no other

24 AR assays. So they would be predicted to be acting through some

25 sort of specific interference with that particular assay were

26 also active in all the other nuclear receptor assays. So not

27 all of them, but say, they have an average of four and a half
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1 hits across the nuclear receptor battery versus an average of

2 less than one, for chemicals that were inactive in the AR

3 assays. 

4 So we would say that these compounds could be

5 potentially promiscuous across the multiple nuclear receptor

6 assays or they could be like that class of surfactants where

7 they are preferentially affecting the Nova Screen assay

8 technology by a denaturing protein or however they're acting.

9 In Tox21, these show the cross-reactivity

10 results across the different technologies to the BLA or the

11 luciferase. And as we would expect here, cytotoxicity is the

12 driver for the vast majority of this, so anytime you are looking

13 at an antagonist pathway, most of your assays are going to be

14 loss of signal assays and those are inherently going to be

15 confounded by cell stress and by cytotoxicity. 

16 So you see a large number of pseudo-receptor

17 pathways, and even your model, so your predicted antagonist,

18 when they're compared across all of the other Tox21 BLA

19 antagonist assays, you can see that they actually are probably

20 being driven largely by cytotoxicity -- I don't want to say

21 they're being driven by cytotoxicity, they're responses are

22 being confounded by cytotoxicity because that antagonist

23 response is happening in that same region, that same

24 concentration region. 

25 So we also looked at the PAINS, which I think

26 is a really appropriate acronym. So the pan assay interference

27 compounds that simply are just active across many, many assays
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1 and may be related to cell stress or cytotox as well. So again,

2 we just looked across all of the assays. And so we looked at --

3 I'll just walk you through one example.

4 If you look in this low Z area, so that's in

5 the cytotoxicity region of bioactivity, you have your model

6 antagonist chemicals being an active in an average of almost 10

7 percent of all non-AR ToxCast assays. So that's across the

8 other 800 assays, whereas, the chemicals that are inactive in

9 the AR assays. So they're not active against the AR model, are

10 only active in an average 2 percent of non-AR ToxCast assays. 

11 And I would say that those 10 percent of non-AR ToxCast assays

12 would be almost exclusively cytotoxicity assays because we have

13 cytotoxicity assays that run the entire gamete or the entire

14 spectrum of technologies. 

15 So from human primary cells to all different

16 kinds of cytotox assays. And then if you go into that high Z

17 region, you exclude the cytotoxicity region, you exclude

18 anything that has a Z-score of less than three, you still see

19 that potentially cell stress-driven response. So you still see

20 that the antagonist -- so the compounds that are only active in

21 the Tox21 antagonist assays are still active in 7 percent of all

22 non-AR assays, whereas, chemicals that are inactive, are only

23 active in an average of 2 percent.

24 So we're still figuring out what this actually

25 means. This is a lot of diagnostic test to help us try and

26 understand what's actually driving these assay-specific

27 responses versus these pathway-based responses. Then we went
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1 through and asked the question about structural classes. Are

2 they more likely to be associated with true or pseudo-receptor

3 activities? These are not super interesting. 

4 The only significant findings are with the

5 agonist class. We already knew that, that they are steroid

6 pharmaceuticals. One interesting thing that helps point us to

7 sort of chemistry reason for why we would see a pseudo-receptor

8 activity specifically in this Nova Screen biochemical assay, we

9 see an association with the structural class of phenyl sulfuric

10 acid dyes, which are apparently stuffing up this particular kind

11 of assay technology.

12 Some more associations between

13 physico-chemical properties that we saw in the ER pathway model

14 and this just needs further investigation. And hopefully, the

15 QSAR models that are being developed will help us -- they may

16 incorporate some physico-chemical properties and help us kind of

17 tease out some more of these associations.

18 So the AR QSAR model is nowhere near, it's not

19 even comparable to the effort that is being put forward for the

20 ER QSAR model. Again, because this is in such early stages of

21 development, but we do have scientists working on it in EPA and

22 in NICEATM, and just the preliminary results for the AR QSAR

23 model, just using a variety of multivariate machine learning

24 approaches and just quick prop descriptors have shown around 80

25 percent cross-validated predictive accuracy against just the

26 ToxCast chemicals. And then we are expanding our library of

27 molecular descriptors and modeling approaches. And as Richard
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1 mentioned, these predictions will be made on the larger set of

2 32,000 chemical structures that include the 10,000 chemicals in

3 the EDSP Universe.

4 So in conclusion, for the AR pathway, we had

5 1,855 chemicals run in nine assays. So not as much redundancy,

6 and therefore, maybe not quite as much confidence as the ER

7 pathway model, but we do still see very good performance against

8 a set of reference chemicals. Again, this is a preliminary set

9 of reference chemicals. This will be further developed as we

10 get expert opinions in on what these reference chemicals should

11 be and what their potency assignment should be.

12 We do have chemicals that are prioritized,

13 environmental chemicals from the ToxCast set that were

14 prioritized to have bioactivity against the pathway. Again, the

15 agonists are compounds that we already know about, whereas, the

16 antagonist that were prioritized do include things that are

17 pesticides, fungicides, plasticizers, anti-microbials, like I've

18 shown you already.

19 So that's it. And I'll take any questions.

20 DR. JAMES MCMANAMAN: All right. Thank you

21 very much. Questions for Dr. Kleinstreuer? 

22 Dr. Weller.

23 DR. GRANT WELLER: One quick question. Your

24 model-fitting procedure, the tuning parameters that go into that

25 penalty function, were those chosen to be the same as for the

26 estrogen receptor models or was it completely independently

27 selected?
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1 DR. NICOLE KLEINSTREUER: Those were the same

2 as the ER pathway model. Let me go back to that slide. And

3 again, you know, as the discussion this morning highlighted, we

4 could do a parameter sweep to look at the influence of those

5 particular parameters on the model, but it seems to be -- it

6 produces responses that are biologically plausible and relevant

7 for the reference compounds that we've looked at, both in the

8 case of ER and an AR.

9 DR. GRANT WELLER: Thank you.

10 DR. JAMES MCMANAMAN: Yes. Dr. Brown.

11 DR. TERRY BROWN: Just a question, what's the

12 ligand choice for the binding assays for the androgen receptor?

13 DR. NICOLE KLEINSTREUER: What's the positive

14 control?

15 DR. TERRY BROWN: The ligand of choice.

16 DR. NICOLE KLEINSTREUER: So the agonist

17 positive control for the model is testosterone propionate. I

18 don't know what the positive --

19 DR. TERRY BROWN: What's the radioligand? 

20 So the radioligand binding assay is correct?

21 Is methyltrienolone a ligand, a radio-labeled

22 ligand?

23 DR. RUSSELL THOMAS: I will find out.

24 DR. TERRY BROWN: Okay. Because what I'm

25 curious about is the estrogen assay is obviously to use 17-beta

26 estradiol, which is the endengeous ligand. Whereas, my

27 impression is that potentially, the androgen receptor binding
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1 assay use labeled methyltrienolone, which is a synthetic

2 compound. It binds very well with the receptor, but it doesn't

3 mirror those activities or those binding characteristics that

4 the endogenous ligand might.

5 DR. NICOLE KLEINSTREUER: Thank you. That's a

6 good point. I'm sorry I don't know the answer, but we will

7 definitely find out for you.

8 DR. JAMES MCMANAMAN: So that answer involved

9 a response from the EPA that couldn't be heard, but they said

10 that they would look into it. Yes, Dr. Dix.

11 DR. DAVID DIX: Dr. Brown, could you clarify

12 that question? You're asking a question concerning specific

13 androgen receptor binding assays?

14 DR. TERRY BROWN: So your ligand binding

15 assays to the receptor --

16 DR. NICOLE KLEINSTREUER: The Nova Screen

17 assays.

18 DR. TERRY BROWN: -- what is the radio-labeled

19 ligand that's being used as the tracer, if you want to say that,

20 put it that way. Because obviously, almost the majority of your

21 assays are looking at antagonism. So your 100 percent you're

22 beginning with is the binding of that radio-labeled tracer and

23 then you're putting in various amounts of the chemical to see

24 what the effect is, in terms of inhibiting that binding.

25 DR. DAVID DIX: Right. And just to be clear,

26 so you're asking what the radioligand used in the two Nova

27 Screen binding assays are? 
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1 DR. TERRY BROWN: Yeah. So what is the ligand

2 that's used and what's the rationale for using non-endogenous

3 ligand, if that's the case, if it is methyltrienolone. 

4 DR. JAMES MCMANAMAN: I guess, what is the

5 labeled ligand. That's correct?

6 DR. TERRY BROWN: Correct.

7 DR. DAVID DIX: For the two Nova Screen AR

8 binding assays.

9 DR. NICOLE KLEINSTREUER: Dr. Thomas, behind

10 me, assures me that he will find out, and apparently has not

11 been able to find out on his Smart phone yet.

12 DR. TERRY BROWN: Okay. I think it's just a

13 point of clarification, to put the assays in context --

14 DR. DAVID DIX: Okay. 

15 DR. NICOLE KLEINSTREUER: Sure.

16 DR. TERRY BROWN: -- to the naturally

17 occurring situation.

18 DR. JAMES MCMANAMAN: Another question, Dr.

19 Brown?

20 DR. TERRY BROWN: I'm curious as to the

21 selection of a cofactor or a co-activator binding assay in the

22 androgen assays versus the absence of a similar assay and the

23 estrogen-receptor assays. 

24 DR. NICOLE KLEINSTREUER: My understanding,

25 and I would welcome any input from EPA and CCT on this is that

26 this is just simply a by-product of what technologies were

27 available. So this is the same company, Odyssey Thera, that
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1 provided the dimerization assays for the ER pathway and that

2 these particular, the SRC1 cofactor recruitment assay was the

3 best assay available for the AR pathway. 

4 Rusty's nodding his head.

5 DR. JAMES MCMANAMAN: I have a clarification

6 question for you or Dr. Judson or both. I thought I understood

7 the answer about -- I had a question, as did Dr. Propper, about

8 those, especially for the ER assay, the notion that compounds

9 can act through the membrane receptor and not really induce a

10 detectable binding, change in binding or effect on binding. 

11 And I thought I understood that these

12 compounds would be detected in your screen. But I thought I

13 heard you also say that you are separating those things as bona

14 fide agonists or antagonist that influence binding from those

15 that are not bona fide. And I just want to get some

16 clarification on what exactly is meant by bona fide because if

17 you're acting through a membrane-associated receptor, that is

18 still bona fide binding, but it may not be detectable in your

19 binding assays.

20 DR. NICOLE KLEINSTREUER: So I apologize if I

21 used the term "bona fide." What I should've said is

22 receptor-driven. So initially, in these pathway-based

23 approaches, we were focusing on receptor-mediated bioactivity

24 through the ER and the AR pathway. 

25 The point that Richard made earlier, and that

26 I'll try to reiterate now is that this model inherently has the

27 ability to also pick up non-receptor-mediated response because
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1 we have assays that map two downstream points in the biological

2 pathway.

3 So if you look at the AR model that's up here

4 on the screen, if you had a compound that did not activate

5 either of the binding assays, A1 or A2, but it did activate A3,

6 A4, and A8 and A9, the model would very likely still predict

7 that to be an AR antagonist, a true antagonist. And the same on

8 the other side, on the flip side, on the agonist pathway, if it

9 didn't bind but still affected all of the downstream

10 agonist-related assays, the model would still predict that to be

11 an agonist. 

12 It is possible that if the effect was only

13 picked up by two assays that belong to the same technology, the

14 model would then predict that to be acting through a technology

15 specific interference pathway, when in fact, it did have true

16 bioactivity along the pathway that may not have been binding

17 mediated or receptor binding mediated. And in that case, I

18 think that these compounds are still being examined further.

19 So I would say no chemical is being discounted

20 as potentially bioactive unless it has an absolute zero score. 

21 Unless it's completely inactive in all of the assays that hit

22 every part of the pathway.

23 DR. JAMES MCMANAMAN: Thank you. I guess it's

24 the concept of receptor-driven. I have a problem with calling

25 these compounds that may not hit one part of the assay, but hit

26 the other at assay-specific. I have a problem with calling them

27 assay-specific and not receptor-driven, when, in fact, they are
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1 receptor-driven, but they're not receptor driven through the

2 classical ER or AR pathway.

3 DR. NICOLE KLEINSTREUER: And that's a fair

4 point. I think that if we could incorporate a membrane-bound

5 receptor-based assay that that would certainly add value.

6 DR. JAMES MCMANAMAN: Thank you.

7 DR. NICOLE KLEINSTREUER: I think Dr. Dix has

8 something to add.

9 DR. JAMES MCMANAMAN: Dr. Dix.

10 DR. DAVID DIX: Concerning this point about

11 the difference between membrane receptor meditated and nuclear

12 receptor-mediated effects, it's an area of continuing research

13 and analysis, both within the ToxCast project as well as

14 consideration for the EDSP. And I think Dr. McManaman, you hit

15 it on the head, at this point, we don't fully understand the

16 relationship between some of these assays performance, vis-à-vis

17 a nuclear receptor or a membrane-bound receptor on activities,

18 especially for specific chemicals. And it's something we would

19 like to actively explore and gain a better understanding so that

20 we can make use of that knowledge in the program. That may be

21 possible without specific membrane-bound receptor assays through

22 some more inference-driven approaches.

23 I have a clarifying question. I'd like to get

24 a little better understanding on the question from Dr. Brown

25 concerning the AR cofactor recruiting assays.

26 DR. JAMES MCMANAMAN: Sure.

27 DR. DAVID DIX: Again, I just want to make
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1 sure that I understand the question you are asking. 

2 DR. TERRY BROWN: I guess it was just there's

3 an incongruence between the battery of assays that are being

4 applied to the estrogen receptor, versus those that are being

5 applied to the androgen receptor in the sense that there's no

6 co-activator interaction with the estrogen receptor among the

7 battery of test, other than endogenous co-activators that may be

8 present in a cell-based assay; whereas, for the androgen

9 receptor, there's a specific intent to perform, I guess, two

10 assays that I assume use the co-transfection, an SRC1 expression

11 vector. 

12 And that in combination with the fact that

13 there are a whole hosts of these co-activator proteins, SRC1 is

14 one of those that could be interacting with the receptor in an

15 endogenous situation, why SRC1? 

16 And also the fact that SRC1, the combination

17 of a transactivation assay with SRC1 present in this kind of a

18 situation seems to imply that there's a different dynamic range

19 when you add a co-activator to an assay, transactivation assay

20 versus an assay where there is not necessarily the addition of a

21 specific co-activator.

22 If you look up here, A5, 6, and 7, I assume

23 are assays in the relative absence of a co-activator; whereas A3

24 and 4 specifically utilize the presence of a co-activator, which

25 actually should amplify the response range. I guess the real

26 question then becomes is how does this modify your

27 interpretation when you're taking into account the area under
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1 the curve, which, in theory, and what we've been told, combines

2 all these different assays into a single interpretation and

3 whether there's really parallelism between what's being done in

4 the estrogen receptor framework for I think 18 assays versus

5 what's being done in the androgen receptor framework for these

6 nine assays. I mean, it's just a question to think about.

7 DR. RICHARD JUDSON: Let me make an attempt. 

8 It's kind of a deep question. It's partly a technology question

9 and partly a sort of deep biology question.

10 The company, the group that actually developed

11 both the cofactor recruitment assays and the dimerization assay,

12 Odyssey Thera, it's all using this FRET technology. And they

13 had the estrogen receptor assays running when we approached

14 them. We said we'd also like the cofactor recruitment assays. 

15 They tried to get the equivalent estrogen-receptor SRC1 assay

16 running and they just couldn't get it running. So that's why we

17 would like it.

18 I think they tried to get the AR dimerization

19 assays running and they just couldn't get them running. So we

20 would like to have had the full complement for both ER and AR

21 and it was just a technology, you know, given the time we had to

22 get assays running, we just couldn't get it running. So there

23 was no deep reason for doing cofactor recruitment in one and

24 dimerization in the other, which is unfortunate that we didn't

25 have all of that.

26 Second issue is, the A5, 6, and 7 and the

27 equivalent ones in the estrogen pathway, there has to be
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1 cofactors there to make the transcription factors, who knows

2 what they are. They're the co-factors that are relevant or the

3 native cofactors that are in those cell types, whatever they

4 are. And you know, there's always the potential we don't see

5 activity because somehow the way the ligand binds doesn't cause

6 cofactor recruitment for that particular chemical. That's

7 another issue we haven't talked about. 

8 But why SRC1? I think it was the obvious

9 first choice to try to get an assay running. There was no deep

10 reason, it's just that the assay developer knew something about

11 that. So let's try to get SRC running first. And I mentioned,

12 we're working with a group who has this whole panel of cofactors

13 for, in this case, the estrogen receptor. So we're going to

14 start, hopefully run some limited number of chemicals to see how

15 much difference in activity we see as a function of what

16 cofactors are available.

17 We'll get some answer. Then there was at

18 least one more key -- oh, okay. So for that sitter assay, A3

19 and A4, the only cofactor available is SRC, because that's all

20 that's been engineered there. At least I think that's right. 

21 So if the particular ligand needs some

22 cofactor other than SRC to operate, which I can imagine some

23 scenario, then it wouldn't activate. So it might light up A1

24 and A2 so it's actually binding and it causes transcription, so

25 it gets A5, 6 and 7, but it wouldn't get A3 and A4. So one

26 could imagine a scenario like that. We haven't even thought

27 about looking at that. Or I haven't. Is that -- how close are
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1 we to --

2 DR. TERRY BROWN: That's helpful. I guess the

3 real crux of my question was not so much -- I mean, SRC1 is very

4 promiscuous, so it will interact with a lot of these different

5 receptors and with various ligands-bound and so forth.

6 The other aspect of what I was asking, I think

7 I was trying to get at was does putting in semi-artificially

8 SRC1 into the mix of proteins available as a co-activator, does

9 it skew, in some way, the interpretation of the results because

10 it really amplifies the signal significantly over a cell-based

11 assay, where you're just dealing with endogenous cofactors that

12 may be interacting with that receptor in a transactivation

13 assay?

14 So instead of your range being zero to 100 in

15 an endogenous cell, when you put in SRC1, it may be zero to

16 100,000.

17 DR. RICHARD JUDSON: Okay. There is kind of

18 an interesting story out of there. So if we go back to the

19 dimerization assays in the estrogen model -- so forget this for

20 the moment -- what we actually see is agonists go zero to 100

21 percent. And antagonist in that dimerization assay actually

22 goes up to about 200 percent. And we believe what the biology

23 is, and we've talked to other nuclear receptor experts who say

24 that makes sense, is that one of the reasons agonists and

25 antagonists are different is there's this timing of the --

26 there's the on and off rate of the dimerization, as well as the

27 on and off rate binding to the DNA. 
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1 So antagonist tend to make the dimers stickier

2 and you need that cycling to get activity because they spend

3 more time together, you get more photons out and so the activity

4 is higher. So we actually see this antagonist signature in

5 those assays all by themselves. 

6 We had hoped -- and that's actually a nice

7 gain of signal, antagonist, which is good. We had hoped that A3

8 and A4, the SRC1, we would see that same antagonist super

9 response, you get up to 200 percent and we don't. It's really,

10 those get up to the true agonist or the true antagonist go up to

11 100 percent. But what I don't know that we've looked at, are

12 there chemicals that get beautifully up to 100 percent and

13 they're not doing anything in other assays where you see partial

14 agonism or partial antagonism in other assays.

15 DR. NICOLE KLEINSTREUER: Actually, a good

16 example is that is fluroanthene, the one reference chemical that

17 was not predicted by the model to be an agonist or antagonist,

18 is active in both A3 and A4, so both the time points of that

19 cofactor recruitment assay. And that may be a function of that

20 assay essentially being more sensitive-driven by the biology

21 that he just explained.

22 DR. DAVID DIX: I have a follow-up or

23 continuation of this response.

24 DR. JAMES MCMANAMAN: Sure. 

25 DR. DAVID DIX: Just to make sure that we're

26 all clear that the technology, this is a protein complementation

27 assay that we're looking at from the Odyssey Thera source. So
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1 the nature of the interaction is specific to the androgen

2 receptor and the cofactor; in this case, SRC1. So it's

3 measuring the proximity of the cofactor and the receptor. So it

4 is specific to the androgen receptor in this particular assay.

5 I just wanted to make sure we were on the same

6 page. So as well as a different cell type, as well as a very

7 different technology than in the other assays in this suite. 

8 DR. TERRY BROWN: I agree it is, it's a

9 different technology, it's a different suite of assays. I think

10 the question is -- my comments really revolve around the

11 interpretation of the data that comes out of that. This might

12 impact in a way how you view a pure antagonist versus maybe a

13 so-called SARM in relation to the cofactor interaction.

14 Dimerization is a very different activity than

15 receptor co-activator interaction. I mean, those are very

16 different things that are being measured. They're

17 protein/protein interactions, but it's a very different dynamic.

18 And the androgen receptor is very peculiar in the sense that,

19 as you probably know, these co-activators bind to the so-called

20 AF1 activation, the activation function of one reach and then

21 the interim is as opposed to co-activators binding with many of

22 the other nuclear receptors in this so-called AF2 region, which

23 is the configuration that the receptor takes when the ligand

24 binds to it in the carboxy-terminus. 

25 So it's just a matter of how you think about

26 these assays and how you put them in the context of your testing

27 that I'm really kind of probing at because it really changes the
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1 dynamics of the system by putting in an SRC1 protein as opposed

2 to the context in a cell-based assay or particularly in a

3 cell-free based assay, where you're just looking at binding

4 interactions.

5 DR. DAVID DIX: One final piece to this

6 response to the question then. We really appreciate this

7 question and the continued thoughts around it and would really

8 value feedback. As Dr. Kleinstreuer mentioned, the AR model and

9 the AR dataset is in an earlier stage of development than the

10 ER, though you've pointed out an important difference between

11 the two in this cofactor recruitment aspect. 

12 But from a more programmatic perspective, we

13 are confident and we would like feedback on whether this is a

14 justifiable confidence that for these datasets where we have

15 over 1,800 chemicals and many dozens of reference chemicals to

16 analyze, we think we have at least the data to inform

17 development and use of the model for the endocrine screening

18 program. And that gets back to what both Drs. Kleinstreuer and

19 Judson have mentioned some ongoing research, but when they get

20 to the point where they deliver these large datasets and they've

21 been analyzed across 1,800 plus compounds, dozens of different

22 reference compounds, this I where we gain a level of confidence

23 in their performance that we think is suitable for the

24 prioritization, and at least, as we've approached it in a

25 step-wise fashion, some parts of the screening step in the

26 program. And that's where we're right now asking you to focus. 

27 DR. JAMES MCMANAMAN: And I think that is part
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1 of one of the charge questions. So I think we'll get to those

2 details.

3 DR. TERRY BROWN: I'm sorry. I didn't mean to

4 get into the details and I didn't mean to be critical. I just

5 meant to comment and try to parse out some of the more detailed

6 aspects of the assays that are proposed. I'm sorry.

7 DR. JAMES MCMANAMAN: Other questions? Dr.

8 Doerge.

9 DR. DANIEL DOERGE: Unfortunately, this

10 doesn't really relate to the androgen receptor, not the least of

11 which because I don't know very much about that, but more to the

12 androgen receptor and the sort of uncertainty aspects and

13 complexity aspects, really, that we're starting to talk about. 

14 And I'd like a little clarification. Again, it starts at the

15 philosophical level, particularly when you're comparing with

16 nuclear receptor binding potency and the uterotrophic ER

17 alpha-driven endpoints. And now you start to ask questions

18 about what does this mean for the membrane receptor.

19 DR. JAMES MCMANAMAN: Dr. Doerge, can you move

20 your microphone a little closer? Thank you.

21 DR. DANIEL DOERGE: Sorry. The membrane

22 receptors obviously is an extremely complicated area,

23 encompassing a number of aspects that may be interrogated,

24 effectively, by this system you've got in place. That is those

25 where ER alpha and beta are targeted to the membrane by specific

26 lipid modifications at a conserved cysteine residue or in their

27 membrane receptors, the GPR30, which is a transmembrane receptor
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1 that binds estrogens into something totally different, which

2 you'd have to capture with different apical -- or not apical,

3 but different downstream effector sites.

4 So what I'm hearing is we're really talking

5 about a classical nuclear receptor-oriented ER alpha largely

6 driven process here. And I guess the clarification I look for

7 is, you know, how are these additional levels of complexity

8 going to be considered as you go forward?

9 DR. DAVID DIX: This is exactly this step-wise

10 approach and making a careful comparison to the existing Tier 1

11 battery. So at this point, the estrogen receptor model and

12 assay data, we made the comparison to the uterotrophic response,

13 which does incorporate, potentially, a nuclear and membrane

14 receptor-mediated effects. And we'll continue to take this

15 step-wise approach across the battery of Tier 1 screening assays

16 that include intact neuroendocrine systems.

17 DR. RICHARD JUDSON: I made this comment

18 before, but I just want to reiterate, in order to probe that

19 mechanism, the membrane-bound receptor mediated activity, we

20 actually need compounds which are known to act through that

21 mechanism and we've done some probing, through the literature,

22 and there isn't very much known. 

23 So there's a hypothesis that there may be some

24 environmental chemicals that act through that, but until we have

25 a test battery to play with, it's hard to make much progress.

26 DR. JAMES MCMANAMAN: Okay. Given that we are

27 running a little behind time, I think what we're going to do
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1 today is we're going to have Dr. Browne's presentation and then

2 hold off on the remaining two presentations from the EPA until

3 tomorrow morning.

4 As lucid as Dr. Wambaugh is, there are still

5 51 slides. So that might take a little time to get through. Go

6 ahead if you're ready.

7 DR. PATIENCE BROWNE: I could talk about that

8 slide, but I don't know much about it.

9 DR. JAMES MCMANAMAN: I guess there are three

10 presentations, tomorrow, so we'll finish them up tomorrow

11 morning when everybody's fresh.

12 

13 ANDROGEN RECEPTOR (AR) INTERPRETATION AND APPLICATION

14 

15 DR. PATIENCE BROWNE: Okay. Well, thank you. 

16 The distinction of being the last presentation today, which is

17 always a somewhat auspicious distinction. Again, we're going to

18 do something quite similar to what we did for the ER model and

19 we're going to talk about the efforts on the program office side

20 for the AR model. You've heard a fair amount about the research

21 efforts and then we'll talk a bit about how we're putting those

22 into effect for our purposes for prioritization and testing. 

23 So back to our roadmap. We're still in the

24 estrogen receptor bioactivity discussion, and I think you heard

25 in the last talk and you'll hear in this one as well that though

26 this effort mirrors the ER model, the AR model is still being

27 developed. So this is somewhat a focus status report and also a
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1 path forward on this. 

2 Just to touch on the major points of this

3 section before we get into all of this, as I said, this is

4 really under development, but we are following a similar

5 approach. As you saw, the mathematics are very similar to the

6 ER model. The expectation with any environmental chemicals that

7 act through the androgen receptor is that most will do so as

8 antagonists, and that, in and of itself, brings a certain level

9 of difficulty discriminating that antagonist effect from both

10 cytotoxicity and any cell-based assays and from overt toxicity

11 in vivo. So we'll talk about how we're dealing with that and

12 how, perhaps, it's going to be handled in the future.

13 Again, we're doing this in a very

14 methodological manner in evaluating the computational tools in a

15 fit-for-purpose manner, inconsistent with the goals that were

16 outlined in the White Paper. So as you heard, we used the exact

17 same template for the ER model with some differences that I

18 think were pointed out by Dr. Brown.

19 We have nine assays that are integrated into a

20 network model. There were 23 in vitro reference chemicals. As

21 we said, there are some limitations to the in vitro reference

22 chemicals in that we don't have clearly defined potencies on

23 that yet. And we'll talk about how that limits our current

24 progress about what we plan for the future.

25 As we developed this model, we plan on

26 increasing that in vitro reference chemical set to define

27 potency. And again, I think we touched upon the fact that that
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1 was a fairly rigorous process for the ER model that we had a

2 peer review process on that and we had some consistent results

3 with multiple different labs and multiple different assays to

4 have consistent AC50, or at least within an acceptable range. 

5 And we're looking for, really, the same level of scrutiny for

6 those in vitro reference chemical potencies for the AR model.

7 We'll also talk about an effort to develop in

8 vivo reference chemicals. So when we talk about the objectives

9 for the AR model for programmatic use, I think it's very

10 important to note that in our iterative process of defining the

11 scientific data behind this and how robust the model is, we're

12 really focused, exclusively, on prioritization of chemicals for

13 additional screening and testing at this point. We'll talk

14 about, perhaps, touching on those second and third goals as the

15 model develops. We certainly plan to circle back to those.

16 Again, as we mentioned, we expect that most

17 environmental chemicals will act as androgen antagonists. And

18 so six of the nine ToxCast assays are relevant to that

19 antagonist pathway. So we see, again, the same thing that

20 you've been seeing all day today. We see the dose response of

21 each of those assays in the ToxCast model, integrated into the

22 network model, normalized against, in this case, testosterone

23 propionate. And we see that red line that represents the

24 antagonist action or the R2 activity, in this case. And we'll

25 be using this to prioritize chemicals in the EDSP Universe.

26 We have the same criteria for scientific

27 confidence for any new tools and will touch on each of those and
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1 give a status report on those. So this includes other reference

2 chemicals that are quickly detected and that would be for both

3 in vitro reference chemicals and in vivo reference chemicals and

4 how well do the new methods compare with those that are

5 currently in use and that would be the results of the ToxCast

6 Tier 1 results.

7 So you heard from Dr. Kleinstreuer in the last

8 talk that again, the model performance against in vitro

9 reference chemicals is excellent. When we look -- and I

10 understand this is a bit difficult to see -- but when we look at

11 the balance accuracy here, we have .97, 97 percent balance

12 accuracy is an exceptionally high, and actually, slightly higher

13 than we saw for the ER model. 

14 At this point, we don't have the performance

15 of ToxCast against a set of in vivo reference chemicals, but

16 I'll talk about efforts that are currently underway to establish

17 those.

18 We are in the midst of evaluating Hershberger

19 assays from the literature. And this is really an effort that

20 closely mirrors the same analysis of the uterotrophic

21 literature. So we have a number of search criteria for a

22 peer-reviewed literature studies, and the results of these

23 Hershberger bioassays are compared with seven minimum criteria

24 to determine whether studies are guideline-like. And our

25 definition of guideline-like is that if we were to run a Tier 1

26 guideline study, we would expect the same results.

27 So this includes defining the animal model,
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1 the sample size, the route of test chemical administration, the

2 number of dose levels, the duration of exposure, and the tissues

3 examined. I should note that the Hershberger bioassay is

4 considerably more complicated than the uterotrophic bioassay. 

5 There are five accessory sex tissue, the weights of which are

6 analyzed relative to a control. And the Tier 1 guideline study

7 has both agonist and antagonist portions, so those are compared

8 to vehicle control, the positive internal control as the

9 testosterone propionate-treated group and then the positive

10 control for an antagonist testosterone propionate, plus

11 flutamide.

12 So it's a larger assay, it's a more

13 complicated assay, and again, when we evaluate what we expect to

14 be a largely antagonist response due to environmental chemicals,

15 that can be quite difficult to discriminate from overt or

16 systemic toxicity.

17 At this point, we're in the developmental

18 stage. We've identified about 25 chemical article combinations

19 and those have all been evaluated by two independent reviewers. 

20 We have a spreadsheet that somewhat mirrors the example that's

21 included in the appendix of the White Paper for the uterotrophic

22 database, although it's substantially less mature. 

23 We're currently in the phase of having an EPA

24 secondary review to make sure that the Agency agrees with those

25 evaluations. And as I said, this will take more time. This is

26 due to the complexity of the assay. The hope is that we will

27 identify chemicals with more than one positive or negative, but
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1 not contradictory results that is consistently positive in

2 multiple assays with no negative results or consistently

3 negative in multiple assays, with no positive results. And

4 identify a set of in vivo reference chemicals from them.

5 Until that is complete, we're not comfortable

6 comparing the ToxCast output with the Tier 1 data since we

7 really want to know how well the model is performing relative to

8 our set of in vivo reference chemicals, as a point of reference.

9 But future efforts will be to compare ToxCast

10 model output with the single AR binding assay, and that's a low

11 throughput-binding assay and with the Tier 1 Hershberger assay. 

12 Both of those are specific to AR interactions. As we said

13 before, we really stopped out analysis at that point, even

14 though we have pubertal male responses and fish responses, which

15 would likely incorporate an anti-androgenic response, but

16 because these are neuroendocrine intact animals and there's

17 quite a bit more complexity potentially there, we'd like to do

18 this in an iterative fashion and stop at just the AR-mediated

19 response at this point.

20 So as I said, this is our path forward in a

21 really a status report. These are our analyses we expect to

22 undertake in the next calendar year, and certainly hope to

23 report by the next SAP meeting. And I think Steve will touch on

24 that in the future directions of it as well. We anticipate that

25 there will be some additional complexity in teasing the results

26 apart.

27 A point that I made for the ER model response
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1 I'm going to make again for the AR model response, and I'll make

2 it a little more slowly to make sure it's clear. I think I

3 might've gone through this a bit quickly.

4 In our EDSP chemical universe, we have

5 somewhere between 1,400 and 1,500 chemicals for which we have

6 ToxCast data. We are going to report out on how many of those

7 are bioactive and either the androgen agonist or antagonist

8 response of those List 1 and List 2 chemicals, but I need to be

9 very clear that we don't have a full set of data for either of

10 those lists. We have 49 of the 52 List 1 chemicals and 56 of

11 the 107 List 2 chemicals. So we're not talking about all data

12 for all chemical populations.

13 So when we look at our graph here, we see that

14 we have a few more categories to deal with because we have our

15 red chemicals here, which are our reference antagonists. We

16 have some black chemicals which are referenced inactives and

17 then we have these sort of yellowish chemicals, which a little

18 hard to see in the room right now which are SARMs, which have

19 both agonists and antagonist activities.

20 When we look at our List 1 chemicals, which

21 are shown in blue here, we do see some AUC scores that are above

22 that 100-micromolar point of reference. And just to clarify,

23 there are no chemicals that would be off the table for

24 consideration. We're interested in all bioactivities; however,

25 we define these bright lines here as just really a point of

26 reference for an AC50, to keep in mind.

27 We have some List 2 chemicals which also have
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1 some modest AUC scores as well, but what we see, most

2 importantly, once again, is that we have this set of chemicals

3 in the larger universe for which we have ToxCast data that have

4 AUC scores that are in the same range, or in this case, exceed

5 what we have for reference antagonist. And again, these would

6 be our highest priority chemicals.

7 When we call some of those out, we see our

8 highest chemical in the EDSP universe is mifepristone here. And

9 interestingly, it sort of brings up an issue that was brought up

10 in the ER, mifepristone is a progesterone receptor and

11 glucocorticoid receptor antagonist. We do we see some crossover

12 activity and perhaps, this is contradictory to the expectation

13 of what we'd expect for this synthetic steroid, however, it's

14 well supported by the published literature, as is the response

15 that we see for BPA. We think of BPA as typically being an

16 estrogen receptor agonist. We do see some modest AR receptor

17 antagonist in here. And again, this is also well supported by

18 literature.

19 So we have vinclozolin here, which is one of

20 our reference chemicals that was called out previously. Just to

21 give you a point of reference, it happens to be a List 2

22 chemical and is the highest-ranking List 2 chemical. We have

23 our conazoles, one of which is I believe -- I want to say it's a

24 List 2 chemical as well, but I can't quite tell from here. I

25 apologize.

26 And we have our triclosan and our

27 triclocarban. We talked about triclosan in the case studies
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1 that Dr. Kleinstreuer presented, being somewhat androgenic and

2 triclocarban being the alternative with lower androgenicity. 

3 Again, we have the supplementary data for those that are

4 interested in delving deeper into this topic.

5 But just to back up a step and evaluate the

6 model performance, we still have this very rigorous

7 fit-for-purpose analysis. And I think, despite the fact we're

8 dealing with fewer assays here, nine assays as opposed to 18 in

9 the ER model, we still have greater coverage of the ToxCast

10 pathway than we may have in the Tier 1 battery. We have a

11 single AR binding assay. 

12 We don't have an ARTA assay in the Tier 1

13 battery, whereas we do have that covered in the ToxCast assays. 

14 So we have nine assays that use multiple different technologies

15 to detect multiple different points of signaling along that

16 pathway. And there's a good chance that we get greater

17 sensitivity out of the model.

18 Again, though, we don't have the same coverage

19 for the AR model at this point. We do plan on expanding that

20 set of reference chemicals. We currently have those 23 I

21 mentioned earlier and Nicole mentioned before me. When we look

22 at the Tier 1 interlab validation study, we had 10 chemicals for

23 the binding assay and there were 13 chemicals used in the

24 Hershberger in the lab validation, which was actually conducted

25 by OECD. 

26 I should point out that there's overlap

27 between those 10 and 13, and almost entirely those up to 13
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1 chemicals are covered in the 23 reference chemicals we used as

2 well. So we have current ongoing efforts to expand that

3 reference chemical set and to define potency. And again, that

4 will be a rigorous process. Currently, the reference chemicals

5 include a lot of pharmaceutical chemicals. And these, of

6 course, are specifically designed to target the AR. Thirteen of

7 out of those 23 reference chemicals are actually steroids or

8 synthetic steroids. We expect them to target the air with very

9 high affinity. 

10 So we know the model performs very well

11 against these. That is not terribly surprising; they were

12 designed to do exactly that. But the take-home point on this is

13 though we need to expand our reference chemical set to include

14 less potent chemicals and some negatives -- and this was one of

15 the direct recommendations of the July 2013 with regard to the

16 ER model performance, and that was done for ER, we plan on

17 undertaking the same exercise for AR.

18 At this point because we're using such potent

19 reference chemicals, we expect that any chemical in the EDSP

20 Universe that has an AUC score that's in that same range to be

21 very likely bioactive. In other words, we're probably very,

22 very good at detecting the strong components right now.

23 So we have strong confidence in using this

24 model for prioritization and that's one of the reason that we're

25 limiting the uses at this point, until we undertake those other

26 efforts.

27 So revisiting the objectives, just kind of in
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1 summary, we've said that we feel, based on the reference

2 chemical performance, the model is suitable for prioritization

3 of chemicals for further EDSP screening and testing. Of the

4 List 1 chemicals for which we have data, we have no agonist

5 that's expected. We really don't expect that of the AR

6 interactions for environmental chemicals. We have eight

7 antagonists and about 82 percent with no activity. 

8 And again, that no activity is a true zero

9 model output. We have a List 2 zero agonist, six antagonists,

10 about 90 percent with no activity. And then the greater

11 universe, we have less than 1 percent agonist. We have just

12 under 8 percent antagonist, which is 144 chemicals, and 88

13 percent which are zero values. 

14 So the zero activities are fairly similar in

15 the List 1 and List 1 in universe chemicals. We're finding the

16 vast majority of chemicals have a zero model score and what we'd

17 really like to do is prioritize those universe chemicals that do

18 have some level of bioactivity.

19 Again, I moved relatively quickly through

20 that, but I would welcome questions from the panel.

21 DR. JAMES MCMANAMAN: Okay. Are there

22 questions for Dr. Browne?

23 All right. Thank you very much.

24 DR. PATIENCE BROWNE: Thank you.

25 DR. JAMES MCMANAMAN: So I think that will

26 conclude today's session. We'll begin tomorrow morning at 9:00

27 a.m. So I look forward to the next set of presenters and some
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1 great data. Thanks.

2 (Whereupon, the meeting was adjourned for the

3 day)

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1 

2 DR. JAMES MCMANAMAN: Good morning. Time for

3 day-2 of this session on Integrated Endocrine Bioactivity and

4 Exposure-Base Prioritization and Screening. 

5 So I'm going to have the panel introduce

6 themselves in just a minute. But first off, the feedback we

7 were getting yesterday from some of the mics were because

8 they're cordless mics. So the one in front of Veronica and the

9 one in front of Ed and Tom and Grant, those are all cordless, so

10 please turn those off unless you're going to use them, because

11 that's where the feedback was coming from yesterday. The ones

12 with the wired mics, they're fine. You can leave them on.

13 But I encourage everyone to bring out their

14 inter-lounge singer and hold your mics like I am close so that

15 we can record everything. Okay. 

16 So with that, I'm Jim McManaman. I'm a

17 Professor of Reproductive Sciences at the University of

18 Colorado, and I am the session Chair. And I'll let the other

19 members of the panel introduce themselves now.

20 DR. BARRY DELCLOS: Barry Delclos from the

21 FDA's National Center of Toxicological Research.

22 DR. DANA BARR: Dana Barr, Professor of

23 Exposure Science and Environmental Health, Emory University,

24 Atlanta, Georgia.

25 DR. DAVID JETT: Dave Jett, NIH, Director of

26 the Chemical Defense Program there.

27 DR. VERONICA BERROCAL: Veronica Berrocal,
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1 Assistant Professor of Biostatistics at University of Michigan.

2 DR. TERRY BROWN: Terry Brown, Professor of

3 Biochemistry and Molecular Biology, Johns Hopkins School of

4 Public Health.

5 DR. ROBERT DENVER: Bob Denver, Professor and

6 Chair of Molecular Cellular Developmental Biology, University of

7 Michigan.

8 DR. CATHERINE PROPPER: Cathy Propper,

9 Professor, Department of Biological Sciences, Northern Arizona

10 University.

11 DR. DANIEL DOERGE: Dan Doerge, National

12 Center for Toxicological Research.

13 DR. EDWARD PERKINS: Ed Perkins, U.S. Army

14 Corp of Engineers, Ecotoxicology.

15 DR. THOMAS POTTER: Thomas Potter, USDA

16 Agricultural Research Service.

17 DR. GRANT WELLER: Grant Weller. I'm a

18 Research Scientist and Statistician at Savvy Sherpa in

19 Minneapolis, Minnesota.

20 DR. DANIEL SCHLENK: Dan Schlenk, University

21 of California, Riverside.

22 DR. JAMES MCMANAMAN: Okay. Thank you

23 everyone, all the panel members again for participating in this

24 very interesting panel session. So with that, we'll go back to

25 the EPA, and Dr. John Wambaugh will do the first presentation.

26 

27 HIGH THROUGHPUT EXPOSURE; TOXICOKINETIC/DOSIMETRY
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1 

2 DR. JOHN WAMBAUGH: Good morning. I'm John

3 Wambaugh from the Office of Research and Development at the U.S.

4 EPA. I am actually going to be presenting two chapters from the

5 White paper in this talk. So first we'll hit on the exposure

6 chapter, and then we will actually also cover the toxicokinetics

7 and dosimetry chapter. So these are chapters 4 and 5 in the

8 White paper.

9 So chapter 4 covers what we refer to as the

10 results of the ExpoCast project. And so this is a sister

11 project to the ToxiCast project where, instead of trying to

12 forecast to toxicity -- it's a ToxiCast, just toxicity

13 forecasting -- we're trying to forecast exposure. And so we can

14 think of exposure both from exterior to the home sources; so

15 emissions both to air, to water, bioaccumulations to the food

16 web.

17 And so, yes, you could have exposure from

18 inhalation. You could have exposure from food products,

19 residues on those products, as well as consumer use of products.

20 That is a source of exposure; the objects that we deliberately

21 use and put in the home, and then also the objects that are just

22 in the home that we indirectly get exposed to.

23 All of this research was just presented to a

24 scientific advisory panel in July. That was the new

25 high-throughput methods to estimate chemical exposure. I know

26 that some of you were there in July and I really appreciate you

27 returning and seemingly being cheerful about seeing this
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1 reviewed again. And I really do appreciate your continued

2 involvement here.

3 ExpoCast is trying to develop approaches,

4 tools and data to rapidly predict exposure for thousands of

5 chemicals. And so this comes back to our fit-for-purpose. 

6 ExpoCast is not about trying to do a great job for a chemical

7 that we've been studying for 30 years. It's really to address a

8 gap in data, and I'll elucidate that gap a little bit more. But

9 thousands of chemicals is the important part, and so I like this

10 T.S. Eliot quote at the bottom.

11 All cases are unique or all chemicals are

12 unique and very similar to others. And so what we're trying to

13 do is, from the chemicals that are data-rich, learn what we can

14 about the chemicals that are data-poor.

15 So why are we doing this? Why are we

16 presenting this in the meeting today? Well this is, I think, a

17 figure that leads in the direction of the talk that Richard

18 Judson will give just a little bit later this morning. But the

19 basic idea is that, if you can take toxicity data and convert it

20 into an oral-equivalent dose.

21 And so go from the frank concentration that

22 was measured in vitro, so five micromolar or three micromolar,

23 and convert it to a dose that might cause that as has been done

24 in this figure. This is from Judson et al., 2011, where these

25 box and whisker plots here are really -- I think that each dot

26 is a ToxiCast assay. 

27 And so you have doses milligram per kilogram
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1 per day for a range of pesticides, so that's ToxiCast there. 

2 Then you're comparing with in vivo animal studies. So the solid

3 squares are the low effect level group. The triangles are the

4 no-effect level group. And you can see that that actually does

5 seem to track, for this subset of pesticides, with the ToxiCast

6 data.

7 And actually, if you go to the no-effect

8 group, divide it by 100, yet it seems to be below, the no-effect

9 group is a little bit above the ToxiCast. And then you can

10 compare with actual estimates of exposure for humans to those. 

11 And you can see, generally, you have some margin between the

12 estimated human exposure and the activity from ToxiCast.

13 And so we have basically an entire talk on

14 this idea, but this is where you'd want to go. You want to have

15 kinetics, toxicokinetics that allow you to convert the

16 concentrations from ToxiCast into human doses, and then you want

17 to be able to compare it with exposure.

18 Unfortunately, as was pointed out by Barbara

19 Wetmore in a paper in 2012, as you look for chemicals that you

20 have ToxiCast -- in this case, they looked at almost 250

21 ToxiCast chemicals on which data was available at the time. 

22 Almost 200 of them had some sort of exposure estimate, and I

23 think it was 11 of them had some sort of toxicokinetic data.

24 So if you want to do that comparison that I

25 showed you on the previous slide, you could only really do that

26 for 11 chemicals. 

27 What Barbara Wetmore did, and this is the
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1 subject of chapter 5 -- and I'll go into this in greater detail

2 in a little bit. But basically she used pharmaceutical

3 techniques to do in vitro toxicokinetic estimates. And she was

4 able to push up that number of chemicals that have toxicokinetic

5 data up to basically the same number that she looked at from

6 ToxiCast.

7 So you had for almost 200 chemicals some

8 estimate of exposure, some estimate of bioactivity, and the

9 pharmacokinetics necessary to convert from the in vitro

10 bioactivity to a human dose that might cause that bioactivity.

11 Unfortunately, what she found as we went on to

12 the next set of ToxiCast chemicals, is that the first set of

13 ToxiCast chemicals were deliberately chosen to be enriched for

14 pesticides. These are chemicals that we know a lot about and we

15 have a lot of exposure estimates for those chemicals.

16 As we went into the next phase of ToxiCast, a

17 greater number of industrial chemicals were chosen. They're

18 chemicals that really are the more unknown sorts of chemicals. 

19 And even though we were able to get in vitro pharmacokinetics

20 for all of those, we had no exposure estimate to use. 

21 And this is a problem that was nicely pointed

22 out in a paper in 2012 by Peter Egeghy where, basically, looking

23 at the exposure data landscape for manufactured chemicals. And

24 basically what you see is that we do have gross national

25 production volumes for about 14,000 chemicals, so that just

26 tells you how much of the chemical was manufactured or imported

27 into the United States.
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1 We have some sort of idea of how almost 8,000

2 of these chemicals are used. That number has changed somewhat

3 since 2012 because of new databases we developed. But then as

4 you really get down to water concentrations or air

5 concentrations or biomonitoring data, we're down to hundreds and

6 dozens of chemicals.

7 And so if we are trying to take on a list like

8 the EDSP universe of roughly 10,000 chemicals, we need to do

9 something to address that data, especially if we want to do the

10 sorts of comparisons I was showing you earlier between

11 bioactivity and exposure.

12 And so this was, to some extent, addressed by

13 the 2012 NRC report on Exposure Science in the 21st Century. 

14 It's a large book. Of particular interest was the need for new

15 tools for screening and prioritization for exposure, a better

16 focus on monitoring studies and better quantification of

17 population vulnerability.

18 So this is the figure that was much discussed

19 yesterday. I will not identify the artist responsible but let's

20 just say we're close. So what was correctly pointed out about

21 this figure is that this figure has a very heavy exposure accent

22 to it, partially because it came out of the exposure

23 considerations.

24 It is completely possible that biological

25 activity is moving around. And what really happens for any

26 given chemical is that the median estimate of both the exposure

27 and the bioactivity move around. If we illustrated all the
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1 permeations, it would get pretty complicated, but we can

2 certainly do that. But everyone figured this out yesterday,

3 eventually.

4 The other thing to look at though -- and this

5 comes out of a couple of the public comments that I was able to

6 preview, thanks to them being posted early -- is that there is

7 the consideration of whether or not we should just compare the

8 central tendency of these estimates or the tails of these

9 estimates. So, there are pros and cons of anything that you

10 would want to do there.

11 The pro of comparing the tails is that, what

12 I've drawn here, with the exception of this one exposure

13 estimate there, everything seems to have the same 95 percent

14 interval. Well as we develop our tools, we are trying to get to

15 the point for chemical class A where it's very well-known. We

16 might have this nice tight confidence interval where we really

17 feel confident that exposure somewhere or bioactivity somewhere

18 within that interval.

19 As we get to chemical class B that's

20 relatively unknown, that might be much, much broader. So you

21 could actually have two chemicals that have the same central

22 tendencies, but you're much less confident about that second

23 chemical.

24 And so I would say, if the distributions are

25 overlapping, even if the central tendencies seem to be the same,

26 that would be a higher priority chemical. That doesn't mean

27 it's the highest priority chemical. But basically we want to be
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1 able to take into account our confidence in our predictions. 

2 And so that gets down to the two statements

3 down here. And I've stolen this from Nate Silver, who has a

4 great book; The Signal and the Noise -- but basically a

5 prediction as to his specific statement. And if you have

6 multiple models, each model could have a prediction. One could

7 say up and one could say down. 

8 A forecast is a probabilistic statement where

9 we're trying to incorporate as many of those predictions into a

10 single distribution of what goes on. And so one way you could

11 look at it is, if you had 10 different models and they all

12 agreed on where the exposure for a chemical was, that might be a

13 very tight distribution.

14 If those same 10 different models span a range

15 of predictions, your forecast is going to be wider. And that's

16 why we're really interested not just in the central tendencies

17 of these estimates, but what is the distribution. Of course,

18 then the question becomes how do you get this distribution, and

19 that's what a lot of what we'll be talking about for the rest of

20 the day.

21 So our goals for ExpoCast are to incorporate

22 multiple models into consensus predictions for thousands of

23 chemicals. And so we are open to any and all models that think

24 that they can predict exposure. We are happy to basically

25 accept them, however, we are in the business of looking gift

26 horses in the mouth. 

27 So yes, we appreciate any model that can make
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1 predictions for thousands of chemicals, but then we want to

2 evaluate and calibrate that model to see how well it does for

3 any available data that we have. And then we want to

4 empirically estimate uncertainty in those predictions.

5 And so I'm representing arbitrary mystery

6 chemical X from the EDSP list as a pile of powder. And what I

7 want you to think of is that, really, from the exposure point of

8 view, we have 10,000 chemicals on this EDSP list and most of

9 them we do not have an idea of what the exposure should be. So

10 we have thousands of chemicals to look at and we want to figure

11 out how can we use ExpoCast or any other technique to predict

12 exposure. And then, how do we handle uncertainty?

13 And so the particular study that we have

14 focused on using is from the Centers for Disease Control

15 National Health and Nutrition Examination Study, their survey. 

16 And so, primarily, I believe this study is known for providing

17 the biometrics about the American population; average height,

18 average weight, lots of different health indicators. But it

19 does monitor the presence of chemicals in spot urine samples and

20 blood draws for, depending on the chemical, roughly 2,000

21 people.

22 And so this is not all of America but this is

23 actually a very nice rolling survey of Americans. Now the

24 survey is conducted in a representative manner, so a single

25 individual sample might actually represent 18 people, depending

26 on their demographic, or fewer people that's -- it is a complex

27 study design but we've put a lot of work into trying to --. 
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1 All that information is made available by the

2 CDC and we've put a lot of work into incorporating that study

3 design information into our analysis. So the CDC has monitored

4 a subset of the EDSP universe chemicals. It's a couple of

5 hundred. I can't guarantee that they're all in the EDSP

6 universe. But we have a couple hundred chemicals that have been

7 monitored by the CDC in the urine and blood of Americans.

8 And so what they find is that some of these

9 chemicals seem to have relatively high exposure. There are

10 metabolites of these chemicals, or the chemicals themselves that

11 are present in the urine of most Americans. Other chemicals

12 have some sort of moderate exposure. You can show that there's

13 a distribution and they're present in a lot of samples, maybe

14 not all of the samples and maybe not a high levels. And then

15 some of the chemicals are below the limit of detection.

16 Now that's always an issue, is that if you're

17 using analytic chemistry, you've got a limited urine sample,

18 you're trying to look at a large number of chemicals. So there

19 is a limit of detection associated with each chemical. It's

20 chemical specific. And in some cases, all you can do is say,

21 well, that chemical isn't there at the limit of detection. It

22 could be 90 percent of it, it could be a billionth of it. We

23 don't know but it's below some level. 

24 What we are doing with ExpoCast is taking what

25 we call high level information, so very course descriptors that

26 we think might be related to chemical exposure, and trying to

27 group the CDC chemicals or any monitoring chemicals into
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1 chemicals that are below the limit of detection, moderate

2 exposure and high exposure.

3 And we're using this very high level

4 information, in particular, what we call the ACToR UseDB, which

5 is available for tens of thousands of chemicals. 

6 So the question is not to say, well pick one

7 chemical that we know a lot about that's high and we'll figure

8 out exactly why that chemical is high. It's, take the

9 information that we have available for the entire EDSP universe

10 and try to use that information to organize these NHANES

11 chemicals, because what we can then do is apply that same

12 information to these chemicals that have never been tested. 

13 And so what we're able to do is use this high

14 level information to group some chemicals that have no exposure

15 information into -- these look a lot like the high chemicals in

16 the NHANES list. These look a lot like the chemicals that are

17 low in the NHANES list. These are the moderate ones. And we're

18 actually doing this in a continuum. This isn't a class-based

19 thing but this is the general idea.

20 And so there's two things that are really

21 important from this. One, we were basically able to identify a

22 list of chemicals that potentially looked very much like the

23 chemicals that are high in the samples of most Americans. We'll

24 still have chemicals left on the table that we can't say

25 anything about.

26 And so, if this was going to be the end of

27 things, that might seem unsatisfying. If, however, we've got
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1 10,000 chemicals to look at, we're talking hundreds of chemicals

2 that end up in this high exposure list here. 

3 And so, as far as a fit-for-purpose

4 prioritization, I can tell you today the chemicals that look

5 like they fit in that high bin. That doesn't mean that there

6 are some of these chemicals we haven't looked at that won't also

7 eventually want to be examined. But if you need to go out

8 tomorrow or next year and start a study, ExpoCast is intended to

9 be a tool to point you in the right direction tomorrow and

10 deliver then our best estimate.

11 So why is this so complicated, or at least

12 trickier than -- not that it's trickier. Why is our analysis

13 really cruder than what was presented yesterday for the

14 biological space? So there's two basic issues. 

15 One, everything is confounded by a human

16 activity. So it would be kind of like, if the ER pathway could

17 chose to sleep in that day and not go into work or the ER

18 pathway was a workaholic and decided not to sleep at night, we

19 have that issue when it comes to exposure. We also don't have

20 the sort of data we want to evaluate this. 

21 And so, exposure space for human receptor,

22 basically we thought of -- and like we described this earlier --

23 there is chemical manufacturing release to the environment and

24 that can end up in food. It can end up in air soil and water

25 and that can lead to the human. It can also end up in consumer

26 products, which can either be thrown into the environment or

27 actually directly used, such as a lotion might be used by a
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1 human or outgassing from flooring materials or articles of

2 clothing. There's basically many ways that chemicals can end up

3 reaching a human sample.

4 And so we describe any pathway where you'd go

5 from a product to a human as an exposure pathway. And so this

6 lotion use we call near-field direct. So near-field refers to

7 the source of admission. Near-field is if it's in the home. 

8 Far-field is more of an environmental release. It could be the

9 factory up the river, it could be a factory in the next state

10 over.

11 So far-field release is more of this

12 environmental release. Near-field is what's in the home. And

13 near-field direct is when you're deliberately applying it. 

14 Near-field indirect is outgassing just into your environment. 

15 Then there's also a dietary pathway, which is a lot like the

16 near-field direct pathway, but basically you're taking food so

17 you could have residues on the food that are then getting into

18 the person.

19 So the problem with doing all of this analysis

20 is that this exposure event in the blue box is largely

21 unobservable. It's really hard to know exactly how much someone

22 breathed in or it consumed. I always like to think of it in

23 terms of potato chips. You can know that the serving size on a

24 bag of potato chips is 15 chips, but it's pretty unknowable

25 whether or not the person ate the whole bag of chips. 

26 And honestly, for most of us, we probably

27 don't want people in this room sitting around knowing whether or
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1 not we ate the whole bag of potato chips. So this event is hard

2 to see, so we have ways around this. But this blue box, this

3 are here, that's the data you want to be evaluating the exposure

4 models with, and so that's seemingly unknowable. But we can get

5 around that to some extent.

6 We have what I call forward models, which take

7 data about the presence of chemicals in environmental media or

8 presence of chemicals in products and then construct the model

9 on how those products are used, consumed, how those media are

10 interacted with and make a prediction of exposure. And there's

11 a suit of models available that do that. And so that's one way

12 of getting at this problem.

13 Then we also have reverse models. And that's

14 basically where we take something like the CDC data that I was

15 describing earlier and we look at biomarkers of exposure. And

16 so the benefit of that is that any biomarker of exposure that

17 you have available reflects all the pathways. 

18 So let's say there's a pathway in this

19 schematic that I haven't thought of that does lead to exposure. 

20 The biomarker for that chemical might very well then be present

21 in the urine. Now biomarkers themselves are confounded by

22 kinetics. You know, if something is rapidly metabolized and

23 wasn't there in the sample, we're not going to see it.

24 So reverse modeling has as many drawbacks,

25 maybe more, as forward modeling. But the two tools together can

26 be used to provide an evaluation. And so we refer to this as

27 the systematic empirical evaluation of models. And all we're
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1 really trying to do is use a combination of forward prediction

2 of exposure. So we have any available model that can predict

3 forward exposure based on the data we have.

4 We infer exposure using the reverse models and

5 then we systematically evaluate these two sets of data against

6 each other. And whatever we can learn from the chemicals that

7 we have monitoring data on, we extrapolate to the model

8 predictions that we have for chemicals with no modeling data.

9 And to do this, we've been using a Bayesian

10 formulism using a multivariate linear regression. And I'll go

11 into that a little bit more. Unfortunately, in the July SAP,

12 this was an entire talk basically on this one slide here. There

13 is detail available in the White paper and there are a series of

14 publications on this approach, 2013 to 2014 Environmental

15 Science and Technology. 

16 But if you have questions, I'll be happy to

17 fill them. But basically, the schematic of what we're doing is

18 on the next slide. This is Figure 4.1 from the White paper. 

19 And this is just a graphical description of what I hopefully

20 just described where we basically have the EDSP chemical

21 universe that we want to know about, and we have a subset of

22 those chemicals that we actually have monitoring data.

23 And so we use some sort of technique to do

24 exposure inference, and I'll describe that a little bit more,

25 but that gives us some sort of estimate of what actual inferred

26 exposure would be. And then we have a series of models that

27 make predictions.
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1 And so what you're hoping to see is some sort

2 of systematic correlation between the model predictions and the

3 inferred exposures. So bad news would be if it's completely

4 flat. That would be basically meaning that a model was not

5 predicting anything. But the weight you would then assign to

6 that model is zero. You'd say we don't want to use that model. 

7

8 And of course you'd go back and let the

9 modeler know right now we're not showing that that model

10 predicts anything. And sometimes it's an easy fix and sometimes

11 it's not. But if you do get some sort of correlation between

12 the models, you can apply that correlation to the other models.

13 You basically say, okay, I believe this

14 combination of models seems to be predictive based on the

15 monitoring data that we have, so that's our better than nothing

16 estimate of exposure for all of these other chemicals.

17 What you also get, and this is, to me,

18 actually more important than just getting a calibrated estimate

19 for the other chemicals, is an estimate of uncertainty. You

20 never want to believe a modeler, and I say this as a modeler and

21 I love modelers. You never want to believe a modeler where all

22 the points are perfectly on the line. Something's wrong there.

23 Instead, what you want to see is what is the

24 scatter around the line? You know, you hope there's a trend but

25 you want to estimate what is the scatter on that trend. And

26 that is our empirical estimate of uncertainty. That says, using

27 the data that we have, these inferred exposures, how wrong are
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1 the models? Because we can extrapolate that uncertainty also to

2 the other chemicals.

3 And so, this is where that bell curve is

4 coming from in our exposure estimates. It's an empirically

5 estimated distribution of our best guesses of what the inferred

6 exposures were around the predictions of our models. 

7 So we've been continuing on this process for

8 some time now, so this first generation model was a Wambaugh et

9 al. publication in Environmental Science and Technology in 2013.

10 And in that case, we took all comers. But basically there were

11 two available models that could make high-throughput predictions

12 of exposure at that point. 

13 These were far-field models, so they predicted

14 the fate and transport of models to the environment,

15 accumulation to the food web and exposure to humans from those

16 sources. And then we had a zero or one indicator of is the

17 chemical in the house or not. 

18 And with that information, we compared it with

19 the NHANES urine data and we found a very modest correlation,

20 but a statistically significant correlation with the inferred

21 exposures. And almost all of that correlation was driven not by

22 these Fate and Transport models, but actually by that zero or

23 one indicator of is the chemical in the house or not. That

24 actually outdid these models that had all of these physical

25 chemical information and models for the environment.

26 And so our next generation, we said, well that

27 seems to indicate how a chemical is used in the home, is a big
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1 driver. And so what we then wanted to do is we broke out in the

2 home or not into a series of yes or no questions of is the

3 chemical used in consumer products; is the chemical used as a

4 colorant; is it an inert ingredient in certain things, and tried

5 to identify which of those seemed to best correlate with the

6 NHANES urine data. And then we got a much best better model

7 that I'll be presenting today. 

8 And then, you know, we're not satisfied with

9 that. The second-generation analyses was published just in

10 September in the Wambaugh et al. in Environmental Science and

11 Technology. And then we are already working on our

12 third-generation model. I mean that's how you know I'm a

13 modeler, I've always got my next generation model.

14 And so there, we have the advantage of a real

15 breakthrough from the Sheds program, so that's the stochastic

16 human exposure dose simulator. This is an EPA already product

17 that has traditionally been used over the last decade or so to

18 give extremely precise estimates for a specific chemical of what

19 the distribution of exposures would be. 

20 It's almost like playing the game of Life. 

21 You simulate all the events in a person and they're actually

22 parameterized so that you get a distribution for a population of

23 people on what their exposures might be.

24 Christian Isaacs (phonetic) in the Office of

25 Research and Development has streamlined the Sheds program so

26 that it has now been run on 2500 chemicals. So you go from

27 Sheds having been run on a couple of dozen chemicals in its
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1 first 10 years of existence to now it's been run on 2500

2 chemicals.

3 Certainly there are approximations that had to

4 be made to make that happen, but we are comparing it with the

5 biomonitoring data, the urine data. And we're actually adding

6 blood data so that we will have some evaluation that we can

7 actually show that those assumptions were appropriate and that

8 we're able to make these predictions for larger datasets.

9 So how do we do this biomonitoring inference? 

10 Well we're generalizing the model that was developed by LaKind

11 Naiman for looking at bisphenol A. And basically there, they

12 would look at bisphenol A glucuronidate in the urine and say,

13 well, for every molecule of this metabolite of bisphenol A that

14 I see in the urine, there must have been a molecule of parent

15 exposure.

16 Now this is a steady-state assumption. And we

17 know that that's wrong but, again, that's why we're doing this

18 empirically where we're trying to evaluate how well these things

19 work. Well basically, in a steady-state assumption you'd say

20 how many milligrams of chemical do I see per gram of creatinine;

21 how many grams of creatinine are excreted per day? 

22 And this is, again, where we use this very,

23 very detailed information that the CDC provides. We're actually

24 able to do individual-specific estimates for each sample of the

25 grams of creatinine per day for that individual.

26 What we then do is we take any metabolite we

27 see and we link it back to a parent exposure so that we would
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1 have an observed mg per kg per day from the CDC, and we link

2 that back with the parent exposure. Now, in some cases, you

3 might have a parent chemical that has multiple metabolites. And

4 we are just adding those metabolites back up with some fraction.

5 We don't know how much of each metabolite came from that

6 particular parent chemical. 

7 So if we knew this fraction, which is

8 somewhere between zero and one, we could do this perfectly with

9 relatively no uncertainty. Unfortunately, we don't know that

10 fraction. So we are uncertain about the fraction and we're

11 uncertain about the parent chemical exposure. But having these

12 unknowns and having this equation, we are able to use

13 computational techniques to simulate a range of plausible

14 values.

15 So because we can write the equation even

16 though we don't know the precise values, we find a range of

17 values that are consistent with the data. And we're using

18 Bayesian analyses via Markov Chain Monte Carlo in order to do

19 this.

20 Now part of the reason we've got to use a

21 computer to do is, I wish it was always parent chemical leading

22 to a single analyte in urine. And so red are the parent

23 chemicals and blue is the analyte in urine. But the actually

24 CDC chemicals by design, to some extent, are much more

25 complicated.

26 So for instance, the CDC was very clever. 

27 They monitored one metabolite of organophosphate pesticides that
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1 maps to at least 17 different parent chemicals. And so, if the

2 design of your study is to determine whether or not people get

3 exposed to organophosphate pesticides, that's a brilliant

4 metabolite to monitor. If you're trying to do what I'm trying

5 to do, it makes things a little difficult because you don't know

6 which of those 17 chemicals necessarily went to that.

7 However, as you can see here, I believe this

8 is a cluster of ethylates. There are chemicals that can lead to

9 multiple metabolites. And different chemicals can lead to

10 different metabolites but then it's not everything to

11 everything. There's actually some structure there. 

12 I would not want to do this with pencil and

13 paper, but you can begin with this Markov Chain Monte Carlo

14 techniques to try to scan the space of possible permutations of

15 exposures that were consistent with what the CDC measured. And

16 so we have lots of unknowns, but we basically have a technique

17 that can try lots of these possibilities.

18 And we have one other major issue that we have

19 to address when we're looking at the NHANES samples, and that's

20 the limit of detection. And so, for each of these chemicals, we

21 are actually trying to estimate the median U.S. population

22 exposure. 

23 So we're not necessarily getting at the upper

24 confidence limits yet and that's because, for 2000 people, we

25 have a pretty good dataset for getting to the median. But as

26 you start to look at 95th or 99th percentile, that 2000 people

27 starts to shrink to a smaller and smaller group.
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1 So for each chemical, we assume that the 2000

2 people that the CDC looked at were log-normally distributed. 

3 And that's a pretty good assumption for the chemicals that are

4 above the limit of detection that looks to generally be true.

5 So for the chemicals that are mostly below the

6 limit of detection, we make the exact same assumption. We

7 assume that there's this log-normal distribution of exposure,

8 but the mean and the standard deviation are mostly below the

9 limit of detection. So we don't know is it a big peaky bell

10 curve or is it a short very broad bell curve? We don't know the

11 mean and the standard deviation.

12 But again, because we're using this Markov

13 Chain Monte Carlo technique, we can basically say, well, find us

14 the range of mean and standard deviations that would be

15 consistent with the data. And so we will actually consider this

16 both this bell curve and a range of other bell curves. And so,

17 we are still assuming that all the chemicals are log-normally

18 distributed. We just know a lot less if the chemical is mostly

19 below the limit of detection.

20 So all of this has, at least to me, gives the

21 appearance of being very complicated in some sense, but I think

22 it's really important to remember that really what we're doing

23 is a linear regression. We're saying we have some model

24 predictions. We have to do some arithmetic to infer the

25 exposure. But ultimately what we want to do is see do our

26 inferred exposures track with our model predictions.

27 And so, it's literally going to be an
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1 exposure. It's going to have an intercept value. It may not be

2 zero. That intercept value is basically the average exposure

3 across all the chemicals. It's not explained by the rest of

4 these variables. And we do transform these variables to make

5 that true.

6 We have model predictions and each one of

7 those could have a slope, so you could have lots of different M

8 values. If you have 10 different models, you'd have 10

9 different M values. We have additional factors for these zero

10 one use indicators; is it in cosmetics or not? And then, I

11 think this is the most important part; we have this residual

12 arrow, this epsilon, which is anything that's unexplained by the

13 model.

14 And so, even if none of our models worked, we

15 would basically be able to give you the average value for all of

16 the bio monitored data, and the noise, how many standard

17 deviations around that do you need to take things into account. 

18 So any pathways that are missed by the forward models will

19 increase this epsilon term. 

20 So what we can do then is basically rank

21 chemicals based on exposure. So we have predicted exposure,

22 close to 8000 chemicals at this point. All of these chemicals

23 have been tested by NIH Tox-21, ultra-high-throughput toxicity

24 testing. So we have some idea of the in vitro toxicity or

25 bioactivity of all of these chemicals. 

26 And what we're plotting here for each chemical

27 -- and note that this is transformed so that our first 10
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1 chemicals take up about a fifth of the plot. The next hundred

2 chemicals take up about, the next fifth of the plot, got a

3 thousand here. And then this next half of the plot is 7,000

4 chemicals that are all bunched together.

5 And what we we're able to do is, for each

6 chemical, chemical number one, we're plotting the median and the

7 upper 95th percentile confidence interval. And we're showing

8 both the total CDC sample, so a model calibrated to that. And

9 we've broken out for children ages six to 11, which is the

10 youngest demographic that we have urine samples from the CDC, so

11 we have a range of predictions for each of those. And this is

12 in mgs per kg per day. And so we have a median and a 95th

13 percentile for each of these chemicals. 

14 So you'll notice that we don't plot the lower

15 95th percentile bar. And one of the reasons for that is it was

16 already a fairly complicated plot. But the other reason is that

17 these horizontal lines that I just added to the figure are the

18 distribution of the limits of detection across all the chemicals

19 for the CDC samples, so the median and the quintiles for that

20 limit of detection.

21 And so what you actually see is that, for

22 almost all of the chemicals, the lower 95th percentile is below

23 the limit of detection. But actually, for a lot of these

24 chemicals -- and remember this is a transformed plot, so we have

25 thousands of chemicals that we're basically predicting with 95

26 percent confidence that if the CDC went to look for them, they

27 wouldn't find them, at least given the typical limits of
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1 detection that the CDC has used.

2 As far as what chemicals are involved in this,

3 well we have a distribution value. So each arrow represents the

4 chemicals that we have information on. And so you see that the

5 highest overall chemical is a chemical that's monitored by the

6 CDC, and it is high in most Americans. And actually our ninth

7 highest chemical is also monitored by the CDC.

8 But then, the benefit of this approach is that

9 -- when we have seven other chemicals here that seem very much

10 like those chemicals that have never been looked for. And so,

11 as far as a prioritization, those chemicals have trickled to the

12 top right away.

13 Now if you think this is a confidence interval

14 here, really what we have in a lot of ways is maybe a

15 hundred-way tie at the top. We have a lot of chemicals that are

16 predicted to be very high. And that is still extremely useful

17 information because we also have thousands of chemicals that are

18 now looking very much like the chemicals that are below the

19 limit of detection of the CDC. And so we have a lot of data on

20 those chemicals.

21 And so these are chemicals that I would

22 prioritize on an exposure bases as lower. The area of concern,

23 and just for comparison with the previous slide, we had this big

24 stretch of chemicals in the middle that don't really look like

25 the CDC chemicals, so we don't know really know anything about

26 them.

27 And you can see that they're given neither
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1 high values nor the low values. Right now they're just given

2 the average value because that's all we have about them. These

3 are also chemicals that you would actually want to study

4 because, basically, we don't have information on what they look

5 like. 

6 Now the CDC have not been just grabbing

7 chemicals at random. They've looked at the chemicals that we

8 think you're most likely to be exposed to. So in addition to

9 the very high chemicals, these chemicals are somewhat

10 interesting. 

11 So just a summary of what we got from

12 exposures, so basically we have compared factors that are

13 available for all of the EDSP universe, and so these are very

14 course descriptors of potential exposure. To a subset of

15 chemicals that we actually have monitoring data on, and we found

16 which factors are most predictive of exposure.

17 We've then used this calibrated mode model to

18 actually make predictions for close to 8,000 chemicals. There's

19 really no conceptual reason we couldn't use this model for tens

20 of thousands of more chemicals. I'm just giving you what was

21 just published in September. 

22 And the structure here has not led to any

23 particular exposure model or any particular data source. If you

24 give me a new monitoring data source, we can revise our

25 estimates using that data source. If you give me a new model,

26 we can include that model. And that was deliberately by design

27 because we knew that things like Sheds high-throughput were
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1 coming, and so that's why we're incorporating that model now.

2 And so, in future direction, everything has

3 been urine-based so far, and that gives you a certain suite of

4 chemicals. The chemicals that are only in the blood, by the

5 CDC, typically are the much longer half-life chemicals, so that

6 will open up a new space of chemicals for evaluation for us.

7 We are developing new data sources to

8 characterize the sources of exposure from consumer products

9 because that's turned out to be important. We are developing

10 new models that better explain human exposure, such as the Sheds

11 high-throughput model. And we're actually coordinating with

12 some planned and ongoing biomonitoring studies to make sure that

13 they look for some of these chemicals that I've just suggested

14 to you were critical to look for, such as the very high

15 chemicals and the chemicals the chemicals that we don't have

16 information on.

17 And so, at this point I'll just take a brief

18 pause. That was chapter 4, and then I will dive into chapter 5

19 when you're ready. But if anyone had a question on anything

20 leading up to this point, I'd be happy to take it.

21 DR. JAMES MCMANAMAN: Yes. It's a good place

22 to break, so let's ask some questions.

23 DR. VERONICA BERROCAL: I wanted to ask a

24 question about your Bayesian modeling. So usually when you do

25 Bayesian modeling, the choice of the prior is very important. 

26 So I was wondering if, in your priors, if you have complete

27 information on what parent chemicals are going to have an effect
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1 on the metabolite and if that's based on chemical understanding?

2 DR. JOHN WAMBAUGH: That's an excellent

3 question. So in general, for our Bayesian analyses, we have

4 used what we would characterize as -- minimally informative

5 priors is the correct term because every prior is a little

6 informative. And we have done posterior checks to say we think

7 this prior is minimally informative. And then whatever result

8 we get, we compare it to see whether or not it was an

9 unnecessary constraint.

10 The only place we use informative priors is

11 exactly what you identified, is with the mapping of parent

12 chemicals to metabolites. So if you have a hundred metabolites

13 and a hundred parent chemicals, you could potentially think you

14 would have 10,000 possible relationships between them.

15 What we have done is we have actually used

16 information created by the CDC already, and then further

17 augmented by ourselves on these -- certain chemicals have

18 absolutely no chance of leading to certain metabolites. So

19 bisphenol A is not going to lead to mono-ethylhexyl phthalate. 

20 But if there's multiple ethylates that can lead to

21 mono-ethylhexyl phthalate, we will estimate the fraction that

22 lead to those metabolites.

23 So in the case, for instance, of that

24 pesticide metabolite that had 17 different parents, there are 17

25 different numbers that are estimated. For bisphenol A

26 glucuronide, there's only one number that's estimated, and

27 that's the fraction of bisphenol A that led to that metabolite. 
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1 So we have used that information to constrain the space into a

2 solvable problem. 

3 And so that information was curated initially

4 by the CDC. In their presentation, they said this is why we're

5 monitoring this chemical in urine; we think it leads to these

6 chemicals. And then we had a computational chemist by hand

7 review all of the structures of all of the parent molecules and

8 all of the metabolites that were monitored and basically just

9 give us a thumbs up or thumbs down on I believe that this parent

10 molecule could potentially lead to this metabolite. And so that

11 is really the only informative prior that we put into our

12 Bayesian analyses.

13 DR. JAMES MCMANAMAN: Other questions? Okay. 

14 Dr. Jett?

15 DR. DAVID JETT: And if you could go back to

16 one of the NHANES ranking slides? And this is a problem we're

17 facing in our ranking efforts at the Homeland Security and HHS. 

18 And that is, with those data, given the variability, how are you

19 going to bin these chemicals, or is it even possible with those

20 data? What would be the cut-off points and so forth?

21 DR. JOHN WAMBAUGH: So to my mind, you asked

22 two questions. One is how you bin and one is how you do the

23 cut-offs. And I would always punt on how you do the cut-offs

24 because I think that's very hard and that's very subjective.

25 How you would bind them is basically like I

26 was suggesting in that, you know, you have a plateau at the top

27 here where the median or even the 95th percentiles is roughly
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1 consistent for the top hundred or so chemicals. And it might be

2 the top 200 chemicals. And so I don't know for a fact that

3 chemical number 200 is going to be higher than chemical number

4 one, but I have a lot of confidence that chemical number 200 is

5 going to be higher than chemical number 7,000.

6 And so I would say, in terms of

7 prioritization, these chemicals up here seem to look most like

8 the chemicals that are higher. And so if you wanted to call

9 that a bin, I would draw a fuzzy line somewhere in there. And

10 like I said, it's very subjective where you'd draw the line. 

11 But any chemical that has a higher prediction here, I would be

12 more interested in.

13 And the bin of non-interest is really the

14 largest bin. And because this plot's transformed, you don't

15 quite see it. But there are really literally thousands of

16 chemicals in this predicted to be below the limit of detection

17 with 95 percent confidence bin. So that's another bin of

18 chemicals that have a lower priority.

19 So we've got a higher priority and a lower

20 priority. And then the only caveat to that is these chemicals

21 that are in the middle here, you know, that really is kind of

22 the here there be dragons. We don't know. And so I would

23 hesitate to call this the only high priority bin here. I think

24 both of those things come in. And that information is

25 incorporated into the sorts of rankings that Richard Judson will

26 be showing later today.

27 DR. JAMES MCMANAMAN: DR. VERONICA BERROCAL:
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1 DR. VERONICA BERROCAL: These plots again,

2 maybe I didn't understand correctly, but I was expecting that

3 the chemicals for which you don't have any information would

4 give you wider intervals than the chemicals for which you have

5 data. And it seems here that the intervals has always about the

6 same size.

7 DR. JOHN WAMBAUGH: You're completely correct.

8 In our second-generation model, we have a single error term. 

9 We are not able right now in that model to distinguish this as a

10 chemical class that we know a lot about from a chemical class

11 that we don't know a lot about. And that's just due to the lack

12 of sophistication of the models that we're using in this

13 second-generation analyses.

14 These models are highly predictive. And I

15 should've stated this. The R-squared we get for explaining the

16 CDC exposures is about .5, so that means we're explaining

17 roughly half of the chemical-to-chemical variability in exposure

18 to the CDC chemicals using our model. But our model is four yes

19 or no questions in production volume, so it's extremely simple.

20 So it's one of those things where, if I came

21 to you and I just said that's my model, you'd laugh me out of

22 the room. But if I say that's my model and I can explain half

23 of the chemical-to-chemical variability, that's a very useful

24 tool, but that's a course tool. 

25 When we bring Sheds high-throughput and any

26 other additional model and then we can start to say, here's a

27 chemical that we really understand well; it's more than four yes
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1 or no questions in production volume, and here is a chemical

2 that we don't understand at all; we don't have Sheds

3 high-throughput. In that case, I think we will be able to

4 support a more complicated error term.

5 But we've been trying to do the simplest model

6 that we can that is still predictive, and so right now the

7 uncertainty is constant across those. And so what that'll mean

8 in our third-generation model is that we'll actually be much

9 more confident about the chemicals that are at the high end of

10 the list. And so those error terms will shrink, and the

11 chemicals that we know not so much about, those error terms will

12 increase. And the confidence interval we have right now is

13 basically the average across those two.

14 DR. JAMES MCMANAMAN: Okay. Dr. Potter, yes?

15 DR. THOMAS POTTER: Nice to see you again,

16 John. I can remember your presentations from the meeting in

17 July, and it's nice to get an update. We may have answered this

18 question back in July, but I thought I needed to come back to at

19 least refresh my memory. Where do workplace exposures fit into

20 this matrix? 

21 Clearly, in universal pesticides, I'll offer

22 that, that applicator and operator exposure tends to be much

23 higher than for the rest of the population. So we're talking

24 about perhaps a more susceptible part of the population that may

25 have exposure, so just your thoughts on that, please.

26 DR. JOHN WAMBAUGH: So basically what we ended

27 up having to say in July is, it's very important, and we don't
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1 have the data to do anything systematic about it right now. And

2 because of the ongoing efforts of the CDC, we have a dataset

3 that can speak to the average American and actually speak to

4 certain demographics, like the children age six to 11 or women

5 of reproductive age. 

6 But I'm not aware of a single dataset that we

7 can analyze that says this is what occupational exposure looks

8 like for hundreds of chemicals. The approach we're using is,

9 you need that sort of dataset. So right now we just have to

10 list it as a caveat; that these exposure estimates speak to an

11 average exposure and we are not yet able to take into account

12 the extremes that might be an occupational exposure.

13 Now if it's an occupation that is so common

14 that that 2,000-person sample that the CDC looked like is going

15 to have a representative number that would be included. But if

16 it's an occupational exposure that's not reflected in that

17 sample, we're just not going to have it yet.

18 DR. THOMAS POTTER: -- a number of studies,

19 and I'll try to put them in the record, looking at exposures,

20 particularly to farm workers and farm families, and again,

21 regarding pesticide-active ingredients. So one would think that

22 those would be a natural area to start from.

23 DR. JOHN WAMBAUGH: Yes, I agree. And really

24 what is needed, and there are certainly people undertaking these

25 efforts, is efforts very similar to what NICEATM presented

26 yesterday in mining the literature, building this database that

27 allows you to do the evaluation. But having two people look at
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1 700 different papers and each paper getting looked at by two

2 people, yes, that was a monumental effort. And a similar effort

3 would bear a ton of fruit, but that hasn't been done yet.

4 DR. JAMES MCMANAMAN: Other questions? Okay. 

5 Well then let's move on to the second part.

6 DR. JOHN WAMBAUGH: All right. So now we're

7 crossing into chapter 5 from the White paper, and this is on

8 Toxicokinetics and Dosimetry. And in particularly, we're

9 talking about high-throughput toxicokinetic approaches. But in

10 general, toxicokinetics provides a bridge between bioactivity

11 and exposure. 

12 You can look at it in two different ways. If

13 you have a toxicokinetic model and you know that someone was

14 exposed to one milligram per kilogram of body weight per day,

15 then you can actually predict what their tissue concentrations

16 are. 

17 Likewise, from ToxiCast or any other in vitro

18 method, if you have a suspicion that a certain tissue

19 concentration of so many micromolar is potentially of interest,

20 you can run a reverse toxicokinetic model and say what dose

21 would I have to give a subject in order to achieve that

22 concentration.

23 So there's nothing high-throughput, necessarily, about that. 

24 That's just what toxicokinetics provides; it's how does the

25 chemical get distributed and cleared from the body.

26 High-throughput methods have been adapted from

27 pharmaceutical industry techniques to determine the range of an
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1 efficacious dose for a clinical trial. And so the situation

2 they might be in that I'd like to think of; I'm an allergy

3 sufferer and so you have both Zyrtec and Allegra and they're

4 both effective. I've used both of them. Zyrtec is this tiny

5 little pill and Allegra is a big pill and I have to take it with

6 a lot of water every morning. They're both efficacious. You

7 just need different doses to get that same effect.

8 And so, what you want to do, if you're going

9 to conduct a clinical trial if you have a drug, is you want to

10 figure out do I need to give my test subjects the horse pill or

11 do I need to give them the tiny little pill. You know, there's

12 a lot of safety issues involved in that, as well.

13 And so, what these high-throughput

14 toxicokinetic methods do is basically give you an in vitro

15 technique to get a ballpark estimate of what dose would be

16 efficacious for a person. And the industry claim -- and there's

17 research papers. There's a Wong et al. 2010 paper basically

18 supporting the idea that, typically, you can get within a factor

19 of three using these in vitro techniques for pharmaceutical.

20 And so what we've done is said, basically, we

21 need that sort of information for environmental chemicals, and

22 so we will use these techniques and try to use it for

23 environmental chemicals. And this is the bridge that allows us

24 to compare exposure estimates and bioactivity estimates.

25 And one reason you might want to do this --

26 and this is from Barbara Wetmore's 2012 paper on Toxicological

27 Sciences -- is that, as you look across the ToxiCast assays --
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1 so we have 250, roughly, ToxiCast compounds with very, very

2 small writing on the X axis here. And then for each chemical we

3 have plotted the AC50 value. So that was the concentration that

4 gave 50 percent activity in that particular ToxiCast assay.

5 And so you can first see that, across these

6 chemicals, there are some chemicals that were not terribly

7 active. They maybe only lit up a dozen assays, and all the rest

8 of them were flat. And so that chemical might only have eight

9 points on this chart.

10 There's other chemicals that were much more

11 promiscuous and they were active across a hundred assays, and so

12 they have a hundred points on here. And then you look at the

13 range of concentrations that were active. Basically you see the

14 study design here. And so this is, again, where these AUC

15 limits came through. We didn't really test above a hundred

16 micromolar. We didn't really test below about ten thousandth of

17 that, so all the concentrations are basically in about the same

18 area.

19 So how do we relate those concentrations to

20 some sort of plasma concentration in a human? Well we're using

21 a fairly simple model. I mean it's simple enough to be

22 parameterized and hopefully complicated enough to be useful. 

23 We basically say that, if we know how the

24 chemical binds to human plasma protein and we have some estimate

25 of how the liver, how the hepatic metabolism clears it from the

26 body, we can make a steady-state estimate of exposure to that

27 chemical. So again, it's similar. This is slightly more
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1 complicated but it's similar to that LaKind Naiman estimate that

2 was done to infer bisphenol A exposure from biomonitoring data.

3 And so, you are a tube in this model. Every

4 molecule that comes out of the body is immediately replaced with

5 a molecule coming into the body. You're at steady-state, and

6 we're not making any estimate of oral absorption or

7 bioavailability but we can make conservative assumptions there. 

8 We can assume that it's rapidly absorbed and that a hundred

9 percent of it ends up in your body. And it's never going to be

10 higher than that. It's probably lower than that.

11 And so this is the model that we have, but

12 then we have to get in vitro measurements of this plasma

13 protein-binding and metabolic clearance. And so the plasma

14 protein-binding assay uses a method developed by Waters et al. 

15 For pharmacokinetics, it's high-throughput. Of course, low

16 throughput for pharmacokinetics can take years and hundreds of

17 animals. This is not as high-throughput as a ToxiCast

18 high-throughput assay, but for pharmacokinetics, this is very

19 high-throughput. 

20 Basically you have a double well experiment. 

21 You have a membrane between two wells that allows chemical to

22 move between the two wells but does not allow large molecules

23 like plasma protein to move. 

24 You add plasma protein to just one of the two

25 wells, allow the chemical to come to equilibrium between them

26 and then you assay the concentration in the two wells. So in

27 the well with protein, you have one concentration to chemical,
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1 and in the well without protein, you have a second

2 concentration. That gives you how much is not bound by plasma

3 protein.

4 The rate limiting step here is basically the

5 analytical chemistry. You need to be able to measure the

6 concentration in those two wells. This is very different from a

7 lot of the very clever high-throughput bioactivity assays that

8 have florescent compounds and can just take an image and tell

9 you how much of a substrate has been displaced, or how much of

10 the ligand has been displaced by your chemical.

11 This is not a generic readout across all

12 chemicals. Basically, an analytic chemist has to develop a

13 method for each one of these chemicals, and that's really why

14 this is a lower throughput methodology than high-throughput

15 toxicity screening, but you're able to do a much higher

16 throughput than a traditional pharmacokinetics.

17 And so this data has been initially collected

18 by the Hamner Institute. There's a series of papers reporting

19 over the last couple of years, and now some of this data

20 collection has actually moved into the ToxiCast program itself.

21 So we also, coupled with that plasma protein

22 binding, which basically gives you how much chemical is free for

23 the body to do something with, we have a measure of intrinsic

24 hepatic clearance. So there, you have a well that has a

25 suspension of a 10-donor pool of human hepatocytes.

26 And so, this is deliberately designed to not

27 get at human biological variability. There's lots of
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1 variability in how humans metabolize drugs. And we're

2 deliberately trying to average over 10 different people to try

3 to get a representative, healthy adult metabolism rate. 

4 You then add a chemical to that well and then

5 you remove aliquots at regular intervals. Again, you have to

6 measure the concentration in the well using analytic chemistry,

7 so you need that method. But what you can get is basically the

8 rate of disappearance of just the parent compound. You're not

9 determining the metabolite. But remember, this is intended to

10 be high-throughput. You get how fast that chemical is cleared

11 by those metabolites.

12 And so what you can do with those two things

13 is parameterize this one equation here, which is basically a

14 linear model. This is a steady-state solution to a

15 pharmacokinetic model that gives you the concentration at

16 steady-state, if I know the fraction is bound due to plasma --

17 that's this FUB that was measured in vitro -- and the intrinsic

18 metabolic clearance that was measured in vitro.

19 You then also have what's glomerular

20 filtration, so this is passive secretion by the kidney. 

21 Basically your kidney is actually quite clever in how it

22 operates. Almost everything is initially dumped out of the

23 body, and then the useful things are taken back into the body. 

24 And so the kidney doesn't have to recognize things that it

25 doesn't want, it just has to recognize the things that it does

26 want.

27 And so glomerular filtration is a passive
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1 process, but only the chemical that is not bound to plasma is

2 available to be dumped that way. So that's kidney excretion,

3 renal excretion, and then you have metabolic clearance by the

4 liver. And so this is the blood flow, like QL -- it's in liter

5 per hour -- to deliver. 

6 And the chemical is then metabolized in the

7 liver according to how much of it is available. And so we're

8 making the assumption there that if the chemical is highly bound

9 to the plasma then it's not available to be metabolized.

10 Using those two numbers, we can basically

11 predict what concentration would be in the plasma for a one

12 milligram per kilogram per day dose. And the interesting thing

13 about this model, although it seems very simple, is that it is

14 very simple; it's linear. So if I have predicted the

15 concentration for one milligram per kilogram per day, I also

16 know the concentration for 10 milligrams per kilogram per day. 

17 I'd just have to multiply that number by 10.

18 And I can do any sort of transformation I

19 want. Once I've done it once, I can predict the concentration

20 resulting from any dose. And so, no kinetics is not necessarily

21 linear always, but yes, kinetics is typically linear for a

22 regime and usually a low-dose regime, like we're interested in. 

23 So if I have this linear model, then I can swap the axes. And

24 this is the in vitro/in vivo extrapolation that we're interested

25 in. 

26 So basically, all I've done in these two

27 things is I've taken dose from the X axis and I put it on the Y
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1 Axis. And now I can say that I have an in vitro AC50 that I'm

2 really interested in. I used the exact same slope, the line,

3 and go up and I find the dose that I would need to cause that

4 concentration at steady-state in a human. And so that is the

5 verse toxicokinetics that we're using and that's how we can

6 convert from an in vitro concentration to an in vivo

7 concentration. 

8 So what we're able to do is take, for

9 instance, these 250 chemicals in the Barbara Wetmore paper and

10 convert from a Y axis that is micromolar concentration to a Y

11 axis that is milligrams per kilogram per day to produce a

12 steady-state concentration.

13 And you can see that, now we have basically 12

14 orders of magnitude and separation between the dose that's

15 needed to cause the most potent ToxiCast bioactivity and the

16 least potent ToxiCast bioactivity. And we're really ranging

17 from micrograms per kilogram body weight per day up to grams per

18 kilogram body weight per day. And so that's extremely useful

19 information if you're trying to compare with exposure and you

20 want to know how much you're actually going to need to cause

21 this activity.

22 We do try to incorporate some biological

23 variability into this model. So we have in vitro measurements,

24 and we use those to do a basic simulation of a single adult

25 human that, of course, doesn't actually exist. It has no gender

26 and is 70-kilograms, supposedly, on average. And, I don't know,

27 I haven't been 70 kilograms on average in a long time.
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1 But what we can then do is simulate

2 variability using Monte Carlo's simulation on those parameters

3 to simulate a variation in an adult population. Now again, that

4 is some part of variability. It's not everything. But what

5 that does give us is populations that are more sensitive to

6 these doses. And so, another way of looking at this is

7 basically, we're able to simulate variation in renal clearance,

8 variation in hepatic metabolism. Our equation doesn't depend on

9 anything else, so we can't really simulate anything else right

10 now.

11 Now, for instance, liver size, cardiac output

12 all impact the liver flow rate. They can impact glomerular

13 filtration, so there is some biological variability in there. 

14 But if it's not in this equation, it's really hard to do

15 anything with.

16 A typical Monte Carlo simulation, we basically

17 take the parameters that do vary, we assign a distribution to

18 them, and then we rerun this equation with multiple draws from

19 these different distributions to end up with a distribution to

20 see steady-state values. And so here is this line that I was

21 showing you earlier where the red line is the median predicted. 

22 And then we have an upper 95th percentile predicted and a lower

23 95th percentile. 

24 And the reason this is important is so you

25 have a fixed micromolar concentration you're interested in. You

26 saw bioactivity at three micromolar and you want to know what

27 dose you need. Well if you use the median line, you'll get one
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1 dose. If you use the lower 95th percentile, you actually see

2 those people need to get exposed to a much higher dose to cause

3 that concentration in their serum, three micromolar. 

4 And then this upper 95th percentile is our

5 most sensitive of this population. They need a smaller dose to

6 cause an equivalent three-micromolar concentration in their

7 plasma.

8 And so this is our variability analyses. And

9 of course, like I said, there's variability we haven't included

10 but this at least gives us an initial handle on what would a

11 more sensitive person need to be exposed to. And this is

12 treated at much greater length both in this White paper and the

13 July White paper. But these techniques were developed for the

14 pharmaceutical industry where the claim is that they give you a

15 value roughly on the order, plus or minus a factor of three.

16 What we have done is an analyses. And this is

17 actually a recommendation of the July SAP to use a random forest

18 analysis. But basically, when you compare an in vitro

19 prediction with actual in vivo data, where it's available, which

20 factors predict chemicals will be on the order, within this plus

21 or minus three times, in which chemicals -- the predictions will

22 be kind of lousy.

23 And so what we've done is we've put the

24 chemicals that we have data on into these bins, and we find that

25 the plurality of the chemicals are in this bin of on-the-order. 

26 So this is like the pharma space here. And these are the

27 chemicals where we really expect high-throughput toxicokinetics
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1 to do a great job.

2 And then we see that there are chemicals where

3 it's between three and 10 times overestimated or greater than 10

4 times overestimated. There are a lot of chemicals in those

5 bins. But an overestimate of the concentration resulting from

6 an exposure is actually conservative. That's going to

7 overestimate the concentration that you get from a fixed dose,

8 and so that is an error but that is a conservative error. 

9 Now we do have a smaller subset of chemicals

10 where we don't have that sort of error. We have the opposite

11 problem. But what we can do with this technique is basically

12 identify on a per chemical basis whether or not we think these

13 approaches are going to be useful.

14 So just to summarize, we're taking techniques

15 that were developed by the pharmaceutical industry and we've

16 adapted them for environmental chemicals because we need that

17 information to bridge bioactivity and exposure.

18 We can, if you have that in vitro data, make

19 those predictions. And all of our methods and data and intended

20 to be publically available. I should slightly modify this one

21 bullet. 

22 We have not quite uploaded our R package yet,

23 but basically the pharmacokinetic model and the data will be

24 available in the next few weeks as a package called, cleverly,

25 HTTK, High-Throughput Toxicokinetics, so that any researcher on

26 Earth can, and with the click of a button, have our dataset,

27 have our models, reproduce any of these results, including the
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1 Monte Carlo analysis. All of that code is included in that.

2 And then we have two manuscripts for peer

3 review, one describing the analysis that I showed in the final

4 figure where we put chemicals into bins based on their

5 predictive ability, and a second as a more mythological

6 description of how we did all of this.

7 And again, we can do hundreds of chemicals per

8 year with this technique, which compared to traditional

9 pharmacokinetics, is blistering speed. But it's not the

10 high-throughput of what NCGC in Maryland is able to do with

11 their high-throughput robot doing tens of thousands of chemicals

12 in weeks. So, that's a summary.

13 In future direction, we are continuing to

14 collect new data, and we are using an improved methodology that

15 actually gets us more confidence and more commonly gives us

16 estimates of the plasma binding. We are working to expand

17 population variability to include more sensitive populations in

18 life stages and try to actually use this to go a little bit

19 beyond NHANES. 

20 With the urine data, we're limited to children

21 age six to 11. If we have good simulations of biological

22 variability, we can push below that. Of course, that's

23 modeling. And then we are currently evaluating the performance

24 of the model using a literature curation of available in vivo

25 studies. And that's very heterogeneous and it's over many

26 decades.

27 And so, there's an ongoing effort in the
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1 Office of Research and Development to collect in a systematic

2 study design limited in vivo data in rats. They're actually

3 taking in vitro pharmacokinetic data in rat and then seeing how

4 well it extrapolates to rats for environmental chemicals. 

5 So all of this is known to work pretty well

6 for pharmaceuticals but we're actually trying to probe

7 environmental chemical space. And as with the ExpoCast, as we

8 get better models, as we get better data, we expect to be able

9 to refine our estimates of uncertainty and apply those as we go

10 along.

11 So that concludes the review of section 5 from

12 the White paper and I'm happy to take any questions.

13 DR. JAMES MCMANAMAN: Okay, thank you. Any

14 questions for Dr. Wambaugh? Yes, Dr. Doerge?

15 DR. DANIEL DOERGE: Yes. I'm interested in

16 how your thinking has incorporated the differences that humans

17 encounter in their near- and far-field exposures that were

18 translated in a kinetic sense to differences in routes of

19 administration. 

20 And the reason I'm asking this is because the

21 model you've chosen to evaluate your kinetics in vitro sure

22 looks to me like presenting a bowl of concentration hepatocytes

23 in a well, analogous to a parental route of administration,

24 either dermal, injection, inhalation or something like that,

25 clearly distinct from oral. 

26 And again, on a philosophical level, since

27 you've translated in formalism now that these are oral
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1 equivalent doses, to me they look -- and clearly, that's a

2 conservative assumption, as you've clearly pointed out. But

3 that's going to change the nature of the exposure estimate

4 depending on, again, simple questions we know about how people

5 are exposed.

6 So how is that incorporated into your modeling?

7 DR. JOHN WAMBAUGH: Thank you for the

8 question. So one effort that is briefly discussed in this White

9 paper and extensively discussed in the July White paper is that

10 we've actually used the high-throughput data we have to

11 parameterize more of a physiologically based dynamic model, and

12 that's allowed us, first, to simulate -- and this is even just

13 for oral exposures. 

14 But rather than these constant infusion

15 scenarios, bursting activity. So you only eat meals at certain

16 times of the day, so actually simulate non-steady-state

17 processes and see how much of an impact that has. 

18 And for the chemicals that we have just for

19 the oral doses that the kinetic models have had, an order of

20 magnitude impact, but not a huge impact in what the peek

21 concentrations should get. And so the idea would be, rather

22 than getting your one milligram per kilogram per day as a

23 constant dose all day long, let's say you just got that at

24 breakfast. And so we've been able to do some Monte Carlo

25 simulations to test those things, and we haven't seen a big

26 difference there. 

27 What we're thinking will have a much bigger
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1 difference will be exactly like you suggested, inhalation routes

2 of exposure and dermal routes of exposure. 

3 Again, all of these models have to be

4 basically a Swiss army knife model. They have to have some

5 predictability for most of our chemicals. We tend not to be

6 super interested in them, so we are working on a Swiss army

7 knife, dermal model, and a Swiss army knife inhalation model

8 that should allow us to get a better estimate of what sort of

9 exposure you get.

10 Now there are researchers that have been

11 looking for a smaller class of chemicals, general models. 

12 There's a group of University of Michigan, Olivia Juliet's

13 (phonetic) group that's done a lot of work on exposure to

14 parabens, which are an important chemical class that are

15 commonly exposed to. And there, they have a dermal model that

16 seems to broadly apply across parabens. And there, they have a

17 much better read on how that model works across those groups.

18 And so in general, if we have a model that

19 works across every chemical, we plan to just incorporate it into

20 our R package. And that's actually one of the reasons that we

21 went with a package, is we expect to get new data and we expect

22 to incorporate new models. 

23 And basically, if you're a researcher anywhere

24 on earth and you want to update and get our newest version or

25 model, again, you just click the button again and you'll get the

26 latest and greatest. But right now we do not have that dermal

27 model yet. That's a future direction that we'd love to have. 
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1 And we're not there yet but we're working toward it. Does that

2 answer your question?

3 DR. DANIEL DOERGE: Yes. I mean the

4 dermal-specific routes are important for some chemicals. I mean

5 I don't see that you're missing anything from a dermally exposed

6 chemical, at this point, with the model you're using. 

7 Frankly, it's the oral chemicals I see that

8 are receiving shrift (phonetic), if you will, not only for the

9 periodicity of dosing in food, but also, I mean huge impact, the

10 first past metabolism both in the GI tract and the liver before

11 it gets to your plasma where there's proteins and is subject to

12 renal excretion and things like that.

13 So yes, obviously you can't incorporate a

14 model for every compound. I understand it's got to be one size

15 fits all. And really I'm thinking out loud now along with you,

16 really, and figuring out how one might conceptualize that. But

17 I see, the main thing is an overestimate for a lot of orally

18 administered chemicals.

19 DR. JOHN WAMBAUGH: So, one approach we've

20 taken to that was with the comparison to literature data. So

21 we've built these models for rats using rat in vitro data. And

22 for the hand-full of chemicals where we have rat in vivo data,

23 for an actual oral administered dose we're able to look and see

24 systematically how much do we over predict. And so some of

25 those chemicals will be metabolized in the gut and never even

26 reach the liver, at least in the parent form.

27 And what we have found so far, and this is not
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1 a huge enough dataset to completely believe it -- it was 20

2 something chemicals -- is that, on average, we're over

3 estimating the dose by about five times. 

4 Now, as we get this new dataset that's in the

5 same lab systematically controlling that, and we've deliberately

6 chosen a diverse family of chemicals, we might be able to see

7 that, for certain chemical classes, that number is a hundred and

8 other chemical classes it's not. But we don't know that yet.

9 That factor is actually discussed in the July

10 paper. It's, on average, five times overestimate. And so

11 that's one place where I think just empirical data collection

12 will really help this.

13 And one other opportunity, and I'm sorry I've

14 neglected to mention this, the National Toxicology Program has

15 performed pharmacokinetic studies on hundreds of chemicals at

16 this point. And so they have an ongoing effort to get this into

17 a computable format very similar to what Nycedum did with the

18 uterotrophic studies. 

19 When that's available, I will be able to

20 basically say, well here is my predictions for these 500

21 chemicals. And if the number is 500, I'm going to feel very

22 confident about the -- if it's on average, we're overestimating

23 five times, I'd feel pretty good about that. And we may find

24 that that number doesn't hold up. But that, I think, is the

25 path forward.

26 As we organize the data that we have out

27 there, we'd maybe collect additional targeted data but,
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1 basically, just keep this empiricism in mind. I agree, I want

2 to know the answer. I unfortunately don't know it today.

3 DR. JAMES MCMANAMAN: Okay. Dr. Weller?

4 DR. GRANT WELLER: Thanks, Dr. Wambaugh, for

5 that presentation, especially because I wasn't here in July. 

6 I'm curious about your Monte Carlo approach to getting

7 population variability in steady-state concentrations on slide

8 47. 

9 So it looks like, based on the figure there,

10 that the inputs to this Monte Carlo simulation are, normal

11 distributions are log-normal distributions. I'm wondering,

12 number one, are those inputs assumed to be independent of each

13 other? And not knowing the science that well, does that make

14 sense? 

15 And secondly, I'm curious about the resulting

16 distribution of steady-state concentrations that come out, like

17 what's the variability to mean ratio or like a signal-to-noise

18 ratio on that?

19 DR. JOHN WAMBAUGH: Thank you. So yes, we

20 have assumed log-normal distributions for those parameters

21 which, for any one parameter, is generally pretty good for a

22 human population. There are certainly exceptions to that. 

23 And then what you get at is something -- so

24 there's a great paper on virtual populations, basically Monte

25 Carlo simulations of people by Kevin McNally (phonetic) who is

26 in the U.K., and he has what he calls the monsters of Monte

27 Carlo which are, you know, if you have these uncorrelated
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1 distributions, yes, you can get someone who is 95th percentile

2 on weight and second percentile on height, which tends not to

3 happen, but it can happen.

4 And so right now, our Monte Carlo technique is

5 not correlated, so all of these parameters are uncorrelated. A

6 potential future direction would be to actually take the

7 distributions from the CDC's NHANES data, which are correlated

8 and are even better real, and use those to draw parameter

9 estimates. And if the panel has any other recommendations on

10 how to do that, we would be happy to do it. This is a first

11 step. And so, yes, there's certainly other things that we'd

12 want to do with the distributions.

13 Now, that's for the analysis that I have

14 presented. The published analysis in the Wetmore 2012 paper

15 actually drew from a SIMSET (phonetic) tool that is developed by

16 the pharmaceutical industry that does have a proprietary

17 database of properly co-varying metrics. And in that database,

18 I'm not sure that things like weight -- it's really hard to get

19 data on people's live weights, at least of intact people, so I'm

20 not sure if those things vary.

21 But if we had information on particular

22 enzymes that were responsible for metabolism, that's what that

23 tool is actually designed to do, is actually capture that

24 covariance. But right now, the model we have is too simple to

25 use that sort of information.

26 As far as the change that we get out of it,

27 we're assuming basically a coefficient of variation of about .3,
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1 and that basically propagates right through the model so simple

2 and linear that, at that point, that just propagates right

3 through and then we get basically a coefficient of variation of

4 3. 

5 So another way of putting that is that we're

6 seeing what we're assuming, in terms of the variability. But

7 the model that we have is so simple right now that that's

8 generally going to be true. So as long as we believe the values

9 that we're assuming, that's what we're going to get back.

10 DR. JAMES MCMANAMAN: Other questions? Yes,

11 Dr. Berrocal?

12 DR. VERONICA BERROCAL: Well actually, I'm

13 wondering if, assuming that they're independent, it would give

14 you a greater uncertainty than assuming they are dependent. And

15 so, in some ways, assuming that they are independent, it would

16 give you more uncertainty, more mobility, which is on the

17 conservative side. 

18 DR. JOHN WAMBAUGH: I agree it would give us

19 greater uncertainty. It's still possible that it's not ideally

20 conservative, especially as you want to identify that

21 unfortunate, most susceptible individual, that the right

22 combination of parameters -- so it definitely does increase your

23 uncertainty. Thank you.

24 DR. JAMES MCMANAMAN: Other questions? All

25 right. Thank you very much. So I have down Mr. Judson as the

26 next speaker. 

27 
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1 INTEGRATED (RTK) BIOACTIVITY EXPOSURE RANKING (IBER, MODELING

2 UNCERTAINTY

3 

4 DR. RICHARD JUDSON: Okay. Last night the EPA

5 team decided we would like, at the end of my presentation, to

6 actually bring up the dashboard and look at just a little bit of

7 data. I knew, at least yesterday, that we were setup with a

8 computer that had internet access. So I may just come over and

9 sit there for a few minutes at the end of the presentation, if

10 that's okay. 

11 Okay. So, this talk really summarizes

12 everything you've heard for the last day and a little bit and

13 puts it all together to answer the question; can we take a

14 relatively large set of chemicals and say, okay, does it look

15 like these chemicals are interacting with the estrogen or

16 androgen receptor? And you can obviously carry that forward to

17 any other pathway or target, give us a quantitative dose where

18 that activity might occur, and then compare that to predicted

19 exposure.

20 And from all those pieces, we can prioritize

21 chemicals to say, the chemicals that are both active, inactive

22 at doses where people might be exposed -- so you actually would

23 activate those pathways in normal daily living -- those would be

24 the chemicals we would proceed with first to take do further

25 testing or some other kind of action. Having said that, you've

26 already seen that we're on the path to being able to do that. 

27 And so, what I'm really going to show you is
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1 the approach we're thinking about using. We're not planning to

2 make decisions based on any of this tomorrow or next month. But

3 what we want to get feedback on is does this basic approach to

4 put all this together make sense, and are there recommendations

5 to do a better job of that.

6 Okay. Having said that, let's jump back in. 

7 So you've seen this diagram before. And the goal is to say, I'm

8 going to have chemicals that -- and forget those left-2 things

9 which don't make any sense, actually. So we're going to look at

10 the low priority. So these two distributions are far apart. 

11 And in particular, the extreme points of the distributions are

12 far apart.

13 There's lots of uncertainty, as you've already

14 seen. There's lots of uncertainty in both where the bioactivity

15 occurs and where the exposure occurs. So to be

16 health-protective, we're going to look at those extremes: Are

17 the extremes far from one another, are they close or are they

18 definitely overlapping? And the ones that are definitely

19 overlapping, those are the ones where -- let's go do something

20 about those chemicals first, those chemicals in those exposure

21 routes. 

22 And so the pieces we're going to use are --

23 we've talked about the in vitro activity which gives us a

24 concentration in the well, the microtiter plate well, which

25 we're going to say is really equivalent to the free

26 concentration in plasma is where we get from the

27 pharmacokinetics. And so we use the pharmacokinetics to reverse
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1 pharmacokinetics to say what oral dose. And the examples I'm

2 going to use is the steady-state oral dose that John was just

3 talking about, and then compare that with the modeled exposure.

4 Okay. So as we go through this, a few points

5 to remember is we really want to make sure we're using worse

6 case scenarios to be health protective. And so we need to worry

7 about these confidence intervals that have to do with

8 variability and uncertainty. And you've heard a little bit

9 about that. I'll say a little bit more.

10 Getting really good estimates of those, that's

11 really a work in progress. And for people who do stand

12 risk-assessment, that's a hard problem even when you're doing

13 standard risk-assessment. And here, we have maybe some extra

14 challenges, although on the other hand, maybe it's a little bit

15 easier in some cases.

16 And then we're going to use 99 percent ends of

17 the confidence interval or 95 percent using the pharmacokinetics

18 and the pharmacodynamics to figure out what those worse cast

19 scenarios are, and then we just prioritize. We're not saying we

20 should ban this chemical. We're prioritizing going and doing

21 the next bit of workout on those chemicals where we had close

22 overlap.

23 And a little bit more summary. So I talked

24 about the bioactivity from ToxiCast. John just told you about

25 the high-throughput toxicokinetics which gives you the dose

26 where bioactivity is happening. John just told you about

27 exposure. And now we then calculate this integrated bioactivity
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1 exposure ranking or ratio. We change this periodically, what

2 the acronym stands for. But anyway, it's really some measure of

3 how close bioactivity and exposure are. 

4 And from that, we would prioritize chemicals

5 that might go first into tier 1 testing. And then if you saw

6 activity in the tier 1 testing, which are the screening assays,

7 it would go into the tier 2 test. So this is probably not the

8 best way to think about it, but all of what we're doing is kind

9 of tier 0. So tier 1 is the 11 in vivo/in vitro batteries. All

10 of this prioritization work we're talking about is kind of the

11 tier 0 to feed into that. Okay. 

12 So just a little bit more recap. So the tools

13 we're using, and you've seen most of this today. So, we did

14 pharmacokinetics. That's using the high-throughput assays and

15 two very simple, probably the simplest pathways we could

16 approach the estrogen and androgen where people know a lot about

17 the structure of those pathways. 

18 And in particular, we actually we get some

19 uncertainty, which you haven't heard about yet, so I'll touch on

20 that, how we think about uncertainty in the pharmacodynamics. 

21 John talked about how we do the pharmacokinetics, which has us

22 actually going in and measuring intrinsic clearance in

23 hepatocytes, plasma, protein binding. 

24 We can do modeling that actually puts

25 population variability into the model. And so we get a

26 distribution that has distribution of that transformation from

27 in vitro concentration to in vivo dose. And then we come up
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1 with some kind of a distribution of doses where bioactivity

2 might occur, and then we put that together with the exposure

3 modeling, which you just heard about.

4 Okay. So we can calculate these central

5 tendencies, but equally important are the distributions of

6 uncertainty and variability. And so this just summarizes where

7 we are and this is really a work in progress. So we have three

8 science areas of pharmacodynamics, pharmacokinetics and

9 exposure. 

10 One advantage this approach has over typical

11 rodent testing is, almost all the testing we're doing is

12 actually in human cells. And so we don't have to think too much

13 about species extrapolation. We have to think about in vitro to

14 in vivo extrapolation, which is a big issue. But we are doing

15 mostly human-derived work.

16 So the biggest uncertainty in the

17 pharmacodynamics is the -- this is the next slide, so I'll go

18 into that. But, probably, you can think about this as model

19 uncertainty. So our model is this a microtiter plate where we

20 have some engineered cells to detect estrogen receptor activity

21 to actually have in vivo estrogen response or androgen pathway

22 response, there's a lot of other biology going on that's simply

23 not included here.

24 For the human population variability in the

25 pharmacodynamics, certainly what we've shown you has nothing

26 there. These are engineered cell lines from some person long

27 ago. There's some really interesting work that Yvonne Russon
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1 (phonetic) at Texas A&M and collaborators are doing where

2 they're taking the HapMap cell lines -- so these are cell lines

3 derived from normal human populations, immortalized lymphocytes

4 that are totally genotype.

5 So we know the genomes of, I think there are

6 about a thousand individuals, and they've started doing in vivo

7 cytotoxicity assays and also measuring some other biomarkers in

8 response to chemicals in starting to see how much population

9 variability there is in response to chemicals. And so there is

10 a technology route out there to start estimating that.

11 On pharmacokinetics, the data uncertainty

12 piece, the biggest one is the plasma protein binding and the

13 intrinsic clearance. And in particular -- I think John

14 mentioned this -- is where you have chemicals where the fraction

15 unbound is close to zero. It's very, very small.

16 Then the uncertainty in how much is unbound

17 gets very large. The relative uncertainty gets very large. And

18 in fact, the chemicals where it's really hard to say do we have

19 a good estimate or not for the pharmacokinetics for those

20 chemicals, if the fraction unbound is less than one percent, it

21 could be .1 percent or one percent, and so you have a big

22 relative variability. And so those tend to be the chemicals

23 with the biggest uncertainty.

24 John mentioned the population variability that

25 can be built into these estimates having to do with, for

26 instance, liver function and SIPs (phonetic) and so on. 

27 Exposure, there is uncertainty. A lot of that
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1 comes out of the uncertainty in the biomonitoring data are

2 uncertainty in the reverse kinetics trying to map from what is

3 actually measured, the metabolites in urine and blood back to

4 what the parent was.

5 The biggest variability though is -- and this

6 is the biggest component of variability all over this -- is we

7 are all very different; you know, I eat Cheerios for breakfast

8 and somebody else eats pancakes for breakfast, right? And so

9 there's a lot of variability in how we're exposed to chemicals

10 as we walk through our day. So those are all components we need

11 to think about, and we have beginning approaches to deal with

12 all of those.

13 Okay. So pharmacodynamics, this is the one

14 place where uncertainty wasn't really touched on, and so I just

15 want to mention this. This is data for estrogen receptor for

16 bisphenol A, just as an example. This is what the raw data

17 looks like. I haven't shown you any actual raw data, so this is

18 from one of the NCGC estrogen receptor assays where we ran in

19 many, many replicates. 

20 This is one of these gain/loss curves. You

21 see the data comes up, it plateaus and then it drops off. So

22 the curve fits through the center of the points pretty well but

23 there's a lot of variability from replicate to replicate. So

24 there is that kind of uncertainty.

25 But then even bigger is if we look here. So

26 you've seen this plot before, but we have 16/18 estrogen

27 receptor assays run in different cell types, different
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1 technologies that are all essentially measuring the same thing. 

2 But the way the assays run, we actually see about a little over

3 an order of magnitude spread in where the characteristic

4 concentration in the AC50 is. 

5 And this uncertainty and exactly where this

6 activity turns on actually swamps a lot of the other kinds of

7 uncertainty we see. Okay? So that's a factor that we'll bring

8 in. And I will actually come back to this slide in a little

9 bit. Okay. And in fact, yes, I'm going to go to the next

10 slide. So we actually have two case studies so you can kind of

11 see how we put all of this stuff together.

12 One case study is a success and one case study

13 is kind of a failure. And I think this is useful to understand

14 how this works and doesn't work. Okay. So case study one is

15 where we want to put the bioactivity, the pharmacokinetics and

16 the exposure together. And we're going to do bisphenol A, which

17 is what I showed you on the last slide.

18 So look at the table on the right for a

19 moment. So I've talked about AC50s before. These are what are

20 called ACCs. And if we go back to this slide -- so, I'm not

21 showing it here, but every assay, there's sort of a background

22 baseline noise level. And the ACC is where this fitted hill

23 curve goes above that baseline noise. So it's a point of

24 departure at the low concentration into these curves. Okay?

25 So for each one of these curves, we fit a hill

26 curve to it and then we calculate the ACC; where is the first

27 point along this curve, the first concentration where you get
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1 above that baseline. And so that's what this table is. And in

2 some of these tables, you see these million values. That's just

3 a surrogate for inactive. And then this is just plot in

4 micromolar of where those values are. So you see there's quite

5 a spread there. Okay?

6 What we do is we take the log median to get

7 sort of the median estimate for a chemical from all of the

8 active assays, and so that's that single point. So if you go

9 back and forth, you can kind of see that that point is about the

10 median of that. Then, because we have that big spread, we can

11 actually calculate a distribution. And I think this line is one

12 mag, needing absolute deviations, so that gives us an estimate.

13 And I'm showing just the lower confidence,

14 which is the end where we're going to compare with exposure. 

15 Then we do the reverse toxicokinetics conversion. We convert

16 from concentration to dose, and that adds extra uncertainty and

17 variability to it. 

18 So now, if we notice going from this slide,

19 which is micromolar to this slide, which is mgs per kgs per day,

20 then we're going to shift the axis up and shrink it a little

21 bit. But in these two slides, this dotted line is exactly the

22 same as this one up here. So we have a median estimate of

23 exposure to bisphenol A. And John Wambaugh talked about the

24 five parameters that go into the model to get that median

25 estimate. 

26 So does it have consumer and industrial use;

27 yes. Is it industrial use only; no. Is it a pesticide inert;
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1 no. Is it a pesticide active; no. What's the production

2 volume; it's greater than a billion pounds a year. Those five

3 parameters go into the model and that produces that point

4 estimate, this central tendency. Okay. 

5 Then there is this chemical-independent

6 variability and uncertainty estimate on exposure, and so that's

7 the red. And so then the IBER, if you think about the ratio or

8 the distance, that's your margin exposure in this world. Okay. 

9 So there is no overlap for those 95 percent confidence

10 intervals. 

11 Okay. So how well does the exposure estimate

12 work? So for this chemical, for bisphenol A, there was a paper

13 by LaKind Naiman which went and actually did some population

14 variability analysis for what the actual exposure was. And

15 these values are in nanogram per kilogram per day. And then

16 there was the ANSIS (phonetic) also. 

17 Okay. But these number, you see, they are 30

18 or 40. Okay. The median estimate or the mean of the median --

19 you can ask John about this. But anyway, that red point is 20

20 to 60. So we're within about a factor of two of what this good

21 actual data is. And then these upper 95 percent confidence

22 intervals, they're about two orders of magnitude above that.

23 So clearly, at least from overall population

24 exposure, you would conclude that bisphenol A would not be

25 activating the estrogen receptor. Whether that's true or not,

26 that's what this data tells you. Okay? So that's kind of a

27 success, things make sense.
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1 Okay. So now let's go to the next example,

2 which is triclosan. And as you saw yesterday from Nicole

3 Clangsure's (phonetic) talk, it is classified as being an

4 androgen receptor antagonist. And so here again, these are the

5 concentrations, the ACCs instead of the AC50s, and we see a

6 range of concentrations there. We take the median value, and

7 notice that it actually is one of the points there. So instead

8 of a mean, it's a median.

9 You add the uncertainty which actually comes

10 from this distribution here. We've now done the in vitro to in

11 vivo, so now we're on the dose scaling. The point estimate for

12 exposure -- and this is, again, the model -- triclosan has

13 consumer exposure; yes. Industrial use only; no. Pesticide

14 inert; no. Pesticide active; yes. Production volume; very low.

15 And so what we get here is the point estimate

16 and the uncertainty intervals and uncertainty variability

17 intervals, and you see you still have too much of an overlap. 

18 So you'd say, okay well, at normal, the average human, triclosan

19 wouldn't be activating the androgen receptor. 

20 But now if we actually look at some real data,

21 so there are actually three studies that all give the exposure,

22 and these are in microgram per kilogram body weight per day. 

23 They're on the order of 1-2-3, and our exposure is very low. 

24 It's two to three orders of magnitude too low. So the actual

25 real data is more up at the top of this, or even beyond that, so

26 this is kind of a failure from the exposure model. 

27 And what's interesting, the reason for the
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1 model failure, if we go back to this model -- so it's actually

2 because triclosan is called a pesticide-active ingredient. If

3 you look in NHANES which is, again, where all the calibration

4 comes from, they measure lots of pesticide-active ingredients. 

5 But pesticides are regulated in a way and used in a way that

6 people are really not exposed. You know, they are setup so to

7 keep exposure down.

8 And so the general rule is, if you're

9 pesticide-active, people won't be exposed, and so your level is

10 way below the limit of detection quite often. And so that

11 heuristic, in this case, doesn't work. And it doesn't work

12 because, you see, even though this is a pesticide-active, it's a

13 pesticide-active you put on your toothbrush and stick in your

14 mouth and brush with. Okay. So it shows a type of failure of

15 the model but one that we could understand and fix in later

16 versions. Okay. So that's kind of the basic idea. 

17 So now then, let's actually look at all of the

18 data. So we have a total of 290 chemicals where we have the

19 estrogen receptor model data, we have the pharmacokinetics data,

20 the high-throughput pharmacokinetics data, and we have exposure

21 data. And for each of the chemicals, we see this same pair of

22 exposure estimates where the dot is the geometric median of the

23 mean or mean of the median, and the 95 percent confidence

24 interval and then the median and the confidence intervals for

25 the ACC corrected for toxicokinetics.

26 And what you see is that almost all of the

27 chemicals, there is no overlap. And they're actually ranked
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1 from the farthest distance from the bottom line to the top line.

2 And over here at the left, we do see a handful of chemicals

3 where there is overlap. And it's a little hard to read but

4 these are all pharmaceuticals which, going back to the model --

5 the model doesn't have a component -- is this something you'd

6 intentionally put in your mouth. 

7 Maybe it's a little surprising that the

8 pharmaceuticals actually show up here, although, the main reason

9 they're showing up here is not because the estimated exposure is

10 higher. Notice the exposure for the most part is about the

11 same, but that the bioactivity of those compounds is very high. 

12 They're a very potent compound, so that's really what's driving

13 the overlap.

14 Okay. Same example for androgen receptor,

15 it's the same 290. And notice we're not showing 290 chemicals

16 here. The only chemicals that are being shown out of the 290

17 are the ones that were classified with the estrogen or androgen

18 model as being positive. And again, most chemicals, the

19 difference here is very, very large.

20 And it's important to see the scales. It's a

21 little hard to see. But the main bands, those are three orders

22 of magnitude in mg per kg per day. And so here it's 3-6. And

23 at the far right, it's about eight orders of magnitude

24 difference, very, very large. But again, there's a handful of

25 chemicals down here at the left, and the overlap is mainly being

26 driven by the high potency of these pharmaceuticals or natural

27 hormones.
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1 One other thing to just point out, remember

2 that one of the key components to this model is, is there a

3 near-field exposure, yes or no. The red bars are chemicals

4 where there is a near-field exposure. And so the chemicals that

5 tend to poke up out of the mass there are the ones that have the

6 near-field exposure. 

7 If we go back to the estrogen, it's the same

8 way. All of the more highly predicted to be exposed chemicals

9 are red, although some of those with near-field, they're also

10 down in the large mass. Okay. So that's the proposed path

11 we're going to take to put all of this data together. Clearly,

12 there's a lot of work to be done.

13 But what we're showing here is this method to

14 rank chemicals by this exposure to bioactivity ratio, and it's

15 using the in vitro data from the pathway, the in vitro

16 toxicokinetics. We are making protective conservative worst

17 case scenario estimates. So you're really looking at do the 95

18 percent lowest confidence interval for the activity in the upper

19 95 for the exposure, do those start to overlap. If they do,

20 then let's look at those chemicals a little more closely.

21 It's using models to estimate exposure, and

22 relatively simple models. In SEEM three that you'll hear about

23 probably next year or the year after that is going to make much

24 more sophisticated near-field modeled use. And certainly, the

25 variability is really important.

26 And for ER and AR, at least, most chemicals

27 that show this overlap in exposure are drugs or natural hormones
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1 where you're actually supposed to have bioactivity, although the

2 overlap is more driven by the potency of the chemicals than by

3 increased exposure estimates, because we know that you're

4 supposed to be taking a pill of this a day. Okay, so that's the

5 talk. 

6 If I can go and spend just a few minutes. Do

7 we want to take questions?

8 DR. JAMES MCMANAMAN: Yes, why don't we take

9 questions first? 

10 DR. RICHARD JUDSON: Okay. Okay.

11 DR. JAMES MCMANAMAN: I think that there are

12 some interesting issues related to what you just presented. 

13 Questions from the panel? Okay, well I do. What do you mean by

14 close?

15 DR. RICHARD JUDSON: That's a regulatory -- 

16 DR. JAMES MCMANAMAN: No, no, no. You're

17 using that in a sense that -- and I understand the regulatory

18 implications. So your estimates of 95 percent confidence

19 interval, that's based on an estimate of a bioavailability and

20 activities and all these things which admittedly could be a

21 factor of three difference.

22 So for bisphenol A, if you take the 95th

23 percent confidence interval and you increase the bioavailability

24 by a factor of three, I mean they could begin to overlap. 

25 And so I'm asking from a standpoint of

26 binning, which gets binned? I mean this bisphenol A, is that a

27 compound that would be flagged as something that would be of
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1 interest or is it something that you think that there is

2 sufficient separation, going back to your little diagram where

3 the two things -- I'm trying to understand where the cutoffs

4 would be here.

5 DR. RICHARD JUDSON: So I'd maybe go back to

6 John Wambaugh's comment. He showed the distribution of

7 exposures for 8,000 chemicals or so. And somebody asked the

8 question, how would you bin that? So a big piece of uncertainty

9 here is model uncertainty. We push that under the rug a little

10 bit. And so we know that, with what we know, the uncertainties

11 are this, but there are things we don't know. So Donald

12 Rumsfeld would be right at home here.

13 DR. JAMES MCMANAMAN: Right. 

14 DR. RICHARD JUDSON: So the things we don't

15 know could easily be another factor of three or 10. Okay. So

16 as a good scientist we'd say, okay, if the overlap is less than

17 a factor of three or 10, we'd say we don't know the difference

18 between a factor of 10 overlap and a factor of zero overlap.

19 And then you would take a make rough bins

20 without any bright lines and say -- I would take this slide here

21 and I would say, okay, I would start everything else being

22 equal. I would start from the left and move to the right. But

23 at some point you'd get so that you're 10 to the sixth, 10 to

24 the eighth overlap, I would say. 

25 We cannot think of any reasonable way where

26 we're eight orders of magnitude off, so we'd really stop at some

27 point and go worry about some other problem. So the answer is,
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1 there certainly are other things we haven't accounted for in the

2 uncertainty and variability here, and so those actual confidence

3 intervals may be bigger. But there's no good way to set a

4 bright line where we'd say we worry or don't worry.

5 DR. JAMES MCMANAMAN: I understand about the

6 ones that are really far off. That's the thousand down on the

7 right side of that graph. But it's the ones that are in the

8 gray area. Because it all depends on how accurate your

9 estimates -- and these are only estimates -- are for both

10 exposure and pharmacokinetic and pharmacodynamics. And each one

11 of those things can be off by a factor of three to 10, which

12 then makes that gray area a little more problematic.

13 DR. RICHARD JUDSON: Right. 

14 DR. JAMES MCMANAMAN: And would it be more

15 conservative to assume the worst case, to go up by a factor of

16 three and down by a factor of three so that you maximize the

17 potential for overlap and use that as a -- 

18 DR. RICHARD JUDSON: Okay.

19 DR. JAMES MCMANAMAN: Am I missing the point

20 here?

21 DR. RICHARD JUDSON: No, no, no. Those are

22 actually goods suggestion.

23 DR. DAVID DIX: These are, I think, good

24 questions and relevant as we continue to consider ways to

25 improve this science. Some of these considerations, I think,

26 will be informed as we continue to make more comparisons with

27 additional screening level data and get a better handle on how
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1 those compare and the relevant uncertainties, if you will, with

2 the different approaches.

3 But the question of binning and classification

4 and these types of things isn't something that we're looking at

5 this tool for right now. If you look at the charge questions,

6 we're asking about considerations of this use for

7 prioritization, so it's really a start at the top and work your

8 way down. 

9 We're not really looking, at this point, at

10 this point in time, to use this tool beyond prioritization. 

11 We're not asking you all to look at it or consider it in that

12 context, in part, because we're at this stage of development of

13 the science.

14 DR. JAMES MCMANAMAN: Yes. I may have

15 misspoke when I said binning. I was just trying to bring it

16 back to a concept that had been developed earlier, because I

17 wasn't really asking a regulatory question, I was asking a

18 scientific question. And it seemed like binning was more

19 scientific than regulatory. Yes, Dr. Doerge?

20 DR. DANIEL DOERGE: Yes. Actually it turns

21 out to be kind of a follow-up question to the previous one. And

22 that is, I know since you guys have inquiring minds, you've

23 taken this beyond your prioritization ratio that you've got

24 here. So tell me what's been your experience with the test

25 compounds that you've looked at? I know you've just showed us a

26 couple today. 

27 But comprising your margins of exposure
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1 obtained with your ratio plots with high-quality rodent bioassay

2 data that are available for some compounds, obviously not all --

3 but in those cases where you've got your predictions and you've

4 got high quality consensus bioassay data, how well do these

5 margins relate to each other?

6 DR. DAVID DIX: One of the things that's

7 important, we want to be careful with our language and we don't

8 consider these margins of exposure. 

9 We're looking at estimates of bioactivity in

10 comparison to estimates of exposure. And from a problematic

11 perspective, we don't consider that a margin of exposure. We

12 consider margins of exposure related to adversity. And that's

13 really the long-term answer to some of the questions that you're

14 asking on how do we address this. We go to a higher level of

15 testing where we can determine dose response for adverse

16 outcomes. 

17 The question is still a good one, just using a

18 different term, considering this comparison of bioactivity and

19 exposure, which is what we term it in the bioactive exposure

20 ranking or ratio.

21 DR. JAMES MCMANAMAN: Okay. But let me

22 address Dr. Doerge's question. So earlier today you saw there

23 was an example for the conazole fungicides where the phenotype

24 -- and it's not adverse -- the phenotype was liver hypertrophy. 

25 And this was in a publication two or three years ago. And the

26 liver hypertrophy is hypothesized to be driven by interaction

27 with PXR. Okay?
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1 So we have bioassays for PXR; we can do the

2 pharmacokinetic to convert from in vitro PXR activity to in vivo

3 activity, and then we can compare that to the dose where you

4 actually see liver hypertrophy in the rodents. And across that

5 class of compounds, we were always, I'm going to say eight out

6 of 10 compounds, we were below. So we estimated that a dose,

7 you would see activity below where you actually see activity,

8 but within a factor of 10. So we tended to track very well the

9 dose there. 

10 So that doesn't have anything to do with

11 exposure, that's just getting the pharmacokinetics, the in vitro

12 bioactivity and the in vivo response together. So that's one

13 published example. But we're now kind of at the point where we

14 can do more examples like that to find out how well we do. Do

15 you want to add more?

16 DR. JOHN WAMBAUGH: I just wanted to circle

17 back to the discussion by Dr. McManaman and Dr. Dix. To me,

18 looking at this figure on slide 23, the question we're asking is

19 really, does it make sense to read, to some extent, from left to

20 right? And right now, we're not asking necessarily where do we

21 stop reading.

22 But taking the uncertainty in the models we

23 have right now doesn't make sense to start reading from the

24 left, and it's a policy choice where you stop reading.

25 DR. JAMES MCMANAMAN: So Dr. Wambaugh, let me

26 follow-up on that. So if we go to slide three, because I think

27 this gets at the cruxes, this is a question of prioritization,
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1 and that the figure that is second from the right, the one

2 that's medium priority. So I mean it takes real world

3 experiences here. You presented two examples where the models

4 failed, one where the models is soon to be successful and one

5 where it failed.

6 So how does that fit into the prioritization

7 scheme? Because it would seem to me that, based on that

8 analyses, that you're not very close to being able to fit any of

9 these things into a real defined prioritization scale because

10 you've got one where it failed and one where it was successful. 

11 And even in the one that was successful, it's unclear whether

12 that would be a medium priority or a high priority or a low

13 priority?

14 DR. JOHN WAMBAUGH: I think in both case

15 studies, the difference between the overlap is somewhere around

16 a factor of 10. And when you read those charts we were looking

17 at earlier, on the left-hand side you have the pharmaceuticals

18 that are potent and intended to be bioactive, and they're

19 overlapping. And on the far side, you have chemicals where you

20 have six to seven to eight orders of magnitude.

21 And so, the reason I'm putting it in terms of

22 reading from left the right is that, in that context, if I'm

23 looking at hundreds of chemicals and I see that they're not

24 quite overlapping but it's a smaller distance between them, that

25 to me is a higher priority. And so, to my mind, your highest

26 priority are the ones that are overlapping. 

27 But at some point -- and again, that's why
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1 we're saying it's a policy decision. But at some point, you

2 want to take a close overlap and investigate that before you'd

3 investigate an eight order of magnitude difference between the

4 two.

5 And so in that sense, the conclusion I

6 would've drawn from bisphenol A and triclosan would probably be

7 very similar. Because in the context of the chemicals, those

8 are both close enough that you might want to think about it a

9 little bit more than the four yes or no questions and some in

10 vitro tests.

11 DR. JAMES MCMANAMAN: Okay. Thank you. I

12 don't remember who had their hands up first. Dr. Brown?

13 DR. TERRY BROWN: In your presentation, one

14 aspect applies to this because we're already discussing the

15 confidence in both exposure levels as well as bioactivity. And

16 so if you go to slide eight, which is where you showed the

17 bisphenol A data relative to the bioactivity assays, I think one

18 of the things that we're being asked to assess in this meeting

19 is the reliability of those bioactivity assays. And yet, to my

20 knowledge, this is the first time that we've actually seen real

21 data that shows the variability in those bioassays, unless I'm

22 incorrect.

23 We weren't provided with proficiency,

24 reliability, variability, information related to either the

25 androgen or the estrogen receptor assays that are being used,

26 other than the fact that if you include enough assays around one

27 or the other of those parameters, you can make, I guess, a best
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1 knowledge-based assessment of yes it is bioactive or no it

2 isn't, and where it kind of lies in the framework of differences

3 along the spectrum.

4 DR. RICHARD JUDSON: So, I mean you've seen a

5 number of these plots. I didn't point out the fact there is

6 uncertainty in that quantitative value. Basically, the model

7 that I and Nicole Clangsure showed -- ultimately we were saying,

8 does this chemical look like it's really active against estrogen

9 receptor. A secondary point was where is it active, what's the

10 concentration of where it's active, which is just being brought

11 up today.

12 We do have lots of quality metrics for all of

13 the assays. And it is true, we didn't bring any of that with us

14 or present it here. All of the assays we're presenting, though,

15 sort of pass a quality metric. That's what we can say. And all

16 of that QC data is publicly available. You have to really want

17 to dig in and find it. I have to say that. 

18 And we could make it available to the panel if

19 it would be helpful for understanding this. We don't have any

20 presentations about that. Yes, you're correct, we didn't give

21 you all of that technical performance data.

22 DR. TERRY BROWN: So I understand that we'd

23 probably be overwhelmed with data. But in past iterations of

24 some of these SAP meetings, we've actually been asked to assess

25 the reliability of certain assays. It's come a long way from,

26 for instance, the androgen receptor rat cytosol assay that we

27 saw many years ago to the high-throughput assays and the
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1 recombinant protein in vitro type of high-throughput assays that

2 we're seeing now.

3 But again, it comes back to the quality

4 controls that are hopefully in place related to these assays in

5 producing reliable data that is going to be the input,

6 particularly, in the bioactivity aspect of this.

7 DR. DAVID DIX: If I could continue the

8 response for that question?

9 DR. JAMES MCMANAMAN: Sure.

10 DR. DAVID DIX: These are really good points,

11 Dr. Brown, and I appreciate them. And it was a balance of time

12 of a sort. But one thing I would point out, we've taken a

13 little bit more of a performance-based approach to analyzing the

14 results from these high-throughput screening data, and that was

15 the point of the comparisons to their performance against

16 reference chemicals as well as the comparison of their

17 performance against other bioactivity assays, such as the

18 uterotrophic assay. 

19 And so we're moving. And I think we

20 collectively, including with many partners through OECD and

21 other consortia, to a performance-based approach to test

22 guidelines. So you look at what you're trying to measure. At

23 the end of the day, we're not trying to get a reproducible

24 result from an individual assay.

25 Really, in the EDSP, what we're trying to do

26 is have a collection of data that tells us whether or not this

27 compound is likely to be bioactive in that pathway, so we'd
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1 likely end up looking at many different assays at once. And so

2 that's where we're at a point where we're making comparisons in

3 the high-throughput groups of assays against the more low

4 throughput and animal-intensive assays like the uterotrophic. 

5 And next we'll go on to make comparisons to the pubertal and the

6 fish short-term reproductive assay as well.

7 But your points are well taken in terms of

8 understanding the reliability and reproducibility of the

9 individual assays, as well. Some of that will be addressed in

10 at least a very quick passing fashion in looking at the EDSP-21

11 dashboard, which we'd like to do.

12 And then, some of the other points that are

13 being raised in question in terms of how this might be used in

14 prioritization and things I think will be addressed in the next

15 presentation by Patience Browne, which will show a proposed

16 application of using the IBER method to prioritize chemicals and

17 some additional comparisons of those results to other data.

18 DR. JAMES MCMANAMAN: If the panel is okay

19 with this, and since your questions might be answered, as Dr.

20 Dix just implied, in the next set why don't we hear Dr. Brown's?

21 So the dashboard, does it fit in better now than after Dr.

22 Brown? I'm trying to figure out the best way, because we could

23 go on and ask the questions now or they could be answered by Dr.

24 Brown's presentation. So I'm just trying to decide what would

25 be the best.

26 DR. RICHARD JUDSON: Okay. So I think the EPA

27 group would like to go ahead and do the dashboard. It'll be
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1 very quick.

2 DR. JAMES MCMANAMAN: So you have the

3 questions from the panel related to this specific top. Why

4 don't we go ahead and ask those now? So we'll start with Dr.

5 Berrocal.

6 DR. VERONICA BERROCAL: So I actually,

7 conceptually think that the approach of looking at the two

8 distributions of the bioactivity and exposure and see whether

9 the overlap is a good approach. I think it's conditional

10 though. It's conditional on your estimates of the (inaudible)

11 of this distribution, as well as the uncertainty.

12 And from yesterday's discussion, I actually

13 did not have the impression that, going back to slide eight,

14 that you had so many different possible curves and you were

15 estimating the bioactivity based on the fitted Hill model.

16 So I'm wondering if your uncertainty in ACC-50

17 is actually underestimated by the fact that you're using only

18 that fitted Hill curve to estimate what is the concentration and

19 what is the implication in terms of just uncertainty in the

20 bioactivity.

21 DR. RICHARD JUDSON: So we do have uncertainty

22 estimates around each one of the ACC's AC50s there. And so if

23 we look at the top curve where we had the real data, the ACC is

24 this blue line in this -- I can't even read that. Anyway let's

25 pretend that's the ACC there. So there is obviously some

26 uncertainty around that. And we did not include that

27 uncertainty in the examples we were showing subsequently, so
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1 that would be another factor that we could throw in there. All

2 I can do is guarantee that it's significantly smaller than this

3 uncertainty from curve to curve. So it could easily add another

4 factor of three though.

5 DR. JAMES MCMANAMAN: Is there a follow-up

6 question, Dr. Berrocal?

7 DR. VERONICA BERROCAL: Nothing.

8 DR. JAMES MCMANAMAN: Okay. I saw Dr. Jett

9 had his hand up.

10 DR. DAVID JETT: Yes. So I think that the

11 evidence of the variability is really important. I just noticed

12 in some of the ER assays that there appear to be some outliers. 

13 Are you guys considering -- and maybe I'll get thrown out the

14 room by a statistician -- but considering some level of

15 variability that triggers a replication of a specific assay for

16 a specific chemical?

17 DR. RICHARD JUDSON: That's a good point, and

18 we have certainly discussed that. So the question is, if we

19 have a bunch of assays like this that turn on -- and

20 occasionally we have an example where one of the assays is an

21 order of magnitude lower in concentration than everything else;

22 so would we follow-up and rerun that assay?

23 And if we were in a low-throughput mode where

24 we had our own labs and we could tell the grad student to just

25 go rerun that for me, we would do that. But in this

26 high-throughput mode, it's really cost-prohibitive to do that. 

27 And so really, the route we do is, you run every assay once or
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1 with replicates as appropriate for a thousand chemicals, and you

2 use the estimates you get from the whole panel of assays to

3 really throw those outliers out and say, I just won't use that.

4 DR. JAMES MCMANAMAN: Dr. Barr?

5 DR. DANA BARR: Yes. Mine was kind of a

6 question just to see if I'm interpreting it correctly. But

7 first of all, I wanted to thank you for a couple of things. 

8 One, I think you did a great job of finally explaining that one

9 figure that we were having problems with, and I think I

10 understand it quite well now. And it illustrates the point very

11 well, I think, so nice job, Dr. Wambaugh.

12 The other thing is I appreciate you giving us

13 good case and worst case scenarios because it is something that

14 we asked for last time, which would enable us to kind of see how

15 it works well and how it doesn't work well. 

16 So basically on that figure again, I just

17 wanted to clarify if I'm thinking about it correctly, that

18 you're kind of looking at the worst case scenario for bioactive

19 and the worst case scenario for exposure and seeing if they

20 overlap, and then making those the highest priority, correct?

21 DR. RICHARD JUDSON: Correct.

22 DR. DANA BARR: Okay, got it. Thanks.

23 DR. DAVID DIX: Let me add to that answer. I

24 don't want us to get fixated on this overlap issue, because if

25 there's a real overlap between the 95 percent confidence level

26 for bioactivity and exposure, we'd probably be a little bit more

27 than just concerned. So it's the distance or the way we're
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1 presenting, and it will be presented in the next talk as a ratio

2 to represent that.

3 But problematically, we would stay away from

4 saying the only thing we're concerned -- certainly, there's a

5 lot more chemicals that don't overlap that we still have a high

6 priority interest in, so just reacting to that.

7 DR. DANA BARR: Yes. The closer they are,

8 though, to matching. Yes, thank you.

9 DR. JAMES MCMANAMAN: Yes, Dr. Weller?

10 DR. GRANT WELLER: So you've done a lot of

11 work to quantify the uncertainty, and basically, you get what

12 looks like posterior distribution estimates for both bioactivity

13 and exposure. And now you're comparing, as Dr. Dix said, a

14 ratio of the upper limit for bioactivity and lower limit for

15 exposure. 

16 I'm just wondering, given the work that you've

17 done to quantify the uncertainty in each of those pieces

18 individually, have you thought about just going the extra step

19 in getting a distribution of that ratio and then framing it in

20 terms of like a hypothesis test that that ratio is larger than

21 one, for example; getting like a posterior probability estimate

22 of that?

23 DR. JOHN WAMBAUGH: I think that sounds like

24 an excellent recommendation from the panel.

25 DR. JAMES MCMANAMAN: Well, good. With a

26 positive recommendation, and unless Dr. Weller has another

27 comment, I think we'll move on to the dashboard.
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1 (Whereupon a break was taken)

2 

3 DR. RICHARD JUDSON: For somebody who leads,

4 sometimes, the software development group, this is always the

5 moment of truth; will the software work when I need it to? So

6 the URL -- and this was on one of the slides -- it's actor like

7 the guy on the stage; actor.epa.gov/edsp21. Okay? And so any

8 of you can go and follow along.

9 So the basic idea is we have a list of

10 chemicals. And this is all of the chemicals that are in the

11 EDSP universe, only a fraction of which we have all this data

12 for, and so you have to kind of know what chemical you're

13 looking for. It's all the EDSP universe chemicals plus any

14 chemical not in that universe, which is in the ToxiCast dataset.

15 Okay. And I'm just going to walk through one chemical, which

16 is bisphenol A.

17 And so, what you do is go up here. There's a

18 little text box that says "Chemical name". I'll blow this up

19 enough so that we can see a little bit. Okay. So you can start

20 typing in and then you just select the chemical.

21 And so we see the structure of bisphenol A on

22 the summary tab. And here at the bottom, these are the AUC

23 scores that I and Nicole talked about yesterday. So it has an

24 estrogen score of 0.665 for the agonist and an androgen

25 antagonist of .136. Okay? And so that really summarizes the

26 model. All of these fancy plots of the model are not here yet,

27 but it says the model summarizing all of the ER and AR data
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1 gives a positive and relatively large value.

2 The other panel over here shows the

3 categorization from the estrogen receptor QSAR model, and all

4 we're doing is summarizing. And it says the consensus is active

5 as an agonist and antagonist and a binder. And then from the

6 literature database, it's also an active agonist and an active

7 binder. I guess there's no data for antagonist. We didn't have

8 enough data there. 

9 So the bioactivity summary summarizes

10 androgen, estrogen and thyroid. And the thyroid, we just had

11 the receptor, and I wouldn't put any store on that data. But

12 what we see here in these little dancing pie plots are the

13 active assays. And the more potent an assay is for that

14 particular endpoint, the bigger the pie slice. And then you can

15 see the actual potency values.

16 And if you click there, if you actually want

17 to know a little summary of what that assay is actually doing,

18 you can get the summaries of the assays for the androgen and the

19 estrogen assays. Okay.

20 But then the real bioactivity, and I showed

21 that one plot for bisphenol A for one of the assays, here, we

22 have the androgen assays at the top and the estrogen at the

23 bottom. And if we click, it goes and grabs the data for each of

24 the individual assays, and we see the concentration response

25 plot.

26 For every assay, every chemical, we fit three

27 curves. There is a flat curve where nothing happens, and that's
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1 this yellow line down here. There is a hill curve, which is the

2 red one, and this gain-loss, which is blue. And the one which

3 is solid, and it's a little hard to see here, is the winner. 

4 And it's winning based on this Akaike information criteria, and

5 so the gain-loss is one. But you can see all the data points

6 and you can see all three curves.

7 And we show the AC50 value for the winning

8 one, and then this is the point where the cytotoxicity, that's

9 the cytotoxicity region. So for bisphenol A for this assay,

10 we're well outside of that region. And actually, we also have

11 multiple samples quite often. For bisphenol A, we've run

12 bisphenol A multiple times. And as you scroll down, you get

13 more or less the same results.

14 As we go into the estrogen model that I've

15 shown you, we have chosen a representative sample here. And

16 there is kind of a complicated algorithm for choosing which

17 representative one. But essentially we said, okay, were more of

18 them active than inactive, in which case we chose an inactive

19 one. If more inactive than active, we call it inactive. And if

20 it's a tie, we go towards inactive.

21 And if we're choosing one of the active ones,

22 we chose one closet to the median AC50 rather than one of the

23 outliers. Okay. One other point I want to make, I think if I

24 go to one of these tox21 assays, they're actually two different

25 samples for bisphenol A. 

26 And this is actually a little bit of

27 interesting biology that I could easily get people excited
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1 about, but we won't go into it, is that bisphenol A can act as a

2 partial agonist in certain assay context. So instead of going

3 up to a hundred percent, it reliability goes up to, in this

4 case, 40 or 50 percent. And we don't really understand why

5 that's so but it's a known phenomenon.

6 But what I wanted to point out is that not

7 only do we do the three-fifths, but we also have a whole series

8 of what we call caution flags where we're looking at chemicals

9 plate-wise level; or do we have striping or do we have edge

10 effects on the plates or so on. And some of these assays,

11 chemicals that are very potent will actually be so bright in the

12 florescence assays that they cause neighboring wells to appear a

13 little bit light. Okay. 

14 And so we don't throw the data out but we

15 attach warning flags. And in this case, there was a stripe on

16 the plate. Okay. And so it's just a warning that this chemical

17 is sitting in a stripy place. And so those sort of warnings and

18 other quality information, we could potentially build into more

19 sophisticated activity models, as Dr. Brown was at least making

20 me think about doing. So we do have that kind of extra data. 

21 So that's the bioactivity, and you can run through all of the 18

22 estrogen and the nine androgen assays.

23 Then we also have the ExpoCast data, and this

24 is very summarized data here. But we have the rank plots, so I

25 think in John Wambaugh's presentation, you essentially saw a

26 version of this where these are the median estimated exposure

27 for the 8,000 chemicals, which is most of the EDSP universe and
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1 bisphenol A. 

2 The chemical we're worrying about is shown

3 here with its median and its upper estimates. You can say,

4 okay, this chemical is way up in that top exposure set and you

5 can get some estimate of where it is. 

6 Then these are the estimates which is not very

7 interesting by different population groups. And I'm not sure,

8 we should actually see some more data, so there, that's not

9 working like it should. But anyway, what it should do is give

10 the estimates from the model for these different subpopulations,

11 again, with the median and the upper 95 percent confidence

12 interval.

13 And finally, we have this very large database

14 of how chemicals are used from many hundreds of sources out on

15 the internet. And so you could actually dig in and say, okay,

16 how is bisphenol A or whatever chemical you chose, what are its

17 uses which is going to inform the more sophisticated exposure

18 models that are coming along.

19 And then finally the last tab, just to let you

20 go and dig into the assay descriptions a little bit more. So

21 that's really all I wanted to show and just encourage people if

22 they have their favorite chemical they have questions about. 

23 You can dig in and you can see the assay data and the summary of

24 the exposure of models.

25 DR. JAMES MCMANAMAN: Thank you. I think

26 we'll take a short break here, 10 minutes. We're running a

27 little bit behind time, so a bio break and some coffee if you
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1 want, and then we'll come back in here to Dr. Browne's

2 presentation.

3 

4 (Whereupon a break was taken)

5 

6 DR. JAMES MCMANAMAN: So here is Dr. Patience

7 Browne from the EPA. 

8 

9 IBER INTERPRETATION AND APPLICATION

10 

11 DR. PATIENCE BROWNE: So once again, I am here

12 to really represent the program office's perspective on the

13 tools that you've just heard. And I think a lot of the tools

14 that we've seen today have been discussed previously. And I'll

15 mention that this integrated bioactivity exposure ranking is

16 really new, and what we're asking the panel to consider is the

17 proposed approach for using this method.

18 I want to take a little bit of time on this. 

19 We really appreciate your feedback on a lot of your thoughtful

20 comments, particularly on the last couple of talks, but I'd like

21 to point out that these computational tools have been discussed

22 in previous peer reviewed SAPs. 

23 Part of the reason for sort of the relatively

24 rapid review of those is the amount of data involved in those is

25 just so voluminous. There was a three-day meeting to discuss

26 the high-throughput ER model in January 2013. There was a

27 three-day meeting to discuss the high-throughput exposure
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1 estimates in July 2014. All of the intricacies of those methods

2 are well beyond the time we're allotted for this particular

3 meeting.

4 I'd also like to point out that many of these

5 have been peer reviewed in publication. There are tens of

6 publications from the ToxiCast group on bioactivity and several

7 publications on high-throughput exposure and the high-throughput

8 toxicokinetics, as well.

9 So consistent with both the approach of the

10 White paper and the charge questions, what EPA is really asking

11 of the panel today is to review the proposed approach for using

12 these computational models in prioritization of chemicals for

13 EDSP screening and testing. So I think we'll go back through

14 some of the data. 

15 But the point here is that, identifying

16 chemicals with lower exposure bioactivity ratios allow EPA to

17 more efficiently use resources and put in the words of our

18 assistant administrator what was potentially a multi-century

19 project can literally become multi-year using these

20 high-throughput techniques, and that's very exciting for us.

21 So once again, just to bring back comments

22 from the SAPs that I mentioned previously, the panel

23 recommendations in a report from the 2013 SAP said that the

24 approach presented is appropriate for prioritizing chemicals to

25 identify ER interactions. And we mentioned that that 2013

26 meeting was a more abbreviated set of the ER high-throughput

27 assays. It was eight, we've now expanded to 18. And there is
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1 the model approach, the network model approach that Dr. Judson

2 spent a great deal of time going over.

3 In the 2014 report, the panel reported that

4 the same framework represented quality science adequate for

5 testing or validating predictions using the conservative worst

6 case scenarios. I think we've just demonstrated that that is

7 the approach we've adopted, and that the high-throughput

8 exposure should be simultaneously used with the high-throughput

9 bioactivity.

10 And so I think, what we've really demonstrated

11 is that we've taken the recommendations from the previous SAP

12 meetings and integrated that into our proposed approach

13 presented to you today.

14 So, as I've done with the other presentations

15 I've given, I'd like to really point out the objectives of this

16 section and I'd like to really emphasize what we're talking

17 about is a prioritization of chemicals for EDSP screening and

18 testing based on the rank of ER bioactivity in estimate exposure

19 and a prioritization of chemicals for further EDSP screening and

20 testing based on the rank of AR bioactivity and exposure.

21 And once again, the request to review the

22 proposed approach is in part due to the expectation that these

23 models will be continually refined, as there are more data

24 available, as more assays are integrated and as the mathematics

25 and the model are refined, as well. So these actual numbers may

26 be a moving target. In fact, it may improve considerable. 

27 However, at this point, what we're looking at is the relative
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1 rank.

2 So, we've seen this figure once before. What

3 we're talking about is taking bioactivity and having a bridge

4 between the high-throughput exposure model or, another way of

5 thinking about this is the in vitro to in vivo extrapolation

6 using the high-throughput toxicokinetics and then looking at the

7 distance between those two estimates.

8 So computational tools are integrated into

9 these three critical steps. I've shown you this figure before. 

10 This is our estrogen agonist bioactivity. So one way of

11 prioritizing chemicals would be based on simple bioactivity. 

12 And as we said, nothing is really off the table at this point. 

13 There are no bright lines, no cut offs, but

14 our higher AUC scores would be higher priority chemicals. And

15 once again, without getting into the analyses of how each

16 high-throughput assay is performing, I'd like to bring your

17 attention back to the balanced accuracy of the model performance

18 against in vitro reference chemicals which was about 95 percent,

19 really remarkable, and in keeping with our approach to use

20 performance-based analysis rather than the reliability of

21 individual assays.

22 And I'd like to point out that, I think,

23 probably unexpected to EPA, our balanced accuracy for predicting

24 the in vivo response of the uterotrophic was in a similar

25 ballpark of about 95 percent. So we've really demonstrated that

26 this is a scientifically robust tool for us, and I think the

27 application may extend beyond prioritization.
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1 One more thing. I think there's been some

2 conversation about nonylphenol, and nonylphenol is that last

3 point there just above the hundred micromolar. And that was the

4 linear, by the way, and we were talking about the relative

5 estrogenicity of that. That would certainly be picked up by

6 this technique. It would certainly be a chemical that we would

7 be interested in. 

8 But just in comparing that as sort of a method

9 check, I would say, the average lowest observed effect level for

10 the in vivo nonylphenol uterotrophic assays was about 130

11 micromolars. So this really is a suggestion that this model is

12 performing extremely well.

13 So again, showing you the androgen antagonist

14 bioactivity, this is a figure I've shown you before. I've

15 called out certain chemicals on this. Another thing I'd like to

16 point out once again, here is the performance of this model. 

17 We're using fewer assays. We know that there are some potential

18 challenges to differentiating antagonism from in vitro

19 cytotoxicity. 

20 However, the balanced accuracy of this model

21 was also in the 95 percent range. Again, performance-based

22 analysis suggest this is a very robust tool. So both of these

23 are potential methods for prioritizing chemicals for EDSP

24 screening and testing. 

25 Integrating the recommendations from previous

26 SAP panels, and including the high-throughput exposure data, we

27 come up with a ranking. And I'll show you figures you've seen
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1 and then a new way of looking at these. So right now we're

2 talking about using this ranking for prioritization for

3 additional screening and testing. I want to be very clear about

4 that. 

5 In the future, we may include an approach for

6 going down to the screening level and perhaps testing level as

7 the models are refined, and as additional bioactivities beyond

8 just receptor-base are included.

9 So this figure that we've seen, I'm going to

10 go through it one more time because I do think it's more

11 complicated than the simplicity might suggest. We've seen a lot

12 of AUC data and those are 0 to 1 values, with the higher

13 priority chemicals being, potentially, the more potent

14 chemicals. 

15 When we talk about bioactivity in this figure,

16 I'd like to point out that this is the converted human oral

17 dose-equivalent, so we're talking about an AC50. So these are

18 not the same data. And I'd like to point out in the future

19 figures I'm going to show you, lower is more potent for the

20 AC50. So we have to change directions. And I think when we

21 switch back and forth, that can become very confusing.

22 So when we look at these chemicals, Dr. Judson

23 sort of dismissed the no priority, but I think they're worth

24 mentioning because we do see chemicals for which we have a

25 predicted exposure, and we basically have no bioactivity from

26 the model outputs. I'll give you an example in just a second.

27 In addition, we see chemicals for which there
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1 is a predicted bioactivity and there is no detectable exposure. 

2 And then as I think we've discussed quite extensively in the

3 last few minutes, what we're really interested in is the

4 distance between these. And as the distance gets smaller

5 between the two distributions, the priority increases. 

6 So, giving you some real chemicals to talk

7 about, this is the ER bioactivity exposure ranking, IBER

8 exposure ranking, if you will. And again, I want to point out

9 that this is the human oral dose-equivalent. So, we have zero

10 here. But as we get to smaller numbers, we're talking about

11 more potent chemicals.

12 So we see a chemical over here, dimethyl

13 phthalate for which there is no detected ER activity; true zero

14 here. And here is our exposure estimate. And our IBER ranking

15 would be greater than a million. And for the purposes of this

16 talk, our cutoff was a million fold separation between the two

17 distributions.

18 Here we have vinclozolin. And keep in mind,

19 we're talking about ER bioactivity. We know that this is a

20 potent AR chemical. There is some potency associated with ER

21 but it's relatively low. We're talking about almost 40 mgs per

22 kg of body weight per day.

23 There is a very low detected exposure for

24 this, so again, we have an IBER ranking that's greater than a

25 million. These would be low on the priority list, or perhaps

26 no-based on the relative bioactivity and exposure, respectively.

27 Cyproconazole, we have an increasingly potent
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1 chemical here. We have a relatively low exposure. Our IBER

2 ranking is still greater than a million, despite the fact that

3 we have populated data for both of these.

4 As we move to the right, 2-phenylphenol, we

5 see an increased in potency for ER here. We see a bit of an

6 increase in exposure and we have an IBER ranking that's two to,

7 I think, three. So we've got about a two thousand fold

8 separation between these two. And I'll talk about how we might

9 interpret that in just a second.

10 And then Pyridaben, our last chemical here, we

11 actually have an IBER ranking that's less than five-fold apart. 

12 And this would be something that we would consider extremely

13 high-priority based on these values.

14 This is a figure that you've just seen in Dr.

15 Judson's talk. And as Dr. Wambaugh said very eloquently, I

16 think the way to read this figure is from left to right, in

17 terms of our interest and prioritization of these. 

18 Same thing for AR, as was pointed out, there

19 is some uncertainty around these measures, and I believe

20 triclosan, I want to say, is right here. So we saw that there

21 is perhaps some variability, some error to these measures. So I

22 think that's important for interpreting the figure I'm going to

23 show you in just a second.

24 I'm going to put some of these data in

25 context, but I'd like to point out once again with the

26 complicated Venn diagrams that we have about 290 chemicals for

27 which there is high-throughput toxicokinetic data. So we have,
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1 by no means, data on all of the chemicals we've discussed today.

2 We're showing you a fairly small subset.

3 But when we look at these, this is another way

4 of looking at exactly the same data you've seen, and this is

5 really the exposure ranking at this point. So, first of all, I

6 want to point out on this scale that this would be one. This

7 would be the same bioactivity and exposure value here. So the

8 chemicals that are below one are obviously the highest priority.

9 And again, we are essentially reading this

10 direction, and we cut off at one million here. But when we're

11 talking about any chemical for which the estimated exposure is

12 greater than a concentration that would cause a bioactivity

13 response, these are the highest priority chemicals. And in

14 fact, most of these are pharmaceutical chemicals. This is

15 something you've heard from Dr. Judson and others.

16 So I call out some chemicals of interest. 

17 Here, as I said, we have a pharmaceutical chemical. We have 17

18 alpha-estradiol. Relative to estradiol, we have actually even a

19 lower ratio, higher concern. Triclosan, as I said, we have

20 about a four-fold difference between the proposed bioactivity or

21 the estimated bioactivity in exposure. 

22 We know that there are some variability around

23 those. We know that in the case of triclosan, because it is a

24 pesticidal chemical that is actually applied often to a person,

25 that this is perhaps an underestimate for exposure. And I think

26 the take-home point from this figure is not necessarily where

27 the cutoffs are or the bright lines, but where the priorities
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1 lie.

2 Knowing that there are variability in the

3 estimates, knowing that there is variability in the human

4 population, we would consider all of these -- the highest

5 priority would be the lowest value, but nothing is off the

6 table. So we would be moving this direction in terms of

7 chemicals we'd like to prioritization.

8 And what we ask of the panel is, is this a

9 legitimate way to use these high-throughput computational tools

10 for us to nominate chemicals for additional screening and

11 testing for a prioritization approach of the 10,000 chemicals in

12 the EDSP universe.

13 There are some obvious advantages to this,

14 hopefully, everybody will recognize. We do expect that these

15 models will continue to be revised and improved as more data are

16 available. High-throughput toxicokinetic data right now is the

17 rate-limiting step. There are plans with ORD and also other

18 research partners to increase that dataset. Also, Dr. Wambaugh

19 talked about some revisions to, particularly, the exposure

20 models that are planned.

21 These numbers are essentially a moving target.

22 But the ranks, as I said, are what we're really interested in. 

23 So I think, most attractively, the exposure predictions provide

24 predictions for thousands of chemicals for which they are really

25 no other available data sources. And for us, that's an

26 incredibly rich potential of how to consider environmental

27 chemicals.
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1 It allows our resources to be focused on

2 chemicals that are most likely to have endocrine effects. It's

3 taken essentially four years and an extensive amount of money to

4 go through the List 1 chemicals, and I think that we've

5 demonstrated that those chemicals have very limited estrogen

6 and/or androgen receptor-mediated bioactivity. And I want to

7 stress the receptor-mediated on that.

8 There are indications that there are some

9 non-receptor-mediated effects. And I don't want to dismiss

10 that, nor do I want, at any point, for people to consider that

11 the List 1 chemicals were unimportant for other reasons. We've

12 discussed specifically the receptor-mediated effects. 

13 And I'll also point out that when we consider

14 this IBER approach, we intend to develop models for things other

15 than receptor bioactivities. So as we talk about

16 steroidogenesis and metabolism in our future and other in vivo

17 responses in neuroendocrine in-tact animals, we may have

18 additional rankings based on different bioactivities.

19 At this point, what we're asking the panel to

20 review is if the proposed method is an appropriate approach for

21 prioritizing chemicals based on bioactivity and exposure. And I

22 think we've shown you three different methods of prioritizing

23 chemicals; one specifically on just ER bioactivity, one

24 specifically just on AR bioactivity, and then the third, the

25 integrated bioactivity exposure ranking.

26 So, just revisiting the objectives and putting

27 these in context, when we think about using this method to
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1 prioritize chemicals to give you some prospective on the List 1

2 chemicals, the IBER method detected four chemicals based on ER

3 bioactivity, four chemicals based on AR bioactivity with ratios

4 less than one million. And as I said, that's just our cutoff

5 for this demonstration. That's not a bright line.

6 For the List 2 chemicals, again, for which

7 there are data, we have zero chemicals that have ER bioactivity

8 and only two chemicals that have AR bioactivity with ratios less

9 than one million. And for the universe, we have about 58 and 55

10 respectively. And again, based on this method, those would be

11 our highest priority chemicals to pursue.

12 So I thank the panel for your attention and I

13 welcome any questions.

14 DR. JAMES MCMANAMAN: Okay. Any questions for

15 Dr. Browne? Dr. Propper had a question that she had for the

16 previous speakers. So if there are no specific questions for

17 Dr. Browne -- Dr. Propper?

18 DR. CATHERINE PROPPER: This is just a quick

19 follow-up on Dr. McManaman's question earlier just for the

20 charge to the panel, which is, we are being asked, I understand,

21 to state whether or not this approach is good for prioritizing

22 the compounds. But that does lead into decision-making for

23 whether they go into future tiered assays. This is correct,

24 right? And that's according to several graphics we've seen

25 today in that prioritization scheme, it's prioritization, and

26 then Tier 1 and Tier 2.

27 So we can't take our prioritization question
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1 completely out of our understanding of what happens after

2 prioritization. Is this correct?

3 DR. PATIENCE BROWNE: So, I think that's

4 correct. We're asking you to evaluate whether this is an

5 appropriate prioritization technique for additional screening,

6 but not exclusively Tier 1 screening. Additional data

7 gathering, let's say that.

8 DR. JAMES MCMANAMAN: So Dr. Potter, go ahead.

9 DR. THOMAS POTTER: I was on the July panel

10 and one of the universal comments we had from panelists -- I

11 can't speak for everybody -- I speak only for myself -- was that

12 we really would like to have seen more examples of model

13 performance. And Dr. Judson shared two examples for us today,

14 one which looked like the models performing reasonably

15 effectively and one where it wasn't.

16 And so I just wanted to come back to that

17 point that the devil is in the details. It's performance

18 metrics that are going to ultimately drive decisions about

19 whether these are effective tools or not.

20 DR. PATIENCE BROWNE: And I think that's a

21 fair point, so maybe I can respond to that in a couple of ways,

22 not discussing model performance specifically. But I think that

23 this is a continual process. What we would like to do is

24 evaluate the tools as they exist now with the recognition that

25 this doesn't tie us to this particular prioritization or

26 ranking. 

27 As the models improve as we have other
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1 estimates of bioactivity, we will continue this process and may

2 prioritize other chemicals. Chemicals that are low priority now

3 may shift up, and vice versa. We may find other bioactivities

4 which would increase the priority of a particular chemical. So

5 we expect this to be a very dynamic and iterative process. But

6 the point that I would like to make is we'd like to get started

7 going after what seem to be the most endocrine-active chemicals.

8 DR. JAMES MCMANAMAN: Other questions? Yes,

9 go ahead.

10 DR. EDWARD PERKINS: I'm not sure if this

11 quite fits in, but once you talk about sort of adaptive

12 management approach of trying to adapt the models as you go, it

13 increase resolution, depending on what bioactivity you're

14 interested or what pathway, the priority is going to change. 

15 Is there a plan to sort of integrate across

16 pathways? How are you going to get the universal priority

17 number? Are you going to end up with a priority where it's the

18 intention to get a priority for E-A-T separately for development

19 or different areas? Or is one of the intent is to try to pull

20 this all together in more of a unified prioritization to

21 coordinate efforts?

22 DR. PATIENCE BROWNE: Well I think Steve Knott

23 will address that a little bit in the future directions of how

24 we plan to put this in an adverse outcome pathway framework. 

25 And I think you make a good point that some of the distinction

26 between endocrine pathways are somewhat artificial, and there is

27 a lot of cross-talk between those.
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1 Exactly how those will be ranked is unclear at

2 this point, I think. And I would allow Dr. Dix to interject at

3 any point on this. Right now, as I think was pointed out

4 previously, the receptor binding is the low-hanging fruit. It's

5 the easiest thing to go after. And we can certainly identify

6 some chemicals that are high priority based on that bioactivity

7 at this point.

8 Steve Knott will talk about planned projects

9 for the office in the next calendar year, and perhaps what will

10 be at the next SAP, which would include a lot more integration

11 and a lot more cross-talk across the pathways, I believe.

12 DR. JAMES MCMANAMAN: Yes, Dr. Dix, you have a

13 follow-up on that?

14 DR. DAVID DIX: A follow-up answer to that

15 question and to the previous question, we just wanted to

16 emphasize that we, in the previous July SAP, did provide quite a

17 bit more information in the associated white paper and

18 publication since then on the exposure model performance. 

19 And over the course of this SAP, we'd remind

20 the panel that we're really trying to draw focus and we're

21 focusing on model performance, not necessarily by individual

22 assays or even individual chemicals, but looking across many

23 chemicals, particularly focused on how the models perform

24 against reference chemicals and how they perform against other

25 established guideline tests for bioactivity.

26 DR. JAMES MCMANAMAN: Dr. Denver has a

27 question.
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1 DR. ROBERT DENVER: Just to follow up on that,

2 and also this is a question for Dr. Judson. You may have

3 already done this and thought about this, but I wonder if

4 there's more information in your high-throughput datasets that

5 could help you understand the variability or uncertainties.

6 So for example, you have a couple of cell-free

7 assays that, you know, if they show clear hits and yet those

8 don't show any activity in the cell-based assays, does that for

9 example indicate that there are limitations to cellular uptake

10 of those chemicals or the ability for those cells to actually

11 see those chemicals inside the cell?

12 Conversely, if you see some assays that show

13 clear hits -- so for example, in your -- in the assays, there's

14 five different cell lines that are used. And say you, for

15 example, see hits in the cell-free assay, the receptor-binding,

16 and then you see a hit in say the Hep-G2 cells but not the other

17 cell lines, does that tell you something about cell variability

18 and uptake or metabolism that can perhaps, number one, explain

19 the variability and the uncertainties in the high-throughput

20 data; and number two, provide some information to future

21 analyses of these particular chemicals?

22 DR. RICHARD JUDSON: So I zoomed through

23 yesterday some of these diagnostics trying to approach those

24 sort of issues. And there are certainly some factors that help

25 us understand why a chemical only activates the cell-free assays

26 or only is active in the Hep-G2 cells, for instance. And we're

27 working on that. We haven't answered that question.
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1 One piece of variability, which we have

2 actually looked at which I thought was going to solve a bunch of

3 problems, which turned out not, was, naïvely, for instance, you

4 would expect a cell-free assay to be activated at a lower

5 concentration than a cell-based assay because you have to get

6 the chemical into the cell. And so the cell-free assay should

7 always be left-shifted relative to the cell-base. In some

8 chemicals, that's true; in some chemicals, that's not.

9 And it may be there is some signal in there,

10 but it's not a universal thing. So that would be another kind

11 of a factor to look at. So we have lots and lots of data that I

12 think is going to help us sort these issues out, but we haven't

13 figured it out yet.

14 DR. ROBERT DENVER: Okay. And you are

15 considering them with regard to the cell line that is used for

16 that particular assay? So, for example, that one example that

17 you mentioned that you see higher activity with the cell-based,

18 does that represent, say, the metabolic conversion of that

19 compound in that particular cell?

20 DR. RICHARD JUDSON: That could very well be

21 the case. The other factor that hasn't really -- I mean I think

22 it got brought up -- is some of these assays are run with the

23 folding receptor and some are run with just the login binding

24 domain. That's a factor that we haven't really looked at to see

25 does that explain some of the variability, and I suspect it does

26 in some cases.

27 DR. JAMES MCMANAMAN: Dr. Perkins, I think,
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1 had his hand up first.

2 DR. EDWARD PERKINS: Yes. This is a question

3 for Richard Judson again. Following up on the uncertainty, in

4 trying to project uncertainty for biological pathway activation,

5 dropping down to the lowest, most sensitive assay from the mean

6 or median, depending on what you had, have you done a similar

7 comparison when you're just looking at the bioactive assays?

8 If I remember correctly, it seems that most of

9 the comparisons were between the median AUC versus the lowest

10 effect level in the performance comparisons rather than trying

11 to incorporate the uncertainty across the AUCs of the different

12 assays and then maybe looking at, say, a benchmark dose

13 comparison to extrapolate between the no-effect level and the

14 low does effect level, which might give a much better

15 approximation of performance and incorporate more uncertainty.

16 DR. RICHARD JUDSON: So let me rephrase the

17 question. So you have many assays turning on, but at slightly

18 different points. And you could actually incorporate not only

19 the hill curve, which is all that goes into the model, but you

20 could go and incorporate all of the raw data. So we actually

21 have one model where we've done all of that, brought all of the

22 raw data together and kind of fit a super curve there.

23 And this is something which is actually on the

24 table that's going to happen in the next year, is to essentially

25 try to put all of that into a benchmark dose formulation. And

26 so, can we really estimate from all of the uncertainty which

27 comes from different cell types, different cell-free/cell-based
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1 and so on, and come up with an equivalent benchmark dose,

2 something like a benchmark dose formulation?

3 DR. EDWARD PERKINS: Right. Well I was

4 thinking more directly of trying to incorporate the uncertainty

5 in the bioactivity profiling analysis, much as you did when

6 you're trying to do the comparison to exposure pathways or

7 amount; try to incorporate the range of the curve fitting to

8 each assay and have the most sensitive assay curve be sort of

9 the lower bound and the least sensitive be the higher bound. 

10 And then you'd have more of an interval

11 compared to the uterotrophic test in which you can get some

12 uncertainty for that dose since you just used the lowest effect

13 level, which is not necessarily really accurate on what's there.

14 So it may give more interesting results.

15 DR. RICHARD JUDSON: So I think I do see what

16 you're talking about. So with the IBER approach we're saying,

17 for prioritization, let's just look at the end of the interval

18 whereas the interval itself is interesting.

19 DR. EDWARD PERKINS: Yes. Yes. 

20 DR. RICHARD JUDSON: Right. 

21 DR. JAMES MCMANAMAN: Dr. Brown?

22 DR. TERRY BROWN: First, I'd like to

23 complement you on the dashboard. I thought that was wonderful. 

24 And really seeing the data for individual assays and everything

25 else, I think it's going to be a real resource for everyone, and

26 the transparency of it also.

27 So this is for any of you who wish to respond,
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1 and that is, how do you decide about either inclusion or

2 exclusion of specific assays? And you've already mentioned that

3 this is an evolving process where you'll be adding assays

4 probably. But I'm just kind of curious as to what kind of

5 parameters or what kind of judgments you're making as this

6 process evolves to either include an assay or exclude an assay. 

7

8 And there's also, obviously, redundancy that

9 we've talked about too in specific assays. There might be three

10 binding assays for ER; there's two for AR, and down the line. 

11 What's the process you go through in making the judgments about

12 the inclusion or alternatively, the exclusion of specific

13 assays?

14 DR. RICHARD JUDSON: I wish I could say there

15 was a really scientific answer to that, but it's kind of

16 practical. And I think I mentioned it yesterday, but just to

17 reiterate; the way we work partly as being a government agency,

18 we decided with the whole ToxiCast program to not develop assays

19 ourselves. So in order to move forward relatively quickly and

20 say, would the whole in vitro approach work, we wanted to

21 generate a lot of data quickly.

22 So we put out requests for proposals to

23 whoever could give us assays in various suites. And so the

24 assays that were included were essentially ones from companies

25 who proposed them. And so that's partly why we have lots of

26 estrogen receptor assays, because a number of companies had them

27 and were able to supply those to us.
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1 So the first inclusion criteria was could get

2 the data, and would the people who are running the assays, could

3 they have some performance data to show that that assay should

4 work. And most of these assays were developed to support

5 pharmaceutical development. And so they were already selling

6 these assays to the pharmaceutical companies.

7 But again, these assays were run for

8 pharmaceutical-like compounds. And so now we take them and we

9 just put all this garbage into them, you know, every kind of

10 chemical you can imagine. And we start looking at how variable

11 the background is and so on when they actually run it for our

12 chemicals.

13 And there are assays that just, they just

14 didn't work. We were told they were going to work and they just

15 didn't work. They had very low signal-to-background, very high

16 noise. They were very unreproducible. And so we actually had

17 more estrogen receptor and androgen receptor assays than we

18 showed you, and the ones that just didn't perform, we threw

19 away.

20 But notice, we kept some of the assays that

21 had a lot of like false positives, partly because we wanted to

22 understand the cause of the false positives, and partly because

23 we think that that false positivity is actually telling us some

24 biology other than estrogenicity. And so it's a little bit of

25 an ad-hoc process, is the real answer.

26 DR. PATIENCE BROWNE: If I could just

27 interject a little bit of a biological perspective, I think
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1 we're talking a lot about, first of all, the redundancy of the

2 assays. And I think, perhaps, that's a little bit of a misnomer

3 that a lot of them aren't truly redundant. We're talking about

4 different cell backgrounds. We're talking about login binding

5 domains versus the native receptor. We're talking about

6 different species.

7 And I think when I look at this, what I'm

8 really impressed with is the performance overall and predicting

9 chemicals that we expect to be positives. And particularly,

10 when we look at the androgen data at the prediction of the

11 SARMs; I mean part of the reason they're selective androgen

12 receptor modulators is due to this cell type they're tested in,

13 and then so-called redundancy of the suite is picking up both of

14 those activities.

15 So I think, at this point, just again, I'm

16 speaking from a biological perspective, we'd be interested in

17 anything that improved our ability to detect chemicals that seem

18 to have positive effects in vivo. But the suites of assays is

19 performing very well. 

20 So unless we could identify either a gap that

21 we needed to cover or an overly promiscuous, if you will, assay,

22 the suite we're using seem to be very effective for our needs,

23 and perhaps not truly redundant in the sense that we're using

24 it. There are some biological distinctions between those that

25 are important to recognize.

26 DR. JAMES MCMANAMAN: Dr. Dix, if we could

27 have a brief comment?
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1 DR. DAVID DIX: So to close our response to

2 that question, I think the last talk, the Future Directions talk

3 by Steve Knott will address some of these issues as well. And

4 we'd encourage moving on to that as soon as possible.

5 DR. JAMES MCMANAMAN: That was my point,

6 exactly. So I think, unless there are any really burning

7 questions that haven't been addressed partially or completely, I

8 think we will move on to Mr. Knott and his presentation. And

9 then following that, we'll break for lunch and come back in

10 order to provide the public commenters, some of which have to

11 leave earlier, plenty of time for their presentations. We'll

12 have an hour lunch break after. Mr. Knott? Maybe 50 minutes,

13 I'm being told. 

14 

15 FUTURE DIRECTIONS

16 

17 DR. STEVEN KNOTT: I think I can be pretty

18 brief, although a lot of things have been pointed toward future

19 directions over the last day. 

20 So returning to our roadmap, we've arrived at

21 section 7 of the White paper, which addresses the future

22 directions. And the intention of this section was really to

23 provide that context. We've put the charge in terms of

24 fit-for-purpose.

25 If you go back to the EDSP-21 work plan, the

26 conceptual model established there, is that we were taking this

27 step-wise iterative approach and that we would be bringing tools
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1 that are fit for data purpose to bear on the program as soon as

2 they're ready and really making those advances. But there

3 continues to be a lot of work in the background, a lot of

4 significant research in various areas such as the non-receptor

5 mediated effects, the thyroid bioactivity exposure. 

6 So we wanted to make sure that that context

7 was present, in terms of considering the charge. And a lot of

8 the comments that have come up over the last day or so really do

9 point to some of these future and ongoing activities.

10 So again, to kind of layout the history where

11 we are for this meeting and where we see some things going in

12 the future, and one thing that's missing from here is the

13 January 2013 SAP meeting, which really brings the estrogen

14 receptor tools, the eight assays along with the expert system

15 and some physical chemical property considerations as sort of

16 the initial look at prioritization tools.

17 Last summer in July 2014 we, again, brought

18 the exposure model in high-throughput toxicokinetic again with

19 an eye towards prioritization. And that brings us to today

20 where we are looking at expanding the ToxiCast assays, as you've

21 heard over the last day to, again, support this integrated

22 bioactivity exposure approach, but also looking at the expansion

23 in the bioactivity domains too in the AR and where we might be

24 able to go with the advances that have been made.

25 Looking to the future, again, we want to keep

26 this in this fit-for-purpose context. So work will continue on

27 the estrogen pathway, the androgen pathway. We also have
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1 ongoing work and beginning to bring tools to the thyroid pathway

2 as well, and again, considering application for prioritization,

3 but also for beginning to support weight of evidence

4 determinations, and then where we can substitute for specific

5 endpoints in the tier 1 battery. And these would be potentially

6 topics for future SAP meetings.

7 So we had this list broken out by the ER and

8 AR pathways and the exposure, which really summarizes some of

9 the future work and ongoing work that's noted in the White

10 paper. And some of this has come up in the discussion with the

11 panel over the last day or so. But I'll just walk through this.

12

13 For both ER and AR, of course there is a focus

14 on expanding the chemical space for the chemicals that have been

15 evaluated for bioactivity. And I think, for AR in particular,

16 that would include identifying additional reference chemicals to

17 represent a range of potencies. 

18 I would also say too that we could consider

19 here an issue that's come up, which is also beginning to look at

20 mixtures. And I think the idea is, once we've progressed far

21 enough to an understanding of a larger set of individual

22 chemicals, they'll be more opportunities to also then begin to

23 look at the mixtures.

24 There's also the idea of looking at additional

25 assays. I think we already talked a little bit about that in

26 the redundancy issue, but you'd have a better understanding of

27 things like the cell cytotoxicity and whatever information that
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1 can be brought forward for on those areas.

2 And then also it was mentioned, I think the

3 presentations including that CERAPP project looking at the QSAR

4 models for the ER pathway. And then there is a similar effort

5 underway for the AR pathway. For the exposure area, you just

6 heard today about a lot of the future directions there with the

7 next generation of this SEEM framework to bring new exposure

8 models into that framework to be able to make refinements to the

9 exposure assessments.

10 And I would also add that this would include

11 not just refinements to the human exposure, but also beginning

12 to bring models into the framework to look at ecological

13 targets. We specifically mentioned fish in the bullet here. 

14 And then you also heard about the high-throughput toxicokinetics

15 in the ongoing work there. 

16 And I think one thing that may not be clear

17 here, but part of this is also the species extrapolation issue,

18 which has been brought up by the panel over the last day, and

19 beginning to broaden that understanding of using these models,

20 again, not just for human health issues but also for the broader

21 ecological space.

22 So then, as I mentioned earlier, the one area

23 of particular focus is with the thyroid pathway beginning to

24 identify assays, and generate high-throughput screening data and

25 building the models such as been built for the ER and AR

26 pathway.

27 We list some of the specific targets that are
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1 under consideration, including the thyroid hormone receptor,

2 thyroid releasing hormone, the symporter. I think that's

3 multiple deiodinase. I think that's a typo in that slide. And

4 again, looking at parameterizing the models for both humans and

5 rats; and similarly, the modeling for the steroidogenesis

6 pathway, and I think some of that was covered by Richard earlier

7 in the talks yesterday.

8 So one additional issue that came up during

9 our discussion and was sort of a recent release of the SAP

10 report from the July meeting, we pulled this particular

11 recommendation about using this information to inform

12 dose-setting. And we actually -- and I think this came up with

13 the panel members -- but have pulled out this point that this

14 idea of using the exposure modeling to set doses and it could

15 avoid overt toxicity and other confounders. 

16 And this is true also with the high-throughput

17 bioactivity assays as well. The specific recommendation from

18 July was to apply this for the Tier 1 studies, but we think it

19 also has a bearing with the high-throughput assays.

20 So looking down the road and even further, we

21 mentioned at the beginning that we really want to bring this

22 into an adverse outcome pathway framework. And we've

23 highlighted here for the estrogen pathway where we believe we

24 are for this meeting, for the fit-for-purpose charge for this

25 meeting, the particular endpoints that we believe are now well

26 covered based on the comparisons that we've made.

27 But the future really focuses on expanding
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1 this, continuing to work through the adverse outcome pathway. I

2 think it came up yesterday why or when were we going to bring in

3 the pubertals, the fish short-term reproduction assays, so it is

4 in the path, again, taking the step-wise approaching, according

5 to our work plan.

6 And these would be the subject of a future SAP

7 potentially. And in fact, the 2015 line there is intended to

8 capture, at least where we think we might get to in the next

9 year and maybe back with the panel to have further discussions.

10 I think this was also another topic that came

11 up yesterday that was about some of the historical work with,

12 for instance, the rainbow trout. And again, looking at species

13 extrapolation issues and expanding through this pathway is where

14 I think some of that would be addressed. And then, similarly

15 for the androgen pathway. 

16 Again, what's been presented here up to this

17 point is really that we're following a parallel course to the

18 estrogen pathway. We were not quite as far along but, again,

19 the path forward is the same, and I think the charge speaks to

20 getting it put on the path forward.

21 So I think that was all that I had in a very

22 brief overview. I'd asked my colleagues with ORD and the

23 division patience to remain here with me, given the number of

24 future direction questions and comments that have come up here

25 over the last day just in case there's some further questions

26 along those lines.

27 DR. JAMES MCMANAMAN: Thank you, Mr. Knott. 
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1 Any follow-up questions? Dr. Perkins?

2 DR. EDWARD PERKINS: What are the plans for

3 incorporating feedback mechanisms and feedback control

4 mechanisms that might establish the thresholds for sensitivity? 

5 I was curious about how will you plan incorporating either

6 models or systems, especially when you start getting into a

7 complete endocrine system with the neural feedback loops. How

8 are you going to incorporate threshold and feedback control in

9 both the models and in the assays of assessments, which has a

10 really big impact on environmentally relevant doses?

11 DR. RICHARD JUDSON: So that's a big question.

12 But I can tell you one project we're working on that's trying

13 to get to that, and that goes to the thyroid work. So there are

14 various organs where the thyroid targets happen or sit. And so

15 what we're doing is building a full PBPK model that's going to

16 have metabolism, have circulation, have time-dependent dosing. 

17 And we will ask the question, okay, what kind of a dosing regime

18 would potentially light up deiodinase, a particular deiodinase. 

19

20 So the first part of the model is just, what

21 dosing PK context would get you so you're activating that? But

22 then we're building virtual tissue models where we actually are

23 then taking -- the first virtual tissue model is of the thyroid

24 itself where we're going down to the cell level. And the cells

25 are these autonomous agents that have all of the feedback

26 mechanisms within them. 

27 And so, then we would take the basic
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1 compartment model for the whole body and then replace the

2 thyroid with this virtual tissue. I can't promise we're going

3 to have anything to show anybody in 2015, but that's sort of

4 over the next three years that we're working on that.

5 DR. DAVID DIX: So one thing I've been curious

6 about and it's a little bit of a confounding issue with trying

7 to assess the approaches, is that, on the one hand, you want us

8 to judge the usability and utility of what you have right now,

9 but also on the other hand, it's a work-in-progress and you're

10 going to continually refine it and add to it and maybe change

11 it. And then in some cases, potentially dramatically if you

12 start adding in complex modeling systems that try to mimic

13 feedback loops in the system.

14 How do you plan to incorporate your framework

15 now, which is based on this sort of dose-response area under the

16 curve framework with future anticipated improvements in modeling

17 the systems, or are you just going to try to evolve the whole

18 framework as it goes? Is this something we should start looking

19 at now? Is this a flexible enough framework that you have to

20 incorporate future developments, or is this sort of the first

21 iteration, then you're going to go do a whole second-generation

22 type of effort to incorporate not more novel, more relevant

23 information?

24 DR. RICHARD JUDSON: I see the framework as

25 being very modular, so there is the on pathway bioactivity. And

26 again, estrogen and androgen are very simple. Steroidogenesis

27 is a little more complicated. Thyroid is yet more complicated. 
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1 But it's really asking the question, given some dosing regimen,

2 when would you turn on the pathway. So that's really the

3 question we're focusing on in the short-term.

4 And so we can improve the modeling of the

5 pathways independent of improving the model of the

6 pharmacokinetics. We can improve the modeling in the

7 pharmacokinetics with its variability in uncertainty,

8 independent of the pathways. And then we can improve the

9 modeling of the exposure independent of those two. So it's kind

10 of three independent teams. And when you finally put it all

11 together, it's relatively simple to take the models and

12 integrate them, I think. I think it's relatively simple.

13 But then, for instance, if you say, okay; well

14 how do you incorporate all of the physiology with all of the

15 feedback and go from did I activate the receptor or some target

16 to can I predict adversely, which is ultimately where you'd like

17 to be. That really goes way beyond the simple modularity that

18 we're doing today. And that's all I can say about that right

19 now.

20 Let me finish up. Okay. There are many

21 different things we could do for bioactivity, toxicokinetics and

22 exposure. And we would really appreciate -- you've heard some

23 potential ideas. If you have specific things, that particular

24 thing would be most productive to go down, you'd think. We'd

25 really appreciate those kind of comments.

26 DR. DAVID DIX: To flip that over onto the

27 application side, I would point out, this is as good a slide as
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1 any to point you to. The method we're really asking you to

2 focus on aren't the particulars of the underlying assays and

3 models, it's their performance vis-à-vis, established screening

4 for bioactivity test guidelines associated with the program. 

5 And so we showed you that example of results

6 for the estrogen receptor bioactivity model, vis-à-vis, the

7 uterotrophic. That's the method we're asking. Is that a solid

8 path forward, and some advice in terms of when that performance

9 is good enough, vis-à-vis, the established screening assays? 

10 And so that's the comparisons we would make. 

11 And those screening assays, especially on the

12 right-hand side where you're looking at in-tact animals,

13 incorporate inherent feedback, and then perhaps associated

14 thresholds.

15 DR. PATIENCE BROWNE: If I can, again, speak

16 to the biology a little bit in maybe the last two questions that

17 you had. Dr. Judson has built very lovely performing models. 

18 But where we started in January 2013 was really just looking at

19 if you got a hit or not with eight ER assays. And I think the

20 expectation on my part is that we'll have additional data to

21 examine this adverse outcome pathway that will include

22 steroidogenesis and perhaps some metabolism and additional ways

23 of perturbing endocrine pathways.

24 And I think, as Dr. Dix eluded to, what we're

25 really interested in is how well do these predict the various

26 tier 1 and tier 2 endpoints. And when you talk about the

27 negative feedback effects that obviously is going to be quite
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1 complicated. And we have a selected number of endpoints that

2 might address that, including things like pituitary weight and

3 various neuroendocrine hormones that are measured in both the

4 amphibian metamorphosis assay and also in the pubertal assay. 

5 So rather than, perhaps, modeling the complex

6 biology, what we really want to do is predict the endpoints that

7 we have to assess. And the better our prediction, however,

8 that's built into the models that Dr. Judson and others might

9 build, what we're really interested in is, as Dr. Dix said, the

10 performance and how we can use these high-throughput tools to

11 hopefully, both accelerate the process and replace animal use.

12 DR. JAMES MCMANAMAN: Okay. I think that, in

13 lieu of the fact that we do have to have some lunch, and we do

14 have our public commenters to consider, I think that we'll wrap

15 this portion up. 

16 When we come back, I think we can probably

17 squeeze in an hour for lunch. We'll be back at 1:30 and we'll

18 start with Patricia Bishop as our first public commenter.

19 

20 (Whereupon a lunch break was taken)

21 * * * * *

22

23

24

25

26

27
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1 

2 PUBLIC COMMENTS PERIOD

3 

4 DR. JAMES MCMANAMAN: Time to move on to the

5 public commenters. The first one is Patricia Bishop from the

6 People for Ethical Treatment of Animals.

7 MS. PATRICIA BISHOP: So I'm Patricia Bishop

8 from PETA, and these comments are submitted on behalf of PETA,

9 the Physicians Committee for Responsible Medicine and the Humane

10 Society of the United States. We are national animal protection

11 and science advocacy organizations with a combined constituency

12 of more than 13 million Americans who share the common goal of

13 promoting reliable and relevant regulatory testing methods and

14 strategies that protect human health and the environment while

15 reducing and ultimately eliminating the use of animals.

16 So I'm just going to start with some general

17 comments and then go into a few specifics. So I have to say,

18 overall our organizations are very impressed with and

19 appreciative of the amount of work that the EPA has performed in

20 developing and implementing computational and high-throughput

21 screening tools to predict both chemical activity in the

22 estrogen receptor and the androgen receptor pathways and

23 environmental exposure. We are very excited about the progress

24 that has been made and the strong likelihood that these tools

25 may soon be ready to replace the uterotrophic and the

26 Hershberger EDSP Tier 1 in vivo assays as well as the ER and AR

27 receptor binding assays that, while considered to be in vitro
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1 assays, do rely on the collection and use of animal tissues.

2 There are two important improvements over the

3 current EDSP encapsulated by the proposed approach. The

4 evaluation and presentation of chemical activities as

5 probabilities and the inclusion of exposure estimates in

6 prioritization, i.e., the integrated bioactivity and exposure

7 ranking, also known as IBER. Such approaches to prioritizing

8 and screening the universe of EDSP chemicals will lead to a

9 better understanding of the potential endocrine activity of the

10 broad spectrum of chemicals in the EDSP chemical universe as

11 well as to a more efficient and more humane way to rapidly

12 identify those substances that have the greatest potential for

13 causing adverse effects in human and wildlife populations and

14 will ultimately result, we think, in the use of far fewer

15 animals in testing under the EDSP.

16 The ability of HTS screening to replace the

17 mechanistic in vivo Tier 1 assays, i.e., the uterotrophic and

18 Hershberger, is promising. The EPA should consider additional

19 ways the information could be used to avoid in vivo testing. 

20 For example, as described in the White Paper, the utility of the

21 in vivo Tier 1 uterotrophic and Hershberger assays appears to be

22 related to chemical activation and detoxification. Therefore,

23 it may be possible in the near term to skip Tier 1 altogether

24 and move on to further evaluation if it can be shown that

25 detoxification is unlikely to play a role in high IBER scoring

26 chemicals. In addition, as the HTS assays improve and expand

27 coverage and the IBER approach evolves, the true utility of
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1 other Tier 1 assays must continue to be reassessed.

2 While we understand that the focus of the EDSP

3 SAP meetings is on the adequacy and appropriateness of the

4 science, there is often an accompanying lack of clarity with

5 regards to policy, in other words, in this case, a description

6 of how exactly IBER results will be used to inform decision

7 making for purposes other than prioritization such as informing

8 additional Tier 1 and Tier 2 testing. Because of the large

9 animal numbers involved, particularly in Tier 2, consideration

10 of how these methodologies will be used to inform future testing

11 decisions is integral to this ongoing discussion. For example,

12 does the EPA intend to require Tier 2 testing for high-ranking

13 IBER chemicals, or would another level of intermediate screening

14 or testing be considered to further exclude chemical substances?

15 Suggestions have been made for more nuanced tiering or

16 integrated strategies, including addition of a Tier 1.5 or use

17 of a strategy based on the OECD conceptual framework which could

18 provide a basis for applying IBER information along with other

19 scientifically relevant information, or OSRI, to inform

20 potential further testing. Can the EPA envision situations in

21 which regulatory decisions could be made with IBER and Tier 1

22 assays, avoiding Tier 2 altogether?

23 So we urge the agency to consider and share

24 with the public how prioritization and screening results from

25 these methodologies could inform a more integrated approach to

26 testing and assessment. And I'm sure that's being done, but we

27 think maybe this is a possibility, that it could be discussed at
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1 this point in this meeting, to some extent.

2 So just a few specific comments. 

3 Consideration of receptor-specific activity versus the potential

4 pseudo-receptor activity, which is discussed on page 37, as well

5 as representation of chemical activity as a probability, is

6 particularly helpful in providing additional information

7 regarding uncertainty and/or specificity of chemical behavior

8 and provides a clear advantage of the HTS approach versus

9 conventional testing.

10 With respect to the ER model's concordance

11 with results of reference chemicals run in the uterotrophic

12 assay, the false negative and positive rates of less than 10

13 percent are excellent. The two chemicals that were not

14 predicted correctly appear to be activated or deactivated by

15 metabolic processes not available in the HTS assays used. This

16 underscores the need for more work on reliable methods and

17 incorporate metabolism into the testing system. We urge the EPA

18 to continue its investigation in this area and to make it a

19 priority for future work.

20 On page 52, there's mention of a concordant

21 mathematical relationship between in vitro AUC values and the

22 level of in vivo potency in the uterotrophic assay. We suggest

23 that the EPA further investigate whether this relationship is

24 maintained between the ER pathway model and higher-tier tests in

25 order to facilitate avoidance of Tier 2 testing and move closer

26 to using in vitro and in silico approaches for risk assessment.

27 The development of metabolism and in
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1 toxicokinetic models, as described later in the paper, are

2 important part of this goal, but lack of such models need not be

3 a barrier to using the ER pathway model for quantitative

4 prediction in appropriate cases. We also see real value in the

5 prioritizing of chemicals for further testing by adding the

6 exposure element to the model. On page 59, for example, 73 out

7 of about 1,800 chemicals were predicted to have ER activity

8 above the AUC cutoff designated for positive estrogen activity. 

9 This number was reduced to about 6 after applying IBER rankings

10 highlighting this approach's utility and focusing attention on

11 only those chemicals with the most potential for adverse

12 effects.

13 Understanding that the work on the AR model is

14 not as far along as that for the ER, we feel greater

15 clarification is needed for the statements on page 75 which say

16 that the AR pathway analyses are currently underway, and at this

17 time, we propose that adequate confidence has been demonstrated

18 only for the first goal which is prioritization. It goes on to

19 say additional resolution of performance of the AR models and

20 potential utility for contributing to the weight of evidence

21 determination of AR bioactivity will likely be improved by

22 adding additional reference chemicals. While there's been no

23 timeframe given in the paper for the completion of the AR work,

24 we question why the results of the AR approach could not be

25 used, at least in the short term, as part of a weight of

26 evidence, particularly if OSRI is available for a particular

27 chemical that is concordant with those results. It is possible
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1 the information on AR bioactivity gathered thus far could be

2 used to help inform decisions about List 2 chemicals, and we

3 hope it could be utilized for that purpose.

4 We also note the value of current efforts in

5 helping to identify and prioritize what additional exposure and

6 biomonitoring data will be needed for better predictions of

7 human exposure to chemicals. This will hopefully assist in

8 directing and focusing monitoring efforts to provide more useful

9 information in the future.

10 As the Agency is aware, improved biomonitoring

11 is a cornerstone of the 2007 National Academy's Toxicity Testing

12 in the 21st Century report. With respect to the work being

13 described on the thyroid pathway, we acknowledge the EPA as an

14 international leader in attempts to model this pathway and

15 develop useful assays but would also encourage Agency staff to

16 stay engaged with the OECD and global efforts underway in order

17 to maximize and speed progress in this area in enhanced

18 development of the model.

19 Finally, we are pleased to see there are plans

20 to model the other various pathways, for example,

21 steroidogenesis, self-stress and so forth, and that the EPA will

22 initiate systematic reviews and curation of the pubertal, AMA

23 and fish reproduction assays. And we look forward to future

24 presentations of these analyses at upcoming SAP meetings.

25 So just to wrap up, we are truly excited by

26 the enormous strides the EPA is making in developing and

27 implementing 21st century toxicology and risk assessment methods
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1 and the eventual effect this will have both on reducing the use

2 of animals in regulatory testing and increasing the efficiency

3 and accuracy of identifying chemicals of concern.

4 Thank you.

5 DR. JAMES MCMANAMAN: Thank you. Any

6 questions?

7 All right. Okay. Our next presenter is Mr.

8 Becker -- or Dr. Becker, rather, from the American Chemistry

9 Council. And I think everyone has a handout from this

10 organization.

11 DR. RICHARD BECKER: Thank you. I appreciate

12 the opportunity to provide comments to the Science Advisory

13 Panel and to EPA and to the members of the public who are

14 present today. I want to provide some comments on the White

15 Paper and draw your attention, of course, to also that we did

16 submit written comments as well.

17 In terms of the activity so far, it's very

18 clear, and I think everyone should be appreciative of the effort

19 that EPA has put in from a standpoint of the scientific

20 investigations to get us to this point. Clearly, the conceptual

21 approach that EPA has put forward is an advancement in the use

22 of this methodology and will be improved -- will be an

23 improvement to the program, particularly an improved basis for

24 priority setting. The approach represents a significant advance

25 in bringing such measures to the forefront for decision making

26 in a regulatory context, and I might add, for product

27 stewardship. So we appreciate that. And a note here, as noted
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1 in our comments and as I'll expand a little bit more, some

2 additional work by EPA should be considered to improve the

3 scientific basis of the methods used and to more fully document

4 the model and its performance.

5 In terms of recommendations for a path

6 forward, improving the transparency and the availability of the

7 information and refining the methodology is important. We

8 clearly understand the Agency intends to release its model codes

9 in the future, but at the present time, the lack of that

10 information, the fact that it wasn't available with the White

11 Paper, makes it difficult to conduct a comprehensive and

12 independent verification of the modeling by outside experts or

13 by peer reviewers.

14 Our comments that we submitted, written

15 comments, include an analysis prepared by Dr. Tony Cox for ACC,

16 and specific comments are provided in that, and we'd like to

17 draw the attention of the Science Advisory Panel to those

18 comments, and request that you give those due consideration and

19 that the Agency as well give those consideration as they look to

20 improve the IBER approach.

21 And now some more specifics in terms of a

22 little perspective and then maybe additional understanding of

23 where some improvements might be warranted for consideration. I

24 think I presented some of this earlier to one of the SAP panels,

25 but we published a paper earlier this year where we looked

26 exposure activity profiling. We focused in this paper on the

27 uterotrophic assay and matching that up to human biomonitoring
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1 data to get an idea where the activity corresponded in terms of

2 the actual human exposures. But in that paper, we also made

3 some recommendations for utilizing that exposure activity

4 profiling or similar to the IBER approach for using that

5 information for high-throughput screening information as well.

6 We used a slightly different approach than

7 what EPA has put forward, and I want to just spend a little bit

8 of time talking about that, and bring your attention to this. 

9 This paper has been accepted for publication, but I can't

10 distribute it just yet. So as soon as it's available, we'll

11 send it out.

12 We used a metric called the exposure activity

13 ratio which is basically the inverse of the IBER. It's simply

14 the exposure divided by the activity. In our very simple

15 approach, we used the AC50 oral equivalent doses and also the

16 exposures oral equivalent doses. We also used a metric called

17 the EAR, the exposure activity ratio, in which case we grounded

18 that for genistein, if you would, as a genistein exposure

19 compared to the genistein activity, and then we derived

20 something we called the relative endocrine exposure activity

21 quotient, was simply the EAR of a chemical compared to the EAR

22 of genistein. We used genistein as a benchmark.

23 And of course, we can see this very similar

24 approach drawing from the exposures work that was done by --

25 published by Wetmore et al.

26 This is a graphic that we all recognize. This

27 is taken from EPA ToxCast information. It's a graph of the AC50
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1 values in one of the ER assays averaging the ERa TRANS assay,

2 and it just shows here for a set of chemicals the relative --

3 the activities as AC50s. One thing to note, of course, on the

4 log scale is that you see four or five orders of magnitude

5 difference in terms of those AC50s. And I want to come back to

6 that point, which is why I draw your attention to that.

7 We were then able to use that to calculate the

8 exposure activity ratios, which is shown in this slide. Again,

9 the log scale, and you see there's quite a difference in terms

10 of the pattern of ratios, if you would, compared to what you see

11 for activities. Genistein is the one that's highlighted in red

12 there as the benchmark. But again, to point out that you still

13 have several orders of magnitude difference between the exposure

14 activity ratios of a number of these compounds and the benchmark

15 genistein. And of course, if you normalize to genistein, you

16 have the same pattern as the EARs, except you just basically

17 have normalized that to a reference compound.

18 Now the utility of that, of course, is that if

19 genistein is a compound in our diet, for which we're all exposed

20 to, and for which is a normal component of a healthy diet, it

21 makes good sense, I think, to perhaps consider that as a

22 benchmark for evaluating relative exposure activity profiles of

23 other substances to help you make decisions about what's

24 important to go forward in terms of additional screening. And

25 as you see here, there are some compounds that have several

26 orders of magnitude difference in terms of their exposure

27 activity ratios compared to genistein which means, I think, that
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1 in some cases one would conclude that those are probably

2 low-priority candidates for further evaluation.

3 Now that brings me to the discussion of the

4 AUC metric. I think we all struggled a bit with trying to

5 understand that AUC metric based upon the information as

6 described in the EPA White Paper. I think we also agreed -- I

7 learned a lot yesterday from the presentations that EPA made and

8 helped clarify some of that, in my mind, about how that

9 transformation related to AC50s. But I think that nonlinear

10 transformation makes it very difficult to do comparisons and for

11 interpretation, increases complexity, and I think that's

12 compounded when you overlay that with exposure information which

13 is linear.

14 And I'll give an example of that. Literature

15 values for relative potency in the estrogen assays, as mentioned

16 in the comments from the EPF, indicate several orders of

17 magnitude difference between weak agonists and strong agonists. 

18 But that rationale, when they compressed the AUC for weak and

19 strong agonists, I don't think has been well described or

20 discussed yet. So I'd ask that the SAP evaluate these issues,

21 and the Agency consider addressing these in its response to

22 comments and revision of the IBER profiling method.

23 Now here's the example. The two pictures, of

24 course, are ones that we've seen from EPA, taken from their

25 appendix. On the left is, of course, is diethylstilbestrol

26 which is a strong agonist, and the AUC is given as 1.01. On the

27 right is genistein which is described as a weak agonist, ER
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1 agonist. Both are inactive in terms of antagonists. And you'll

2 see that in terms of the AUCs, they differ by what, 20 percent,

3 30 percent. But if you look at the relative potencies compared

4 to 17-beta estradiol, you know, I think it's a binding assay or

5 a transcriptional activation assay. EPF comments contain that. 

6 There are four orders of magnitude difference between the

7 potencies of those chemicals. And yet, in the AUC approach,

8 there's only 10 or 20 percent difference, 30 percent difference.

9 And last night, of course, then I think I've

10 got it right. On the far left-hand side, I've inserted my

11 understanding of the graphic which shows the compression of the

12 potency scores, if you would, or the AUCs for the agonists at

13 the top because of the top left-hand side of that AUC graph

14 versus AC50, you'll see where diethylstilbestrol and genistein

15 are basically on that same flat part of the curve or just on the

16 asymptote, and yet, so you get a value of .77 for genistein or

17 1. And that really doesn't do justice, I think, to the biology

18 which shows that really, in terms of potency, these are both

19 agonists. They differ by four orders of magnitude or so.

20 So that compression makes it very difficult

21 when you then combine that information with exposure to separate

22 out. And what you do is you're really compressing at the top

23 end of that curve very weak substances, I think. So I think

24 that's something that's worthwhile looking at.

25 Now I haven't had time to look at the model. 

26 We really don't have the algorithm. But it would be interesting

27 to kind of look at that and say are there other ways one could
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1 approach modeling, where you maintain the understanding of the

2 biology in that kind of scoring system?

3 Now we did a very simple thing. We just used

4 AC50s. But I understand when you're looking at different

5 interactions along that pathway that there's perhaps a reason to

6 consider some kind of transformation. But I think this

7 transformation ends up skewing our vision and actually giving a

8 misimpression, at times, of what's going on in the biological

9 space.

10 Then I'd also like to consider one last part

11 in terms of -- one last suggestion, and this is in terms of

12 improving our understanding of the confidence of these methods. 

13 We published a method when we looked at some previous work in

14 terms of prediction modeling using high-throughput screening

15 assays which we call a scientific confidence framework. It's

16 not rocket science. It's simply good science. You know,

17 qualify in terms of understanding your approaches. You need to

18 have a good understanding of analytical performance,

19 qualification understanding of the models and then a good

20 justification for utilization.

21 Analytical validation, I think EPA has done a

22 better job this time around than they've done in the past. We

23 heard about the number of reference compounds that they

24 identified and have utilized in their modeling performance, so I

25 think that's good. It still is not clear to me that they've

26 laid it out clearly in terms of the elements that you'd really

27 like to understand when you're going to apply this to a broader
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1 space of chemistry, particularly the domain of applicability.

2 In terms of model performance, obviously, one

3 would like to have a defined algorithm. There shouldn't be

4 black boxes. We'd like to understand goodness-of-fit,

5 predictability in prediction models. Certainly these are

6 quantitative. What are the limitations, alternative models? 

7 What's the sensitivity of these particular parameters or

8 parameters with particular model? And then, you know, enough

9 detail so that an independent review or reconstruction, or one

10 could actually do a sensitivity analysis on your own would be

11 helpful. I think that this is an area where we would like to

12 see further improvement. I think EPA is on the path to doing

13 that. We saw the release or the demonstration of the EDSP 21

14 dashboard, and I think that will go farther toward that, but I

15 think some more attention to these details in this specific

16 approach would be helpful.

17 Finally, utilization is really putting the

18 model and the approaches into the appropriate context. There's

19 been some discussion about this by the SAP of how this

20 information is going to be used. And I think that's

21 particularly important. You know, it's this issue of fit for

22 purpose, enough precision; someone else has said enough

23 precision for the decision. And that's a very important aspect

24 of the work that SAP is doing -- will be doing here is providing

25 that kind of recommendation to EPA. So we think a kind of a

26 structured framework like this for documenting scientific

27 confidence would be a good recommendation for the Agency to
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1 consider.

2 So finally, in conclusion, clearly, the

3 approach that EPA has presented for IBER represents a

4 significant advancement in using high-throughput screening

5 exposure information to support decision making. But we do

6 think that some additional work by EPA should be considered to

7 improve the science basis of the methods used and more fully

8 document model development and performance.

9 Thank you for your attention.

10 DR. JAMES MCMANAMAN: Thank you. Questions?

11 All right. Oh. Dr. Weller.

12 DR. GRANT WELLER: I just have a quick

13 clarification question. On your alternative methods of

14 transformations, you're talking about the different

15 transformations that could yield different distributions. But

16 in terms of just ordering, is it correct to state that it would

17 not change that?

18 DR. RICHARD BECKER: You know, I haven't

19 looked at that. I'm not sure because you remember the AUC is a

20 way of -- and we didn't look at this. But the AUC combines the

21 AUC from many different assays. We didn't look at how you would

22 combine that information, so I think you could have some changes

23 in ordering, depending upon how you do that. But I didn't look

24 at that yet. But I think that might be a good way of looking at

25 these models in terms of understanding sensitivity.

26 DR. GRANT WELLER: Thank you.

27 DR. JAMES MCMANAMAN: Okay. Oh. Dr. Barr.
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1 DR. DANA BARR: I just have one quick question

2 for clarification. By de-prioritization, do you mean removing

3 from consideration altogether or just moving further down the

4 line for priority?

5 DR. RICHARD BECKER: Well, I mean, you ran

6 into this situation. I think de-prioritization, at some point,

7 makes a decision about it's such a low priority that it's not

8 worthwhile pursuing. And I think that, you know, we make these

9 decisions contextually depending upon lots of different

10 parameters, resources, time, you know, potential for exposures

11 and risks, et cetera. But I do think that at some point

12 de-prioritization means, you know, it's a low enough priority

13 not to have to be considered any further for a particular

14 program such as EDSP.

15 But I do think, again, from a policy

16 standpoint, it's important to have these discussions. What does

17 that mean? What would that appropriate exposure activity

18 profile look like that would say it's such a low priority? Now

19 we suggested genistein because we know that, you know, if you're

20 two or three orders' magnitude below the exposure activity ratio

21 for genistein, that really does mean that in terms of the

22 contribution to, if you would, an estrogenic effect by such a

23 substance at such an exposure would be miniscule compared to

24 your background natural exposures. So does it make sense, at

25 that time, to pursue further evaluation of such substances, or

26 do you have enough decision with this scientific basis

27 information to say that is not a priority for further action at
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1 this time?

2 Thank you.

3 DR. JAMES MCMANAMAN: Thank you. So next we

4 have three speakers from the Endocrine Policy Forum. Dr. Ellen

5 Mihaich will be the first speaker followed by Dr. Sue Yi and

6 then Dr. Chris Borgert. The total allotted time for these

7 presenters is an hour. So use your time wisely.

8 DR. ELLEN MIHAICH: Thank you. Hi. I'm Dr.

9 Ellen Mihaich, and I am the scientific coordinator for the

10 Endocrine Policy Forum. Many of you have heard us speak at the

11 past SAPs. We usually get in here and do that, and my

12 colleagues and I will be speaking on behalf of the Endocrine

13 Policy Forum.

14 Just for those of you that don't know us, the

15 Endocrine Policy Forum is a consortium, a self-funded

16 consortium, of better than 95 percent of the List 1 test order

17 recipients. So we're the ones that did all of the studies for

18 the regulation, and we received the test orders. So we've done

19 the 11 screening assays that I'm sure you've all heard about.

20 So we also have some other stakeholders such

21 as Crop Life America, the American Chemistry Council, the

22 American Cleaning Institute and the Consumer Products Safety

23 Association, along with we're now starting to gather in some of

24 the potential test order recipients for List 2. So we have been

25 over the past four years or so, I guess, been working to help

26 our members address regulatory and policy issues, technical

27 issues with it. And we've also done a lot in the last couple of
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1 years with evaluating the data that's come out of this process. 

2 We have a couple of papers that we've presented here and

3 published also on weight of evidence, and we're hoping by the

4 end of this year we'll have another one out on just the control

5 data from all of those. So we're trying to utilize the data as

6 best we can to help move the science forward.

7 So I'm just going to do a few slides on some

8 overarching comments, and then my colleagues are going to take

9 off from here. We're really focusing specifically on the charge

10 questions that you all were given, and so that's where our

11 comments are coming from.

12 Conceptually, we support EPA's data-driven

13 approach to combine the high-throughput bioactivity and exposure

14 predictions for prioritization. We think it's really a nice way

15 to go forward. The ER model certainly appears to perform very

16 well as currently parameterized, and they've done a good job of

17 that. We've seen some good steps forward on that.

18 We really like the reproducible, systematic

19 approach to separate the bioactivity from selective cell stress.

20 That's certainly something that we've seen in actually doing

21 the studies, that we've had a lot of issues with systemic

22 toxicity. And so if we were able to bring in the cell stress

23 part of this that it may help us, you know, do screening better

24 and to understand what's really an endocrine issue versus more

25 of a systemic toxicity.

26 And we are very pleased with EPA's

27 acknowledgement of the importance of rigorous data quality for
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1 the evaluation of this process. Have done a nice job on that.

2 There's some limitations that we saw, again,

3 if you focus just on what we were given with the -- what, a

4 month ago now, with the White Paper and the appendices. There

5 really was insufficient information to support an independent

6 review of the methodology. There are lack of model details, the

7 things that we didn't have, the user interface, the R-code, that

8 sort of thing, prevented full evaluation of the adequacy,

9 predictability and transferability of the model. Of course,

10 these were written before. It was really nice to see the

11 dashboard. We weren't aware -- we've been checking, but we

12 weren't aware that it was actually active. So it will be good

13 to be able to work with that and see how that works.

14 We think that the AUC/potencies, if you will,

15 really need to be anchored to biologically relevant activity in

16 vivo. I'm not just talking about an uterotrophic study. I'm

17 talking about things like multigenerational studies, other kinds

18 of in vivo studies that are out there, because ultimately, even

19 though it's not necessarily what we're talking about here, we

20 all have heard, you know, we've been listening to the

21 development of adverse outcome pathways, and we need to know

22 that these are going to be useful all the way down the line

23 ultimately. So we need to anchor that information to in vivo

24 studies.

25 We think the application of IBER scores needs

26 to be demonstrated, and that's come up a number of times in your

27 questions. What's a priority, and what's not a priority? I
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1 think that' going to be important to try to understand. I mean,

2 we know that it's sort of a continuum, and you start at one end

3 and keep moving down. But as the question to Dr. Becker just

4 now, at some point, there probably is diminishing returns on

5 that.

6 And clearly, we know that we need to be

7 working -- that EPA needs to be continuing to work on the AR

8 model and compare it with in vivo data. But that has already

9 been acknowledged.

10 One thing that we think is kind of important

11 is that we really encourage EPA to update the White Paper with

12 additional information and important clarifications that you all

13 have heard. The bottom line is this is on the record now, and

14 people will go back and look at this a year later who have not

15 been sitting in this room and have not heard the additional

16 information that we got yesterday which I think really did help

17 clarify things. And I think it's very important to complete

18 this document and have an accurate and current record so that

19 when people go back and look at it, they see all of the work

20 that's done because a lot of work has been done, and I would

21 hate for that to be missed.

22 Just as my final slide, looking at List 1

23 lessons learned and some data considerations, the Tier 1

24 screening of List 1 chemicals we all have heard has confirmed

25 many negative results identified by higher-tiered testing. And

26 we just think that higher negative results from these studies

27 really need to be used to help deprioritize, and I use that term
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1 loosely because I don't want us to think we're never going to

2 look at it again, maybe to put something aside for a while. But

3 not just fit, and the issue here is we don't want to say okay,

4 now we have these IBER scores and the bioactivity. Let's not go

5 backwards. We kind of did that for List 1, where we had very

6 data-rich compounds in the pesticides, but then we went back to

7 the screening level. We shouldn't do that. If we have negative

8 results, we need to be using those as part of our assessment.

9 And we would like the opportunity to comment

10 on the draft prioritization because it could also help with some

11 refinements in this. And right now, we can't really -- since we

12 don't know where chemicals are specifically going to be

13 prioritized, we can't really address this. But industry, with

14 an acknowledgement by EPA that they'll look at the information,

15 industry could provide additional information that could help

16 inform prioritization, things like guideline studies and other

17 published literature that maybe is not easily obtained. And

18 certainly, I think, the bigger one is looking at refined

19 exposure estimates that -- and I know that we might be able to

20 help out with some of that, too.

21 So with that, I'm going to now just turn it

22 over to my colleague, Dr. Sue Yi, who will be working through

23 the estrogen and androgen systems.

24 DR. SUE YI: Good afternoon. My name is Sue

25 Yi. I work for Syngenta Crop Protection. I am speaking on

26 behalf of the Endocrine Policy Forum. So before I get started

27 with the slides, I'll just remind you that these comments are
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1 really directed towards the White Paper as opposed to the

2 presentations we saw yesterday and today because they have

3 actually clarified some of the questions and points that we made

4 that have been clarified.

5 So in regard to question 1.a. and 2.a., so I

6 will probably or I am going to attack these both for the ER and

7 the AR together, as they relate fairly well. And the

8 overarching comments for these charge questions are very similar

9 for each of the ER and the AR questions.

10 Okay. So for question 1.a. and 2.a., how

11 clearly has EPA described the computational tools used to

12 estimate ER and AR agonist/antagonist bioactivity? We

13 acknowledge and actually commend the EPA for pursuing a really

14 scientific, data-driven approach to prioritization. I think

15 it's really important that they move towards this and

16 incorporated the cytotoxicity as one of the confounders for in

17 vitro ER and the AR activity. This was discussed back in --

18 sometime ago. I can't remember which SAP, but we did bring this

19 up in terms of cytotoxicity and how confounding this could be in

20 terms of the ER/AR activity.

21 So one of the limitations or several of the

22 limitations really went back to descriptions within the White

23 Paper regarding how they came up with the ER/AR under AUC model

24 functions. So quite a bit of the description is incomplete for

25 us to go back and do an independent evaluation.

26 There was some clarification as to how the

27 AUCs were calculated, so it was a composite of AUCs across the
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1 board. And we're really not sure whether that is -- that that

2 actually complicates the comparison that you can make to a true

3 pharmacological potency. And so I'll bring some potency up. 

4 However, my colleague will actually address the potency as a

5 specific issue. So in regard to the AUC calculation, it would

6 really be helpful, actually, if we had a very specific example

7 of how those calculations were done.

8 In addition, it really would be important to

9 cite which version of the ToxCast and ExpoCast are being

10 utilized for these analyses because there are several versions

11 out there.

12 And then in terms of being able to evaluate

13 the ER and the AR AUC model, there are several components that

14 we felt were necessary that weren't actually in the White Paper

15 that would have been very helpful in evaluating the models. So

16 I'll highlight a few and not really go through this entire list.

17 But in terms of explaining the specific model terms and choices

18 that were -- that would be quite useful in evaluation, such as

19 the Z-scores. It would be nice to have a sample calculation of

20 the Z-scores and have all of the Z-scores presented in an

21 appendices, for instance. The penalty terms would be nice to

22 have including the set levels for the alpha and the SR.

23 And then on page 22 of the White Paper,

24 there's a mathematical description of the AUC. Part of that

25 equation is the sign slope, and I won't read the quote for you. 

26 But we felt that discounting any effect -- discounting these may

27 affect the predicted bioactivity. And so it would really
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1 actually be useful for us to have what those sign (slopes) and

2 the specifics of the description of the sign (slope).

3 And then I'll also point out that the model,

4 it was -- we need further explanation as to how the model was

5 corrected to handle the detailed observations of crosstalk and

6 cross-reactivity as was shown in pages 34 and 35 of the White

7 Paper.

8 And then in terms of question 1.b. and 2.b.,

9 again, we'll reiterate the fact that incorporation of the

10 cytotoxicity and nonselective cell stress being confounders to

11 in vitro endocrine activity was a major strength. And inclusion

12 of many assays, so this is 18 assays across the board for

13 binding as well as transactivation, really helps to

14 contextualize the potential for false positives and false

15 negatives that could occur due to assay interference. As it was

16 pointed out by EPA that there are different technologies,

17 different recombinant forms that are being measured. So this is

18 a nice complement so that we're not relying upon just one assay,

19 for instance.

20 And we saw that EPA plans to incorporate or do

21 a pathway analysis, really, for the steroidogenesis. We felt

22 that the effects of the steroidogenesis pathway is not currently

23 incorporated into this ER AR AUC, and that might also be

24 actually quite useful.

25 The mathematical relationship between the

26 potency and the AUC was unclear, and so as such, it is unclear

27 how the AUC can be used to prioritize and distinguish chemicals
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1 associated with the different levels of concern. And I think

2 Chris will probably touch a little bit more in detail on that

3 point.

4 And then in terms of the discussion regarding

5 the structural classes, we're limited, and therefore, we would

6 like to see a little bit more details such as how many

7 structural classes were assigned in the library of test

8 chemicals, and do the reference structures evaluated reflect the

9 understanding of the diversity of the structures that are likely

10 to perturb the ER or the AR pathway? And so it would be nice if

11 we could have reference chemicals that span the wide range of

12 potencies to strengthen confidence in these models.

13 So in terms of question 1.c. and 2.c., the

14 systematic review, and so this goes along the curation of the

15 literature with the methods and quality standards for in vivo

16 studies. The systematic review of the literature to evaluate

17 concordance of the ER and AR AUC model we believe is a critical

18 step toward acceptance of these models. So the literature has

19 to include chemicals that span the range of potencies and

20 represents sufficient true positives and true negative

21 chemicals.

22 So in terms of rating the literature, the

23 manuscript, for instance, we would advocate perhaps using

24 something like a ToxRTool approach so that we have the

25 availability of the test article purity as being included

26 because I think that that's actually quite an important aspect

27 of evaluating the quality of the study itself.
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1 Attention to systemic toxicity is actually

2 quite important, and this goes back to the incorporation of

3 cytotoxicity in the in vitro studies. We'd like to see that

4 systemic toxicity in the in vivo studies are also incorporated

5 into the assessment.

6 So as an example, if you look at the

7 uterotrophic assays, if you have a sufficiently depressed body

8 weight gain or decreases in body weight, this needs to be

9 specified because this can distinguish the endocrine activity

10 from the systemic toxicity because, as you know, that body

11 weight can actually have quite a bit of an impact on the

12 endocrine activity.

13 Specification of the birth data and the age of

14 the animals used in the -- age of the animals, actually, is very

15 imperative in the uterotrophic immature assay. As you know,

16 maturity of the animal can affect the relative response of the

17 uterotrophic response to various chemicals.

18 So the criteria for determining the study

19 outcomes should be very clearly specified in the White Paper. 

20 So we give an example of the Hershberger, for instance, where

21 according to guidelines, there needs to be actually

22 statistically significant changes in two or more accessory sex

23 tissue weights in the appropriate direction and not just any

24 changes that are noted on page 73 of the White Paper.

25 And then while we recognize the fact that this

26 might be rather difficult to do because some of these are from

27 academic labs, laboratory proficiency determination is also very
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1 important because as some of you may have been involved in the

2 Tier 1 SAP discussions, laboratory proficiency is actually quite

3 important in terms of test performance.

4 So in terms of question 1.d., what the

5 strengths and limitations of using the ER AUC model to

6 distinguish and prioritize chemicals based on the potential for

7 estrogen bioactivity in reference to or in consideration with

8 the reference chemicals, the strength really of the ER AUC

9 model, as it stands, is that the model performance relative to

10 the uterotrophic reference chemical appears to be very

11 excellent. As it was pointed out by the EPA, near 95 percent, I

12 believe, is actually quite remarkable, and therefore, it seems

13 to be very specific and sensitive. Improvement would be

14 anticipated based on addition of the metabolic capacity to the

15 battery.

16 In terms of the limitations, again, we'll go

17 back to the potency, and there was not really a consideration of

18 the relative potency cutoff. So that is to say, .1 model

19 positive may not relate to in vivo receptor activity. And so

20 it's unclear as to whether or not that makes the biological

21 sense. It's tying it back to the in vivo biological activity.

22 So perhaps benchmarking to a chemical with in

23 vitro estrogenic activity that does not -- sorry. Let me start

24 that again. Benchmarking to a q with in vitro estrogenic

25 activity does not necessarily demonstrate in vivo activity based

26 on low potency. Right. So it would really be helpful to

27 understand and perhaps benchmark against known chemicals.
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1 Explanation of the ER AUC scores that

2 translate to in vivo relevance would really help with increasing

3 the confidence that the ER AUC model can be used for

4 prioritization and even further down the line. Explanation of

5 the steps in prioritization, and this was mentioned a couple of

6 times, would be actually quite helpful. So what happens to

7 down-selected chemicals based on the T-score and the Z-score

8 filtering, as was discussed in page 34 of the White Paper? And

9 does the ER AUC actually distinguish low and high ER potency for

10 prioritization? And what would be an example of a priority

11 chemical for further evaluation?

12 So moving on to question 2.d., the AR AUC

13 model as presented represents actually very encouraging results,

14 albeit it was acknowledged that it was an incomplete result, and

15 we look forward to seeing the completed version of the AR AUC. 

16 And again, comparison to the in vivo response would be very

17 helpful which I know that they're planning to do with the

18 Hershberger assays and the pubertals as well. Again, relative

19 potency measures has to be addressed, and Chris will talk about

20 that further.

21 Okay. In terms of question 1, Charge Question

22 1.e.: What really were the strengths and limitations of the ER

23 AUC model that contributes to the weight of evidence in

24 determining the chemical for -- the potential for estrogen

25 bioactivity; we feel that the ToxCast bioactivity data and the

26 ER AUC model should be considered in the weight of evidence for

27 potential estrogen bioactivity because these data do appear to
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1 inform relevant molecular initiating event hypotheses.

2 The limitation really is -- and questions

3 regarding how would an incomplete set of assay data be used with

4 the ER AUC model or independent of the ER AUC model. So what

5 would happen? So in terms of looking at this, we have a suite

6 of 18 assay inputs that are being used to determine. So how

7 would removing assays, for instance, contribute to the model

8 performance? Would it performance less well or better or just

9 as the same? And that could be quite informative.

10 Methods also must be transferrable before

11 application to a weight of evidence. So in terms of determining

12 weight of evidence for the Tier 1 assays, it needs to be much

13 more transferrable as well as more detailed for independent

14 review. And again, further explanation of how potency is going

15 to shift the effect of the overall AUC scores are needed.

16 Question 1.f. is replacing or substituting for

17 the Tier 1 ER binding, ER transactivation or the uterotrophic

18 assay using the ER AUC model. So there's a high degree of

19 concordance between the ER AUC model and the uterotrophic

20 results, as shown by the EPA, again, the 93 percent concordance

21 which is actually quite remarkable. But the focus was

22 predominantly on negative uterotrophic results. So in this

23 case, more positive chemicals or more chemicals in general might

24 need to be incorporated to help with the confidence level. And

25 the Agency actually stressed this point is that uterotrophic is

26 not where they're stopping. They're actually going to move on

27 with the pubertal and the FSTRA, and we just made that point as
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1 well. So we look forward to seeing the Agency's results

2 regarding the comparison with the pubertal assays as well as the

3 FSTRA in points.

4 So overall, the ER and the AR AUC models are

5 quite encouraging, and it's a big step forward and based on very

6 scientifically driven data. It does require more accessibility

7 and transferability for evaluation with respect to their

8 utility, not only for prioritization but also for weight of

9 evidence. So the models need to be further described and

10 described really fully in detail within the White Paper. The

11 relationship between the AUC and the potencies need to be well

12 understood as well. Instances of incomplete datasets should be

13 also defined. And then separation of the cytotoxicity and the

14 endocrine bioactivity is an appropriate step, but that also

15 needs to be clearly defined as well. We'd like to see

16 steroidogenesis be considered for estrogen and androgenic

17 activity prioritization and concordance with the in vivo

18 androgenic response. We look forward to seeing those results. 

19 And finally, really considering benchmarking to a chemical of

20 low or no in vivo concern or determining an AUC threshold would

21 actually be helpful so that the stakeholders can understand how

22 the prioritization process will actually occur.

23 I would also like to direct you to our written

24 comments, and I believe you have had those for some time now, to

25 provide you with the further details regarding the comments made

26 here today. Thank you.

27 DR. CHRIS BORGERT: My name is Chris Borgert. 
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1 I'm with Applied Pharmacology and Toxicology in Gainesville,

2 Florida. I'm a consultant to the Endocrine Policy Forum. I

3 also have a faculty appointment at the University of Florida.

4 Incidentally, I served on the EDSTAC back in

5 what seems like 100 years ago now. I was just one of among many

6 voices, but I was a member of the Screening and Testing Advisory

7 Committee subgroup -- excuse me, the Screening and Testing

8 Workgroup, which is a subcommittee of the plenary. I served on

9 both of those. And so even though I served on both of those,

10 both the plenary committee and the subcommittee, I was just one

11 of many voices. Many of us thought that improvements were

12 needed back then, so I just want to be encouraging in my

13 comments and say that I'm delighted to see that the Agency has

14 been diligent about trying to look for better and more

15 streamlined approaches. So I want my comments here to be viewed

16 in a positive vein, not in a negative vein at all.

17 But I want to sort of underscore the

18 overarching point that I think potency has been underutilized in

19 the current method. I think there's a lot of information that's

20 being generated by this system, and I think more can be pulled

21 out of it. So that's an encouragement to use it to even greater

22 degree, perhaps, than it's currently being used.

23 And what I mean by that, we've already talked

24 a little bit about the need to benchmark some of these

25 computational tools against not only in vivo screens, which was

26 delighted to hear from Dr. Judson who mentioned that, you know,

27 the uterotrophic assay itself is a bit of a contrived assay that
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1 makes it very, very sensitive, and that's appropriate for a

2 screen. But we need to benchmark these against, you know,

3 longer term studies in intact animals given chronic exposures

4 because that's really where we determine hormonal activity

5 that's actually manifest in a whole organism under normal

6 circumstances.

7 So want to bring your attention then to some

8 points that have been made, but let's look at them in detail. 

9 The AUC methodology appears to compress some of the potency

10 ranges. And just for example, we've got here some reference

11 chemical values and the AUCs associated with them. We've got

12 the endogenous hormone 17-beta estradiol and DES. Dr. Becker

13 already mentioned that those are actually normally separated

14 pharmacologically by an order of magnitude in potency. Here

15 they're -- that difference is a bit compressed.

16 Then we have chemicals like phytoestrogens not

17 much further down. The one that really concerns me are the

18 androgens which have AUC scores well above .1, but these are, in

19 fact, not estrogenic. And you know, anyone that disputes that

20 needs to go into a magazine store and look at some of the muscle

21 and fitness magazines. Those chemicals do not have feminizing

22 effects.

23 So we know clinically that these androgens are

24 not functioning as estrogens, and I think that needs to be taken

25 into consideration. I don't mean to be flippant, but we

26 actually have, you know, clinical information for a lot of these

27 types of chemicals. And I think some additional use can be made
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1 either of the AUC or maybe a subset of the assays to get more

2 explicitly at this issue of potency.

3 So I think potency could be used to a greater

4 advantage. We need to make potency comparisons between key in

5 vitro assays, and as I mentioned, critically, the reproduction

6 developmental studies would provide a sound basis for maybe

7 establishing some potency thresholds below which we understand

8 that the AUC score or the potency estimate is really telling us

9 that biological activity is highly unlikely. And so then

10 prioritizing those kinds of chemicals for further screening, not

11 saying take them off the table necessarily. I understand that

12 from a policy standpoint, that can be difficult, but at least

13 that should tell us these are very, very unlikely to ever have

14 any real biological activity.

15 Some other questions that arise, you know,

16 just in terms of understanding how the AUC relates to potency,

17 if you had to chemicals with the same AUC but are actually

18 separated by potency, how does that come out, and how would that

19 be prioritized?

20 So let's move on then, and let me talk a

21 little bit more in depth about just what is potency. So I'm a

22 pharmacologist by training, so this is where my brain goes. So

23 let's look at a simple measurement of pharmacologic potency. 

24 This is competitive binding displacement curve. We have

25 tritiated binding of the endogenous ligand on the Y axis, and

26 across the X axis we have here increasing concentrations in

27 terms of molarity. And so this is the typical kind of full-dose
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1 response curves that you often see in classic pharmacology

2 experiments. And over here to the far left, we see that

3 chemicals like estradiol and DES have very high affinity. This

4 is an assay that primarily measures affinity. It doesn't

5 characterize the efficacy of the chemical at the receptor, but

6 this is an important component of potency.

7 So chemicals like the endogenous ligand like

8 pharmaceutical such as DES will exhibit high potencies with EC50

9 values down here in a very low range. And I realize that I'm

10 covering basics that all of us understand, but you know, let's

11 go back to some of the basics, and think about what this might

12 mean for the program under consideration.

13 As we move to the right then, less potent

14 chemicals like genistein, other phytoestrogens, which have lower

15 affinity for the receptor, it takes higher concentrations to

16 displace the natural tritiated ligand from the receptor. Then

17 we move further to the right, and we get less and less potency

18 up until the point where we get to the point of 100 micromolar

19 where basically everything will act as an antagonist and block

20 the receptor just by sheer numbers along. It's a nonspecific

21 effect.

22 But in this region, between chemicals such as

23 the phytoestrogens which arguably might not have any adverse

24 estrogenic effects in a human, there are papers to that effect,

25 but that's sort of on the line. I think that's why Dr. Becker

26 and his colleagues benchmarked their approach against genistein.

27 We get into this region where we've got potencies that reflect
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1 chemicals that are simply natural products of metabolism. They

2 are precursors of hormones, metabolites of hormones, sterols,

3 fatty acids, proteins, et cetera. These are present in the

4 bloodstream, and you can find this information in classic

5 textbooks of endocrine pharmacology. These are present in the

6 bloodstream at concentrations 10 to the 5th to 10 to the 9th

7 fold molar excess relative to natural hormones. So if you

8 compare the potency differences here, just in terms of affinity,

9 one glimpse on potency, with this molar excess, you would

10 conclude gee, these must be sending endocrine signals, but we

11 know they don't. And in fact, you know, if they did, we

12 couldn't have a functioning endocrine system because it would

13 hear all the noise from all the other metabolites.

14 Interestingly, some of these have actually

15 been tested clinically. There were actually clinical studies

16 done on essential fatty acids as potential natural treatments

17 for postmenopausal symptoms. You know, now I'd like to track

18 down who funded that study because I got a lot of studies I'd

19 like to get funded and maybe can talk them into that. You can

20 probably talk them into anything. Think about it. Chemicals

21 with this potency are not going to manifest endocrine activity. 

22 And lo and behold, even at very high doses, they fail to

23 manifest any change, any estrogenic change in clinical trial

24 participants. So we have some real-world examples that

25 chemicals in this range do not manifest endocrine activity.

26 So one of the reasons for this is that, of

27 course, and I realize again I'm stepping us through stuff we all
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1 know, but let's go back to basics and think about it. Affinity

2 relates very tightly to receptor occupancy. And in a paper we

3 published about a year ago, little more than a year ago now, we

4 stepped through such calculation just to show that this concern

5 over maybe a few molecules of a chemical with very low potency

6 would, in fact, not elicit an endocrine effect. And you can

7 convince yourself of this by classical endocrine pharmacological

8 calculations.

9 So if you have a chemical -- I won't step

10 through all of this, but I think I've got enough time to finish

11 these slides adequately. So let's look at this in a little bit

12 of detail. If you have a chemical with the same affinity as the

13 endogenous ligand, and you consider how much additional ligand

14 it would be required to increase receptor occupancy from just 1

15 percent to 2 percent, and I'm not saying that that's a

16 biologically relevant increase in receptor occupancy; I don't

17 think that's ever been demonstrated that that small a change in

18 receptor occupancy would be biologically significant. But

19 nonetheless, just to effect a 1 percent change, you'd need about

20 2 trillion molecules of that ligand in the water compartment of

21 an average-size female. Now 2 trillion molecules isn't all that

22 many, but it's a lot more than a few, and here, we're

23 considering a chemical that has affinity similar to the

24 endogenous ligand. If you go out six orders of magnitude less

25 potency, it becomes an insurmountable number of molecules. And

26 so you can understand why essential fatty acids won't treat

27 postmenopausal symptoms. It's very clear, and this can be
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1 calculated.

2 So now I want to turn some attention to

3 another example, and you got to pick an example. So I saw that

4 D4 was used or was mentioned in the paper as a false negative

5 result. And so I want to illustrate here how additional potency

6 considerations might apply to these interpretations and maybe

7 even improve and clarify the interpretations. So the White

8 Paper gave some very cogent reasons why D4 may have been a false

9 negative. The first reason given was that in vitro metabolic

10 conversion to an active compound might not be replicated in the

11 in vitro system, but you know, it's a false negative because

12 other reports from in vitro systems from the literature did find

13 activity. I didn't put it up here. It was a later version that

14 didn't get to you, but actually, metabolites of D4 have been

15 examined in the uterotrophic assay and in receptor binding

16 assays, and they're negative. So maybe there's a metabolite we

17 don't know about, but at least that doesn't seem to be the

18 likely reason although, you know, I don't think the White Paper

19 tried to say that it was. They just listed the possible

20 reasons, and they did a thorough job.

21 The uterotrophic bioactivity might be mediated

22 by a non-ER mechanism, but He et al., for instance, was one of

23 the papers that showed the activity of D4 and showed that it's

24 blocked by ICI and that it also doesn't have any uterotrophic

25 effect in ER knockout mice, so it does seem to be operating

26 through that effect, at least in screening assays, does seem to

27 be mediated through the ER. But here we get to some real
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1 possibilities. The White Paper notes that the weak in vivo

2 activity might require serum concentrations that are too high to

3 be replicated by an in vitro system, and in fact, D4 fails to

4 elicit the typical pattern of either estrogenic or

5 anti-estrogenic responses. In long-term reproduction studies,

6 you don't see the changes you would expect in uterine weights in

7 either FC or F1 females. There are no effects in males. It has

8 very weak potency, and I'm going to show you on the next slide,

9 based on these screening level assays.

10 There also is a possibility that because of

11 its extreme volatility, and this was mentioned yesterday by

12 representatives from EPA, that it's very difficult to maintain

13 adequate concentrations in the test system. So this is another

14 very plausible reason. But let's just think about this

15 anchoring to the reproduction study, and I want to show you some

16 of that potency information, then, in the next slide, in the

17 next couple of slides.

18 So in this slide, I'm simply returning to

19 something I presented over a year ago at the SAP on this topic. 

20 Let's consider then some relative potencies of a variety of

21 compounds. Here we have from the Chen et al. paper, this is

22 Katz and Ellenbogen's research group, endogenous estrogens,

23 although not the primary estrogens, and not the ones that are

24 responsible for primary estrogen activity during reproductive

25 years of a human female, have relative potencies a couple orders

26 of magnitude below 17-beta estradiol. Testosterone is a much

27 lower relative potency.
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1 The ICCVAM validation study on the BG1-Luc

2 assay assessed some androgens as well, and note that even

3 non-aromatizable androgens have potencies similar to the

4 aromatizable androgens in these assays. So the affinity here,

5 the -- these actually are potencies because these are

6 transcription activation assays, all of them. So these

7 potencies are not due to aromatization of an aromatizable

8 androgen to an estrogen structure. Non-aromatizable androgens

9 are in the same potency range. And as I say, physiologically we

10 know they don't function as estrogens.

11 Tamoxifen here, as an agonist, has fairly low

12 potency. Here are the phytoestrogens mentioned again, and then

13 here are, again, by these methods, some -- an endogenous

14 estrogen, a couple of pharmaceutical estrogens. So again, that

15 one order of magnitude potency difference is important to

16 consider.

17 And now let's keep those in mind for a moment

18 as we look at some of the potencies for D4. So here we have

19 various assays that D4 was run in. And here is the positive

20 control chemical against which the relative potency was

21 assessed. Now they're not all, in fact, none of them -- only

22 one of them, actually, is 17-beta estradiol. So remember, these

23 are being benchmarked for relative potency against chemicals

24 that have a little bit less potency than 17-beta estradiol, at

25 least by a number of assays. So this is the rat uterotrophic

26 assay in Fischer 344 rats and Sprague Dawley rats. This is the

27 mouse uterotrophic assay, and uniformly, we see that with
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1 respect to pharmaceutical estrogens, D4 has extremely low

2 potency. When you compare it to a phytoestrogen, it's still a

3 couple orders of magnitude lower than coumestrol. You get

4 similar results in the mouse, whether you look at uterine weight

5 or peroxidase activity, and for the estrogen receptor alpha

6 transactivation assays, you see again a fairly low potency, has

7 no activity at all against or at the estrogen receptor beta.

8 So again, very, very low potencies, but as I

9 mention, in long-term reproduction studies, we can show that D4

10 does not, you know, certainly any chemical will have some

11 adverse effect. But they're not the typical pattern that you

12 would see at all from an estrogen, so again, suggesting that

13 chemicals with this kind of potency simply don't manifest

14 estrogenic responses in vivo, and it's that kind of concept that

15 I think is possible to pull out of this computational system,

16 and it's important to do that because it's not going to be

17 efficient to move forward chemicals that have potency that

18 simply can't manifest activity in vivo.

19 We have to remember that potency is a density

20 function. It's not a weight function. Let me give you an

21 example. I can weigh Styrofoam on something that's fairly

22 sensitive like a scale. But that doesn't mean that if I put 10

23 tons of Styrofoam out in the ocean, it will sink. Potency is a

24 bit like that. We can measure the potency of chemicals in

25 sensitive assays. That doesn't mean they're going to have any

26 impact when we put them into a biological system complete with

27 hormones and other processes going on.
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1 So I think that the potency is not fully

2 utilized by the current methodology. It could be utilized to

3 greater benefit. I think I've illustrated that with a couple

4 examples. I won't go back through these bullets here. I think

5 I've covered D4 pretty adequately. I'm not disputing that D4

6 may have been, you know, a false negative by the AUC methodology

7 because there again, you're benchmarking the result against some

8 other sensitive screening level assays. But if you benchmark

9 the negative against long-term bioassays, they're actually

10 consistent. No activity in the AUC methodology and no

11 estrogenic activity in these long-term studies, where you would

12 expect to see effects if it was truly working as an estrogen.

13 So I think there's a possibility for

14 establishing some clear potency thresholds where we can

15 acknowledge endocrine activity in an intact functioning organism

16 is highly unlikely. And I don't know if that's extracting a

17 subset of the assays from the computational system to look at. 

18 I'll leave that for others to figure out. But I think you're

19 generating enough information from the system that these kinds

20 of considerations could be made, and it would greatly enhance

21 the practical utility of this at least, not for taking chemicals

22 off the table completely but at least for acknowledging which

23 ones just are insufficiently potent to be of concern.

24 So with that, then I'd like to just finish off

25 with some points about the IBER methodology I want to point out

26 that most of these have already been covered in the July SAP so

27 I'll just -- I'll touch on them briefly. But I refer you to
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1 presentations that were -- a lot more detail was given there,

2 especially by Mike Bartels' presentations.

3 So we look at Charge Question 3.a.: How

4 clearly has EPA described the computational models in Section 6

5 to develop IBER values, et cetera? Clearly, IBER scores are

6 going to depend on the results of high-throughput exposure

7 simulations, the HTTK predictions and the bioactivity pathways

8 and activity distributions. For the high-throughput exposure,

9 the approaches for exposure estimates have been well defined,

10 again, referring you to the July SAP and our comments there.

11 I want to note, however, that there still

12 exists a wide range of variability in these exposure

13 predictions, especially as was mentioned for the far-field

14 scenarios and that there are currently no predictions for

15 non-oral exposure routes, or at least at the point we read the

16 White Paper, there aren't. Some further clarifications were

17 made today, but we think that's important.

18 You know, I'd note that there are very robust

19 exposure databases, for instance, for pesticides such as the

20 pesticide handler exposure database that EPA uses for

21 registration. So I don't know how these methods are being

22 benchmarked against those kinds of more specialized and robust

23 databases, but I think that could be a help.

24 With regard to the high-throughput

25 toxicokinetic modeling in question 3.a., how clearly has EPA

26 described those computational tools, the pharmacokinetic model

27 used to predict systemic exposures involved steady-state blood
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1 levels was, again, well discussed at the July meeting. The use

2 of higher-end pharmacokinetic modeling, such as the SimCYP tool,

3 would allow for a probabilistic distribution of the systemic

4 exposures. The specific details of the three-compartment PK

5 model have not yet been made public. EPA has mentioned those. 

6 We think that would be a vast improvement over the second

7 generation and really encourage its development. But cost

8 issues will limit access to some of the tools, such as SimCYP,

9 and so it might be worthwhile to explore less costly

10 alternatives so that that would be more transferrable in

11 practical terms to folks who want to use the models.

12 The HTTK model as defined assumes linear

13 kinetics for absorption, metabolism and excretion. We would

14 encourage utilization of models that will allow for nonlinear

15 properties, especially for metabolism. While for many chemicals

16 the low end of the curve does involved linear kinetics, that's

17 not always the case. An obscure chemical some of you may be

18 familiar with called ethanol actually has nonlinear kinetics in

19 the low-dose region instead of the higher-dose region. So there

20 are some exceptions to the liner low-dose rule, and a model that

21 would be able to account for those would be helpful. And I

22 think, as I may have mentioned already, no predictions for

23 non-oral exposure routes are given. So that would be an

24 improvement. But we think that third-generation model has a lot

25 of promise.

26 And then my last slide, I believe, here is

27 addressing questions 3.b. and 3.c.: What are the strengths and
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1 limitations of using the IBER approach to prioritize chemicals

2 for further EDSP screening based on the ratio between the ER, AR

3 bioactivity dose ranges and the expected exposure ranges? 

4 Again, we agree that the IBER screening approach will provide a

5 means of prioritizing compounds with the highest ER, AR

6 biological activity, especially true for those chemicals that

7 fall out at the very high end of the range. But that's specific

8 to selected in vitro and in vivo assays. Again, the IBER

9 approach could be benchmarked against longer-term studies.

10 We think that the use of upper bounds of

11 exposure and lower bounds for bioactivity can indeed account for

12 uncertainty and variability, but we also need to acknowledge

13 that upper bounds and lower bounds are not as stable as central

14 estimates, and they might tend to be more a function of the

15 database that's used and the particular system that's used to

16 gather and produce those data. So that caveat needs to be kept

17 in mind.

18 Comparisons across compounds within the

19 program, maybe ought to consider both or perhaps using central

20 tendencies of exposure more so than the upper and lower bounds. 

21 We have to remember that these are -- I think EPA has mentioned

22 this several times, and I want to underscore it. You know,

23 these are somewhat crude tools in the larger perspective. We're

24 not looking for absolute biological precision with respect to

25 potency or the potency activity and exposure ratios. We're

26 looking for tools that provide useful screening and give us

27 information. And so maybe a more stable central tendency
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1 estimate is better here than trying to account for variability

2 that might not be possible for the databases that are being

3 used.

4 And again, just to underscore that prior to

5 final decisions for the EDSP evaluations based on the IBER

6 rankings, in other words, which chemicals might actually be

7 passed through and require more animals and more resources both

8 from the private sector and from EPA, the stakeholders would

9 like to be able to -- especially potential test recipients

10 should be allowed to submit refined IBER assessments that

11 utilize measured exposures, other toxicokinetic databases that

12 they may have at their disposal and that might provide more

13 complete information to the Agency.

14 So with that, I thank you for your attention

15 to all of us and, again, very much appreciate the opportunity to

16 present this and hope it's given you some food for thought for

17 your comments. Thank you.

18 DR. JAMES MCMANAMAN: Okay. Are there any

19 questions for the Endocrine Policy Forum's presenters?

20 All right. Thank you very much.

21 Next presenter is Dr. Katie Paul Friedman from

22 Syngenta, Dow AgroSciences and Bayer CropScience. So before you

23 begin, we're going to take a 15-minute break.

24 

25 (Whereupon a brief recess was taken)

26 

27 DR. JAMES MCMANAMAN: So I think the panel has
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1 had their bio break, and they're ready to concentrate now. Dr.

2 Friedman, go ahead, please.

3 DR. KATIE PAUL FRIEDMAN: While the slides are

4 coming up, I'm Dr. Katie Paul Friedman. I'm a regulatory

5 toxicologist at Bayer CropScience. I want to thank the panel

6 for allowing me to share the perspectives that several

7 scientific colleagues and I have been developing at Bayer Dow

8 AgroSciences and Syngenta about the use of high-throughput

9 screening information as a line of evidence for endocrine

10 activity evaluation. And as part of this, I will show our

11 approach and an overview of an example using data-rich

12 chemicals, pesticides, specifically triazole fungicides.

13 And the objectives of our collaboration are of

14 interest to this Scientific Advisory Panel because we endeavor

15 to use high-throughput screening information as a first line of

16 evidence for determining the priority to identify endocrine

17 activity. And the initial goal of our collaboration is to

18 demonstrate a roadmap; it's actually somewhat similar to the

19 roadmap that EPA has shown on a couple of occasions so far

20 today, and I think you'll see the resemblance. Our roadmap is

21 specifically for data-rich chemicals, like pesticides, as they

22 move through the EDSP, with the idea of maximizing transparency,

23 utilizing hazard and exposure data, organizing information based

24 upon the OECD Conceptual Framework for Endocrine Information,

25 which was mentioned by the first public commenter today, and

26 using a structured systematic data review that really considers

27 the level of biological relevance of the data for making an
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1 endocrine determination. And soon we'll share our approach via

2 a manuscript describing our case-study. And I'll just show you

3 briefly some of the high points today.

4 So we propose a very simplistic roadmap for

5 prioritization of data-rich chemicals, including pesticide

6 active ingredients, whereby high-throughput hazard and exposure

7 information should be utilized first, followed by consideration

8 of Part 158 guideline toxicity study information and other

9 scientifically relevant information, or OSRI, in order to make

10 endocrine priority determinations. This is very much in line

11 with the IBER approach that's been presented to you by the EPA. 

12 This simply gives a broader context and talks about some next

13 steps for this particular group of chemicals.

14 The OECD conceptual framework was used to

15 organize the reviewed data from multiple sources into bins of

16 increasing biological relevance for endocrine hazard. Note that

17 we've labeled all of the high-throughput screening bioactivity

18 data, i.e., ToxCast and high-throughput exposure prediction

19 information from ExpoCast as Level 1 prioritization information

20 that could be considered in this framework. Validated or

21 low-throughput in vitro assays we've classified as Level 2

22 information, and as you increase in level, you see that there's

23 increasing confidence that these data correspond to

24 demonstration of endocrine activity and ultimately adversity.

25 The nuanced differences between Level 3 and

26 Level 4 data may not be immediately evident, but this relies on

27 whether or not the endocrine system is intact. So for instance,
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1 the disrupted system in the uterotrophic assay would be Level 3

2 information, whereas the intact fish reproductive system would

3 be Level 4. There's some more difficult Tier 1 assays to parse,

4 for instance, the pubertal assays we've separated by endpoint,

5 and really this is with regard to whether or not the endpoint

6 demonstrates the potential for endocrine activity or actual

7 endocrine adversity like delayed sexual maturity.

8 You can see that in addition to Tier 1

9 specific information across this conceptual framework, there are

10 also data from screening as well as pesticide registration

11 studies and literature, other scientifically relevant

12 information. So you can see this in the far-right column where

13 I've listed some of the Part 158 guideline study information

14 that would be available for pesticides.

15 I want to mention that for our particular case

16 study, we identify what Level 5, or this top tier of

17 information, would be. But none of these were available for the

18 pesticides we considered. These are the types of data that

19 would give you in vivo endocrine adversity information over

20 multiple life cycles. So EDSP Tier 2 studies would be

21 EDSP-specific information, and then newer, two-generation

22 reproduction study or the extended one-generation reproduction

23 study or other lifecycle studies might come from guideline

24 toxicity testing.

25 And I want to emphasize that adaptation of the

26 OECD conceptual framework to develop this table is a really

27 important systematic review of available data, accomplishes
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1 several objectives, including transparency and using appropriate

2 context for this information in line with international

3 perspectives on endocrine activity.

4 In interest of time, I won't show you all of

5 the endpoints we considered across each line of evidence with

6 high-throughput information being one line of evidence, EDSP

7 Tier 1 study information being a second line of evidence and

8 guideline toxicity study or OSRI information being a third line

9 of evidence. But I want to show you a table of some of the

10 high-throughput information from ToxCast that we used as our

11 Level 1 information in trying to understand the utility of this

12 line of evidence. And this is an example of assay data that we

13 considered for androgen, anti-androgen and steroidogenesis

14 hypotheses. And I just highlight some differences from the

15 approach that was presented today just to show that there's a

16 slight variation.

17 So we've considered all three ToxCast Phase II

18 androgen receptor binding assays. Yesterday, we heard that the

19 rat AR assay was excluded. We didn't make this distinction in

20 our qualitative review. And then we also considered

21 steroidogenesis as part of this overall androgen and

22 anti-androgen pathway. And so we included the high-throughput

23 screening assay information that were available predominantly

24 for aromatase inhibition, which is really just one endpoint in

25 steroidogenesis but an important line of information.

26 Oh, I forgot to add, I think it's important,

27 these in vitro screening data may suggest apical effects on
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1 steroidogenesis or androgen homeostasis, right? All of these

2 assays point to potential molecular initiating events by which

3 perturbation might occur. But all of this could either be

4 confirmed or negated using higher levels of information. So we

5 don't stop at Level 1. We go on to compare to available data

6 for these pesticide actives.

7 In order to compare the relative utility of

8 lines of evidence, we really needed a scoring system to track

9 expert confidence in the strength of each level of information

10 available. And so we developed a qualitative scoring system to

11 evaluate endocrine relevance. I want to note that this is a

12 preliminary ranking system to describe the available hazard

13 information, and compare the relative utility of these

14 information for ascertaining a potential endocrine effect. This

15 qualitative scoring ranges from 0 to 5, with 0 being a complete

16 lack of evidence of perturbation of an endocrine pathway, and 5

17 being definitive evidence for endocrine adversity. Distinctions

18 for scoring 1 to 4 are described in detail here, and the panel

19 has these slides, and so I won't read through all of these

20 criteria. But it's our effort to try to distinguish essentially

21 the relevance of any piece of information for making the

22 determination that an effect is endocrine specific.

23 The higher the qualitative score, the more

24 confidence one would have that observed endpoint change was

25 specifically due to an endocrine effect, and not resultant to

26 systemic or general toxicity. And this qualitative scoring

27 enables us to compare the relative utility of these "lines of
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1 evidence" like high-throughput screening information in a visual

2 way that I'll briefly walk through.

3 This is just an example of the three triazole

4 fungicides that we considered in our case study, myclobutanil,

5 propiconazole, and triadimefon. And this initial slide

6 describes the data available across three lines of evidence for

7 androgen, anti-androgen and steroidogenesis pathways. The lines

8 of evidence here on these sort of heat map quilts are delineated

9 on the bottom here. So high-throughput screening information,

10 the only block that's ever available is Level 1 because we've

11 distinguished this as Level 1 information. In the middle of

12 each quilt is the EDSP Tier 1 information, as all three of these

13 chemicals were subject to List 1. And then on the far right of

14 the quilt are the Part 158 and OSRI data that we've manually

15 curated actually using the ToxRTool approach for data quality.

16 And I don't want to get necessarily into the

17 specifics of the data so much as to highlight for you that for

18 these three lines of evidence, the high-throughput screening

19 information were pretty predictive. At Level 1 and even Level

20 2, what we found were typically changes in aromatase, aromatase

21 inhibition. And then at Levels 3 and 4, we observed sort of

22 inconsistent, potentially endocrine-related effects that were

23 coincident typically with systemic toxicity or other toxicities

24 that would really drive the point of departure. And so if the

25 only information you had had was high-throughput screening

26 information and the data that was necessary to register the

27 pesticides, you would likely come to the same determination than
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1 if you had had -- without the EDSP Tier 1 information. That was

2 our general conclusion walking through these case studies was

3 that the high-throughput and in vitro information were useful

4 for identifying a potential molecular initiating event. And

5 then due probably to low-potency action or high doses needed to

6 necessitate any effects in vivo, they're really obfuscated by

7 systemic toxicity. So a system of high-throughput screening

8 information and Part 158 information really worked well together

9 in concert.

10 The pattern of findings on this next slide is

11 for estrogen and anti-estrogen and steroidogenesis effects, and

12 it's similarly demonstrated here with the heat map quilt

13 delineating negative through very strong evidence, and again,

14 Level 1 and 2 evidence and findings at Levels 3 and 4 often

15 coincided with systemic toxicity and other primary toxicities

16 that may not be endocrine. Importantly, what these visual

17 demonstrations illustrate for this case study are that Tier 1

18 studies were often not really needed to make determinations of

19 priority for further endocrine testing when high-throughput

20 screening information and Part 158 guideline toxicity studies

21 and OSRI were available for data-rich chemicals. And separating

22 these lines of evidence in this way really shows their utility

23 and highlights the potential role for high-throughput screening

24 information as a data stream for prioritizing chemicals as well

25 as the protective nature of safety information available already

26 via Part 158 guideline toxicity studies.

27 Overall, through this exercise, what we
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1 concluded was that high-throughput bioactivity data is a useful

2 line of evidence for prioritizing the need for endocrine

3 evaluation. With increasing levels of information for these

4 fungicides, it is clear that endocrine hazard observations were

5 coincident with other toxicities in vivo, suggesting that

6 endocrine toxicity was likely not the primary effect for these

7 particular triazoles and that further endocrine information,

8 hazard information, would likely not refine the point of

9 departure for risk assessment of these chemicals.

10 Now in our slides thus far, we have focused

11 simply on a systematic hazard evaluation using activity

12 information. And the focus of this SAP really is the

13 integration of these activity information with exposure

14 information, and we really like the demonstration of the IBER

15 approach for indicating priority for further endocrine testing.

16 So I'm going to show our favorite slide of the

17 day. This is on page 12 of the SAP White Paper, and I agree

18 with many of the other people who've shown this slide that it's

19 actually a pretty informative graphic in terms of demonstrating

20 visually the IBER prioritization concept, that chemicals of

21 greatest priority will have overlapping distributions of

22 bioactivity and exposure predictions. We agree with this idea

23 for prioritization but further assert, as others have today,

24 that potency in some cases may be low enough that exposure at

25 any level may have no biological result. And actually, I think

26 that's represented to the left on this graphic. So if you look,

27 even though exposure may be high for this particular instance,
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1 the bioactivity is very low, so it doesn't matter, or in this

2 case, there's negligible exposure in the presence of high

3 bioactivity. So this concept is here, but I'd just like to

4 underscore that idea of a potency threshold.

5 And then the other caveat that we note is that

6 conservative thresholds to distinguish levels of priority for

7 further testing are maybe not yet clear, and so this will be an

8 area for growth. And I think the way this will grow is through

9 demonstration of case studies. And so that's what we seek to

10 provide in showing you this information.

11 Here we show a similar approach to the IBER

12 concept for triadimefon as an example of the triazoles. And

13 actually, it's pretty representative for most of the conazoles

14 as well, except for conazoles that are used pharmaceutically and

15 have a very different exposure pattern. First, what we've done

16 is plot a frequency distribution of all of the ToxCast assay hit

17 information from Phase I that were converted to oral equivalent

18 doses, megs per kg per day by Barber, Wetmore et al. in a 2012

19 publication. So that's this distribution here.

20 I want to note that this is not limited to

21 endocrine activity. Simply, we've plotted all of the assay hits

22 as they were converted to oral equivalent doses and shown you

23 the distribution. And so actually, these hits that are

24 occurring at lower doses are related to metabolism. As many of

25 you know, triazoles and conazoles activate a number of

26 cytochrome P450s. There's a number of metabolic effects. And

27 so that's actually what the majority of these hits are to the
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1 left of I think what Richard Judson would call the burst. 

2 Right. So these higher concentrations and doses probably

3 correspond to some level of nonselective stress.

4 Then we've compared these to the 2013 version

5 of ExpoCast. This is from Wambaugh et al., 2013, and this is

6 the predicted human oral exposure range from low to upper 95

7 percent confidence limit here. And what I want to point out is

8 that there is a 4 to 5 order of magnitude separation between the

9 bioactivity exposure predictions and the human oral exposure

10 predictions.

11 Now superimposed on the same graph, I also put

12 up the EPA exposure estimates for triadimefon from a 2009 risk

13 assessment document. Please note that the use patterns for

14 triadimefon have changed, and there's a number of refinements

15 that could occur. But these are some numbers to start with for

16 what we think oral exposures might be. And I've also plotted

17 the dose at which any of the effects that are maybe endocrine or

18 maybe systemic toxicity related occur. This is predominantly at

19 1,800 ppm in the diet for rodents which is equivalent to roughly

20 100 milligrams per kilogram per day. And I want to highlight

21 that the separation is remarkably similar between these

22 predicted exposure estimates from EPA using more conventional

23 methodology and the dose that would be required to elicit really

24 any of the endocrine effects in vivo in rodents.

25 So I thought this was a great demonstration of

26 how the IBER approach worked and here would suggest that

27 triadimefon would be somewhere between no to low priority
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1 depending on the separation that was needed. And it actually

2 reflects well the separation that's present through a protective

3 Part 158 guideline toxicity process.

4 And finally, I just want to note that using

5 the IBER approach is concordance. So the IBER for triadimefon

6 for both estrogen and androgen receptor pathway activity was

7 negative. So this would agree with that determination.

8 So a roadmap for pesticide actives emerges

9 based on this information or other data-rich chemicals through

10 the EDSP. This roadmap would begin with consideration of

11 high-throughput in vitro activity information and

12 high-throughput exposure prediction information, followed by

13 consideration of Part 158 guideline toxicity data required for

14 pesticide active registration. At this point, it should be

15 determined whether additional hazard or exposure information

16 would revise the point of departure for risk assessment of the

17 chemical. Then hazard information perhaps could be selected for

18 specific questions that remain, and the high-throughput

19 screening bioactivity information could be useful for

20 determining potential adverse outcome pathways, and thus should

21 direct any further endocrine testing in perhaps a targeted

22 manner.

23 And we suggest that to this side of the

24 roadmap here where if more hazard information was thought to be

25 needed to perhaps change the point of departure in the risk

26 assessment, you could choose EDSP Tier 1 battery, some subset of

27 it, alternative mechanistic endpoints that might be more
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1 informative for your actual questions or perhaps the EDSP Tier 2

2 battery. On the other side, we demonstrate that in some cases

3 perhaps more hazard information wouldn't present any

4 refinements, and what you might need is more exposure

5 information. And so there should be a pathway for providing

6 that in order to inform additional EDSP-related risk assessment

7 decisions. So a direct path through this roadmap would suggest

8 that perhaps endocrine activity has already been identified

9 through the studies completed for registration or that these

10 effects really demonstrate an appropriate margin of exposure

11 using screening information.

12 In conclusion, we found that combining

13 high-throughput hazard and exposure information was adequate for

14 determining priority for EDSP Tier 1 evaluation. Standard

15 testing guidelines are sufficient to inform the potential

16 endocrine activity of tested molecules in many cases. And so

17 these data should be incorporated.

18 And finally, a combined evaluation of

19 high-throughput exposure and hazard data for these three

20 triazoles suggests a low or absence of priority for testing

21 through the EDSP Tier 1 battery. So I guess it's a case of if

22 only we had known then what we know now. The priority for

23 chemicals moving through the EDSP process based on IBER will

24 continue to be delineated, and I think using examples like this

25 on other pesticides where we do have a data-rich environment

26 will begin to demonstrate what appropriate margins are.

27 And with that, I want to thank all of my
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1 collaborators at Bayer CropScience, Syngenta and Dow

2 AgroSciences in what I think is an important cross-industry

3 effort to participate in developing further screening and

4 prioritization science. And I want to thank you for the

5 opportunity to present our perspective, and I'm happy to take

6 any questions that you might have.

7 DR. JAMES MCMANAMAN: Thank you. Any

8 questions for Dr. Friedman?

9 Okay. Thanks a lot.

10 The next presenter is Dr. Sue Marty from Dow

11 Chemical Company.

12 DR. SUE MARTY: Hi. My name is Dr. Sue Marty.

13 I'm a senior toxicology leader at the Dow Chemical Company. 

14 The title of my presentation is A Margin of Exposure Approach: 

15 An Alternative Approach to Evaluate Bioactivity and Exposure. 

16 As Dr. Dix alluded to, I'm already getting off on the wrong

17 foot. Margin of exposure is actually more commonly used to

18 refer to adverse outcomes, and in fact, even internally we call

19 this MoEBA, the margin of exposure for biological activity. And

20 that's really what I'm going to talk about today.

21 When we found out that the EPA was interested

22 in looking at ToxCast bioactivity data in light of exposure and

23 internal dosimetry, we became interested in looking at these

24 data for our List 1 and List 2 chemicals for EDSP, and we wanted

25 to see what those data told us. So fortunately, our laboratory

26 has some experience with toxicokinetics and PBPK modeling, so I

27 should acknowledge the contributions of my colleagues, Drs.
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1 Chaudhuri and Bartels, who did much of the PBPK work that's in

2 this presentation.

3 What you'll notice about our MoEBA is that it

4 is a simplified version of what the EPA is doing. We weren't

5 aware of the AUC approach prior to the White Paper, so you'll

6 see that our approach is much more simplified. But we think

7 it's a good initial approach to put the data into context.

8 So what you'll see with our MoEBA approach is

9 it's conceptually similar to the EPA's, but it differs from the

10 IBER approach that they're using. We did not use AUC scores for

11 bioactivity, nor did we use ExpoCast data for exposure

12 assessments. In our alternative approach, we took advantage of

13 existing data on exposure for pesticides, so there's a lot of --

14 exposures for pesticides are reasonably well characterized. So

15 we were able to use our own exposure data as opposed to relying

16 on ExpoCast. We used the exposure data. We focused on

17 exposures to the general public. We took the estimated

18 exposure, and from that we used PBPK modeling to determine an

19 internal human dose. That in vivo estimated internal dose was

20 then compared to the in vitro concentrations at which

21 bioactivity was seen in the ToxCast assays.

22 Our thinking was pretty simple. If there's a

23 sufficient MoEBA between the lowest AC50 concentration and the

24 ToxCast assays and the internal dose based upon estimated human

25 exposures, then presumably the compound would be of lower

26 concern for additional ER and AR screening. Overall, I would

27 say that when you look across our portfolio of ag chemicals from
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1 List 1 and List 2, our conclusions are generally consistent with

2 the EPA's IBER approach.

3 So here's how our approach goes. We

4 downloaded the active results from ToxCast. This was roughly

5 the September 2014 timeframe. We separated the bioactivity data

6 into endocrine-related and non-endocrine-related assays. We

7 used the lowest AC50 values for the endocrine assays and the

8 non-endocrine assays. We selected AC50 values because we felt

9 they were more stable. We start looking toward the ends of the

10 dose response curve. There's more variability, and we felt that

11 there's also potential for change depending on your curve

12 fitting. In future determinations, we could easily go to an AC5

13 approach or a point of departure approach.

14 We considered cell cytotoxicity when there

15 were paired assays. So what I mean, some of the assays have

16 partner assays that go with them that address specifically

17 cytotoxicity. If the AC50 of that partner cytotoxicity assay

18 was lower than the AC50 for bioactivity, then we removed those

19 assays from consideration. We did not take into account cell

20 stress because at this point in time, it's still unclear what 35

21 assays the Agency is using to assess cell stress. So we didn't

22 take that into account. We did use PBPK model to take the

23 measured external dose and predict human blood concentrations,

24 and then from there, we compared that internal dose

25 concentration to the in vitro positive results from ToxCast.

26 We based our MoE on the highest exposure

27 group. So when we used our exposure data, we took that highest
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1 exposure group. And then we also used the PBPK model to adjust

2 TK parameters for age-related physiology. So the margin of

3 exposure, we compared the lowest AC50 concentration in the

4 ToxCast assays with the internal blood concentrations from human

5 exposures.

6 So basically, you've seen this diagram before.

7 This is the EPA's approach. So just to clarify, we did not use

8 ExpoCast data. We used our ag chemical exposure data. We did

9 not use the AUC approach. Instead, we used the lowest AC50

10 values. We did not use the high-throughput TK approach. 

11 Instead, we used PBPK modeling. And instead of getting an IBER

12 score, what we have is a margin of exposure for biological

13 activity.

14 So ag chemical exposure data, ag chemical

15 exposures are very well characterized. In fact, it's a

16 requirement as part of the registration process for chemicals. 

17 The EPA has a much larger universe of chemicals that they have

18 to address. So for them, ExpoCast fills definite critical need.

19 But in this case, we have more what we think might be

20 definitive exposure data. And using our exposure data allowed

21 us to evaluate age group, so we focused on the age groups with

22 the highest predicted exposure.

23 I wanted to walk through this with an example

24 compound, so I selected benfluralin. As you heard yesterday,

25 most of the List 1 and List 2 compounds have little or no

26 bioactivity in the ER- and AR-related assays. Benfluralin is

27 one material which we have which had weak ER bioactivity, so I
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1 thought it would make a good example for this particular

2 illustration.

3 So what I've included here, next you'll see in

4 this -- we have the ExpoCast estimated exposures. I just put

5 these in for comparison. This actually came from the recent

6 Wambaugh paper, so you can see the 95th percentile was 6 times

7 10 to the minus 6. Then you can see next the data that comes

8 from our risk assessment document with the EPA, where we have

9 the highest exposed group being the 20- to 50-year-old males or

10 20- to 50-year-olds, males or females, with an exposure of 1.4

11 times 10 to the minus 5 milligrams per kilogram per day. I also

12 included, just so you could see for comparison that we could do

13 it, we could look at the 1- to 2-year-olds who happen to have an

14 exposure of 7 times 10 to the minus 6 milligrams per kilogram

15 per day.

16 Instead of using the AUC, we used the lowest

17 AC50 value. This is from ToxCast Phase II data, so not from the

18 re-release. Again, for comparison, I put the AUC value, the

19 EPA's AUC value in the first column. The ER agonist benfluralin

20 had an AUC value of .04, so it didn't meet the .1 criterion that

21 was highlighted in the White Paper, but it did have activity.

22 For our ToxCast Phase II data, the lowest AC50

23 values were found, where in one of the Odyssey Thera assays for

24 an endocrine related assay, we found a 9 micromolar AC50. This

25 is an Odyssey Thera ER DNA binding assay.

26 For non-endocrine assay, we had 11.7

27 micromolar as the lowest AC50 value. This is an ATG NRF2 assay
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1 which probably suggests oxidated stress.

2 Now Dr. Wambaugh did a great job of talking

3 about the high-throughput toxicokinetic work that was done by

4 Wetmore to help with doing reverse dosimetry. So these include

5 such assumptions as the whole body is at the same concentration,

6 environmental exposure is constant and uniform, 100 percent

7 absorption of the test material, plasma concentrations are at

8 steady state.

9 One of the really nice aspects of this is that

10 they included elimination due to liver metabolism, and this was

11 experimentally determined by substrate depletion by primary

12 hepatocytes. They also included elimination via passive

13 glomerular filtration, and as Dr. Wambaugh described, this was

14 also experimentally determined with rapid equilibrium dialysis. 

15 Adjustments for plasma protein binding were made. So the steady

16 state plasma concentration was predicted for a dose of 1

17 milligram per kilogram per day. And then the linear dose was

18 adjusted to get the estimated external exposure that yielded the

19 bioactive concentration in ToxCast, and then they did Monte

20 Carlo to simulate the human variability in TK in adults.

21 For our approach, what we used was a

22 commercially available software package called GastroPlus. This

23 considered an oral absorption model attached to a PBPK model. 

24 We feel like this helped us to refine predictions of plasma

25 concentrations as well as the fraction absorbed from GI tract.

26 So we started out with the highest exposure

27 that we've already shown you from the risk assessment document. 
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1 We split this into three daily exposures to mimic 3 meals a day.

2 We assumed this was administered for a period of 28 days. We

3 integrated the parameters for metabolic clearance, plasma

4 protein binding, and renal clearance that were determined in

5 Wetmore, so we took those values for benfluralin from Wetmore

6 and put them into the model. We used variables in a generic

7 PBPK model with age-appropriate physiology. We used a metabolic

8 clearance rate of 1 or 10 micromolar, depending on the AC50

9 values. We ran the PBPK model under fed conditions. 

10 Permeability was based on physiologically relevant QSAR

11 predictions of GI permeation in humans. And we used oral

12 absorption with an Advanced Compartmental Absorption and Transit

13 model to predict passive absorption across the gut. And this

14 becomes important for a compound like benfluralin where the

15 absorption is only about 31 percent.

16 So this is the MoEBA values for benfluralin. 

17 So here you can see the IBER scores I have included on the left

18 for comparison purposes, 79,000 for the ER, a million for AR. 

19 And then you can see the age ranges. So we have the 20- to

20 50-year-olds, which are our highest exposure group for

21 benfluralin, and then the 1- to 2-year-olds for comparison. We

22 have the MoEBA for endocrine assays, which are the E's, and the

23 non-endocrine assays which are the NEs. And so you can see the

24 margins of exposure by the Wetmore approach, and then the next

25 column is when we adjust for the fraction absorption, so the

26 fact that only 31 percent is absorbed. And then we did the

27 margin of exposure again using the GastroPlus PBPK model where
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1 we just took the experimentally determined variables from

2 Wetmore and used the PBPK model. So you can see all of these

3 are fairly large MoEs.

4 This diagram was just kind of -- I hope I'm

5 not misleading folks with this one. What I did was I put the

6 margins of exposure for more traditional tox endpoints at the

7 bottom, and that's what the X-axis applies to, external

8 exposure. So you know, things like that, we have 1,000 X margin

9 of exposure in between estimated human exposures and our chronic

10 reference dose. We have 100,000 margin of exposure in between

11 our estimated exposure and our NOAEL of our tox data package.

12 Now the ones at the top, these are not meant

13 to be used on the same axis. These are just the MoEBAs. So

14 these are from internal exposures. But I just wanted to show,

15 once again, that we have very large margin of exposures which

16 are consistent with other data that we know about these

17 compounds.

18 So in conclusion, options to consider for

19 bioactivity exposure profiling would be the use of the most

20 refined exposure data. I think the EPA is fully supportive of

21 that, and I think they would welcome more refined exposure data

22 when it's available. For toxicokinetics, maybe use PBPK models

23 that can account for percent absorption, absorption rate and

24 age-related physiology.

25 As with the AUC approach, we still need to

26 have that regulatory decision about what a suitable MoE would be

27 to differentiate chemicals that need additional data from
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1 chemicals that have sufficient data. And all in all, I would

2 say that the alternate approach yielded similar conclusions to

3 the IBER approach that many of our ag chemicals are low priority

4 for additional EA and AR screening. Thank you.

5 DR. JAMES MCMANAMAN: Thank you, Dr. Marty. 

6 Any questions?

7 All right. Thank you very much.

8 Our next presenter is Dr. Anna Mazzucco from

9 the National Center for Health Research.

10 DR. ANNA MAZZUCCO: Hi. My name is Dr. Anna

11 Mazzucco. Thank you for the opportunity to address the panel

12 today. I'm speaking on behalf of the National Center for Health

13 Research and also the Cancer Prevention and Treatment Fund,

14 which is one of our major projects. I have my Ph.D. in cell

15 biology from Harvard Medical School, and I conducted

16 postdoctoral research at the National Cancer Institute, so those

17 are the perspectives I'm speaking from today. Our organization

18 conducts public health research and shares information with

19 health professionals and consumers. So that's part of why I'm

20 here.

21 We just wanted to applaud the efforts of the

22 EPA to protect the public from harmful chemicals in their food,

23 water, air and products they use every day. And we strongly

24 support the Agency's efforts to test chemicals more efficiently

25 and with the best possible scientific methods. And as we all

26 know, research has implicated endocrine disrupting chemicals

27 potentially in cancer, infertility and other health problems. 
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1 And so it's important to be able to quickly identify such agents

2 in order to prevent them from entering our food and environment

3 and the health of generations to come after us.

4 So we recognize that use of high-throughput

5 technologies is critical to expediting this chemical screening,

6 and we support the Agency in these efforts. We have a just a

7 few points regarding the high-throughput screening approach as

8 described in the White Paper, and several of these points have

9 already come up in other comments today, so I'll just be brief.

10 We just wanted to raise the issue of -- as has

11 been discussed this afternoon by multiple other people, just how

12 this model will be used really in prioritizing specific

13 chemicals. Kind of more explicit information on exactly which

14 thresholds will be used in decision making I think would be

15 informative. Another point is that we know from a lot of the

16 biology that chemical exposures, particularly during sensitive

17 developmental periods such as prenatal adolescent development,

18 et cetera, can really impact health in adulthood. And so we

19 just wanted to suggest just that the Agency could think about

20 other ways that these critical windows could be prioritized; for

21 example, in biomonitoring or population-based studies that will

22 be used for looking at exposure because we really think that

23 this approach will not only help protect the most vulnerable

24 demographics but also ensure the greatest public health benefits

25 for everyone.

26 Several other stakeholders today have

27 expressed concern over kind of clarifying the relationship
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1 between the area into the curve model and traditional potency,

2 and so we just wanted to also raise that issue, in addition to

3 the issue of just making sure that the model has been tested on

4 a sufficiently diverse set of chemical structures and potencies;

5 that was already raised this afternoon. And we also just wanted

6 to say that we just wanted to emphasize that the screening

7 assays to be relevant to real-world exposures, that broad

8 biomonitoring and exposure studies are really vital just to

9 making sure that the extrapolation models that are used are

10 really going to be meaningful.

11 And just our last two points, from a

12 biological perspective, which is my training, just as

13 high-throughput assays continue to be developed and refined,

14 just that the most orthogonal assays possible as far as kind of

15 downstream readouts of activation of these biological pathways

16 should be used just to make sure that we're really able to

17 capture the broadest possible range of mechanism of action, you

18 know, not just looking, for example, at binding to the receptor

19 but making sure we're really capturing kind of the full

20 reflection of activation of the pathway. And just the last

21 point which the EPA and others have raised, just the importance

22 of being able to account for metabolites, also precursors, to

23 make sure that those things wouldn't be missed, as we are

24 relying more on in vitro rather than in vivo studies. And

25 lastly, others have suggested it would just be helpful to have

26 more of the computational model, the details to be fully

27 accessible for others to use the program.
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1 And so we just, in conclusion, hope that the

2 Agency will just have all the resources that it needs to really

3 continue in this very important effort, and we're glad to see

4 that that's happening. So thank you very much for the

5 opportunity to address the panel.

6 DR. JAMES MCMANAMAN: Thank you very much. 

7 Thank all the public presenters.

8 Before we move on, two items. The panel has

9 been notified of this, but I just wanted to read it into the

10 record, is that in response to Dr. Browne's question yesterday,

11 the AR binding assay is tritiated methyltrienolone, and that was

12 reported back to us by the EPA. Dr. Thomas reported that. So

13 let's read that into the record.

14 And then Dr. Berrocal and Dr. Weller had

15 questions for the EPA regarding one of the figures. So before

16 we begin with the charge questions, let's one of you ask that

17 question.

18 DR. GRANT WELLER: Yeah, thanks. There was

19 just one clarification question on -- it's Slide 6 of the 10th

20 presentation, Dr. Browne's presentation. It's a slide that

21 shows a diagram of the proposed methodological approach where on

22 the left there's bioactivity forecasting; on the right is

23 exposure forecasting, and integrating those together in the

24 IBER. And the question was is data from the HTTK piece, the

25 yellow box on that slide, which is incorporated in the

26 bioactivity estimation, is data from that also incorporated in

27 exposure forecasting as well in the reverse dosimetry modeling?
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1 DR. PATIENCE BROWNE: The answer is no. So

2 the bioactivity is based on the first curves that we showed

3 yesterday for ER and AR, are based on the AUC scores which are

4 an in vitro bioactivity. The high-throughput toxicokinetics is

5 converting that in vitro bioactivity to a human oral dose

6 equivalent, and that was the reverse toxicokinetics that Dr.

7 Wambaugh spoke of today. So that's using the hepatic clearance

8 and the plasma protein binding, converting the in vitro to an in

9 vivo response or an in vivo dose equivalent, if you will. And

10 that's a pivotal piece to get a value that's basically in the

11 same units for humans as the exposure. So that human dose

12 equivalent would be the numerator, and the exposure would be the

13 denominator for the IBER approach.

14 DR. JAMES MCMANAMAN: Okay. 

15 DR. GRANT WELLER: Thank you.

16 DR. JAMES MCMANAMAN: Thank you very much.

17 So I think we're going to move on to the

18 charge questions, and what we'll do is do -- there are three

19 charge questions, and there are multiple parts. We will do

20 Charge Questions 1.a. through c., and at the end of that, then

21 we'll go back to the EPA and see if you understood the answers,

22 the recommendations of the panel.

23 Okay. So if we could have the first charge

24 question read into the record and then Charge Question 1.a. 

25 We'll start with that.

26 

27 CHARGE QUESTION: 1.A
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1 

2 DR. PATIENCE BROWNE: Sure. EPA's proposed

3 approach for quantifying a chemical's potential estrogen

4 bioactivity is based on a computational model integrating data

5 from 18 high-throughput ToxCast assays measuring several

6 endpoints along the estrogen, or ER, signaling pathway. The

7 computational model outputs are expressed as area under the

8 curve, or AUC, scores for ER agonist, or R1, and antagonist, or

9 R2, bioactivity. Before routinely using the ER computational

10 model in the Endocrine Disruptor Screening Program, or EDSP,

11 framework, EPA is reviewing the scientific strengths and

12 limitations of the ER model described in the White Paper to: i)

13 prioritize chemicals for further EDSP screening and testing

14 based on estimated bioactivity, ii) contribute to the weight of

15 evidence evaluation of a chemical's potential bioactivity, and

16 iii) substitute for specific endpoints in the EDSP Tier 1

17 battery. Please address the following charge questions relevant

18 to Section 2 of the White Paper and estrogen bioactivity:

19 Charge Question 1.a.: How clearly has EPA

20 described the computational tools in Section 2.1 (i.e., high

21 throughput assays and models) used to estimate ER agonist and

22 antagonist bioactivity?

23 Charge Question 1.b.: What are strengths and

24 limitations of the ER AUC model's ability to identify reference

25 chemicals that include a variety of structures and have a wide

26 range of in vitro ER bioactivities?

27 And Charge Question 1.c.: EPA used data from
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1 published in vivo studies that are methodologically consistent

2 with EDSP Tier 1 guidelines to evaluate concordance between ER

3 AUC model scores of in vitro bioactivity, and the in vivo

4 uterotrophic response studies (Section 2.2.1). What are

5 strengths and limitations of the curation method and quality

6 standards used for evaluating published in vivo studies?

7 DR. JAMES MCMANAMAN: Okay. So the

8 discussants on Charge Question 1.a. are Dr. Perkins, Dr.

9 Delclos, Dr. Denver and Dr. Potter. Dr. Perkins is the lead

10 discussant and will summarize deliberations.

11 Not what you're doing? Go ahead. Do whatever

12 you want to do.

13 DR. EDWARD PERKINS: I think what we'll be

14 doing is each discussant's going to be presenting what their

15 personal view is of this question, and then once we -- we'll

16 summarize everything for the White Paper. But we wanted to give

17 you individual responses to how people perceive the description.

18 So I'll go first. So I get the clean slate. 

19 I'm not repeating anything. I thought it was actually

20 relatively clearly written. There were some errors in it, but

21 those didn't really bother me too much. I guess what's really

22 clear about it, it's a very complicated subject. A lot of

23 confusion was cleared up through the oral presentation, so I

24 think a lot more effort can be put into explain things a little

25 more in depth such as using specific working examples to clarify

26 how you're actually going through and calculating the curves.

27 One thing that might also be put in there,
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1 since this is obviously planned to change over time, is to have

2 versioning on your documents or versioning on the models, so you

3 know which strategy are you referring to as you start adding and

4 removing and doing different things with it.

5 I felt there needed to be further explanation

6 of the efforts to assess alternative approaches for weighting or

7 the impact of weighting on the different results. It was

8 mentioned in there but just very briefly. It was discussed more

9 thoroughly at the presentation.

10 Clearly, there's some confusion on the dose to

11 the area under the curve approach. I thought it was actually

12 very, very nice and very simply allowed you to combine multiple

13 activities of very different types and get a median, and I think

14 you can actually get an activity range across multiple doses,

15 sort of get a dose range effect that might -- since you don't

16 know when you're using -- later on for point of departure,

17 generally those can be taken either as the most sensitive one,

18 depending on what organizational level you're interested in. So

19 you can get a range of those. That might be more interest than

20 comparing to different endpoints when you're looking in vivo.

21 There might be some better explanation of what

22 flags are there. There were a number of things that were

23 presented, other SAPs that were somewhat glossed over, some more

24 either detailed references, or I know in some places there were

25 -- the earl (phonetic 5:37:04) for the documents were listed,

26 and that was good, too.

27 So an overview of the AUC derivation would
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1 help. Another thing would be really nice, in the very

2 beginning, in the conceptual -- it would be a conceptual module

3 approach of how you're envisioning these computational models to

4 work together. I was a little confused when there was really --

5 I thought the model and the approach with the network model was

6 very, very elegant, and then a lot of work was going into

7 understanding why it didn't work for certain chemicals and

8 talking about how you might start to use QSAR to investigate

9 that. Seems like you might want to have that in first before

10 you start doing it because when you start doing the bioactivity

11 profiles, it's unclear to me how you're going to know what

12 possible false negatives to look at, so see why they didn't

13 work, while you had the advantage of looking at chemicals how --

14 you knew how they worked. What my assumption is that you want

15 to minimize the false negatives and don't mind a few extra false

16 positives.

17 So I think there needs to be some explanation

18 of a strategy to minimize that, present how are you going to try

19 to look at metabolism first. I know you already had the data

20 for a lot of these things, but if you want to use these models

21 in a modular format, there probably needs to be some conceptual

22 rationale for application of these things. You can't afford to

23 use expert judgment just to look at that -- right at that -- the

24 questionable layer because if you have one that's a false

25 negative, and it's way, way down the line, you may never get to

26 it.

27 I think that's it pretty much overall. I
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1 think it's a very elegant approach, and it's vastly, vastly

2 improved from what was presented in the past. It has a lot of

3 promise for potentially integrating other assays of multiple

4 types at different levels of biological organization, so I'm

5 very excited about that but again it's -- the implications of

6 what the area end of the curve to dose is relative to actual

7 pathway activation at different scales probably needs to be

8 explored and explained a little better, perhaps a few more

9 conceptual diagrams, cartoons for people like me, at least, to

10 help figure this out. Might really help set up the background

11 so that these more complex relationships, the basis is better

12 understood when you try to pull them together and integrate the

13 different data types.

14 That's about all I have.

15 DR. JAMES MCMANAMAN: That was Dr. Perkins. 

16 Dr. Delclos is next.

17 DR. BARRY DELCLOS: Yeah. I really don't have

18 a lot to add. I agree with most of those comments. And to me,

19 when I read the paper, the issue presented for discussion was

20 not whether there were other models that might perform better,

21 although it would be useful to consider those in the future, of

22 course, but how well this model served the purpose for

23 prioritization of the chemicals that have potential to interact

24 with nuclear estrogen receptors for further testing.

25 And as several people have said today, I

26 thought that the overall approach was very clearly described in

27 the White Paper. When I read it in the White Paper, there were
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1 a lot of details that were obscured in the Z-scores utility,

2 penalty term, explanation of discounting and so forth. But I

3 think these issues were really well -- the combination of the

4 White Paper and the presentations clarified that to the largest

5 extent.

6 Then the last thing that I would say was that,

7 you know, I know the main goal of the presentations was to

8 assess the utility and the model and the assays as they exist

9 now, and I know that those will probably be changing over time. 

10 But it was stated in the White Paper and several, I think,

11 public commenters raised the issue that it was not likely that

12 all the chemicals that are evaluated have all 18 assays

13 available for assessment so that it would -- some investigation

14 and discussion of what substantive assays might be most useful

15 might have been helpful. That's really all I have to say.

16 DR. JAMES MCMANAMAN: Thank you. Dr. Denver.

17 DR. ROBERT DENVER: I agree with Dr. Perkins

18 and Dr. Delclos. I feel that the derivation of the model wasn't

19 fully explained in the White Paper, but many of the

20 uncertainties were clarified during the discussion yesterday.

21 I agree with the comments by members of the

22 SAP and public commenters that the compression of the potency

23 range by the AUP approach needs to be addressed and explained.

24 I also want to just mention that I would have

25 welcomed greater description of the ToxCast high-throughput

26 screening assays in the White Paper, at least a summary of the

27 principles and performance of these assays. I realize that
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1 evaluating these assays is not the focus of this SAP, but since

2 the model is based on data from these assays, it would have been

3 valuable to us to understand better their functioning.

4 DR. JAMES MCMANAMAN: Thank you. Dr. Potter.

5 DR. THOMAS POTTER: I would like to echo what

6 all my colleagues have said, that you know, the White Paper and

7 the presentations combined provide a much clearer picture of the

8 status of both the ToxCast endocrine effort as well as the IDR

9 ADR (phonetic 5:43:19) program in general. And I did hear a

10 recommendation that came during the public comment period that

11 perhaps there could be some effort to incorporate materials from

12 the presentation into the White Paper so that the document is

13 kept current and provides an accurate record of discussions and

14 deliberations that have been had at the meeting here. I think

15 that's critically important and certainly would emphasize that.

16 You know, and also, again, to agree with my

17 colleagues, again, this is an impressive and outstanding effort.

18 Obviously, it's cutting-edge science and, you know, so in that

19 sense, the work is commendable. I have a few comments that I

20 think might, at least from my perspective, help make the White

21 Paper, in particular, clearer with regard to the testing

22 protocols.

23 And the first point I would make is that I

24 think it would be appropriate to provide some historical context

25 in particular regard to computational methods. I, you know,

26 looked in the literature, and there are a number of

27 publications, some of which some of the EPA team are co-authors
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1 on that have been published in recent years. They appear to

2 describe, in some cases, alternate computational or ranking

3 schemes. I think it will be helpful to know why there has been

4 a transition, perhaps, or perhaps there hasn't been. But is

5 there a transition from one approach and basically to help us

6 understand how this process is maturing because obviously,

7 that's what's going on. This is an effort that's moving forward

8 perhaps exponentially in terms of, you know, doing, you know,

9 thorough data-quality assessments and whatnot. So I think

10 something, some brief summary of what's gone on in the past

11 would be very helpful, certainly from my perspective, in terms

12 of understanding what has transpired.

13 Another area which, again, I think, again,

14 would add to clarity and, again, help understand all of the

15 computational processes that have gone into this effort is

16 there's some discussion of the quality control and assurance

17 procedures in ToxCast. I recognize that that would probably be

18 a major undertaking given the amount of data that's available. 

19 I spent a few hours this morning trying to download some of the

20 information from the ToxCast website, and it was interesting. I

21 got lots of massive spreadsheets with lots of information and

22 certainly would be helpful if there's some effort to digest

23 that.

24 One of the things I thought was interesting is

25 I found a summary as of, let's see, I can't remember the date on

26 that one, but the detailed chemical purity and molecular weight

27 validation of chemicals in the ToxCast universe. After they
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1 were entered, there were like 13,000 entries. Fifty-seven

2 percent were flagged as past, thirty-six percent as not

3 determined, meaning the analyses are ongoing, and six percent as

4 caution. So you know, there's that universe of chemicals, while

5 my next question is what about the universe of chemicals that

6 went into developing ToxCast. Again, relatively small effort,

7 hopefully, could be used to extract some metrics out of there

8 which, again, would add credibility and I think improve

9 transparency and clarity of the effort.

10 I heard earlier, again, that there would be

11 helpful to have some more discussion about all the deliberations

12 that went into weighting the different ER assays that went into

13 ultimately devising the AUC score. Again, a computation would

14 be helpful so that we really can see where the arithmetic and

15 the math is headed with that. Certainly, more detail is needed

16 in the text, and I would fully expect that Dr. Judson could, you

17 know, do a little bit of homework on that, given what I've heard

18 him say at the meeting here and, you know, provide some clarity

19 there.

20 I think that something needs to be said about

21 redundancy. Whether there is redundancy or not, I think there

22 are a lot of tests here, and certainly, the obvious question

23 that what -- particularly someone coming in from the outside on

24 this, is are these tests redundant or not? Are they all needed?

25 And what I really thought was interesting --

26 again, this comes from one of the presentations from Dr. Judson

27 -- was the linkage between the assays. That really hadn't leapt
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1 out at me until I saw the diagram on the board and with the idea

2 that these assays could light up sequentially in terms of

3 binding cellular activation, et cetera. And so again, clarity

4 and transparency and consistency will tremendously help by

5 getting that information into the White Paper and ultimately

6 into the record of the meeting. That's all I have here.

7 DR. JAMES MCMANAMAN: Thank you, Dr. Potter. 

8 Go back to Dr. Perkins, if he has any last -- any summarization.

9 DR. EDWARD PERKINS: No. I think that's about

10 it for this one.

11 DR. JAMES MCMANAMAN: Okay. Then I'll open it

12 up to the rest of the panel for any comments, recommendations.

13 Yes, Dr. Weller.

14 DR. GRANT WELLER: So I'd just like to make a

15 couple comments about the modeling approach and the estimation

16 approach described for calculating the area under the curve. 

17 And thanks to the EPA team for explaining these in the oral

18 presentations. But I just wanted to point out the methodology

19 for computing the AUC using this penalized least squares

20 approach, I mean, it's rather ad hoc, which is not inherently

21 problematic, given especially the fit for purpose criterion and

22 also the performance of the model that has been demonstrated by

23 the EPA. I just wanted to make the EPA team aware of references

24 in the statistical literature around ridge regression and lasso

25 problems penalized least squares estimation that are out there

26 some of which -- there's one reference which I'll include in the

27 write-up that actually addresses this type of problem directly.
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1 And other than that, the main question that

2 comes up, and is not, I don't think, fully addressed in the

3 White Paper, is model fit or, you know, and model fit and

4 checking the tuning parameters used in the penalty term. And

5 the reason that's important is that when you get this AUC value

6 that is then used as inputs to the bioactivity prediction, one

7 wonders what the sensitivity of the choice of those parameters

8 in the model, how that affects the results further along in

9 propagating these estimates through to bioactivity forecasting.

10 One other minor comment, I want to commend the

11 EPA for the SERAP (phonetic 5:51:00) program in integrating the

12 predictions from multiple models together. It's well known that

13 using weighted combinations of predictions is generally going to

14 improve performance over one single model, and that's great that

15 that's -- that program has been done.

16 In the White Paper, I would have liked to see

17 a little more discussion about how the results from those models

18 were combined into a consensus forecast, in particular, handling

19 where some models were qualitative, and some were quantitative. 

20 Just a bit more detail about the way those models were combined.

21 And that's the extent of my comments on this section.

22 DR. JAMES MCMANAMAN: Other comments?

23 Dr. Potter, you may.

24 DR. THOMAS POTTER: The thoughts I had about

25 QA/QC metrics and something that would be of interest is, you

26 know, I noted in almost all the presentations that the AUC

27 values were reported as unit values. And I did hear Dr. Judson
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1 indicate that a number of these chemicals were run through the

2 assay multiple times. So what is the result of that analysis in

3 terms of some uncertainty estimates that might be associated

4 with AUC values would be very helpful?

5 Am I correct in saying that there were a

6 number of --

7 DR. RICHARD JUDSON: You got that correct.

8 DR. THOMAS POTTER: Okay. Good. Yeah. Okay.

9 Good. You weren't shaking your head, so I wasn't sure I was on

10 good ground there but go ahead.

11 DR. JAMES MCMANAMAN: All these nonverbal cues

12 we need.

13 All right. Other comments?

14 Okay. Then if I could get Dr. Browne to read

15 in Charge Question 1.b. once again.

16 DR. PATIENCE BROWNE: May I ask a clarifying

17 question on that?

18 DR. JAMES MCMANAMAN: Pardon me?

19 DR. PATIENCE BROWNE: May I ask a clarifying

20 question on 1.a.?

21 DR. JAMES MCMANAMAN: Sure.

22 DR. PATIENCE BROWNE: Okay. There were

23 several comments about potency, and it was one of the things

24 that came up from the panel as well. I'm not sure whether it

25 was clearly described in the talks, but the AUC scores basically

26 translate to log potency AC50s, and I believe both Dr. Judson,

27 Drs. Kleinstreuer and Casey also acknowledged that there is some
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1 compression towards the top of that scale.

2 In the context of EPA's interests, we're

3 largely concerned with environmental chemicals which are likely

4 to have potencies well below the pharmaceutical or native

5 steroids that are towards the top of that scale. And I guess

6 I'd just like to take a moment to ask the panel if they think

7 that the compression toward the top of the scale is appropriate

8 and the tools, as they are, useful for prioritizing

9 environmental chemicals.

10 DR. JAMES MCMANAMAN: Yeah. If that could be

11 covered in another -- and in the context of one of the charge

12 questions, that's fine, but as an additional question, it may

13 not be appropriate at this time. So if we could go on with 1.b.

14 DR. PATIENCE BROWNE: Sure. So Charge

15 Question 1.b.

16 DR. DAVID DIX: Excuse me, Chair?

17 DR. JAMES MCMANAMAN: Yes?

18 DR. DAVID DIX: Could I ask a clarifying

19 question concerning 1.a.? First is would you prefer that Dr.

20 Browne read the questions 1.a., 1.b., 1.c. separately?

21 DR. JAMES MCMANAMAN: Yes.

22 DR. DAVID DIX: Rather than all in total? 

23 Okay. 

24 DR. JAMES MCMANAMAN: She's already read them

25 but we'll just --

26 DR. DAVID DIX: Right.

27 DR. JAMES MCMANAMAN: -- as a way of
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1 separating them.

2 DR. DAVID DIX: Okay. So we'll do that in the

3 future.

4 Now I do have a clarifying question on 1.a.,

5 if I could.

6 DR. JAMES MCMANAMAN: Sure.

7 DR. DAVID DIX: Something Dr. Potter said. He

8 mentioned chemical QC in an example that came from a web source.

9 And we can wait and get this in the written record, but could

10 you clarify what that was?

11 DR. THOMAS POTTER: Hopefully, you can get the

12 ExpoCast webpage on the Agency's website. Yeah. I spent most

13 of my morning hanging out there.

14 DR. JAMES MCMANAMAN: Dr. Potter needs to get

15 a life.

16 (Laughter)

17 DR. JAMES MCMANAMAN: All right. So that was

18 Dr. Dix addressing those, and if that clarified it, then we'll

19 move on to 1.b.

20 

21 CHARGE QUESTION: 1.B

22 

23 DR. PATIENCE BROWNE: Charge Question 1.b.: 

24 What are strengths and limitations of the ER AUC model ability

25 to identify reference chemicals that include a variety of

26 structures and have a wide range of in vitro ER bioactivities?

27 DR. JAMES MCMANAMAN: Sorry for the ex parte. 
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1 So we'll go on with that. Then I'll come back to another --

2 address Dr. Potter's response. So the discussions on 1.b. are

3 Dr. Berrocal -- she's lead discussant -- Dr. Delclos and Dr.

4 Denver.

5 Dr. Berrocal.

6 DR. VERONICA BERROCAL: So we have come

7 together and wrote a document that we wrote a document that we

8 have come together, and then if there's such a discussion want

9 to add something to it, please do so.

10 So first of all, we wanted to applaud the EPA

11 for having developed a statistical model and a computation tool

12 based on a series of in vitro and cell-based assays to predict

13 chemicals' estrogenic activities. We see several strengths and

14 some limitation to the ER AUC's model presented.

15 So we start with the strengths. It's always

16 good to start with the good things. Commenting on the general

17 merit of the ER AUC model, we believe that the major strength of

18 the approach is that it will allow to derive information on the

19 potential estrogenic activity of chemicals and should

20 considerably reduce the need for more expensive animal testing.

21 The approach will allow analysis of several

22 chemicals in parallel, thus being more efficient from both a

23 computational and time perspective.

24 Commenting on the specific characteristic of

25 the ER AUC model, we believe that the employment of a

26 combination of assays is a great strength in that it allows for

27 detection of technology-specific false positive and negative
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1 activities, and allows for a more robust assessment of the in

2 vitro estrogenicity of a chemical than what can be obtained

3 using a single assay.

4 The ER AUC model is very elegant and

5 mathematically very simple. Its formulation is based on the

6 assumption that the assay signal represents a true, nuclear

7 receptor-mediated process, and there is a linear relationship

8 and no loss of information in the transmission from the receptor

9 to the assay signal. Statistically, the equation of the

10 receptor signal is conducted using a constrained least square

11 minimization, highlighting the resemblance between the

12 statistical approach proposed with the machine learning concept

13 of penalized least square regression, or "ridge regression". 

14 Despite its simplicity, the material presented in the White

15 Paper and in the oral presentation indicate that the model

16 displays a strong ability to detect reference chemicals that act

17 as agonists. In particular, the results reported indicate that

18 the model is able to detect weak agonists better than the Tier 1

19 assays.

20 With respect to antagonist reference

21 chemicals, we observe that the model has a slightly less robust

22 capacity of detection than for agonists due to the confounding

23 effect of cytotoxicity and cellular stress.

24 In general, we believe that the results

25 produced by the model are good or better than results obtained

26 using the three assays that it is intended to replace in the

27 existing Tier 1 screening battery.
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1 The simplicity, parsimony, and

2 interpretability of the ER AUC model is a great strength.

3 As indicated in the public comments, several

4 competing model parameterizations might exist, assuming

5 different physiological relationships between the cellular

6 signaling pathway and assay signal. It is possible that more

7 sophisticated and complex models might yield a slightly better

8 performance when evaluated on the reference chemical, especially

9 with respect to antagonist chemicals. However, as the priority

10 of the EDSP chemical screening is on estrogen agonists, the

11 panel feels that it is unlikely that more complex models would

12 yield significantly better results than the ER AUC model

13 presented.

14 We believe that in the presence of equivalent

15 predictive ability on reference chemicals yielded by various

16 alternative and competing models, priority should be given to

17 models that are more parsimonious and succinct, as identified by

18 Occam Razor's principle.

19 So now one of the limitation has been already

20 addressed by Dr. Waller, and it refers to the penalization

21 constant. In the White Paper and during the public

22 presentation, it was indicated that the penalty term has a

23 functional form fixed a priori that depends on the estimated

24 pathway signals which are iteratively re-evaluated. Commonly,

25 in penalized estimation approaches, the penalty term is

26 independent of the model parameters being estimated and is

27 determined via cross-validation. The magnitude of the penalty
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1 term has an effect on the shrinkage towards zero of the model

2 parameters. We believe that sensitivity analysis should be

3 conducted to determine how sensitive ER AUC values are on the

4 penalty term adopted.

5 We hypothesize that large ER AUC values are

6 probably not going to be highly influenced by the penalty term,

7 and thus the penalty term would not affect results significantly

8 for the high-activity chemicals. However, it might have effects

9 on the moderate or low-activity chemicals with ER AUC values

10 that are closer to the threshold.

11 The threshold of ER AUC equal to .1 has not

12 been clearly motivated in the White Paper. The definition of

13 true/false positive and similarly true/false negatives depends

14 on the threshold value of ER AUC equal to .1. Given the

15 uncertainty around the estimated receptor signals, which in turn

16 propagates to uncertainty on the ER AUC values, it is possible

17 that those reference chemicals that are near the threshold might

18 be incorrectly classified, changing the accuracy of the ER AUC

19 model on the reference chemicals. We again highlight that the

20 uncertainty in the estimated receptor signals will likely affect

21 the predicted classification of the moderate/low-activity

22 chemicals which may not be the priority chemicals.

23 As mentioned in a public comment, and in our

24 remark regarding the strength of the model, the ER AUC model is

25 based on the assumption of a linear relationship between

26 receptor activity and assay signal. While this is a good

27 starting assumption, there might exist different functional
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1 relationships between assay and receptor signals. We encourage

2 the EPA to investigate different types of functional

3 relationships, especially if this endeavor might improve the ER

4 AUC model predictive performance for antagonist chemicals.

5 This has also been addressed, I think, today

6 by Dr. Potter, but the panel recognized that the ER AUC values

7 are based on Hill, Gain-Loss or constant models that are fitted

8 to data arising from replicates of assay experiments. One of

9 these three models is subsequently selected using model fitting

10 criteria such as the Akaike Information Criteria or AIC. While

11 Hill or Gain-Loss models might have been used previously in the

12 literature, the panel feels that using the raw data arising from

13 each replicate to derive the curves relating a chemical

14 concentration to an assay signal can yield a range of ER AUC

15 values from which mean/median and confidence intervals could be

16 derived, thus communicating the uncertainty in the estimated

17 bioactivity of the chemical.

18 And finally, the last limitation comment is

19 the ER AUC model yields very nice predictive results on the

20 reference chemicals. The reference chemicals constitute the

21 training set, which has in some sense guided the selection of

22 the assays. A possible limitation that has been discussed and

23 raised during the public presentations and question session is

24 the focus on nuclear estrogen receptors. There is uncertainty

25 on how well the chemicals examined to this point sufficiently

26 cover the structural classes that are present in the entire

27 universe of chemicals to be assessed.
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1 DR. JAMES MCMANAMAN: Are you -- is that --

2 DR. VERONICA BERROCAL: I'm done. Yeah.

3 DR. JAMES MCMANAMAN: You're finished? Okay. 

4 Thank you.

5 Dr. Delclos?

6 DR. BARRY DELCLOS: My comments were

7 incorporated.

8 DR. JAMES MCMANAMAN: Thank you.

9 Then Dr. Denver?

10 DR. ROBERT DENVER: I think that Dr. Berrocal

11 did a great job in summarizing our comments, and I have nothing

12 further to add.

13 DR. JAMES MCMANAMAN: All right. I'll open it

14 up to the other panel members.

15 Okay. If we could then read Charge Question

16 1.c. 

17 

18 CHARGE QUESTION: 1.C

19 

20 DR. PATIENCE BROWNE: Charge Question 1.c.: 

21 EPA used data from published in vivo studies that are

22 methodologically consistent with EDSP Tier 1 guidelines to

23 evaluate concordance between ER AUC models of in vitro

24 bioactivity, and the in vivo uterotrophic response studies

25 (Section 2.2.1). What are strengths and limitations of the

26 curation methods and quality standards used for evaluating

27 published in vivo studies?
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1 DR. JAMES MCMANAMAN: Okay. The discussants

2 on this charge question are Dr. Schlenk, Dr. Delclos and Dr.

3 Propper. Dr. Schlenk is the lead discussant.

4 DR. DANIEL SCHLENK: I just want to thank my

5 co-discussants, associate discussants, for our discussions on

6 this particular question. I've separated this into strengths

7 and limitations and have essentially itemized these to sort of

8 provide this, and I will again pass on to the associate

9 discussants after to add specific points if they need to.

10 So the strengths, I think, with a few

11 exceptions, which we'll provide below here, the methods that

12 were used to curate and standardize the literature studies to

13 compare the in vivo uterotrophic endpoints from the literature

14 with the ER AUC model were in most cases sufficient. This was a

15 monumental task -- I think that was pointed out several times --

16 on the amount of data that had to be evaluated. And the studies

17 that were looked at seemed to be consistent with that approach. 

18 So again, applause to the Agency for putting that together. I

19 think that was a valiant effort in trying to do that.

20 What we felt were particularly good were the

21 separation of the studies between the injection and the oral

22 dosing strategies which identify potential study design

23 discordancy, and also the separation of the rats from the mice

24 at different developmental stages I think was also warranted to

25 also detect these discrepancies. Just a recommendation; perhaps

26 it might be better to focus on the rats per se, given the

27 insensitivity of the mice results that seemed to be present at
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1 least in the data that was provided there.

2 Overall, it was remarkable, I felt, especially

3 given some of the limitations that we'll discuss here in a

4 minute that the estimates were just off the charts in terms of

5 -- particularly for the low-potency compounds. That was very

6 encouraging. The balance accuracy of over 90 percent for

7 positive predicted value and negative predicted value are all

8 incredibly remarkable. In fact, brings to point, I think, Dr.

9 Wambaugh's statement of never trust a modeler because it seems

10 fairly amazing that you can get that good predictive

11 capabilities there.

12 So in terms of the limitations, again, and

13 I'll stress again to sort of take this -- if we had to weight

14 the strengths, I would weight the strengths more than I would

15 the limitations. These are just sort of -- you know, obviously,

16 to improve 10 percent is not that big, but some of the things, I

17 think, that just came out were a little bit shocking, and I'm

18 not sure how to explain this, so I'm going to put it on the

19 record for that.

20 So while the literature valuations indicated

21 strength of comparisons for high-potency compounds, it's unclear

22 whether valid comparisons with low-potency compounds could be

23 utilized for accurate comparisons primarily due to the

24 inter-laboratory variability. Again, the performance standard

25 contrasts that. It's completely opposite of that. Reading

26 through the document, the first thing I saw was out of 70

27 compounds were evaluated, 18 were discordant which pointed to a
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1 26 percent discordancy value. So the first thing I thought is

2 like oh, well, that's not very good. I mean, if I'm running and

3 study, and my controls are 26 percent variance, that's -- how

4 could that ever work? But yet, the model predicts 90 percent. 

5 So I don't know, again, how that happened, but I would not have

6 predicted that to happen with a 26 percent discordancy. So

7 again, I think that sort of points to the previous question of

8 sort of the black boxes in the model and how those numbers got

9 from here to there. But I would not have predicted a 90 percent

10 prediction value on a 26 percent discordancy. But apparently,

11 that seems to be the case.

12 So while we agree with the Agency this is

13 likely due to differences in design and the number of studies

14 conducted for each chemical, some variability could be due to

15 the following errors which, again, may or may not be determined

16 based upon the literature that you had but I think should be

17 thought about in future evaluations because obviously you want

18 to build on this database and do additional -- fill in some of

19 the gaps we'll talk about here a bit later.

20 It's been brought up in the previous questions

21 about chemical purity and availability and dosing materials. I

22 think this is a very valid endpoint that needs to be addressed,

23 not just in literature studies but also in, I think, the entire

24 ToxCast system. I had conversations with Robert Tanguay at

25 Oregon State who has done some high-throughput studies with

26 zebra fish embryo assay, and he has -- in fact, he gave

27 presentation on this, and it will be in -- he is submitting it
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1 for publication this coming year but basically found that a

2 contaminant that was in flame-retardant material that was tested

3 was less than 1 percent of that material actually elicited the

4 biological responses that he was seeing. So they were chasing,

5 again, the parent compound, but it was actually a degradant or a

6 contaminant that was in that material. So again, that's just an

7 example of how chemical purity is, I think, incredibly important

8 in any evaluation whether it's ToxCast or even a

9 literature-based evaluation.

10 For these particular studies, I think the site

11 of injection is something that also can be evaluated. I haven't

12 done rat studies since I was in grad school but -- or rodent

13 studies in general but -- so I went on YouTube and actually

14 looked up some how you do subcu injections. And it's actually

15 incredibly variable on how -- particularly if you're looking at

16 different academic labs on how they do that. Most actually

17 inject in the neck, pinch up the neck and actually do it to

18 neck. But there's a fair number that actually do abdominal

19 subcu injections which is, you know, millimeters away from an IP

20 injection. So consequently, that may have led to some of the

21 discrepancies that you see with some of the injection values

22 that were presented. So again, something to think about. 

23 Again, I don't know how you'd get that off of a literature

24 review, but I know the EPA has contacted me on studies for

25 certain things, so again, contacting the author to confirm the

26 dosing strategy might be something that could be possible.

27 Several of the items that were provided in the
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1 White Paper were expanded upon during the oral presentation by

2 Dr. Casey, and so that cleared up a few things. So some of

3 these may be moot based upon some of the things that were in the

4 presentations. But there were likely -- we thought the

5 differences in solvent or carrot (phonetic 6:13:47) controls

6 weren't really well discussed, and possibly that could have led

7 to variability, particularly with some of the injection studies.

8 Again, in the document, I wasn't necessarily pinpointed that

9 strain differences were looked at, particularly in some of the

10 rodents, although in the presentation slides that were provided,

11 the strain was listed on there. Again, one of the questions I

12 forgot to ask yesterday, I tried to get through them all, but

13 one was, you know, how many of the studies were actually looked

14 at in Sprague Dawleys which was sort of the rat model that you'd

15 typically done, I guess, for uterotrophic responses.

16 One endpoint that came up amongst our

17 discussion was the limited sample size that was allowed, I

18 guess, for some of the evaluations. I think I asked a question

19 during the presentation, what was the minimum treatment number,

20 and I think I was told it was five. But in the text, it stated

21 that control values actually were allowed to be three. And

22 this, again, may lead to some of the variance between some of

23 the studies, obviously.

24 And then I think Dr. Propper pointed out the

25 original Tier 1 guidelines suggest a minimum sample size of six,

26 actually, for all treatments. And again, given the number of

27 treatments, a sample size of three in a control provides, I
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1 think, very little statistical power perhaps.

2 One thing that was brought up today, I think,

3 by the public commenters was -- I think is very important is

4 obviously the systemic toxicity evaluations. Again, I'm not

5 sure whether that was evaluated or not, but I think that's

6 critical, again, given some of the issues we've had with overt

7 toxicity confounding some of the responses, particularly in the

8 in vivo assays.

9 Other, again, sort of minor things that I

10 think will be -- as they are living documents and living studies

11 that continue to be built upon, there seemed to be a little lack

12 in some of the responses, particularly for the LELs, between 0.1

13 and 1, which was slide 16 in Dr. Casey's presentation. Seems to

14 be you've got a lot of compounds on either end, but the ones in

15 the middle are somewhat lacking perhaps, so it would be a little

16 bit more -- I would feel a lot more trusting of the model, I

17 guess, if we had some of those points filled in in the middle. 

18 And for the reference for Figure 2.12, the reference compounds,

19 at least in the document there are only 3 values between .2 and

20 .001. The rest were all negative. So again, to build something

21 on three values in between positives, I think, you want to at

22 least maybe pursue compounds or at least studies that have

23 values that are in those -- in that range.

24 For the nonreference compounds, in fact, for

25 Figure 2.15, there was only one value between .02 and .001. So

26 again, I don't know how important that is. But at least when

27 you look at the data outputs there's these gaps that were -- I
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1 think Dr. Propper had pointed out as well.

2 And again, I assume and we assume based on

3 some of the comments that these literature-based approaches will

4 also occur with some of the other Tier 1 in vivo studies, so

5 FSTRA and the pubertal assays. So again, everything that I

6 mentioned before should also be evaluated, I think, in those

7 assays as well, if possible.

8 And we would hope that, again, in future

9 panels that those data would also be provided rather than just

10 the uterotrophic. I think we were a little -- it was a little

11 shocking just to see the rat uterotrophic or the rodent

12 uterotrophic response initially.

13 So overall, with a high degree of variability

14 but only having one false positive and one false negative out of

15 42 compounds, which was taken from Figure 2.12, the predictions

16 are surprisingly excellent. I'd say you probably want to focus

17 future studies on compounds that are metabolically activated, I

18 think, particularly to see how strong and how powerful the model

19 is. So I would again look at things like methoxychlor that

20 wasn't one of the compounds. I don't know, maybe it already

21 was. It's hard to tell based on the data that we got. But

22 methoxychlor would obviously be one. Some of the pyrethroids

23 with some work that we'd done I know that are -- seem to be

24 metabolically activated would also be something that might be of

25 interest there as well. So that's all I got.

26 DR. JAMES MCMANAMAN: Thank you, Dr. Schlenk.

27 Dr. Delclos?
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1 DR. BARRY DELCLOS: I have nothing to add.

2 DR. JAMES MCMANAMAN: Okay. Dr. Propper.

3 DR. CATHERINEN PROPPER: I only have one thing

4 to add, and it may be more relevant to 1.d., but since it was

5 brought up, was some of the potency considerations. I do think

6 active, not active leads to a zero, one response when you're

7 doing the uterotrophic assay curation. And potency is important

8 here. So I think taking into consideration what the dose was

9 that gave an active response ultimately needs to be placed into

10 the models.

11 DR. JAMES MCMANAMAN: Okay. Comments from

12 other panel members?

13 All right. Then we'll go back to the Agency. 

14 Did you understand the answers and the recommendations to these

15 subcharge questions?

16 DR. PATIENCE BROWNE: We're going to take a

17 moment to confer, if that's okay.

18 DR. JAMES MCMANAMAN: Sure. That's fine.

19 DR. DAVID DIX: Sorry for that delay. We have

20 a little un-clarity in some of the comments, but we think that

21 probably in the written comments it will be clearer, some of the

22 points that were made. You know, it might be a misunderstanding

23 of the comments and/or, you know, we're not -- we have a little

24 un-clarity concerning this 26 percent discordance issue relating

25 to the uterotrophic assays. But I don't know. I'll leave it to

26 the Chair and Dr. Schlenk.

27 DR. JAMES MCMANAMAN: Go ahead and ask the
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1 question. As it in a clarifying -- if you can ask it so that

2 it's clarification and not an additional question, that's fine.

3 DR. DAVID DIX: Our observation is that for

4 each chemical, there is, you know, relatively good concordance,

5 nothing like 26 percent discordance in however many uterotrophic

6 assays there are for those assays. I think maybe we

7 misunderstood.

8 DR. DANIEL SCHLENK: Let me just quote from

9 the White Paper. "Of the 70 chemicals in the database with at

10 least two reported uterotrophic studies, 18, 26 percent had at

11 least one study with discordant outcome resulting in chemical

12 being classified as both active and inactive." That's what I

13 was basing that number off of.

14 DR. DAVID DIX: Okay. So I think we

15 understand that then. So you weren't making -- but then we

16 still derived an activity column that we, you know, active or

17 inactive by uterotrophic, that we then made the comparison to

18 the toxic assay you see.

19 DR. DANIEL SCHLENK: Right. And again, my

20 point was -- looking at that initially was how could you get

21 such good numbers? But --

22 DR. DAVID DIX: Right, right.

23 DR. DANIEL SCHLENK: -- but you did, so

24 obviously, there's other issues that are involved, and so

25 consequently, the comments that I had were more if you wanted to

26 fix that, or as more studies become evaluated to fit those gaps,

27 you might want to be looking at those issues.
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1 DR. DAVID DIX: So our clarifying question

2 would be that if you could -- and it might be something just to

3 take away and consider in your written -- if there's suggestions

4 because we are dealing with those discordants from the

5 uterotrophic and, you know, we're very likely to see it in the

6 other data types which is what --

7 DR. DANIEL SCHLENK: Right.

8 DR. DAVID DIX: -- is there suggestions you

9 might make to us, how to deal with the discordance from these in

10 vivo study types --

11 DR. DANIEL SCHLENK: That's what's in the

12 limitations. So those points that I met were basically how you

13 could deal with discordancy basically.

14 DR. DAVID DIX: Thank you.

15 DR. DANIEL SCHLENK: Sorry that wasn't clear.

16 DR. JAMES MCMANAMAN: All right. Everything

17 else is understood?

18 DR. PATIENCE BROWNE: Yes. Thank you.

19 DR. JAMES MCMANAMAN: Okay. So Dr. Potter had

20 made a comment about a particular website where he found the

21 data. And we'd like to read that into the public record. If

22 you don't have it now we can do it --

23 DR. THOMAS POTTER: I have it.

24 DR. JAMES MCMANAMAN: Okay. Very good.

25 DR. THOMAS POTTER: Found it on my phone.

26 DR. JAMES MCMANAMAN: Good thing for phones.

27 DR. THOMAS POTTER: Yeah, really. 
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1 www.epa.gov/ncct/toxcast.

2 DR. JAMES MCMANAMAN: Okay. 

3 DR. THOMAS POTTER: And that's the master

4 website, and I enjoyed cruising around there.

5 DR. JAMES MCMANAMAN: So tomorrow we'll start

6 at 9:00 a.m., and we'll begin with Charge Question 1.d. And I

7 think we'll complete everything tomorrow. So thank everyone for

8 their presentations and for staying until the end and all the

9 panel members. Thank you very much.

10 * * * * *

11 (Whereupon, the meeting was adjourned for the day)

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1 

2 DR. JAMES MCMANAMAN: Welcome to the third day

3 of this FIFRA Meeting. I don't think it's necessary to

4 introduce panel members again. Should we do that? 

5 All right. So let's just go around one more

6 time. I'm Jim McManaman, a Professor of Reproductive Sciences

7 at the University of Colorado and this session's Chair. 

8

9 DR. BARRY DELCLOS: Barry Delclos from the

10 FDA's National Center of Toxicological Research.

11 DR. DANA BARR: Dana Barr, Emory University.

12 DR. DAVID JETT: Dave Jett, National

13 Institutes of Health and Chemical Defense. 

14 DR. VERONICA BERROCAL: Veronica Berrocal,

15 Assistant Professor of Biostatistics, University of Michigan.

16 DR. TERRY BROWN: Terry Brown, Department of

17 Biochemistry and Molecular Biology, Johns Hopkins School of

18 Public Health.

19 DR. ROBERT DENVER: Bob Denver, University of

20 Michigan.

21 DR. CATHERINE PROPER: Cathy Proper,

22 Professor, Department of Biological Sciences, Northern Arizona

23 University.

24 DR. DANIEL DOERGE: Dan Doerge, National

25 Center for Toxicological Research.

26 DR. EDWARD PERKINS: Ed Perkins, Senior

27 Research Scientist, Army Corps of Engineers.
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1 DR. THOMAS POTTER: Tom Potter, USDA

2 Agricultural Research Service.

3 DR. GRANT WELLER: Grant Weller, I'm a

4 Research Scientist at Savvy Sherpa in Minneapolis, Minnesota.

5 DR. DANIEL SCHLENK: Dan Schlenk, University

6 of California, Riverside.

7 DR. JAMES MCMANAMAN: Okay. So I think we

8 left off with Charge Question 1c, so we can begin with Charge

9 Question 1d. If that can be read into the minutes.

10 CHARGE QUESTION: 1.D

11 

12 DR. PATIENCE BROWNE: Charge Question 1.d. 

13 Based on all the data presented in Section 2 on ER AUC model

14 performance including characterization of reference chemicals

15 and concordance with in vivo uterotrophic results, what are

16 strengths and limitations of using the ER AUC model to

17 distinguish and prioritize chemicals based on potential estrogen

18 bioactivity?

19 DR. JAMES MCMANAMAN: Okay. The discussants

20 for this charge question are Dr. Schlenk as Lead Discussant and

21 Dr. Delclos as Associate Discussant.

22 DR. DANIEL SCHLENK: Thank you. So I itemized

23 this question, again, into strengths and weaknesses, so I'll be

24 reading from that perspective. In terms of strengths, I think

25 the assumption here is that, again, you're assuming a linear ER

26 binding and/or activation process through nuclear receptors, and

27 I think that's an important assumption that you need to do. And
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1 as long as you're making that assumption, I think there is

2 excellent performance for the reference chemicals that are

3 provided. The outcomes for List 1 and List 2 and the universe

4 actually seems consistent with active and non-active

5 descriptions.

6 As opposed to what other people mentioned

7 yesterday, I actually think redundancy is a good thing. 

8 Redundancy in power of the multiple high through put signals for

9 that process or that pathway, I think, is a valid mechanism. 

10 We've actually addressed that I believe in some of the earlier

11 EDSP validation panels that it was good to have redundancy. So

12 I would actually promote that. And in fact, it actually

13 provides a confirmation of the hits.

14 Again, I mentioned this yesterday already, the

15 strong predictive accuracy greater than 90 percent for different

16 outcomes. Very strong correlation across potency categories for

17 all the reference compounds that were provided in the text. 

18 Very, very strong correlations there. 

19 Again, limitations, I think these were already

20 brought up, and nothing really surprising here. Metabolism,

21 again, is something that obviously needs to be included. I

22 think the Agency mentioned that they had done some preliminary

23 studies with S9 to look at plus and minus S9 fractions prior to

24 some of the assays. I would just make sure that that would

25 include human and rat S9 for those studies if possible.

26 And then there's a number of different

27 commercial labs that actually provide a humanized S9 fraction
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1 that could be used. Again, some of the compounds that require

2 metabolic activation would be good to use to make some of these

3 comparisons, and I mentioned these yesterday, pyrethroids,

4 methoxychlors. I did not mention some of the PCBs and PAHs that

5 -- obviously there was one I think they used that actually did

6 have a hydroxylated metabolite motive action, but again, it

7 would be good to explore some of those compounds.

8 And I think the comment was made, while the

9 uterotrophic assay may actually enhance clearance with greater

10 metabolism, I think a lot of that depends on the organism that

11 you're using. And maybe for that uterotrophic assay that's the

12 case, but not for some of the other assays. I'm thinking mostly

13 FISTRA and definitely in developmental assays. 

14 I think activation to estrogenic metabolites

15 may be a very quick even that takes place and is very important

16 in certain developmental windows, depending on the model you're

17 working with, of course, but obviously, for the Tier 1 assays

18 and the battery that's proposed right now, it's probably not

19 that big a deal. But at least in terms of development, I think

20 it's a real big motive action issue. So again, metabolism, I

21 think, just needs a little bit better emphasis added at some

22 point.

23 The other limitation I would say is, again,

24 the exclusivity on nuclear receptors. And again, this was

25 brought up. I don't think that's anything new there. So other

26 alternative pathways that can change estrogenic activity, and I

27 think the Agency has also mentioned steroidogenesis and so we'll
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1 just put that on the record as well, because obviously that

2 changes estrogen levels which could actually cause estrogenic

3 activity. So it's kind of an obvious choice there too. And

4 they had said that they're working towards that, so that's not a

5 big deal.

6 And then thirdly, the only limitation this is

7 mostly, I think, just in the presentation of the materials, some

8 of the potency categories I thought were a little bit vague

9 until you actually got to Dr. Judson's presentation and Slide 63

10 of 65 where he actually does numerically categorize those

11 potencies. 

12 I would highly recommend the Agency, at least,

13 in any future correspondence provide some sort of numerical

14 categorization instead of just calling it modest and very weak

15 and weak. 

16 Again, for me, I always get after my students

17 all the time for using high and low in terms of descriptors. 

18 It's always nice to actually have what does high mean, what does

19 low mean, what does modest mean. So at least some sort of

20 definition for what that means.

21 Overall, I think minor limitations for

22 compounds, particularly mostly those that require metabolic

23 activation. But it seems to be appropriate for chemical

24 prioritization, I'd say for List 1, List 2 and the universe

25 compounds, at least, that were provided that the data looked

26 really good, at least for the in vitro part. Let's put it that

27 way. We'll address the uterotrophic assays specifically in
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1 those results in 01-F, but at least in terms of overall

2 prioritization, if we're stopping at that definition, I would

3 say definitely a step forward.

4 DR. JAMES MCMANAMAN: Thank you, Dr. Schlenk. 

5 Dr. Delclos?

6 DR. BARRY DELCLOS: I concur with what Dan

7 said. I really have nothing to add.

8 DR. JAMES MCMANAMAN: Okay. Other panel

9 members? Dr. Perkins.

10 DR. EDWARD PERKINS: I wanted to comment on

11 the performance comparison versus uterotrophic results. Based

12 on the data with the high throughput toxicokinetics, it seems

13 that comparing the AUC value to a lowest effect level is a bit

14 like comparing oranges to apples. A more meaningful comparison

15 might be to be able to compare oral dose equivalents based on

16 biological pathway activation concentrations and revers

17 toxicokinetics to the equivalents in the uterotrophic assays. 

18 And you have all the tools to do that, so it just seems that a

19 natural thing to compare that and you would be more closely

20 approximating the systems. That and trying to look not just at

21 the lowest level dose administered that caused an effect, but

22 also trying to do a similar DM benchmark dose level comparison.

23 DR. PATIENCE BROWNE: Thank you for the

24 comment.

25 DR. JAMES MCMANAMAN: Other comments from the

26 panel? Yes, Dr. Jett.

27 DR. DAVID JETT: This may be more of a
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1 question, but when I think about an appropriate screening

2 program paradigm in the pharmaceutical industry, the redundancy

3 part is a good thing with orthogonal assays, but there's also

4 counter screens put into place to look at selectivity and

5 specificity. I'm not sure if that applies in this context, but

6 I did want to raise that issue.

7 DR. JAMES MCMANAMAN: Is that addressed to Dr.

8 Schlenk or to Dr. Delclos, or just a comment? Is it a question?

9 DR. DANIEL SCHLENK: Was that a question or is

10 that just a comment?

11 DR. DAVID JETT: Well, yeah, I guess it's

12 both. I could be a question that maybe it's built in there in

13 already and I just didn't see it. But I'll say it's a question.

14 DR. JAMES MCMANAMAN: Okay. Would any of the

15 panelists like to respond that?

16 DR. DANIEL SCHLENK: As far as I know, the

17 redundancy was one of the main issues for EDSTAC when they

18 started meeting was to try to have that redundancy, but I don't

19 know if there's a counter. I mean, you have antagonism,

20 obviously. 

21 DR. DAVID JETT: Well, for instance, putting

22 in assays to look at other receptor types, other targets that

23 may be of relevance. 

24 DR. EDWARD PERKINS: ToxCast has all the

25 receptors and also that's where a lot of the cross talk comes in

26 with some of the assays that they already have, where you have

27 platform-specific noise in the multiplex systems. So in this
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1 one analysis, they do actually somewhat address specificity by

2 looking at technology specific hits, or that's where some of the

3 pseudo receptor stuff comes in trying to address that type of

4 issue. Is it a technology related thing or somewhat

5 non-specific relative to the expected one. 

6 And I think that's where it goes back to

7 having multiple orthogonal, different assays testing slightly

8 differently or very differently at multiple biological scales,

9 what's happening, helping to do that.

10 DR. DANIEL SCHLENK: I think that the term

11 that was used before was redundancy in complementarity. That's

12 what I was after. That's what I was saying, that I actually

13 think that's a good thing that you have redundancy in

14 complementarity for those processes. 

15 DR. JAMES MCMANAMAN: Dr. Potter.

16 DR. THOMAS POTTER: Some of the conversation

17 we had on day one in the Q & A with the Agency, I think there's

18 been some discussion back and forth about whether the battery of

19 assays are redundant or not. I'm not familiar enough with them

20 to say, so I would make a recommendation. I certainly think

21 this would be a useful addition to the White Paper or to the

22 minutes of the meeting to have some discussion of the assays by

23 the agencies so that around this topic of redundancy and why

24 it's important to the ultimate result.

25 DR. JAMES MCMANAMAN: So would you concur with

26 Dr. Perkins' and Dr. Schlenk's view or assessment of that

27 question?
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1 DR. THOMAS POTTER: Yes. Absolutely. 

2 DR. JAMES MCMANAMAN: Okay. 

3 DR. DAVID JETT: And just to be clear, I

4 concur as well. The redundancy is an important component of it.

5 I was specifically talking about selectivity in the sense of

6 using other screens with other targets that should hopefully

7 come up negative. 

8 DR. JAMES MCMANAMAN: Right. And the examples

9 that Dr. Perkins and Dr. Schlenk gave, you think would fulfill

10 that?

11 DR. DAVID JETT: Yes.

12 DR. JAMES MCMANAMAN: Okay. Other comments? 

13 All right. We'll go back to the Agency. Did you understand the

14 panel's suggestions and comments?

15 DR. DAVID DIX: I have one clarifying question

16 concerning Dr. Perkins' comments concerning use of pathway

17 points of departure. This was specific to prioritization, if

18 you could expand on or clarify that for us, just a little.

19 DR. EDWARD PERKINS: This is specific to the

20 performance evaluation of the part of the AUC ranking versus the

21 uterotrophic assays.

22 DR. JAMES MCMANAMAN: Everything clear?

23 DR. RICHARD JUDSON: So I have a clarifying

24 question about one of the points Dr. Schlenk brought up. So I

25 understood your saying that understanding metabolism, the

26 ability to be metabolically activated was more important during

27 development? Somehow that issue was not clear to me.
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1 DR. DANIEL SCHLENK: I'm sorry. I was using

2 that as worst-case scenario. I think the potential -- it was

3 brought up earlier that duration, the time that you actually

4 decided to run the assays the duration of exposure in the

5 assays, obviously, is dependent upon the assay that you're

6 using. If we at some point, and again I'm using zebra fish,

7 perhaps as an assay that may be added at some point down the

8 road for whatever reason. 

9 The timing of exposure is critical and the

10 metabolic activation that occurs during that timing is critical.

11 So consequently, with the uterotrophic assay, which is a longer

12 duration of exposure type of assay, metabolism may not be that

13 important because you probably would clear everything

14 beforehand. But if you're looking at a developmental endpoint

15 or another cellular endpoint that occurs more rapidly than

16 metabolic activation, you don't need it to be quantitatively

17 huge at some point. It's a time selective metric at that point.

18 So, consequently, as the assays become developed, incorporate

19 that. You might be able to see endpoints that occur that more

20 rapid stages. Does that make sense? 

21 I'm thinking in terms of the comment that was

22 made about uterotrophic assays. Metabolism's not really a big

23 point of that because by the time you actually get the change in

24 uterine growth, the compound's gone or it's been cleared. But

25 if you're looking at an endpoint like development that where you

26 see end response immediately, biotransformation, even a minor

27 amount of biotransformation can be very significant, if that
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1 makes sense.

2 DR. RICHARD JUDSON: Yeah. So I think maybe

3 you are suggesting in the in vitro situation, when get around to

4 running metabolically competent assays, we may want to look at

5 different time in the readout.

6 DR. DANIEL SCHLENK: Yes. Duration. I think

7 a time-point duration would be -- I think that was brought up

8 earlier, too. Duration of exposure experiments are definitely

9 important. And again, I would also even add that to not just

10 even the constructed cell lines, but even particularly some of

11 the integrated systems, cellular systems, where you are not,

12 again, more development related perhaps if those ever become

13 online. I know they will be for Tier 2 and some of the other

14 assays down the road, but I think metabolic transformation is

15 very, very important in those sensitive windows. And I think we

16 were kind of missing that. 

17 DR. JAMES MCMANAMAN: Other comments or

18 clarifications necessary? All right. We'll then move on to

19 Charge Question 1.e.

20 

21 CHARGE QUESTION: 1.E

22 

23 DR. PATIENCE BROWNE: I don't have the ability

24 to advance. Could someone please? Thank you.

25 Charge Question 1e. Based on all the data

26 presented in Section 2 on ER AUC model performance, including

27 characterization of reference chemicals, concordance with in
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1 vivo uterotrophic results, and comparison with Tier 1 assay

2 endpoints, what are the strengths and limitations of the ER AUC

3 model to contribute to the weight of evidence determination of a

4 chemical's potential estrogen bioactivity?

5 DR. JAMES MCMANAMAN: Okay. The discussants

6 for this charge question are Dr. Schlenk as the lead discussant.

7 Dr. Delclos and Dr. Propper are associate discussants. Dr.

8 Schlenk.

9 DR. DANIEL SCHLENK: So again, we've sort of

10 been communicating back and forth on some of these and I think

11 some of these we actually have some consensus. Some there may

12 be a little non-consensus, so we can discuss that as we go

13 through the question. Again, this is kind of like a sequential

14 question. So you've started with literature based, and now

15 we're moving into everything that was in Section 2. So this may

16 be a little redundant, just to kind of give you a little

17 warning. We tried to focus this mostly on weight of evidence,

18 so there's a lot of different ideas in this response, I guess,

19 just to give you a little warning. 

20 So the strengths, I think the ER AUC showed

21 outstanding performance in characterization of reference

22 chemicals concordance with the selected chemicals for the in

23 vivo uterotrophic responses. And again, this is from the

24 literature and the Tier 1 assays, or Tier 1 endpoints that were

25 provided. There are some issues related to that, which I'll

26 talk about here in a minute. 

27 I think the use of battery of the ER AUC fits
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1 the adverse outcome pathway, so I think that's a real strength

2 in maintaining that adverse outcome pathway paradigm. Again, as

3 long as the assumption is that that's a linear pathway at this

4 point, which again, I think is maybe a little over simplified. 

5 But at this point, I think that's the way you've got to go. So

6 that assumes ER activation, DNA binary receptor transactivation,

7 yadda, yadda, yadda, for the AOP pathway. 

8 I think, and this has always been my thought

9 of the AOP paradigm, I think it will, and this was brought up I

10 think in some of the questions earlier, what do we do about

11 mixtures. I honestly think the AOP pathway allows us to look at

12 mixtures and will allow us to look at mixtures. I've said this

13 many times. If you think about when a physician prescribes

14 seven or eight pharmaceutical agents to a patient, he does it in

15 a motive action approach. That's how you combine all that

16 therapy. So if we can do that before chemical exposure, if we

17 can do it for pharmaceutical treatments, we should be able to do

18 it for chemical exposures at some point. 

19 Again, I think at that point it becomes a

20 computing issue at some point. You know, when can we do that? 

21 But I'm optimistic that the AOP pathway will allow mixtures once

22 we can assess combined modes of action or antagonistic pathways

23 that can complete those interaction analyses. 

24 And again, the redundancy and complementarity

25 endpoints, I think, are strengths, as I mentioned earlier. So

26 those are the strengths.

27 Some of the limitations, and I've had some
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1 conversations with Dr. Judson about this as well as mentioned

2 this earlier in some of my questions, was I'm a little puzzled

3 about why the rainbow trout liver slice data set the ER expert

4 system is not included in particularly this process. The reason

5 why I think it's a strength is because it fits the AOP process

6 so well, and it includes metabolic activation as a basis in that

7 assay. It's not as high throughput, obviously, as some of the

8 other assays are, but at least the data that's been generated

9 for I don't know how many compounds that they actually looked

10 at. I want to say a couple hundred, if I remember correctly. I

11 think that's useful data and that needs to be integrated not

12 only for ecological wildlife perspectives, but also for human

13 health as well. It fits the adverse outcome pathway to a T and

14 actually gives you a protein to look at the end for evaluation,

15 if you want to go that far. 

16 So I would highly recommend that that be, and

17 I don't know what the issues are of why it hasn't been, but I

18 would highly recommend that that be added back to the docket, if

19 you will, in terms of mining and data addition. In follow up to

20 the July 2013 meeting regarding weight of evidence evaluations,

21 and again, this is sort of focused on weight of evidence, I

22 would just reiterate that I think a more transparent, and I

23 think the key word is objective description of the weight of

24 evidence process needs to be provided for prioritization. 

25 We had suggested certain aspects of that in

26 that previous essay piece, so again, I would just reiterate that

27 don't lose track of the fact that it is weight of evidence, so
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1 how various endpoints and potencies are weighted and evaluation

2 is very important. I think at some point it's going to be

3 necessary to quantify those modular relationships in the AOP

4 pathway. Actually, Dr. Perkins gave a shared accession at CTAC

5 a couple weeks ago where, I think that was brought up. If we

6 look at those arrows between the boxes, we're going to have to

7 get to a point where we objectively have to quantify that and to

8 weight that, and that process needs to be transparent. 

9 Again, this was brought up earlier and

10 probably will be again, but I think it would have been really

11 good to compare the AUC data with FISTRA and the pubertal assays

12 and not just uterotrophic assays. In light of particularly Ann

13 Clean Gray's paper where the FISTRA and pubertal assays are

14 actually proposed as the gatekeeper, I guess, is one of the

15 terms that were utilized in terms of gatekeeper assays. 

16 I think it would have been really good to look

17 at positive responses in that regard. And once we get to F,

18 we'll talk a little bit about the uterotrophic responses. I

19 think we thought that those were a little too negative, per se. 

20 It would have been nice to have some intermediate points on that

21 curve, which I kind of brought up yesterday.

22 This is one that we probably may have a little

23 discussion on, and I brought this up the other day, and it has

24 to do with nonmonotenicity. I actually went through the

25 database the other night and Dr. Judson is correct. There is

26 not a lot of nonmonotonic responses that are present there,

27 which is pretty surprising to me. 
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1 So I bring it up because there's just been so

2 much work done that has shown that there is nonmonotonic

3 responses in endocrine aspect. And I don't know whether that's

4 just a QA/QC issue related to ToxCasts. And maybe they test

5 doses that are so low that you never see that response. I don't

6 know why, but I do know it's an issue. 

7 It's a real issue that has been addressed in

8 the World Health Organization and in the EEC's State of the

9 Science, and Vandenberg's, et.al. review article on this. And

10 it's not just in vivo. There are in vitro nonmonotonic patterns

11 that are there that are not related to cytotoxicity, and so I

12 just question that. I'm just not sure it's just a disconnect

13 and I don't know that I would recommend the Agency somehow

14 figure out why that is or a least justify why that is not an

15 issue related to ToxCast, per se, or that if it is an issue,

16 it's totally related to cytotoxicity and not related to

17 crosstalk interactions or all the other multiple receptor

18 subtype potencies that you get in multiplex systems and that

19 type of thing. I just find that sort of fascinating from an

20 academic perspective, but it's going to come back and it keeps

21 coming back. So I'll throw it out there again just to let you

22 guys deal with that, I guess. 

23 But overall, I think estrogenic activity still

24 narrowly defined here as this linear nuclear ER activation. 

25 Again, this is a little redundant. We should be looking at

26 other estrogenic pathways, perhaps, that may not occur through

27 ER binding. 
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1 And again, the metabolic capability thing is

2 another issue that keeps coming up. It seems to be a tolerable

3 deficiency if the question is limited to the potential for

4 nuclear receptor mediated activity, but again, it obviously

5 would be tolerable in cases where only a metabolite of the

6 compound was biologically active. So again, these are issues

7 that keep coming up over and over. 

8 That is all I have for E.

9 DR. JAMES MCMANAMAN: Okay. Thank you Dr.

10 Schlenk. Dr. Delclos.

11 DR. KENNETH DELCLOS: So for the most part, I

12 agreed with what Dan said. I was not in agreement with the

13 concern over the nonmonotenicity issue. I thought that the

14 performance and the comparisons with the standard chemicals was

15 good and that there were nonmonotonicity in the absence of

16 toxicity at the high doses. 

17 I wasn't disturbed by that for this purpose of

18 prioritization because of the agreement. And I think that if

19 there were a lot of examples of low dose nonmonotonicity, then

20 that would beg some research as to explaining the mechanisms

21 behind that because I don't think that's clearly defined. 

22 There's some speculation and hypotheses, but that hasn't been

23 clearly defined, and also if that were the case, I think using

24 the AUC would be problematic if we're comparing potencies.

25 So anyway, I thought, you know, the dose

26 response issues will be dealt with in later aspects, but I

27 wasn't disturbed as Dan was about that issue.
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1 DR. JAMES MCMANAMAN: Thank you, Dr. Delclos. 

2 Dr. Propper.

3 DR. CATHERINE PROPPER: I have nothing more to

4 add.

5 DR. JAMES MCMANAMAN: Okay. I'll open it up

6 to other panel members. No one cares about nonmonotonicity

7 other than Dr. Schlenk. 

8 DR. CATHERINE PROPPER: It's not that I don't

9 care about it, but I don't --

10 DR. JAMES MCMANAMAN: You just think it's

11 monotonous.

12 DR. CATHERINE PROPPER: No. I think it's

13 actually not monotonous. I think it's very interesting, but I

14 do feel that for weight of evidence at the moment, the way the

15 assays pick up estrogenicity covers that. And I guess what I'd

16 like to say is the Agency shouldn't ignore it if it happens, but

17 I'm not sure that as long as the weight is put on the effects

18 you see rather than on the exact shape of the curve, that that's

19 going to be important.

20 DR. JAMES MCMANAMAN: Okay. Dr. Doerge.

21 DR. DANIEL DOERGE: Yeah. I must say with

22 respect to the potential for nonmonotonic dose responses, I've

23 personally found the responses from the Agency quite refreshing

24 in that they critically evaluated the data that they generated. 

25 And at this point that's a lot of data. And the data they have

26 shows no evidence for this. That means they were looking for

27 it. 
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1 So to me, that's quite comforting, actually,

2 and again, the really compelling thing is, as Dr. Delclos

3 pointed out, is that these can only be evaluated in the terms of

4 discreet mechanisms and chemicals that act through these

5 well-known pathways then can be evaluated, vis-à-vis, other

6 unknowns. So I really was quite comforted by the Agency's

7 treatment of the nonmonotonic issue.

8 DR. JAMES MCMANAMAN: Okay. Dr. Schlenk.

9 DR. DANIEL SCHLENK: Don't get me wrong, I

10 looked to see if there was and there isn't. I mean, it's very

11 clear at least in the data that's provided it's not there, but I

12 also know that it's in the literature and in systems that are

13 very comparable to this. And I guess my concern is I think you

14 do need to be concerned about the dose because you're using an

15 AOC, that AOC process using a 50-percentile of the dose response

16 curve. 

17 So if you're getting a dose response curve

18 that doesn't give you a clear AUC in some of the partial

19 agonists even, you're using that as your main point for that, as

20 I understand the model for that metric, you've got to be sure

21 that that's on the curve and that it's the 50-percentile

22 component and not something that occurs early on and goes away

23 and then comes back again. 

24 Again, the data is clear and I agree with Dr.

25 Doerge. That's the thing that is somewhat puzzling to me is

26 that it's so clear that there's essentially none that's been

27 observed when you see it in the literature. That's the only
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1 thing that I'm bringing up. There's a discrepancy there, and it

2 may not be important, but it just seems to me that there's a

3 discrepancy there.

4 DR. JAMES MCMANAMAN: That was Dr. Schlenk. 

5 Dr. Propper.

6 DR. CATHERINE PROPPER: So I am agreeing with

7 everything everybody else is saying here, but I do think some of

8 this might become more of an issue when you look at the other in

9 vivo systems, because the chance of seeing the nonmonotonicity,

10 while you do see it in in vitro systems, it's when you start

11 getting all the compensatory mechanisms and feedback mechanisms

12 in play that that really starts shooting up. And that's where I

13 see it in my lab as well, is in my in vivo systems. And so that

14 kind of leads to for the weight of evidence, including those

15 other in vivo assays that have been conducted or are going to be

16 conducted.

17 DR. JAMES MCMANAMAN: Thank you Dr. Propper. 

18 Dr. Schlenk.

19 DR. DANIEL SCHLENK: I totally agree. I think

20 the propensity, the possibilities of that definitely are

21 enhancing in vivo systems with feedback loops and all of that,

22 but I also know that there's a lot of in vitro studies that see

23 the same, particularly in integrated cell systems. 

24 So those would be the ones that I would

25 highlight some of the multiplex systems. Particularly where

26 you've got multiple receptor possibilities in different

27 affinities for those receptors, it seems odd that you don't see
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1 anything in those systems. So that's puzzling. That's all I'm

2 saying.

3 DR. JAMES MCMANAMAN: So Dr. Schlenk mentioned

4 limitations in terms of not including the trout liver slice

5 assay and the pubertal assays. Is there a general concurrence

6 from the panel about the lack of these assays or is there a

7 general concurrence that they should be included? Dr. Perkins.

8 DR. EDWARD PERKINS: Part of the issue is that

9 they provide another orthologous bridge along the way of this

10 AOP concept of linking high throughput in vitro assays to in

11 vivo responses. And I think that's part of making these more

12 quantitative linkages between going from in vitro very simple

13 systems to more complex systems. 

14 At least on a proof of concept basis need to

15 show those linkages relationship for maybe just using chemicals

16 that are done already, to demonstrate that that does transition

17 and that would provide some of this linkage from along that AOP

18 from going to a complex system with a complete endocrine system

19 to determine just how far can you start replacing things. 

20 So I think it's essential to start

21 incorporating that type of data especially when you're going

22 towards a weight of evidence, take whatever information you can

23 find and still if it's consistent with what you have, then it

24 should be fine.

25 DR. JAMES MCMANAMAN: Okay. Any other

26 comments regarding this charge question? Any other discussion? 

27
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1 All right. I'm back to the Agency. Were

2 those comments and positions clear? 

3 DR. DAVID DIX: I have a clarifying question

4 concerning our use of the uterotrophic data. We'd like to

5 better understand the comment that's come up a few times

6 concerning the use of positive and negative reference chemicals

7 and/or other chemicals in the data set. 

8 What we presented was data on 42 reference

9 chemicals, 29 actives, 13 inactives, and so we're having trouble

10 understanding the comments around too many negatives. I think

11 we're clear on the data gap issue. I think in your earlier

12 comments yesterday you pointed out in that chart in one of the

13 slides that Dr. Casey presented, but the issue about not enough

14 positives, we're struggling to understand that.

15 DR. JAMES MCMANAMAN: Asking the panel

16 members, will that be addressed in Charge Question 1f?

17 DR. DAVID DIX: Yeah. It will be addressed in

18 F.

19 DR. JAMES MCMANAMAN: And if it isn't

20 sufficiently address, then we can come back to that, but maybe

21 we could move on to F if there are no other clarification

22 questions from the agency.

23 DR. PATIENCE BROWNE: No other questions.

24 DR. JAMES MCMANAMAN: Okay. Let's move on to

25 1f, then.

26 

27 CHARGE QUESTION: 1.F
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1 

2 DR. PATIENCE BROWNE: Charge Question 1.f. 

3 Based on all the data presented in Section 2 on ER AUC model

4 performance, including characterization of reference chemicals

5 concordance with in vivo uterotrophic results, and comparison

6 with Tier 1 assay endpoints, what are the strengths and

7 limitations of using the ER AUC model to substitute for EDSP

8 Tier 1 ER biding ER transactivation or uterotrophic assays for

9 the purpose of characterizing a chemical's potential estrogen

10 bioactivity?

11 DR. JAMES MCMANAMAN: Okay. The discussants

12 on this charge question are Dr. Propper. She is the lead

13 discussant. Dr. Delclos and Dr. Schlenk, associate discussants.

14 DR. CATHERINE PROPPER: Thank you. So I've

15 found that there are several strengths in this assay and I've

16 also, since it's the first time I'm up as lead discussant, I

17 want to say I'm very impressed with how all of this is coming

18 together and I appreciate all the work you put into it. 

19 In general, the AUC model will capture many of

20 those estrogenic compounds in that universe of endocrine

21 disrupting compounds. I think, given especially those that are

22 going to be functioning through those nuclear receptor

23 mechanisms, the need to rapidly evaluate compounds with

24 estrogen-like activities critical in the EPA's effort to develop

25 this process is really commendable. 

26 Overall, the replacement of the Tier 1 in

27 vitro ER endpoints, both the ER binding and the ERTA, with the
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1 estrogen receptor AUC model will likely be more effective in

2 sensitive measure for the occurrence of estrogenically active

3 compounds that occur through that nuclear receptor, binding in

4 activation. 

5 Regarding whether the ER AUC model can replace

6 the Tier 1 uterotrophic assay is going to be more complicated,

7 as I think we've now heard over the course of a number of days. 

8 The replacement of this assay is desirable both from an animal

9 welfare and cost perspective, so we fully appreciate that. 

10 The data comparing the ER AUC model to the

11 uterotrophic assay were strong for the reference compounds that

12 are clearly estrogenic, those with those high AUC values, and

13 we'll talk a little about the compression issue in a minute, or

14 clearly not estrogenic, those at the very low end of the AUC

15 values. 

16 Although there were an occasional fast

17 negative and positive as shown in Figure 2.12, and I'll refer to

18 the fixtures that are in the White Paper rather than the

19 fixtures that were presented as slide numbers because I think

20 everybody has access to all of that, the EPA did a good job of

21 explaining the reasons for these potential false positives and

22 negatives. 

23 While in an ideal world we'd like to see a

24 model fit that hits the response data perfectly, we're never

25 going to get there as we all know as scientists, and so I think

26 we recognize that the concordance of the model was very high,

27 especially at the and low end of those AUC spectrum. 
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1 We weren't just concerned as some in the

2 public domain here with the compression of the AUC model values

3 at the top of the curve, because the compounds that fall into

4 that compressed range already ones we know are pretty estrogenic

5 and really were just used as high-end reference points. 

6 Furthermore, those above the .4 AUC values

7 also appear to perform consistently well. Also, and I'll get in

8 to the below the .4 AUC range as well with some of the

9 non-reference compounds that were curated. 

10 The issue of potency for the uterotrophic

11 assay results was considered by the EPA when comparing the

12 results of the AUC model values, and this is Figure 2.14, which

13 I think I questioned earlier, but I understand a lot better now.

14 If the compounds in the intermediate AUC ranges only got active

15 hits at very high LEL levels of the compound, the reality of the

16 compound actually having exposure linked relevancy to biological

17 activity as we'll discuss when we get to Question 3 may be

18 questioned. 

19 And as the EPA pointed out, this information

20 then can be taken into account when trying to determine the

21 model's value for potentially substituting in the uterotrophic

22 assays. 

23 However, concordance between the uterotrophic

24 results and the other in vivo assay potencies needs to be

25 considered. So we're just coming back now to really seeing that

26 whole picture using some of the other in vivo assays. Such an

27 approach was used for those curated non-reference chemicals that
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1 had AUC values below .1, but not for those that had positive

2 uterotrophic assay results between .1 and .4. And that was

3 shown in Table 2.10. 

4 On the other hand, the chemicals that provided

5 negative uterotrophic assay results with AUC model values

6 between .1 and .4, those data suggest those may be true

7 negatives given that the no effect level was so high as shown in

8 that table. So these kinds of considerations suggest that the

9 potency is being taken into account in the model. 

10 Okay. I'm going to move on to limitations. 

11 The data were less straight forward when the AUC model for

12 non-referenced chemicals was compared to the uterotrophic

13 studies where the data were limited or discordant. And this in

14 Figure 2.15. 

15 In particular, when the AUC value is below .4,

16 and even sometimes below the cut off of .1, there were a mix of

17 active and inactive results that suggest the predictability of

18 the model based on the curation approach is limited when those

19 values are below .4. 

20 Furthermore, the data for all of the assays

21 utilizing the List 1 and List 2 battery are below the AUC cutoff

22 of .1. And I think this is where we were getting into all the

23 data were negative. It's just for those List 1 and List 2

24 compounds where you really see that absolute dead-on negative,

25 which leads to 100 percent concordance; but I think we're a

26 little bit concerned that then you're missing anything that's a

27 bit higher than that .1 cutoff where the data were a lot less
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1 concordant when you were using both the reference and

2 non-reference compounds. 

3 But to summarize this point of concern,

4 currently the data represents a spearman carbora-like statistic

5 and in this case we have the data being symmetrically being

6 active or inactive. Right? So you're getting a lot of

7 positives at the high end and a lot of negatives at the low end,

8 but it didn't really take into account as much what do we do

9 with the model data that show up in the middle. 

10 A second but related concern remains regarding

11 the ability of the ToxCast in vitro methods to assist estrogenic

12 effects that may be impacted, again, by absorption metabolism

13 feedback systems and compensation or may result through the

14 non-nuclear estrogen receptor signaling pathways. And I think

15 you've done a great job of identifying that as a limitation as

16 well. But these important physiological processes aren't even

17 well understood for a number of chemicals. So you have a few

18 where we do understand these and then a lot where we don't. And

19 again, this is where the in vivo assays can be important. 

20 The panel recognizes that you're currently

21 working towards similar evaluations of these other in vivo

22 assays and you've reminded us of that several times, which has

23 made me more comfortable. But we do want to encourage you to

24 encourage those other assays. 

25 So at the moment, we can't recommend the

26 uterotrophic assay be substituted. I think the in vitro assays

27 you actually have a more powerful model with ToxCast than with
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1 the original Tier 1 assays but we still have concerns about

2 using this approach to substitute out the uterotrophic assay. 

3 One possible suggestion would be conduct

4 limited uterotrophic assay testing using the original

5 guidelines, I think we identified some of the weaknesses in the

6 guideline like approach, or curating some of that literature

7 using some of the curated literature with a little bit more

8 stringency for a limited number of compounds that are in that

9 low to middle AUC range. And that will help really sort out the

10 model. You can go to that universe of chemicals that's already

11 been put through ToxCast is shown in Figure 2.16, and I think

12 that would be helpful. It would then add to the weight of

13 evidence that may support substituting the uterotrophic assay in

14 the future. So I think you do have a potential path to getting

15 there, but we're not fully comfortable with it yet.

16 DR. JAMES MCMANAMAN: Thank you Dr. Propper. 

17 Dr. Delclos.

18 DR. BARRY DELCLOS: I have nothing further.

19 DR. DANIEL SCHLENK: Nothing to add.

20 DR. JAMES MCMANAMAN: Other panel members. 

21 All right. We can go back to the Agency to see if these

22 comments were understood.

23 DR. PATIENCE BROWNE: We have a clarifying

24 question. 

25 DR. JAMES MCMANAMAN: Sure.

26 DR. PATIENCE BROWNE: Thank you for that

27 explanation. I think you brought up some valid points and some
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1 concerns on our part as well. 

2 As you pointed out the model performance very

3 well for AUC scores greater than .4 and for zero, basically, if

4 there's very good agreement with the ToxCast data and the

5 uterotrophic, and in the Agency's interest to reduce animal use,

6 would you recommend replacement of the uterotrophic assay for

7 AUC scores greater than .4 and zero, that is the extreme ends

8 that we displayed?

9 DR. CATHERINE PROPPER: We haven't discussed

10 this as a group, so I will speak only for myself. 

11 DR. JAMES MCMANAMAN: I think that that

12 extends beyond the charge question, so I think we'll have to --

13 it's a new type of question. So I think that this Agency

14 understands the panel's comments regarding Charge Question F, is

15 there any clarification needed regarding that specific charge

16 question?

17 DR. PATIENCE BROWNE: No, thank you.

18 DR. JAMES MCMANAMAN: Okay. All right. Well,

19 it looks like we're on to Charge Question 2. So if we could

20 have that summary question read in and we'll start with Charge

21 Question 2a.

22 

23 CHARGE QUESTION: 2.A

24 

25 DR. PATIENCE BROWNE: Charge Question 2.a. 

26 EPA's proposed approach for quantifying a chemical's potential

27 androgen bioactivity is based on a computational model
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1 integrating data from nine high throughput ToxCast assays

2 measuring several endpoints along the androgen receptor (AR)

3 signaling pathway. The computational model outputs are

4 expressed as an area under the curve (AUC) scores for AR agonist

5 (R1) and antagonist (R2) bioactivity. 

6 Before routinely using the AR computational

7 models in the EDSP framework, EPA is reviewing the scientific

8 strengths and limitations of the AR AUC model described in this

9 White Paper to: i) prioritize chemicals for further EDSP

10 screening and testing based on estimated bioactivity, and ii)

11 contribute to the weight of evidence evaluation of a chemical's

12 potential bioactivity. Please address the following charge

13 questions relevant to Section 3 of the White Paper and androgen

14 bioactivity: The EPA's proposed approach for quantifying.

15 Charge Question 2a. How clearly has EPA

16 described the computational tools in Section 3.1 (i.e., high

17 throughput assays and models) used to estimate AR agonist and

18 antagonist bioactivity? 

19 DR. JAMES MCMANAMAN: Okay. The discussants

20 on this charge question are Drs. Brown, Denver, Doerge and

21 Potter. Dr. Brown is the lead discussant.

22 DR. TERRY BROWN: Okay. So, I'm the lead

23 discussant on all aspects of Question 2, so I'll try to

24 summarize as best I can the thoughts of the group, and others

25 can contribute then.

26 I'd like to begin by commending the EPA

27 collaborative team. I think it is truly a collaborative effort
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1 of many of you and I want to compliment you on the progress that

2 you made and the information that you provided in the White

3 Paper and, in particular, the explanatory information that you

4 provided here in the World Presentations that really have

5 allowed us to better understand the methodologies. 

6 In particular, the adaptation and

7 implementation of a panel of in vitro high throughput androgen

8 receptor bioactivity pathway assays are a major advancement in

9 the EDS program in represent a response to concepts that I think

10 have been advocated in the past by the SAP review groups. It's

11 been a while for me since I've served in one of the SAP groups. 

12 I go back a fair time, four or five years ago at least, and it's

13 taken me -- one, I appreciate the progress that's been made, but

14 secondly it's taken me a while to catch up with actually where

15 things are in the interim and to gain an understanding of how

16 things have transpired to date.

17 The presentations during the meeting, I think,

18 have provided clarity into much of the information provided in

19 the draft White Paper, and a lot of my comments will probably

20 parallel comments that were made in sections of Charge Question

21 1. But as mentioned by other panel members yesterday in our

22 initial discussion of the charge questions related to the

23 estrogen receptor bioactivity information, I think that final

24 editing of the White Paper would benefit significantly by having

25 the EPA team provide the same level of clarity to the final

26 version of the White Paper that was provided here in oral

27 discussions and presentations so that we can better understand
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1 the bioactivity assays as well as the assumptions made in terms

2 of the modeling. 

3 Understandably, in contrast to the estrogen

4 receptor bioactivity assays, the androgen receptor bioactivity

5 assessments, we made a work in progress and I think that's what

6 we're addressing today, is that work in progress. I would

7 encourage further building on the panel of androgen receptor

8 bioactivity assays to maximize the capture of each of the

9 appropriate linear steps in the androgen receptor bioactivity

10 pathway. 

11 I think you've flushed out a little bit more

12 of the activity steps in the ER pathway than what has been done

13 in the AR pathway. This includes a continuing assessment and

14 refinement of the model, and particularly the AUC approach with

15 particular attention to the contribution of each of the

16 component assays in the final assignment of the relative AR

17 bioactivity. So some of my comments will actually kind of

18 address that AUC model. 

19 So I think some questions remain about the

20 derivation of a value for potency based upon AUC modeling and

21 whether this provides adequate distinction of AR bioactivity

22 between chemicals of a particular interest in the screening

23 process. For instance, do the current assays capture direct

24 actions of androgen receptor bioactivity as opposed to indirect

25 effects? And this is in some of the cell based transcription

26 assays that are time dependent in some ways, whether in fact,

27 what's being captured, or really the direct activities as
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1 opposed to potentially indirect activities when you're looking

2 at longer time periods. 

3 The absence of information for each of the

4 androgen receptor bioactivities led to some misunderstanding in

5 the lack of clarity during our meeting. For instance, in cited

6 the ligand binding assays and what the radio ligand is in those

7 assays. In addressing the redundancy, I think how much of

8 redundancy in the assay design is beneficial is a question, and

9 when does redundancy lead to some skewing of the data which may

10 result when one step in the AR pathway is over or under

11 represented in the final AUC calculation. 

12 I don't debate the necessity for some

13 redundancy, but I think too much redundancy at a particular step

14 in the pathway can lead to a skewing of the calculation of the

15 AUC values, at least in my mind. I may not understand the model

16 well enough to appreciate the nuances of it. 

17 Additional information that is currently

18 available in the White Paper regarding the validation of the

19 individual AR bioactivity assays should be readily available. 

20 It took me a while to catch up with exactly what some of the

21 assays were capturing, in essence, and how the component parts

22 were contributing to the pathway. The transparency provided by

23 the EDSP 21 Dashboard shown yesterday, but Dr. Judson, I

24 thought, was critical to the overall success of the program,

25 gaining the confidence of the stakeholders and scientific public

26 and other scientists while also providing opportunities, I think

27 in particular, for other interested scientists and individuals
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1 to provide further input and refinement for the methods, the

2 models and the approaches employed in these analyses. 

3 I applaud the fact that this is publically

4 available and will allow others to assess the information that's

5 there in the model and the assays that are being used.

6 Particular attention should be given to issues

7 related to assay interference and factors and chemicals that

8 contribute to cytotoxicity and self- stress in the AOR

9 bioactivity assays. This is particularly important because many

10 of the compounds or chemicals reflected in the AOR bioactivity

11 assays are in fact AOR antagonists as opposed to agonists. I

12 think one thing we didn't maybe discuss at any length was

13 whether there's careful assessment of solvent and test chemical

14 effects in the in vitro assays that need to be considered,

15 particularly related to whether or not the assay is a common

16 protein assay as opposed to a cell-based assay. And I think

17 these factors are critical for the androgen receptor bioactivity

18 assays due to the predominance, again, of chemicals apart from

19 the known pharmaceutical AR agonists, they're predominantly

20 expressed antagonist activity rather than agonist activity.

21 And whereas the current focus is on the AR

22 nuclear receptor genomic activity pathway, I think there is a

23 need to continue to give some attention to the development of

24 alternative androgen receptor related assays that do not follow

25 the classical genomic nuclear receptor pathway. 

26 So I'll stop there and let other members of

27 the --
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1 DR. JAMES MCMANAMAN: Thank you, Dr. Brown. 

2 Dr. Denver.

3 DR. ROBERT DENVER: I concur with Dr. Brown's

4 summary of the group's comments and I have nothing further to

5 add.

6 DR. JAMES MCMANAMAN: Dr. Doerge.

7 DR. DANIEL DOERGE: I have nothing to add.

8 DR. JAMES MCMANAMAN: Dr. Potter.

9 DR. THOMAS POTTER: I concur. I just have a

10 few thoughts that are basically a reflection of things that were

11 said with regard to the ER assay and these come in the form of a

12 recommendation, basically to help strengthen the document in

13 terms of both clarity, and the document in this case, the White

14 Paper, both in terms of clarity and transparency. 

15 I, for one, would like to see a testing that

16 was done placed some historic and scientific context. I think

17 that would be helpful for comprehension and understanding by

18 non-specialists in the field. 

19 I believe there's a need and I would recommend

20 strongly that there was some discussion of data quality metrics

21 that would include things such as chemical purity. We heard

22 something about solvent effects in assays, those are certainly

23 important. Further along that line, some need to look at

24 reproducibility of results and uncertainty associated with AUC

25 scores. Again, we recommended this yesterday. 

26 I believe it would be very helpful to provide

27 a sample AUC computation and discuss any and all deliberations
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1 that went into weighting or the lack of it in terms of

2 individual assay results. And finally, again, we've hit up on

3 this several times over the last few says, discuss the concept

4 of redundancy in the assay battery and/or the lack of it and why

5 it's important. 

6 DR. JAMES MCMANAMAN: Thank you, Dr. Potter. 

7 Other panelists? Since the issue of redundancy has been brought

8 up several times, I would like to ask the panelists do they have

9 any specific recommendations regarding this question. It's one

10 thing to say that it's an issue. It's another thing, do we have

11 clear ideas about how the issue should be evaluated. 

12 Yes, Dr. Perkins?

13 DR. EDWARD PERKINS: Several ways it could

14 potentially be evaluated and if it recall correctly, one of the

15 drivers was the ToxCast system. Whether they add more assays is

16 now the question. They already have all of these assays. As

17 far as redundancy, my impression of when you start taking these

18 things and try to do more legal based decisions, you're actually

19 required to have orthogonal assays to show that another type of

20 test also shows this proof, that this is really a valid test. 

21 So it's really required in some law cases to do this. So you

22 really don't want to get rid of too much. It's not really

23 redundancy when it's a complementary assay measuring the same

24 phenomenon but in a different way. 

25 Secondly, there are some approaches, some

26 tools, that you can use to try to evaluate whether more or less

27 information adds any value to the decisions, such as Bayesian
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1 approaches where, I guess, it's post hoc analysis or something

2 like that, where you can actually look at if smaller subsets of

3 models give you similar answers or larger ones. So you can look

4 at whether adding or subtracting assays in a system actually

5 adds value to your decisions. 

6 So those are some other types of approaches

7 that can be addressed in the future when, especially as several

8 of the public commenters pointed out, how much testing do you

9 really need to do? 

10 If you do more testing, will it add any value

11 to your information? That may be more appropriate for the later

12 IBER case. So the question is basically, how many assays do you

13 need to do to find what's going on? I think that's one

14 significant area that could be looked at that would be very

15 valuable at least for establishing confidence of what's

16 happening.

17 DR. JAMES MCMANAMAN: Other panel members have

18 views on this, because I think we have two varying viewpoints. 

19 One is that having more assays would be good and others may be

20 thinking we have too many assays. So there's this dichotomy

21 that -- if you ask a guitarist how many guitars does he need, he

22 says one more. So we don't want to fall into that sort of trap.

23

24 Dr. Denver, were you just raising or

25 scratching?

26 DR. ROBERT DENVER: I just want to agree with

27 the comments of Dr. Perkins that this needs to be looked at in a
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1 systematic way and whether adding more actually adds value or

2 subtracting reduces how robust the conclusions are.

3 DR. JAMES MCMANAMAN: Dr. Propper.

4 DR. CATHERINE PROPPER: So, and I concur as

5 well and there's got to be a statistical approach where you can

6 take a look at whether or not the AUC values really start moving

7 as you run through iterations. However, I also do agree that

8 some of this redundancy is important, and I think that's one of

9 our points with in vivo assays as well. Right now, we don't see

10 redundancy in the in vivo assays, and that's important,

11 especially because they're not truly redundant. They'll give

12 you information across species and potential mechanisms.

13 DR. JAMES MCMANAMAN: Thank you, Dr. Propper. 

14 Dr. Berrocal had a comment, too.

15 DR. VERONICA BERROCAL: Yes. I think that

16 whether we should add or -- I don't think we should subtract any

17 assays. I actually think how many assays which you use really

18 depends on the variability that you have and the results that

19 you get from these assays. And if the variability depends on

20 the assays themselves and the response of the chemicals, then I

21 don't know if there is a way that you can determine a specific

22 number.

23 DR. JAMES MCMANAMAN: I think Dr. Perkins was

24 alluding to that they may be using some of the Bayesian types of

25 analyses. Would you agree with that that would be a

26 possibility?

27 DR. VERONICA BARROCAL: I'm not sure what he
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1 meant by Bayesian analysis in that context.

2 DR. THOMAS PERKINS: Well, there's been some

3 approach for integrated testing strategies such as for skin

4 sensitization where a Bayesian network has been developed and

5 within this context, if you can just do some of the assays

6 within the suite that they have defined and you can determine

7 whether any more added information can actually get a more exact

8 answer, I guess. 

9 And so, my question is, if you want to start

10 adding, especially in a flexible modular format, if you wanted

11 to think, well, how many more assays do I want to add in here,

12 does it make sense to do that. There should be some statistical

13 way to determine if that's actually of value to do and just

14 adding more information.

15 DR. VERONICA BARROCAL: Well, I do think that

16 one could do some sort of hypothesis testing and say all these

17 assays are giving me significantly different responses. The

18 problem I have is that I don't think they have explored the

19 possible chemicals that are out there. 

20 So you don't have any idea what's the

21 variability in the response of the chemicals that are out there

22 to the assays. So you can only do that for the chemicals that

23 you know in the reference set, maybe. 

24 DR. THOMAS POTTER: Yes, that's true, but also

25 in the context, the way the network is designed that they're

26 currently using follows a biological pathway and so whether you

27 add more elements in there, how much more information will it
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1 bring into the system. 

2 DR. VERONICA BARROCAL: Sure. Conditional on

3 that pathway, then, would be you can test, but the problem is, I

4 thought, was for some of the chemicals that they haven't

5 collected data yet, they don't know if that's the correct

6 pathway.

7 DR. THOMAS POTTER: Oh. No, no. I was more

8 referring to how many more tests you need to do to be confident

9 on any one particular chemical. The estrogen pathway, they have

10 18 assays, whereas, there's only nine for the androgen.

11 DR. VERONICA BARROCAL: Yeah. So you could do

12 some sort of hypothesis testing and see.

13 DR. THOMAS POTTER: Right. Basically like

14 that, so some type of approaches like that.

15 DR. VERONICA BARROCAL: Some experiment. 

16 DR. THOMAS POTTER: And then when other

17 information is brought in or you have chemicals that are on the

18 border, how much more information do you want to get? Are you

19 going to prioritize for more in vitro testing or do you need to

20 go on to the next one? 

21 In my viewpoint, that would help answer some

22 of the questions that the public comments had about how much

23 more testing do you have to do to get confident about it. If

24 you have something that's negative right now, how many more

25 times do you need to test it before you're confident?

26 DR. VERONICA BARROCAL: Yeah. There should be

27 maybe a baseline number.
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1 DR. JAMES MCMANAMAN: Okay. That was Dr.

2 Perkins and Dr. Berrocal. Dr. Brown and then Dr. Jett, then Dr.

3 Weller.

4 DR. TERRY BROWN: I don't want to mislead you

5 in my comments that redundancy is universally bad, in my

6 opinion. What I was trying to get at was how many binding

7 assays are appropriate? If you have a rat, a mouse, a chimp, a

8 human binding assay in the battery, and then those four assays

9 contribute, say, four out of the 12 components that you're using

10 to compute the AUC model result, then it seems like those

11 binding assays by themselves are disproportionately contributing

12 to the calculation of that AUC, if I understand it

13 appropriately. So I'm not saying that redundancy is bad. 

14 Obviously, there is some need for redundancy

15 for proof of principal and replication of data, and I think

16 that's already been expressed. And in androgen receptor

17 activity assays, because there's only nine versus there's 18 in

18 the ER bioactivity assays, I think there's some scaling there

19 that needs to be appropriately applied across both the ER and

20 the AR bioactivity assessments so that all the different steps

21 in that linear formulation are actually captured. 

22 I think there's weaknesses presently in the

23 bioactivity assays that are being offered for the androgen

24 receptor, particularly with respect to the wider range of steps

25 in the bioactivity assay model that are captured in the ER

26 bioactivity assays. 

27 DR. JAMES MCMANAMAN: Now, Dr. Jett.
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1 DR. DAVID JETT: I think you may have answered

2 the question, because I was trying to figure out whether or not

3 when you add an assay that interrogates a different mechanism,

4 does that count as an orthogonal type of assay or different

5 assay but it appears as though it doesn't matter because they

6 all go into calculation of the AUC. And so that's sort of a

7 question and it looks like from your standpoint that they all go

8 into calculation of the AUC, so it could skew the data in that

9 sense.

10 DR. TERRY BROWN: My understanding is they

11 contribute to the AUC calculation, but if you were assessing

12 different steps in that overall linear pathway from androgen

13 receptor binding to some kind of an assay that assesses effect

14 down at the bottom, you're capturing different components. And

15 I think that's important, and that's kind of what the ER

16 bioactivity assays do, I think, in a better way than what the

17 present panel of androgen receptor bioactivity assays. 

18 And this is a circumstance where the agency

19 really needs to look at what those assays are actually assessing

20 and make some decisions about whether they want to prioritize

21 contracting with some company or some other resource out there

22 that can provide them with assays to capture other components of

23 that pathway that aren't presently available. 

24 I know that you're dealing with companies or

25 offers of, these are the assays, you contract them out for

26 proficiency testing and so forth, and then make decisions about

27 whether they're doing what they shouldn't be doing. But I think
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1 there's also reasons to say, we want an assay that captures this

2 particular aspect of the bioactivity pathway and ask who can

3 come up with an assay that will satisfy the proficiency and

4 replication and adequacy that we want to capture in producing

5 this AUC model. Because all of your data is ultimately captured

6 in an AUC model, and I think what you want to do is maximize

7 input into that model to make it more valid.

8 DR. JAMES MCMANAMAN: Yes, Dr. Weller?

9 DR. GRANT WELLER: So just two quick comments.

10 One is that agreeing with Dr. Brown in that this issue of

11 redundancy is dependent upon the goal, the purpose of the use of

12 the assays. I think it's important to just keep that in mind. 

13 And second is just, in addition to Dr. Perkins' and Dr.

14 Berrocal's comments, I just wanted to make the Agency aware of

15 the terminology "variable selection." And I think that

16 ultimately, in its simplest form, this is a variable selection

17 problem and can be formulated that way, and there's lots of

18 standard procedures and literature out there to do that.

19 DR. JAMES MCMANAMAN: Yes. Thank you, Dr.

20 Weller. Dr. Denver.

21 DR. ROBERT DENVER: I just want to comment

22 that I think the AR suite of assays is actually in pretty good

23 shape. You have five that are transactivation, or gene

24 expression readout, which are more apical. And you have a

25 couple that are self-rebinding assays and two that are

26 intermediate, the protein-protein interaction assays. If it was

27 reversed, if you had five or seven that were receptor-binding
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1 assays, I'd be concerned; but you're picking up things that are

2 not relevant to apical endpoints. But I think that if you were

3 going to add anything to these assays, I would say maybe think

4 about some of the intermediate steps in the biological pathway. 

5 Actually, the mix is pretty good. 

6 DR. JAMES MCMANAMAN: Thank you, Dr. Denver. 

7 Other comments about this charge question? All right. I'll go

8 back to the Agency. Were these recommendations clear?

9 DR. PATIENCE BROWNE: Thank you. I think we

10 have no additional questions.

11 DR. JAMES MCMANAMAN: Okay. Thank you very

12 much. Okay. We'll then move on to Charge Question 2b.

13 CHARGE QUESTION: 2.B

14 

15 DR. PATIENCE BROWNE: Charge Question 2.b. 

16 What are strengths and limitations of the AR AUC model's ability

17 to identify reference chemicals that include a variety of

18 structures and have a wide range of in vitro AR bioactivities?

19 DR. JAMES MCMANAMAN: Okay. The discussants

20 for this charge question are Drs. Brown, Denver and Doerge. Dr.

21 Brown is the lead discussant.

22 DR. TERRY BROWN: Okay. I've organized my

23 comments in two categories, either strengths or limitations. So

24 I'll start with the strengths. An obvious strength that uses in

25 vitro reconstituted recombinant protein and cell based assays

26 with relevant reduction in in vivo animal usage, I think that's

27 certainly an end that we've been trying to achieve, includes
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1 assays of AR binding that parallel the EDSP Tier 1 studies of AR

2 binding that were previously prescribed in the right prostate

3 cytosol binding assays. And these binding asset studies now in

4 the battery are complimented by additional assays within the AR

5 pathway. So these are all positives.

6 The assays use various cell lines with

7 different inherent properties, which may allow some subtle

8 differences in chemical properties to actually be ascertained. 

9 In the conduct of those studies by careful

10 assessment of the assay performance and the results, not all

11 cells, obviously, are the same. Assays are high throughput and

12 probit linear array of AR dependent bioactivities in a framework

13 that integrates the different pathway steps. 

14 The redundancy of assays, and I come back to

15 that again, putting it forth as a positive, represent different

16 species and/or assays or steps in the pathway that employ

17 different protein components and biochemical molecular

18 quantitation detection methods, all which is a good thing, I

19 think. And the assays have shown ability to distinguish both

20 agonist and antagonist properties of chemicals. 

21 Okay. So, limitations. Some limitations that

22 I want to mention, again, I'll come back to the AUC, which is

23 derived from a network of assays with some redundancy in the

24 assay endpoints and some variation degree of specificity for the

25 AR dependent pathway that may contribute unequally or

26 disproportionately to the mean calculated AUC value. 

27 The AUC value range is narrow and lacks, I
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1 think, some discrimination between AR bioactivity values

2 assigned to different chemicals. The lack of clarity of how the

3 penalty term is applied to the AUC calculation seem to be within

4 the draft White Paper may need some further explanation. How

5 that penalty is exercised and calculated in the final AUCs. 

6 There may be a need to include a wider range

7 of chemicals. These are preliminary studies. We recognize

8 that. Among different structural classes to inform future

9 studies. Most chemicals that are interacting with the AR have

10 antagonistic activity, which I've already mentioned, so that the

11 assays and AUC values have to distinguish, obviously, between

12 self-toxicity, self-stress and really capture truly AR

13 antagonism. 

14 Metabolism of chemicals and non-classical

15 androgen receptor mechanisms should be considered in the model

16 design in some way. Consideration should be given to the ligand

17 of choice for the androgen receptor binding assays. So I've

18 kind of referenced back to Dr. Border, who's in the back, I see,

19 some of the comments he made yesterday in kind of giving us a

20 classical lecture about the binding kinetics of ligands to these

21 receptors. And because Methyltrienolone is a synthetic

22 compound, its peculiar binding characteristics to the androgen

23 receptor at the molecular level may be different and probably

24 are different from the binding of the endogenous compounds. 

25 So, the endogenous compounds being

26 testosterone and dihydrotestosterone. So in conducting binding

27 assays with potentially biologically active environmental
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1 compounds, is it really most appropriate to conduct these

2 binding assays in a competitive binding type of assay using a

3 synthetic non-natural, non-endogenous ligand as opposed to the

4 natural ligand. And cell based assays does become an issue,

5 because Methyltrienolone is used because it has high affinity,

6 it doesn't get metabolized, et cetera. 

7 So you don't have to worry about whether it's

8 testosterone or dihydrotestosterone, the natural ligands being

9 converted to some other androgenic or non-androgenic compound

10 during the timeframe of the assay. 

11 So there is value in Methyltrienolone, but I

12 wonder whether, in fact, whether you're trying to define very

13 fine levels of chemical interactions with the receptor,

14 particularly in antagonistic mode, whether or not

15 Methyltrienolone is the most appropriate ligand. I don't want

16 to belabor that, but that's really why I asked the question

17 originally during the meeting.

18 And the other aspect is just to come back,

19 just briefly, to the absence of the nonmonotonic responses in

20 the androgen receptor pathway. This may be in some ways, in my

21 mind at least, related to the predominance of AR antagonism

22 reported to date in the studies. And this is an activity that's

23 most often expressed at higher chemical dose, such that receptor

24 mechanisms are very sensitive to even low doses of compounds and

25 it may be that the nonmonotonic responses in receptor related

26 mechanisms that do, at least in the literature, occur at low

27 doses, may be obscured in the assessment of the bioactivity
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1 assays that are being conducted where you're really getting

2 values related to chemicals that are being observed at higher

3 concentrations. 

4 At least, that's kind of the way I looked at

5 it. I'm not saying that you're misinterpreting the data. 

6 You're not. And the data is very clear, but there may be an

7 underlying aspect of these nonmonotonic responses that could

8 still be present that aren't being captured in the bioactivity

9 assays the way they are now that do occur when you look at in a

10 physiologic situation.

11 DR. JAMES MCMANAMAN: Okay. Thank you, Dr.

12 Brown. Dr. Denver.

13 DR. ROBERT DENVER: I concur with Dr. Brown's

14 summary of the group's comments and I have nothing more to add.

15 DR. JAMES MCMANAMAN: Thank you. Dr. Doerge.

16 DR. DANIEL DOERGE: I have nothing to add.

17 DR. JAMES MCMANAMAN: Other panelists? Dr.

18 Brown, you mentioned about distinguishing between true

19 antagonist activity versus non-specific disruption of binding. 

20 Do you have specific recommendations about how that could be

21 done?

22 DR. TERRY BROWN: Well, I think the Agency is

23 already aware of that and they pointed that out in the

24 presentations, that they're paying particular attention and have

25 a battery of cytotoxic self-stress related assays. So I think

26 it's already being addressed, and probably very appropriately,

27 but I think it needs constant vigilance in the sense that like
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1 most of these agents, these chemicals are generating

2 antagonistic activities and those are typically being seen at

3 higher concentrations or higher dosage levels. So I think it's

4 something that has to be distinguished, and I think they're

5 making a great attempt at trying to keep that in perspective.

6 DR. JAMES MCMANAMAN: All right. Okay. If

7 there are no other questions or comments from the panel, I'll go

8 back to the Agency and ask if these recommendations were

9 understood. 

10 DR. PATIENCE BROWNE: Thank you. I don't

11 think we have any clarifications, necessarily, but we would ask

12 the panel to recommend any reference chemicals that they think

13 have been omitted from ones that are included in the White Paper

14 for the AR pathway.

15 DR. JAMES MCMANAMAN: Okay. All right. At

16 this point, I think we'll take a break for 15 minutes and come

17 back to Charge Question 2c.

18 (Brief recess.)

19 DR. JAMES MCMANAMAN: I think we've started

20 again. So we'll begin with Charge Question 2.c. If we could

21 have that read into the minutes.

22 

23 CHARGE QUESTION: 2.C

24 

25 DR. PATIENCE BROWNE: Charge Question 2.c. 

26 EPA plans to use data from published in vivo studies that are

27 methodologically consistent with EDSP Tier 1 guidelines to
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1 evaluate concordance between AR AUC model scores of in vitro

2 bioactivity, and the in vivo androgenic and antiandrogenic

3 responses (Section 3.2.1). What are strengths and limitations

4 of the planned curation methods and quality standards for

5 evaluating published in vivo studies?

6 DR. JAMES MCMANAMAN: All right. The

7 discussants on this charge question are Drs. Brown, Denver and

8 Doerge. Dr. Brown is the lead.

9 DR. TERRY BROWN: Okay. So again, I'll

10 organize my comments in terms of strengths and then limitations.

11

12 Preliminary correlations with the Hershberger

13 androgen bioactivity assay are promising and the in vitro AR

14 bioactivity assays appear to replicate pretty well the published

15 in vivo chemical bioactivities. The curation methods and

16 quality standards are well considered and strong. Approximately

17 40 chemicals, articles are currently undergoing secondary

18 analysis and that should give appropriate levels of information,

19 although somewhat more limited than the ER uterotrophic assays. 

20 The Hershberger in vivo assay and in vitro AR bioactivity assays

21 are designed with parallel objectives and seem to be detecting

22 both agonist and antagonist activities.

23 Limitations. Hershberger in vivo assays are,

24 by nature, more complex and involve assessments of mobile

25 androgen dependent tissue weights, either in the agonist or

26 antagonistic mode. This was appropriately pointed out in the

27 White Paper. 
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1 The general expectation is that in vitro AR

2 bioactivity assays will measure consistent agonist or

3 antagonistic activity, whereas, evaluation of published results

4 from Hershberger assays may show considerably more variation and

5 response among multiple tissues that are assessed. This could

6 be related to the pSARM activity as well as to alternative or in

7 vivo physiologic effects observed in the Hershberger assay that

8 aren't captured in the androgen receptor bioactivity panel of

9 assays. 

10 The curated study in numbers may be low, due

11 to the relative scarcity of chemicals with defined agonist,

12 antagonist and androgenic activity. 

13 Congruence of in vivo study designs may vary

14 significantly. This is an assay that was designed, I think,

15 back in the '30s and been applied in a somewhat more routine

16 fashion since the '70s, but we're going back several decades,

17 obviously, to curate the literature and obviously a lot of

18 variation occurs in the way this test is performed. 

19 The congruence of in vivo study designs may

20 vary significantly, which is kind of what I just alluded to. In

21 vivo test chemical purity rather than administration, dosage,

22 age, and strain of rats, etcetera, may vary significantly and

23 contribute to some of this variation between studies, and

24 thereby complicate the data interpretation for studies performed

25 over several decade of time.

26 And the systematic toxicity may results. 

27 Again, this requires information on animal body weight, feeding
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1 behavior, etcetera, within those studies to exclude those

2 complicating factors. That information may or may not be

3 available in the published literature. And so will be essential

4 in Hershberger assay evaluation to distinguish what's truly AR

5 dependency as compared to some kind of more overt toxicity. 

6 That's all I have to say.

7 DR. JAMES MCMANAMAN: Thank you, Dr. Brown. 

8 Dr. Denver.

9 DR. ROBERT DENVER: I concur with the summary

10 of Dr. Brown of the group's comments and I have nothing further

11 to add.

12 DR. JAMES MCMANAMAN: Dr. Doerge.

13 DR. DANIEL DOERGE: I have nothing to add.

14 DR. JAMES MCMANAMAN: Other panelists. All

15 right. We'll then move on to Charge Question 2d. Oh, I'm

16 sorry. I forgot to go back to the Agency. I'm getting ahead of

17 myself. All right. 

18 DR. PATIENCE BROWNE: Thank you. There are no

19 clarifying questions.

20 DR. JAMES MCMANAMAN: Okay.

21 

22 CHARGE QUESTION: 2.D

23 

24 DR. PATIENCE BROWNE: Charge Question 2.d. 

25 Based on the data presented in Section 3 on AR AUC model's

26 performance, what are strengths and limitations of using the AR

27 AUC model to distinguish and prioritize chemicals based on
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1 potential androgen bioactivity?

2 DR. JAMES MCMANAMAN: The discussants for this

3 charge question are Drs. Brown, Propper and Denver. Dr. Brown

4 is the lead discussant.

5 DR. TERRY BROWN: Okay. So I'm almost done

6 now. So I only have a few strengths and limitations. I think a

7 lot of my thoughts have already been captured in responses to

8 the ABC part of this question. So I'll start with strengths. 

9 So the androgen receptor AUC model displays considerable value

10 in preliminary evaluations and we, again, consider these as

11 preliminary at this stage of AR bioactivity with standard

12 chemicals. 

13 So the preliminary results look very

14 promising. The availability and transparency of the

15 computational model will allow for additional scientific input

16 to the model design and assessments, and I think this is a

17 positive. And again, referring to the online dashboard and the

18 provision of the actual data and the modeling that goes into

19 that. 

20 Another strength is that it includes

21 contributions from diverse assay designs and endpoint analyses

22 as well as filters for cytotoxicity and self-stress and I think

23 that all of those are appropriate. 

24 Limitations, a few limitations. 

25 Discrimination of AR antagonist activity from the self-stress

26 toxicity measures. Lack of clarity regarding the relationship

27 between the AUC model and potency as indicated by more
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1 traditional assays of AR bioactivity. I think that this has

2 been brought up previously and I think we need to really be able

3 to assess on a more dynamic range how this AUC model helps us

4 understand AR bioactivity across the range. And the results are

5 preliminary, which I've already alluded to, and lack adequate

6 representation of the diverse chemical structures. And again,

7 this responds to what Dr. Browne asked for earlier, and that is

8 the panel to suggest more chemicals that could be used that

9 would be relevant to these assays. 

10 And again, we recognize that the results are

11 preliminary and will require comparisons with curated in vivo

12 responses and other data as we go forward. 

13 DR. JAMES MCMANAMAN: Dr. Propper.

14 DR. CATHERINE PROPPER: I concur and have

15 nothing to add.

16 DR. JAMES MCMANAMAN: Dr. Denver.

17 DR. ROBERT DENVER: I agree with the comments

18 and have nothing further to add.

19 DR. JAMES MCMANAMAN: Other panelists. So,

20 Dr. Brown, you mentioned that a limitation might be that the

21 pathway steps could be fleshed out a little better in the AR

22 pathway. Do you have some specific recommendations for how that

23 might be done or assays that they might be included in

24 strengthening the overall AUC?

25 DR. TERRY BROWN: So I guess this goes back to

26 the other parts of the charge question. So I think, like Dr.

27 Denver mentioned, we have initial AR binding activity assays,
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1 and we have apical assays that kind of capture both ends of the

2 pathway, but we kind of lack a continuity of assays that capture

3 different steps along the way as well as in comparison to the ER

4 bioactivity assays. 

5 I think the one assay in the AR bioactivity

6 panel that's in there is the recombinant AR protein interaction

7 with SRC 1. I know that's an assay that obviously is out there

8 that you've evaluated and included, but I find it kind of

9 unexpected that for some reason that particular assay would be

10 included. Because I think it's a recombinant protein/protein

11 interaction, and I'm not sure really the details of that assay

12 whether they're really full-length recombinant protein

13 interaction assays that are being evaluated there. An SRC 1 is

14 one of many, many hundreds of coactivator proteins, and I think

15 one of the aspects that I've alluded to also is tissue specific,

16 organ specific responses in the physiologic context that

17 obviously won't be dependent upon one coactivator like SRC 1. 

18 An SRC 1 is a coactivator when in fact, many of the chemicals

19 that you're looking at are antagonist, and thereby they would

20 exclude the coactivator interaction, but include a corepressor

21 interaction, for instance. 

22 So it's kind of curious that two of the assays

23 have been included that specifically look at a coactivator

24 protein interaction. And again, I'm not clear on exactly what

25 it's being compared with. Is it being compared with the AR

26 protein component in the presence of what ligand, I'm not sure. 

27
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1 Is it Methyltrienolone again or is it one of

2 the endogenous androgens? Obviously, that's the standard or the

3 control to which the chemicals are being compared, but I think

4 each of these molecular interactions that occur, either at the

5 protein level or at the ligand receptor level, have very subtle

6 aspects to them. And that's really where I come from and that's

7 the molecular aspect that any permutations of these molecular

8 components' entities and their interactions can be really skewed

9 in different ways by the binding of different chemicals or even

10 the different parent ligands. I think it's really important to

11 keep that in perspective as you go forward. 

12 I think what I'd like to see are other steps

13 in the pathway that really put various aspects of the pathway

14 into perspective, more like the ER bioactivity panel does as

15 compared to the panel that's presently constituted for the

16 androgen receptors.

17 DR. JAMES MCMANAMAN: But specifically being

18 able to distinguish between coactivator and corepressor

19 interactions.

20 DR. TERRY BROWN: Well, in this case, yeah, in

21 the specific thing that I'm alluding to, but there's other

22 aspects that really look more at DNA binding of dimerization. 

23 There's a lot of different data out there on dimerization with

24 the androgen receptor and how it's an antiparallel dimer

25 formation. There's also information out there on how androgen

26 receptors in a rack with different response elements in the DNA,

27 whether they're inverted repeats that's a typical response
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1 element, or whether they're direct repeats, etcetera. And all

2 of these are permutations on what's specific for androgen

3 receptor versus its carryover to a homology with glucocorticoid

4 and progesterone receptors. And many of the compounds that's

5 you've looked at are actually relatively strong pro-gestational

6 agents, like cyproterone acetate, progesterone itself, et

7 cetera, have pro-gestational activity, so there's a lot of

8 overlap there between those particular receptor components,

9 meaning the AR, GR and PR and their dynamic interactions with

10 DNA and therefore their ability to modulate gene expression.

11 DR. JAMES MCMANAMAN: Thank you. Dr. Denver.

12 DR. ROBERT DENVER: This discussion just

13 brought to mind a question that I had. Perhaps a comment that

14 maybe the Agency has access to a high throughput protein

15 structure assay that might allow one to distinguish these false

16 positives at the outset for both the ER and the AR binding

17 assays. For example, it was discussed that you can get a false

18 positive if the protein denatures. And is there any high

19 throughput assay that could distinguish that at the outset and

20 help you sort through some of these false positives?

21 DR. JAMES MCMANAMAN: Thank you. Dr. Doerge.

22 DR. DANIEL DOERGE: I guess I have a

23 clarifying question for Professor Brown. Am I hearing a

24 recommendation outside of the more general one about developing

25 new assays that might incorporate different protein components,

26 but something that might actually be feasible at this stage of

27 the assay development is a more extensive validation, if you
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1 will, of the ligands used? 

2 Is this something that particularly with this

3 non-metabolizing systems that are currently employed, could a

4 more critical evaluation of the ligands visa vi the endogenous

5 hormones be done? Is that something that would be feasible and

6 useful in an assay validation?

7 DR. TERRY BROWN: Well, I don't want this

8 really to focus on specific interests of an academic lab. I

9 want this really to be put in the perspective of a large-scale

10 screening program that can be valuable. And I think in large

11 respect that has been captured. I think what I've done maybe,

12 and maybe overemphasized to some degree, the particular

13 molecular details of this that really need to be considered and

14 thought about in assessing the assays, but may be out of the

15 realm of reality in terms of is it really critical for

16 incorporation.

17 DR. JAMES MCMANAMAN: Dr. Propper.

18 DR. CATHERINE PROPPER: So this is back to the

19 question you all had about other potential reference compounds

20 that could be used, and this integrates in with the issue of

21 metabolic conversion. One possibility would be to take a look

22 at Vinclozolin, which you had as an antagonist, but it gets

23 metabolized into two compounds, very long names that even in the

24 literature is referred to as M1 and M2. That M1 and M2 may be

25 really nice reference molecules to add to the ToxCast battery to

26 see how they perform with respect especially to Vinclozolin. 

27 And I have an article, if you'd bear with me, though, my phone
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1 keeps closing up, that I just looked up that's one of the

2 original citations on this just as a starting point. And that's

3 Kelsey, et. al., and it was in Toxicology and Applied

4 Pharmacology, June 1994. So this has been around a bit, and

5 perhaps even my knowledge of the current literature on this is

6 weak, so it may be already well beyond what we need. But it's

7 Volume 126, page 276 is the start point.

8 DR. JAMES MCMANAMAN: Thank you, Dr. Propper. 

9 Other panelists. Okay. I encourage the details of this

10 discussion to be put into your reports so that the Agency has a

11 sufficient amount of information to follow up on your

12 suggestions. With that, I'll go back to the Agency. 

13 DR. PATIENCE BROWNE: We'd like to thank the

14 panel for all their input and thoughtful commentary on this. I

15 think there were some excellent points raised, but just to

16 circle for a second back to the charge question, given the

17 performance of the model against the relatively potent reference

18 chemicals which recognized are largely made up of, particularly

19 on the high end of the potency, pharmaceutical chemicals. 

20 We ask if they think the model is suitable for

21 prioritizing chemicals in the EDSP universe based on the

22 demonstrated performance and model for androgen bioactivity.

23 DR. JAMES MCMANAMAN: Seems like a fair

24 question. Clarification. Does the panel have a consensus about

25 this? There was a nod.

26 DR. TERRY BROWN: Well, I think to boil down

27 all my comments, I think we're very positive in our view. At
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1 least I am, personally, very positive in my overall view of what

2 you've accomplished to date, and I think that certainly the

3 impetus is there to move forward in a way that you've been

4 doing. And I don't know whether everyone else concurs or not.

5 DR. ROBERT DENVER: I concur.

6 DR. CATHERINE PROPPER: I concur.

7 DR. DANIEL DOERGE: I think the way Professor

8 Brown put it as acknowledging this is a work in progress and the

9 androgen receptor work is somewhat more preliminary in nature

10 than the ER. But recognizing that these things can go on in

11 parallel, I definitely concur with everyone's opinion that it's

12 fit for that purpose.

13 DR. PATIENCE BROWNE: Thank you for the

14 clarification.

15 DR. JAMES MCMANAMAN: All right. So given the

16 limitations, I think we're ready to move on to the next charge

17 question, Charge Question 3. 

18 

19 CHARGE QUESTION: 3.A

20 

21 DR. PATIENCE BROWNE: Charge Question 3.a. 

22 For Endocrine Disruptor Screening Program (EDSP) chemicals with

23 ToxCast estrogen receptor (ER) and androgen receptor (AR)

24 bioactivity scores (Sections 2 and 3), and ExpoCast high

25 throughput toxicokinetics and exposure estimates (Sections 4 and

26 5), the IBER approach was used to rank chemicals based on the

27 ratio between the bioactivity dose range, and the expected
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1 exposure range (Section 6). 

2 The IBER approach extends point estimates of

3 bioactivity, toxicokinetics, and exposure for a chemical, to

4 distribution ranges based on uncertainty and population

5 variability. Chemical rankings are based on the ratio of the

6 lower range of the bioactive dose, to the upper dose of the

7 exposure estimates. Please address the following charge

8 questions relevant to Section 6 of the White Paper and the IBER

9 approach.

10 Charge Question 3.a. How clearly has EPA

11 described the computational tools in Section 6 to develop IBER

12 values, including modeling uncertainty and population

13 variability? 

14 DR. JAMES MCMANAMAN: Okay. Thank you. The

15 discussants for this charge question are Dr. Berrocal, Drs.

16 Barr, Weller and Perkins. Dr. Berrocal is the lead discussant.

17 DR. VERONICA BARROCAL: I will actually just

18 report my own opinion. We have decided that each of

19 independently are going to reflect our own opinion.

20 I think that at a very general level the White

21 Paper illustrates quite clearly the Agency's approach to derive

22 IBER values. As for any section of the White Paper, oral

23 presentations have clarified any remaining doubts I personally

24 had.

25 I believe that Figure 1.3 as a pictorial

26 representation of the IBER approach and it conveys the

27 underlying and main point that IBER is trying to account for
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1 uncertainty and population variability in both the chemical

2 bioactivity and population exposure to the chemical.

3 I think that it might be worth clarifying in

4 Figure 1.3 that the ranking is based on the ratios and not on

5 the overlap of the two distributions. I think that it's

6 important to re-iterate that the goal is to rank chemicals based

7 on that ratio and not by setting some threshold values. It

8 seems that this point has been raised several times and maybe

9 there is not clarity that it is based on the rankings of the

10 ratios. 

11 Going back to Figure 1.3, I wanted to address

12 some comments on uncertainty.

13 The figure illustrates two distributions: a

14 blue one which represents the distribution of bioactivity, and a

15 second, green one that represents the distribution of exposure.

16 The distribution of bioactivity incorporates

17 population variability by adopting a Monte Carlo approach in the

18 pharmacokinetics component of bioactivity that handles the

19 individual heterogeneity in metabolic clearance, body weight,

20 liver flow, etc. as well as the variability in the fraction of

21 chemical unbound. Accounting for all these sources of

22 variability, addresses the population variability. 

23 I don't think there is clear indication in the

24 White Paper about whether model uncertainty is addressed

25 relatively to the pharmacokinetics bioactivity component of

26 IBER. Table 6.1 indicates that model uncertainty is not

27 accounted for, and maybe there should be in the White Paper a
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1 more elaborate discussion about how EPA intends to address this

2 aspect of uncertainty for the pharmacokinetics aspect. 

3 Table 6.1 indicates that model uncertainty and

4 population variability in the pharmacodynamics aspect of

5 bioactivity are accounted for in the default way. I am

6 extrapolating by the use the word default, that default refers

7 to standard deviation or a median deviation around the ACC, but

8 I am not sure there is any discussion in the paper. I don't

9 seem to remember about what default means.

10 The Table refers to Judson (2011). But I

11 think that adding some discussion and detail in the paper about

12 what is this default approach would be good.

13 I think that the uncertainty in the

14 pharmacokinetics-pharmacodynamics model should been accounted

15 jointly. It seems to me that right now it is accounted in a

16 two-step approach and that tends to underestimate the

17 variability in the bioactivity. So one way to jointly account

18 for uncertainty in the pharmacokinetics-pharmacodynamics

19 component of the IBER approach would be to propagate the

20 uncertainty in the pharmacokinetics part through the

21 pharmacodynamics model by establishing a single Bayesian model. 

22 And then, by a Monte-Carlo approach, the EPA could get the

23 population variability. So this is for the bioactivity

24 component. 

25 For the exposure component, I think that there

26 are some gaps on the derivations of the exposure in the White

27 Paper that are left too general. 
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1 So I think that maybe there could be some

2 added detail on the prediction model that Dr. Wambaugh presented

3 in Slides 23-25. So that one could have an idea of what is the

4 equations in the Bayesian model that links parent chemical

5 concentration to measured chemical biomass concentration. I

6 think that it would also be important to specify what are the

7 priors that are used in those Bayesian models, and in particular

8 to specify what is the difference between the priors used for

9 those chemicals for which there is a clear knowledge in the

10 literature about a metabolic pathway and for those for which

11 there is not much knowledge.

12 I also think that although Table 6.1 indicates

13 that model uncertainty has been accounted for in exposure, I am

14 not sure it has been appropriately accounted for.

15 Based on figure 4.4, I suspect that the

16 chemical parent concentration predicted by the Bayesian model

17 for HTE might have been potentially incorporated in the SEEM

18 only in their form of posterior summary, meaning posterior mean

19 or posterior median. I am assuming that that is the case

20 because the SEEM model yields the same level of uncertainty for

21 exposure for those chemicals for which there is information in

22 the biochemical component and the biomodeling data versus those

23 ones for which there is not that information. And that is

24 probably an underestimation of uncertainty. So probably a

25 better way to do this would be to unite the Bayesian model used

26 to derive the parent chemical concentration to the SEEM model in

27 one Bayesian model. This would allow to have greater
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1 uncertainty in the exposure values for those chemicals for which

2 no data is available.

3 Regarding population variability, right now in

4 the current description, it has not yet been accounted in the

5 exposure component, but Dr. Wambaugh mentioned during the

6 presentation that the EPA plans to use SHEDS stochastic exposure

7 to mix those simulator models to address this issue, and maybe

8 it might be worth mentioning that in the White Paper. 

9 And finally, my last comment is that I would

10 like to stress there are different uncertainties at play here. 

11 There is estimate uncertainty, which is what can be accounted

12 through confidence interval and deviations. There is modeling

13 uncertainty, which is the uncertainty in the statistical model

14 that is used to make predictions, like the linear model in SEEM,

15 the HTE model, the ER AUC model, et cetera, and reduce

16 population variability or heterogeneity in the population. 

17 I think that currently the IBER is only

18 addressing the estimate uncertainty and the population

19 variability, but not the modeling uncertainty since any

20 prediction of both bioactivity and exposure data have been

21 derived conditional on the adopted model, either the ER AUC

22 model or the SEEM model. So I encourage EPA to make clear that

23 the modeling uncertainty they are referring in IBER is really

24 estimate or model parameter uncertainty rather than modeling

25 uncertainty.

26 Modeling uncertainty would be to consider

27 different statistical models to derive predictions of
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1 bioactivity and exposure and bring all these different

2 statistical approaches through different matter, such as super

3 learners or Bayesian model averaging, boosting, and so forth for

4 which I'm glad to give you references. That's all.

5 DR. JAMES MCMANAMAN: All right. Thank you

6 for those very thorough and precise comments. Dr. Barr is next.

7 DR. DANA BARR: I only have a few more

8 comments to add. First of all, I think you did a very good job

9 of describing the approaches for the IBER in the White Paper,

10 those items that weren't particularly clear, I think you

11 clarified in the presentations yesterday and the day --

12 yesterday and the day before. And I think it would be very

13 important to go back and address those specific items and try to

14 sure those up in the White Paper so they are clear for more

15 people.

16 In particular, I appreciate the comment that

17 Dr. Browne had yesterday regarding the relation of the AUC to

18 potency. And I think that that was lost on a lot of people in

19 the White Paper. And so it would be really important to clarify

20 that in the White Paper.

21 One thing that I was interested in is trying

22 to understand how, once you've ranked these IBERs and you have

23 the top ones -- and I don't know if you're going to classify

24 them as high moderate and below the limit of detection or

25 however you clarify -- however you do rank those, how they will

26 be used in trying to go back and further assess them. Are you

27 going to assess the top third? Are you going to go in rank
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1 order? Is there some process you have in mind? Or if that

2 hasn't been defined yet, at least state that as well.

3 Now, this goes back to the SEEM approach. In

4 the note that we discussed the SEEM approach in a great deal, I

5 think it was July. And one thing that Dr. Wambaugh presented

6 yesterday, and it just kind of struck something with me, and it

7 wasn't quite clear for me was how creatinine excretion or

8 creatinine was used in the models for developing exposure. And

9 I wondered if racial, age, and sex differences in creatinine

10 excretion had been taken into account. 

11 And then just wanted to offer the possibility

12 of using urinary excretion rate because those are data now that

13 are available in NHANES that you could potentially use. And I

14 wondered, it wasn't really clear whether mixtures were going to

15 be considered. Obviously, chemical exposures are not occurring

16 in isolation and many of these chemicals have similar

17 bioactivities and so that would be interesting to address as

18 well.

19 I really appreciate very much, and I have in

20 the next section, I'll comment more on this emphasis on

21 fit-for-purpose because I think that is something that we need

22 to consider here. I think you've done a great job in trying to

23 document this and then trying to develop it. I think obviously,

24 there are refinements that can be made, but we have to consider

25 if it's worth the time and efforts just for the FIFRA purpose

26 here and just ranking whether or not these chemicals should go

27 on for further testing. And those are all my comments.
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1 DR. JAMES MCMANAMAN: Thank you, Dr. Barr. 

2 Dr. Weller.

3 DR. GRANT WELLER: So I concur with Dr.

4 Berrocal's comments, and where my comments overlap, I'll try to

5 be quick on that part. In general, I found the work described

6 in Section 6 to be thorough, with appropriate justification for

7 the choices of models and evaluation metrics. I just have a few

8 suggestions on parts that could be improved in the White Paper.

9 The first one is, as Dr. Berrocal touched on,

10 the IBER metric developed in Section 6 uses results of tools and

11 data that were developed in Sections 2 through 5 -- described in

12 Sections 2 through 5 of the Paper. And as these are pushed

13 forward into the IBER development, it becomes unclear to me

14 which uncertainties and sources of variability are estimated and

15 accounted for in that IBER construction and which are not.

16 So I acknowledge that Table 6.1 begins to get

17 at this, but it would be helpful for stakeholders, and

18 particularly those who are not statistical experts for the

19 Agency to be clear about which sources of variability and

20 uncertainty are modeled and accounted for in that IBER

21 construction and being clear about which potential sources of

22 uncertainty are not modeled, are not accounted for and providing

23 justification for why they weren't.

24 Just to use a particular example of that, it

25 appears that estimation and uncertainty for the ER AUC values

26 are not propagated through to that final distribution or

27 predicted distribution for bioactivity. Again, I concur with
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1 Dr. Berrocal that it's troubling to me that in the exposure

2 predications that the variability for chemicals for which there

3 is biomonitoring data is the same as chemicals for which there

4 is not biomonitoring data. 

5 Further discussion, given some of the public

6 comments we're heard, it think some further discussion about the

7 confidence of the IBER metric for the extreme cases. So the

8 obviously bioactive chemicals, or perhaps, the very low exposure

9 chemicals I think would be helpful in the White Paper. 

10 The public discussion suggests that, to me,

11 from the Agency's perspective that these cases are not of high

12 interest because high scientific confidence and the priority

13 levels of these extremes seems to already exist. And if that's

14 true, the practical impacts of the compression, for example, at

15 the upper limit of the AUC distribution become less important,

16 in terms of the prioritization and the ranking of

17 fit-for-purpose methodology. And that, I think, should be noted

18 in the White Paper.

19 In general, I find the motivation for and

20 description of the IBER metric is worst-case scenario to be well

21 described and it's a natural metric for a step to achieve this

22 prioritization ranking.

23 Finally, it's important, I think, to emphasize

24 that the method and the IBER metric is a tool for prioritization

25 which should not, you know, simply be blindly applied. And the

26 Agency's discussion about the triclosan example was a good

27 example of that. And the Agency has demonstrated that they
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1 understand that it's worth maybe emphasizing in the Paper; and

2 in particular, the results, the IBER metric is conditional on

3 the choice of models used, the data incorporated and the

4 uncertainties that are accounted for in its construction. 

5 And those are all my comments from 3a.

6 DR. JAMES MCMANAMAN: Thank you, Dr. Weller. 

7 Dr. Perkins.

8 DR. EDWARD PERKINS: I don't have much more to

9 add. I just wanted to compliment and thought that it was really

10 nicely done.

11 DR. JAMES MCMANAMAN: All right. Other panel

12 members?

13 All right. I'll go back to the Agency. Were

14 those comments clear?

15 DR. PATIENCE BROWNE: I think they were very

16 clear and very extensive. Thank you.

17 DR. JAMES MCMANAMAN: Thank you. All right. 

18 We'll move onto Charge Question 3.b.

19 

20 CHARGE QUESTION: 3.B

21 

22 DR. PATIENCE BROWNE: Charge Question 3.b. 

23 What are the strengths and limitations of using the IBER

24 approach to prioritize chemicals for further EDSP screening

25 based on the ratio between ER bioactivity dose range and the

26 expected exposure range?

27 DR. JAMES MCMANAMAN: Thank you. The
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1 discussants are Drs. Propper, Berrocal, Schlenk, Perkins, Weller

2 and Potter. Dr. Propper is the lead discussant.

3 DR. CATHERINE PROPPER: And I'll give a basic

4 summary of what we all think some of the comments really have

5 already been addressed in 1a, because there is just overlap in

6 the logical development of these issues. So I'll try to not be

7 too repetitive there as well, and then I'm going to let my

8 colleagues state their own -- anything extra that they have to

9 say over what I'm stating.

10 So the systemic empirical evaluation of

11 models, the SEEM effort appears to be very logically developed. 

12 It provides a very nice strong theoretical framework and it does

13 have the potential to prioritize further EDSP screening of

14 compounds with estrogenic activity through this IBER approach.

15 Furthermore, the EPA's attempts to utilize the

16 variation to identify the lower bioactivity range and higher

17 potential exposure ranges suggest that prioritization will be

18 conservative and we, on the panel, appreciated that.

19 And it will be for inclusion of compounds that

20 may have this endocrine disrupting capacity, but are in that

21 intermediate AUC range that we discussed for earlier questions. 

22 Using the IBER ratio value is, in theory, potentially very

23 useful for prioritization. There are some limitations, most of

24 which have already been discussed, but the need to have better

25 exposure monitoring to tighten the model. And you all recognize

26 this and put it forward, both in the White Paper and in

27 particular, in the presentations.
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1 The panel was concerned about the limited

2 amount of exposure data available for mining the NHANES study

3 for incorporation into the model in comparison to the data

4 that's available through the ToxCast model. So the model

5 assumes linearity of the toxicokinetics and I think these folks

6 have addressed some of that already, but we'll come in and

7 discuss it a little bit more. 

8 Given that they're both liver and

9 kidney-associated parameters involved in the toxicokinetics,

10 we're a little concerned about the linear approach of the model.

11 But as not a person that does toxicokinetics, again, I'm going

12 to let some of my panel members address this further.

13 The assumptions and parameters used to develop

14 the exposure model may have potential problems, and a number of

15 those were addressed, I think the triclosan, worst-case scenario

16 example really showed how just making one false assumption can

17 really change the dynamic of the model. And that Dr. Berrocal

18 and Dr. Weller, I think, will address this more in their

19 section.

20 Overall, the panel is appreciative that the

21 EPA recognized the uncertainty and variability that need to be

22 taken into account for the IBER approach, which we just

23 discussed again extensively, prior to launching this platform

24 for prioritization, since some of those issues really do need to

25 be addressed, we emphasize this.

26 Last, we feel the model, as it relates to the

27 endocrine disruptor screening program would probably need that
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1 next step validation processes has been done with the AUC model

2 approach. Just two certain reference compounds, evaluate them

3 through ToxCast and ExpoCast, get the ratio value and compare

4 the results to the literature and to Tier 1 in vivo output. And

5 then we've discussed again, mixtures, and Dr. Barr will probably

6 bring that up again here. 

7 So to summarize, really, we think the IBER

8 approach is on track for providing a potentially powerful

9 mechanism for prioritizing chemicals for further EDSP

10 evaluation, but it does need further refinement and then

11 validation.

12 This model is also predicted on human exposure

13 data and I want, in the future, to hope that the panel takes the

14 lessons learned from this approach and applies what they have

15 learned from human health risk to comparative Tier 1 assays and

16 potentially be incorporated into looking at wildlife exposure

17 types of data.

18 DR. JAMES MCMANAMAN: Thank you, Dr. Propper. 

19 Dr. Berrocal.

20 DR. VERONICA BERROCAL: So I actually have

21 addressed already some of the limitations for the model, for the

22 IBER approach before, but I want to start again with the

23 strengths. And first of all, I wanted to applaud the EPA for

24 proposing an approach to prioritize chemicals that takes into

25 consideration the uncertainty, and brought information regarding

26 the bioactivity and the exposure, as well as the variability in

27 the level of exposure experienced by the population.
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1 I believe that the problem that the EPA is

2 trying to address is extremely complex, given the lack of

3 information in the vast majority of the chemicals, as well as

4 the large number of chemicals to be considered. Conceptually, I

5 think that that the IBER approach is a good attempt to develop

6 an approach that recognizes uncertainty and goes beyond taking a

7 simple ratio of uncertainty -- central tendencies, as it was

8 suggested during the public comments.

9 I believe that taking the ratio of central

10 tendency would not be reflective of the large amount of

11 uncertainties on both sources of data and it would not attain

12 the goal of protecting the health and welfare of the portion of

13 the population that is exposed to the highest level of chemicals

14 -- no, that are more highly exposed. And even if they're

15 exposed to the lowest dose of chemicals, that would stimulate

16 bioactivity. 

17 Conceptually, as I mentioned, the IBER

18 approach is a valid strategy, in practice, its applicability and

19 how reliable it could be at identifying really high priority

20 chemicals depends on how well and appropriate distributions on

21 bioactivity and exposures are derived. And so I think that

22 stress should be placed on trying to account for all possible

23 sources of uncertainty. And as I said, there are three major

24 sources of uncertainty: uncertainty in the model parameters,

25 uncertainty in the modeling strategies undertaken, and

26 population viability.

27 So I think that the EPA has done a good job at
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1 trying to address the population viability, but in the current

2 and the future approaches by doing Monte Carlo simulation and

3 planning to use SHEDS, what think at the moment lacking is

4 addressing the modeling uncertainty and the fact that there are

5 possible other ways to derive estimates of bioactivity and of

6 exposure. For example, for exposure, some of the questions that

7 I had when I was looking at the SEEM production model was, is

8 the relationship really linear? Is that a thrashable effect?

9 Although, some of those covariates including

10 the model, the only covariates that we can include in the model.

11 So I think that a more robust approach for both bioactivity and

12 exposure would be to consider a large number of potential

13 statistical models and combine them together, as I mentioned

14 before.

15 And then the last comment, which I think Dr.

16 Weller is going to elaborate a little bit more on is that it

17 seems to be that the IBER approach is one step short. The EPA

18 has been trying to address the uncertainty in both the

19 bioactivity and exposure, recognizing uncertainty in both the

20 differential process and the population viability, but yet, at

21 the end, the approach will rise to reduce everything to a single

22 number, not accounting for the uncertainty in that value. And

23 so I think Dr. Weller is going to elaborate about how the EPA

24 could go about accounting for that uncertainty.

25 DR. JAMES MCMANAMAN: Thank you, Dr. Berrocal.

26 Dr. Schlenk?

27 DR. DANIEL SCHLENK: Yeah. I don't really
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1 have much to add that hasn't already been discussed and

2 depreciating questions, since this is sort of compendium of

3 things that have already been talked about from the strengths

4 and limitations perspective. I will say this, though, that

5 again, having served on the July panel, it's really good to see

6 how the information that was presented in that panel is actually

7 going to be applied. That was very encouraging to see that,

8 especially having been here from, you know, the beginning, so to

9 speak, for a lot of these things.

10 So that, I'm encouraged. I think the strength

11 is also, in terms of utilizing the quotient-based method,

12 although it may be a little bit oversimplified, I think it's the

13 way to go because it incorporates both the biology and the

14 exposure side. I think early on in the days when we were all

15 talking about the effects, the effects, the effects, and

16 everyone sort of let exposure go and again, the July meeting

17 sort of addressed that. 

18 And obviously, there were strengths and

19 limitations that were presented in that meeting that probably

20 haven't had a chance to be totally incorporated into what we've

21 been able to talk about today, so I'll leave those still to be

22 evaluated. I think some of the other panel members may bring up

23 some of those issues, particularly related to database use and

24 exposure aspects.

25 But I'm excited, particularly, I really am

26 excited about the third phase for the SEEM model that

27 incorporates the kinetics into that. I think that will be very,
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1 very, exciting to do that, which will allow also exposure to be

2 assessed in sensitive age groups. I think that's something that

3 will be very, very powerful when that comes around. 

4 So, you know, this is just a rah-rah

5 statement, basically just to say, you know, great job. I love

6 the approach. Obviously, there's some issues related to

7 uncertainty, which will always be there, I guess, with most

8 models, but I think those can be addressed. I'm very optimistic

9 for the future and look forward to implementation of a lot of

10 some of the comments that may help do this. 

11 So I think it's a great tool for screening, at

12 least for ER. The data looks solid in that prioritization sort

13 of box, and I'm really excited about that. I think that's --

14 and it's only going to get better. 

15 DR. JAMES MCMANAMAN: Thank you, Dr. Schlenk. 

16 Dr. Perkins.

17 DR. EDWARD PERKINS: Things have been said

18 already and I just want to emphasize that I really like the

19 approach of what's happening. I've taken previous panels'

20 suggestions and been able to incorporate them into your

21 prioritization strategies. 

22 DR. JAMES MCMANAMAN: Thank you, Dr. Perkins. 

23 Dr. Weller.

24 DR. GRANT WELLER: I concur with the previous

25 discussants in that the proposed approach is fit-for-purpose

26 here and scientifically sound. And I particularly want to

27 applaud the Agency for demonstrating the effort to quantify
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1 uncertainty in the ToxCast and ExpoCast predications as well. I

2 believe that that adds to the scientific rigor and credibility

3 of the methods. 

4 So the main strengths of the approach are that

5 it incorporates the estimates of uncertainty and population

6 variability into the methodology in a systematic and an

7 automated way. The models, although there's been some questions

8 raised about the appropriateness of a linear model, the

9 advantage of that is they're straightforward to generalize,

10 which could allow for incorporation of additional sources of

11 uncertainty or additional data.

12 And the IBER metric, as currently constructed,

13 represents a worse case or conservative scenario, as others have

14 said, and is a sensible metric to use for prioritization.

15 As Dr. Berrocal alluded to, in addition to

16 other limitations I discussed in 1a, one possible limitation is

17 that the derived quantity is just -- it's a point estimate and

18 there was some discussion yesterday about should we use a point

19 estimate of central tendency or should we use the extremes here?

20 I would suggest that another way to look at

21 this is to consider this ratio as a risk measure and if you

22 have, you know, considering that you have an uncertainty

23 distribution of both exposure and bioactivity, you could define

24 that ratio of the two, and then actually just propagate the

25 uncertainty for it into a distribution of that ratio in the

26 population. 

27 And then if you're, you know, this sort of
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1 suggests a purely Bayesian framework, which it seems the methods

2 aren't quite that but are close. But if you have that, you can

3 have a distribution of this ratio in the population and then

4 make probability statements about it. Say, things like the

5 probability that this ratio is greater than one or greater than

6 five orders of magnitude. 

7 And so I would just present that as

8 suggestion, a possible alternative, which you basically get for

9 free, given the work that's already been done, you could just

10 propagate that last step of uncertainty into this ratio. So I

11 would just put that forth as a suggestion that could be looked

12 at. And that's all my comments.

13 DR. JAMES MCMANAMAN: Thank you, Dr. Weller. 

14 Dr. Potter.

15 DR. THOMAS POTTER: I've been asked by the

16 panelists here to speak more clearly in the microphone. Is this

17 better? Okay. Great.

18 Well, I don't have a lot to offer on this. 

19 Certainly, I agree with everything that's been said by my

20 colleagues. I will say with regarding the strengths that one of

21 the great advantages of the IBER approach is that it's a very

22 scientifically sound conceptual model. I believe there's

23 consensus throughout the room on that. We've heard that

24 throughout this session that's a great strength.

25 And the other strength, obviously, is that

26 substantial progress has been made in the development of the

27 computational tools, recognizing again that some of this is a
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1 work in progress and will be evolving in the future. So

2 certainly, those make this effort, the IBER effort in general

3 very strong and sound. 

4 I had a few thoughts regarding limitation. 

5 Some of this dates back to the July 2014 meeting, where I had an

6 opportunity to participate and in particular, the triclosan

7 example that was shown to us by Dr. Judson yesterday, I believe

8 one of the recommendations from the panel from 2014 was that in

9 the case of pesticides, that they be divided up into categories

10 which would involve agricultural use and other uses such as

11 domestic or uses in a school scenarios and whatnot. This idea

12 of sorting out the near-field and the far-field exposure I think

13 is critically important, particularly with that subclass of

14 chemicals. It may be with others, but certainly, it leaps out

15 to me that that's important in pesticides.

16 Another thought with regard to pesticides, and

17 again, I brought this up several times, and again, it was also

18 discussed in the 2014 meeting that the model, as its evolving,

19 likely needs to include worker exposure and develop worker

20 exposure scenarios that allow those types of exposures, which

21 may be relatively high; again, in particular, with regard to

22 pesticides. I think it's critically important that they be

23 included. 

24 I will offer, and again, back to the July

25 meeting, there was some discussion about the need to consider

26 exposure from particularly sensitive groups. This would include

27 pregnant women, infants. And those categories were indeed
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1 endocrine disruption may have the most serious implication in

2 terms of adverse health outcomes. 

3 There are certainly was to approach that, you

4 know, including developing different heuristics in the way in

5 which chemicals are sorted, relative to their production volume.

6 And I think that that's -- it would be critically important and

7 add a very important feature to the model as we go forward.

8 One other thought about, again, the July

9 meeting, Dr. Wambaugh showed us yesterday that he has retained

10 the lognormal approach to dealing with limit of detection

11 results. I think the panel, at that time, was somewhat

12 skeptical of that approach. I think there are alternates that

13 need and should be considered, and this could include something

14 as simple as range-finding, either assigning all values that are

15 LOD, their zero, or assigning them all to be the limit of

16 detection. I think that would help us have a little bit more

17 confidence in the results, and perhaps, something that can be

18 done, at least on a pilot scale.

19 So again, I think the whole effort is very

20 strong, powerful, conceptual model. Again, I think we have

21 consensus on it. Great computational tools that are evolving

22 and that there need to be some tweaks, particularly, I think, in

23 my perspective on the exposure side, that would really improve

24 the effort.

25 DR. JAMES MCMANAMAN: Thank you, Dr. Potter. 

26 This is now open to other panelists. Dr. Berrocal.

27 DR. VERONICA BERROCAL: I didn't add an
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1 additional comment that I wanted to make about the limitation. 

2 Can I?

3 DR. JAMES MCMANAMAN: Sure.

4 DR. VERONICA BERROCAL: So -- and I cannot

5 find what I wrote down in my text. One limitation that I also

6 found was regarding the exposure prediction. Since there is

7 very limited data on biomonitoring of dose metabolite of those

8 chemicals, it seems to me that somebody can actually call those

9 not really predication, but more extrapolation. And from a

10 statistical perspective, sometimes extrapolations are not really

11 well looked upon, especially in this case where the majority of

12 your predications are for chemicals for which you don't have any

13 biomonitoring data. 

14 So I think it's something that has to be taken

15 into consideration if there is any possibility to get more

16 information about biomonitoring for other chemical behind the

17 NHANES would be great.

18 DR. JAMES MCMANAMAN: Thank you. Other

19 panelists? Dr. Propper.

20 DR. CATHERINE PROPPER: I just want to

21 emphasize Dr. Potter's point about making sure pregnant women

22 and children, possibly infants, are taken into account in the

23 model or even have some separate values attached to it just

24 because that is such a sensitive time period and the general

25 assumptions may not apply as well. So you might want to err on

26 the on the side of caution.

27 DR. JAMES MCMANAMAN: Thank you, Dr. Propper. 
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1 Dr. Potter, you mentioned other exposure groups. Do you have

2 specifics that you can refer to? If you can't off the top of

3 your head

4 DR. THOMAS POTTER: Okay. That was a mirror

5 on the ceiling. I'll stick with the workers and the pregnant

6 women and infants, at this point. I think those are two

7 critically important.

8 DR. JAMES MCMANAMAN: Is there information

9 available for those groups that could be used?

10 DR. THOMAS POTTER: Well, certainly from the

11 point of view of workers. And again, with regard to pesticides,

12 I mean, these are routinely -- the exposures are routinely

13 accessed in the registration process, so there's a wealth of

14 data out there that can be extracted from registration documents

15 that would actually provide some data that can be used from all

16 the calibration. So I think that in the case of workers, yes,

17 absolutely. 

18 In the case of women and infants category,

19 it's a little bit more difficult, but again, I think there's

20 some brainstorming that can be done around that when you're

21 looking at clothing and those kinds of things that -- lotions or

22 whatever that may have some primary use for that segment of the

23 population that I think could be dealt with and we could develop

24 some rankings associated with that.

25 DR. JAMES MCMANAMAN: Thank you. Dr. Propper.

26 I'm sorry. Dr. Jett. I didn't see your hand up quick enough. 

27 So if it's okay, we'll go with Dr. Propper first.
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1 DR. CATHERINE PROPPER: The literature can

2 probably also be mined or curated for some of the worker

3 exposures. There's the general registration data, but there is

4 some literature out there. It's not extensive for a number of

5 pesticides, but agricultural workers, there's some data there

6 for pesticide applicators for people working on farms and then

7 also within the pesticide formulation side of the equation.

8 DR. JAMES MCMANAMAN: And you could -- either

9 you or Dr. Potter could supply those references at some point?

10 DR. CATHERINE PROPPER: I'll look up a couple

11 of references.

12 DR. JAMES MCMANAMAN: The URLs.

13 DR. CATHERINE PROPPER: Yes.

14 DR. JAMES MCMANAMAN: Okay. Dr. Jett.

15 DR. DAVID JETT: There are several

16 organizations of pesticide farm worker organizations that model

17 exposure: Texas, California, Florida. And there are also

18 several studies at the NIH of funding, looking at facts and

19 biomarkers and things like that, where they're modeling exposure

20 as well.

21 DR. JAMES MCMANAMAN: Dr. Barr, did you have a

22 comment?

23 DR. DANA BARR: No.

24 DR. JAMES MCMANAMAN: Okay. All right. So

25 I'll go back to the Agency. Were these comments understood and

26 clear?

27 DR. JIM WAMBAUGH: We do agree with the
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1 importance of modeling exposures for children and pregnant

2 women. So what we have attempted to do with the NHANES data is

3 we were limited by the children, age 6 to 11, is the youngest

4 group that we had data on. We did try to model women of

5 reproductive age, but in the sample of 2,000, only so many are

6 going to be pregnant.

7 Does the panel -- can you clarify any data

8 sources that you think we should be using to evaluate any model

9 that we might develop?

10 DR. JAMES MCMANAMAN: Yeah. I think that that

11 clarification was in my comments to Dr. Potter and Dr. Propper.

12 DR. JIM WAMBAUGH: Okay. Thank you.

13 DR. JAMES MCMANAMAN: Dr. Barr.

14 DR. DANA BARR: And I'm also including some of

15 that information in parts to you as well, but you mentioned the

16 2,000 or so for NHANES, that's per cycle. Now there are data

17 available for many, many cycles. So that could be closer to

18 10,000 or 12,000 by now.

19 DR. JIM WAMBAUGH: Thank you very much.

20 DR. JAMES MCMANAMAN: Okay. Back to the

21 Agency.

22 DR. PATIENCE BROWNE: Again, I think the

23 Agency would like to thank all the panelists for their very

24 detailed comments and suggestions. I think particularly, the

25 references and things like that would be much appreciated. But

26 I would just like to briefly clarify and circle back to the

27 charge question, I think our impression, but just to confirm,
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1 was that the panel agrees -- or we would like clarification on

2 whether the panel agrees that using the IBER approach to

3 prioritize chemicals for EDSP screening based on ER bioactivity

4 and the existing exposure models with the recognition both will

5 be refined, are suitable for prioritizing EDSP chemicals.

6 DR. JAMES MCMANAMAN: Okay. So --

7 DR. PATIENCE BROWNE: And if I might comment,

8 we do have record of Dr. Propper's comments, specifically on

9 some things that need to be addressed.

10 DR. JAMES MCMANAMAN: So I guess if we could

11 have each of the panelists that were on this question affirm or

12 deny their confirmation of the charge question.

13 DR. CATHERINE PROPPER: I'll affirm that in

14 theory, we really, really like it. Well, I do. I'll speak just

15 for myself here. But that in practice, it still needs that

16 further refinement and validation. I hope that answers your

17 question.

18 DR. JAMES MCMANAMAN: Do other panelists agree

19 or disagree with Dr. Propper?

20 DR. VERONICA BERROCAL: Yes, I agree with Dr.

21 Propper. I think that the only limitation I would have is that

22 if you want to use it right now to prioritize chemicals, I would

23 feel uneasy using it on those chemicals for which the exposure

24 data is not really validated in any way.

25 DR. JAMES MCMANAMAN: Dr. Weller.

26 DR. GRANT WELLER: I'll just note that I

27 concur with Dr. Berrocal.
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1 DR. JAMES MCMANAMAN: So I think that there's

2 a general affirmation that it is -- you're headed in the right

3 direction, but there are -- there may be detours -- not detours,

4 but bumps in the road that need to be clarified. Dr. Potter.

5 DR. THOMAS POTTER: Well, I just want to say

6 that I think proof of concept in fit-for-purpose has been

7 demonstrated. I mean, clearly, the IBER approach can sort out

8 and identify the highly active pharmaceuticals. Yeah, that's

9 great.

10 My follow-up question on that is what happens

11 with all of those chemicals that are in the mean? They're on

12 the flat part of the curve. Whether or not the model is going

13 to be effective, given uncertainties and ranking or prioritizing

14 chemicals in that region, I think remains to be demonstrated.

15 DR. JAMES MCMANAMAN: Dr. Schlenk.

16 DR. DANIEL SCHLENK: I mean, again, you have

17 two components here. You have exposure and you have the

18 bioactivity components. I mean, I think we've gone through the

19 bioactivity components and have been positive for

20 prioritization. That's solid. I think, and I'm speaking for

21 myself, but I'm pretty sure most people agree with the

22 bioactivity side. It's the exposure side I think that still has

23 a little bit of tweaking to do. 

24 So I think once, you know, we've only had one

25 panel on that. So it's going to take another couple, perhaps,

26 to get that side tweaked. But I think, at least from the

27 bioactivity side, I'm totally on board with that myself.
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1 DR. JAMES MCMANAMAN: I think that what I

2 heard from the panelists were what Dr. Schlenk just mentioned

3 about the exposure not being enough information so far on that

4 and that requires some development. But also, the models, I

5 think that there was some concerns about the models and how best

6 to apply those models. So I think that overall, there is, you

7 know, encouragement, but limitations were recognized and we hope

8 the Agency understands those limitations and is able to act on

9 those. 

10 Okay. We're down to Charge Question 3c, which

11 is our last charge question. So if we could read that into the

12 minutes. 

13 Dr. Dix, did you have a --

14 DR. DAVID DIX: Yes. I really appreciate the

15 time and quality and usefulness of the responses but in Dr.

16 Berrocal's last response, we had a little lack of understanding

17 on our side. You talked about validation -- it was a little

18 unclear. You talked about chemicals that had monitoring data or

19 didn't have monitoring data. We got muddled in understanding

20 that.

21 DR. VERONICA BERROCAL: Okay. I think I'm

22 actually referring to the slide -- I don't remember what slide

23 it was -- that Dr. Wambaugh showed about the predications of the

24 exposure with the median and 95 percentile for all the

25 chemicals. And it turned out that the majority of the chemicals

26 that were predicated were not chemicals for which you had

27 information; it was just a very small fraction that was based on
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1 NHANES data.

2 So I feel it's a big leap of faith to derive

3 predictions for chemicals for which you don't have any way to

4 calibrate your exposure prediction model. So that is my

5 concern. If the IBER approach is going to take into account

6 both bioactivity and exposure and the bioactivity part is pretty

7 solid, but the exposure part is not solid, I'm not sure that you

8 can get meaningful ratios.

9 DR. DAVID DIX: That was Figure 4.4 in the

10 White Paper.

11 DR. VERONICA BERROCAL: Yes. Yes. 

12 DR. JAMES MCMANAMAN: All right. I was

13 distracted. Are we okay on the --

14 DR. DAVID DIX: So I just want to clarify. 

15 What is the panel recommending we do with chemicals that do

16 appear to have high exposure under the current model? Should we

17 ignore that information for the time being?

18 DR. JAMES MCMANAMAN: Wait a minute. I think

19 that that may go beyond the charge question. I think it goes

20 beyond the charge question about exactly how to do that. So if

21 we could limit it to the charge question, I think we should do

22 that.

23 DR. VERONICA BERROCAL: Can I add again a

24 remark? But it's not directly answering that question. So I

25 mentioned for my response to Charge Question 3a that there are

26 approaches that can be taken to account for the uncertainty and

27 the two steps that go into deriving the distribution of
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1 exposure, especially for those chemicals for which there is no

2 biomonitoring data.

3 So I think taking those steps might lead to a

4 better representation of uncertainty, and Figure 4.4 might

5 change.

6 DR. DAVID DIX: So I do think we need to

7 clarify that the White Paper in July did cover a full Bayesian

8 analysis in evaluating the models.

9 DR. JAMES MCMANAMAN: Yes. There has to be a

10 question.

11 DR. DAVID DIX: Is the panel aware of the

12 appendix that was provided?

13 DR. JAMES MCMANAMAN: We'll have it read into

14 the panel that the appendix does exist. Minutes. All right. 

15 Yes, Dr. Propper.

16 DR. CATHERINE PROPPER: Again, not in direct

17 response to your question, but as part of the validation scheme,

18 it might be that you want to look at those compounds where you

19 have high exposure and include them into the validation section

20 of the model development.

21 DR. JAMES MCMANAMAN: Thank you. Dr. Weller.

22 DR. GRANT WELLER: Just to come back to the

23 actual charge question, I think the panel is in agreement that

24 the IBER approach, conceptually, is strong.

25 DR. JAMES MCMANAMAN: I think that that's

26 true. So at this point, unless there are additional

27 clarification questions from the Agency, I've been asked to say
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1 that there are some people that are hungry and they would like

2 to go to lunch. So I think we will come back to Charge Question

3 3c.

4 I'd suggest it be an hour, but if we've got

5 one question in the afternoon, you know, if we were a little

6 later than an hour I think we'll be okay too. 

7 (Whereupon, a luncheon recess was taken.)

8 * * * * *

9 

10 

11 

12 

13 

14 

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16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 DR. JAMES MCMANAMAN: Welcome back from lunch
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1 or exercise or both. Dr. Jenkins says now that we've had lunch,

2 we can come back and finish strong. All right, we're going to

3 finish strong.

4 I think we can read Charge Question 3.c into

5 the record. 

6 

7 CHARGE QUESTION: 3.C

8 

9 DR. PATIENCE BROWNE: Charge Question 3.c. 

10 What are the strengths and limitations of using the IBER

11 approach to prioritize chemicals for further EDSP screening,

12 based on the ratio between AR bioactivity dose range and the

13 expected exposure range?

14 DR. JAMES MCMANAMAN: The discussants on this

15 are Drs. Propper, Berrocal, Barr, Perkins and Weller. Dr.

16 Propper is the lead discussant.

17 DR. CATHERINE PROPPER: And I will be short

18 because I think we've covered most of the issues in the previous

19 two Part 3 questions.

20 So the strengths are largely the same as those

21 identified in 3a and b for the ER active compounds. 

22 Theoretically, the model is sound, but it does need refinement,

23 followed by validation, prior to putting the model into

24 practice. More so with the AR than with the ER, just because of

25 the already identified issues we've discussed in the last couple

26 of days.

27 Currently, the models for both androgens and
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1 estrogens will probably be predicative, as they currently stand,

2 they'll only be predictive for those compounds that are either

3 very strongly or weakly estrogenic or androgenic. So with

4 respect to this particular question for the androgenic side, the

5 limitations are specific to the androgen assays are that the

6 assays and the data for exposure modeling are not as far along

7 in their development and it makes it difficult to evaluate

8 whether the approach will work for this set of compounds. And

9 it's also more complexity because you do have a lot more of the

10 agonist and the antagonist side of the question.

11 There's a high risk, therefore, of not

12 prioritizing chemicals because of the exposure side of the model

13 just not being as predictive as it could be.

14 DR. JAMES MCMANAMAN: Okay. Thank you. Dr.

15 Berrocal.

16 DR. VERONICA BERROCAL: I don't have anything

17 else to add.

18 DR. JAMES MCMANAMAN: Thank you. Dr. Barr.

19 DR. DANA BARR: I have a few comments to add. 

20 First of all, I do think that this system is a lot better than

21 any of the alternative systems that have been used because it

22 does combine now the bioactivity and exposure. Regardless of

23 some of the calculations, especially as noted in the exposure

24 calculations, it's still a giant leap forward from where we were

25 before. 

26 I do have some suggestions of things that I

27 think could strengthen the approach, and they primarily have to
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1 do with the exposure side. The SEEM approach really does focus

2 on the biomonitoring data from NHANES and that's a great

3 resource. It's a lot of data. It's representative of a the

4 U.S. population; it's available in many cycles now. 

5 As noted earlier, we have more than just the

6 2,500 per cycle, but there are several cycles available and so

7 probably 10,000 more samples available for that. But some of

8 the limitations of NHANES is that it doesn't target susceptible

9 populations, typically, like pregnant women, fetuses and small

10 children under 6 years of age. 

11 And so there may be opportunities to evaluate

12 other datasets, such as datasets from the NIEHS EPA Children's

13 Center that focuses largely on those younger population or

14 pregnant populations.

15 Also, chances to evaluate data specific to

16 states, such as California biomonitoring data and New York

17 biomonitoring data that have fairly strong programs and are

18 developing data specific for their states.

19 The other thing is I think that NHANES is very

20 good at capturing baseline exposure. In many instances, there's

21 this really dietary or continual exposures and it does a rather

22 poor job of capturing episodic exposures, especially those that

23 you might experience during critical windows of a development. 

24 So again, it's really important, I think, to

25 get some of the exposures that might be obtained in or exposures

26 that might happen in at-risk populations. So NHANES doesn't

27 necessarily get at all those data.
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1 So I guess what I'm just getting to is I think

2 it's important to branch out and include other datasets other

3 than just NHANES, although it's probably one of the most

4 complete datasets we have. There are other limitations to

5 NHANES that I just think you need to understand that because of

6 the sampling logistics, it prevents the collection of data in

7 less dense population areas. So certain exposures may be under

8 represented in NHANES.

9 And also, for some of the short-lived

10 chemicals, for many of you that might be familiar with the

11 NHANES process in collecting samples, they sometimes can spend

12 eight to ten hours in the global examination center unit while

13 they are collecting samples, getting their biometry data

14 collected and getting the anthropometric data collected, and

15 during that time, many of the short-lived chemicals may have

16 eliminated from their body. And so it might not be an

17 appropriate representation as some of the short-lived chemicals.

18

19 And there are other logistics. For example,

20 they sample the north in the summertime and the south in the

21 wintertime and so seasonal exposures may not be captured there

22 as well. So I just think it's important to investigate

23 alternative datasets to include in this evaluation process. 

24 I brought up before the use of creatinine and

25 the adjusted values and when I mentioned urinary excretion rate,

26 I saw that Dr. Wambaugh was shaking his head. So I imagine this

27 is something that you've already considered, but I do think it's
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1 important to consider alternative measures of your dilution when

2 getting your exposure estimates.

3 The remaining two things I have, I think are

4 two of the biggest things, to me, and one is that

5 biotransformation products haven't been considered. And

6 metabolites in particular, for some chemicals, are believed to

7 be the bioactive chemical, phthalates, for example. And I saw

8 that a lot of the examples that we had that some of the

9 phthalates were not very responsive to the assays; however,

10 there have been many reports that the metabolites are more

11 bioactive. So I think it's important to investigate those as

12 well.

13 And then, again, addressing this issue of

14 chemical mixtures, it's an uncomfortable subject and no one

15 really wants to do it because it's so complex, but I think since

16 we're looking at similar bioactivity here, it's really important

17 to look at complimentary exposures in the bioactivity that they

18 may have.

19 Otherwise, I think this is a great approach. 

20 I think it's a great step and I'm really encouraged and happy to

21 see that EPA is really pushing this as a part of its risk

22 evaluation.

23 DR. JAMES MCMANAMAN: Thank you, Dr. Barr. 

24 Dr. Perkins.

25 DR. EDWARD PERKINS: I agree with what

26 everyone else has said. I think it's a great approach. I think

27 it's the right direction. It is clearly useful for prioritizing
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1 chemicals based on exposure and bioactivity and you're able to

2 rank those now to some utility; however, that's depending upon

3 the caveats that people have already spoken about. As a

4 preliminary approach for trying to direct further

5 investigations, I think it's fairly satisfactory.

6 DR. JAMES MCMANAMAN: Thank you, Dr. Perkins. 

7 Dr. Weller.

8 DR. GRANT WELLER: The same comments I made to

9 Charge Question 3b also apply to Charge Question 3c, so I won't

10 add anything else except to just kind of summarize and remind

11 that the results of the IBER approach are conditional upon the

12 uncertainty and air propagation in the previous steps leading up

13 to it for both bioactivity. And as the panel has talked

14 extensively about, particularly the exposure prediction.

15 DR. JAMES MCMANAMAN: Thank you, Dr. Weller. 

16 Other panel members? All right. Back to the Agency. Were

17 these comments clear? Understood?

18 DR. RICHARD JUDSON: So we do have a

19 clarification, both in 3b and 3c, the idea of validating the

20 IBER needed to be validated. And it's not clear to us what that

21 might look like. So if the panelists could clarify what they

22 mean, is that acceptable?

23 DR. JAMES MCMANAMAN: Sure. Who specifically

24 mentioned that? Dr. Propper, are you going to give it a shot?

25 DR. CATHERINE PROPPER: I will. I think

26 several of us have brought it up. But for what I mean by that

27 would be to take those IBER values that you want to use -- you
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1 decide to move forward because of the prioritization scheme. 

2 And then send them through either the in vivo Tier 1 or

3 prioritize them into Tier 1I. So it's for that. And see how

4 that functions. 

5 So first, do it for low, medium and high IBER

6 values and see how they function through the assays to validate

7 that they are, in fact, predicative for your outcomes because if

8 they're only predictive at the very high end, then that's

9 important so you can move forward with at least those compounds

10 that end up having those high values. If they're predicative

11 throughout, that gives you some information as well and it again

12 allows either narrowing or widening of that prioritization

13 scheme across your IBER values. Does that make sense?

14 DR. RICHARD JUDSON: Just to be clear, the

15 example you gave is really validating the bioactivity piece of

16 that as opposed to the exposure piece of that -- of IBER.

17 DR. CATHERINE PROPPER: It's validating it,

18 but within the context of your decision-making process for

19 prioritization.

20 DR. JAMES MCMANAMAN: That was Dr. Judson and

21 Dr. Propper. 

22 DR. DAVID DIX: I'll go ahead and jump in and

23 say that we appreciate the panel's advice and we have no further

24 questions to clarify.

25 DR. PATIENCE BROWNE: Yes. Thank you.

26 

27 SUMMARY AND PANEL COMMENTS
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1 

2 DR. JAMES MCMANAMAN: All right. Thank you

3 very much. Well, at this stage, in the last few minutes, we can

4 go through and make some final comments to the Agency. Do you

5 want to start?

6 I want to thank all the members of the Agency

7 and their colleagues who presented, Drs. Wambaugh, Judson, and

8 Browne. Dr. Casey, Dr. Kleinstreuer. These were excellent

9 presentations and I think that all the panel -- I can speak for

10 the panel in saying that it helped clarify the White Paper and

11 they certainly helped clarify in our minds what was hoped to be

12 gained through these studies. And as usual, they're very

13 stimulating. And for me, it's always fun to see where you guys

14 are going with this. It's very good science.

15 I'd also like to thank Dr. Dix and Mr. Knott

16 for their leadership in this because it clearly -- considering

17 where we were in July 2013, my gosh, you guys have made a lot of

18 progress and this is really beginning to really shape up to be

19 an major effort from the EPA. 

20 Considering all the potential people who you

21 might consider your constituents, from the folks who don't want

22 you to use animals, from the folks who want you to cheapen

23 everything, from the folks who want you to expand everything,

24 from the folks -- I mean, you've got so many constituents;

25 people who are so-called stakeholders in the process that is

26 really very remarkable that you've done this and it shows

27 remarkable set of leadership. So I congratulate you all. It's
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1 very nice.

2 I'll turn it over to Dr. Delclos.

3 DR. BARRY DELCLOS: I'll just echo what was

4 already said by everybody, and it's to praise the amount of work

5 that went into this and the value of what you're doing and the

6 progress that you've made. Thank you very much.

7 DR. DANA BARR: Clearly, everything you're

8 doing is very important to not only the EPA, but the community

9 at large. And a lot of work went into the development of the

10 White Paper and especially the presentations as well. And I

11 always feel fortunate because I sometimes -- not sometimes, I

12 always feel like I learn more here than I actually sometimes

13 give, but thank you for educating me on a lot of these things.

14 DR. DAVID JETT: I jotted down a few general

15 comments that I thought about and I'll just say those and then a

16 couple of things that is more specific and may be more covered. 

17 The general comments were, you know, this is, I think, a logical

18 strategy for addressing the need. And I think any approach like

19 this has to start with intrinsic toxicity and exposure and I

20 think that the IBER approach generally does this in a systematic

21 and careful manner.

22 I think while a data-driven process should

23 always be the goal, the challenge is to generate it in a

24 rigorous manner that reduces and minimizes the errors that are

25 inevitable for a large dataset. And the EPA White Paper

26 describes the process whereby these factors are carefully taken

27 into consideration. I think this is the major strength of the
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1 overall work plan.

2 The only couple of things that I thought maybe

3 were covered, I think you should continue your efforts to

4 examine a concordance of data generated by the models with known

5 datasets, including comparison of the in vitro bioassay data to

6 the new and previously published in vivo data. The initial

7 comparisons have been very encouraging, so I encourage you to

8 continue that.

9 Let's see. The other thing again, the Agency

10 should be commended for plans to incorporate other mechanistic

11 targets related to influencing endocrine activity. I think the

12 Agency should also consider targets important in fetal and

13 post-natal development regulated by the endocrine system; for

14 example, possible disruptive effects of these kinds of chemicals

15 on the development of the brain.

16 And then finally, the only other thing I can

17 see is -- well, actually, that was it. So thank you. 

18 DR. VERONICA BERROCAL: So first of all, I

19 thought that the problem was very interesting and fascinating. 

20 I am not usually exposed to these type of problems, so I was

21 actually very enthusiastic about your approach. I think it

22 incorporated certainty and that's refreshing to see.

23 I think that the IBER approach relies a lot on

24 toxicology and exposure and the bioactivity and the exposure. I

25 feel that you've done great work on the bioactivity part and I

26 think, for me, the focus should now be mostly on the exposure

27 component. But I think it's a great undertaking. I also was
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1 mentioning to Dr. Weller that I'm impressed by the amount of

2 work that you were able to produce in a short amount of time. I

3 was not involved with the panel in July, but I'm thinking that

4 this is a pretty fast pace to get results going and a large

5 amount of results.

6 DR. TERRY BROWN: As I mentioned previously, I

7 think this is a tremendous collaborative team effort that you've

8 displayed here and I think from my perspective, of not having

9 been involved in the SAP reviews in the most recent years, it's

10 come a long, long, long way since I was involved a number of

11 years ago.

12 And I have to compliment you in that the

13 bioactivity assays, I think you're definitely on the right

14 track. Things are moving ahead and I think you've really got a

15 good handle on it. It's apparent to me, at least, that the

16 exposure estimates are somewhat problematic, going forward. 

17 And I think the only other thing I mention is

18 that we need to keep in mind as we go forward in this effort

19 that it's really an integrative function; meaning, physiology

20 and biology are much, much more complex than breaking it down

21 into the individual components, which is really what we do in

22 most of these meetings. But my compliments to you.

23 DR. ROBERT DENVER: Really impressive effort. 

24 I'm really happy to see that we've moved beyond the limitations

25 of some of the Tier 1 assays. I remember when we reviewed them

26 they were state of the art, 1990s era assays. And I'm really

27 happy to see that the high throughput screens are paying off,
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1 and I hope that our comments have been helpful.

2 DR. CATHERINE PROPPER: Like everybody else,

3 I'm just wowed by the amount of data you've pulled together and

4 how well it's potential predictive value can be. As somebody

5 who came in skeptically, years ago, when ToxCast started because

6 I was afraid of how it would integrate with in vivo outcomes and

7 the complexity of the physiology, I like the approach of being

8 able to compare the outcomes to the in vivo data. And I think

9 it's just such a great way to go.

10 I was very skeptical with the tier, and I've

11 sat on several panels. So I was skeptical about how are we ever

12 going to get this universe of chemicals to really begin to

13 evaluate potential human and wildlife health risks. And I think

14 we're on a path to doing that, in a way that surprises me and

15 pleases me. I walk out of here feeling much more comfortable

16 about the potential for us to begin to understand these risks

17 than when I started on these panels a few years ago.

18 DR. DANIEL DOERGE: I'd just like to say that

19 I think this is a highly logical framework that's being -- where

20 several different high throughput data streams are being put

21 together in a very comprehensive manner. While I think the

22 White Paper was quite clear, I'd like to call out the component

23 sign as for the excellent job of publishing really well

24 documented peer-reviewed literature papers. I think that really

25 helps those of us out in the inner lands quite a bit. 

26 Having said that, as much information and

27 supplementary as you typically provide in those papers, even
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1 enhanced in the White Paper presentation, I really found that

2 some key elements never really got through to me, anyway, until

3 the personal touch was added. So I'm most appreciative of that

4 aspect. But it's sort of like a light in the darkness. It's

5 starting to get a little closer. So thank you.

6 DR. EDWARD PERKINS: Like everyone else, I'm

7 very surprised about how well everything has pulled together. I

8 was on the January 2013 SAP, and this is light years away from

9 that. So it's very exciting. And I'm also very excited about

10 the potential of some of the approaches for integrating the data

11 to be open to integrating other types of data, maybe even Tier 1

12 responses into that to get other more combined scores, potency

13 at higher levels and things like that. And interested to see

14 how you will be able to propagate this up into intact organisms

15 and account for some of these threshold and feedbacks. 

16 I think you've really come up with a credible

17 solution to try to prioritize things and start focusing efforts.

18 So I commend you for that.

19 DR. THOMAS POTTER: I would like to add onto

20 all the superlatives. Clearly, this is an outstanding,

21 cutting-edge effort. I would particularly like to thank the

22 Agency and all the scientists in the back who I know have

23 contributed your creativity, your focus, your insight.

24 I'd also like to give thanks for those things

25 to fellow panel members and to the public commenters. I really

26 felt that this was a very positive session, one where there was

27 excellent dialogue and insight. I certainly learned a lot from
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1 it and I think it greatly advances the whole process.

2 DR. GRANT WELLER: Like Dr. Berrocal, I also

3 haven't done much or any work in this area before, and so I want

4 to thank the panel, first of all, for helping me to understand

5 the science behind all of this in very clear and very thorough

6 presentations and I really appreciate that.

7 From a statistical and computational

8 perspective, the combined amount of work and degree of

9 difficulty in what's been presented today is really impressive

10 and I applaud the Agency for doing the work that they've done

11 and getting to this point.

12 My only main comment is we were talking, I was

13 talking with Dr. Berrocal and Dr. Propper yesterday and we were

14 talking about the use of models, statistical and computational

15 models and understanding that no model is ever going to be right

16 and that it's going to get it exactly right. But the goal, we

17 concluded, was just to be less and less wrong as we go along. 

18 So along those same lines, you know, thinking

19 about the use of models and trying to understand and be clear

20 about what the potential limitations of the models of what's

21 captured and what's not captured. What else is out there that

22 could be lurking is how I think about the use of models and I

23 hope that the EPA will think about those things and be clear to

24 stakeholders, going forward. So again, that's the extent of my

25 comments and thank you very much.

26 DR. DANIEL SCHLENK: As usual, just to

27 reiterate what everyone said, of course, really thankful for
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1 letting me be on this thing, even though I keep thinking you

2 guys are going to get tired of looking at me at some point, but

3 I appreciate the opportunity to participate. Obviously, a

4 tremendous learning experience. 

5 I thank former panel members, their work and

6 other panel members who have participated, really appreciate

7 that. The presentations were amazing, which is -- some of the

8 panels that you go through, the presentations aren't so good and

9 these are actually really amazing. So I'm just very, very

10 encouraged. I would send that out as an encouragement as well,

11 but it's great to see how far you've come and how close you are.

12 I think we're really close. It's very exciting. So thank you

13 and congratulations.

14 DR. JAMES MCMANAMAN: Before turning it over

15 to the Agency for final comments from them, I want to also

16 extent my thanks to the public presenters. There was some very

17 interesting data presented by them and I think it really helped

18 define what we would like to see this go, in terms of being a

19 useful way of evaluating these chemicals. So thanks again to

20 the public presenters, as well as the EPA for excellent

21 presentations and a lucid description of the process as it

22 stands and as where you intend it to go.

23 So with that, I will turn the mics over to the

24 Agency for final comments.

25 DR. PATIENCE BROWNE: Thank you. I'd just

26 like to thank all the panel members for their time, for their

27 attention and for their very thoughtfully considered comments. 
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1 We do hear you. As you can see, from the July 2014 SAP, we

2 incorporate those as quickly as we can and we feel that our

3 science has improved by your input. So thank you very much.

4 I'd also like to thank all of my fellow

5 colleagues, who have put in so much hard work and still answer

6 my emails, over the last couple of months, despite the fact I've

7 barraged them. So thank you all for your time and your hard

8 work as well.

9 MR. STEVEN KNOTT: And I just want to add to

10 those comments from Patience to thank the panel members, also

11 the public for the comments they presented here, and definitely

12 our colleagues and ORD, and see them throughout OCSPP for all

13 their all the hard work that went into this and we do really

14 appreciate the feedback from the panel.

15 DR. RICHARD JUDSON: I just want to echo my

16 thanks. Even though we all like to be perfect, right, and

17 everybody to love us, lots of great comments. And I was talking

18 to John Wambaugh before he had to take off, and the downside is

19 both of us now have five or six cool new projects to go work on

20 with our data and your ideas. So it could be good, it could be

21 bad. Especially, you know, I'm going to take these nice

22 compliments back to all of the folks back in RTP and elsewhere

23 who actually did a lot of the work. 

24 This really is a big team behind this. And

25 John and I and Lauren and Nicole are really the front men for

26 the -- a lot of good workers behind. Thanks.

27 DR. DAVID DIX: I'll just follow on, again,
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1 express appreciation to the panel. We greatly value these

2 reviews, and what we consider invaluable advice. We, as a

3 program, as the lead Office for Chemical safety and Pollution

4 Prevention for the Endocrine Disruptor Screening Program. We're

5 very appreciative of the opportunity to have a public meeting

6 like this and have public comment add to the quality of the

7 process.

8 And then finally, again, speaking for OCSPP,

9 we highly value our partners across the program, from the Office

10 of Water and from the Office of Research and Development, and

11 more recently, from the National Institute of Environmental

12 Health Sciences. 

13 As you can see, we're accelerating the

14 translation of some critical research from both within and

15 without the Agency, into practice to bringing the Endocrine

16 Disruptor Screening Program sort of up to speed and to meet the

17 goals of the program in a really expeditious and timely fashion

18 over the next few years. And get to the point where we are all

19 very happy with your support and your help in scientifically

20 refining the program to identify where the real potential

21 bioactivity exposure, and ultimately risk, for endocrine

22 disruption and human health and wildlife lies. So we appreciate

23 your contributions to that goal. Thank you.

24 DR. JAMES MCMANAMAN: Okay. At this point,

25 I'd like to introduce Fred Jenkins, the DFO, and he will make a

26 few final comments and adjourn the meeting. 

27 DR. FRED JENKINS: First, I want to start by
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1 thanking Dr. McManaman for his great leadership in chairing this

2 FIFRA SAP session. I thank you so much for agreeing to chair

3 this session.

4 Thank you to this entire panel; you all did a

5 fantastic job deliberating on these charge questions that were

6 posed to you this week. On behalf of the Agency, I wanted to

7 extend my thanks for your time and your commitment and working

8 hard throughout this whole entire process to make a successful

9 meeting.

10 I want to thank Dr. David Dix for his

11 leadership, Director of Office of Science Coordination and

12 Policy, for his leadership in this entire effort. Thanks to

13 Steven Knott, Director of Exposure Assessment and Coordination

14 Policy Division. 

15 And a special thanks to Dr. Patience Browne. 

16 Patience served as the OSCP lead scientist who worked with me in

17 helping to prepare for this meeting. It was great working with

18 you, Patience. You did a great job. I really appreciate the

19 opportunity to work with you in preparing for this.

20 Thank you to our ORD partners, again, Dr.

21 Richard Judson and DR. Jim Wambaugh. And thank you to all the

22 Agency scientists that supported the work that went into making

23 this meeting happen.

24 Again, as mentioned, I want to thank Dr.

25 Warren Casey, our NICEATM partner as well as the contractor,

26 Nicole Kleinstreuer. And a special thanks to ORD management,

27 represented by Tina Bahadori and Rusty Thomas.
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1 Thank you to Bill Jordan, Jim Calls

2 (phonetic), Jeff Dawson, Jack Housenger from the Office of

3 Pesticide Programs. And again, as mentioned, this is a very

4 large agency, office-wide effort, so thank you again to OCSP,

5 OPP, ORD, OPPT, Office of Water, and thank you also to the NIH

6 partners. 

7 Thank you to our public commenters that

8 participated in this meeting. Thank you for all those who came

9 from the public to this meeting. Thank you to those who

10 participated by listening to us on our webcast. 

11 And I can't forget to thank my FIFRA SAP

12 colleagues, under the leadership of Laura Bailey, the FIFRA

13 SAP's executive secretary, Dr. Sharlene Matten, Alva Daniels,

14 who served on detail briefly in our office, in preparing for

15 this meeting. Donald Wood, our administrative team lead, and

16 Shirley Percival. 

17 Thank you to our contractor, who supported us

18 throughout this meeting. And the last thing I have to say is

19 the meeting minutes will be completed within 90 days of the

20 close of this meeting.

21 And with that said, this meeting is official

22 adjourned. Everyone have a nice evening.

23 (Whereupon, the meeting was adjourned.)

24

25

26

27
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A

A-16 41:8

A-predicted

42:8

A&M 248:1

a.m 187:27

410:6

A1 36:17,21

62:25 143:26

152:3 155:24

166:5 170:23

A16 41:13

A2 36:21 62:26

166:5 170:24

A3 36:18,21

62:26 166:5

168:23 170:18

170:25 172:7

172:18

A4 65:8 155:24

166:6 170:19

170:25 172:8

172:18

A5 65:8 168:22

169:26 170:25

A6 152:7

A7 143:27 152:7

A8 155:24 166:6

A9 152:3 166:6

abbreviated

278:26

ABC 463:8

abdominal

403:18

ability 83:11

85:16 95:5,14

165:27 234:5

292:10 298:17

311:16 380:24

393:24 395:16

396:15 419:26

421:23 437:11

454:16 455:19

467:10

able 17:22,26

30:23 57:11

63:6 66:6

67:11 69:5

80:4,8 84:12

84:22 85:27

96:17 100:3

122:1 164:11

193:17 194:4

194:19 196:5

197:1 200:13

200:21 208:8

208:24 209:12

212:15 213:5

220:9 221:3

222:11 227:5

227:15 230:8

231:7 234:10

235:8 236:24

238:23 239:6

239:19 243:26

263:8 296:27

300:24 302:8

319:7 327:22

328:13 330:19

332:12 352:21

354:9 368:15

376:1 377:16

377:22 395:18

416:15 420:19

423:17 464:2

466:18 486:21

487:20 498:8

507:1 512:2

513:8 514:14

absence 118:18

164:22 168:23

366:20 427:15

443:3 457:19

absolute 23:23

73:15 154:5

166:20 251:12

353:24 436:24

absolutely 52:19

66:12 217:18

419:1 493:17

absorbed 226:8

372:25 373:26

absorption

226:6 352:13

372:7,23

373:12,12,13

373:15,25

374:23,23

437:12

AC-50 39:24

74:4

AC/AC50

132:26

AC5 369:12

AC50 63:11,15

63:23 72:3

73:12 74:23

91:26 129:6

131:4 137:1

148:27 149:1

179:4 183:26

225:3 230:1

250:4 274:7,22

282:17,20

318:15,27

321:14 368:23

369:7,8,17,18

370:3,9 371:17

371:22,24,27

373:8

AC50s 74:19

250:19 253:5

268:22 319:3,5

320:9 322:4

391:26

academic

335:27 403:16

426:20 468:8

Academy's

315:11

ACC 250:22,26

254:25 268:23

268:25 317:15

473:7

ACC's 268:22

ACC-50 268:16

accelerate 17:18

309:11

accelerating

518:13

accent 195:21

accept 197:25

acceptable

179:4 507:22

acceptance

334:18

accepted 103:14

105:23 146:1

318:9

access 18:3 69:4

243:8 352:8

434:20 467:14

accessed 493:13

accessibility

51:24 52:2

339:6

accessible

377:27

accession 425:4

accessory 181:5

335:22

accidental 96:14

accommodate

127:6

accompanying

312:4

accomplished

470:2

accomplishes

357:27

account 69:26

76:14 113:3

168:27 197:1

212:17 222:11

352:21 353:11

354:1 369:19

369:22 374:23

377:22 435:20

436:9 437:8

471:27 473:17

477:10 482:22

484:22 492:22

499:5,26

514:15

accounted 259:1

472:27 473:5

473:14,15

474:13,14

475:4,11

478:15,20,22

480:4

accounting

472:21 485:22

485:24

ACCs 250:20

253:5

accumulation

206:15

accuracy 88:14

89:9 118:11

129:22 160:25

180:11,12

280:17,23

281:20 316:3

397:18 401:6

413:15

accurate 52:18

52:19 259:8

295:13 329:18

386:13 401:23

ACEA 38:27

57:19

acetate 82:8

467:6

achieve 223:21

454:27 479:21

acid 160:10

acids 344:3,16

345:26

acknowledge

315:13 331:13

350:15 353:12

367:27 478:16

acknowledged

329:9 337:14

391:27

acknowledge...

327:27 330:14

acknowledging

350:22 470:8

acronym 158:26

246:2

act 144:4 149:25

151:18 165:9

176:20,24

178:7 179:17

275:1 343:19

395:16 429:4

498:8
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acting 143:22

144:8,11,20,23

145:1 153:12

154:19 156:26

157:24 158:8

165:17 166:14

action 25:4

179:24 243:25

325:27 361:5

377:17 414:6

414:20 423:15

423:22

actions 25:6,7

94:18 442:24

activate 96:5

144:11,12

166:4,5 170:23

243:23 307:15

363:25

activated 143:24

143:27 144:23

149:19 150:5

293:4 313:14

406:17,24

419:26

activates 292:25

activating

119:23 149:5

252:25 253:19

305:21

activation 16:22

52:14 55:1

76:24 77:6

130:5 142:18

173:20,20

294:4 311:22

321:5 348:6

377:15,20

384:7 389:3

412:26 414:2

414:14 415:23

416:16 420:10

420:16 423:6

424:6 426:24

434:4

active 15:4

39:22 40:4,6

55:5 70:8 71:3

73:25 85:14,14

87:26 94:9

97:22 98:2

99:1,19,20

111:2,18 112:6

113:11,12,13

113:26 114:14

114:17 115:4

115:11,14

116:20 117:2,7

117:15,16

118:3 119:3

120:21,22

121:2 131:5,6

137:16 143:7,9

143:10 147:11

150:21 151:8

151:15 155:26

157:15,23,26

158:27 159:6,9

159:10,20,21

159:23 172:18

225:7,11,13

243:21 251:8

252:1 253:14

265:8,9,10

273:4,6,6,13

274:18,19,21

292:26 328:12

346:10 356:6

365:14 369:4

407:6,6,9

408:12,16

413:4 427:6

434:2 435:14

436:17 437:6

456:27 497:8

502:21

actively 167:19

actives 69:10

70:8 71:6 72:5

83:7 84:3

115:18 117:9

157:15 359:6

365:8 432:9

activities 55:4

112:14 126:1,4

160:3 163:3

167:17 183:19

187:14 257:20

298:14 300:9

311:4 319:3,11

382:13 394:13

395:1 433:24

442:27 443:1

459:2 460:22

activity 9:8

11:10 29:26,27

34:22 37:2,2,3

44:21 45:4

46:2,7 47:22

47:23 48:16,25

48:26,27 49:13

49:17 52:17

55:7,7,19

56:11,19,25

65:15 69:6

71:17,20 72:2

72:12 74:1,18

76:1,23 77:4

77:26 78:12

79:14,20 80:3

80:7,14,15

82:12,27 83:7

83:8,14 85:10

85:16 86:15

89:1 90:17

91:17 93:1

99:25 100:8

101:25 102:6

112:7 113:6

116:9 117:12

121:22 125:27

127:9 128:15

128:15,17

135:23 137:2

143:3 145:8,12

145:22,25

146:27 147:25

147:26 149:9

149:16 150:15

150:25 151:10

151:11,14

152:10,10

153:2,4,8,20

153:21,25

154:21 155:10

155:11,20

156:11 160:8

170:5,15 172:2

172:3 173:14

176:19 179:24

184:12 187:7,8

187:10 193:12

195:25 201:16

225:4 230:21

236:15 243:18

244:23 246:6

247:20 250:6

256:18 262:2,3

262:7,7 275:19

283:13 292:8

293:17 310:21

311:9 313:3,4

313:5 314:7,8

316:17 317:26

318:1,3,12,14

318:17,19,20

319:8,14,22,27

325:17,20

328:15 331:17

331:20 333:11

335:9,12

336:19,21,23

336:25,25

339:17 341:4

342:9,14

344:21,25

346:13,23

347:2,24 349:5

349:7,18

350:10,11,15

351:8 353:6,25

355:10,17

356:24 357:6

358:3 362:11

362:13 363:21

365:6,11 366:8

366:16 367:19

370:13 371:21

382:14 394:19

397:26 408:16

414:26 415:3

426:23 427:4

435:17 442:12

444:20,20,22

451:17 456:10

457:22 458:19

461:3,6,12

463:25 464:27

467:7 481:14

511:11

actor 200:4

272:6

actor.epa.gov/...

272:7

actual 22:22

31:26 44:17

54:11 57:5

97:24 142:10

144:2 193:10

204:25 232:19

238:23 249:17

252:14,21

253:24 259:2

273:15 279:25

318:2 357:6

366:1 384:6

463:18 500:23

ad 5:8 389:20

ad-hoc 297:25

adapt 290:12

adaptation

357:25 441:6

adapted 223:26

233:16

adaptive 290:11

add 22:26 45:13

56:3,12 93:15

100:12 104:1

167:5,8 168:19

226:24 228:4

253:9 262:15

269:3 270:23

302:10 306:10

316:26 358:26

384:18 387:14

388:8 394:9

399:12 400:9

407:1,4 416:7

421:9 428:4

438:12,19
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445:5,7 446:15

447:10 448:16

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464:15,18

468:25 476:8

480:9 486:1

491:7,27

499:23 503:17

503:19 507:10

514:19 517:9

518:6

added 42:24

60:19 126:1

136:12 153:15

213:17 414:21

420:7 424:18

449:7 474:2

514:3

adding 92:24

208:5 209:4

296:3 306:12

314:5,22 382:3

447:4 448:1

449:10,14

473:11

addition 13:12

126:18 131:6

168:20 215:8

282:27 311:26

312:16 332:8

336:14 357:8

377:2 418:21

424:19 453:13

488:15

additional 9:13

9:16 19:25,26

21:18 125:27

130:7,15,26

133:5 136:5

139:7 176:7

179:13 182:25

212:9 220:26

239:27 259:27

267:17 282:3,7

286:10 287:18

289:5,6 301:16

301:24 303:8

308:20,22

311:18 312:8

313:6 314:19

314:22 315:5

317:2,22

319:24 324:6

329:12,15

330:15 341:27

345:13 346:5

365:15 366:6

368:26 374:27

375:4 392:12

402:18 408:2

443:17 454:10

455:4 463:15

488:10,11

492:1 500:26

additions 26:23

additive 27:7

95:3,4 144:18

address 15:20

17:15 21:18

24:3 25:27

27:1 93:4

94:27 103:20

121:21,21

192:7 195:9

210:19 261:14

261:22 290:23

299:3 309:2

326:26 330:13

332:4 369:16

370:18 375:11

378:5 380:17

394:2 415:27

418:1,3 432:20

440:12 442:18

471:7 472:11

473:1 475:7

476:13 477:17

482:12,18

484:2 485:1,18

addressed 68:20

108:23 129:15

195:12 267:9

267:14 299:7

304:14 337:19

385:23 390:2

396:20 398:5

402:22 413:10

417:7 426:7

432:16,17

447:7 458:26

472:24 481:5

482:6,15,25

483:21 486:17

487:8 496:9

addresses 12:22

12:24 13:1

101:19 299:21

389:27 472:22

addressing

320:21 352:27

393:18 442:6

443:7 475:18

485:4 506:13

510:18

adds 119:8

251:16 446:27

447:5 448:1

488:2

adequacy 312:3

328:8 453:4

adequate 279:4

314:17 347:13

366:13 442:21

464:5

adequately

345:11 350:5

adjourn 518:26

adjourned 188:2

410:11 520:22

520:23

adjust 370:1

373:25

adjusted 372:18

505:25

Adjustments

372:15

administered

106:1 238:18

238:23 373:2

416:21

administration

181:1 235:19

235:23 461:21

administrative

2:3 3:1,11

520:15

administrator

278:18

administrators

126:1

admission 202:7

admittedly

257:20

adolescent

376:17

adopt 103:4

adopted 103:13

103:16 279:7

397:4 475:21

adopting 472:17

ADR 386:9

adult 228:3

230:24 231:3

adulthood

376:18

adults 372:20

advance 7:21

316:24 421:24

Advanced

373:12

advancement

316:21 324:4

441:8

advances 16:14

17:16 107:18

300:2,24 515:1

advantage

207:14 247:10

313:8 342:4

368:12 383:13

488:9

advantages

16:14 17:20

286:13 489:21

adverse 8:26

18:4 20:3

261:15,24

290:24 303:22

304:1 308:21

311:13 314:11

328:21 343:23

349:11 365:20

367:18 423:1,2

424:13 491:2

adversely

307:16

adversity 9:16

261:12 356:24

357:7,19

359:17

advertised 21:15

advice 3:19

308:8 508:23

518:2

advised 14:10

14:25

advisement

51:19

advisory 1:2 3:4

3:14,18 14:10

14:24 191:24

316:12 317:17

340:6 355:14

advocacy 310:11

advocate 334:23

advocated

441:10

AF1 173:20

AF2 173:22

affect 95:14

332:27 335:16

397:7,20

affinities 430:27

affinity 36:1

186:9 343:3,4

343:15 344:8

345:1,12,23

348:4 457:5

affirm 496:11,13

affirmation

497:2

afford 383:22

afraid 513:6

afternoon

102:14 125:2

125:24 126:22

330:24 376:11
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 4

377:5 501:5

ag 368:27 370:8

370:14,14

375:3

age 222:4,5

234:21 335:13

335:14 370:21

370:21 373:19

461:22 477:9

487:2 495:3,5

504:10

age-appropriate

373:7

age-related

370:2 374:24

agencies 18:6

103:4,6,10

146:13 418:23

agency 1:13

3:13,20,23,24

7:19 14:22

15:20 16:17

18:10 19:18

25:9,18 60:24

181:24 296:17

312:23 315:10

315:15 317:8

317:19 320:21

323:27 338:25

340:13 354:13

369:21 376:6

376:19 378:2

400:18 402:12

407:13 413:22

414:27 415:12

418:17 419:13

426:13 428:16

428:23 432:1

432:22 438:21

439:13 452:18

453:14 454:8

458:22 459:8

462:16 467:14

469:10,12

478:19 479:27

480:13 487:27

494:25 495:21

495:23 498:8

500:27 507:16

509:4,6 511:9

511:12 514:22

515:10 516:15

516:24 518:15

519:6,22 520:4

Agency's 12:1

14:24 24:26

339:1 375:24

393:12 429:6

439:5 471:21

479:11,26

agenda 4:16

agenesis 25:25

agents 305:25

376:1 423:14

459:1 467:6

ages 213:9

ago 31:26 43:13

43:13 52:12

87:12 103:12

140:23 247:27

261:25 265:27

328:4 331:18

340:5 345:3,3

347:19 425:5

441:12 512:11

513:5,17

agonism 147:18

152:13,13

172:14

agonist 2:7 17:3

33:27 35:4

36:23 37:2

38:26 41:22

44:3,7,25 45:4

46:19 48:24,25

48:26 49:11

55:12,13 71:2

80:24 81:27

83:17 84:25

87:20 89:25

96:4 115:23

117:13,25

124:26 126:4

128:6,10,12,14

128:17 130:19

130:23 131:2,5

131:13 132:8

132:11 137:5

139:20 141:15

142:21 143:19

143:22,26

144:15 145:6,7

145:9 146:27

147:5,15,19,23

147:25 148:1

148:10,13,17

148:20,23

150:13,25

151:3,5,10,14

152:1,11,17

156:7 160:5

162:16 166:8

166:11 172:10

172:17 181:7

183:7 184:16

187:4,9,11

272:24 273:5,6

275:2 280:10

320:26,27

321:1 348:11

371:19 380:8

380:21 440:4

440:17 444:20

455:20 460:22

460:25 461:2

461:11 503:10

agonist-related

166:10

agonist/antag...

331:12

agonists 16:21

40:11,13,24

50:2,12,12

55:17 81:18,22

82:9 83:20

85:2,4 94:24

97:20 100:19

118:6 125:27

135:21,24

136:27 137:3

144:7 146:23

147:9 157:1

161:15 165:14

171:20,24

183:19 320:17

320:17,19

321:12,19

395:17,18,22

396:10 429:19

444:11,19

agree 80:7

116:23 173:8

222:23 240:1

242:18 353:4

362:17,22

365:7 384:18

385:17,21

386:16 402:12

429:24 430:19

447:26 448:7

448:25 464:17

489:19 494:27

496:18,20

497:21 506:25

agreed 127:13

197:12 320:6

427:12

agreeing 430:6

453:10 519:2

agreement

131:15 427:12

427:18 439:4

500:23

agrees 181:24

496:1,2

agricultural

6:16 190:16

412:2 490:10

494:5

AgroSciences

354:22 355:8

367:2

ahead 20:18

26:10 28:24

68:21 177:6

267:27 268:4

289:8 290:9

355:2 381:11

391:10 407:27

462:16 508:22

512:14

AIC 398:10

aiming 69:2

air 15:17 186:8

191:15 195:4

201:24 375:23

507:12

Akaike 274:4

398:10

al 192:24 206:9

207:10 224:17

226:14 318:25

346:22 347:21

363:18 364:5

469:3

albeit 337:14

Alex 87:3

algorithm 42:14

274:16 321:26

323:3

alike 39:13,15

aliquots 228:5

Allegra 224:3,5

allergy 224:2

allotted 278:2

326:6

allow 50:18

99:22 193:15

226:22,25

237:8 278:16

291:2 352:3,14

394:18,21

423:12,21

444:4 455:7

463:15 467:15

474:27 487:1

488:10 490:20

allowed 236:12

354:10 370:20

382:12 404:17

404:21 441:5

allowing 355:6

allows 59:9

222:27 224:23

226:21 287:1

394:26 395:1

423:11 508:12

alluded 367:16

461:20 464:5

465:15 488:15
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alluding 448:24

466:21

alpha 34:18

43:12 65:12,12

65:13,15 66:26

67:2 175:25

176:5 332:22

349:5

alpha-alpha

34:18 65:12

alpha-beta

34:19

alpha-Cyclode...

46:9,13

alpha-driven

175:17

alpha-estradiol

285:18

alternate 55:26

55:27 375:2

387:2

alternates

491:12

alternative

140:22 146:12

185:2 323:6

324:13 365:27

367:15 368:12

382:6 396:16

414:26 444:24

461:6 489:8

503:21 505:23

506:1

alternatively

296:12

alternatives 18:1

352:10

altogether

311:23 312:22

325:3

Alva 520:13

AMA 315:22

amazed 124:2

amazing 116:25

401:10 516:7,9

America 198:22

326:21

American

198:17 222:3

316:8 326:21

326:22

Americans

80:23 198:23

199:7,11

200:23 214:6

310:12

amount 33:8

51:6 60:15

93:15,15 140:3

177:20 277:24

287:3 295:7

310:19 387:18

400:16 420:27

469:11 482:2

484:10 510:4

512:1,2,5

513:3 515:8

amounts 76:18

163:23

amphibian

309:4

amplifies 171:10

amplify 168:25

analogous

235:23

analyses 66:14

182:21 207:9

209:18 217:3

218:12 220:13

232:8,16 263:8

280:15 292:21

314:16 315:24

332:10 388:3

423:23 444:2

448:25 463:21

analysis 23:25

26:21 54:12

66:15 102:3

116:15 136:12

154:24 167:13

180:20 182:13

185:7 199:3

201:12 202:19

232:18 234:1,3

241:13,14

252:14 280:20

281:22 295:5

317:15 323:10

333:21 391:2

394:21 397:2

418:1 447:1

449:1 460:18

500:8

analysts 109:24

analyte 209:22

209:23

analytic 199:17

227:12 228:6

analytical 227:5

322:18,21

analyze 27:13

174:16 222:7

analyzed 174:21

181:6

analyzing 11:7

266:13

anchor 328:23

anchored

328:15

anchoring

347:15

and/or 26:27

100:15 287:6

313:7 407:23

412:26 432:7

446:4 455:16

androgen 2:8,8

8:22 12:24

13:9,18 16:10

16:27 17:2,3

19:26 25:27

26:23 29:10

30:23 33:15

75:1 78:5 81:2

81:20,20

125:23 140:13

140:16,24

141:2,4 143:21

146:25 150:24

162:12,27

163:13 164:22

168:5,8 169:5

173:1,4,18

175:10,12

177:13 178:7

179:17 183:7

243:16 246:16

247:21 253:4

253:19 255:14

255:17 264:25

265:26 272:24

273:10,18,22

275:22 281:13

287:6 297:17

298:10,11

300:27 304:15

306:26 310:22

330:23 348:8

358:13,18,21

359:1 360:7

365:6 439:27

440:2,13 441:7

442:4,7,9,24

443:4 444:17

444:24 450:10

451:16,23

452:12,17

456:15,17,22

457:20 460:13

460:25 461:8

463:1,9 466:16

466:24,25

467:2 469:22

470:9,23 503:5

androgenic

90:23 101:24

185:1 339:16

339:18 457:9

460:2 461:12

503:3,4

androgenicity

185:2

androgens

341:18,23

348:2,3,4,8

466:2 502:27

anecdote 95:27

animal 15:15

17:27 103:5

119:3 126:20

180:27 193:2

309:11 310:10

311:1 312:9

335:16 394:20

434:8 439:5

454:26 461:27

animal-based

27:17 138:15

animal-intensi...

267:4

animals 123:1

182:16 226:17

287:17 308:12

310:6,15

311:15 316:2

335:14,14

341:3 354:7

509:22

Ann 425:12

Anna 375:8,10

375:10

annually 10:7

ANSIS 252:16

answer 30:2,3

31:11 42:22

53:5 57:11

68:21 70:14,23

88:12 91:23

95:19 98:15

100:1,13 116:1

116:13 122:1

163:6,8 165:7

170:17 238:2

240:2 243:13

258:27 261:13

270:23 291:14

296:15 297:25

379:1 449:8

450:21 517:5

answered 37:6

50:18 221:17

267:19,23

292:27 452:1

answering

499:24

answers 30:5

61:2 70:14

84:4 379:21

407:14 447:3

496:16
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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antagonism

147:18 163:21

172:14 281:18

417:19 456:13

457:21

antagonist 34:1

34:27 35:1

37:2 38:26

40:9 44:4,8

46:6,8,19

48:24,25,27

49:12 71:2

79:1,2 80:25

81:23,25 82:1

84:25 87:20

89:26 128:12

128:15 130:24

131:6 135:25

136:27 137:3,5

141:16 142:21

143:27 144:6

144:11,13,22

145:10,20,22

146:24,27

147:6,10,15,19

147:23,26

148:1,11,14

149:12,23,24

149:25 150:3,7

150:11,14,25

151:3,5,9,10

151:14 152:1

152:18,22

153:1,3,4,18

154:19 156:1,2

156:7,13 157:1

158:13,17,19

158:22 159:6

159:20,21

161:16 165:14

166:7,7 171:21

172:1,4,7,8,10

172:17 173:12

178:9 179:19

179:24 181:7

181:10,14

183:7,19 184:5

184:11,17

187:12 253:4

272:25 273:5,7

281:13 343:19

380:8,22

395:20 396:9

398:4 440:5,18

444:20 455:20

458:19 460:22

461:12 463:25

465:19 468:22

503:10

antagonistic

423:22 456:10

457:14 459:2

460:26 461:3

antagonists 50:3

50:4 81:18,19

81:21,24 94:24

97:20 135:22

144:4 156:3

171:25 178:8

179:17 183:15

187:7,9 321:1

444:11

anthropometric

505:14

anti-androgen

358:13,22

360:7

anti-androgenic

182:15

anti-cancer 32:5

anti-estrogen

361:11

anti-estrogenic

347:5

anti-microbial

153:24

anti-microbials

161:17

antiandrogenic

460:2

anticipate 97:25

182:24

anticipated

14:20 306:16

336:14

anticipating

114:2

antimicrobials

153:17

antiparallel

466:24

anxious 61:7

anybody 40:11

47:14 85:21

129:25 306:3

anytime 106:3

158:12

anyway 38:3

46:13 48:10

75:10 81:11

86:22 99:13

129:17 246:2

252:19 268:24

276:9 427:25

514:2

AOC 429:15,15

AOP 122:9

423:7,9,11,21

424:5 425:3

431:10,17

AOR 444:8,10

444:11

apart 36:4 65:18

82:4 151:17

182:26 244:10

244:12 284:11

444:18

apical 176:2,2

358:27 453:24

454:2 465:1

apologize 165:20

184:25

apoptosis 77:5

apparent 17:9

512:15

apparently

160:10 164:10

402:10

appear 61:2

269:12 275:12

313:14 337:27

387:1 435:7

460:14 499:16

appearance

211:21

appears 65:15

311:21 327:15

336:10 341:9

452:5 478:25

481:11

appendices

130:27 328:4

332:21

appendix 133:26

135:7 181:21

320:25 500:12

500:14

applaud 375:21

394:10 444:3

483:23 487:27

515:10

applause 400:18

apples 416:14

applicability

323:1 484:18

application 2:8

2:10 13:19

18:15 26:21

97:12 125:18

126:8 177:13

267:16 277:9

280:27 301:2

307:27 328:25

338:11 383:22

applicator

221:22

applicators

494:6

applied 30:24

141:1 142:26

168:4,5 285:24

340:1 451:19

456:3 461:15

469:3 479:25

486:7

applies 264:14

374:7 417:5

483:14

apply 152:15

200:11 205:12

235:9 237:16

303:18 322:27

346:6 492:25

498:6 507:9

applying 18:8

202:13 312:18

314:9

appointment

340:3

appreciate 7:17

7:19 52:9

61:10 174:6

191:26 192:1

197:27 266:11

270:12 277:19

307:22,25

316:11,27

354:15 433:18

434:9 441:13

443:16 476:16

477:19 498:14

508:23 515:6

516:3,6 517:14

518:22 519:18

appreciated

481:18 495:25

appreciation

518:1

appreciative

310:19 316:18

482:20 514:3

518:5

approach 8:23

9:10 11:7 13:2

13:20,26,27

15:14,17 16:1

17:7,13 19:20

19:27 20:21,27

21:9 22:8,8,24

24:6,9 29:8

30:22 51:7

53:2 94:20

103:24 126:15

128:7 132:19

176:10,15

178:5 204:14

214:8 222:8

238:19 240:6

244:1,3 246:16

247:10 266:13
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 7

266:21 268:7,9

277:17 278:9

278:11,24

279:1,1,7,12

279:22 280:19

282:5 286:11

287:14,20

288:21 290:12

292:23 295:16

296:20 299:27

300:22 311:3

311:27 312:25

313:8 314:24

316:21,24

317:20 318:4,6

318:15,24

321:7 322:1

323:16 324:3

327:13,19

331:14 334:24

343:26 353:1,4

353:9 355:11

356:1,11

358:15 360:15

362:15 363:11

364:26 365:5

367:14,15

368:5,6,7,8,10

368:12 369:2,3

369:13,13

370:7,9,10

372:21 373:24

374:25 375:2,3

376:7,23

378:21 379:13

380:3 382:11

383:3,5 384:1

384:26 385:23

387:5 389:15

389:16,20

394:18,21

395:12 400:17

423:15 435:27

436:18 438:2,6

439:26 440:14

442:14 448:5

449:3 470:26

471:2,9,21,26

472:17 473:12

473:16,19,22

477:3,4 480:24

481:14 482:10

482:22 483:2,8

483:14,22,24

484:5,6,18

485:11,17,21

487:6,19,25

488:4 489:21

491:3,10,12

496:2 497:7

499:5 500:24

502:11 503:8

503:27 504:1

506:19,26

507:4,11

510:18,20

511:21,23

513:7

approach's

314:10

approached

169:13 174:24

approaches 5:5

24:27 26:1,7

126:23 160:24

160:27 165:23

167:22 192:3

223:9 233:13

249:11 260:2

306:7 311:7

313:26 322:17

323:18 340:15

351:9 382:6

396:25 406:3

444:2 446:25

447:1,6 450:14

476:2,9 485:2

499:26 514:10

approaching

142:12,13

304:4

appropriate

3:24,25 9:13

29:5 61:14,16

126:16 139:16

158:26 208:7

270:1 278:24

287:20 289:5

314:4 323:18

325:17 335:23

339:14 341:1

358:1 366:10

366:26 386:24

392:7,13

415:23 417:1

442:9 447:11

451:7 457:1,15

460:18 463:23

478:6 484:20

505:17

appropriately

40:23 97:19

451:13,19

458:26 460:26

474:14

appropriateness

312:3 488:8

Approximately

460:16

approximates

27:14

approximating

416:20

approximation

294:15

approximations

208:3

aquatic 6:21

AR 2:8 13:18,23

17:4 137:14

141:15,19,26

142:2,4,5,6,8

142:11,15,17

142:27 143:3

143:14,26

144:4,10,22

145:5,20,26

146:1,5,17,27

147:15 149:9

149:25 150:22

152:11,13,21

153:17,26

154:17,19

155:17 157:23

157:24 158:2

159:9,9 160:18

160:22 161:4

162:8 164:7

165:3,24 166:3

166:7 167:2,25

169:18,20

174:8,9 177:13

177:20,26

179:6,9 182:10

182:12 183:1

184:16 185:11

185:19 186:6

186:17 187:5

256:26 272:27

279:20 283:20

284:18 287:24

288:3,8 296:10

300:23 301:8

301:13,15

302:5,25

310:26 314:13

314:16,19,21

314:23,24

315:1 329:7

331:7,9,12,17

332:13 333:23

334:10,17

337:12,15

339:4 353:2,5

358:19 368:26

373:18 375:4

378:11 379:3

440:2,4,6,8,17

442:13,16,21

443:10,19

444:19,21

451:20 453:22

454:16,18

455:1,1,4,12

455:25 456:1,9

456:12 457:21

459:14 460:1

460:13,20

461:1 462:4,25

462:26 463:11

463:25 464:1,4

464:21,27

465:5,6,25

467:9,16

470:23 502:12

502:24

AR-mediated

182:18

AR-reference

146:19

AR-related

370:26

AR-STTA

146:14

arbitrary 198:5

area 2:5 13:3,16

13:25 20:26

21:26 22:22

43:19 44:9,10

45:5,6,9,23

55:8 62:6

69:18,23,27

93:5,6 97:11

102:23 139:20

159:4 167:12

168:27 175:22

214:22 222:22

225:18 259:8

259:12 302:5

302:22 306:15

313:18 315:17

323:11 363:8

377:1 380:7

382:11 384:6

387:13 389:16

440:4 447:14

515:3

areas 5:9 19:8

20:22 21:5,6

92:22 149:6

247:8 290:19

300:4 302:1

505:7

arena 129:25

arguably 343:23

argue 119:21

151:21

argument 80:10

arising 398:8,12

arithmetic
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 8

211:24 388:14

Arizona 6:9

190:9 411:22

Arkansas 5:18

ARLINGTON

1:17

army 6:14

190:13 237:4,6

237:7 411:27

aromatase

358:24 360:20

360:20

aromatizable

348:4,7

aromatization

348:7

aromatized

137:24

arose 121:22

arrangements

4:8,11,12

array 455:12

arrived 122:7

299:20

arrow 41:13

62:23,23 63:1

212:12 214:3

arrows 41:9,23

62:26 75:3

425:6

art 105:19

107:16,27

512:26

ARTA 185:12

article 181:18

334:25 426:9

468:27

articles 202:1

460:17

artifact 54:5

artificial 290:26

artist 24:16

195:19

ascertained

455:8

ascertaining

359:14

aside 32:27

330:2

asked 37:5 68:15

77:17 91:2

98:23 160:1

218:21 258:7

264:18 265:24

270:14 288:20

304:22 404:18

457:16 464:7

489:15 500:27

asking 61:12

90:13 163:12

163:26 168:1

171:6 174:26

235:20 257:25

260:6,11,17,17

261:14 262:18

262:20 277:16

278:10 287:19

289:4 307:1

308:1,7 432:15

aspect 21:11

22:18 104:17

171:6 174:11

264:14 266:6

323:23 334:26

426:3 453:2

457:18 458:7

466:7 473:2,2

473:4 514:4

aspects 18:1

22:17 110:5

129:20 175:6

175:12,13,23

372:9 424:25

427:26 440:23

465:15 466:6

466:13,22

486:24

assay 16:22

27:11 29:15,17

30:1,2,7 31:13

32:10,11,11,15

32:16,17 33:2

34:12,24,26

35:13,17,20,23

36:9 38:12,14

38:15 39:1,20

39:22,23,26

41:4,6,8,12,19

41:19,24,27

42:12,17 46:13

46:19,27 48:18

49:14,22 54:11

54:18 57:3,19

57:19,22,24

58:1,2,6,6,15

58:15,16 59:9

59:17,18,26

60:1,14,16

66:20 67:8,10

69:4,8 71:19

71:20 72:9,10

72:11 73:24

74:3,23 75:10

76:3,20 78:20

80:27 81:27

82:20,21 84:20

86:7 92:19

99:14 103:12

104:16,25

105:6,11,15

108:19 109:15

110:11 113:1,7

113:22 115:27

116:24 118:15

119:6,9 121:17

121:17,24

124:3 128:20

129:6,6,19

130:2,4,5,5,7

130:11,18

131:8,12

134:10,11,19

134:23 136:12

136:13 138:6

138:19 139:10

141:11 142:5,6

142:8,10,11,17

142:18 143:9

143:12,13

146:14 148:10

148:19,21

149:6 151:19

151:20 154:25

156:20,22

157:8,23,25

158:7,26 160:8

160:11 162:20

162:25 163:1

164:21,22

165:2,3,8

166:25 167:5

168:8,17,19,19

168:20 169:11

169:15 170:9

170:10,18

171:11,13,21

172:19,20,27

173:4 174:2,3

176:12 181:12

181:13,26

182:10,11,11

185:11,12,23

192:26 225:4

226:14,18,26

250:21 265:26

266:18,24

267:6 269:15

269:22,27

273:13,17,26

274:9 275:2

276:20,23

280:16 292:15

293:4,5,6,16

294:5 295:8,8

296:6,6 297:3

298:21 309:4,4

313:12,22

317:27 319:1

321:4,5 333:15

333:18 335:15

338:3,6,18

340:27,27

343:4 346:15

348:2,26,27

357:1 358:12

358:19,23

363:16,21

369:17 371:24

371:25,26,27

378:11 391:2

395:3,6,9

396:6 397:26

398:1,8,14

402:26 407:7

408:18 414:9

414:11 420:5,7

420:11,12

422:1 424:7

431:5 433:6,15

434:6,8,11

435:11,24

436:2,5 437:26

438:2,4,13

439:6 443:8

444:7,15,16,16

445:11 446:2,4

446:23 451:8

451:25 452:3,4

452:5,13 453:1

453:3 455:10

455:24 457:2

457:10 460:13

460:20 461:7

461:14 462:4

463:21 465:5,7

465:9,11

467:15,19,27

468:6

assay-by-assay

54:11

assay-specific

144:24 148:15

149:21 152:4

155:26 156:26

159:26 166:26

166:27

assaying 57:1

96:6

assays 2:6 9:14

19:20,25,25,26

25:3,10,14,20

25:21 26:8,12

26:14,19,24,27

27:9,11 29:4,9

29:9,10,12,17

29:21,21,24

30:8,26,27

31:3,4 32:3,9

32:12,23,25

33:23,24 34:14
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 9

34:15,16,19,23

34:24,25 35:1

35:7,7,12,17

35:21 36:8,13

36:17,18,20,24

37:1,7,10,15

37:16,18,21,22

37:24,26 38:3

38:4,5,10,23

38:24 39:5,10

39:23 40:2,4,5

40:6,8,9,14,16

40:18,19,20,22

42:1,8 43:27

44:6,17,17,18

44:19,24 45:15

45:19,25,26

46:14,15,22,24

46:25 48:16,17

49:7,11,17,25

51:7,8,14

52:23 53:9,10

53:12,24,24

54:8 55:3,6,12

55:12,15 56:7

57:8,12,26

59:19 60:2

61:13,15,21,26

61:27 62:7,7,8

62:10,18 63:1

63:6,15,22,23

64:4,6,12,17

64:18 65:7

66:27 69:25

70:3,17,20

71:6,19,24,25

72:1,16,17

73:11 74:17,19

74:24,27 75:5

75:5,7,14 76:8

76:9,26 77:11

77:22,23 78:5

78:6,8,15,18

79:2,12,18

80:13,22 81:24

81:25,26 82:27

86:2 87:27

89:18,22 90:16

90:18 94:23

95:8,9,19,20

95:25 96:6,27

97:1 99:5,6

100:17 101:27

102:5 103:17

103:20,22

104:12 107:19

107:22,23,25

109:1,3,13

110:19 111:2,3

112:10 113:19

114:17 115:23

116:10 118:6

119:8,10,12

124:5,25 126:6

126:25 127:11

128:9,11 129:4

129:5 130:2,11

131:18,19

134:10,12,19

137:17 138:9

139:19 141:6,6

141:7,10,19,27

142:4,5,13,18

142:20,27

143:1,3,5,7,8

143:10,15,16

143:27 144:12

144:13,14,14

145:3,8,13,25

148:8,9,20

149:8,15,16,19

149:23 150:1,4

150:5,13 151:6

151:9,9,16

152:5 153:10

153:15,22,26

154:14,17,27

155:1,8 156:18

156:25 157:18

157:24,26

158:3,6,13,14

158:19,27

159:2,7,8,9,10

159:11,12,13

159:16,21,22

161:5 162:12

163:13,15,17

163:21,27

164:8,13,22,23

165:1,19 166:1

166:5,10,13,21

167:16,21,25

168:3,10,23

169:2,4,6,11

169:13,14,19

169:22 171:19

172:5,13,14

173:7,9,26

175:6 176:15

178:10,19

179:3,18,21

180:19 182:2,3

185:8,8,13,14

224:27 225:7

225:11 227:7

246:6,14 248:7

249:18,27

250:2 251:8

264:17,19,25

264:26 265:13

265:14,25,27

266:1,4,17

267:1,3,4,9

269:12,19,20

270:2 273:13

273:18,19,21

273:22,24

274:24 275:10

275:12,22

278:27 279:24

280:21 281:10

281:17 288:23

291:22 292:7,8

292:12,13,25

293:22 294:7

294:12,17

295:24 296:2,3

296:9,10,13,18

296:23,24,26

297:2,4,6,7,13

297:17,20

298:2,18

300:14,20

301:25 302:24

303:17,19

304:3 305:9

308:2,9,11,19

310:26,27

311:1,17,21,26

312:1,22

313:15 315:15

315:23 319:1

320:15 322:15

324:21 326:19

333:12,12

335:7 337:18

338:7,12 339:2

342:1,5 346:16

346:26 347:9

348:4,6,19,25

349:6,25 350:8

350:17 353:8

356:21 357:3,4

358:18 359:2

368:21,24

369:6,7,8,15

369:15,16,19

369:21 370:4

370:26 371:23

373:22,23

377:7,13,14

380:5,21 384:3

385:8,12,14,26

385:27 386:1,2

388:12,27

389:2 394:12

394:26 395:19

395:26 398:22

405:8 406:5,7

407:25 408:6,6

413:24 414:12

414:13,17

415:27 416:17

417:3,22,26

418:7,19,22

419:21 420:4,5

420:18,22

421:4,14

422:24 424:8

425:11,12,13

425:15 428:15

430:15 431:5,6

431:10 433:8

435:22,26

436:20 437:19

437:22,24,26

438:1 440:1,17

441:8 442:1,4

442:8,16,23,26

443:6,7,19,21

444:5,9,11,14

444:18,24

445:22 446:15

446:16,19

447:4,12,19,20

448:9,10,17,17

448:19,20

449:5,11,17,22

450:10 451:7,8

451:11,17,18

451:23,26

452:16,17,19

452:22,25

453:12,22,25

453:26 454:1,3

454:25 455:1,3

455:4,6,11,14

455:16,19,23

456:11,17,27

457:2,4 458:1

458:9,25

460:14,19,20

460:23 461:2,4

461:9 464:1,9

464:23,27

465:1,2,4,13

465:22 467:17

467:25 468:14

483:15 503:5,6

506:9 508:6

512:13,25,26

assert 362:23

assess 104:20

264:18 265:24

306:7 309:7

369:21 382:6

385:8 423:22

444:4 464:3

476:26,27

assessed 348:2
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 10

348:21 398:27

461:5 487:2

assesses 452:13

assessing 26:8

452:11,19

468:14

assessment

11:24 14:19

20:14 21:14

125:4 265:1

312:26 313:26

315:27 330:8

335:5 362:9

364:13 365:16

365:26 366:6

371:8 372:27

385:13 395:1

418:26 442:13

444:13 455:10

457:27 519:13

assessments

21:13 61:17

302:9 305:9

354:10 368:12

387:9 442:5

451:20 460:24

463:16

asset 455:3

assign 110:15

205:5 231:17

assigned 5:13

146:2 334:7

456:2

assigning 491:14

491:15

assignment

161:11 442:16

assist 315:7

437:11

assistant 5:27

190:1 278:18

411:15

associate 400:5

400:8 412:21

422:7 433:13

associated 1:4

17:23 112:20

113:1 131:13

135:8 160:2

199:19 283:20

291:17 308:4

308:13 334:1

341:11 391:3

445:24 493:24

association

160:9 326:23

associations

160:12,17

associative

155:14

assume 41:18

66:10 127:13

168:10,22

211:1,7 226:8

259:15 406:2,2

assumed 240:12

240:20 373:2

assumes 352:12

423:6 482:5

assuming 23:15

211:17 241:27

242:6,9,13,14

242:15 396:4

412:25 474:19

assumption

66:15 75:21

76:2 208:16,19

211:3,6 229:8

236:2 383:14

395:6 397:25

397:27 412:25

412:27 413:1

423:3 482:16

assumptions

208:7 226:7

372:5 442:1

482:13 492:25

assurance

387:16

assures 164:10

asymptote

321:16

asymptotes

155:22

asymptotic

93:26 94:1

at-risk 504:26

ate 202:25 203:1

ATG 371:27

Atlanta 5:22

189:24

attach 275:15

attached 34:15

372:23 492:23

attack 331:6

Attagene 38:11

40:18,19 44:18

57:26 58:6

75:16 90:16

142:17 157:7

157:18,21

attain 484:11

attempt 169:7

459:5 484:5

attempted 495:2

attempts 315:14

481:15

attending 3:15

3:17

attention 136:4

154:11 280:17

288:12 314:10

316:15 317:17

318:8 319:6

323:15 324:9

335:1 341:7

346:2 354:14

442:15 444:6

444:23 458:24

516:27

attractively

286:23

attributable

111:13

AUC 2:6 44:11

45:18,24,26

48:5,6,7,9 49:2

49:4 50:13

53:19,27 69:18

78:1 86:16

91:5,19 92:5

92:14,25 93:12

93:21 102:24

102:27 114:20

115:5,9 117:1

117:14,22,23

117:25 118:23

128:18 132:26

135:23 136:26

137:5 145:7,7

145:12,20,23

147:3 153:1,3

183:21 184:1,4

186:20 225:14

272:22 280:14

282:12 294:9

313:21 314:8

320:4,5,18,26

321:7,13

324:19,20,21

331:23 332:5

332:13,24

333:23,26,27

334:17 336:5,8

337:1,3,9,12

337:15,23,26

338:4,4,15,18

338:19 339:4

339:11,20

341:9,18 342:1

342:8,16,17

350:6,10 368:5

368:10 370:9

371:16,18,19

371:20 374:25

379:3 380:8,24

381:3 382:27

388:13 389:19

390:5,26 391:4

391:25 393:24

394:17,25

395:4 396:2,12

397:3,5,9,11

397:14,16,18

397:24 398:4,6

398:14,19

399:23 400:14

412:13,16

416:13 419:20

421:26 422:2

422:20,27

425:11 427:24

429:18 433:3,7

433:19 434:1,5

434:10,12,14

434:27 435:2,6

435:8,12,14

436:1,5,11,15

436:21 438:9

439:3,7 440:4

440:8 442:14

442:18,20

443:11,15

445:24,27

448:6 451:10

451:12 452:6,8

452:11 453:5,6

454:16 455:22

455:26,27

456:3,11 460:1

462:25,27

463:9,27 464:3

464:24 475:15

475:21 476:17

478:25 479:15

481:21 483:1

AUC's 394:14

AUC/potencies

328:14

AUCs 294:11

321:2,12

331:27,27

341:11 456:5

audio-visual

102:15

augmented

217:17

AUP 385:23

auspicious

177:17

author 403:25

authority 3:22

automated

488:7

automatically

36:22

autonomous

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availability 52:2

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139:27 164:27

165:3 170:16

170:19 171:8

193:21 198:3

199:1 200:5,9

203:11,17

204:2,13

206:11 215:13

229:2,7,9

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233:24 234:24

239:19 261:2

265:16,18

279:24 286:16

286:25 313:15

314:26 317:10

318:10 357:14

357:17,27

358:23 359:5

359:10,12

360:6,10

361:21,25

372:22 374:22

385:13 387:18

443:18,19

444:4 452:23

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63:10,15 73:19

88:14 114:7

117:17 156:10

157:27 158:1

159:6,10,23

198:17,18

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221:13 222:3

222:11 228:2

230:26,27

239:2,10,22

253:18 281:9

average-size

345:21

averaging 319:1

476:3

avoid 92:27

303:15 311:19

avoidance

313:25

avoiding 312:22

aware 222:6

315:10 328:11

328:12 368:5

389:23 453:14

458:23 500:11

axes 154:10

229:23

axis 91:8 225:2

229:27 230:1

230:10,11

251:20 342:25

342:26 374:13

B

b 196:19 502:21

back 11:1,12

12:20 14:2

17:6 20:4

21:24 33:7

37:5,14,17

40:27 43:20,21

45:9,21 48:17

54:4,24,27

59:25 61:4

62:3,20 63:5

64:22 67:26

68:11,25 69:15

69:17 75:12

81:20 83:19

87:19 91:18

93:22 102:10

102:16 117:24

117:27 118:18

122:14 125:17

129:16 134:2

135:18 150:18

162:2 171:18

174:18 177:23

179:15 185:5

190:24 192:5

205:8 208:27

209:2,4 218:15

221:18,18

228:23 242:9

244:6 249:3

250:8,20 251:9

254:1 255:4

256:7 258:2,5

260:16 262:17

266:3 268:13

277:1 278:13

278:21 280:17

282:21 289:16

299:9,25 304:9

309:16,17

319:5 329:14

329:19 330:6

331:17,22,25

335:2 336:17

336:21 340:4

340:12 343:11

345:1 350:4

378:12 379:21

389:8 394:1

407:13 418:6

418:18 419:13

422:10 424:18

426:20,21

429:23 432:1

432:20 435:25

438:21 441:12

454:8 455:14

455:22 456:18

456:18 457:18

459:8,17

461:15,16

462:16 464:25

468:18 469:12

469:16 472:11

476:13,26

477:3 480:13

490:5,24

494:25 495:20

495:26 500:22

501:2,27 502:2

507:16 514:22

517:22,22

background

4:16 126:11

250:21 297:11

300:3 325:24

384:10

backgrounds

298:4

backwards

330:5

bad 205:3 451:5

451:13 517:21

bag 202:24,25

203:1

Bahadori

519:27

Bailey 520:12

balance 88:24

110:7 122:24

123:3 129:21

180:11,11

266:11 401:6

balanced 88:14

280:17,23

281:20

ball 96:13

ballpark 132:27

133:17 224:15

280:25

ban 245:20

band 40:3,6,7,13

bands 255:21

bar 213:15

Barbara 193:18

193:27 224:26

230:9

Barber 363:18

bare 76:13

Barr 5:20,20

21:22,23 22:9

23:8 90:6,7

98:23 189:22

189:22 270:4,5

270:22 271:7

324:27 325:1

411:11,11

471:16 476:6,7

478:1 483:5

494:21,23

495:13,14

502:15 503:18

503:19 506:23

510:7

barraged 517:7

barrier 314:3

BARROCAL

448:27 449:15

450:2,11,15,26

471:17

Barry 5:17,17

66:25 189:20

189:20 384:17

399:6 407:1

411:9,9 416:6

438:18 510:3

bars 114:7,9

256:3

Bartels 368:1

Bartels' 351:2

base 41:19 64:11

84:10 129:20

based 8:24 15:1

19:20 26:24

30:17 49:6,17

58:18 61:3

91:4 95:24

103:12 104:11

113:4,25 118:8

121:22 127:23

128:18 131:17

136:7 138:16

138:19,25

139:4 174:3

187:1 204:3

205:14 212:21

217:1 234:4

236:11 240:9

244:2 257:19

263:7 268:15

274:4 279:18

279:20 280:11

284:13 287:18

287:21 288:2,3
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416:11,15

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442:20,25

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457:4 462:25

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474:15 480:25

496:3 498:27

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250:23 251:1

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bases 214:22

basic 85:1

192:19 201:14

230:24 244:3

254:16 272:9

305:27 481:3

basically 18:8,8

18:12,14 19:7

83:5 92:21

121:8 135:21

136:23 137:19

144:21 193:13

194:2,5,22,24

196:27 197:4

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201:22 202:2

202:16 203:14

204:12,17,20

205:4,13

206:10 208:11

208:19 210:16

211:13 212:2

212:15,20

213:25 215:4

215:12 218:8

218:25 221:13

221:26 224:14

224:17,20

225:13,17,23

226:20 227:4

227:12,22

228:7,13,21

229:10,26

230:13 231:7

231:16 232:18

233:11,23

237:4,23

239:20 240:1

240:24 241:27

242:1,3 265:6

270:16 271:11

282:25 318:13

319:16 321:15

343:19 370:6

379:10 387:5

391:25 403:1

409:12,13

439:3 445:10

445:12 447:12

450:13 487:5

489:8

basics 49:23

116:11 343:10

343:11 345:1

basing 408:13

basis 7:22

233:12 312:18

316:23 317:3

324:7 325:26

342:6 384:11

424:6 431:14

batteries 246:9

battery 9:14

11:5,6 13:16

13:24 16:5,7

16:20 17:4,9

18:20,22,23

19:1 20:13,25

76:3 126:3

127:26 128:1

135:12,13

157:13,19

158:1 168:3,7

176:11,15,25

185:10,13

301:5 336:15

365:26 366:2

366:21 380:17

395:27 414:18

418:18 422:27

436:21 446:4

451:8 455:4

458:25 468:25

Bayer 354:22

355:5,7 367:1

Bayesian 204:9

209:18 216:24

216:25 217:3

218:12 446:27

448:24 449:1,4

473:21 474:4,7

474:16,25,27

476:3 489:1

500:7

bear 33:13 223:3

300:1 468:27

bearing 303:19

beautifully

172:12

Becker 316:8,8

316:11 324:18

325:5 329:3

341:12 343:25

bed 31:7

beg 427:20

began 15:23

19:6

beginning 18:18

20:23 163:22

249:11 301:1,3

301:19 302:11

302:19,23

303:21 383:2

486:8 509:18

begins 478:16

behalf 7:19,20

310:8 326:12

330:26 375:12

519:6

behavior 42:12

79:2 313:7

462:1

behold 344:22

belabor 457:16

believe 18:26

19:19 50:3

71:8 76:16

81:19 83:20

126:10 154:18

154:26 171:22

184:23 198:16

205:13,20,21

210:7 218:9

239:1 242:8

284:19 291:11

303:23,25

334:17 336:12

339:24 352:26

391:26 394:17

394:25 395:24

396:14 397:2

413:10 445:19

445:26 471:25

484:1,9 488:2

489:22 490:7

believed 506:6

bell 206:3 211:9

211:10,16,16

belong 143:10

152:5 166:13

benchmark

120:4 294:12

294:25 295:1,2

318:22 319:12

319:14,22

336:27 340:24

341:2 350:8

416:22

benchmarked

343:26 348:23

351:22 353:9

benchmarking

336:22,24

339:19 350:7

bend 23:22

beneficial 443:8

benefit 203:16

214:8 350:3

441:24

benefits 17:26

376:24

benfluralin

370:24,26

371:19 373:5

373:14,16,21

benzanthracene

99:16

benzene 79:6

99:16

benzyl 80:5

100:26

Berrocal 5:26,26

67:18 68:2,3

68:13 189:27

189:27 216:23

219:27 220:1

242:11,12

268:5,6 269:6

269:7 378:14

394:3,5,6

399:2,10

411:14,14

448:14,15

451:2 471:15

471:16 478:9

479:1 481:1

482:17 483:19

483:20 485:25

488:15 491:26

491:27 492:4

496:20,27

498:21 499:11

499:23 502:15

503:15,16

511:18 515:2

515:13

Berrocal's

453:14 478:4
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375:25 440:24

498:5

beta 34:18 38:25

65:13,15 66:26

67:1,4 142:20

175:25 349:7

beta-beta 34:19

65:13

beta-estradiol

92:20

beta-specific

65:18

better 22:27

24:9 27:12

31:5 53:5 60:3

60:9 88:20

89:16 115:7

116:27 118:16

134:4 136:7,26

156:14 167:19

167:24 195:15

195:16 205:15

207:6 216:10

235:8,8 237:8

237:17 241:8

244:5 259:27

267:21 294:14

301:26 309:7

311:9 315:6

322:22 326:16

327:23 338:8

340:14 354:1

382:21 384:8

384:11,20

386:3 395:18

395:25 396:7

396:12 400:26

414:21 432:5

435:13 441:5

441:27 452:16

464:21 474:25

481:24 487:14

489:17 500:4

503:20

beyond 16:12

45:25 52:4,16

52:22 96:8

234:19 253:25

260:10,23

278:2 280:27

282:7 307:17

439:12 469:6

484:6 499:19

499:20 512:24

BG1 103:13

121:23

BG1-Luc 348:1

bias 69:24

bid 37:25

biding 70:3

433:8

big 29:2 37:12

40:6,17 47:15

62:20 72:11

74:18 76:5

84:8 85:26

86:18 87:9

89:24 206:27

211:9 214:23

224:5 236:25

247:14 248:21

251:10 258:8

305:10,11

339:5 401:16

414:19,20

415:5 420:22

499:2 517:24

bigger 40:13

236:27 249:25

259:3 273:14

330:18

biggest 47:13

71:18 78:21

247:16 248:12

248:23 249:5,6

506:4

Bill 520:1

billion 252:2

billionth 199:22

bimodal 75:18

76:13 157:2

bin 201:5 218:19

218:22 219:9

219:13,14,17

219:17,23

232:25 258:8

binary 423:6

bind 35:4,5 41:7

55:5 58:23

69:1 76:21

166:9 173:19

218:25

binder 273:5,7

binding 16:22

17:4 34:1,16

34:23 35:18,20

36:1,7,7 37:18

46:25 47:8

52:13,16,23

55:15 58:23

61:13,15,27

62:18 87:20

97:25 99:11,23

103:23 129:6

130:4 134:19

134:23 135:15

136:12 139:9

142:4,5,5,6,8

142:11 143:21

144:10,12

148:8 149:15

149:19 150:4

151:9 153:21

154:27 155:8

157:23 162:12

162:20,27

163:3,13,14,22

163:24,27

164:8,21

165:10,10,10

165:14,18,19

166:5,16,17

170:24 171:27

173:21 174:3

175:16 182:10

185:11,23

227:22 234:16

246:23 248:12

291:4 293:23

296:10 298:4

310:27 321:4

333:13 338:17

342:24,25

346:15 358:18

371:25 372:15

373:4 377:18

378:11 379:8

389:3 412:26

426:27 433:27

434:3 443:6

451:6,8,11

452:13 455:1,2

455:3,3 456:17

456:20,22,24

456:26 457:2,2

458:19 464:27

466:9,22

467:16

binds 33:14 34:3

35:5 76:24

143:24 163:2

170:5 173:24

176:1 225:24

binned 257:26

binning 257:26

260:3,15,18

bins 23:24 24:8

49:3,4 50:11

91:24 232:24

233:5 234:4

258:19 356:15

bio 212:16

276:27 355:1

bioaccumulati...

191:15

bioactivated

90:22

bioactive 104:21

104:23 119:1

131:17,19

138:22,24

139:4 166:20

183:7 186:21

261:19 263:18

265:1 266:27

270:18 294:7

372:19 471:6

479:8 506:7,11

bioactivities

126:5 136:3

138:16 183:24

282:7 287:15

287:18 290:3

380:26 393:26

443:4 454:18

455:12 460:15

477:17

bioactivity 1:5

2:7,9 3:6 8:24

8:25 9:7,10

10:17 13:2,9

13:10,20,21,26

14:16 19:10,11

19:27 20:6,12

20:20,21,27

21:8,25 22:4,6

22:12,20,25

23:14,17 24:5

101:24 104:5

104:18 124:26

125:19 127:18

127:23,25

128:6 132:9

136:22,24

138:19 143:13

145:19 151:2

151:26 152:19

155:2,18 159:5

161:14 165:23

166:16 177:24

187:18 189:3

194:8,10,10

195:11,27

196:17 212:25

223:10 224:24

227:7 230:15

230:16 231:26

233:17 243:1

244:14 245:24

245:26,27

246:3 247:1

250:15 255:11
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273:20 275:21

277:15 278:6

278:16 279:9

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280:3,10,11

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287:6,21,23,24

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300:5,22,23

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314:21 315:1

327:13,19

330:4 331:12

332:27 336:7

337:25,25,27

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351:7 353:3,11

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370:26,27

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379:5 380:4,9

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398:17 399:24

412:18 422:4

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440:5,10,12,14

440:18 441:8

441:23 442:1,4

442:4,8,9,17

442:21,24

443:19 444:9

444:10,17

451:18,20,23

451:25,26

452:16,17

453:2 456:1

457:27 458:8

460:2,13,14,20

461:2,8 463:1

463:11 464:1,4

465:4,5 466:14

469:22 470:24

470:27 471:3

472:2,14,16,18

472:25 473:5

473:17,23

475:20 476:1

478:27 480:25

481:16 483:26

484:16,21

485:5,11,19

488:23 496:3

497:18,19,22

497:27 499:6,6

502:12 503:22

506:16,17

507:1,13

508:15 511:24

511:25 512:13

518:21

bioassay 115:2

118:3,21

129:14 181:3,4

261:1,4 511:5

bioassays 54:19

180:23 262:1

264:21 350:9

bioavailability

106:12 116:4

226:7 257:19

257:23

biochemical

142:4 160:8

455:17 474:22

biochemistry

6:3 190:3

411:17

biological 15:15

29:22 42:26

107:21 141:8

141:14 143:16

166:1 190:9

195:24 201:14

227:27 230:22

231:13 234:21

294:4 297:27

298:16,24

322:8 336:20

336:21 342:9

342:14 349:26

353:6,24

355:27 356:16

362:25 367:19

370:12 377:12

377:15 384:4

403:4 411:22

416:16 418:8

435:16 449:26

454:4

biologically

68:10 141:16

152:12 162:6

328:15 345:16

345:18 427:6

456:27

biology 6:3,7

26:8 29:14

30:7 32:4

33:11 43:11

54:6 71:10

75:26 107:19

144:3 169:9

171:22 172:20

190:3,6 247:22

274:27 297:24

308:16 309:6

321:17 322:2

375:15 376:16

411:17 486:13

512:20

biomarker

203:16,20

biomarkers

203:15,21

248:7 494:19

biomass 474:5

biometrics

198:17

biometry 505:13

biomodeling

474:22

biomonitoring

195:5 208:5,9

216:12 226:2

249:1 315:6,10

317:27 376:21

377:8 479:3,4

492:7,13,16

500:2 504:2,16

504:17

BioSeek 76:4

Biostatistics

5:27 190:1

411:15

biosynthesis

25:6

biotransforma...

90:10 420:26

420:27 506:5

birth 335:13

Bishop 309:18

310:5,7,7

bisphenol 53:21

74:15 208:11

208:12,13

217:19,25,27

226:2 249:16

250:16 251:23

252:12,24

257:22,26

264:6,17

272:16,21

273:21 274:9

274:11,12,25

275:1 276:1,16

bit 12:5 15:14

16:17 19:3

21:27 30:11,13

31:17 33:13

37:20 39:6,9

42:27 43:11,13

46:16,17,17,17

48:22,23 51:3

56:5 69:23

74:1 85:19

102:9 103:6,8

112:13 113:17

122:5,5 124:18

125:5,15

126:11 132:12

132:24 133:22

134:6,20

136:16,17

138:3,9 143:17

177:21 180:10

182:17 183:3

192:8,18 193:9

194:2 204:11

204:24 234:18

243:6,12 245:8

245:9,14,21,23

246:12 250:9

251:21 258:10

264:9 266:13

270:26 272:19

274:26 275:13

276:20,27

277:18 284:5

290:23 291:17

297:24,27

298:2 301:25

306:6 308:16

317:1 318:7

320:4 331:24

334:2,6 335:11

340:24,27

341:15 342:21

345:11 348:24

349:24 388:17

390:20 401:17

402:19 405:16

414:21 415:8

416:13 425:18

436:26,27

438:7 442:11

469:4 482:7
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 15

485:16 486:12

491:16 493:19

497:23 513:25

BLA 157:20

158:10,18

black 41:23

44:13 132:21

148:8 183:16

323:4 402:8

blame 24:18

blindly 479:25

blistering 234:9

block 343:19

360:10

blocked 346:24

blood 198:20

199:7 208:6

216:4 229:4

249:3 351:27

369:23 370:4

bloodstream

344:4,6

blow 272:18

blue 33:11,11,25

33:27 41:8

44:2,10 46:17

48:22 62:6,13

62:14,17,23

63:1 74:27

77:25 78:3,16

79:13,15 88:14

93:13,13 111:2

111:18 115:17

128:14 132:18

148:12,23

183:21 202:20

203:2 209:23

268:24 274:2

472:14

board 14:24

117:3 332:1

333:12 389:1

497:27

Bob 6:5 190:5

411:19

body 106:16

223:14,25

225:26 226:4,5

226:9 227:23

228:23,23

230:17,18

253:22 283:22

306:1 335:7,8

335:10 372:5

461:27 472:19

505:16

bogged 67:26

boil 469:26

bolus 74:18

bona 165:13,15

165:16,18,21

book 195:14

197:4

boosted 90:17

boosting 476:3

border 450:18

456:18

Borgert 326:6

339:27,27

born 103:19

bother 381:21

bothers 138:3

bottom 39:3

40:1,7,18 41:8

45:10 46:9

48:12,15 62:13

63:3 66:21

73:5 76:3 78:3

79:14,19 80:26

83:18 84:26

107:1 114:10

117:13 146:25

147:6,13

148:24 156:7

192:10 255:1

272:22 273:23

329:13 360:9

374:7 452:14

bottom-left

79:19

bottom-right

41:12 70:3

79:13

bought 38:1

bound 114:5

227:2 228:16

229:1,8 295:9

295:9

boundaries 92:2

bounds 353:10

353:11,13,13

353:20

bovine 37:19

53:14,17

bowl 235:22

box 49:27 75:8

192:25 202:20

203:2 272:18

378:25 487:13

box-and-whis...

49:1

boxed 48:20

boxes 20:17

323:4 402:8

425:6

BPA 46:3,4

106:20 111:9

112:14,17,20

133:10 184:15

184:15

brain 342:22

511:15

brainstorming

493:20

branch 79:11

505:2

branched 79:9

133:12 139:5

branchy 79:7

break 50:17,18

50:24 67:14,19

102:8 140:8,8

216:22 272:1

276:26,27

277:4 299:9,12

309:20 354:23

355:1 459:16

breakfast

236:24 249:7,8

breaking 512:20

breakthrough

207:15

breathed 202:22

bridge 223:10

224:23 233:17

280:3 431:9

brief 13:4 67:16

94:15 122:14

128:4 140:11

216:17 298:27

299:18 304:22

354:25 376:9

387:10 459:18

briefly 12:4

236:8 350:27

356:3 360:2

382:8 457:19

495:26 520:14

bright 91:27

183:25 258:20

259:4 275:11

280:13 285:27

288:5

brilliant 210:3

bring 15:23 20:2

21:12 26:5

53:1 89:4

122:10 189:13

220:25 243:6

250:7 260:15

265:13 278:21

280:16 301:1

302:7,12

303:21 304:2

318:8 327:22

331:18 332:3

341:7 426:1

450:1 476:1

483:6 486:22

bringing 9:27

19:27 26:7

299:27 316:25

430:1 518:15

brings 11:9

178:8 184:9

300:13,19

320:3 401:8

broad 29:23,24

109:16,17

211:10 311:10

377:7

broaden 302:19

broader 19:12

21:2 25:12

76:5 196:20

302:20 322:27

356:12

broadest 377:17

broadly 10:13

237:16

broke 207:1

broken 213:9

301:7

brought 5:8

14:22 19:15,18

19:22 21:3

27:8 184:9

265:10 293:22

294:21 300:17

302:1,18

402:20 405:2

407:5 413:20

414:25 419:24

420:3 421:7

423:9 425:5,9

425:21,23

438:27 446:7

450:17 464:2

467:13 483:25

490:17 505:24

507:26

Brown 6:2,2

26:9,10 97:15

97:17 98:6,13

98:16,19,22

100:5 137:7,8

137:8,12,15

162:10,11,15

162:19,24

163:11,14,18

164:1,6,12,16

164:19,20

167:24 168:2

171:2 173:8

175:3 178:18

190:2,2 264:12

264:13 265:22

266:11 267:22

275:19 295:21

295:22 411:16
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 16

411:16 440:20

440:21,22

445:1 451:2,4

452:10 453:10

454:20,21,22

458:12,18,22

460:7,8,9

462:7,10 463:3

463:3,5 464:20

464:25 466:20

467:23 468:7

469:26 470:8

512:6

Brown's 29:4

37:6 100:11

267:20,24

445:3 458:13

Browne 124:23

125:2,3,9,12

136:20,25

137:6,11,13,18

138:13 139:2

139:24 140:1

177:7,15

187:22,24

267:15 277:7

277:11 288:15

288:17 289:3

289:20 290:22

297:26 308:15

379:1 380:2

391:14,16,19

391:22 392:14

392:20 393:23

399:20 407:16

409:18 412:12

416:23 421:23

432:23 433:2

438:23,26

439:17,25

454:9,15

459:10,25

462:18,24

464:7 469:13

470:13,21

476:17 480:15

480:22 495:22

496:7 502:9

508:25 509:8

516:25 519:15

Browne's 177:1

277:1 378:10

378:20

brush 254:14

build 19:8 78:27

84:9 86:4,10

122:4 275:18

309:9 402:18

405:20

building 1:15

18:25 19:13

122:6 125:14

222:26 302:25

305:15,22

442:7

built 31:8 64:4

64:15 86:19,24

86:26,26 89:21

108:26 238:21

248:25 302:25

308:17 309:8

405:11 417:12

bullet 10:13

233:21 302:13

bullets 350:4

bumps 497:4

bunch 77:3

269:19 293:2

bunched 213:4

bunks 45:14

burning 299:6

burst 72:12,15

72:18 73:3,4

73:26 78:11

148:21 149:27

150:16 153:21

153:26 154:4

156:6 364:1

bursting 73:27

236:15

bursts 73:10

business 197:25

butlyparaben

111:13

button 233:26

237:25

butyl 80:5

100:26

by-product

164:26

C

c 116:18 120:9

142:9 379:20

calculate 43:19

44:10 45:5

245:27 247:4

250:26 251:11

319:7

calculated

331:27 346:1

455:26 456:5

calculating

381:26 389:16

calculation 45:9

332:5,19 345:4

443:11,14

451:12 452:6,8

452:11 456:3

calculations

53:19 332:7

345:8 503:23

503:24

calendar 182:22

291:9

calibrate 198:2

499:4

calibrated

205:18 213:8

215:17

calibration

45:22 145:4,6

145:11 254:3

493:16

calibrations

145:18

California 6:22

190:21 412:6

494:17 504:16

Caliper 38:1

call 20:8 33:2

38:3 39:24

49:21 73:22,23

84:26 88:6,8

89:3 92:1

117:2 133:7

184:7 199:25

200:4 202:6

203:6 219:8,23

274:19 275:8

285:16 322:15

364:1 367:18

492:8 513:22

called 38:27

41:17 55:13

73:10 76:4

81:23 86:14

148:1 184:20

233:24 250:20

254:2 281:15

318:12,16,20

352:18 372:22

calling 12:6

36:11 56:6

117:8 166:24

166:26 415:14

calls 83:8 117:7

240:26 520:1

Canada 103:8

Canadian 87:11

Canadians

87:12

cancer 375:13

375:16,27

candidate 15:26

candidates

320:2

capabilities

401:11

capability 427:1

capacity 17:23

336:14 395:22

481:20

capture 24:17

24:21 25:12,14

80:19 96:7

130:3 176:2

241:23 304:8

377:17 433:19

442:8,23

452:22 453:4

456:12 465:1,2

captured 110:3

442:27 451:21

451:25 453:5

458:8 461:8

463:7 468:11

505:21 515:21

515:21

captures 453:1

capturing

377:19 443:21

452:14 504:20

504:22

carbora-like

437:4

carboxy-termi...

173:24

cardiac 231:11

care 69:1 428:9

careful 176:10

261:7 444:13

455:9 510:21

carefully 510:26

cares 428:6

Carlo 209:18

210:13 211:13

231:16 234:1

236:24 240:6

240:10,25,27

241:4 372:20

472:17 485:2

Carlo's 231:2

carrot 404:5

carry 243:16

carryover 467:3

cartoon 33:5

70:2 71:22

74:15

cartoons 384:9

cascade 56:19

56:22

case 24:12 27:16

32:25 39:8

46:18 51:19

62:12 107:8

108:13 118:11

119:12 129:18

141:1,6,15

144:27 145:17
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 17

152:6,14

155:24 162:8

164:3 166:17

170:13 173:2

179:22,24

184:4,27

193:20 206:10

217:23 221:3

245:6 250:10

250:12,12,14

254:11 256:17

259:15 263:14

270:13,13,18

270:19 274:18

275:4,15 279:6

285:23 293:21

304:25 312:5

318:17 338:23

352:17 357:15

360:4 361:2,17

363:2,9 366:21

370:19 402:11

414:12 427:23

437:5 445:13

447:12 466:20

474:19 488:13

490:9 492:11

493:16,18

case-study 356:2

cases 23:13 66:4

81:10 153:27

192:11 199:20

209:2 245:15

261:3 293:26

306:11 314:4

320:1 362:24

366:2,16 387:2

400:14 427:5

446:21 479:7

479:11

Casey 32:14

91:23 98:14,17

102:17,18,26

119:27 120:6

120:15,24

121:1,6,10,20

122:20 123:12

123:14,21

124:10,20

140:23 391:27

404:2 432:13

509:8 519:25

Casey's 405:13

cash 87:15

cast 245:18

catch 441:14

443:20

categories

183:14 413:16

415:8 454:23

490:9,27

categorization

273:3 415:14

categorize

415:10

category 493:18

CATHERINE

6:8 62:3,16,21

62:24 63:4,14

63:18,24 67:6

67:12 121:14

123:10,13,15

124:15,17

139:15,25

190:8 288:18

411:21 428:3,8

428:12 430:6

433:14 439:9

448:4 464:14

468:18 470:6

481:3 492:20

494:1,10,13

496:13 500:16

502:17 507:25

508:17 513:2

CATHERINEN

407:3

Cathy 6:8 190:8

411:21

cause 72:25,27

83:11,14 116:7

157:9 170:5

192:23 194:10

230:3,15,20

232:2,6 275:12

285:12 297:22

415:2

caused 416:21

causes 35:6 36:3

36:13 40:19

55:19 76:24

77:5 104:24

106:26 143:22

143:24,25

170:24

causing 40:16

72:15 311:13

caution 275:8

388:4 492:26

cautious 33:1

caveat 135:9

219:20 222:10

353:16 363:5

caveats 135:2

507:3

CCL 15:27

CCT 164:25

CDC 199:2,3,7

199:27 203:14

208:23 209:1

209:24,26

210:15 211:2

213:8,10,19,26

214:1,6,7,19

214:25 215:6

216:5 217:16

218:4 220:16

220:18 222:2

222:14

CDC's 241:7

ceiling 493:5

cell 31:4 34:26

35:13,14 36:15

40:20 46:26

47:1 49:18

57:21,23 58:24

59:2,14 64:11

71:26 72:18,18

72:23 73:3

76:2,19 77:2,3

77:4 90:16

103:13 153:11

158:15 159:1

159:19 170:3

171:15 173:6

247:26 248:2,2

249:27 292:11

292:14,17,17

293:6,15,19

294:27 298:4

298:12 301:27

305:24 327:19

327:22 333:10

369:14,19,21

375:14 421:10

430:23 442:25

454:25 455:6

457:4

cell-base 293:7

cell-based 53:17

71:25 95:9,22

154:17 168:8

171:10 174:2

178:10 292:8

293:5,17

394:12 444:16

cell-free 35:20

53:9,11 71:25

174:3 292:6,15

292:25 293:4,6

cell-free/cell-b...

294:27

cell-stress 29:26

71:21 75:27

cells 32:6 34:9

35:5 37:19

39:2 57:1

71:26 72:21,25

72:26,27 73:1

75:16 76:4,10

76:11,26 80:1

81:3 95:11,23

159:15 247:12

247:20 292:10

292:16,26

305:24 455:11

cellular 6:6 25:7

105:12 190:6

292:9 389:3

395:23 396:5

420:15 421:11

censoring 91:14

center 1:14 5:18

6:12 73:10

140:21 189:21

190:12 249:22

375:9,12

411:10,25

504:13 505:12

centered 154:6

Centers 198:14

central 196:8,21

196:25 197:16

247:4 252:4

353:13,19,27

484:7,9 488:19

century 195:13

315:12,27

CERAPP 86:14

302:3

certain 40:14

66:4 72:25,26

83:25 93:15

108:17 110:5

111:8 144:21

144:23 145:1

147:9 178:8

207:4 216:3

217:17,18

222:4 223:18

236:15 239:7

265:25 275:2

281:15 383:7

403:25 414:16

424:25 483:2

505:7

certainly 23:27

36:23 40:11

42:10 46:10

47:15,16 60:17

61:9,11,11,27

64:9 82:9

95:27 100:2

123:18 134:15

137:24 145:17

145:23 146:18

150:20 154:20

167:5 179:15

182:22 196:2

208:3 222:24
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 18

240:22 241:11

247:25 256:24

259:1 269:18

271:4 281:5,6

291:5 292:24

323:5 327:15

327:20 330:18

349:10 386:15

387:11,22

388:15,22

418:20 445:22

454:27 470:2

489:19 490:2

490:14 491:3

493:10 509:11

514:27

certainty 511:22

cetera 26:14

325:11 344:3

351:5 376:18

389:3 457:6

467:7 475:15

Chain 209:18

210:13 211:13

chair 4:22 5:5

6:6 11:12

28:14 189:18

190:6 392:16

407:26 411:7

519:2

chairing 519:1

challenge 510:23

challenges

245:14 281:18

challenging

145:27

chance 64:2

185:16 217:18

430:9 486:20

chances 504:15

change 68:17

110:13,13

114:20,20,22

117:5 165:10

236:3 241:26

246:1 282:20

290:14 306:10

324:17 344:23

344:23 345:17

345:19 359:24

365:25 369:11

382:1 414:26

420:23 482:17

500:5

changed 195:2

364:14

changes 26:22

173:27 324:22

335:22,24

347:6 360:20

415:2

changing 39:4

142:9 385:9

397:18

chapter 191:6,7

191:9 194:1

216:18,18

223:7

chapters 191:4,7

characteristic

74:9 250:3

394:24

characteristics

118:14 163:3

456:22

characterizati...

412:14 421:27

422:21 433:4

characterizati...

24:22

characterize

47:20 100:27

216:8 217:4

343:5

characterized

113:7 368:14

370:15

characterizing

433:9

charge 2:13,14

2:15,16,17,18

2:19,20,21,22

2:23,24,25

5:12 12:9,15

12:23,25 13:17

13:25 16:23

17:5 20:22

21:12 68:20

89:12 126:10

175:1 260:5

278:10 288:20

299:23 300:7

303:24 304:19

327:9 331:8

337:21 351:3

378:16 379:18

379:19,20,23

379:24,27

380:17,19,23

380:27 381:8

391:15 392:11

392:14 393:21

393:23 399:15

399:18,20

400:2 410:6

412:8,8,10,12

412:20 421:19

421:21,25

422:6 431:26

432:16,27

433:2,12

439:12,14,15

439:19,20,23

439:25 440:12

440:15,20

441:20,22

454:7,12,13,15

454:20 459:17

459:20,23,25

460:7 462:15

462:22,24

463:3 464:26

469:16 470:16

470:17,19,21

471:7,10,15

480:18,20,22

495:27 496:12

498:10,11

499:19,20,21

499:25 500:23

501:2 502:4,7

502:9 507:9,9

519:5

chart 47:16

225:9 432:12

charts 263:16

401:4

chasing 403:4

Chaudhuri

368:1

cheap 86:20

cheapen 509:22

check 281:9

checking 328:11

390:4

checks 217:6

cheerful 191:27

Cheerios 249:7

chelater 46:10

chemical 5:25

7:14,20,26

11:8,26 19:20

31:9 32:10,27

33:14 34:11

35:19 36:16

39:19,25 41:3

42:6,19,22,23

43:4 44:19

48:1,3,20 52:3

55:11,19 63:21

65:27 69:1,12

71:24 72:5,6

72:12,13,14,24

73:6,22 74:9

74:14,15 76:23

77:1,19 78:23

80:21,26 81:5

83:11,13,14,16

83:21 84:15,16

84:20 86:1,19

87:8,9,17 88:5

88:26 89:4

90:11,22 91:10

91:12,16,26

96:18 98:11

99:8,11,21,23

104:21 105:20

106:15,16

110:24 111:1,7

111:10 113:6

113:11,15

114:8 116:1

119:16,22,23

120:3,10

128:16,18

129:7 130:23

131:2,21 133:2

133:11,16,21

139:18 141:25

144:19 152:25

153:16 161:2

163:23 166:19

170:6 172:16

178:26 179:6

181:1,18 183:4

183:12 184:8

184:22,22,24

186:3,13,19

187:2 189:26

191:25 192:6

194:26 195:26

196:15,19,23

196:26,27

197:12 198:6

198:20 199:19

199:20,21,26

200:7,8 201:23

203:20 204:20

206:17,23,25

206:27 207:3,3

207:18 208:20

209:3,6,11,21

211:1,18

212:26 213:6,6

214:5,5,7

217:1 218:5

219:3,3,4,5,11

220:10,10,27

221:1 223:25

225:2,8,24,27

226:21,25,27

227:10,22

228:4,10 229:1

229:6,8 233:12

235:7 237:14

237:19 238:6

239:7,8 245:20

251:7 252:12

265:8 269:16

272:12,14,15
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 19

272:18,20

273:26 275:16

276:2,4,16,22

281:6 283:12

283:20 284:1

284:10 285:11

285:17,24

290:4 292:25

293:6 297:10

300:15 301:14

310:21 311:4

311:10,22

312:14 313:5,7

314:27 318:21

336:10,22

337:11,24

339:19 341:11

343:5 345:5,9

345:12,23

348:20 349:10

352:17 365:17

367:11,13

370:8,14,14

376:5,16 377:4

387:26 393:8

395:2 396:8,10

398:13,17

402:14,21

403:7 408:4,11

411:13 415:23

423:16,18

444:13 445:21

450:9 455:8

457:13,23

460:15 461:21

464:6 471:3,5

472:1,2,21

474:4,5,16,26

477:15 492:16

506:7,14 518:3

chemical's

127:25 380:3

380:15 422:4

433:9 439:26

440:11

chemical-inde...

252:5

chemical-to-c...

220:17,23

chemicals 2:7

10:14,15,19,23

11:3 14:20

15:4,5,8,14,18

15:21 16:2,3

17:12,15,22

25:1 27:1

29:11,27 30:14

30:17,21 31:21

31:22 35:12

36:17 38:22,23

39:11 40:10,12

42:22 47:4,6,8

47:12,14,26

49:3,7,9,10,11

49:12,23,25

50:1,4,10,11

52:24 53:3

55:21 56:13,22

57:16 60:10

61:2,4,20

65:17,17 69:14

71:1,23 72:25

73:4,8,20,21

74:8 75:15,17

76:11 79:3,4

79:21 80:18

82:6,8,26 83:1

83:6,7,19,22

83:25 84:7,13

84:17 85:6,9

85:13,18 86:1

86:11,20,21

87:10,18,24,25

88:3,11 89:2,7

89:18,23,26,27

90:9,13,21,26

91:24,25 92:8

99:13,15

101:15 104:11

106:18 108:5

108:11 109:9

109:10,12,14

109:17 110:8,9

110:23 111:8

112:2 113:2,3

113:5,9,24,26

113:27 114:3

114:26,27

115:6,7,10

118:27 119:1,4

121:19 122:25

124:27 125:26

126:13,16

127:1,4,6,10

127:22 128:22

128:23,27

129:1,2,3,9,12

129:12,23,24

130:19,19,20

131:7,10,17,24

131:27 132:2,6

132:15,16,18

132:20,24,27

133:2,5,14,19

134:17,17,20

134:21,24,26

135:1,5,20,22

135:25,26

136:3,5,8

138:18 141:17

141:20 142:27

143:3,7,9

145:26 146:2,6

146:8,10,15,17

146:19 147:2,6

147:7,8,15,17

151:4 152:10

152:14,19,21

152:24 157:22

158:2 159:6,8

159:22 160:26

161:2,5,8,9,10

161:12,13

167:18 170:14

172:12 174:15

174:15 176:24

178:6,20,22

179:8,12,17,25

180:2,3,3,9,15

181:14,27

182:4,8 183:5

183:8,10,11,15

183:16,17,20

183:23,27

184:2,6,20

185:20,22,23

186:1,1,4,5,7

186:14,19

187:3,4,6,12

187:15,16,17

192:5,9,11,13

192:14 193:19

193:21,26

194:4,7,12,13

194:14,15,17

194:18,18,23

194:25 195:2,6

195:8 196:21

197:23 198:1,8

198:10,19

199:4,6,9,10

199:10,11,15

199:18,27,27

200:1,5,11,12

200:14,15,16

200:22,23,24

201:1,1,4,6

202:2 203:7,8

204:6,8,22

205:16,19

206:2 207:26

207:27 208:2

209:23,24

210:1,6,8,9,20

211:3,5,17

212:3,21,22,22

212:25 213:1,2

213:4,13,18,22

213:24,25

214:2,4,9,10

214:11,15,17

214:18,20,21

214:24,25

215:3,7,7,9,9

215:15,18,20

216:4,4,5,6,13

216:15,15,15

216:27 217:12

217:13,17

218:6,19 219:1

219:2,7,8,16

219:18,20

220:3,4,18

221:9,11 222:8

224:21,23

225:6,6,10

227:12,13

230:9 232:20

232:21,24,25

232:27 233:2,4

233:9,16 234:4

234:7,11 235:4

236:18 237:5

237:11 238:4,7

238:18,22,25

239:2,6,15,21

243:14,15,21

243:21,24

244:8,20,20

245:21 246:4

248:8,9,14,18

248:20,22

249:9 254:18

254:21,27

255:2,15,16,18

255:25 256:3,4

256:8,14,20,26

257:2 258:7

263:19,23

264:7 266:16

267:16 270:1

271:5 272:10

272:10,13

275:8,11,27

276:14 278:12

278:16,24

279:17,19

280:11,14,18

281:15,23

282:13,14,22

282:24,27

283:6,11

284:26 285:1,8

285:13,14,16

286:7,10,11,24

286:27 287:2,4

287:5,11,21,23

288:1,2,2,3,6,7

288:8,11 290:2

290:2,7 291:6
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 20

291:22,23,24

292:10,11,21

293:8,8 297:12

298:9,17

301:14,16,22

311:8,10,26

312:13 313:11

313:13 314:5,7

314:11,22

315:2,7 316:3

319:2 321:7

329:24 330:12

333:27 334:8

334:11,19,21

335:17 336:6,8

336:27 337:7

338:23,23

341:16,21,27

342:10,17

343:3,7,14,22

344:1,20,25

348:23 349:13

349:17,24

350:21 352:15

353:1,6 354:6

355:12,21

356:5,13

360:13 361:21

361:24 362:9

362:20 365:9

366:23 367:24

368:27 370:16

370:17 374:27

375:1,3,22,24

375:26 376:13

380:13,25

383:7,13

384:23 385:12

387:27 388:4,5

391:1 392:3,9

393:25 394:19

394:22 395:16

395:21 396:9

396:15 397:8,9

397:17,19,22

397:22 398:4

398:20,20,25

398:27 408:9

412:14,17

413:2 421:27

422:22,22

427:14 429:4

431:15 432:6,7

432:9 433:4

435:27 436:4

436:12 437:17

438:10 440:9

442:22 444:7

444:10,18

448:20 449:19

449:21,22

450:4,17

454:17 455:20

456:2,7,9,14

458:2 459:1,12

460:17 461:11

462:27 463:12

464:8 465:18

466:3,9 469:18

469:19,21

470:22,26

472:6 474:9,21

475:1 477:16

477:26 479:2,3

479:8,9 480:24

483:9,24 484:3

484:4,13,15,20

490:14 491:5

492:8,12 496:3

496:5,22,23

497:11,14

498:18,25,25

498:26 499:3

499:15 500:1

502:11 503:12

505:10,15,17

506:6 507:1

511:14 513:12

516:19

chemicals'

394:13

chemist 6:12,16

218:6 227:12

chemistries

138:12

chemistry 29:20

32:19 71:10

155:7 160:7

199:17 227:5

228:6 316:8

323:1 326:21

chemists 79:5

Chen 347:21

chest 18:9

chew 124:18

children 213:9

222:4 234:20

492:22 495:1,3

504:10

Children's

504:12

chimp 142:6,11

157:23 451:7

chips 202:23,24

202:24,25

203:1

choice 50:8

113:3 162:12

162:15 170:9

216:25 262:24

390:7 415:3

456:17 480:3

choices 332:17

478:7

choose 27:15

365:26

choosing 89:7

113:2 274:16

274:21

chose 68:9 95:24

122:19 201:17

274:18,22

276:16

chosen 95:8

161:25 194:13

194:17 235:21

239:6 274:15

Chris 326:6

334:2 337:19

339:27,27

Christian

207:24

chromosome

76:20

chronic 341:3

374:9

chunk 89:25

circle 129:16

179:15 262:16

469:16 495:26

circled 75:4,5

circling 135:18

circulation

305:16

circumstance

452:18

circumstances

341:6

cis 38:12 57:27

58:9,16,18

59:9 75:16

citations 469:2

cite 332:9

cited 95:23

443:5

claim 224:16

232:14

Clangsure 265:7

Clangsure's

253:3

clarification

21:23 28:14,20

94:5 164:13

165:5,16

175:14 176:6

314:15 324:13

325:2 331:26

378:19 408:2

432:21 439:15

469:24 470:14

495:11 496:1

500:27 507:19

clarifications

329:12 351:16

421:18 459:11

clarified 331:3,4

385:4,20

393:18 471:23

476:11 497:4

clarify 22:10

52:16 53:8

163:11 183:22

270:17 320:8

329:17 346:7

370:7 381:25

393:10 419:18

476:19,25

495:7,26

499:14 500:7

507:21 508:24

509:10,11

clarifying

167:23 376:27

391:16,19

392:18 393:4

408:1 409:1

419:15,23

432:3 438:23

462:19 467:23

472:3

clarity 312:4

387:14 388:9

388:18 389:3

441:18,25

443:5 445:13

445:14 456:2

463:26 472:9

class 29:18

83:13 152:25

158:6 160:5,9

196:15,19

220:10,10

237:11,14

262:5

class-based

200:18

classes 82:26

83:14,16,21

100:6 160:1

239:7,8 334:5

334:7 398:26

456:8

classic 343:1

344:4

classical 167:2

176:5 345:7

444:25 456:20

classification

260:3 397:21

classified 55:17
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 21

253:3 255:17

356:21 397:18

408:12

classify 113:23

476:23

classifying 71:1

120:4

clean 381:18

425:13

cleaned 85:5

Cleaning 326:22

clear 28:25 34:4

46:18 55:2

91:3 127:16

128:17 136:11

138:19 163:25

172:26 183:2,9

282:3 292:7,13

302:16 313:8

316:18 322:25

345:27 350:14

362:4 363:7

381:22 409:15

419:3,22,27

420:13 429:11

429:18,24,26

432:2,11

446:11 454:8

458:6 465:24

472:23 474:9

475:22 476:10

476:14 477:7

477:14 478:19

478:21 480:14

480:16 494:26

507:17,20

508:14 513:22

515:5,19,23

clearance

226:13 227:24

228:18 229:3

231:7 246:22

248:13 373:3,4

373:8 379:7

414:9 472:19

cleared 51:3

112:17 116:5

223:25 228:10

381:23 404:2

420:24

clearer 386:7,21

407:21

clearly 25:13

49:11,12 56:13

76:9 85:18

99:19 151:3

178:22 221:21

235:25 236:1,2

252:23 256:11

316:20 317:8

322:26 324:2

329:6 331:11

335:19 339:15

351:4,5,25

380:19 381:20

382:10 384:26

391:25 397:12

427:21,23

434:12,14

440:15 471:10

471:21 489:16

497:7 506:27

509:16 510:7

514:20

clears 225:25

clever 209:26

227:7 228:21

cleverly 233:24

click 233:26

237:25 273:16

273:23

clicking 33:7

clinical 224:1,9

341:26 344:15

344:23

clinically 341:23

344:15

close 39:14

58:13 61:2

75:9 112:11

115:6 170:27

189:14 195:20

212:22 215:18

244:17 245:21

246:3 248:15

257:14 263:8

264:2,8 299:1

489:2 516:11

516:12 520:20

close-up 143:14

closely 145:24

151:25 180:20

256:20 416:19

closer 22:21

125:11 138:20

154:9 175:20

271:7 313:25

397:10 495:17

514:5

closet 274:22

closing 469:1

clothing 202:2

493:21

cluster 39:15

40:3 133:1

143:6 210:8

clustered 65:7

clustering 39:12

40:16 64:24

142:26

co-activator

164:21 168:6

168:13,19,21

168:23,24

171:8 173:15

co-activators

168:7 173:19

173:21

co-authors

386:27

co-culture 95:21

co-discussants

400:5

co-factors 170:2

co-localized

132:20

co-transfection

168:10

co-varying

241:17

coactivator

465:14,17,18

465:20,23

466:18

code 234:1

codes 317:8

coefficient

241:27 242:3

cofactor 33:23

34:2 69:5,8,11

70:5 142:14

143:23 144:12

148:9 151:16

151:20 155:1

164:21 165:2

167:25 169:11

169:14,23

170:6,19,22

172:19 173:2,3

173:13 174:11

cofactors 33:18

35:5 69:8

170:1,3,12,16

171:11

coffee 276:27

cogent 346:8

coincided

361:15

coincident

360:23 362:5

collaboration

38:20 355:13

355:17

collaborations

140:6

collaborative

9:5 60:26

440:27,27

512:7

collaborators

248:1 367:1

colleague 330:22

332:4

colleagues 52:5

87:2 101:20

304:22 326:12

327:8 343:26

355:7 367:27

386:6,17 481:8

489:20 509:7

517:5,12

520:12

collect 234:14

235:1 239:27

collected 86:22

227:17 450:5

505:14,14

collecting

505:11,13

collection 76:8

227:20 239:11

266:26 311:1

505:6

Collective 86:15

collectively

266:20

color 74:25,26

Colorado 189:18

411:7

colorant 207:4

colored 39:19

colors 74:26

column 357:12

371:19 373:25

408:16

columns 39:11

39:13,13,18

combination

22:3 49:9

105:13 168:12

168:16 204:1

205:14 242:22

385:3 394:26

combinations

181:18 390:13

combine 87:23

321:21 324:22

327:13 382:12

423:15 485:13

503:22

combined 14:12

97:19 310:11

366:18 386:7

390:18,20

423:22 514:12

515:8

combines 141:5

169:1 324:20

combining

366:12
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 22

come 9:2 12:20

26:19 30:12

37:14,17 38:8

42:21 43:16,20

43:20 44:15

47:10 49:5

54:24 62:3

64:10 74:25

81:20 84:12

90:25 117:3,6

118:8 120:3

219:24 221:18

226:25 240:16

243:8 246:27

250:8 265:25

277:1 281:27

289:16 295:1

299:9 300:8

301:10,19

304:24 309:16

319:5 327:1

328:26 342:18

357:23 360:27

376:3,9 394:1

394:6,8 419:7

426:20 432:5

432:20 445:11

453:3 455:14

455:22 457:18

459:16 466:6

482:6 500:22

501:2 502:2

512:10 514:16

516:11

comers 206:10

comes 29:17

45:2 54:8

62:14 76:23

91:25 93:6,10

100:5 108:8

173:11 192:5

196:5 201:19

226:4 249:1,21

253:9 254:4

266:3 294:27

371:7 388:26

390:2 417:25

418:3 429:23

487:3

comfortable

71:11 182:5

437:23 438:15

513:15

comforted 429:6

comforting

429:1

coming 5:1

22:20,20 47:7

54:9 73:27

78:17 84:9

119:19 189:11

206:4 216:1

226:5 276:18

327:11 355:4

388:23 403:1

426:21 427:2,7

433:17 435:25

commend

331:13 390:10

514:18

commendable

386:19 433:25

commended

511:10

commending

440:26

comment 4:6,7

7:2,5 22:16

29:4 30:25

52:6 59:26

68:4,26 100:11

175:5 176:17

258:6 271:27

298:27 330:9

386:10 390:10

397:23 398:18

409:20 414:8

416:10,24

417:8,10

420:21 432:5

448:14 453:21

467:13 475:9

476:16 477:20

485:15 492:1

494:22 496:7

515:12 518:6

commentary

469:14

commenter

309:18 355:25

commenters

299:10 309:14

310:5 385:11

385:22 405:3

447:8 514:25

520:7

Commenting

394:16,24

comments 2:12

2:26 4:8,13,13

7:8 8:17 21:21

126:14 173:10

196:5 277:20

278:21 289:10

300:8 304:24

307:25 310:2,8

310:17 313:2

316:12,14,16

317:1,14,15,16

317:18 320:16

320:22 321:5

327:8,11

330:27 331:8

339:24,25

340:13,15

351:10 354:17

376:9 384:18

385:21 386:19

389:12,15

390:21,22

391:13,23

396:3 399:6,11

406:3 407:11

407:20,21,23

408:25 416:25

419:12,14,16

421:17 431:26

432:2,10,12

438:22 439:14

441:19,20

442:17 445:4

447:27 450:22

451:5 453:9,14

454:7,23

456:19 458:14

459:7 460:10

462:10 464:17

469:27 472:12

476:6,8 477:27

478:4,4 479:6

480:5,14 481:4

484:8 487:10

489:12 494:25

495:11,24

496:8 503:19

507:8,17

508:27 509:4

510:15,17

513:1 515:25

516:15,24,27

517:10,11,17

518:26

commercial

413:27

commercializa...

107:19

commercially

372:22

commitment

61:5,8 519:7

committed

51:23

committee 3:14

3:18 14:10

146:11 310:9

340:7,10

common 86:10

86:11 222:13

310:12 444:15

commonly

234:15 237:15

367:17 396:24

communicating

398:16 422:10

community 86:9

510:8

companies

37:23,25,26

107:20 296:24

296:26 297:6

452:24

company 37:27

38:27 76:4

164:27 169:10

367:11,13

452:21

comparable

160:19 429:13

comparative

483:15

comparator

155:27,27

156:6

compare 73:8,21

78:24 104:2,8

104:11 108:1

114:11 129:17

130:1 180:4

182:9 193:10

193:17 196:7

217:8 224:24

230:19 232:18

243:18 245:3

251:14 260:1

262:3 329:8

344:8 349:2

359:5,7,13,27

400:13 416:15

416:19 425:11

483:3 513:8

compared 27:9

158:18 180:23

181:7 206:18

215:12 234:8

295:11 318:19

318:21 319:10

319:27 321:3

325:23 364:4

368:20 369:24

370:3 436:12

462:5 465:25

465:25 466:3

466:15

comparing 48:9

66:8 175:15

182:6 193:2

196:11 208:4

271:13 281:8

382:20 416:13

416:14 427:24
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 23

434:10 435:11

comparison 2:5

2:6 22:24

102:22 110:17

124:25 130:9

176:10,12

193:24 214:23

238:20 261:10

261:18 266:16

294:7,13 295:6

332:2 337:16

339:2 371:5,12

371:18 373:18

373:21 408:17

416:11,14,22

422:1 433:5

465:3 482:3

511:5

comparisons

13:22 16:18,26

60:8 130:15

131:21 134:18

195:10 259:26

266:15 267:2,5

267:17 294:9

294:10 303:26

308:10 320:10

342:4 353:18

401:21,22,23

414:3 427:14

464:11 511:7

compartment

306:1 345:20

Compartmental

373:12

compelling

123:16 429:2

compendium

486:2

compensation

437:13

compensatory

430:11

competent 90:18

421:4

competes 35:24

competing 46:12

396:4,16

competitive 36:7

342:24 457:2

compilation

84:22

compiled 87:11

91:25

complement

169:20 295:23

333:18

complementar...

418:11,14

423:24

complementary

446:23

complementat...

37:21 38:5

46:24 67:4

142:12,14

172:26

complete 118:18

182:5 216:26

305:7 329:17

349:26 354:13

359:15 410:7

423:23 431:18

505:4

completed 16:18

38:9 337:15

366:9 520:19

completely

27:13 121:10

151:12 161:26

166:21 195:24

205:3 220:7

239:1 289:1

299:7 350:22

401:25

completion

314:23

complex 26:8,8

27:16,16 95:21

101:23,26

122:16 128:27

143:24 198:26

306:12 309:5

384:11 396:7

396:11 431:13

431:18 460:24

484:2 506:15

512:20

complexity

175:13 176:7

181:26 182:17

182:25 320:11

503:9 513:7

complicate

461:24

complicated 5:4

28:6 29:22

30:10 32:4

75:26 83:5

132:5 175:22

181:4,13 196:1

201:11 209:25

211:21 213:16

221:4 225:22

226:1 274:16

282:11 284:26

306:27,27

309:1 381:22

434:6

complicates

332:2

complicating

462:2

compliment

441:1 480:9

512:12

complimentary

38:17,18

506:17

complimented

455:4

compliments

512:22 517:22

component

19:13 64:16

249:6 255:5

319:20 343:6

419:4 429:22

442:16 443:21

465:26 472:18

472:25 473:19

473:24,25

474:22 475:5

511:27 513:22

components

64:7 108:19

186:22 249:10

256:2 332:13

451:9 452:14

452:22 455:17

467:8,25

497:17,18,19

512:21

components'

466:8

comports 131:4

composite

331:27

compound

35:16 55:9

94:18,19 99:2

138:5,7,24,27

144:27 148:5

149:12,24,26

150:4,11,12,22

150:24 151:12

151:15,18,21

151:23 154:26

157:11 163:2

166:4 228:8

238:14 255:12

257:27 266:27

293:19 319:17

319:19 346:10

368:25 370:24

373:14 403:5

427:6 435:15

435:16 456:22

457:9

compound's

420:24

compounded

320:12

compounds

10:18 29:19

37:11,11 47:20

47:21,22 61:6

80:6 82:20,24

94:17,26 97:19

97:20,22,25

98:1,5,12,26

98:27 99:1,25

100:15 104:3

117:15,22

118:1 137:10

138:11,22

144:3,6,8

146:22,23,24

146:25,26

147:10 148:5,7

151:27 152:8

155:13,25

156:2,26 158:4

158:27 159:20

161:15 162:7

165:8,12

166:18,25

174:21,22

176:20 225:1

227:8 255:11

260:25 261:2

262:5,6 288:22

297:8 319:14

319:25 322:23

330:6 347:21

353:5,18

370:25 374:17

401:5,21,22,27

405:14,18,22

405:24 406:15

406:17,20

413:17 414:1,7

415:22,25

424:9 433:20

433:21,23

434:3,11 435:3

435:9,14

436:24 437:2

438:8 444:10

456:24,25

457:1,24 467:4

468:19,23

481:14,19

483:2 500:18

502:21 503:2,8

508:9

comprehension

445:17

comprehensive

10:5,11 317:11
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 24

513:21

compress 341:9

compressed

114:20 320:18

341:15 435:4

compressing

321:22

compression

321:11,20

385:22 392:1,7

434:13 435:2

479:14

comprising

260:27

computable

18:2 239:17

computation

388:13 394:11

445:27

computational

9:27 10:22,25

11:3 16:19

17:1,16,21

18:1,14,19,23

18:27 24:26

125:22 126:14

128:8 144:16

178:14 209:13

218:6 277:21

278:12 280:8

286:9 310:20

331:11 340:25

349:15 350:17

351:4,26

377:26 380:4,7

380:9,20 383:3

386:25 387:2

387:15 394:23

439:27 440:3,6

440:16 463:15

471:11 489:27

491:21 515:7

515:14

compute 451:10

computer 42:4

42:10 209:21

243:8

computing

389:19 423:20

conazole 261:23

conazoles

152:26,26

153:7,12

184:23 363:13

363:14,25

concentrate

355:1

concentration

35:9 36:5,27

36:27 39:24

42:19 43:17,27

44:20,21,24

45:15,20 55:16

55:27 56:3

62:10,12 63:11

68:17 72:4,9

74:9 77:1,24

78:19 91:15

93:2,10,25

95:24 107:8

116:12 120:10

145:1 149:18

149:18,22

150:5,6,19

153:27 156:12

158:24 192:21

223:19,22

225:3,20

226:26,27

227:2,6 228:6

228:15 229:11

229:15,16,19

230:4,6,7,10

230:12 231:25

232:3,6 233:5

233:7 235:22

244:24,26

246:27 250:4

250:24,27

251:16 265:10

268:18 269:21

273:24 285:12

293:5 368:23

369:25 370:3

372:5,16,19

398:14 474:5,5

474:16,26

concentrations

44:14 62:13

72:7,8 78:14

92:21 95:2

96:4,8,23

115:27 116:2

137:21 148:20

149:15 153:4

153:14,22

154:7 155:21

193:16 195:4,5

223:15 225:13

225:17,19

236:21 240:7

240:16 253:5,6

342:26 343:15

344:6 347:2,13

364:2 368:20

369:23 370:4

372:7,25

416:16 458:3

459:3

concentric

111:19

concept 106:24

108:21 117:27

141:11 166:24

260:16 349:14

362:20 363:3

363:12 395:12

431:10,14

446:3 497:6

concepts 441:9

conceptual

14:14 20:4

24:7 215:19

299:26 312:17

316:20 355:24

356:14 357:9

357:26 383:2,2

383:21 384:9

489:22 491:20

conceptualize

238:16

conceptually

268:7 327:12

368:9 484:4,17

500:24

concern 144:2

214:22 285:19

316:3 334:1

339:20 345:4

350:23 368:26

376:27 427:13

429:13 437:3

437:10 499:5

concerned 22:22

51:17 92:17

141:17 270:27

271:4 392:3

429:14 435:1

436:26 454:1

482:1,10

concerning

163:12 167:10

167:25 392:19

407:24 419:16

419:16 432:4,6

concerns 341:17

438:1 439:1

498:5

concert 361:9

conclude 36:19

187:26 252:24

320:1 344:10

concluded 362:1

515:17

concludes

235:11

conclusion

89:20 110:11

111:26 135:18

161:4 264:5

324:2 361:2

366:12 374:18

378:1

conclusions 32:1

118:9 119:24

122:18 369:1

375:2 448:2

concordance

108:16 109:2

117:11 131:16

136:9 139:18

313:10 334:17

338:19,20

339:17 365:5

381:2 399:23

408:4 412:15

421:27 422:22

433:5 434:26

435:23 436:25

460:1 511:4

concordant

313:20 314:27

437:1

concur 416:6

418:25 419:4

445:3,9 448:4

458:13 462:9

464:14 470:5,6

470:11 478:3

478:27 487:24

496:27

concurrence

431:5,7

concurs 470:4

conditional

268:9,10 450:2

475:21 480:2

507:11

conditions 373:9

conduct 224:9

317:11 438:3

455:9 457:1

conducted 122:8

126:19 185:24

198:24 375:15

395:10 397:3

402:14 430:15

430:16 458:1

conducting

456:26

conducts 375:18

confer 52:8

407:17

conference 1:14

124:9

confidence

18:25 19:8,14

26:4 30:21

119:8,12 140:3

161:6 174:14
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 25

174:22 179:27

186:23 196:16

197:1 210:24

213:7,26

214:13 219:4

219:17 221:12

234:15 245:7

245:17 251:13

252:9,21

254:23,24

256:18 257:18

257:23 259:2

264:15 270:25

276:11 314:17

322:12,15

323:27 334:12

337:3 338:24

356:23 359:9

359:24 364:7

398:15 443:25

447:15 475:12

479:7,12

491:17

confident 32:27

174:13 196:17

196:22 221:9

239:22 450:8

450:23,25

configuration

173:23

confining 130:8

confirm 87:27

403:25 495:27

confirmation

113:11 413:13

496:12

confirmatory

56:24

confirmed 116:1

329:24 359:4

Conflict 3:27

conflicting

107:13 113:21

confounded

81:26 150:14

153:8,20

156:15 158:15

158:22 201:15

203:21

confounders

303:15 331:16

333:10

confounding

57:5 130:13

153:13 306:6

331:19 395:22

405:7

confused 28:10

383:4

confusing 21:27

22:9,10 282:21

confusion

381:23 382:10

congratulate

509:27

congratulations

516:13

congruence

97:23 461:13

461:19

connect 121:24

connecting

41:20

connects 41:23

cons 196:9

consensus 80:11

86:26 89:16

127:8 197:22

261:4 273:4

390:18 422:11

469:24 489:23

491:21

consequence

32:4 35:13

50:7 54:9

consequences

33:22

consequently

403:20 408:25

420:11,18

conservative

226:7 233:6,8

236:2 242:17

242:20 256:16

259:15 279:5

363:6 481:18

488:13

conserved

175:26

consider 25:5

90:10 97:7

136:14 139:3

211:15 259:24

260:11 261:8

261:11,12

277:16 284:12

286:4,26

287:10,13

301:18 309:14

311:18 312:23

319:21 320:21

322:6,10 324:1

345:13 347:20

348:16 353:19

374:18 384:21

409:3 463:10

475:26 477:22

477:24 485:12

488:21 490:25

506:1 509:21

511:12 518:2

considerable

279:26 463:9

considerably

181:4 394:20

461:4

consideration

10:21 51:25

101:17 119:20

167:14 183:24

196:7 303:1

312:9 313:3

317:18,19,23

325:3 336:7,17

341:25 343:12

356:7 365:10

365:13 369:19

407:8 456:16

483:25 492:15

510:27

considerations

15:2,2,7

195:23 259:25

260:6 300:15

329:23 346:6

350:20 407:5

436:8

considered

121:18 131:8

131:17,18

132:21 137:3

145:24 154:7

176:8 310:27

312:14 317:2

324:6 325:13

337:26 339:16

356:20 357:18

358:5,13,17,20

360:4 369:14

372:23 435:11

435:25 444:14

456:15 460:16

468:13 477:15

484:4 491:13

505:27 506:5

516:27

considering

10:17 15:24

25:10 114:17

115:14,14

261:18 269:13

269:14 293:15

300:7 301:2

339:19 345:23

488:22 509:16

509:20

considers 355:26

consistency 9:19

389:4

consistent 52:24

84:6 88:17

109:6 110:26

112:15,17

123:26 126:3

127:11,17

129:4,13 136:2

137:25 146:7

146:10,16

153:18 179:2,4

209:17 210:15

211:15 219:1

278:9 350:10

369:1 374:16

381:1 399:22

400:17 413:4

431:23 459:27

461:2

consistently

85:13 90:1

182:1,2 435:7

consists 16:8

consortia 266:21

consortium

326:15,16

constant 40:7

68:17,18 221:7

236:14,23

372:6 396:21

398:7 458:27

constituency

310:11

constituents

509:21,24

constitute

398:20

constituted

466:15

constrain 218:1

constrained

42:17 144:25

395:10

constraint 42:26

217:9

construct 203:8

constructed

421:10 488:12

construction

140:27 478:15

478:21 480:4

consultant 340:2

consultation

47:10

consumed

202:22 203:9

consumer

191:19 201:25

207:3 216:8

251:26 253:13

326:22

consumers
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 26

375:19

consumption

15:9

contact 15:11

contacted

403:24

contacting

403:25

contain 321:5

contained

108:18

containing

15:10

contaminant

403:2,6

contaminate

15:26

contaminates

15:23,24,26

content 142:23

142:24

context 2:2 21:3

84:21,23 85:16

104:1,15

135:11 153:3

164:13 173:26

174:2 260:12

263:22 264:7

275:2 284:25

287:27 299:23

300:6,26

305:21 316:26

323:18 356:12

358:2 368:7

386:24 392:2

392:11 417:5

445:16 449:1,5

449:25 465:16

508:18

contexts 92:16

contextualize

333:14

contextually

325:9

continual

289:23 504:21

continually

279:23 306:10

continuation

172:23

continue 10:22

13:6 18:6,7

176:14 259:24

259:26 266:7

286:15 290:1

300:26 312:1

313:18 366:24

377:13 378:3

405:11 444:23

511:3,8

continued 9:7

15:20 25:21

52:1 97:12,12

174:7 192:1

continues 52:25

300:3

continuing

51:17,27 54:26

167:12 206:7

234:13 304:1

329:7 442:13

continuity 465:2

continuous 24:7

24:11

continuum

200:18 329:2

contract 452:25

contracting

140:20 452:21

contractor

140:20 519:25

520:17

contradictory

135:6 182:1

184:12

contrast 153:23

442:3

contrasts 401:25

contribute 13:14

127:24 136:10

338:7 380:14

422:3 440:11

440:25 444:8

451:9 452:11

455:25 461:23

contributed

514:23

contributes

337:23

contributing

45:15 314:20

443:22 451:11

contribution

325:22 442:15

contributions

7:22 26:26,26

367:27 463:21

518:23

contrived

340:27

control 52:4

105:7 106:21

106:21 148:18

162:14,17

181:6,8,8,10

198:14 305:3,8

327:4 348:20

387:16 404:21

404:27 466:3

controlling

239:5

controls 266:4

402:3 404:5

conventional

313:9 364:22

convents 47:17

converge 66:18

66:20

convergence

66:19

conversation

67:27 281:2

418:16

conversations

6:27 67:18

402:24 424:1

Conversely

292:12

conversion

98:26 251:15

293:18 346:10

468:21

convert 192:19

192:23 193:15

194:9 230:6,10

251:15 262:2

converted 74:4

282:16 363:17

363:22 457:9

converting

379:5,8

conveys 471:26

convince 345:7

convincing

119:16

cool 517:19

cooperation

105:16

coordinate 73:9

74:11 290:21

coordinating

12:1 146:11

216:11

Coordination

7:13,25 11:24

11:25 125:4,5

519:11,13

coordinator

326:9

copies 33:16

copular 38:7

cordless 189:8,9

corepressor

465:20 466:18

corner 111:15

114:6

cornerstone

113:4 315:11

Corp 190:14

Corps 6:14

411:27

correct 53:9

59:7,20,23

65:25 92:7

120:18,19

162:20 164:5,6

217:5 220:7

265:20 270:20

270:21 288:23

289:2,4 324:16

391:5,7 425:25

450:5

corrected

254:25 333:5

correction 142:7

correctly 22:1

53:26 147:16

147:18,21,22

147:25,26

195:20 220:2

270:6,17 294:8

313:14 424:10

446:14

correlate 207:5

correlated 91:13

241:5,7

correlation 49:5

117:11 205:2

205:11,12

206:19,20,21

413:16

correlations

413:18 460:12

correspond

34:14 356:23

364:3

corresponded

318:1

correspondence

415:13

correspondingly

29:12

corresponds

33:26

cosmetics

212:10

cost 64:16 86:3

352:7 434:9

cost-prohibitive

269:26

cost-wise 64:19

costly 352:9

could've 68:6

coumestrol

349:3

Council 316:9

326:21

Counsel 4:3

count 452:4

counter 64:5
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 27

counterparts

103:7

couple 3:10 6:24

9:22 12:10

43:20 51:4

52:11 53:7

93:4 106:14,23

111:5 114:13

115:25 123:27

132:9 139:2

196:5 199:4,6

207:27 227:19

260:26 270:7

277:20 289:21

292:6 326:27

327:2 337:5

347:17,25

348:14 349:3

350:3 355:19

389:15 424:10

425:5 453:25

494:10 497:25

502:25 510:16

511:2 517:6

coupled 227:21

course 8:11 9:18

26:21 60:7

100:13 133:9

139:5 146:14

156:13 186:6

197:17 199:25

205:8 215:14

220:24 226:15

230:25 232:9

234:22 291:19

301:13 304:17

316:15 318:23

319:3,15,18

320:24,25

321:9 328:9

344:27 384:22

414:17 434:7

515:27

covariance

241:24

covariates 485:9

485:10

cover 39:3 114:1

134:13 152:6

191:6 298:21

398:26 500:7

coverage 93:25

126:4 185:9,18

311:27

covered 89:24

128:5 185:13

186:1 303:6,26

350:5,26

392:11 502:18

510:16 511:3

covering 16:9

141:7 343:10

covers 106:6

191:9 428:15

coveted 103:1

Cox 317:15

crazy 29:20 31:6

76:12 80:27

create 87:13

created 217:16

creates 33:20

creatinine

208:20,21,25

477:7,8,9

505:24

creativity

514:23

credibility 388:8

488:2

credible 514:16

criteria 66:19

109:25 110:4,5

113:8 117:2,8

120:7 122:21

122:27 152:15

179:26 180:21

180:23 274:4

297:1 335:18

359:20 398:10

398:10

criterion 92:14

371:20 389:21

critical 26:2

33:4 61:19

81:19 175:4

216:14 280:9

334:17 370:18

376:5,20 405:6

420:9,10

433:24 443:24

444:17 468:4

468:15 504:23

518:14

critically 342:5

386:15 428:24

490:13,22

491:6 493:7

Crop 326:21

330:25

CropScience

354:22 355:5

367:1

cross 417:25

cross-industry

367:2

cross-reactivity

155:13 157:7

157:17,18

158:9 333:6

cross-talk

290:27 291:11

cross-validated

160:25

cross-validation

396:27

crossing 223:7

crossover

184:11

crosstalk 155:12

157:6 333:5

426:17

crude 353:23

cruder 201:13

cruising 410:4

crux 171:3

cruxes 262:27

CRYSTAL 1:16

CTAC 425:4

cues 391:11

curate 400:12

461:17

curated 2:5

86:24,25

102:22 218:3

360:15 435:9

435:27 438:7

461:10 464:11

494:2

curating 87:3

438:6

curation 121:18

121:18 234:24

315:22 334:14

381:5 399:26

407:7 436:18

460:4,15

curious 51:10,12

53:25 54:2

57:2 69:22

90:9 162:25

164:20 240:6

240:15 296:4

305:5 306:5

465:22

current 13:16

20:18 129:18

132:3 141:20

178:23 186:2

311:3 315:4

329:18 340:19

350:2 386:13

442:23 444:21

469:5 475:4

485:1 499:16

currently 15:24

103:22 130:1

180:5,16

181:23 185:20

186:4 234:23

314:16 327:16

333:22 340:22

351:14 437:4

437:20 443:17

449:26 460:17

468:3 475:17

488:12 502:27

503:1

curve 2:5 21:26

22:11 42:15,16

43:19 44:2,9

44:10,11 45:5

45:9,13,23,23

46:7 53:23

55:8 62:7,13

62:14,15 63:10

65:22 69:18,23

69:27 72:2

77:26 78:1,16

79:13 92:23

93:5,6,13,13

93:18,20,22,23

102:23 107:1,4

114:14 139:20

145:6,11 150:7

169:1 206:3

211:10,10,16

249:22 250:23

250:26,27

268:18,23

269:3,3 273:27

274:1 294:19

294:22 295:7,8

306:16 321:15

321:23 342:24

352:16 369:10

369:11 377:1

380:8 382:11

384:6 389:16

425:21 428:18

429:16,17,21

435:3 440:4

497:12

curves 22:18

43:17 45:1

46:4 54:22

72:3 78:17

99:24 145:4

148:5 155:21

211:16 249:20

250:24,25

268:14 273:27

274:6 343:1

379:2 381:26

398:13

cut 51:11 280:13

285:10 436:16

cut-off 218:20

cut-offs 218:23

218:23

cutoff 46:1
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 28

131:3 133:23

137:1 145:15

283:16 288:4

314:8 336:18

436:21,27

cutoffs 258:3

285:27

cutting-edge

386:18 514:21

cycle 495:16

504:6

cycles 357:20

495:17 504:4,6

cycling 172:2

Cyproconazole

283:27

cyproterone

467:6

CYPs 90:17

cysteine 175:26

cytochrome

363:26

cytoplasm 33:17

cytosol 265:26

455:3

cytotox 72:17

73:11 78:7

79:25 80:8

149:27 153:15

153:26 154:22

155:12 159:1

159:16

cytotox-driven

153:11

cytotoxic 83:11

94:19 96:5

101:25 153:23

458:25

cytotoxicity

32:26 49:18

54:10,15,18

63:21,22,23

71:21 72:16,19

73:10,27 74:10

74:19 75:4,5

75:20 79:21

81:26 82:3,11

83:9 85:14

94:26 95:15,16

95:25,26

141:14 148:20

148:27 149:2,3

149:10,26

150:8,14,16,18

153:8,9,13,20

153:21 154:1,4

154:5,8,16

155:4 156:5,12

156:15 157:4

158:11,15,20

158:21,22

159:5,12,13,17

178:10 248:7

274:8,9 281:19

301:27 331:16

331:19 333:10

335:3 339:13

369:14,17,17

395:23 426:11

426:16 444:8

463:22

D

D 155:7

D4 115:21 116:9

116:11 117:5,7

346:4,8,14,23

347:3 348:18

348:19 349:1,9

350:5,5

daily 243:23

373:1

Dan 6:11,20

190:11,20

411:24 412:5

416:6 427:12

427:27

Dan's 54:27

Dana 5:20,20

21:23 22:9

23:8 90:7

189:22,22

270:5,22 271:7

325:1 411:11

411:11 476:7

494:23 495:14

503:19 510:7

dancing 273:12

DANIEL 6:11

6:20 51:2

52:10 53:7,14

53:18 54:17,25

91:1,18 92:4,9

92:12 93:26

120:2,13,16,20

120:26 121:4,8

121:11 138:1

138:23 175:9

175:21 190:11

190:20 235:15

238:3 260:20

400:4 408:8,19

408:23 409:7

409:11,15

411:24 412:5

412:22 417:9

417:16 418:10

420:1 421:6

422:9 428:21

429:9 430:19

438:19 445:7

458:16 462:13

467:22 470:7

485:27 497:16

513:18 515:26

Daniels 520:13

dark 39:20

darkness 514:4

dashboard

31:25 54:12

243:6 267:11

267:21,27

271:27 295:23

323:14 328:11

443:23 463:17

data 2:6,6,8 9:1

9:4,6,7,9,12,13

14:11 18:3

20:6,8,14 26:4

27:8 28:2 31:5

31:23,26 32:1

32:22 49:17

51:18,18,24,27

54:13,21 56:17

56:21 61:21

71:23 80:15

81:7,8,8,17

84:1,5,7 85:5

85:20,23,24

86:4,16 87:16

89:15 95:22

97:17,21,24,24

97:26 98:26

99:4 102:24

104:2 106:13

107:6 112:3

115:1 118:19

119:18 122:25

124:25 128:21

129:10 130:18

130:21,26

132:7,12

133:26 135:11

135:27 136:9

136:13,17

138:1,3,25,26

139:5,26

140:13,16

142:11 173:11

174:16 176:12

179:11 182:6

183:6,9,11

184:3 185:3

187:4 188:1

192:4,8,19

193:6,21,23

194:5,23 195:5

195:9 198:3

201:20 203:3,7

203:14 204:3,5

204:7,8,22

205:15,27

206:19 207:6

208:5,5,6

209:17 211:15

212:16 214:19

215:15,23,24

215:25 216:7

218:18,20

220:5 222:1

226:2 227:17

227:19 232:19

232:24 233:18

233:19,23

234:14,20

235:2,3,8

236:10 237:21

238:20,21,22

239:11,26,27

241:7,19 243:7

248:11 249:1

249:15,16,17

249:21 252:21

252:26 253:20

253:25 254:18

254:19,19,20

254:21 256:11

256:15 259:27

261:2,4 264:17

264:21 265:16

265:21,23

266:5,14,26

267:17 268:23

272:11,27

273:7,8,11,23

274:5 275:14

275:20,23,24

276:8,23

277:24 278:14

279:23 281:26

282:12,18

284:3,24,27

285:1,4 286:15

286:16,25

288:7 289:6

292:20 293:11

294:20,22

295:24 296:21

297:2,3 298:10

300:1 302:24

308:20 315:6

318:1 327:1,5

327:5,27 329:8

329:23 335:13

337:25,27

338:3 339:6

353:16 355:23

355:26,27

356:15,18,23

356:26 357:10
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 29

357:18,27

358:12,27

359:5 360:6,14

360:15,17,26

361:24 362:1

365:13 366:17

366:19 367:22

367:24,25

368:7,11,13,15

368:16 369:5

369:27 370:8,8

370:14,20,20

371:7,17,22

374:11,16,20

374:21,27

375:1 378:24

378:26 380:4

380:27 383:19

384:13 386:2

387:18 398:8

398:12 399:21

400:16 401:1

405:27 406:9

406:21 409:6

409:21 412:13

415:25 416:12

421:25 424:3,8

424:11,19

425:11 428:24

428:25,25

429:11,24

431:21 432:4,7

432:8,11 433:3

434:10,24

436:6,11,13,20

436:23,27

437:4,5,9

439:4 440:1

443:9 445:20

450:5 451:15

452:8 453:5

458:5,6 459:26

461:24 462:25

463:18 464:12

466:23 474:22

475:2,20

477:12 478:11

479:3,4 480:3

482:2,3 483:13

483:17 484:11

487:12 488:11

492:7,13

493:14,15

494:3,5 495:2

495:4,7,16

496:24 498:18

498:19 499:1

500:2 503:6

504:2,3,15,16

504:17,18,27

505:6,13,14

511:4,5,6

513:3,8,20

514:10,11

516:17 517:20

data-driven

327:12 331:14

510:22

data-poor

192:14

data-quality

387:9

data-rich 192:13

330:6 355:11

355:21 356:5

361:21 365:9

366:25

database 84:9

87:9 104:14

109:27 110:10

110:15,22

113:9 121:23

181:22 222:26

241:17,17

273:6 276:13

351:20 353:15

402:18 408:9

425:25 486:23

databases 195:3

351:19,23

354:2,11

dataset 26:21

31:8 37:12

52:24 53:4

60:16,20 86:18

86:25 88:17

98:21 99:3

100:14 104:9

104:10 174:9

210:25 222:2,6

222:9 233:26

239:1,4 272:14

286:18 510:25

datasets 10:16

27:9 53:1 61:3

174:14,20

208:8 292:4

339:12 504:12

504:12 505:2,4

505:23 511:5

date 16:12 97:21

387:25 441:16

457:22 470:2

dates 490:5

Dave 5:23

189:25 411:12

David 5:23 7:12

7:12 11:27

12:4,10 14:2

18:10 19:16

20:5 22:15

24:1,2,18

25:17 26:18

51:16 52:19

55:23 60:5

64:1,13 96:24

96:25 100:12

101:5,11

121:20 124:10

136:21 163:11

163:25 164:7

164:14 167:10

167:27 172:22

172:25 174:5

176:9 189:25

218:15 259:23

261:6 266:7,10

269:10 270:23

291:14 299:1

306:5 307:26

392:16,18,22

392:26 393:2,7

407:19 408:3

408:14,22

409:1,8,14

411:12 416:27

417:11,21

419:3,11,15

432:3,17 452:1

494:15 498:14

499:9,14 500:6

500:11 508:22

510:14 517:27

519:10

Dawley 348:26

Dawleys 404:14

Dawson 520:2

day 5:2 78:22

85:23 106:16

111:24 112:8,8

112:22,24

118:22 179:20

188:3 193:1

197:20 201:17

208:21,25

209:1 213:12

223:14 229:12

229:15,16

230:11,17,18

236:16,22,23

243:12 249:10

251:19 252:15

253:22 255:22

257:4 266:23

283:22 299:19

300:8,21

301:11 302:18

304:25 362:17

363:18 364:20

371:11,15

372:17 373:1

375:23 410:11

411:2 418:17

425:23 476:11

476:12

day-2 189:3

days 106:13

373:2 434:7

486:14 502:26

520:19

days' 77:16

DDT 106:19

de-prioritizati...

325:2,6,12

deactivated

313:14

dead-on 436:24

deal 32:18 54:2

79:22 126:26

133:25 139:25

141:11 183:14

249:11 279:2

409:9,13

414:19 415:5

426:22 477:4

dealing 24:27

53:19 132:5

171:11 178:11

185:8 409:4

452:24 491:10

dealt 138:8

427:26 493:23

debate 443:12

decade 60:25

207:17 461:25

decades 17:10

234:26 461:16

December 1:18

11:5 122:7

decide 48:23

57:9 81:10

97:27 267:24

296:1 508:1

decided 45:7

101:9 201:18

243:5 296:18

420:4 471:18

decision 51:10

117:2,8 122:24

264:1 312:6

316:25 323:23

324:5 325:7,26

374:26 376:14

decision-maki...

3:22 26:15

288:22 508:18

decisions 51:15

244:2 289:18

312:11,21

315:2 319:23
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 30

325:9 354:5

366:7 446:18

446:27 447:5

452:20,26

decreased 73:1

78:8

decreases 114:6

335:8

deemed 105:25

deep 169:8,9,23

170:9

deeper 69:13

185:4

deer 28:24 29:1

default 473:5,6

473:6,9,12

Defense 5:25

189:26 411:13

deficiency 427:3

define 128:2

178:26 183:25

186:3 457:12

488:23 516:18

defined 23:23

52:21 92:2

127:21 135:19

178:22 263:9

323:3 339:13

339:15 351:9

352:12 426:24

427:21,23

449:6 461:11

477:2

defines 73:20

defining 63:7

91:3 127:16

179:10 180:27

definite 370:18

definitely

139:20 163:7

242:22 244:18

244:18 414:13

416:3 421:8

430:20 470:11

512:13 517:11

definition 108:6

180:25 397:12

415:20 416:2

definitive

359:17 370:20

degradant 403:5

degree 135:16

338:18 340:22

406:13 455:24

468:12 515:8

degrees 116:18

DEHP 80:10

96:3

deiodinase

303:3 305:18

305:18

delay 68:21

407:19

delayed 357:7

Delclos 5:17,17

66:24,25

189:20,20

381:9 384:16

384:17 385:18

394:3 399:5,6

400:2 406:27

407:1 411:9,9

412:21 416:5,6

417:8 422:7

427:10,11

428:1 429:2

433:13 438:17

438:18 510:2,3

deliberately

135:13 191:20

194:13 202:13

215:26 227:26

228:2 239:5

deliberating

519:5

deliberations

381:10 386:14

388:11 445:27

delighted 136:17

340:13,26

delineated 360:8

366:24

delineating

361:13

deliver 61:7

174:20 201:10

229:5

delving 185:4

demographic

198:26 213:10

demographics

222:4 376:24

demonstrate

9:15 14:25

19:8 22:4

336:25 355:18

366:2,10,26

431:16

demonstrated

138:19 279:6

279:10 280:25

287:5 314:17

328:26 345:17

361:12 389:22

469:22 479:27

497:7,14

demonstrates

115:1 117:19

357:6

demonstrating

362:19 487:27

demonstration

128:7 288:5

323:13 356:24

362:14 363:9

364:25

demonstrations

127:14 361:17

denature 36:3

denatures

467:18

denaturing

36:17 158:8

denominator

379:13

dense 505:7

density 349:19

Denver 6:5,6

24:24,25 56:26

56:27 57:14,21

57:25 58:14,20

58:25,27 59:8

59:14,18,21

190:5,5 291:26

292:1 293:14

381:9 385:16

385:17 394:4

399:9,10

411:19,19

440:20 445:2,3

447:24,26

453:20,21

454:6,20

458:12,13

460:7 462:8,9

463:3 464:16

464:17,27

467:11,12

470:5 512:23

deny 496:12

Department

190:9 411:16

411:22

departure

250:24 360:24

362:9 365:16

365:25 369:13

382:16 419:17

depend 24:14

42:22 52:4

231:8 351:6

dependency

462:5

dependent 56:1

242:14 420:5

442:26 453:11

455:12,25

460:25 465:17

depending 69:6

107:2 198:20

198:25 236:4

290:13 294:6

324:23 325:9

365:1 369:11

373:8 382:18

414:16 507:2

depends 23:19

23:24 259:8

396:23 397:13

414:10 448:18

448:19 484:20

depict 22:8

depletion 372:11

depreciating

486:2

depressed 335:7

deprioritize

329:27

depth 342:21

381:25

Deputy 4:2

derivation

382:27 385:18

442:20

derivations

473:26

derive 394:18

398:13 471:21

474:26 475:27

485:5 499:2

derived 119:14

248:3 318:19

398:16 408:16

455:23 475:21

484:21 488:17

deriving 499:27

dermal 106:8

235:24 237:2,7

237:15,26

dermal-specific

238:4

dermally 238:5

DES 341:12

343:3,8

describe 202:4

204:24 359:12

387:2

described 16:17

16:25 94:20

107:26 108:26

116:26 201:22

204:20 311:20

314:1 315:13

320:6,19,27

331:11 339:9

339:10 351:4

351:26 359:18

372:13 376:8

380:12,20

384:26 389:16
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 31

391:25 440:8

440:16 471:11

478:5,11

479:21

describes 360:6

510:26

describing

203:15 234:3

356:2 476:9

description

204:19 234:6

312:5 331:24

332:24 333:2

381:17 385:25

424:23 475:4

479:20 516:21

descriptions

276:20 331:22

413:5

descriptors

109:23 110:2

160:24,27

199:25 215:14

415:17

deserves 154:20

design 107:2

111:6,13,15,27

112:3 113:1

120:27 198:27

199:3 209:24

210:2 215:26

225:14 235:2

400:22 402:13

443:8 456:16

463:16

designated 3:9

314:8

designed 29:21

32:18 135:13

186:6,12

227:26 241:23

449:25 460:21

461:14

designs 110:15

111:10 112:5

120:27 461:13

461:19 463:21

desirable 434:8

desire 110:7

despite 185:7

284:2 395:14

517:6

detail 71:15

120:12 126:27

194:1 204:13

323:9 334:2

339:10 341:8

345:12 351:1

359:18 388:15

390:20 473:11

474:2 520:14

detailed 14:16

175:5 208:23

333:5 338:13

382:24 387:26

495:24

details 67:20

131:23 133:25

175:2,4 289:17

323:15 328:6

334:6 339:25

352:4 377:26

385:1 465:11

468:13 469:9

detect 19:10

83:4 185:15

247:20 298:17

395:16,18

400:25

detectable

165:10,18

283:1

detected 131:13

165:12 180:2

283:13,23

288:2

detecting 83:1

96:4 186:22

460:21

detection 35:19

57:4 102:6

199:15,19,21

200:1 210:20

211:4,6,9,19

213:18,20,23

214:1,19

219:16 254:10

394:27 395:22

455:18 476:24

491:10,16

detects 19:11

determination

27:10 314:21

335:27 356:1

359:22 360:27

365:7 422:3

determinations

10:17 13:14,23

20:24 301:4

356:10 361:18

369:12

determine

105:20 109:11

135:15 180:24

210:2 223:27

261:15 338:6

341:4 368:18

397:3 431:19

435:20 448:21

449:6,13

determined

365:15 372:11

372:14 373:4

374:1 388:3

396:27 402:15

determining

228:9 335:18

337:24 338:11

339:20 355:16

365:20 366:14

detours 497:3,3

detoxification

311:22,25

develop 26:5

52:8 53:5

104:10,14

109:8 116:16

179:7 192:3

196:14 227:12

287:14 296:18

315:15 351:5

357:26 433:24

471:11 477:23

482:13 484:5

490:19 493:23

495:9

developed 8:27

9:8 10:23

13:22 19:15

25:19 29:17

104:16 109:25

110:4 113:10

160:15 161:9

169:10 177:27

178:25 195:3

208:10 226:14

232:13 233:15

241:15 260:16

297:4 359:10

377:13 394:11

420:18 449:4

478:10,11

481:11

developer

170:10

developing 9:9

21:12 216:7,9

310:20 315:26

355:7 367:3

388:6 467:24

477:8 491:4

504:18

development 8:2

16:4 25:21

51:20 52:5,6

60:23 105:16

160:21 174:9

174:17 178:4

191:3 207:25

235:1 260:12

272:4 290:18

297:5 313:27

315:18 324:8

328:21 352:7

376:17 414:19

419:27 420:25

421:12 444:23

467:27 478:13

481:6 489:26

498:4 500:20

503:7 504:23

510:9 511:13

511:15 518:10

developmental

6:7 121:25

181:17 190:6

342:6 376:17

400:24 414:13

414:16 420:14

developments

306:20

develops 179:15

deviation 73:15

73:16,16,18,18

73:19 211:8,11

473:7,7

deviations 73:24

73:25 154:6

211:14 212:17

251:12 475:12

devil 289:17

devising 388:13

DFO 3:12 28:6

518:25

DHEP 100:26

DHT 133:16

137:24

Di-n-butyl 48:21

diagnostic 83:3

155:9 159:25

diagnostics

82:16,16,18

84:2 292:23

diagram 18:11

21:5 22:2 23:7

24:7 132:5

244:7 258:2

370:6 374:4

378:21 389:1

diagrams 284:26

384:9

dialogue 514:27

dialysis 372:14

diazinon 133:19

Dibutyltin 82:7

dichotomy

97:23 447:20

Dicofol 48:14,15

die 72:24,25

diet 122:16,23
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 32

123:5,6 319:19

319:20 364:19

dietary 202:15

504:21

diethylhexyl

48:14 100:27

101:1

diethylstilbest...

124:5 320:25

321:14

differ 321:2,19

difference 41:11

42:12 65:14

69:10 70:7,23

83:6 100:23

105:8 114:23

144:26 155:25

156:9,16,25

167:11 170:15

174:10 236:26

237:1 255:19

255:24 257:21

258:17 263:15

264:3 285:20

319:5,9,13,26

320:17 321:6,8

321:8 341:15

348:15 474:8

differences

95:14 178:17

235:16,18

265:2 344:8

356:25 358:14

402:13 404:5,9

455:8 477:9

different 9:1

12:14 15:14

23:24 26:14

29:6,7,27 30:4

31:4 34:20

37:24 38:24

43:5,5,6 46:23

49:3 56:3 64:8

64:10,10,12

66:27 69:6,7

72:17,18 74:26

75:13 77:19

86:6 87:19

89:22 91:8

92:15 95:1,12

95:13 96:13,23

100:7 106:4

109:1,24

110:14 111:14

111:15 119:8

119:13,13,14

120:22,27

129:4,5 131:21

132:12 133:8

134:14,14

135:17 141:1,7

141:8,9 142:15

148:15 153:7

154:11 157:17

158:10 159:15

168:18 169:2

171:4,25 173:6

173:7,9,9,14

173:16,17

174:21 176:1,2

176:3 179:3,3

185:14,15

197:11,14

198:18 210:1,9

210:10 212:7,8

212:9 217:24

217:25 223:1

223:12 224:7

227:6 228:2

231:19 249:7

249:27,27

260:2 261:18

267:1 268:14

274:24 276:7

276:10 287:18

287:22 290:19

292:14 294:11

294:18,27,27

298:4,6 307:21

318:6 322:4

324:14,15,21

325:9 333:16

333:17 334:1

363:15 382:4,7

382:13,20

384:4,7,13

388:12 396:5

397:27 398:2

400:24 403:16

413:15,26

418:7 421:5

422:18 430:26

446:24 449:17

451:20 452:3,4

452:12,14

455:7,13,15,17

456:2,8,23,24

465:3 466:9,9

466:10,23,26

467:25 475:10

475:27 476:1,2

491:4 513:20

differential

485:20

differentiate

116:21 118:2,6

121:2 124:4

143:12 374:27

differentiating

114:26 117:21

281:18

differentiation

115:8

differently 29:26

418:8,8

differs 368:9

difficult 53:1

110:15 113:17

130:17 180:10

181:15 210:5

317:11 320:10

321:20 335:26

342:12 347:12

357:3 493:19

503:7

difficulty 178:9

515:9

dig 69:13 82:14

85:23 265:17

276:15,20,23

digest 387:22

digging 85:17

dihydrotestost...

137:9 456:26

457:8

diligent 340:14

dilution 506:1

dimer 466:24

dimerization

34:1,16,21

99:6 143:22

165:1 169:11

169:18,24

171:19,21,26

173:14 466:22

466:23

dimerize 33:17

38:9

dimerizes 35:4,5

dimers 34:19,19

172:1

dimethyl 46:14

283:12

diminishing

329:4

direct 57:6

110:17 186:15

202:6,13,16

339:23 365:21

366:7 442:23

442:27 467:1

500:16 507:4

directed 28:14

331:1

directing 315:8

direction 23:27

56:14 192:17

201:9 216:2

234:13 237:27

241:6 285:10

286:6 304:24

335:23 497:3

506:27

directional

125:6

directions 2:11

13:5 19:2

182:24 282:20

290:23 299:2

299:15,19,22

302:6

directly 34:15

62:25 104:2,8

104:12 201:27

295:4 389:27

499:24

director 5:24

7:13 11:24

58:6 140:24

189:25 519:11

519:13

disagree 496:19

disappearance

228:8

Disclosure 4:1

disconnect

426:12

discordance

111:6 407:24

408:5 409:9

discordancy

400:23 402:1,6

402:10 409:13

discordant

110:23 111:11

111:12 112:3

401:27 408:11

436:13

discordants

409:4

discount 45:8

50:14 150:17

discounted

166:19

discounting

332:26,26

385:2

discovered

82:22

discreet 429:4

discrepancies

400:25 403:21

discrepancy

84:19 112:14

430:1,3

discriminate

181:15

discriminating

178:9

discrimination
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 33

456:1 463:25

discuss 5:12

125:15,24

132:10 277:25

277:27 401:3

422:12 435:17

444:12 445:27

446:3 482:7

discussant

381:10 394:3

400:3 412:20

412:21 422:6

433:13,16

440:21,23

454:21 463:4

471:16 481:2

502:16

discussant's

381:14

discussants

381:8 400:1,5

400:9 412:19

422:5,7 433:11

433:13 440:19

454:19 460:7

463:2 471:15

481:1 487:25

502:14

discussed 5:5,10

133:15 195:18

236:8,9 239:9

269:18 277:14

277:21 283:2

285:1 287:12

312:27 313:4

320:20 331:17

337:8 352:1

376:11 382:8

398:22 404:6

439:9 467:17

477:4 481:21

481:24 482:23

483:5 486:1

488:16 490:18

502:25

discussing 50:16

264:14 289:22

discussion 126:2

126:7,21 162:3

177:24 262:17

268:12 301:10

303:9 312:11

320:3 323:19

334:4 384:19

385:14,20

387:16 388:11

390:17 394:8

404:17 418:18

418:22 425:23

431:26 441:22

445:20 467:12

469:10 473:1,8

473:11 479:5,6

479:10,26

488:18 490:25

discussions

106:24 110:10

126:22 304:9

325:16 336:2

386:13 394:2

400:5 441:27

Disease 198:14

dishes 95:10

dismiss 287:9

dismissed

282:23

displace 343:16

displaced 36:1

227:9,10

displacement

342:24

displaces 35:25

display 138:11

displayed

128:16 439:8

512:8

displays 395:16

463:9

disposal 354:12

disproportion...

451:11 455:26

disputes 341:19

disputing 350:5

disrupt 61:6

76:12

disrupted 357:1

disrupting

104:19 375:26

433:21 481:20

disruption 25:1

458:19 491:1

518:22

disruptive

511:14

disruptor 2:7

7:23,24 8:8

12:2 14:9

25:23 60:27

84:10 100:8

124:26 380:10

470:22 482:27

518:4,16

distance 22:5

23:21 252:8

255:1 263:24

270:27 280:7

283:4,4

distances 23:23

distinct 28:6

59:3,15 235:25

distinction

24:10 177:16

177:17 290:25

358:19 442:21

distinctions

298:24 359:17

distinguish

47:22 58:22

89:25 220:9

333:27 335:9

336:6 337:9

359:20 363:6

412:17 455:19

456:11 462:4

462:27 466:18

467:15,19

distinguished

360:11 459:4

distinguishing

458:18

distracted

499:13

distribute

318:10

distributed

211:2,18

223:25

distribution

22:17 24:7,12

75:18 76:5,13

156:21,21

157:2 197:10

197:13,17,18

199:13 206:5

207:19,22

211:7 213:18

214:3 231:17

231:19 240:16

246:26,26

247:1 251:11

253:10 258:6

268:11 271:12

271:19 352:3

363:16,19,23

471:4 472:14

472:15,16

478:26,27

479:15 488:23

488:25 489:3

499:27

distributional

66:15

distributions

156:17,24

196:24 231:19

240:11,11,20

241:1,7,12

244:10,11

247:5 268:8

283:5,17

324:15 351:8

362:21 472:5

472:13 484:20

disturbed

427:17,27

dive 216:18

diverse 239:6

377:4 463:21

464:6

diversity 334:9

divide 193:8

divided 143:11

146:23 153:6

318:14 490:9

divides 151:27

division 11:25

11:27 115:20

125:4 304:23

519:14

Dix 7:8,12,13

11:15 22:15

24:2,18,20

25:17 26:18

51:11,16 52:19

59:26 60:5

96:25 100:10

100:12 163:10

163:11,25

164:7,14 167:7

167:9,10,27

172:22,25

174:5 176:9

259:23 261:6

262:17 266:7

266:10 267:20

270:23 271:13

291:2,12,14

298:26 299:1

306:5 307:26

308:24 309:9

367:16 392:16

392:18,22,26

393:2,7,18

407:19 408:3

408:14,22

409:1,8,14

419:15 432:3

432:17 498:13

498:14 499:9

499:14 500:6

500:11 508:22

509:15 517:27

519:10

DM 416:22

DNA 33:20 34:3

34:6,23 35:6

38:18 46:24

77:12 143:25

171:27 371:25

423:6 466:22
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 34

docket 1:8,10

4:15,16,18,19

8:14 12:16,16

424:18

docking 86:8

87:21

document 10:4

10:7 12:11

317:3 324:8

329:18 364:13

371:8 372:27

386:12 394:7,7

401:26 404:8

405:19 445:12

445:13 477:23

documented

123:6 513:24

documenting

323:26

documents 4:17

12:11 31:20

51:4 382:2,25

405:10 493:14

Doerge 6:11,11

92:11,12 93:26

175:8,9,19,21

190:11,11

235:14,15

238:3 260:19

260:20 411:24

411:24 428:20

428:21 429:25

440:20 445:6,7

454:20 458:15

458:16 460:8

462:12,13

467:21,22

470:7 513:18

Doerge's 261:22

doing 26:2 30:15

36:25,26 42:11

42:15,16,25

43:5,6 45:10

69:13 75:23

80:26 81:1,2,4

82:8 84:22

89:18 90:24

101:19 102:3

102:27 114:25

117:26 121:22

169:23 172:13

178:13 192:15

199:24 200:18

202:19 204:17

208:17 211:22

234:11 245:12

245:20 246:8

247:11,14

248:1,6 273:4

273:17 275:20

305:15 307:18

323:12,24,24

327:20 368:4

372:4 381:11

381:14 382:4

383:10,10

387:8 407:7

452:27,27

470:4 485:2

510:5,8 513:14

515:10

dollars 60:18,25

domain 293:24

323:1 435:2

domains 298:5

300:23

domestic 87:11

490:11

Donald 258:11

520:15

donated 102:15

dosage 459:3

461:21

dose 8:25 9:15

14:17 20:15

31:26 35:9

53:22 54:22

95:4 99:23

104:24 106:15

106:26,27

107:4,10,11,15

111:17 112:12

112:12,20,22

112:23 150:19

151:17 179:20

181:2 192:20

192:23 194:10

207:16 223:20

224:1,15

229:12,20,27

230:3,14

231:27 232:1,2

232:5 233:7

236:23 238:23

239:3 243:17

245:1,2,25

246:27 251:16

253:11 261:15

262:3,6,9

294:12,25

295:1,2,12

353:3 364:17

364:23 368:19

368:19,24

369:10,23,24

372:16,17

374:10 379:5,9

379:11 382:10

382:15 384:6

407:8 416:15

416:21,22

427:19,25

428:22 429:14

429:15,17

457:23 470:27

471:6,6 480:25

484:15 492:7

502:12

dose-equivalent

282:17 283:9

dose-response

306:15

dose-setting

303:12

doses 107:1,14

192:27 193:16

224:7 231:6

236:1,19

243:22 247:1

303:14 305:10

318:15,16

344:22 361:5

363:18,22,24

364:2 382:14

426:5 427:16

457:24,27

dosimetry 191:7

223:8 367:23

372:4 378:27

dosing 106:1,5,6

106:12 111:26

238:9 305:16

305:17,21

307:1 400:22

402:21 403:26

dot 112:5 192:25

254:22

dots 78:4 79:15

112:19 114:7

115:17,18

132:21

dotted 41:10

251:21

double 226:20

doubles 101:16

doubts 471:23

Dow 354:22

355:7 367:1,10

367:13

down-selected

337:7

download

387:19

downloaded

369:4

downside

119:18 517:18

downstream

56:18,23 81:7

96:22 101:22

166:1,9 176:3

377:15

downward

53:27

downweight

70:21

downweighted

70:20

dozen 207:27

225:7

dozens 174:15

174:21 195:6

Dr 3:3 4:21,27

5:17,20,23,26

6:2,5,8,11,13

6:15,17,20,24

7:8,12 11:14

11:14,20 21:20

21:21,23 22:9

22:15 23:8,10

24:2,16,18,23

24:24,25 25:17

26:9,9,10,18

27:18,19 28:4

28:12,15,18,21

28:23,26 29:4

37:6 50:16,20

50:22,23,25,27

51:1,1,2,11,16

52:10,19,20,26

53:6,7,13,14

53:16,18 54:4

54:17,20,25,26

55:10,22,23

56:9,26,26,27

57:7,14,17,21

57:23,25 58:5

58:12,14,17,20

58:25,25,27

59:4,8,12,14

59:16,18,20,21

59:22,24,24,25

59:26 60:5,22

61:9,18 62:2,2

62:3,9,16,19

62:21,22,24,25

63:4,8,14,16

63:18,19,24,26

63:26 64:1,9

64:13,16,21,22

64:25,27 65:2

65:4,5,6,10,19

65:21,23,24

66:1,8,11,13

66:17,23,24,24

66:25 67:3,5,5

67:6,9,12,13

67:17,18,18,18

67:22,24,25

68:2,3,3,13,15
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 35

68:18,19,24

69:16,16,17,20

69:21 70:1,16

70:19,25,27

90:6,6,7,12,27

90:27 91:1,7

91:18,22 92:4

92:7,9,10,10

92:12 93:3,26

94:1,3,4,5,11

94:14,22 95:7

95:18 96:1,2

96:10,25 97:15

97:15,17 98:3

98:6,7,13,14

98:16,17,19,20

98:22,23 100:5

100:9,10,10,11

100:12 101:3,3

101:5,9,11,18

102:7,13,18,19

102:26 119:26

119:27 120:1,2

120:6,13,15,16

120:19,20,24

120:26 121:1,4

121:6,8,10,11

121:12,12,14

121:20 122:12

122:13,20

123:8,8,10,12

123:13,14,15

123:21 124:8

124:10,13,13

124:15,16,17

124:19,20,22

125:2,9,9,12

130:6 136:19

136:20,20,21

136:25 137:6,6

137:7,8,8,11

137:12,13,15

137:18,27,27

138:1,8,13,23

139:2,13,14,15

139:24,25

140:1,7,9,12

140:13,18

161:20,21,22

161:23 162:1,9

162:10,10,11

162:13,15,16

162:19,23,24

163:5,8,10,11

163:11,14,16

163:18,25

164:1,4,6,7,9,9

164:12,14,15

164:16,18,18

164:20,24

165:5,6,7,20

166:23 167:3,6

167:7,7,9,9,10

167:14,24,26

167:27 168:2

169:7 171:2,17

172:15,22,24

172:25 173:8

174:5,8,27

175:3,7,7,9,19

175:19,21

176:9,17,26

177:1,4,7,9,15

178:18 180:7

185:1 187:21

187:22,24,25

189:2,20,22,25

189:27 190:2,5

190:8,11,13,15

190:17,20,22

190:25 191:2

216:21,23

217:2 218:13

218:14,15,21

219:27,27

220:1,7 221:14

221:14,15,26

222:18,23

223:4,6 235:13

235:14,14,15

236:7 238:3,19

240:3,3,4,4,19

242:10,11,12

242:18,24

243:4 257:8,10

257:11,15,16

258:5,13,14

259:5,13,14,18

259:19,21,23

260:14,19,20

261:6,21,22

262:16,17,17

262:25,25

263:14 264:11

264:12,13

265:4,22 266:7

266:9,10,11

267:18,19,20

267:21,23,26

268:2,4,6,21

269:5,6,7,8,8

269:10,17

270:4,4,5,11

270:21,22,23

271:7,9,9,10

271:13,23,25

271:26 272:3

275:19 276:25

277:1,6,6,11

279:1 282:22

284:14,15

285:15 286:18

288:14,15,15

288:17,17,18

288:19 289:3,8

289:8,9,13,20

290:8,10,22

291:2,12,12,14

291:26,26

292:1,2,22

293:14,20,27

293:27 294:2

294:16 295:3

295:15,19,20

295:21,21,22

296:14 297:26

298:26,26

299:1,5,17

304:27 305:1,2

305:11 306:5

306:24 307:26

308:15,17,24

309:8,9,12

310:4 316:5,8

316:11 317:15

324:10,11,12

324:18,26,27

324:27 325:1,5

326:3,4,5,6,8,8

329:3 330:22

330:24 339:27

340:26 341:12

343:25 354:18

354:21,27

355:1,3,4

367:7,8,10,12

367:12,16

372:2,13 375:5

375:5,8,10,10

378:6,10,12,14

378:14,18,20

379:1,6,14,15

379:16 380:2

381:7,8,8,9,9,9

381:13 384:15

384:15,16,17

385:16,16,17

385:17,18

386:4,4,5

388:16,26

389:7,7,8,9,11

389:13,14

390:22,23,24

390:27 391:7,8

391:11,14,16

391:18,19,21

391:22,26

392:10,14,16

392:17,18,19

392:21,22,24

392:26,27

393:2,6,7,7,11

393:14,14,17

393:18,23,27

394:2,3,3,3,5,6

396:20 398:6

399:1,2,3,5,6,8

399:9,10,10,13

399:20 400:1,2

400:2,2,3,4

401:8 404:2,24

405:13 406:1

406:26,26,27

407:1,2,2,3,11

407:16,18,19

407:26,27

408:3,8,14,19

408:22,23

409:1,7,8,11

409:14,15,16

409:18,19,19

409:23,24,25

409:26,27

410:2,3,5

411:2,9,11,12

411:14,16,19

411:21,24,26

412:1,3,5,7,12

412:19,20,21

412:22 415:9

416:4,4,5,6,8,9

416:10,23,25

416:26,27

417:7,7,8,9,11

417:14,16,21

417:24 418:10

418:15,15,16

418:25,26,26

419:1,2,3,8,9,9

419:11,12,15

419:16,19,22

419:23,24

420:1 421:2,6

421:17,23

422:5,6,7,7,7,9

424:1 425:4,25

427:9,9,10,11

428:1,1,2,3,5,7

428:8,10,12,20

428:20,21

429:2,8,8,9,24

430:4,4,5,6,17

430:17,18,19

431:3,3,7,8,25

432:3,13,15,17

432:19,23,24

433:2,11,12,13

433:13,14

438:16,16,17

438:18,19,20
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 36

438:23,25,26

439:9,11,17,18

439:25 440:19

440:21,22

443:23 445:1,1

445:2,3,3,6,6,7

445:8,8,9

446:6,6,12,13

447:17,24,26

447:27 448:3,3

448:4,13,13,14

448:15,23,23

448:27 449:2

449:15,24

450:2,7,11,13

450:15,16,26

451:1,1,2,2,2,2

451:4,27,27

452:1,10 453:8

453:8,9,10,13

453:13,19,19

453:20,21

454:6,6,9,11

454:15,19,20

454:22 456:18

458:11,11,12

458:13,13,15

458:15,16,17

458:17,22

459:6,10,15,19

459:25 460:6,8

460:9 462:7,7

462:8,9,10,12

462:12,13,14

462:18,20,24

463:2,3,5

464:7,13,13,14

464:16,16,17

464:19,20,25

464:26 466:17

466:20 467:11

467:11,12,21

467:21,22

468:7,17,17,18

469:8,8,13,23

469:26 470:5,6

470:7,13,15,21

471:14,15,16

471:17 474:2

475:5 476:5,6

476:7,17 477:5

478:1,1,2,3,3,9

479:1 480:6,6

480:7,8,11,15

480:17,22,27

481:2,3 482:17

482:18 483:5

483:18,18,19

483:20 485:15

485:23,25,25

485:26,27

487:15,15,16

487:17,22,22

487:23,24

488:15 489:13

489:13,14,15

490:7 491:9,25

491:25,26,27

492:3,4,18,19

492:20,21,27

492:27 493:1,4

493:8,10,25,25

493:26,27

494:1,8,9,10

494:12,13,14

494:14,15,21

494:21,23,24

494:27 495:10

495:11,11,12

495:13,13,14

495:19,20,22

496:6,7,8,10

496:13,18,19

496:20,20,25

496:25,26,27

497:1,4,5,15

497:15,16

498:1,2,13,14

498:15,21,23

499:9,11,12,14

499:18,23

500:6,9,11,13

500:15,16,21

500:21,22,25

501:27 502:1,9

502:14,15,17

503:14,14,16

503:18,18,19

505:26 506:23

506:23,24,25

507:6,6,7,8,15

507:15,18,23

507:24,25

508:14,17,20

508:20,21,22

508:25 509:2,8

509:8,15 510:2

510:3,7,14

511:18 512:1,6

512:23 513:2

513:18 514:6

514:19 515:2,2

515:13,13,26

516:14,25

517:15,27

518:24,27

519:1,10,15,20

519:21,24

520:13

draft 330:10

441:19 456:4

dragging 65:18

dragons 219:22

dramatically

306:11

draw 219:9,10

241:8 291:20

316:15 317:17

319:6

drawbacks

203:24

drawing 318:24

drawn 196:12

264:6

draws 24:13

198:20 231:18

drew 241:15

drinking 15:16

15:25

drive 1:16 111:5

151:1 289:18

360:24

driven 69:7

71:20,21

141:13 155:3,4

155:5,6 158:20

158:21 167:1

176:6 206:21

255:26 257:2

261:26 339:6

driver 87:4

158:12 207:1

drivers 16:12

155:18 446:15

drives 43:7

driving 32:22

159:26 255:12

drop 118:27

dropping 294:5

drops 249:21

Drs 174:18

367:27 391:27

440:20 454:20

460:7 463:3

471:15 481:1

502:15 509:7

drug 46:6 224:9

drugs 32:5,7

228:1 256:27

ducktail 18:3

due 54:18 59:5

105:11 134:9

155:20 156:20

181:14,26

220:11 228:16

279:22 298:12

317:18 333:15

348:7 359:25

361:5 372:10

395:22 401:23

402:13,14

444:18 461:10

Duluth 101:20

dumped 228:22

229:2

duration 181:2

420:3,4,12

421:6,7,8

dyes 160:10

dynamic 115:2

124:3 168:18

173:17 236:11

290:5 464:3

467:9 482:17

dynamics 174:1

E

E 427:8

E's 373:22

E-A-T 290:18

EA 375:4

EAR 318:17,21

318:21

earl 382:25

earlier 19:16

44:18 50:8

51:18 101:6

108:26 114:19

117:20 128:5

129:22 130:7

133:12 134:24

140:26 141:22

148:25 154:25

165:25 174:9

185:21 195:10

201:22 203:15

231:21 260:16

261:22 263:17

288:19 299:11

302:22 303:6

317:24,25

388:10 413:10

420:3 421:8

423:10,25

424:2 425:9

432:11 435:13

464:7 481:21

504:5

early 28:9

160:20 196:6

429:22 486:14

earnest 19:6

EARs 319:16

earth 233:26

237:24

easier 23:6

43:21 245:15

easiest 291:5

easily 105:8

110:18 258:15
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 37

269:3 274:27

330:17 369:12

easy 47:20 56:15

205:10

eat 236:15 249:7

eats 249:8

EC50 343:8

echo 386:5

510:3 517:15

ecological

302:12,21

424:12

economic

105:16

ECOTOX 96:12

101:19

ecotoxicology

6:21 190:14

Ed 6:13 189:9

190:13 411:26

edge 275:9

editing 441:24

editorialize

96:11

EDS 441:9

EDSP 2:7 9:14

9:26 10:3,5,23

11:2 12:6

17:12,19 20:13

26:22 47:15

51:16,21 52:3

86:1 87:8

103:14 124:27

125:25 126:13

127:19,22

128:1,8 131:24

132:1 133:2

161:3 167:14

179:25 183:4

184:8 186:19

187:3 195:8

198:6,8 199:4

199:5 200:9

204:20 215:13

266:25 272:11

272:13 275:27

278:13 279:17

279:19 281:23

286:12 310:26

311:3,8,10,15

312:2 323:13

325:14 353:2

354:5 355:22

357:20 358:6

360:12 361:1

365:10,26

366:1,14,21,23

367:24 380:10

380:13,16

381:2 396:10

399:22 413:11

433:7 440:7,9

443:23 455:1

459:27 469:21

470:22 480:24

481:13 483:9

496:3,5 502:11

EDSP's 14:4

EDSP-21 267:10

299:25

EDSP-related

366:6

EDSP-specific

357:21

EDSP21 18:11

EDSP21-4 85:21

EDSTAC 126:2

340:4 417:17

educating

510:13

EDWARD 6:13

69:17,21 70:16

96:2 190:13

290:10 294:2

295:3,19 305:2

381:13 389:9

411:26 416:10

417:24 419:19

431:8 446:13

480:8 487:17

506:25 514:6

EEC's 426:8

effect 82:5 93:7

104:25 105:4

106:25 107:9,9

107:16 111:16

111:23 112:10

114:2,4,8,9

117:17 118:21

123:19,21

163:24 165:10

166:12 177:22

178:9 193:3

216:27 224:7

281:9 294:10

294:14 295:12

316:1 325:22

332:26 338:15

343:21,24

345:6,19

346:25,26

349:11 359:14

359:22,25

362:6 382:15

395:23 397:1

416:13,21

436:7 452:13

485:8

effective 224:4

289:19 298:22

434:1 497:13

effectively 19:9

19:11 28:16

93:11 175:24

289:15

effector 176:3

effects 8:26

19:12 20:9

29:25 57:4,5,6

130:14 150:14

167:12 176:14

275:10 287:2,9

287:12 298:18

300:5 308:27

311:13 314:12

333:22 341:22

343:24 347:7

350:12 358:27

360:22 361:6

361:11 363:26

364:17,24

366:10 397:8

428:17 437:12

442:25 444:14

445:22 461:7

486:15,15,15

511:14

effects-related

14:13

efficacious 224:1

224:6,16

efficacy 343:5

efficiency 27:6

316:2

efficient 27:4

53:2 311:11

349:17 394:22

efficiently

126:19 278:17

375:24

effort 61:22

83:27 91:8

94:23 104:14

129:3 160:19

177:26 179:7

180:19 223:2,2

234:27 236:8

239:16 302:4

306:22 316:18

359:20 367:3

378:3 381:24

386:8,11,17

387:7,15,22

388:6,9 400:19

433:24 440:27

481:11 487:27

490:2,2 491:19

491:24 509:19

512:7,18,23

514:21 519:12

520:4

efforts 136:5

177:19,21

180:16 182:9

186:2,26

218:17 222:2

222:25,25

290:21 315:4,8

315:16 375:21

375:24 376:6

382:6 477:25

511:3 514:17

Egeghy 194:22

eight 19:19

34:20,22,24

43:10 57:3

88:11,12

126:25 187:6

225:8 255:23

258:26 262:5

263:20 264:3

264:16 268:13

278:27 300:14

308:19 423:14

505:12

eighth 258:24

either 36:14

46:10,27 56:24

79:7 100:15

105:1 106:1

109:27 113:11

166:5 183:7,9

201:26 235:24

264:24 296:1,6

298:20 305:5

342:1 347:4,7

359:3 382:17

382:24 405:14

454:23 460:25

466:4 475:21

491:14 494:8

503:2 508:2,12

elaborate 473:1

485:16,23

elegant 383:6

384:1 395:4

element 76:25

314:6 467:1

elements 322:26

449:27 466:26

514:2

elicit 345:6

347:4 364:23

elicited 403:3

eliminate 26:16

eliminated 51:9

505:16

eliminating

310:15

elimination
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 38

372:10,12

Eliot 192:10

Ellen 326:4,8,9

Ellenbogen's

347:22

eloquently

284:15

elucidate 192:8

eluded 308:24

emails 517:6

embarrassing

90:12

embryo 402:26

emerged 146:4

emerges 365:8

emissions

191:15

Emory 5:21

189:23 411:11

emphasis 14:6

414:21 477:20

emphasize

128:25,26

141:21 279:16

291:16 357:25

377:6 386:15

479:23 482:25

487:18 492:21

emphasized

21:10

emphasizing

480:1

empirical

203:27 205:26

239:11 481:10

empirically

70:19 198:4

206:4 208:18

empiricism

240:1

employ 455:16

employed 444:2

468:3

employment

394:25

enable 270:14

enables 359:27

encapsulated

311:3

encompassing

175:23

encounter

235:17

encourage

189:13 276:21

299:4 315:15

329:11 352:7

352:14 398:1

437:23,24

442:7 469:9

475:22 511:7

encouraged

486:10 506:20

516:10

encouragement

340:21 498:7

516:10

encouraging

337:13 339:5

340:12 401:6

486:7 511:7

endeavor 355:14

398:3

ended 113:25

221:26

endengeous

162:26

endocrine 1:5

2:7 3:6 7:23,24

8:8,19 10:17

11:10 12:2

14:9 19:10,11

19:12 25:1,13

25:23 60:27

61:6 84:10

100:8 104:19

124:26 126:5

174:17 189:3

287:2 290:26

305:7 308:23

311:9 318:20

326:4,10,12,15

327:24 330:26

333:11 335:9

335:12 339:14

340:2 344:5,10

344:12,21,25

345:6,7 350:15

354:19 355:9

355:16,24

356:1,10,16,24

356:27 357:6,7

357:19 358:3

359:11,14,16

359:17,22,25

361:16,19

362:2,4,6,7,15

363:21 364:17

364:24 365:21

366:8,16 369:7

371:24 373:22

375:26 380:10

386:8 426:3

431:18 433:20

470:22 481:20

482:27 491:1

511:11,13

518:4,15,21

endocrine-acti...

290:7

endocrine-bas...

53:23

endocrine-rel...

141:2 360:22

369:6

endocrinology

6:9

endogenous

58:10 105:1

163:4 168:7,15

171:11,15

341:12 342:25

343:7 345:13

345:24 347:22

348:13 456:24

456:25 466:2

468:4

endpoint 105:11

109:2 273:14

357:4,5 358:24

359:24 402:22

404:16 420:14

420:15,25

463:21

endpoints 13:15

16:9,20 17:2

18:20 19:1

20:25 25:16,25

25:25 75:17

128:1,2 130:2

130:7 134:14

135:10,12,14

175:17 301:5

303:25 308:26

309:1,6 358:5

365:27 374:6

380:6,16

382:20 400:13

420:19 422:2

422:24 423:25

425:1 433:6,27

440:2 454:2

455:24

ends 45:16

226:9 245:16

322:7 369:9

439:7 465:1

enforce 42:25

engaged 315:16

engineered

76:19,26 77:7

170:20 247:20

247:26

Engineers 6:14

190:14 411:27

enhance 350:20

414:9

enhanced

315:17 514:1

enhancing

430:21

enjoyed 410:4

enormous 60:15

315:26

enriched 194:13

ensure 3:25 4:3

376:24

ensuring 3:13

entered 388:1

entering 376:2

enthusiastic

511:21

entire 3:15

159:13,13

193:13 200:9

204:12 332:16

398:26 402:23

519:4,8,12

entirely 34:4

46:18 185:27

entities 466:8

entries 388:1

environment

95:17,17

201:23,26

202:14 206:14

206:25 310:14

366:25 376:2

environmental

1:12 5:21 6:9

6:22 8:5 97:3

125:26 141:17

148:6 152:21

152:24 153:16

161:13 176:24

178:6 179:17

181:14 187:6

189:23 202:8

202:12 203:7

204:14 206:9

207:10 224:21

224:23 233:16

235:4,7 286:26

310:23 372:6

392:3,9 456:27

518:11

environmenta...

305:10

envision 23:6

312:20

envisioned 14:8

envisioning

383:3

enzymes 241:22

EPA 3:21 4:18

5:11 7:15,15

9:5,7 14:25

18:5 86:4

103:13 105:22

106:10 109:27
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 39

112:3 121:27

125:7 126:1,21

127:19 160:21

163:9 164:25

177:2 181:23

190:25 191:4

207:16 243:4

267:26 277:7

278:10,16

280:23 310:19

311:18 312:12

312:20 313:17

313:23 315:13

315:21,26

316:13,19,21

317:2 318:7,27

320:6,7,24

322:21 323:12

323:25 324:3,6

329:7,11

330:14 331:11

331:13 333:16

333:20 336:11

338:20 347:12

351:4,20,25

352:5 353:21

354:8 355:19

356:11 364:12

364:22 367:21

368:4 370:17

371:8 374:20

375:22 377:21

378:12,15

379:21 380:11

380:19,27

386:27 389:17

389:23,23

390:11 394:10

398:2 399:21

403:24 434:20

435:11,19

440:7,15,26

441:25 459:26

471:10 473:1

473:22 475:6

475:22 482:21

483:23 484:1

484:27 485:17

485:23 504:12

506:21 509:19

510:8,25

515:23 516:20

EPA's 327:12,26

368:9 369:2

370:7 371:19

380:2 392:2

433:24 439:26

440:14 481:15

EPA-HQ-OP...

1:8

EPF 320:16

321:5

episodic 504:22

epsilon 65:26

212:12,19

epsilons 66:2

equal 258:22

397:11,14

equally 247:5

equation 41:1

41:18 42:3,8

43:1 45:11

209:12,15

228:13 231:8

231:14,18

332:25 395:9

494:7

equations 36:26

44:9 67:26

474:4

equilibrium

226:25 372:14

equilin 133:10

equipment

102:15

equivalent 63:22

73:16 75:8

103:25 145:8

145:12,21

169:15,27

232:6 236:1

244:25 295:1

318:15,16

363:17,22

364:19 379:6,9

379:12 396:14

equivalents

416:15,17

ER 2:5,6,6 16:21

16:22 67:8

77:2,12 86:4

86:16 102:23

102:27 103:17

103:24 117:13

117:25 124:25

124:25 125:21

127:18,23

128:6,12 129:4

129:5 130:4,23

131:2,8,13,17

131:18 132:8

132:10,20

134:3,8,13,19

135:9,12,15,15

136:11,12

137:11,12

139:9,19 141:5

141:6,23 142:2

142:24 145:5

145:27 152:12

160:13,20

161:6 162:2,8

165:1,8,24

167:2 169:20

174:10 175:16

175:25 176:5

177:18,26

178:6,17 179:1

180:13 182:27

184:10 185:9

186:16,16

201:16,17

256:26 269:12

272:27 277:26

278:25,26

279:18 283:7

283:13,19,20

284:5 287:23

288:2,7 296:10

301:7,13 302:4

302:25 308:19

310:26 313:10

313:24 314:3,7

314:14 319:1

320:27 327:15

331:6,9,12,17

332:13 333:23

334:10,17

336:5,8 337:1

337:3,9,9,22

337:26 338:4,4

338:17,17,18

338:19 339:4

346:25,27

353:2,5 368:26

370:27 371:19

371:25 373:18

379:3 380:6,8

380:9,12,21,24

380:26 381:2

388:12 393:24

393:26 394:14

394:17,25

395:4 396:2,12

397:3,5,9,11

397:14,16,18

397:24 398:3,6

398:14,19

399:23 400:14

412:13,16,25

421:26 422:2

422:20,27

423:6 424:3

426:24,27

433:3,7,8,8,27

433:27 434:5

434:10 442:12

445:11 451:18

451:19,25

452:15 460:19

465:3 466:14

467:16 470:10

470:23 475:15

475:21 478:25

480:25 487:12

496:3 502:21

502:24

ER- 370:26

ER-based

103:22

ER-binding

130:24 134:10

ER-mediated

126:17

ER/AR 331:20

331:23

era 319:1 512:26

err 492:25

error 43:3 66:15

105:27 220:8

221:4,10,11

233:8,8,10

284:21

errors 381:20

402:15 510:24

ERTA 130:24

131:12 134:10

134:23 135:16

136:13 139:9

433:27

ESDP 89:24

127:5

especially 9:25

107:22 111:10

153:21 165:8

167:18 195:9

240:5 242:20

305:6 308:11

351:2,13

352:15 353:6

354:9 389:21

396:8 398:3

401:2 431:21

433:21 434:27

447:7 448:11

449:10 468:26

486:8 492:11

500:1 503:23

504:22 510:10

517:21

essay 424:26

essence 443:21

essential 24:27

344:16 345:26

431:20 462:3

essentially 29:18

33:26 39:10,12

40:14 41:2

44:1 50:11

58:7,19 62:11
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 40

63:10 69:26

73:14 103:21

103:25 117:22

147:4 148:27

151:13 153:1

154:4,18

157:14,22

172:20 250:1

274:17 275:25

285:9 286:21

287:3 294:24

296:24 359:20

400:7 429:26

establish 9:15

108:10 109:6

180:16 305:4

established 8:19

108:8 125:26

291:25 299:26

308:3,9

establishing

342:7 350:14

447:15 473:21

establishment

9:19

estimate 11:8

191:25 193:22

194:8,8,20

195:26 196:13

198:4 201:10

204:25 205:16

205:18,19,25

205:26 210:21

217:21 224:15

225:24,26

226:1,6 236:3

237:8 248:19

251:7,12,22,25

252:4,6,11,18

253:11,15

256:21 257:19

268:18 271:21

276:5 279:18

283:14 294:26

331:12 342:8

354:1 380:21

440:17 475:11

475:18,24

488:17,19

estimated

127:23 136:22

136:24 193:12

206:5 217:25

217:26 255:9

262:6 275:26

285:11,21

368:17,19,24

371:4 372:18

374:9,11

380:14 396:23

396:26 397:15

397:20 398:16

440:10 478:14

estimates 68:14

193:10 194:3

194:15 196:8,9

197:17 206:4

207:18 208:24

215:25 222:10

224:24,24

234:16 235:9

241:9 245:10

248:25 254:22

256:17 257:3

257:18 259:9,9

261:9,10

268:10,22

270:2 271:12

276:3,6,10

278:1 280:7

286:3 290:1

311:5 330:19

351:9 353:14

364:12,22

390:9 391:3

401:4 470:25

471:2,7 485:5

488:5 506:2

512:16

estimating 239:3

248:10 268:15

estimation

104:23 378:26

389:15,25

396:25 478:25

estradiol 35:23

46:11 77:20

78:7 92:20,25

105:8 124:4

133:9 162:26

285:18 321:4

341:12 343:3

347:26 348:22

348:24

estrogen 2:5

8:22 12:22

13:9,12 16:9

16:20,21 19:19

19:25 25:27

26:14 28:2

30:9 31:2,10

32:23 33:15,21

34:18 35:4,18

35:24,25 40:5

43:25 47:8,22

49:11,12 51:6

52:13,17 55:1

55:5,19 56:20

56:24 64:6

65:12 66:9,9

69:6 71:19

72:10 74:17

75:1,10 78:4

78:13 79:10

80:3 81:2,8

82:9 83:7 84:7

86:8,15 90:1

94:25 99:14

100:19 102:23

103:12 105:1,5

108:6 113:15

118:2 119:7

125:27 126:4

127:9 128:9,14

128:17 130:3

137:17 161:26

162:25 168:4,6

169:4,13,27

170:13 171:19

176:11 177:24

184:16 243:15

246:16 247:20

247:21 249:15

249:18,26

252:25 254:19

255:17 256:7

264:25 265:8

272:24 273:3

273:10,19,22

274:14 275:22

280:10 287:5

296:26 297:17

300:13,27

303:23 304:18

306:26 308:6

310:22 314:8

320:15 330:23

336:7 337:24

337:27 339:16

347:24 348:8

348:14 349:5,7

349:12 350:12

361:11 365:6

380:3,6,18

384:24 396:10

398:24 412:17

415:2 422:4

433:9 434:1

437:14 441:23

442:3 450:9

470:23

estrogen-based

30:8

estrogen-free

123:5

estrogen-like

433:24

estrogen-rece...

125:19 164:23

169:15

estrogenic 31:13

32:24,25 33:1

33:2 37:11

55:7 68:27

71:7,9,11

76:17 79:17,24

79:26 80:7,12

81:6 83:22

90:22 95:3

99:17,18 100:3

100:15,16

101:24 102:1

105:21 131:8

133:13 325:22

336:23,24

341:19 343:24

344:23 347:4

349:14 350:11

394:13,19

414:14,26

415:2 426:23

426:26 433:20

434:12,14

435:4 437:11

481:14 503:3

estrogenically

434:2

estrogenicity

80:1 101:1

127:13 281:5

297:24 395:2

428:15

estrogens 66:11

80:5,19 95:1,1

95:1 98:11

104:26 105:3

115:4 118:3

176:1 341:24

347:22,23

348:10,14

349:1 503:1

et 26:14 192:24

206:8 207:10

224:17 226:14

318:25 325:11

344:3 346:22

347:21 351:5

363:18 364:5

376:18 389:3

457:6 467:6

469:3 475:15

et.al 426:9

etcetera 461:22

462:1 467:1

ethanol 352:18

Ethical 310:6

ethics 3:25 4:2,3

ethylates 210:8

217:20

Europe 87:2
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 41

103:8

evaluate 125:26

127:18,19

128:19,21

131:15 134:2

134:18 136:11

181:13 185:5

198:2 201:20

204:5 208:18

235:21 289:4

289:24 320:20

332:12 334:16

359:11 367:15

370:21 381:2

399:23 433:23

446:26 460:1

483:2 495:8

503:7 504:11

504:15 513:13

evaluated

120:17 127:9

134:16,19,23

134:25 141:19

181:19 301:15

334:8 385:12

396:8 400:16

401:27 403:11

405:5 406:6

408:26 428:24

429:3,5 446:11

446:14 465:8

465:13 486:22

evaluating 30:20

120:2 126:8

178:14 180:18

203:3 234:23

319:22 327:1

332:15 334:27

381:6 386:1

399:26 460:5

500:8 516:19

evaluation 88:2

127:25 140:22

203:26,27

208:6 216:6

222:27 311:4

311:24 320:2

325:25 328:1,8

331:25 332:18

337:11 339:7

355:10 362:3

362:11 366:14

366:18 380:15

403:8,9 419:20

424:14 425:1

440:11 461:3

462:4 468:4

478:7 481:10

483:10 505:23

506:22

evaluations

20:24 181:25

354:5 402:17

404:18 405:4

424:20 437:21

463:10

evaporated

116:19

evening 520:22

event 69:25

70:17,17

202:20 203:1

338:1 361:4

events 126:17

207:21 359:2

eventual 316:1

eventually 17:26

103:13 196:3

201:7

everybody 74:11

86:25 87:25

286:14 289:11

430:7 434:20

510:4 513:2

517:17

everybody's

177:11

everyone's

470:11

evidence 10:16

11:7 13:14,14

13:23 20:24

21:7 56:24

119:4 127:25

136:10 269:11

301:3 314:20

314:26 327:3

337:23,26

338:11,12

339:9 355:9,16

358:5,6,7,9,12

359:8,16,17

360:1,6,8,18

361:13,14,22

362:2 380:15

422:3,17

424:20,21,24

424:27 428:14

428:26 430:14

431:22 438:13

440:11

evident 356:26

evolution 12:6

14:4 17:6 52:1

evolutionary

17:17

evolve 51:17

306:17

evolved 51:21,23

107:17

evolves 296:6

311:27

evolving 16:12

296:3 490:1,18

491:21

ex 393:27

exact 112:2

150:9 178:16

211:6 230:2

428:18 449:7

exactly 24:21

38:2 59:16

64:6 78:13

82:2 94:11

118:12 165:16

176:9 186:12

200:8 202:21

217:11 237:1

250:5 251:21

285:4 291:1

299:6 312:6

376:13 443:20

465:24 499:20

515:16

examination

154:21 198:15

505:12

examine 308:21

511:4

examined

145:24 150:21

166:18 181:3

201:7 346:15

398:25

example 43:22

43:24 55:11

61:14 72:6

74:14 100:21

100:25 105:4

109:21 111:9

111:21 125:22

129:5 141:5

143:19 145:5

148:4,5 150:4

150:18,23

151:7 152:6,23

153:16 154:27

156:10 157:8

157:11,22

159:3 172:16

181:20 249:16

253:1 255:14

261:23 262:13

269:20 271:21

282:26 292:6,9

292:13,15

293:16,16

308:5 311:20

312:11 314:6

315:20 320:14

320:23 332:6

335:6,20

337:10 341:10

346:3,3 349:21

355:11 358:12

360:3 363:12

370:23 371:1

376:21 377:18

393:8 403:7

467:17 478:24

479:14,26,27

482:16 485:6

490:7 505:19

506:7 508:15

511:14

examples 55:14

55:18 56:1

77:16,16,19

80:6 81:11

143:19 245:1

262:14 263:3

268:27 289:12

289:13 344:24

350:4 366:24

381:25 419:8

427:19 506:8

exceed 184:4

Excel 39:10

excellent 118:10

118:14 180:9

217:2 271:24

313:13 336:11

406:16 413:2

469:15 509:8

513:23 514:27

516:20

exception 33:23

35:8 42:21

90:15 96:12

196:12

exceptionally

180:12

exceptions 48:11

147:23 240:22

352:20 400:11

excess 344:7,9

excited 274:27

310:23 315:25

384:5 486:25

486:26 487:13

514:9

exciting 5:3

278:20 487:1

514:9 516:12

exclude 51:18

123:4 159:17

159:17 296:6

312:14 462:1

465:20

excluded 152:9
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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358:19

excluding 51:26

exclusion 296:2

296:12

exclusively

152:16 159:12

179:12 289:6

exclusivity

414:24

excreted 208:21

excretion 229:2

229:3 238:12

352:13 477:7

477:10,12

505:25

excuse 125:9

340:7 392:16

executive 520:13

exercise 61:16

61:24 65:22

66:14 134:27

138:22 186:17

361:27 502:1

exercised 456:5

exercises 125:14

exhibit 343:8

exist 95:14

230:25 289:24

385:8 396:4

397:27 479:13

500:14

existence 208:1

existing 17:24

17:27 18:20

127:19 134:5,9

134:11 176:10

368:13 395:27

496:4

exists 351:12

exogenous 105:3

expand 16:16

25:22 186:2,13

234:16 311:26

317:1 419:18

509:23

expanded

278:27 404:1

expanding 18:17

160:26 185:19

300:20 301:14

303:27 304:13

expansion 21:6

126:24 300:22

expect 29:23

30:3 56:22

75:9 90:2 99:1

149:5 152:17

158:11 179:16

180:26 181:13

182:21 184:13

186:8,19 187:5

232:27 235:8

237:21,21

286:14 290:5

293:4 298:9

347:6 350:12

388:16

expectation

178:6 184:12

279:22 308:20

461:1

expectations

51:21

expected 65:6

106:7 116:3

134:22 146:26

147:5,9 152:11

187:5 353:3

418:5 470:27

480:26 502:13

expecting 106:8

220:2

expediting 376:5

expeditious

518:17

expensive 69:8

394:20

experience

16:11 49:6

103:9,9 260:24

367:26 504:23

516:4

experienced

483:27

experiences

263:3

experiment

101:18 107:2

226:20 450:15

experimentally

372:11,14

374:1

experiments

95:6 112:9

138:15 343:2

398:8 421:8

expert 49:4 51:7

57:8 86:5

89:13 92:5

161:10 300:14

359:9 383:23

424:3

expertise 5:9

experts 33:12

34:4 48:1,26

49:5 80:6,11

86:23,23 89:14

90:20 146:1

171:23 317:12

478:18

experts' 84:6

explain 69:22

82:10 90:8

91:9 92:19

136:26 216:10

220:22 292:18

293:25 381:24

401:18

explained 77:15

172:21 212:3

384:8 385:19

385:23

explaining

102:27 220:15

220:16 270:8

332:17 389:17

427:20 434:21

explanation

65:9 76:16

333:4 337:1,4

338:14 382:5

382:21 383:17

385:2 438:27

456:4

explanatory

441:3

explicit 376:13

explicitly 52:21

126:10 128:2

342:2

explore 167:19

352:9 414:7

explored 384:8

449:18

ExpoCast 9:6

19:23 20:7

21:3 191:10

192:3,6 197:21

198:11 199:24

201:8 235:7

275:23 332:9

356:19 364:5

368:11,16

370:8,18 371:4

393:12 470:24

483:3 488:1

exponentially

387:8

exposed 87:13

191:22 210:3

215:8 223:14

232:2,11 236:5

237:15 238:5

243:22 249:9

254:6,9 256:8

319:19 371:9

484:13,14,15

511:20

exposure 2:9,9

5:21 8:24 9:6

9:11 11:8,24

12:26 13:2,10

13:20,27 15:1

15:9 19:22,27

20:7,9,21,27

21:8,25 22:3,5

22:10,13,20,25

23:13,16 24:5

78:24 96:15,20

97:4,8 106:7,8

106:9 110:21

116:7 125:3

181:2 189:23

190:27 191:5

191:13,14,17

191:18,20,25

192:4 193:10

193:12,17,22

194:8,15,20,23

195:11,13,15

195:21,22,26

196:12,17

197:12,24

198:7,9,12

199:9,12,26

200:2,2,14

201:2,19,21

202:5,20 203:3

203:10,15,16

203:19 204:2,3

204:4,24,26

205:16 206:4

206:12,15

207:16 208:15

208:27 209:2

209:11 210:22

211:7,25 212:1

212:2,21,21

214:22 215:14

215:16,23

216:8,10

220:17 221:22

221:25 222:7

222:10,11,12

222:16 223:11

224:24 225:26

226:2 230:19

233:6,17 236:3

237:2,2,9,13

243:1,19

244:15,20

245:3,27 246:1

246:3 247:2,9

248:27 250:16

251:14,23

252:6,8,11,14

252:24 253:12

253:13,21,23

253:26 254:7

254:20,22
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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255:9,10 256:3

256:4,6,14,19

256:21,27

257:3 259:10

260:27 261:8

261:10,11,12

261:19,19

262:11 264:15

268:8 270:19

270:26 271:13

271:15 275:26

276:4,17,24

277:15,27

278:7,16 279:8

279:18,20

280:4 281:26

282:25 283:1,7

283:8,14,23,26

284:1,6 285:5

285:7,11,21,25

286:19,23

287:21,25

291:18 295:6

300:5,18,22

301:8 302:5,7

302:9,11

303:14 307:9

307:22 310:23

311:5,6 314:6

315:5,7 317:26

318:3,12,14,17

318:18,20

319:8,13,22,26

320:12 321:21

324:5 325:17

325:20,23

327:13 330:19

351:6,8,9,12

351:15,19,20

352:23 353:3

353:11,20,25

355:23 356:6

356:18 362:13

362:22,24,27

363:2,15 364:6

364:9,9,12,22

365:12,15

366:4,10,13,19

367:14,15,17

367:19,22

368:11,13,15

368:16,18

369:26,27

370:1,3,8,12

370:14,20,20

370:22 371:10

371:14 372:6

372:18,26

373:20,24,27

374:6,8,9,10

374:11,19,20

374:21 376:22

377:8 378:23

378:27 379:11

379:12 420:4,9

420:12 421:8

423:16 435:16

470:25 471:1,3

471:7 472:2,15

473:25,26

474:13,21

475:1,5,6,20

476:1 477:8

479:1,8 480:26

481:17,25

482:2,14

483:12,16,26

483:27 485:6,6

485:12,19

486:14,16,24

487:1 488:23

490:12,19,20

490:26 491:23

492:6 493:1

494:17,19

496:4,23

497:17,22

498:3,24 499:4

499:6,7,16

500:1,19

502:13 503:6

503:12,22,23

504:1,20 506:2

507:1,14

508:16 510:19

511:24,24,26

512:16 518:21

519:13

Exposure-Base

189:4

exposure-based

1:5 3:6 11:10

exposure-relat...

14:12

exposures 96:14

97:3 205:3,27

206:6,21

207:19,23

210:15 211:26

215:12 220:16

221:19 222:19

235:17 236:13

258:7 318:2,16

318:24 325:10

325:24 341:3

351:27 352:4

354:11 364:16

368:14,17,25

370:5,15 371:4

373:1 374:9,14

374:15 376:16

377:7 423:18

477:15 484:21

490:20 493:12

494:3 495:1

504:21,22,25

504:25 505:7

505:21 506:17

express 518:1

expressed

376:27 380:7

440:4 444:20

451:16 457:23

expression 95:9

168:10 453:24

467:10

extend 3:15,16

4:20 7:21

56:15 280:27

519:7

extended 357:22

extends 439:12

471:2

extensive 126:4

127:1 287:3

467:27 480:16

494:4

extensively

236:9 283:2

482:23 507:14

extent 195:12

203:5 209:24

262:19 313:1

385:5 390:21

515:24 516:16

exterior 191:14

external 8:3

369:23 372:18

374:7

extinguished

57:6

extra 107:6

245:13 251:16

271:18 275:20

383:15 481:8

extract 388:7

extracted 109:22

493:14

extracting

350:16

extrapolate

204:7 206:1

294:13

extrapolated

93:9

extrapolates

235:4

extrapolating

473:6

extrapolation

229:24 247:13

247:14 280:5

302:17 304:13

377:9 492:9

extrapolations

492:10

extreme 244:11

347:11 439:7

479:7

extremely 60:20

115:2 175:22

207:18 214:16

220:19 230:18

281:12 284:12

349:1 484:2

extremes 222:12

244:16,17

479:13 488:19

eye 300:19

F

F 425:17 432:18

432:21 439:14

F1 347:7

facilitate 313:25

facilitates 18:2

facing 82:23

218:17

fact 52:13 55:14

57:13,17 80:12

95:13,21 97:27

107:2 119:7,18

127:14 132:21

137:9,16

166:15,27

168:12,16

178:27 185:7

219:2 248:18

250:9 264:26

265:5 268:17

279:26 284:2

285:14 304:7

309:13 317:10

333:9 335:25

341:19 344:11

345:6 347:3

348:21 367:18

370:15 373:26

401:8 402:26

405:24 413:12

424:27 442:26

444:3,11

457:12 465:18

485:4 508:7

517:6

factor 33:19,20

34:3,6 74:5

76:21 143:24

224:18 232:15

239:9 250:7
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252:20 257:21

257:24 258:15

258:17,18,18

259:11,15,16

262:8 263:16

269:1,4 293:11

293:21,24

factors 49:19

111:5 155:3

170:1 212:9

215:12,16

232:20 292:24

444:7,17 462:2

510:26

factory 202:9,9

facts 494:18

faculty 340:3

fail 344:22

failed 14:25

263:4,5,10

fails 347:3

failure 250:13

253:26 254:1

254:14

fair 33:8 51:6

167:3 177:20

289:21 403:18

441:12 469:23

fairly 17:7

104:25 113:12

114:20 116:14

123:26 128:17

129:25 132:5

140:3 179:1

187:14 213:16

225:21 285:2

331:7 348:11

349:6,21 374:3

401:10 504:17

507:5

faith 499:2

fall 93:2 100:25

132:2 149:6

353:7 435:3

447:22

falls 36:4 132:9

137:9

false 32:3,20,21

49:22 116:27

141:12,13

297:21,22,23

313:12 333:14

333:14 346:4,8

346:11 350:6

383:12,15,15

383:24 394:27

406:14,14

434:21 467:15

467:17,20

482:16

familiar 14:4,5

121:16 142:25

352:18 418:19

505:10

families 222:20

family 43:2

239:6

fancy 272:26

fantastic 519:5

far 32:11 35:8

36:20 51:16

55:15 61:11

65:8 77:26

97:21,24

110:18 120:3

124:3 141:23

144:3 149:6

201:3 214:2,11

216:3 238:27

241:26 244:10

244:12,17

255:23 259:6

263:19 301:20

304:18 311:14

314:14 315:1

316:17 321:10

343:2 355:19

360:13 362:10

377:14 417:16

424:15 431:19

446:17 498:3

503:6 516:11

far-field 202:8

202:11 206:13

235:17 351:13

490:12

far-right 357:12

farm 222:20,20

494:16

farms 494:6

farther 323:14

farthest 255:1

fascinating

426:19 511:19

fashion 55:20

76:27 144:11

174:25 182:18

267:10 461:16

518:17

fast 228:10

434:16 512:4

fat 80:23

fate 206:14,22

fatty 344:3,16

345:26

favor 50:8

favorite 74:15

276:22 362:16

FC 347:7

FDA 87:2

FDA's 5:18 6:11

84:9 189:21

411:10

feasible 467:26

468:5

feature 491:7

fed 373:9

federal 3:9,14,18

3:27 9:5 18:6

60:26 146:13

feed 246:11

feedback 97:5

174:8,13 189:6

189:11 244:3

277:19 305:3,3

305:7,8,25

306:13 307:15

308:13,27

430:11,21

437:13 517:14

feedbacks

514:15

feeding 461:27

feel 37:16 61:1

61:14 71:11

187:1 196:17

239:21,23

314:14 337:25

372:24 385:18

405:16 428:14

482:26 496:23

499:2 510:11

510:12 511:25

517:2

feeling 513:15

feels 396:11

398:12

fellow 514:25

517:4

felt 332:14,26

333:21 369:8

369:10 382:5

400:20 401:2

514:26

female 105:2,2

345:21 347:25

females 347:7

371:10

feminizing

341:21

fetal 511:12

fetuses 504:9

fewer 14:20 88:1

88:1 185:8

198:26 281:17

311:14

fide 165:14,15

165:16,18,21

field 123:16

445:18

FIFRA 1:2,9 3:4

3:18 4:22 7:15

11:23 12:8,27

16:6 19:14

411:3 477:25

519:2 520:11

520:12

fifth 213:1,2

Fifty-seven

388:1

figure 22:16

36:12 48:17

62:4 91:19

105:4 124:11

124:15 131:23

132:10 139:17

192:17,24

195:18,21,21

198:10 200:7

204:18 213:17

224:10 234:4

240:9 245:18

262:18 263:1

267:22 270:9

270:16 280:2,9

281:14 282:9

282:15 284:14

284:16,22

285:26 350:18

384:10 405:18

405:25 406:15

426:14 434:17

435:12 436:14

438:11 452:2

471:25 472:4

472:11,13

474:15 499:9

500:4

figured 196:2

293:13

figures 132:22

281:27 282:19

378:15

figuring 159:24

238:16

Fij 41:22

fill 204:17

402:18

filled 405:17

fills 370:18

filter 122:21

filtering 337:8

filters 463:22

filtration 228:20

228:27 231:13

372:13

final 45:17 70:25

84:26 130:21

174:5 234:3

329:22 354:5
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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441:23,25

442:16 443:11

456:5 478:26

509:4 516:15

516:24 518:26

finalizing 10:16

finally 10:26

11:9 43:18

78:24 88:10

89:20,21 119:5

270:8 276:13

276:19 307:10

315:19 323:17

324:2 339:19

365:4 366:18

398:18 446:2

475:9 479:23

511:16 518:8

find 63:7 68:5

93:4 118:15

143:26 162:23

163:7 164:10

164:11 199:8

209:16 211:13

213:27 230:3

232:24 239:23

262:14 265:17

290:3 344:4

346:12 426:19

431:23 447:13

465:8 479:19

492:5

finding 83:16

85:1 91:9

187:15

findings 160:4

361:10,14

fine 50:25

189:12 392:12

407:18 408:2

431:24 457:13

finish 177:10

307:20 345:10

350:24 502:2,3

finished 7:1

399:3

fire 157:9,9

firmly 51:23

first 7:3 8:23

10:6,13 11:2

12:21 15:3

18:15 33:16

36:22 44:21

55:6 65:24

76:2 92:16

93:4 125:21

127:22 129:20

130:20 138:22

139:3 140:19

170:9,11 189:6

190:25 191:5

194:12 206:8

208:1 212:27

225:5 236:12

238:10 241:10

243:24 244:20

246:5 250:26

250:27 257:9

264:12,20

270:7 285:5

294:1 295:22

297:1 298:1

305:20,23

306:20 309:18

310:5 314:18

326:5 346:9

355:15,25

356:7 363:15

371:19 379:2

379:23 381:18

383:9,19

386:23 392:19

394:10 401:26

402:1 433:16

476:8 478:9

483:23 493:27

503:20 508:5

511:18 515:4

518:27

fiscal 15:22

Fischer 348:26

fish 15:16 69:2

96:22 121:17

130:11 139:10

182:14 267:6

302:13 304:3

315:23 357:2

402:26 420:6

FISTRA 414:13

425:11,13

fit 55:8 66:16

68:5 72:1,2

201:5 221:19

250:25 263:6,8

267:21 273:26

294:22 300:1

323:21 330:3

389:21 390:3,3

408:26 434:24

470:12

fit-for-purpose

21:11 108:4

117:27 126:9

134:4 178:15

185:7 192:5

201:3 299:24

300:26 303:24

477:21 479:17

487:25 497:6

fitness 341:21

fits 238:15

249:22 290:11

422:27 424:5

424:13

fitted 250:22

268:15,18

398:7

fitting 42:16,16

65:22 295:7

369:12 398:9

five 4:14 16:8

55:24 65:11,11

75:21 85:3

87:12 106:19

109:5 111:22

111:23 119:12

119:13,14

120:14,15,16

181:5 192:22

239:3,10,23

251:24 252:2

292:14 319:4

404:20 441:12

453:23,27

489:6 517:19

five-fold 284:11

fix 205:10

254:15 408:26

fixated 270:24

fixed 231:25

233:7 396:23

fixtures 434:18

434:19

flagged 118:5,25

257:27 388:2

flagging 114:27

117:25 124:6

flags 275:8,15

382:22

flame-retardant

403:2

flat 205:4 225:8

273:27 321:15

497:12

flavor 71:16

fleshed 464:21

flexible 306:19

449:10

flip 43:20 166:8

307:26

flippant 341:25

flipped 23:1,6

floor 140:13

flooring 202:1

florescence

275:12

florescent 227:8

Florida 340:2,3

494:17

flow 229:4

231:12 472:20

fluoresce 35:16

35:17 71:26

76:25

fluorescent

35:16

fluorophore

38:9

fluorophores

38:7

fluroanthene

147:27 151:11

172:16

flushed 442:11

flutamide

181:11

focus 12:8 13:7

20:14,15 21:5

24:26 33:10

47:26 86:12

136:4 174:26

177:27 195:16

291:20 301:13

302:23 308:2

312:2 328:3

338:21 362:12

386:1 398:24

400:26 406:16

422:17 444:21

468:8 504:1

511:26 514:23

focused 8:20

14:27 16:4

19:24 20:12,19

20:23 52:13

121:26 144:5

179:12 198:14

287:1 291:23

317:26 362:10

368:16 370:21

424:21

focuses 20:22

25:3 107:20

303:27 504:13

focusing 165:23

291:21 307:3

314:10 315:8

327:9 514:17

fold 283:16

284:7 344:7

folding 293:23

folks 29:2 31:27

39:8 41:16

47:10 61:20

86:10 87:1

90:20 91:23

121:27 352:11

374:5 482:5

509:21,22,23

509:24 517:22
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 46

follow 12:13

33:3 57:14

64:2 67:6

147:12 272:8

292:1 424:19

444:24 469:11

517:27

follow-up 22:15

31:16 89:18

98:23 172:22

260:21 262:26

269:5,22

288:19 291:13

291:14 305:1

497:10

Follow-ups

55:22

followed 18:13

326:5 356:7

365:12 502:23

following 82:25

113:8 118:9

126:2 141:4

145:5 178:4

294:3 299:9

304:17 380:17

402:15 440:12

471:7

follows 449:26

follows-up 89:3

food 15:9,16

191:15,18

201:24 202:16

202:17 206:15

238:9 354:16

375:22 376:2

fooled 79:24

foot 367:17

forecast 191:12

191:13 197:8

197:15 390:18

forecasting

191:13 378:22

378:23,27

390:9

forefront 316:25

forest 232:17

forget 91:20

171:19 244:8

520:11

forgot 358:26

404:12 462:16

form 33:19 64:4

79:8,9 116:20

119:1 133:13

139:5 143:22

238:26 396:23

445:11 453:16

474:18

formalism

235:27

format 18:2 58:8

58:9,10 95:23

142:19 239:17

383:21 449:10

formation

466:25

former 516:5

forms 333:17

formulated

453:17

formulation

294:25 295:2

395:5 451:21

494:7

formulism

204:10

forth 45:21 64:5

64:7 171:5

218:20 251:9

282:21 315:21

385:2 418:18

422:10 452:26

455:15 476:3

489:11

fortunate

510:11

fortunately

367:25

Forty 131:27

Forty-nine

132:3

Forum 326:4,10

326:13,15

330:26 340:2

Forum's 354:19

forward 7:23

16:27 30:21

37:9 43:20

51:25 60:4

61:15 84:11

85:19 86:17

89:9 122:19

160:19 176:8

178:1 182:20

187:27 203:6

203:25 204:1,3

212:18 239:25

243:16 296:19

302:1 304:19

304:20 308:8

315:23 316:21

317:6 318:7

319:24 327:6

327:15,17

337:15 339:1,5

339:18 349:17

387:7 416:3

436:11 464:12

466:11 470:3

478:13 481:26

487:9 491:7

503:24 508:1,9

512:16,18

515:24

found 94:17

112:22,23

194:11 206:19

215:15 238:27

360:20 366:12

367:21 371:23

371:24 387:25

403:1 409:20

409:25 428:23

433:15 478:5

492:6 514:1

founding 107:20

four 46:4 65:10

65:11 85:3,7

85:11 94:23

95:1 99:16

105:17 123:2

142:18 157:27

220:18,27

264:9 287:3

288:2,3 319:4

321:6,19

326:25 441:12

451:8,9

four-fold 285:20

Fourteen 152:27

fraction 25:15

98:9 99:9

209:4,7,10,10

217:21,27

228:16 248:14

248:20 272:11

372:25 373:25

413:27 472:20

498:27

fractions 413:23

framework 18:4

20:3 122:9

127:19 128:8

169:4,5 265:2

279:4 290:24

302:7,8,12

303:22 306:14

306:16,18,19

306:24 312:17

322:15 323:26

355:24 356:14

356:20 357:9

357:26 380:11

440:7 455:12

481:12 489:1

513:19

framing 271:19

frank 192:21

Frankly 238:7

Fred 3:3,8 28:12

28:18 518:25

518:27

free 37:17 42:20

227:22 244:25

489:9

frequency

363:16

fresh 177:11

FRET 38:5

46:25,27

169:12

Friedman

354:21 355:2,3

355:4 367:8

frogs 69:3

front 40:2 58:26

189:8,9 517:25

fruit 26:3 223:3

291:4

FSTRA 338:27

339:3 406:5

FUB 228:17

fulfill 419:9

full 18:22 130:22

142:9 169:20

183:9 305:15

328:8 377:19

500:7

full-dose 342:27

full-length

465:12

fully 167:15

317:3 324:7

339:10 350:1

374:20 377:26

385:19 388:16

390:2 434:9

438:15

fun 509:13

function 33:26

39:5 68:7

141:23 161:25

170:15 172:19

173:20 248:26

348:10 349:20

349:20 353:14

508:6 512:19

functional

396:23 397:27

398:2

functioning

341:24 344:12

350:15 386:3

433:22

functions 331:24

508:4

Fund 375:13

funded 344:18

344:19
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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funding 494:18

fungicides

161:17 261:23

355:12 360:4

362:4

further 10:21

13:22 44:24

52:6,20 56:18

73:7 93:22,23

97:26 102:3

109:26 122:9

127:22 135:20

146:19 154:20

160:14 161:9

166:18 187:3

217:16 243:24

279:19 303:20

304:9,25

311:24 312:14

312:20 313:23

314:5 320:2

323:12 325:3

325:13,25,27

333:4 337:4,11

337:20 338:14

339:9,25

341:17 342:10

343:17 351:16

353:2 361:19

362:7,15,23

363:7 365:21

367:3 380:13

382:5 384:24

390:8 399:12

438:18 440:9

442:7 444:1

445:4,23 456:4

462:10 464:18

476:26 477:27

479:5,6 480:24

481:13 482:12

483:9,10

496:16 502:11

507:4 508:23

Furthermore

435:6 436:20

481:15

fusion 58:21,22

59:1,3,11,15

future 2:11 13:5

13:6 19:2,4

20:2 23:7

51:14 60:8

67:7 122:8,11

130:15 138:17

140:6 178:12

178:24 182:9

182:24 216:2

234:13 237:27

241:6 282:5,18

287:16 288:23

290:23 292:20

299:2,15,18,21

300:9,12,25

301:6,9 302:6

303:27 304:6

304:24 306:16

306:20 312:10

313:19 315:9

315:23 317:9

369:12 384:21

393:3 402:17

406:8,17

415:13 438:14

447:7 456:8

483:13 485:2

487:9 490:1

fuzzy 219:9

G

gain 82:1 106:17

167:19 172:7

174:22 335:8

441:15

gain-loss 274:2,5

398:7,11

gain/loss 72:3

249:20

gained 509:12

Gainesville

340:1

gaining 443:25

GAL4 58:21,23

58:23 59:1,3,5

59:11,15

game 207:20

gamete 159:13

gamma 80:24,24

gap 192:8,8

298:20 432:11

gaps 402:19

405:27 408:26

473:26

garbage 297:9

GastroPlus

372:22 373:27

gatekeeper

425:14,15

gather 326:23

353:16

gathered 315:1

gathering 289:7

GC 116:15

gee 344:10

gender 230:25

gene 38:24

76:20 77:7

95:8 131:11

453:23 467:10

general 4:2

21:21 22:15,19

30:22 59:27

100:4,14

200:19 217:3

223:10 237:11

237:18 254:8

310:16 338:23

359:26 361:2

368:17 386:9

394:16 395:24

403:13 431:5,7

433:19 461:1

467:24 471:20

473:27 478:5

479:19 490:2

492:24 494:3

497:2 510:14

510:17

generalize 488:9

generalizing

208:10

generally 193:11

211:4 240:21

242:8 369:1

382:17 390:13

510:20

generate 61:16

99:9 296:21

302:24 510:23

generated 16:2

340:20 424:8

428:24 511:4

generating

20:14 350:19

459:1

generation

206:8,26

207:13 302:7

352:7

generations

376:3

generic 29:7,25

227:11 373:6

generous 131:11

genes 56:19,21

56:23 77:6

genetic 42:13

genistein 106:20

318:18,18,19

318:22,22

319:11,15,15

319:19,27

320:27 321:14

321:16 325:19

325:21 343:14

343:26

genomes 248:5

genomic 444:22

444:25

genotype 248:4

geometric 114:7

117:17 123:22

254:22

Georgia 189:24

getting 20:18

26:10 27:6

53:22 55:4

67:25 68:25

85:25 86:24

88:17 189:7

202:17 203:12

205:18 210:23

210:25 223:1

236:22 240:6

245:10 262:11

271:19,21

280:15 304:20

305:6 367:16

370:11 389:5

429:17 430:11

436:22 437:6

438:14 458:1

462:16 505:1

505:13,14

506:2 515:11

GI 238:10

372:25 373:11

giant 503:24

gift 197:25

give 31:10 43:2

52:7 66:22

68:10 71:16

91:24 99:4

102:16 104:6

109:5 133:7

180:1 184:21

192:18 207:18

212:15 215:24

215:25 218:9

220:4 223:21

224:10,11,14

231:5 232:14

242:13,16,18

243:17 253:21

265:20 276:9

282:26 288:1

294:14 295:14

296:23 317:18

317:19 320:14

335:20 349:20

353:26 357:19

381:16 422:16

422:19 429:18

444:23 447:3

448:11 460:18

476:4 481:3

507:24 510:13

514:24

given 41:18

46:16 59:10
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 48

61:1 69:15

72:5 84:15

113:6 127:4

136:6 139:21

169:21 176:26

195:26 213:27

214:27 215:1

218:18 271:16

279:15 304:23

307:1 314:23

320:26 327:10

328:3 341:3

346:9 351:1

352:23 354:16

387:18 388:17

389:21 396:16

397:14 400:26

401:3 404:26

405:6 433:21

436:7 444:6

456:16 469:16

470:15 479:5

482:8 484:2

489:9 497:13

gives 27:14 36:4

42:7 45:22

61:26 63:13

154:9 157:21

204:25 211:20

216:3 227:2,22

228:15 232:10

234:15 244:23

245:25 251:12

273:1 356:12

424:14 508:11

giving 40:24

56:14 102:4

215:20 270:12

283:6 322:7

449:17 456:19

GL 120:17

glad 378:3 476:4

glasses 48:13

glimpse 344:9

global 39:16

315:16 505:12

glomerular

228:19,27

231:12 372:13

gloomy 5:1

glossed 382:23

glows 38:9

glucocorticoid

75:1 78:6

184:11 467:3

glucuronidate

208:12

glucuronidated

116:5

glucuronide

217:26

go 3:10 12:4

17:6 20:4

21:24 23:27

28:18,24 30:12

31:23,27,27

37:9,9 41:6

42:18 44:15

45:9,25 47:5

50:17,19 54:24

56:8 59:25

62:19,26 63:5

64:22 68:11,21

71:14 72:14

75:12 76:12

77:17 81:16

82:24,25 83:18

87:18,26 89:4

89:5 91:18

93:7,22 97:1

97:26 114:6,21

118:4,18 136:2

141:27 149:10

154:23 159:16

161:24 162:2

171:18,20

172:10 176:8

177:5 190:24

192:21 193:7

193:14 194:1

201:7,17 202:4

204:10 205:8

207:26 218:15

230:3 234:18

235:9 244:19

245:4 246:5,7

247:17 250:9

250:20 251:8

251:24 252:3

253:1 254:1

256:7 257:6

258:5,27

259:15 261:14

262:26 264:16

267:5,23,27

268:4 269:25

272:8,17

274:14,20,24

275:1 276:20

278:13 282:10

287:4 288:23

289:8 290:9,12

291:5 294:20

296:11 299:25

300:24 306:21

307:15,24

310:17 319:24

323:14 327:15

329:14,19

330:4 331:25

332:16 336:16

341:20 343:11

345:1,24 350:4

355:2 359:5

369:12,16

379:21 381:11

381:18 389:8

391:10 392:13

394:1 407:13

407:27 411:5

419:13 422:12

423:5 424:15

438:10,21

441:12 450:20

452:6,7 454:7

459:7 462:16

464:12 466:11

469:12 470:10

476:13,26,27

477:26 480:13

485:24 486:13

486:16 491:7

493:27 494:25

499:19,27

501:2 508:22

509:4 512:18

513:9 515:17

516:8,18,22

517:19

goal 27:2 86:16

118:2 119:5

124:4,7 244:7

310:12 314:2

314:18 355:17

385:7 453:11

472:6 484:12

510:23 515:16

518:23

goals 9:20,27

18:21 126:21

127:21 178:15

179:14 197:21

518:17

goes 41:14 48:7

48:8 54:3,8,14

55:23 67:26

125:17 147:3

171:22 197:10

250:23 273:23

275:3 294:19

305:13 306:18

307:17 314:18

334:14 335:2

342:22 369:3

418:6 429:22

463:18 464:25

477:3 484:6

499:19

going 5:15 10:10

12:3,14,20

17:10 26:16

29:2 30:6,9,13

30:13 31:21

32:4,13,15

33:2,8 35:4,17

36:4,17 37:2

37:15 41:5,18

41:26,27 42:14

43:18,19,20,26

45:8,13,18

46:1 49:19,23

51:13,14 52:14

58:3 61:20,23

64:3 70:4,5

71:14,15 72:15

72:19,23 75:11

76:12 77:5,13

77:17 79:21

80:18 81:9,16

85:18 89:2,4,5

92:1,23 95:5

96:10,11,21

99:19 101:6,7

104:13 109:20

114:4 116:15

117:3,5,6,15

124:9 125:10

125:23 137:23

140:24 143:17

144:5 146:2

148:4 150:9,10

150:18 151:24

152:4,6 154:23

156:14 157:9

157:12 158:13

158:14 170:13

176:8,27 177:1

177:17,19

178:12 183:1,6

189:5,10 191:4

197:15 200:26

203:23 211:27

212:1 216:27

217:19 218:19

219:3,5 222:14

222:17 224:8

226:9 230:20

233:6,13 236:3

239:21 242:8,9

243:27 244:8,9

244:16,22,25

245:2,16,20

246:22 247:22

250:9,16

251:14,18,20

255:4 256:11

256:23 258:2

262:5 266:5

268:13 271:18

272:15 275:2
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 49

276:17 279:2

282:6,9,19

284:22,24

289:18 290:7

290:14,16,17

293:2,12

294:24 295:25

297:14 300:11

304:2 305:8,15

305:24 306:2

306:10,17,21

308:27 310:16

322:8,27

323:20 327:7,8

328:22 329:1

330:1,12,21

331:6 338:14

338:26 344:21

347:8 349:16

349:25,27

351:6 354:23

362:16 367:20

377:10 379:17

381:14,26

383:6,11,18

387:7 390:13

397:6 401:18

407:16 425:2,6

426:20 428:19

430:15 431:12

431:18,21

433:22 434:6

434:25 436:10

447:13 450:19

454:3 461:16

471:19 472:11

476:23,27,27

477:14 481:7

482:11 485:16

485:23 486:7

487:14 495:6

497:12,25

499:5 502:2

507:24 509:14

512:4,16

513:12 515:15

515:16,24

516:2 517:21

Gold 32:16

108:12 129:13

135:3

good 3:3 4:27

6:5,20 7:12

10:9 11:21

23:7 26:4 28:4

31:7 32:25

35:7 39:17

40:24 49:4

55:20 61:26

64:3,15 70:1

70:13 82:11

85:1,4 88:18

89:26 90:21,24

90:25 102:4

104:9 105:4

111:9 114:25

115:3,3,5,19

116:5,24 117:7

117:21,26

118:7 121:7

123:4 125:2

131:15 138:14

140:1 141:24

161:7 163:6

172:7,15

185:16 186:22

189:2 191:2

210:25 211:3

216:21 234:21

239:23 240:21

245:10 248:19

252:20 258:16

259:3,23

261:17 266:10

268:9 269:17

270:13 271:25

288:21 290:25

307:27 308:9

319:21 322:16

322:18,19,25

323:27 324:24

327:16,17

328:12 330:24

368:7 371:1

382:26 391:8,9

391:10 394:16

394:16 395:25

397:26 400:20

401:10 402:2

408:4,21

409:24,26

413:7,11 414:2

414:7 415:26

417:3 418:13

425:11,16

427:15 434:20

439:4 447:19

453:22 454:5

455:18 473:12

476:8 479:26

484:5,27 486:5

504:20 509:14

512:15 516:8

517:20,26

goodness-of-fit

323:4

goods 259:22

gosh 84:27

509:17

gotten 10:27

56:17

government

296:17

government-b...

51:22

GPR30 175:27

GR 467:9

grabbing 46:11

215:6

grabs 273:23

grad 269:24

403:12

gradient 42:14

gram 208:20

grams 208:21,25

230:17

Grant 6:17,17

64:22,27 65:4

65:6,20,21,24

66:13,23 94:5

161:23 162:9

189:9 190:17

190:17 240:4

271:10 324:12

324:26 378:18

379:15 389:14

412:3,3 453:9

478:3 487:24

496:26 500:22

507:8 515:2

graph 39:8

77:25 114:2

115:17 128:10

135:8 183:13

259:7 318:27

321:13 364:11

graphic 318:26

321:11 362:19

362:26

graphical

204:19

graphics 288:24

graphs 23:13

74:21 77:22

gratitude 7:21

gray 36:10 41:12

63:20,20 73:20

74:1,16,18

75:8 79:14

132:25 149:27

153:9 259:8,12

Gray's 425:13

grayed 21:5

great 28:26,26

28:27 50:27

53:6 57:7 65:5

95:4 102:27

124:6 126:26

133:25 139:19

188:1 192:6

197:4 233:1

240:24 270:8

279:2 364:25

372:2 390:14

394:26 396:2

399:11 437:15

459:5 477:4,22

487:5,11

489:17,21,24

491:21 492:17

497:9 504:2

506:19,20,26

511:25,27

513:9 516:11

517:17 519:1

519:17,18

greater 12:6

16:13 18:13

127:12 134:8

185:9,16

187:10 194:1

194:17 232:12

233:3 242:14

242:19 252:2

283:15,24

284:2 285:12

314:14 340:21

342:3 350:3

385:25 413:15

414:9 439:3,7

474:27 489:5,5

greatest 237:26

311:12 362:21

376:24

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grounded

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group 65:8 79:6

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growth 104:27

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guarantee 54:21

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guess 5:2 21:15

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54:1 67:20

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138:12,24,25

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168:25 171:2

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326:25 366:21

381:21 392:5

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guidance 47:9

guidebook 108:9

guided 398:21

guideline 32:16

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guideline-like

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180:25

guidelines 16:8

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381:2 399:22

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guitarist 447:21

guitars 447:21

gut 112:18

238:25 373:13

guy 272:7

guys 124:9

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426:22 509:13

509:17 516:2

H

half 38:6,7 84:17

84:17 85:8,8

111:12 112:2

132:3 157:27

213:3 220:17

220:22

half-a-dozen

31:8

half-life 216:5

halfway 63:11

72:4 107:3

133:11

Hamner 227:18

hand 27:12

28:11 106:10

127:4 152:18

218:6 245:14

269:9 294:1

306:7,9 436:4

493:26

hand-full 238:22

handful 65:17

90:14 99:15

255:2,24

handle 63:17

198:12 232:10

259:27 333:5

512:15

handled 178:12

handler 351:20

handles 472:18

handling 390:18

handout 316:9

hands 68:25

264:12

hanging 393:13

HapMap 248:2

happen 5:15

34:10 41:26

46:21 56:10

208:4 241:3,3

294:24 305:14

338:5 371:13

402:6 504:26

519:23

happened 402:5

happening

34:11 63:21

72:19,19 73:3

75:20,26 77:27

78:7 79:20

80:1,8 83:8

95:26 119:4

149:1,3 150:16

154:21 156:12

158:23 245:26

378:4 418:9

447:16 487:19

happens 33:14

33:27 34:2,2

34:21 35:3

36:8 49:10

77:6 184:21

195:25 273:27

289:1 337:6

428:16 497:10

happy 21:18

60:20 87:6

101:10 119:24

197:24 204:16

216:20 235:12

241:10 367:5

506:20 512:24

512:27 518:19

hard 7:17 27:9

31:12 81:12

82:3 90:25

110:17 113:18

132:24 140:5

145:15 151:16

176:25 183:18

202:21 203:1

218:24 231:14

241:18 245:12

248:18 255:3

255:21 274:3

406:21 517:5,7

517:13 519:8

harder 47:22

harmful 375:22

harmonized

16:8

Harvard 375:15

hate 28:7 329:21

hazard 355:23

356:6,16

359:12 362:4,8

362:11 365:15

365:17,24

366:3,13,19

head 66:14

82:22 165:4

167:15 391:9

493:3 505:26

headed 59:27

103:11 388:15

497:2

headlights 28:24

29:1

health 5:21,24

8:6,21 97:14

189:23 190:4

198:15,18

245:6 302:20

310:14 375:9

375:12,18,19

375:27 376:3

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411:13,18

424:13 426:8

483:15 484:12

491:2 513:13

518:12,22

health-protect...

244:16

healthy 228:3

319:20

hear 23:20,26

24:19 61:19

78:21 141:21

177:25 256:22

267:20 340:26

344:13 386:9

390:27 517:1

heard 59:26

103:2 125:6,19

126:7,12,21,26

126:27 127:8

128:23 129:22

129:27 131:24

132:1 133:12

134:12 135:2

140:23,26

163:9 165:13

177:20,24

178:16 180:7

243:12 245:8

246:19 247:3

277:13 285:15

300:21 302:6

302:14 307:22

322:23 326:10

326:19 328:20

329:13,15,24

358:18 370:24

388:10,17

434:7 445:21

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helpful 104:15

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helps 63:4,25

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464:3 513:25

Hep-G2 292:16

292:26

hepatic 225:25

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hepatocytes

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246:23 372:12

HepG2 73:1

75:16 76:2

90:16

Hershberger

17:4 148:19

180:18,23

181:3 182:11

185:24 310:26

311:18,21

335:20 337:18

460:12,20,23

461:4,7 462:4

hesitate 219:23

heterogeneity

472:19 475:16

heterogeneous

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heuristic 45:16

254:11

heuristics 491:4

HHS 218:17

Hi 326:8 367:12

375:10

hierarchical

39:12 142:26

high 2:9 9:3,27

10:21,24 11:8

12:7 14:11,23

14:26 15:5,13

16:13,19 17:1

17:21 18:13,19

18:27 19:19,23

20:20 21:4

24:11,26 25:10

25:14 26:13

38:16 51:13

57:3 67:10

69:9 72:8

76:27 80:20

88:16 89:6,15

96:6,23 97:4

103:16 108:22

118:4,25 127:5

128:17 131:17

133:16 134:8

137:21 152:27

153:2,14 157:3

159:16 180:12

186:9 190:27

199:9,14,25

200:2,3,7,8,13

200:15,23

201:2,5 214:6

214:16 215:1,9

216:14 219:23

221:9 255:11

255:26 261:4

263:12 271:5

291:6 297:15

311:25 337:9

338:18 343:3,8

344:22 347:2

353:7 356:3

361:5 362:27

363:2 380:20

406:13 413:8

415:17,18

416:12 424:7

427:16 431:10

434:12,26

435:15 436:7

437:7 440:1,16

441:7 455:11

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467:18 469:19

470:24 476:24

479:11,12

484:19 490:21

499:16 500:19

503:11 508:5,8

508:10 512:27

513:20

high-activity

397:8

high-end 435:5

high-potency

401:21

high-priority

284:13

high-quality

86:19 261:1

high-ranking

312:12

high-through...

191:25 206:11

215:27 216:11

220:25 221:3

223:9,23,26

224:13 226:15

226:17,18,19

227:7,14

228:10 232:27

233:25 234:10

234:11 236:10

245:25 246:14

254:20 265:27

266:1,14 267:3

269:26 277:26

277:27 278:7,7

278:20,26

279:7,8 280:4

280:6,16

281:26 284:27

286:9,16 292:4

292:19 300:18

302:14,24

303:16,19

309:10 310:20

318:5 322:14

324:4 327:13

351:6,8,24

355:8,15 356:6

356:17,18

358:6,10,22

360:1,9,18,25

361:3,7,19,23

362:1 365:11

365:12,18

366:13,19

370:10 372:3

376:4,7 377:13

379:4 380:5

385:25 402:25

higher 22:11

88:4,4 106:12

114:18 119:11

127:13 156:13

172:4 180:12

196:26 219:3,5

219:8,11,19

221:23 226:10

227:15 232:2

255:10 261:14

263:25 280:14

280:14 282:12

285:19 293:17

295:9 329:26

343:15 359:4

359:23 364:2

436:27 457:23

458:2 459:3,3

481:16 514:13

higher-dose

352:19

higher-end

352:2

higher-tier

313:24

higher-tiered

329:25

highest 24:13

112:12 114:21

114:22 115:10

133:5,18

155:21 184:6,8

196:27 214:5,7

263:25 270:20

285:8,13 286:4

288:11 353:5

369:26,27

370:22 371:9

372:26 373:20

484:13

highest-ranking

131:2 133:20

133:20 184:22

highlight 12:7
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highlighting

314:10 395:11

highlights 18:14

361:23

highly 38:11

114:27 116:16

118:24 119:3

220:14 229:8

256:8 342:9

350:16 397:6

415:12 424:16

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518:9

hill 72:2 250:22

250:25 268:15

268:18 274:1

294:19 398:7

398:11

hints 80:16

histogram 72:5

74:23 75:14

historic 445:16

historical 12:5

14:3 304:11

386:24

historically

108:18

history 300:10

hit 74:3 78:5

82:25 90:23

149:23 150:1

166:21,25,25

167:14 191:5

292:16 308:19

363:16 446:2

hits 74:16 75:15

109:19,19,21

139:23 148:19

154:9,12,15

157:3,4 158:1

292:7,13,15

363:21,23,27

413:13 418:2

434:24 435:15

hitting 151:6

hoc 5:8 389:20

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hold 63:17 67:11

177:2 189:14

239:24

home 132:6

191:14,21,22

202:7,12

206:27 207:2

258:12

Homeland

218:17

homeostasis

359:1

homework

388:17

homology 467:3

honestly 202:26

423:11

hope 140:25

181:26 182:22

205:24 315:3

354:16 374:4

378:1 406:8

483:13 496:16

498:7 513:1

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hoped 25:12

172:6,7 509:11

hopefully 47:27

139:11 160:14

170:14 204:19

225:22 266:4

286:14 309:11

315:7 388:7

393:11 419:6

hoping 69:5

205:1 327:3

Hopkins 6:3

190:3 411:17

horizontal 149:4

213:17

hormonal 341:4

hormone 25:6

25:26 303:1,2

341:12

hormones

255:27 256:27

309:3 344:2,2

344:7 349:27

468:5

horrible 96:23

horse 224:10

horses 197:26

hosts 168:13

hour 28:8 102:9

229:5 299:12

309:17 326:7

501:4,6

hours 34:20,20

34:22 35:26

57:3,20 142:16

387:19 505:12

house 206:17,23

Housenger

520:2

HPTE 100:22

HTE 474:17

475:15

HTS 311:16,26

313:8,15

HTTK 233:25

351:7 352:12

378:24

http://www.ep...

1:9

huge 127:6

236:20 238:9

239:1 420:17

huh 44:22

human 8:21

15:15,16 37:18

76:4 96:13

97:14 108:6

119:14 142:5

159:15 193:12

193:16 194:10

201:15,21,25

202:1,3,5

207:16 216:10

225:20,24

227:25,27

230:4,25

240:22 247:12

247:24 248:3

253:18 282:16

283:9 286:3

302:11,20

310:14 311:13

315:7 317:27

318:2 343:24

347:25 364:6,9

368:19,24

369:23 370:4

372:20 374:9

379:5,11

413:25 424:12

451:8 483:12

483:15 513:13

518:22

human-derived

247:15

humane 310:9

311:11

humanized

413:27

humans 34:17

61:7 97:3

193:10 206:15

228:1 235:16

303:4 373:11

379:11

hundred 199:5,6

213:1 217:12

217:13 219:1

225:11,12,15

226:8 239:7

275:3 281:3

424:10

hundred-way

214:15

hundreds 53:3

195:5 201:1

222:8 226:16

234:7 239:15

263:23 276:14

465:14

hungry 501:1

hunting 133:27

hydrogen 46:15

hydroxyfluta...

149:13

hydroxyl 79:6

99:18

hydroxylated

414:6

hypertrophy

261:24,26

262:4

hypotheses

80:22 338:1

358:14 427:22

hypothesis 34:7

56:10 70:22

72:21 73:5

80:13 176:23

271:20 449:16

450:12

hypothesize

73:26 397:5

hypothesized

261:26

I

i.e 311:6,17

356:18 380:20

440:16

IBER 2:9,10

22:23 243:1

252:7 267:16

277:9 283:7,14

283:24 284:1,6

284:11 287:14

288:2 295:16

311:7,25,27

312:6,13,18,21

314:9 317:20

318:4,13

320:22 324:3

328:25 330:4

350:25 351:5,5

353:1,4,8

354:5,10

356:11 362:14

362:20 363:11

364:26 365:5,5

366:23 368:10
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472:26 473:19

475:17,23

476:9 478:10

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480:2,23

481:14,22

482:22 483:7

483:22 484:5

484:17 485:17

488:12 489:21

490:2 496:2

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500:24 502:10

507:11,20,27

508:5,13,16

510:20 511:23

IBERs 476:22

IC50s 91:26

ICCVAM 129:3

146:13 348:1

ICI 346:24

idea 23:2 29:3

29:15 32:20

34:12 39:7

82:21 86:6

192:19 193:14

195:1 198:9

200:19 212:24

224:18 236:21

254:16 272:9

301:20,24

303:14 318:1

355:22 362:22

363:4 389:1

449:20 474:3

490:11 507:19

ideal 434:23

ideally 86:3

123:5 134:4

242:19

ideas 90:26

307:23 422:18

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IDENTIFICA...

2:3 4:25

identified

147:16,18,21

147:22,25,26

147:27 181:18

217:11 322:24

329:25 366:8

396:17 438:5

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identifies 10:13

identify 108:5

109:14 118:5

119:6 126:16

181:27 182:4

195:19 200:21

207:5 233:12

242:20 278:25

291:5 298:20

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identifying 15:8

278:15 301:16

316:3 361:4

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iffy 139:21

ignore 54:14

428:16 499:17

ii 358:17 371:17

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440:10

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illustrate 346:5

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illustrated

195:27 350:3

illustrates

270:10 471:21

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illustration

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image 8:16

227:8

imagine 35:3,15

36:2,16,19

45:19 69:13

71:27 76:19

109:19 170:22

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immature 105:1

105:24,25,26

105:27 110:18

110:20 111:21

112:4 335:15

immediately

226:4 356:26

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immortalized

248:3

impact 70:18

173:12 231:12

231:12 236:17

236:20,20

238:9 305:10

335:11 349:26

376:18 382:7

impacted 437:12

impacts 8:21

25:6,12 479:14

impendence

39:3,4

imperative

335:15

impetus 470:3

implement

127:16

implementation

3:22 12:1 14:5

441:7 487:9

implemented

17:13

implementing

14:27 17:20

125:15 310:20

315:27

implicated

375:26

implication

268:19 491:1

implications

257:18 384:5

implied 267:20

imply 168:18

implying 24:8

importance

327:27 377:21

495:1

important 3:20

8:8 18:5 19:13

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81:21 85:23

104:17 106:24

107:10 117:24

119:19 142:3,6

145:14,16

146:6 156:19

174:10 179:10

192:9 200:21

205:18 211:22

212:11 216:9

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231:24 237:14

238:4 247:5

255:20 256:25

261:7 269:11

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325:16 329:1

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331:15 332:8

334:26 335:2

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376:1 378:3

386:15 390:5

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414:15 418:24

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506:11,16

508:9 510:8

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importantly

60:14 132:23

184:2 361:16

imported 194:26

impressed 298:8

310:18 433:17

512:1

impression

162:27 268:13

446:17 495:27

impressive

61:11 386:17

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improve 259:25

279:26 289:27

307:4,6,8

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317:20 324:7

346:7 388:8

390:14 398:3

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improved

234:14 286:15

298:17 314:21

315:10 316:22

316:23 384:2

478:8 517:3

improvement

316:23 323:12

336:13 352:6

352:24
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improving 27:6

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imprudent

27:10

in-tact 287:17

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inactivate

119:22

inactive 39:21

48:15 49:10

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94:9 99:27

107:15 111:3

112:20 113:12

113:17,26

115:12,15,24

117:2,9 132:21

144:1 146:24

147:12,17,17

147:19 148:1

152:8 157:16

158:2 159:8,22

166:21 243:21

251:3 274:18

274:18,19,19

274:20 321:1

408:12,17

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inactives 115:18

183:16 432:9

inaudible

268:10

Incidentally

340:4

include 14:9

17:26 54:15

155:11 161:2

161:16 176:16

186:5,13

215:26 234:17

264:26 268:26

282:5 291:10

296:6 301:16

302:10 308:21

317:15 334:19

372:4 380:25

389:26 393:25

413:25 445:21

454:17 456:6

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included 15:8

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126:6,15 130:8

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139:17 150:27

181:21 222:15

232:9 234:1

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includes 14:15

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including 13:5

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281:26 302:3

303:1 309:2

312:16 332:22

356:5 358:1

412:14 421:26

430:14 431:4

433:4 471:12

485:9 491:4

495:14 511:5

inclusion 296:1

296:12 297:1

311:5 333:11

481:19

inclusive 123:4

incomplete

331:24 337:14

338:3 339:12

incongruence

168:3

inconsistent

110:26 178:15

360:22

incorporate 19:6

25:24 53:2

56:7 67:7

160:16 167:4

176:13 182:15

197:9,21

230:22 237:19

237:22 238:13

294:11,15,18

294:20 295:4,7

305:8 306:14

306:20,22

307:14 308:13

313:17 333:20

386:11 420:18

467:25 487:20

511:10 517:2

incorporated

9:2,17 219:25

235:16 236:6

331:16 333:23

335:4 338:24

366:17 378:25

378:26 399:7

474:17 480:3

483:16 486:20

511:22

incorporates

9:12 472:16

486:13,27

488:5

incorporating

22:17 199:2

216:1 305:3,5

431:21

incorporation

333:9 335:2

468:16 482:3

488:10

incorrect 264:22

incorrectly

397:18

increase 18:8

22:13 27:5

39:3 105:10,13

106:17,26

110:8 112:25

149:22 150:6

212:19 221:12

242:22 257:23

284:6 286:18

290:4,13

345:14,16

356:22

increased 17:22

17:23 257:3

284:5

increases 283:5

320:11

increasing 23:1

134:15 178:26

316:2 337:2

342:26 356:16

356:23 362:3

increasingly

283:27

incredible 34:9

incredibly

128:27 129:26

137:13 286:26

401:8 403:7,15

incubate 99:8

independence

39:2

independent

3:19 113:11,19

120:24 121:3

121:22 181:19

240:12 242:13

242:15 307:5,8

307:9,10

317:12 323:9

328:5 331:25

338:4,13

396:26

independently

85:15 122:2

161:26 471:19

indicate 112:19

206:27 292:9

320:16 391:1

395:15,17

indicated 396:3

396:22 401:20

463:27

indicates 111:3

472:26 473:3

474:12

indicating

362:15

indication

472:23

indications

287:8

indicative

118:27

indicator 206:16

206:23

indicators

198:18 212:10

indirect 57:4

202:14 442:24

443:1

indirectly 62:26

191:22

individual 55:3

59:2 88:19,20

89:17 111:19

152:4 198:25

208:25 242:21

266:24 267:9

273:24 280:21

291:21,22

295:24 301:21

381:17 443:19

446:2 472:19

512:21

individual-spe...

208:24

individually

271:18

individuals

248:6 443:27

indoor 15:17

induce 165:9

induction

131:11
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industrial

194:17 251:26

251:27 253:13

industry 18:7

82:23 98:11

223:27 224:16

232:14 233:15

241:16 330:13

330:15 417:2

inert 15:5,13

207:4 251:27

253:14

inevitable

510:25

infants 490:27

492:22 493:6

493:18

infer 204:4

211:24 226:2

inference 204:24

208:9

inference-driv...

167:22

inferred 204:25

205:3,27 206:5

206:20 211:26

infertility

375:27

infinite 91:15

influence 153:13

153:15 162:4

165:14

influenced

111:27 397:6

influencing

511:11

inform 23:27

26:7 174:16

276:17 303:11

312:6,10,19,25

315:2 330:16

338:1 366:6,15

456:8

information

9:24 12:23

13:5 14:12,13

14:18 18:2

20:1,9,10,15

21:18 22:3,4

27:12 40:25

51:9 61:17

81:14 90:8

92:27 102:5

103:21,25

109:2 113:14

113:21 118:23

127:27 130:3

133:25 140:26

199:1,3,25

200:4,9,10,12

200:14,15

206:18,25

208:23 214:4

214:17 215:4

216:16,27

217:16 218:1,3

219:24 220:3

224:21 230:19

233:17 241:21

241:25 264:24

274:4 275:18

291:17 292:4

292:20 301:27

303:11 306:23

311:19 312:18

312:19 313:6

315:1,9 317:7

317:10 318:5,5

318:27 320:5

320:12 321:21

323:20 324:5

324:22 325:27

328:5,23

329:12,16

330:14,15

340:19 341:26

344:4 347:16

350:19 353:27

354:13 355:9

355:15,23,24

356:7,8,9,19

356:19,22

357:2,9,12,13

357:17,19,21

358:2,6,7,8,10

358:11,23,25

359:4,9,13,14

359:21 360:1,9

360:11,12,19

360:25,26

361:1,3,8,8,20

361:24,25

362:3,7,8,12

362:13,14

363:10,17

365:9,11,12,15

365:17,19,24

366:3,5,11,13

375:18 376:13

387:20,21

389:5 394:18

395:8 398:10

431:22 435:19

441:2,3,18,23

443:3,17 444:4

446:27 447:11

448:12 449:7

449:14,27

450:17,18

460:18 461:27

462:2 466:25

469:11 474:21

474:23 483:25

484:3 486:6

492:16 493:8

495:15 498:3

498:27 499:17

508:11 513:26

informative

119:10 217:4,6

217:7,10

218:11 338:9

362:19 366:1

376:15

informed 259:26

informing 312:7

informit 60:1

infusion 236:14

ingestion 106:7

ingredient 207:4

254:2

ingredients 15:4

15:6 222:21

254:4 356:6

inhalation 106:9

191:18 235:24

237:1,7

inherent 308:13

455:7

inherently

158:14 165:26

389:20

inhibiting

163:24

inhibition

358:24 360:21

initial 13:22

25:2 84:26

126:2 135:19

155:17 232:10

300:16 355:17

360:5 368:7

441:22 464:27

511:6

initially 134:19

165:22 218:3

227:17 228:22

406:12 408:20

initiate 126:17

315:22

initiated 16:26

initiating 338:1

359:2 361:4

inject 403:17

injection 106:2

106:3,4,11

110:21 111:22

111:25 112:4

112:15,21,25

121:5,7 235:24

400:21 403:11

403:20,21

404:7

injections

403:14,19

inner 513:25

innovative

125:13

input 164:25

266:5 444:1

453:7 463:15

469:14 517:3

inputs 240:10,12

338:6 390:6

inquiring

260:22

insensitive 89:7

insensitivity

400:27

inserted 321:10

inset 48:22

inside 292:11

insight 514:23

514:27

instance 23:3

26:13 40:17

41:7 56:9 62:1

93:17 95:15

209:26 217:23

230:9 231:11

248:26 265:26

292:26 293:3

304:12 307:13

332:21 333:19

334:23 335:20

338:7 346:22

351:19 356:27

357:4 362:27

417:21 442:23

443:5 465:21

instances 339:12

504:20

Institute 227:18

326:22 375:16

518:11

Institutes 5:24

8:5 411:13

insufficient

328:5

insufficiently

350:23

insurmountable

345:25

intact 130:11

176:16 182:16

241:19 341:3

350:15 356:27

357:2 514:14

integral 45:11

312:11
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integrate 30:10

31:5 69:25

141:18 290:15

307:12 384:12

513:6

integrated 1:4

2:9 3:5 13:10

19:27 20:21

21:8 126:26

140:20 178:19

179:21 189:3

243:1 245:27

277:15 279:12

279:24 280:8

287:25 300:21

311:6 312:16

312:25 373:3

421:11 424:11

430:23 449:3

integrates

455:13 468:20

integrating

44:12 62:27

128:7 141:9

281:25 378:23

380:4 384:3

390:11 440:1

514:10,11

integration

291:10 362:13

integrative 5:4

13:1,20,26

24:4 26:25

512:19

intend 287:14

312:12 516:22

intended 21:13

22:4 201:8

228:9 233:19

263:18 304:7

395:26

intends 317:8

473:1

intent 60:2

108:24 168:9

290:19

intention 290:18

299:22

intentionally

255:6

inter-laboratory

401:24

inter-lounge

189:14

interact 41:11

108:5 113:15

119:6,17 171:4

384:23

interacted

203:10

interacting 31:9

168:14 171:12

243:15 456:9

interaction

66:27 168:6

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261:26 423:23

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465:11,13,20

465:21,24

interactions

19:10 130:3,9

130:10 135:15

136:11 173:17

174:4 182:12

187:6 278:25

322:5 426:17

457:13 466:4,8

466:19 467:9

interactive

94:21

interacts 41:3

Interagency

140:21 146:11

intercept 212:1

212:2

interest 3:27

58:7 85:20

130:22 195:14

223:19 258:1

271:6 284:17

285:16 355:14

358:4 382:19

390:25 406:25

439:5 442:22

479:12

interested 10:11

51:17 86:21

126:8 129:1

130:8,27

133:24,27

183:24 185:4

197:16 219:12

229:22,24

230:2 231:25

235:15 237:6

281:7 283:3

286:22 290:14

298:16 308:25

309:9 367:21

367:23 382:18

443:27 476:21

514:13

interesting 5:4

38:15 48:20

54:25 75:18

79:3 81:10

95:18 96:18,25

148:6 149:13

150:27 160:3,6

171:18 190:24

215:10 229:12

247:27 253:27

257:12 274:27

276:7 295:14

295:18 321:26

387:20,24

388:25 428:13

477:17 511:19

516:17

interestingly

76:7 147:20

151:14 184:9

344:14

interests 392:2

468:8

interface 328:7

interference

29:17 30:1,7

31:14 36:14

38:15 40:15,22

46:20,27 48:19

82:20,21

141:12 143:13

144:24 148:16

149:21 151:19

154:25 156:27

157:25 158:26

166:15 333:15

444:7

interfering 36:8

36:24

interim 173:21

441:15

interject 291:2

297:27

interlab 134:22

185:22

intermediate

139:22,26

312:13 425:20

435:14 453:26

454:4 481:21

internal 10:8

181:8 367:23

368:19,19,24

369:24 370:4

374:14

internally 84:5

367:18

international

103:6 315:14

358:2

internationally

103:10

internet 243:8

276:15

interpret 284:9

interpretability

396:2

interpretation

2:8,10 168:27

169:2 171:9

173:11 177:13

277:9 320:11

461:24

interpretations

346:6,7

interpreting

270:6 284:22

interrogate 67:1

interrogated

175:23

interrogates

452:3

interrupted

130:12

interval 196:14

196:16,18

213:7 214:13

221:12 245:17

254:24 256:18

257:19,23

276:12 295:10

295:17,18

475:12

intervals 220:4,5

228:5 245:7

252:10,22

253:16,17

254:24 259:3

398:15

intricacies 278:1

intrigued 137:9

intriguing 138:2

intrinsic 227:23

228:17 246:22

248:13 510:19

intro 122:5

introduce 4:20

5:16 7:3 189:5

189:19 411:4

518:25

introduced

69:24

introduction 2:4

11:18 12:3,22

45:17 75:10

INTRODUCT...

2:3 4:25

introductory

8:17 30:25

intuitive 117:15

invaluable 518:2

inverse 41:17

42:2 318:13

inverted 466:27

invested 60:24

60:27

investigate
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264:2,3 313:23

383:8 398:2

505:22 506:11

investigation

160:14 313:18

385:13

investigations

316:20 507:5

investment 61:5

involve 5:10

460:24 490:10

involved 101:21

163:8 214:2

224:12 277:24

312:9 336:1

351:27 352:16

408:24 482:9

512:3,9,10

involvement

192:2

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irrelevant

121:15

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isolation 477:16

issue 24:3 28:17

55:1 64:2

70:23 78:20

91:14 94:27

96:24 97:2

101:12,19

103:19 108:22

122:16 169:26

170:7 184:9

199:16 201:19

210:18 247:14

270:24 301:19

301:26 302:17

303:8 306:6

323:21 327:24

330:3 332:5

342:2 376:10

377:2,3 384:19

385:11 407:24

414:20 417:6

418:4 419:27

423:20 426:4,6

426:7,15,15

427:2,13,27

429:7 430:8

431:8 432:11

432:13 434:13

435:10 446:7

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506:13

issued 18:10

issues 1:4 3:20

24:14 32:12

52:22,23 53:4

80:2 99:12

156:22 201:14

224:12 257:12

292:24 293:12

299:3 302:20

304:13 320:20

326:26,27

327:21 352:8

385:3 405:6

408:24,27

417:17 422:25

424:17 427:6

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481:6 482:24

486:23 487:6

502:18,25

it'll 7:4 8:14

267:27

itemized 400:7

412:22

items 3:11 378:8

403:27 476:10

476:13

iteration 306:21

iterations

265:23 448:7

iterative 26:7

122:6 179:10

182:18 290:5

299:27

iteratively 42:10

396:24

J

Jack 520:2

James 4:21,27

11:14 21:20

23:10 24:16,23

26:9 27:18

28:23 50:16,22

50:25 51:1

53:6 54:26

55:22 56:26

58:12,25 59:24

62:2 63:26

64:21 65:19

66:8,24 67:5

67:13,17,24

68:19 69:16

70:25 90:6,27

92:10 94:3,14

95:7 96:1

97:15 100:10

101:3 102:7,13

102:19 119:26

121:12 122:12

123:8 124:8,13

124:16,19

125:9 136:19

137:6,27

139:13 140:7

140:12 161:20

162:10 163:8

164:4,18 165:5

166:23 167:6,9

167:26 172:24

174:27 175:7

175:19 176:26

177:9 187:21

187:25 189:2

190:22 216:21

218:13 219:27

221:14 223:4

235:13 240:3

242:10,24

257:8,11,16

258:13 259:5

259:14,19

260:14 261:21

262:25 264:11

266:9 267:18

268:2 269:5,8

270:4 271:9,25

276:25 277:6

288:14 289:8

290:8 291:12

291:26 293:27

295:21 298:26

299:5 304:27

309:12 310:4

316:5 324:10

324:27 326:3

354:18,27

367:7 375:5

378:6 379:14

379:16 381:7

384:15 385:16

386:4 389:7,11

390:22 391:11

391:18,21

392:10,17,21

392:24,27

393:6,14,17,27

399:1,3,8,13

400:1 406:26

407:2,11,18,27

409:16,19,24

409:26 410:2,5

411:2 412:7,19

416:4,8,25

417:7,14

418:15,25

419:2,8,12,22

421:17 422:5

427:9 428:1,5

428:10,20

429:8 430:4,17

431:3,25

432:15,19,24

433:11 438:16

438:20,25

439:11,18

440:19 445:1,6

445:8 446:6

447:17 448:3

448:13,23

451:1,27 453:8

453:19 454:6

454:11,19

458:11,15,17

459:6,15,19

460:6 462:7,12

462:14,20

463:2 464:13

464:16,19

466:17 467:11

467:21 468:17

469:8,23

470:15 471:14

476:5 478:1

480:6,11,17,27

483:18 485:25

487:15,22

489:13 491:25

492:3,18,27

493:8,25 494:8

494:12,14,21

494:24 495:10

495:13,20

496:6,10,18,25

497:1,15 498:1

499:12,18

500:9,13,21,25

501:27 502:14

503:14,18

506:23 507:6

507:15,23

508:20 509:2

516:14 518:24

January 11:1

19:18 51:5

85:27 126:12

277:26 300:13

308:18 514:8

Japan 103:8

Jeff 520:2

Jenkins 3:3,8

28:12,18 502:1

518:25,27

Jett 5:23,23

55:23 63:27

64:1,13 101:4

101:5,11

136:20,21

189:25,25

218:14,15

269:8,10

411:12,12
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417:11,21

419:3,11 451:2

451:27 452:1

493:26 494:14

494:15 510:14

Jim 6:24 189:16

411:6 494:27

495:12,19

519:21 520:1

job 39:17 70:14

85:1 90:24

102:27 114:26

117:21,26

118:7 124:6

192:6 233:1

244:5 270:8,11

322:22 327:16

328:1 346:20

372:2 399:11

434:20 437:15

476:8 477:22

484:27 487:5

504:22 513:23

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John 96:15 97:9

190:25 191:2,2

217:2 218:21

220:7 221:16

221:26 222:23

223:6 236:7

238:19 240:19

242:18 245:2

245:24,26

246:21 248:13

248:24 251:23

252:19 258:6

262:16 263:14

271:23 275:25

517:18,25

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411:17

joint 14:23

jointly 473:15

473:17

Jordan 520:1

jotted 510:14

journey 34:9

judge 306:8

judgment

383:23

judgments

296:5,11

Judson 27:20

28:4,5,15,21

28:26 50:20,23

50:27 52:20,26

53:13,16 54:4

54:20 55:10

56:9 57:7,17

57:23 58:3

60:22 61:18

62:9,19,22,25

63:8,16 64:9

64:16,25 65:2

65:5,10,19,23

66:1,11,17

67:3,9,19,22

67:25 68:3,18

68:24 69:20

70:1,19,27

90:12 91:7,22

92:7 93:3 94:1

94:11,22 95:18

96:10 98:3,7

98:20 100:9

101:9,18

120:19 138:8

165:6 169:7

171:17 174:19

176:17 192:18

192:24 219:25

242:25 243:4

257:10,15

258:5,14

259:13,18,21

265:4 267:26

268:21 269:17

270:21 272:3

279:1 282:22

285:15 289:13

292:2,22

293:20 294:3

294:16 295:15

295:20 296:14

305:11 306:24

308:17 309:8

340:26 364:1

388:16,26

390:27 391:7

391:26 419:23

421:2 424:1

425:25 443:23

473:10 490:7

507:18 508:14

508:20 509:7

517:15 519:21

Judson's 284:15

415:9

Juliet's 237:12

July 11:4,9 13:1

19:22 21:3

97:4 121:15

186:15 191:24

191:26 204:11

221:17,18,27

232:13,17

236:9 239:9

240:5 278:1

289:9 291:16

300:17 303:10

303:18 350:26

351:10 352:1

424:20 477:5

486:5,16 490:5

490:24 491:8

500:7 509:17

512:3 517:1

jump 34:9 244:6

508:22

June 11:4 469:4

just-so 76:17

justice 321:17

justifiable

174:14

justification

322:20 478:6

478:23

justify 426:14

K

kaempferol

115:21,21,26

116:2,4,7

Katie 354:21

355:3,4

Katz 347:22

keep 7:4 120:17

150:9 183:26

240:1 254:7

283:18 300:25

329:3 348:17

427:7 453:12

459:5 466:11

512:18 516:1

keeping 280:19

keeps 119:19

426:20 427:2

469:1

Kelsey 469:3

KENNETH

427:11

Kepone 80:4

kept 297:20

353:16 386:13

Kevin 240:25

key 12:11 19:17

170:18 256:2

342:4 424:23

514:2

kg 111:24 112:7

112:8,22,24

118:22 209:1

213:12 255:22

283:22 363:18

kgs 251:19

kidney 72:26,26

228:20,21,24

229:2

kidney-associ...

482:9

kill 32:6 72:26

76:10,11 78:9

killing 76:9 80:1

81:3

kilogram 106:16

192:27 223:14

229:12,15,16

230:11,17,18

236:22 252:15

253:22 364:20

371:11,14

372:17

kilograms

230:27

kilopascals

116:17

kind 22:3 26:2

29:26 30:19

35:13,13 40:22

41:16 42:15

44:7 45:1,7

47:14,20 49:24

50:21 51:12

52:27 61:19

63:14,17,23

68:9 69:22

70:10,21 71:27

73:21,26 74:10

76:11 77:5,15

77:21 78:13

80:21 81:12

84:1 88:27

89:9 90:2 91:9

91:14,27 95:24

96:20 98:23

99:7,10,11,20

100:18 101:23

122:17,17

128:4 133:27

137:16 139:21

139:26 149:9

150:9 160:10

160:16 168:17

169:8 171:17

173:27 186:27

201:16 219:21

232:22 243:25

246:8,10 247:1

249:24 250:10

250:13 251:9

252:26 253:26

254:16 260:21

262:13 265:2

270:5,14,18

272:12 274:16

275:20 293:10

294:22 296:4,4

296:5,15 297:9

300:10 305:17

307:9,25
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236:19

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229:21 235:21

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140:10,13,18

140:19 161:21

162:1,13,16

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164:15,24

165:20 167:3,7

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174:18 180:7

185:1 391:27

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112:27 113:18

160:5 170:10

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243:7 383:14

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346:25

knocks 35:25

Knott 8:18

11:15,20,23

21:20 22:2,26

23:18 122:4

130:16 290:22

291:8 299:3,8

299:12,17

304:27 509:15

517:9 519:13

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16:24 23:15,20

24:20 25:5,9

28:8 29:16,18

29:22 30:16,23

31:16,21 32:8

32:23 33:12

34:6,10 37:5,9

41:27 42:1,4

45:10 46:12,21

47:6 49:3 50:2

53:23 55:18,27

56:1,5,8 60:24

61:24 77:4

78:24,26 82:26

84:7,20 87:10

90:24 94:15

95:19 96:12

101:12 102:2

102:16 112:17

113:23 116:12

116:13 122:6

138:1,5,8

144:3 145:20

149:17 157:8

161:15 162:3

162:18 163:6

169:21 170:4

172:11 173:19

175:11 176:7

177:8 182:7

186:10 191:25

194:14 199:23

200:7 202:21

202:23 203:22

204:21 205:9

205:24 207:8

207:12 208:17

209:5,9,16

210:5 211:9,10

211:18 214:25

214:25 218:26

219:2,21,22

220:10,11

221:11 223:13

224:11 225:23

228:16 229:16

230:20,26

231:26 236:4

239:8 240:2,2

240:27 246:16

248:5 249:7

254:6 257:3

258:10,10,11

258:15,17

260:22,25

272:12 273:17

281:17 283:19

285:22,23

292:7 297:9

321:4 322:16

323:8,21

324:18 325:8

325:10,12,19

325:19 326:14

327:23 328:20

328:21 329:2,6

330:12,19

335:10,15

337:17 340:26

341:2,19,23,26

342:15 343:10

344:11,11,17

345:1 346:11

346:17,18

348:10 349:10

350:6,16

351:18,21

353:22 363:25

366:22 374:8

374:16 375:26

376:15 377:18

382:3,16,24

383:11,19

385:7,7,9

386:6,16,18,25

387:3,8,8

388:4,17,18

390:3,26

401:15 402:5

403:19,23,24

404:13 405:26

406:20,23

407:22,23,25

408:4,16 409:5

417:16,19

421:13 423:20

424:9,17 426:3

426:6,6,13

427:25 429:12

430:22 434:25

435:4 448:21

449:23 450:5

452:24 465:7

470:4 476:23

479:25 486:8

487:4,5 488:22

488:27 491:4

497:24 498:7

501:5 510:17

514:22 515:18

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240:13 286:2,3

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79:8 90:21

91:21 95:10

98:20,27 100:3

116:4 150:25

176:20,22

198:16 235:5

275:5 311:7

336:27 366:22

390:12 444:19

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lab 57:18 75:22

75:23 84:20

85:7,15 101:20

108:19,20,25

185:24 239:5

430:13 468:8

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48:1,3 163:1

164:5 356:17

laboratory

140:20 335:27

336:2 367:25

labs 85:7 88:12

91:15,15,16

105:18 109:5,5

113:13 119:13

129:4 179:3

269:24 335:27

403:16 413:27

lack 116:4

220:11 312:4

314:2 317:9

328:6 359:16

405:11 431:6

443:5 446:1,4

456:2 463:26

464:5 465:2

484:2 498:16

lacking 405:15

485:3

lacks 455:27

lactamase 38:25

142:20

laid 9:26 322:26

LaKind 208:10

226:1 252:13

lands 513:25

landscape

194:23

lane 147:2

language 43:24

261:7

large 123:16,26
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468:9

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larger 10:23

17:15 18:3

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lastly 377:25

latest 237:26

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Laughter

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launching

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Lauren 517:25

law 446:21

laws 4:1

layer 95:20

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layout 300:10

lays 18:12

lead 12:1 57:4

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217:20,22

218:10 288:22

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440:22 443:9

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500:3 502:16

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leader 315:14

367:13

leadership

509:16,27

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leading 209:21

216:20 217:18

507:12

leads 105:27

192:17 218:5

272:3 407:6

430:14 436:25

leap 499:2

503:24

leaps 490:14

leapt 388:27

learn 37:8

192:13 204:6

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learned 29:8

35:11 320:7

329:23 483:14

483:15 514:27

learners 476:3

learning 29:13

30:16 47:21

160:23 395:12

516:4

least-square

42:2

least-squares

68:4

leave 23:4

189:12 299:11

350:18 407:25

486:21

leaving 54:1

lecture 456:20

led 15:3 215:22

217:27 403:20

404:6 443:4

left 4:21 35:8

36:21 40:8

44:6 47:26

62:10 63:3

72:6,7 73:4,24

75:7,8,16 76:3

76:3 77:26

78:3,25 79:15

88:25 93:14,15

93:16,19,20

105:7 114:4

115:11 117:20

121:17 122:17

149:2 151:27

156:22 200:24

255:2,25

258:22 262:19

262:24 263:22

284:16 320:25

343:2 362:26

364:1 373:17

378:22 412:8

473:27

left-2 244:8

left-hand 43:25

43:26 44:17

46:14 48:2,4

54:7 63:10

71:23 114:5

148:21 263:17

321:10,13

left-shifted

293:7

legal 446:18

legend 148:7

legends 148:15

legislation 8:20

legitimate 286:9

LEL 107:4,5

123:18,23

435:15

LELs 124:12

405:12

length 232:12

444:12

lessons 329:23

483:14

let's 28:18 33:10

37:7 44:10,16

45:19,21,21

47:26 62:19

67:14 69:15

70:13,26 71:5

75:12 86:10

93:7,22 102:9

170:11 195:19

203:18 216:22

223:5 236:23

244:6,19 253:1

254:17 256:20

268:24 289:7

295:17 330:4

341:8 342:20

342:23 343:10

345:1,11

347:14,20

348:17 378:13

378:16 387:25

411:5 415:26

432:24 511:9

letting 516:1

level 1:14 9:15

61:16 91:5

96:6,20 106:25

107:16 111:16

111:23 112:10

114:4,15

115:13 118:21

119:11 120:11

123:19 140:4

174:22 175:15

178:8 179:5

187:18 193:3,4

199:23,25

200:3,14

235:26 250:22

254:9 259:27

261:14 269:14

270:25 275:9

281:9 282:6,6

294:10,13,14

295:13 305:24

312:13 313:22

330:7 338:24

347:9 350:8

355:27 356:19

356:21,22,25

356:26 357:1,3

357:16 358:11

359:5,9 360:10

360:11,19,19

361:14 362:25

364:3 382:18

416:13,21,22

436:7 441:25

456:23 466:5,5

471:20 474:20

483:27 484:13

levels 112:20

114:3,8,9

117:18 176:7

181:2 199:14

264:15 332:22

334:1 352:1

359:4 360:21

361:14 362:3

363:6 384:4

415:2 435:15

457:13 459:3

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514:13

liaison 3:12

library 152:27

160:26 334:7

lie 286:1

lies 265:2 518:22

lieu 309:13

life 9:18 58:4

207:20 234:18

326:21 357:20

393:15

lifecycle 357:23

ligand 77:10,21
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72:10,11 78:5

80:13,21,24,27

liked 390:16

likelihood 44:27

310:24

likes 98:12

Likewise 223:17

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211:4,6,9,19

213:20,23

214:19 219:16

254:10 271:14

271:14 352:8

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491:15 499:21

limitation 338:2

394:14 396:19

398:18,22

414:23 415:6

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488:16 490:4

492:1,5 496:21

limitations

17:24 104:4

178:21 292:9

323:6 328:2

331:21,22

336:5,16

337:22 353:1

380:12,24

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399:25 400:7

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409:12 412:16

413:19 415:21

422:2 423:27

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462:26 463:6

463:24,24

470:16 480:23

481:23 483:21

486:4,19

488:16 498:7,8

502:10 503:5

504:8 505:4

512:24 515:20

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122:26 123:1

170:14 199:17

234:20 235:2

287:5 334:5

363:20 404:17

427:3 436:13

436:18 438:4,8

460:19 482:1

492:7 495:3

limiting 109:21

186:25 227:4

limits 178:23

210:24 213:18

213:27 225:15

line 24:14 26:20

41:4,10,20

58:4 62:6,13

62:17 63:5,6

63:13 73:13,22

75:7 83:18

90:16 100:25

103:13 111:17

111:18 115:12

115:20 126:20

128:14,15

132:19 133:4

136:4,21

138:20 147:13

148:12,13,23

148:26 149:1,2

149:4 179:23

205:22,24

219:9,10 230:2

231:20,21,27

251:11,21

255:1,1 259:4

268:24 274:1

288:5 293:15

296:10 304:7

325:4 328:22

329:13 337:4

343:25 355:9

355:15 356:10

358:2,5,6,7,8

358:12,25

362:2 383:25

445:23

linear 65:25

79:7,8,18

93:12,15,21,25

132:11 133:13

144:17 204:10

211:23 228:14

229:14,21,21

229:23 242:2

281:4 320:13

352:12,16

372:17 395:7

397:25 412:25

423:3 426:24

442:9 451:21

452:12 455:12

475:14 482:10

485:8 488:8

linearity 482:5

lined 74:7

liner 352:20

lines 41:2,5,5,6,6

41:23 71:26

72:18 74:7

91:27 111:19

148:8,9 183:25

213:17 247:26

248:2,2 258:20

280:13 285:27

292:14,17

304:26 359:8

359:27 360:6,7

360:18 361:22

421:10 455:6

515:18

link 208:27

209:1

linkage 388:27

431:17

linkages 431:12

431:15

linked 435:16

linking 431:10

links 21:17

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151:8

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lipid 175:26

list 2:6 10:14,14

10:19 15:3,4

15:17,20,26

47:9,11,15

87:11 109:12

110:2,3 124:22

124:25 130:18

130:21,22

131:2,7 132:3

132:4,15,16,18

132:18 133:19

133:19,20,21

135:21,22,24

136:23,23,26

136:26 146:1

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183:20,27

184:21,22,24

187:4,9,15,15

195:7 198:6,8

200:16,17,22

201:2 221:10

222:10 272:9

283:25 287:4

287:11 288:1,6

301:7 302:27

315:2 326:16

326:24 329:22

329:24 330:5

332:16 360:13

367:24,24

369:1,1 370:25

370:25 413:3,3

415:24,24

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82:7 155:11

346:19 357:13

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listening 125:13

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listing 19:16

91:19

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109:10 142:8

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219:15 278:19
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320:14 330:17

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400:12,13

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44:18 46:16,17

46:17,17 47:23

47:27 48:22,22

48:23 52:6

56:5 62:23

69:23 73:9

74:1 75:11

84:23 85:19

86:6 99:4

102:9 113:17

117:14 122:5,5

125:5,10,15

126:11 129:21

132:12 136:16

136:17 138:3,9

141:10 143:17

149:11 167:24

175:14,20

176:27 177:5

183:2,17 192:8

192:18 193:9

194:2 204:11

204:24 210:5

217:5 224:5,11

234:18 243:6

243:12 245:8,9

245:14,23

246:12 250:2,8

251:20 255:3,7

255:21 256:20

258:2,9 259:12

264:9 266:13

270:26 272:18

272:19 273:12

273:17 274:3

274:26 275:13

276:20,27

277:18 290:23

297:24,27

298:2 301:25

306:6,27

308:16 317:1

317:22 318:7

334:2,6 340:24

342:21 345:3

345:11 348:24

370:25 381:24

383:4 384:8

388:17 390:17

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406:10,10

407:20,23

414:21 415:8

419:18 422:12

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423:4 424:2

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426:25 434:13

436:26 438:7

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491:16 493:19

497:23 498:16

498:17 501:5

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229:4,7 231:11

231:12 238:10

238:26 248:26

261:24,26

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230:27 236:23

242:8 247:26

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98:10 100:25

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193:19 196:4

197:11 198:10

199:18 200:15

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214:24 215:4

216:13,14

219:7 222:27

223:12 224:27

225:12 236:1

238:23 243:6

243:14 244:9

244:16 249:25

250:18 253:20

254:3,17

256:20 260:5

260:11 265:8

266:22 268:23

282:22 285:3

293:11 295:17

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300:16 301:19

301:23 302:12

315:23 317:19

321:3,25,27

324:20,21,23

325:18 329:14

329:19 330:2

330:14 335:6

337:15 339:1

339:18 340:14

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348:18 349:4

350:17 351:3

362:26 368:27

371:13 383:12

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405:27 406:19

413:23 416:20

417:4,22 421:4

423:11,12

424:14 425:6

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448:6 452:19
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109:22 151:24

157:6 158:25

159:2,2 162:7

172:11 193:20

194:5 200:22

201:6 211:2

214:10 215:7

222:14 223:1

260:25 289:14

293:2,24

317:25 322:13

324:19 386:26

400:17 403:14

404:9,13

415:25 424:9

429:10 447:14

447:27 458:4

467:5 469:1

489:11 492:11

looking 9:3,10

9:23 13:8,12

13:19,26,27

15:21 16:21,27

19:7 20:5,26

23:3,21 25:19

25:24 29:6,10

29:11,23,25

33:25 34:14

35:11 36:27

39:1,23 47:25

52:23 57:13

77:11 85:12

90:20 100:21

101:5,14

121:18 123:18

123:24 128:8

133:4 138:21

139:16 143:2

144:5 149:17

152:13 155:11

155:12,12,13

155:14,18

158:12 163:21

170:27 172:27

174:3 179:5

194:22 197:25

208:11 210:19

214:18 222:19

231:6 237:11

256:17 260:4,9

261:9 262:18

263:16,23

267:1,10 268:7

270:18 272:13

275:8 279:27

280:6 282:1

285:4 291:22

294:7,12

297:10 300:20

300:22,25

301:24 302:3

303:4,20

304:12 306:18

308:12,18

321:24 322:4

324:24 329:22

330:18 338:5

353:24,26

367:22,23

369:9 376:22

377:18 382:20

383:13 403:15

408:20,27

418:2 420:14

420:25 426:25

428:26 443:1

465:19 483:16

485:7 493:21

494:18 506:16

516:2

looks 5:3 30:10

36:6 65:24

83:9 102:4

138:2 142:13

211:4 222:7

235:22 240:9

249:17 271:12

439:19 452:7

487:12

loops 305:7

306:13 430:21

loosely 153:6

330:1

lose 138:27

424:27

loss 81:25,27

82:2,3 99:11

158:14 395:8

lost 28:8,11,21

64:1 93:19

476:18

lot 28:27 29:2

30:6 44:21

45:6,19,27

47:6,9,21

49:20 53:23,25

62:5 63:4

65:14,16 71:13

71:13,14 76:14

80:10 82:15,25

84:7,9 90:8

91:15 96:18

102:4 105:27

107:19 112:27

119:8 120:7

122:22 123:7

125:13 126:7

129:8,9,11

130:13 139:26

140:26 147:8

152:25 153:19

155:20 157:3,3

159:25 171:4

186:5 194:14

194:15 197:19

198:27 199:2

199:13 200:7

200:15,16

202:15 211:18

213:23 214:14

214:15,19

219:4 220:10

220:11 224:6

224:12 227:7

233:4 237:13

238:17 246:16

247:22 248:27

249:9,23 250:6

256:12 271:5

271:10 277:13

277:19 282:11

290:27 291:10

291:11 296:21

297:21 298:1,3

299:18 300:3,3

300:7 302:6

320:7 326:27

327:21 329:20

340:19 341:26

344:18 345:22

351:1 352:24

367:9 368:13

376:15 381:22

381:24 383:6

383:20 384:2

384:18 385:1

388:22 405:14

405:16 414:10

417:25 422:18

425:26 427:19

428:25 430:22

435:13 436:27

437:6,7,18

441:19 461:17

463:7 466:23

467:7 476:18

486:9 487:9

489:18 503:9

503:20 504:3

506:8 509:17

510:9,13

511:23 514:27

517:23,26

lotion 201:27

202:6

lotions 493:21

lots 29:16 31:3

35:12 40:5

42:5 49:6,7

50:9 54:20,21

54:21 71:24

74:16 77:1,16

78:3,11 81:11

86:7,11,11

87:1 89:14,22

98:14,18

198:18 210:16

210:17 212:7

227:27 244:13

244:14 254:4

265:12 293:11

293:11 296:25

325:9 387:21

387:21 453:17

517:17

loud 238:15

lousy 232:22

love 67:9 205:21

237:27 487:5

517:17

lovely 308:17

low 16:11 17:7,8

23:12,13,14

24:11 44:14

45:14 72:7,9

76:15 116:9

118:23 119:2

130:4,4 131:19

134:11 138:15

138:17 149:18

149:18 150:5

155:19,19

156:5 159:4

182:10 193:3

200:17 215:1

226:15 244:10

250:24 253:14

253:23,24

263:12 267:3

283:21,23,25

284:1 290:2

294:14 297:15

325:7,12,18

336:26 337:9

339:20 343:9

345:5 348:11
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352:16 362:24

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457:24,26

461:10 479:8

508:5

low-activity

397:9

low-dose 229:22

352:19,20

low-hanging

26:3 291:4

low-potency

361:5 401:5,22

low-priority

320:2

low-throughput

269:23 356:21

lower 56:3 95:16

102:5 107:7,15

114:5,26

117:23 123:18

145:11 185:2

213:14,22

214:22 219:18

219:19 226:10

227:14 231:22

232:1 251:13

269:21 271:14

278:16 282:19

285:19 293:4

295:9 343:14

347:27 349:3

353:11,13,20

363:24 368:25

369:18 471:6

481:16

lowest 24:13

77:24 104:24

106:25,25

107:15 111:16

111:23 112:10

114:2,4,8,9,9

115:11 117:17

118:21 256:18

281:9 286:5

294:5,9 295:12

368:23 369:7

370:3,9 371:16

371:22,27

416:13,21

484:15

lucid 177:4

516:21

luciferase 38:25

157:20 158:11

luciferase-based

142:20

lunch 102:8,10

125:20 299:9

299:12 309:13

309:17,20

501:2,27 502:1

luncheon 102:11

501:7

lunchtime 103:1

lurking 515:22

lymphocytes

248:3

M

M 212:7,9

M1 468:24,24

M2 468:24,24

machine 30:16

160:23 395:12

machines 29:22

mag 251:12

magazine

341:20

magazines

341:21

magic 45:11

magnitude

99:24 230:14

236:20 250:3

252:22 253:24

255:22,23

258:26 263:20

264:3 269:21

319:4,13,26

320:17 321:6

321:19 325:20

341:14 345:24

347:26 348:15

349:3 364:8

396:27 489:6

main 93:5

238:17 255:8

255:21 385:7

390:1 417:17

429:19 471:27

488:4 515:12

maintain 322:1

347:12

maintained

313:24

maintaining

423:2

major 13:2,7,25

20:26 32:2

125:21 178:2

210:18 333:11

375:14 387:18

394:17 441:8

484:23 509:19

510:27

majority 54:23

143:7 144:6

147:22 154:13

158:12 163:20

187:16 363:27

484:3 492:11

498:25

making 8:7

18:13 20:12

22:24 37:6

46:12 84:3

176:10 226:6

229:8 256:16

267:2 270:20

275:19 296:5

296:11 300:2

312:7 315:26

316:25 324:5

355:27 359:21

376:14 377:3,9

377:19 408:15

413:1 431:11

459:5 482:16

492:21 519:22

male 182:14

males 347:7

371:9,10

mammalian

107:18

managed 7:25

management

10:5,8,11

79:23 290:12

519:26

manager 81:6

manifest 341:5

344:21,23,25

349:13,18

manner 53:2

69:26 126:9

178:14,15

198:24 365:22

510:21,24

513:21

manually 360:14

manufactured

194:23,26

manufacturing

201:23

manuscript 95:2

334:23 356:2

manuscripts

234:2

map 39:9 49:24

49:25,26 65:1

65:2 142:22,26

143:1,16 152:9

166:1 249:2

360:8 361:12

mapped 87:15

mapping 217:11

maps 151:20

210:1

margin 193:11

252:8 261:11

366:10 367:14

367:17,19

370:2,12

373:27 374:8

374:10,15

marginal 130:23

margins 31:11

31:11 260:27

261:5,8,12

366:26 373:24

374:6

Markov 209:18

210:13 211:12

marks 148:26

149:4

Marty 367:10

367:12,12

375:5

Maryland

234:10

mass 116:15

256:5,10

massive 24:27

387:21

master 410:3

matched 142:24

matching 271:8

317:27

material 31:18

370:27 372:7

395:14 403:2,3

403:6

materials 4:17

12:17 202:1

386:11 402:21

415:7

math 69:19,19

69:20 388:15

math-challeng...

69:22

mathematical

141:18 151:1

313:21 332:24

333:25

mathematically

42:27 395:5

mathematics

178:5 279:24

matrix 95:12

221:20

Matten 520:13

matter 116:25

117:1 128:21
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matured 8:27

maturing 387:6

maturity 335:16

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max 72:4 96:27

maximal 92:24

maximize

259:16 315:17

442:8 453:6

maximizing

355:22

maximum 39:25

107:10,14

111:17

maxing 92:21

Mazzucco 375:8

375:10,11

McManaman

4:21,27 11:14

11:21 21:20

23:10 24:16,23

26:9 27:18

28:23 50:16,22

50:25 51:1

53:6 54:26

55:22 56:26

58:12,25 59:24

62:2 63:26

64:21 65:19

66:8,24 67:5

67:13,17,24

68:19 69:16

70:25 90:6,27

92:10 94:3,14

95:7 96:1

97:15 100:10

101:3 102:7,13

102:19 119:26

121:12 122:12

123:8 124:8,13

124:16,19

125:9 136:19

137:6,27

139:13 140:7

140:12 161:20

162:10 163:8

164:4,18 165:5

166:23 167:6,9

167:14,26

172:24 174:27

175:7,19

176:26 177:9

187:21,25

189:2,16

190:22 216:21

218:13 219:27

221:14 223:4

235:13 240:3

242:10,24

257:8,11,16

258:13 259:5

259:14,19

260:14 261:21

262:17,25

264:11 266:9

267:18 268:2

269:5,8 270:4

271:9,25

276:25 277:6

288:14 289:8

290:8 291:12

291:26 293:27

295:21 298:26

299:5 304:27

309:12 310:4

316:5 324:10

324:27 326:3

354:18,27

367:7 375:5

378:6 379:14

379:16 381:7

384:15 385:16

386:4 389:7,11

390:22 391:11

391:18,21

392:10,17,21

392:24,27

393:6,14,17,27

399:1,3,8,13

400:1 406:26

407:2,11,18,27

409:16,19,24

409:26 410:2,5

411:2,6 412:7

412:19 416:4,8

416:25 417:7

417:14 418:15

418:25 419:2,8

419:12,22

421:17 422:5

427:9 428:1,5

428:10,20

429:8 430:4,17

431:3,25

432:15,19,24

433:11 438:16

438:20,25

439:11,18

440:19 445:1,6

445:8 446:6

447:17 448:3

448:13,23

451:1,27 453:8

453:19 454:6

454:11,19

458:11,15,17

459:6,15,19

460:6 462:7,12

462:14,20

463:2 464:13

464:16,19

466:17 467:11

467:21 468:17

469:8,23

470:15 471:14

476:5 478:1

480:6,11,17,27

483:18 485:25

487:15,22

489:13 491:25

492:3,18,27

493:8,25 494:8

494:12,14,21

494:24 495:10

495:13,20

496:6,10,18,25

497:1,15 498:1

499:12,18

500:9,13,21,25

501:27 502:14

503:14,18

506:23 507:6

507:15,23

508:20 509:2

516:14 518:24

519:1

McManaman's

288:19

MCMANAN...

6:24

McNally 240:25

meals 236:15

373:1

mean 26:15

29:16 30:26

45:18 64:9

72:24 73:17

92:14 93:5

100:5 108:14

114:7 117:17

123:22 135:6

138:4 154:3

169:6 171:3

173:15 175:3,4

175:18 196:26

201:5 207:12

211:8,11,14

221:7 225:21

238:3,4,9

240:17 252:18

253:8 254:23

254:23 257:13

257:24,26

263:2 265:4

293:21 294:5

298:11 325:2,5

325:17,21

329:1 340:23

341:25 343:12

349:22,25

369:15 389:20

402:2 415:18

415:19,19

417:19 429:10

455:26 474:18

493:12 497:7

497:11,16,18

507:22,26

509:24

mean/median

398:15

meaning 92:15

153:3 205:4

388:3 467:9

474:18 512:19

meaningful

68:11 377:10

416:14 499:8

means 24:21

39:12 112:9

153:2 156:5,11

159:25 220:16

285:1 319:27

325:12 353:5

415:20 428:26

473:9

meant 22:7

23:21 165:16

175:5 374:12

449:1

measure 35:26

42:9,13 109:3

227:5,23 228:6

246:2 254:4

266:22 349:24

434:2 461:2

488:21

measured

105:11 145:2

173:16 192:22

210:15 228:17

228:18 249:3

309:3 333:17

354:11 369:23

474:5

measurement

39:2 342:23

measurements

226:12 230:23

measures 284:19

284:21 316:25

337:19 343:4

463:26 506:1

measuring 34:25

34:26,26 35:1
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173:3 246:22

248:7 250:1

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mechanism

176:19,21

346:22 377:17

413:9 452:3

483:9

mechanisms

77:3 305:3,4

305:26 427:20

429:4 430:11

430:11 433:23

448:12 456:15

457:24,26

mechanistic

311:17 365:27

511:10

media 203:7,9

median 63:13,23

73:12,15,17

148:27 153:27

154:5 156:12

195:26 210:21

210:25 213:6

213:12,19

218:27 231:21

231:27 251:6,7

251:10,22,24

252:18,18

253:6,8 254:22

254:23,24

274:22 275:26

276:3,11 294:6

294:9 382:13

473:7 474:19

498:24

mediated 166:17

166:17 176:19

300:5 346:21

346:27 427:4

Medical 375:15

Medicine 310:9

meditated

167:11

medium 23:12

23:13,15,16

24:11 263:2,12

508:5

meet 371:20

518:16

meeting 1:4 2:3

3:1,5,5,9,15,16

3:17,26 4:5,15

4:22 7:4,6 12:8

12:9,12 14:23

19:24 20:15,19

51:5 52:12

61:18 126:12

126:15,24

127:4 182:23

188:2 192:16

221:16 264:18

277:25,27

278:3,26

300:11,13

303:10,24,25

313:1 352:1

386:14 388:18

389:6 410:11

411:3 417:18

418:22 424:20

441:17 443:5

457:17 486:16

486:19 490:5

490:18,25

491:9 518:5,26

519:9,17,23

520:8,9,15,18

520:19,20,21

520:23

meetings 19:17

21:11 265:24

279:12 301:6

312:3 315:24

512:22

megs 363:18

member 340:6

members 2:3

3:26 4:10,25

5:8,8,15 8:6

11:22 33:6

189:19 190:23

303:13 316:13

326:26 385:21

399:14 407:12

410:9 411:4

416:9 428:6

432:16 438:20

441:21 444:26

447:17 480:12

482:12 486:22

507:16 509:6

514:25 516:5,6

516:26 517:10

membrane 67:8

165:9 167:11

175:18,21,25

175:27 176:13

226:21

membrane-as...

165:17

membrane-bo...

167:4,17,21

176:19

memorized

48:12

memory 221:19

men 517:25

mention 38:15

82:19 122:15

124:11 239:14

249:15 277:15

313:20 349:9

357:15 385:24

414:4 455:22

512:17

mentioned

11:27 12:10,19

18:10 20:5

21:7 25:11

51:11 96:3

114:19 128:26

144:17 146:22

161:1 170:11

174:8,19

179:16 185:21

185:21 248:14

248:24 278:22

278:25 293:17

296:2,16 302:2

302:13,22

303:21 320:15

337:5 340:26

341:13 342:5

346:4 347:11

348:12 351:13

352:5,22

353:21 355:25

382:8 393:8

397:23 406:6

413:6,14,22

414:3,27

423:25 424:1

431:3 441:21

456:10 458:18

464:20,27

475:5 484:17

485:13 493:1

495:15 498:2

499:25 505:25

507:24 512:6

519:24 520:3

mentioning

282:24 475:8

512:1

mercuric 82:7

merit 394:17

messages 156:19

messy 131:23

met 3:14 4:4

409:12

metabolic 98:25

226:13 228:18

229:3 293:18

313:15 336:14

346:9 363:26

373:3,7 414:2

415:22 420:10

420:16 421:14

424:6 427:1

468:21 472:19

474:10

metabolically

90:18 406:17

406:24 419:26

421:4

metabolism 25:7

25:25 112:17

119:19 225:25

228:3 231:8

238:10 241:22

287:16 292:18

305:16 308:22

313:17,27

344:1 352:13

352:15 363:24

372:10 383:19

413:20 414:10

414:20 419:25

420:12 437:12

456:14

Metabolism's

420:22

metabolite

100:2,23

208:13,26

209:5,27 210:4

217:1,24,27

218:10 228:9

346:16 414:6

427:5 492:7

metabolites

90:10,14 98:27

99:2,9 100:7

199:10 209:3,4

210:9,10

217:12,12,18

217:22 218:8

228:11 249:3

344:2,13

346:14 377:22

414:14 506:6

506:10

metabolize

228:1

metabolized

100:15 116:20

119:1 203:22

229:6,9 238:25

457:6 468:23

metals 29:19

32:6

metamorphosis

309:4

method 30:16

108:7,12,20

109:4,7 116:16
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288:2,10 308:1

308:7 320:22

322:13 340:19

381:5 479:24

486:11

methodological

178:14 378:21

methodologic...

381:1 399:22

459:27

methodologies

26:5 312:10,25

441:5

methodology

30:22 227:14

234:14 316:22

317:7 328:6

341:9 350:2,6

350:10,25

364:23 389:18

479:17 488:6

methods 10:22

10:25 11:8

19:9 51:24

67:20 94:19

103:4,9 108:22

108:24 109:5

127:5,20 130:1

134:9 135:20

140:22 146:4

146:12 180:4

191:25 223:26

224:14 233:19

278:1 281:23

287:22 310:13

313:16 315:27

317:3 322:12

324:7,13

334:15 338:10

348:13 351:21

375:25 386:25

399:26 400:11

437:11 444:1

455:18 460:4

460:15 488:3

489:1

methoxychlor

100:22 106:20

406:19,22

methoxychlors

414:4

methyltrienol...

162:21 163:1

164:3 378:11

456:21 457:5

457:11,15

466:1

metric 73:21

265:15 318:12

318:16 320:4,5

420:17 429:20

478:10 479:7

479:20,21,24

480:2 488:12

488:14

metrics 241:17

265:12 289:18

388:7 390:25

445:20 478:7

mg 112:22

118:22 209:1

255:22

mgs 111:24

112:7,8,24

213:12 251:19

283:21

mic 58:13

mice 105:25,26

109:21 346:25

400:23,27

Michigan 6:1,7

190:1,7 237:12

411:15,20

microarray

56:21

microgram

253:22

micrograms

230:17

micromolar

45:25 48:10

72:12,13,27

73:6 74:4

78:25,26 80:17

96:17,27 97:2

131:4 132:13

137:1 145:9,13

145:21 153:5

192:22,22

223:19 225:16

230:10 231:25

231:26 232:3

251:4,19 281:3

343:18 371:24

371:27 373:8

micromolars

45:24 80:14

281:11

microphone

6:25,26 58:26

125:10 175:20

489:16

microtiter

244:24 247:19

mics 189:7,8,12

189:14 516:23

middle 63:2

70:11,21

214:24 219:21

360:11 405:15

405:17 437:9

438:9

midrange

137:10 140:5

midst 180:18

mifepristone

184:8,10

might've 183:3

Mihaich 326:5,8

326:9

Mike 351:2

milligram

192:27 223:14

229:12,15

236:22 372:17

milligrams

106:15 208:20

229:16 230:11

364:20 371:11

371:14

millimeters

403:19

millimolar 96:19

million 54:22

82:24 251:2

283:15,16,25

284:2 285:10

288:4,9 310:12

373:18

millions 60:25

mimic 35:24

43:25 306:12

373:1

mimicking

35:19

mind 24:8,17

100:5 183:26

218:21 240:1

263:25 283:18

320:8 348:17

353:17 383:15

443:15 453:12

457:21 467:13

477:1 512:18

minds 260:22

509:11

Mine 270:5

mined 494:2

minimal 37:10

43:8

minimally 217:4

217:7

minimization

42:2,14,17

65:26 144:26

395:11

minimize 42:12

383:15,18

minimizes

510:24

minimum 61:25

106:12 109:25

180:23 404:19

404:25

mining 34:15

55:12 77:10

222:26 424:19

482:2

miniscule

325:23

Minneapolis

6:19 190:19

412:4

Minnesota

190:19 412:4

minor 390:10

405:9 415:21

420:26

minus 48:8,8

66:3,3 92:1

99:6,10,14,19

232:15,21

371:7,11,14

413:23

minute 17:6

115:1 132:23

189:6 401:4

422:26 434:13

499:18

minutes 3:10

4:14 7:5 55:24

57:1,20 243:9

257:6 276:26

283:3 299:12

412:9 418:22

459:16,21

498:12 500:14

509:3 520:19

mirror 163:3

493:4

mirrors 177:26

180:20 181:20

misimpression

322:8

misinterpreting

458:5

mislead 451:4

misleading

134:20 374:5

misnomer 298:2

missed 104:3

133:21 136:22
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mobility 242:16

mode 25:4 35:1

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215:17 269:23

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model 13:13,13

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36:25 37:4

39:7 41:2,15

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44:1,20,22

45:6,17 46:18

47:3,4 48:15

48:18,23 49:13

50:12 52:21

54:3,6,13

56:13 57:16

60:6,9,16,21

60:22 61:10

62:11 66:16,22

67:26 70:2,12

71:5,7,8 79:23

79:26,27 80:3

80:8,12,25

81:12,13,15,20

81:24 83:4,8

83:10 84:11

85:17 86:4,7

86:27 87:20

88:10,18,19

89:16,17,22

95:3,4,20

102:27 103:17

103:24 104:9

107:26 108:25

108:27 109:4

110:18,19

112:4,21

114:25 115:5

117:6,20

118:10,19,24

124:5 125:14

125:21,22,23

126:24,26

127:8,10,11,18

128:13 129:17

134:3,8,16

136:6 138:16

140:25,27

141:4,18,19,22

141:23,26

142:2,11,25

143:14 144:17

144:18 145:20

146:20 147:21

147:23 148:2

148:13,14,14

148:22,23

149:14,17,23

150:7,21,26

151:1,4,10,12

152:1 156:3

158:17 159:5,9

160:13,18,20

160:23 161:7

162:2,5,17

165:26 166:3,6

166:10,14

171:19 172:17

174:8,17

176:11 177:18

177:20,26,26

178:6,17,20,25

179:1,6,9,11

179:15,21,22

180:8,13,27

182:7,10,27

183:1 185:6,9

185:17,19

186:10,16,24

187:2,9,16

197:6,27 198:2

203:8 204:2,7

205:2,4,6,6,9

206:8 207:6,12

207:13 208:10

211:23,26

212:6,13 213:8

215:17,19,23

215:25,26

216:1,11 220:8

220:9,18,18,21

220:22,26

221:5,8 223:13

223:20 225:21

226:3,11

228:14,15

229:13,23

230:23 233:23

234:24 235:21

236:11 237:4,7

237:7,15,17,18

237:25,27

238:6,14

241:24 242:1,7

246:25 247:18

247:19 251:24

252:3 253:12

253:26 254:1,1

254:15,19

255:4,5,18

256:2 258:9

265:6 268:15

272:26,26,27

273:3 274:14

276:10 277:26

279:1,1,25

280:4,17

281:11,16,20

282:26 289:12

289:22 291:18

291:21 294:19

294:21 299:26

300:18 305:15

305:20,23

306:1 307:5

308:6 313:24

314:3,6,13

315:14,18,20

317:4,8 321:25

323:2,8,18

324:8 327:15

328:6,9 329:8

331:23 332:13

332:17 333:3,4

334:17 336:5,9

336:9,18 337:3

337:13,23,26

338:4,4,7,18

338:19 351:26

352:5,12,20,24

369:22 370:1

372:23,23

373:6,7,9,13

373:27 374:2

376:12 377:1,3

377:26 380:4,7

380:10,12

381:3 383:5,5

384:22 385:8

385:18 386:2

389:22 390:3,3

390:8,14

393:24 394:11

394:14,17,25

395:4,15,18,21

395:25 396:2,4

396:12,26

397:1,19,24,24

398:4,9,19

400:14 402:4,8

404:14 405:16

406:18 412:13

412:16 414:16

421:26 422:3

429:20 433:3,7

433:19 434:1,5

434:10,24,26

435:2,12 436:5

436:9,11,18

437:9,27

438:10 439:2

439:27 440:3,8

442:14,18

443:15 444:5

451:10,25

453:5,6,7

456:15 460:1

462:27 463:9

463:15,16,27

464:3 469:17

469:20,22

472:24,26

473:3,14,21,21

474:2,4,13,16

474:20,25,26

474:27 475:13

475:14,15,15

475:21,22,22

475:24 476:3

481:25 482:3,4

482:4,10,14,17

482:26 483:1

483:12,21

484:24 485:7

485:10,10

486:26 488:8

489:22 490:18

491:7,20

492:23 494:16

495:4,8 497:12

499:4,16

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modeling 2:9

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89:14 91:10,11

91:12 94:7

105:23 117:12

125:19 127:12

134:5 140:13

140:16 147:16

147:19 160:15

161:26 197:6

197:11,14,22

197:23 203:4,6

203:11,13,27

204:4,26

205:12,12,14

206:1,6,11,13

206:14,22,24

206:25 212:8

212:14,18

216:10 220:12

220:14 233:27

235:8 236:19

237:3,11,22

238:21 256:21

256:22 262:22

263:3,4 275:19

276:18,24

278:12 279:23

282:7 286:15

286:20 287:14

289:14,27

290:12 291:23

302:4,8,12,19

302:25 303:4

305:6,9,22

307:11 308:3

308:17 309:8

314:1,2,19

322:19 323:5,6

324:25 332:15

334:12,18

339:4,9 351:4

352:11,14

374:22 377:9

380:21 382:2

383:3,20

384:20 390:12

390:17,19,20

396:7,11,16,17

398:7,9,11

399:23 407:10

440:7,17 444:2

447:3 474:7

475:7,27 477:8

478:7 480:3

481:11 485:13

487:8 488:7

496:4 498:4,5

498:6 500:8

502:27 511:4

515:14,15,19

515:20,22

moderate 47:18

88:27 91:4,20

115:22 199:12

200:1,17 397:9

476:24

moderate/low-...

397:21

moderately

115:22

modes 25:5,7

423:22

modest 131:10

131:10 137:2

184:1,16

206:19 415:14

415:19

modifications

175:26

modified 18:11

modify 168:26

233:20

modular 306:25

383:21 425:3

449:10

modularity

307:17

modulate

467:10

modulator

150:24

modulators

146:26 298:12

module 383:2

MoE 369:26

374:26

MoEBA 367:19

368:3,8,23

373:16,22

MoEBAs 374:13

MoEs 374:3

molar 344:7,9

molarity 342:27

molecular 6:3,6

107:19,21

160:27 190:3,6

338:1 359:2

361:4 387:26

411:17 455:17

456:23 466:4,7

466:7 468:13

molecule 208:13

208:14 218:10

226:4,5

molecules 218:7

226:22 345:5

345:20,21,25

366:16 468:25

moment 33:10

37:15 44:16

146:21 171:20

250:19 272:5

348:17 392:6

407:17 428:14

437:25 485:3

momentarily

43:17

Monday 5:1

money 60:27

82:25 287:3

monitor 198:19

210:4

monitored 199:3

199:7 209:27

212:16 214:5,7

218:8

monitoring

15:15 20:7

195:16 199:27

204:7,22

205:15 215:15

215:24 218:5

315:8 481:25

498:18,19

mono-ethylhe...

217:19,21

monotonic

93:24

monotonous

428:11,13

monsters 240:26

Monte 209:18

210:13 211:13

231:2,16 234:1

236:24 240:6

240:10,24,26

241:4 372:19

472:17 485:2

Monte-Carlo

473:22

month 70:6

244:2 328:4

months 517:6

monumental

223:2 400:15

moot 404:3

morning 3:3

4:27 5:1,2 6:5

6:20 7:12

11:21 28:4

32:14 125:20

126:7 128:11

140:27 162:3

177:3,11

187:26 189:2

191:2 192:18

224:6 387:19

393:13

motivated

397:12

motivation

479:19

motive 414:6,20

423:15

mouse 37:19,19

105:24,25,27

348:27 349:4

451:7

mouth 197:26
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310:4 311:24

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331:15 338:26

342:20 343:13

343:17 349:17

355:22 378:8

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421:18 432:21

432:24 436:10

454:12 462:15

470:3,16

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moved 187:19

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moving 7:23

18:24 22:14

30:21 52:16,17

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195:25 266:19

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multi-century

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multi-develop

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multi-generati...

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multi-year

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multigenerati...

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29:3 33:5

61:13,15 89:13

134:13 135:17

141:5,9 158:5

179:3,3 182:2

182:3 185:14

185:15 197:6

197:22 209:3

210:9 217:20

231:18 274:11

274:12 303:3

356:15 357:20

376:11 379:19

382:12,14

384:3 390:12

391:2 413:8

418:7,8 426:17

430:26 461:5

multiplex 38:12

64:18 157:8

417:27 426:18

430:25

multiplexed

58:1,2,16,17

58:24 59:2,6,7

59:13,19

101:27

multiplexes

64:17

multiplexing

38:16 59:5,9

multiplied 74:5

multiply 229:17

multivariate

160:23 204:10

muscle 341:20

myclobutanil

360:4

mystery 198:5

mythological

234:5

N

n 110:6,11

N1 62:27

N2 62:27

Naiman 208:11

226:1 252:13

naïve 144:18

naïvely 293:3

name 3:8 11:23

92:14,17 125:3

140:19 272:18

330:24 339:27

367:12 375:10

named 33:26

names 37:27

468:23

nanogram

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narrow 455:27

narrowed

109:19

narrowing

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narrowly 426:24

Nate 197:3

national 5:18,24

6:11 8:5 15:25

38:20 103:3

140:21 189:21

190:11 194:24

198:15 239:14

310:10 315:11

375:9,12,16

411:10,12,24

518:11

native 133:9

170:3 298:5

392:4

natural 77:20

222:22 255:26

256:27 325:24

343:16 344:1,7

344:16 416:19

457:4,8 479:21

naturally 118:1

164:16

nature 155:7

173:1 236:3

361:25 460:24

470:9

NCCT 58:6

NCGC 38:20

234:10 249:18

near 160:18

311:23 336:11

397:17

near- 235:17

near-field 202:6

202:6,7,12,13

202:14,16

256:3,4,6,9,24

490:12

necessarily 24:8

25:2,3 30:8

52:14 72:22

102:2 132:2

139:6 150:17

151:22 168:20

210:6,23

223:23 229:20

262:20 285:26

291:21 295:13

328:19 336:25

342:11 360:16

404:8 459:11

504:27

necessary

126:20 194:9

332:14 360:26

411:3 421:18

425:3

necessitate

361:6

necessity 443:12

neck 403:17,17

403:18

necrosis 77:5

need 6:25 31:2

33:9 37:9

40:26 64:2

81:7,24 106:27

113:20 128:21

172:2 176:20

183:8 186:13

195:8,14 201:7

212:17 222:9

224:7,10,11,21

227:5 228:7

230:3,20

231:27 232:2,5

232:11 233:16

245:6 249:10

272:5 313:16

314:2 322:17

328:15,21,23

329:6,27 330:8

333:4 338:24

339:9,11

340:24 341:2

342:4 345:19

353:12 362:2

366:4 370:18

374:25,27

391:12 394:20

400:9 412:27

420:16 429:14

431:14 433:23

444:14,23

445:19,23

447:9,13,21

450:8,19,25

451:14 456:4,6

464:2 468:13

469:6 477:21

481:24 482:21

482:24,27

483:10 490:25

491:13,22

496:9 497:4

500:6 502:22

505:5 510:18

512:18

needed 17:13

96:5 109:8,8

113:2,5,10,18

113:19 221:18

222:24 230:15

298:21 314:15

315:6 338:15

340:12 359:8

361:5,18 365:1

365:25 382:5

388:15,24
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negated 359:4

negative 47:18

48:3 71:2

84:17,19 85:8

88:8,27 106:21

107:13 111:17

111:18,26

112:11,22

117:4 118:13

130:25 143:1

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182:2,3 308:27

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340:16 346:4,9

346:11,16

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365:7 383:25

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419:7 425:19

432:6 434:17

436:5,23,24

450:24

negatives 32:3

32:21 47:16

84:16 113:18

113:22 141:13

186:14 333:15

383:12,15

397:13 432:10

434:22 436:7

437:7

neglected

239:14

neglecting

135:14

negligible 363:2

neighboring

275:12

neither 151:13

214:27

NEs 373:23

network 126:26

178:20 179:22

279:1 383:5

449:4,25

455:23

neural 305:7

neuroendocrine

130:11,13

176:16 182:16

287:17 309:3

never 54:18

96:19 103:2

116:6,19

200:12 205:20

205:21 214:10

226:9 238:25

330:1 383:25

401:9 426:5

434:24 514:2

nevertheless

55:6

new 11:8 17:12

17:13 29:1

42:23 51:18

102:14 103:4

103:24 107:21

108:21 109:13

128:20,22

134:3 135:20

136:17 179:27

180:4 191:24

195:3,14

215:24,25

216:6,7,10

234:14 237:21

237:22 239:4

277:16 282:1

302:7 414:25

439:13 467:25

504:16 511:6

517:19

newer 357:21

newest 237:24

news 205:3

NHANES

200:10,16,17

206:19 207:6

210:19 218:16

234:19 241:7

254:3 477:13

482:2 492:17

495:2,16 499:1

504:2,8,19,26

505:3,5,8,11

nice 36:5 38:14

79:13 88:27

89:10 90:2,7

150:7 172:6

196:16 198:23

221:15,17

270:11 327:14

328:1,10

332:19,21

333:18 334:10

372:9 382:12

383:1 398:19

415:18 425:20

468:25 481:12

510:1 517:21

520:22

NICEATM

47:10 91:23

103:2 140:22

160:22 222:25

519:25

nicely 18:3

133:14 150:26

194:21 480:10

nicer 88:24

Nicole 140:18,19

162:1,13,16

163:5,16 164:9

164:15,24

165:20 167:3,7

172:15 185:21

253:2 265:7

272:23 517:25

519:26

NIEHS 504:12

night 201:18

243:4 321:9

425:25

NIH 38:19 87:2

189:25 212:23

494:18 520:5

nine 19:25 29:9

43:10 65:9

141:7 143:15

154:12 161:5

169:6 178:19

179:18 185:8

185:14 275:22

440:1 450:10

451:17

Ninety-nine

106:2

ninth 214:6

no-based 283:26

no-effect 193:4,7

193:8 294:13

no-screen 76:7

NOAEL 374:11

nod 469:25

nodding 165:4

node 41:12

62:27,27

nodes 33:25,27

36:10

noise 30:1,4 32:4

60:3 141:13

149:10 155:5

155:12 156:23

197:4 212:16

250:22,23

297:16 344:13

417:27

nominate 129:1

286:10

nominated

103:15

nomination

129:10

non-active 413:4

non-androgenic

457:9

non-animal 18:1

non-AR 159:7

159:10,11,22

non-aromatiz...

348:3,8

non-classical

456:14

non-consensus

422:12

non-cytotox

78:12

non-endocrine

369:8 371:26

373:23

non-endocrin...

369:6

non-endogeno...

164:2 457:3

non-ER 346:22

non-estrogenic

37:11 101:15

non-governme...

18:7

non-interest

219:13

non-metaboliz...

468:3

non-monotonic

53:22 54:19

non-monotoni...

53:20 54:2,5,7

non-natural

457:3

non-normal

156:23

non-nuclear

437:14

non-oral 351:15

352:23

non-phenol 79:5

non-phenols
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non-receptor

55:1 300:4

non-receptor-...

26:1 29:13

55:20 56:7,11

71:20 82:12,27

165:27 287:9

non-reference

139:18 435:9

435:27 437:2

non-referenced

436:12

non-specialists

445:18

non-specific

76:18 81:3

418:5 458:19

non-steady-st...

236:16

non-zero 123:19

123:21 145:23

150:20

nonlinear 320:9

352:14,18

nonmonotenic...

425:24 427:13

nonmonotonic

425:26 426:2

426:10 428:22

429:7 457:19

457:25 458:7

nonmonotonic...

427:15,19

428:6 430:9

nonreference

405:24

nonresolved

155:22

nonselective

333:10 364:3

nonspecific 77:6

343:20

nonverbal

391:11

nonylphenol

106:20 133:12

138:3 281:2,2

281:10

normal 240:10

243:23 248:3

253:18 319:20

341:5

normalize 78:2

319:15

normalized

145:6,9 179:22

319:17

normally 157:2

341:13

north 505:20

Northern 6:9

190:9 411:22

note 3:20 106:14

106:25 107:10

117:25 120:6

122:21 127:3

130:20 131:20

134:26 135:10

142:3,6 145:14

146:6 179:10

181:3 212:27

315:4 316:27

319:3 348:2

351:11,18

356:16 359:11

363:5,20

364:13 365:4

477:4 496:26

noted 16:10 17:7

301:9 316:27

335:24 390:26

479:17 503:23

504:5

notes 347:1

noteworthy

106:23

notice 40:6,23

44:12 45:10

46:6 48:6,15

107:6 110:4

114:18 118:18

142:23 213:14

251:18 253:7

255:10,15

297:20 368:3

noticed 51:8

269:11

notified 378:9

notion 165:8

notorious 80:21

Nova 157:19,22

158:7 160:8

163:16,26

164:7

Novascreen 38:1

38:3

novel 306:22

November 8:13

NRC 195:13

NRF2 371:27

NTP 125:7

nuanced 312:15

356:25

nuances 443:16

nuclear 33:12,15

58:22 59:1,2,3

59:10,15 71:25

74:27 75:7

76:8 78:4

80:22 137:19

149:8 150:1

154:13 157:10

157:19,26

158:1,5 167:11

167:17 171:23

173:22 175:16

176:5,13

384:24 395:6

398:24 412:26

414:24 426:24

427:4 433:22

434:3 444:22

444:25

nucleus 33:18

143:23

number 1:8 4:16

12:17 17:15

25:1 28:6

30:24 43:1,6

43:15 45:18

46:15 49:19

50:13 52:25

61:3 69:24

77:14 82:13

85:9 86:5

89:27 91:11,12

94:25 100:7

110:9,27 111:1

111:2,3,7,18

111:19 127:6

133:21 134:20

135:27 150:12

152:15 155:9

155:23 158:16

170:14 175:23

180:21 181:2

194:4,5,17

195:2 199:18

213:6 217:26

219:3,3,4,5

222:15,18

229:17 239:7

239:21,24

240:12 252:17

265:5 290:17

292:18,20

296:26 304:23

309:1 314:9

319:14 322:23

328:26 345:25

348:25 363:25

363:26 364:14

382:22 386:26

391:1,6 402:13

403:18 404:19

404:26 408:13

413:26 434:7

437:17 438:8

448:22 450:27

482:14 484:4

485:12,22

494:4 512:10

numbers 45:19

66:12 68:10

87:15 98:4

111:16 116:25

217:25 229:10

279:25 283:10

286:21 312:9

343:20 364:15

402:8 408:21

434:19 461:10

numerator

379:12

numerical

415:13

numerically

415:10

Nutrition

198:15

Nycedum

239:17

O

obfuscated

361:6

objective 44:8

424:23

objectively

425:7

objectives

127:16 135:19

179:8 186:27

279:15 287:26

355:13 358:1

460:21

objects 191:20

191:21

obscure 352:17

obscured 385:1

457:27

observation

100:14 408:3

observations

333:5 362:4

observe 145:2

153:23 154:8

395:21

observed 209:1

281:9 359:24

360:21 429:27

458:2 461:7

obtained 261:1

330:17 395:2

395:25 504:25

obvious 44:22

87:26 110:23

111:4 170:8

286:13 388:22
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323:2 382:1

386:18 387:6

401:15 402:17

404:23 405:4

406:22 408:24

413:21 414:5

414:17 415:1

417:20 420:5

424:7 427:4

451:14 455:11

456:11 461:17

461:17 465:7

465:17 466:2

477:15,23

479:8 486:18

487:6 489:25

516:3

Occam 396:18

occasional

434:16

occasionally

36:12 269:20

occasions

355:19

occupancy

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occupation

222:13

occupational

15:11 222:7,12

222:16

occur 57:1

243:18 247:2

333:15 339:22

359:3 364:15

364:18 406:4

420:19 426:26

434:3 457:26

458:9 466:4

occurrence

434:2

occurring 118:1

153:26 157:4

164:17 363:24

477:15

occurs 244:15

244:15 420:10

420:15 429:22

461:18

ocean 349:23

OCSP 520:4

OCSPP 517:12

518:8

odd 91:9 430:27

Odyssey 37:27

38:4 46:24

142:13 151:15

151:20 156:20

164:27 169:12

172:27 371:23

371:25

OECD 47:9

103:7,15

105:16,22

106:5,13 108:9

109:9,27 129:3

146:14 185:25

266:20 312:17

315:16 355:24

356:14 357:26

offer 221:21

477:11 489:18

490:24

offered 451:23

offers 452:25

office 4:2 7:13

7:14,20,25,26

7:27,27 8:2,2

11:26 51:19

52:2,4,6 103:3

125:4 177:19

191:3 207:24

235:1 291:9

518:3,9,10

519:11 520:2,5

520:14

office's 277:12

office-wide

520:4

Officer 4:2

offices 8:1

125:16

official 3:9

520:21

officials 3:25

offs 280:13

offset 74:6

oftentimes 129:5

oh 32:9,23 36:22

37:22 48:7

58:17 65:2

79:6,24 98:17

149:17 170:18

324:11,27

358:26 402:2

450:7 462:15

oils 87:17

okay 6:24 7:7

11:16,20 28:10

30:8 31:1,12

33:4 37:13

39:5 42:6

45:17,23 46:19

50:20,22 55:22

57:18 59:21

61:25 62:22

63:8,14,18,24

64:25 65:5

66:24 67:13,24

68:24 69:20

70:24,25,27

71:9 72:3,15

79:27 81:15

84:11 88:5,9

89:2 90:6

91:10 93:4

96:1 98:22

99:17 102:1

119:26 120:20

121:11 124:9

124:13,19

125:12 138:23

139:2 145:15

162:24 164:12

164:14 170:18

171:17 176:26

177:15 187:21

189:15 190:22

205:13 218:13

221:14 223:4

235:13 240:3

243:4,10,11,14

244:6 245:4

246:11 247:4

249:13 250:7,9

250:14,24

251:5 252:4,8

252:11,17,18

252:26 253:1

253:18 254:14

254:16 255:14

256:10 257:4

257:10,10,13

258:15,16,21

259:18 261:21

261:27 264:11

267:18,26

269:8 270:22

272:7,15,19,25

273:19 274:17

274:23 275:13

275:16 276:4

276:15 288:14

293:14 305:17

307:13,20

309:12 316:7

324:27 330:3

331:10 337:21

354:18 367:9

379:14,23

381:7 389:11

391:8,8,14,22

392:23 393:2

399:3,15 400:1

407:2,11,17

408:14 409:19

409:24 410:2

412:7,19 416:8

417:14 419:2

419:12 422:5

427:9 428:5,20

429:8 431:25

432:24 433:11

436:10 439:18

440:19,22

451:1 454:11

454:12,19,22

455:21 458:11

459:6,15 460:9

462:20 463:5

469:9 471:14

489:17 493:4

493:27 494:14

494:24 495:12

495:20 496:6

498:10,21

499:13 501:6

503:14 518:24

older 109:4

Olivia 237:12

omitted 459:13

on-the-order

232:25

once 43:21

69:23 85:5

87:13,23 88:6

110:25 114:14

184:2 229:19

229:19 267:1

269:27 277:11

278:21 279:21

280:2,15

281:16 284:25

290:11 301:20

374:15 381:15

391:15 423:21

425:17 476:22

497:24

One's 64:10

one-generation

357:22

ones 31:12,15

36:20 50:1,14

66:27 70:8

81:12,12 82:6

83:22 85:2,12

85:13 87:16,26

88:1 93:1,1

99:26,26 100:2

147:12 169:27

189:11 200:17

244:18,19
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476:23

ongoing 27:2

174:19 186:2

216:12 222:2

234:27 239:16

300:9 301:1,9

302:15 312:11

388:3

online 9:2 26:7

421:13 463:17

open 18:2

127:15 197:23

216:6 389:11

399:13 428:5

491:26 514:11

OPENING 2:3,4

3:1 7:10

operate 170:22

operates 228:22

operating

346:25

operator 221:22

opinion 451:6

470:11 471:18

471:19

opinions 161:10

OPP 1:10 520:5

opportunities

301:22 443:26

504:11

opportunity 4:5

17:17 28:13,20

239:13 316:12

330:9 354:15

367:5 375:11

378:5 490:6

516:3 518:5

519:19

opposed 53:27

152:12 173:21

174:1 185:8

331:1 368:15

413:6 442:24

443:1 444:11

444:16 457:3

508:16

opposite 116:9

117:5 233:10

401:25

OPPT 520:5

opt 70:11

optimal 39:15

optimistic 17:11

423:21 487:8

options 66:22

374:18

oral 111:25

112:4,16 121:4

121:6 226:6

235:25,27

236:13,19

238:7,23 245:1

245:2 282:16

283:9 318:15

318:16 363:17

363:22 364:6,9

364:16 372:23

373:11 379:5

381:23 389:17

395:15 400:21

404:1 416:15

441:26 471:22

oral-equivalent

192:20

orally 106:1

238:17

orange 132:16

132:19

oranges 416:14

ORD 286:17

304:22 517:12

519:20,26

520:5

order 23:22 53:3

61:16 73:8

81:23 89:3

110:27 114:3

115:10 176:18

209:18 223:21

232:15,20

236:19 250:3

253:23 264:3

269:21 296:19

299:10 313:25

315:16 326:16

326:24 341:14

348:15 356:9

359:7 364:8

366:6 376:2

477:1

ordering 324:16

324:23

orders 15:18

99:24 230:14

252:22 253:24

255:21,23

258:26 263:20

319:4,13,26

320:16 321:6

321:19 326:18

345:24 347:25

349:3 489:6

orders' 325:20

Oregon 402:25

organ 465:16

organism 341:5

350:15 414:10

organisms

514:14

organization 8:6

105:16 316:10

375:17 384:4

426:8

organizational

382:18

organizations

18:7 310:11,18

494:16,16

organize 200:10

239:26 356:15

460:10

organized

454:22

organizing

355:23

organophosph...

209:27 210:3

organs 69:12

305:14

orient 147:2

148:3

origin 74:10

92:23

original 126:23

126:24 404:25

438:1,4 469:2

originally 14:8

125:26 457:17

orthogonal 64:4

377:14 417:3

418:7 446:19

452:4

orthologous

431:9

OSCP 519:16

OSRI 312:19

314:26 356:9

358:8 360:14

361:21

ought 353:19

outcome 18:4

20:3 108:17,17

109:15 111:27

128:24 131:7

290:24 303:22

304:1 308:21

328:21 365:20

408:11 423:1,2

424:13

outcomes 27:15

104:22 105:22

109:11 110:24

112:3,7 115:16

121:24 127:11

129:4 261:16

335:19 367:18

413:3,16 491:2

508:7 513:6,8

outdid 206:24

outgassing

202:1,14

outlier 116:15

outliers 115:20

123:25 269:12

270:3 274:23

outlined 126:9

127:21 132:13

178:16

output 44:1

128:11 130:18

139:4 182:6,10

187:9 231:11

483:4

outputs 13:13

97:8 128:12

138:19 282:26

380:7 405:27

440:3

outreach 103:7

outs 64:7

outset 467:16,19

outside 74:16

79:14 274:10

317:12 388:23

467:24

outstanding

386:17 422:21

514:20

ovariectomized

105:2,24,24

overall 13:26,27

14:14 20:26

21:8 22:8

214:5 252:23

298:8 310:18

338:15 339:4

358:21 361:27

368:26 383:27

384:26 401:2

406:13 415:21

416:1 426:23

433:26 443:24

452:12 464:24

470:1 482:20

498:6 511:1

overarching

327:8 331:8

340:18

overcoming

17:24

overemphasized
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overestimate

233:5,7 238:17

239:10

overestimated

233:3,4

overestimating

93:1 239:22

overexpressing

58:7,10

overlap 23:13,16

84:25 131:22

131:26 147:8

152:4 185:26

245:22 252:9

253:17 254:27

255:3,13,25

256:19,27

257:2,24

258:16,18,18

258:24 259:17

263:15 264:2

268:9 270:20

270:24,25

271:5 467:8

472:5 478:4

481:5

overlapping

196:25 244:18

244:19 263:19

263:24,26

362:21

overlay 115:16

320:12

overly 298:21

oversimplified

486:12

overt 178:10

181:15 303:15

405:6 462:5

overview 5:14

104:6 110:14

304:22 355:11

382:27

overwhelmed

265:23

owned 38:2

oxidated 77:2

83:11 372:1

oxidative 77:12

oxidized 99:17

oxychlor 107:7

P

P450s 363:26

pace 17:18

512:4

package 233:22

233:24 237:20

237:21 372:22

374:11

page 173:6

313:4,20 314:6

314:15 332:23

335:24 337:8

362:17 469:7

pages 333:6

PAHs 414:4

painful 31:22

83:2,8

painfulness 83:4

pains 82:20

83:10 155:13

158:25

pair 79:3 254:21

paired 369:15

pairs 100:2

pan 82:20

158:26

pancakes 249:8

panel 1:2 2:3,26

3:4,13,15,21

3:26 4:25 5:7,8

5:12,15 7:19

8:13 11:22

12:18 14:25

19:16 21:12,19

21:21 28:9,13

28:23 33:6

43:25,26 44:17

49:1 51:8 52:9

69:5,8 75:6

92:5 101:10

110:1 121:15

137:17 146:18

147:4,6 170:12

187:20 189:5

189:19 190:23

190:24 191:24

241:9 257:13

265:18 267:18

268:3 270:2

271:24 273:2

277:16 278:11

278:22 279:3

286:8 287:19

288:12,20

289:9 291:20

301:11 302:18

303:13 304:9

316:13 317:17

354:27 355:5

355:14 359:18

375:11 378:5,8

379:22 389:12

391:24 392:6

396:11 398:6

398:12 399:14

407:12 410:9

411:4 416:8,26

428:6 431:6

432:15 437:20

438:20 441:7

441:21 442:7

447:17 452:17

459:7,12 461:8

464:8 465:6

466:14,15

469:14,24

480:11 481:18

482:1,12,20

483:13 486:5,6

486:22 490:8

491:11 495:7

496:1,2 497:25

499:15 500:11

500:14,23

507:13,16

508:27 509:9

509:10 512:3

514:25 515:4

516:5,6,26

517:10,14

518:1 519:4

panel's 419:14

439:14 508:23

panelists 289:10

417:15 446:7,8

458:17 462:14

464:19 469:9

489:16 491:26

492:19 495:23

496:11,18

498:2 507:21

panels 281:26

317:24 406:9

413:11 513:11

513:17 516:8

panels' 487:19

paper 8:12

12:12,15,16,21

16:17,26 31:19

49:20 71:14

92:3 116:17

122:14,17

123:22 124:11

127:17,21

130:27 142:7,8

178:16 181:21

191:5,8 193:19

194:22 204:13

204:18 210:13

223:1,7 224:17

224:26 230:9

232:12,13

235:12 236:9,9

239:10 240:24

241:14 252:12

278:10 291:17

299:21 301:10

311:20 314:1

314:23 316:15

317:11,25,26

318:2,9 320:6

328:4 329:11

331:1,23

332:14,23

333:7 335:19

335:24 337:8

339:10 345:2

346:4,8,18

347:1,21

351:16 362:17

368:5 371:6,21

376:8 380:12

380:18 381:16

384:19,27,27

385:4,10,19,26

386:6,12,21

389:5 390:3,16

395:15 396:21

397:12 404:1

408:9 418:21

425:13 434:18

440:9,13 441:3

441:19,24,26

443:18 445:14

456:4 459:13

460:27 471:8

471:21,22

472:24,27

473:8,11,27

475:8 476:9,14

476:19,20

478:8,12 479:9

479:18 480:1

481:26 499:10

500:7 509:10

510:10,25

513:22 514:1

papers 109:22

109:23 120:7

123:7 223:1

224:17 227:18

327:2 343:24

346:23 513:24

513:27

para 138:3

paraben 133:15

parabens 237:14

237:16

paradigm 417:2

423:2,9

parallel 35:10

304:17 394:22

441:20 455:1

460:21 470:11

parallelism

169:3

parameter 68:5
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parameter-free

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parameterizat...

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parameterize

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parameterized

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parameterizing

303:4

parameters

30:17 37:25

42:20 43:12

68:8,15 161:24

162:5 231:2,17

240:20 241:5

242:22 251:24

252:3 264:27

296:5 323:7,8

325:10 370:2

373:3 390:4,7

396:26 397:2

482:9,13

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Pardon 391:18

parent 90:11,13

98:27 99:2

100:2,23

208:14,27

209:2,3,6,11

209:21,22

210:1 216:27

217:11,13

218:7,9 228:8

238:26 249:4

403:5 466:10

474:4,16,26

parental 235:23

parentheses

111:16

parents 100:16

217:24

parse 60:2 175:5

357:3

parsimonious

396:17

parsimony

396:1

part 5:6 17:12

27:2 37:23

38:19 41:1

51:14 64:17,23

66:2 75:2 88:3

91:7 93:18,20

94:22 103:24

121:18,21

122:1 166:22

166:25 174:27

192:9 209:20

212:11 221:24

223:5 231:4

255:10 260:12

277:23 279:22

298:11 302:17

305:20 308:20

314:2,25

321:15 322:10

327:23 330:8

332:24 355:10

356:8 357:13

358:21 360:14

361:8,20,26

365:3,13

370:16 375:19

415:26 417:3

419:20 427:11

431:8,11 439:1

463:8 473:20

478:5 497:12

499:6,7 500:17

502:19 506:21

511:25

parte 393:27

partial 172:13

172:14 275:2

429:18

partially 195:22

299:7

participant 4:1

participants

87:1 344:24

participate

367:3 490:6

516:3

participated

105:18 516:6

520:8,10

participating

3:16 11:22

190:23

particular 3:5

12:8,15 14:21

15:22 16:5,14

16:21 17:3

18:19 25:4

26:22 29:9

32:26 35:13

36:14,15 43:8

51:8 69:25

76:3,25 82:19

94:8 107:5

118:11 120:3

142:3 146:20

152:25 157:25

160:10 162:5

165:2 170:6,21

173:4 195:14

198:13 200:4

209:6 215:23

215:23 225:4

241:21 244:11

246:18 248:13

273:14 278:2

289:25 290:4

292:21 293:16

293:19 301:15

302:23 303:10

303:25 305:18

307:23 314:26

323:7,8 325:13

353:15 356:13

357:15 362:7

362:27 371:1

386:21,25

390:18 395:17

400:6 403:10

409:20 436:15

441:3,6 442:15

442:22 443:13

443:27 444:6

450:9 453:2

458:24 465:9

467:8 468:12

474:7 476:16

478:24 480:2

481:27 490:6

490:21 503:4

506:6

particularly 9:4

9:13 25:26

53:21 131:5

174:2 175:15

222:20 223:8

266:6 277:20

286:19 291:23

298:9 312:9

313:6 314:26

316:23 323:1

323:21 376:16

388:23 400:20

401:5 403:15

404:7,9 405:7

405:12 406:18

415:22 421:10

424:4 425:12

430:23,25

442:14 444:9

444:15 451:24

457:14 468:2

469:18 476:10

478:18 486:23

486:25 487:26

490:13,26

491:22 495:24

507:14 514:21

particulars

308:2

parties 10:11

partly 32:17

169:8,9 296:17

296:25 297:21

297:22

partner 18:6

369:16,17

519:25

partners 8:8

38:19 125:7

266:20 286:18

518:9 519:20

520:6

partnership

7:26 8:5

partnerships 8:3

parts 5:10

174:25 379:19

443:21 464:26

478:8 495:15

pass 14:21

265:15 400:8

passed 8:20

354:7

passing 267:10

passive 228:20

228:27 372:12

373:13

path 16:27

178:1 182:20

239:25 243:26

256:10 304:4

304:19,20

308:8 317:5

323:12 366:7

438:14 513:14

pathologists

110:11

pathology 73:3

pathway 2:8

12:22,24 13:18

16:27 19:12

26:1 28:2 29:7

30:9,27 31:2

32:26 41:5

55:25,26 56:6

56:12,16,20,24

66:21 90:23

128:10 134:13

140:13,16

141:1,2,3,8,19

141:26 142:2

142:11,24

143:8,16,19

144:3,9,15,24

145:5,5,10,22

146:5,27

147:10,11

149:14,25

150:22,26
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 77

151:2,19

152:11,17,22

155:2,18,27

158:13 160:13

161:4,7,14

162:2 165:1,3

165:24 166:2,8

166:15,16,22

167:2 169:27

179:19 185:10

185:16 201:16

201:18 202:4,5

202:15,16

203:18 243:17

247:21 256:15

266:27 290:14

290:24 294:4

300:27,27

301:1 302:4,5

302:23,26

303:6,22,23

304:1,13,15,18

306:25 307:2

308:21 313:24

314:3,16

315:13,14

322:5 333:21

333:22 334:10

358:22 359:16

365:6 366:5

377:20 380:6

384:7 396:6,24

413:9 416:16

419:16 423:1,2

423:3,7,11,21

424:13 425:4

440:3 441:8

442:10,12,13

443:10,14,22

444:22,25

449:26 450:3,6

450:9 452:12

452:23 453:2

454:4 455:5,13

455:16,25

457:20 459:14

464:21,22

465:2 466:13

466:13 474:10

pathway-based

29:8 30:22

159:27 165:22

pathways 8:22

15:9 16:10

29:23 30:24

126:1,5 148:16

152:2,6,11

154:25 156:8

156:27 158:17

203:17 212:18

243:23 246:15

246:17 290:16

290:26 291:11

295:6 301:8

307:5,8 308:23

310:22 315:20

328:21 351:7

360:7 365:20

377:15 414:26

423:22 426:26

429:5 437:14

patience 125:2,3

125:12 136:25

137:11,13,18

138:13 139:2

139:24 140:1

177:7,15

187:24 267:15

277:6,11 289:3

289:20 290:22

297:26 304:23

308:15 379:1

380:2 391:16

391:19,22

392:14 393:23

399:20 407:16

409:18 412:12

416:23 421:23

432:23 433:2

438:23,26

439:17,25

454:9,15

459:10,25

462:18,24

469:13 470:13

470:21 480:15

480:22 495:22

496:7 502:9

508:25 516:25

517:10 519:15

519:16,18

patient 423:14

Patricia 309:18

310:5,7,7

patted 82:22

pattern 36:27

56:14 78:14

319:10,16

347:4 349:11

361:10 363:15

patterns 36:19

151:17 364:13

426:10

Paul 354:21

355:3,4

pause 216:18

pay 64:17

154:11

paying 458:24

512:27

PBPK 305:15

367:26 368:1

368:18 369:22

370:1,11

372:23 373:7,9

373:27 374:2

374:22

PCBs 414:4

peak 75:19,20

75:27 76:5

peaky 211:9

peculiar 173:18

456:22

peek 236:20

peer 3:19 19:14

23:20 179:2

234:2 277:22

278:5 317:13

peer-reviewed

129:2 180:22

513:24

penalization

396:20

penalize 68:4

penalized 68:23

389:19,25

395:13 396:25

penalty 42:24

43:7 68:1,15

68:16 150:27

161:25 332:21

385:2 390:4

396:22,25,27

397:4,6,7

456:3,5

pencil 210:12

people 4:6 10:4

28:21 30:14

32:9 47:19

48:7 69:2

81:16,21 82:14

84:8 85:24

86:8 87:5

95:21 96:11

98:8 110:10

128:27 198:21

198:25,26

202:27 207:23

210:2,24,26

211:2 222:24

222:27 223:2

228:2 232:2

236:4 240:25

241:19 243:22

245:11 246:16

254:6,9 274:27

276:21 287:10

297:2 310:6

329:14,19

362:18 376:11

381:17 384:9

384:25 413:6

476:15,18

494:6 497:21

501:1 507:3

509:20,25

people's 241:19

perceive 381:17

percent 39:25

75:9 77:23

79:20 80:27

82:2 88:17

98:5,9 106:2

110:19,20

118:11 129:22

129:23,24

131:11,22

135:22,25

136:1,3,7,9

144:21 146:7

149:4,6 150:2

159:7,10,11,21

159:23 160:25

163:21 171:21

171:22 172:9

172:11,12

180:11 187:7

187:10,11,12

187:13 196:13

199:22 213:26

219:17 225:4

226:9 245:16

245:17 248:20

248:21,21

252:9,21

254:23 256:18

257:18,23

270:25 275:3,4

276:11 280:18

280:25 281:21

313:13 321:2,3

321:8,8 326:16

336:11 338:20

345:15,15,19

364:7 372:6

373:15,26

374:23 388:2,2

388:3 401:6,16

402:1,3,4,6,9

402:10 403:3

407:24 408:5

408:10 413:15

436:25

percentile

210:26 213:7

213:13,15,22

231:22,23

232:1,4 241:1

241:2 371:6

498:24
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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percentiles

218:27

Percival 520:16

perfect 29:15,16

32:17,17 73:20

78:18 85:18

86:7,7 93:25

517:16

perfectly 205:22

209:8 434:24

perform 60:3

95:15 134:4

168:9 291:23

291:24 297:18

327:15 384:20

435:7 468:26

performance

11:6 26:24,27

60:1,21 61:10

61:26 68:7

92:19 108:12

113:5 116:23

116:23 117:7

118:10,14,16

134:3,16 136:7

136:8 147:1

148:13,14

161:7 167:16

174:23 180:8

180:14 185:6

186:16 187:2

234:23 265:21

266:15,17

280:17 281:16

289:13,17,22

291:18,21

294:10,15

297:3 298:8

308:3,8 309:10

314:19 317:4

322:18,24

323:2 324:8

336:3,9 338:8

338:8 385:27

389:22 390:14

396:8 398:4

401:24 412:14

413:2 416:11

419:20 421:26

422:21 427:14

433:4 439:2

455:10 462:26

469:17,22

performance-...

266:13,21

280:20 281:21

performed

21:14 95:10

119:12 239:15

310:19 461:18

461:24

performing 60:9

182:7 280:16

281:12 289:14

298:19 308:17

performs 60:6

60:12 129:18

186:10

period 2:12 4:7

105:5 120:3,9

127:7 310:2

373:2 386:10

492:24

periodically

33:7 246:1

periodicity

238:9

periods 376:17

443:2

Perkins 6:13,13

69:16,17,21

70:16 96:2

190:13,13

290:10 293:27

294:2 295:3,19

305:1,2 381:8

381:9,13

384:15 385:17

389:8,9 411:26

411:26 416:9

416:10 417:24

419:9,19 425:4

431:7,8 446:12

446:13 447:27

448:23 449:2

451:2 471:16

480:7,8 481:1

487:16,17,22

502:15 506:24

506:25 507:6

514:6

Perkins' 418:26

419:16 453:13

permanent 5:8

Permeability

373:10

permeation

373:11

permeations

196:1

permutations

98:1 210:14

466:7 467:2

peroxidase

349:5

perplexed

137:16

person 24:19

202:18,25

207:21 224:16

232:11 247:26

285:24 482:11

personal 381:15

514:3

personalities

141:10

personally 52:10

428:23 470:1

471:23

perspective 12:5

13:5,19 14:3

52:3,3,27 53:1

60:5,20,23

100:20,24

174:12 261:11

277:12 297:27

298:16 317:22

353:23 367:5

377:12 386:20

387:11 394:23

412:24 426:20

434:9 459:5

466:11,14

468:9 479:11

486:4 491:23

492:10 512:8

515:8

perspectives

355:6 358:3

375:17 424:12

perturb 334:10

perturbation

359:3,16

perturbing

308:23

pesticidal

285:24

pesticide 3:20

8:1 15:4,10,11

15:18,21

217:24 251:27

252:1 253:13

253:14 351:20

356:5 357:10

359:6 365:8,14

494:6,7,16

520:3

pesticide-active

222:21 254:2,4

254:9,12,13

pesticides 32:6

90:14 161:17

193:1,5 194:14

209:27 210:3

221:21 254:5

330:6 351:19

355:12,21

357:14,18

360:27 366:25

368:13,14

490:9,15,16,22

493:11 494:5

PETA 310:8,8

Peter 194:22

Ph.D 375:14

pharma 232:26

pharmaceutical

29:18 82:23

98:21 132:2

133:10 186:5

194:2 223:27

224:19 232:14

233:15 241:16

285:14,17

297:5,6 343:8

348:14 349:1

392:4 417:2

423:14,17

444:19 469:19

pharmaceutic...

297:8

pharmaceutic...

363:14

pharmaceutic...

29:18 98:18

144:7 152:16

152:20 160:6

235:6 255:4,8

255:26 263:17

497:8

pharmacodyn...

245:18 246:20

247:8,17,25

249:13 259:10

473:4,21

pharmacokine...

228:15 233:23

235:3 239:15

259:10 262:2

351:26 352:2

pharmacokine...

194:9,19

226:15,16,18

227:16 234:9

244:27,27

245:1,17

246:14,21

247:8 248:11

248:19 250:15

254:19,20

262:11 307:6,7

472:18,25

473:2,20

pharmacokine...

473:14,18

pharmacologic

342:23

pharmacologi...

118:1 332:3

345:7
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pharmacologi...

115:4 118:3

341:14

pharmacologist

342:22

pharmacology

340:1 343:1

344:5 469:4

phase 14:17,21

18:15,17,26

19:3 20:6

21:16 181:23

194:16 358:17

363:17 371:17

371:22 486:26

phases 14:7,20

18:15,24

phenol 43:24

phenolic 97:22

98:10,12

phenols 83:18

phenomenon

75:25 82:10

149:13 275:5

446:24

phenotype

261:23,24

phenyl 160:9

philosophical

92:13 175:15

235:26

philosophies

106:5

phone 164:11

409:25 468:27

phones 409:26

phonetic 207:24

237:13 238:8

240:25 241:15

248:1,26

252:16 253:3

382:25 386:9

390:11 404:5

520:2

phosphate 46:15

photons 172:3

phthalate 48:14

48:21 80:10

100:26,27

101:1 217:19

217:21 283:13

phthalates 100:6

100:24 506:7,9

phys-chem

83:24

physical 19:20

206:24 300:15

physician

423:13

Physicians

310:9

physico-chemi...

155:15 160:13

160:16

physiologic

458:10 461:7

465:16

physiological

396:5 437:16

physiologically

236:11 348:9

373:10

physiology

107:18 307:14

370:2 373:7

374:24 512:19

513:7

phytoestrogen

122:27 349:2

phytoestrogen...

122:23

phytoestrogens

122:15 341:16

343:14,23

348:12

pick 58:13 80:4

80:8 138:11

165:27 200:6

346:3 428:15

picked 151:10

151:14 166:13

281:5

picking 83:10

115:3,5 298:13

454:1

picks 150:26

pictorial 471:25

picture 40:27

62:20 105:7

386:7 435:26

pictures 43:26

320:23

pie 273:12,14

piece 56:16

71:18 99:4

118:23 174:5

248:12 258:8

293:1 359:21

378:24 379:10

424:26 508:15

508:16

pieces 56:12

65:18 243:20

244:22 271:17

pile 198:6

pill 224:5,5,10

224:11 257:4

pilot 491:18

pinch 403:17

pink 44:13,16,17

44:21 45:2,6

62:15 75:3,4

Pinocchio 24:20

pinpointed

404:8

pituitary 309:2

pivotal 379:10

PK 305:21 352:4

place 16:15

19:18 26:4,16

31:3 41:13

145:19 153:25

175:24 216:21

217:10 239:11

249:14 266:4

275:17 414:15

417:4

placed 14:6

407:9 445:16

484:22

places 12:8

382:24

plan 9:26 10:5,6

10:11 94:20

97:26 125:23

178:24,25

179:15 185:19

186:16 237:19

290:15,24

299:25 304:5

305:5 306:14

511:1

planned 140:6

216:12 286:20

291:8 382:1

460:4

planning 244:1

337:17 485:3

plans 67:7

130:15 286:17

305:2 315:19

333:20 459:26

475:6 511:10

plasma 225:20

225:24 226:12

226:13,23,24

227:2,21

228:16 229:1,9

229:11 232:7

234:16 238:11

244:26 246:23

248:12 372:7

372:15,16,24

373:3 379:8

plastic 95:10

plasticizers

161:17

plate 244:24

247:19 275:16

plate-wise 275:9

plateau 218:26

plateaus 249:21

plates 275:10

platform 482:23

platform-spec...

417:27

plausible 76:16

162:6 209:13

347:14

play 55:21 85:22

86:10 176:25

311:25 430:12

475:10

playing 70:6

207:20

please 4:8 6:25

7:2,3 189:10

221:25 355:2

364:13 380:17

394:9 421:24

440:12 471:7

pleased 5:6

315:19 327:26

pleases 513:15

plenary 340:8

340:10

plenty 299:11

plot 72:5 74:23

74:24 78:10,11

88:13 111:14

111:15 135:7

147:3 213:1,2

213:3,14,16,24

249:26 251:3

273:21,25

363:16

plot's 219:14

plots 49:2 80:26

89:5 135:8

148:21,24

192:25 220:1

261:1 265:5

272:26 273:12

275:24

plotted 21:26

75:14 225:3

363:21 364:16

plotting 74:23

91:11 212:26

213:6

plurality 232:25

plus 9:13 25:21

42:14 52:24

87:9 99:6,8,14

99:20 174:21

181:10 232:15

232:20 272:13

413:23

point 4:20 9:3

9:22 10:14,26
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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11:1 13:8

16:16 17:17

23:23 25:13

28:7 37:22

41:1 49:21

51:20,26 52:8

54:27 56:14

57:6,15 60:8

60:15,19 63:19

70:1 76:20

81:19 82:15

84:14 85:17

98:4 101:6

102:1,3 114:13

128:6 130:8

131:1,9,23

132:6,9 133:8

133:18 135:14

139:8 140:3

142:25 160:6

163:6 164:13

165:25 167:4

167:10,15

174:20 176:11

179:13 180:14

181:17 182:8

182:13,19,27

183:22,25

184:21 185:19

185:26 186:12

186:18,25

196:15 198:7

201:9 206:12

212:22 216:17

216:20 238:6

239:16 242:2

250:23,27

251:8,9 252:3

252:19 253:11

253:15 256:1

258:23,27

259:19 260:9

260:10 262:13

263:27 264:1

265:5,9 266:12

266:15 267:2

269:17 270:10

274:8,23 275:6

277:21 278:4

278:15 279:15

279:27 280:12

280:22 281:3

281:16 282:16

282:18 283:8

284:25 285:5,6

285:26 287:10

287:13,19

289:17,21

290:6,25 291:2

291:3,7 298:15

299:5 300:9

303:13 304:17

307:27 308:1

313:1 316:20

319:6,12 325:6

325:11 329:4

333:3 334:3

338:25,27

340:18 343:18

343:18 350:25

351:15 359:2

360:24 362:8

364:7 365:14

365:16,25

369:13,20

376:15 377:21

382:16 386:23

389:18 398:25

401:8 408:20

414:22 420:6,7

420:17,17,23

423:4,5,18,19

423:20 425:2,7

428:25 429:19

437:3 459:16

469:2,7 471:2

471:27 472:8

488:17,18

492:21 493:6

493:11 494:9

500:26 515:11

516:2 518:18

518:24

pointed 57:18

174:10 178:18

193:18 194:21

195:20 236:2

284:18 291:3

299:18 333:16

336:11 400:15

401:27 404:24

406:1 429:3

432:12 435:19

439:2 447:8

458:23 460:26

pointing 89:2

points 6:25 29:7

57:2,10 62:25

125:21 141:7

142:16 143:16

166:1 172:18

178:2 185:15

205:22 218:20

225:9,12

244:11 245:4

249:22 253:7

266:10 267:7

267:12 274:5

294:18 331:3

339:3 341:8

350:25 356:3

376:7,8 377:11

400:9 402:7

405:17 407:22

409:12 419:17

419:24 425:20

435:5 438:27

448:9 469:15

poised 18:26

poke 256:5

policy 7:14,25

11:25,25 24:15

125:4,5 262:24

264:1 312:5

325:15 326:4

326:10,13,15

326:26 330:26

340:2 342:12

354:19 519:12

519:14

Pollution 7:14

7:20,27 11:26

518:3

pool 227:25

poor 26:27

504:22

pop 98:15

popular 111:8

populated 284:3

population

195:17 198:17

207:22 210:21

221:23,24

231:3 232:5

234:17 240:7

240:22 246:25

247:24 248:8

248:24 252:13

252:23 276:7

286:4 471:4,12

472:1,2,17,22

473:4,23 475:3

475:16,16,18

483:27 484:13

484:26 485:1

485:20 488:5

488:26 489:3

493:23 504:4

504:13 505:7

population-ba...

376:21

populations

131:21 183:12

231:5 234:17

240:24 248:3

311:13 504:9

504:14,26

portfolio 368:27

portion 24:4

309:15 484:12

portions 181:7

posed 519:6

positions 432:2

positive 39:19

40:8,24 48:2

71:9,10 84:17

84:18 85:8,12

88:1,6,7,8,8

89:3,8,18 90:1

91:11,13

100:19 106:21

107:13 111:22

111:25 112:7

112:13,23

113:23 115:21

117:1,3,4

118:11 120:22

132:27 137:20

138:5 139:10

139:11,23

148:18 162:13

162:17,18

181:8,9,27

182:1,3 255:18

271:26 273:1

298:18 313:12

314:8 336:19

338:23 340:16

348:19 369:25

394:27 397:13

401:7 406:14

425:17 432:6

434:17 436:1

455:15 463:17

467:18 469:27

470:1 497:19

514:26

positives 32:3,21

47:15,19 48:11

49:22 73:11

84:15 98:10

100:16 106:19

141:12 297:21

297:22 298:9

333:14 334:20

383:16 405:21

432:14 434:21

437:7 455:5

467:16,20

positivity 297:23

possibilities

210:17 347:1

430:20,26

possibility 19:4

20:2 51:27

116:6,18 134:7

137:21 312:27

347:10 350:13

448:26 468:21

477:11 492:15
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 81

possible 4:19

10:17 27:4,13

41:25 54:15

68:11 90:15

107:12 110:8

123:4 142:24

166:12 167:21

195:24 210:14

217:14 218:19

242:19 268:14

299:4 311:23

314:27 346:19

349:15 354:2

375:25 377:14

377:17 383:12

396:6 397:16

398:22 403:26

406:7 413:25

438:3 449:19

484:22 485:5

488:16 489:8

511:14

possibly 117:8

404:6 492:22

post 447:1

post-factor 47:2

post-natal

511:13

post-validate

89:15

postdoctoral

375:16

posted 196:6

posterior 217:6

271:12,21

474:18,18,19

postmenopausal

344:17 345:27

potato 202:23

202:24 203:1

potencies 114:21

114:24 118:2

129:10 146:2

178:22 179:6

301:17 321:3,7

334:12,19

339:11 343:8

343:27 347:20

347:25 348:3,5

348:7,18 349:8

377:4 392:4

415:11 425:1

426:18 427:24

435:24

potency 45:21

48:6,8,10 49:3

49:5 69:27

78:23 91:5,13

93:12,13,21

104:24 114:6

114:22,26

117:11,23

118:25 161:11

175:16 178:27

186:3 255:26

257:2 273:15

283:20 284:5

313:22 320:15

321:12,18

332:3,3,4

333:26 336:17

336:18,26

337:9,19

338:14 340:18

341:9,14 342:2

342:3,4,7,8,16

342:18,21,23

343:6,17 344:8

344:9,21 345:5

345:25 346:5

347:8,16,27

348:9,12,15,20

348:23,24

349:2,6,13,17

349:19,23,24

350:1,14

353:25,25

362:24 363:4

377:1 385:22

391:23,26

407:5,7 413:16

415:8 435:10

436:9 442:20

463:27 469:19

476:18 514:12

potent 39:20

45:22 79:9,12

92:27 99:26

114:4,5,27

118:24 133:2

137:3,14

157:11,12

186:14,18

230:15,16

255:12 263:18

273:13 275:11

282:13,19

283:11,20,27

343:13 350:23

469:17

potential 8:21

16:2 25:1,5,10

25:15 53:4

60:17 61:6

98:24 101:14

104:21 105:21

108:5 113:15

119:6,17

127:25 128:19

141:11 150:13

170:4 215:14

241:6 259:17

281:17,23

286:26 307:23

311:9,12

312:20 313:3

314:11,20

325:10 326:24

333:14 336:6

337:24,27

344:16 354:9

357:6 359:2,14

361:4,23

365:20 366:15

369:11 380:3

380:15 384:23

394:19 400:22

412:17 420:2

422:4 427:3

428:22 433:9

434:21 438:14

439:26 440:12

448:12 463:1

468:19 478:21

481:13,17

482:14 485:12

509:20 513:4

513:13,16

514:10 515:20

518:20

potentially

55:26 97:10,19

99:2,9 100:8

123:4 133:4

135:6 150:20

150:21 153:4

154:19 155:1

155:19 156:22

157:10 158:5

159:19 162:27

166:20 176:13

182:17 200:22

217:13 218:10

223:19 275:18

278:18 282:13

301:5 304:7

305:18 306:11

360:22 375:27

384:3 435:21

443:1 446:14

456:27 474:17

477:13 481:22

483:8,16

potentiate 94:18

potentiation

94:17 101:15

POTOMAC

1:15

Potter 6:15,15

59:24,25 61:9

122:13 190:15

190:15 221:14

221:15 222:18

289:8,9 381:9

386:4,5 389:7

390:23,24

391:8 393:7,11

393:14 398:6

409:19,23,25

409:27 410:3

412:1,1 418:15

418:16 419:1

440:21 445:8,9

446:6 449:24

450:7,13,16

481:2 489:14

489:15 491:25

493:1,4,10

494:9 495:11

497:4,5 514:19

Potter's 394:2

492:21

pounds 252:2

powder 198:6

power 127:12

405:1 413:8

powerful 406:18

437:27 483:8

487:3 491:20

PPAR 75:1

80:24,24 81:2

101:25

ppm 364:19

PR 467:9

practical 296:16

350:21 352:11

479:14

practice 24:15

25:23 26:5

27:3 60:11

484:18 496:15

502:24 518:15

practices 10:8

praise 510:4

precise 21:13

207:18 209:16

476:6

precision 323:22

323:23 353:24

precursors

344:2 377:22

predicated

42:12 498:26

predication

492:9

predications

479:2 488:1

492:12 498:23

predicative

503:1 508:7,10
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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predict 31:9

60:7 88:26

136:23 144:27

149:14 166:6

166:10,14

192:4 197:24

198:11 204:2

223:15 229:11

229:19 232:20

238:24 307:16

308:25 309:6

310:21 351:27

369:23 373:13

394:12

predictability

237:5 323:5

328:9 436:17

predicted 95:26

115:21 150:13

151:5,18

152:10 153:17

156:2 157:24

158:17 172:17

206:13 212:21

214:16 219:16

229:14 231:21

231:22 243:18

256:8 282:25

283:1 313:14

314:7 332:27

364:6,22

370:22 372:16

397:21 401:7,7

402:6,9 474:16

478:27 483:12

predicting 27:15

205:5 213:25

280:23 298:8

prediction 42:7

86:15 148:23

150:7 197:5,6

203:10 204:1

219:11 232:19

298:10 309:7

314:4 322:14

323:5 356:18

365:12 390:6

402:10 474:2

475:20 492:6

499:4 507:14

predictions

27:14 148:22

152:1 161:1

197:1,9,15,22

198:1,4 204:8

204:27 205:2

206:6,11 208:8

211:24,26

212:6 213:11

215:18 232:21

233:19 239:20

261:3 279:5

286:23,24

315:6 327:14

351:7,13,14

352:22 362:22

364:9,10

372:24 373:11

390:12,13

406:15 475:14

475:27 499:3

predictive 17:23

118:12,13

127:12 160:25

205:14 215:16

220:14 221:6

234:5 360:19

396:15 398:4

398:19 401:10

413:15 503:2

503:13 508:8

513:4

predicts 149:24

205:10 402:4

predominance

444:18 457:21

predominantly

338:22 358:23

364:18 444:19

prefer 392:19

preferentially

158:7

pregnant 490:27

492:21 493:5

495:1,6 504:9

504:14

preliminary

5:14 7:8 102:3

160:22 161:8

359:12 413:22

456:7 460:12

463:10,11,13

464:5,11 470:9

507:4

prenatal 376:17

preparation

7:18

prepare 519:17

prepared 317:15

preparing

519:19 520:14

prescribed

455:2

prescribes

423:13

presence 168:24

198:19 203:7,8

363:2 396:14

465:26

present 10:13

51:9 138:25,26

168:8,17

199:11,13

203:20 265:14

300:7 316:14

317:9 344:3,5

354:16 365:2

366:3 367:5

383:18 398:26

400:27 425:26

452:17 458:8

489:7

presentation

5:11 19:2

22:23 23:4

24:4 28:15,19

28:27 31:24

37:16 51:3

90:8 97:18

108:16 125:17

140:9,25

141:22 142:23

144:5 177:1,16

190:25 218:4

240:5 243:5,9

264:13 267:15

267:24 275:25

277:2 299:8

311:4 367:14

368:2 378:20

378:20 381:23

382:9 386:12

395:15 396:22

402:27 404:1

404:10,19

405:13 415:7,9

475:6 514:1

presentations

4:18 8:4,7,11

8:15 12:13,13

12:13,19 26:11

27:1 28:13,16

60:7 97:8

177:2,10

221:16 265:20

279:14 299:11

302:3 315:24

320:7 331:2

351:1,2 385:4

385:7 386:7

388:26 389:18

390:26 398:23

404:4 410:8

441:4,17,27

458:24 471:23

476:11 481:27

509:9 510:10

515:6 516:7,8

516:21

presented 8:13

11:2 12:5 14:3

14:15 17:16

20:5 24:12

55:2 126:15,23

185:1 191:23

201:13 222:25

241:14 257:12

263:3 271:1

278:24 279:13

304:16 317:24

324:3 327:2

332:20 337:13

347:19 356:11

358:15 382:23

384:2,19

394:14 395:14

396:13 403:22

412:13 421:26

432:8,13 433:3

434:19 462:25

474:2 477:5

486:6,19 509:7

515:9 516:17

517:11

presenter 27:20

124:22 140:12

316:7 354:21

367:10 375:8

presenters 5:11

7:18 187:27

326:7 354:19

378:7 516:16

516:20

presenting

126:25 191:4

192:16 207:7

235:22 265:14

271:1 381:14

presently 451:22

452:23 466:15

pressure 116:17

presumably

23:14 95:9

368:25

pretend 268:25

pretty 39:16

49:4 50:5 60:6

65:8 72:9 85:1

89:16 100:25

102:4 118:20

119:16 121:15

124:2 142:25

144:23 150:15

152:16 196:1

202:24 210:25

211:3 235:5

239:23 240:21

249:22 299:17

350:5 360:19

362:19 363:13
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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368:22 383:27

425:27 435:4

453:22 454:5

460:14 497:21

499:6 512:4

prevent 104:27

376:2

prevented 328:8

Prevention 7:14

7:27 11:26

375:13 518:4

prevents 505:6

preview 196:6

previous 16:6

49:25 64:27

74:14 139:16

193:25 214:23

260:21 277:22

279:11 281:25

288:16 291:15

291:16 322:13

402:7,20

424:26 487:19

487:24 502:18

507:12

previously 98:24

113:10 114:13

184:20 277:14

278:22 291:4

398:11 455:2

464:2 511:6

512:6

primaries 72:18

primarily 10:7

27:16 51:19

97:21 128:9

198:16 343:4

401:23 503:27

primary 15:25

71:26 72:26,26

76:4 159:15

347:23,24

361:15 362:6

372:11 493:22

principal 104:25

451:15

principle 58:14

396:18

principles

385:27

prior 4:7,11,12

49:6 102:5

153:22 216:25

217:5,7 218:11

354:4 368:5

413:23 482:23

502:23

priori 396:23

priorities 13:10

14:1,13,26

15:1,7 16:1

17:8 20:10,22

22:6 285:27

prioritization

1:5 3:6 8:24

9:3,10,20,21

11:11 14:9,10

14:15 20:6,16

21:7,15 101:8

126:13,16

138:22 151:22

174:24 177:22

179:12 186:24

187:2 189:4

195:15 201:4

214:11 219:7

246:10 260:7

260:10,23

262:27 263:6,9

267:14 278:12

279:17,19

280:27 282:2

284:17 286:7

286:11 288:25

288:25,27

289:2,5,25

290:20 295:17

300:16,19

301:2 311:6

312:7,24

314:18 327:14

330:10,16

331:14 337:4,5

337:10 339:8

339:17,22

356:5,19

362:20,23

367:4 384:23

415:24 416:2

419:17 424:24

427:18 479:16

479:22,24

481:17,23

482:24 487:12

487:21 488:14

497:20 508:1

508:12,19

prioritize 55:9

127:22 179:25

187:17 214:22

243:20 245:19

246:4 267:16

288:1 290:2

315:5 333:27

336:6 353:1

380:13 412:17

440:9 450:19

452:20 462:27

480:24 481:13

483:24 496:3

496:22 502:11

508:3 514:17

prioritized

10:20,24

161:12,14,16

330:13 342:19

376:20

prioritizing 11:2

135:20 138:18

245:20 278:24

280:11 281:23

287:21,22

288:21 311:7

314:5 342:10

353:5 361:24

362:2 376:12

392:8 469:21

483:9 496:5

497:13 503:12

506:27

priority 18:16

18:18 22:12

23:12,15,15,16

23:22 27:10

128:18 133:5

133:16 138:21

184:6 196:26

196:27 219:18

219:19,20,23

244:10 263:2

263:12,12,13

263:25,26

270:20 271:6

280:14 282:13

282:23 283:5

283:25 285:8

285:13 286:5

288:11 290:2,4

290:14,16,17

290:18 291:6

313:19 316:24

325:4,7,12,18

325:27 328:27

328:27 337:10

355:16 356:10

361:19 362:15

362:21 363:6

364:27 366:14

366:20,22

375:3 396:9,16

397:22 479:12

484:19

priors 216:26

217:5,10 474:7

474:8

private 354:8

pro 196:11

pro-gestational

467:5,7

probabilistic

197:8 352:3

probabilities

27:15 45:1

311:5

probability

144:19 271:21

313:5 489:4,5

probably 22:27

23:5 24:3

25:15 29:27

31:8,15 34:5

36:23 40:4,11

43:5,6 46:26

48:18 63:9

66:1 75:25,27

78:5 79:27

80:7,19 81:13

82:9 83:2

91:14 96:5,19

101:13 103:2

104:17 118:4

118:21 122:16

158:19 173:19

186:21 202:26

226:10 246:7

246:15 247:18

256:23 264:6

265:23 270:26

280:23 296:4

309:16 320:1

329:4 331:6

334:2 344:20

361:5 364:2

372:1 383:21

384:7 385:9

387:17 397:6

406:16 407:21

414:18 420:13

425:10,22

441:19 456:23

458:26 474:24

474:24 482:27

483:5 486:19

494:2 503:1

504:7 505:3

probe 176:18

235:6

probing 77:10

173:27 176:21

probit 455:12

problem 32:8

37:8 41:18

42:17,26 99:22

104:9 144:26

166:24,26

194:21 202:19

203:12 218:2

218:16 233:11

245:12 258:27

389:27 449:18
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 84

450:3 453:17

484:1 511:19

problematic

259:12 261:10

389:21 427:24

512:16

problematically

271:3

problems 57:12

94:21 270:9

293:3 375:27

389:25 482:14

511:20

procedure

161:24

procedures 2:3

3:1 387:17

453:18

proceed 243:24

process 17:10

26:11,15,17,19

34:21 36:11,13

40:19 62:9

86:23 87:3

97:5 101:8

108:7 122:6

176:6 179:1,2

179:10 186:4

206:7 229:1

289:23 290:1,5

296:3,6,11

297:25 309:11

327:1 328:1

339:22 365:3

366:23 370:16

387:6 395:7

412:26 413:9

424:4,5,24

425:8 429:15

433:25 442:23

477:1 485:20

493:13 505:11

505:23 508:18

509:25 510:22

510:26 515:1

516:21 518:7

519:8

processes 56:27

236:17 313:15

349:27 387:15

418:14 437:16

483:1

procymidone

150:8

produce 107:8

112:16 116:10

134:22 154:6

230:11 353:16

512:2

produced 97:24

104:7 111:22

112:13 117:5

395:25

produces 115:22

119:11 162:6

252:3

producing 266:5

453:4

product 202:5

207:16 316:26

production 15:5

15:13 56:4

105:1 143:25

194:25 220:19

221:1 252:1

253:14 485:7

491:5

productive

307:24

products 15:11

34:25,26 90:10

191:18,19,19

201:26 203:8,9

207:3 216:8

326:22 344:1

375:23 506:5

professionals

375:19

professor 5:21

5:27 6:2,6,9,21

189:17,22

190:1,2,5,9

411:6,15,22

467:23 470:7

proficiency

264:23 335:27

336:2 452:26

453:3

profile 325:18

profiles 43:27

319:22 383:11

profiling 295:5

317:26 318:4

320:22 374:19

profound 105:4

progesterone

75:2 78:6

184:10 467:4,6

program 2:7

5:25 7:23,24

8:8,9,17,19,27

9:4,20,24 10:1

10:8,10,10,12

12:2 14:4,7,8

14:20,27 15:1

15:3,24 16:3

16:13 17:7

18:14 19:7

24:14 25:19,22

25:24 27:2,3

53:1 61:1 64:3

94:6 97:13

103:3,15

105:17 124:26

128:20,20

129:17 133:6

140:21 167:20

174:18,26

177:19 189:26

207:15,25

227:20 239:14

277:12 296:18

300:1 308:4

316:23 325:14

343:12 353:19

377:27 380:10

386:9 390:11

390:15 417:2

441:9 443:24

468:10 470:22

482:27 518:3,4

518:9,16,17,20

program's 9:18

programmatic

60:19,23

100:20,24

125:16 174:12

179:9

programs 8:1

64:15 504:17

520:3

progress 13:6

49:16 71:15

84:3 155:16

176:25 178:24

245:11 247:7

310:23 315:17

441:1,13 442:5

442:6 470:8

489:26 490:1

509:18 510:6

progressed

301:20

progressing

122:9

project 9:7

25:19 26:20

60:26 86:14,15

101:21 103:19

167:13 191:10

191:11,11

278:19 294:4

302:3 305:12

projects 103:11

291:8 375:14

517:19

proliferate 39:2

proliferation

34:27 35:2,6

56:4 57:20,22

57:23 73:1

78:8,8 105:12

promiscuity

157:10

promiscuous

158:5 171:4

225:11 298:21

promise 306:2

352:25 384:3

promising

311:18 460:13

463:14

promote 413:12

promoting

310:13

prone 40:23

proof 431:14

446:20 451:15

497:6

prop 160:24

propagate

473:19 488:24

489:10 514:14

propagated

478:26

propagates

242:1,2 397:16

propagating

390:9

propagation

507:12

propensity

430:20

Proper 411:21

411:21

properly 241:17

properties 19:20

83:24 155:15

160:13,16

352:15 455:7,8

455:20

property 300:15

propiconazole

360:5

propionate

148:18 162:17

179:23 181:10

propionate-tr...

181:9

proportional

48:6 49:2

proposals 37:24

296:22

propose 89:17

314:17 356:4

proposed 22:24

100:8 128:7

175:6 256:10

267:15 277:17

278:11 279:12
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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279:22 285:20

287:20 296:25

311:3 378:21

380:2 395:12

414:18 425:14

439:26 440:14

487:25

proposing

483:24

Propper 6:8,8

62:2,3,16,21

62:24 63:4,14

63:18,19,24

67:5,6,12

121:13,14

123:9,10,13,15

124:13,15,17

139:14,15,25

165:7 190:8,8

288:15,17,18

400:3 404:24

406:1 407:2,3

422:7 428:2,3

428:8,12 430:5

430:6,17

433:12,14

438:16 439:9

448:3,4,13

463:3 464:13

464:14 468:17

468:18 469:8

470:6 481:1,2

481:3 483:18

492:19,20,27

493:25,27

494:1,10,13

495:11 496:13

496:19,21

500:15,16

502:15,16,17

507:24,25

508:17,21

513:2 515:13

Propper's 130:6

496:8

proprietary

241:16

pros 196:9

prospective

288:1

prostate 455:2

protect 310:14

375:22 376:23

protecting 8:21

69:2 97:14

484:12

protection 1:13

310:10 330:25

protective 245:6

256:16 361:25

365:2

protein 33:21

34:26 35:1,6

35:23 36:3

37:20 38:4,7

38:23 46:11,24

56:4 66:27

67:3 94:23

99:10,11,23

143:25 158:8

172:26 174:1

225:24 226:23

226:24,27

227:1,3,21

246:23 248:12

266:1 372:15

373:4 379:8

424:14 444:16

454:25 455:17

465:6,12,24,26

466:5 467:14

467:18,25

protein-binding

46:14 226:13

226:14

protein-protein

453:26

protein/protein

173:17 465:10

proteins 38:8

76:13 155:7

168:13 171:8

238:11 344:3

465:14

protocols 386:22

prototype 99:7

prove 113:18

provide 4:12

13:4 14:17

52:8 103:21,25

126:11 203:26

286:23 291:16

292:20 299:10

299:23 312:18

315:8 316:12

316:14 330:15

339:25 342:6

353:4,26

354:12 363:10

386:7,24

388:18 400:8

400:11 413:27

415:13 431:9

431:17 441:25

444:1 445:26

452:22 493:15

513:27

provided 3:27

14:3 165:1

264:23 317:16

401:1 403:27

404:10 406:9

413:3,17

415:25 422:25

424:24 429:11

436:4 441:2,4

441:18,18,26

443:22 500:12

provides 3:19,21

4:5 12:23

119:9 208:23

223:10,24

313:8 386:13

404:27 413:13

442:21 481:12

providing 109:2

198:16 313:6

323:24 366:5

443:26 478:22

483:8

provision

463:18

provisions 3:14

3:27

proximity 173:3

prudent 57:15

pSARM 461:6

pseudo 36:11

37:3 39:7 44:2

44:13 71:4

143:11 144:22

148:15 149:20

151:24 152:2

155:10,18,23

155:26 418:3

pseudo-Attag...

44:23

pseudo-receptor

41:11 44:23

45:2 49:13,16

55:25 56:6,13

66:5 71:6,17

76:15 78:17

83:15,25 84:3

85:15 149:14

152:5 156:27

157:15 158:16

160:2,7 313:4

pseudo-recept...

49:27 50:6

pubertal 130:10

139:11 182:14

267:5 309:4

315:22 338:27

339:2 357:4

406:5 425:11

425:13 431:5

pubertals 304:3

337:18

puberty 121:17

public 2:12 3:17

4:6,7,8,13,15

5:11 11:22

12:16,18 31:27

51:24 85:20,21

85:22 190:4

196:5 299:10

309:14,18

310:2,5 312:24

316:13 352:5

355:25 368:17

375:18,22

376:24 378:7

385:11,22

386:10 396:3

396:21 397:23

398:23 405:3

409:21 411:18

435:2 443:25

447:8 450:22

479:5,10 484:8

514:25 516:16

516:20 517:11

518:5,6 520:7

520:9

publically 8:13

10:9 233:20

444:3

publication 9:24

10:2 206:9

261:25 278:5

291:18 318:9

363:19 403:1

publications

204:14 278:6,7

386:27

publicly 265:16

published 10:3,6

10:20 86:8

137:25 184:14

207:9 215:21

241:14 262:13

317:25 318:25

322:13 327:3

330:17 345:3

381:1,6 387:1

399:21,27

459:26 460:5

460:14 461:3

462:3 511:6

publishing

513:23

pull 82:3 290:19

349:15 384:12

pulled 303:10,13

340:20 513:3

514:7

pulling 37:5

punt 218:23

pure 146:23,23
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147:9,14,15

148:17,17

173:12

purely 54:5

146:24 489:1

purity 120:7

123:1 334:25

387:26 402:21

403:7 445:21

461:21

purpose 93:8

108:8 135:11

300:1 315:3

323:22 384:22

389:21 427:17

433:9 453:11

470:12 477:25

purposes 177:22

283:15 312:7

373:18

pursue 288:11

325:25 405:22

pursuing 325:8

331:13

push 194:4

234:22 258:9

pushed 70:11

478:12

pushing 506:21

put 23:24 37:23

55:3 58:26

74:11 77:17

84:22 85:16

87:10 91:24

92:5 98:20

99:16,18 107:6

107:26 108:25

109:13 119:2

121:23 122:27

135:8 138:24

139:6 160:19

163:20 164:13

171:15 173:26

191:21 198:27

199:2 218:11

222:19 229:27

232:23 234:4

244:4 247:2

250:11,15

254:13 255:6

256:11 273:11

278:17 284:24

290:24 294:25

296:22 297:9

299:23 304:20

307:10 316:19

316:21 318:7

330:2 346:13

349:22,26

364:11 368:7

371:4,18 373:6

374:5 381:24

381:27 401:18

413:8 415:1,26

417:4 428:17

433:18 438:11

466:13 468:9

469:10 470:8

481:26 489:11

513:20 517:5

PutA 99:23

puts 243:13

246:24

putting 18:2,4

21:2 43:11

88:23 110:22

163:23 171:7

174:1 177:21

242:5 263:21

287:26 323:17

400:18 417:21

455:15 502:23

puzzled 424:2

puzzling 429:25

431:1

PXR 261:27

262:1,2

pyrethroids

406:22 414:3

Pyridaben

284:10

Q

QA/QC 390:25

426:4

QC 116:1

265:16 393:8

QL 229:4

QSAR 19:21,26

20:7,12 83:1,4

83:10,10 85:26

86:4,8,17

87:21 89:5

91:7 94:6

97:17,24,26

98:7,10,21

101:6 160:15

160:18,20,22

273:3 302:3

373:10 383:8

QSAR-ready

86:19,24

quadrant 79:16

154:14

qualification

322:19

qualify 322:17

qualitative 15:1

15:8 22:7

24:10 94:8,8

94:12 116:22

358:20 359:10

359:15,23,26

390:19

quality 88:4,17

89:15 261:4

265:12,15

266:3 275:18

279:4 327:27

334:15,27

360:15 381:5

387:16 399:26

445:20 460:4

460:16 498:15

518:6

quantification

195:16

quantified 39:23

quantify 271:11

271:17 425:3,7

487:27

quantifying

380:3 439:26

440:14

quantitation

455:18

quantitative 9:8

14:19 21:14

22:7 87:22

88:26 92:14

94:8,9,12

100:23 110:16

118:17 243:17

265:6 314:3

323:6 390:19

431:12

quantitatively

420:16

quantity 116:6

488:17

quarter 135:4

question 2:13,14

2:15,16,17,18

2:19,20,21,22

2:23,24,25

12:23 13:17

16:24 17:5

21:24 23:11

24:25 26:12

28:5 29:4 37:6

52:1 53:18

57:7,26 59:5

59:27 61:4

63:26 64:5

65:21 66:25

68:14 70:14,15

70:26 89:12

90:13 91:1

92:12,24 94:6

94:16 95:8,19

97:16 98:15,23

100:1,11

101:12,23

103:24 107:27

113:20 122:13

128:16 130:6

138:2,14,17

140:1 157:7

160:1 161:23

162:11 163:12

163:12 164:18

165:6,7 167:23

167:24 168:1

168:26 169:6,8

169:8,9 171:3

173:10 174:6,7

197:18 200:6

216:19,24

217:3 221:18

236:8 238:2

243:13 258:8

260:3,17,18,21

261:17,22

262:18,27

266:8 267:13

269:6,18 270:6

288:15,19,27

291:15,15,27

292:2,27 294:2

294:17 299:2

305:11,17

307:1,3 314:24

324:13 325:1

329:3 331:5,10

333:8 334:13

336:4 337:12

337:21,21

338:16 351:3

351:25 378:10

378:17,19,24

379:24,24,27

380:19,23,27

381:8,15 388:5

388:22 390:1

391:15,17,20

392:12,15,19

393:4,21,23

398:23 399:15

399:18,20

400:2,6 402:7

404:18 408:1,2

409:1 410:6

412:8,9,10,12

412:20,23

417:1,8,9,12

417:13 418:27

419:15,24

421:19,21,25

422:6,13,14

426:12 427:3
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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431:26 432:3

432:16,27

433:2,12

435:17 438:24

439:12,13,14

439:16,19,20

439:21,23,25

440:15,20,23

441:20 443:8

446:9,16

447:12 449:9

452:2,7 454:7

454:12,13,15

454:20 457:16

459:17,20,23

459:25 460:7

462:15,22,24

463:3,8 464:26

467:13,23

468:19 469:16

469:24 470:17

470:17,19,21

471:10,15

480:18,20,22

495:27 496:11

496:12,17

497:10 498:10

498:11 499:19

499:20,21,24

499:25 500:10

500:17,23

501:2,5 502:4

502:7,9 503:4

503:10 507:9,9

questionable

383:24

questioned

435:13,18

questions 5:12

11:14 12:15,25

21:19,21 23:10

27:18 28:14

37:17 50:18,24

51:5 53:8

54:27 57:11

67:14 68:19,20

77:19 90:5,27

92:10 94:3,14

94:15 101:3

102:7 119:25

119:26 121:12

122:12 123:8

124:8,19

126:10 136:18

136:20 139:13

140:7 157:14

161:19,21

175:1,7,17

187:20,22

204:16 207:2

216:22 218:13

218:22 220:19

221:1 223:4

235:12,14

236:4 242:10

242:24 257:7,9

257:13 259:24

260:5 261:13

264:9 267:19

267:23 268:3

276:22 278:10

288:13,14,16

290:8 299:7

304:24,25

305:1 308:16

316:6 324:10

327:10 328:27

331:3,8,9

338:2 342:15

352:27 354:19

365:18 366:1

367:6,8 375:6

378:15,16

379:18,19,20

380:17 392:12

392:20 402:20

404:11 407:15

423:10 424:2

432:22,23

440:13 441:22

442:19 450:22

454:10 459:7

462:19 471:8

481:21 485:6

486:2 488:7

500:27 502:19

508:24 519:5

quick 10:12

57:25 91:1

160:24 161:23

267:10 268:1

288:18 324:12

325:1 414:15

453:9 478:5

493:26

quickly 34:22

37:15 67:23

68:9 126:19

141:27 149:11

180:2 183:3

187:19 296:19

296:21 376:1

517:2

quilt 360:12,14

361:12

quilts 360:8

quintiles 213:19

quirks 141:10

quite 24:17

41:15 48:23

51:3 58:1 68:4

82:3 95:12,13

103:6,8 110:5

112:13 123:15

126:10 131:12

133:22 156:21

161:6 177:18

181:15 182:17

184:24 219:15

228:21 233:22

251:4 254:10

263:24 270:10

274:11 283:2

290:11 291:16

304:18 308:27

319:9 331:24

332:18 333:24

334:26 335:2

335:11 336:2

336:12 337:6

338:9,21 339:5

428:23 429:1,6

471:21 477:7

489:2 513:22

513:25

quote 192:10

332:25 408:8

quotient 318:21

quotient-based

486:11

R

R 48:17 67:1,4

142:9 233:22

237:20

R-code 328:7

R-squared

220:15

R1 41:8,22 62:8

62:16,17,22,27

136:27 137:4

143:20 148:23

151:27 380:8

440:5

R2 137:5 144:9

144:20 149:25

152:1 153:1

179:24 380:9

440:5

R3 152:3

R4 48:16 65:7

155:24

R6 40:18

R7 152:3,6

156:10

R8 41:11

racial 477:9

rack 466:26

radar 111:14

135:7,8

radio 443:6

radio-labeled

162:21 163:18

163:22

radioactive

35:22

radioactivity

35:26

radiolabeled

35:22,25

radioligand 36:5

37:18 142:4

155:8 162:19

162:20 163:26

radioligand-bi...

35:20 76:9

rah-rah 487:4

rainbow 51:6

304:12 424:3

raise 28:11

376:10 377:2

417:6

raised 25:2

267:13 377:5

377:21 385:11

398:23 469:15

472:8 488:8

raising 447:24

Raloxifene 46:5

79:1 93:7,19

ran 38:1 94:25

94:25 99:5,13

110:10 249:18

325:5 373:9

random 215:7

232:17

range 25:21

45:26 55:16

78:15,26 84:15

91:17 93:2

95:25 112:14

114:1,10,19

115:2 123:24

123:24,26

129:7 132:26

139:22 150:19

152:19 154:4

168:18,25

171:14 179:4

184:4 186:20

193:1 197:14

209:13,16

211:14,16

213:11 223:27

225:13 253:6

281:21 295:7

301:17 334:11

334:19 343:9

344:25 348:9

351:12 353:7
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380:26 382:14

382:15,19

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451:24 454:18

455:27 456:6

464:3,4 470:27

471:1,6 480:25

480:26 481:16

481:21 502:12

502:13

range-finding

491:14

ranged 112:7

ranges 49:5 81:1

96:26 97:10,13

117:23 341:10

353:3,3 359:15

373:19 435:14

471:4 481:17

ranging 230:16

rank 110:27

114:3 115:10

212:20 256:14

275:24 279:18

279:20 280:1

470:26 472:6

476:25,27

507:2

ranked 254:27

291:1 476:22

ranking 2:9 13:2

13:10,20,21,27

20:21,27 24:5

133:19 218:16

218:17 243:1

246:1 261:20

277:15 281:27

282:2 283:7,8

283:14,24

284:2,6,11

285:5 287:25

289:26 311:7

359:12 387:2

419:20 472:4

477:26 479:16

479:22 497:13

rankings 219:25

287:18 314:9

354:6 471:5

472:9 493:24

ranks 286:22

rapid 277:24

372:14 420:20

rapidly 17:22

116:5 192:4

203:22 226:8

311:11 420:15

433:23

rat 105:24,24

106:16 110:18

110:20 111:21

112:4 124:3

142:8 235:3

238:21,22

265:26 348:25

358:19 403:12

404:14 406:11

413:25 451:7

rate 171:26,27

227:4 228:3,8

231:12 373:8

374:23 477:12

505:25

rate-limiting

286:17

rates 34:6

313:12

rating 334:22

ratio 22:24 91:4

91:5 240:17,18

246:1 252:7

256:14 260:23

261:1,20 271:1

271:14,19,20

285:19 318:13

318:17 325:20

353:2 470:27

471:5 472:7

480:25 481:22

483:3 484:7,9

488:21,24,25

489:3,5,10

502:12

ratio-based 24:6

rationale 39:7

96:20 164:2

320:18 383:22

rationalize

23:17

ratios 24:12

278:16 288:3,8

319:8,10,14,27

353:25 472:4

472:10 499:8

rats 109:21

235:2,4 238:21

303:5 348:26

348:26 400:23

400:26 461:22

raw 54:13 80:15

249:16,17

294:20,22

398:12

Razor's 396:18

re-evaluated

396:24

re-iterate 472:6

re-release

371:18

reach 111:26

114:15 173:20

238:26

reaching 202:3

reacting 271:6

reaction 77:12

read 33:10 47:16

48:12 64:7

130:17 237:17

255:3 262:19

263:16 268:24

284:16 332:25

351:15 359:19

378:9,13

379:24 384:19

384:27 391:14

392:20,24

399:15 409:21

412:9 439:20

459:21 498:11

500:13 502:4

read-out 35:15

35:16 38:24

read-put 40:21

readability

130:22

readily 17:9

443:19

reading 48:13

262:21,23,24

263:22 285:9

401:25 412:24

readout 38:16

71:27 227:11

421:5 453:24

readouts 38:13

71:27 72:17

157:9,16

377:15

ready 26:6 87:7

177:6 216:19

300:2 310:25

355:1 470:16

real 27:4 30:7

33:11 36:23

71:23 74:14

75:25 78:13

103:24 134:7

135:2 141:14

141:15 155:10

168:25 171:3

207:14 241:8

253:20,25

263:2,9 264:20

268:23 270:25

273:20 283:6

295:25 297:25

314:4 342:14

346:27 414:20

423:1 426:7

518:20

real-time 39:1

57:19

real-world

344:24 377:7

reality 435:15

468:15

realize 22:26

343:9 344:27

385:27

realized 53:19

121:27

really 5:3 13:8

17:16 18:15,25

20:2,9,16 22:3

22:5,6,10,19

25:5 27:7 29:7

30:7,11,27

31:2,12,12,13

32:24 33:1,4,8

35:18 36:26

37:4,10,16

38:14 42:16,25

44:5,25 45:1

45:21,22 46:2

46:7,19 48:6,9

49:15,21 55:25

57:10 61:23

62:18 66:12

68:9 69:7 71:8

71:11,12,15,20

72:21 73:26

74:5,26 76:15

79:9,16,21,23

80:17 81:24

82:14 83:10,19

83:21,26 87:4

87:27 88:12,18

90:25 91:16

93:5 98:4 99:3

99:3,26,26

100:1 103:18

104:9 105:13

107:3,21,26

108:3 109:3,10

109:12 113:23

114:15,22

115:20 116:20

116:25 118:7

118:22 119:17

123:3 124:7

125:18 126:8

126:22 127:5

127:19 129:8

129:25 130:8

131:4,15 132:6

133:1,4 135:11

136:2,14
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149:5 150:20

153:22 158:26

165:9 169:3

171:10 172:9

173:10,27,27

174:6,7 175:10

175:13 176:4

178:4 179:5,12

180:19 182:7

182:13,21

183:25 187:5

187:17 191:26

192:1,7,25

193:25 194:18

195:4,25

196:16 197:16

198:7 200:20

201:13 202:21

204:1 211:22

211:22 214:14

214:24,25

215:19 218:11

219:13,15,21

220:27 222:23

225:15,16

227:13 230:2

230:16 231:9

231:14 232:27

238:15,16

239:12 241:18

243:11,27

244:25 245:5

245:10,11

246:2 247:7,27

248:18 249:14

254:6 255:12

256:17,25

258:26 259:6

260:7,9,17

261:13 262:19

265:8,16

266:10,25

269:11,26,27

270:3 272:25

275:4 276:21

277:12,16,19

278:10 279:10

279:15,16

280:12,19,25

281:11 283:3

285:5 286:22

286:24 289:12

291:20 293:21

293:24 294:26

295:13,24

296:15 298:8

299:6,22 300:2

300:8,13 301:8

303:21,27

304:17 305:10

307:1,2,17,22

307:25 308:1

308:18,25

309:6,9 321:17

321:18,22,26

322:26 323:17

325:21 327:9

327:14,18,24

328:5,10,15

329:11,16,27

330:11,13

331:1,13,15,22

332:1,6,8,16

332:27 333:13

333:21 336:8

336:17,26

337:2,22 338:2

339:10,19

341:4,17 342:8

352:7 355:26

357:5,26

358:24 359:8

360:24 361:6,8

361:18,22

362:12,14

364:23 366:10

367:20 372:9

376:12,18,22

377:8,10,16,19

378:2 381:21

381:21 383:1,4

384:10,17

385:3,15

388:14,25,27

404:6 409:27

413:20 415:26

416:7 420:22

425:10,16

429:2,6 430:12

433:25 435:5

435:25 436:24

437:8 438:9

441:4 442:27

446:20,21,22

446:22 447:9

448:6,17

452:19 456:12

457:1,16 458:1

464:2 465:11

465:12 466:6,8

466:10,13,22

468:8,9,13,15

468:25 475:23

476:19 477:14

477:19 480:9

481:4 482:16

482:17,24

483:7 484:19

485:8,27 486:5

486:25 487:13

487:18 491:23

492:9,10

496:14,14,24

498:14 504:1

504:21,24

506:15,16,20

506:21 508:15

509:18,18,26

512:14,19,21

512:23,24,26

513:12,23,24

514:1,2,16,25

515:6,9,27

516:6,9,12,17

517:13,24,25

518:17 519:18

realm 468:15

realms 97:27

reason 30:26

32:20 74:8

82:12 103:18

111:9 116:3

160:7 169:23

170:10 186:24

209:20 213:16

215:19 224:25

231:24 235:20

253:27 255:8

263:21 277:23

298:11 322:5

346:9,18

347:14 390:5

420:8 424:4

465:9

reasonable

258:25

reasonably

133:16 137:21

289:14 368:14

reasons 24:6

25:18 32:19

34:3 50:14

86:3 90:15

96:3 100:18

115:25 171:24

213:15 237:20

287:11 344:26

346:8,20

434:21 453:1

reassessed 312:1

recall 446:14

recap 136:16

246:12

received 15:18

326:18

receiving 238:8

receptor 2:5,8

13:13 19:19,25

19:26 25:20

26:23 31:2,10

33:12,15,15,16

33:17,21 34:1

34:16,18 36:3

36:7 41:3,7,8

41:20,20,22,24

41:26 42:1,4,7

47:8 52:13

55:5 56:2,20

58:7,10,22

59:1,2,3,6,11

59:15 62:11

64:6 65:7,12

69:6 71:19

72:10 74:17,27

75:7,10 76:8

76:24 77:10

79:10 80:22

81:20,21 82:24

83:7 84:7 86:8

86:15 90:1

96:19 99:14

102:23 103:12

108:6 113:16

119:7,17

125:23 130:3

137:17,20

140:24 141:2,4

143:22 144:10

144:20 145:1

146:25 149:8

149:20 150:1

150:24 151:2

152:2 154:13

155:18,26

157:12,19,26

158:1,5 161:26

162:12,27

163:2,13,15

165:9,17

166:17 167:1

167:11,17,17

167:21 168:4,5

168:6,9,14

169:4,5,13

170:13 171:12

171:23 173:2,3

173:4,15,18,23

175:10,12,16

175:18,27

176:11,19

177:13,24

178:7 184:10

184:11,16,16

201:21 243:16

247:20 249:15

249:18,27

252:25 253:4

253:19 254:19

255:14 264:25
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265:9,26 273:3

273:11 287:15

291:4 293:23

296:26 297:17

297:17 298:5

298:12 300:14

303:1 307:15

308:6 310:22

310:22,27

336:19 343:5

343:15,16,20

345:2,14,16,18

346:15 349:5,7

358:18 365:6

377:18 395:8

395:10 397:15

397:20,26

398:1 417:22

418:3 423:6

426:17 427:4

430:26 433:22

434:1,3 437:14

440:2 441:8,23

442:4,4,7,9,24

443:4 444:17

444:22,24,25

451:16,24

452:13,17

456:15,17,23

457:13,20,23

457:25 461:8

463:9 466:5,24

467:3,8 470:9

470:23,23

receptor-base

282:8

receptor-based

25:20 167:5

receptor-bindi...

26:14 79:10

292:15 453:27

receptor-driven

165:22 166:24

166:27 167:1

receptor-medi...

19:10 25:4

29:12,25 76:1

130:10 165:23

167:12 176:14

287:6,7,12

395:7

receptor-orien...

176:5

receptor-relev...

130:9

receptor-speci...

313:3

receptors 36:11

37:3 39:7

42:11 43:9

44:1,2,13

49:27 50:9

66:5 71:4,26

78:4 143:11

144:22 148:15

151:24,24

155:24 157:11

157:13 171:5

173:22 175:22

175:27 384:24

398:24 412:26

414:24 417:25

430:27 456:21

466:16,26

467:4

recess 67:16

102:11 140:11

354:25 459:18

501:7

recipients

326:17,24

354:9

recognition

289:24 496:4

recognize

122:16 125:25

134:3,7 228:24

228:25 286:14

298:25 318:26

335:25 376:4

387:17 434:26

456:7 464:10

481:25

recognized

398:6 469:18

482:21 498:7

recognizes

149:23 437:20

484:6

recognizing

470:10 485:19

489:27

recombinant

53:12,17 59:5

64:11,11 266:1

333:17 454:25

465:6,10,12

recombinant-...

53:10

recommend

146:5 415:12

424:16,18

426:13 437:25

439:6 445:19

459:12

recommendati...

232:17 271:24

271:26 303:11

303:17 323:25

323:27 386:10

400:25 418:20

445:12 467:24

recommendati...

3:21 121:16

126:2 146:7,18

186:15 241:9

244:4 278:23

279:11 281:25

317:5 318:3

379:22 389:12

407:14 446:9

454:8 458:20

459:8 464:22

490:8

recommended

97:7 445:25

recommending

499:15

recommends

106:6,10

reconsider 24:3

reconstituted

454:25

reconstruction

323:9

record 7:4,5

67:18 68:22

189:15 222:19

329:13,18

378:10,13

379:24 386:13

389:6 393:9

401:19 409:21

415:1 496:8

502:5

recorded 7:1

recruit 33:18

recruiting

167:25

recruitment

33:24 34:2

69:11 142:15

143:23 144:13

151:16,20

155:1 165:2

169:11,14,23

170:6 172:19

174:11

recruitments

70:5

recruits 35:5

red 39:20,21

40:10 44:3,3

46:7,17 48:3

48:23 63:20,22

73:13,22 74:7

74:18 75:4

76:20 82:7

88:13 111:2,17

112:19 115:18

128:15 147:12

148:13 179:23

183:15 209:22

231:21 252:7

252:19 256:3,9

274:2 319:11

redder 39:20

reduce 103:5

110:9 126:19

394:20 439:5

475:15 485:21

reduced 314:9

reduces 448:2

510:24

reducing 310:15

316:1

reduction

454:26

redundancy

26:12,17 27:5

52:22 53:4

59:26 64:2,4

64:15,23 68:26

68:27 69:13

134:6,9,15

135:16 161:5

296:8 298:1,13

301:26 388:21

388:21 413:7,8

413:11 417:2

417:17,18

418:11,13,23

419:4 423:24

443:7,8,9,13

443:13 446:4,7

446:17,23

448:8,10 451:5

451:13,14

453:11 455:14

455:23

redundant 21:1

298:3,23

388:24 418:19

422:16 426:25

448:11

refer 10:4,10

52:26 191:9

203:26 350:27

367:18 434:17

493:2

reference 30:17

47:4,5,7,11,14

47:26 55:21

60:9 61:4 79:4

84:6 89:26

90:21,25 91:24

92:8,18 99:20

104:10 108:11

109:9 110:8,10

113:2,3,9,26
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113:27 114:3

115:10 119:4

121:19 127:1,9

128:22,23,27

129:2,9,12,23

129:24 131:27

132:20,27

133:2,8,10,14

133:16 134:17

134:17,20,21

134:24,26

135:1 136:8

141:20,25

145:26 146:2,5

146:9,17,21

147:2,5,7,15

148:5 149:12

150:3,12 151:3

151:12 161:8,9

161:10 162:7

172:16 174:15

174:22 178:20

178:21,26

179:6,8 180:1

180:3,3,9,15

182:4,8,8

183:15,22,26

184:5,20,21

185:20 186:1,3

186:4,7,13,19

187:1 266:16

280:18 291:24

301:16 313:11

314:22 319:17

322:23 334:8

334:11 336:7,8

336:10 341:10

374:10 380:24

389:26 393:25

395:16,20

396:8,15

397:17,19

398:20,20

405:18,18

412:14 413:2

413:17 421:27

422:21 432:6,8

433:4 434:11

435:5 437:1

449:23 454:17

459:12 468:19

468:25 469:17

483:2

referenced

26:27 30:14,20

183:16 456:18

references 9:22

92:26 382:24

389:23 476:4

494:9,11

495:25

referred 24:19

96:26 468:24

referring 351:10

382:3 450:8

463:17 475:23

498:22

refers 106:4

202:6 396:20

473:6,10

refine 103:5

109:26 138:15

235:9 306:10

362:8 372:24

refined 10:23

279:23,25

282:7 330:18

354:10 374:20

374:21 377:13

496:5

refinement

126:23 442:14

444:1 483:10

496:16 502:22

refinements

302:8,11

330:11 364:14

366:4 477:24

refining 317:7

518:20

reflect 334:8

343:27 471:19

reflected 126:6

222:16 444:10

reflection

377:20 445:10

reflective 484:10

reflects 203:17

365:2

refresh 221:19

refreshing

428:23 511:22

regard 186:15

293:15 331:5

332:5 351:24

357:5 386:21

386:25 425:17

445:11 490:16

490:21 493:11

regarding 51:5

222:21 313:7

331:23 334:4

338:3 339:2,25

376:7 378:15

397:24 424:20

431:26 434:5

437:10 439:14

439:15 443:18

446:9 463:26

475:3 476:17

483:25 489:20

490:4 492:6

regardless 73:2

503:22

regards 312:5

regime 229:22

229:22 305:17

regimen 307:1

region 63:20,21

72:22 73:20

74:1,16,18

75:27 76:15

78:7,12 79:14

80:8 83:9

85:14 96:5

149:2,9,27

150:8,16 153:9

153:20 154:16

154:22 156:6

157:3,4 158:23

158:24 159:5

159:17,17

173:22 274:9

274:10 343:22

343:27 352:19

352:19 497:14

register 360:26

registration

15:21 351:21

357:10 365:14

366:9 370:16

493:13,14

494:3

registry 87:15

regression 65:25

204:10 211:23

389:24 395:13

395:13

regular 228:5

regulated 15:24

56:20 254:5

511:13

regulation 15:25

326:18

regulations 3:25

regulatory 15:2

27:3 60:11

79:22 257:15

257:17 260:17

260:19 310:13

312:21 316:2

316:26 326:26

355:4 374:26

reinforce 140:25

reiterate 165:26

176:18 296:17

333:9 424:22

424:26 515:27

reiterates 19:17

relate 12:25

175:10 225:19

261:5 331:7

336:19

related 4:17

12:25 26:12

159:1 199:26

257:12 261:12

264:24 266:4

268:3 311:22

320:9 363:24

364:18 371:24

418:4 421:12

422:25 426:4

426:11,15,16

426:16 437:10

441:22 444:7

444:15,24

457:21,25

458:2,25 461:6

486:23 487:6

511:11

relates 64:23

97:2 122:14

342:16 345:2

482:26

relating 24:14

398:13 407:24

relation 173:13

476:17

relationship 9:8

93:12,21,24

118:17 167:16

313:21,23

333:25 339:11

376:27 395:7

397:25 431:15

463:26 485:8

relationships

217:14 384:11

396:5 398:1,3

425:3

relative 26:27

36:1 60:13

168:23 181:6

182:7 248:17

248:22 264:17

279:27 281:4

283:26 285:18

293:7 318:20

319:2,22

320:15 321:3

335:16 336:9

336:18 337:18

344:7 347:20

347:25,27

348:20,23

359:7,13,27

384:6 418:5

442:16 461:11

491:5
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relatively 34:21

44:14 84:24

89:7,27 126:19

127:7 134:16

152:27 153:2

153:14 187:19

196:20 199:9

209:9 243:14

256:22 273:1

277:23 283:21

284:1 296:19

307:11,12

381:20 388:6

408:4 467:5

469:17 472:25

490:21

release 201:23

202:8,11,12

303:9 317:8

323:13

released 16:7

releasing 303:2

relevance 108:7

108:10,15,15

337:2 355:27

356:16 359:11

359:21 417:23

relevancy

435:16

relevant 8:15

12:23 20:8

96:9 97:11,14

109:20 127:27

128:9 135:12

141:16 151:6

152:12 154:17

162:6 170:2

179:18 259:24

260:1 305:10

306:22 310:13

312:19 328:15

338:1 345:16

356:9 357:11

373:10 377:7

380:17 407:4

440:13 454:2

454:26 464:9

471:8

reliability 108:7

108:11,14,18

108:26 109:3,6

264:19,24

265:25 267:8

275:3 280:20

reliable 61:3

107:23 109:15

266:5 310:13

313:16 484:19

relies 356:26

511:23

rely 311:1

relying 58:9

333:18 368:15

377:24

remain 304:23

365:18 442:19

remained

123:26

remaining 177:2

471:23 506:3

remains 3:22

437:10 497:14

remark 397:24

499:24

remarkable

129:26 280:19

336:12 338:21

401:2,8 509:26

509:27

remarkably

364:21

REMARKS 2:4

7:10

remember 52:14

64:3 67:27

84:27 148:8

211:22 213:24

221:16 228:9

245:5 256:1

264:12 294:8

324:19 331:18

348:22 349:19

353:21 387:25

424:10 473:9

498:22 512:25

remind 148:12

154:3 291:19

330:27 507:10

reminded

437:22

remove 228:5

removed 130:25

143:2 369:18

removing 104:4

325:2 338:7

382:4

renal 229:3

231:7 238:12

373:4

repeat 154:2

repeatable

108:19

repeating

381:19

repeats 466:27

467:1

repetitive

143:17 481:7

rephrase 294:16

replace 17:27

103:5 306:1

309:11 310:25

311:16 395:26

434:5

replaced 226:4

replacement

433:26 434:8

439:6

replacing 18:21

338:16 431:19

replicate 249:23

249:23 398:13

460:14

replicated

346:10 347:3

replicates

249:19 270:1

398:8

replication

269:15 451:15

453:4

report 4:1 84:13

84:21 85:4

90:19 99:5

107:9,15 120:7

126:3,14 127:8

132:22 146:14

177:27 180:1

182:21,23

183:6 195:13

278:23 279:3

303:10 315:12

471:18

reported 85:11

107:5 110:25

112:14,20

113:13 114:8

122:22 123:7

123:23 279:3

378:12,12

390:27 395:17

408:10 457:22

reporter 38:24

58:18,19,20

59:6,9 76:19

77:7

reporters 38:25

59:3

reporting

227:18

reports 4:3

137:25 146:3,6

146:11,16

153:18 346:12

469:10 506:10

represent 22:16

22:23 111:16

114:7,9 198:25

271:2 277:12

293:18 301:17

441:9 455:15

representation

8:14 22:19

313:5 464:6

471:26 500:4

505:17

representative

62:6,17,18

198:24 222:15

228:3 274:15

274:17 363:13

504:3

representatives

146:12 347:12

represented 8:4

16:20 21:25

22:13 62:8

112:5 279:4

362:26 443:11

505:8 519:27

representing

115:11 198:5

represents 8:12

21:25 111:2,15

112:5 114:2

115:9,13

117:13 179:23

214:3 316:24

324:3 334:20

337:13 395:6

437:4 472:14

472:15 488:13

reproduce

233:27

reproducibility

267:8 445:24

reproducible

30:3 266:23

327:18

reproduction

304:3 315:23

342:5 347:5,15

349:9 357:22

357:22

reproductive

121:25 189:17

222:5 267:6

347:24 357:2

411:6 495:5

request 37:24

279:21 317:18

requests 296:22

require 312:12

339:6 347:2

354:7 414:1

415:22 464:11

required 106:12

123:1 345:14

364:23 365:13

446:19,21
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requirement

370:16

requirements

4:4 51:22

requires 17:25

18:24 110:6

461:27 498:4

rerun 231:18

269:22,25

research 5:18

6:12,12,14,16

6:16,18 8:2

25:19,22 26:20

27:3 51:20

52:5,6 60:25

125:7,13 140:6

141:24 167:12

174:19 177:20

189:21 190:12

190:16,18

191:3,23

207:25 224:17

235:1 286:18

300:4 347:22

375:9,13,16,18

375:26 411:10

411:25,27

412:2,4 427:20

518:10,14

researcher

233:25 237:23

researchers

237:10

resemblance

355:20 395:11

residential 15:10

residual 212:11

residue 15:10

175:26

residues 191:19

202:17

resolution

290:13 314:19

resolve 140:2,6

resource 10:9

295:25 452:21

504:3

resources 17:25

278:17 287:1

325:10 354:7

378:2

respect 313:10

315:12 339:7

349:1 353:24

395:20 396:9

428:22 451:24

468:11,26

503:4

respective

147:16

respectively

283:26 288:10

respond 52:20

60:22 289:21

295:27 417:15

responds 464:7

response 8:20,26

9:16 14:17

20:15 25:17

31:26 35:9,9

36:6 39:25

43:17,27 52:8

53:23 54:19,22

68:6 99:24

104:26 106:27

107:4,8 112:12

112:16 116:8

117:6 124:3

141:16 144:6

149:7,18 150:9

151:17 153:23

158:23 159:19

163:9 165:27

168:25 172:9

172:23 174:6

176:12 179:20

181:14 182:15

182:19,27

183:1,8 184:14

247:21,22

248:8,9 261:15

262:12 266:8

273:24 280:24

285:13 299:1

320:21 335:16

335:17 337:16

339:18 343:1

369:10 378:10

379:9 381:4

394:2 399:24

406:12 407:6,9

420:26 422:18

426:5 427:26

429:15,17

434:24 441:9

448:20 449:21

461:5 466:26

466:27 498:16

499:25 500:17

responses 92:21

114:1 116:10

129:13 141:13

141:14 153:7

153:11 158:21

159:27,27

162:6 182:14

182:14 287:17

347:5 349:14

381:17 403:4

404:15 405:7

405:12 422:23

425:17,18,26

426:3 428:22

428:23 431:11

449:17 457:19

457:25 458:7

460:3 463:7

464:12 465:16

498:15 514:12

responsibility

142:9

responsible 3:13

195:19 241:22

310:9 347:24

responsive 506:9

rest 11:4 108:15

134:27 148:14

149:11 197:19

212:3 221:23

225:7 389:12

405:20

result 87:23

89:10 90:3

100:26 106:11

111:25,26

112:13 113:13

119:10 139:10

139:11 151:4

217:7 266:24

311:14 337:14

346:5 350:7

362:25 391:2

418:24 437:13

443:10 451:10

resultant 359:25

resulted 105:21

107:22

resulting 14:11

129:7 229:20

233:5 240:15

408:11

results 11:7

18:27 27:16

40:12 47:25

69:24 81:15

84:11 97:11

100:13 104:2,7

104:8 105:6

107:13 108:1

108:20 109:6

109:26 110:25

111:11,12,23

114:11 127:26

129:19 130:25

134:22 135:3,6

139:21 141:5

146:10,17

147:5 155:17

157:21 158:10

160:22 171:9

179:2 180:5,6

180:22,26

182:1,2,3,25

191:10 233:27

266:14 267:17

274:13 295:14

308:5 312:6,24

313:11 314:24

314:27 329:25

329:26 330:8

337:13 338:20

338:22 339:1

339:18 349:4

351:6 369:4,25

382:7 390:8,17

395:17,24,25

396:12 397:7

398:19 400:27

412:15 416:1

416:11 422:1

433:5 435:11

435:12,24

436:2,5,17

445:24 446:2

448:18 455:10

461:3,26

463:13 464:4

464:10 478:10

480:2 483:4

491:11,17

507:11 512:4,5

retain 61:15

retained 491:9

retention 105:12

retesting 150:19

return 61:5

returning

191:27 299:20

347:18

returns 329:4

revers 416:16

reverse 12:26

19:24 21:4

203:13,24

204:4 223:20

244:27 249:2

251:15 372:4

378:27 379:6

reversed 117:14

453:27

review 2:5 3:19

7:22 10:15

12:27 13:8

15:21,22 20:19

23:20,26 25:3

32:15 102:22

122:7 128:4

179:2 181:24

218:7 234:3

235:11 277:24
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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278:11 279:21

287:20 323:9

328:6 334:14

334:16 338:14

355:26 357:27

358:20 403:24

426:9 441:10

reviewed 4:3

10:22 192:1

277:22 278:5

356:15 512:25

reviewers

181:19 317:13

reviewing

100:13 380:11

440:7

reviews 16:6

19:14,15 86:4

315:22 512:9

518:2

revise 215:24

365:16

revised 286:15

revision 320:22

revisions 286:19

revisitation

144:16

revisiting

186:27 287:26

revolve 173:10

rhythm 73:12

rich 286:26

Richard 28:4,5

28:15,21,26

50:20,23,27

53:6,13,16

54:4,20 55:10

56:9 57:7,17

57:23 58:3

61:18 62:9,19

62:22,25 63:8

63:16,19 64:9

64:16,25 65:2

65:5,10,19,23

66:1,11,17

67:3,9,22,25

68:3,18,24

69:20 70:1,19

70:27 90:12

91:7,22 92:7

93:3 94:1,11

94:22 95:18

96:10,26 98:3

98:7,20 100:9

101:9,18

102:26 107:26

108:26 114:12

116:26 120:19

127:8 128:5,11

128:23 140:27

143:18 144:17

154:2,24

160:27 165:25

169:7 171:17

176:17 192:17

219:25 243:4

257:10,15

258:5,14

259:13,18,21

265:4 267:26

268:21 269:17

270:21 272:3

292:22 293:20

294:3,16

295:15,20

296:14 303:6

305:11 306:24

316:11 324:18

325:5 364:1

391:7 419:23

421:2 507:18

508:14 517:15

519:21

rid 27:6 446:22

ridge 389:24

395:13

right 4:10,11

23:5,18 24:23

27:19 28:26

37:14 44:1,3

45:1,7,8 46:3,9

47:3 49:1

53:16 55:15

57:13 59:12,13

62:4,6,21,24

63:3,5,8,16

67:17 68:24

72:7 74:6 76:2

77:25 78:11

79:19 88:13

89:1 93:3,14

93:16,17,18

94:2,11 96:13

98:6,19 99:25

100:9,9 105:7

106:19 107:27

114:5,6 115:12

116:1 117:16

120:25 121:11

124:5,15,20

125:20 128:10

131:16 132:14

132:21,25

136:25 137:13

137:14 139:24

140:7 149:1,4

150:16 153:10

154:14 156:12

157:4 161:20

163:25 170:20

174:26 183:18

186:22 187:23

201:9 205:9

214:12 215:1

220:9 221:6,12

222:1,9 223:6

231:9 237:26

241:4,24 242:1

242:2,7,21,25

249:8 250:18

255:23 258:12

258:13,22

259:7,13 260:5

262:20,20,23

263:1,22 282:1

284:4,16,20

286:16 288:24

291:3 295:3,20

306:8 307:18

316:7 320:27

321:10 324:11

330:11 336:26

343:13,17

354:20 359:1

360:13 364:2

375:7 378:22

383:23 391:13

392:26 393:17

399:13 407:13

408:19,22,22

409:7,16 411:5

414:18 419:8

419:13 421:18

432:1 437:6

438:21 439:18

448:9 450:13

450:24 454:7

455:2 459:6,15

460:6 462:15

462:17 470:15

473:15 475:3

476:5 480:11

480:13,17

494:24 496:22

497:2 499:12

500:14 502:2

506:27 507:16

509:2 512:13

515:15,16

517:16

right-hand 75:6

148:22 308:12

rights 32:10

rigor 127:20

488:2

rigorous 87:6

179:1 185:6

186:4 327:27

510:24

rigorously

128:21

ring 79:6

rings 99:16

rise 485:21

risk 14:19 20:14

21:14 79:22

81:6 143:17

313:26 315:27

362:9 364:12

365:16,25

366:6 371:8

372:27 483:15

488:21 503:11

506:21 518:21

risk-assessment

245:12,13

risks 325:11

513:13,16

river 202:9

Riverside 6:23

190:21 412:6

Rj 41:20,20

RNA 33:20,20

34:4,25 35:6

38:10,18

143:25

road 101:13

303:20 420:8

421:14 497:4

roadmap 8:10

12:4,20 125:17

177:23 299:20

355:18,19,20

356:4 365:8,10

365:24 366:7

Robert 6:5

24:25 56:27

57:14,21,25

58:14,20,27

59:8,14,18,21

190:5 292:1

293:14 385:17

399:10 402:24

411:19 445:3

447:26 453:21

458:13 462:9

464:17 467:12

470:5 512:23

robot 234:11

robust 107:22

107:23 179:11

280:26 281:22

351:18,22

395:1,21 448:2

485:11

rocket 322:16

Rockville 38:20

rodent 105:23

115:1 117:20

118:3,21,24
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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247:11 261:1

403:12 406:11

rodents 109:21

262:4 364:19

364:24 404:10

role 311:25

361:23

rolling 198:23

room 183:18

202:27 220:22

269:14 329:15

489:23

rough 258:19

roughly 49:2

195:8 198:20

218:27 220:17

225:1 232:15

364:19 369:4

round 68:10

route 106:5,6,7

110:21 181:1

235:23 248:10

269:27

routes 235:18

237:1,2 238:4

244:21 351:15

352:23

routine 66:19

461:15

routinely 380:9

440:6 493:12

493:12

rows 39:11,13

39:14,22 84:26

143:1

Rs 68:14

RTK 2:9 243:1

RTP 517:22

rug 258:9

rule 83:26 254:8

352:20

rules 83:1

Rumsfeld

258:12

run 30:1,2,4,18

34:20 37:22,26

38:12,22,26

40:20 46:25,26

49:8 61:21

69:5,9 71:24

75:16 76:3

81:25 83:3

86:1,20 89:23

90:16 95:20

99:6 101:17,27

129:4,7 137:20

142:13,19,20

159:13 161:5

170:14 180:25

207:26,27

208:1 223:20

249:27 250:2

269:27 274:11

275:21 293:22

293:23 297:7

297:11 313:11

348:19 391:1

420:4 448:7

running 38:22

38:24 44:20

49:6 82:13

102:9,27

136:12 138:14

169:13,16,16

169:19,19,22

169:22 170:9

170:11 176:27

276:26 297:2

402:2 421:4

runs 38:27

RUSSELL 58:5

58:17 59:4,12

59:16,20,22

162:23

Russon 247:27

Rusty 58:5

519:27

Rusty's 165:4

S

S9 99:6,8,8,10

99:14,19,20

413:23,23,25

413:27

safe-sided

106:11

safety 7:14,20

7:26 11:26

52:3 224:12

326:22 361:25

518:3

sample 181:1

198:25 199:17

202:3 203:23

208:24 213:8

222:14,17

274:15 332:19

404:17,25,27

445:27 495:5

505:20

samples 94:24

101:21,22,26

198:19 199:13

199:14 200:23

210:19 213:10

213:19 274:11

274:25 504:7

505:11,13

sampling 505:6

SAP 1:2,9 3:18

4:9,10,22 7:16

8:4 11:1,11,23

12:8,27 13:6,8

14:24 16:6

19:14 20:19

97:4 126:12

152:25 182:23

204:11 232:17

265:24 278:23

279:11 281:26

291:10,16,19

300:13 301:6

303:9 304:6

312:3 315:24

317:24 320:20

323:19,24

331:18 336:2

347:19 350:26

351:10 362:12

362:17 385:22

386:1 441:10

441:11 512:9

514:8 517:1

519:2 520:11

SAP's 520:13

SAPs 11:4 89:13

277:22 278:22

326:11 382:23

SARM 147:24

147:27 150:23

151:3,7,13

173:13

SARMs 147:20

147:22 183:18

298:11

sat 513:11

satisfactory

507:5

satisfied 3:26

207:8

satisfy 453:3

Savvy 6:18

190:18 412:4

saw 99:15 106:3

111:12 112:25

114:12 127:10

160:13 178:5

180:13 231:26

246:5 253:2

261:22 265:27

269:8 275:25

284:20 323:13

328:2 331:2

333:20 346:3

389:1 401:26

505:26 506:7

saying 22:27

37:1,24 54:17

62:17 65:25

71:11 79:24

99:21 119:11

119:15 211:23

245:19 264:1

265:7 271:4

295:16 342:11

345:15 391:5

418:12 419:25

430:7 431:2

451:13 458:5

509:10

says 41:3 48:18

49:13 50:12

62:11 71:7

79:23,26 80:12

80:25 81:13

87:25 118:19

118:26 205:26

222:7 272:18

272:27 273:4

446:3 447:22

502:1

scale 44:11

132:11 133:11

263:9 285:6

319:4,9 349:22

392:1,5,7

491:18

scales 117:14

255:20 384:7

418:8

scaling 74:5

253:11 451:18

scan 210:14

scarcity 461:11

scatter 78:10

205:24,25

scenario 170:23

170:26 256:17

270:18,19

420:2 479:20

482:15 488:13

scenarios 236:15

245:6,19

270:13 279:6

351:14 490:11

490:20

scheduled 28:19

schedules 15:21

15:22

schematic

203:19 204:17

scheme 151:22

263:7 288:25

500:17 508:1

508:13

schemes 70:7

387:3

Schlenk 6:20,21

51:1,2 52:10

53:7,14,18
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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54:17,25 90:27

91:1,18 92:4,9

120:1,2,13,16

120:20,26

121:4,8,11

137:27 138:1

138:23 190:20

190:20 400:2,3

400:4 406:26

407:26 408:8

408:19,23

409:7,11,15

412:5,5,20,22

416:4 417:8,9

417:16 418:10

419:9,24 420:1

421:6 422:6,8

422:9 427:10

428:7 429:8,9

430:4,18,19

431:3 433:13

438:19 481:1

485:26,27

487:15 497:15

497:16 498:2

515:26

Schlenk's

418:26

school 190:3

375:15 403:12

411:17 490:11

science 3:4 5:21

7:13,25 11:25

14:24 19:15

51:17,20,22,23

125:5 189:23

195:13 204:15

206:9 207:10

240:13 247:8

259:25 260:13

279:4 310:11

312:4 316:12

317:17 322:16

322:16 324:7

327:6 367:4

386:18 426:9

509:14 515:5

517:3 519:11

Sciences 8:6

189:17 190:9

224:27 411:6

411:22 518:12

scientific 1:2,4

3:19 7:22

10:27 26:3

127:20 179:11

179:26 191:24

260:18,19

296:15 316:19

317:3 322:15

323:26 325:26

326:9 331:14

355:7,14

375:25 380:11

440:7 443:25

445:16 463:15

479:12 488:2

scientifically

19:9 20:8

127:27 280:26

312:19 339:6

356:9 357:11

487:26 489:22

518:19

scientist 6:14,18

190:18 258:16

411:27 412:4

519:16

scientists 160:21

434:25 443:26

443:27 514:22

519:22

score 94:10,12

114:18 115:6

115:11,12

117:1,14,22,23

118:20,24

119:2 128:18

130:24 131:3,4

131:13 145:21

145:23 148:24

151:23 152:14

154:6 156:5

166:20 186:20

187:16 272:24

342:8 359:23

370:12 388:13

scores 86:16

115:9 130:19

132:11,26

135:23 136:26

137:5 138:16

150:21 153:3

183:21 184:1,4

272:23 280:14

321:12 328:25

330:4 337:1

338:15 341:18

351:5 368:10

373:17 379:3

380:8 381:3

391:25 439:3,7

440:4 445:25

460:1 470:24

514:12

scoring 152:21

311:25 322:2

359:8,10,15,18

359:26

scratching

447:25

screen 17:22

61:6 82:24

103:14,22

104:5,18

157:19,22

158:7 160:8

163:16,27

164:7 165:12

166:4 341:2

screening 1:6

2:7 3:7 7:23,24

8:9,23,25 9:4

9:12,14,21

10:16,21 11:6

11:11 12:2

13:11 14:6,9

14:11,16,16

15:3 16:5 17:9

17:18 18:20,22

20:11,13,16

21:16 24:26

25:24 61:1

64:3 89:6

94:23 96:26

97:10,13

103:16 124:26

126:18 127:22

133:5 135:20

136:5 139:7

174:17,25

176:15 179:13

187:3 189:4

195:15 227:15

246:6 259:27

266:14 278:13

279:17,19

281:24 282:3,6

286:10 289:5,6

302:24 308:3,9

308:11 310:21

311:8,16

312:13,24

318:5 319:24

322:14 324:4

326:19 327:23

329:24 330:7

340:6,7 342:10

346:26 347:9

350:8 353:2,4

353:26 355:9

355:15 356:17

357:10 358:23

358:27 360:1,9

360:18,25

361:7,20,23

365:19 366:11

367:3 368:26

375:4 376:5,7

377:6 380:10

380:13 385:26

395:27 396:10

417:1 440:10

442:22 468:10

470:22 480:24

481:13 482:27

487:11 496:3

502:11 518:4

518:16

screenings 16:9

screens 40:2

47:27 64:5

340:25 417:4

419:6 512:27

scroll 274:12

scrutiny 129:8

179:5

se 400:26 425:19

426:15

search 109:16

109:17 180:21

seasonal 505:21

seated 4:10

second 8:24

18:17 36:23

42:3,8 75:20

127:24 131:9

169:26 179:14

196:22 223:5

227:1 234:5

241:2 263:1

282:26 284:9

284:23 352:6

358:7 437:10

453:13 469:16

472:15

second-genera...

207:9 220:8,13

306:21

secondary

181:24 265:9

460:17

secondly 92:17

240:15 441:14

446:25

seconds 57:1

secretary 520:13

secretion 228:20

section 12:21,22

12:24 13:1

16:17,25 22:23

28:16 89:21

125:21 178:3

235:11 279:16

299:21,22

351:4 380:18

380:20 381:4

390:21 399:25

412:13 421:26

422:15 433:3
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 97

440:13,16

460:3 462:25

471:1,8,11,22

477:20 478:6

478:10 482:19

500:19

sections 8:12,16

12:14,21,25

441:20 470:24

470:25 478:11

478:12

sector 354:8

Security 218:17

see 4:18 22:23

27:4 28:24,27

30:23 31:23

32:20 33:5

34:22 36:6,19

37:7,8 39:4

40:1,2,3,13

42:20 43:26

44:2 45:14,18

47:17,27 49:4

49:9,27 52:11

53:27 54:5,8,9

54:12,13,18

56:22 57:12,20

57:21 60:6,12

60:15 62:26

69:10 70:2

71:18,19 72:2

72:6,11,14

73:3,4,5 74:13

74:16,17 75:5

75:6,11,17,21

75:23,26 76:5

76:13 77:22,25

78:3,11,11,13

78:15,16 79:12

79:13,15,18

80:16,16 81:27

82:2,2,17 83:6

83:6 84:15,17

85:10 87:1

88:16 92:9

93:3 99:19,25

99:27 102:14

105:8 106:3

110:18 111:24

115:19 118:23

118:26 119:3

119:18 122:10

123:24,24

124:1 128:10

128:16 129:21

131:2,5,6,11

131:14 132:24

132:27 133:13

135:21 144:7

145:11 148:19

148:24 149:3,8

149:13,16,19

149:26,27

150:1,4,8,8

153:11,25

155:23 156:8

156:21 157:1,2

157:2,10

158:16,19

159:18,19

160:7,9 161:7

163:23 170:4

170:14,15

171:20 172:4,8

172:13 179:19

179:20,23

180:10 183:13

183:18,21

184:1,7,11,15

184:16 193:4

193:11 194:24

198:2 203:2,23

205:1,23

208:14,20,27

210:7 211:25

213:21 214:4

214:27 217:8

219:15 221:15

225:5,13

230:13 231:20

232:1 233:2

236:17 238:5,7

238:17,23

239:6 248:8

249:21 250:2,7

250:11 251:2,4

251:9 252:17

253:5,17

254:12,21,26

255:2,20,21

262:4,7,7

263:23 268:8

270:6,14

272:19,21

273:12,15,24

274:3,5,6

276:8,23

282:24,27

283:12 284:5,5

292:11,12,15

292:16 293:17

293:24 295:15

300:11 306:24

314:4 315:19

318:23 319:4,9

319:10,25

321:2,14

323:12 328:10

328:13 329:19

334:6 335:3

339:15 340:13

343:1,2 347:6

348:27 349:6

349:12 350:12

355:20 356:22

357:8,12

367:25 368:6,8

371:3,6,7,12

373:17,19,23

374:2 378:3

379:21 383:12

387:25 388:14

390:16 394:13

403:21 406:11

406:18 408:18

409:5 417:13

420:19,26

426:5 428:18

429:10,27

430:10,13,22

430:27 434:23

436:24 438:21

445:15 448:9

450:12 456:18

466:12 468:26

486:5,7 493:26

506:21 508:3,6

509:13 511:9

511:17,22

512:24,27

514:13 516:11

516:18 517:1

517:12 518:13

seeing 8:11

44:21 54:13

73:2 80:15

85:15 139:22

145:22 155:20

179:20 191:27

235:3 242:6

266:2 270:19

295:24 337:15

339:1,18 403:4

430:9 435:25

seek 363:9

seemingly

107:12 191:27

203:4

seen 39:8 54:19

61:11 77:21

91:16 93:10

95:2 132:10,12

137:26 142:1

148:25 153:19

153:22 236:25

243:26 244:7

244:14 246:13

249:26 264:20

265:4 277:14

280:2 281:27

282:9,11

284:14 285:4

288:24 289:12

320:24 327:17

327:20 368:21

370:6 459:2

segment 493:22

select 272:20

selected 69:13

161:27 309:1

353:8 365:17

369:8 370:24

398:9 422:22

selecting 113:9

145:26

selection 57:2

92:14 128:26

129:11 164:21

398:21 453:15

453:16

selective 146:25

150:24 298:11

327:19 420:17

selectivity 417:4

419:5

self- 444:8

self-funded

326:15

self-proliferati...

33:22 39:1

self-rebinding

36:18 453:25

self-stress

315:21 456:12

458:25 463:22

463:25

self-toxicity

456:12

selling 297:5

semi-artificially

171:7

send 318:11

508:2 516:10

sending 344:10

senior 6:13

367:13 411:26

sense 26:25 43:4

50:19,23

100:22 107:14

168:5 171:24

173:18 211:21

235:18 240:14

244:4,9 252:27

257:17 262:19

262:23 264:5

298:23 319:21

325:24 336:21

386:19 398:21

419:5 420:20

421:1 449:12
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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508:13

sensible 488:14

sensitive 40:25

44:19 68:14

88:21,22 105:3

105:20 112:16

115:2 124:2

231:5 232:5,11

234:17 294:5

295:8,9 336:13

341:1 349:22

349:25 350:8

376:16 382:17

397:3 421:15

434:2 457:24

487:2 490:26

492:24

sensitive-driven

172:20

sensitivity 88:13

88:15,18

118:12 134:8

134:15 185:17

305:4 323:7,10

324:25 390:7

397:2

sensitization

449:4

sent 86:26

separate 116:16

133:13 155:10

321:21 327:19

492:23

separated

341:13 342:18

357:4 369:5

400:6

separately

109:24 290:18

392:20

separates 23:14

separating

117:26 165:13

361:21 393:1

separation

23:17 230:14

258:2 283:16

284:8 339:13

364:8,21 365:1

365:2 400:21

400:23

September

207:10 215:21

369:5

sequencing

38:11,17,17

sequential

422:13

sequentially

389:2

SERAP 390:11

series 8:15 9:26

11:4 19:14

33:11 57:11,15

204:13,26

207:2 227:18

275:7 394:12

serious 491:1

serum 232:3

347:2

serve 3:12 8:14

93:8 121:15

135:11 140:25

served 340:4,8,9

384:22 441:11

486:5 519:16

520:14

Service 6:16

190:16 412:2

serving 4:21

202:23

session 4:22,22

5:4,6,7,10

11:23 102:14

187:26 189:3

189:18 190:24

398:23 489:24

514:26 519:2,3

session's 411:7

set 13:10 14:13

17:8 20:21

22:6 26:23

30:17,27 32:27

37:10 40:17

41:9 43:8

47:13 49:12

60:2,9 61:25

86:10,11 88:4

92:25 99:4

104:10 107:6

108:11 109:25

127:1 129:2,9

134:17,27

141:20 153:17

155:27 161:1,8

161:8,13

178:26 180:15

182:4,8 183:9

184:2 185:20

186:3,13

187:27 194:12

194:12 243:14

259:3 267:20

276:4 278:26

301:21 303:14

319:2 332:22

338:3 377:4

384:10 398:21

424:3 432:7

449:23 503:8

509:27

sets 141:25

204:5

setting 14:1,26

15:1,7 16:1

17:8 18:16,18

20:10 57:17

316:24 472:7

setup 243:7

254:6

seven 8:12,16

53:9 106:18

109:9,12,12

134:25 154:15

180:23 214:9

263:20 423:14

453:27

seventh 122:20

122:27

sex 181:5 335:22

477:9

sexual 357:7

shaking 391:9

505:26

shape 428:18

453:23 509:18

share 310:12

312:23 355:6

356:1

shared 289:13

425:4

shares 375:18

Sharlene 520:13

Sheds 207:15,25

207:27 215:27

216:10 220:25

221:2 475:6

485:3

sheer 343:20

Sherpa 6:18

190:18 412:4

shift 93:14,16,16

93:19 125:6,15

251:20 290:3

338:15

shifted 55:15

99:21,24 127:5

shifting 93:14

Shirley 520:16

shocking 401:17

406:11

shooting 430:12

short 10:2 105:5

127:7 211:10

276:26 314:25

485:17 502:17

512:2

short-lived

505:9,15,17

short-term

267:6 304:3

307:3

shot 23:19

507:24

should've 48:13

91:2 142:10

165:21 220:15

show 8:16 23:21

30:6 31:22

32:16 46:1

71:3,22 76:2

83:25 90:1

114:27 120:10

132:14 149:26

156:17 158:9

199:12 208:7

243:27 255:8

256:27 267:15

274:7 276:21

281:27 282:19

284:23 292:7,8

292:12 297:3

306:3 345:4

347:8,15 349:9

355:10 356:2

358:4,9,15

362:16 363:11

374:14 431:15

437:9 446:19

461:4

showed 114:12

124:11 135:4,6

152:10 193:25

234:3 250:17

258:6 260:25

264:16 265:7

273:20 297:18

308:5 346:23

346:23 379:2

422:20 482:16

491:9 498:23

showing 31:18

39:8,22 48:5

88:13,25

100:16 123:22

145:24 153:8

195:10 205:9

213:7 219:26

231:21 250:21

251:13 255:9

255:15 256:13

268:27 281:13

285:2 363:10

shown 81:11

106:22 112:10

115:17,23

123:23 131:8

132:15,25

136:6 160:24
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sick 72:22 77:4

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signal 46:16

60:3 81:25,27

82:1,18 83:26

115:23 158:14

171:10 172:7

197:4 293:9

395:6,9,10

396:6 397:26

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signal-to-back...

297:15

signal-to-noise

240:17

signaling 134:13

185:15 380:6

396:6 437:14

440:3

signals 344:10

396:24 397:15

397:20 398:1

413:8

signature 79:16

172:4

signed 4:1

significant 9:25

25:25 83:16

100:7 104:25

106:26 110:13

112:25 151:23

155:24 156:9

156:15,18,25

160:4 206:20

300:4 316:24

324:4 335:22

345:18 420:27

447:14

significantly

171:10 269:2

396:12 397:7

441:24 449:17

461:14,20,22

silico 313:26

Silver 197:3

SimCYP 352:2,8

similar 91:19

129:7 134:18

135:27 141:18

142:22 148:24

151:17 164:22

177:18 178:4,5

187:14 192:12

222:25 223:2

225:27 226:1

239:17 264:7

280:24 294:6

302:4 318:4,23

331:8 345:23

348:3 349:4

355:18 363:11

364:21 368:9

375:2 416:22

437:21 447:3

477:16 506:16

similarly 16:25

20:11 135:24

303:5 304:14

361:12 397:13

simple 30:11

41:16 42:2

63:9 130:14

220:19 225:21

225:21 229:13

229:14 236:4

241:24 242:1,7

246:15 256:22

280:11 306:26

307:11,12,17

318:14 322:3

342:23 368:22

395:5 431:12

484:7 491:14

simplest 41:25

70:12 221:5

246:15 453:16

simplicity

282:11 395:14

396:1

simplification

74:2

simplified 368:4

368:6 423:4

simplistic 356:4

simply 30:20

31:20 41:23

45:19 63:12

72:4 73:23

75:14 80:14

89:1 144:17,19

158:27 164:26

247:22 318:13

318:21 322:16

344:1 347:18

349:13,18

356:12 362:11

363:21 382:12

479:25

SIMSET 241:15

simulate 207:21

209:13 231:1,3

231:7,9 236:12

236:16 372:20

simulated 22:16

simulation

230:24 231:2

231:16 240:10

485:2

simulations

234:21 236:25

240:25 351:7

simulator

207:16 475:7

simultaneously

279:8

singer 189:14

single 27:11

58:24 59:13

80:12 112:5

134:10,10

135:7 169:2

182:10 185:11

197:10 198:24

209:22 220:8

222:6 230:24

251:8 390:14

395:3 473:21

485:21

sink 349:23

SIPs 248:26

sister 8:1 191:10

sit 243:9 305:14

site 1:9 58:23

92:24 403:10

sites 176:3

sits 33:20

sitter 170:18

sitting 33:17

77:24 202:27

275:17 329:15

situation 22:21

107:3 164:17

168:15,18

224:1 325:6

421:3 458:10

situations

312:20

six 16:9 34:16

43:9 65:8 70:5

70:20,21,22

75:21 85:3

123:1 124:1

179:18 187:9

213:9 222:4

234:21 263:20

345:24 388:3

404:25 517:19

sixth 70:6

258:23

size 101:16,16

105:13 120:13

181:1 202:23

220:6 231:11

238:14 404:17

404:25,27

skeptical 491:12

513:10,11

skeptically

513:5

skew 171:9

452:8

skewed 156:22

466:8

skewing 322:7

443:9,14

skin 449:3

skip 311:23

slapped 68:25

slate 381:18
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slides 25:11

31:18,20 33:6

33:10 43:20

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71:13 85:21

93:4 101:6

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148:4 154:3,23

177:5 218:16

251:21 272:6

327:7 330:27

345:11 347:17

355:3 359:19

362:10 404:10

432:13 474:3

slight 358:16

slightly 64:8,12

180:12 225:27

233:20 294:17

318:6 395:21

396:7 418:7

slope 45:12

53:27 212:7

230:2 332:25

333:2

slopes 333:1

sloppy 75:22

slot 103:1

slowly 183:2

small 25:15

27:11 37:23,26

43:6 53:1 66:3

66:7,12 84:25

89:27 99:3

105:25,25,26

129:21 131:12

225:2 248:15

285:2 345:17

388:6 498:27

504:9

smaller 61:3,3

210:27,27

232:5 233:9

237:11 263:24

269:2 283:4,10

447:2

smallish 43:15

Smart 164:11

smattering 81:9

SMP 19:4

snapshot 141:25

so-called 37:3

173:13,19,22

298:13 509:25

Society 310:10

software 272:4,5

372:22

soil 201:24

solid 26:3 41:10

193:2 274:3

308:7 487:12

497:20 499:7,7

soluble 46:12

solution 35:27

39:16 43:1,8

43:14 44:22,25

45:3 151:1

228:14 514:17

solutions 42:11

43:2,14,15

50:8

solvable 218:2

solve 293:2

solvent 404:5

444:13 445:22

solvents 32:5

solving 144:25

some-odd 49:17

somebody 28:8

28:10 55:18

56:10 82:17

249:8 258:7

272:3 492:8

513:4

someplace 92:3

Something's

205:22

somewhat 18:11

23:25 79:9

126:5 177:17

177:27 181:20

185:1 195:2

215:9 290:26

353:23 355:18

382:23 405:15

418:1,4 429:25

460:19 461:15

470:9 491:11

512:16

soon 4:19 263:4

299:4 300:1

310:25 318:10

356:1

sophisticated

42:27 256:24

275:19 276:17

396:7

sophistication

220:12

soring 39:12

sorry 58:22,27

63:8 91:4

125:12 129:21

132:17,26

136:22,25

137:24 147:5

154:10 163:6

175:3,6,21

239:13 336:23

393:27 407:19

409:15 420:1

462:16 493:26

sort 19:2,17 21:1

22:8 23:21

26:25 29:24

31:13,16 37:10

40:15 42:23

46:27 51:7

52:17 53:22,24

53:27 55:25

56:6 57:11

59:27 60:3

73:3 76:25

79:2 85:5,15

92:12 94:20

129:13 130:22

138:16 141:12

141:18,25

155:6,10

157:25 160:7

169:9 175:12

183:17 184:9

193:22,23

195:1 199:12

201:20 204:23

204:25 205:1

205:11 222:9

224:21 225:20

229:18 233:10

237:8 241:25

250:21 251:7

265:15 266:12

275:17 277:23

281:8 282:23

290:11,15

292:24 293:12

295:8 300:15

303:9 306:3,15

306:20 328:8

329:2 340:17

343:25 360:8

360:21 382:15

400:7 401:13

401:15 402:7,8

404:14 405:9

415:13,19

422:9 424:21

426:19 438:9

447:22 449:16

450:12 452:6

467:20 486:2

486:16,17

487:12 488:27

497:7 514:4

518:16

sorted 77:13

491:5

sorting 133:27

490:12

sorts 29:20,22

37:26 70:6

77:4 80:26

81:4 82:8

194:18 195:10

219:25

sound 19:9

342:6 487:26

489:22 490:3

502:22

sounds 271:23

soup 99:9

source 78:21

172:27 191:20

202:7 215:23

215:24,25

393:8

sources 9:9

191:14 206:16

216:7,8 276:14

286:25 356:15

472:21 478:14

478:19,21

484:11,23,24
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south 1:15,16

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space 74:4

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323:1

span 197:14

334:11,19

speak 6:25 26:20

222:3,3,10

289:11,11

308:15 326:10

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509:9

speaker 11:13

139:16 242:26

326:5

speakers 288:16

326:4

speaking 4:6

298:16 326:12

330:25 375:12

375:17 497:20

518:8

speaks 304:19

spearman 437:4

spec 116:15

special 519:15

519:26

specialize 103:4

specialized

351:22

species 64:10

247:13 298:6

302:17 304:12

448:12 455:16

specific 5:9

13:15 16:19

17:2 19:1

26:26 35:14,15

37:17 40:22,25

56:10,12 76:18

76:23 83:14

88:21,22 92:15

108:8 128:1

137:17 143:13

144:15 145:1

157:25 163:12

166:15 167:18

167:21 168:9

168:21 173:1,4

175:25 182:12

197:5 199:20

207:18 268:3

269:15,16

288:16 296:2,9

296:12 301:4

302:27 303:17

307:23 313:2

317:16 323:15

332:5,6,17

336:13 352:4

353:7 357:9

359:22 365:18

376:12 380:16

381:25 394:24

400:9 418:2

419:17,19

439:15 446:9

448:21 458:20

464:22 465:15

465:16 466:21

467:2 468:8

476:13 503:5

504:15,18

510:16

specifically 37:5

61:13 67:1

105:14 125:27

160:8 168:24

186:6 287:12

287:23,24

289:22 302:13

327:9 330:12

355:12,21

359:25 369:16

415:27 419:5

465:23 466:17

496:8 507:23

Specification

335:13

specificity 85:5

88:14,15,18

118:13 313:7

417:5 418:1

455:24

specifics 133:8

310:17 317:21

333:2 360:17

493:2

specified 335:9

335:19

specify 474:6,8

spectrum 159:14

265:3 311:10

434:27

speculate 65:11

speculation

427:22

speed 17:22

234:9 315:17

518:16

spellings 109:18

spend 33:8

49:20 82:25

172:2 257:6

318:7 505:11

spent 51:6 52:11

62:5 70:6

108:16 279:2

387:19 393:12

spitting 96:23

split 373:1

spoke 128:24

379:7

spoken 507:3

spoofing 49:22

spot 198:19

Sprague 348:26

404:14

spread 78:12,20

250:3 251:5,10

spreads 117:23

spreadsheet

39:10,19

181:20

spreadsheets

387:21

SQAR 127:8

square 65:26

395:10,13

squares 193:3

389:19,25

squeeze 309:17

SR 332:22

SR-zero 43:12

SRC 170:11,19

170:22 465:7

465:13,17,18

SRC1 142:15

165:2 168:10

168:13,15,16

168:17 169:15

170:8 171:3,8

171:15 172:8

173:2 174:1

stable 353:13,27

369:9

staff 4:9,10

315:15

stage 61:14

174:9 181:18

260:12 272:7

463:11 467:26

509:3

stages 141:23

160:20 234:18

400:24 420:20

stakeholders

326:20 339:21

354:8 376:26

443:25 478:17

509:25 515:24

stand 60:16

245:11 503:1

standard 32:16

73:14,16,18,18

73:19,24,25

83:4 108:12

129:13 135:4

211:8,11,14

212:16 245:13

366:14 401:24

427:14 453:18

463:11 466:2

473:7

standardize

400:12

standards

334:15 381:6

399:26 460:4

460:16

standpoint

144:2 257:25

316:19 325:16

342:12 452:7

stands 246:2

336:9 516:22

stared 46:22

54:20

stars 34:13

143:15

start 13:7 27:13

28:5 44:24

45:21 64:14

73:2 74:25

81:27 82:1

92:13 93:18

94:16 101:8

102:13 105:26

116:11 130:14

149:16 150:6

153:11,23

154:8 170:14

175:17 201:8

210:26 220:26

222:22 248:10

256:19 258:21

258:22 260:7

262:23 268:4

272:19 297:10

305:6 306:12

306:18 309:18

310:16 329:2

336:23 364:15

369:9 379:25

382:3 383:8,10

383:10 394:15

394:16 410:5

430:10 431:19

431:20 439:20

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starting 26:2

101:6,14 102:1

102:2 114:4

175:13 248:8

326:23 397:27

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starts 149:14

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430:12

state 18:6

105:19 107:16

202:9 288:21

324:16 372:8

372:16 402:25

426:8 477:2

481:8 512:26

state-of-the

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state-of-the-art

108:1

stated 220:15

385:10 404:20

statement 7:3

197:5,8 401:9

487:5

statements

197:2 314:15

489:4

states 1:12

194:27 310:10

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stating 481:9

statistic 437:4

statistical 67:20

389:24 394:11

395:12 405:1

448:5 449:12

475:13,27

476:2 478:18

485:13 492:10

515:7,14

statistically

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106:26 110:13

112:25 206:20

335:22 395:9

statistician 6:18

190:18 269:14

statistics 67:19

110:12 116:24

status 177:27

180:1 182:21

386:8

stay 271:3

315:16

staying 410:8

stays 22:12

steady 372:8,15

steady-state

208:16,19

225:26 226:5

228:14,16

230:4,12

231:20 240:7

240:16 245:2

351:27

stem 95:22,23

step 8:23 9:21

9:21,21 14:11

14:15 33:16

39:9 42:14,18

122:7 129:16

134:2 174:25

185:5 227:4

241:11 271:18

286:17 334:18

339:5,14 345:9

416:3 443:10

443:13 479:21

483:1 485:17

489:10 506:20

step-wise 174:25

176:9,15

299:27 304:4

stepped 345:4

stepping 344:27

steps 9:2,20

10:27 129:15

280:9 327:17

337:5 356:13

442:9,12

451:20,24

452:12 454:4

455:13,16

464:21 465:3

466:12 499:27

500:3 507:12

steroid 25:24

133:10 137:19

144:7 152:16

152:20 160:5

184:13

steroidal 97:22

steroidogenesis

90:23 287:16

303:5 306:26

308:22 315:21

333:21,22

339:16 358:13

358:21,25

359:1 360:7

361:11 414:27

steroids 25:26

83:18 186:7,8

392:5

sterols 344:2

Steve 8:18 11:23

130:15 182:23

290:22 291:8

299:3

Steven 11:20

22:2,26 23:18

122:4 299:17

517:9 519:13

stewardship

316:27

stick 254:13

493:5

stickier 172:1

stimulate 104:27

484:15

stimulating

509:13

stochastic

207:15 475:6

stolen 197:3

stop 28:24 50:17

50:21 61:23

182:18 258:26

262:21,24

359:5 444:26

stopped 182:13

stopping 338:26

416:2

store 273:11

341:20

story 76:17

171:18

straight 41:6

51:13 436:11

straightforward

104:26 113:13

488:9

strain 404:9,11

461:22

strange 155:6

strategic 17:14

126:21

strategies

310:14 312:16

400:22 449:3

484:25 487:21

strategy 17:13

96:7 109:14

312:17 382:3

383:18 403:26

484:18 510:18

stream 361:24

streamline 26:17

streamlined

51:12 207:25

340:15

streams 9:1,12

20:6 513:20

strength 333:11

336:8 359:9

394:17,26

396:2 397:24

401:21 423:1

424:5 454:24

463:20 486:10

489:24,25

510:27

strengthen

140:26 334:12

445:12 503:27

strengthening

464:24

strengths 27:4,5

336:5 337:22

352:27 380:11

380:23 381:5

393:24 394:13

394:15 399:25

400:6,10

401:14,14

412:16,23,24

422:2,20

423:25,26

433:6,15 440:8

454:16,23,24

460:3,10

462:26 463:6,8

480:23 483:23

486:3,18 488:4

489:20 502:10

502:20

stress 35:14

49:18 77:2,2

77:12,12 83:12

153:11 158:15

159:1 287:7

327:19,22

333:10 364:3

369:20,21

372:1 395:23

401:13 444:8

475:10 484:22

stress-driven

159:19

stressed 338:25

stretch 214:24

strict 122:26

strides 315:26

stringency 438:8

stripe 275:15

striping 275:9
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 103

stripy 275:17

strong 47:17

88:27 91:4,20

92:1 106:21

115:3,6 117:25

124:6 129:26

131:14 140:3

146:3 156:3

186:22,23

310:24 320:17

320:19,26

361:13 395:16

406:18 413:15

413:16,18

434:11 460:16

467:5 481:12

490:3 491:20

500:24 502:2,3

504:17

stronger 117:21

strongly 375:23

445:20 503:3

struck 477:6

structural 9:8

97:27 98:1

160:1,9 334:5

334:7 398:26

456:8

structure 87:3

87:14 116:14

155:15 210:11

215:22 246:17

272:21 348:8

467:15

structured

323:26 355:26

structures 86:19

86:24,25 87:7

87:14,17 161:2

218:7 334:8,9

377:4 380:25

393:26 454:18

464:6

struggle 53:24

struggled 320:4

struggling

432:14

student 269:24

students 415:16

studies 17:4

104:8,14 106:3

109:26,27

110:27 111:1

111:18,19,20

111:22,23,25

112:19 120:3

120:23,24,26

121:3 123:2,4

123:17,20

134:22 139:17

180:22,24

184:27 193:2

195:16 216:12

222:18 234:25

239:15,18

250:10 253:21

263:15 303:18

326:17 327:21

328:17,18,24

329:26 330:16

334:16 335:3,4

341:3 342:6

344:15,18

347:5 349:9

350:11 353:9

357:11,20,23

361:2,18,20,26

363:9 366:9

376:21 377:8

377:24 381:1,4

381:6 399:21

399:24,27

400:12,16,21

402:13,23,25

403:10,12,13

403:24 404:7

404:13,23

405:10,22

406:4,17

408:10,26

413:23,25

430:22 436:13

455:1,3,9

456:7,9 457:22

459:26 460:5

461:23,24

462:1 494:18

509:12

study 16:23

105:18 106:14

107:5 110:3,14

111:6,10,13,14

111:15,24,27

112:3,4,6,24

113:1,4 120:8

120:17,17,18

129:1 135:5

139:18 180:26

181:6 185:22

198:13,15,16

198:27 199:2

201:8 210:2

215:3 225:14

235:2 250:12

250:12,14

328:16 334:27

335:18 344:18

347:15 348:1

356:8 357:13

357:16,22,23

358:7,8 360:4

361:17 400:22

402:3 408:11

409:10 461:10

461:13,19

482:2

studying 192:7

stuff 51:13

250:11 344:27

418:3

stuffing 160:10

Styrofoam

349:21,23

subcharge

407:15

subclass 490:13

subcommittee

340:8,10

subcu 403:14,19

subcutaneous

106:8,11

subgroup 340:7

subject 12:27

13:16 16:6,23

17:5 194:1

223:21 238:11

304:6 360:13

381:22 506:14

subjective

218:24 219:10

subjects 224:10

submit 316:16

354:10

submitted 310:8

317:14

submitting

402:27

subpopulations

276:10

subq 106:2,3,4

subsequent

126:22

subsequently

14:27 268:27

398:9

subset 87:16

193:5 199:4

204:21 215:14

233:9 285:2

342:1 350:17

365:26

subsets 447:2

substance

325:23

substances

87:11 311:12

312:14 319:23

321:23 325:25

substantial

489:26

substantially

181:22

substantive

385:14

substitute 13:15

127:27 301:4

380:16 433:7

438:2

substituted

437:26

substituting

18:18,22,27

20:25 136:15

338:16 435:21

438:13

substitution

13:24 16:18

17:1 21:8

139:8

substrate 227:9

372:11

subtle 455:7

466:5

subtract 42:9

45:14 74:5

448:16

subtracting

53:26 447:4

448:2

subtractions

26:24

subtype 426:18

success 250:12

252:27 443:24

successful 263:4

263:10,11

519:8

succinct 396:17

suddenly 72:11

82:21

Sue 326:5

330:22,24,24

367:10,12,12

sufferer 224:3

sufficient 109:13

110:12 113:14

113:21 116:6

258:2 334:20

366:15 368:23

375:1 400:14

469:11

sufficiently

335:7 377:4

398:25 432:20

suggest 24:2

281:22 282:11

313:22 358:27

364:26 365:23

366:7 376:19

404:25 436:6,8
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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436:17 464:8

481:17 488:20

501:4

suggested

216:13 237:1

325:19 377:25

424:25 484:8

suggesting

218:26 349:12

362:5 421:3

suggestion

259:22 281:11

322:11 438:3

489:8,11

suggestions

101:10 312:15

409:3,8 419:14

469:12 478:8

487:20 495:24

503:26

suggests 82:17

97:18,21

366:20 372:1

479:10 489:1

suit 203:11

suitable 174:23

187:2 374:26

469:20 496:5

suitcase 102:15

suite 27:11

29:10,23,24

38:23 126:6

173:7,9 216:3

298:13,22

338:5 449:6

453:22

suites 26:8

296:23 298:18

sulfuric 160:9

sum 21:2

summaries

273:18

summarization

69:18 389:8

summarize

67:22 86:13

233:14 381:10

381:16 437:3

440:24 483:7

507:10

summarized

71:1 275:24

summarizes

69:23 243:11

247:6 272:25

273:9 301:8

summarizing

272:27 273:4

399:11

summary 2:26

10:2 89:11

187:1 215:11

234:12 245:23

272:22 273:9

273:17 276:23

385:26 387:10

387:25 439:20

445:4 458:14

462:9 474:18

481:4 508:27

summer 300:17

summertime

505:20

super 33:1 160:3

172:8 237:6

294:22 476:2

superimposed

364:11

superlatives

514:20

supplementary

31:20 133:26

185:3 513:27

supply 296:27

494:9

support 13:22

16:18 21:14

110:12 129:10

221:4 297:4

300:21 301:3

324:5 327:12

328:5 375:24

376:6 438:13

518:19

supported

184:14,17

519:22 520:17

supporting 17:1

51:21 224:18

supportive

374:20

suppose 125:6

137:22

supposed 32:6

35:23 48:3

116:13 147:11

147:12,17

257:1,4

supposedly

230:26

suppression

144:14

sure 6:26 7:1

21:27 24:2

38:2 41:16

54:1 55:10

57:12 62:4

64:15 98:13

113:5,6,27

115:26 118:20

123:17 139:15

164:15 167:26

168:1 172:24

172:25 173:5

181:24 183:2

216:12 235:21

241:18,20

245:5 266:9

276:7 290:10

300:6 312:26

324:19 326:19

332:1 377:3,9

377:16,19,23

380:2 391:9,21

391:24 392:14

393:6 401:18

405:5 407:18

413:24 417:5

426:12 428:17

429:20 438:25

448:27 450:2

465:11,26

473:8 474:14

476:14 492:3

492:21 497:21

499:7 507:23

surfaces 15:12

surfactant 36:2

surfactants

29:20 32:5

76:11 155:7

158:6

surprised 84:14

514:7

surprises 83:20

513:14

surprising 95:5

123:25 186:11

255:7 413:20

425:27

surprisingly

406:16

surrogate 251:3

survey 38:14

198:15,23,24

susceptible

221:24 242:21

504:8

suspect 293:25

474:15

suspects 83:17

suspension

227:25

suspicion 223:18

swamps 250:6

swap 229:23

sweep 68:5,12

162:4

Swiss 237:4,6,7

switch 282:21

switches 6:26

symmetrically

437:5

symporter 303:2

symptoms

344:17 345:27

synergism 95:5

Syngenta 330:25

354:22 355:8

367:1

synthetic 163:1

184:13 186:8

456:21 457:3

system 26:13

38:21 74:12

78:18 86:5

89:13 97:22

98:2,25 99:21

100:17 108:2

109:4 130:12

130:13 174:1

175:24 300:14

305:7 306:13

313:17 322:2

340:20 344:12

346:11 347:3

347:13 349:15

349:26 350:17

350:19 353:15

356:27 357:1,2

359:8,10,12

361:7 402:24

424:4 431:18

431:18 446:15

447:4 450:1

503:20 511:13

systematic

203:27 205:2

222:1 235:1

315:22 327:18

334:14,16

355:26 357:27

362:11 448:1

461:26 488:6

510:20

systematically

204:5 238:24

239:5

systemic 116:7

181:16 327:21

327:25 335:1,4

335:10 351:27

352:3 359:26

360:23 361:7

361:15 364:18

405:4 481:10

systems 16:4,11

17:8,21,25,27

25:13,26 27:17

61:6 95:21
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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122:10 140:20

176:16 305:6

306:12,17

330:23 346:12

416:20 417:27

421:11,11

426:18 429:12

430:9,10,13,21

430:23,25

431:1,13,13

437:13 468:3

503:21

T

T 156:21 424:13

T-score 337:7

T-scores 155:19

T.S 192:10

tab 272:22

276:19

table 2:2 50:10

61:12 83:5

142:8,10

183:23 200:24

250:18 251:1

280:12 286:6

294:24 342:11

350:22 357:26

358:9 436:3,8

472:26 473:3

473:10 474:12

478:16

tables 251:2

tail 60:24 79:7,7

tails 196:8,11

take 3:10 11:1

17:10 23:18

26:16 27:12

30:13 32:22

39:9 42:3

50:24 51:18

62:9 63:12,12

64:19 67:14

69:9 72:1,16

73:11,11,12,14

73:18,19 74:22

74:23 81:15

86:16 90:4

93:13 101:10

101:17,26

102:9 113:2

119:24 132:6

134:2 136:17

140:8,8 142:8

148:4 161:19

176:14 177:5

181:25 192:19

195:7 197:1

200:8 203:6,14

208:26 212:17

213:1,2 216:17

216:20 222:11

224:5 226:16

227:8 230:8

231:17 235:12

241:6 243:13

243:24 251:6

253:6 256:11

257:7,8,22

258:19,20

264:2 276:26

277:18 288:27

297:8 305:27

307:11 327:8

342:11 354:23

367:5 369:19

369:22,22

392:6 401:13

407:16 409:3

431:22 437:8

448:6 459:16

468:21 497:25

499:5 507:27

517:18,21

take-home

186:12 285:26

takeaway 143:5

147:14

taken 15:14

16:14 19:18

30:26 44:6,7

74:3,7 75:13

102:11 135:10

154:5 228:23

229:27 238:20

260:23 266:12

267:7 272:1

277:4 279:11

287:3 309:20

318:27 320:24

341:24 354:25

382:17 406:15

435:20 436:9

441:13,14

477:10 482:22

487:19 492:14

492:22 499:26

501:7 510:26

takes 173:23

263:2 343:15

414:15 483:13

483:24

talk 12:5 19:3

20:1 31:17

32:13,15 37:27

39:6 47:23

86:22 89:12

96:15,16

104:13 125:5

125:14 127:15

131:20 175:13

177:7,19,21,25

178:11,23

179:7,8,13

180:8,16 191:5

192:17 193:13

204:12 243:11

253:3 257:5

271:1 282:15

283:6,16 284:8

284:15 287:15

290:11 291:8

299:2,2 308:26

337:19 342:20

344:19,20

367:20 402:19

417:25 422:26

425:18 434:13

486:21

talked 37:20

82:11 122:5

134:6 170:7

171:23 184:27

244:23 245:23

246:21 250:19

251:23 272:23

286:19 296:9

301:25 340:23

486:3 498:17

498:18 507:13

talking 73:17

85:25 96:2

108:3 117:19

121:27 122:8

125:7,18 128:2

133:1 138:14

139:8 176:4

183:11 197:19

201:1 221:23

223:9 245:3

246:10 279:16

280:3 281:4

282:2,17

283:10,19,21

285:11 295:16

298:1,3,4,5

318:8 324:14

328:16,17,19

372:2 383:8

419:5 486:15

515:12,13,14

517:17

talks 127:15

140:23 277:20

303:7 356:12

391:25

Tamoxifen

348:11

Tanguay 402:24

target 69:12

146:5 157:8

186:6,8 243:17

279:26 286:21

307:15 504:8

targeted 17:14

175:25 239:27

365:21

targeting 25:20

targets 25:11,16

302:13,27

305:14 417:22

419:6 511:11

511:12

task 127:4

400:15

tax 95:12

taxa 95:12

team 243:5

386:27 389:17

389:23 440:27

441:25 512:7

517:24 520:15

teams 307:10

tease 160:17

teasing 182:25

technical 86:2

92:13 99:22

265:21 326:26

technically

92:26

technique

198:11 204:23

210:16 211:13

224:15 233:11

234:8 241:4

281:6 289:5

techniques

107:21 126:18

194:3 209:13

210:14 223:27

224:19,22

232:13 233:14

278:20

technologies

29:6 33:24

46:23 75:13

119:13 134:14

135:17 141:8

142:1 157:17

157:20 158:10

159:14 164:26

185:14 250:1

333:16 376:5

technology

35:15,15,19

36:9,15,24

38:5,11 40:3

40:15,17 46:27

49:14 75:15,22

75:23 78:20
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 106

143:6,10,12,13

151:21 156:20

156:27 158:8

160:11 166:13

166:14 169:8

169:12,21

172:26 173:7,9

204:15 206:9

207:11 248:10

418:2,4

technology-sp...

144:24 151:19

152:3 394:27

Telephone 1:10

telescope 33:9

tell 32:11 41:26

60:8 63:9

69:11 81:16

137:18 151:16

184:24 201:4

227:8 260:24

269:24 292:17

305:12 342:13

406:21

telling 297:23

342:8

tells 35:27

194:26 252:26

266:26

template 178:17

ten 87:12 225:16

505:12

tend 22:11 39:14

39:14 40:2

50:8 83:24

128:27 137:20

172:1 237:5

248:22 256:5

353:14

tended 88:20

262:8

tendencies

196:22,25

197:16 247:5

353:20 484:7

tendency 196:8

252:4 353:27

484:10 488:19

tends 35:14

40:16 221:22

241:2 473:16

tens 200:5

215:19 234:11

278:5

terbufos 133:20

133:21

term 18:21

42:24 55:24

60:17 66:15

68:1 131:11

165:21 212:19

217:5 220:8

221:4 261:18

261:19 311:23

314:25 329:27

341:3 385:2

390:4 396:22

396:25 397:1,4

397:6,7 418:10

456:3

terminology

453:15

terms 23:12

26:11 53:19

55:7 60:3,22

97:13,25 98:27

109:17 120:2,4

135:19 138:8

150:27 163:24

202:23 219:6

221:10,11

242:6 263:21

267:7,13

268:19 271:20

284:17 286:6

299:23 300:7

308:8 316:17

317:5,21 318:1

319:5,9,24,26

321:1,2,18

322:11,11,14

322:17,26

323:2 324:16

324:25 325:21

331:19,20

332:12,17,17

332:21 333:8

334:4,13,22

336:3,4,16

337:21 338:5

338:11 342:16

342:27 344:8

352:11 362:19

387:8,11 389:2

391:3 401:4,12

412:24 414:19

415:17 416:1

420:21 424:19

425:15,15

429:3 431:4

442:1 445:13

445:14 446:1

460:10 468:15

479:16 486:11

491:2 516:18

terribly 186:11

225:6

Terry 6:2,2

26:10 97:17

98:6,13,16,19

98:22 100:5

137:8,12,15

162:11,15,19

162:24 163:14

163:18 164:1,6

164:12,16,20

168:2 171:2

173:8 175:3

190:2,2 264:13

265:22 295:22

411:16,16

440:22 451:4

452:10 454:22

458:22 460:9

463:5 464:25

466:20 468:7

469:26 512:6

test 15:18 16:4,5

16:8,11 17:8

17:21,24,27

30:27 31:7

45:24,27 47:5

47:9,15 77:27

84:20 90:26

91:15 93:17

95:24 98:25

103:14 104:18

108:7,9 111:9

122:10 146:4

159:25 168:7

176:25 181:1

224:10 225:15

225:16 236:25

246:7 260:24

266:21 271:20

295:11 308:4

326:16,18,24

334:7,25 336:3

347:13 354:9

372:7 375:24

426:4 444:13

446:20,20

450:3,25

461:18,21

tested 29:11

73:6,7 77:24

78:15 84:16,18

88:6,7,11 90:9

90:13,15 91:10

91:17 92:20

98:4,7 106:15

106:18 107:7

107:11,15

110:24 111:7

111:17 112:4

112:24 115:26

116:2,19 135:5

155:21 157:16

157:17 200:12

212:23 298:12

344:15 366:16

377:3 403:2

testing 8:25 9:15

9:21 10:18

14:7,10,17

17:14,18 20:13

29:8 33:3

47:21 70:22

89:19 96:8,12

97:10,10,13

103:5 111:8

114:16 115:27

126:20 127:23

133:6 135:21

136:5 139:7

173:26 177:22

179:13 187:3

212:24 243:25

246:5,6 247:11

247:11 261:15

278:13 279:5

279:18,20

281:24 282:3,6

286:11 310:13

311:15,19

312:8,10,12,14

312:20,26

313:9,17,25

314:5 315:11

316:2 329:25

340:6,7 357:24

361:19 362:15

363:7 365:21

366:15,20

380:13 384:24

386:21 394:20

418:7 438:4

440:10 445:15

447:8,10 449:3

449:16 450:12

450:19,23

452:26 477:27

testosterone

148:18 162:17

179:22 181:9

181:10 347:26

456:26 457:8

testosterones

144:8

tests 9:16 11:5

112:1 155:9

264:10 291:25

313:24 388:22

388:24 450:8

Texas 248:1

494:17

text 71:14

272:18 388:16

404:20 413:17

492:5
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thank 4:22

11:20,21 21:19

21:20 23:8

24:23 63:5,24

66:23 67:12

90:4,7,13 96:1

102:26 124:20

136:19 161:20

162:9 163:5

166:23 167:6

175:20 177:15

187:23,24

190:22 235:13

236:7 240:19

242:23,25

264:11 270:7

271:8 276:25

288:12 304:27

316:4,5,11

324:9,10,26

326:2,3,8

339:26 354:14

354:17,20

355:5 366:27

367:4,7 375:4

375:5,7,11

378:4,6,7

379:15,16

385:16 386:4

389:7 399:4,8

400:4 406:26

409:14,18

410:7,9 412:22

416:4,23

421:24 427:9

428:1 430:17

433:14 438:16

438:26 439:17

445:1 446:6

448:13 453:19

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458:11,15

459:10 462:7

462:18 467:11

467:21 469:8

469:13 470:13

471:14 476:5

478:1 480:6,16

480:17,27

483:18 485:25

487:15,22

489:13 491:25

492:18,27

493:25 495:12

495:19,23

500:21 503:14

503:18 506:23

507:6,15

508:25 509:2,6

509:15 510:6

510:13 511:17

514:5,21 515:4

515:25 516:5

516:12,25,26

517:3,4,7,10

518:23 519:2,4

519:10,20,21

519:24 520:1,4

520:5,7,8,9,11

520:17

thankful 515:27

thanking 519:1

thanks 3:15,17

4:21,27 51:2

54:25 92:9

121:11 188:1

196:6 240:4

270:22 367:9

378:18 389:17

514:24 516:16

516:19 517:16

517:26 519:7

519:12,15,26

that' 329:1

theme 50:21

71:12 84:1

themes 29:2

32:2

theoretical

481:12

Theoretically

502:22

theory 169:1

481:22 496:14

Thera 37:27

38:4 46:24

142:13 151:16

151:20 156:20

164:27 169:12

172:27 371:23

371:25

therapy 423:16

thin 87:25

thing 21:10

36:25 37:1

42:23 60:4

80:3 82:19

90:19 104:20

111:7 112:15

117:10 118:26

119:11,16

127:3 129:27

131:20 142:2

143:18 150:8

156:4 160:6

179:19 196:4

200:19 229:12

238:17 250:1

256:1 266:12

270:12 271:4

281:1,15

284:18 291:5

293:10 300:12

302:16 306:5

307:24 319:3

322:3 328:8

329:10 381:27

383:1 385:6

401:26 402:1

405:2 407:3

409:26 413:7

416:19 417:3

418:4,13

426:19 427:1

429:2,25 430:1

444:12 446:10

446:10 455:18

466:21 476:21

477:5 504:19

511:9,16

512:17 516:1

520:18

things 23:1,22

23:24 27:6

28:24 29:22

34:10 43:5,5,6

43:7 47:1

51:11,12 52:12

55:3 60:2

68:27 69:25

70:10,13 72:22

74:10 76:12

77:11 80:16,27

81:4,9 82:8,13

87:12,17 96:7

96:21 100:18

100:20 105:13

106:14,23

107:17 109:20

111:4 113:27

114:13,17

119:6 120:8

122:22 127:7

128:5,5 132:9

133:27 139:3

140:4 161:16

165:13 173:16

200:20,27

207:4 208:18

210:5 212:17

215:27 219:24

220:20 228:12

228:23,24,25

229:27 236:25

238:12 241:11

241:18,20

244:8 252:27

257:20 258:3

258:11,14

259:1,11 260:4

261:6 263:9

264:18 267:14

270:7 287:14

299:18 300:11

301:27 307:21

307:23 309:2

328:7,17

329:17 330:16

374:8 377:23

381:24 382:4

382:22 383:20

383:22 387:24

391:23 394:16

401:16 403:25

404:2,3 405:9

406:19 431:19

441:15,16

445:10,21

446:18 454:1

470:10 486:3,9

487:17 489:4

493:21 494:19

495:25 496:9

503:26 506:3,4

510:13,16

511:2 512:14

514:13,17,24

515:23

think 4:18 7:7

12:16 19:11

22:2,7,27 23:5

23:8,19,22,24

23:26 24:5,9

25:11,13,18,22

26:6,10,22

27:1,10,12

30:15 31:16

33:7 37:12,19

38:6 42:6

43:21,21 44:5

44:9,27 45:16

47:1,11,13,20

51:11 52:20,26

52:27 53:8

55:23,25 56:6

58:12 60:5,6

60:19 61:19,23

64:1,2,13

68:26 70:2,4

70:23 71:5

72:16 74:11

76:11 77:18

80:4 82:14

85:24 87:5,12

89:11,21 90:20

92:2,23 96:27

97:9,18 100:10

100:24 101:10

102:8,13,17
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152:24 156:18

158:25 164:12

166:18 167:4,7

167:14 169:4,6

169:18 170:8

170:20 171:6

173:9,25

174:16,23,27

175:1 176:27

177:24 178:18

178:27 179:9

182:23 183:2

184:15 185:7

187:25 191:14

192:16,25

193:23 197:23

198:7 199:26

202:22 211:21

212:11 214:13

215:8 217:6,13

218:5,24

219:23 221:3

222:21 224:2

233:12 239:11

239:24 246:8

246:20 247:12

247:13,18

248:5,13

249:11 250:13

251:11 252:7

257:11 258:1

258:25 259:23

259:25 262:26

263:14 264:8

264:17 266:19

267:14,26

268:7,9 269:10

270:8,9,11

271:23,27

274:23 275:20

275:25 276:25

277:13 278:13

279:6,10

280:22,26

281:1 282:10

282:20,23

283:2 284:7,16

284:22 285:25

286:23 287:4

287:22,27

289:3,20,22

290:22,25

291:2,3 293:12

293:21,27

295:15,25

296:16 297:23

297:27 298:2,7

298:15 299:2,6

299:8,17

301:15,20,25

302:2,16 303:2

303:3,6,12,18

304:2,8,10,14

304:19,21

307:12,12,24

308:19,24

309:12,14,16

311:14 312:27

316:9,18

317:24 319:21

319:27 320:4,6

320:9,11,19

321:4,9,17,23

321:23 322:6

322:21,25

323:11,12,14

323:15,20,25

324:6,22,24

325:6,8,11,15

327:14 328:14

328:25 329:1

329:10,16,17

329:26 330:1

330:18 331:14

334:1,26

340:18,19,20

341:24,27

342:3 343:11

343:25 344:20

345:1,10,17

346:18 347:14

349:15 350:1,3

350:4,13,18

351:17,23

352:6,22,24

353:10,21

354:27 355:20

358:26 362:25

363:8 364:1,16

366:24 367:2

368:6 370:19

374:20,21

376:14,19,22

379:17 381:13

381:24 382:13

383:17,27

384:1 385:3,10

386:14,20,24

387:3,9,13

388:8,20,21

389:9 390:2

392:6 398:5

399:10 400:10

400:15,19,24

401:8,17 402:7

402:16,22,23

403:7,10,22

404:18,20,24

405:1,2,3,5,10

405:21 406:1,6

406:10,18

407:5,8,20

408:6,14 410:7

411:3 412:7,24

412:27 413:1,7

413:9,19,22

414:5,8,10,14

414:19,21,25

414:27 415:7

415:21 417:1

418:6,10,13,17

418:20 419:9

420:2 421:2,6

421:7,14,15

422:10,20,27

423:1,4,5,8,9

423:10,11,13

423:19,25

424:5,11,22,23

425:2,5,10,16

425:19 426:23

427:18,21,23

428:10,12,13

429:13 430:7

430:19 431:11

431:20 432:10

432:11 433:21

434:7,19,25

435:13 436:22

436:25 437:14

437:26 438:5

438:11,14,27

439:11,12,13

440:27 441:9

441:17,23

442:5,11,19

443:7,13,26

444:12,16,22

445:16 447:13

447:18 448:8

448:15,16,17

448:23 449:11

449:15,18

451:15,18,22

452:1,15,16,27

453:6,12,15,22

454:2,3,9,26

455:19 456:1

458:22,25,27

459:3,4,11,12

459:16,19

461:14 463:6

463:16,22

464:1,2,26

465:5,10,14

466:3,10,12

468:10,11

469:15,20,26

469:27 470:2,7

470:16 471:20

472:3,5,23

473:11,13,25

474:1,6,12

475:17 476:8

476:10,12,18

477:5,21,22,23

479:6,9,17,23

480:15 481:4

482:5,15,18

483:7 484:5,21

484:27 485:3

485:11,15,23

486:10,12,14

486:22,27

487:2,8,11,13

490:12,22

491:6,11,12,16

491:19,20,22

492:14 493:6

493:16,19,23

495:8,10,22,24

495:27 496:21

497:1,6,14,18

497:20,22,24

497:26 498:1,5

498:6,21

499:18,19,21

500:3,6,23,25

501:2,6 502:4

502:18 503:20

503:27 504:19

504:24 505:1,5

505:22,27

506:3,11,15,19

506:20,26,26

507:5,25 509:9

510:17,18,20

510:22,27

511:3,11,21,23

511:26,27

512:7,8,13,14

512:17 513:8

513:13,19,21

513:24 514:16

515:1,22,23

516:12,17

thinking 23:12

55:24 64:14

76:17 84:2

101:13 235:16

236:27 238:15

244:1 270:17
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129:16 134:21

179:14 287:24

358:8 411:2

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third-generati...

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thirdly 415:6

Thirteen 186:6

thirty-six 388:2

this-- 136:22

Thomas 6:15

58:5,5,17 59:4

59:12,16,20,22

59:25 61:9

122:13 162:23

164:9 190:15

190:15 221:15

222:18 289:9

378:12 386:5

390:24 391:8

393:11 409:23

409:25,27

410:3 412:1

418:16 419:1

445:9 449:2,24

450:7,13,16

489:15 493:4

493:10 497:5

514:19 519:27

thorough 346:20

387:9 476:6

478:6 515:5

thoroughly

382:9

thought 23:7

30:11 50:17

59:22 109:25

113:14,20

115:27 165:6

165:11,12

170:26 201:22

203:19 221:18

271:18 292:3

293:2 295:23

340:11 354:16

364:25 365:24

371:1 381:19

382:11 383:5

384:26 387:24

388:25 402:1

402:17 404:4

415:8 423:8

425:19 427:13

427:25 443:24

450:4 468:14

480:9 490:16

491:8 510:15

511:2,19

thoughtful

277:19 469:14

thoughtfully

516:27

thoughts 61:10

138:7,10 174:7

221:25 390:24

440:24 445:10

463:7 490:4

thousand 99:13

213:3 248:6

259:6 270:1

284:7

thousands 97:19

97:20 98:8

109:19 192:4,9

197:22 198:1

198:10 200:5

213:25 214:17

215:20 219:15

234:11 286:24

thousandth

225:16

thrashable

485:8

three 9:2,20

13:3 18:15

19:8 26:13

28:16 34:15

37:18 43:9

46:14 52:23

55:6 61:27

62:7,18 64:6

70:3 71:23

73:18,25 75:13

76:6 88:7

103:22 105:23

106:13 109:4

112:21,21,23

127:20 130:2

144:14 154:5,7

154:12,16

159:18 177:9

192:22 224:19

231:26 232:3

232:15,21

233:3 247:7

253:21,24

255:21 256:22

257:21,24

258:15,17

259:11,16,16

261:25 262:26

269:4 273:26

274:6 280:9

284:7 287:22

296:9 306:4

307:10 325:20

326:4 358:17

360:3,6,12,18

366:19 373:1

379:18 395:26

398:9 404:21

404:27 405:21

484:23

three-compart...

352:4

three-day

277:25,27

three-dimensi...

95:11,11,16,23

three-fifths

275:7

three-microm...

232:6

three-part 8:23

three-quarters

137:4

three-year 77:14

threshold 138:4

149:10 150:2,2

305:8 339:20

363:4 397:10

397:11,14,17

472:7 514:15

thresholds

145:15 305:4

308:14 342:7

350:14 363:6

376:14

threw 297:18

throughput 2:9

9:4,27 10:21

10:24 11:8

12:7 14:11,23

14:26 16:11,13

16:19 17:1,7,8

17:21,24 18:13

18:19,27 19:19

19:23 20:20

21:4 24:26

25:10,14 26:13

38:21 51:13

57:3 67:10

69:9 89:6 97:4

102:5 103:16

108:22 118:4

127:5 130:4,4

131:18,19

134:8,11

142:19 190:27

226:16 227:14

227:16 267:4

380:21 416:12

424:7 431:10

440:1,17 441:7

455:11 467:14

467:19 470:25

512:27 513:20

throughput-bi...

182:11

throw 29:3,19

29:19 30:26

32:5 35:16

37:7 58:3 77:1

86:11 87:26

137:19 138:26

145:17 269:1

270:3 275:14

426:21

throwing 32:19

36:2 56:5 85:6

thrown 139:5

201:26 269:13

thumb 83:1

thumbs 218:9,9

thyroid 8:22

16:10 26:1

273:10,10

300:5 301:1

302:23 303:1,2

305:13,14,23

306:2,27

315:13

tie 214:15

274:20 289:25

tier 9:14 10:15

10:18 11:5,6,7

13:11,16,24

14:6,6,13

15:18 16:5,5,7

16:20 17:4,9

18:20,22 20:13

20:14,25 25:3

103:14,22

126:3 127:26

128:1 129:19

130:2,2,18,19

130:24 131:8

134:11,18,23

134:25,26

135:9,12,15

136:9,12,13

176:10,15

180:6,25 181:6

182:6,11

185:10,12,22

246:5,6,7,9,9

246:11 288:26

288:26 289:6

301:5 303:18
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357:16,20

358:7 360:12

361:1,17

365:26 366:1

366:14,21

380:16 381:2

395:18,27

399:22 404:25

406:4 414:17

421:13 422:1

422:24,24

433:6,8,26

434:6 438:1

455:1 459:27

483:4,15 508:2

508:3 512:25

513:10 514:11

tiered 288:23

tiering 312:15

ties 12:20

tight 196:16

197:13

tighten 481:25

tighter 150:19

tightly 345:2

time 9:3 10:13

14:23,25 16:3

17:25 19:19

26:16 27:5

31:24 33:9

39:5,5 49:20

51:6 52:7,11

57:2,9,10,13

57:15 60:8,27

62:5,11 79:20

81:3 84:18,18

85:20,25 102:9

105:6,19 127:7

141:26 142:15

153:10 169:21

172:3,18

176:27 177:5

181:25 189:2

193:21 206:8

230:27 260:10

264:20 266:11

270:14 276:27

277:18 278:2

279:2 282:10

299:11 310:4

314:17 317:9

318:8 321:25

322:22 325:10

325:25 326:1,6

326:7 339:24

345:10 358:4

369:20 382:1

385:9 392:13

394:23 411:6

415:17 420:3

420:17,23

421:5 433:16

441:12 442:26

443:2 461:25

477:25 491:11

492:24 498:15

499:17 505:15

512:2 516:26

517:7 519:7

time-dependent

305:16

time-point 421:7

timeframe

314:23 369:5

457:10

timely 518:17

times 28:27 33:5

34:20 38:2

85:3,3,3,4,11

86:5 88:7,11

111:7 122:22

141:22 232:21

233:3,4 236:16

239:3,10,23

274:12 322:8

328:26 337:6

353:22 371:6

371:11,14

391:2 400:15

423:13 432:5

437:22 446:3,8

450:25 472:8

490:17

timing 171:25

420:9,10

Tina 519:27

tiny 224:4,11

tired 85:25

516:2

tissue 69:7 95:12

119:14 181:5

223:15,18

305:22,23

306:2 335:23

460:25 465:15

tissue-based

53:12

tissues 15:15,16

119:13 181:2

311:1 461:5

title 3:5 367:14

TK 370:2,10

372:20

today 4:8,13

5:10 9:25

10:22,27 11:10

12:3,20 19:2

30:2,4 31:18

38:2 60:12

86:12 97:9

103:19 126:21

128:24 129:16

129:22 133:12

134:6,12

148:25 177:1

177:16 179:20

192:16 201:4

207:7 219:26

240:2 246:13

260:26 261:22

265:11 277:14

278:11 279:13

285:1 288:25

289:13 300:19

302:6 307:18

316:14 331:2

339:26 351:17

355:20,25

356:3 358:15

362:23 367:20

375:12,17

376:9,26 379:7

384:25 398:5

405:2 442:6

486:21 515:9

today's 11:23

12:12 187:26

told 95:2 128:11

129:11 134:24

169:1 245:24

245:26 297:14

299:13 367:25

404:20

tolerable 427:2

427:5

Tom 6:15 189:9

412:1

tomorrow 19:2

30:2,4 177:3

177:10,10

187:26 201:8,9

244:2 410:5,7

ton 223:3

tons 349:23

Tony 317:15

tool 18:9 61:1

122:1 128:20

128:22 201:9

220:24,24

241:15,23

260:5,10

280:26 281:22

352:2 394:11

479:24 487:11

tools 9:27 11:2,3

12:7 14:23,26

16:13,19 17:2

17:21 18:8,8

18:14,16,19,23

18:25 19:1,7

19:23 20:20,24

21:13,15 26:6

37:12 83:1

101:7 108:4

125:16 126:9

126:14 127:16

128:8 178:14

179:27 192:4

195:15 196:14

203:25 246:12

277:13,13,21

280:8 286:9

289:19,24

299:27 300:14

300:16 301:1

309:10 310:21

310:24 331:11

340:25 351:26

352:8 353:23

353:26 380:20

392:8 416:18

440:16 446:26

471:11 478:10

489:27 491:21

toothbrush

254:13

top 6:26 34:14

40:3,6 41:18

63:1,11 66:20

72:6 73:4

74:24,25 75:8

75:15 76:3

77:25,26

117:16 147:4

149:4,7 155:19

155:25 156:17

214:12,15

218:26 219:1,2

253:25 255:1

260:7 268:3,23

273:22 276:4

321:13,13,22

357:16 374:12

392:1,5,7

435:3 476:23

476:27 493:2

top-left 46:4

50:1 77:21

78:16

top-right 46:5

62:23

topic 85:27
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213:8 254:18

326:6 392:22

totality 55:4

totally 28:10

30:4 39:27

48:18 49:10

71:2 91:8

176:1 248:4

426:16 430:19

486:20 497:27

toto 26:25

touch 125:20

127:1 130:6,16

178:2 179:27

182:23 246:19

334:2 350:27

514:3

touched 154:24

178:27 249:14

478:9

touching 129:20

179:14

tough 79:21

tox 9:5 374:6,11

Tox-21 212:23

tox21 38:19

60:26 94:23

100:14,17

103:15 142:18

148:10,10

151:9 157:20

158:9,18

159:21 274:24

ToxCast 2:5,6

9:4 20:7,12

21:6 25:19

60:25 87:16

97:1 100:13,17

102:23 103:21

104:8,12

107:25 109:13

114:17 115:22

115:23,27

116:10 117:12

118:6 124:5,25

126:6 128:9

130:21,24

131:25 134:13

134:16,27

135:27 136:13

146:16 152:26

159:7,10,11

160:26 161:13

167:13 179:18

179:21 180:5

180:15 182:6,9

183:6 184:3

185:9,13

318:27 332:9

337:25 356:18

358:10,17

363:16 367:22

368:21,24

369:4,25 370:4

371:17,22

372:19 380:5

385:25 386:8

387:17,20,27

388:6 402:24

403:8 417:24

426:15 437:11

437:27 438:11

439:4 440:1

446:15 468:25

470:23 482:4

483:3 488:1

513:5

ToxCasts 426:4

toxic 138:9

408:18

ToxiCast 191:11

191:12 192:26

193:1,5,9,12

193:16,20,21

194:6,12,13,16

223:17 224:27

225:1,4 226:17

227:20 230:15

230:16 245:24

272:14 278:6

296:18 300:20

toxicities 360:23

361:15 362:5

toxicity 104:18

121:25 178:10

181:16 191:12

191:12 192:19

212:23,24

227:15 303:15

315:11 327:22

327:25 335:1,4

335:10 356:8

357:24 358:8

359:26 360:23

361:7,15,20,26

362:6 364:18

365:3,13 405:4

405:7 427:16

461:26 462:5

463:26 510:19

toxicokinetic

21:4 193:23

194:3,4 223:9

223:13,20

224:14 284:27

286:16 300:18

314:1 351:25

354:11 372:3

TOXICOKIN...

2:9 190:27

toxicokinetics

12:26,26 19:23

19:24 21:4

191:6 193:15

223:8,10,24

230:5 232:27

233:25 245:25

251:15 254:25

256:16 278:8

280:6 302:14

307:21 367:26

374:22 379:4,6

416:12,17

470:25 471:3

482:5,9,11

Toxicological

5:18 6:12

140:22 189:21

190:12 224:26

411:10,25

toxicologist 5:19

355:5

toxicology 6:22

11:3 17:16

103:3 140:21

239:14 315:27

340:1 367:13

469:3 511:24

toxics 7:27

ToxRTool

334:24 360:15

trace 41:2,4

tracer 163:19,22

track 7:4 12:14

193:5 211:26

262:8 344:17

359:8 424:27

483:8 512:14

tract 238:10

372:25

tracts 9:17

trading 86:11

traditional

227:16 234:8

374:6 377:1

464:1

traditionally

14:18 21:14

207:17

train 30:16

86:17

trained 47:4

88:21

training 30:15

86:16 88:4

342:22 377:12

398:21

trans 38:12 58:2

58:6,8,10,15

59:17,18,18

142:17 319:1

transactivation

51:7,14 103:12

103:23 121:24

129:6 168:17

168:19 171:12

333:13 338:17

349:6 423:6

433:8 453:23

transcribes

143:25

transcription

33:19,19 34:2

34:5,6 38:10

76:21 142:17

143:24 144:14

148:10 170:1

170:24 348:6

442:25

transcriptional

16:22 55:7

130:5 321:5

transfer 108:20

108:24

transferability

108:22,24

328:9 339:7

transferrable

338:10,13

352:10

transform 212:4

transformation

73:9 98:24,25

229:18 246:26

320:9,10 322:6

322:7 421:14

transformations

324:14,15

transformed

212:27 213:24

219:14

Transit 373:12

transition 19:6

127:7 387:4,5

431:16

translate 25:23

337:2 391:26

translated

235:18,27

translates 92:18
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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translation

518:14

translocate

33:18

translocation

143:23

transmembrane

175:27

transmission

395:8

transparency

51:23 52:2,22

295:26 317:6

355:22 358:1

388:9 389:4

443:22 445:14

463:14

transparent

424:22 425:8

transpired

387:12 441:16

transport 25:6

206:14,22

trap 447:22

treat 345:26

treated 15:11

232:12

treatment 310:6

375:13 404:19

429:7

treatments

344:16 404:26

404:27 423:17

tremendous

512:7 516:4

tremendously

389:4

Trenbolone

137:24

trend 88:27

205:24,25

trends 155:14,14

triadimefon

360:5 363:12

364:12,14,27

365:5

trial 224:1,9

344:23

triangles 193:3

triangulation

119:9

triazole 355:12

360:3

triazoles 362:7

363:12,25

366:20

Tributyltin

80:20 82:7

trickier 201:12

201:12

trickled 214:11

triclocarban

153:24 154:15

154:20 184:27

185:2

triclosan 153:17

153:25 154:13

154:19 184:26

184:27 253:2

253:12,18

254:2 264:6

284:20 285:19

285:23 479:26

482:15 490:6

tried 169:15,18

207:4 346:19

404:12 422:17

triggers 269:15

trillion 345:20

345:21

trim 60:17

trimming 64:14

triples 101:16

tritiated 342:25

343:16 378:11

Tropsha 87:4

trouble 432:9

troubling 479:1

trout 51:6

304:12 424:3

431:4

true 29:27 30:18

35:3 40:5,24

41:22 42:4

45:3 46:6 50:2

54:6 56:24

62:14 64:20

66:11 78:23

93:21 117:5

120:8 127:14

132:22 135:10

135:25 136:1

137:23 140:4

143:13,26

144:7,21 145:6

145:9 149:9,24

155:2 156:1,6

156:7 157:1,1

160:2 166:7,15

172:10,10

187:8 211:4

212:5 242:8

252:25 265:13

283:13 293:8

303:16 311:27

332:2 334:20

334:20 353:6

395:6 436:6

449:24 458:18

479:14 500:26

true/false 397:13

397:13

truly 47:22 80:7

149:5 298:3,23

315:25 350:12

440:27 448:11

456:12 462:4

truncating

92:27 93:18

truncation 93:7

trust 79:27

401:9

trusting 405:16

truth 27:11

34:10 42:9,10

61:5 86:17

135:3 272:5

try 28:22 49:16

64:14 94:20

121:24 143:12

151:1 155:10

159:25 165:26

170:9,11 175:5

200:10 210:14

210:17 222:19

224:22 228:2

230:22 234:18

290:19 294:25

295:7 306:12

306:17 329:1

359:20 383:18

384:12 417:18

440:23 446:18

446:26 476:13

478:4 481:6

495:4 514:17

trying 22:6,16

24:21 32:21

42:25 57:9,26

66:13 71:16

82:16,17

103:20 104:20

105:26 123:3

138:27 140:2

171:7 191:11

191:13 192:3,6

192:12 195:7

196:14 197:9

198:27 199:18

199:26 204:1

208:18 210:4,4

210:21 221:5

228:2 230:19

235:6 249:2

258:3 260:15

266:22,23,25

267:22,24

290:12 291:20

292:23 294:4

294:10 295:4,6

305:12 306:6

320:4 327:5

340:14 354:1

358:11 387:19

400:19 416:20

416:22 418:3

435:20 451:6

452:2 454:27

457:12 459:5

471:27 476:21

476:26 477:22

477:23 484:2

484:22 485:1

485:18 507:4

515:19

tube 226:3

Tuesday 5:2

tuning 161:24

390:4

turn 7:7 8:17

11:12 14:2

42:8 44:18,24

45:20,20 55:16

56:11,23 78:19

79:12,19 81:25

150:6 189:10

269:19 307:2

330:21 346:2

397:15 510:2

516:23

turned 7:1 23:2

33:21 43:9

44:14 71:6

77:7 84:24

149:15 216:9

293:3 498:25

turning 17:17

44:23 45:2

46:15,22 78:14

78:15 80:16

150:12 153:15

294:17 516:14

turns 42:7 45:3

45:14,24 48:10

72:23 74:9

77:9 80:21

89:6 91:12

96:18 98:11

250:6 260:20

tweaked 43:12

497:26

tweaking 497:23

tweaks 491:22

twice 33:5

109:23

two 5:10 16:2

22:18 33:16

34:20,23,24,25

34:27 35:8

36:19 38:6,7,8
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

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40:8,18 43:9

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63:3 64:11

65:8,18 70:6

71:6 73:4

77:13 81:24

84:26 88:7

94:15,24,24,27

99:24 103:23

104:2,22 105:9

105:21 106:27

107:12 108:19

109:24 113:13

113:19,19,20

113:22 114:21

114:23 115:20

116:21 119:10

120:22 121:2

129:15,20

137:4 140:23

142:4,15

143:10,19,27

146:6,16 153:6

154:11 156:16

163:26 164:7

166:1,13 168:9

174:11 177:2

181:19 191:4

196:21 197:2

200:20 201:14

203:25 204:5

206:11 218:22

221:13 222:27

223:1,12

226:21,22,24

226:26 227:6

228:12 229:10

229:26 234:2

244:10 246:15

250:10 251:21

252:20,22

253:24 258:3

261:25 263:3

264:4 268:7

274:24 280:7

283:5,16 284:6

284:7,8 288:8

289:13 292:20

296:10 307:9

308:16 311:2

313:13 320:23

325:20 335:22

377:11 378:8

408:10 447:18

453:9,25

454:23 465:22

468:23 472:5

472:13 483:2

488:24 493:6

497:17 499:27

502:19 506:3,4

two-dimensio...

95:17

two-generation

357:21

two-step 473:16

tying 336:21

type 20:14 24:9

35:14 36:15

40:20 46:26

47:1 69:7

143:10,12

154:24 173:6

254:14 266:1

298:12 306:22

389:27 418:3

420:12 426:19

431:21 439:13

446:19 450:14

452:4 457:2

511:20

types 31:5 72:18

97:25 98:1

111:14 119:14

129:5 132:7

138:11 153:7

170:3 249:27

260:4 294:27

341:27 357:18

382:13 384:4

384:13 398:2

409:6,10

417:22 447:6

448:24 483:17

490:20 514:11

typical 30:16

41:17 46:4

83:21 98:10

213:27 231:16

247:10 342:27

347:4 349:11

466:27

typically 14:18

66:20 92:22

132:11 184:15

216:5 224:18

229:21 360:20

360:23 404:15

459:2 504:9

513:27

typing 272:20

typo 303:3

U

U.K 240:26

U.S 7:15 103:6

103:10 190:13

191:3 210:21

504:4

U.S.A 87:2

ultimate 105:11

418:24

ultimately 14:17

18:21 60:2

211:25 265:7

289:18 307:16

310:15 311:14

328:18,23

356:24 388:13

389:5 407:9

453:5,16

518:21

ultra-high 38:21

142:19

ultra-high-thr...

212:23

un-clarity

407:20,24

unambiguously

100:19

unbound 248:15

248:16,20

472:21

UNC 87:4

uncertain

209:10,11

uncertainties

258:10 260:1

292:5,19

385:20 475:10

478:14 480:4

484:11 497:13

uncertainty 2:10

22:18 23:25

78:22,27

175:12 198:4

198:12 205:19

205:26 206:1

209:9 221:7

235:9 242:14

242:16,19,23

243:2 244:13

244:14 245:8

246:19,20

247:6,16,19

248:11,16,17

248:23,27

249:1,2,14,24

250:5,7 251:16

252:6 253:9,16

253:16 258:8,9

259:2 262:22

265:6 268:11

268:16,19,21

268:26,27

269:3 271:11

271:17 284:19

294:3,4,11,15

294:26 295:4

295:12 307:7

313:7 353:12

391:3 397:15

397:16,20

398:16,24

445:24 471:4

471:12 472:1

472:12,24,26

473:2,3,13,18

473:20 474:13

474:20,24

475:1,11,13,13

475:18,19,23

475:24,25,26

478:20,22,25

482:21 483:25

484:6,7,23,24

484:24,25

485:4,18,19,22

485:24 487:7

488:1,5,11,22

488:25 489:10

499:26 500:4

507:12

unclear 263:11

291:1 333:26

333:26 336:20

369:20 383:11

401:21 478:13

498:18

uncomfortable

506:14

uncorrelated

240:27 241:5

underestimate

285:25 473:16

underestimated

268:17

underestimati...

474:24

undergoing

10:15 460:17

underlying

308:2 458:7

471:27

underscore

340:17 353:22

354:4 363:4

underscores

313:16

understand

21:27 29:24

31:1,2,4 32:21

40:26 41:17

42:27 49:16,22

57:26,27 58:1

58:14,15,16,21
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58:24 59:1,4,7

59:8,10 62:4

71:10,16 72:23

98:24 99:12

104:15,17

138:23 145:18

159:26 167:15

168:1 180:10

220:2,27 221:2

238:14 250:13

254:15 257:17

258:3 259:5

265:22 270:10

275:4 288:20

292:5,25

297:22 312:2

317:8 320:5

322:4,27 323:4

327:24 329:1

336:27 339:21

342:7,11

343:10 345:26

358:11 386:3

387:6,14

407:14 408:15

419:13 429:20

432:5,14

435:13 437:18

441:5,27

443:15 451:12

464:4 476:22

480:1 505:5

513:16 515:4

515:19

Understandably

442:3

understanding

53:26 82:15

84:2 97:12,18

107:18 154:25

164:24 167:19

167:24 217:1

265:19 267:8

289:1 301:21

301:26 302:19

311:9 314:13

317:22 321:11

322:1,12,17,18

322:19 324:25

334:9 342:16

383:7 387:12

419:25 432:10

441:15 445:17

452:10 498:16

498:19 515:15

understands

439:14 498:8

understood

59:22 165:6,11

339:12 379:21

384:12 409:17

419:25 437:17

438:22 459:9

494:25 507:17

undertake

182:22 186:25

undertaken

484:25

undertaking

186:17 222:24

387:18 511:27

underutilized

340:18

underway

180:16 302:5

314:16 315:16

underwent 15:3

uneasy 496:23

unequally

455:25

unexpected

280:23 465:9

unexplained

212:12

unfortunate

169:24 242:21

unfortunately

33:9 57:8

175:9 193:18

194:11 204:11

209:9 240:2

unified 290:20

uniform 372:6

uniformed 53:4

uniformly

348:27

unimportant

287:11

unique 43:1

87:14 116:14

151:2 192:11

192:12

unit 390:27

505:12

unite 474:25

United 1:12

194:27 310:10

units 379:11

universal 221:21

289:10 290:16

293:10

universally

451:5

universe 2:7

10:23 11:2

16:3 17:12

86:1 87:8,9

89:24,25

101:16 124:27

126:13 127:5

131:24,25

132:1,3,24

133:2,15

135:26 161:3

179:25 183:4

184:3,8 186:20

187:11,15,17

195:8 199:4,6

200:9 204:21

215:13 272:11

272:13,14

275:27 286:12

288:9 311:8,10

370:17 387:27

388:4,5 398:27

413:3 415:24

433:20 438:10

469:21 513:12

University 5:22

6:1,4,7,10,22

189:17,23

190:1,6,10,20

237:12 340:3

411:7,11,15,19

411:23 412:5

unknowable

202:24 203:4

unknown

194:18 196:20

unknowns

209:12 210:16

429:6

unnecessary

217:9

unobservable

202:21

unregulated

15:25

unrelated

101:12

unreproducible

297:16

unsatisfying

200:27

unsettled 101:1

unsupervised

64:24

unweighted

69:26 70:13

upcoming

315:24

update 10:12

221:17 237:24

329:11

updated 10:6,7

uploaded 233:22

upper 114:16,19

155:21 210:23

213:7 231:22

232:4 252:21

256:18 271:14

276:3,11

353:10,13,20

364:6 471:6

479:15

upper-left 79:15

upstream

101:22

uptake 25:7

292:9,18

urge 312:23

313:17

urinary 477:12

505:25

urine 198:19

199:7,11,17

203:21 206:19

207:6 208:5,12

208:14 209:22

209:23 213:10

218:5 234:20

249:3

urine-based

216:3

URL 85:21

272:6

URLs 494:12

usability 306:8

usage 454:26

USDA 6:16

190:15 412:1

use 12:7 15:10

16:13 17:22

18:13 20:10,12

20:23 23:7

30:9 51:22,27

55:6 57:10

61:13 74:10

85:24 86:3

87:9,10 89:15

94:20 98:12

101:7 104:3

106:5,6,7,11

108:4 109:14

113:24 116:26

122:1 130:1

134:9 135:17

162:25 163:1

167:20 168:10

174:17 179:9

180:5 185:14

189:10 191:19

191:21 194:20

198:11 200:10

200:13 202:6

204:1,23 205:6

208:22 209:12

209:20 212:10

215:19 217:10

224:22,22
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230:24 231:27

232:1,17

234:18 241:8

241:25 244:22

244:27 245:2

245:16 251:26

251:27 253:13

256:24 259:17

260:6,10 270:2

270:3 278:17

280:19 286:9

309:10,11

310:15 311:1

311:14 312:16

316:1,21 319:7

326:7 329:27

340:21 341:27

352:1,11

353:10 355:8

355:15 364:13

368:10,11,15

369:22 370:7,9

370:10 374:19

374:22 375:23

376:4 377:27

383:8,20,23

414:2 419:16

422:27 432:4,6

439:5 446:26

448:17 453:11

455:6 459:26

473:6 475:6

477:13 478:24

485:3 486:23

488:14,18,19

490:10 493:22

496:22 505:24

507:27 509:22

515:14,19,22

UseDB 200:4

useful 9:23

45:16 73:21

74:8,22 150:20

214:16 220:23

225:22 228:23

230:18 233:13

250:13 315:8

315:15 328:22

332:18 333:1

333:24 353:26

361:3 362:1

365:19 384:21

385:14 392:8

418:21 424:11

468:6 481:23

506:27 516:19

usefulness

498:15

user 328:7

uses 15:16 47:14

186:25 226:14

276:17 351:20

454:24 478:10

490:10,11

USEtox 52:21

usual 83:17

509:12 515:26

usually 112:12

216:24 229:22

326:11 511:20

uterine 106:17

109:18 112:26

347:6 349:4

420:24

uterotrophic

16:23 32:15

60:14 103:25

104:1,4,7,14

104:15 108:13

108:17 109:11

109:15,17,18

112:1 113:1,7

115:16,18,18

115:24 116:4,8

116:10 117:4

117:12 119:18

119:22,23

121:25,26

127:11 130:5

130:12,25

132:4 134:25

135:3 136:13

138:6,26 139:9

139:19,21

175:16 176:12

180:20 181:4

181:21 239:18

266:18 267:4

280:24 281:10

295:11 308:7

310:25 311:17

311:21 313:11

313:22 317:27

328:16 335:7

335:15,17

336:10 338:17

338:19,22,25

340:27 346:15

346:21,24

348:25,27

357:1 381:4

399:24 400:13

404:15 406:10

406:11,12

407:7,25 408:5

408:10,17

409:5 412:15

414:9,11

415:27 416:11

416:17 419:21

420:11,22

422:1,23

425:12,18

432:4 433:5,8

434:6,11

435:10,21,23

436:2,5,12

437:26 438:2,4

438:13 439:5,6

460:19

UTEROTRO...

2:5 102:22

uterus 104:26,27

105:2,5,7,14

105:27

utility 14:26

306:8 311:20

311:27 314:10

314:20 319:18

339:8 350:21

358:11 359:7

359:13,27

361:22 385:1,8

507:2

utilization

322:20 323:17

352:14

utilize 168:24

327:5 354:11

481:15

utilized 9:9

315:3 322:24

332:10 350:2,2

356:7 401:23

425:15

utilizing 318:3

355:23 436:21

486:11

V

vague 415:8

valiant 400:19

valid 401:22

402:22 413:9

438:27 446:20

453:7 484:18

validate 19:6

84:11 86:18

89:15 103:4

109:13 146:19

508:6

validated 105:15

118:15 356:20

496:24 507:20

validating 18:7

47:14 103:9

279:5 507:19

508:15,17

validation 83:27

85:19 105:18

105:21 106:14

108:4,6,9,21

109:9 113:4

134:22,27

146:4,12

185:22,24

322:21 348:1

387:27 413:11

443:18 467:27

468:6 483:1,11

496:16 498:17

500:17,19

502:23

valuable 118:22

386:3 447:15

468:10

valuate 30:14

valuations

401:20

value 27:7 48:5

48:9 60:16,19

63:12 74:24,25

91:4,5,5 104:1

110:9,16

114:14,21,22

114:23 118:12

118:13 132:13

144:20 145:7,8

145:12 149:4

156:13 167:5

174:8 212:1,2

212:15 214:3

215:2 225:3

232:15 253:6

265:6 273:1

274:7 285:7

286:5 314:4

315:4 321:16

371:17,18,19

371:20,27

379:10 390:5

397:14 401:7,7

402:1,10

405:25 416:13

435:21 436:15

442:20 446:27

447:5,10 448:1

449:13 455:26

455:27 457:11

463:9 481:22

483:3 485:22

510:5 513:4

518:1,9

values 42:18

91:6,21 92:19

139:20 145:7

145:10,15

153:1 155:19

155:25 156:18

187:13 209:14
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 116

209:16,17

212:8,9 215:1

215:1 231:20

242:8 251:2,4

252:15 273:15

282:12 284:13

313:21 319:1

320:15 341:11

343:9 351:5

369:7,8 370:10

371:23 373:5,9

373:16 390:27

390:27 391:4

397:3,5,9,16

398:6,15

403:21 404:21

405:19,21,23

434:12,15

435:2,6,12

436:1,5,19

443:15 448:6

456:1,11 458:2

471:12,22

472:7 475:1

478:25 491:14

492:23 505:25

507:27 508:6

508:10,13

Vandenberg's

426:9

variability 22:17

112:27 218:18

220:17,23

227:27 228:1

230:23 231:2,4

231:13 232:8,9

234:17,22

240:7,17 242:6

245:8 246:25

247:6,24 248:9

248:22,24

249:5,6,9,23

251:17 252:6

252:14 253:16

256:25 259:2

264:21,24

269:11,15

284:21 285:22

286:2,3 292:5

292:17,19

293:1,25 307:7

351:12 353:12

354:1 369:10

372:20 401:24

402:14 404:7

406:13 448:18

448:19 449:21

471:5,13 472:1

472:17,20,22

472:22 473:4

473:17,23

475:3,16,19

478:14,19

479:2 482:21

483:26 488:6

variable 32:16

297:10 403:15

453:15,16

variables 110:17

212:4,4 373:6

374:1

variance 402:3

404:22

variant 90:16

variation 231:3

231:7,8 241:27

242:3 358:16

455:24 461:4

461:18,23

481:16

variety 9:1

24:14 60:13

86:2 141:8

160:23 347:20

380:25 393:25

454:17

various 5:12

12:21 14:20

15:9 37:26

99:1 109:18

111:10 133:14

143:16 163:23

171:5 296:23

300:4 305:14

308:25 309:3

315:20 335:17

348:19 396:15

425:1 455:6

466:13

vary 231:17

241:20 461:13

461:20,22

varying 447:18

vast 54:23 144:6

158:12 187:16

352:6 484:3

vastly 384:1,1

vector 168:11

vehicle 181:8

vein 340:16,16

Venn 284:26

verification

317:12

Veronica 5:26

5:26 68:2,13

189:8,27,27

216:23 219:27

220:1 242:12

268:6 269:7

394:6 399:2

411:14,14

448:15,27

449:15 450:2

450:11,15,26

471:17 483:20

491:27 492:4

496:20 498:21

499:11,23

503:16 511:18

versa 147:10

290:3

verse 230:5

version 30:23

38:7 237:24

275:26 332:9

337:15 346:13

364:4 368:4

441:26

versioning 382:2

382:2

versions 31:20

33:6 254:16

332:10

versus 53:3

65:12 70:17

72:18 76:18

93:1 94:8,9,9

98:27 117:12

122:25 126:25

145:2 155:27

156:27 158:1

159:27 164:22

168:4,20 169:4

173:12 294:9

298:5 313:3,8

321:14 327:24

416:11 419:20

451:17 458:19

467:3 474:22

vertical 63:6

148:26 149:1,2

vetted 141:25

146:1

vi 468:4

viability 484:26

485:1,20

vice 147:10

290:3

view 154:9

173:12 198:8

381:15 418:26

469:27 470:1

493:11

viewed 340:15

viewing 134:1

viewpoint

450:21

viewpoints

447:18

views 447:18

vigilance 458:27

vinclozolin

150:3 184:19

283:18 468:22

468:26

VIRGINIA 1:17

virtual 240:24

305:22,23

306:2

vis-à-vis 61:3

100:26 167:16

308:3,6,9

429:5

visa 468:4

vision 322:7

visual 360:1

361:16

visually 105:8

362:20

vital 377:8

vitro 16:8 32:8

47:8 49:7

53:24 60:13

69:2 83:23

87:27 89:19,22

97:9 98:4

103:20 104:1,4

107:23 108:4

108:18 114:12

116:24 118:15

119:2 128:23

129:2,9,23

135:15 136:7

138:9 141:5,9

145:13 146:4

178:20,21,26

179:6 180:3,8

192:22 194:3,9

194:19 212:24

223:17 224:14

224:19 226:12

228:17,18

230:1,6,23

232:18 233:18

235:3,21

238:21 244:23

246:9,27

247:13 253:10

256:15,15

262:2,11

264:10 266:1

280:5,18

281:18 296:20

310:27 313:21

313:26 331:17

333:11 335:3

336:23,24

342:5 346:9,11

346:12 347:3

353:8 356:21
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 117

358:27 361:3

365:11 368:20

369:25 377:24

379:4,5,8

380:26 381:3

393:26 394:12

395:2 399:23

415:26 421:3

426:10 430:10

430:22 431:10

431:12 433:27

437:11,26

441:7 444:14

450:19 454:18

454:25 460:1

460:13,20

461:1 511:5

vitro/in 229:24

vivo 16:9 47:8

49:7 60:14

69:1 83:23

97:10 108:2

114:24 129:12

129:24 136:8

138:25 178:11

179:8 180:3,15

182:4,8 193:2

229:24 230:6

232:19 234:24

235:2 238:22

246:27 247:14

247:21 248:6

253:11 262:2

262:12 280:5

280:24 281:10

287:16 298:18

310:26 311:17

311:19,21

313:22 328:16

328:18,23

329:8 334:15

335:4 336:19

336:21,25

337:2,16

339:17,20

340:25 347:1

349:14,18

353:8 357:19

361:6 362:5

364:24 368:19

377:24 379:9,9

381:1,3,6

382:20 399:21

399:24,27

400:13 405:8

406:4 409:10

412:15 422:1

422:23 426:10

430:9,13,15,21

431:11 433:5

435:24,26

437:19,21

448:9,10

454:26 459:26

460:2,5,15,20

460:23 461:7

461:13,19,21

464:11 483:4

508:2 511:6

513:6,8

vivo/in 246:9

voices 340:6,11

volatile 116:17

volatility 347:11

volume 15:5,13

220:19 221:1

252:2 253:14

469:7 491:5

volumes 194:25

voluminous

277:25

vulnerability

195:17

vulnerable

376:23

W

wait 393:9

499:18

walk 37:12,15

41:16 143:18

159:3 249:10

272:15 301:11

360:2 370:23

513:15

walking 361:2

wall 48:2,4

Waller 396:20

Wambaugh

96:15 97:9

177:4 190:25

191:2,3 206:8

207:10 217:2

218:21 220:7

221:26 222:23

223:6 235:14

236:7 238:19

240:4,19

242:18 251:23

262:16,25

263:14 270:11

271:23 284:15

286:18 364:5

371:6 372:2,13

379:7 474:2

475:5 477:5

491:9 494:27

495:12,19

498:23 505:26

509:7 517:18

519:21

Wambaugh's

258:6 275:25

401:9

wander 48:17

want 3:3 4:20

6:27 21:27

22:10 24:3

31:23 32:22

37:7,24 39:6

41:1 42:1,17

52:15 59:25

61:12,19 68:25

71:8 77:18

78:24 82:14

88:11 96:24

102:26 106:25

113:27 114:13

117:10 118:4

120:5 123:17

125:20 132:9

136:2 158:20

163:19 165:15

167:27 176:18

182:7 184:23

193:14,14,16

193:24 195:9

196:10,27

198:1,3,7,10

201:7,20

202:27 203:3

204:21 205:6

205:20,21,23

205:25 210:12

211:25 215:3

224:8,9,25

228:25,26

229:19 230:20

231:26 237:24

240:1 241:12

242:20 244:3

245:5 249:15

250:15 257:7

261:7 262:15

264:2,8 265:16

270:24 273:16

274:23 277:1

277:18 282:3

283:8 284:20

285:6 287:6,9

287:10 300:25

303:21 306:7

309:6 316:14

318:7 319:5

330:1,3 340:12

340:15,17

341:7 346:2,5

347:15 350:25

351:11 352:11

353:22 355:5

357:15,25

358:9 359:11

360:16 363:20

364:7,20 365:4

366:27 367:4

381:12 383:9

383:14,20

385:24 390:10

394:8 400:4

402:17 405:21

406:16 408:27

417:6 421:4

424:10,15

433:17 437:23

441:1 446:22

447:22,26

449:9,11

450:18 451:4

452:20 453:1,4

453:6,21

455:22 457:15

468:7,9 483:13

483:22 487:18

487:26 492:20

492:25 496:22

497:5 499:14

500:18 507:27

509:5,6,21,22

509:23 515:3

516:15 517:9

517:15 518:27

519:10,24

wanted 8:16

12:11 53:8

68:22 89:12

100:12 104:6

114:11 120:4

121:23 124:10

130:14 133:18

173:5 207:1

216:23 219:8

262:16 270:7

270:17 275:6

276:21 289:16

291:15 296:20

297:21 300:6

367:24 370:23

374:14 375:21

376:10,19

377:2,5,6

378:9 381:16

389:18,23

394:10 408:25

416:10 449:10

453:14 472:11

477:11 480:9

483:23 492:1

519:6

wants 506:15

warning 275:15
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 118

275:16 422:17

422:19

warnings 275:17

warranted

317:23 400:24

Warren 32:14

91:22 98:14,17

102:17,18,26

120:6,15,24

121:1,6,10,20

122:20 123:12

123:14,21

124:10 129:11

134:24 135:4

140:23 519:25

wasn't 63:6

109:12 115:26

116:7 122:22

123:6 203:23

240:5 249:14

260:17 289:15

317:10 385:18

391:9 404:8

406:20 409:15

427:17,27

477:7,14

watch 34:10

water 8:2 15:10

15:16,23,25

101:20,22

105:12 191:15

195:4 201:24

224:6 345:20

375:23 518:10

520:5

Waters 226:14

wax 96:13

way 17:14 24:12

28:7 35:23

38:6 40:10

41:9 44:8

58:18 68:26

72:24,25 74:20

74:21 78:25

81:3 87:27

92:19,23 93:17

95:27 103:27

105:19,20

107:25 108:25

112:8 118:4

132:16 143:26

144:18 147:4

163:20 170:5

171:9 173:12

197:10 203:11

229:2 231:6

242:5 246:8

250:2 254:5,5

254:10 256:8

258:25 259:3

260:8 265:25

267:22 270:27

276:4 280:4,10

281:4 282:1

284:16 285:3

286:9 296:17

307:17 311:11

324:20,24

327:14 328:22

360:2 361:22

363:8 383:25

383:25 390:20

392:27 415:27

423:5 428:14

431:9 446:24

448:1,21

449:13,25

452:16 453:17

456:16 458:4,9

461:18 465:3

470:3,7 473:5

473:17 474:25

486:13 488:7

488:20 491:4

496:24 499:3

512:10 513:9

513:14 516:19

waypoint 61:19

ways 31:6 42:5

70:12 202:2

203:2 214:14

223:12 242:15

259:24 289:21

308:22 311:19

321:27 376:20

442:26 446:13

457:20 466:9

485:5

we'll 7:7 13:4

20:1 27:19

28:17 37:14

43:17 50:17,17

53:5 68:20

78:21 81:19

96:14 97:8

102:8,13,16

122:8 124:20

125:24 140:8,9

148:6 152:24

170:17 175:1

176:14 177:10

177:21 178:11

178:23 179:7

179:13,24

187:26 190:24

191:5 197:19

200:7,23 221:8

250:7 267:5

268:4 271:27

276:26 277:1

278:13 299:9

299:11 308:20

309:14,17,17

318:10 327:4

333:9 336:16

356:1 379:18

379:21,25

381:13,15

392:25 393:2

393:18 394:1

400:11 401:3

402:19 407:13

410:5,6,7

414:27 415:27

419:13 421:18

425:18 434:13

435:17 439:12

439:20 454:12

459:16,20

462:15 480:18

482:6 493:27

500:13 501:6

we're 8:15 9:3

10:16 12:6,20

13:12,19 16:21

18:4,26 20:5

20:12 21:10,11

23:3,11 26:2

29:6,9,13,25

30:9,13,13,15

30:20,23 31:18

32:19 33:2

40:27 41:18

42:16 43:11,18

45:18 46:2

47:20 51:18,23

51:26 52:16,16

52:23 54:4,15

57:17 60:20

61:7,22 69:2,5

69:22 71:16

75:11 77:11

80:14 82:13,15

82:16,17 84:3

84:22 85:17,18

89:4,5 90:24

91:11 92:1,18

96:11 101:19

102:8 103:18

103:19 104:18

108:3 115:13

115:14 125:15

126:8,24 128:2

128:6,8 131:3

132:5 133:1,4

135:13 137:1

138:21 139:8

139:22 140:2,5

141:17,19

143:2 145:21

146:22 152:12

159:24 170:12

170:13 172:25

172:27 174:26

175:13 176:4

176:27 177:1

177:17,19,21

177:23 178:11

178:13 179:5

179:11 181:17

181:23 182:5

183:11,24

185:7 186:18

186:21,24

187:15 191:13

192:12 195:5

195:20 197:9

197:16 200:3

200:13,17

201:1 203:23

203:27 204:17

205:9 207:8

208:5,8,10,17

208:18,23

209:10,17

210:19,23

211:12,22,23

212:26 213:5,6

213:7,25 216:1

216:11 218:4

218:16 220:12

220:16 221:23

222:8,17 223:6

223:8 225:20

226:6 228:1

229:7,22,24

230:5,8,16

231:7 233:14

234:20 235:6

236:27 238:1,1

238:23 239:2

239:22 241:27

242:5,6,9,9

243:26 244:1,1

244:9,16,22,25

245:5,16,19,20

246:8,10,13

247:11 249:9

250:16 251:14

251:20 252:20

253:11 255:15

256:11,13

258:26 260:4,6

260:9,11,12

261:9 262:13

262:18,20

264:1,14,18

265:14 266:2

266:19,23,25

267:2,2 270:27
FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614

Page 119

271:4 273:4

274:10,21

275:8 276:2,26

277:16 278:2

279:16,27

280:3 281:17

282:1,17 283:3

283:10,19,21

285:2,10

286:22 287:19

289:4 291:20

291:20 292:26

295:16 298:1,3

298:4,5,22,23

304:17 305:12

305:15,22,24

306:2,4 307:3

307:18 308:1,7

308:24 309:9

319:19 326:17

326:23 327:3,5

327:9 328:19

330:1 332:1

333:18 334:5

345:22 353:23

353:25 354:23

377:16,19

378:3 379:17

392:2 407:16

407:23 409:5

416:2 422:15

425:6 427:24

432:9,11,14

434:24 435:25

436:25 438:15

439:19 442:6

461:16 469:27

470:16 479:6

482:10 498:10

502:2 506:16

513:14 516:12

518:4,13

we've 19:15 29:8

29:11 30:24

32:11 43:22

46:3,5 49:8

55:15,17 57:8

61:11,21 65:15

67:10 71:24

73:5,8,9 74:3,6

74:7 75:12

78:2 82:12

83:27 85:5

89:21,22,24

90:20 93:19

95:20 98:4

118:8 122:7

125:13 134:6

135:10 136:6

137:26 138:14

141:17 151:27

153:19 154:5

155:9 156:4

162:7 169:1

171:23 172:11

174:24 176:21

181:18 187:1

192:7 198:27

199:2 200:27

204:9 206:7

209:20 213:9

215:17 219:19

221:5 224:20

232:23,23

233:15 236:10

236:24 238:19

238:21 239:5

244:23 247:25

253:10 264:20

265:24 266:12

274:11 277:14

278:27 279:6,7

279:10,11

280:2,25 282:9

282:11 283:2

284:7 285:1

287:4,11,22

288:24 294:21

296:9 299:20

299:23 301:20

303:22,26

320:24 326:18

326:27 327:2

327:17,20,21

328:11,20

340:23 341:10

341:11 343:27

356:17,21

357:4 358:17

360:10,14

363:15,21

364:4 372:27

405:6 413:10

422:9 434:7

446:2 454:27

459:19 483:5

486:20 489:23

497:18,24

501:4 502:1,18

502:25 512:24

weak 45:22

47:18,18 48:21

50:14 79:8

80:5,6,19,25

83:26 88:27

91:3,3,16,19

91:19 99:27

106:19 115:22

131:13 146:3

320:17,18,27

321:23 347:1,8

370:27 395:18

415:14,15

469:6

weaker 45:27

93:1 115:7

118:6

weakly 78:5

100:15 503:3

weaknesses

412:23 438:5

451:22

wealth 493:13

web 1:9 83:4

191:16 206:15

393:8

webcast 520:10

webpage 393:12

website 10:3

31:25 57:27

387:20 393:12

409:20 410:4

weeding 141:12

week 19:24

20:16,19 21:6

23:20,27 31:25

48:18 62:5

519:6

week's 13:8

weekly 31:13

50:9

weeks 233:24

234:12 425:5

weigh 70:4

105:26 349:21

weight 10:16

11:6 13:13,14

13:23 20:24

21:7 70:5,12

105:10,14

106:16,17

109:18 112:26

127:24 136:10

198:18 205:5

223:14 230:17

230:18 241:2

241:18 253:22

283:22 301:3

309:2 314:20

314:25 327:3

335:8,8,11

337:23,26

338:11,12

339:8 349:4,20

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2 10:18,19 11:5

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2008 15:23

2009 16:8

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2010 224:17

2011 9:25 18:10

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2012 10:6 19:6

193:19 194:22

195:3,13

224:26 241:14

363:18

2013 11:1,4

19:18 51:5

54:27 85:27

126:12 127:3

186:15 204:14

206:9 277:26

278:23,25

300:13 308:18

364:4,5 424:20

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21 9:5,26 123:11

323:13 443:23

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315:12,27

22 87:19 332:23

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23 134:19

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28 373:2

29 113:26 432:9

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2c 459:17

2d 462:15

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3 2:3 8:13 12:24

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3-6 255:22

3-day 105:6

3.1 440:16

3.2.1 460:3

3.a 2:23 351:3

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30 152:26,27

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305-5805 1:10

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31 373:15,26

32 94:26

32,000 86:20

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33 87:19

339 2:10

34 108:9 333:6

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344 348:26

35 72:17 333:6

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37 72:17 313:4

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3rd 48:9

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4 12:25 66:3

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48 57:20

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5 2:3 12:25 66:3

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578 2:16

58 288:9

59 314:6

591 2:17

5th 344:6

6

6 13:1 91:5

110:6 168:22

169:26 170:25

314:9 351:4

371:6,7,14

378:19 471:1,8

471:11 478:6

478:10 495:3

504:10

6.1 472:26 473:3

474:12 478:16

6:13:47 404:5

60 94:6 252:20

607 2:18

617 2:19

63 91:2 415:9

638 2:20

645 2:21

649 2:22

65 415:10

660 2:23

67 15:17

674 2:24

7

7 159:21 168:22

169:26 170:25

299:21 371:14

7,000 84:12

213:3 219:5

70 110:19,20

111:23 230:27

401:26 408:9

70-kilograms

230:26

700 25:21

109:22 223:1

703 1:10

704 2:25

70s 461:16

713 2:26

73 135:22 314:6

335:24

75 91:6,6 314:15

77 321:16

79,000 373:18

8

8 2:4 92:1

118:20 142:16

187:12

8,000 195:1

215:18 258:7

275:27

8,200 38:22

94:24

80 38:13 136:1

160:24

800 49:17 159:8

8000 212:22

82 38:13 187:7

821 29:10 32:12

35:12 71:24

86 135:25

88 187:12

8th 48:8

9

9 91:6 114:23

371:24

9:00 187:26

410:6

90 88:17 187:10

199:22 401:6

402:4,9 413:15

520:19

93 338:20

94 129:22

95 118:11

129:23,24

136:7 196:13

213:25 219:17

245:17 252:9

252:21 254:23

256:17,19

257:18 270:25

276:11 280:18

280:25 281:21

326:16 336:11

364:6 498:24

95th 210:26

213:7,12,15,22

218:27 231:22

231:23 232:1,4

241:1 257:22

371:6

97 50:11 69:10

70:7 81:18

83:19 98:5

133:3 136:3

180:11,11

98 132:4

99 245:16

99th 210:26

9th 344:6