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### FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

MEETING ON SCIENTIFIC ISSUES ASSOCIATED WITH INTEGRATED ENDOCRINE BIOACTIVITY AND EXPOSURE BASED 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 , 201 **4**FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614



### TABLE OF CON**TENTS**

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 3 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

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 5 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 7 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 8 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 9 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 10 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 13 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 14 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 15 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 16 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 17 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 19 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 20 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 21 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 23 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 24 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 25 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 26 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 27 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 28 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 29 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 30 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 31 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 32 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 33 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 34 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 35 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 36 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 37 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 38 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 39 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 40 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 41 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 42 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 43 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 44 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 45 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 46 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 47 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 48 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 49 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 50 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 51 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 52 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 53 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 54 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 55 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 56 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 57 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 58 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 59 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 60 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 61 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 62 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 63 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 64 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 65 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 66 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 67 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 68 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 69 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 70 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 71 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 72 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 73 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 74 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 75 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 76 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 77 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 78 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 79 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 80 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 81 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 82 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 83 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 84 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 85 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 86 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 87 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 88 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 89 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 90 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 91 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 92 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 93 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 94 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 95 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 96 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 97 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 98 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 99 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 100 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 101 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 102 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 103 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 104 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 105 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 106 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 107 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 108 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 109 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 110 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 111 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 112 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 113 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 114 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 115 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 116 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 117 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 118 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 119 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 120 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 121 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 122 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 123 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 124 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 125 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 126 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 127 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 128 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 129 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 130 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 131 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 132 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 133 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 134 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 135 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 136 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 137 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 138 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 139 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 140 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 141 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 142 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 143 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 144 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 145 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 146 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 147 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 148 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 149 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 150 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 151 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 152 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 153 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 154 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 155 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 156 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 157 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 158 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 159 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 160 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 161 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 162 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 163 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 164 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 165 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 166 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 167 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 168 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 169 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 170 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 171 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 172 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 173 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 174 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 175 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 176 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 177 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 178 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 179 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 180 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 181 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 182 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 183 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 184 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 185 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 186 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 187 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 188 1 great data. Thanks. 2 (Whereupon, the meeting was adjourned for the 3 day) 4 * * * * * 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 189 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 190 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 191 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 192 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 193 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 194 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 195 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 196 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 197 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 198 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 --.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 199 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 200 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 201 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 202 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 203 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 204 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 205 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 206 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 207 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 208 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 209 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 210 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 211 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 212 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 213 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 214 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 215 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 216 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 217 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 218 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 219 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:

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 220 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 221 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 222 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 223 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 224 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 --

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 225 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 226 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 227 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 228 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 229 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 230 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 231 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 232 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 233 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 234 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 235 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 236 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 237 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 238 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 239 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 240 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 241 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 242 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 243 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 244 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 245 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 246 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 247 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 248 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 249 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 250 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 251 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;

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 252 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 253 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 254 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 255 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 256 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 257 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 258 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 259 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 260 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 261 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 262 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 263 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 264 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 265 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 266 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 267 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 268 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 269 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 270 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 271 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 272 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 273 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 274 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 275 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 276 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 277 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 278 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 279 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 280 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 281 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 282 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 283 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 284 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 285 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 286 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 287 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 288 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 289 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 290 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 291 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 292 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 293 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 294 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 295 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 296 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 297 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 298 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 299 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 300 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 301 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 302 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 303 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 304 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 305 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 306 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 307 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 308 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 309 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 310 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 311 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 312 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 313 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 314 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 315 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 316 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 317 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 318 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 319 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 320 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 321 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 322 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 323 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 324 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 325 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 326 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 327 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 328 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 329 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 330 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 331 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 332 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 333 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 334 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 335 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 336 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 337 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 338 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 339 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 340 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 341 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 342 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 343 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 344 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 345 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 346 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 347 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 348 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 349 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 350 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 351 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 352 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 353 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 354 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 355 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 356 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 357 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 358 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 359 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 360 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 361 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 362 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 363 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 364 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 365 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 366 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 367 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 368 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 369 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 370 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 371 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 372 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 373 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 374 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 375 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 376 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 377 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 378 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 379 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 380 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 381 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 382 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 383 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 384 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 385 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 386 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 387 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 388 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 389 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 390 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 391 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 392 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 393 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 394 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 395 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 396 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 397 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 398 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 399 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 400 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 401 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 402 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 403 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 404 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 405 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 406 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 407 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 408 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 409 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 410 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) 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 411 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 412 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 413 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 414 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 415 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 416 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 417 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 418 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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 419 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 420 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 421 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 422 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 423 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 424 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 425 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 426 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 427 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 428 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 429 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 430 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 431 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 432 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 433 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 434 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 435 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 436 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 437 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 438 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 439 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 440 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 441 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 442 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 443 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 444 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 --

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 445 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 446 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 447 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 448 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 449 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 450 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 451 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 452 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 453 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 454 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 455 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 456 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 457 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 458 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 459 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 460 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 461 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 462 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 463 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 464 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 465 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 466 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 467 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 468 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 469 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 470 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 471 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 472 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 473 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 474 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 475 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 476 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 477 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 478 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 479 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 480 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 481 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 482 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 483 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 484 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 485 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 486 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 487 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 488 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 489 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 490 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 491 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 492 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 493 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 494 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 495 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 496 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 497 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 498 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 499 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 500 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 501 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 15 16 17 18 19 20 21 22 23 24 25 26 27 DR. JAMES MCMANAMAN: Welcome back from lunch

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 502 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 503 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 504 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 505 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 506 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 507 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 508 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 509 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 510 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 511 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 512 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 513 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 514 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 515 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 516 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 517 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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 518 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 519 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 520 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 1 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 2 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 3 445:5 ,7 446:15 447:10 448:16 449:11 ,27 452:3 454:3 458:14,16 462:11 ,13 464:15 ,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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 5 **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 Page 6 **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** 305:25 **availability** 52:2 141:24 317:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 11 334:25 402:21 463:14 **available** 4:17 4:19 10:3 ,9 12:16,17 14:23 18:9 ,25 20:10 31:3 67:10 107:24 110:1 ,9 133:26 136:14 139:27 164:27 165:3 170:16 170:19 171:8 193:21 198:3 199:1 200:5 ,9 203:11 ,17 204:2,13 206:11 215:13 229:2,7 ,9 232:19 233:20 233:24 234:24 239:19 261:2 265:16,18 279:24 286:16 286:25 313:15 314:26 317:10 318:10 357:14 357:17 ,27 358:23 359:5 359:10,12 360:6,10 361:21 ,25 372:22 374:22 385:13 387:18 443:18 ,19 444:4 452:23 462:3 475:2 477:13 482:2,4 493:9 495:17 504:4,6,7 **average** 44:7 63:10,15 73:19 88:14 114:7 117:17 156:10 157:27 158:1 159:6,10,23 198:17 ,18 212:2,15 215:2 221:13 222:3 222:11 228:2

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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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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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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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

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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

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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

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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

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**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

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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

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**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

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**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

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**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

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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

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**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

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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

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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

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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

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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

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**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

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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

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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

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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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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

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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 Page 47 **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

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**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

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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 **greatly** 7:19 350:20 515:1 518:1 **green** 22:11 46:18 48:1 63:6,13 88:13 147:11 148:9 472:15 **gross** 52:27 73:3 194:24 **ground** 26:3 391:10 **grounded** 318:17 **group** 65:8 79:6 79:6 84:8 86:23 87:5 99:18 109:24 120:13 152:20 152:23 169:10 170:12 181:9 193:3 ,4,8 ,9 199:27 200:14 210:27 237:12 237:13 267:27 272:4 278:6

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109:27 120:8 122:26 135:5 180:26 181:6 291:25 330:16 356:8 357:13 357:23 358:8 361:20,26 365:3 ,13 438:6 **guideline-like** 110:3 ,4 180:24 180:25 **guidelines** 16:8 105:22 110:6,6 266:22 308:4 335:21 366:15 381:2 399:22 404:25 438:5 459:27 **guitarist** 447:21 **guitars** 447:21 **gut** 112:18 238:25 373:13 **guy** 272:7 **guys** 124:9 260:22 269:13 426:22 509:13 509:17 516:2 **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 ,19 375:27 376:3 376:18 ,24 411:13 ,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 ,24 358:18 370:24 388:10,17 434:7 445:21 479:6 489:23 498:2 **hearing** 152:24 176:4 467:23 **heart** 97:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 51 **heat** 39:8 49:24 49:25 ,26 64:27 65:2 142:22,26 143:1 152:9 360:8 361:12 **heavily** 111:27 **heavy** 195:21 **height** 198:17 241:2 **help** 7:4 37:10 64:18 140:6 143:12 145:18 159:25 160:15 160:16 239:12 292:5 ,24 293:12 315:2 319:23 326:25 327:6,23 329:16,27 330:10,15 ,20 337:2 338:24 351:23 372:4 376:23 383:1 384:10,10 386:20 387:5 387:14 389:4 438:9 445:12 450:21 467:20 487:10 491:16 518:19 **helped** 90:8 320:8 372:24 509:10,11 516:17 **helpful** 104:15 171:2 265:19 313:6 323:11 323:16 332:6 332:15 336:26 337:6,17 339:21 352:21 377:25 385:15 387:3 ,11 ,22 388:11 ,14 391:4 438:12 445:17 ,26 478:17 479:9 513:1 **helping** 103:4

315:5 418:9 515:4 519:17 **helps** 63:4,25 98:16 109:6 160:6 333:13 464:3 513:25 **Hep-G2** 292:16 292:26 **hepatic** 225:25 227:24 231:8 379:7 **hepatocytes** 227:25 235:22 246:23 372:12 **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** 234:25 **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 ,16 441:7 455:11 457:5 467:14 467:18 469:19 470:24 476:24 479:11 ,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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 52 12:11 332:16 358:14 360:17 364:20 397:19 430:25 **highlighted** 17:3 162:3 303:23 319:11 371:21 **highlighting** 314:10 395:11 **highlights** 18:14 361:23 **highly** 38:11 114:27 116:16 118:24 119:3 220:14 229:8 256:8 342:9 350:16 397:6 415:12 424:16 424:18 484:14 497:8 513:19 518:9 **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** 5:8 389:20 447:1 **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** 140:25 181:26 182:22 205:24 315:3 354:16 374:4 378:1 406:8 483:13 496:16 498:7 513:1 515:23 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 53 369:2 370:11 373:17 375:3 378:24 379:13 447:12 470:26 471:2,8 ,11 ,22 471:26,27 472:26 473:19 475:17 ,23 476:9 478:10 478:13 ,15 ,20 479:7 ,20,24 480:2,23 481:14,22 482:22 483:7 483:22 484:5 484:17 485:17 488:12 489:21 490:2 496:2 497:7 499:5 500:24 502:10 507:11 ,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

446:11 517:20 **IDENTIFICA ...**  2:3 4:25 **identified** 147:16,18 ,21 147:22,25 ,26 147:27 181:18 217:11 322:24 329:25 366:8 396:17 438:5 502:21 ,25 **identifies** 10:13 **identify** 108:5 109:14 118:5 119:6 126:16 181:27 182:4 195:19 200:21 207:5 233:12 242:20 278:25 291:5 298:20 302:24 311:12 315:5 355:16 357:16 376:1 380:24 393:25 400:22 454:17 481:16 497:8 518:20 **identifying** 15:8 278:15 301:16 316:3 361:4 437:15 484:19 **IDR** 386:8 **iffy** 139:21 **ignore** 54:14 428:16 499:17 **ii** 358:17 371:17 371:22 380:14 440:10 **iii** 380:16 **illustrate** 346:5 361:17 **illustrated** 195:27 350:3 **illustrates** 270:10 471:21 472:13 **illustration** 371:2

**image** 8:16 227:8 **imagine** 35:3 ,15 36:2,16,19 45:19 69:13 71:27 76:19 109:19 170:22 170:26 297:10 505:26 **immature** 105:1 105:24,25 ,26 105:27 110:18 110:20 111:21 112:4 335:15 **immediately** 226:4 356:26 420:26 **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 37:22 80:3 ,4 81:21 85:23 104:17 106:24 107:10 117:24 119:19 142:3 ,6 145:14,16 146:6 156:19 174:10 179:10 192:9 200:21 205:18 211:22 212:11 216:9 216:25 221:27 231:24 237:14 238:4 247:5 255:20 256:25 261:7 269:11 284:22 298:25 311:2 314:2 317:7 319:24 323:21 ,23 325:16 329:1 329:10,12,17 331:15 332:8 334:26 335:2 336:1 ,3 343:6 348:15 349:16 351:17 357:27 358:25 ,26 367:2 373:14 376:1 378:3 386:15 390:5 403:7 405:3 ,26 407:7 412:27 414:15 418:24 419:4,26 420:13 421:9 421:15 425:2

428:19 430:2 437:16,19 444:9 445:23 446:5 448:8 ,10 452:15 453:12 466:10 472:6 474:6 476:13 476:19 479:15 479:23 490:13 490:15 ,22 491:6,7 493:7 504:24 505:2 505:22 506:1 506:11 ,16 508:9 510:8 511:12 **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** 61:11 386:17 512:23 515:9 **improve** 259:25 279:26 289:27 307:4,6,8 311:26 317:2 317:20 324:7 346:7 388:8 390:14 398:3 401:16 491:23 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 54 **improvements** 306:16 311:2 317:23 340:11 **improving** 27:6 307:5 317:6 322:12 **imprudent** 27:10 **in-tact** 287:17 308:12 **inactivate** 119:22 **inactive** 39:21 48:15 49:10 70:8 ,9 87:24 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 ,19 274:20 321:1 408:12,17 436:17 437:6 **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

372:4 380:25 389:26 393:25 413:25 445:21 454:17 456:6 465:20 485:10 490:19 ,26 491:13 500:19 505:2,23 **included** 15:8 87:3 120:22 126:6,15 130:8 130:26 134:27 139:17 150:27 181:21 222:15 232:9 234:1 247:23 282:8 296:24 334:25 358:22 371:3 371:12 372:10 372:12 373:17 413:21 424:4 431:7 459:13 464:23 465:8 465:10,23 490:23 **includes** 14:15 180:1 ,27 424:6 442:13 454:27 463:20 **including** 13:5 15:9 19:18 233:27 266:20 281:26 302:3 303:1 309:2 312:16 332:22 356:5 358:1 412:14 421:26 430:14 431:4 433:4 471:12 485:9 491:4 495:14 511:5 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 55 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 56 **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** 66:27 168:6 173:1 ,13 ,15 261:26 423:23 453:26 465:6 465:11 ,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**

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 57 264:2,3 313:23 383:8 398:2 505:22 506:11 **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 **IP** 106:2 107:20 403:19 **irrelevant** 121:15 **Isaacs** 207:24 **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 ,27 429:7 430:8 431:8 432:11 432:13 434:13 435:10 446:7 446:10,11 453:10 457:4 468:20 475:7 506:13 **issued** 18:10 **issues** 1:4 3:20 24:14 32:12 52:22,23 53:4 80:2 99:12 156:22 201:14 224:12 257:12 292:24 293:12 299:3 302:20 304:13 320:20 326:26,27 327:21 352:8 385:3 405:6 408:24,27 417:17 422:25 424:17 427:6 427:26 444:6 481:6 482:24 486:23 487:6 502:18 ,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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 58 417:11 ,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 ,11 322:22 327:16 328:1 346:20 372:2 399:11 434:20 437:15 476:8 477:22 484:27 487:5 504:22 513:23 519:5 ,18 **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 **Johns** 6:3 190:3 411:17 **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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**knockout** 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 **know** 4:9 ,9 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 ,17 426:3 426:6,6,13 427:25 429:12 430:22 434:25 435:4 448:21 449:23 450:5 452:24 465:7 470:4 476:23 479:25 486:8 487:4,5 488:22 488:27 491:4 497:24 498:7 501:5 510:17 514:22 515:18 517:21 **knowing** 202:27 240:13 286:2,3 **knowledge** 84:10 167:20 264:20 469:5 474:9 ,11 **knowledge-ba ...**  265:1 **known** 46:13 79:8 90:21 91:21 95:10 98:20,27 100:3 116:4 150:25 176:20,22 198:16 235:5 275:5 311:7 336:27 366:22 390:12 444:19 511:4 **knows** 36:12

170:1 **Korea** 103:8 **L 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 **labeled** 40:4,18 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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201:25 203:19 210:8 ,9 217:19 217:20,22 218:10 288:22 311:8 381:9 394:3 400:3 404:22 412:20 422:6 433:12 433:16 440:21 440:22 443:9 443:14 454:21 460:8 463:4 471:16 481:2 500:3 502:16 518:3 519:16 520:15 **leader** 315:14 367:13 **leadership** 509:16,27 519:1 ,11 ,12 520:12 **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** 37:8 192:13 204:6 510:12 **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 ,9 117:18 176:7 181:2 199:14 264:15 332:22 334:1 352:1 359:4 360:21 361:14 362:3 363:6 384:4 415:2 435:15 457:13 459:3 460:18 479:13 514:13 **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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200:1 210:20 211:4,6,9,19 213:20,23 214:19 219:16 254:10 271:14 271:14 352:8 364:7 476:24 479:15 491:10 491:15 499:21 **limitation** 338:2 394:14 396:19 398:18 ,22 414:23 415:6 437:15 464:20 488:16 490:4 492:1 ,5 496:21 **limitations** 17:24 104:4 178:21 292:9 323:6 328:2 331:21 ,22 336:5 ,16 337:22 353:1 380:12,24 381:5 393:24 399:25 400:7 401:3 ,12,15 409:12 412:16 413:19 415:21 422:2 423:27 431:4 433:7 436:10 440:8 454:16,23 455:21 ,21 460:3 ,10,23 462:26 463:6 463:24,24 470:16 480:23 481:23 483:21 486:4,19 488:16 498:7 ,8 502:10 503:5 504:8 505:4 512:24 515:20 **limited** 122:26 122:26 123:1 170:14 199:17 234:20 235:2

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** 21:17 474:4 **linuron** 147:24 151:8 **linurons** 147:24 **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 ,26 136:26 146:1 183:8 ,8 ,10,11 183:20,27 184:21 ,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 ,3 415:24,24 436:21 ,21 ,23 436:23 **listed** 4:16 82:6 82:7 155:11 346:19 357:13 382:25 404:11 **listening** 125:13 328:20 520:10 **listing** 19:16 91:19 **lists** 16:2 87:13 109:10 142:8 146:5 183:10 **lit** 55:11 225:7 **liter** 229:4 **literally** 211:27 219:15 278:19

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 62 **literature** 2:5 60:10 84:8 ,9 84:13 ,14,22 85:2,5 ,11 86:18 ,25 88:3 91:13 ,25 ,27 100:19 ,27 102:22 104:11 109:14,16 120:11 121:17 153:19 176:21 180:19 ,21 ,22 184:14,18 222:26 234:24 238:20 273:6 320:14 330:17 334:15 ,16,18 334:22 346:12 357:11 386:26 389:24 398:12 400:12,13 401:20 402:16 402:23 403:23 422:14,24 429:12,27 438:6,7 453:18 457:26 461:17 462:3 468:24 469:5 474:10 483:4 494:1 ,4 513:24 **literature-based** 403:9 406:3 **little** 12:5 14:3 15:14 16:16 19:3 20:1 ,18 21:27 29:1 30:11 ,12 31:17 31:24 33:5 ,13 37:20 39:6,9 42:27 43:11 ,13 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 ,7 255:21 256:20 258:2,9 259:12 264:9 266:13 270:26 272:18 272:19 273:12 273:17 274:3 274:26 275:13 276:20,27 277:18 290:23 297:24,27 298:2 301:25 306:6,27 308:16 317:1 317:22 318:7 334:2,6 340:24 342:21 345:3 345:11 348:24 370:25 381:24 383:4 384:8 388:17 390:17 401:17 405:1 405:11 ,15 406:10,10 407:20,23 414:21 415:8 419:18 422:12 422:16,16,19

423:4 424:2 425:18 ,19 ,22 426:25 434:13 436:26 438:7 442:11 464:21 482:7 ,10 485:16 486:12 491:16 493:19 497:23 498:16 498:17 501:5 514:5 **live** 31:25 241:19 **liver** 225:25 229:4,7 231:11 231:12 238:10 238:26 248:26 261:24,26 262:4 372:10 424:3 431:4 472:20 482:8 **living** 243:23 405:10,10 **lo** 344:22 **LOBBY** 1:14 **LOD** 491:15 **log** 44:11 48:6 48:10 49:2 63:12 73:12 251:6 319:4,9 391:26 **log-normal** 211:7 240:11 240:20 **log-normally** 211:2,17 **logical** 33:3 481:6 510:17 513:19 **logically** 481:11 **login** 293:23 298:4 **logistics** 12:4 505:6,19 **lognormal** 491:10 **logs** 114:21 ,23 123:27 124:1

132:11 **long** 43:13 46:22 79:6 85:25 101:17 113:21 230:27 236:23 242:8 247:26 265:25 413:1 423:3 428:17 468:23 512:10 512:10,10 **long-term** 261:13 347:5 349:9 350:9 ,11 **longer** 17:25 18:21 60:17 216:5 341:3 420:11 443:2 **longer-term** 353:9 **look** 9:15 15:8 18:18 22:5 25:27 28:21 ,24 32:1 34:25 36:22 39:13 ,14 40:1 ,12 43:18 44:16 48:22 55:16 66:18 68:7 74:20 77:21 79:19 80:25 81:1 ,7 ,8 81:8 ,9,16 82:4 82:18 83:5 ,19 84:25 85:2,23 85:24 95:8 98:10 100:25 103:27 104:7 107:14 111:5 116:16,22 117:10,20,27 119:5 123:2,10 132:8 ,15 ,23 135:26 142:25 144:26 159:4 162:4 163:10 166:3 168:22 176:6 180:9 ,10 183:13 ,20 185:21 187:27

193:19 196:4 197:11 198:10 199:18 200:15 200:16 201:1 ,4 203:15 208:12 210:26 213:26 214:24 215:4 216:13 ,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 ,17 256:20 260:5 260:11 265:8 266:22 268:23 282:22 285:3 293:11 295:17 298:7 ,10 300:16 301:19 301:23 302:12 315:23 317:19 321:3 ,25 ,27 324:20,21 ,23 325:18 329:14 329:19 330:2 330:14 335:6 337:15 339:1 339:18 340:14 341:8 ,20 342:23 345:11 348:18 349:4 350:17 351:3 362:26 368:27 371:13 383:12 383:19 ,23 405:27 406:19 413:23 416:20 417:4,22 421:4 423:11 ,12 424:14 425:6 425:16 430:8 445:23 447:2,3 448:6 452:19

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 63 458:9 463:13 465:23 466:22 468:21 487:9 488:20 494:10 500:18 506:17 507:21 **looked** 11:5 15:15 26:24 30:15 32:9 ,12 43:22 46:3 ,5 55:17 57:27 65:16 67:10 80:15 83:13 85:3 ,7 ,7 87:8 109:22 151:24 157:6 158:25 159:2,2 162:7 172:11 193:20 194:5 200:22 201:6 211:2 214:10 215:7 222:14 223:1 260:25 289:14 293:2,24 317:25 322:13 324:19 386:26 400:17 403:14 404:9 ,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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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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 65 139:6 173:25 363:1 452:5 476:2 **mature** 33:19 125:22 181:22 **matured** 8:27 **maturing** 387:6 **maturity** 335:16 357:7 **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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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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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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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 ,20 492:23 494:16 495:4,8 497:12 499:4,16 500:20 502:22

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 69 502:23 503:12 515:15 **model's** 313:10 380:24 435:21 454:16 462:25 **model-fitting** 161:24 **model-free** 39:27 **modeled** 245:3 256:24 478:20 478:22 **modeler** 205:9 205:20,20,21 207:13 401:9 **modelers** 205:21 **modeling** 2:9 31:1 ,7 61:22 85:26 89:14 96:15 97:5 ,8 97:17 160:27 203:24,25 204:8 216:24 216:25 234:23 236:6 243:1 246:24 247:3 303:5 ,14 306:12,16 307:4,6,9 309:5 317:12 322:1 ,14,24 351:25 352:2 367:26 368:18 370:11 378:27 389:15 442:2 442:20 463:18 471:12 475:12 475:19 ,23 ,24 475:26 484:25 485:4 494:19 495:1 503:6 **models** 2:8 9:9 17:23 20:7 ,12 21:11 28:2 31:8 ,10 50:1 51:27 66:5 78:27 86:8 ,12 86:17 ,26,27 87:20,20,21 ,21

87:21 ,23 ,23 88:1 ,10,16,20 88:26 89:5 ,8 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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31:19 **multi-year** 278:19 **multigenerati ...**  328:17 **multiple** 9:17 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** 252:15 **narrow** 455:27 **narrowed** 109:19 **narrowing** 508:12 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 71 439:15 507:20 **needing** 251:12 **needs** 108:23 127:19 160:14 170:21 298:22 328:25 329:7 335:8 ,21 338:12 339:15 341:20,24 353:16 378:2 383:17 ,21 384:7 385:23 388:20 393:14 402:22 407:9 413:21 414:21 424:11 ,24 425:8 435:24 447:27 451:19 452:19 458:27 490:19 496:15 **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 ,22 117:4 118:13 130:25 143:1 147:9 181:27 182:2,3 308:27 313:12 329:25 329:26 330:7 334:20 338:22 340:16 346:4,9 346:11 ,16 350:6,9 361:13 365:7 383:25 394:27 401:7 405:20 406:14 419:7 425:19 432:6 434:17 436:5 ,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**

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 72 79:4 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 73 **obviously** 12:12 24:27 92:15 111:6 137:15 162:25 163:20 175:22 238:13 243:16 261:2 268:25 285:8 296:8 308:27 323:2 382:1 386:18 387:6 401:15 402:17 404:23 405:4 406:22 408:24 413:21 414:5 414:17 415:1 417:20 420:5 424:7 427:4 451:14 455:11 456:11 461:17 461:17 465:7 465:17 466:2 477:15 ,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** 345:2,14,16,18 **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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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**

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 75 468:12 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 76 240:21 241:8 475:24 **parameter-free** 30:19 **parameterizat ...**  396:4 **parameterize** 228:13 236:11 **parameterized** 207:22 225:22 327:16 **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 ,13 484:24 **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

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**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

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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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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

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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 Page 82 **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 Page 83 368:22 383:27 425:27 435:4 453:22 454:5 460:14 497:21 499:6 512:4 **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

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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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 86 147:9 ,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

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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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**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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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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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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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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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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 93 **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

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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

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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

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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

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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 Page 98 452:9 458:27 508:13 **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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504:1 **sign** 45:12 332:25 333:1 ,2 513:23 **signal** 46:16 60:3 81:25 ,27 82:1 ,18 83:26 115:23 158:14 171:10 172:7 197:4 293:9 395:6,9,10 396:6 397:26 398:14 **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 100 **sleep** 201:17 ,18 **slice** 87:25 273:14 424:3 431:4 **slices** 88:5 **slide** 8:10 14:2 21:2,24 24:20 24:20 33:4 41:2 43:23 ,24 45:21 53:8 54:5 64:23 ,25 65:22 71:1 ,3 88:2,24 89:1 91:2,20 94:6 112:6 114:12 118:18 123:10 130:17 142:3 154:2 162:2 177:8 193:25 204:12,18 214:23 240:7 247:17 250:8 250:10,17 ,20 251:18 ,19 258:20 262:18 262:26 264:16 268:13 303:3 307:27 319:8 329:22 347:8 347:16,18 352:26 360:5 361:10 362:16 362:18 378:19 378:20,25 405:13 415:9 434:19 498:22 498:22 **slides** 25:11 31:18 ,20 33:6 33:10 43:20 45:9 68:25 71:13 85:21 93:4 101:6 104:13 122:15 148:4 154:3 ,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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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 ,10 383:10 394:15 394:16 410:5 430:10 431:19 431:20 439:20 446:17 448:6 449:9 454:24 463:8 469:7 483:22 509:5

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448:5 449:12 475:13 ,27 476:2 478:18 485:13 492:10 515:7 ,14 **statistically** 83:15 104:24 106:26 110:13 112:25 206:20 335:22 395:9 **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

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**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

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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

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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

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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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 107 **textbooks** 344:5 **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 ,9 412:22 416:4,23 421:24 427:9 428:1 430:17 433:14 438:16 438:26 439:17 445:1 446:6 448:13 453:19 454:6,9,11 458:11 ,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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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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2014-0614 Page 109 280:5 295:4 368:22 414:12 420:21 447:20 512:3 515:18 516:1 **thinks** 92:22 149:17 **third** 8:25 13:25 15:26 70:4 113:22,22 129:16 134:21 179:14 287:24 358:8 411:2 476:27 486:26 **third-generati ...**  207:12 221:8 352:24 **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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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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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 Page 112 **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

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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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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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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

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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

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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

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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

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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 ,12 339:8 349:4,20 380:14 387:26 401:13 ,14 422:3 ,17 424:20,21 ,23 424:27 425:8 428:14,17 430:14 431:22 438:12 440:11 461:27 472:19 **weighted** 390:13 425:1 **weighting** 70:7 382:6,7 388:12 446:1 **weights** 181:5 241:19 335:23 347:6 460:25 **welcome** 2:4 3:4 7:10,15 82:18

146:18 164:25 187:20 288:13 374:21 411:2 501:27 **welcomed** 385:25 **welfare** 434:9 484:12 **well-character ...**  108:12 **well-known** 43:25 196:15 429:5 **Weller** 6:17 ,17 64:22,27 65:4 65:6,21 ,24 66:13 ,23 67:18 68:15 94:4,5 161:22,23 162:9 190:17 190:17 240:3 ,4 271:9 ,10,26 324:11 ,12,26 378:14,18 379:15 389:13 389:14 412:3 ,3 451:3 453:8 ,9 453:20 471:16 478:2,3 480:6 481:1 482:18 485:16,23 487:23 ,24 489:13 496:25 496:26 500:21 500:22 502:15 507:7 ,8 ,15 512:1 515:2 **wells** 38:13 226:21 ,22,25 226:26 227:6 275:12 **went** 19:16 37:23 86:9 ,25 99:27 122:18 159:27 194:11 194:16 210:6 213:26 237:21

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333:6 335:19 335:24 337:8 339:10 346:7 346:18 347:1 351:16 362:17 368:5 371:21 376:8 380:12 380:18 381:16 384:27 ,27 385:4,10,19 ,26 386:6,12,20 389:5 390:3 ,16 395:14 396:21 397:12 404:1 408:9 418:21 434:18 440:9 440:13 441:2 441:19 ,24,26 443:18 445:13 456:4 459:13 460:27 471:8 471:20,22 472:24,27 473:26 475:8 476:9 ,14,19 ,20 478:8 479:9 ,18 481:26 499:10 500:7 509:10 510:10,25 513:22 514:1 **who've** 32:9 362:18 **Whoops** 132:16 **wide** 25:20 66:21 81:1 84:15 334:11 351:12 380:25 393:26 454:18 **widely** 145:27 **widening** 508:12 **wider** 152:19 197:15 220:4 451:24 456:6 **wiggly** 41:6 **wildlife** 8:21 61:7 97:3 ,14 311:13 424:12 483:16 513:13

518:22 **willing** 51:18 **windows** 376:20 414:16 421:15 504:23 **winner** 274:3 **winning** 274:4,7 **winnow** 27:13 **wintertime** 505:21 **wired** 189:12 **wisely** 326:7 **wish** 3:14 4:8 209:21 295:27 296:14 **women** 222:4 490:27 492:21 493:6,18 495:2 495:4 504:9 **wonder** 292:3 457:12 **wondered** 22:13 477:9 ,14 **wonderful** 295:23 **wondering** 55:2 68:16 92:16 94:7 122:17 216:26 240:11 242:13 268:16 271:16 **wonders** 390:7 **Wong** 224:17 **Wood** 520:15 **word** 424:23 473:6 **words** 186:21 278:17 312:5 354:6 **work** 3:24 7:17 7:21 9:26 18:5 35:24 47:7 49:15 58:2 60:15 61:25 66:4,5 71:15 87:27 102:4 103:5 ,7 ,7 121:23 122:8

122:11 155:16 198:27 199:2 201:17 208:19 235:5 237:13 245:11 246:10 247:7 ,15 ,27 250:14 252:12 254:11 ,11 256:12 260:7 270:15 271:11 271:16 272:5 296:17 ,20 297:4,14,14,15 299:25 300:3 300:26 301:1 ,9 301:9 302:15 304:1 ,5 ,11 305:13 310:19 313:16,19 314:13 ,23 315:12 317:2 318:24 322:13 323:24 324:6 328:13 329:7 329:19 ,20 330:25 368:1 372:3 383:4,6 383:7 ,13 386:19 402:4 406:23 426:2 433:18 442:5 ,6 470:8 ,9 478:5 489:9 490:1 503:8 510:4,9 511:1 ,25 512:2 515:3 ,8 ,10 516:5 517:5 ,8 517:13 ,19 ,23 519:19 ,22 **work-in-progr ...**  306:9 **workaholic** 201:18 **worked** 31:5 58:1 66:1 77:14 84:6,8 99:21 212:14 361:8 364:26

383:14 519:16 **worker** 490:19 490:19 494:2 494:16 **workers** 222:20 493:5 ,11 ,16 494:5 517:26 **Workgroup** 340:8 **working** 19:5 95:20 101:20 140:5 146:22 160:21 170:12 207:11 234:16 237:6 238:1 276:9 292:27 305:12 306:4 326:25 329:7 330:22 350:12 381:25 414:17 415:4 437:21 494:6 519:7 ,17 **workout** 245:21 **workplace** 221:19 **workplan** 10:3 18:11 ,12 19:5 **works** 95:4 129:25 237:17 237:19 250:14 270:15 328:13 **world** 86:9 98:11 252:8 263:2 426:8 434:23 441:4 **worry** 74:26 80:2,2 81:21 245:6 258:27 259:4,4 457:7 **worrying** 96:21 276:2 **worse** 88:20 245:5 ,18 488:13 **worst** 256:16 259:15 270:13 270:18 ,19

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147:3 374:7 **Y Y**  229:27 230:10 230:10 342:25 **Y-axis** 21:24 22:13 106:17 111:1 117:17 **yadda** 423:7 ,7 ,7 **YARD** 1:15 **yeah** 24:18 40:12 63:16 124:10 137:15 164:1 378:18 384:17 391:8 392:10 393:12 399:2 409:27 417:11 421:2 428:21 432:17 450:11 ,26 466:20 485:27 495:10 497:8 **year** 11:9 19:22 21:3 182:22 201:8 234:8 252:2 256:23 256:23 291:9 294:24 304:9 317:25 327:4 329:14 345:3 ,3 347:19 403:1 **years** 9:1 10:20 14:5 15:22 43:13 47:7 52:12 60:18 77:14 82:23 87:12 103:9 ,11 105:17 107:18 192:7 208:1 226:16 227:19 261:25 265:27 287:3 306:4 326:25 327:1 340:5 347:25 387:1 441:12 504:10 512:9 512:11 513:5 513:17 514:8

518:18 **yellow** 146:24 274:1 378:25 **yellowish** 183:17 **yes/no** 87:22 94:12 116:22 121:9 **yesterday** 189:7 189:11 195:19 196:2 201:13 222:26 243:7 253:2 272:23 292:23 296:16 303:7 304:2,11 320:7 329:16 331:2 347:11 358:18 370:24 378:10 379:3 385:20 404:12 413:7 ,14 414:3 425:21 432:12 441:21 443:23 445:25 456:19 476:11 ,12,17 477:6 488:18 490:7 491:9 515:13 **yesterday's** 268:12 **Yi** 326:5 330:22 330:24,25 **yield** 324:15 396:7 ,12 398:14 **yielded** 15:17 372:18 375:2 396:15 **yields** 398:19 474:20 **York** 504:16 **younger** 504:13 **youngest** 213:10 495:3 **YouTube** 403:13 **Yvonne** 247:27 **Z**

**Z** 76:15 154:6 156:5 ,24 159:4 159:16 **Z-coordinate** 74:6 **Z-score** 73:23 154:7 ,12,15 156:11 157:3 159:18 337:7 **Z-scores** 156:5 332:19 ,20,20 385:1 **Z-space** 73:23 75:12 **zebra** 402:26 420:6 **zero** 41:24 42:18 43:3 65:27 66:6,9 73:23 74:25 75:19 ,27 76:5 81:27 93:9 94:12 117:6 130:24 135:21 ,21 ,23 135:24,25 147:4 166:20 171:14,15 ,20 187:8 ,9,13 ,14 187:16 205:6 206:16,22 209:8 212:2,9 248:15 258:18 283:9 ,13 288:7 397:1 407:6 439:3 ,7 491:15 **zeros** 132:22 135:26 136:1 137:4 140:4 **zoom-in** 148:7 **zoomed** 292:22 **Zyrtec** 224:3 ,4 **0 0**  246:9 ,11 282:12 359:15 359:15 **0.1** 45:24 405:12 **0.665** 272:24

**001** 405:20,25 **01-F** 416:1 **02** 405:25 **04** 131:3 371:20 **05** 145:12,21 152:14 153:3 **06** 133:22 **1 1**  2:6 9:14 10:14 10:14,15 11:6 11:7 12:23 13:11 ,16,17 ,24 14:6,13 15:18 16:5 ,7 ,20,24 17:4,9 18:20 18:22 20:13 ,22 20:25 25:3 45:26 46:1 48:7 50:12,13 70:11 ,11 78:25 92:25 103:14 103:22 111:6 114:15 ,18 ,23 115:13 ,20,26 116:18 118:22 118:25 124:25 126:3 127:26 128:1 129:19 130:2,2,18 ,18 130:19 ,21 ,24 131:2,3 ,8 132:3 ,13 ,15 ,18 132:19 133:4 133:19 ,20 134:11 ,18 ,23 134:25 ,26 135:9 ,12,15 ,15 135:21 ,22 136:4,9,12,13 136:23 ,26 137:1 139:22 140:4 145:8 152:15 176:10 176:15 180:6 180:25 181:6 182:6,11 183:8

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112:12,24 374:8 **1,400** 89:23 183:5 **1,450** 131:25 133:3 135:26 **1,500** 109:17 183:5 **1,800** 38:22 39:11 47:21 50:11 52:24 75:17 83:22 89:23 ,27 98:5 131:25 174:15 174:21 314:7 364:19 **1,812** 31:21 ,22 49:9 **1,855** 151:26 161:5 **1.01** 320:26 **1.3** 471:25 472:4 472:11 **1.4** 371:10 **1.5** 312:16 **1.a** 2:13 331:5 331:10 379:20 379:24,27 380:19 381:8 391:20 392:19 392:20 393:4 **1.b** 2:14 333:8 380:23 391:15 392:13 ,15 ,20 393:19 ,21 ,23 394:2 **1.c** 2:15 334:13 380:27 392:20 399:16,18 ,20 **1.d** 2:16 336:4 407:4 410:6 412:10,12 **1.e** 2:17 337:22 421:19 ,21 **1.f** 2:18 338:16 432:27 433:2 **1/TIER** 2:6 124:25

**1:20** 102:10 **1:30** 309:17 **10** 43:5 48:8 ,8 66:3 ,3 72:12 78:25 84:16 89:8 92:1 95:1 98:9 112:22 159:6,11 185:22,27 197:11 ,14 208:1 212:8 ,8 212:27 228:2 229:16,17 233:3 ,3 258:15 258:17 ,18 ,23 258:23 259:11 262:6,8 263:16 276:26 313:12 321:8 344:6,6 349:22 371:7 371:11 ,14 373:8 401:16 **10-donor** 227:25 **10,000** 16:3 17:11 86:1 89:24 127:4 131:24 161:2 195:8 198:8 201:1 217:14 286:11 495:18 504:7 **100** 31:19 43:6 45:24,25 72:13 72:27 73:6 75:9 77:23 79:19 80:14,16 82:2 96:17 ,27 131:4,16,22 132:13 136:8 137:1 144:21 145:9 ,12,21 146:7 149:6 153:5 163:21 171:14,20 172:11 ,12 193:8 340:5 343:18 364:20 372:6 436:25

**100-micromolar** 114:15 133:22 183:22 **100,000** 171:16 374:10 **107** 10:19 132:4 183:11 **10th** 378:19 **11** 85:10 193:23 193:26 213:9 222:4 234:21 246:9 326:19 495:3 **11.7** 371:26 **12** 64:23 ,26 134:24 230:13 362:17 451:9 **12,000** 495:18 **125** 116:17 **126** 469:7 **13** 67:15 113:26 185:23 ,27 ,27 310:12 432:9 **13,000** 388:1 **130** 281:10 **136** 272:25 **14** 2:4 65:22 **14,000** 194:25 **143** 2:6 **144** 187:12 **15** 9:1 47:7 103:9 202:24 459:16 **15-mintue** 140:8 **15-minute** 67:14 354:23 **158** 356:8 357:13 360:14 361:8 ,20,26 365:3 ,13 **16** 21:24 43:23 43:24 109:1 ,5 119:7 ,12 128:9 128:11 142:16 405:13 **16/18** 249:26 **17** 210:1 ,6 217:24,24

285:17 **17-beta** 77:20 92:25 162:25 321:4 341:12 347:26 348:22 348:24 **174** 2:7 16:2 **18** 19:25 29:9 30:26 39:5 ,10 39:18 43:5 ,14 43:27 45:19 77:22 126:25 134:12 141:6 169:4 185:8 198:25 275:21 278:27 333:12 338:6 380:5 385:12 401:27 408:10 450:10 451:17 **195** 2:8 **1990s** 512:26 **1994** 469:4 **1996** 8:20 **1998** 8:20 **1999** 14:22 16:15 105:17 107:16,24 108:1 **1a** 481:5 488:16 **1c** 412:8 **1d** 412:9 **1e** 421:25 **1f** 432:16,25 **1I** 508:3 **2 2**  10:18 ,19 11:5 12:22,25 14:6 15:20 16:5 ,17 17:5 18:26 20:14,22 54:21 112:7 116:19 132:4,16,18 133:19 ,21 135:24 136:23 136:26 159:10

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454:13 ,15 **2.c** 2:21 334:13 459:20,23 ,25 **2.d** 2:22 337:12 462:22,24 **20** 47:7 89:8 ,8 91:10 105:18 239:1 252:19 321:2,8 **20-** 371:9 ,10 373:19 **200** 97:2 171:22 172:9 193:22 194:7 219:2,3 219:4 **2000** 210:24,26 211:1 **2007** 15:22 315:11 **2008** 15:23 **2009** 16:8 364:12 **2009/2010** 15:19 **2010** 224:17 **2011** 9:25 18:10 19:5 192:24 473:10 **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 ,25 300:13 308:18 364:4,5 424:20 509:17 514:8 **2014** 9:25 10:6 13:1 60:24 204:14 278:1 279:3 300:17 369:5 490:5 ,8 490:18 517:1

**2015** 1:18 304:7 306:3 **21** 9:5 ,26 123:11 323:13 443:23 **21st** 195:13 315:12,27 **22** 87:19 332:23 **22202** 1:17 **23** 134:19 146:15 ,22 178:20 185:20 186:1 ,7 262:18 **23-25** 474:3 **24** 34:20 35:26 57:3 **24-hour** 34:24 **248** 2:8 **25** 107:17 116:17 131:11 152:13 181:18 **250** 193:20 225:1 230:9 **2500** 207:26 208:1 **26** 402:1 ,3 ,6,10 407:24 408:5 408:10 **266** 2:9 **276** 469:7 **2777** 1:16 **28** 373:2 **29** 113:26 432:9 **290** 254:18 255:15 ,15 ,16 284:26 **2a** 439:21 440:15 **2b** 454:12 **2c** 459:17 **2d** 462:15 **3 3**  2:3 8:13 12:24 15:27 16:25 19:3 25:13 118:20 133:26 241:27 242:4 356:25 357:1

360:21 361:14 373:1 405:19 435:17 440:13 462:25 470:17 470:24 502:19 **3-6** 255:22 **3-day** 105:6 **3.1** 440:16 **3.2.1** 460:3 **3.a** 2:23 351:3 351:25 470:19 470:21 471:10 **3.b** 2:24 352:27 480:18 ,20,22 **3.c** 2:25 352:27 502:4,7 ,9 **30** 152:26,27 192:7 252:17 321:3 ,8 **30,000** 127:9 **305-5805** 1:10 **30s** 461:15 **31** 373:15 ,26 **32** 94:26 **32,000** 86:20 87:14 98:8 161:2 **33** 87:19 **339** 2:10 **34** 108:9 333:6 337:8 **344** 348:26 **35** 72:17 333:6 369:20 **37** 72:17 313:4 **387** 2:10 **3a** 480:5 499:25 502:21 **3b** 507:9 ,19 **3c** 498:10 501:3 507:9 ,19 **3D** 95:21 **3rd** 48:9 **4 4**  12:25 66:3 156:11 168:24 191:7 ,9 216:18

356:26 357:3 359:18 360:21 361:14 364:8 435:6,8 436:2 436:6,15 ,19 439:3 ,7 470:25 **4.1** 204:18 **4.4** 136:2 474:15 499:9 500:4 **40** 252:18 275:4 283:21 460:17 **40,000** 87:10 **400** 143:2 152:9 **418** 2:11 **42** 87:19 109:22 406:15 432:8 **43** 113:25 **433** 2:12 **45** 47:11 128:23 131:27 134:17 135:1 **455** 47:9 **47** 240:8 **48** 57:20 **49** 130:21 183:10 **5 5**  2:3 12:25 66:3 89:7 98:5 ,8 110:6,12 191:7 194:1 216:18 220:16 223:7 235:11 357:16 359:15 ,16 364:8 371:11 470:26 478:11 478:12 **5:37:04** 382:25 **5:43:19** 386:9 **5:51:00** 390:11 **50** 39:24 80:27 149:4 150:2 225:4 275:4 299:12 **50-odd** 75:17 **50-percentile**

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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