### FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

**OPEN MEETING**

**PRIORITIZING THE UNIVERSE OF ENDOCRINE DISRUPTOR SCREENING**

**PROGRAM CHEMICALS USING COMPUTATIONAL TOXICOLOGY TOOLS**

**DOCKET NUMBER: EPA-HQ-OPP-2012**-0**818 FIFRA SAP WEB SITE http://www.epa.gov/scipoly/sap/**  **OPP Docket Telephone: (703) 305-5805**

**UNITED STATES ENVIRONMENTAL**

**PROTECTION AGENCY**

**CONFERENCE CENTER LOBBY LEVEL ONE POTOMAC YARD (SOUTH BUILDING)**  **2777 SOUTH CRYSTAL DRIVE ARLINGTON, VIRGINIA 22202**  **JANUARY 29 TH** 

### - JANUARY 31, 2013

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1 **DR. SHARLENE MATTEN:**Good morning. My name 

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

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

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

### 5 four-day meeting. 

### 6 The SAP will consider and review scientific 

### 7 issues associated with prioritizing the Universe of Endocrine 

### 8 Disruptor Screening Program chemicals using computational 

### 9 toxicology tools. 

### 10 As a designated federal official for this 

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

### 12 and am responsible for ensuring the provisions of the Federal 

### 13 Advisory Committee Act. 

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

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

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

### 17 for coming and appreciate their time waiting for the Washington 

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

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

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

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

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

### 23 in the Scientific Advisory Panel Staff for their commitment and 

### 24 assistance with the Scientific Advisory Panel Meetings. 

### 25 The FIFRA Scientific Advisory Panel is a 

### 26 Federal Advisory Committee that provides independent scientific

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1 peer review and advice to the Agency on pesticides and 

### 2 pesticide-related issues regarding the impact of proposed 

### 3 regulatory actions on human health and the environment. 

### 4 The FIFRA Scientific Advisory Panel only 

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

### 6 make comments on regulatory decision making, regulatory policy 

### 7 and implementation of any of the recommendations that the Panel 

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

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

### 10 from this meeting will be included in a report. 

### 11 As a designated federal official for this 

### 12 meeting, a critical responsibility is to work with appropriate 

### 13 Agency officials to ensure that all appropriate ethics 

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

### 15 briefed on the provisions of the Federal Conflict of Interest 

### 16 laws. 

### 17 In addition, each participant has filed a 

### 18 standard government financial disclosure form. I, along with 

### 19 our Deputy Ethics Officer for the Office of Science Coordination 

### 20 and Policy and in coordination and consultation with the Office 

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

### 22 ethics requirements are met. 

### 23 For members of the public requesting time to 

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

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

### 26 those who have not preregistered and wish to provide public

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1 comments, please contact me or another member of the FIFRA 

### 2 Advisory Panel staff. The public comment period will be 

### 3 tomorrow morning starting at approximately 9:15. 

### 4 All of the background materials, agencies 

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

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

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

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

### 9 that information is provided on the agenda. 

### 10 All presentations made by the Agency, all 

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

### 12 and any other documents provided during the discussion by the 

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

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

### 15 agenda prepared for this meeting lists all the contact 

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

### 17 don't hesitate to ask me. 

### 18 After this meeting is concluded, the SAP must 

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

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

### 21 considering all of the background materials, presentations and 

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

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

### 24 meeting, no later than about May 1st. 

### 25 Again, I wish to thank the Panel for their 

### 26 participation. I'm looking forward to this meeting and all of

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1 the challenging interesting discussions that will occur over the 

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

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

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

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

### 6 people that can help you with that. 

### 7 With that, I am going to introduce the Chair 

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

### 9 you. 

### 10 **DR. DANIEL SCHLENK:**Thanks Sharlene. Good 

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

### 12 Prioritizing the Universe of Endocrine Disrupting Screening 

### 13 Program Chemicals using Computational Toxicology Tools. At this 

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

### 15 members introduce themselves, their affiliation and a brief 

### 16 description of their area of expertise. 

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

### 18 the Department of Environmental Sciences at the University of 

### 19 California Riverside. My expertise is primarily mechanisms, 

### 20 modes of actions of chemicals in aquatic organisms and 

### 21 particularly endocrine disrupting compounds. 

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

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

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

### 25 ecotoxicologist and I conduct toxicology research and aquatic 

### 26 ecosystem risk assessment. Oh, and I'm a member of the

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1 Permanent Panel. 

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

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

### 4 Health Hazard Assessment, which is part of the California 

### 5 Environmental Protection Agency. I am chief of the Cancer 

### 6 Toxicology and Epidemiology Section. Carcinogenesis is my 

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

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

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

### 10 a reproductive biologist and molecular biologist with interest 

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

### 12 Permanent Panel. 

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

### 14 Prakash Nagarkatti, Vice President for research at the 

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

### 16 area of immunotoxicology. That is how environmental chemicals 

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

### 18 permanent member of this Panel as well. 

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

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

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

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

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

### 24 Associate Division Director for Computational Sciences and 

### 25 Mathematics at the Pacific Northwest National Laboratory. My 

### 26 expertise is in computational toxicology.

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1 **DR. MARK CRONIN:**Mark Cronin, Professor of 

### 2 Predictive Toxicology at the School of Pharmacy at John Moores 

### 3 University in England. My expertise is in computational 

### 4 toxicology for environmental and human health effects. 

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

### 6 Schultz, Professor Emeritus at the University of Tennessee. 

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

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

### 9 Toolbox and the implementation of the Adverse Outcome Pathway 

### 10 Program within OECD. 

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

### 12 profession at the College of Veterinary Medicine at Mississippi 

### 13 State University, pesticide toxicologist with special interest 

### 14 in neurotoxicology and metabolism. 

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

### 16 Associate Director for Chemical Risk Assessment at the 

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

### 18 of my career in regulatory risk assessment with Health Canada 

### 19 and have a background in toxicology. 

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

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

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

### 23 and endocrine disruption. 

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

### 25 professor at the Scripps Research Institute. My area of 

### 26 expertise is pharmacology of nuclear receptors.

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1 **DR. MICHAEL KEISER:**Michael Keiser, Chief 

### 2 Operating Officer of SeaChange Pharmaceuticals. My area is 

### 3 network pharmacology in computer science and other predictive 

### 4 tools for drug off-target effects. 

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

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

### 7 and drug development and endocrine disruption. 

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

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

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

### 11 environmental media. 

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

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

### 14 European Food Safety Authority and my background is on 

### 15 computational toxicology, molecular toxicology, endocrine 

### 16 disruptors and reproductive toxicology. Thanks. 

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

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

### 19 comments from Louise Wise, who is the Deputy Assistant 

### 20 Administrator from the Office of Chemical Safety and Pollution 

### 21 Prevention. 

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

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

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

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

### 26 This is such an important topic for us. It

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1 has a lot of precedent for how we move forward on a lot of 

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

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

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

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

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

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

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

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

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

### 11 it over to Steve Bradbury. 

### 12 **DR. DANIEL SCHLENK:**Thank you very much. 

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

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

### 15 **DR. STEVEN BRADBURY:**Thanks and I want to 

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

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

### 18 talking about the computational toxicology efforts that we are 

### 19 undertaking in terms of prioritizing chemicals for that program. 

### 20 I want to thank Sharlene and all the staff 

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

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

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

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

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

### 26 happen, so we greatly appreciate their efforts.

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1 Also of course, I want to thank all of you in 

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

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

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

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

### 6 that you spend. 

### 7 I also want to thank the public for the time 

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

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

### 10 11 I think I can't state it enough how important 

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

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

### 14 Pollution Prevention. The administrators made it very clear 

### 15 that science is the backbone, the foundation of the 

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

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

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

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

### 20 science and the best available input from an external 

### 21 peer-review process. 

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

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

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

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

### 26 forward are transparent so everybody can see what's happening,

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1 how the science is being developed, what ideas are being offered 

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

### 3 comments into the steps as we go forward. 

### 4 Very good science without transparency isn't 

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

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

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

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

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

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

### 11 of approaching computational toxicology tools and how we might 

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

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

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

### 15 computational toxicology techniques as we move forward. 

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

### 17 Science Advisory Panels, the concept of using integrator 

### 18 approaches to testing and assessment is a principle or 

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

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

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

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

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

### 24 concepts in technology. 

### 25 A couple of concepts that I think are 

### 26 important in all that paper and all those discussions, and one

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1 of them is not really 21st century per se, but it's more about 

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

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

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

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

### 6 in using computational toxicology, 21st century toxicology, 

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

### 8 answer because there are lots of different questions that are 

### 9 embedded within this emerging technology and approaches. There 

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

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

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

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

### 14 undertaking. 

### 15 Certainly as we see moving forward with 21st 

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

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

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

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

### 20 appropriately as we go forward. 

### 21 In that context, we have different kinds of 

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

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

### 24 it could be actually changing the risk assessment foundation or 

### 25 the approaches to risk assessment and moving into a whole 

### 26 different knowledge system for doing our risk assessments.

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1 But today, what we're focusing on -- this week 

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

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

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

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

### 6 alphabetical order to start going through chemicals. Having 

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

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

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

### 10 kinds of technologies could help us solve. 

### 11 I think another important theme that comes out 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### 26 thoughts as you go forward.

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1 So our concepts in going forward are that 

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

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

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

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

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

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

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

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

### 10 today. 

### 11 The 2009 SAP that looked at the first cut at 

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

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

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

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

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

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

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

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

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

### 21 really important. 

### 22 So some of the specifics as we go forward, 

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

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

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

### 26 to really focus on the underlying in vitro information that's

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1 the knowledge base for the Expert System, and knowing as you 

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

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

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

### 5 the ones that do generally have very low potency. 

### 6 So it's understanding how to interpret the 

### 7 empirical information that underlies the Expert System is really 

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

### 9 in vitro information may be generated and getting some feedback 

### 10 on understanding that empirical information as it forms our 

### 11 category approach will be really important. Especially in the 

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

### 13 seem to have activity. 

### 14 So getting back to decision criteria, having 

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

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

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

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

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

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

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

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

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

### 24 pathway. 

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

### 26 for all your effort in the next several days as we go through

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1 the presentations and into the charge questions. Also as Louise 

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

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

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

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

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

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

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

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

### 10 where we are today. 

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

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

### 13 and context. 

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

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

### 16 Assessment Coordination and Policy Division in the Office of 

### 17 Science Coordination Policy. 

### 18 **MS. MARY MANIBUSAN:**Good morning everybody, 

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

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

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

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

### 23 of endocrine disruption chemical issues for your advice and 

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

### 25 for the opening and keynote address. 

### 26 As Dr. Bradbury had indicated, the work that

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1 we'll be bringing to you today and you'll be hearing about is 

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

### 3 across Office of Pollution Prevention and Toxics, our pesticide 

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

### 5 Research and Development colleagues from our National 

### 6 Computational Tox Center as well as our National Health Effects 

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

### 8 will be speaking over the course of today. 

### 9 Today as Dr. Bradbury had indicated is a 

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

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

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

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

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

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

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

### 17 program, walk you through our current activities and then 

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

### 19 computational toxicology to move us faster, quicker and more 

### 20 efficiently towards our mission. 

### 21 Overlaid on top of this evolutionary timeline 

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

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

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

### 25 please. 

### 26 So the EDSP Mission is very focused. Our

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1 mission is to protect public health and wildlife by screening 

### 2 and testing chemicals for endocrine activity, very specific 

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

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

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

### 6 really focusing on application for regulatory objectives. 

### 7 We accomplish this mission under two 

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

### 9 Cosmetic Act, specifically directed the Agency to develop this 

### 10 screening program using appropriate validated test methods and 

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

### 12 activity and highlighting estrogenic effects for the human 

### 13 population. 

### 14 The 1996 Safe Drinking Water Act Amendments, 

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

### 16 drinking water where there is substantial human populations of 

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

### 18 under. 

### 19 Shortly after the 1996 statutes, we had formed 

### 20 EDSTAC, the Endocrine Disruptor Screening and Testing Advisory 

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

### 22 These recommendations really formed where the program is to 

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

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

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

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

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1 2 It also made the recommendation to expand 

### 3 beyond the estrogenic effects to be inclusive of androgen and 

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

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

### 6 We also had the recommendation that is 

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

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

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

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

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

### 12 whether that interaction occurs to have evidence as an apical 

### 13 adverse endpoint for risk assessment, as well as quantifying 

### 14 that dose-response relationship. 

### 15 Here's what the current Tier 1 screening 

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

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

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

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

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

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

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

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

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

### 25 as well as thyroid axes as well. 

### 26 For those chemicals where weight of evidence

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1 determination has been made based on the Tier 1 battery of assay 

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

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

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

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

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

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

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

### 9 of the Tier 2 will be necessary. 

### 10 The Mammalian Two-Generation Reproduction and 

### 11 Extended One-Generation Reproduction Studies -- these are 

### 12 already validated and OECD validated as well. They are 

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

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

### 15 Invertebrate Multi-Generation Reproduction Studies as well as 

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

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

### 18 plan to bring that before the SAP in 2013. 

### 19 Here's a quick timeline snapshot of where the 

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

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

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

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

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

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

### 26 List 1 chemicals as comprised of 67 chemicals, primarily active

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1 ingredients and a few high production volume inert ingredients. 

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

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

### 4 water contaminants. 

### 5 Shortly after, we refocused our program to 

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

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

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

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

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

### 11 clerical document, the Weight of Evidence Document, that 

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

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

### 14 and OSRI information to make a decision of whether chemicals 

### 15 should and need to advance forward to Tier 2. 

### 16 Highlighted in red, which is not quite visible 

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

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

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

### 20 This comprehensive management plan was issued 

### 21 in June of 2012. It really provides that strategic 

### 22 implementation plan or guidance document for internal use, for 

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

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

### 25 on how do we advance forward with utilization of new 

### 26 technologies, but how do we operationally, as a program, move

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1 forward with our data review processing as well as making some 

### 2 of these critical decisions. 

### 3 As a supplement to the comprehensive 

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

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

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

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

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

### 9 Important to note that this management plan is 

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

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

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

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

### 14 for 2013. 

### 15 So between the time of spring and summer of 

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

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

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

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

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

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

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

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

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

### 25 battery as well as some possibilities for optimization. 

### 26 We also plan to bring before the SAP our

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1 Weight of Evidence analysis on merging all of the Tier 1 and 

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

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

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

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

### 6 to review. 

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

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

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

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

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

### 12 foundation for how we visually plan to move forward in 

### 13 implementing this work plan. 

### 14 The work plan objectives are several. The 

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

### 16 current information as well as new information in emerging 

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

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

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

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

### 21 1 battery. 

### 22 We're proposing a multi-tier testing and 

### 23 assessment framework utilizing multi-disciplinary tools that 

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

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

### 26 building confidence as we go. So the term of learning by doing

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1 and taking significant steps is really critical. When I talk 

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

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

### 4 transdisciplinary, the confidence that our risk managers make 

### 5 and understanding the uncertainty and the bounds of uncertainty 

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

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

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

### 9 But anchoring all of these particular 

### 10 decisions and utilization of tools is a firm understanding of 

### 11 the biological mechanism. Making sure that we understand what 

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

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

### 14 toward that apical endpoint. 

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

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

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

### 18 which is utilizing high through put and in silico technologies 

### 19 to help us prioritize the universe of chemicals, determining 

### 20 which chemicals to move forward first for screening, 

### 21 understanding that we have limited resources. 

### 22 The second phase is taking the high through 

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

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

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

### 26 replacement phase.

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1 And then the final phase is to explore the 

### 2 opportunity of doing a wholesale replacement of the Tier 1 

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

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

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

### 6 timeframes. The timeframes are only dependent on the confidence 

### 7 that we exhibit and demonstrate as we go forward. 

### 8 Moving form vision mission to implementation, 

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

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

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

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

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

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

### 15 acceptable for where you are in that risk management 

### 16 determination phase. 

### 17 For us, the clarity of programmatic goal is 

### 18 determining in what order we screen chemicals for endocrine 

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

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

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

### 22 negative. So minimizing false negatives as well as false 

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

### 24 Having a defined domain of application and 

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

### 26 again understand what is our universe of chemicals, what's

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1 encompassing that domain of applicability for regulatory 

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

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

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

### 5 optimization. 

### 6 But overall, building transparency in the 

### 7 strategy and ensuring scientific soundness is really critical. 

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

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

### 10 flexible and general enough to apply for all computational tox 

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

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

### 13 significant and measurable and what is appropriate for use in 

### 14 regulatory decision making. 

### 15 The last bullet, to ensure scientific 

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

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

### 18 here today doing with the SAP. 

### 19 So the EDSP Universe of Chemicals -- the 

### 20 Universe of Chemicals as well as the General Validation 

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

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

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

### 24 FFDCA as well as the Safe Drinking Water Act. 

### 25 This is the inventory of the Universe of 

### 26 Chemicals. It amounts to about 10,341 unique chemicals. About

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1 1500 are the active ingredients and 5000 are the inert 

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

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

### 4 Water Act Chemicals. These are both the unregulated chemical 

### 5 lists as well as the regulated drinking water lists that 

### 6 combines to form that 3000. 

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

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

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

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

### 11 high-production volume inert ingredients. 

### 12 Since then, about 15 have decided to leave the 

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

### 14 registrants approximately two years to generate that data. So 

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

### 16 There has been some extensions due to laboratory capacity 

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

### 18 data. 

### 19 From the time of test order issuance to the 

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

### 21 almost about five years. 

### 22 So based on the current pace and trajectory, 

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

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

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

### 26 computational toxicology and clear advancements in that area.

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1 It's really heralding and important evolutionary trending point. 

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

### 3 accelerating the testing pace and movement of the endocrine 

### 4 program. 

### 5 So we're here today with a proposal to 

### 6 prioritize our chemical universe using a number of different 

### 7 tools, both current and emerging technologies. But this 

### 8 approach of prioritizing chemicals is not new to the endocrine 

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

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

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

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

### 13 was not ready for regulatory prioritization efforts, they 

### 14 understood that that future would be coming. 

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

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

### 17 data from high through put assays and thereby expanding the 

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

### 19 recommendation that we've taken to heart. 

### 20 In 2007, NAS put forward their critical vision 

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

### 22 all of the existing knowledge and employing both in silico 

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

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

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

### 26 the ER Expert System, and confirmed that that was going to be an

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1 important tool to how we move forward as a program for ER 

### 2 binding mechanism. 

### 3 SAP in 2011 advocated for the use of the 

### 4 Adverse Outcome Pathway. As that umbrella organizing principle 

### 5 for us to demonstrate the utilization of these computational tox 

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

### 7 validation principles to ensure that there is general acceptance 

### 8 and utilization of these tools in a transparent manner. 

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

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

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

### 12 and building upon current and emerging technologies, looking to 

### 13 physico-chemical properties, exposure information, together with 

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

### 15 effects-based chemical categorization approaches using both low 

### 16 and high through put in vitro assays. 

### 17 The prioritization criteria that are 

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

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

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

### 21 believe might be straddling the line. 

### 22 Today, we are demonstrating a prioritization 

### 23 approach that is focused on ER mediated Adverse Outcome Pathway 

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

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

### 26 move us forward with addressing the androgen and thyroid

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1 pathways, understanding that the thyroid pathways would be quite 

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

### 3 generic approach. 

### 4 What emphasized that the prioritization 

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

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

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

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

### 9 assessments that are being described only to demonstrate the 

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

### 11 prioritized and moved forward for Tier 1 screening. 

### 12 Of course, I want to highlight that the OECD 

### 13 Validation Principles was our guiding principle for how we look 

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

### 15 have clear biological mechanism as we move forward, 

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

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

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

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

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

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

### 22 Here is our proposed chemical prioritization 

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

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

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

### 26 have a FIFRA Registration Review Schedule for these compounds.

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1 So every 15 years, we will revisit each pesticide and ask the 

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

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

### 4 assessment. But that workflow process is already establishes 

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

### 6 Tier 1 as well as subsequent Tier 2 tests. 

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

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

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

### 10 Water Act chemicals as well as the inert ingredients where 

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

### 12 move that forward. 

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

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

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

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

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

### 18 animals for consideration in the screening approach. 

### 19 So the first filter is on physico-chemical 

### 20 properties. This filter is asking the question whether the 

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

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

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

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

### 25 screening through the EDSP. 

### 26 So physico-chemical properties is not a new

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1 concept. This concept was raised in the EDSTAC report as 

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

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

### 4 need to be included for screening and testing under the 

### 5 endocrine program. 

### 6 Subsequent to that as we prepare to issue our 

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

### 8 federal register notice where we laid out exclusion criteria. 

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

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

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

### 12 of entry issues before we even see systemic effects. 

### 13 So all of those had been articulated 

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

### 15 boundaries on how we define each of those physical chemical 

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

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

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

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

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

### 21 resources in declaring whether it has anticipation of being an 

### 22 endocrine-active chemical. 

### 23 As those chemical move forward, they move into 

### 24 the exposure potential criteria box. That is only applicable 

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

### 26 upon the candidate contaminate list occurrence information. We

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1 draw upon the public water systems monitoring data, the ambient 

### 2 groundwater and surface water concentrations as well as 

### 3 environmental release and public release and production 

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

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

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

### 7 priority for endocrine screening. 

### 8 Chemicals that would advance forward moves 

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

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

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

### 12 chemical coverage at that time included food use inert 

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

### 14 particular in vitro assays that were highly optimize to identify 

### 15 weak bindings, again because that domain of coverage really 

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

### 17 purpose. 

### 18 So the two in vitro assays that were built 

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

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

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

### 22 When we move the chemicals from the exposure 

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

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

### 25 identifying about 5% that had relative binding affinity that 

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

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1 screening. 

### 2 What remained was about 1700 chemicals. Here 

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

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

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

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

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

### 8 In doing so, the White Paper had provided two 

### 9 critical analyses. The high through put analysis against the 

### 10 reference chemicals, so 37 reference chemicals were identified 

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

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

### 13 System was built on to do that. 

### 14 The second analysis that you'll hear more 

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

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

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

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

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

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

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

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

### 23 of chemicals. 

### 24 When there are chemicals that the ER Expert 

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

### 26 through put data on, we've developed a strategic testing

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1 approach. You'll hear more about that from Dr. Pat Schmieder 

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

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

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

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

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

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

### 8 have moved through this prioritization process, before it 

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

### 10 regulatory programs need to confirm that the chemical meets the 

### 11 Safe Drinking Water Act Amendment Requirement in terms of having 

### 12 a substantial exposure to human population. 

### 13 We also need to ensure that that chemical is 

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

### 15 where we receive our authority to issue test orders. 

### 16 What we learned on the right-hand side in 

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

### 18 in terms of considering read across and formation of 

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

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

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

### 22 looking for opportunities to do so. 

### 23 That completes my presentation of the overview 

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

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

### 26 Just want to note here the order of subsequent USEPA

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1 Presentations throughout the day. Following me will be Dr. Ray 

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

### 3 chemical properties issue and where we've established some 

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

### 5 Following Dr. Kent will be Rick Kolanczyk and 

### 6 Dr. Schmieder who will emphasize the Adverse Outcome Pathway 

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

### 8 System beyond the food use inerts and antimicrobials to also 

### 9 capture the nonfood use inert ingredients. 

### 10 Then Dr. Richard Judson from our NCCT Group 

### 11 will provide an overview of the high through put technologies 

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

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

### 14 using reference chemicals. 

### 15 Dr. Mike Hornung will talk about the 

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

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

### 18 training set in vitro to in vitro. 

### 19 Then finally, we'll hear from Rick Kolanczyk 

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

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

### 22 strategic testing approach to get at the unknown binding 

### 23 potential chemicals. 

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

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

### 26 today's presentation and a good look forward on the next few

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1 days in terms of some of the recommendations that we'll be 

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

### 3 4 **DR. DANIEL SCHLENK:**Thank you for the 

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

### 6 the general overview? Dr. Chambers? 

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

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

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

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

### 11 the others? 

### 12 **MS. MARY MANIBUSAN:**We look to our statutory 

### 13 authority under FFDCA for pesticide active ingredients that 

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

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

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

### 17 it moves into EDSP screening. 

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

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

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

### 21 **MS. MARY MANIBUSAN:**We will not be 

### 22 considering exposure for the prioritization of inerts. We will 

### 23 move those forward for prioritization. 

### 24 **DR. DANIEL SCHLENK:**Dr. Schultz? 

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

### 26 can you give me an Agency definition of what lower priority

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1 means? 

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

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

### 4 particular SAP is to help identify those chemicals that should 

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

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

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

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

### 9 prioritization, but in a subsequent list perhaps. 

### 10 **DR. STEVEN BRADBURY:**Another example going 

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

### 12 mixtures, it recommended first take a look at individual 

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

### 14 information on the individual compounds before trying to figure 

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

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

### 17 lower priority with the idea of following up on that 

### 18 recommendation, being discrete individual chemicals first. 

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

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

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

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

### 23 the morning. 

### 24 **DR. RAY KENT:**Good morning. 

### 25 **DR. DANIEL SCHLENK:**Excuse me just a minute 

### 26 Dr. Kent. Can I just remind those of you that are on the phone

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1 to please mute your line? We're getting some static through on 

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

### 3 line. Thank you. 

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

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

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

### 7 that says physico-chemical properties approximately N equals 

### 8 5000. 

### 9 So I think it's long been recognized that 

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

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

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

### 13 objective criteria to determine which ones should be tested and 

### 14 which ones that do not need testing. 

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

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

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

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

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

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

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

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

### 23 Mary covered this to some extent is the 

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

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

### 26 chemicals. In the Federal Register Notice of September of 2005,

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1 there actually were a list of chemicals that were actually 

### 2 specified, including polymers with number average molecular 

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

### 4 reactive and corrosive chemicals among them, defer mixtures and 

### 5 then some others as well. 

### 6 What we'll be talking about today are the 

### 7 exclusion principles, primarily the second two bullets, 

### 8 exclusions based on ionization and electronic charge, and then 

### 9 on the third bullet exclusions based on chemical reactivity. 

### 10 The ionization and electronic charge would include strong acids 

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

### 12 reactivity where chemicals that are corrosive as well as 

### 13 chemicals that hydrolyze in the environment. 

### 14 For the first set, for the exclusions based on 

### 15 ionization and electronic charge, the criteria are based 

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

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

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

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

### 20 chemistry. I promise not to do very much. 

### 21 So for our purposes, an acid is a proton 

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

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

### 24 For the acids and the acid dissociation constant can 

### 25 characterize that equilibrium. What we typically use for the 

### 26 acid dissociation constant is not the constant itself, but the

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1 negative log of the constant, which we call the pKa. 

### 2 The base is a proton acceptor. Ammonia is our 

### 3 example here. It also has a base dissociation constant 

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

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

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

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

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

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

### 10 For strong acids and bases, the concern is for 

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

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

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

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

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

### 16 molecule itself. 

### 17 The criteria for acids and bases that we're 

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

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

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

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

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

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

### 24 We have a couple of examples. Phosphoric acid 

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

### 26 and 12.9. The lower pKa of these is 2. So, we would exclude

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1 this chemical. On the other hand, guanidine is a strong base. 

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

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

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

### 5 would exclude that. 

### 6 For charged species at pH 7, the basis for 

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

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

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

### 10 initiating event of that Adverse Outcome Pathway. The natural 

### 11 substrate for the estrogen receptor and many of the inhibitors 

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

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

### 14 to interact with the estrogen receptor. 

### 15 So our criterion for exclusion is if less 1 

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

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

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

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

### 20 for bases. 

### 21 Again, an example is barbituric acid has 3 

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

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

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

### 25 of information is ChemAxon. ChemAxon actually enables you to 

### 26 look directly at the percent charged or uncharged species at any

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1 given pH. Direct calculation from the ChemAxon website 

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

### 3 7. So this would be excludable. 

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

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

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

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

### 8 website, the calculation shows 1.65 percent uncharged species at 

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

### 10 We have criteria then based on chemical 

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

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

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

### 14 looking for chemicals that are particularly unstable in a 

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

### 16 relatively short half-life in the environment. 

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

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

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

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

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

### 22 already seen these as examples of chemicals that were excludable 

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

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

### 25 The dodecyltrimethylammonium chloride is a 

### 26 very strong cationic detergent. It does not have a function of

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1 what you can consider like a reactive functional group as many 

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

### 3 corrosive chemical. Potassium permanganate is a well-know 

### 4 strong oxidizing agent. 

### 5 For hydrolyzable chemicals, we came up with an 

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

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

### 8 very little difference. Some examples of estimated hydrolysis 

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

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

### 11 excludable under our current criterion. Aspirin with a 

### 12 half-life of 61 days would not. 

### 13 This is a very important slide in the 

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

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

### 16 the question, is this really scientifically supportable and 

### 17 biologically plausible. 

### 18 So this is sort of a summary then of the 

### 19 chemicals that were excluded applying each of these filters. 

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

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

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

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

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

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

### 26 or greater than 9.

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1 We're keeping them separate at this point 

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

### 3 criteria. One is the testability or the tissue reactivity 

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

### 5 initiating event. Thank you very much. 

### 6 **DR. DANIEL SCHLENK:**Okay. Any questions or 

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

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

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

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

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

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

### 13 natural substrate for the estrogen receptor is nonpolar. So 

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

### 15 likely to interact with the estrogen receptor than the natural 

### 16 substrates or many of the known inhibitors of the estrogen 

### 17 receptor. 

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

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

### 20 **DR. RAY KENT:**Yes. 

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

### 22 for clarification again, you talked about exclusions and needing 

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

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

### 25 regard to interpreting the physico-chemical cutoffs. 

### 26 **DR. RAY KENT:**I think the intent is that

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1 we're going to have a group of expert chemists and perhaps 

### 2 toxicologists look at each of these predictions and determine 

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

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

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

### 6 algorithms are before we actually accept them as valid. 

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

### 8 **DR. JAMES MCMANAMAN:**Yes, Jim McManaman. 

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

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

### 11 made these exclusion criteria, you were thinking only about 

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

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

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

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

### 16 in particular. 

### 17 **DR. JAMES MCMANAMAN:**I was thinking that a 

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

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

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

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

### 22 pathway. 

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

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

### 25 about as far as excluding if their number average molecular 

### 26 weight are typically those that you might think of as industrial

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1 polymers where they're made up of subunits and you have a 

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

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

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

### 5 particular kinds of polymers would have any particular 

### 6 biological activity such as a peptide hormone might have. 

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

### 8 to respond to that too? 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### 23 identifying which chemicals would have the ability to interact 

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

### 25 but also understanding the downstream pathways. 

### 26 So today, we're focusing on the AOP for the ER

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1 Expert System recognizing that these physical chemical 

### 2 properties might have application across multiple pathways. 

### 3 **DR. DANIEL SCHLENK:**Dr. McManaman? 

### 4 **DR. JAMES MCMANAMAN:**So the reason I bring 

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

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

### 7 would normally exclude as being direct activators of the 

### 8 estrogen receptor can activate the estrogen system indirectly. 

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

### 10 molecular initiator is because at the molecular initiating event 

### 11 is direct interaction with estrogen receptor, then these other 

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

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

### 14 alternate pathway or if you have something in mind. 

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

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

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

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

### 19 estrogen binding, ligand binding domain, realizing there could 

### 20 be other pathways that could initiate perturbations in the 

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

### 22 This conversation was just getting that if there was a 

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

### 24 different exclusion parameters that would make sense or not make 

### 25 sense in the context of different initiating pathways. 

### 26 Getting feedback from the panel will be

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1 important in terms of to what extent is something that is highly 

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

### 3 broadly usable whereas there could be other components of these 

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

### 5 advice that are appropriate for this specific initiating event, 

### 6 but they may not be appropriate for a different initiating 

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

### 8 trying to hope to get some advice from you. 

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

### 10 Safe. 

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

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

### 13 Adverse Outcome Pathway specific series of criteria for your 

### 14 exclusion factors. Have you thought about having additional 

### 15 exclusion factors that maybe other molecular properties, not 

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

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

### 18 other exclusion principles for reactivity, maybe you could get 

### 19 that down to 4000 or 2500. 

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

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

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

### 23 there that would be helpful. 

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

### 25 Safe? 

### 26 **DR. STEPHEN SAFE:**Yes, Steve Safe. I was a

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1 little bit -- not worried, but interested in the one category or 

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

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

### 4 activating the estrogen receptor and maybe even some other 

### 5 receptors. 

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

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

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

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

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

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

### 12 basis. 

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

### 14 Chambers? 

### 15 **DR. JANICE CHAMBERS:**Jan Chambers. Real 

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

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

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

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

### 20 21 **DR. RAY KENT:**Ray Kent. I think for the 

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

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

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

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

### 26 For the charge that pH 7, again that was more -- that was

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1 perhaps not as robust in terms of definition, but we felt that 1 

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

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

### 4 exclusion. 

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

### 6 Ms. Manibusan? 

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

### 8 considering the kind of holistic process of applying the 

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

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

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

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

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

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

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

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

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

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

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

### 20 determination whether based on weight of evidence, whether it 

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

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

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

### 24 None of the filters are independently applied. Everything moves 

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

### 26 about physico-chem properties at the onset, we'll be hearing

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1 more about that in the ER Expert System and how each one will 

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

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

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

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

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

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

### 8 resources on the remaining chemicals that really require a lot 

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

### 10 focus on those chemicals with higher priority that we should 

### 11 move into the Tier 1 screen. 

### 12 **DR. DANIEL SCHLENK:**Thank you. Any other 

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

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

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

### 16 come back at 20 till 11. 

### 17 (WHEREUPON a recess was taken) 

### 18 **DR. DANIEL SCHLENK:**Okay. Our next 

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

### 20 Kolanczyk. 

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

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

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

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

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

### 26 the pathway and where we are. As you see, we're down here

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1 talking about the Expert System. The topics I am going to cover 

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

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

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

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

### 6 principles from that review because they are very important to 

### 7 understanding the Expert System approach and to our expansion of 

### 8 the Expert System. 

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

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

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

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

### 13 physico-chem filtering. 

### 14 So under the history of the development of the 

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

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

### 17 regulatory domain was built specifically to cover food use inert 

### 18 ingredients and antimicrobials. 

### 19 Also in the review, we had talked about in 

### 20 2009, some initial steps to automate the system we'll bring 

### 21 around and tell you where we are with that, and then again, 

### 22 expanding the domain of the system, further evaluations of 

### 23 chemical groups. Rick will go through how we do that, and again 

### 24 building of version two. 

### 25 So the whole approach is really moving 

### 26 incrementally. You're building from initial steps and

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1 addressing new challenges. The ultimate challenge we want to 

### 2 talk about here is addressing the EDSP Universe, which it not 

### 3 only has food use inerts, but also nonfood use, fragrances and 

### 4 the Safe Drinking Water Act chemicals. 

### 5 So as we went into this, we were actually 

### 6 addressing five different goals. The first one was from again, 

### 7 Expert System version one and I don't want to get confusion here 

### 8 on the different versions. So we took to 2009 SAP basically was 

### 9 what we're calling version one of the Expert System. So then we 

### 10 were asked, now we have a new universe. So how did that -- how 

### 11 does that system cover that universe? 

### 12 We also have been working on expanding to 

### 13 Expert System version two. We're also going to talk about how 

### 14 that covers this new universe. Then, we're going to identify 

### 15 the remaining chemicals of unknown binding potential that are 

### 16 outside the model domain. Then, this is also we tried to 

### 17 determine where the high through put data could possibly cover 

### 18 those unknown binding potential chemicals, and then also the 

### 19 question of determining if and then how we would use the high 

### 20 through put data to expand training sets to cover unknown 

### 21 binding potential chemicals. As you see, the different sections 

### 22 of the White Paper are addressed here. It is kind of going to 

### 23 be our road map to lead you through where we're going through 

### 24 all the talks today. 

### 25 So the mechanistic basis of the Expert System, 

### 26 again you've heard about the Adverse Outcome Pathway context.

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1 I'm going to talk more about that, the (Q)SAR principles, and 

### 2 also in vitro assays designed for a purpose, this specific 

### 3 purpose here. So just for those that might not be familiar with 

### 4 Adverse Outcome Pathways, it's a conceptual approach. 

### 5 To look at chemical effects across levels of 

### 6 biological organization where -- and the levels of organization 

### 7 are where you can make measures, but what it is, is to really 

### 8 try to look at that linkage so that when you measure something 

### 9 that is the initiation, the chemical interacting at the receptor 

### 10 in this case. That's the initiation of a pathway. It doesn't 

### 11 mean everything will always go all the way down that pathway, 

### 12 but it's a way of starting to build linkages and understand so 

### 13 that when we understand more about chemicals that can start the 

### 14 initiating event, we start to then try to be able to get to 

### 15 where we can, in this case, prioritize chemicals that might 

### 16 eventually -- and there's plausible linkage. 

### 17 There's a plausibility that a chemical that 

### 18 acts at the receptor could be an induce vitellogenin, for 

### 19 instance, in fish. Then at the tissue level, it will alter the 

### 20 proteins and the hormones. At another tissue, in the gonad, you 

### 21 might get ovotestes. Then even in the individual, it might 

### 22 initiate sex reversal or altering of behavior because of 

### 23 changing in secondary sex characteristics, decreasing in 

### 24 reproduction. There can be population consequences of that. 

### 25 So the pathway -- it helps you understand 

### 26 where you are and where you have information, and where you

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1 don't have as much information or where there is more 

### 2 uncertainty, but it gives you a place, as you can see, the 

### 3 greater toxicological understanding is usually to the left side 

### 4 of the pathway where we can really look more at mechanisms. But 

### 5 the risk assessment relevance on the right side, what's 

### 6 happening to the individual and to the population. 

### 7 So using the ER binding affinity as an 

### 8 indicator of potential reproductive effects, we're not 

### 9 predicting in vivo, but we're looking at -- we try to use this 

### 10 to have mechanistic linkages. We know that they exist between 

### 11 the endpoint of ER mediated impairment and the hazard endpoint 

### 12 of just binding to the receptor. 

### 13 The foundation of the assays used to build the 

### 14 Expert System is the AOP, the molecular initiating event we're 

### 15 looking at in the pathway is identified. It is ER binding. The 

### 16 Expert System identifies which chemical structures can initiate 

### 17 that pathway. The pathway context provides again the conceptual 

### 18 model used to generate testable hypotheses. It also provides 

### 19 decision making rationale for the regulatory community again, 

### 20 understanding how much you know at different points along the 

### 21 pathway. 

### 22 This particular pathway also is an area focus 

### 23 consistent with the legislative directive. Estrogenicity was 

### 24 the initial legislative directive behind endocrine disruptors. 

### 25 Chemical binding to the ER is known to have potential to cause 

### 26 adverse effects. Evidence existed that very many diverse

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1 chemical structures could bind to the ER. So it seemed like a 

### 2 good logical place to start. 

### 3 It's important again in earlier discussion 

### 4 that was had here already is to understand that yes indeed, it's 

### 5 the ER binding -- it's this pathway we're talking about and it's 

### 6 not other ways of causing disrupting the estrogen pathway that 

### 7 what might work through a different molecular initiating event. 

### 8 As was discussed, that's important because the chemical 

### 9 structural drivers, that chemical biological interaction, will 

### 10 be different, although the downstream effects may be very 

### 11 similar if you're initiating event is different than the 

### 12 chemical drivers getting you to that initiating event will be 

### 13 different. 

### 14 So the two levels that we're looking at, if we 

### 15 talk about the in vitro assays or our training set, our focus 

### 16 area is we're looking at receptor binding. We're also verifying 

### 17 within these chemical structural groups that we're looking at. 

### 18 We're verifying that that binding can translate to a 

### 19 tissue-level effect. In this case, it's altered vitellogenin 

### 20 protein. But the focus area of our Expert System or the (Q)SAR 

### 21 part of it is on that receptor binding itself. 

### 22 So we're working at two different levels along 

### 23 the pathway, but it's really the initiating event that we're 

### 24 trying to predict, what chemicals can potentially start that -- 

### 25 well can start the pathway, but potentially lead to other 

### 26 effects.

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1 Okay. So in applying the OECD Principles, the 

### 2 desired outcomes are really transparency and usefulness. I will 

### 3 walk through these and what this means in the context of how 

### 4 we're using it. 

### 5 So first, can the (Q)SAR estimates be 

### 6 explained mechanistically. The (Q)SAR, the OECD principles for 

### 7 (Q)SAR validation include a defined endpoint. Well, we're 

### 8 talking about in this context, a well-defined endpoint or what 

### 9 we called in the last review we called the bioassay domain, so 

### 10 that you start to understand the endpoint both from the context 

### 11 of the biology but also the chemistry and the interaction. So 

### 12 it informs an important risk assessment endpoint, the AOP ending 

### 13 in impaired reproduction as we talked about, plausible linkage 

### 14 of that initiating event to higher level, adversity, 

### 15 interpreting the measurements, understanding what it is in those 

### 16 assays that you measure and confounding factors to those assays. 

### 17 18 On the chemistry side, using in vitro assays 

### 19 that allow testing of the types of chemicals found on that 

### 20 regulatory inventories, the range of the chemical properties 

### 21 then that are imparted by those chemicals. Again as we'll talk 

### 22 about, these are really the very low affinity chemicals. 

### 23 Understanding the chemical form and the concentration in the 

### 24 assays, we also had talked about and had additional review on 

### 25 how important that was. 

### 26 Mechanistic interpretation, being able to

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1 explain the predictions mechanistically again with both respect 

### 2 to the chemistry and the biology, the relationship of the 

### 3 predicted parameters to a regulatory question, the linkage 

### 4 through the AOP, the relationship of chemical parameters to 

### 5 biological activity, are they reasonable, do they make sense to 

### 6 similar chemicals. 

### 7 The model applicability domain -- so that is 

### 8 the training set measurements that are used to then build the 

### 9 logic-based rule Expert System is the model. So how well does 

### 10 that cover the regulatory domain, which is the EPA question, the 

### 11 chemicals on the list of the regulatory domain -- so 

### 12 understanding that. That Expert System again is decision tree, 

### 13 it's logic, rules. The local in vitro training sets are what 

### 14 these rules are based upon. The Expert System model domain 

### 15 should adequately cover the regulatory domain. Again at that 

### 16 time, it was the food use inerts and antimicrobials. We 

### 17 completely covered that domain when we built the first system. 

### 18 Appropriate measures of goodness of fit, 

### 19 robustness, ability to predict -- those measures that are 

### 20 appropriate for regression models are not appropriate when 

### 21 you're looking at the Expert System. When you're looking at 

### 22 logic rules, when you're looking at these -- in fact the 

### 23 chemical categories and then the sub-models -- and as was talked 

### 24 about in the 2009 review, the Expert System is actually a series 

### 25 of different smaller sub-models. 

### 26 And an unambiguous algorithm -- there again,

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1 the system is logic tree, it's rules, it's chemical queries and 

### 2 supporting information behind that. 

### 3 So another one of the outcomes -- how 

### 4 reasonable a question you can ask, how reasonable is the 

### 5 estimate compared with data for similar chemicals? So let's 

### 6 talk a little bit about chemical similarity when we're doing 

### 7 chemical similarity in the context of effects-based chemical 

### 8 categories. So building effects-based categories -- structural 

### 9 similarity is in the context of the biological activity and in 

### 10 this case, that molecular initiating event, so chemical binding 

### 11 to the estrogen receptor. 

### 12 But actually it turns out that there are 

### 13 multiple ER-interaction types. You can interact at different 

### 14 points within the estrogen receptor and chemicals interacting at 

### 15 different points have different properties. The chemicals that 

### 16 initiate the molecular initiating event at the same point within 

### 17 the receptor are similar. That's what our similarity is. It's 

### 18 getting the same type of effect. Then you group those chemicals 

### 19 and then you start trying to understand what the properties are. 

### 20 Chemicals sharing similar structural features are grouped if 

### 21 they impart the same biological activity. 

### 22 So the figure here is the inside of the 

### 23 estrogen receptor with estradiol located in there, which 

### 24 interacts at two points within the receptor. But the chemicals 

### 25 that we're talking about and that we built the original Expert 

### 26 System for do not interact. They are not the steroid type

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1 chemicals. They interact -- if they interact at all -- they 

### 2 interact at one point within the receptor. 

### 3 So for instance, here we have alkylphenols 

### 4 that interact at this point and the charge on the oxygen is 

### 5 similar to how estradiol interacts at that point. You can also 

### 6 look at our lead series as we built this system, which was the 

### 7 alkylanilines with a different charge. So you have hydrogen 

### 8 bond donating and accepting type behavior at these different 

### 9 points, but it imparts different types of activity. So that was 

### 10 the basis of identifying our chemical similarity. 

### 11 We're also -- okay. Let's talk about a little 

### 12 bit of the desired outcome is usefulness of your predictions. 

### 13 Are they applicable to all the chemicals of regulatory concern? 

### 14 So in the initial system, prioritizing chemicals with the food 

### 15 use and antimicrobial inventories again were covered. There 

### 16 were no steroidal type chemicals on it. So the interactions 

### 17 that we saw were very low-affinity interactions. 

### 18 So to look at those kind of chemicals, we had 

### 19 to take the in vitro assays that we used to build the training 

### 20 set and optimize them to detect any indication of binding and 

### 21 then verified with gene activation in a metabolically competent 

### 22 tissue slice to increase the confidence in the non-binders. So 

### 23 to do this, we test the chemical solubility within that assay or 

### 24 if it's a cell-based assay, you look at cytotoxicity if that 

### 25 happens first. Otherwise, you keep testing the chemical to the 

### 26 solubility within the assay. A lot additional information is

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1 used in the data interpretation and that will be covered more in 

### 2 section seven also. 

### 3 So this is just to go through what the assays 

### 4 are, the estrogen receptor binding displacement assay. 

### 5 Estradiol was run with every chemical. Negative chemicals look 

### 6 like this and again, they stop at different points because they 

### 7 have different solubilities. They have different properties. 

### 8 Again, the positive control and what test chemicals look like. 

### 9 You can get full displacement curves for a very soluble chemical 

### 10 up to very high concentrations. Then again, you go into the 

### 11 tissue slice assay and you verify that that actually can 

### 12 translate to gene activation. In this case, it is vitellogenin 

### 13 induction using a QPCR technique, again the positive control, 

### 14 estradiol is always used, example of some negative chemicals on 

### 15 the right side, estradiol and some positive chemicals that are 

### 16 giving induction. 

### 17 I also talked about the importance of 

### 18 understanding something about the chemistry. We look at the 

### 19 chemical identity and concentration within and try to understand 

### 20 different types of chemical within the binding assay. These are 

### 21 zero and 20 hours at the beginning and end of the incubation. 

### 22 These are stock solutions. These chemicals across the dose 

### 23 response range are what we would call well behaved. In the 

### 24 series B, you have an initial loss. You've got binding to the 

### 25 glassware and stuff, but you still have a well behaved. The 

### 26 loss happens right away and then it continues throughout the

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1 assay. 

### 2 When we see these kind of relationships where 

### 3 you get a drop from the initial concentration, we've actually 

### 4 gone back and now we know that this or the one on the right is 

### 5 even more exaggerated. It's an indiction that there's 

### 6 metabolism occurring, that there's a transformation of that 

### 7 initial parent compound because these here are showing the 

### 8 parent compound. So again, having some idea of what's happening 

### 9 across the broad range of chemicals with different chemical 

### 10 properties so that you can help to interpret what you see in the 

### 11 assays. 

### 12 Another important thing is the assay total of 

### 13 protein affects chemical bioavailability. So an ER binding 

### 14 assay is not an ER binding assay. You have to look at the 

### 15 components within the assay and how the assay is run. But the 

### 16 total chemical concentration, for instance this was some work at 

### 17 Utrecht University by a student of Joop Hermens, and they looked 

### 18 at different increasing chemical -- or total protein 

### 19 concentrations in the assay. They also looked at what was the 

### 20 free fraction. In each case, they're showing you to get the 

### 21 response, you have to increase the applied concentration because 

### 22 you've got so much nonspecific binding, but in each case, where 

### 23 you're getting the effect, the free fraction is the same. 

### 24 So that will be discussed more as we talk 

### 25 about different assays and comparing across assays and comparing 

### 26 total protein concentrations. This is just another example of

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1 that. Again, the binding assay we've used mostly to build the 

### 2 Expert System is a cytosolic preparation of trout receptors with 

### 3 a total protein concentration of 4 mg/mL. We also have a 

### 4 recombinant rtER alpha and in that system, you only have 0.01 

### 5 total protein. 

### 6 So this curve right here, it's the same 

### 7 chemical and that's for the recombinant receptor. Everything is 

### 8 shifted to the left because the free fraction is the same in 

### 9 both of these, but you need to apply a lot more. When you've 

### 10 got the total protein concentration, you've got a lot of 

### 11 nonspecific binding. So you increase the chemical concentration 

### 12 and the free fraction is the same on both of these. So when 

### 13 comparing assays you have to have some idea of what you're 

### 14 comparing. What's within the assay so that you're comparisons 

### 15 are apples to apples if you will. 

### 16 Another thing that we talked about in the 

### 17 other review was using an effects based chemical category and 

### 18 the read across approach allows you to extrapolate from a 

### 19 smaller number of training set compounds to a broader inventory. 

### 20 What we had determined for the chemicals that we were testing 

### 21 is that for the ones that were binding, we found a relationship 

### 22 with LogKow, relative binding affinity LogKow. Again, we talk 

### 23 about those two different interaction sites and we got a little 

### 24 bit different -- and this line is just drawn in there. It's not 

### 25 a fitted line. But what we see and across all of these is that 

### 26 we get increased to a certain level and then it levels off, but

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1 there is a relationship with LogP. 

### 2 So if you test at the higher end and you test 

### 3 maybe one or two chemicals in the middle and at the lower end, 

### 4 you can extrapolate to the ones in between. That's what having 

### 5 a category-based approach allows you do that. We also found 

### 6 that there were a lot of chemicals. We didn't find anything to 

### 7 bind below this LogP cutoff. That's also a part that was 

### 8 incorporated into the Expert System rules. 

### 9 Okay. And then finally, does the model and 

### 10 the Expert System answer your regulatory question? Well we did 

### 11 cover -- with that approach we covered all of the inventory of 

### 12 the food use and antimicrobials and about five percent of those 

### 13 chemical were prioritized to be looked at first. 

### 14 We also talked about that we looked at some 

### 15 interspecies comparisons. This is a plot of rat uterine cytosol 

### 16 binding and trout cytosol binding for 55 very diverse chemicals. 

### 17 You can see that there's a good correlation. The reason we 

### 18 looked at those two together was their similar chemical 

### 19 bioavailability in both of those systems. Again, just reminding 

### 20 you here with recombinant receptors versus cytosolic receptors 

### 21 just within the trout, I'm going to show you here what we also 

### 22 looked at for a series of alkylphenols, we looked at recombinant 

### 23 trout and recombinant human. This top line is their basically 

### 24 on top of each other, the human and trout. So the species 

### 25 wasn't making the difference. It was the cytosolic protein 

### 26 versus the recombinant receptor and the amount of protein there.

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1 So again, the species correlation seemed very 

### 2 strong with the alkylphenols. We did the same with our other 

### 3 lead series, which was the alkylanilines. Again, the 

### 4 recombinant receptor systems are the top line and the cytosolic 

### 5 are the bottom line. 

### 6 So we also talked in that review, we talked 

### 7 about how can we expand to additional inventories, other data 

### 8 types and additional endpoints. We also talked some about early 

### 9 attempts to think about automating that Expert System. So I'm 

### 10 going to turn it over now to Rick, and he is going to talk about 

### 11 automating the Expert System and moving on to then building 

### 12 Expert System version two. 

### 13 **MR. RICK KOLANCZYK:**Okay. Thank you. I 

### 14 thought we'd start out with the conceptual slide. This is the 

### 15 decision tree as we left off from the 2009 SAP review. Roman 

### 16 numerals one through seven, I believe, on here the idea of seven 

### 17 models within a model. Maybe just want to point out that we 

### 18 were looking at the issue of charge. Ray went into some 

### 19 description in his presentation a bit earlier. Also the A type 

### 20 and the B type groups that Pat had mentioned, this idea of the 

### 21 LogKow cutoff, and then also a special rules group. We'll be 

### 22 touching on that a little bit later in some of the 

### 23 presentations. 

### 24 So moving from a drawn-up conceptual diagram 

### 25 to an actual piece of automated stand-alone software. We 

### 26 achieved this by a collaborative effort between EPA, OECD and

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1 LMC, the Laboratory of Mathematical Chemistry, headed up by 

### 2 Ovanes Mekenyan. I just want to maybe point out that this 

### 3 Expert System version one is available to the public through the 

### 4 most recent version of the OECD Toolbox. So you can get at the 

### 5 model there yourselves. 

### 6 I just want to maybe mention that this -- and 

### 7 Pat had led into this, that it's a rules-based logic decision 

### 8 tree. So you're moving through a series of nodes starting at 

### 9 this point and going through a series of yes/no questions to be 

### 10 asked at each of the nodes. I just want to point out that the 

### 11 goal of this system was to be transparent to the users. What 

### 12 you see is what you get, and in addition to that to be a fully 

### 13 expandible system. So when you encounter new groups or new 

### 14 nodes, you can easily accommodate those and put those into the 

### 15 various branches of the model. 

### 16 What I've got is another screen shot of how 

### 17 you're actually going to use the system. This happens to be a 

### 18 means of loading up a single chemical. You would profile this 

### 19 starting out at our start point where the boxes turn green is an 

### 20 affirmative answer or a yes answer to that particular question 

### 21 being asked. Red boxes denote no. You will continue through 

### 22 the tree until you reach a decision. 

### 23 Listed over here below the target structure is 

### 24 the decision for that. This happens to be unknown binding 

### 25 potential. So it resides outside of the domain of the model. 

### 26 You might equally see greater than or a less than an RBA cutoff.

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1 We chose that since this is a prioritization model to simply 

### 2 state that we did see binding higher than the lowest potential. 

### 3 We ran through -- in the training set, we've got a lower 

### 4 threshold, the lowest threshold for a binding affinity. 

### 5 This slide represents the ability to 

### 6 incorporate an inventory list so you can load up an entire 

### 7 inventory, run those through in a batch mode, if you will. This 

### 8 happens to be a single structure that you are highlighting at 

### 9 the time and the predicted potential for that. If we were in 

### 10 the view for single mode as you went through these, you would 

### 11 see -- as we saw on the previous slide with the green and the 

### 12 red boxes, taking you through the tree as to where it would 

### 13 parse out and eventually settle in. This happens to be the show 

### 14 for the entire inventory. This entire inventory contained 211, 

### 15 I believe somewhere in that neighborhood, structures and you'll 

### 16 notice that there are numbers in between each of the purple 

### 17 nodes, and that is kind of to give you a rough idea of which 

### 18 branch and which direction your chemicals might be parsed into 

### 19 as far as coverage. 

### 20 This is the meat and potatoes behind this. 

### 21 This is the boundaries as described. So search criteria in the 

### 22 form of structural fragment, in this case for an alkylaniline 

### 23 might also include exclusionaries in here, masks to exclude 

### 24 particular fragments. In here, you can also include things such 

### 25 as LogKow cutoff and just want to maybe state here that this 

### 26 really ties in the structural component for each of the groups

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1 that we're talking about as well as the binding results from the 

### 2 rainbow trout assay. 

### 3 Also within each of the nodes, and this one 

### 4 happens to be for a group of salicylates, target structure here, 

### 5 LogKow, CAS number, the training set that went behind the logic 

### 6 that was introduced here, and just with the structures. So you 

### 7 can see where potentially your chemical might fall into line 

### 8 with what has been tested. 

### 9 Also, this is another view of that training 

### 10 set. It happens to be for a different node. These anilines in 

### 11 a list fashion rather than a grid still contains all of the 

### 12 information. What I really want to point out down below is that 

### 13 we've included notes on interpretation here. This set happened 

### 14 to be so that aniline itself was a negative for binding, but the 

### 15 methylaniline and subsequent structures higher up on that group 

### 16 were positive. What if you happen to have another structure 

### 17 that was maybe beyond this, but we hadn't tested up to that 

### 18 particular LogKow range. You would still have the basis of 

### 19 information here so that you could reach your own assumption 

### 20 based on these notes on interpretation. 

### 21 So that took us through actually Expert System 

### 22 version one, an automated stand alone piece of software. Next, 

### 23 I had mentioned about the transparency and the expandability. 

### 24 We moved into building an Expert System version two, taking on a 

### 25 new inventory of nonfood use inerts. 

### 26 So Expert System one as it was stated before

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1 based on food use inerts, antimicrobials, moved into a set of 

### 2 nonfood use, 1500 or so, actually discrete structures that when 

### 3 into this. Maybe run through the steps that were used to 

### 4 develop Expert System version one were then just expanded to 

### 5 version two. So we acquired the specific inventory, the nonfood 

### 6 use inerts. Next in line was to characterize those structures. 

### 7 Now I mentioned about using discrete structures. So we went 

### 8 through the SOP that was included in the 2009 SAP. We stayed 

### 9 away from mixtures at this point, polymers and nondiscrete 

### 10 substances. We checked on verification of the structures as 

### 11 well as CAS numbers to make sure we were all set to go at that 

### 12 point. 

### 13 Next stage here was to actually apply the 1500 

### 14 or chemicals to the Expert System version one. What we wanted 

### 15 to do was to focus on those chemicals for which we had an 

### 16 unknown binding potential. Those which resided outside of the 

### 17 domain of the model since we were interested in expanding said 

### 18 model. Now obviously we're going to encounter chemicals that 

### 19 were found in known classes, but maybe they were just outside of 

### 20 the LogKow range. So that was one angle that we took on. 

### 21 Secondary to this, I think I want to maybe 

### 22 take a step back here and talk a little bit about the model. As 

### 23 you filter through and very easy to come up with nice discrete 

### 24 classes of chemicals, but what happens when you run into a 

### 25 structure that has multi-functionalities associated with it. So 

### 26 in other words if you have a phenol, but it also has a halogen

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1 or a carboxylic acid or a sulfonic acid, it gets very 

### 2 complicated which discrete group does it fall into. Until we 

### 3 can formulate a new group to capture those chemicals, you'll 

### 4 fall into two categories that we're called mixed phenols and 

### 5 mixed organics. We've separated these out out of importance for 

### 6 the phenol functionality being the three position of the 

### 7 estradiol, the endogenous ligand itself. So we've found a 

### 8 number and the majority of those unknown binding potential 

### 9 chemicals within those mixed groups. 

### 10 Next was to strategically test those potential 

### 11 new groups or new categories that may arise out of the mixed 

### 12 phenols and the mixed organics, and then eventually expand upon 

### 13 the model. 

### 14 So again, collect structures with multiple 

### 15 functionalities, we went through that, may encounter specific 

### 16 functionalities that will render a specific structure positive 

### 17 or negative. Really, this might fall into play as if you have 

### 18 one of those mixed groups that I was talking about and you might 

### 19 happen to have -- and I'll throw in an example out there such as 

### 20 sulfonic acid. The sulfonic acid at pH 7 or in this case at pH 

### 21 76 of the assay pH would end up as a charged species. It 

### 22 actually impairs binding for us. I just want to point out here 

### 23 too that it's a series of generating questions, coming up with a 

### 24 hypothesis and testing those hypotheses. 

### 25 I think the best way to illustrate this is to 

### 26 take you through an example. So delved into a group of mixed

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1 phenols, I grabbed five said structures out of those mixed 

### 2 phenols, and this whole idea of grouping chemicals with respect 

### 3 to structural similarity. Now I wanted to look for the largest 

### 4 commons structural fragment. In this case, common to all the 

### 5 structures was this 2-hydroxybenzophenone. I think it would be 

### 6 a mistake to look at it as a phenol or just a ketone or an 

### 7 alkoxyphenol, but we're looking that largest fragment. 

### 8 Next was to go through some strategic testing. 

### 9 This is the idea of formulating that question. So of interest 

### 10 is the root structure. We've just applied an acronym here, HBO 

### 11 for this. It actually was a non-inventory chemical, but does it 

### 12 bind to the receptor? Pink here would illustrate an 

### 13 affirmative, a positive. 

### 14 Next question, what about additional hydroxyl 

### 15 groups on the A-ring? That too was positive. I'll keep rifling 

### 16 through these looking at about multiple hydroxylation on both 

### 17 rings, and again a positive effect there. What if capped by a 

### 18 4-methoxy group in place of the second phenol functionality, 

### 19 that too, positive. 

### 20 Where it gets interesting is what about the 

### 21 effect of a longer chain alkyloxy? We had negative result on 

### 22 this. About all I can formulate is that when you run into a 

### 23 longer chain like this, you get the back folding after about 

### 24 four carbons or so. I think you might run into some steric 

### 25 hindrance on the hydroxyl group may be responsible for binding. 

### 26 There is a feature of attenuation involved here. I will take

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1 you through just one more. What about the effect of a sulfonate 

### 2 acid group. That too was negative for binding to the receptor. 

### 3 4 What you can do is come up with a conclusion 

### 5 here that the 2-hydroxybenzophenones seem to be a positive 

### 6 group. There are some exceptions here due to attenuation. In 

### 7 the first case, there is the idea of the steric hindrance. 

### 8 Second was the idea of the charged species. 

### 9 Another example looking at phenones within the 

### 10 rainbow trout training set. I've sorted these according to 

### 11 LogKow, increasing left to right. When we apply the results of 

### 12 the rainbow trout assay upon these, again green are negatives 

### 13 and pink were positives for binding. The LogKow relationship 

### 14 didn't seem to hold, but what you do notice here is maybe best 

### 15 not to look at phenones as a single group. In other words split 

### 16 these into those that are branched and those that have a 

### 17 straight n-alkyl chain. 

### 18 So in expanding from version one to version 

### 19 two posed some new groups. In the interest of time, we'll just 

### 20 move through. Another example -- I gave you the one previous, 

### 21 the 2-hydroxybenzophenones series, benzotriazole phenols and 

### 22 down below, just to point out the inventory chemicals within the 

### 23 series, these in the training set, we actually did test two of 

### 24 these out of the group, one at the lowest Kow for the series, 

### 25 this one up at 7.63, so near the top. We tried to get coverage 

### 26 across these and a negative group for us.

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1 Phenolsulfonphthaleins, again just to point 

### 2 out maybe a limited representation within the inventory, but 

### 3 what I want to maybe point out -- and you'll notice we end up 

### 4 with some mixed results positive for this, negative. The 

### 5 negative may be due to some attenuating factor. These large 

### 6 bromine groups on either side of the hydroxyl groups imparting 

### 7 some form of steric hindrance there as well. 

### 8 Also expanded, moving out of the mixed 

### 9 organics from Expert System version one to two, again an example 

### 10 here, amino anthracenediones. In this instance, we grabbed 

### 11 three adequate LogKow coverage of the series, three out to 

### 12 eight. So we want to make sure we test everything out to cover 

### 13 the inventory. 

### 14 Phosphoric acid esters -- it's very interest 

### 15 set in that it did not meet the criteria that Pat laid out about 

### 16 the A-type binders and the B-type binders for the atomic charge 

### 17 on what we perceived were what could be the responsible atom or 

### 18 site for binding here. The charges on the oxygen and the 

### 19 phosphorus just do not meet that. 

### 20 What I want to point out is I rolled in a 

### 21 couple of new colors for you here. This tan, we could not find 

### 22 this chemical. In other words, I could not procure that for 

### 23 testing. So we invoke the use of a surrogate, this phos here. 

### 24 If you'll notice the other two members within the training set 

### 25 that we tested were positive. This was a negative, and I think 

### 26 what we're running into is that idea of a charged relationship.

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1 So in other words, the PKa for this OH group unesterified is 

### 2 another attenuating factor, which would render that negative 

### 3 within the series. 

### 4 We're a little short on LogKow coverage, so 

### 5 the additional color I brought in here, yellow, is for future 

### 6 testing to go after LogKow 8. If you'll notice, we've kind of 

### 7 truncated the LogKow calculation using KOWWIN out to 8. I know 

### 8 the program calculates things much higher. A LogKow of 17 for 

### 9 some structure, in particular some of the pHs and the like. For 

### 10 purpose, we've kind of cut off at 8 for realistic view of 

### 11 things. 

### 12 Back to the decision tree and what I've added 

### 13 in here are where those perspective new groups that I've 

### 14 incorporated, where they might reside within the model. Any 

### 15 additions to known groups that we have that we would just simply 

### 16 extend the LogKow coverage to account for those. 

### 17 Now, what goes into forming a new group? And 

### 18 I threw up a series of slides that when you keep trying to pull 

### 19 out new group after group and what constitutes a group, and it's 

### 20 really a play on words and something that I've assigned. I call 

### 21 them analogs. It's kind of a precursor to that. Eventually, 

### 22 you're going to run into that bottom of the pit, those mixed 

### 23 organics and mixed phenols. In a perfect world you're not -- 

### 24 well it may be in a perfect world, but not in this world. You 

### 25 can't eventually define every single group there is. So, you're 

### 26 eventually going to run into the bottom dwellers, if you will,

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1 and the way that we've been handling the mixed phenols and the 

### 2 mixed organics as a group within the tree is by exact match. In 

### 3 other words, gut it out and test them. 

### 4 But in the interim, what you try and do is the 

### 5 best you can is to pull these analogs out. It could be the 

### 6 precursors for eventual groups. So I guess I've furthered those 

### 7 inventory chemicals from the 2009 nonfood inerts and attempted 

### 8 to parse out maybe the next step here. 

### 9 This was a very important slide for me to look 

### 10 at the aldehydes and ketones. In other words, these structures 

### 11 are going to come back to us later only. Primarily, and I hate 

### 12 to jump too far ahead because it will be the subject of another 

### 13 presentation, but aldehydes and ketones are going to be found in 

### 14 your fragrances for the EDSP Inventory of Chemicals. 

### 15 So you can see that I've got a good start here 

### 16 on starting to pull things out for new groups. Eventually, you 

### 17 run into something such as this slide where you've only got 

### 18 really two representatives and pair of chemicals. So, now 

### 19 you're down to is it really a true group, or are you just using 

### 20 a surrogate here? It's a work in progress, an integrative 

### 21 process, and hopefully continue to work on this. 

### 22 I think I'd like to maybe turn this back to 

### 23 Pat. Were you going to start with this slide or the next? 

### 24 **DR. PAT SCHMIEDER:**I'm using this then to 

### 25 wrap up what Rick was talking about again where the position of 

### 26 those new groups that we know something about.

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1 So now, okay. So this is Expert System 

### 2 version, what we're calling version two. So let's look at the 

### 3 coverage of that, of the EDSP Universe with that version of the 

### 4 Expert System. In the White Paper, we went through both version 

### 5 one and that coverage, but I'm just going to talk about version 

### 6 two here. Again, as we think about this, what Rick talked about 

### 7 in that inventory -- let me describe this newer inventory and 

### 8 what we had worked on because that's important to understand 

### 9 that there are some differences there. So I'm going to try to 

### 10 talk to -- we talked about determining the coverage of version 

### 11 two and then identifying the remaining unknowns. 

### 12 So the composition of this new universe is 

### 13 it's not exactly the food use inerts that we built the system 

### 14 on. This is a new list in 2012. The nonfood use that Rick just 

### 15 talked about to expand it to Expert System version two, this is 

### 16 a new list in 2012. So there is definitely a lot of overlap, 

### 17 but there are some additional structures on it. 

### 18 What we hadn't tested at all or hadn't 

### 19 specifically looked and tried to cover a list of fragrances used 

### 20 in inert ingredients, and then the Safe Drinking Water Act 

### 21 chemicals. There's the overlap with these lists up here, and so 

### 22 we probably would cover those pretty well, but then there were 

### 23 these new chemicals on the SDWA list that we did not 

### 24 specifically try to build the model domain to cover. 

### 25 So, let's look at what's in some of these 

### 26 inventories. So we're talking about again not -- these are the

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1 discrete chemicals, so they're not the mixtures, they're not 

### 2 undefined. They're not polymers. If you look at, if you want 

### 3 to take from the right side, here is the more pure class, if you 

### 4 will. So a chemical that is only used a food use inert and it's 

### 5 not also on the SDWA list, it's not nonfood, it's not on the 

### 6 fragrance list, would that be these 239 here. Four more here 

### 7 are Food use and also fragrances. This was just a way to build, 

### 8 to parse out the inventory so you get some idea of that there's 

### 9 a lot of overlaps in these groups. 

### 10 So overall, the inerts were about 2600. The 

### 11 chemicals that were not also inerts that were on the SDWA list 

### 12 but not also inerts are down here just to give you an idea of 

### 13 what the makeup is. So 43 percent of this inventory are those 

### 14 inerts from those classes, including the SDWA overlaps, and 57 

### 15 percent were only on that Safe Drinking Water Act list. 

### 16 If we look at the predictions then, as I think 

### 17 Mary said earlier, 71 percent coverage from Expert System two of 

### 18 this list of around 6000 chemicals. The first version was 68 

### 19 percent coverage. 

### 20 Similar coverage, whether it was inerts or the 

### 21 Safe Drinking Water Act chemicals actually, 71 percent, 72 

### 22 percent here, again 72 for the total group, all the inerts group 

### 23 together. So very similar. If we parse those out by use 

### 24 classes, 97 percent of the food use inert alone, those kind of 

### 25 chemicals, were covered, which is not surprising because it 

### 26 tells you, I would guess, a couple things that we built the

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1 initial system to cover those and that the list probably didn't 

### 2 change that much from when we built the initial system to 2012. 

### 3 We got 86 percent coverage of the nonfood use 

### 4 inerts with expanding to Expert System version two. We didn't 

### 5 cover the fragrances as well. Again, we did not have this 

### 6 inventory to specifically target, but overall, we've got 72 

### 7 percent coverage of all inerts, 71 percent coverage of the 

### 8 Drinking Water Act chemicals that are not also inerts. So that 

### 9 was pretty good, even though we didn't specifically necessarily 

### 10 target those. So overall, 71 percent coverage. 

### 11 The largest increase from version one to 

### 12 version two were for nonfood use inerts as expected because 

### 13 that's what we were building version two to cover. There're 

### 14 modest increases in coverage for the other groups, but the 

### 15 initial coverage was pretty good. If you just look at then -- 

### 16 and that was the total coverage, but if you look at which 

### 17 chemicals are in the model domain and where you can make 

### 18 predictions for whether they bind or don't bind, overall they're 

### 19 four percent predicted to be binders. 

### 20 But if you look within the groups, one percent 

### 21 of the food use inerts, four percent of the nonfood, and this is 

### 22 interesting, the 10 percent that we saw in the fragrances. 

### 23 Again, Rick talked about some of the groups of concern there, 

### 24 the aldehydes and ketones that need more looking at because 

### 25 we're getting a higher percentage of predicted binders. Within 

### 26 the Drinking Water Act chemicals, only three to four percent.

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1 Again, the new groups that Rick talked about, 

### 2 building on from version one to two, I just highlighted some of 

### 3 these that were actually containing some of the binders here, 

### 4 but these were the overall groups that we had added. 

### 5 So now let's talk about the ones that are not 

### 6 in the model domain, the unknown binding potential chemicals. 

### 7 There are 1704 within the Expert System version two groups, and 

### 8 they're distributed across the system, as you can see here, 

### 9 although number wise, the vast majority are mixed organics and 

### 10 mixed phenols, not surprisingly. We'll come back to that and 

### 11 talk more about those specifics of the unknown binding potential 

### 12 chemicals in section eight. 

### 13 So I'm going to move on to talking then, okay 

### 14 so if you apply that inventory and that assessment was before 

### 15 any application of any physico-chem filters, so how does that 

### 16 affect the system. Well one of the things that we did was we 

### 17 applied the Expert System to predict the chemicals that were 

### 18 filtered out. As Ray emphasized, the physico-chem filters are 

### 19 subject to further examination. The expert chemistry group is 

### 20 going to look at this, but we thought this was a good exercise 

### 21 to get some idea of what types of chemicals were being filtered 

### 22 out. I think as Mary talked about before, 92 percent of those 

### 23 filtered out chemicals were predicted to not have any binding 

### 24 affinity. 

### 25 There's another group of those that were 

### 26 unknown binding potential. To me, this helps you target where

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1 to look. Especially if you look at ones that we did predict to 

### 2 have some binding potential and another group of unknowns, they 

### 3 were the perfluorinated chemicals. As a chemist, they look at 

### 4 these and say, oh yeah, perflourinated chemicals have very 

### 5 unique chemistry. It makes sense to not just rotely filter them 

### 6 out, but to take a lot harder look at those kind of chemicals, 

### 7 but there is a reason to because they have such unique 

### 8 chemistries. 

### 9 Actually this alkylphenol here -- it's 

### 10 interesting that it's cresol and -- there's ortho-, meta- and 

### 11 para-cresol individually, and then a mixture of all of them. 

### 12 Two of them had been filtered out and two hadn't. It had to do, 

### 13 I believe with, having an automated filter that looked at 

### 14 corrosivity being indentified on an MSDS sheet, and for some 

### 15 reason, two of them were filtered out and two others weren't. 

### 16 We tested them. So the fact is, they are testable in these kind 

### 17 of systems and we did -- or we at least, I don't know which 

### 18 form. One of the forms we did test. 

### 19 So again, using this as a way to raise 

### 20 hypotheses to say these are the ones that we need to look at 

### 21 more closely, and these are the ones down below here that we're 

### 22 pretty confident that we don't have to look at, perhaps, really 

### 23 closely. Again, or at least we're pointing you where to look. 

### 24 This is a list of those chemical groups where those nonbinders 

### 25 were predicted. It's a wide range of chemical categories, and 

### 26 you'll note that many chemicals in the same class were not

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1 excluded by the filters. 

### 2 So if you look at then what happened to the 

### 3 inventory -- it went from 5975 to about 5000. The inerts 

### 4 decreased to 2080. The SDWA only decreased here and you can see 

### 5 the whole breakout there obviously. 

### 6 So since about 950 chemicals with predictions 

### 7 were removed by the filter, the apparent coverage decreases, the 

### 8 number of binders remain the same because you're filtering out 

### 9 all those nonbinders and a few of the unknowns. So the 

### 10 percentages change and it looks like you're not covering as 

### 11 much. It's just a matter of what you've left in and what you've 

### 12 taken out, if you will. 

### 13 The predictions then, again now it's 

### 14 percentage wise is two percent. The same number -- the same 

### 15 chemicals are predicted to bind here as they were before you 

### 16 filtered the inventory, which tells you you're not filtering out 

### 17 a lot of binders. Overall results using the inventory, there 

### 18 were 165 chemicals overall that were prioritized. 

### 19 So in summary, the ER Expert System 

### 20 development is an effects-based chemical categories approach. 

### 21 It's designed around the ER-mediated Adverse Outcome Pathway. 

### 22 It incorporates the (Q)SAR principles. It can be expanded to 

### 23 cover additional inventories, as we've demonstrated here. The 

### 24 results are before the P-chem filtering. The version two 

### 25 covered 71 percent of this larger list, 165 predicted to bind, 

### 26 1704 unknown, and then after filtering. The only thing that

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1 changes is the number of the percentage coverage because you've 

### 2 taken out a lot of nonbinders and a the number of unknowns, but 

### 3 again, it's the same number of chemicals predicted to bind. 

### 4 This is just the road map again to tell you 

### 5 where we're going. It's actually the next talks after lunch. 

### 6 Hopefully, we'll address the study to talk about the high 

### 7 through put data and how you could use this to expand, and then 

### 8 we'll actually kind of come back to number four -- well in 

### 9 section eight, we'll talk about where the high through put 

### 10 covers the unknowns and also how you could use that data 

### 11 strategically. 

### 12 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 13 or clarification from the Panel? Dr. Schultz? 

### 14 **DR. TERRY SCHULTZ:**I want to thank you two 

### 15 for an excellent presentation having been involved in this in 

### 16 the 2009. I've seen where you've gone. I have a number of 

### 17 questions I would like to ask. First of all, in 2009 you said 

### 18 one of the first rules you're using for exclusion was whether it 

### 19 was a cyclic or a noncyclic chemical. After having two more 

### 20 years of experience with this, do you still feel comfortable 

### 21 with that as your first triangle square, whatever it is in your 

### 22 Expert System? 

### 23 **MR. RICK KOLANCZYK:**Kind of stealing a little 

### 24 bit of the thunder on this afternoon's presentation, but 

### 25 following Panel recommendations from 2009, we were directed to 

### 26 look at P-phos and P-fluor, again those perfluorinated

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1 compounds. So in fact, we did those so quickly that we 

### 2 incorporated those into Expert System version one. I still 

### 3 believe it. At that time, I should say that the Panel was in 

### 4 agreement for the inventories of concern. Those speaking of the 

### 5 food use inerts and the antimicrobials, our coverage was 

### 6 adequate. Our model was correct in saying that the acyclics 

### 7 could be ignored so to speak. I think later this afternoon, I'm 

### 8 going to maybe illustrate a couple more examples of some more 

### 9 cautionary steps that I'd want to look at further. 

### 10 As far as where it belongs in the actual tree 

### 11 itself, I think we could leave it right up front as long as we 

### 12 include nodes to account for binders. So in other words, I'm 

### 13 not using it to say that all acyclics are negative in that 

### 14 portion of the tree. There obviously is the box, as we 

### 15 accounted for as I mentioned before for P-phos and P-fluor. It 

### 16 will give you a positive. So if you ran it through the system, 

### 17 you went off on your own, pulled up that version of the tree in 

### 18 the OECD toolbox, plugged that chemical in, ran it, you would 

### 19 get an affirmative answer for binding. Anything else you want 

### 20 to expand on, Pat? 

### 21 **DR. DANIEL SCHLENK:**That was Rick Kolanczyk. 

### 22 23 **DR. TERRY SCHULTZ:**Another question. You had 

### 24 mixed phenols, you had mixed organics, the logical question is 

### 25 why not mixed anilins? 

### 26 **MR. RICK KOLANCZYK:**Just lack of grouping. I

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1 guess I put higher precedents on the mixed organics themselves 

### 2 -- I'm sorry the mixed phenols themselves because of the 

### 3 3-hydroxyl group on the estradiol itself. There appears to be a 

### 4 little higher affinity, I believe, on one of Pat's charts that 

### 5 showed that there's a little bit higher affinity for the phenols 

### 6 over the anilin series. As far as B-type receptors, you're 

### 7 going to see other functionalities other than just anilin 

### 8 itself. I'm think of a couple -- phthalates would fall under 

### 9 that area. Cyclohexanol, there're just more examples, and then 

### 10 you run into the issue well now you've got mixed phthalates or 

### 11 mixed cyclohexanol groups. I guess just for a simpler way of 

### 12 grouping, we kept it that way. 

### 13 **DR. DANIEL SCHLENK:**Again, that was Rick 

### 14 Kolanczyk. 

### 15 **DR. TERRY SCHULTZ:**Lastly, could you 

### 16 comment on the testing of the Expert System? As you know when 

### 17 you start to take extended fragment rules and start to program 

### 18 them up into a software, sometimes there is a translational 

### 19 problem between the toxicological test and the delta squiggle 

### 20 squiggle that is put into the code. I noticed in here that this 

### 21 is a pretty sophisticated program software. 

### 22 **MR. RICK KOLANCZYK:**Again, this is Rick 

### 23 Kolanczyk. I get the thumbs up. Just to elaborate on that, the 

### 24 initial coding of this, we relied quite heavily on the 

### 25 programmers, our collaborators in Burgas, Bulgaria LMC. I felt 

### 26 confident enough that in going from Expert System version one to

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1 two, I wrote those changes myself into the program. So you can 

### 2 learn with time, I guess. But you're right, talking about 

### 3 introducing masks, it can get a little more complicated the more 

### 4 you learn, but by the same token, it's all there. All of the 

### 5 boundaries are defined within the system. Do you want to add 

### 6 anything? 

### 7 **DR. PAT SCHMIEDER:**This is Pat Schmieder. We 

### 8 also did an exercise that relates to this, where we took Expert 

### 9 System version one and we took additional data that we developed 

### 10 that we did the additional test results that we're going to use 

### 11 to build version two and ran it through version one to see how 

### 12 it did. Now you can -- that kind of analysis approach is often 

### 13 used and you can get numbers on it, but I think the most 

### 14 important -- there are a couple of things. Not to sidetrack, 

### 15 because I will get back to your question. 

### 16 One of the things we found is that most of the 

### 17 chemicals were unknown binding potential, which is why we were 

### 18 testing them. Just to do that analysis rotely, I don't know 

### 19 that that's the best to evaluate this type of model, this 

### 20 decision tree model. But one thing, the only chemical that was 

### 21 not correctly predicted out of, I think the 46, around 50 that 

### 22 were in the domain of the model, was one in which the mask -- 

### 23 and Rick can describe this better. The chemist should be 

### 24 talking about this. It had two hydroxyl groups on it. One of 

### 25 the hydroxyl groups was 26 substituted around it, but the second 

### 26 one, there was just a hydrogen. It was substeal on one side and

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1 not the other side, and that showed us that we had to write that 

### 2 specific query, and then it was captured well. 

### 3 **DR. DANIEL SCHLENK:**This is Dan Schlenk. I 

### 4 have a question just for a followup on that. Your model seems 

### 5 to be all in two dimensions. I'm curious, is there any interest 

### 6 in terms of developing any stereoisomer components in this in 

### 7 terms of three dimensions? Because we have some ____ centers on 

### 8 a lot of the molecules, and I wonder if that could actually, is 

### 9 that part of the next step, I guess, or has that even been 

### 10 considered? 

### 11 **MR. RICK KOLANCZYK:**That's a very good 

### 12 question. In fact, we have looked at some isomers in our 

### 13 training set. I think -- I'm trying to think of the situation, 

### 14 stereoisomer. Still beam, trans and cis -- you get completely 

### 15 different activity, but we have accounted for that. It's 

### 16 currently a group, and again, kind of stealing a little bit of 

### 17 thunder from this afternoon's presentation, I've got a series of 

### 18 slides. Could I defer that until later and then we could talk 

### 19 through that? 

### 20 **DR. DANIEL SCHLENK:**Yes, definitely. 

### 21 **MR. RICK KOLANCZYK:**Okay. I think as far as 

### 22 looking R versus S type stereochemistry differences, while we've 

### 23 looked at the fipronil isomers and we haven't seen any 

### 24 differences between -- of course they were both negative. I 

### 25 just want to state that maybe the software has the capability of 

### 26 incorporating that into very sophisticated smiles so that we are

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1 prepared to handle that situation when it does arise. I think 

### 2 it's going to fall into play later on when we get into those 

### 3 multi-cyclic hydrocarbons. We'll get to it later. 

### 4 **DR. DANIEL SCHLENK:**Great. Thanks. Any 

### 5 other questions? Yes, Dr. Keiser? 

### 6 **DR. MICHAEL KEISER:**This is Michael Keiser. 

### 7 So I have three quick, just sort of brief technical questions. 

### 8 One of them is going back a little bit. What would you say was 

### 9 the key rationale motivating a decision tree based approach 

### 10 compared to some of the other (Q)SAR or three-dimensional 

### 11 approaches out there? 

### 12 **DR. PAT SCHMIEDER:**This is Pat Schmieder. We 

### 13 wanted the system to actually be as transparent as we could get 

### 14 it. So while there are a lot of other approaches that gather in 

### 15 large numbers of chemicals as we're going and testing. As we 

### 16 encounter the inventory that we had to address, these are not 

### 17 the typical kind of chemicals that weren't really potent 

### 18 chemicals. So as we're going, we determine we had actually, 

### 19 early on, been using some broader-based, more typical (Q)SAR 

### 20 approaches, but determine that we were seeing activities within 

### 21 these chemical categories and the chemical groups within them 

### 22 that seemed to make logical sense. By using the decision tree, 

### 23 we could incorporate a lot of the other information around the 

### 24 assays. Does that make sense? 

### 25 **DR. MICHAEL KEISER:**It does thank you. My 

### 26 second question was in terms of molecular similarity that has a

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1 couple of different definitions out there. It seems to me that 

### 2 the way you guys were approaching it is in terms at looking at 

### 3 common substructures again as a way that would make sense from a 

### 4 minicell chemistry perspective and for transparency, I'm 

### 5 imagining, but I was curious too if that is indeed the key way, 

### 6 you're grouping the different chemicals into the decision tree 

### 7 nodes or if you've also looked at things such as molecular 

### 8 descriptors, fingerprints, ways like that. 

### 9 **DR. PAT SCHMIEDER:**Yes, at this point, I 

### 10 think it is a better way of describing it that we're basically 

### 11 using substructures, but we're also letting the biology tell us 

### 12 if that's right. Our initial hypothesis is around the classical 

### 13 chemical classes and the substructures at letting the biology 

### 14 help us. The molecular descriptor that we found to be most 

### 15 important here was within the smaller groups, there was a 

### 16 relationship with LogKow. So we did not try to look for a lot 

### 17 of additional descriptors because for what we were doing, we 

### 18 could answer our question in a way that we thought was pretty 

### 19 transparent without trying to pull in additional descriptors. 

### 20 **DR. MICHAEL KEISER:**Thank you. That answers 

### 21 that question. And then the third one and final question that I 

### 22 had was looking in terms of as you begin to acquire more 

### 23 information for any particular decision tree nodes, some of the 

### 24 examples you've shown where you have things that deem for the 

### 25 relative binding affinity above or below the cutoff. Have you 

### 26 looking into ways to assigning confidence or other predictive

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1 numerical scores to the particular nodes that you result in the 

### 2 decision tree? I'm just curious what your path forward is to 

### 3 take the empirical data as you acquire it and consider weighting 

### 4 particular nodes in the tree once you've reached them? 

### 5 **DR. PAT SCHMIEDER:**This is Pat Schmieder. I 

### 6 guess I would ask for a little clarification or advice from the 

### 7 panel however it's formulated to say to advise on what we would 

### 8 gain by weighting specific nodes. At this point, a chemical 

### 9 comes in and it's handled in a series of yes/no questions within 

### 10 the tree and we get a prediction on whether the chemical is 

### 11 positive or negative. I'm not certain how weighting a node 

### 12 would help that frankly. 

### 13 **DR. MICHAEL KEISER:**So I get the impression 

### 14 that perhaps this might be for later discussion in detail to 

### 15 clarify my question only. I was asking whether assigned any 

### 16 different confidences to a yes/no prediction dependent on which 

### 17 node that prediction comes from. 

### 18 **DR. PAT SCHMIEDER:**No we don't. The chemical 

### 19 goes within the chemical class and the group we look at the 

### 20 training set of chemicals within that group and you look at the 

### 21 chemical target. We don't assign any weight to that other than 

### 22 trying to say is it in this group or is it outside, and is it 

### 23 within the LogKow range or outside for the activity, and if it 

### 24 does not fit within this group, it is given an unknown and 

### 25 predicted as being unknown. But we are not weighting any of the 

### 26 particular nodes within the tree.

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1 **DR. MICHAEL KEISER:**Thank you. 

### 2 **DR. DANIEL SCHLENK:**Other questions? Okay. 

### 3 Let's go ahead and break for lunch. We're right on time, so 

### 4 let's be back at 1 p.m. Thank you. 

### 5 (WHEREUPON lunch was taken) 

### 6 **DR. DANIEL SCHLENK:**Okay. Our next 

### 7 presentation is by Dr. Judson. He's from the National Center 

### 8 for Computational Toxicology and ORD. 

### 9 **DR. RICHARD JUDSON:**Okay. Great. Thank you 

### 10 very much. So, you've seen this slide before. What I'm going 

### 11 to be speaking to is the high through put assay data. What our 

### 12 group was asked to do was not to tell you everything we know, 

### 13 but to really focus on the beginning steps of using the high 

### 14 through put screening data, which is to say, okay, how do we 

### 15 gather the data, how do we analyze it, how do we think about 

### 16 doing quality control on it, and very little on what we're 

### 17 actually going to do with the data. We're going to take baby 

### 18 steps here with this group. 

### 19 However, given that, there is some context 

### 20 that I'll have two or three slides of context. So why did we 

### 21 generate the big data set that we're presenting to you here? So 

### 22 in my time here, I'm going to talk a little bit about, like in 

### 23 the background, for this assay battery we're developing, I'll 

### 24 very briefly go through the eight assays that we're presenting 

### 25 to you, which is a subset of the total set of assays, talk about 

### 26 how we selected the chemicals we've tested, how we process

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1 those, a reasonable amount about our data analysis and 

### 2 interpretation methodology. None of this data is complete. We 

### 3 got another 100 chemicals worth of data yesterday. 

### 4 Okay. So the data processing is still 

### 5 preliminary and we're actually looking for feedback on how we're 

### 6 doing that. This is a good time to actually be in front of this 

### 7 group. And then finally, talk a little bit about the kinds of 

### 8 analyses on assay performance we're using, developing. 

### 9 Okay. So the goal of the high through put ER 

### 10 data, estrogen receptor data, we're using and this is really -- 

### 11 the estrogen receptor work we're going to talk about here is 

### 12 kind of a model of how we're looking at pathways in general. So 

### 13 we have equivalent data for the androgen receptor for 

### 14 steroidogenesis for the P-par pathways and so on. So with ER, 

### 15 so much is known about that pathway that this is good starting 

### 16 point for understanding high through put screening and data in 

### 17 this context -- high through put data in this context, I'm not 

### 18 allowed to say screening in this context, so sorry. 

### 19 Okay. ORD is evaluating a very large battery 

### 20 of endocrine-related assays. So we have 21 estrogen receptor 

### 21 assays. We have five or 10 androgen receptor, we have a number 

### 22 of in the steroidogenesis and so on. 

### 23 The assay data is coming out of two programs 

### 24 that many of you've hear about, but some of you may have not, 

### 25 called the ToxCast and the Tox21 program. So ToxCast is an 

### 26 EPA-only program. It's been going for about six years. The

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1 goal was to bring in high through put assays and think about how 

### 2 they could be used in toxicity testing for prioritization, one 

### 3 day maybe for replacement, but it's a big research program where 

### 4 we are looking of hundreds of assays. We're up to about 900 

### 5 assays total and we're up to about 1800 chemicals that we've 

### 6 tested in at least some of those assays. The ER data we'll be 

### 7 talking about today is a very small but very important piece of 

### 8 the ToxCast program. 

### 9 Tox21 is a cross-Agency program, which has the 

### 10 same goals as ToxCast, but we have collaborators at the National 

### 11 Toxicology Program, the NIH Chemical Genomic Center and the FDA. 

### 12 Some of the assays I'll be talking about today are coming out 

### 13 of the NIH NCGC Lab. 

### 14 Okay. So if we focus down on the estrogen 

### 15 receptor assays, we're going to talk specifically about just a 

### 16 subset of eight. We're not going to talk about the whole 

### 17 battery. I'll mention what those other assays are, but these 

### 18 are again being used to illustrate the process for gathering the 

### 19 data, analyzing it, doing quality control and so on. These 

### 20 assays were all run at either contract labs, and I'll mention 

### 21 the names of the labs as we go through, or the NIH Chemical 

### 22 Genomic Center up in Rockville. They're a subset of the 900 

### 23 ToxCast assays. This little graphic is just a good way to help 

### 24 understand what the chemical space. It is even confusing for us 

### 25 sometimes. 

### 26 So ToxCast went in two phases, phase one and

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1 phase two. Phase two, we're just finishing up and there are 

### 2 some publications coming out of that. That will bring us to 

### 3 about 1000 chemicals across the whole set of assays. We ran 

### 4 another 800 assays in the endocrine-related subset, and in the 

### 5 8200 chemical library, which is what's being run at the NCGC is 

### 6 covering about 15 to 20 assays per year. We've done estrogen, 

### 7 androgen, some steroidogenesis to get started. 

### 8 Okay. So the high through put assay overview, 

### 9 I'm going to again talk about just this eight assays. All of 

### 10 the details of the assay protocols, at least at a summary level 

### 11 are in Appendix F. So maybe tomorrow if you want questions, 

### 12 we'll go back and forth referring back to some of the details in 

### 13 Appendix F. I'm going to put very little detail in today's 

### 14 presentation. 

### 15 We were asked to simply say okay, what is high 

### 16 through put mean? It's not all clear what that actually means. 

### 17 It's just as a working definition. It's assays that can get 

### 18 through somewhere between 1000 and 100,000 chemicals in a 

### 19 reasonable period of time. That's a real wishy-washy -- it 

### 20 could be anything you want. All of the assays we're talking 

### 21 about, the labs can run thousands of chemicals a year in these 

### 22 assays, at least 10,000 chemicals a year if they devoted all 

### 23 their time to doing that. They're run in microtiter plates, 

### 24 they're typically run using robots, and they can either be 

### 25 single-plex or they can be multiplex readouts. We have some of 

### 26 both. This is just an example applied. I'll talk about this in

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1 more detail later. It's the results from one of the NCGC 

### 2 estrogen receptor assays where they ran 30,000 samples in a week 

### 3 for that one assay. 

### 4 Okay. So as we plan this project over the 

### 5 last two or three years, we decided to use estrogen receptor as 

### 6 a test case for how you really think about pathways. So for 

### 7 those of you, if there's anybody on the Panel that actually 

### 8 doesn't know all the details of the estrogen receptor, this 

### 9 cartoon is there. So a ligand, which may be an activated ligand 

### 10 what gets dosed to the cells or the animal, may not be the 

### 11 active ligand that gets metabolized. So the active ligand binds 

### 12 to the estrogen receptor, estrogen receptor dimerizes, 

### 13 translocates to the nucleus, cofactors are recruited, the mature 

### 14 transcription factor binds to DNA, then you have RNA produced, 

### 15 protein produces and with the estrogen receptor, at least one 

### 16 downstream consequence, you can have cell proliferation. 

### 17 So we actually have assays from top to bottom 

### 18 here. We have binding assays, dimerization assays, DNA binding 

### 19 assays, RNA and protein expression assays as well as 

### 20 self-proliferation. We're only going to focus on the boxes that 

### 21 are in yellow here. So Novascreen ran the DNA binding assays, 

### 22 and then we have transcription factor assays from Attagene, NCGC 

### 23 and Odyssey Thera we'll talk about. But all the others are 

### 24 there. 

### 25 A question was raised earlier in the day. 

### 26 Okay, what happens if you have chemicals which perturb the

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1 pathway? They make something happen to the pathway, but they're 

### 2 not hitting, not actually binding to the standard binding site. 

### 3 Well in principle by looking at the Weight of Evidence 

### 4 downstream from the binding assays, if everything else lights 

### 5 up, that's at least some evidence. So we're thinking about how 

### 6 you actually make predictions about off pathway estrogen 

### 7 receptor non-ligand mediated activity. 

### 8 What is really important and a lot of what 

### 9 I'll talk about later on are these side pathways, which cause 

### 10 the assays to light up, but they have nothing to do with 

### 11 estrogen receptor activity. So these are assay interference 

### 12 pathways. You're causing false positives. So a big issue that 

### 13 we have to deal with in the quality control and all the data 

### 14 filtering is how you deal with those false positives. We'll 

### 15 talk about the preliminary approaches we're developing to do 

### 16 that. 

### 17 Okay. So starting off at the top of the 

### 18 pathway, we have three binding assays. These are cell-free 

### 19 assays, radioligand displacement assays, very standard that have 

### 20 been around for forever. What's special about the way we run 

### 21 these is they're quite expensive to run, so we can't afford to 

### 22 run all chemicals in full concentration response from the get 

### 23 go. If we did, we couldn't run other assays. So fall the 

### 24 self-rebinding assays and we have 230 or so in our whole panel, 

### 25 we run everything at 25 micromolar to begin with. If you see 

### 26 some level of activity and there's some statistical metric that

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1 we use to say what that is, then we follow up in concentration 

### 2 response. 

### 3 So as a consequence and later in the next 

### 4 talk, Dr. Hornung was going to talk about comparing this data to 

### 5 their really well-run binding assays. We miss chemicals that 

### 6 they see largely because they're looking up at 100 or 200 

### 7 micromolar, and those we don't even see any activity. 

### 8 One of the rationales for doing this is with 

### 9 the whole ToxCast program, sort of a base principle is that what 

### 10 we're really worried about are chemicals that people are being 

### 11 exposed to at relatively low doses. So you have to have 

### 12 relatively potent chemicals to see any activity. It's a design 

### 13 choice that may or may not be right in certain circumstances. 

### 14 That's what leads to at least one thing that leads to the 25 

### 15 micromolar prescreen. 

### 16 Anyway, so then we follow up and go up to 50 

### 17 micromolar. Again, not terribly high, but if you're assuming 

### 18 you have to have pretty potent chemicals to see an effect, 

### 19 that's a reasonable choice. 

### 20 For each of the assays, I'm going to show one 

### 21 of these little concentration response plots. Later on, I will 

### 22 show one blown up so that you can actually see what all the 

### 23 symbols are, and we can talk about that more tomorrow. But the 

### 24 basic thing is the black curve is the test chemical we're 

### 25 looking at and in all of these, I'm using bisphenol A for all of 

### 26 the assays. The red is 17 beta-estradiol. So just kind of

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1 orient your eyes as you're going through all these. 

### 2 Okay. So the rest of the assays are 

### 3 transcription factor assays. The first is a beta-lactamase 

### 4 assay being run by NCGC. They run from six nanomolar up to 92 

### 5 micromolars, so a little bit higher in concentration, run a 

### 6 15-point concentration response series. Here they're measuring 

### 7 two channels, a blue and a green channel and then what you 

### 8 actually call the estrogen receptor activity is the ratio. One 

### 9 of the issues that's kind of special about these, again we talk 

### 10 about that more later, is that if you have cytotoxicity, quite 

### 11 often cytotoxicity can mimic activity. Because if you goof up 

### 12 the channel in the denominator of the ratio, it actually looks 

### 13 like something is active. So inactive in this assay, you need 

### 14 to check against some other assay. That's a kind of assay 

### 15 interference pathway we'd worry about. 

### 16 Also run at the NCGC is the luciferase BG-1 

### 17 assay. Again, all of their assays are run at the 6 nanomolar to 

### 18 the 92 micromolar. What's interesting specifically about this 

### 19 assay is there is BG1 assay that was just validated by OECD and 

### 20 ECCVAM for use -- I guess it could be used in the endocrine 

### 21 program or other programs. That validation process took seven 

### 22 years to get that assay validated. So it was kind of a heroic 

### 23 effort. The number that people worked on validating that low 

### 24 through put method has been looking at this data set. So this 

### 25 data set is available to all of our collaborators, and they're 

### 26 quite impressed. You're getting all of the reference chemicals

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1 right that you got with the low through put method. This is 

### 2 something they were able to run 10,000 chemicals in a week on, 

### 3 so very high through put. It doesn't have quite the dynamic 

### 4 range of the lower through put assay, but it's doing a pretty 

### 5 good job despite that. 

### 6 Okay. An important thing that will come up in 

### 7 some of the potential technical discussions about the NCGC 

### 8 assays is they're run in a little bit different way than most 

### 9 high through put assays. Typically, you'd have a 96 or 384-well 

### 10 plate, and you concentration response series would be down a row 

### 11 or down a column. Instead what they do is they actually start 

### 12 off with 384-well plates and then they merge those. They 

### 13 interlead those to create the 1536 well-plates. So all of their 

### 14 assays are run in 1536. I'll point out later why it's important 

### 15 that they actually run three replicates where each of these four 

### 16 quadrants are shuffled. So each chemical tends to have 

### 17 different neighbors from each run, because there tends to be 

### 18 some slop over or there can be signal slop. That's a technical 

### 19 term. I'll talk a little bit more about that later and how we 

### 20 try to deal with that. 

### 21 So they take a master plate, the 384, they 

### 22 have three different interlead versions of that for the 1536 and 

### 23 then what they do is the concentration response is done by 

### 24 taking the master plate at the top and diluting down. So 

### 25 there're actually 15. Of these 15, 36-well plates that they 

### 26 dilute down. Those are all put on a robot and then away you go.

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1 All of the details will be important as I come back to talk 

### 2 about some of the assay interference issues. 

### 3 Okay. The next assay is another 

### 4 Transactivation assay, very much like the BG1, but it's run by a 

### 5 company called Odyssey Thera. It's run up to 100 micromolar. 

### 6 It's a luciferase readout, nothing particularly special about 

### 7 that. 

### 8 Okay. The next and I think this is a final 

### 9 set of assays, these are a set of transcription factor assays 

### 10 run by a company called Attagene. What's particularly 

### 11 interesting about these is they're very, very high multiplex. 

### 12 They actually run just two assays or two wells per chemical 

### 13 concentration. In one well, they get 52 readouts. In the 

### 14 other, they get 29 readouts. They run up to 200 micromolar. So 

### 15 this is the assay in this panel anyway where we run to the 

### 16 highest concentration and start looking for potentially the 

### 17 weakest compounds. 

### 18 They way they do the multiplexing is that 

### 19 readout is actually a sequencing-based readout rather than a 

### 20 fluorescence-based readout. So they have many transcription 

### 21 factors and the cells are engineered to have many of these 

### 22 reporters. The reporter, RNA construct, all have a single tag 

### 23 that has different cut sites. 

### 24 So what you do is you take this big mess of 

### 25 RNA that comes off of the cells. You digest them with enzymes. 

### 26 The report from each transcription factor has a different

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1 length. You run that out on a capillary electrophoresis 

### 2 machine, so therefore you can read quantitatively how many 

### 3 transcripts you are getting from the 52 or the 29. The 

### 4 difference between the cis and the trans is these are run in 

### 5 HepG2 cells and many of the transcription factors are 

### 6 endogenously expressed. All of those you can run, and that's 

### 7 the Cis assay. 

### 8 The trans assay, you actually transfect, 

### 9 transiently transfect the transcription factors that are not 

### 10 native there in the cells. For a number of cases, they have the 

### 11 same assay, and particularly for estrogen receptor, they have it 

### 12 in both the trans and the cis assay. So again, you'd want both 

### 13 of those assays to light up to feel like you have true activity. 

### 14 So that's a very very quick, high-level 

### 15 overview of what the assays are and we can talk about that more 

### 16 later. 

### 17 Okay -- so the chemicals. We start off with 

### 18 this very large set of chemicals that were nominated for the 

### 19 ToxCast program. Many stakeholders within the Agency, outside 

### 20 we had academic groups, folks from the OECD, folks from the 

### 21 industry who wanted chemicals tested. For example, everyone of 

### 22 the chemicals that's in the 10,000 chemical library that we 

### 23 spoke about earlier was put in as a nomination. The total list 

### 24 was about 20,000 chemical nominated, 20,000 unique cast numbers 

### 25 by the time you looked at physical chemical property. So we 

### 26 can't test volatiles. We can't test compounds, which are

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1 insoluble for instance. We don't even try those. We chose not 

### 2 to test mixtures initially. 

### 3 We came to about 10,000 chemicals that we went 

### 4 to our chemical vendor. There's a vendor compound focused 

### 5 incorporated, who actually did all of this. We said okay, go to 

### 6 Sigma and go to one other company. I don't remember what 

### 7 company two was. Because those are trusted vendors and get 

### 8 everything you can. They were only able to get about one-third 

### 9 of those 10,000 compounds. 

### 10 The National Toxicology Program had their 

### 11 10,000 compounds that they nominated. There was some overlap 

### 12 with ours. They had about the same experience. They went to a 

### 13 couple of other vendors, but this is actually a very practical 

### 14 issue that we don't quite know how to deal with yet, is that 

### 15 they're a lot of chemicals that are out in the environment. You 

### 16 could probably buy a carload, but you can't buy 100 microgram at 

### 17 enough purity to do this sort of testing. We haven't figured 

### 18 out what to do with that. 

### 19 Our requirements were that you had to have 90 

### 20 percent purity and 80 percent of the samples we got, at least on 

### 21 the certificate of analysis were at 95 percent. We're not 

### 22 requiring 99.99 percent purity just because you can't get that 

### 23 for lots of these chemicals. Most everything we got reported to 

### 24 be 95 percent purity. 

### 25 They were solubilized in 20 micromolar DMSO, 

### 26 put onto 96-well plates and frozen and then shipped to vendors

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1 as requested. So again, you'll hear a little bit more later 

### 2 about the Duluth Protocol where they carefully test every 

### 3 chemical that they've tested up to the limit of solubility. 

### 4 From a practical standpoint, it's really really hard to do. 

### 5 It's no longer high through put if you have to figure out what 

### 6 the limit of solubility is for every one of 10,000 compounds. 

### 7 Okay, so that's why we have this very standard protocol. We'll 

### 8 solubilize up to 20 micromolar. If it's not soluble there, we 

### 9 would try a 10 micromolar. If it's not soluble at 10 

### 10 micromolar, it's out. We don't worry about it. 

### 11 Because you can't always trust the certificate 

### 12 of analysis, the entire 10,000 chemical library that's going to 

### 13 the NCGC is going through further analytical QC. So we take the 

### 14 master plates, the 20 millimolar master plates, and they're 

### 15 going out. Right now, we only have about 30 percent of QC 

### 16 results for that. That data is slowly rolling in. 

### 17 Okay. We have chemicals, we send them off to 

### 18 the various labs who are going to run them, and they send us 

### 19 back data. For the entire ToxCast program, there're about 15 

### 20 labs that generate the data over the last three years or so, and 

### 21 every lab sends this data in a different format. This is a huge 

### 22 informatics issue. Because of this, we have many many, hundreds 

### 23 of assays, lots of different formats, lots of ways that people 

### 24 would like to analyze the data. We had to put together a robust 

### 25 and highly automated informatics system to deal with all of 

### 26 that. We've tried to make most of the assays fit a little bit

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1 of a one-size-fits-all. 

### 2 An important point -- we didn't make up a 

### 3 bunch of statistical methods. All the statistical approaches 

### 4 and curve-fitting approaches are things that are really standard 

### 5 in the high through put field. The total data set, which is 

### 6 almost complete, is about 28 million data points and it's about 

### 7 1.5 million chemical assay pairs, 1.5 million curves, like the 

### 8 curves we'll talk about. We've developed this eight-level 

### 9 process and I'll go into more detail on the next slide. 

### 10 The final output of this is all the data 

### 11 finally flows into a database, and the database is in part of 

### 12 what we call the Actor System. So if you go to actor.epa.gov, 

### 13 that's where all of our data is. Once the data is ready -- it's 

### 14 clean and we have a process for actually doing the data release, 

### 15 which is going to happen during this calendar year, all the data 

### 16 goes up on the website and everybody can see all the data. We 

### 17 make the data not only available to browse through the website, 

### 18 but we provide all of the data files including if people want 

### 19 all the back to the raw data. If somebody wanted to start from 

### 20 scratch and really wanted to deal with everything there to the 

### 21 end, we would provide all that data to them. 

### 22 Okay. So the pipeline -- there're these eight 

### 23 steps. In a very cartoonish way, I'll walk through this and 

### 24 will be happy to talk about it more. We get raw data files, 

### 25 which are typically at a plate level. So here's the number of 

### 26 counts, photon counts per well on each of the plates. We take

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1 that and we do some mapping. The vendors only see blinded 

### 2 chemical data. They have no idea what chemicals are on the 

### 3 plates. 

### 4 So at step two, we actually map the blinded 

### 5 chemical tracking IDs back to what was there, and we move things 

### 6 into a standard format instead of dozens of different formats 

### 7 here. Standard format, then we do data normalization. So we 

### 8 typically normalized for estrogen receptor to 100 percent of 

### 9 estradiol on the plates, then we turn that into 

### 10 concentration-response files. 

### 11 We do outlier detection. Throughout outliers, 

### 12 then in step six, we do the curve fitting and hit-calling. We 

### 13 say okay. If you look at just that curve for just that 

### 14 chemical, just that assay, it looks like there is activity. In 

### 15 level seven, which I'll spend a fair amount of time here talking 

### 16 about, we do systematic filtering on there. Looking at data 

### 17 than just that chemical, just that assay, we don't believe that 

### 18 it's a hit anymore. 

### 19 And then finally, we have a level eight, which 

### 20 we haven't done any of here where if it seems appropriate, we'll 

### 21 actually have manual intervention where some set of experts will 

### 22 sit there and they'll look at every curve and say yes, I believe 

### 23 that or I don't. But we're really striving for level one 

### 24 through seven to be very objective and there's no bias. Okay I 

### 25 know that chemical is supposed to be positive, so you have to 

### 26 make it positive. There's enough noise in the data that I think

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1 that would be a bad thing to do. The data is what the data is. 

### 2 3 Okay. So the curve fitting steps -- this is 

### 4 level six -- so we just use a four parameter Hill function. 

### 5 We're using R, which is an open source code. So again, we can 

### 6 hand anybody in the world all of our software. They could start 

### 7 from the beginning. We ask, do you see any significant 

### 8 activity? I can talk about thresholds there. If it does, then 

### 9 we calculate an AC50, what's the concentration where you get to 

### 10 50 percent of the maximum you get in that assay. Then we 

### 11 calculate various confidence intervals around that. Then there 

### 12 is an Emax and the T is the top of the Hill fit parameter. 

### 13 An important parameter is what we call the 

### 14 response cutoff. Every assay has noise around the baseline. 

### 15 Some of them are very noisy. Some of them are really, really 

### 16 tight. In order to call a chemical active in an assay, the mean 

### 17 response at some concentration has to be above what we call the 

### 18 response cutoff, which is 10 median absolute deviation. It's 

### 19 kind of like standard deviation around the baseline noise. So 

### 20 it's been conservative, conservative meaning that we would 

### 21 rather call something inactive if it's kind of in the noise 

### 22 rather than calling it active prematurely, because we seem to be 

### 23 having in general, too many false positives in some of these 

### 24 assays. But they merge all the assays together ultimately and 

### 25 deal with those issues. 

### 26 As we go on, we may see more and more of

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1 these, so let's just go through one in a little bit of detail, 

### 2 one of these concentration response plots. This is for the NCGC 

### 3 betalactamase assay and it's bisphenol A. 

### 4 So the black curve is the test chemical and 

### 5 the whiskers are one standard deviation. In this case, there 

### 6 were three technical replicates. That's the number up there. 

### 7 The AC50 is the concentration at which you got 50 percent up the 

### 8 curve to wherever it got to. There is actually also a relative 

### 9 AC50, and we had a bunch of discussion, is this the relative or 

### 10 the absolute, and I don't remember, which is what concentration 

### 11 did it take to get to 50 percent of estradiol. So that's 

### 12 another parameter. 

### 13 The estradiol curve is the red and again with 

### 14 the standard deviation. For assays where we had concurrent 

### 15 cytotoxicity, that's given in gold, again with the whiskers for 

### 16 one standard deviation. This dotted-dashed line is the response 

### 17 cutoff, the 10 MAD. Notice that this assay, the variation 

### 18 around the curve is very, very tight. This is probably the 

### 19 cleanest assay in terms of just technical variability. This 

### 20 little green tick is what we call the LEC, the lowest effective 

### 21 concentration. It's the first testing concentration where you 

### 22 got above the response cutoff. For every chemical, we have a 

### 23 picture like this and we have the table, and it gives the Hill 

### 24 fit parameters, various other fit parameters and confidence 

### 25 intervals. Okay. So that's how to read one of those plots. 

### 26 Okay. So we do this very standard, hands-off

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1 process to get to an initial call. We think in this assay, this 

### 2 chemical is either inactive or it is active. Then we go into 

### 3 level seven, where we start bringing in other information. We 

### 4 bring in cytotoxicity data or autofluorescence data where that 

### 5 is appropriate. We can turn active calls into believe them 

### 6 active. We may say no, I believe that's inactive. We may say 

### 7 set aside, and set aside means that the computer algorithm just 

### 8 doesn't know what to do with that data. So a human needs to 

### 9 look at that. We admit that the algorithms are smart enough to 

### 10 know that they're not as smart as they could be. So that's what 

### 11 set aside is. 

### 12 Finally, we may say discard. The only time we 

### 13 discard data is when we subsequently get the chemical QC data 

### 14 and it turns out what was in the well was not what was reported 

### 15 to be in the well. So I'll go through a little bit about this 

### 16 level seven filtering. 

### 17 A next step which is planned, but we've not 

### 18 actually implemented, is to now take what we bring out of level 

### 19 seven I'll talk about and start bringing in the chemical 

### 20 information, which is something the Duluth group has done quite 

### 21 a bit of. That will help us again decided finally, okay 

### 22 something is called active, does it really look like an active 

### 23 chemical or can I make a case that physically there's no way it 

### 24 would actually bind to the estrogen receptor. That's work in 

### 25 progress. 

### 26 Okay. So here's an example. I'll show you

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1 several examples of each rule. This comes from this Attagene 

### 2 assay. Again, this is the highly, multiplexed assay. We have 

### 3 lots of readouts going on all of the time. One of the things we 

### 4 see in this assay is that quite often as you get toward 

### 5 cytotoxicity, chemicals that are going to be cytotoxic, you see 

### 6 this big burst of activity, lots of the endpoints are turning on 

### 7 at relatively high concentrations, and none of those are 

### 8 probably receptor mediated. It's false activity. This is an 

### 9 are-you-awake test. 

### 10 So normally you go these plots from low 

### 11 concentration to high. Here we did it the other way around. So 

### 12 this is high concentration to low, and just look at the black 

### 13 spiky curve there. It's a histogram of the number of readouts 

### 14 as a function of concentration. So you see at relatively high 

### 15 concentration, and this is about 100 micromolar, lots and lots 

### 16 of endpoints are turning on. So it's sort of nonspecific 

### 17 activity. 

### 18 Included in this assay are three readouts for 

### 19 cell stress, oxidative stress assays. So when those turn on, 

### 20 you know the cells are being very unhappy. Instead of just 

### 21 having a little rule that says if at concentrations where you 

### 22 have those cell stress assays are turning on and you have this 

### 23 big burst of nonspecific activity, I'm just going to ignore any 

### 24 specific readouts. I think, and this is bisphenol A, that 

### 25 estrogen receptor assays are hitting these two little green 

### 26 symbols here. So they are at concentrations much below where

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1 this nonspecific stuff is going on. So we would believe that. 

### 2 Here's an example where you see some activity 

### 3 in the estrogen receptor assay, the black curve, but the gold 

### 4 bar is where we have that threshold. So any activity above 

### 5 where that gold bar is, I'm not going to believe. So this would 

### 6 be a case of level seven filter out. Level six said active, 

### 7 level seven says inactive. I have to say, this whole process of 

### 8 dealing with this, we're not done with that. We're still 

### 9 defining this process of filtering out these high concentration 

### 10 bursts. 

### 11 Okay. There are a number of assays, and I 

### 12 mentioned the betalactamase assay earlier, where cytotoxicity 

### 13 can give you a false readout. So again, the betalactamase 

### 14 assay, you have two channels and you're taking the ratio 

### 15 cytotoxic compound could actually cause a decrease in the bottom 

### 16 channel, which looks like an increase in the ratio. So here's 

### 17 an example of a compound where you start to see cytotoxicity, 

### 18 the gold at that same time you're seeing the activity in the 

### 19 true channel. So we would say this we don't believe is truly 

### 20 active. We believe this is an assay artifact caused by 

### 21 cytotoxicity. 

### 22 Again, the betalactamase assay is a 

### 23 fluorescence-based assay. So if you have a fluorescent 

### 24 compound, you could get fooled there. So the NCGC has run red, 

### 25 blue and green assays, both with and without cells. So we have 

### 26 the AC50 for everyone of the 10,000 chemicals in all of those,

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1 and we use that as a guide. 

### 2 If this is a chemical that's going to glow 

### 3 blue, or glow green or red, do we really believe that that's 

### 4 active based on the potential for this assay interference. 

### 5 Because you can't absolutely say that oh, this has being active 

### 6 because of that autofluorescence. We typically call those a 

### 7 SETASIDE. So a human has to go in and look and make some 

### 8 decision about whether that's really active or not. 

### 9 Here's an ongoing problem with the NCGC assay, 

### 10 something that is very specific to this technology. We call 

### 11 that flare. So the assays are running in 1536-well plates and 

### 12 especially in the BG1 assay. An active compound would be very, 

### 13 very bright. It will cast this flare across the neighboring 

### 14 wells on the plate. So if you actually have a plate that has a 

### 15 bunch of active compounds on it, and there're reasons why that's 

### 16 actually true, and if they're all in the same region on the 

### 17 plate, we see these bright, flarey regions. This is the plate 

### 18 and a green circle. You can't really see it here in the room, 

### 19 but a green circle is a positive activity. You see these big, 

### 20 like a big lamppost, which is spreading light all over. 

### 21 So essentially what we do is we detect these 

### 22 flares and any chemical, which is in the shadow of that flare, 

### 23 we just delete the data and say you can't trust the data. We 

### 24 take that out. That does a pretty good job of filtering that 

### 25 out. There are a handful of compounds that are still in the 

### 26 data set, which are potentially flare-related activity. So we

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1 have some filters to try to say, okay we think that that 

### 2 activity is coming from a neighboring well. 

### 3 Again going back, I talked about the way the 

### 4 plates, the three to four well plates are shuffled in the three 

### 5 different replicates. It's because in each one of those cases, 

### 6 some of the neighbors of the well are different. So you're not 

### 7 always sitting -- each of those three replicates are not always 

### 8 sitting next door to something that is really bright. Using 

### 9 that triplicate, we're actually able to solve, we believe, most 

### 10 of the -- correctly call a chemical being active is either in 

### 11 the flare or not in the flare. This is an example of where 

### 12 there is some activity at high dose in this particular compound, 

### 13 and turns out this point on the plate is sitting next to some 

### 14 really bright compound. So we believe this little activity is 

### 15 caused by flare. 

### 16 Okay just in summary, there're actually a lot 

### 17 of reasons you can have assay interference, false positives, 

### 18 false negatives, and none of these reasons are really 

### 19 potentially -- except for that flare issue -- have anything 

### 20 specific to high through put. These are things that really have 

### 21 to do with in vitro screening. We just list these because 

### 22 they're ones that we're having to, through our level seven to 

### 23 level eight, having to deal with. 

### 24 Cytotoxic or cytostatic compounds, they can 

### 25 cause various kinds of assay interference. Surfactants -- so in 

### 26 the cell free binding assays, you can get beautiful binding

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1 curves, whether you're in high through put or low through put if 

### 2 you put a surfactant in there. So we need to have filters that 

### 3 are based on chemical structure. This chemical lights up all 

### 4 those assays and you look and it's a surfactant, so I don't 

### 5 believe that. Flare we talked about. Chemical contamination -- 

### 6 we're going through and getting all of that data to deal with 

### 7 those. 

### 8 In these assays, we see a number of compounds 

### 9 and the bisphenols are a good example. Across many assays, you 

### 10 get this partial agonist behavior. They come up to 50 percent 

### 11 of E2 or 25 percent or 40 percent of E2. It's very 

### 12 reproducible. It happens across multiple assay technology. So 

### 13 there is something real about that. But if you were in an assay 

### 14 where the noise level is at sort of 20, 22 percent and you top 

### 15 out at 25 percent, do I call that active or not. Then you 

### 16 really want confirmation from other assays to decide what to do 

### 17 with that. 

### 18 I'll talk a little bit about chemical batch 

### 19 discrepancy. It's part of the Tox21 Program. We have maybe 

### 20 about 1000 compounds where we independently procured the 

### 21 samples. The NTP bought it, we bought it, the NCGC bought it, 

### 22 and so we can actually say here's supposedly the same compound. 

### 23 It's the same chemical structure, but it two different batches, 

### 24 two different vendors, and you get different answers. Okay, how 

### 25 do you deal with that? We haven't solved that problem yet. 

### 26 Activity of weak compounds -- I've said

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1 something about that. If chemicals are not soluble in DMSO, we 

### 2 simply can't deal with them. We've talked about building water 

### 3 plates, being able to run assays in water. That's a 

### 4 possibility. We can't deal with volatile compounds. 

### 5 Then metabolism -- we'll talk more later this 

### 6 afternoon about how you deal with that. Some of the assays 

### 7 we're investigating, not the ones we're talking about today, can 

### 8 be run plus or minus S9. So, we can actually see if there is a 

### 9 metabolic activation, something going on there. Those assays 

### 10 are not ready for primetime, but it's something being 

### 11 investigated. 

### 12 Okay, so we now have lots of data, now we'll 

### 13 start seeing how well these assays run. So I'll get a little 

### 14 more specific there. 

### 15 We selected a set of 37 reference compounds, 

### 16 all agonists, although we have some antagonist assays also. 

### 17 Then we're going to talk about overall accuracy and something 

### 18 about reproducibility. Then because we have all of these 

### 19 assays, all of these chemicals, in all cases we've run at least 

### 20 1800 chemicals and in some cases we've run 10,000 chemicals 

### 21 through these assays. We actually have a lot of statistics of 

### 22 assays performance that we can start looking at. 

### 23 Okay this is an eye test. This is the list of 

### 24 37 reference chemicals. These came -- they largely came from 

### 25 the OECD, ICCVAM and ECCVAM validation processes on estrogen 

### 26 receptor in vitro assays. Susan Laws did a very careful look to

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1 try to make sure these were ones that not only -- is there a lot 

### 2 of in vitro data, but is there corresponding in vivo data. 

### 3 Again, the details -- you're not expected to read all of this, 

### 4 but what we're going to look at is there are four colors here. 

### 5 White says that the assay -- and these are the 

### 6 eight assays -- the assay agreed with the expected call. Okay 

### 7 so the NCGC BG1 assay agreed on all 37 chemicals. A red cell 

### 8 says the assay was inactive where it was supposed to have been 

### 9 active. So the betalactamase assay missed a couple of the 

### 10 actives. A blue says it was active where it should have been 

### 11 inactive, and a yellow says there's some ambiguity about the 

### 12 call. I'll talk about that a little bit. 

### 13 Okay by just glancing at this, we see that the 

### 14 BG1 assay got everything corresponding. The betalactamase assay 

### 15 missed a couple of the actives. The Attagene assay has got all 

### 16 of the actives. It had a couple, two or three, inactives that 

### 17 showed up activity, and I'll talk about those. This Odyssey 

### 18 Thera transactivation assay -- that's just a bad assay. So it 

### 19 missed a lot of the active and it has a very high noise level. 

### 20 So that is an example of an assay, once we've gone through this 

### 21 process, we just won't use that anymore probable. 

### 22 Then finally the binding assays, the last 

### 23 three columns, there's lots of red. So they're missing a lot of 

### 24 the weak -- I know again you can't ready this -- but going from 

### 25 the top, these are the strong positives, moderate strong, 

### 26 moderate weak, very weak, negative. So what it's doing is it's

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1 missing a lot of these very weak compounds, and again that's 

### 2 primarily because of the assay protocol. We didn't run these 

### 3 chemicals up. We ran all of these in concentration response, 

### 4 but only up to 50 micromolar. A lot of these compounds you only 

### 5 see activity at 100 micromolar or more. So that's a quick 

### 6 summary. 

### 7 Let's look at a little bit of detail. These 

### 8 are the betalactamase -- the chemicals in the betalactamase 

### 9 assay, which should have been positive; that was the nominal 

### 10 call, and were either negative or questionable. Here on the 

### 11 left-hand side, this is methoxychlor and bisphenol B. Here we 

### 12 see, these are examples of these sort of partial agonist 

### 13 activity. They're very reproducible. You get a nice 

### 14 concentration response profile, but the top is right around 

### 15 where our statistical threshold is. So the algorithm says you 

### 16 didn't meet the threshold or you're right at the threshold. So 

### 17 if we go back and look at this, you'd say there really is 

### 18 activity, but our noise level is such that it's a little hard to 

### 19 see. 

### 20 This was a chemical -- this is really bad, so 

### 21 even I can't read these. This is a chemical which was called 

### 22 initially level seven fail. We think it's inactive because it 

### 23 hits the blue channel. Okay so you'd say if it's hitting the 

### 24 blue channel about the same time you see the real activity, but 

### 25 again, this is something people know, this chemical really is 

### 26 positive, so we're going to call that positive.

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1 Finally, this was another one of those yellow 

### 2 ones were for ethylparaben, there were two samples independent 

### 3 procured, one of which met the criteria. It was very weak, but 

### 4 it did meet the threshold for activity and one of the sampled 

### 5 didn't. This point out this issue of we have a lot of data for 

### 6 two different samples. We can get different results from the 

### 7 two differently procured samples. So a work in progress to deal 

### 8 with these sort of issues. 

### 9 Okay. The Attagene assays, all of the 

### 10 positive reference chemicals, they got all of those, they got 

### 11 the call, but they had a number of blue things, so they were 

### 12 positive where chemicals were expected to be negative and those 

### 13 were corticosterone, progesterone and haloperidol. 

### 14 Corticosterone and progesterone were active in both of those 

### 15 assays. Haloperidol was only active in the CIS assay. This 

### 16 one, I've no idea what's going on there. That's a question 

### 17 we're digging into to decide whether that's really a believable 

### 18 result. 

### 19 I'm going to dig a little bit more into the 

### 20 progesterone. This is a chemical where you can come up with a 

### 21 story where you go through a metabolic transformation and you 

### 22 have something estrogenic. Okay I see some nodding head or some 

### 23 shaking heads there. These HepG2 cells were selected to be -- 

### 24 they're not terribly metabolically competent, but they have some 

### 25 metabolic capacity there. So, that's one possibility. 

### 26 Now if you dig into the rest of the assays

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1 though, it's not just the Attagene assays that are lighting up 

### 2 progesterone. The Attagene assays are both on the left. 

### 3 They're positive. Two of the binding assays are positive. The 

### 4 BG1 shows activity, and it shows activity not in just one 

### 5 sample, but in three independently procured samples. So there 

### 6 is something about progesterone, which is at relatively high 

### 7 concentrations. These are high concentrations where you're seen 

### 8 activity is doing something in multiple cell backgrounds to the 

### 9 estrogen receptor. So the only point about this is that picking 

### 10 true negative reference compounds can be tricky sometimes. 

### 11 Again, a work in progress to understand what all this means. 

### 12 Okay. If we just summarize that table with 

### 13 all the reds, the blues and the yellows, and just ask what is 

### 14 the concordance, and that's the second line. The concordance 

### 15 for all of these assays with the reference compounds, the Tox21 

### 16 BG1 got them all right, 94 percent for the betalactamase, the 

### 17 Attagene were both 90 some odd. This Odyssey Thera luciferase 

### 18 was at 80 percent and the binding assays were middling to bad. 

### 19 Again, that's because we're missing a lot of those weak 

### 20 positives. A table -- we can talk about this more, but these 

### 21 are simply some summary statistics about the standard deviation 

### 22 around from the technical replicates for some of the reference 

### 23 compounds. They really mimic what we see across the whole 

### 24 compound library. So there is nothing specifically noisy or 

### 25 unnoisy about the reference compounds. 

### 26 So that we actually now have lots and lots of

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1 data on these assays so we can actually do a little bit more 

### 2 looking at quality reproducibility. What I'm showing you here 

### 3 is again for the Tox21 assays and it's the betalactamase on the 

### 4 left and the BG1 on the right, we have a 1000 or 1400 compounds 

### 5 which were procured independently. We have sample one and 

### 6 sample two. We can say how to those agree with one another? 

### 7 Ideally to read the plot, only plot against these is the AC50. 

### 8 So what concentration got you half way up the 

### 9 curve and the X axis is one replicate, the Y axis is the other 

### 10 replicate. It's one point per pair of chemicals. If everything 

### 11 was right, everything would be on the diagonal. So compounds 

### 12 which are really potent are down here at the bottom left. This 

### 13 cluster stuff at the top right are all the inactives. Those are 

### 14 inactive in both assays. Just for plotting purposes, there's a 

### 15 little jitter put on it so all the points are not sitting on top 

### 16 of one another. The colors have to do with the strength of the 

### 17 reference compounds. The reference compounds are colored by 

### 18 their proportion strength. 

### 19 So things on the diagonal were all happy. 

### 20 These that are off the diagonal, this group up here -- so along 

### 21 the top and on the right are ones that were active in one 

### 22 replicate, but not in the other. So that's where you start to 

### 23 worry about something. Okay what we see is most of the density 

### 24 there are things that are really weak. They're one micromolar, 

### 25 10 micromolar, 100 micromolar, AC50 in one replicate and they 

### 26 miss in the other. That says they're really borderline in one

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1 replicate and they miss the other one. So we're not too 

### 2 concerned about that. 

### 3 There are a handful of compounds where they 

### 4 are close to nanomolar activity in one replicate and inactive in 

### 5 the other. Those are ones where we're going to do the followup. 

### 6 We haven't done that yet. The data, again, going through final 

### 7 clean up and we'll do that. This gives you some idea of how 

### 8 well you can trust. The other thing we haven't done is filter 

### 9 out all of the chemicals which are simply not what they're 

### 10 supposed to be. We don't have all of the analytical QC data. 

### 11 This is tool. Once we have all that in, we can quickly focus in 

### 12 on the chemicals that something really funny is going on. 

### 13 Okay then we can ask the same sort of question 

### 14 about comparing two different assays from the same lab or two 

### 15 different assays slightly different technologies. Let's focus 

### 16 on the left. These are the Attagene system trans. Again, you 

### 17 would like to have, if it's active in one, it would be active in 

### 18 the other at about the same AC50, so you would like everything 

### 19 to be on the diagonal. Once again we see a big group of 

### 20 chemicals that are weakly active on one, but inactive in the 

### 21 other. So they are kind of on the border one way or the other, 

### 22 but a handful of chemicals which are really active in one, but 

### 23 inactive in the other. Once we do all the rest of our 

### 24 filtering, we'll go back and look at those again. 

### 25 You see more of these real heavily discordant 

### 26 compounds looking at the betalactamases versus the BG1s, so

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1 things that are sub-nanomolar in one and inactive in the other. 

### 2 We're digging into those. 

### 3 Okay in standard assay validation, you always 

### 4 think about transferability. We were asked, okay at least think 

### 5 about what would transferability be here. We're not asking some 

### 6 other lab to rerun these assays, but you can at least ask 

### 7 somebody else to take the data and analyze it. Would you get 

### 8 the same answer with your assay analysis protocol as we did? So 

### 9 we're actually running an experiment with our Tox21 partners. 

### 10 The folks at the NCGC are taking the raw data. The folks at the 

### 11 NTP are taking the raw data. The folks at the FDA are taking 

### 12 the raw data. We're all analyzing and seeing what do we agree 

### 13 and disagree on the activity calls, the AC50s and so on. There 

### 14 is a manuscript that is being prepared on that. 

### 15 Okay finally in summary, we're showing you 

### 16 some of the real baby steps in dealing with the high through put 

### 17 data for using for prioritization around the endocrine program. 

### 18 We looked at eight of 21 assays. Already there were orthogonal 

### 19 -- we have other orthogonal assays. We believe it's really 

### 20 important -- there's no new here. You have to use this as a 

### 21 battery. There is no perfect assay. 

### 22 One of the analyses we're going to be able to 

### 23 do once we get all the assays together is to say what is the 

### 24 minimal set of assays to get, to meet some criteria? You 

### 25 certainly don't need 21 assays. Do you need four assays? Do 

### 26 you need eight assays? Do you need two assays? So we can ask

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1 questions like that. Again, we're not asking is this chemical 

### 2 an endocrine disruptor, we're simply asking does this chemical 

### 3 interact with the estrogen receptor in vitro. That's what we're 

### 4 trying to say. 

### 5 We've shown that you can analyze generate data 

### 6 on thousands of compounds. You can analyze all the data in a 

### 7 very systematic objective way. It's not perfect. We're looking 

### 8 for better ways and advice from this group is very welcome. The 

### 9 next challenge once all the data is together is how you actually 

### 10 combined it into this final prediction. Again, something we're 

### 11 just starting on. Okay. That's what I had. I'm happy to take 

### 12 any questions. 

### 13 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 14 for Dr. Judson? Dr. Safe? 

### 15 **DR. STEVEN SAFE:**The betalactamase assay is 

### 16 with the GAL4 and the GAL4 response element promoter? 

### 17 **DR. RICHARD JUDSON:**I'm going to throw out a 

### 18 lifeline and ask Keith Houck, who's our assay expert, to come up 

### 19 and sit next to me. He can address some of the technical 

### 20 questions about the assays, if that's okay. 

### 21 **DR. STEVEN SAFE:**Well what is the assay, is 

### 22 that what it is? 

### 23 **DR. KEITH HOUCK:**It is. It's the GAL4 

### 24 mammalian 1 hybrid system. 

### 25 **DR. DANIEL SCHLENK:**Can you just state your 

### 26 name please again?

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1 **DR. KEITH HOUCK:**Keith Houck. 

### 2 **DR. DANIEL SCHLENK:**Thanks. 

### 3 **DR. STEVEN SAFE:**All estrogenic compounds 

### 4 basically of these xenoestrogens are selective estrogen receptor 

### 5 modulators. Basically what you've done is you're using two 

### 6 assays, a GAL4 ERS assay, where you're activating a yeast 

### 7 reporter and an ERE luciferase assay, right? I mean, those are 

### 8 the only two assays you're using. So it's not eight assays, 

### 9 it's really only two, but you've got different cell contexts, et 

### 10 cetera. Is that correct? 

### 11 **DR. KEITH HOUCK:**Well there're also the 

### 12 binding assays. 

### 13 **DR. STEVEN SAFE:**Binding is separate, that's 

### 14 fine. That's straight forward. 

### 15 **DR. KEITH HOUCK:**Two different formats of the 

### 16 transactivation assay. 

### 17 **DR. STEVEN SAFE:**That's really all -- it's 

### 18 not eight different assays, it's really two assays. Two assays 

### 19 in a different context. All I'm saying is that this shouldn't 

### 20 be the same. There's no way. If there's selective estrogen 

### 21 receptor modulators, cell context and whatever is around it, 

### 22 dictates differences because the compounds are different because 

### 23 they're selective, estrogen receptor modulators. All of them 

### 24 are. The fact that -- don't waste any time by looking at why 

### 25 they're different. They should be different. 

### 26 **DR. DANIEL SCHLENK:**Dr. Safe? I think those

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1 are things we would save for the comment period after the fact. 

### 2 So we just want questions at this point. 

### 3 **DR. STEVEN SAFE:**Okay. I won't make a 

### 4 speech. 

### 5 **DR. DANIEL SCHLENK:**Dr. Burris? 

### 6 **DR. THOMAS BURRIS:**Tom Burris. I have a 

### 7 question concerning of prioritization using (Q)SAR. Given the 

### 8 capabilities of these assays and dealing with a 5000-compound 

### 9 library, it seems like the prioritization could be based in 

### 10 empirical data, not just modeling. I'm not sure why that 

### 11 strategy has been taken to even consider the (Q)SAR. 

### 12 **DR. DANIEL SCHLENK:**Does anyone want to 

### 13 answer that? Yes, Ms. Manibusan? 

### 14 **MS. MARY MANIBUSAN:**So as the endocrine 

### 15 program moves forward, what we're stressing is that we're taking 

### 16 incremental, small steps in integrating some of these 

### 17 computational tox tools into the regulatory decision making, 

### 18 starting first off the block with chemical prioritization. What 

### 19 we're bringing forward to the Panel is that demonstration, but 

### 20 the question is how does the Agency go forward in comparing and 

### 21 understanding how to use high through put along side with the 

### 22 benchtop assays. 

### 23 The empirical data for sure, but what we're 

### 24 demonstrating again is how do we begin to evaluate this new set 

### 25 of data. What are the uncertainties that we should just be 

### 26 aware of? As Richard said, that assays are imperfect. There

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1 are going to be strengths and limitations. The agency needs to 

### 2 understand what those limitations and strengths are before we 

### 3 move forward to fully adopting high through put in whatever 

### 4 fashion the Panel is suggesting that we can at this point. 

### 5 So there're no limitations, but what we're 

### 6 bringing forward again is just an incremental step-wise 

### 7 progression demonstrating how can we evaluate this new set of 

### 8 data alongside with our low through put. 

### 9 **DR. RICHARD JUDSON:**Can I just make one -- 

### 10 another problem. This issue of the 30 percent of chemicals 

### 11 we're able to procure. There're some real practical issues. 

### 12 There're chemicals that you want to test, but you can't get MSDS 

### 13 or you can't even buy. So there's a point of diminishing 

### 14 returns trying to go buy every chemical and run every chemical. 

### 15 So you need something to extrapolate where it's really hard to 

### 16 get the data. 

### 17 **DR. DANIEL SCHLENK:**That was Dr. Judson. 

### 18 Yes, Dr. Burris? 

### 19 **DR. THOMAS BURRIS:**This is Tom Burris again. 

### 20 That was actually my follow-up question. Given that if only 30 

### 21 percent of these compounds are accessible, is there a pathway 

### 22 for synthesis that the Agency has, or is this something -- in 

### 23 that case a prioritization would be very valuable. Which ones 

### 24 would you synthesize first? But I don't know. This is the 

### 25 first I've heard that these compounds are not available. 

### 26 **DR. RICHARD JUDSON:**Just to be a little more

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1 specific on numbers, which doesn't change the basic issue. 

### 2 Between all of the Tox21 partners, we're able to buy -- it's 

### 3 about 45 or 50 percent of the 5000 compounds we're worrying 

### 4 about. Still, there's 50 percent that we can't. The NCGC has 

### 5 synthesized compounds, but that's really in a research context. 

### 6 I don't know that we've exhausted from a research standpoint. 

### 7 We weren't tasked to go find every last chemical. So it may be 

### 8 that if we tried harder, we could get up to 75 percent if 

### 9 somebody was tasked with that. It's an issue that has to be 

### 10 dealt with at some point though. 

### 11 **DR. DANIEL SCHLENK:**That was Dr. Judson. 

### 12 Again, just to remind you, if you could just state your name 

### 13 before you respond, that would be great, thanks. Dr. Waters? 

### 14 **DR. KATRINA WATERS:**High, it's Katrina 

### 15 Waters. I have a couple of questions. One thing you mentioned 

### 16 was using the estradiol as the 100 percent normalization 

### 17 standard on each plate. Did you have a threshold that that had 

### 18 to cross in order for it to be a positive plate with the 

### 19 positive control? 

### 20 **DR. RICHARD JUDSON:**Yes, there are various 

### 21 steps for throwing out plates. Unfortunately, our colleague who 

### 22 knows all those details is home sick and wasn't able to make it 

### 23 here. If a plate is bad because of the positive control didn't 

### 24 work well -- and typically I think there were be sort of a Z 

### 25 score between the positive controls and the negative controls, 

### 26 we would just throw the plate out.

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1 **DR. KATRINA WATERS:**Right. I mean you should 

### 2 have enough replicates of the positive to be able to says that a 

### 3 certain plate is below the standard. 

### 4 **DR. RICHARD JUDSON:**Yes, all the assays have 

### 5 that on them. 

### 6 **DR. KATRINA WATERS:**Okay and my second 

### 7 question is you mentioned using the interference measurement 

### 8 with the cell stress and oxidative burst and all of that as a 

### 9 way to minimize false positives, but yet I thought earlier we 

### 10 described a goal of being minimizing false negatives. Could you 

### 11 comment on how that affects your false negative? 

### 12 **DR. RICHARD JUDSON:**So we're not done yet. 

### 13 So let me just talk -- this again Dr. Richard Judson. Let me 

### 14 talk about the way we're thinking about doing that. If we can 

### 15 definitely say, okay I have a good scientific reason to believe 

### 16 that is a false positive, I think that's a good thing to do, and 

### 17 we'll throw those things out. There's some parameters you tweak 

### 18 that can let you say, okay I'm going to err on the side of 

### 19 having more false positives than false negatives. Definitely, 

### 20 almost certainly, you can say that chemical is a false positive. 

### 21 I understand that. I want to throw it out, but your point is 

### 22 really important that I would be easy to say none of these 

### 23 assays are perfect so we're not going to use any of the data and 

### 24 we'll have nothing but false negatives, right. So it's a little 

### 25 bit of a judgement call that we're learning about how to do. 

### 26 **DR. DANIEL SCHLENK:**Okay. Dr. Nagarkatti?

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1 **DR. PRAKASH NAGARKATTI:**I had a question 

### 2 about the transcription factor assay. So in those assays, are 

### 3 you looking at just the ERE, detection of ERE binding or are you 

### 4 looking at other transcription factors as well? The reason I'm 

### 5 asking is that because it is known that ligand activation of 

### 6 estrogen receptor can also directly activate other transcription 

### 7 factors like AP-1, NF kappa B. So it is not clear from your 

### 8 mechanistic studies whether you do take into consideration other 

### 9 transcription factors are all you're looking at is only the 

### 10 Attagene. 

### 11 **DR. KEITH HOUCK:**This is Keith Houck. So we 

### 12 do look at a panel of transcription factors in both the cis and 

### 13 the trans. It is a multiplex system, so all of those results 

### 14 come along with the ER results. One clarification, so in the 

### 15 cis system, we're using an ERE and there's greater likelihood of 

### 16 crosstalk with some of the other transcription factors. The 

### 17 trans system is the mammalian one hybrid system, the GAL4 

### 18 system, which is a little less sensitive to that type of 

### 19 crosstalk. 

### 20 In terms of the analysis for this specific 

### 21 project, I don't believe we're really using the activity for 

### 22 those other transcription factors other than for that 

### 23 cytotoxicity filter that we heard about. 

### 24 **DR. PRAKASH NAGARKATTI:**Right. When you take 

### 25 into consideration the wide area of these chemicals, do you know 

### 26 like what proportion of them would trigger the classical ERE

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1 pathway versus the nonclassical activation of the transcription 

### 2 factors? The reason I'm asking is just to see whether -- are we 

### 3 missing out on some chemicals that might bypass or go through 

### 4 the alternative pathway, is that a significant number that we 

### 5 need to be concerned about or is it just not something that 

### 6 needs to be taken into consideration? 

### 7 **DR. KEITH HOUCK:**This is Keith Houck. That's 

### 8 not a trivial question to answer. We have a lot of data that 

### 9 will, I think, help us get a handle on that, but we're not 

### 10 really at that level of analysis of the data at this point. 

### 11 **DR. PRAKASH NAGARKATTI:**Thank you. And I had 

### 12 one more question. When you're comparing the assays, correct me 

### 13 if I'm wrong, but you said that in order the minimize the 

### 14 concentration levels, you first take into consideration maybe 

### 15 like 25 micromole. If that turns out positive, then you do a 

### 16 dose response, maximizing up to 50 micromoles. If when you do 

### 17 that, what I'm wondering is then at 50 micromole it comes out 

### 18 positive, where as the first screen at 25 micromole turns out to 

### 19 be negative, wouldn't that impact your correlation between the 

### 20 two assays? So my question was why in the first place that 

### 21 single dose was not used at 50 micromole, if I understood right. 

### 22 **DR. RICHARD JUDSON:**This goes back to -- this 

### 23 was a design decision for the program, three or four years ago. 

### 24 The main focus, again the estrogen receptor endocrine focus is a 

### 25 tiny part of this. A lot of what we're looking at are pathways 

### 26 that are hit a relatively potently in targets we don't really

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1 know much about. So we're screening lots and lots of targets 

### 2 that are not their traditional ones that everybody knows about. 

### 3 4 Again, these assays are quite expensive. If 

### 5 we were to run that at 100 micromolar, say that was our first 

### 6 screen and you get lots of false positives there, then we spend 

### 7 a lot of money following up. So if you say what we really cared 

### 8 about, at least for that assay set or the ones that are pretty 

### 9 potent, doing the 25 micromolar first screen seemed reasonable. 

### 10 11 In this context, I think we're not there yet 

### 12 to be able to definitely say this, but if we look at what is the 

### 13 minimum set of assays we would run all the chemicals on. So if 

### 14 we were going to run 5000 or 10,000 chemicals, we wouldn't run 

### 15 the binding assays first. We would run these transcription 

### 16 factor assays or the protein complementation assays first 

### 17 because they're relative to the binding assays are inexpensive. 

### 18 You get a lot of data out of them, and then take consistent 

### 19 positives there and go follow up in the binding assays. 

### 20 **DR. DANIEL SCHLENK:**That was Dr. Judson. Dr. 

### 21 Houck, do you have something to add? 

### 22 **DR. KEITH HOUCK:**Yes, Keith Houck. Just to 

### 23 clarify, it was only the binding assay where we did the single 

### 24 concentration and initial screen. All the others were run in a 

### 25 full concentration response the first time through. 

### 26 **DR. DANIEL SCHLENK:**Okay. Dr. Jacobs?

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1 **DR. MIRIAM JACOBS:**Miriam Jacobs. Related 

### 2 really to some of the questions that were coming earlier, I just 

### 3 wondered what the rationale was originally for this election of 

### 4 the 21 assays, and then the eight assays that are being 

### 5 presented here? 

### 6 **DR. RICHARD JUDSON:**I'll start. Richard 

### 7 Judson. So we don't -- for this program, with the exception of 

### 8 the NCGC, none of these assays are custom. These are 

### 9 off-the-shelf assays you can go out and buy. So the ToxCast 

### 10 Program, the way that's run is we have several rounds where 

### 11 we've put out requests for proposals. Companies would say -- 

### 12 for these class of assays -- and companies would come in, okay 

### 13 we have assays that will do this, that or the other with 

### 14 particular technologies. So the assays, the total battery, the 

### 15 21 assays are coming from companies who bid on these proposals 

### 16 that met the kinds of assay batteries we were interested in. 

### 17 So that's where the 21 comes from. In 

### 18 particular, estrogen receptor pathway is an interesting testing 

### 19 ground. I mentioned this earlier. For understanding how you 

### 20 can use lots of assays across a pathway to figure out what's 

### 21 going on. So we wanted to have at least one pathway where we 

### 22 had every assay we could imagine run. Then you can start asking 

### 23 these questions, what is the minimal set. So that was the 

### 24 rationale for having lots of different assays. 

### 25 Now why do we choose eight of the 21 to bring 

### 26 to this Committee? It was partly because they were the same

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1 technologies as the Duluth Lab has used to train the Expert 

### 2 System. So binding assays, transactivation assays, and partly 

### 3 the workgroup, the ORD Workgroup who went through this for the 

### 4 last six months, there was just a limited bandwidth to sort of 

### 5 dig in and learn all the details about all the assays, and you 

### 6 felt like we could do a reasonable job having the group look at 

### 7 the eight assays that were kind of familiar to everyone and not 

### 8 worry about the others for this meeting, anyway. 

### 9 **DR. MIRIAM JACOBS:**A couple of more points 

### 10 just of clarification from the slides earlier run. Sorry, 

### 11 Miriam Jacobs. On slide 143, you said 20 micromolar, but on the 

### 12 slide it was 20 millimolar. So I just wondered which one was 

### 13 correct. 

### 14 **DR. RICHARD JUDSON:**What's on the slide is 

### 15 correct. This is correct, yes. So 20 millimolar is correct. 

### 16 **DR. MIRIAM JACOBS:**Thank you. 

### 17 **DR. DANIEL SCHLENK:**Okay, Dr. Denslow? 

### 18 **DR. NANCY DENSLOW:**Nancy Denslow. Most of 

### 19 your assays appear to be activators of the estrogen receptor. 

### 20 Have you looked ever at antagonists of the estrogen receptor, 

### 21 and are they included in any of the chemicals? 

### 22 **DR. RICHARD JUDSON:**So in the 21, there are 

### 23 actually four antagonist assays. We didn't talk about them, but 

### 24 they're part of the panel. The NCGC, each of their assays they 

### 25 ran in both agonist and antagonist mode. Then what were the 

### 26 other two? Odyssey Thera ran the other two. There are two more

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1 antagonist assays. I'm drawing a blank. 

### 2 Okay, but then the other set of assays, which 

### 3 are interesting is the protein complementation assays. Again, 

### 4 we didn't talk about that so I just mention this as a sort of an 

### 5 interesting scientific point. Antagonist in those. So these 

### 6 are assays when the two dimers come together. All of the 

### 7 classic antagonists, instead of coming up to 100 percent of E2, 

### 8 they go to 200 or 300 percent of E2. There is a specific 

### 9 antagonist signal there. So we are screening in our model for 

### 10 antagonist as well as agonist. The antagonist assays have a 

### 11 problem though. They are very much more sensitive to 

### 12 cytotoxicity compounds. So trying to sort out cytotoxicity from 

### 13 antagonism is tough. We're not done with that. 

### 14 **DR. DANIEL SCHLENK:**That was Dr. Judson. 

### 15 **DR. NANCY DENSLOW:**And I have one more 

### 16 question. Nancy Denslow again. So like the Attagene assay, it 

### 17 sounds wonderful, but there's only one company that's doing this 

### 18 assay and it's all proprietary. So how do you validate that the 

### 19 assay would work in the same way if somebody else was doing the 

### 20 assay? Isn't that something that needs to be done for the 

### 21 validation of assays? 

### 22 **DR. RICHARD JUDSON:**Richard Judson. So that 

### 23 -- I could talk for an hour about that, but we don't want to do 

### 24 that. There's been a lot of discussion, and I'm just going to 

### 25 point to a paper which was just published this week that came 

### 26 out of a cross agency worldwide work group looking at exactly

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1 that issue. 

### 2 It came out in Alltech. Judson is the first 

### 3 author. Looking at how do you think about validating these 

### 4 Tox21 assays. There were representatives from EPA, ORD, from 

### 5 the regulatory offices from NTP, from all the Tox21 partners, 

### 6 people who had been in ICCVAM, ECCVAM and OECD industry 

### 7 academia. There were some ideas that we've put out on the table 

### 8 of how you deal with that that I'd be happy to talk about, but 

### 9 it's outside of the scope of what we're discussing today. 

### 10 **DR. DANIEL SCHLENK:**Thanks. Okay. Dr. 

### 11 Waters and then Dr. . 

### 12 **DR. KATRINA WATERS:**Katrina Waters. So I 

### 13 have a question about your set of reference chemicals that you 

### 14 chose. There're only 37 and only eight that are negative based 

### 15 on your expert classification of them as being positive or 

### 16 negative. I'm surprised you don't have more negative chemicals 

### 17 available to you out of the hundreds that you have screened that 

### 18 would have some animal data available. 

### 19 **DR. RICHARD JUDSON:**Susan Laws, Dr. Laws is 

### 20 here. She worked on that, and I think she would be a good 

### 21 person to address those issues. 

### 22 **DR. DANIEL SCHLENK:**Will you please state 

### 23 your name for the record? 

### 24 **DR. SUSAN LAWS:**Hi, I'm Susan Laws. Yes, you 

### 25 are correct. What we did when we started this, our strategy was 

### 26 to first look at chemicals that were historically recognized as

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1 being ER agonist. This was just a starting point for his whole 

### 2 performance exercise. Of those, we used chemicals that we not 

### 3 only had both confirmation in vitro, in vivo and other assays, 

### 4 they were also harmonized with some of the assays that we had 

### 5 validated during the early work within the Agency for the Tier 1 

### 6 screening battery and they were also harmonized with the OECD's 

### 7 test guidelines, specifically the updated test guideline 455, 

### 8 which is an ERTA assay. 

### 9 Now the next phase would be -- there are 

### 10 certainly many negatives. The question with some of those 

### 11 negatives, we don't have as much strong documentation that they 

### 12 really were supposed to be negative. Even with the progesterone 

### 13 example, a negative may not be a negative all the way across the 

### 14 battery of assays. 

### 15 The next stage and partly what will be 

### 16 discussed in the next presentation is looking at all of the 

### 17 negatives that were produced here, and then we'll be able to 

### 18 look -- and looking at their consistency with being negative 

### 19 across the battery. So I think ultimately, there will be more 

### 20 negatives and there will be more to base a performance. 

### 21 **DR. DANIEL SCHLENK:**Okay. Dr. Burris? 

### 22 **DR. THOMAS BURRIS:**I have a question 

### 23 concerning the criteria used to call something as active, this 

### 24 threshold is 10 fold over median absolute deviation. In 

### 25 particular, based on what you had mentioned about some of the 

### 26 partial agonists potentially having enough noise that they may

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1 be at the level of at least statistical activity, at least as 

### 2 it's defined here. I'm just curious if there has been any type 

### 3 of evaluation of a variety of statistical tools to make sure 

### 4 that compounds such as those weak partial agonists that still 

### 5 have toxicological relevance don't get missed. 

### 6 **DR. RICHARD JUDSON:**Richard Judson. So we 

### 7 did a lot of sensitivity analysis over choosing that threshold, 

### 8 worrying about that and looking at this enormous dataset that we 

### 9 can then look at to say okay, if you change it from 10 MAD to 12 

### 10 MAD, or 10 MAD to 8 MAD. What changes across 1000 compounds or 

### 11 so, and so I guess the first answer is that we're getting ready 

### 12 to go back through one final iteration on setting these 

### 13 parameters. So 10 MAD may turn into 8 MAD. 

### 14 The more sophisticated, at least what I think 

### 15 is the more sophisticated thing we're doing is we're coming up 

### 16 with a consensus call. We're not going to say, we're not just 

### 17 going to count how many assays -- was it positive to say okay 

### 18 that's the consensus I believe this chemical is estrogenic. 

### 19 We're doing a variety of ways to do that. One way is to forget 

### 20 the call. Let's just take all of the concentration response 

### 21 profiles for all 10 assays or 12 assays or 15 assays, whatever 

### 22 we're going to use and say are they all consistently going up. 

### 23 If they're all consistently going up, but none 

### 24 of them do anything more than just tickle that threshold, we say 

### 25 yes, we really believe something is going on and it's really 

### 26 just a weak partial agonist. That's kind of a research effort

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1 to deal with that issue, but we recognize this is an issue. So 

### 2 those would be real false negatives. They really are doing 

### 3 something, but we set that threshold and lost it. 

### 4 **DR. DANIEL SCHLENK:**Dr. Houck, would you like 

### 5 to add something? 

### 6 **DR. KEITH HOUCK:**Keith Houck. Yes, and we 

### 7 also have the antagonist data as well as the receptor 

### 8 dimerization data, which we haven't discussed today. But those 

### 9 partial agonists, which may be potent, raloxifene is a good 

### 10 example -- raloxifene has minimal activity in this agonist 

### 11 assay, but it's clearly a potent bioactive estrogen compound, 

### 12 and it shows up very clearly in the antagonist assays and the 

### 13 receptor dimerization assay. So again, once we've got the whole 

### 14 battery together, we'll be able to pick those things out 

### 15 readily, I believe. 

### 16 **DR. DANIEL SCHLENK:**Okay. Dr. Keiser, I saw 

### 17 your hand up earlier. Do you still? 

### 18 **DR. MICHAEL KEISER:**Michael Keiser. So two 

### 19 quick questions. One is just a very quick clarification or 

### 20 confirmation. I heard you say earlier that the only case of the 

### 21 single dose was for the binding assay, and that's the NVS, and 

### 22 that the remainder ones are done all in full dose response in 

### 23 the very first pass, is that correct? 

### 24 **DR. RICHARD JUDSON:**That is correct. 

### 25 **DR. MICHAEL KEISER:**And then the second 

### 26 question because of this issue of antagonist has come up is I

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1 was curious what your approach has been to dealing with 

### 2 promiscuous aggregation from small molecule, promiscuous 

### 3 inhibition -- excuse me -- from small molecule aggregation in 

### 4 terms of detergents, looking at hill slope coefficients, 

### 5 anything like that. 

### 6 **DR. RICHARD JUDSON:**We haven't solved that 

### 7 problem yet. That's on the queue. I can tell you how -- what's 

### 8 written down is the plan to deal with those. So we know that 

### 9 there are certain issues like that where we know you can have 

### 10 technology-specific interference. So these self-rebinding 

### 11 assays, detergent sort of things that everyone knows, those will 

### 12 fall apart and give you a signal. If we break the assays into 

### 13 these technology groups, the self-rebinding assays that Attagene 

### 14 is kind of its own group. The antagonists are their own group. 

### 15 You see strong activity, but only in one of those groups. 

### 16 Then you take that sort of chemicals and you 

### 17 say, okay what structural classes are they part of, and if you 

### 18 find out -- my guess is the ones that are positive only in those 

### 19 binding assays. They're going to tend the detergents and we see 

### 20 a series of chelators that are positive there. You throw in a 

### 21 couple of different kinds of data, which we haven't shown you 

### 22 today how we're actually doing. 

### 23 **DR. MICHAEL KEISER:**Just one followup on that 

### 24 is then in terms of things such as dyes or other cases where you 

### 25 might in fact want to consider a detergent or to look at the 

### 26 slope of the curve, are there any ideas for that that are

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1 already on the docket? 

### 2 **DR. RICHARD JUDSON:**One of the problems with 

### 3 looking at the slope of the curve -- so we get a slope is that 

### 4 for most of the assays, we don't have enough tightly enough 

### 5 spaced points to really resolve that. The slope has pretty big 

### 6 confidence intervals. One of the guys in the group has spent a 

### 7 lot of time trying to convince us that there's a lot of 

### 8 information there. I think the confidence intervals are so wide 

### 9 that there isn't enough information to say something specific, 

### 10 but it may be there're chemical classes that would let us prove 

### 11 yes or no that that is an issue or there's information there to 

### 12 be able to say this is a false positive versus a true positive. 

### 13 **DR. DANIEL SCHLENK:**That was Dr. Judson. Dr. 

### 14 Jacobs? 

### 15 **DR. MIRIAM JACOBS:**Miriam Jacobs. Just 

### 16 coming back to the antagonist assays again. I wondered if it 

### 17 was perhaps the BG1 assay that you've been running in that 

### 18 antagonist mode as well because that's a test guideline TG457. 

### 19 Is that one of the assays? 

### 20 **DR. RICHARD JUDSON:**We do run both -- the 

### 21 NCGC runs both the BG1 and the betalactamase in the antagonist 

### 22 mode. So, yes they do have that. I believe it's Odyssey Thera 

### 23 that has the other two antagonist mode assays. 

### 24 **DR. DANIEL SCHLENK:**That was Dr. Judson. Dr. 

### 25 Safe? 

### 26 **DR. STEVEN SAFE:**Just a comment on some of

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1 your comments. With the exception of the estrogen receptor 

### 2 binding, the transactivation assays in a different cell context 

### 3 would not necessarily -- a false negative in one and a positive 

### 4 in the other, they don't have to be consistent because cell 

### 5 context dictates for these selector receptor modulators. 

### 6 **DR. RICHARD JUDSON:**This is Richard Judson 

### 7 again. This is a great dataset because we have so many 

### 8 chemicals that we can start actually addressing some of those 

### 9 issues. Are these false positives or is it some really 

### 10 interesting biology that we didn't have a chance to test before? 

### 11 12 **DR. STEVEN SAFE:**I agree. 

### 13 **DR. DANIEL SCHLENK:**That was Dr. Safe. Any 

### 14 other questions or clarification here? Dr. Judson, you will be 

### 15 available tomorrow, is that correct? 

### 16 **DR. RICHARD JUDSON:**I'm here the next two 

### 17 days. 

### 18 **DR. DANIEL SCHLENK:**Great. So again, just to 

### 19 remind the Panel, we will allow you to stew on this overnight if 

### 20 other questions come up in your deliberations. We will bring 

### 21 the Agency before the Panel again. So just to let you know 

### 22 that. 

### 23 I have a quick question. In looking at the 

### 24 metabolic profile that was in some of the documentation that 

### 25 Attagene actually has a certain P450 profile that's been, I 

### 26 guess, inserted into the cell are enriched, it sounds like. I

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1 was curious. Is there any rationale for the levels of the 

### 2 specific isoforms that are present there? Because I noticed 

### 3 they are very different than what you'd expect in say human 

### 4 liver microsomal sample, for example, three As like one of the 

### 5 lower content where as 2E1 is very high. I'm just curious if 

### 6 you have any information on why that profile is the way it is, 

### 7 if they provided any of that information. 

### 8 **DR. KEITH HOUCK:**Keith Houck. They selected 

### 9 for that, an enrichment in the set activity. I don't remember 

### 10 the details of how they did that, but they didn't transfect in 

### 11 cDNAs, so there wasn't any specific rationale for what 

### 12 proportion of level they would get in the cell line. 

### 13 **DR. DANIEL SCHLENK:**Thanks. Okay. Dr. 

### 14 Denslow, one more? 

### 15 **DR. NANCY DENSLOW:**Nancy Denslow. My 

### 16 question is with the Attagene assay. Some of the chemicals will 

### 17 bind to multiple different kinds of receptors, so they're not 

### 18 specific for a particular receptor. So I was wondering in the 

### 19 Attagene assay when they're multiplexing 52 different receptors 

### 20 and you're dealing with very low concentrations of the chemical, 

### 21 are there any interferences from the compound binding to a 

### 22 different receptor that you might miss then because it's sort of 

### 23 used up or something like that. 

### 24 **DR. KEITH HOUCK:**Keith Houck. The chemical 

### 25 wouldn't be used up. I mean the chemical is going to be in 

### 26 excess. There is, with some very potent full agonist for some

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1 of the nuclear receptors, there's some evidence that you see a 

### 2 squelching type of effect on other transcription factors, but 

### 3 it's very secondary to the predominant activity that you see at 

### 4 the primary target of that chemical. There's some evidence that 

### 5 that's going on, but it doesn't seem to be a problem in 

### 6 preventing you from seeing activity on other transcription 

### 7 factors. 

### 8 **DR. DANIEL SCHLENK:**Okay. I've been informed 

### 9 that coffee is available for the panel. At this point, unless 

### 10 there's any other burning questions, we'll take a 15 minute 

### 11 break. We'll be back at 20 till, how about that? 

### 12 (WHEREUPON a break was taken) 

### 13 **DR. DANIEL SCHLENK:**Okay. Our next 

### 14 presentation is by Michael Hornung, who is from Mid-Continent 

### 15 Ecology Division, NHEERL, ORD. 

### 16 **DR. MICHAEL HORNUNG:**Thank you, Dr. Schlenk. 

### 17 What I'm going to present to you is some of the preliminary 

### 18 findings of analysis of the high through put in ER Expert System 

### 19 Training Set Data. Just to put you back in touch with where we 

### 20 are on the road map, we're down in the lower right corner 

### 21 looking at two in vitro assay type systems. 

### 22 In the context of the five goals for the SAP, 

### 23 we're in number five determining if and how the high through put 

### 24 data could be used to expand the Expert System Training Set. 

### 25 Essentially, we are looking at if we have a group of similar 

### 26 chemicals, how do they respond in the training set assays that

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1 we have and the high through put assays that we're beginning to 

### 2 look at. 

### 3 So just a quick outline -- I'm going to talk 

### 4 quickly about the chemicals used in the analysis and then bring 

### 5 some of you up to speed in the in vitro assays used as the 

### 6 Training Set. Dr. Judson talked about the high through put 

### 7 assays and how some of that data is processed. I'm going to do 

### 8 the same for the ER Expert System Training Set assays. And then 

### 9 just look at the results for these common chemicals in the 

### 10 binding and transactivation assays, and discuss some of the 

### 11 factors that can contribute to discordance in these assays. 

### 12 So we looked at 296 chemicals that we had data 

### 13 for in the Training Set assays and the high through put assays. 

### 14 They represent a broad range of chemical classes and selected to 

### 15 cover a LogKow range within that class. These were selected to 

### 16 cover the EPA inventories of food use inerts specifically and 

### 17 antimicrobials. There are other chemicals in there from the 

### 18 nonfood use inventory. These include active and inactive 

### 19 chemicals and classes. So we weren't just looking at one type 

### 20 or another. 

### 21 This bottom panel just shows you -- this is a 

### 22 2-hydroxy benzophenone example. I think Rick showed some of 

### 23 these chemicals. These go from LogKow of 2.2 on the lower left 

### 24 to I think 7.3 up on the upper right. Again, the purplish-pink 

### 25 color are those chemicals that were active in the training set 

### 26 and the chemical on the right in the green was an inactive

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1 chemical in the training set assays. 

### 2 So I think the next two slides are just going 

### 3 to bring a quick review of the assays we looked at. So at the 

### 4 time we started the analysis, we had data available for five 

### 5 high through put assays. These are the three Novascreen assays. 

### 6 In looking at comparing results, we wanted to look at some of 

### 7 the assay parameters. The things we're looking at and thinking 

### 8 about in comparing the results from these assays are the types 

### 9 of receptors. In the mouse ER-alpha is a recombinant ligand 

### 10 binding domain of the mouse ER-alpha receptor. 

### 11 As Pat talked about earlier, we also think 

### 12 about these things in the context of the total protein in the 

### 13 assays. What you can see across these three assays, the total 

### 14 protein amounts can be quite different. The human and bovine 

### 15 assays have full assays that are endogenous to the cells and the 

### 16 MCF7 cells and the bovine uterine, of course, and the proteins 

### 17 are 0.28 mg/mL and 5.6. So all three of these assays use 

### 18 treated E2 displacement, so that's similar across the assays. 

### 19 We talked about the concentration suggested earlier. 

### 20 The two transaction activation assays have 

### 21 been discussed. If we just look at the receptors involved 

### 22 there, the ER-alpha ligand binding domain is in the BLA, and 

### 23 again, it's the endogenous ER-alpha and beta in the BG1 assays. 

### 24 One is fluorescence and one is a luciferase readout. The 

### 25 highest concentration there tested was just under 100 

### 26 micromolar.

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1 In comparison of the trout competitive binding 

### 2 assay, this was an assay very similar to the Novascreen assays 

### 3 in that the treated E2 displacement was used as the endpoint. 

### 4 These were cytosolic receptors prepared from liver cytosol, so 

### 5 whatever endogenous estrogen receptors were expressed in those 

### 6 livers. The highest concentration tested, Pat discussed this a 

### 7 bit earlier. We tested these up to chemical solubility in the 

### 8 assay media. In this assay and in the other binding assays, 

### 9 we're essentially looking at that molecular initiating event of 

### 10 the chemical binding to the estrogen receptor to initiate that 

### 11 ER mediated Adverse Outcome Pathway. 

### 12 The rainbow trout liver slice vitellogenin 

### 13 gene activation assay utilizes rainbow trout liver slices, 

### 14 precision cut slices that respond to E2 by expressing 

### 15 vitellogenin mRNA. That's measured by real time QPCR. The 

### 16 highest concentration tested in these assays is to solubility or 

### 17 to cytotoxicity if noted. These liver slices also have 

### 18 endogenous metabolic capability. We have conducted multiple 

### 19 experiments to look at metabolism in these assays. You can have 

### 20 activation by parent chemical or determine if the activation in 

### 21 that assay is by an active metabolite. 

### 22 So the next couple of slides will take you 

### 23 through the decision process involved in the training set assays 

### 24 with the trout systems. What I want to show here is the first 

### 25 step is running the binding assays. These give five examples of 

### 26 kind of the gradations and the types of responses you can see.

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1 From on the left, a full displacement curve to all the way on 

### 2 the right to a flat line displacement curve. Depending on what 

### 3 those curves look like, assuming you get reproducible binding 

### 4 curves, those will go on to be processed slightly differently. 

### 5 The first example I want to show are those 

### 6 examples where those curves that give greater than 50 percent to 

### 7 full 100 percent displacement of estrogen from the receptor. 

### 8 Now these chemicals that do this, the first thing we make sure 

### 9 that there's a good shape to that binding curve that it looks 

### 10 like -- and these are all done in parallel with an E2 curve. So 

### 11 the first question we ask is it a known active training set 

### 12 class. 

### 13 If it's in a known active class and in the 

### 14 next question, is it within the LogKow range that we know for 

### 15 that class, we'll assign it a binder status. So in the context 

### 16 of knowing something about that chemical class, we can move that 

### 17 chemical fairly quickly along. 

### 18 If there are some questions about the binding 

### 19 curve, if it's very steep, you maybe have one point that tips 

### 20 over and it looks like you just move the concentration up a 

### 21 little bit and you've got full displacement. That raises some 

### 22 questions. If it's known to be in a class that produces false 

### 23 positive results, we'll move that chemical to a non-binder 

### 24 category. If it's not in some known chemical class that gives 

### 25 false binding activity, then the chemical will get moved on to 

### 26 being tested in the liver slice assay.

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1 So the liver slice assay isn't always done in 

### 2 coordination with a binding assay. There are these criteria 

### 3 that it meets to go on to that, although many of them do. 

### 4 In addition, a chemical will go into a binding 

### 5 slice assay if we have uncertainty about where it would fit 

### 6 within a known chemical class. If it's not in a known training 

### 7 set class or if it's in a training set class, but it's outside 

### 8 of the LogKow bounds where we have activity information, we'll 

### 9 test that chemical in the slice assay to try to help further 

### 10 define that chemical class activity boundary. 

### 11 If the chemical gives us an agonist or 

### 12 antagonist response, it will then be called a binder. So that's 

### 13 the confirmation. If it doesn't give agonist or antagonist, we 

### 14 can do other assays like we can perform a Ki binding assay to 

### 15 determine if it's a truly competitive binder. If it is not a 

### 16 true competitive binder in that type of assay, we'll just call 

### 17 it a non-binder. This other arrow that shows going from the Ki 

### 18 category to the binder, we don't have any chemicals that would 

### 19 go that pathway. We haven't seen any. I don't know -- well, 

### 20 I'll just leave it at that. 

### 21 If a chemical gives us, so this is less than 

### 22 50 percent displacement, but we do see some displacement of E2 

### 23 from the receptor, these chemicals unlike the ones that give us 

### 24 full displacement, these chemical won't be called a binder 

### 25 without going through a vitellogenin assay. So we need to have 

### 26 confirmation for these weak displacement chemicals that there's

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1 activity there. 

### 2 So if there is a poor shape to the binding 

### 3 curve or it looks like a questionable binding curve, potentially 

### 4 a false positive. We can go through those same questions as we 

### 5 did earlier. If it's in a chemical group that we know can 

### 6 produce false negatives, we can call that a non-binder. If it's 

### 7 not in that chemical group or there's a lot of uncertainty about 

### 8 it, it will then go on to slice assays. 

### 9 One of the other things we can do with the 

### 10 slice assay or in addition to the slice assay is if we have a 

### 11 weak binder and there is some agonism or antagonism response, we 

### 12 do have a recombinant trout receptor that we can use to test to 

### 13 see if we are getting displacement with that chemical. So that 

### 14 would be a confirmation that we have a binder if in the 

### 15 recombinant trout assay we get activity. 

### 16 This goes back to the slide that Pat showed 

### 17 about the differences and total protein in those assays. The 

### 18 recombinant binding assay has a lot less protein in it than what 

### 19 our cytosolic assay does. The cytosolic trout assay here at 4 

### 20 mg/mL is similar to the range that the Novascreen bovine uterine 

### 21 assay is. We can use this assay, and I think this is just the 

### 22 reminder that by utilizing an assay with a lower amount of 

### 23 protein in it, you'd have an equivalent of free fraction of 

### 24 available chemical to that assay at lower nominal 

### 25 concentrations. 

### 26 The last example here is for a flat line

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1 binder or non-binder. If this chemical belongs to a known 

### 2 inactive class, then we'll call that a non-binder. If it's not 

### 3 in a known inactive class, but there's evidence in the 

### 4 literature, strong evidence that it's a non-binder, then it will 

### 5 go to a non-binder class, but I think we have to have strong 

### 6 evidence for that. If there is evidence in the scientific 

### 7 literature to suggest that there's potential for binding, then 

### 8 we can go on to a liver slice assay. 

### 9 In that liver slice assay -- so the main thing 

### 10 there is if we get activity on the liver slice assay, but 

### 11 remember back we had no flat line binding assay, that might 

### 12 suggest that you're getting metabolism from that non-binder in 

### 13 the slice assay that in the liver slices, it's getting 

### 14 metabolized to a metabolite that can actually bind to the 

### 15 receptor in those slices, activate it and produce a 

### 16 vitellogenin. So if you're seeing that activity, that's 

### 17 suggestive that there's metabolism, and we can also go back to 

### 18 that recombinant assay and just to verify that a parent is 

### 19 inactive and we don't have the sensitivity with the cytosolic 

### 20 receptor for activity. 

### 21 So in these cases, if we run the metabolism 

### 22 assays and we can identify a metabolite as positive, we would 

### 23 call that a binder and call the parent chemical then a 

### 24 non-binder. 

### 25 So I'm going to start off talking about the 

### 26 estrogen receptor binding assays, the four binding assays, the

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1 three Novascreens in the trout. I'm going to give some examples 

### 2 of some of the types of responses we saw with some of these 

### 3 similar chemicals, and then give a little summation of a couple 

### 4 tables at the end. 

### 5 So one of the example chemical sets again we 

### 6 had here was the 2-hydroxy benzophenones. We had four active 

### 7 chemicals in that class in the training set. What I've done on 

### 8 these curves -- these curves show four rainbow trout binding 

### 9 assay results, and I've superimposed on it the 50 micromolar 

### 10 cutoff line that the high end of the dose response curve for the 

### 11 Novascreen binding assays. Assuming that -- remember these 

### 12 assays have the same tritiated E2 displacement response as the 

### 13 endpoint, assuming that these assays are all similarly active 

### 14 and have similar activity, what you might expect from the top 

### 15 two chemicals is that you can see that the displacement occurs 

### 16 to the left or at the lower concentrations below that 50 

### 17 micromolar cutoff. So those two chemicals on top you'd expect 

### 18 everything else being equal, would be positive across all four 

### 19 assays. Those two curves on the bottom, there is just a little 

### 20 short animation here, shows if we would have stopped the trout 

### 21 receptor binding assays at 50 micromolar, we would not have seen 

### 22 any displacement. 

### 23 As we increase the concentration for this 

### 24 chemical, you begin to see displacement of E2 from those curves. 

### 25 So you'd expect that in the binding assays with that cutoff 

### 26 that the two top chemicals would be positive and the two bottom

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1 chemicals would likely be negative. 

### 2 These next couple of examples takes us through 

### 3 those four assays comparing the trout and the Novascreen results 

### 4 for those. As we expected with the trout assay as shown on the 

### 5 left panel, and in this slide and in the subsequent slides after 

### 6 this, if we have three panels in a column, the Novascreen mouse 

### 7 ER-alpha assay is on top and the human in the middle and the 

### 8 bovine on the bottom. Those are oriented in relationship in 

### 9 going from lowest total protein in the mouse assay down to the 

### 10 assay, the Novascreen assay with the highest total protein 5.6. 

### 11 So, just to put them in some type of relationship there. But 

### 12 again, the 50 micromolar cutoff is there, and you can see that 

### 13 for the Novascreen assay, all three of those binding curves fall 

### 14 to the left of that curve and were all active in this assay. 

### 15 For that benzophenone that was in the upper 

### 16 right corner in the first slide, you can see that that 50 

### 17 micromolar cutoff is -- this chemical had a little lower 

### 18 affinity than the first one, but most of that competition 

### 19 binding curve is still at the lower concentrations below that 50 

### 20 micromolar cutoff. So what we see in the Novascreen assay is 

### 21 was yes, that these did produce active calls in these assays. 

### 22 The one on top, I just wanted to note, when I first looked at 

### 23 this, this looked active. I just assumed all three were active, 

### 24 but looking at this one is one that gets called as inactive. 

### 25 You can see that the top displacement points just reach that 

### 26 threshold level.

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1 So that's something where Richard talked a 

### 2 little bit about manually looking at some of the curves and also 

### 3 the replication is an N of 1 in this. The replication for the 

### 4 binding assays and the reference chemicals, I think, were an N 

### 5 of 2 or 3, but some of these test chemicals, it's just an N of 

### 6 1, which gives you -- when there are questions about the data, 

### 7 it can lead to a little bit of uncertainty. 

### 8 So this slide shows the two chemicals that 

### 9 were the two lower ones on that first slide of the four trout 

### 10 bindings assays. What we see here is that where these binding 

### 11 curves were to the right in the trout assays of that 50 

### 12 micromolar cutoff, these two chemicals were inactive in the 25 

### 13 micromolar prescreen for all three of those Novascreen assays. 

### 14 So as expected for this lower affinity chemical, it was inactive 

### 15 in the Novascreen assays due to that cutoff. 

### 16 The next couple of slides here talk about a 

### 17 couple of other things that we saw with these binding assays. 

### 18 This was a potent chemical, hyphenated chemical in the trout 

### 19 assays. When I talked about earlier about replication and some 

### 20 questions about it, this assay showed -- what I wanted to just 

### 21 bring out here, I think, for the first time is that in the 

### 22 training set binding assays, there're two technical replicates 

### 23 per concentration per run and at least two runs per chemical. 

### 24 For some of the test chemicals in the Novascreen assays due to 

### 25 some of the concerns Richard brought out earlier, those are 

### 26 limited to one technical replicate and one run per chemical.

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1 What we see here on the right might be 

### 2 something you might expect if that total protein and fee 

### 3 available chemical is playing into the response of these assays 

### 4 where the mouse ER-alpha assay has the least amount of total 

### 5 protein, we are seeing a binding curve there, but for the human 

### 6 and the bovine, you're not seeing activity. If you look at 

### 7 where that 50 micromolar cutoff is, that is right at the bottom 

### 8 of that shoulder of that binding curve. So you might expect to 

### 9 see that type of response at the mouse ER-alpha assay, maybe 

### 10 more sensitive because of more free fraction chemical available 

### 11 due to the lower protein concentration. 

### 12 The replication question comes up especially 

### 13 for something like there where we're comparing the assays or 

### 14 looking at the responses in these assays where the activity, the 

### 15 binding affinity and the chart assay falls right on that 50 

### 16 micromolar curve. In this curve, this also looks like, on the 

### 17 right hand side in the Novascreen assays that we are going from 

### 18 a mouse assay that looks potentially more sensitive to a human 

### 19 assay where I think there's one point that just touches the 

### 20 significance threshold there, and then the bovine not showing 

### 21 activity. 

### 22 So it looks like for some of these chemicals, 

### 23 this type of relationship with protein may hold out, but for 

### 24 some of them, it doesn't. The chemical on the left is 

### 25 chlorhexidine diacetate. This one chose a response that you 

### 26 might expect for that protein free chemical relationship.

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1 However, on the left is a chemical, pentyl-cyclohexanone, for 

### 2 which there is no activity in the mouse, but there is activity, 

### 3 almost full displacement curves in the bovine or human. 

### 4 So some of these things raise questions about, 

### 5 we try to apply these principals of trying to understand how the 

### 6 binding assays -- what the binding assays are telling us and why 

### 7 they are positive or negative in one assay versus another. In 

### 8 some of those, they make some sense and others, maybe it's 

### 9 question about the ligand binding domain versus the full 

### 10 receptor. The human and the bovine in this one are full 

### 11 receptor where the mouse is just the ligand binding domain. So 

### 12 maybe that plays a role in this binding assay or in the response 

### 13 to this chemical sort. 

### 14 So I have two slides with tables on here that 

### 15 kind of summarize the activity of the training set chemicals 

### 16 among all the binding assays. What we see is in the upper table 

### 17 are those out of the 296, there were 172 chemicals in the 

### 18 training set that were inactive. So we give them an RBA equal 

### 19 to zero. About 90 percent of those inactives were also inactive 

### 20 in the Novascreen assays. That's fairly good concordance across 

### 21 those assays, but the other thing to keep in the back or your 

### 22 mind is because of that cutoff, you may be -- because of the 50 

### 23 micromolar cutoff or the 25 micromolar cutoff, you may see 

### 24 having more negatives by default showing up in that because 

### 25 you're not testing as high as what was tested in the trout 

### 26 assays.

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1 The bottom table shows out of that 296 

### 2 chemicals tested, 124 were positive in the training set. Out of 

### 3 these are about 30 percent that were active in the high through 

### 4 put assays. So there's less agreement here. This is where I 

### 5 think the next table kind of discusses where some of that 70 

### 6 percent of discordance was. What we see is if we took the 

### 7 training set active chemicals and bend them into RBA groups, 

### 8 each is one 10 fold RBA group, with the highest affinity 

### 9 chemicals on top and the lowest ones on the bottom, what we see 

### 10 is that for the high affinity chemicals, there's really good 

### 11 concordance across the binding assays. As you go to the lower 

### 12 and lower affinity chemicals, the binding affinity, the 

### 13 agreement, the concordance between the training set call and 

### 14 those Novascreen binding assays tends to drop off. Most of that 

### 15 we can explain. We can explain for a lot of these by that 

### 16 cutoff. 

### 17 One little note here, when we looked at these 

### 18 assays, it looked like the human ER assay was actually picking 

### 19 up more chemicals than the bovine and the mouse. It's a limited 

### 20 data set of these 296, but that seemed to pick up or identify 

### 21 slightly more chemicals as positive. 

### 22 So I'm going to switch gears right after the 

### 23 binding assay summary. So the concentration cutoff seemed to 

### 24 play a big role where the discordance was between the training 

### 25 set binding assays and the Novascreen binding assays. Some of 

### 26 that where that didn't explain the discordance, then questions

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1 about whether the ligand binding domain receptor of the full 

### 2 receptor played a role in there or chemical bioavailability 

### 3 based on those proteins, but there didn't seem to be any real 

### 4 clear answer there. Really digging into that deeper isn't 

### 5 something that we've done. One of the other things that jumped 

### 6 out at us in looking at this was when you do have limited 

### 7 replication, that can lead to some uncertainty in looking at 

### 8 making these calls across these assays. 

### 9 Now I'm going to switch gears and include the 

### 10 gene activation assays. This again is just a slide to just as a 

### 11 reminder of the transactivation assays, the BLA assay. So 

### 12 instead of the full set of eight chemicals -- remember this is 

### 13 five chemicals -- we had the three binding assays and the two 

### 14 Tox21 gene activation assays that we compared to the trout 

### 15 assay. So we have the BLA assay again with the ligand binding 

### 16 domain, a fluorescence readout, the BG1 with a luminescence 

### 17 readout and the training set liver slice assay with the 

### 18 vitellogenin readout by QPCR. 

### 19 So I'm going to do the same thing with these 

### 20 assays, give you some examples of some of the things we looked 

### 21 at, and then some summary tables at the end. But just up front, 

### 22 some of the factors in interpreting these assays Dr. Judson 

### 23 touched on some of these things in his presentation. The 

### 24 concentration test can affect these assays. Chemical 

### 25 interferences, cytotoxicity, chemical solubility and media pH 

### 26 are all things to think about, and they get back to some of the

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1 things that Dr. Kent talked about with some of the chemical 

### 2 P-chem parameters. We'll also discuss a little bit about 

### 3 metabolism and how that plays into interpreting these assays. 

### 4 So the concentration cutoff that we saw in the 

### 5 binding assay with the 25 and 50 micromolar prescreen may also 

### 6 affect interpretation of discordance between different assays in 

### 7 the gene activation assays. If assays are run to different 

### 8 toxicity limits -- not toxicity limits sorry -- concentration 

### 9 limits, some assays may not be at a high enough concentration 

### 10 assuming everything else is equal in those assays as well to 

### 11 pick up active chemicals. 

### 12 What we see here on the left is the trout 

### 13 liver slice vitellogenin mRNA response for pentylaniline. You 

### 14 can see at the -4 Log molar, so 100 micromolar is where we first 

### 15 get the first significant induction by pentylaniline and 

### 16 vitellogenin RNA in the liver slices. That's a very significant 

### 17 response near to that of E2. In the BLA and the BG1 assays, it 

### 18 went up to 92 micromolar, so very similar concentration. If 

### 19 everything else was similar in those assays, you'd expect that 

### 20 we'd start to see some induction with those chemicals at those 

### 21 concentrations. It might be that we're just right at the tip of 

### 22 seeing some activity there. 

### 23 So I have two examples here to look at 

### 24 interpreting activity within chemical groups. This first 

### 25 example is alkylbenzenethiol. These are going to be where we 

### 26 have two chemicals, structurally similar chemicals that we've

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1 classified in the same chemical group. In one set of assays, 

### 2 they're inactive across the assays that we had data for. For 

### 3 the other similar chemical, there was some active and some 

### 4 inactive activity. 

### 5 So for this example, we had p-toluene thiol or 

### 6 methylbenzenethiol. That chemical gave us a ski slope-shaped 

### 7 binding curve. So when we push at the high concentrations, 

### 8 there is something that goes on with that assay. We call that a 

### 9 negative binding. It doesn't look like what Pat showed earlier 

### 10 with what a binding curve should look like, what you've seen in 

### 11 the previous ones. Butylbenzenethiol gives the same type of 

### 12 response. In the butylbenzenethiol, that was associated -- that 

### 13 uptake and that curve was associated with precipitation forming. 

### 14 Sometimes that curve is associated with precipitation, but no 

### 15 displacement of E2 until we got to that displacement. 

### 16 For the toluene thiol, that was inactive in 

### 17 the 25 micromolar prescreen, but in the three Novascreen assays, 

### 18 that showed a strong indication that of displacement of E2 from 

### 19 that receptor. 

### 20 In the BLA assays for this chemical, there was 

### 21 really no indication that there was any activity in either the 

### 22 methyl or butylbenzenethiol. In the BG1 assay, which is the 

### 23 luciferase based assay, you see that for the methylbenzenethiol, 

### 24 there's no activity. You can see here that this assay may be a 

### 25 little noisier than the BLA assay. There is some activity in 

### 26 the butylbenzenethiol, but you can see those red asterixes there

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1 are just notations that there're some flare effects in that 

### 2 plate or associated with that. That just raises a little yellow 

### 3 flag that when you see those things that you need to look at 

### 4 that a little more closely. 

### 5 So interpretation of these benzenethiol 

### 6 results where we're looking at a chemical class, but we're 

### 7 getting discordant activity within those classes, one chemical 

### 8 is inactive across all assays. The higher LogKow 

### 9 butylbenzenethiol was inactive in the binding assay, the trout 

### 10 binding assay, but it was active in the Novascreens and inactive 

### 11 in the BLA assay. In the BG1, it was active, but there was some 

### 12 indication of flare effect, so it raises a little question 

### 13 there. 

### 14 So if there is truly activity with the 

### 15 butylbenzenethiol, how do you go about trying to discern that? 

### 16 One of things to do, the second bullet down here is to expand 

### 17 the chemical in that class to establish greater confidence in 

### 18 the structure activity relationships. You can bound these two 

### 19 chemicals by adding a straight chain butylthiol or increasing 

### 20 the LogKow, go one chemical to either side of it, or go ahead 

### 21 and test chemicals in other assays. We could include the trout 

### 22 liver slice assay. We didn't test this in the trout liver slice 

### 23 assay when we started doing these because these were not 

### 24 inventory chemicals. The only chemical in this group if 

### 25 benzenethiol. So depending on where you want to put your 

### 26 efforts in what you're looking for, I think, can determine which

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1 chemicals you're testing. So if this were a group with a lot of 

### 2 inventory chemicals, it would make more sense to pursue those 

### 3 things further. 

### 4 On the next example are the benzotriazole 

### 5 phenols. Again, two structurally similar chemicals and really 

### 6 it's just the extent of alkylation on the phenol group on there 

### 7 that's different. In this one, you can see that in this case, 

### 8 the higher LogKow chemical is inactive across all the assays and 

### 9 the lower LogKow is active in two assays. Again, these are the 

### 10 trout receptor binding assay for those two chemicals showing a 

### 11 flat line binding curve, no indication of toxicity up to 

### 12 solubility. 

### 13 In the Novascreen assays, there was no 

### 14 activity for either of these chemicals except for the mouse 

### 15 ER-alpha assay. I think this was an N1. It is hard for me 

### 16 looking at that if that curve looks like that's a little flat 

### 17 for a binding curve, but it's hard to tell based on those two E2 

### 18 points. 

### 19 The benzotriazoles in the BLA assay, all three 

### 20 of those were clearly negative. As you remember Dr. Judson 

### 21 talked about, these came from three replicate sources. So that 

### 22 would a strong indication that that was a negative chemical, but 

### 23 in the BG1 assay, again these are three replicate chemical 

### 24 sources and there's strong activity for the methylphenyl 

### 25 chemical here and no activity in the octylphenyl, but you can 

### 26 see in some of those curves, there's a little bit of flare

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1 effect there. 

### 2 In two of those curves for the methylphenyl, 

### 3 there's a little red asterisk at the top concentration for the 

### 4 metylphenyl at the top of that curve, so showing that there's 

### 5 some flare activity in the plates associated with those curves. 

### 6 7 So based on chemical categories, it would be 

### 8 great if your chemical categories or your assays respond 

### 9 similarly for similar chemicals that are in the same category. 

### 10 As you can see, that's not always the case. When we get 

### 11 equivocal responses among the assays, from this one, the parent 

### 12 chemical based on the binding assays that are not binding to a 

### 13 receptor, there's a flare effect in the BG1. We might 

### 14 hypothesize because we had three chemical sources for the BG1 

### 15 assay and they gave strong responses, even with that flare 

### 16 effect signal there, you might have hypothesize that there may 

### 17 be metabolic activation, but I think you'd want to have 

### 18 verification of that. 

### 19 So a couple other artifact types of signals 

### 20 that we saw or that kind of came up in our analysis of some of 

### 21 this data. Charge species are chemicals that we'll see in these 

### 22 inventories. In 2009 SAP, we talked about octylbenzene sulfonic 

### 23 acid, and this dataset that we looked at, we had the 

### 24 dodecylbenzwnesulfonic acid that produced similar false binding 

### 25 curves we can see across the four binding assays. They all gave 

### 26 very strong evidence of displacement of E2 from the receptor.

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1 So that would like a positive chemical if we didn't know 

### 2 anything else, but looking at the trout vitellogenin slice assay 

### 3 on the left, we can see that in the agonist mode, this did not 

### 4 produce any vitellogenin signal above background. 

### 5 In the vitellogenin slice assays, we run these 

### 6 in antagonist mode. You can see it on the far right in that 

### 7 middle panel, there is a red line there that says LDH. The 

### 8 antagonism dropped off, but that's at the same concentration 

### 9 that we saw activity or cytotoxicity as measured by the leakage 

### 10 of lactate dehydrogenase into the culture media with that liver 

### 11 slice. So that would be evidence that that decrease in E2 

### 12 signal in that antagonism figure in the middle is a false 

### 13 antagonist effect. 

### 14 The other thing to bring up is that for this 

### 15 charge species, if we put this through the PKa filter that Ray 

### 16 talked about earlier, this has a PKa of -1.84. So this chemical 

### 17 would get flagged if this were an inventory chemical to test. 

### 18 Another thing that we know can affect our 

### 19 assays as we have run these and looked at some of the different 

### 20 parameter is pH. Hydroxyquinoline sulfate is a chemical that in 

### 21 the human ER binding assay and the trout liver cytosol binding 

### 22 assay gave displacement of E2 from the receptor. One of the 

### 23 things that we measured in the liver cytosol prep is pH. As we 

### 24 push these concentrations, the pH dropped well below 7, which 

### 25 isn't compatible with getting good binding data. So that can 

### 26 produce false negative data.

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1 We found pH can also effect, which may not be 

### 2 surprising, can also effect the gene response in the slices. 

### 3 This quinoline sulfate does produce a decrease in pH as shown in 

### 4 the panel on the right. Again on the left is the vitellogenin 

### 5 agonism. There is no activity there. In the middle panel, 

### 6 that's our antagonist assay. One of the things we started doing 

### 7 is running antagonist assays with two different concentrations 

### 8 of E2. So if you have a true antagonist, those antagonism 

### 9 curves should be separated by an equivalent amount of -- so if 

### 10 you have 10 to the minus 6 E2 and 10 to the minus 8, you'd 

### 11 expect your antagonism curves to be sifted by two orders of 

### 12 magnitude by two log units. So we don't see that. 

### 13 We also found that the vitellogenin induction 

### 14 in these slices is actually more sensitive to pH change than 

### 15 what the binding assays were. That does go down a little bit as 

### 16 seen on the slide on the right. Another thing to note is in the 

### 17 filtering, this chemical would be highlighted, would be flagged 

### 18 by the PKa filter. That is a very low PKa. There is only 

### 19 ionized forms at the assay pH. 

### 20 So the last example or thing to look at for 

### 21 understanding the activity of the parent chemicals in these 

### 22 assays is metabolism where you get no binding, but potential 

### 23 gene activation. Here we had methyltestosterone as an example. 

### 24 You can see in the Novascreen mouse ER-alpha assay and the 

### 25 bovine ER-alpha, the bovine ER assay, there is no displacement 

### 26 and in human and the trout on the bottom, there is just a low

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1 amount, let's say partial displacement or limited displacement. 

### 2 But when these chemicals are tested in the BG1 

### 3 assay and the BLA assay, they give very strong responses. So 

### 4 the no activity in the binding but strong activity in the cell 

### 5 based assay suggests that there's metabolism going on. 

### 6 In the trout assays, we've determined that 

### 7 17-methyltestosterone gets metabolized to 17-methylestradiol, 

### 8 and in running binding assays with 17-metylestradiol, this 

### 9 chemical has very high affinity for the fish ER. So we would 

### 10 call 17-methyltestosterone a non-binder and 17-methylestradiol a 

### 11 binder. So where we had apparent discordance between a call in 

### 12 the training set, 17-methyltestoserone was called inactive, the 

### 13 parent chemical would be considered inactive, but it's the 

### 14 metabolite that would be or a non-binder and the metabolite 

### 15 would be called a binder. So that discordance would actually be 

### 16 agreement if the metabolic conversion is to that bioactive 

### 17 compound. In thinking about developing a chemical category and 

### 18 structure-based activity, you want to be assigning the correct 

### 19 structure to the activity. 

### 20 So I think there's just a last table or two to 

### 21 kind of summarize some of these things. One of the things we 

### 22 wanted to look at is if we had discordant calls, was there a 

### 23 pattern to it. With the things we knew about LogKow and binding 

### 24 relationships, did it match up with LogKow, were there specific 

### 25 classes that were effected more than others, and generally what 

### 26 we saw was similar to what we saw at with the binding assays

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1 that the discordant calls were mostly training set chemicals 

### 2 with low affinity. You could see a lot of these RBAs from the 

### 3 training set assays have three or four zeros in them. 

### 4 I just want to give an example. I just have 

### 5 some overlays with alkylphenols was a group where there were a 

### 6 lot of chemicals for these five assays. These clear boxes or 

### 7 the white boxes are those chemicals that were training set 

### 8 actives but high through put assay inactives. These were going 

### 9 from low RBA from the left to higher RBA on the right, 

### 10 continuing on that second row on the bottom. What you see is 

### 11 these low affinity alkylphenols were HTP inactives. There were 

### 12 two of them in here that were active in the BLA assay, so they 

### 13 gave a significant response. 

### 14 In the alkylphenols, if we continue that set 

### 15 up to the higher LogKow and higher affinity LogKow chemicals, 

### 16 you can see that these pink boxes again or purple show the high 

### 17 through put transactivation or the Novascreen assays where they 

### 18 were positive again going from low LogKow to high LogKow. For 

### 19 the most part, these were detected as active with just missing 

### 20 or having some discordance with the training assay on that ortho 

### 21 para t-butylphenol. 

### 22 So I think this is last table slide here. In 

### 23 these assays, we're looking at two levels along the adverse 

### 24 outcome pathway, at binding and at the transactivation level in 

### 25 a pathway that Dr. Schmieder showed earlier. 

### 26 So in looking at the assays, the concordance

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1 between the training set assays and the high through put assays, 

### 2 if we look at the first column, in the training set assays, 

### 3 there were 172 chemicals inactive, and 134 of these were 

### 4 inactive in all five of those high through put assays. There 

### 5 was pretty good concordance between those inactive groups. 

### 6 Thirty percent of the high through put inactive calls were 

### 7 training set active in that box below the 134. That row shows 

### 8 -- the middle row in the table -- we have 124 active training 

### 9 set chemicals, 57 of those were inactive in the high through put 

### 10 assay, but 34 were active and 33 were active in at least one or 

### 11 two of the binding and gene activation assays in the high 

### 12 through put mode. 

### 13 So I think the bottom line here was that 

### 14 probably for the 43 chemicals active and at least one binding 

### 15 and one gene activation assay, we had good agreement with the 

### 16 high through put actives. Eighty percent of those actives agree 

### 17 with the training set. I think 62 chemicals were active in the 

### 18 binding or a transactivation assays. So that was kind of a less 

### 19 constrictive categorization or they weren't active in both. 

### 20 Fifty-three percent of those high through put actives agreed 

### 21 with the training set actives. So it was about a 50 percent 

### 22 inactive versus active agreement when we reduce that criteria. 

### 23 So the preliminary assessment of our analysis 

### 24 of these data in looking at the training set assay and the five 

### 25 high through put assays, the five high through put assays 

### 26 detected the moderate to high affinity chemicals well, but not

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1 the low affinity chemicals based on what we saw with the 

### 2 training set. That is likely largely contributed to the 

### 3 chemical concentration cutoffs. pH and charge effects can play 

### 4 a role in producing false positive and false negative effects, 

### 5 but I think we have ways of looking at the data and looking at 

### 6 across assays to try to determine where those types of responses 

### 7 and looking at the P-chem data where those types of responses 

### 8 fall out. 

### 9 We can look at metabolism across assays and 

### 10 verification of metabolism to try to understand what is the 

### 11 parent chemical or what is the chemical that's producing the 

### 12 estrogenic activity. So on further analysis like we talked 

### 13 about earlier, this is a preliminary analysis of a small set of 

### 14 these high through put assays. With further analysis of 

### 15 chemicals where we have discordant responses in these assays 

### 16 will help to verify the activity of the chemicals and improve 

### 17 our interpretation of the high through put in vitro and training 

### 18 set in vitro assay results. 

### 19 **DR. DANIEL SCHLENK:**Okay. Thank you. Any 

### 20 questions or clarification? Dr. Schultz? 

### 21 **DR. TERRY SCHULTZ:**A lot of information. I'm 

### 22 attributing it to the lateness in the afternoon why I didn't 

### 23 capture it all has nothing to do with my age, but it sounds to 

### 24 me like we have two things here. One, you're using the rainbow 

### 25 trout cytosolic binding assay and then the liver slice to find 

### 26 your gold standard that you're going to compare the high through

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1 put assays too. Is that correct? 

### 2 **DR. MICHAEL HORNUNG:**This is Mike Hornung. 

### 3 What we're doing initially here is looking at where we have the 

### 4 data for the trout assays where we've optimized it for these low 

### 5 affinity chemicals by pushing the concentrations up and doing 

### 6 these other things to look at how the high through put assays 

### 7 that we're talking about using for prioritization. Are we 

### 8 getting the same types of responses for these assays? 

### 9 **DR. TERRY SCHULTZ:**Correct, but you're saying 

### 10 that whatever my rainbow trout assay, that is what I'm saying. 

### 11 If it's positive in that, that's a positive. If it's negative, 

### 12 it's negative and I'm comparing the other one to it, correct? 

### 13 **DR. MICHAEL HORNUNG:**I think -- this is Mike 

### 14 Hornung again. I think some of the discussion we had earlier 

### 15 about the different assays and what we see in that, you may get 

### 16 discordance in assays where, for one reason or another, one 

### 17 assay doesn't give you a correct -- not a correct response. 

### 18 There is no correct response. Where a chemical may be positive 

### 19 in one assay and positive in another. I don't think you should 

### 20 look at it as assay versus assay and one being right or one 

### 21 being wrong. I think you have to look at how they're performed 

### 22 for answering the question that you're asking. Do we have them 

### 23 optimized? Answer the question for looking for assays that act 

### 24 via this mechanism or do this. Do we have our assays optimized 

### 25 for the questions we're asking? We're going to get discordance 

### 26 across assays. If we look across any assays, we're going to get

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1 -- some of those topics came up today. So I don't know -- I 

### 2 tend to trail off. I don't know if I answered your question 

### 3 quite right. 

### 4 **DR. TERRY SCHULTZ:**I agree with you. I get 

### 5 very concerned when we start to basically be dealing with 

### 6 statistics in order to make these mechanistically relevant 

### 7 comparisons. So I do agree with you on that. The second 

### 8 question I have, if I may, it sounds to me like the number one 

### 9 discrepancy that you have is because of the concentrations that 

### 10 are used within the different assays. The high through put 

### 11 being defined at 50 or 25, and then going down as opposed to the 

### 12 Duluth Protocol where basically you go up to side (inaudible) 

### 13 limits. 

### 14 **DR. MICHAEL HORNUNG:**This is Mike Hornung 

### 15 again. I would agree. I think that is our major finding that 

### 16 the concentration differences in the assays can play a big role 

### 17 in whether you're detecting low affinity binders or not. There 

### 18 are other chemicals that are discordant across the assays where 

### 19 we may have other questions that we can deal with in the future, 

### 20 but for a lot of these chemicals that we saw, and I think that 

### 21 was one of those numbers with 30 percent concordance and 70 

### 22 percent discordance, I think a lot that value is from the 

### 23 different concentrations tested in the assays. So to be able to 

### 24 compare those assays, and this might have been mentioned earlier 

### 25 also, if you're not comparing the same concentrations and kind 

### 26 of getting back to what Dr. Schmieder talked about, the free

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1 fractions, those types of things, if you're not comparing 

### 2 chemical at site of action in equivalent amounts across the 

### 3 assays, that raises some questions about the comparisons that 

### 4 you're doing and how you need to take that into consideration in 

### 5 interpreting the results from those assays. 

### 6 **DR. TERRY SCHULTZ:**Thank you. 

### 7 **DR. DANIEL SCHLENK:**Dr. Denslow? 

### 8 DR. NANCY DENSLOW? I'm Nancy Denslow. So one 

### 9 of my questions is like with the rainbow trout estrogen receptor 

### 10 binding assay and the transactivation assay for vitellogenin, 

### 11 the liver probably has more than just estrogen receptor-alpha 

### 12 being expressed. It has beta -- well in rainbow trout, there 

### 13 are four estrogen receptors and then there's a membrane receptor 

### 14 as well. So the other assay is really estrogen receptor-alpha 

### 15 specific. So I was just wondering if some of the differences 

### 16 could also be ascribed to these other receptors being in some of 

### 17 the assays? 

### 18 **DR. MICHAEL HORNUNG:**Mike Hornung. There may 

### 19 be some of that in there. The rainbow trout liver slice assay 

### 20 has all the endogenous receptors. In that liver cytosolic prep, 

### 21 we also have a mix of receptors. So comparing the activity 

### 22 between the cytosolic prep and the liver prep, you have a fairly 

### 23 uniform comparison of receptors across those assays. So the 

### 24 slice assay works very well as a verification of the activity -- 

### 25 yes, the slice assay looking at vitellogenin induction is a very 

### 26 good verification based on the receptor population that's in the

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1 cytosol activity. 

### 2 In the other transactivation assays, right. 

### 3 Some of those may have human estrogen receptor alpha expressed. 

### 4 I think the BG1 cells -- that has whatever endogenous alpha, 

### 5 beta, gamma, whatever receptors those cell lines express. So 

### 6 there may be more similarity there. Some of the data that I 

### 7 think Dr. Schmieder showed earlier about differences between rat 

### 8 cytosolic receptor binding and a trout receptor binding that a 

### 9 lot of the differences in the activity was more related to the 

### 10 assay type and the amount of protein that may be in some of 

### 11 these factors, although receptor abundance in numbers may be 

### 12 different, but I guess as a first blush looking at an estrogen 

### 13 receptor assuming alpha mediated responses, I think we need to 

### 14 start somewhere at that molecular initiating event and think 

### 15 about the pathway that we're looking at in induction of that 

### 16 receptor type. 

### 17 **DR. DANIEL SCHLENK:**Dr. Nagarkatti? 

### 18 **DR. PRAKASH NAGARKATTI:**Nagarkatti. I just 

### 19 wanted to follow up on the previous question about the 

### 20 discordance within the various assays. So my question was 

### 21 again, I'm a little bit confused. You attribute that to the 

### 22 different concentrations that were used in the training set 

### 23 assay versus the other three HTP assays. But then why would it 

### 24 impact only those chemicals that are found to be active by the 

### 25 training set assays, but you got only like 35 percent agreement 

### 26 whereas when you use inactive compounds, you find that there is

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1 88 percent match. So if you can go back to that 7.2.1 table -- 

### 2 you talk about 88 percent versus 35 percent? 

### 3 **DR. MICHAEL HORNUNG:**Back in the binding 

### 4 assay table. 

### 5 **DR. PRAKASH NAGARKATTI:**So my question was if 

### 6 it was due to concentrations, why would it impact only the 

### 7 active compounds? In fact, the only thing that it would be the 

### 8 other way around that it should affect those compounds which 

### 9 were found to be inactive at 25 micromolar, when you raise it to 

### 10 50 micromolar, they may find to be active. So there should be 

### 11 more discordance with the top line there compared to the bottom 

### 12 line. 

### 13 **DR. MICHAEL HORNUNG:**This is Mike Hornung 

### 14 again. I believe you would expect to see -- so in the top line, 

### 15 you would see more agreement, more concordance between these 

### 16 assays. If you are pushing one assay up to -- so assay A goes 

### 17 up to a higher -- 

### 18 **DR. PRAKASH NAGARKATTI:**So the first one is 

### 19 assay A there, 172 chemicals, by the training set were found to 

### 20 be inactive based on 25 micromolar concentrations. 

### 21 **DR. MICHAEL HORNUNG:**So the training set 

### 22 assay are taken up to solubility. So if you've taken them up to 

### 23 solubility and you don't see any activity, you've gone as high 

### 24 as you can test those. So if you are comparing that to an assay 

### 25 where you haven't gone as far, if they have low affinity and 

### 26 you're going to see that activity at higher concentrations, but

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1 default, you're going to call those inactive because you haven't 

### 2 reached the active range yet. You should get more agreement 

### 3 concordance in the inactive set. It would make sense that then 

### 4 where you get the discordance is in looking at the active 

### 5 training set where you've taken those out to that high 

### 6 concentration. So the low affinity chemicals will get detected 

### 7 out there. So you're going to have a higher number of those 

### 8 that are called active. So then if you use a lower 

### 9 concentration cutoff, you're not achieving that same 

### 10 concentration, so you're going to have -- so you will miss some 

### 11 of them. 

### 12 **Dr. PRAKASH NAGARKATTI:**So there's no way 

### 13 that you can compare the data. Like say based on the highest 

### 14 level of solubility to those HTP data, and say that okay, this 

### 15 discordance was -- yes it was due to the differential 

### 16 concentrations that were used. 

### 17 **DR. MICHAEL HORNUNG:**I think -- 

### 18 **DR. PRAKASH NAGARKATTI:**Because you should 

### 19 have that data that you could compare right? 

### 20 **DR. MICHAEL HORNUNG:**I think -- let me go 

### 21 back to the benzophenone table. I don't know if this might 

### 22 help. So this is a chemical class, the 2-hydroxybenzophenones. 

### 23 So this is the rainbow trout data that was taken up to 

### 24 solubility. So those are the curves for the rainbow trout data. 

### 25 But what I've superimposed on there is the 50 micromolar 

### 26 cutoff, which is the high concentration for the dose response

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1 for the Novascreen assays. So if those assays were all equal in 

### 2 sensitivity and everything else, we would expect that those two 

### 3 chemicals on the bottom if just tested up to that solubility 

### 4 line, the red line, would not -- so if we just stop the 

### 5 concentration there, the trout assays would also call those 

### 6 inactive or whatever assay would call those inactive. As you 

### 7 increase that concentration, then those become -- you've gone up 

### 8 into that active range or the concentration where you have the 

### 9 affinity then to displace the E2. So for those bottom 

### 10 chemicals, if you -- for chemicals that are tested to a high 

### 11 concentration, you're hedging your bets for detecting those 

### 12 affinity chemicals. If like in the bottom example there, if you 

### 13 cut that concentration off at 50 micromolar, those would called 

### 14 negative. 

### 15 **DR. PRAKASH NAGARKATTI:**Nagarkatti again. So 

### 16 my question was can you normalize it to the highest level of 

### 17 solubility and then compare the data and say that okay, if you 

### 18 do that, now you're at almost 88 percent or 90 percent 

### 19 concordance. I mean, can you do that? 

### 20 **DR. MICHAEL HORNUNG:**This is Mike Hornung. 

### 21 So I think what you're saying is if we just looked at everything 

### 22 at that 50 micromolar cutoff level or we picked that 

### 23 concentration that was similar across all the assays and said at 

### 24 this concentration, do you give 10 percent or 20 percent or zero 

### 25 percent displacement? I don't know if that would change what we 

### 26 saw with the data.

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1 **DR. PRAKASH NAGARKATTI:**So you attribute the 

### 2 entire discordance due to the fact in one assay used only 

### 3 specific concentration of the highest solubility or whatever, 

### 4 and you used a range in the other one, and you attribute the 

### 5 entire discordance due to that factor. So I was just wondering 

### 6 whether you could revisit the data to address that issue or it's 

### 7 not possible. So that's all I was trying to find out. 

### 8 **DR. DANIEL SCHLENK:**Dr. Judson? 

### 9 **DR. RICHARD JUDSON:**Richard Judson. I want 

### 10 to just make a couple of clarifications about this. So it's 

### 11 really important to stress this is a very preliminary analysis. 

### 12 Let's take one of the specific points that you raised, which was 

### 13 why would the HTP assays be positive when the low through put 

### 14 assays are negative? You can't explain that with this 

### 15 concentration cutoff. 

### 16 I'm going to ask a question of Mike. The 

### 17 calls of positive or negative are kind of a weight of evidence 

### 18 looking at both the trout assays, both the binding and the 

### 19 transactivation. So for instance, one of these surfactants -- 

### 20 we've talked about detergents. They will be called positive in 

### 21 the HTP assays because we haven't done that weight of evidence 

### 22 filtering out yet. So some of those HTP positive -- they're 

### 23 actually positive at very low concentrations because that's what 

### 24 the surfactants do whereas in the trout side, they would have 

### 25 been positive in the trout binding assay just taken as it is, 

### 26 but looking across both the assays and the chemical information,

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1 you say they're negative. 

### 2 So getting sort of down in the weeds of these 

### 3 table comparisons where they finish the weight of evidence on 

### 4 their side and we're looking at this unfiltered data on the HTP 

### 5 side is you're seeing some interesting trends, this 

### 6 concentration trend holds. If you go and add other assays like 

### 7 the Attagene assays, which are taking up to 200 micromolar, we 

### 8 see activity in some even weaker ones. So it lends more weight 

### 9 to this theory. Most of the HTP negative trout positive ones 

### 10 are really just because the trout has run out to these higher 

### 11 and higher concentrations. 

### 12 **DR. DANIEL SCHLENK:**Dr. Safe? 

### 13 **DR. STEVEN SAFE:**Steven Safe. I just wanted 

### 14 to ask you one question. When you use the thiols, you had some 

### 15 discordance, did you use (inaudible) to keep everything reduced? 

### 16 Because thiols can interact and inactivate proteins. 

### 17 **DR. MICHAEL HORNUNG:**This is Mike Hornung. 

### 18 We don't use that in the binding assay or the slice assay, so 

### 19 that is something that -- 

### 20 **DR. STEVEN SAFE:**Yes, that could be a problem 

### 21 with the thiols. 

### 22 **DR. DANIEL SCHLENK:**Any other questions? Dr. 

### 23 Keiser? 

### 24 **DR. MICHAEL KEISER:**Michael Keiser. So two 

### 25 questions about sort of the decision process. Both of them are 

### 26 from earlier on in the slides near the beginning. The first one

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1 is about slide 174, looking at type A and B curves. I was 

### 2 curious and this actually also a general question across it. 

### 3 When you talk about cases of chemical classes or categories that 

### 4 are known to create false positives, obviously that has some 

### 5 consequence here in this case because you might take a 

### 6 relatively strong curve, move it all the way to a non-binding 

### 7 decision. So I was curious how those classes are defined and 

### 8 whether it is more of a binary decision as to class membership 

### 9 or if there is a gradient, maybe computationally or some other 

### 10 approach. 

### 11 **DR. MICHAEL HORNUNG:**This is Mike Hornung. 

### 12 That's a question about the potential false or known false 

### 13 positives and moving them. As I was given that talk, I was 

### 14 thinking about the benzine and sulfonic acids that we had DBS, 

### 15 so the dodecyl that was positive. We know the octyl was. So we 

### 16 have a group there that is a known -- we already have good data. 

### 17 We have Ki data for that chemical class that is negative. 

### 18 You're right that there would be questions 

### 19 about structural similarity. I think getting back to part of 

### 20 what Rick talked about with defining the chemical classes and 

### 21 looking at the chemical class based on activity. If you start 

### 22 getting away from these long alkyl chain sulfonic acids, where 

### 23 would you make that cutoff to say okay, we're still going to 

### 24 call this in this group and move it -- we'll just call that a 

### 25 class that will produce false positives and throw it in that 

### 26 group.

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1 There is kinda of a binary decision. Yes, 

### 2 it's in the group, throw it in there, and no it doesn't fit that 

### 3 classification, leave it out. But part of it is in having 

### 4 confidence and where you define the end of that chemical class. 

### 5 So if you have sulfonic acid that's on an alkyl chain or there's 

### 6 other substructures on that, does that really classify as that 

### 7 type of chemical. Some of those, I think you'd want to go back 

### 8 and take a look at them and verify them. You might have data 

### 9 from P-chem data, PKa data, other things that tell you it's 

### 10 likely not to have activity, but as you get further away from 

### 11 where you have the comfort of what you know is active and you 

### 12 have the tested data, and I think part of that is there's a 

### 13 judgement on having the experience and the expertise in running 

### 14 the assays and seeing what the results are from the different 

### 15 assays and what the activity is when making those types of 

### 16 decisions. 

### 17 When you're confident, you can turn it into a 

### 18 binary decision, but when you starting getting in that realm of 

### 19 questioning yourself whether you really feel confident in 

### 20 including that, then I think you need to do an additional -- 

### 21 move it onto a slice assay or something to verify. 

### 22 **DR. MICHAEL KEISER:**So then just to repeat 

### 23 back to make sure I understand the process, you would say that 

### 24 you would first start with something like a defined substructure 

### 25 match, and then maybe depending on the size of the molecule, 

### 26 other things you have in mind, you might also subject that to

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1 some human decision making process or supervision afterwards, is 

### 2 that correct? 

### 3 **DR. MICHAEL HORNUNG:**Yes, I think so. I 

### 4 don't know if Rich or Pat would have any clarification on that. 

### 5 6 **DR. PAT SCHMIEDER:**This is Pat Schmieder. If 

### 7 I understand your question right, what Mike is talking about 

### 8 here is when we're developing the classes within the training 

### 9 set and the confidence -- when we build the Expert System, we're 

### 10 pretty confident in the calls within that class. So I don't 

### 11 know if we're mixing up, you know, using it afterwards versus 

### 12 building it and trying to make sure that we go through all the 

### 13 steps so that we're really confident when we say this is a class 

### 14 and these are the activities we get within that class. 

### 15 **DR. MICHAEL KEISER:**Okay. Yes, thank you for 

### 16 the answer. I have a second question, which is a little more 

### 17 specific. It should be quicker, which is namely on 177 when 

### 18 we're looking at flat line curves in this case, I was curious 

### 19 just in terms or rough numbers how many cases have been actually 

### 20 resurrected through this decision pathway, and by resurrected I 

### 21 simply mean in this case, how many have been indeed resurrected 

### 22 as metabolites? I was curious how often we end up and the very 

### 23 right most decision point where it's classified as a binder for 

### 24 input to this sort of decision making algorithm. 

### 25 **DR. MICHAEL HORNUNG:**I'm not sure I can give 

### 26 you a number like out of the 296 how many of those would fall

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1 into that category. I think it would be a very small 

### 2 percentage. We'd have to look into that and then I could give 

### 3 you a more concrete answer. 

### 4 **DR. PAT SCHMIEDER:**Pat Schmieder. If I could 

### 5 just add to that. One of the ways when it does occur, and I 

### 6 agree with Mike, it's not that often, but when we do end up 

### 7 pursuing that, it's because within again the chemical category, 

### 8 we look across that category and we get non-binders, for 

### 9 instance. And then all of the sudden, we get a chemical that's 

### 10 doing something else. When I went through pretty quickly 

### 11 earlier on where we look at the chemistry within the section 

### 12 three presentation earlier, we look at the chemistry in the 

### 13 assay, in the binding assay. If we see the zero hour and the 20 

### 14 hour a lot different across that, we get some indication there's 

### 15 something else going on and there's possible metabolism. 

### 16 Now that's just the cytosol, but we can get it 

### 17 into a binding assay. If we don't get consistency across a 

### 18 chemicals class, we really start asking a lot of questions 

### 19 because it's either that we don't have the class specified very 

### 20 well or there's a metabolism or something else going on. Like 

### 21 he's got that whole box. We try to keep pursuing it. Overall, 

### 22 that percentage of when that would happen is pretty low. 

### 23 **DR. MICHAEL KEISER:**Thank you, that does 

### 24 answer my question. 

### 25 **DR. DANIEL SCHLENK:**Any other questions? Dr. 

### 26 McManaman?

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1 **DR. JAMES MCMANAMAN:**So this is a pretty 

### 2 complicated problem. So I can see that there could be several 

### 3 areas where you could have different classes of compounds 

### 4 interacting in various steps along the process. For instance, 

### 5 there's the hormone binding domain of the receptor, but there is 

### 6 also the DNA binding domain component of that. 

### 7 So there are interactions that would affect 

### 8 dimerization and there's also interactions that would affect 

### 9 cooperativity with the DNA elements. So any one of those things 

### 10 could be influenced by a chemical and in fact, some chemicals 

### 11 are known to influence the cooperativity more than the 

### 12 dimerization. 

### 13 I think without a full understanding of what 

### 14 chemicals are doing and how they're affecting the process 

### 15 because each of your assays is going to be -- I mean your 

### 16 binding assay is just going to be binding to the hormone binding 

### 17 domain, but the other assays are going to involve binding to the 

### 18 hormone binding domain and binding to the DNA. So when you set 

### 19 up an assay in which you've got multiple ERE strung together, I 

### 20 think that you're going to may be select for those things in 

### 21 which you're looking at more cooperativity with DNA binding than 

### 22 you are with dimerization. I think that that could explain why 

### 23 you're getting some variability in your assays, but it comes 

### 24 back to you really have to know what the compounds are doing. I 

### 25 just don't see that you really have a handle on that yet. 

### 26 **DR. MICHAEL HORNUNG:**This is Mike Hornung

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1 again. I think yes, it can get very complex looking at all of 

### 2 those different things, but I think what we're trying to do is 

### 3 look at if a chemical can bind to the receptor and we can put it 

### 4 in the context of that molecular initiating event leading to 

### 5 some adverse outcome downstream that that initial binding tells 

### 6 us yes, this may start that process. The multiple EREs that 

### 7 could put in cells in vitro assays, I believe a lot of times are 

### 8 one just to try to increase your signal for the reasons you talk 

### 9 about. 

### 10 So they are artificial systems that look at a 

### 11 lot of different things, but I think if we go back and look at 

### 12 it and say if we have binding in this chemical and this type of 

### 13 structure can bind, then it produces a potential that it can 

### 14 lead to these downstream activation and the downstream signaling 

### 15 and momentum that gets you through some adverse pathway. What 

### 16 all those steps are in the way and what the different assays, 

### 17 how those different assays respond, maybe, I don't want to say 

### 18 isn't as important, but the critical point is understanding that 

### 19 initial binding and activation part of that pathway that does 

### 20 the chemical have the potential to at least initiate that 

### 21 process that leads to some endpoint. 

### 22 **DR. JAMES MCMANAMAN:**Jim McManaman again. So 

### 23 let me give you an example of where you might get some 

### 24 discordance here is that let's say that you have a compound that 

### 25 has weak affinity to the receptor itself, but in the context of 

### 26 an ERE and the cooperativity that you generate from there, you

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1 do get a response because that weak affinity is now compensated 

### 2 for increased cooperativity. So you would get a positive 

### 3 response that would be a positive binder in one assay, but not 

### 4 in the other assay. 

### 5 **DR. MICHAEL HORNUNG:**This is Mike Hornung. I 

### 6 think that gets to one of the points I think we tried to discuss 

### 7 here is trying to understand our assays, how they're responding 

### 8 and what are the components of things in each assay that will 

### 9 lead to a positive or negative response in that assay. I think 

### 10 taking all of that in context helps you decide whether that 

### 11 chemical has the potential to initiate that pathway. What the 

### 12 level of induction is, I don't think for this question is as 

### 13 important as the initial question of can it turn that pathway 

### 14 on. 

### 15 **DR. DANIEL SCHLENK:**Dr. Judson? 

### 16 **DR. RICHARD JUDSON:**Richard Judson. So I 

### 17 mean, addressing questions like you're asking is one reason for 

### 18 having this big battery of assays all up and down the pathway 

### 19 that we have, the high through put assays where we have across a 

### 20 very large set of chemicals. I don't claim to -- I can't answer 

### 21 that question for that today, but we have a lot of data that 

### 22 lets us go address those sort of issues. 

### 23 In just one example, if you look at a series 

### 24 of chemicals that interact with that are positive in most of the 

### 25 assays. They're positive in the binding assays, the 

### 26 dimerization assays, the DNA binding assays, the transactivation

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1 assays, the proliferation assays. Each assay, for instance, the 

### 2 binding assays tend to be left shifted. So you would have an 

### 3 AC50 of 10 nanomolar and in the transactivation assays, it would 

### 4 be 1 micromolar. So they tend to be that way. 

### 5 There are -- for all the phenols, you see a 

### 6 particular trend across assays. But then there're certain 

### 7 chemicals that suddenly you reverse. Some of the 

### 8 transactivation assays suddenly have AC50s which are much more 

### 9 potent in the binding assay. So there is either some assay 

### 10 interference going on, which doesn't have anything to do with 

### 11 the real biology or there is something interesting there. 

### 12 By looking across all these chemicals, we can 

### 13 pull out anomalous chemicals that are worth looking at. But 

### 14 reiterating something a point Mike has made is, are there two 

### 15 kinds of questions we worry about in research. So there are 

### 16 some really important research questions. There are the 

### 17 questions the program offices need. I want to prioritize a 

### 18 bunch of chemicals. What chemicals do I run first? What's 

### 19 really important is there good evidence that this chemical is 

### 20 hitting the estrogen receptor, and we can make everybody believe 

### 21 that. So we would want to go do something with those chemicals 

### 22 first. But you need to understand what's happening in the 

### 23 assays obviously to have confidence in making those decisions. 

### 24 **DR. JAMES MCMANAMAN:**Yes, Jim McManaman 

### 25 again. I bring it up because I think it raises a question about 

### 26 how you strategize your plans for evaluating your compounds

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1 because if you were to use the ER Expert Assay where you're 

### 2 looking only at binding, then you might miss compounds that 

### 3 really are -- especially that you were concerned about the low 

### 4 affinity ones. So you might miss those low affinity compounds 

### 5 and say that they're really negative when in fact they're 

### 6 positive if they were put into a biologic context. 

### 7 So it might be worthwhile to rethink about 

### 8 which assays you could as your first screen in terms of 

### 9 evaluating your compounds. I know that's kind of in general, 

### 10 the (inaudible) of what you're trying to do, but it wasn't clear 

### 11 to me that considering the two components, something that really 

### 12 bound to the ER and something that bound to the ER in the 

### 13 context of DNA were logically separated and put into a logical 

### 14 evaluation. 

### 15 **DR. DANIEL SCHLENK:**Let me just point out, 

### 16 again let's hold the comments until we get to the comment 

### 17 period. This is for questions. Yes, go ahead. 

### 18 **DR. PAT SCHMIEDER:**Okay. So I don't know if 

### 19 I should respond or not, but what I was going to say is what we 

### 20 started here was with one pathway. The ER mediated pathway. 

### 21 Your points are correct. We know there are many other pathways 

### 22 even within the ER system, if you will. But we also knew when 

### 23 we got into this and I think that it's consistent with the 

### 24 legislative mandate to look at estrogenicity. But we also know 

### 25 that there was a lot of evidence that many different wide 

### 26 variety of very diverse structural compounds can bind to the

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1 estrogen receptor. 

### 2 So we started with the ER mediated pathway. 

### 3 By using the AOP concept, the approaches to go to the other 

### 4 pathways and to start understanding which are the assays that 

### 5 give you an idea of something happening through another pathway, 

### 6 and kind of which ones you go to next and how you prioritize 

### 7 that, I think would be in the -- hopefully Mary or Steve would 

### 8 respond also to this -- would be where you think the biggest 

### 9 problem might be, you know, the most chemicals. So do you go to 

### 10 the AR receptor next because you think it could do that more 

### 11 often with the kind of chemicals we're trying to make the 

### 12 decisions on here, or would you go to try to look at the DNA 

### 13 reactivity and do you think there's a big universe of compounds 

### 14 that would do that or is there evidence for a lot doing it that 

### 15 way. 

### 16 So what you're saying is absolutely correct, 

### 17 but we're also trying to prioritize the kind of pathway that we 

### 18 do the work on by, I guess, a guess and it would be good to get 

### 19 the Panel's input on where to go next where you think the 

### 20 biggest problem would be for these inventories of chemicals that 

### 21 are not steroids, that are not designed to do this. 

### 22 **DR. DANIEL SCHLENK:**Okay. Thanks Dr. 

### 23 Schmieder. Any other questions or clarification? Dr. Keiser? 

### 24 This hopefully will be our last one because we're running 

### 25 behind. 

### 26 **DR. MICHAEL KEISER:**Michael Keiser. I

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1 understand. If we could just go to slide 197 please. So for 

### 2 this example and for the one on 198 also, I wanted to ask a 

### 3 clarifying question basically with respect to the earlier 

### 4 discussion about chemical classes. So in these cases, we have 

### 5 examples of chemicals that look quite similar and yet there is 

### 6 some discordance in the results between them. Do you feel that 

### 7 for this example and the one on the next slide, this is more 

### 8 aptly a demonstration of limitations currently in the screening 

### 9 or the approaches that we have, or on the other hand, is it a 

### 10 case where grouping these into the same chemical category might 

### 11 incorrectly bring them together? 

### 12 **DR. MICHAEL HORNUNG:**This is Mike Hornung. I 

### 13 think one of the things here that gets at that is we're looking 

### 14 at chemical categories that are based on two chemicals. For 

### 15 looking at within a chemical class where we know that LogKow 

### 16 plays an important role, if we look at these two chemicals where 

### 17 we have the methylphenol and the octylphenol, there may be 

### 18 something there where as Rick showed for one of the large two 

### 19 hydroxybenzophenones, adding that long alkyl chain pushed it out 

### 20 beyond that activity ring. So maybe something like that is 

### 21 going on here. 

### 22 So where you just start putting a chemical 

### 23 class together and you have two chemicals, that may play a role 

### 24 in some of that discordance. Are you on that edge between where 

### 25 you are structurally in having activity and not having activity? 

### 26 And the structure in all these things can affect free fraction

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1 in the chemical too, so what the solubility is in water and the 

### 2 availability at the site of action. I think there is something 

### 3 to that in looking at the chemical category description, and 

### 4 that, I guess, developing those categories, it's a continuing 

### 5 process. So it's not fun. 

### 6 **DR. MICHAEL KEISER:**Thanks for clarifying 

### 7 that the classes were so small definitely helps. Thank you. 

### 8 **DR. DANIEL SCHLENK:**Okay. Let's go ahead and 

### 9 move on. Again, we'll have another opportunity to ask questions 

### 10 tomorrow morning, so it's not the end of the world if you don't 

### 11 get a question in right now. I think you just need to forward 

### 12 the slides forward to the next presentation. The next 

### 13 presentation is by Dr. Schmieder and Rick Kolanczyk again. 

### 14 **DR. PAT SCHMIEDER:**Thank you. Now we're 

### 15 going to talk a little bit more about the category-based 

### 16 approach and how you can strategically pick chemicals to cover 

### 17 these unknown binding potential areas as you're moving to a new 

### 18 list that we're trying to cover here. Again, we're in the lower 

### 19 right box here, kind of both considering the Expert System and 

### 20 high through put, and we're also trying to address this, 

### 21 determining where the high through put data covers the unknown 

### 22 chemicals and how you could use that to expand the training set. 

### 23 24 So first I'm going to talk a little bit -- 

### 25 this is kind of an outline assessing the distribution of the 

### 26 unknown chemical, putting them in the categories from the Expert

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1 System version two, determining the number of the unknowns 

### 2 within the different groups and how much high through put data 

### 3 is available and the type of data, is there data on the eight 

### 4 assays where you have two levels of the AOP. So you have 

### 5 binding the initiating event and something at a higher level of 

### 6 organization or where you just have the two assays, which is at 

### 7 the higher level and you don't have the binding information. 

### 8 Then we're going to talk some about assessing 

### 9 where the high through put is across the untested ranges within 

### 10 chemical categories and selecting chemicals for testing to cover 

### 11 the ranges within the categories. There are two different parts 

### 12 to this. One is within already defined groups if you're just 

### 13 outside of the LogP range of some examples there and then also 

### 14 going within the larger groups of mixed phenols and mixed 

### 15 organics. 

### 16 So again, we're talking about Expert System 

### 17 version two with these added-in classes. So we assess the 

### 18 distribution of the unknowns based on those classes. We're 

### 19 going to kind of start from the bottom up here. 

### 20 The largest number of the unknowns were in 

### 21 this site B or roman numeral 7 of our Expert System that 

### 22 contains the site B type interaction. There were 1400 chemicals 

### 23 in this or 82 percent of all these unknowns of the 1700 

### 24 unknowns. The high through put data is available for 611 of 

### 25 these chemicals or 44 percent. Thirty percent of that high 

### 26 through put data is for the eight assays again at two levels of

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1 the AOP and the remaining 70 percent is for the two assays. 

### 2 Again, I'm talking about the eight assays and the two ToxCast 

### 3 assays or the NCGC assays, the BLA and the BG1 that Richard 

### 4 spoke about. 

### 5 So that leaves of course, the remainder. You 

### 6 do math, 789 chemicals that don't have any data. Of that, if 

### 7 you break out that site B category, we have unknowns largely in 

### 8 the mixed organic group here, 98 percent of all of these, 80 

### 9 percent of the 1704, but 98 percent within this class. So 

### 10 everything that I say about the mixed organics goes for the 

### 11 whole group obviously. But we will talk a little bit about some 

### 12 of the other categories within that. 

### 13 So the largest majority here is the mixed 

### 14 organics. Rick will talk more about some examples within that. 

### 15 Forty-three percent of those, as I said, it's driving the 

### 16 category. So 43 percent have high through put or 589 chemicals. 

### 17 Then we have the number listed 175 have high through put from 

### 18 eight assays and 414 or 70 percent of high through put from the 

### 19 two assays, leaving 57 percent with no high through put data. 

### 20 As we walk up, we go to the site A the part of the decision 

### 21 tree, which is the second largest number. Now there is eight 

### 22 percent of the unknowns are within this category. Again, the 

### 23 coverage is about 40 some percent of the coverage with high 

### 24 through put data, and the eight assays, you have about 36 

### 25 percent and the two assays you have the large majority of what 

### 26 the high through put data covers, which is no surprise as you

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1 realize there's a lot more of the Tox21 data for a lot more 

### 2 chemicals. Then, 60 percent with no high through put. So 

### 3 again, the mixed phenols are the largest in this category and 

### 4 they're really driving the group. So the percentages are pretty 

### 5 much the same. 

### 6 The third largest number within this are in 

### 7 this special rules category, which is basically they don't 

### 8 interact with the same atomic charge characteristics as the site 

### 9 A or B, but we know they interact with the receptor. So we have 

### 10 88 chemicals in this category. They have a higher 

### 11 representation of high through put data. Of these 58 chemicals, 

### 12 66 percent of them have high through put data, 41 percent of 

### 13 those data for the eight assays and about 60 percent for the two 

### 14 assays, leaving 30 chemicals with no high through put. 

### 15 The groups within this are a large group of 

### 16 multi-cyclic hydrocarbons. While we did have a group of these 

### 17 before based on our earlier inventories, now there's a lot more 

### 18 of these chemicals. So, it's a lot larger group and that will 

### 19 come into play when Rick describes an approach of how to cover 

### 20 these. But they are well covered with high through put data to 

### 21 the extent that the data and the uncertainties in this data can 

### 22 be used to build an Expert System, there is good coverage within 

### 23 that class. 

### 24 The thiophosphate esters, 10 of 14 have high 

### 25 through put data. If you look in the remaining in that if 

### 26 you're looking through the categories for the unknowns, the

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1 remaining are 66 chemicals that belong to these known inactive 

### 2 classes. I will show you a breakout of that in a second, and 

### 3 then the rest are the acyclics. 

### 4 So these are known inactive classes. The 

### 5 unknowns are spread across a lot of these well studied groups, 

### 6 but they're in LogP ranges outside of the well-studied area. 

### 7 The number of the high through put within these -- it varies 

### 8 depending on which group you are. The benzoates have quite a 

### 9 high percentage of, but there are only six compounds we're 

### 10 talking about there. The 27 alcohols, about 11 percent 

### 11 coverage, the mono-cyclic hydrocarbons 31. You can do this 

### 12 breakdown and understand where the data is potentially 

### 13 available. 

### 14 The acyclic perfluoro compounds -- there're 

### 15 these 16 chemical within this group and we have very little 

### 16 information on those, high through put or otherwise. 

### 17 So in summary, 44 percent of the high through 

### 18 put coverage, about 44 percent overall, and it varies between 66 

### 19 percent for one class and one percent for another depending on 

### 20 the chemical class, chemical group, but the larger groups have 

### 21 40 to 60 percent coverage with high through put. There are 509 

### 22 chemicals or 70 percent is for the two assays, the remainder for 

### 23 eight high through put assays, leaving 960 chemical that we have 

### 24 no data for. 

### 25 So let's then talk about, so how can we use 

### 26 this information to select chemicals for testing? First, let's

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1 talk about within defined groups. Again, we're using this 

### 2 example of these classes that we have a lot of information on, 

### 3 but -- so take the alcohols up here. What we've said is we have 

### 4 information on them, but not if the LogKow is above 3.4. I 

### 5 looked at it and all of the chemicals are in that higher range. 

### 6 So we need to cover that higher range of this class for the 

### 7 benzoates for instance. I've broken them out down here and 

### 8 there're two. 

### 9 Our testing is between 3.0 and 4.3, but where 

### 10 we don't know is below that and above it. So we have three 

### 11 chemicals that fall below that of these six unknowns and we have 

### 12 three above. So our testing would be to start covering and 

### 13 expanding through those categories. Everything we've found for 

### 14 these chemical classes has been a non-binder within a class. So 

### 15 you might want to jump right down and get the lowest end and get 

### 16 the highest end. If those are non-binders, the question is, is 

### 17 that enough confidence. 

### 18 But the point is you won't have to test every 

### 19 single thing in here to make a good decision and to expand that 

### 20 class. The same kind of approach is basically used throughout 

### 21 the classes. So we try to test a number of chemicals to get 

### 22 confidence, but not test everything within the classes. 

### 23 This is an example of the 2-alkylanilines. So 

### 24 it's not necessarily a defined group. It's maybe a potential 

### 25 new group. The reason I say it that way -- so we have the 

### 26 training set, which is for the 4-alkylanilines. We have a good

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1 coverage here mostly N and we have a tertiary branch structure 

### 2 and we get a good correlation with LogKow. So the question 

### 3 would be several questions. The two substituted anilines versus 

### 4 the four, two alkylaniline compared to the four. Do ortho 

### 5 substitute alkylanilines behave the same as para-substituted? 

### 6 Another question because of the group, what 

### 7 kind of chemicals are in the unknowns, do 2,6-substituted 

### 8 alkylanilines behave like the two? So depending on what the 

### 9 results are, as you select chemicals and test them, you get the 

### 10 answers back. Then you determine whether you can combine those. 

### 11 They're all one group, or you need a separate group for the 

### 12 2-alkyl versus the 4-aklylanilines. 

### 13 So this just shows how you would pull those 

### 14 out. Our hypothesis would be that the two are maybe closer to 

### 15 the four. So the 4-methylaniline was a binder is 2-methyl, and 

### 16 we can test right there first. If we got a positive there, we 

### 17 might test the higher end of this, but these are the only ones 

### 18 in the inventory, so we'd stop there. We wouldn't have to test 

### 19 all of them. 

### 20 Then take one of the lower end of the 2,6 and 

### 21 see if you get activity, or are you starting because you've got 

### 22 substitution on both sides of that aniline group. Have you 

### 23 gotten rid of the activity? So again, you don't have to test 

### 24 all four, test the low end and the high end. 

### 25 So if you look at where the high through put 

### 26 data is, using that same strategy -- so let's look at the

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1 2-methylanilines. So we have some data here, but we don't at 

### 2 the high end. So you could just pick up one more test point and 

### 3 not have to look through all of it, and the same thing here. 

### 4 But you'd have to look again at -- part of it is like what was 

### 5 presented just before this as look at that type of data. Are 

### 6 the assays that you're looking at catching these chemicals to 

### 7 begin with? But this is a strategy if you're confident in the 

### 8 certainty around those assays, then this is a strategy of how 

### 9 you could test. 

### 10 Those were just a few chemicals, but now we 

### 11 have this larger group. We have 67 unknowns in here. This is 

### 12 actually redefining what had been a very small chemical group, 

### 13 now we have a lot more chemicals in it. So we're going to talk 

### 14 about redefining a large chemical group. I'll turn it over to 

### 15 Rick. 

### 16 **MR. RICK KOLANCZYK:**What I have is the 

### 17 multi-cyclic hydrocarbons. That pretty much followed suit. The 

### 18 way we were handling those was an exact match theory. I just 

### 19 want to point out that it was a relatively small representation 

### 20 across the three inventories of concern at the time. I'm going 

### 21 to go ahead and advance this and show you the activity that we 

### 22 observed within the rainbow trout training set. 

### 23 Green again were represented negatives, pink 

### 24 positives within the series. They're ordered right now with 

### 25 respect to LogKow. That may be better to start to rearrange 

### 26 these to something that might make better sense in terms of a

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1 bridged versus a fused ring structure. Again, apply that same 

### 2 activity here. You get the feel and the flavor. We'll start 

### 3 with the fused ring where they impart no activity. We just 

### 4 wanted to cover the inventory, the unknowns, at this point. I 

### 5 would feel fairly confident that this serves as a good surrogate 

### 6 for these other methyl-substituted forms of naphthaline, a good 

### 7 representative here for the coverage of these other three 

### 8 analogs. Lastly, this structure over here was a close analog 

### 9 for this. 

### 10 So the task at hand was really to cover this 

### 11 structure space. We could adequately do that through an exact 

### 12 match. Then we went through some other expansions of looking at 

### 13 this. 3PE was of great interest to us. We looked at what was 

### 14 done in the Seri, the Japanese. They showed binding here and 

### 15 we're just out to look at, mentioned before about the different 

### 16 modes of binding to the receptor. Not just merely the A type 

### 17 and the B type, but perhaps all I could think of is some type of 

### 18 a pie ring substitution or pie ring activation within the site 

### 19 for hydrogen bonding. 

### 20 I guess I owed somebody an example here 

### 21 looking at these still beans with trans versus the cis and 

### 22 negative versus the positive. So again here, maybe some of that 

### 23 stereochemistry we talked about in this morning's session, and I 

### 24 kind of pushed off. I think there could be something to explain 

### 25 that, why this particular structure is a negative. Again, 

### 26 moving from what was an exact match boundary or criteria for

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1 this set to maybe developing some more concrete rules here 

### 2 involving some stereochemistry aspects. 

### 3 The other negative within this set was 4PE. I 

### 4 think what we ran into here was a solubility issue where we're 

### 5 pushing the envelope of the LogKow and I just don't think we 

### 6 could get enough into solution for the assay to perform to get 

### 7 any type of binding activity. 

### 8 So, in what respect does this apply to the 

### 9 EDSP Inventory? What I've done here is I've pulled some 

### 10 representative structures. Pat mentioned there were 67. This 

### 11 is a subset of those. I guess I'm trying to give you a feel and 

### 12 a flavor of where I would go to expand this set. We only had 

### 13 tested one representative within the inventory. But what I 

### 14 would propose to do is to grab these others, highlight it in 

### 15 yellow to perhaps expand that training set to give greater 

### 16 coverage and greater credence to a hypothesis that perhaps fused 

### 17 ring, multi-cyclic hydrocarbons were going to pose as negative 

### 18 with respect to binding versus some activity that we could find 

### 19 in the bridged. 

### 20 So just to help sort that out, I would propose 

### 21 that we pick these structures out. As far as the structure 

### 22 space, I've added those. Again, yellow proposed testing I feel 

### 23 would give adequate coverage of that set. Beyond that, do the 

### 24 same with the bridged ring again to better resolve that. So 

### 25 again, I could see using the EDSP universe to expand that node 

### 26 into potential future nodes.

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1 So it gives you an idea, I guess down here at 

### 2 the bottom of the slide is where we're taking off. In summary, 

### 3 there are 203 of those unknown binding potential chemicals that 

### 4 are found within defined groups. Seventy-seven of those 

### 5 chemicals have high through put. If we could potentially use 

### 6 some of that data, probably test 10 or 20 percent more of the 

### 7 chemicals to cover that remaining structure space, many of those 

### 8 taking into account LogKow ranges. 

### 9 Okay, so part two, within the mixed functional 

### 10 group classes, again looking at mixed phenols and mixed 

### 11 organics, again another group that we've relied on as far as the 

### 12 exact match, I proposed an expansion of moving from Expert 

### 13 System version two to version three. I've got 16 proposed 

### 14 groups, not limited to that with the EDSP Inventory, but just to 

### 15 give you a flavor and an idea of how the system could be 

### 16 expanded to capture. 

### 17 I've listed those in this table. It will give 

### 18 you some idea as to the representation within the EDSP universe 

### 19 as far as number of chemicals. These first two columns 

### 20 represent some knowledge that we may have already. So in other 

### 21 words, what we've done is I've run these roughly 500 chemicals 

### 22 through the Expert System and came up with a predicted value, 

### 23 either negative or positive. Many o these may have been tested 

### 24 as an exact match, working back from the old systems. So in 

### 25 other words, they were found in one of the other inventories. 

### 26 We ran them through, did an exact match, analyzed and we may

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1 have had positive binding for them or negative binding. The 

### 2 other is that we may not have tested them at all. There may be 

### 3 a LogKow cutoff that was influenced there. That certainly would 

### 4 fall into the negative categories. 

### 5 I guess where we want to really focus where 

### 6 we're getting these positive hits. Those were probably already 

### 7 tested. Aldehydes and ketones, we mentioned those before. 

### 8 Cyclohexanones, those would be on the top of my radar, 

### 9 sulfonamides, I guess we'll work through some of these. The 

### 10 aldehydes, focus in. I'm a big structure guy obviously. That's 

### 11 kind of turned into my life here lately as far as Expert System 

### 12 one, two and proposed three. 

### 13 I guess what I would do is I'd start throwing 

### 14 some of these structures up on a screen. Then you start 

### 15 grouping them. The aldehyde group is branched off of an 

### 16 aromatic ring, a benzine ring. Now here, you've got multiple 

### 17 substitution on that ring. Here in this case ortho, meta and 

### 18 para where you have multiple substitution. Some of the flavor 

### 19 for this is do I need to test all of these structures on this 

### 20 screen. I would say absolutely not. You could grab one or two 

### 21 within a given box here and represent that. I guess what I've 

### 22 attempted to do here is where we had a larger category or class 

### 23 of aldehydes and I've broken them down a little bit only to 

### 24 perhaps build them back up depending on the activity that we see 

### 25 within the assays. 

### 26 The next slide is just an extension, more of

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1 those aldehydes. In this case, cyclohexane ring as the base 

### 2 here rather than a benzene ring. Again same strategy would be 

### 3 applied. What happens when you have a multi-cyclic group with 

### 4 an aldehyde? And then I've got this little orphan here on the 

### 5 right. 

### 6 I'm just going to cycle through some 

### 7 structures. Here are ketones. This could be very important, 

### 8 especially in light of the fragrances, which we really haven't 

### 9 covered in Expert System version one or two. I just went 

### 10 through that same grouping exercise pertaining to the ketones 

### 11 again. 

### 12 Cyclohexanones, just trying to give you a 

### 13 flavor of what's in the inventory and the attempt that I've made 

### 14 to start grouping these things. I think that's a good first 

### 15 cut. 

### 16 Pyridines and quinolines -- now in this case, 

### 17 I've kind of mixed potentially two groups into one really. 

### 18 We've got the pyridines over here and the quinolines. Within 

### 19 this grab paraquat, diaquat, bipyridines, again do I need to 

### 20 test everything on this slide? I've got to keep stressing a 

### 21 strategic approach of grabbing some good representative 

### 22 structures, one or two out of each of these boxes and then 

### 23 proceed through. 

### 24 Anthraquinones -- I haven't grouped them 

### 25 necessarily, but I've got the hydroxylated across the top, more 

### 26 or less started forming them up and again, things looking very,

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1 very similar. Benzothiazoles -- a more limited set. 

### 2 Chlorinated cyclic hydrocarbons -- I show a 

### 3 bit of concern with this group. Again, we haven't seen many of 

### 4 these or any of these at all in the food, nonfood or 

### 5 antimicrobials. Halo benzenes -- again. Halo phenols -- 

### 6 interesting. 

### 7 The one that I do know we've tested within the 

### 8 trout training set is pentachlorophenol, negative. I think the 

### 9 interesting fact here is that when you start looking at 

### 10 chlorinated phenols, the more chlorine rings that you hang on, 

### 11 the further it shifts that PKa value in. The potential of 

### 12 having a charged species at the physiological pH of the assay, 

### 13 fluorophenols, bromophenols, iodaphenols. 

### 14 Isocyanates -- reactive group, in fact. I 

### 15 suspect it would go down to the amean in solution. 

### 16 Diisocyanates, monoisocyanate. Nitro aromatics, again, you pull 

### 17 these things out, start developing them as a class, not 

### 18 necessarily having to test everything. Do we need to test all 

### 19 500 of these? Absolutely not. 

### 20 Nitro phenols, again looking at the influence 

### 21 of the nitro groups. The more nitro groups you hang on here, 

### 22 it's going to influence that PKa value as well, may draw in some 

### 23 idea of a charged relationship here. And lastly, the 

### 24 sulfonamides. Again, a little more diversity here. This might 

### 25 be a little tougher class to actually work through. 

### 26 Bottom line -- really this is a coverage

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1 slide. I put this up here just to give you some flavor of where 

### 2 the high through put coverage is and what we have proposed to 

### 3 add to that, to use the high through put data, possibly another 

### 4 10 to 20 chemicals to look at, at testing. Obviously, you 

### 5 substitute in for the high through put some lower through put 

### 6 testing as well, but just to give you an idea of the coverage. 

### 7 Now I promised somebody something about 

### 8 acyclics this morning too. Expert System version one, 

### 9 consideration of the acyclics as negative, it seemed to be 

### 10 appropriate at that time. We talked a little bit about that 

### 11 this morning during the question period. 

### 12 For expansion into other inventories and most 

### 13 certainly the chemical universe, you require further testing. 

### 14 So I guess the third point there, caution in making that rule 

### 15 that all acyclics are negative. We were steered by the Panel to 

### 16 look at PFOS and PFOA. Immediately, we went after that, 

### 17 probably within a week or two of our departure from DC. 

### 18 As suggested, PFOA and PFOS were both positive 

### 19 in our binding and activation assays. What I would say to look 

### 20 at other structures with respect to the EDSP Inventory. Looking 

### 21 beyond these perfluoro compounds, I would also use caution maybe 

### 22 in looking at some halogenated alkanes. In addition to 

### 23 fluorene, looking at chlorinated and brominated, 

### 24 multi-chlorinated, multi-brominated, again they're a bit foreign 

### 25 to Expert System version two in that we just haven't seen that 

### 26 coverage. They are more of a SDWA type chemical.

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1 The aldehydes and ketones, mentioned those 

### 2 this morning, but they're largely going to be found in the 

### 3 fragrances. We found a couple of representatives here and 

### 4 there. I threw this slide up just to give you a flavor of what 

### 5 some of the perfluoro structures that we're looking at and 

### 6 expanding to version three. 

### 7 In EDSP Inventory, there's this substituted 

### 8 amino perfluoro compound. Some of these charge groups, I don't 

### 9 know how they're really going to play out and behave. But what 

### 10 we do know is that the PFOA and the PFOS were positive in here. 

### 11 Certainly some work ahead here on tackling that. 

### 12 Outside of those preliminary groups for the 

### 13 mixed organics and the mixed phenols, there is high through put 

### 14 data out there with respect to the eight assay up above, 125 of 

### 15 those chemicals. Where there are two assays only, 294, and 576 

### 16 where there is no high through put. There could potentially be 

### 17 some work ahead for those. 

### 18 So in summary, same approach that was used to 

### 19 build the initial Expert System to assess the degree to which -- 

### 20 I hate to just read slides. You can read slides, but what we'd 

### 21 want to do is assess the degree to which the existing Expert 

### 22 System covers the new list. That's looking across the LogKow 

### 23 ranges, the categories that we have before us, and 

### 24 systematically select chemicals for in vitro testing. Again, I 

### 25 just want to reiterate that it's not testing everything. We 

### 26 don't need to test all 5000 chemicals of the EDSP Inventory.

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1 You work through a strategy, good 

### 2 representation, and to do that to extend those areas that are 

### 3 not completely covered with respect to those current classes 

### 4 that we have, namely looking at larger LogKow ranges, we'd 

### 5 expect to maybe test 10 percent or less more. Then to tackle 

### 6 the mixed organics and the mixed phenols, I would estimate maybe 

### 7 another 10 to 25 percent of those to get that coverage down. 

### 8 So the take home summary here is continue the 

### 9 iterative approach. Assess what cannot be adequately predicted, 

### 10 and that's looking at the unknown binding potential result. 

### 11 Develop groups, test within those groups, and then reevaluate 

### 12 the activity within the tested ranged for those chemical groups. 

### 13 So again, that iterative approach, select chemicals to test at 

### 14 bound ranges of unknowns, et cetera. Continue through until you 

### 15 arrive at Expert System version three. And that's it, thank 

### 16 you. 

### 17 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 18 or clarification? Dr. Schultz? 

### 19 **DR. TERRY SCHULTZ:**Terry Schultz. Have you 

### 20 put any thought into developing your scheme for what you're 

### 21 going to be selecting your chemicals? When you flashed up, for 

### 22 example, pyridines and quinolines, I guess what I'm getting at 

### 23 is how do you every get around saying, well we need to do one 

### 24 more test. Have you got a logical program with which you're 

### 25 going to select your chemicals? 

### 26 **MR. RICK KOLANSZYK:**We start out with

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1 basically LogKow. We want to cover that range. Maybe start 

### 2 ordering the chemicals lowest to highest, you want to pick 

### 3 something on the low end, something on the high end, obviously 

### 4 maybe a character in between. I guess I don't go into it saying 

### 5 I'm going to pick these 12 compounds, these six compounds, these 

### 6 three compounds and be done with it. Much of it's the feedback 

### 7 that you get from the result of the biological assays. It's 

### 8 structure activity relationship that we're working through. 

### 9 I guess the hope is that eventually, just as 

### 10 an example, I tried to break them down into the little red 

### 11 boxes. Eventually, hopefully, you can form them back up. Pat 

### 12 even eluded to the fact when she gave her aniline example, 

### 13 looking at the 2-alkylanilines versus the 4-alkylanilines. Is 

### 14 there hope that we could eventually put them together, just deal 

### 15 with alkylanilines? I don't know. I'm rambling a bit. 

### 16 **DR. TERRY SCHULTZ:**We can discuss and we'll 

### 17 have time for comments on this later. I just think you need to 

### 18 come up with a strategy so that it's very clear to everyone how 

### 19 it came about selecting those 10 or 25 chemicals you're going to 

### 20 test. 

### 21 **DR. DANIEL SCHLENK:**Dr. Safe? 

### 22 **DR. STEVEN SAFE:**Steve Safe. Could you go to 

### 23 slide 274? I'm just sort of interested in why you selected the 

### 24 compounds because there are chemicals, polycyclic aromatic 

### 25 hydrocarbons on that slide that are known to be estrogenic 

### 26 published. Methylcholanthrene binds to the estrogen receptor

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1 and induces many different estrogen responses. If you want a 

### 2 positive control for that slide, then you should have 

### 3 methylcholanthrene benzopyrene. These are published papers on 

### 4 polycyclics as estrogens, and they include binding data, 

### 5 transactivation data and even in vivo data. You might want to 

### 6 just check with the polynuclear. 

### 7 **DR. DANIEL SCHLENK:**You'll have ample 

### 8 opportunity to talk about that. Thanks. Yes, just don't 

### 9 forget. 

### 10 **MR. RICK KOLANCZYK:**Can I respond? 

### 11 **DR. DANIEL SCHLENK:**Sure. 

### 12 **MR. RICK KOLANCZYK:**Rick Kolanczyk here. 

### 13 Those examples you just laid out are not part of the inventory 

### 14 here or are they? No. 

### 15 **DR. STEVEN SAFE:**3-methylcholanthrene is 

### 16 there I think if I'm not mistaken on CAS 56-49-5. You should 

### 17 put a yellow thing around that please. 

### 18 **MR. RICK KOLANCZYK:**I value the suggestion, 

### 19 thank you. 

### 20 **DR. PAT SCHMIEDER:**This is Pat Schmieder, if 

### 21 I could also add a comment, and it gets to the other question 

### 22 asked by Dr. Schultz is our strategy overall is because we don't 

### 23 have like everyone else, we don't have unlimited resources. So 

### 24 where we try to do our testing is really targeted at these 

### 25 inventories. And yes, we would look and we haven't done this 

### 26 group and we appreciate any comments you could have on positive

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1 controls to use is really good, but we would also look in the 

### 2 literature. We try to look those up, but we haven't. This is 

### 3 pretty new that we're looking at the multi-cyclic hydrocarbons. 

### 4 Overall, the approach is to really -- and the 

### 5 approach that we took to build the first expert systems were 

### 6 that we are targeting those inventories. So yes, there were a 

### 7 lot of interesting things that we found that we couldn't pursue 

### 8 because what we had to do is -- our question was can you develop 

### 9 these assays, can you do this and build these systems to predict 

### 10 within these targeted inventories. Another reason to do it and 

### 11 to do this approach is you don't know what you're next list is 

### 12 going to be, you don't know for sure whether you're going to be 

### 13 able to procure the chemicals. 

### 14 Another aspect of this is metabolism is a huge 

### 15 question of course. I know one of the charge questions was 

### 16 around that, but we are trying to assemble metabolic maps. So 

### 17 if you have metabolic maps, you might know something about a 

### 18 parent compound or not, but if you have a whole list of 

### 19 metabolites and you're asking the question, is there potential 

### 20 for any of these metabolites to do this, then a (Q)SAR based 

### 21 system will help you see where you have knowledge and where you 

### 22 don't have knowledge without doing any testing because I don't 

### 23 think we're going to be able to test all the metabolites that 

### 24 are potentially we're going to be encountering. 

### 25 The reason to try to build these systems -- 

### 26 and if you remember some from this morning, we looked at the

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1 Safe Drinking Water Act list, and there were over 2000 chemicals 

### 2 on that list that weren't overlapping with inerts. We covered 

### 3 71 percent of them without doing any testing. So there is a 

### 4 reason, I think, from the resource perspective to build some 

### 5 systems so that I can maybe helps us tell where to test, but 

### 6 also give us hopefully some answers in the absence of being able 

### 7 to test. 

### 8 **DR. DANIEL SCHLENK:**Dr. Jacobs? 

### 9 **DR. MIRIAM JACOBS:**Miriam Jacobs. I have a 

### 10 practical question that's perhaps more relevant for the high 

### 11 through put screening too. Some of these compounds that you're 

### 12 proposing to test are quite volatile. Some of the volatility 

### 13 was an exclusion factor that was mentioned earlier on. But the 

### 14 ketones particularly and some of the brominated compounds and 

### 15 chlorinated compounds, how do you propose to deal with those 

### 16 especially with respect to the flares that we saw earlier with 

### 17 the high through put system? 

### 18 **DR. PAT SCHMIEDER:**Well, I can't answer for 

### 19 the high through put systems, but for the systems that we're 

### 20 using on the lower through put side, we have been able to handle 

### 21 chemicals that are more volatile. I know this because we 

### 22 propose some for them to test that they said that they couldn't 

### 23 test because of volatility issue or solubility in DMSO. 

### 24 Another way we can approach this and see if 

### 25 we're able to test them is we do the chemical concentration and 

### 26 we can look. So we might get some loss, but if we have steady

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1 concentration of a more volatile chemical through the time over 

### 2 the period of our assay because we're doing the analytical, then 

### 3 we could use that data. So that might be one way at least to 

### 4 approach it. 

### 5 **DR. MIRIAM JACOBS:**Miriam Jacobs. I can 

### 6 appreciate that very much for the low through put, but what sort 

### 7 of suggestions would you have for dealing with cross 

### 8 contamination with a high through put? 

### 9 **DR. DANIEL SCHLENK:**Dr. Judson, do you want 

### 10 to take a crack at that one? 

### 11 **DR. RICHARD JUDSON:**Richard Judson. So the 

### 12 volatility issue, we haven't made any promises at all. There 

### 13 are some folks in research and development who are looking at 

### 14 doing medium through put systems so essentially, you have -- 

### 15 it's like a mini hood that you put over your maybe 24 well 

### 16 plate. I don't think people have gone beyond 24 well plates. 

### 17 So we can test a few chemicals in a few assays potentially. 

### 18 Then there're some other kind of crazy ideas of testing against 

### 19 fruit flies or other kind of whole animals where you just dose 

### 20 them with the volatile chemicals. That's very much a research 

### 21 effort today for even medium through put they're doing, but the 

### 22 medium through put kind of robotic is -- people are working on 

### 23 it. 

### 24 **DR. DANIEL SCHLENK:**Thank you. Dr. 

### 25 McManaman? 

### 26 **DR. JAMES MCMANAMAN:**So have you considered

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1 using modeling for training? Because the ligand binding domain 

### 2 has been crystalized. Have you thought about trying that 

### 3 approach? 

### 4 **DR. PAT SCHMIEDER:**Yes, in fact we have in 

### 5 our past looked at modeling and building 3D, incorporating 

### 6 conformational flexibility and building models from that. What 

### 7 we realized when we started working with these inventories that 

### 8 the standard approaches at that time and maybe there're better 

### 9 ones now -- because of how they interact only at one part of the 

### 10 receptor, they didn't fit into the standard template models. So 

### 11 we do have a past in trying that, but with these kind of 

### 12 chemicals and the fact that they aren't designed to do this, it 

### 13 may be more appropriate as these domains are expanding to try to 

### 14 go back to some of that. At the time, we found that this 

### 15 approach actually worked better for these types of chemicals 

### 16 that were on the inventories. 

### 17 **DR. DANIEL SCHLENK:** 

### 18 **DR. RICHARD JUDSON:**We're setting up a 

### 19 collaboration with a couple of academic groups who claim to be 

### 20 the best dockers in the world. So we'll find out. We're going 

### 21 to give them -- there's a much larger inventory of chemicals 

### 22 that we're interested in that include these and we'll see what 

### 23 happens. It's a research effort and we may learn something. 

### 24 **DR. DANIEL SCHLENK:**Any other questions? 

### 25 Okay. Thank you. We have one more final -- Mary, do you have 

### 26 something to add?

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1 **MS. MARY MANIBUSAN:**I just want to offer an 

### 2 option to the chair in acknowledgment of the long day and the 

### 3 dense presentations that we could move the summary and recap to 

### 4 the next morning if you'd like, but it's entirely your option. 

### 5 **DR. DANIEL SCHLENK:**Is your morning thing 

### 6 exactly the same sort of presentation, a wrap up essentially? 

### 7 Okay. I'm for that. We can do that for sure. I don't hear 

### 8 anybody complaining. Except for Dr. Schultz, he can stay here 

### 9 while all of us leave of course. With that, I'll turn it over 

### 10 the Sharlene. 

### 11 **DR. SHARLENE MATTEN:**So some updates on the 

### 12 docket. Those who are looking for the presentations today, 

### 13 there's also a presentation of the charge questions, the final 

### 14 roster, final agenda. I put those into the front-end system, 

### 15 the hidden away site where you put things from our end to be 

### 16 approved by those that do the approving in the Office of 

### 17 Environmental Information and I hope I wrote a note to the two 

### 18 people who make those posting things happen. Hopefully, they'll 

### 19 be posted in the next day or two. 

### 20 I wanted to alert any public commenters that 

### 21 are still hanging on and haven't yet made a decision to comment, 

### 22 we're still welcoming comments. You can come see me or anyone 

### 23 in the FIFRA SAP staff. If you've already talked to me, I'm 

### 24 expecting your presentations by 8 a.m. no later than tomorrow 

### 25 morning, or you can email them to me tonight. I will take care 

### 26 of them in the morning. I'm expecting 13 presentations. So I

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1 know the four people that are going to make that happen. So I 

### 2 expect everyone will follow through. We've done so well so far. 

### 3 So I really appreciate that. I also wanted to have the Panel 

### 4 indulge me in about five minutes or so in the room over there 

### 5 and then you're free. 

### 6 With that, we will start up again at 9 a.m. 

### 7 Thank you very much. Bye to those on the phone if you're still 

### 8 there. 

### 9 (WHEREUPON the meeting was adjourned) 

### 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

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1 **DR. SHARLENE MATTEN:**Okay. Good morning. My 

### 2 name is Sharlene Matten. I'm the designated federal official 

### 3 for the Scientific Advisory Panel Meeting. Today is the second 

### 4 day of the Scientific Advisory Panel Meeting on scientific 

### 5 issues associated with prioritizing the Universe of Endocrine 

### 6 Disrupter Screening Program, EDSP chemicals using computational 

### 7 toxicology tools. All of the information that was presented 

### 8 yesterday is now posted to the docket. The docket information 

### 9 is on the top of the agenda and if you need help in finding that 

### 10 information please see me or a member of the EPA staff. 

### 11 We're going to after the EPA's last summary 

### 12 presentation go into the public comment period. If anyone has 

### 13 public comments that hasn't -- if anyone has public comments and 

### 14 they haven't spoken to me please come forward and talk to 

### 15 members of the EPA staff. I'll turn the meeting over to our 

### 16 chair, Dr. Dan Schlenk. 

### 17 **DR. DANIEL SCHLENK:**Thanks. Let's hope all 

### 18 the other questions can be solved that easily huh? Good morning 

### 19 everyone. I'd like to just briefly go around the panel just if 

### 20 you wouldn't mind to state your name and just affiliations 

### 21 before we get going so everybody puts with a face together. My 

### 22 name is Dan Schlenk. I'm from the University of California, 

### 23 Riverside. 

### 24 DR. Stephen Klaine: Steve Klaine with Clemson 

### 25 University. 

### 26 Dr. Martha Sandy: Martha Sandy with the

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1 Office of Environmental Health Hazard Assessment. 

### 2 Dr. James McManaman: Jim McManaman University 

### 3 of Colorado. 

### 4 DR. Prakash NagArkatti: Prakash Nagarkatti 

### 5 from University of South Carolina. 

### 6 DR. Marion Erich: Marion Erich, Virginia 

### 7 Tech. 

### 8 **DR. KATRINA WATERS:**Katrina Waters, Pacific 

### 9 Northwest National Laboratory. 

### 10 DR. Mark Cronin: Mark Cronin, Liverpool John 

### 11 Moores University. 

### 12 DR. Terry Schultz: Terry Schultz, the 

### 13 University of Tennessee. 

### 14 **DR. JANICE CHAMBERS:**Jan Chambers, 

### 15 Mississippi State University. 

### 16 DR. Bette Meek: Bette Meek, University of 

### 17 Ottawa. 

### 18 **DR. EDWARD PERKINS:**Ed Perkins, U.S. Army 

### 19 Corps of Engineers. 

### 20 DR. THOMAS Burris: Tom Burris, Scripts 

### 21 Research Institute. 

### 22 DR. Michael Keiser: Michael Keiser, Sea 

### 23 Change Pharma. 

### 24 DR. Steve Safe: Steve Safe, Texas A&M. 

### 25 DR. Nancy Denslow: Nancy Denslow, University 

### 26 of Florida.

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1 DR. Miriam Jacobs: Miriam Jacobs, European 

### 2 Food Safety Authority. 

### 3 **DR. DANIEL SCHLENK:**All right, thank you 

### 4 everyone. So as we left off yesterday, I believe our next 

### 5 presentation will be from Vicki Dellarco who will give us a 

### 6 summary overview and then what I'd like to do is possibly have 

### 7 the presenters from yesterday be available for the panel if 

### 8 there are any questions at that particular point. But let's 

### 9 first go with a summary. Vicki? 

### 10 **DR. VICKI DELLARCO:**There were a number of 

### 11 presentations that you heard yesterday so I'm going to do a 

### 12 recap of those and I want to begin with the legislative drivers 

### 13 that were mentioned early in the morning yesterday and that is 

### 14 the requirements under the Safe Drinking Water Act and FFDCA as 

### 15 amended by the Food Quality Protection Act to evaluate chemicals 

### 16 for potential endocrine activity. 

### 17 And so given the number of chemicals that are 

### 18 regulated under those statutes has certainly presented a risk 

### 19 management challenge. The inventory was defined around 10,000 

### 20 chemicals or so and so it's that legislative framework and those 

### 21 risk management challenges that help drive the scientific 

### 22 attributes of the scientific framework that we presented. 

### 23 And so what I want to do is go through some of 

### 24 the important attributes of that scientific framework and relate 

### 25 it back to the programmatic context and your advice on the 

### 26 charge questions is going to help us move forward and fine tune

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1 this approach. So the first point that was made yesterday is 

### 2 what is the question? Begin with the end in mind. And 

### 3 beginning with the end mind will ensure clarity over the 

### 4 questions that need to be addressed. So the questions matter 

### 5 and in this case, what chemicals should we move earlier in the 

### 6 program as we look at future lists. 

### 7 It's not only what the question is that 

### 8 matters but how certain the answer needs to be and if you look 

### 9 at the charge questions, there is a number of them that are 

### 10 getting at the issue of uncertainty and increasing the 

### 11 confidence around the approach and when we think of 21st century 

### 12 methods people discuss how the first operational use of those 

### 13 are in a prioritization context. 

### 14 But it's important to point out not all 

### 15 priority setting activities in this Agency are the same nor do 

### 16 they tolerate the same degree of uncertainty. So if I was in a 

### 17 program and I had thousands of compounds and I had to make a 

### 18 decision which ones I was going do risk assessments on first, my 

### 19 focus might be on the more potent ones. But in this case the 

### 20 focus is a little bit different. We want confidence in the 

### 21 negatives that we're seeing with our predictive system because 

### 22 we know from experience that the inventory that we're dealing 

### 23 with, many of those compounds will not be active and those that 

### 24 may be active they will be of low potency not necessarily the 

### 25 high binding affinity like the steroid compounds. 

### 26 So an important aspect and you've seen this in

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1 the charge questions -- several charge questions is having that 

### 2 reasonable certainty -- you can't have absolute certainty but 

### 3 high confidence in identifying low potency compounds, minimizing 

### 4 the negatives with the affects based approach that we're using. 

### 5 Another area that we want high confidence in is around the 

### 6 P-chem filters too. It's a large inventory and pragmatically it 

### 7 would important to see if we can reduce that to a more 

### 8 manageable size. Not every chemical needs to be focused on or 

### 9 evaluated and so we have questions about the P-chem filters in 

### 10 terms of how we might improve them, how do you double check 

### 11 that, how do you use them with care. There are some filters 

### 12 that may be generic in the sense that it doesn't matter that the 

### 13 affect that you're looking at, it is just their inherence 

### 14 chemical properties they're unlikely to pose the systemic risk 

### 15 because they're just simply too corrosive and they're just too 

### 16 reactive. 

### 17 Some of the criteria actually relate to the 

### 18 molecular initiating event that the effect space prioritization 

### 19 systems focused on for ER receptor. And so again your input 

### 20 into how we can increase that confidence and how we can employ 

### 21 those factors in a scientifically sound and transparent way, 

### 22 which takes me to the next point about ensuring transparency. 

### 23 We emphasize this in every review that we do with you no matter 

### 24 what the topic is because the basis of the science methods that 

### 25 we're going to use in our work and that we'll make decisions on, 

### 26 they need to be understood and they need to be transparent in a

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1 manner that maintains that public confidence. 

### 2 And so our methods flow from peer review, like 

### 3 what's happening right now with this expert peer review as well 

### 4 as public participation. So the public comments that you'll 

### 5 hear after our presentation are important and again 

### 6 transparency. So a number of the questions get at that 

### 7 transparency is the rationale around our thinking, around the 

### 8 P-chem filters, clear have we been clear about how we've built 

### 9 the expert system, how we've selected the training sets. Have 

### 10 we been clear about how the in vitro assay systems work? How 

### 11 we're making decisions about what is inactive and what is not 

### 12 inactive. 

### 13 The next point is and Dr. Bradbury mentioned 

### 14 this yesterday, we want to leverage what we've already had and 

### 15 build from that and this relates back to the meeting that we had 

### 16 with you May 2011 when we were talking about our 21st century 

### 17 vision. Because there are existing frameworks and concepts that 

### 18 are internationally accepted that will help promote this 

### 19 transparency and then ensure the utility of the approach to 

### 20 answer the regulatory questions. 

### 21 And this prioritization scheme is actually a 

### 22 real example of those concepts that we discussed a year ago and 

### 23 the first one that I want to come back to that was discussed by 

### 24 a Dr. Schmieder is the OECD (Q)SAR validation principles. There 

### 25 is a paper that we wrote that is a supplemental material in how 

### 26 we look at these validation materials in the matter what the

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1 empirical information is, whether it is low through -- low 

### 2 through put in vitro or high through put in vitro, how broadly 

### 3 applicable they are when you're looking at the utility, the 

### 4 scientific soundness of our approach. 

### 5 And the -- I'm not going to go through all the 

### 6 principles, you heard them yesterday but an important one is 

### 7 this domain of applicability. I mean this gets -- again this is 

### 8 science for a purpose in answering the questions. So the first 

### 9 thing that you want to ensure is your predictive method is 

### 10 representing that chemical space. And so it is important to 

### 11 understand -- that is why it was important for us to define that 

### 12 inventory to be able to move this approach forward to look at 

### 13 the types of chemicals and the range of their properties found 

### 14 in the regulatory inventories. 

### 15 We want in our training sets that we're 

### 16 building the models with to represent that chemical similarity 

### 17 and then what is being measured with the in vitro assays and so 

### 18 the selection of the training sets and the confidence around 

### 19 them, again to answer that question, to have confidence and 

### 20 active means inactive is every important. Another aspect of the 

### 21 principles that I want to highlight is the issue of the defined 

### 22 end point and the mechanist interpretation. 

### 23 Again, the goal that whether a chemical having 

### 24 confidence, a chemical just doesn't have the ability to initiate 

### 25 that molecular event. In this case again we're focusing on the 

### 26 ER pathway and the training sets that were used to build the

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1 expert system were actually developed for that purpose and they 

### 2 were optimized for that purpose and that's why the chemicals 

### 3 were run up to limits of solubility and side of toxicity. But 

### 4 now we're looking at the high through put, which was data -- 

### 5 which was generated for a general research approach, which Dr. 

### 6 Judson discussed and how we might be able to employ that to 

### 7 expand the coverage of the expert system for a chemical 

### 8 inventory, where our rules won't cover those structures. And so 

### 9 there were analyses that we've done to sort of get at the 

### 10 performance of those assays and the confidence that we would 

### 11 have that it would be able to discern that relationship of the 

### 12 chemical structures interacting with the molecular target. 

### 13 We looked at the assays with respect to know 

### 14 reference compounds that had a range of potencies for estrogen 

### 15 agonism, but we also took the rainbow trout training set data 

### 16 where we had a couple of hundred compounds and we wanted to find 

### 17 out how would that match with the comparable ER binding and 

### 18 transactivation assays for the high through put. It's not that 

### 19 we expected everything to match perfectly, we actually expected 

### 20 differences and so an important point is understanding empirical 

### 21 data. So looking at that comparative analysis to understand 

### 22 those differences so we understand how the assays, what the 

### 23 assays are telling us, what the strengths and limitations of the 

### 24 high through put assays are so we can move forward and decide 

### 25 how we can employ that data within this approach. 

### 26 We made it clear yesterday that we're not

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1 finished, there is other assays we haven't done that analysis 

### 2 with and so your advice is timely right now and how we might 

### 3 proceed in doing that analysis and how we might do that 

### 4 differently. 

### 5 The other thing that was mentioned yesterday 

### 6 is integrative approaches to testing and assessment and this is 

### 7 actually a fundamental theme that we took to you in May. It's 

### 8 simply an approach where you promote the use of all existing 

### 9 knowledge in a very integrative structure way and its moving 

### 10 away from sort of a black box empirical approach to more of a 

### 11 hypothesis based approach. The prioritization scheme really is 

### 12 a reflection of this because IATA approaches they have different 

### 13 components and we're employing all of that looking at inherent 

### 14 chemical properties. Trying to incorporate in vitro assay 

### 15 systems to help look at these hypothesis and answer the 

### 16 regulatory question using category-based read-across approaches. 

### 17 18 What you told us in May is that the adverse 

### 19 outcome concept is actually a way to strengthen integrative 

### 20 testing strategies and as was mentioned yesterday it's the 

### 21 conceptual framework on how you look at measuring information or 

### 22 integrating information at different biological scales and 

### 23 understanding those linkages and how they relate to the next 

### 24 event that could be happening. 

### 25 Now I want to make it clear that we're focused 

### 26 more on the mechanistic understanding not at the apical

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1 endpoint. We're not trying to predict an in vivo endpoint. 

### 2 We're just trying to get that mechanistic understanding of how 

### 3 chemical structures can initiate a pathway. Again, it's a 

### 4 defined end point, the ER pathway. And so using this concept 

### 5 allows you to employ lower level testing, information from in 

### 6 vitro assays and places assays and the context of an AOP and 

### 7 provides that confidence in terms of those linkages. So the 

### 8 linkage that we're looking at is the initiation and then after 

### 9 the initiation what may happen at the cellular level or tissue 

### 10 level, the activation of the pathway. 

### 11 And again, having confidence and the training 

### 12 sets that it can discern that relationship and that mechanistic 

### 13 understanding because what that will allow us to do is reliably 

### 14 enable chemical extrapolation approaches. As pointed out 

### 15 yesterday with respect to the inventory and the Expert System 

### 16 there is 1700 chemicals that aren't covered. They're outside 

### 17 that domain. And the point was made, we don't have to test all 

### 18 of those. If we're very strategic and understanding those 

### 19 relationships, selecting the right training set data we may only 

### 20 have to test about 15 percent of that 1700 to finish out our 

### 21 logic based rules. But again we have to have confidence and the 

### 22 assay systems to do that. 

### 23 So an important aspect of the ER Expert 

### 24 System, it is a logic based decision tree that's a chemical 

### 25 category approach too. And again it allows us to really focus 

### 26 government resources and channel them in a smart way.

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1 This is my last slide. I'm trying to give you 

### 2 a break because you saw over 300 slides yesterday. So this is 

### 3 like looking towards the future. This effort focused on one 

### 4 molecular initiating event, the ER pathway, the binding affinity 

### 5 to that. And it was a reasonable starting place for the first 

### 6 AOP. We know the environmental chemicals can interact with that 

### 7 receptor. So it wasn't just that's in accordance with the 

### 8 language and the laws FQPA and so and it's just the means that 

### 9 we're using to take that huge inventory and just how to 

### 10 logically filter it and make priority setting decisions. Again 

### 11 this is a priority setting. 

### 12 So your advice on the questions is important 

### 13 on how to improve this approach. We've expanded the Expert 

### 14 System to version two. We want your input on how we've done 

### 15 that. We've been using the same concepts that were used with 

### 16 version one. Are there any deficiencies in that, do we need to 

### 17 tweak it in some way to improve the confidence around it? We 

### 18 really would like to finish the ER Expert System for the 

### 19 coverage of our inventory very quickly. We want to do that in 

### 20 the short term. A lot of work, a lot of years have been spent 

### 21 on that. And we realize there are other intricate signaling 

### 22 pathways and we want your advice how we take some of the 

### 23 concepts and principles that we've used in building this system 

### 24 and those that may apply in building other predictive systems. 

### 25 A logical next one to look at is the AR 

### 26 receptor. In looking thyroid, although we realize there'll be

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1 some challenges around the thyroid system given that people are 

### 2 still trying to develop the in vitro assays for that not very 

### 3 many if any environmental chemicals act at the ER receptor, I 

### 4 mean the thyroid receptor. And again, not trying to capture 

### 5 every AOP but taking logical ones to again to sort of continue 

### 6 to sort and funnel that inventory and order things up in a 

### 7 logical way. Another issue in terms of looking at the future 

### 8 that we threw at your is the metabolism issue and that came up 

### 9 yesterday. If understanding the interaction the chemical 

### 10 structure bioactivity relationship is important. 

### 11 It's important to understand whether it's the 

### 12 parent compound or the metabolite. I mean that showed up in the 

### 13 high through put data when we were looking at methyl 

### 14 testosterone. It gave a positive but it's likely what happened 

### 15 there was some inherent ability of the cell system being used to 

### 16 metabolize it to methylestradiol. What's important to 

### 17 understand that -- important to understand the inherent ability 

### 18 of metabolism with the ER expert system they were using the 

### 19 liver slices but we're looking again for your thoughts about how 

### 20 we can advance and improve and get at this issue. 

### 21 And so I'll end with saying that it takes time 

### 22 to build approaches and to it takes some time to build the 

### 23 approach around this first molecular initiating event for this 

### 24 AOP but the lessons learned will help us accelerate the work for 

### 25 the next AOP and the next AOP. So this is why it's important to 

### 26 learn by doing. And so we want to hear from you about how we

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1 can move onto these other pathways in a much more efficient way. 

### 2 And I think -- let me ask Mary, my co-chair, on this effort if 

### 3 she wants to add anything or is there anything you want to 

### 4 stress that -- 

### 5 **MS. MARY MANIBUSAN:**That was perfect. 

### 6 **DR. VICKI DELLARCO:**Okay. So we're not done 

### 7 yet. 

### 8 **DR. DANIEL SCHLENK:**Okay. Thanks Dr. 

### 9 Dellarco. Okay at this point in time what I'd like to do is 

### 10 allow the panel to express any other questions that they may 

### 11 have. This concludes the presentations by the agency. So we 

### 12 will have some public comments and then we'll go into the 

### 13 questions. But at this point this is your opportunity to ask 

### 14 any questions that you've been stewing on over the night. I 

### 15 know many of you have met last night to get your ducks in a row 

### 16 so to speak. So if you have any questions that came out of 

### 17 those deliberations now is the time to bring those forward. So 

### 18 anybody have any questions? Dr. Burris. 

### 19 Dr. Thomas Burris: There was a figure, or a 

### 20 series of figures shown in a few of the presentations indicating 

### 21 an A-mode binder and a B-mode binder and I'm curious how that 

### 22 was derived. Is that based on modeling or some other scientific 

### 23 process? 

### 24 **DR. PATRICIA SCHMIEDER:**This is Pat Schmieder 

### 25 the - it was derived first from the theory of the -- the fit of 

### 26 estradiol into the pocket. And some conversations with -- on

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1 the slide it was actually personal communications with John 

### 2 Katzenellenbogen who has done a lot of research on the pocket. 

### 3 And then it was understanding that there're ranges of atomic 

### 4 charges with the fit of estradiol in the pocket in the main 

### 5 sites that estradiol binds. So there was the range. The A type 

### 6 has a range of atomic charges around the oxygen group. I think 

### 7 it's -- I can't remember -- it's a pretty tight range. 

### 8 But we then calculated for the chemicals that 

### 9 we put in that series that they fit it within that range so the 

### 10 alkylphenols, the alkoxyphenols, salicylates and parabens all 

### 11 met within that range. The same thing on the other side, the B 

### 12 side where the anilines, the alkylanilines were binding, our B 

### 13 type lead group if you will. We calculated the range of atomic 

### 14 charges there, which is different on that side. It's 

### 15 electronic, it's hydrogen bond donor versus accepter generally 

### 16 in those two. 

### 17 So we use that calculation to tell us whether 

### 18 those groups of chemicals are within that charge range and would 

### 19 fit within the A type or the B type. And then there is several 

### 20 chemical classes that don't fit either one of those and that's 

### 21 where we have -- we talked some of the multi-cyclic 

### 22 hydrocarbons, which don't fit either one of those and so we have 

### 23 -- we called them at the time special rules until we understand 

### 24 exactly what the interaction is that is driving it. 

### 25 So it was a combination of that calculation to 

### 26 tell us that it fit the criteria of either an A type or a B type

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1 but then the empirical data within those groups that like the 

### 2 alkylphenols the whole group and looking at the relationship of 

### 3 RBA to LogKow that we found within that that allows us to the 

### 4 extrapolation within the category. So I hope that answers the 

### 5 question. 

### 6 **DR. STEVEN BRADBURY:**Steve Bradbury. I'll 

### 7 just expand a little bit from what Pat said. The work with the 

### 8 research group at University of Illinois in Katzenellenbogen, 

### 9 there are papers that are related to the logic of the A site and 

### 10 the B site and some of the questions yesterday about three 

### 11 dimensional modeling and how to look at that. In fact a lot of 

### 12 that research is behind the logic behind the A site and the B 

### 13 site and the distances and the charges associated with that. It 

### 14 was through that work with three dimensional modeling that gave 

### 15 us the hypotheses that we're pursuing and then sort of trading 

### 16 off the CPU time in terms of chugging thousands of chemicals to 

### 17 three dimensional docking versus being able to use categories, 

### 18 but founded on the understanding of the three dimensional 

### 19 aspects of the receptor binding the steric and the electronic 

### 20 characteristics of that three dimensional space. So if you go 

### 21 back to the 2009 SAP there is some literature cited that lays 

### 22 down the foundation for what Pat was just summarizing at this 

### 23 SAP. 

### 24 **DR. DANIEL SCHLENK:**Okay, thank you. Any 

### 25 other questions? Speak now. 

### 26 DR. EdWARD Perkins: This is Ed Perkins. Can

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1 I get a clarification on the reasons why certain compounds 

### 2 weren't tested or obtainable for empirical testing? Because we 

### 3 only had what 30 percent or so? 

### 4 Dr. Richard Judson: Hi this is Richard 

### 5 Judson. So going back to summarizing our protocol. So we had a 

### 6 large 10,000 compounds that were nominated that seemed to 

### 7 Physico-Chemical properties that would allow them to be tested 

### 8 in the high through put so they looked like they would be 

### 9 soluble in DMSO, they wouldn't be volatile. 

### 10 So we went and ordered them in our ordering 

### 11 protocol, to go buy things is we said we have to get compounds 

### 12 from Sigma or Vendor B. And I don't know what Vendor B is who 

### 13 have a reputation for being able to give you compounds of pretty 

### 14 high purity and we told those vendors you need to have -- give 

### 15 us things purity of 90 percent or better. And that's where, 

### 16 it's 30 or 40 percent of compounds that we ask for from reliable 

### 17 vendors we can get. Okay, NTP did the same thing with a 

### 18 different set of vendors and again we're getting the 30 to 40 

### 19 percent. So it really is it's a lot of these compounds are 

### 20 industrial compounds which the standard chemical vendors don't 

### 21 -- they can't give you in those high purities. 

### 22 **DR. PATRICIA SCHMIEDER:**This is Pat 

### 23 Schmieder. With regard to the lower through put assays we did 

### 24 not find this -- we were able to obtain a much higher percentage 

### 25 of the chemicals. There are some chemicals we could not obtain 

### 26 and if so then we looked for surrogates within a chemical class

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1 that would address that. But I can only imagine that the 

### 2 structure space that Richard is talking about is much -- is 

### 3 somewhat different because we were able to obtain a higher 

### 4 percentage. 

### 5 **DR. DANIEL SCHLENK:**Okay any other questions? 

### 6 Oh sorry, Dr. Sandy. 

### 7 **DR. MARTHA SANDY:**This is Martha Sandy. So 

### 8 the high through put assays, the eight that were presented to 

### 9 us, were they done in the same contract laboratories? 

### 10 **DR. RICHARD JUDSON:**No they were done -- so 

### 11 Attagene is one contract lab. They did two of them Odessy Thera 

### 12 another contract lab did one of them, NovaScreen Caliper did 

### 13 three of them and the last two were them were run at the NIH 

### 14 Chemical Genomic Center. And we have a couple of other vendors 

### 15 within the bigger set of assays. 

### 16 **DR. DANIEL SCHLENK:**That was Dr. Judson. Dr. 

### 17 Meek. 

### 18 **DR. BETTE MEEK:**A couple of questions. So 

### 19 Pat the proportion of chemicals that you were able to access 

### 20 then was about you said higher than 30 percent was about but you 

### 21 didn't specify? 

### 22 **DR. PATRICIA SCHMIEDER:**We haven't run the 

### 23 numbers but it's -- oh this is Pat Schmieder - it's definitely 

### 24 over 50 percent -- 

### 25 **DR. BETTE MEEK:**Okay. 

### 26 **DR. PATRICIA SCHMIEDER:**I think it's -- my

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1 guess would be it's over 80 percent. I mean -- and it might 

### 2 even be higher. 

### 3 **DR. BETTE MEEK:**Okay, it's Bette Meek. One 

### 4 additional question, maybe Mary to you, I guess I was trying to 

### 5 determine the chemicals that are not produced and used in the 

### 6 U.S. you said you will not issue testing rules but I wasn't 

### 7 entirely clear where that fit in the -- in the process. 

### 8 **MS. MARY MANIBUSAN:**So under our statutory 

### 9 authority we can only issue test orders to manufacturers or 

### 10 pesticide registrants that are currently manufacturing that 

### 11 chemical in the U.S. So that would come into play when I talked 

### 12 through flow diagram at the end right before it enters the Tier 

### 13 1 for screening officially, we make that determination whether 

### 14 it continues to be manufactured in the United States or not. 

### 15 And if not, then it's removed from the list before we put it 

### 16 forward for Tier 1 screening. Just to add we rely on production 

### 17 information through our Office of Pesticide Pollution Prevention 

### 18 and Toxics. 

### 19 **DR. DANIEL SCHLENK:**Just to add that was Mary 

### 20 Manibusan. Dr. Denslow. 

### 21 DR. Nancy Denslow: Nancy Denslow. Do you 

### 22 have access or contract labs of organic chemists that could take 

### 23 those impure compounds and purify them to higher purity and has 

### 24 that ever been tried? 

### 25 **DR. RICHARD JUDSON:**The EPA/ORD does have 

### 26 lots of chemists who do have the ability to do those sorts of

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1 things. We've never approached them to ask whether they can do 

### 2 that. And it just thinking back on the history of ordering 

### 3 those chemicals, another criteria that I'd forgotten to mention 

### 4 is cost so there is another set I don't -- ten, twenty percent 

### 5 of the compounds we could get at very high cost and is just -- 

### 6 we had only so many slots to run high through put screening 

### 7 assays so we took the less expensive compounds first. There are 

### 8 ways if you're willing to spend more money to get more of the 

### 9 compounds. 

### 10 **DR. DANIEL SCHLENK:**That was Dr. Judson. Dr. 

### 11 Sandy. 

### 12 **DR. MARTHA SANDY:**Martha Sandy. So to follow 

### 13 off with Bette Meeks question, Mary you said that you would look 

### 14 and see if the chemical was produced in the U.S. What about 

### 15 chemicals that are used in the U.S. but imported? 

### 16 **MS. MARY MANIBUSAN:**So sorry, just to 

### 17 clarify. We can issue test orders to manufacturers, pesticide 

### 18 registrants that are producing and using that chemical in the 

### 19 United States so it includes importers as well. 

### 20 **DR. DANIEL SCHLENK:**That was Mary Manibusan. 

### 21 Dr. Chambers. 

### 22 Dr. JANICE Chambers. Now I'm a little bit 

### 23 confused. There are 30 percent of the chemicals on one list and 

### 24 80 percent -- are you using different lists then and if you have 

### 25 more, Pat, can you not share with them with the high through 

### 26 put?

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1 **DR. RICHARD JUDSON:**So very very different 

### 2 lists, so I'll put Pat -- this is Richard Judson -- put words in 

### 3 Dr. Schmieder's mouth that they are specifically looking at the 

### 4 chemicals on the EDSP regulatory inventories. We are looking at 

### 5 a much broader set of chemicals that many other stakeholders 

### 6 requested us to look at. So it's very different. And there is 

### 7 this idea of heroic efforts. So if we were told that you -- if 

### 8 we told our person in our group who manages all the chemicals, 

### 9 you have to get all those chemicals, you know scour the earth, 

### 10 she probably could have gotten 60, 70, 80 percent but it's -- 

### 11 you know it's again a process, a research process that led to 

### 12 the 30 percent number. 

### 13 Dr. Vicki Dellarco: This is Vicki. In fact 

### 14 the 290 some chemicals that were used in the comparative 

### 15 analysis with the rainbow trout data and the high through put 

### 16 data the samples for that to run it through high through put was 

### 17 provide it for the most part from Duluth. 

### 18 **DR. PATRICIA SCHMIEDER:**Yes this is Pat, we 

### 19 did provide them chemicals that they didn't have. 

### 20 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 21 Dr. EDWARD Perkins: Was there a comparison 

### 22 made between the Expert System rule-based classification and the 

### 23 high through put results? 

### 24 **DR. RICHARD JUDSON:**This is Richard Judson. 

### 25 We have started that. So mechanically we have started that, but 

### 26 again the appropriate comparison will be with the final

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1 consensus calls from all of the high through put data which is 

### 2 not there yet. So that is something that needs to happen but it 

### 3 just hasn't happened yet. 

### 4 **DR. DANIEL SCHLENK:**Okay, no further 

### 5 questions? I guess this concludes the presentation by the 

### 6 Agency. Thank you very much. Okay, we're now about to move 

### 7 into our public commenting period. I have a list of public 

### 8 commenter's that are going to be coming forward. Our first 

### 9 presenter is Scott Slaughter he is from the Center for 

### 10 Regulatory Effectiveness. 

### 11 **MR. SCOTT SLAUGHTER:**Good morning. My name 

### 12 is Scott Slaughter and I'm presenting comments today on behalf 

### 13 of the Center for Regulatory Effectiveness. I want to point out 

### 14 at the beginning that we agree with Dr. Bradbury who in his 

### 15 remarks yesterday commented on the importance of this SAP and 

### 16 all SAPs to EPA's actions and processes. And we thank you for 

### 17 the opportunity to comment here today. 

### 18 We also agree with EPA that they're not done 

### 19 yet. The proposed Comp Tox methods that EPA is talking about 

### 20 may be a promising alternative to the current approach to doing 

### 21 EDSP tests. However, these methods must be validated before 

### 22 they can be used for any regulatory purpose. 

### 23 For example, the Information Quality Act and 

### 24 the Paperwork Reduction Act both require that EPA demonstrate 

### 25 that its decision tree generates accurate, reliable and 

### 26 reproducible predictions. As detailed in our written comment,

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1 which I hope you all have by now, we do not believe that the 

### 2 record before this SAP contains this required demonstration for 

### 3 either the decision tree or for the proposed use of HTP data. 

### 4 So we asked this SAP to advise EPA on how to validate its Comp 

### 5 Tox methods so that they can be justifiably be assumed to 

### 6 generate accurate, reliable and reproducible predictions about 

### 7 endocrine effects. 

### 8 In this regard, we'd like to draw your 

### 9 attention to the recent paper by Patlewicz, and I hope I 

### 10 pronounced his or her name correctly, which provides some useful 

### 11 discussion on this validation issue. There is a link to it in 

### 12 footnote one of our written comments I believe. And there is 

### 13 another important point we want to make, in addition to the 

### 14 validation for the Comp Tox methods themselves, they ultimately 

### 15 will be based on the results of experimental tests using more 

### 16 traditional assays. All the underlying assays and experimental 

### 17 data should be derived from properly validated tests, which have 

### 18 shown to be accurate, reliable and reproducible. 

### 19 And we also do not believe that the EPA should 

### 20 use any proprietary algorithms in the EDSP. The Agency has 

### 21 laudably expressed its concern and interest in ensuring 

### 22 transparency. The use of any proprietary algorithms would work 

### 23 against that goal. 

### 24 Finally we request that the SAPs either -- not 

### 25 this one but some other SAP in the future, review every EPA 

### 26 conclusion which states that a test is validated for use in the

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1 EDSP. We believe that this review is necessary because EPA 

### 2 subjected its current EDSP Tier 1 test to external peer review. 

### 3 As discussed in Appendix A to our written comments that you 

### 4 have, we believe that those pier reviews often produce negative 

### 5 reports on the current EDSP Tier 1 test. Yet EPA, on the basis 

### 6 of what we believe to be very negative peer review reports, 

### 7 concluded that those EDSP Tier 1 tests are properly validated 

### 8 and proceeded to use them. And once again this issue is 

### 9 discussed in extreme detail in Appendix A to our written 

### 10 comments. 

### 11 We believe that this Tier 1 test validation 

### 12 issue should be included in the Tier 1 test SAP review that EPA 

### 13 has announced will be scheduled for later this year. We also 

### 14 believe that an SAP should review EPA's validation conclusions 

### 15 for any Tier 2 test and of course, we also believe that another 

### 16 SAP should review any EPA decision that Comp Tox methods have 

### 17 been properly validated for use. Thank you and I'll try to 

### 18 answer any questions you might have. 

### 19 **DR. DANIEL SCHLENK:**Thank you Mr. Slaughter. 

### 20 Any questions from the panel? Okay. 

### 21 **MR. SCOTT SLAUGHTER:**Thank you. 

### 22 **DR. DANIEL SCHLENK:**Thank you. Our next 

### 23 presenter will be Kristie Sullivan from the Physicians Committee 

### 24 for Responsible Medicine. 

### 25 **MS. KRISTIE SULLIVAN:**Hi there, Kristie 

### 26 Sullivan. I'm from the Physicians Committee for Responsible

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1 Medicine. We're a group of physicians and scientists who 

### 2 promote ethics in medicine and science, including for my 

### 3 purposes, regulatory testing. Sorry about that. So I just 

### 4 wanted to remind everybody that the EDSP program is really a 

### 5 huge program in terms of the amount of effort that is being put 

### 6 into it, the numbers of really thousands or hundreds of 

### 7 thousands of animals that could be used and the resources that 

### 8 are being spent and so we think this prioritization effort by 

### 9 EPA is -- should be given kudos. We really appreciate their 

### 10 efforts to speed up the process. 

### 11 Just some initial thoughts. We think 

### 12 prioritization based on exposure effect and physical chemical 

### 13 properties is a reasonable scheme. The tools the EPA is 

### 14 proposing are robust and clear and useful for that purpose and 

### 15 we also think that an effects-based chemical category is a key 

### 16 feature of the plan. So we support and encourage the SAP to 

### 17 support this work. 

### 18 Excuse me, please bear with me I'm getting 

### 19 over a cough. So the rest of my comments are fairly general but 

### 20 I wanted to start with on what EPA cites as one of its 

### 21 motivations. Obviously it stated this program before 2012 but 

### 22 the U.S. House of Representatives put appropriations language 

### 23 into a bill that directed EPA to accelerate ToxCast and other 

### 24 screening methods and to adjust individual test or use those to 

### 25 adjust individual test orders. I wanted to make it clear that 

### 26 the -- if you read the entire language, the intention of the

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1 language is to use the new tools to in -- where possible, to 

### 2 replace the current Tier 1 assays. 

### 3 However, according to the documents that we've 

### 4 seen, it would appear that EPA has created a screen for a 

### 5 screen. Of course it's extremely useful to get from 10,000 

### 6 chemicals to a couple of hundred chemicals. And it's useful for 

### 7 that purpose but we're still seeing this and it's being 

### 8 presented, it seems to us as a new layer in front of the current 

### 9 Tier 1 battery. And then you still have a couple of hundred, or 

### 10 a couple of thousand discreet chemicals still heading into Tier 

### 11 1, which will still take a while to get through with some of the 

### 12 same mechanistic end points that are already addressed in the 

### 13 screening strategy. So I used this -- you have this in front of 

### 14 you and I hope it looks better in front of you than it does up 

### 15 on the screen but I tried to locate the rest of our suggestions 

### 16 on the screening map here. 

### 17 First of all we think that it would be useful 

### 18 for pesticide active ingredients to be folded into the screening 

### 19 process now if they haven't already gone through registration 

### 20 review before being screened in the current Tier 1 battery. We 

### 21 think there is no reason that inerts couldn't also be 

### 22 deprioritized for screening based on exposure considerations. 

### 23 And this has come from the panel from Dr. Keiser is it, 

### 24 yesterday actually it seems kind of like a good idea that you 

### 25 could consider weighting some of the assays -- assay results to 

### 26 rank the chemicals heading into Tier 1 screening. We also

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1 believe that the Expert System and HTPS should have the power to 

### 2 both exclude inactive chemicals from further work and allow very 

### 3 active chemicals to skip further screening. Obviously these 

### 4 comments would pertain to the estrogen pathway at this point. 

### 5 We think it's important to integrate the 

### 6 results from the screening activity into the existing Tier 1 by 

### 7 replacing individual in vitro or in vivo assays on a case by 

### 8 case basis. And that replacing most or all of the Tier 1 assays 

### 9 with higher through put methods should remain a short term goal. 

### 10 In fact the cytosolic binding assays that are currently in the 

### 11 battery consume and inordinate number of animals just to do -- 

### 12 even though they are in vitro assays. 

### 13 Finally, after the high through put or expert 

### 14 system screening but before Tier 1 screening, other available 

### 15 information should be considered. Sometimes this is called 

### 16 other scientifically relevant information, other studies, 

### 17 literature, et cetera. And you could use the AOP framework to 

### 18 organize this available other information. Finally, as many 

### 19 people in this room already know of course the OCD published in 

### 20 2008 a detailed review paper on metabolizing systems for in 

### 21 vitro endocrine disrupters. This is being -- in the process of 

### 22 being up dated for peer review publication and it includes 

### 23 actions that member countries of the OECD like the U.S. could 

### 24 take to increase the metabolic capacity of in vitro assays. 

### 25 From the presentations yesterday, it looks 

### 26 like EPA is taking some of those recommendations but we just

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1 wanted to support this idea and bring it to the Panel's 

### 2 attention. We finally also support the translation of this 

### 3 approach to other pathways. We think it makes sense to do that. 

### 4 And we recommend overall using the information from this 

### 5 screening strategy to save further resources by cutting out 

### 6 current Tier 1 assays for clear positives and negatives. So I 

### 7 can be reached at the email address on this screen and you 

### 8 should have a copy of our written comments for more detailed 

### 9 information. 

### 10 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 11 for Ms. Sullivan? Okay. Thank you very much. Our next 

### 12 presenter is Catherine Willett from the Human Society of the 

### 13 United States. 

### 14 **DR. CATHERINE WILLETT:**Hello that's actually 

### 15 Humane Society, but so we -- not to be confused with human. So 

### 16 I just want to reiterate what Kristie said is we submitted some 

### 17 joint written comments and so my comments will overlap a little 

### 18 bit with Kristi's, but there will be some extra stuff in there 

### 19 that I didn't think about before we submitted our written 

### 20 comments and as with Kristie I'll start off in a positive vein 

### 21 and I hope a positive vein with some other stuff in the middle. 

### 22 Okay. Is this, where is the projector, wait what do I point at? 

### 23 Oh never mind. Okay. 

### 24 So first of all I'd like commend the EPA on 

### 25 these developments to develop tools that evaluate molecular 

### 26 mechanisms of chemical activity as everyone pretty much

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1 appreciates by now, mechanism based approaches are appropriate 

### 2 in the short term for things like prioritization, also to inform 

### 3 chemical categories and structure activity relationships for 

### 4 read across and also to increase the certainty of interpretation 

### 5 of both existing data and new incoming information. And also as 

### 6 also been alluded to in a lot of these discussions, this 

### 7 information can be used to inform testing strategies. 

### 8 In terms of this SAP, the information that 

### 9 we're provided includes a lot of steps forward in the endocrine 

### 10 prioritization and screening process including a rational 

### 11 information-based approach to prioritization so it incorporates 

### 12 the potential hazard along with exposure considerations and also 

### 13 legislative mandates for the prioritization which is a definite 

### 14 step forward. 

### 15 The idea of this iterative classification to 

### 16 cover all chemicals in the applicable inventory, I mean I know 

### 17 EPA has been doing this for some time. This sort of formalizes 

### 18 the process into an increasingly larger chemical space and I 

### 19 think this is a very good way to be able to provide educated 

### 20 guesses at least with respect to covering and identifying the 

### 21 proverbial needles in the environmental chemical haystack. 

### 22 The other thing that is good about this 

### 23 process is that it begins with a prototype, which is the 

### 24 estrogen receptor. You work out the details using this 

### 25 prototype and sort of focus your energies on a prototype. And 

### 26 also this is a nice discussion about a validation that is fit

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1 for purpose. In other words, when is it appropriate to do a 

### 2 formal validation or when is it more appropriate to do something 

### 3 like what is included in the OECD (Q)SAR validation principles 

### 4 and also there is some discussion about the quality control 

### 5 metrics for the HTP assays, which I'll discuss a little bit more 

### 6 in a minute. 

### 7 However, there are some major concerns about 

### 8 this process and from our perspective -- our perspective is we 

### 9 want to streamline this and try to get away from the animal 

### 10 tests and stuff. So the whole discussion is focusing on how do 

### 11 you put things into the lineup to get into the Tier 1? So 

### 12 they're being prioritized for, as Kristie mentioned, additional 

### 13 screening in a Tier 1 that includes the same exact assays. And 

### 14 it's also prioritization to determine the order of testing and 

### 15 to bin chemicals, I mean maybe practically it'll bin chemicals 

### 16 but the discussion is all about how to order them and not to 

### 17 exclude and I think the focus might be -- could be a little 

### 18 stronger on an exclusion part. 

### 19 Also as Kristie mentioned to find that on page 

### 20 five the intention of the bill language is sort of misconstrued 

### 21 and the intention was clearly to adjust Tier 1 -- to adjust the 

### 22 test orders. So it's to use this information for Tier 1 

### 23 information, not to prioritize chemicals for Tier 1 testing at 

### 24 any rate. In this discussion, there hasn't been any mention 

### 25 about how this approach is moving toward the eventual 

### 26 replacement of the Tier 1 battery. As Kristie mention from our

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1 perspective, it is essentially adding another layer to a program 

### 2 without describing how the whole thing is going to be eventually 

### 3 streamlined. 

### 4 Now specific concerns is that the HTP as we've 

### 5 heard many times, I mean the HTPC data is still preliminary. 

### 6 The data that we are trying to use for comparison has not been 

### 7 -- or it wasn't produced for the current purpose so it's kind of 

### 8 difficult to evaluate the comparisons. The assays are still not 

### 9 that well characterized. I'll get to that in a little bit. And 

### 10 the data interpretation filters are still in process so we don't 

### 11 really know what they are and they're not really -- they're not 

### 12 completely transparent, at least not in my reading of this. I 

### 13 mean, they've changed since the last publically available 

### 14 discussions and I'm not sure. So but that's still in process I 

### 15 understand. So it's hard to evaluate that's what I'm trying to 

### 16 say. 

### 17 The other major concerns are that Expert 

### 18 Systems don't currently exist for other endocrine pathways so 

### 19 androgen and thyroid hormone systems primarily -- and to develop 

### 20 a similar expert system approach to each of these would take a 

### 21 very long time. So as Vicki mentioned thankfully that the 

### 22 lessons learned from this evaluation could be used to move 

### 23 forward more quickly and I'll talk a little bit about that in a 

### 24 second hopefully. 

### 25 Okay so some observations from reading this, I 

### 26 thought actually personally that the reference chemicals in

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1 table Susan were actually pretty good and that's just because we 

### 2 have a lot of experience in discussing the old ECCVAM lost and 

### 3 which chemicals have the most supporting information and what a 

### 4 good range of chemicals in terms of activity and chemical space 

### 5 are and I actually thought that was a really good binning of 

### 6 that listing. So and there was fairly good concordance except 

### 7 for that one assay which I was happy to hear yesterday, it was 

### 8 probably going to be jettisoned. The other thing is that a 

### 9 larger set of assays is not necessarily superior to a smaller 

### 10 set of well characterized assays. 

### 11 Having more information is not necessarily 

### 12 better if you don't really -- can't fully appreciate what the 

### 13 information means. And the reference chemicals are a very good 

### 14 way to standardize the methods and the data interpretation 

### 15 procedures. Okay so some more observations are you need to 

### 16 compare similar concentration ranges of the same chemical 

### 17 preparations in order to really evaluate how well the 

### 18 performance is between the tests. 

### 19 And the importance of the replicates 

### 20 especially in the high through put screening and the importance 

### 21 of establishing appropriate data and interpretation data and 

### 22 procedures. So in terms of the appropriateness of the HTPC 

### 23 Assays for expanding the chemical domain, the purpose here as we 

### 24 heard a lot of times is to identify relatively weak binders out 

### 25 of a large population of non binders. The HTP assays may be 

### 26 less sensitive than the ES assays but we don't really know

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1 because the correct comparison hasn't really been made. 

### 2 The current level of discordance is a little 

### 3 bit concerning. I think it's just concerning because we don't 

### 4 fully understand the assays and so there is a lot of explaining 

### 5 that is not quite -- doesn't feel quite confident yet and as 

### 6 we've heard, it's a process. 

### 7 To balance the minimal faults negatives 

### 8 against an over abundance of false positives, it seems to me at 

### 9 some point along the line it would be really useful to know what 

### 10 a biologically relevant signal is and I'm not talking about use 

### 11 of this in regulation but at some point you know is an RBA of 

### 12 0.000001 whatever biologically relevant and are the HTP assays 

### 13 good enough for the purpose? That may not be a question that is 

### 14 really answerable at this point, but there might be a lot of 

### 15 data from the expert system that could give you a handle on that 

### 16 and you probably already know that. But the thing is, now your 

### 17 screen is based on your technology -- your available technology. 

### 18 So you're calling a positive is just based on the sensitivity 

### 19 of your most sensitive assay and is that biologically relevant. 

### 20 Don't know, but it's worth a discussion. 

### 21 Okay, in terms of -- this is way forward 

### 22 really quick for the ER Pathway. It would -- I mean you guys 

### 23 know about this and it would be interesting to share experiences 

### 24 with the ECVAM approach where they're taking the MELN assay and 

### 25 they're using -- they have some standard -- it's in the 

### 26 validation process and they're using high through put screening

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1 to expand the validation of that. And it's similar to what you 

### 2 guys did with the BG1-Luc, and it also incorporates some of the 

### 3 performance-based test guideline principles from the OECD. So 

### 4 the OECD has this whole process where they're developing 

### 5 performance-based test guidelines. You can take some of those 

### 6 principles and apply them in this case. It should be very 

### 7 appropriate. 

### 8 Test appropriate chemical range and 

### 9 concentration in the HTP assays for direct comparison with the 

### 10 expert system which you said you're in the process of doing, I 

### 11 think it would be interesting to characterize, not interesting 

### 12 but important to characterize the metabolic capacity of each 

### 13 test system so you understand when you get discordant results 

### 14 you can understand what the metabolic probabilities are. 

### 15 Incorporate metabolic systems now -- as Kristie mentioned, there 

### 16 has been a paper that reviews the available metabolic systems 

### 17 that was published in 2008. There is an update in process that 

### 18 discusses how one might go about applying those specifically in 

### 19 the realm of endocrine disruption. 

### 20 Okay in terms of the ER pathway, it should 

### 21 soon be possible to compare these data that you are generating 

### 22 with the Tier 1 data for select phase one chemicals that would 

### 23 be appropriate for comparison. And it would be important to 

### 24 review the data interpretations after you've compared all this 

### 25 stuff. Is it appropriate, for example, to base the TA screening 

### 26 on binding first? I mean I don't want to talk about it because

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1 I don't have time. 

### 2 Okay and then review the performance of all 

### 3 the assays to see which ones could be removed from the Tier 1. 

### 4 I'm almost done, sorry. And then in terms of the way forwards 

### 5 for the androgen and thyroid pathways, the development of AOPs 

### 6 and the relevant assays is it's being discussed and is in 

### 7 process in several groups. I just wanted to point there is an 

### 8 OECD thyroid scoping effort, there is an OECD validation 

### 9 management group non animal and the advisory group on molecular 

### 10 screening. So these guys are developing AOPs. They're talking 

### 11 about assays and in terms of a streamline fit for purpose 

### 12 validation that should be applied to these assays that would 

### 13 streamline this whole process, there is the applicable OECD 

### 14 performance based test guideline principles. Then there is a 

### 15 paper from Richard which discusses all of this. Ways that you 

### 16 could streamline this and the important elements of validation 

### 17 and those things that could be jettisons that aren't appropriate 

### 18 for high through put and testing of the reference chemicals as I 

### 19 think is critical for this kind of assessment and the OECD 

### 20 validation studies for many of these they have lists of where 

### 21 you could start. 

### 22 Okay that's it I think it's a -- EPA is 

### 23 walking in the right direction and I think they have a very good 

### 24 handle on what it is they need to do but I think that the 

### 25 information that we're given for this AP is just not there yet. 

### 26 **DR. DANIEL SCHLENK:**Thank you. Any questions

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1 would like to stay for questions or -- if there are any? Any 

### 2 questions for Dr. Willett. Thank you for trying to keep on 

### 3 time. Let's go ahead, we need to take a break at this point. 

### 4 Let's be back at 10:15 please. 

### 5 (WHEREUPON a break was taken) 

### 6 **DR. DANIEL SCHLENK:**Take your seats please. 

### 7 Our next speaker is Richard Becker from the American Chemical 

### 8 Council. Is Dr. Becker around? Oh, Okay. Okay lets we can go 

### 9 ahead and go with Christopher Bogart? No? All right Dr. 

### 10 Becker. Is Dr. Becker here. Okay, thank you. 

### 11 **DR. RICHARD BECKER:**Thank you. I'm sorry to 

### 12 keep everybody waiting. I'll be brief today I think and 

### 13 probably talking at a 10,000 foot level. Other folks will 

### 14 provide much more granularity, I guess. 

### 15 First I want to thank EPA and the Science 

### 16 Advisory Panel for convening the meeting. This is a very 

### 17 important topic obviously. And we submitted public comments, 

### 18 which everybody should have and I urge you please to take a look 

### 19 at those. In our public comments, which are joint comments 

### 20 between ACC and CropLife America, we've actually gone through 

### 21 and tried to address each of the charge questions, provide some 

### 22 analysis and then some recommendations. So I would encourage 

### 23 the Science Advisory Panel members to please look at those. 

### 24 Overall, I think everybody is in support of 

### 25 improving prioritization and the EDSP itself. Obviously it's 

### 26 important for EPA to conduct the research, bring that

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1 information forward and to develop improved scientific tools to 

### 2 improve all aspects of the EDSP. Today we are talking about 

### 3 primarily prioritization but future SAPs and future work we'll 

### 4 be focusing on EDSP itself and Tier 1 and testing as well. 

### 5 I think the other thing that is clear is there 

### 6 has been a lot of progress made and that is a lot of hard work 

### 7 by some dedicated scientists and very challenging. So I think 

### 8 we all have to recognize how important that is. To date on the 

### 9 Expert System and (Q)SAR and HTP really been focusing on the ER 

### 10 but there is an opportunity to expand that going forward. 

### 11 Finally though of course, the goal is to have 

### 12 risk-based prioritization. So obviously there is a whole 

### 13 component of exposure that needs to move forward kind of in a 

### 14 similar vein. So thinking ahead it's important to also consider 

### 15 how the exposure components of prioritization will be developed 

### 16 and to plan accordingly. 

### 17 One of the things that is also clear is the ER 

### 18 Expert System and ToxCast HTP assays are separate research 

### 19 programs. We've seen today, I think and yesterday as well and 

### 20 the documents we're looking at now an attempt to marry these up. 

### 21 But they're clearly not adequately integrated yet. I think one 

### 22 of the problems we've had in looking at the charge questions was 

### 23 they are quite general. And so by having such general charge 

### 24 questions, you're going to end up perhaps providing very general 

### 25 answers or direction and I think what's needed is more 

### 26 specificity here. So I would encourage you to think about how

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1 that integration could occur in a more logical manner than it's 

### 2 occurred today. 

### 3 Obviously to establish scientific confidence 

### 4 in the assays and prediction models is a goal. Everybody has to 

### 5 have that scientific confidence, the Agency to use them, the 

### 6 stakeholders who are going to rely on them or use them for their 

### 7 products and the public who are going to rely on those decisions 

### 8 for the protection of health and the environment. And with that 

### 9 of course, you have to understand for the assays reliability, 

### 10 sensitivity and specificity. 

### 11 One of the problems we've seen is that not all 

### 12 the assays even the ER assays have been run on the same sets of 

### 13 chemicals. So it's kind of hard to mush these things together 

### 14 and make direct side by side comparisons. So one of our 

### 15 recommendations is to go back, look at the recommended list of 

### 16 chemicals that ICCVAM put forward and have a single set, adopt a 

### 17 single set of reference chemicals, if you would, for which all 

### 18 of these assays could be evaluated. That will allow you to make 

### 19 those side by side comparisons, A priori and not post facto I 

### 20 guess I would add. 

### 21 Scott Slaughter cited our paper here which I 

### 22 show and so it's always good to know that at least one person is 

### 23 reading your paper. I would just say that we just published 

### 24 this so it's in press, it's open access and we talk about 

### 25 methods and approaches to conceptually get around some of the 

### 26 challenges with addressing validation of high throughput models.

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1 So I encourage you to look at that. We discuss it somewhat in 

### 2 our comments. 

### 3 In that paper or course we talk about adapting 

### 4 to OECD (Q)SAR principles to HTP assays. I think that's the 

### 5 goal of the Agency but we've also explored that in a little bit 

### 6 more detail how you would do that. Not just do it conceptually 

### 7 but how one could go about doing that. We also point out that 

### 8 it's possible to use the IOM approach, the framework for 

### 9 qualifying biomarkers can be adapted. And that is another 

### 10 complementary approach that might be useful to consider. And 

### 11 then in that paper we discuss how these things can fit within 

### 12 adverse outcome pathways. So I think we made a contribution in 

### 13 that area and would encourage the Agency to look at those and if 

### 14 there are questions about those, call us and we'd be glad to 

### 15 talk about that. 

### 16 In establishing scientific confidence in the 

### 17 prediction models, I think everybody used the word transparency. 

### 18 I don't think anybody who presented from the Agency who 

### 19 summarized from the agency or any of the -- all of the 

### 20 commenters used the same word transparency. I think it's 

### 21 important to have transparency to model algorithms, all the 

### 22 steps, the filters the decision points. Clearly the ER expert 

### 23 system is a good example of working towards that transparency. 

### 24 I don't know if everybody has done it but you can go on the OECD 

### 25 (Q)SAR toolbox, you can download the ER Expert System version 

### 26 one, you can run it. It's very transparent. They've got a user

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1 guide or guidelines you can follow, very helpful. I think that 

### 2 same level of transparency could be mirrored with high through 

### 3 put assays. 

### 4 These high through put models shouldn't be 

### 5 black boxes. All of the data the model construct parameters, 

### 6 model runs to demonstrate performance should be available. I 

### 7 don't know how you can peer review such things unless you have 

### 8 that kind of data. And of course you shouldn't have to reverse 

### 9 engineer those models in order to evaluate performance or 

### 10 conduct sensitivity analysis. 

### 11 And so in looking ahead what could be very 

### 12 helpful for this SAP and to help EPA do is to develop a road map 

### 13 to guide the Agency and a roadmap that they can follow to 

### 14 conduct those specific studies and the analysis, to develop that 

### 15 scientific confidence in the assays and then also in the 

### 16 prediction models for the intended uses. And we've heard that 

### 17 over and over, fit for purpose or intended use. There may be 

### 18 different degrees of this kind of validation or model 

### 19 sensitivity, specificity, applicability for different purposes. 

### 20 It's important to keep that in mind. We're not saying that you 

### 21 have to have one size fits all here. 

### 22 I think for prediction model transparency, I 

### 23 coined the term borrow from the open source folks with respect 

### 24 to model development, applications analyses data, user guides 

### 25 and those kinds of things. I think that would be very important 

### 26 to do in order to improve everybody's understanding and

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1 confidence in these models, and I think we'll allow also outside 

### 2 folks to bring data and information forward to upgrade those 

### 3 models. And obviously of course it will help peer reviewers as 

### 4 well. 

### 5 So and the last slide I just want to talk a 

### 6 little bit about -- this is a picture out of our paper about 

### 7 AOPs and our conceptual framework, which we must remember that 

### 8 of course what we're looking at on these molecular initiating 

### 9 events, and we prefer the term initial molecular events because 

### 10 they really don't initiate anything per se. It's the first 

### 11 event, so it's the initial event. It's not like turning the 

### 12 light switch on and then all the sudden you get an adverse 

### 13 effect. There are a number of steps between that. So typically 

### 14 the initial molecular advance at protein binding or receptor 

### 15 binding, those kinds of things but in order to get effects at 

### 16 organ system or the organism, there are key events that have to 

### 17 occur after those initial events. I think everybody has this 

### 18 kind of concept in mind. We're just trying to suggest that we 

### 19 need to be a little more explicit in our thinking about this and 

### 20 how we talk and deal with the model development and validation 

### 21 of AOPs because they're also not just key events but then dose 

### 22 responses from each of those steps that are important to 

### 23 consider in those key events. 

### 24 So I'll end there and just say that I hope 

### 25 I've stimulated your thinking somewhat and again, I would 

### 26 recommend that you consider helping EPA develop that roadmap.

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1 Thank you. 

### 2 **DR. DANIEL SCHLENK:**Any questions for Dr. 

### 3 Becker? Okay thank you. Our next presenter is Christopher 

### 4 Borgert from Applied Pharmacology and Toxicology. 

### 5 **DR. CHRISTOPHER BORGERT:**Am I on here? Okay 

### 6 thanks. First of all I'd like to thank you for the opportunity 

### 7 to make some comments this morning and I want to start off also 

### 8 by saying that I think that the overall idea here is a good one 

### 9 and I think there is a lot of good thinking that has gone into 

### 10 this. I think we would all support the need to prioritize 

### 11 chemicals and I think the general ideas of how to go about doing 

### 12 that are in place. But I do have some concerns and some things 

### 13 that I hope will be thought about in a little more detail. So 

### 14 what I'm going to do is present some over arching ideas and pull 

### 15 out a few of the details to I hope illustrate my point. 

### 16 So if we could go to the - do I have an 

### 17 advancer here. So I want to step back to, again, more of a 

### 18 10,000 foot level and think about what's going on with this 

### 19 prioritization process. On the one hand it is, and we all 

### 20 understand this, just being used as a screening tool, but on the 

### 21 other hand we're appealing to a confidence and a logic that 

### 22 comes through use of the adverse outcome pathway. So we're kind 

### 23 of dancing between saying well we don't necessarily have to lay 

### 24 out the entire pathway of course because this is for a screening 

### 25 for prioritization, but at the same time we're saying we gain 

### 26 confidence from the logic of the AOP. This is right out of the

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1 EPA document, this is a quote. 

### 2 So the focus is on this molecular initiating 

### 3 event for prioritizing chemicals and then you're going to move 

### 4 up through higher levels of biological organization in the 

### 5 Adverse Outcome Pathway. So that really does imply that we've 

### 6 got a demonstrated pathway and I'm going to now talk about that 

### 7 now and then pick out some just specific details. 

### 8 Okay, so as Rick Becker mentioned, these 

### 9 molecular events are initial but not necessarily initiating and 

### 10 the difference there is that the latter is obligate but the 

### 11 former is not. Okay, so that's an important concept I think we 

### 12 all get it. But I think it's important to think about the words 

### 13 we use as well because it influences the way we do data 

### 14 comparisons and how we think about the data that we're 

### 15 evaluating and how we interpret it. So if we're putting it into 

### 16 this mind set of an adverse outcome pathway it colors the way we 

### 17 think about things a little differently than if we're saying 

### 18 it's all correlational and there is nothing more to it than 

### 19 that. 

### 20 And so I think an important point is here that 

### 21 key events are key to this concept. We have some well worked 

### 22 out frameworks for using key events and a mode of action. But 

### 23 at this point in the EDSP, there is no point at which a causal 

### 24 connection is sought between these logical or assumed or 

### 25 proposed key events. It's not in the proposed prioritization 

### 26 approach. It is nowhere in the EDSP Tier 1 because again that

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1 is just screening, but it's not in EDSP Tier 2 either. So at 

### 2 some point I think it's important to either define that we're 

### 3 not doing that and we're not gaining confidence in an Adverse 

### 4 Outcome Pathway because we've demonstrated it or we do a little 

### 5 more work to try to demonstrate that and there are some good 

### 6 models to follow such as the human relevance framework. 

### 7 So just to put a finer point on that what 

### 8 we're focusing on what I illustrate here as initial molecular 

### 9 events, but these may not be more than an initial event, they 

### 10 may actually not be key. There are very few pathways that I 

### 11 think we can envision where you have a molecular initiating 

### 12 event and just go straight through to an adverse affect without 

### 13 some intervening key nodes where there is a lot of intersection 

### 14 with other pathways and of course this whole diagram is very 

### 15 simplistic. We know that the biology works by complex causal 

### 16 networks. We all know that but again I'm just encouraging 

### 17 everyone to think about what we're actually doing here and what 

### 18 data we need in the context of that thought process and then 

### 19 again, how we're -- this has been mentioned by other speakers 

### 20 but how do we integrate all of what we're doing, not just one 

### 21 piece at time but how does the prioritization then in this logic 

### 22 integrate with Tier 1 screening and Tier 2 testing. I think we 

### 23 need to think about how that all fits together because it is 

### 24 going to impact things like cutoffs and the logic we use to set 

### 25 those. 

### 26 So as I say let me get back to a positive

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1 point I think the overall logic there, the overall idea is good, 

### 2 but we really need to drill down and think carefully about some 

### 3 of these finer points. 

### 4 So one of those detailed concerns is around 

### 5 potency and we hear a lot about low dose hypotheses and all 

### 6 sorts of things these days but potency is a fundamental 

### 7 pharmacological tenant. It's not been disproved. It's one of 

### 8 the most useful characteristics of endocrine-active substances 

### 9 along with concepts like selective estrogen response, 

### 10 modification, et cetera. I think it might be better exploited 

### 11 in this process if we think a little bit more about what 

### 12 information these assays -- different assays are giving us on 

### 13 potency and how that might play into an integration between 

### 14 prioritization and screening. 

### 15 So for instance this 1.0E-07 cutoff, I was not 

### 16 able to completely follow the logic on that. Of course I don't 

### 17 have the raw data to go through and look at that but let's just 

### 18 look at kind of a crude analysis on that. So here is a graph 

### 19 where we're just looking at some crude estimates of relative 

### 20 potency for various test chemicals that are in the validation 

### 21 set so to speak. 

### 22 Now this is paper by Chen et al. is actually 

### 23 Katzenellenbogen's group. So they have looked at the potency of 

### 24 various steroids to the native human estrogen receptor and for 

### 25 instance testosterone has a relative potency. I want to point 

### 26 out those data I just pulled off graphs because I didn't have

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1 the raw data there either. So it's a little different than 

### 2 these where I was actually was able to calculate a mean EC 50 

### 3 quotient relative to 17 beta estradiol. But none the less it 

### 4 tells you that testosterone can be in that range. These are 

### 5 actually data from the ICCVAM BG-1 validation peer review panel 

### 6 report where they reported all the mean EC 50 values. So I 

### 7 understand this is not a true pharmacological demonstration of 

### 8 relative potency but it gives us a ball park. 

### 9 So we have these androgens and there has been 

### 10 a lot of attention paid to methyltestosterone, which is an 

### 11 aromatizable androgen and the explanation for its activity in 

### 12 these assays is it aromatized to an estradiol compound. So it's 

### 13 aromatization that is supposedly responsible for its activity. 

### 14 That might be true also for testosterone but what about 19 

### 15 nortestosterone. That's otherwise known as nandrolone and the 

### 16 long acting nandrolone decanoate ester is one of the favorite 

### 17 anabolic steroids of body builders because it's long acting, it 

### 18 doesn't aromatize and cause estrogenic effects and it has a 

### 19 relative potency in the same ball park. 

### 20 So I'm not completely sure that we're getting 

### 21 all the information that we can out of this potency information. 

### 22 We can look at these various comparators and see the true 

### 23 estrogens even physiological -- physiologically very weak 

### 24 estrogens have a much higher potency, and there are the things 

### 25 in between but I think we need to reconsider some of these cut 

### 26 offs or at least think more fully about what information they

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1 can give us. 

### 2 And as a final note then to pick up on this 

### 3 idea of the integration between priority setting and Tier 1 

### 4 screening. They both have the goal of minimizing false 

### 5 positives and other words relaxing the stringency. So we're 

### 6 doing that step twice, that's kind of a wide open sieve. 

### 7 We use substantially similar assays, binding, 

### 8 transactivation or gene activation assays. So if we're going to 

### 9 use a relaxed stringency in sort of a wide open mode twice, it's 

### 10 really not serving much of purpose. I think it needs to be 

### 11 thought through where we narrow down on some of these aspects 

### 12 like potency and what information the various different assays 

### 13 are giving us rather than you know focus on trying to seek 

### 14 concordance between assays that, as Dr. Safe mentioned, really 

### 15 would be expected to give us different results and see if we can 

### 16 do something like either increase the stringency over here in 

### 17 these in vitro assays or simply eliminate them, think very 

### 18 carefully about our stringency here and whether then we can 

### 19 simply use a very specific in vivo assay like the Uterotropic or 

### 20 the Hershberger then to get a more definitive call. 

### 21 So that's really all I have to say and I 

### 22 appreciate your attention and if there is a question and we have 

### 23 time I'll be happy to entertain it. 

### 24 **DR. DANIEL SCHLENK:**Any questions from the 

### 25 panel? Okay. Oh got one. Dr. Shultz. 

### 26 Dr. TERRY Shultz: So you believe that the cut

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1 off down to the minus seventh is too conservative or not 

### 2 conservative enough? 

### 3 **DR. RICHARD BECKER:**I'm not making a clear 

### 4 call on where the level would be, but it seems to me that from 

### 5 the data that I've looked at that it might be too conservative. 

### 6 You may be -- you may actually and then go back to the slide 

### 7 here, the argument that well that the cut off would include 

### 8 methyltestosterone, which doesn't seem like it ought to be a 

### 9 potential estrogen, but the rationale for that is but we can 

### 10 explain its activity because it's metabolized to an estrogen. 

### 11 That's fine as a logic if you really know the metabolic capacity 

### 12 of that cell line and you can actually demonstrate then that 

### 13 enough should be aromatized to give you an estrogenic response 

### 14 but I don't know how you explain then that a non-aromatizable 

### 15 androgen has -- those are really indistinguishable relative 

### 16 potencies given the statistical power of these assays. So 

### 17 you've got a non aromatizable androgen that isn't exactly the 

### 18 same class. So that logic isn't stacking up for me. I think 

### 19 another interpretation might be that the biological relevance of 

### 20 this potency -- the biological relevance is falling off earlier 

### 21 than ten to the minus seventh at a higher concentration. 

### 22 So that's what I'm suggesting and that's the 

### 23 reasons for it. But I'm not trying to say exactly where the cut 

### 24 off should be because I don't have the raw data to look at 

### 25 myself. 

### 26 **DR. DANIEL SCHLENK:**Okay, any other

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1 questions? All right thank you. Our next presenter is Lisa 

### 2 Ortego from Bayer CropScience. 

### 3 **DR. LISA ORTEGO:**Good morning, my name is 

### 4 Lisa Ortego as Dan said and I'm making comments this morning on 

### 5 behalf of CropLife America. So I don't know how the panel feels 

### 6 but it was quite a challenge for us to get through all of these 

### 7 documents, some of them very heavy with technical information. 

### 8 What is clear is that EPA has worked very hard to document 

### 9 research in this very important area and we really do appreciate 

### 10 that. 

### 11 However, the timing of the document release 

### 12 was such that it left insufficient time for a thorough peer 

### 13 review. And in fact some information that is needed to complete 

### 14 the peer review is still not available and you'll hear other 

### 15 commenters give more specifics on that information later. 

### 16 So what this results in among other things is 

### 17 the inability of the stakeholders to provide their information 

### 18 in time for adequate panel review. And we think that is a bit 

### 19 unfortunate because some of the presentations that you will see 

### 20 today do have technical information that we think would be 

### 21 valuable for the evaluation of the program. 

### 22 You've also heard the EPA say they have two 

### 23 more -- at least two more endocrine SAPs planned for 2013 and we 

### 24 would expect those SAPs to be equally data review intensive if 

### 25 not more. So we have some recommendations going forward that we 

### 26 thing would improve the timing and the availability of time for

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1 peer review. 

### 2 What we would like to recommend is that all 

### 3 the documents should be released for a 60 day public review and 

### 4 comment period. And that the written comment period close about 

### 5 20 days prior to the start of the SAP and that the stakeholder 

### 6 comments be transmitted to the panel members no later than 15 

### 7 days in advance. 

### 8 We think that gives the panel adequate time 

### 9 for their review and it also provides the stakeholders time to 

### 10 get their comments in for the panel to consider them and would 

### 11 give the panel a better opportunity for asking questions during 

### 12 the oral commenting period. 

### 13 Something else that we'd like to recommend not 

### 14 on this slide is also to have all the presentations available on 

### 15 the public docket 48 hours prior to the start of the meeting. 

### 16 My eyes are not as young as they used to be, it's kind of hard 

### 17 to read all of the fine print on some of the slides. 

### 18 So moving forward then, the other topic I'd 

### 19 like to comment on is one of communicating the priority setting 

### 20 results. It's very important moving forward that EPA 

### 21 communicate the results of the priority setting activities in a 

### 22 way that the public understands the potential hazards and risks 

### 23 of the information. And if we don't do that well then we can -- 

### 24 things can happen for example unintended de-selection in the 

### 25 market place, application of the data in other state governments 

### 26 or other countries or within other parts of EPA for which the

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1 data were not intended to be used. And we also can 

### 2 unintentionally promote the use of alternative products 

### 3 presuming a risk of the listed ones, where the alternatives may 

### 4 actually pose a greater risk than the ones that were listed for 

### 5 a priority setting. 

### 6 So those are very important things related to 

### 7 communication and we have some recommendations for that as well. 

### 8 That EPA should consider establishing best practice guidelines. 

### 9 That those would be applicable to all agency programs that 

### 10 would apply to the generation dissemination and characterization 

### 11 of the results, of course generated for the priority setting 

### 12 activities but also by all parts of the endocrine disrupters 

### 13 screening program. We think this is very important. EPA should 

### 14 consider issuing data use guidance for these data both inside 

### 15 and outside of EPA. Now this was a recommendation that the 

### 16 Scientific Advisory Board made for Comp Tox and we also think 

### 17 this is applicable here. So it is very clear what these data 

### 18 are intended to be used for and what they're not intended to be 

### 19 used for. 

### 20 And that is a communication that ensures 

### 21 consistency across the Agency but also enables the public and 

### 22 other governments to understand the activities that EPA is 

### 23 undertaking and what the implications for hazard and risk are. 

### 24 I'd like to finish by commending EPA for their 

### 25 efforts in communicating on list one and draft list two 

### 26 activities and making the public aware that these are not lists

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1 of endocrine disruptors. That is very important to us and we 

### 2 appreciate those efforts. We would like to see that carried 

### 3 forth for the priority setting activities. We would like to see 

### 4 EPA once again confirm that they're not generating lists of 

### 5 endocrine disrupting chemicals. We would like to see EPA 

### 6 communicate that appearance on a list does not -- should not be 

### 7 interpreted to mean the EPA has a significant concern for a 

### 8 significant ecological human health risk at an environmentally 

### 9 relevant concentration. 

### 10 And for substances that have existing data we 

### 11 would like to see EPA communicate to the public clearly that 

### 12 they do have safety data for the compounds that they do, and to 

### 13 use those also in a priority setting process. Thank you. 

### 14 **DR. DANIEL SCHLENK:**Okay Dr. Matten has a few 

### 15 comments to make. 

### 16 **DR. SHARLENE MATTEN:**I just wanted to remind 

### 17 the Panel that their focus is on the charge that the Agency has 

### 18 provided on scientific issues and issues of regulatory policy, 

### 19 regulatory communication, regulatory management are those of the 

### 20 Agency and as I understand the Agency Steve Bradbury, the Office 

### 21 of Pesticide Program Director and two other office directors 

### 22 have written a formal written response. Although it's not in 

### 23 this docket, it certainly could be provided to this Panel so you 

### 24 can see the official response from the Agency about these exact 

### 25 issues. Of course we appreciate the public comment. I just 

### 26 wanted to make sure that you understood that your role is not to

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1 address that sort of areas that were related to regulatory 

### 2 policy, regulatory management decision making. 

### 3 **DR. DANIEL SCHLENK:**Okay, thank you and with 

### 4 that any questions for Dr. Ortego from the Panel? All right 

### 5 thanks Lisa. Okay your next presenter is Ted Simon with Ted 

### 6 Simon, LLC. Can you push the button there please. 

### 7 **DR. TED SIMON:**Okay, I want to thank the EPA 

### 8 for this opportunity for me to present comments to the Panel. 

### 9 I'm here as an individual, as former EPA staffer and as a 

### 10 consultant in toxicology who has provided assistance to both 

### 11 government agencies and to private sector entities. I'm going 

### 12 to discuss today charge questions two and three. 

### 13 Charge question two, which I'm sure you can't 

### 14 read, deals with physical chemical properties and can we do an 

### 15 initial screening with physical chemical properties? There were 

### 16 four items mentioned and I'm going to discuss each of these. 

### 17 The first one was the presence of acids, bases and charged 

### 18 species. Is there some way to eliminate these from the get go. 

### 19 There was a discussion in the prioritization document about the 

### 20 use of pKa and perhaps some pKa screening levels. 

### 21 We would think -- we would say that a pKa 

### 22 value of say five on the low end and nine on the upper end would 

### 23 be appropriate. This would mean that less than one percent of 

### 24 the chemical be present as an uncharged species and thus 

### 25 available to bind to the estrogen receptor. Now that said, 

### 26 there was the discussion in the prioritization document

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1 neglected to include the presence of the ER as something that 

### 2 might possibly change the equilibrium. There was an equilibrium 

### 3 discussion without the estrogen receptor as something in that 

### 4 equilibrium process binding the uncharged species. 

### 5 As far as reactive and unstable chemicals, a 

### 6 value of 40 days for the half life was given and I was left 

### 7 scratching my head about this, was this based on eco 

### 8 considerations, was it based on human health considerations. I 

### 9 mean is this the length of time it would take for biod in the 

### 10 wild to be affected. Is this the length of time perhaps for 

### 11 some developmental critical period? Now those -- those are just 

### 12 two ideas I had but in any case, what is the rationale and that 

### 13 rationale should be put in the document for full transparency. 

### 14 Corrosive chemicals were another category that 

### 15 was mentioned. And there needs to be additional modification of 

### 16 the corrosively filter to take into account certain phenolic 

### 17 chemicals. Creosol is one that comes to mind. It has 

### 18 estrogenic properties. Nonetheless it is corrosive so how do 

### 19 those two match up. So some additional thought is required 

### 20 there. 

### 21 There were some other properties that were 

### 22 mentioned, LogKow that of course has been used in the Expert 

### 23 System. Volatility, physical form, whatever properties are 

### 24 chosen and certainly the Duluth Laboratory at EPA has manifold 

### 25 experience with physical chemical properties and relating those 

### 26 to biological proprieties. Whatever properties you use specific

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1 measures of the property should be provided. 

### 2 Now there was a question in this part of 

### 3 charge question two as to whether weight of evidence for 

### 4 estimates of these chemical properties should be used, and my 

### 5 response would be not at this point and time. Which properties 

### 6 are going to be used as this initial screen should be detailed. 

### 7 These properties should be selected and the sources of the 

### 8 estimates should be selected and at that point it may be you can 

### 9 do a weight of evidence. 

### 10 Now if you do weight of evidence, it should 

### 11 not necessarily be saying software A software B. You need to 

### 12 think about the algorithms involved. If there are some 

### 13 different algorithms that provide different estimates, then 

### 14 those are the -- it's those algorithms that really should 

### 15 provide the differences rather than just say two software 

### 16 packages that implement the same algorithm. 

### 17 The weight of evidence framework that is used 

### 18 needs to be described in a transparent fashion so that the 

### 19 reverse engineering that we've heard about in some of the other 

### 20 comments doesn't have to occur and that users of -- and the 

### 21 public can go back and recreate what was done easily. And so 

### 22 sort of the take home from all of these is to provide a 

### 23 rationale for all of these physical chemical screening methods, 

### 24 the data sources and the cut offs. 

### 25 And I'm going to move now to charge question 

### 26 three, which has to do with the expert system that is based on

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1 the rainbow trout's ER binding assay, the rainbow trout 

### 2 vitellogenin assay and the use of the structural alerts for the 

### 3 chemical categories. 

### 4 Now it's -- the developers of the expert 

### 5 system I think had a really difficult job. The estrogen 

### 6 receptor is eclectic. It binds to many substances, albeit with 

### 7 very low affinity. I think we saw that in some of the data that 

### 8 Dr. Borger presented. So, as I read through the document, it 

### 9 seemed to me that the training set and the test set seemed 

### 10 somewhat ad hoc. They were based on regulatory needs rather 

### 11 than trying to find the training set that included high affinity 

### 12 chemicals, low affinity chemicals and then chemicals somewhere 

### 13 in the middle so that there would be some idea of the 

### 14 performance of the system. So we need a standard test set and 

### 15 the data from this standard test set should be made public. I'm 

### 16 going to come back to this point. 

### 17 How well does this Expert System, based on the 

### 18 rainbow trout estrogen receptor, predict the results -- the 

### 19 activity we see in human-based assays? So in terms of the test 

### 20 system, the ICCVAM 2000 list of validation substances should be 

### 21 used. This is one that is commonly available. I can go find 

### 22 them and we can use that to test the performance of the expert 

### 23 system and indeed the HTP assays. 

### 24 Now following this that data need to be made 

### 25 public and this has been done and I, again, the documents as I 

### 26 got them from the docket did not have this in there and this is

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1 why I'm making this last comment on this slide is that there 

### 2 needs to be a comparison between some of the human based assays 

### 3 and the rainbow trout assays. This could certainly be expand 

### 4 and I think if the ICCVAN 2000 list is adopted then there will 

### 5 need to be additional work in this area. 

### 6 So you could do a comparison between the 

### 7 Novascreen assay results and the rainbow trout ER binding, you 

### 8 could do for transactivation assays that measure messenger RNA 

### 9 synthesis, you could do a comparison between the BG1 assay and 

### 10 the vitellogenin assay. 

### 11 Now in terms of the cut off levels, these 

### 12 seemed and this is specifically related to the Expert System 

### 13 where it looked like they were sort of categories developed 

### 14 based on order of magnitude or two order of magnitude 

### 15 differences in the relative binding affinity. There needs to be 

### 16 some science-based aspect of the cut off levels for designating 

### 17 a substance positive, negative or equivocal. 

### 18 By Dr. Willet in her comments mentioned 

### 19 biological relevance. Dr. Borger provided a really good example 

### 20 of where -- what is a potency level that we should care about in 

### 21 terms of endocrine disruption. And so the thinking about 

### 22 potency that's been expressed today should be incorporated in 

### 23 the determination of these relative binding affinity cut off 

### 24 levels so that we can say that there is true biological 

### 25 relevance here, lack of biological relevance over here and maybe 

### 26 the middle is a bit more equivocal.

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1 Throughout the documentation on the expert 

### 2 system the policy generalization of the expert system being 

### 3 health protected based on some comparisons between the human 

### 4 binding -- human ER binding affinity and rainbow trout ER 

### 5 binding affinity. These were throughout the document and this 

### 6 is really a policy generalization and I would, as far as the 

### 7 document goes, I think the science and the policy should be 

### 8 separated so that you may want to put that somewhere else in the 

### 9 document. Okay and I have a summary of my recommendation for 

### 10 charge question three. 

### 11 First as a validate using the ICCVAM list, 

### 12 provide that comparison with a human based assays from that same 

### 13 list, don't mix policy generalizations with science and as 

### 14 others have mentioned be transparent. Make the data and the 

### 15 analyses available to the public. That's all I have. Thank you 

### 16 so much for this opportunity. 

### 17 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 18 for Dr. Simon? Okay, thank you. Our next presenter is Sue 

### 19 Marty from Dow Chemical Company. Can you press the button 

### 20 there. Thanks. 

### 21 dr. Sue Marty: My name is Sue Marty I'm with 

### 22 the Dow Chemical Company. I just wanted to share a few 

### 23 observations on the program that is being developed here at the 

### 24 Agency. 

### 25 As we learned yesterday, the HTP assays that 

### 26 are relevant for estrogen and signaling pathways are still being

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1 evaluated. At last count there were 21 ER related assays. And 

### 2 I'm a little curious as to the rationale for ER assay selection. 

### 3 So are the assays being selected to evaluate different aspects 

### 4 of ER signaling pathway as we mentioned yesterday, different 

### 5 levels of the AOP. It seems to me that it would be really 

### 6 valuable to have some criteria for assay selection. Otherwise 

### 7 the landscape that we're working will be continually changing 

### 8 and what you can envision from a regulatory context is that 

### 9 compounds will move up and down the prioritization list or in 

### 10 subsequent stages may move on or off of prioritization list just 

### 11 based upon the changing last scope of assays that are included. 

### 12 13 There are multiple assays and it is unclear 

### 14 how data from multiple assays are going to be managed. Are 

### 15 compounds going to be prioritized based upon the number of 

### 16 positive ER assays? Is it based upon the lowest concentration 

### 17 in which a compound had a positive assay result? So it's a 

### 18 little bit unclear yet how these different decisions are going 

### 19 to be made. 

### 20 I think it's important even though it's not 

### 21 critical for prioritization, I think it's important to keep 

### 22 looking at the long term goal. How predictive our in vitro 

### 23 assays for in vivo outcomes. If this is the first tip in an 

### 24 AOP, what's the adverse outcome? How predictive is it of the 

### 25 adverse outcome. So I think it's important that we keep that in 

### 26 mind as we move forward.

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1 So the SAP that the agency is having right now 

### 2 is a great first step. We'd like to encourage that the panel 

### 3 encourage the agency to continue to provide additional 

### 4 information on how the results will be used and how the assays 

### 5 are interrelated. 

### 6 One of the things that make it difficult to be 

### 7 prepared to present on this particular topic is that it's really 

### 8 an evolving field. So in October of 2012 there was an 

### 9 e-publication that came out from the agency from Rotroff using 

### 10 HTP assays to identify endocrine disruptors, so using ToxCast 

### 11 data. Rotroff used nine ER assays in order to evaluate 

### 12 endocrine active compounds. Three months later, the White Paper 

### 13 came out for the SAP. Three of those ER assays have dropped off 

### 14 the plate, and we don't know why the dropped off the plate. Two 

### 15 additional assays were added. The data from those assays, as 

### 16 near as I can tell not publically available so it makes it 

### 17 really difficult to be able to evaluate the process. 

### 18 From a public perspective the procedures are 

### 19 evolving and it's difficult to determine which approaches will 

### 20 ultimately be adopted. So as it's been said already the process 

### 21 is incomplete there are a number of things that are still being 

### 22 developed. But some of these are really critical for 

### 23 understanding the program performance. Things like the final 

### 24 set of filters that are going to be used to combine data across 

### 25 assays. They mentioned a level eight option where you can 

### 26 designate a manual hit call for something that wasn't chosen in

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1 previous steps, and the adoption of performance metrics. So all 

### 2 of these things are really critical. So we would really 

### 3 encourage the agency to continue to host public data summits and 

### 4 review their results and methodologies with the public and allow 

### 5 comment. 

### 6 Several experimental factors can affect assay 

### 7 performance results and interpretation and the Agency has 

### 8 recognized some of these things like cytotoxicity data. But 

### 9 it's not always clear which cytotoxicity assays are being used 

### 10 with which HTP assays and then when they've used the 

### 11 cytotoxicity data to filter HTP results. It's not always clear. 

### 12 And there are a number of other factors that can confound assay 

### 13 results, things like compounds that are not stable in DMSO or 

### 14 for compound inhibits luciferase or causes membrane 

### 15 perturbations or protein denaturation. 

### 16 It's not clear how many of these factors the 

### 17 agency is looking at. They are looking at some and I think 

### 18 that's really -- really commendable and they're developing 

### 19 algorithms for a number of those and so we would really 

### 20 appreciate it those algorithms and the results could be 

### 21 publically available and shared. 

### 22 And I think that is the intent. It's just not 

### 23 always as easy to recreate the results as what the agency has 

### 24 intended. So I'll give you an example. This is in the White 

### 25 Paper. This is using the three stress assays to filter the 

### 26 Attagene hits. So it starts out that you -- they give you a

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1 very nice straight forward four-step process. It starts out by 

### 2 taking the number of assay hits and creating histogram, which is 

### 3 that black solid line against the concentrations at which 

### 4 positive results were seen. Well okay you can see this is an 

### 5 example for bisphenol A. They have 14 hits. Okay, the problem 

### 6 is the White Paper is only talking about eight ER assays and 

### 7 three stress assays. So I don't know where those 14 hits came 

### 8 from. Because of that and what we know now are two of the 

### 9 assays the data are not publically available for. So it's 

### 10 difficult for me to be able to recreate how they did this 

### 11 filtering based upon the information that is available. 

### 12 The data in ToxCast -- I'm curious to know 

### 13 whether or not the data an ToxCast will be annotated to indicate 

### 14 when the EPA has employed filters. Our company downloaded 

### 15 ToxCast data in 2009, and looking at those data in 2013, a 

### 16 number of results have changed for compounds used in the same 

### 17 assays. And there might be a number of reasons for that. I 

### 18 mean it could be QA, it could be data filtering, but it's not 

### 19 clear how the results got from one to the next over you know 

### 20 that span of time. So again we would encourage the agency to 

### 21 make the data available and allow independent peer review. 

### 22 Lastly, the ER expert system and the HTP 

### 23 assays are focused on chemical prioritization so we want to 

### 24 minimize false negatives and that's true but some specificity 

### 25 would still be valuable for efficient and effective 

### 26 prioritization. So we would encourage the Agency to be able to

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1 look at the predictiveness of the ER expert system and the HTP 

### 2 results against the list one chemicals that were screened in the 

### 3 EDSP program. 

### 4 So how often to the HTP data agree with the 

### 5 validated low through put ER binding and ER transactivation 

### 6 assays that we're using Tier 1, and how often do the high 

### 7 throughput assays accurately predict in vivo uterotropic 

### 8 outcomes. In the Rotroff paper, there was balanced accuracy 

### 9 prediction of 89 percent. That 89 percent of the time the in 

### 10 vitro ER results predicted in vivo uterotropic outcomes. Our 

### 11 initial assessment is that it's not going to be as predictive as 

### 12 all that. So it would be nice to see this kind of an analysis 

### 13 done and be made publically available. Thank you. 

### 14 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 15 from the panel. 

### 16 **DR. SHARLENE MATTEN:**Just wanted to -- Sue 

### 17 Marty yes, your oral presentation doesn't match up with your 

### 18 written handout, if you could resend your presentation. You 

### 19 have more slides than are in the photocopy. 

### 20 DR. Sue Marty: You should have seven. I'm 

### 21 sorry I'll check and see what you have and I'll look at it. 

### 22 **DR. SHARLENE MATTEN:**They have five. 

### 23 DR. Sue Marty: They must be missing a page 

### 24 then. Let me check in to that for you. 

### 25 **DR. SHARLENE MATTEN:**Okay. Thanks. 

### 26 **DR. DANIEL SCHLENK:**Okay. Thanks. Our next

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1 presenter is Katy Goyak from ExxonMobil Biomedical Sciences. 

### 2 **DR. KATIE GOYAK:**All right good morning. My 

### 3 name is Katie Goyak. I'm from Exxon Mobil Biomedical Sciences. 

### 4 I'm a toxicologist with that group and I'm happy to have the 

### 5 opportunity to talk with you this morning. 

### 6 So I'm going to be talking today about charge 

### 7 question five, specifically looking at the performance 

### 8 evaluation of the in vitro high through put assays against a set 

### 9 of reference chemicals. So I've placed the full text of the 

### 10 charge questions here for your reference but basically I'm going 

### 11 to be speaking about the choice of references, the individual 

### 12 assay performance and comparative assay performance. And also 

### 13 I'd like to just mention that I'm really just going to be 

### 14 hitting the highlights, if you would like more detail please 

### 15 refer to the written comments that were submitted by ACC and 

### 16 CropLife America. 

### 17 So just as a general comment and some of the 

### 18 other presenters have noted we really agree with the approach 

### 19 for using the reference chemical list to assess assay 

### 20 performance that is laid out in section six -- yes section six 

### 21 of the prioritization White Paper. I think this is really a 

### 22 critical part, a really critical evaluation in order to 

### 23 understand how well the assays perform and start to look at the 

### 24 strength and limitations of the individual assays and this kind 

### 25 of analysis is really consistent with the OECD validation 

### 26 principles. Specifically, it starts to define the biological

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1 endpoint because you're using reference chemicals that are 

### 2 related to estrogen receptor activity and then it also provides 

### 3 a measure of predictiveness of the individual assays. So really 

### 4 what I'm hoping to do today is to provide a few recommendations 

### 5 for how to build on this approach. 

### 6 So one of the first recommendations that we 

### 7 wanted to note is that we think that the classification of each 

### 8 reference chemical as an active or an inactive should really be 

### 9 carefully considered. So just in this evaluation, the reference 

### 10 chemicals are really acting as the gold standard against which 

### 11 in each in vitro assay is going to be assessed. So I just put a 

### 12 few examples here to show the point that I'm trying to make. 

### 13 So when you go back to the ICCVAM background 

### 14 review document and you look to see what data there are to 

### 15 support each of the reference chemical classifications, in a few 

### 16 cases there are classifications made either based on only one or 

### 17 two binding assays or transcriptional activation assays. In 

### 18 some cases there is not any experimental data on either the 

### 19 binding assay or the transcriptional activation assay. So I 

### 20 just think that it should be a point that should be really 

### 21 carefully considered. 

### 22 Actually moving into the actual assessment of 

### 23 the individual assays -- so as I mentioned, this is a providing 

### 24 these kinds of performance statistics. So looking at overall 

### 25 accuracy, sensitivity and specificity of each assay is really 

### 26 helpful to understand the strengths and limitations of each

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1 assay. 

### 2 So our recommendation is really that to take 

### 3 this analysis one step further. So start with these performance 

### 4 statistics and then make an overall interpretation on whether 

### 5 each assay is useful to the overall prioritization scheme or as 

### 6 has been mentioned earlier perhaps to start to rank the assay 

### 7 results. So as an example of what I mean for over all 

### 8 interpretation can be -- I have shown here is that if we look at 

### 9 the results of the three Novascreen binding assays, so there is 

### 10 one assay for human, one for mouse and one for bovine. You can 

### 11 see that the human assay results really have a higher overall 

### 12 accuracy and a higher sensitivity than the mouse and the bovine 

### 13 binding assays. 

### 14 When you breakout which chemicals the mouse 

### 15 and the bovine assays are getting incorrect, you can see that 

### 16 the, which I don't have shown here but the mouse and the bovine 

### 17 assays are really missing the weak and the very weak reference 

### 18 chemicals. So one question to consider might be is considering 

### 19 that there is lower performance of the mouse and bovine assays 

### 20 and that they also missing the weak potency agonist is it really 

### 21 useful to continue to use the mouse and the bovine binding 

### 22 assays or are they bringing in additional benefit to the overall 

### 23 prioritization benefit? 

### 24 And then a brief note on how to address 

### 25 comparative assay performance. So there seems to be two 

### 26 different approaches used in the prioritization White Paper. So

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1 in section seven and also in the Rotroff et al. article that was 

### 2 mentioned earlier, this is an article looking at how to use the 

### 3 in vitro high through put assays to identify potential endocrine 

### 4 disrupting chemicals. There's a use of a specific cut off 

### 5 criteria. So if a chemical is a positive in two out of the 

### 6 eight or two out of the nine assays, then that chemical is 

### 7 called a positive. 

### 8 An alternative approach is as I talked about 

### 9 on the previous slide using summary statistics like specificity 

### 10 and sensitivity to assess the performance of each assay. So our 

### 11 recommendation is that the use of the performance statistics is 

### 12 really more consistent with the validation frameworks. This 

### 13 starts to address analytical performance of each individual 

### 14 assay and also you know measuring how well each assay predicts. 

### 15 And also if you're looking at the reference chemicals, this 

### 16 helps define the biological endpoint for each assay. 

### 17 The cut off criteria approach, while this does 

### 18 recognize that each individual assay measures a specific, has a 

### 19 slightly distinct cell context. You wouldn't expect to have 

### 20 exactly the same kinds of results. But you start lose -- you 

### 21 start to lose a measure of assay performance and also you start 

### 22 to lose what that assay is actually -- the biological signal 

### 23 that it is actually measuring. 

### 24 So overall we recommend the summary 

### 25 statistics. So just in conclusion again we really think that 

### 26 his approach of using the reference chemicals to assess assay

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1 performance is consistent with OECD validation principles and I 

### 2 think this is a great start and you hope we'll consider some of 

### 3 our recommendations to expand on this approach. And also these 

### 4 comments were developed in collaboration with ACC and CropLife 

### 5 America and more detail can be found in our written comments. 

### 6 Thank you. 

### 7 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 8 from the Panel? Okay thank you. Our next presenter is David is 

### 9 it Getter? 

### 10 **DR. DAVID GETER:**Geter. 

### 11 **DR. DANIEL SCHLENK:**Ah, I had a 50/50 chance. 

### 12 Sorry. From Bayer Crop Science. Sorry. Apologies. 

### 13 **DR. DAVID GETER:**Good morning ladies and 

### 14 gentlemen of the board. My name is Dave Geter and I'm from 

### 15 Bayer CropScience and I will be addressing charge question six, 

### 16 which is comments on the analysis of the ER expert training set 

### 17 and the high through put in vitro assay data. 

### 18 Yesterday there was discussion several times 

### 19 on concentrations used within the assays and within the ToxCast 

### 20 cell free assays the top concentration is 50 micromolar. And 

### 21 then when you add cells to some of those high through put assays 

### 22 it goes up to about 200 micromolar. Within the BioReliance, 

### 23 which is a contractor that the Comp Tox within the EPA are 

### 24 running some of their high through put assays. They also use 

### 25 200 micromolar. Within the EPA EDSPR binding guideline, they 

### 26 use a top concentration of 1,000 micromolar. Within the rainbow

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1 trout ER binding, it's about 10,000 micromolar. So just putting 

### 2 these into perspective but you can understand where they chose 

### 3 that 10,000 micromolar because that is the limit dose that is 

### 4 used within guideline in vitro assays, albeit those are 

### 5 predominantly gene tox assays. 

### 6 This is a figure from a paper by Aylward and 

### 7 Hays. The title of the paper is Consideration of Dose in 

### 8 Evaluation of ToxCast Data. Within this figure, you have the 

### 9 box plots which show the plasma and serum concentrations that 

### 10 are relative to the LOAEL and NOAEL for these five compounds and 

### 11 the figures that are used, the symbols on the figure are ToxCast 

### 12 results. At least for these five compounds it shows that within 

### 13 ToxCast that there is a very nice correlation between where 

### 14 you're seeing results within ToxCast and the physiological 

### 15 concentrations that were identified within the study. So when 

### 16 you add in the concentrations that you're looking at within the 

### 17 EDSP, it's starting to climb up a little higher and within the 

### 18 rainbow trout ER binding, it gets even more so. And for some 

### 19 chemicals this could start to enter the range of a 

### 20 supraphysiological concentration. 

### 21 Also yesterday there was some talk about 

### 22 correlation between the high through put assays and rainbow 

### 23 trout ER binding. Within the White Paper, this figure was given 

### 24 as an example of how concentration was very important within 

### 25 their evaluations and that when you looked at the highest 

### 26 concentration that was within ToxCast if you stop there, that

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1 number of compounds that was within the rainbow trout assay 

### 2 would be called negative. It gave within the high through put 

### 3 assays that there was a very small degree of correlation, only 

### 4 about 35 percent. 

### 5 However, if you just use the concentration 

### 6 that would be within the EDSP ER binding assay that you now 

### 7 start to see where you had seven positives, you would have three 

### 8 and this would start to drive that correlation between the 

### 9 positives that you see would make a much stronger correlation 

### 10 between the rainbow trout and the high through put assays. So 

### 11 it's just a suggestion. 

### 12 Now making comparisons between the high 

### 13 through put and rainbow trout ER assays, within ToxCast there 

### 14 are nine ER assays, six reporter gene and three binding. Only 

### 15 two of the six reporter gene assays were used within the 

### 16 comparison and the author cited there was just lack of time to 

### 17 include those other four assays. Also very soon, phase two of 

### 18 ToxCast will release data using 50 endocrine-related assays and 

### 19 there is going to be a thousand plus compounds within there. So 

### 20 EPA has a stable of very well trained top line scientists and I 

### 21 would love to see that they be given enough time, give them 

### 22 enough resources and that they can compare, contrast and adapt 

### 23 this ER Expert System using these data. 

### 24 Now also uncertainties within building up this 

### 25 next version, it would be nice if they could establish a 

### 26 physiologically relevant, limit dose for the prioritization

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1 efforts. This could help compare it to a lot of the other 

### 2 assays. Also if they could include dosimetry as it was given in 

### 3 Wetmore et al. 2012. They also included metabolism within that 

### 4 manuscript. And in addition to comparing our Expert System to 

### 5 the phase two ToxCast, if they could also look at ToxRef data 

### 6 base and also the validated Tier 1 EDSP results. And that's all 

### 7 I have. Thank you for your time. 

### 8 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 9 from the panel for Dr. Geter? Okay thank you. 

### 10 Dr. david Geter: Thanks. 

### 11 **DR. DANIEL SCHLENK:**Our next presenter is 

### 12 Ellen Mihaich from Environmental and Regulatory Resources. 

### 13 **DR. ELLEN MIHAICH:**Okay, good morning. 

### 14 Thanks for letting me speak to you today. I'm going to be 

### 15 talking about a comparison that's been done with the high 

### 16 through put data and actual EDSP Tier 1 data. I'm speaking on 

### 17 behalf of the Endocrine Policy Forum. And I just wanted to 

### 18 briefly tell you who we are. 

### 19 The Endocrine Policy Forum is a consortium of 

### 20 list one test order recipients so the EDSP Tier 1, list one test 

### 21 order recipients and some other interested and involved parties. 

### 22 We represent more than 95 percent of those registrants that 

### 23 receive test orders. So we're the ones that have the experience 

### 24 of running all eleven EDSP Tier 1 studies. It is a self funded 

### 25 group that is administered through CropLife America. And we do 

### 26 have some additional stakeholders there, the American Chemistry

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1 Counselm who will have a very much vested interest in when list 

### 2 two comes out, American Cleaning Institute Chemical Specialty 

### 3 Products Association and some consulting companies. Our 

### 4 objective really is to address regulatory and policy issues as 

### 5 well as technical guidance. We've had very good communications 

### 6 with EPA in trying to help this program go forward, help us 

### 7 understand what we have to do, help us all communicate some of 

### 8 the challenges that we face with doing these eleven screens. 

### 9 So again we have a huge amount of data. And 

### 10 so using that huge amount of data what we've done is we have 

### 11 collected the information from the ER binding, the ER 

### 12 transactivation and the uterotropic studies from our members for 

### 13 40 of the list one chemicals that have been run through this 

### 14 program. There were actually 54 chemicals that were tested and 

### 15 we were able to get information from 40 of them. It was again, 

### 16 unfortunately if we had a little more time we probably could 

### 17 have gotten them all but we didn't. 

### 18 And these are -- this is data from the 890 

### 19 guideline series, which are the required guidelines for doing 

### 20 the Tier 1 studies and in a couple of cases it's from 

### 21 functionally equivalent data which the EPA deemed that it was 

### 22 sufficient to be able to be submitted to satisfy the 

### 23 requirement. 

### 24 Out of this, six chemicals did not have 

### 25 ToxCast data. Six of those 40 did not have ToxCast data. We're 

### 26 assuming that's because they are in a later phase of ToxCast and

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1 so the data wasn't available. So when I'm talking about the 

### 2 information that we're going to present it's really on a total 

### 3 of 34 chemicals, not the 40 because we needed to be able to 

### 4 compare. 

### 5 Now I need to clarify what we're -- qualify 

### 6 what we're doing here. We found it a little difficult to follow 

### 7 some of the process that went on with using say, for example, 

### 8 the cell filter assays. It was a little hard to follow, so we 

### 9 have to make some assumptions. So right up front I want to say 

### 10 that if we did it wrong then I apologize but we did the best we 

### 11 could reading through the White Paper. 

### 12 So we did look at the data using stress 

### 13 filters -- with and without stress filters and we assumed that 

### 14 if two or three of the stress filters were positive, then we 

### 15 removed those two Attagene studies, which is sort of what we 

### 16 were able to read and I think it's Section 7 of the White Paper. 

### 17 We need to also clarify that two of those HTP 

### 18 screens were not publicly available that we could find the data 

### 19 so we were not able to compare our studies to the BG1 or the 

### 20 Odyssey. So we have to limit it to the other three that are 

### 21 available. And we also looked at model predictions from the 

### 22 rainbow trout ER and the Oasis (Q)SAR models and compared them 

### 23 back to our data. So just to remind everybody all of this 

### 24 information is in our written comments that I think you got a 

### 25 couple of days ago at least, so if you want more detail it's in 

### 26 there.

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1 Sue Marty talked about the Rotroff study 

### 2 earlier and so we actually did two different ways of looking at 

### 3 this. One was using the Rotroff criteria, which was that two 

### 4 out of the nine had to be positive in order to call something a 

### 5 positive. And these are the assays that are used in the Rotroff 

### 6 analysis. You have three binding assays and six reporter gene 

### 7 assays. And again -- oh and in that case the cell stress 

### 8 filtering wasn't specifically brought out in their text. There 

### 9 was some discussion of it in the supplemental materials. But it 

### 10 was, again, not really clear, but we went ahead and performed 

### 11 this using the stress filters anyway. 

### 12 And then on the other one is the SAP criteria 

### 13 where we have the eight HTP assays and I just highlighted the 

### 14 three, because there is more discussion on the stress cell 

### 15 filters in the SAP document and just noting again that the two 

### 16 BG1 an the Odyssey were not available for us to use. 

### 17 Okay so the methods that we went through, just 

### 18 as a reminder, is that we had 34 chemicals and we were focusing 

### 19 on the ER binding, the ER transactivation assay and the 

### 20 uterotropic assay. So we have two in vitros and one in vivo. 

### 21 And we again compared that to the hits that came out of the HTP, 

### 22 the rainbow trout ER and the Oasis (Q)SAR models, because there 

### 23 is the two different models that can be looked at. 

### 24 And in the case of using it for the Rotroff 

### 25 methods, two positive hits in any of the nine HTP assays is 

### 26 considered a positive. And what we did just to be a little bit

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1 more conservative is we had some findings within the Tier 1 data 

### 2 that were equivocal and I went ahead and grouped the positives 

### 3 and the equivocals together just to make sure that we didn't 

### 4 miss anything. And in this slide, there is no stress filtering 

### 5 that has been done. And what we found was that of the 34 

### 6 chemicals, there were nine positive HTP hits. Of those nine 

### 7 positive HTP hits when we looked at our data, the data that was 

### 8 turned in to satisfy the Tier 1 requirements, five of those were 

### 9 positive but four of them did not hit any of the three Tier 1 

### 10 screens. 

### 11 Of those, there -- if you then look at how the 

### 12 models performed for the five that were positive, there were 

### 13 three times that the models, either the Oasis model and it would 

### 14 be the Oasis model with S9, the Oasis without the S9 did not hit 

### 15 anything. Three were positive, two were negative. But in all 

### 16 cases the models predicted accurately for those that were 

### 17 negative. 

### 18 Okay so essentially looking at that, we had a 

### 19 44 percent false positive rate, if you look at that from the 

### 20 HTP, and I think I want to point out too that if you ever are 

### 21 going to get to the point where we're going to look at HTP for 

### 22 predictions for in vivo results, which -- and so this contrary 

### 23 to actually what Rotroff found which was a very high correlation 

### 24 or concordance, that it's really only about if you think about 

### 25 it an 11 percent concordance here because we only had one 

### 26 uterotropic positive in our studies but you might think with

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1 that nine HTP actually should that also be predicting an in vivo 

### 2 response and it didn't. 

### 3 If you go to the next with stress filtering, 

### 4 same information there, two or three positives and grouping the 

### 5 equivocals. Here we have a few less positives than the HTP 

### 6 because some of them have been gotten rid of and so we only have 

### 7 six but now it's about a 50 percent false positive rate. 

### 8 However the models, using the Rotroff methods did very well. 

### 9 Where they should have predicted they did. Where they shouldn't 

### 10 have predicted they didn't. However, with the stress filters, 

### 11 we've now lost the uterotropic positive. The stress filters 

### 12 took out that the response from that chemical and so you would 

### 13 have missed the in vivo completely. 

### 14 And I do want to add actually, I have an error 

### 15 in there because you might ask the question about false 

### 16 negatives because I think that is also important. In using the 

### 17 Rotroff methods without stress filtering, there were two false 

### 18 negatives so there was twice that the chemical was missed, and 

### 19 when we used the stress filtering it's an error in our written 

### 20 comments it's actually four false negatives so it missed it what 

### 21 four times where we actually had a response and it didn't get 

### 22 picked up. 

### 23 Here we have using the SAP methods so the same 

### 24 kind of set up with no stress filters here. Because it's only 

### 25 one positive where as the Rotroff was two positives, here you 

### 26 only had one positive and it was that way because even though I

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1 think someone just brought up the fact that in section 7 there 

### 2 were two different ways you could look at the criteria and that 

### 3 is either it's one hit on a transactivation and one hit on a 

### 4 binding or just the hit on the transactivation. We actually did 

### 5 not have any hits on the binding studies in HTP, so they were 

### 6 not predicting very well for our compounds. 

### 7 And so using one positive hit we had 16 

### 8 positive HTP hits but again, when you look at what came out with 

### 9 the actual data, there were seven positives but nine negatives 

### 10 so a false positive rate of 56 percent in that data. And here, 

### 11 unfortunately, the models did not do very well at all. They did 

### 12 not -- they did not match up with what the information was. I 

### 13 will qualify though if I have forgotten to say it the rainbow 

### 14 trout ER only hit once in all of those 34 chemicals but it was 

### 15 -- it predicted correctly all the way through. So we never lost 

### 16 it and it carried through with the compound you would expect it 

### 17 to. 

### 18 But here just like in the -- with no filtering 

### 19 with the Rotroff methods, we did pick up the uterotropic 

### 20 positive but again, you would wonder if there it shouldn't have 

### 21 found more if HTP is going to be predicting 16 positives there. 

### 22 So there is certainly a watch out from the HTP to a prediction 

### 23 of an in vivo result. 

### 24 And then last with the stress filtering on the 

### 25 same data, a few less HTP hits, so 12 but an even worse response 

### 26 with the EDSP data. So here it is now a 58 percent false

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1 positive rate and once again we lost that uterotropic positive. 

### 2 So the stress filters I think are a watch out, but again I could 

### 3 be using the wrong, but it was -- that was unfortunately one of 

### 4 the areas that I think much more transparency needs to be shown. 

### 5 So in conclusion for us, the unavailable data 

### 6 and the poor transparency specifically with the HTP data really 

### 7 kind of probably limited the ability to fully assess the 

### 8 reliability, sensitivity and specificity of the HTP assays and 

### 9 the prediction models. Over all in your charge questions, 

### 10 you're being asked to look at this. So I think it's something 

### 11 to keep in mind. 

### 12 However, just what we were able to do with 

### 13 actual data, we came up with a 44 to 58 percent false positive 

### 14 rate for HTP. And overall, the majority of the in vitro HTP 

### 15 data failed to predict an in vivo response. While it may not be 

### 16 such a big deal for prioritization, I think we have to keep that 

### 17 in mind where these things can go to. 

### 18 But the endocrine policy forum is conceptually 

### 19 supportive of advanced molecular screening and profiling 

### 20 methods. I mean it -- we think it's a great idea. However, 

### 21 we're not sure that the EPA's current approach will achieve the 

### 22 stated goals for affective priority setting at this time. I 

### 23 think we've heard from a lot of people that just said it is 

### 24 still a work in progress. It says that in the document, but 

### 25 still you're being asked to sort of pass some judgment here and 

### 26 I think what Rick Becker said earlier about setting up a road

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1 map would be very helpful in what needs to happen. So things 

### 2 like setting the criteria a priori -- make that decision up 

### 3 front. Don't manipulate it in the end because things can easily 

### 4 get changed. 

### 5 Methodologies, assays and data must be 

### 6 available, transparent and validated and even though you say 

### 7 it's not the charge, I think adequate time needs to be allowed 

### 8 for appropriate peer stakeholder review, as well as appropriate 

### 9 review for the SAP. Thanks. 

### 10 **DR. DANIEL SCHLENK:**Any questions for Dr. 

### 11 Mihaich? Dr. Burris? 

### 12 Dr. Thomas Burris: Did you do an analysis to 

### 13 look for the potential for HTP to miss actives in the EDSP so 

### 14 basically the reverse? 

### 15 **DR. ELLEN MIHAICH:**Yes, that was -- and I 

### 16 mentioned in the -- so we had two -- and I should have done the 

### 17 math really quickly but I didn't. So two out of the 34 were 

### 18 missed in the Rotroff type analysis when there was no filtering, 

### 19 four were missed when there was filtering, and in the SAP type 

### 20 of analysis none were missed without filtering and two were 

### 21 missed with filtering. 

### 22 Dr. Thomas Burris: Okay. 

### 23 **DR. ELLEN MIHAICH:**So there were some false 

### 24 negatives. 

### 25 Dr. Thomas Burris: In the general scheme when 

### 26 you look at the HTP positives, did you do any type of rank order

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1 to look at if the very weak actives were the ones being missed 

### 2 or the ones that were negative in the EDSP? 

### 3 **DR. ELLEN MIHAICH:**You know it's a little 

### 4 hard to do that because really what ended up happening most of 

### 5 the time from -- so again looking at the ToxCast data most of 

### 6 the time the hits were in that two Attagene assays. And the 

### 7 reason that -- in fact almost always were the two Attagene 

### 8 assays there was really very little hit in anything else. Then 

### 9 what kept in or didn't keep it in from the stress perspective is 

### 10 whether or not there was two or three or one stress assay that 

### 11 got hit. 

### 12 So I'm not sure that I'm answering your 

### 13 question right but I don't -- do you want to try that one again? 

### 14 Dr. Thomas Burris: Well I was just concerned 

### 15 that since the HTP assays can many times be much more sensitive 

### 16 than assays that are further downstream, that basically the 

### 17 negatives that are coming out from the EDSP maybe the weak 

### 18 actives that were found HTP, which is what you would expect. 

### 19 **DR. ELLEN MIHAICH:**Oh, I mean I can't -- I 

### 20 can't -- then that would take me having to look at how -- 

### 21 Dr. Thomas Burris: Because my expectation 

### 22 would be that the HTP would detect a number of false positive 

### 23 based on the sensitivity of some of these assays. 

### 24 **DR. ELLEN MIHAICH:**Oh, well definitely. Yeah 

### 25 I mean that's the 44 to 58 percent false positive. So of those 

### 26 they -- I mean those are ones that we ran through the EDSP

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1 studies and they did not -- they were not active. 

### 2 Dr. Thomas Burris: Yes, my concern is that 

### 3 there is no analysis to say that -- if you did an HTP and you 

### 4 have a very active compound and that didn't show up in the EDSP 

### 5 that would more worrisome than if you found a weekly active 

### 6 compound in the HTP and you know it looks like you're not 

### 7 understanding my question. 

### 8 **DR. ELLEN MIHAICH:**Well I guess because I'm 

### 9 trying to figure out how do you know that it's very active in 

### 10 HTP? What's the criteria that says it's very active in HTP? 

### 11 Dr. Thomas Burris: Well you have efficacy and 

### 12 potency. 

### 13 DR. ELLEN MIHAICH: Oh. 

### 14 Dr. Thomas Burris: So it's not just an on 

### 15 off, it's how much on. 

### 16 **DR. ELLEN MIHAICH:**Yeah, I mean really were 

### 17 only able to look at it as was it -- would it have been 

### 18 considered a positive. So no we did not go to that -- and I 

### 19 will, again, and I think it's a fair question and there is 

### 20 absolutely no time to do that. The other problem that we ended 

### 21 up having was and it was something that Sue Marty brought up, 

### 22 which was I did the entire assessment based on a download form 

### 23 ToxCast from 2009, 2010 and a significant number of things that 

### 24 were positive became negative and negative became positive in 

### 25 the 2012 download of it. 

### 26 And again the transparency of why that

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1 occurred, I don't know. So I kind of looked at it was it 

### 2 positive or negative and left it go at that. So -- 

### 3 interestingly though, when you look at the data since I had to 

### 4 do it twice, when I look at the output from what I would have 

### 5 said the first time with the 2009 ToxCast data even though some 

### 6 compounds completely switched around, things that were positive, 

### 7 again were not, it came up with almost the same statistics, 

### 8 which to me is a bit worrisome if we're getting better at curve 

### 9 fitting or criteria setting, whatever to what is a positive and 

### 10 a negative, but I can still come up with about a 50 percent 

### 11 false positive rate. 

### 12 But again so much work has been done and I 

### 13 think it's -- I mean I really commend all of the work that EPA 

### 14 has done. I think maybe part of it could be that if we had a 

### 15 little more transparency about what's going on within HTP, it 

### 16 might help us try to figure out where the, you know, the issues 

### 17 are. Sorry. 

### 18 **DR. DANIEL SCHLENK:**Okay, any other 

### 19 questions? Yes, Dr. Keiser? 

### 20 **DR. MICHAEL KEISER:**Michael Keiser. So I 

### 21 could be getting it a little bit wrong because we only have time 

### 22 to go over it right now but it seems as though what we're 

### 23 basically looking at is positive predictive value. In this case 

### 24 if you think of sensitivity, specificity and negative and 

### 25 positive predictive value and so -- and why I'm motivated by 

### 26 that is we're coming at it from the positive predictions of the

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1 HTP. Although there was some discussion a moment ago about ones 

### 2 HTP might have not predicted positively. 

### 3 I was curious though, more generally, in 

### 4 sensitivity and also in concordance when you begin to take into 

### 5 account true negatives that have come out of that too because of 

### 6 course if you imagine the hay stack that has been referred to 

### 7 earlier, much of the haystack is empty presumably. And so I 

### 8 realize that we don't have the full matrix -- full negative data 

### 9 to compare against the HTP results, but I was curious how some 

### 10 of these rates might change taking into account the true 

### 11 negative predictions that are present in this too. 

### 12 **DR. ELLEN MIHAICH:**Okay so the true negative 

### 13 predictions from the EDSP data or the true negative predictions 

### 14 from the HTP? 

### 15 **DR. MICHAEL KEISER:**It would be from the HTP 

### 16 in this case because that is the starting point that you're 

### 17 using. 

### 18 **DR. ELLEN MIHAICH:**Okay so you're asking if 

### 19 they were truly negative in HTP, what does that predict for -- 

### 20 **DR. MICHAEL KEISER:**How does that change the 

### 21 concordance rates overall? 

### 22 **DR. ELLEN MIHAICH:**Well, I'm trying to figure 

### 23 out how to answer that question. I mean essentially what -- if 

### 24 you take that data and if you figure there were say 16 positives 

### 25 then there is 34 minus 16 negatives in HTP and I had talked -- 

### 26 **DR. MICHAEL KEISER:**I'm sorry I was speaking

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1 more generally beyond that set. 

### 2 DR. ELLEN MIHAICH: Oh. 

### 3 **DR. MICHAEL KEISER:**Because as we saw earlier 

### 4 from some of the screening data and I see that you're 

### 5 restricting this case -- 

### 6 **DR. ELLEN MIHAICH:**Yes, I mean I can't speak 

### 7 any more than to what the 34 chemicals that we've got data for. 

### 8 **DR. MICHAEL KEISER:**Understood. 

### 9 **DR. ELLEN MIHAICH:**Yes, and I don't know that 

### 10 I want to go out there. I mean it could be better. I don't 

### 11 know. All I know is that if you were to look at, I mean these 

### 12 are chemicals that had to be run through the first, you know, 

### 13 Tier 1 of EDSP and so we have that data. We have the data from 

### 14 validated studies and we're trying to sort of back look at if we 

### 15 had put our chemicals through HTP first, what would we have been 

### 16 asked to do. 

### 17 **DR. MICHAEL KEISER:**I understand, the 

### 18 motivation from my question had only been the larger set and the 

### 19 larger space, but I see that you can't address that. 

### 20 **DR. ELLEN MIHAICH:**I can't really -- I can't 

### 21 address that. 

### 22 **DR. MICHAEL KEISER:**So thank you. 

### 23 **DR. ELLEN MIHAICH:**Thanks. 

### 24 **DR. DANIEL SCHLENK:**Okay. Any other 

### 25 questions? Great, thanks Ellen. Our final presenter, according 

### 26 to my list is Terry Quill from Quill Law Group.

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1 **MR. TERRY QUILL:**Good morning, thank you for 

### 2 this opportunity to comment. Let's see if I can figure out -- 

### 3 we're good, okay. I'm Terry Quill I'm commenting on behalf of 

### 4 American Chemistry Council. And my comments are going to be a 

### 5 little shift of focus here. I'd like to comment about the 

### 6 universe of chemicals. And that applies more to your charge 

### 7 question one. Since enactment of the Safe Drinking Water Act 

### 8 amendments and the FFDCA section that we're dealing with here, 

### 9 I've dealt as much with legal regulatory, statutory issues, 

### 10 policy and procedure, as technical issues and I just want to 

### 11 focus on the former and talk about one regulatory issue and a 

### 12 policy issue. 

### 13 Why don't I start out with my conclusion, and 

### 14 maybe that will just make it easier for all to see where I'm 

### 15 going with this. My conclusion is that EPA should determine the 

### 16 scope of its authority prior to prioritization as opposed to 

### 17 during or after prioritization and what do I mean by that. When 

### 18 I say authority looking over my presentation, I don't mean it in 

### 19 a pejorative sense. I'm not challenging EPA's authority. What 

### 20 I'm really talking about are what chemicals are correctly within 

### 21 the scope legally of the EDSP. That's what I'm referring to. 

### 22 And I'm saying they should determine that 

### 23 before they get started instead of after they get started -- 

### 24 after they finish with prioritization and my presentation really 

### 25 deals with, although that sounds like a very simply thing and 

### 26 all it would require is if you recall that chart EPA put up

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1 yesterday where you start out with the universe and there is a 

### 2 few filters, you go through prioritization and at the end EPA 

### 3 determines whether they have the authority to address those 

### 4 chemicals. All I'm saying is just move that up to the beginning 

### 5 -- and there are good reasons to do it. 

### 6 And what I'm going to say here isn't any 

### 7 different than what EPA said yesterday, but I'm going to dig a 

### 8 little bit deeper in that EPA correctly pointed out what its 

### 9 authority was and I assume the reason why they do that is so 

### 10 that they can, in the context of prioritization, they can 

### 11 demonstrate what authority they're acting under and correctly 

### 12 they say the FFDCA, which really is the Food Quality Protection 

### 13 Act and Drinking Water Act amendments. 

### 14 So for pesticide chemicals, the authority is 

### 15 pretty clear. The Act explicitly says the EPA should undertake 

### 16 endocrine testing of pesticide chemicals but for non-pesticide 

### 17 chemicals, there is some language in there that makes a 

### 18 difference. Congress had something in mind and it says that EPA 

### 19 could also -- may also require screening and testing of 

### 20 substances that may be found in sources of drinking water if a 

### 21 substantial population may be exposed to the substance. 

### 22 Well those words have meaning. And those 

### 23 terms or phrases need to be interpreted by the Agency, may be 

### 24 found in sources of drinking water. What's the source of 

### 25 drinking water? Finish water, is it an aquifer under a super 

### 26 fund site? Substantial population, two people, 10,000 people

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1 may be exposed. Should there be a demonstration that it was 

### 2 actually found in drinking water. Those are questions that need 

### 3 to be answered. 

### 4 Unfortunately EPA has not defined those 

### 5 limiting terms of the Safe Drinking Water Act. And keep in mind 

### 6 this is important because EPA has turned to these acts for its 

### 7 authority to address prioritization of substances for the EDSP. 

### 8 And what did Congress mean by this? Well I don't think Congress 

### 9 had I mind a research program. I mean Congress made it very 

### 10 clear the end game was important and EPA said that this morning. 

### 11 At the end of the day EPA wants to determine which substances 

### 12 pose a risk to human health in the environment and they want to 

### 13 regulate them. 

### 14 But what -- maybe the way to look at this is 

### 15 Congress helped out in putting a prioritization filter in here. 

### 16 We're going to ask EPA to look at those things that pose the 

### 17 greatest potential threat because there is a lot to do here. 

### 18 And therefore EPA you figure out which substances those are and 

### 19 address those and at least address those first. 

### 20 Okay, so let's talk about EPA's universe, the 

### 21 current universe. Well it clearly includes substances outside 

### 22 of EPA's authority. And it's a little difficult to tell because 

### 23 EPA has not clearly defined its authority in this regard instead 

### 24 what it did is it relied on some existing lists of chemicals. 

### 25 Again, EPA didn't exactly say how those lists fit within the 

### 26 terms of the statute.

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1 And I'll give you an example. The 

### 2 contaminants in the CCL3 list, well those -- that list was 

### 3 developed with other things in mind. And some of those 

### 4 substances are not in sources of drinking water and it never 

### 5 really determined whether a substantial population is exposed. 

### 6 And for example, in developing the CCL3 list, the EPA looked at 

### 7 things such as TRI releases, production volume, adverse health 

### 8 effects. Those don't really go to the statutory requirements. 

### 9 So what really is EPA's current universe of chemicals? I think 

### 10 it is safe to say that it isn't a list of chemicals that EPA may 

### 11 include in the EDSP because EPA doesn't have authority over some 

### 12 of those chemicals. 

### 13 And it's not clear that EPA disagrees with 

### 14 that. I can't really tell what EPA's definite position is. On 

### 15 one hand it says it has authority over the entire universe 

### 16 including the CCL3 list. On the other hand and I just point out 

### 17 a few things in the White Paper and this was repeated by EPA 

### 18 yesterday morning, it acknowledges that there may not be 

### 19 exposure potential for some of the universe chemicals. But that 

### 20 is a requirement of the Safe Drinking Water Act. 

### 21 For example, EPA says it will determine a 

### 22 universe chemical has the potential for human exposures during 

### 23 prioritization. And that's like paper and there's the page. 

### 24 And EPA will use an exposure filter, again implying that 

### 25 prioritization will be used to determine exposure and therefore 

### 26 authority. And prior to officially advancing chemicals for EDSP

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1 Tier 1 screening, the Agency must make an exposure finding 

### 2 stated in the 1996 Safe Drinking Water Act amendments. 

### 3 And again EPA repeated that yesterday morning 

### 4 and it clearly states that EPA doesn't believe it has the 

### 5 authority for all the chemicals going through prioritization. 

### 6 Well it may sound like a minor point but -- did I miss a slide? 

### 7 No. It is significant for a couple of reasons and let's put 

### 8 aside the first questions to whether EPA has the legal authority 

### 9 to prioritize substances for the EDSP that are not within its 

### 10 EDSP authority. Let's just put that aside for this moment and 

### 11 let's look at why EPA should not, for good policy reasons, 

### 12 prioritize substances outside of its EDSP authority. 

### 13 Well first of all it's just a misallocation of 

### 14 resources. If EPA at some point determines that it doesn't have 

### 15 authority and it can't do anything with those chemicals wouldn't 

### 16 we be better off using those resources to prioritize chemicals 

### 17 that may in fact pose a threat to human health in the 

### 18 environment through exposure? 

### 19 And then secondly and maybe perhaps more 

### 20 importantly as you all know, prioritization -- the assays are 

### 21 designed to minimize false negatives. And what that generally 

### 22 means is there is likely to be a significant number of false 

### 23 positives. Well picture a substance that is identified and it 

### 24 turns out to be a false positive and that EPA determines then it 

### 25 does not have the authority to send to Tier 1 screening or Tier 

### 26 2 testing.

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1 Well generally people believe that well okay, 

### 2 false positives it's a good thing to have because at least we 

### 3 won't lose any chemicals. But they're relying on Tier 1 and 

### 4 Tier 2 to determine whether that is a false positive or not. 

### 5 There won't be that opportunity for those chemicals. So in 

### 6 other words, chemicals will languish on this list of positives 

### 7 and they may very well be false positives. And that will lead 

### 8 to what we call product deselection. And the only things I can 

### 9 think that would solve that problem would be to ensure that you 

### 10 really minimize false positives or do a real good job in 

### 11 communications and try to determine exactly how many false 

### 12 positives are expected in the screening assays. 

### 13 You've got to let people know that these 

### 14 results may not be true and there is not going to be an 

### 15 opportunity to check that. Or, and getting back to my 

### 16 conclusion, actually let me go here first. How should the 

### 17 universe of EDSP chemicals be used? The way it should be used 

### 18 is perhaps as a starting point. 

### 19 Okay, we have the universe the next step 

### 20 should be that EPA would look at those chemicals and determine 

### 21 which fall within its authority. The next step would be then to 

### 22 prioritize the chemicals and move on to Tier 1 screening. That 

### 23 would be the appropriate way of doing it. 

### 24 And so my conclusion again is without reading 

### 25 all these points, is basically why don't we take that last step 

### 26 where EPA determines the scope of the EDSP and its authority,

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1 move it up to the beginning so that we're not misallocating 

### 2 resources for chemicals that EPA has no intention of doing 

### 3 anything with and secondly so that we're not causing product 

### 4 deselection, in other words having chemicals languish on a list 

### 5 of positives when in fact they truly may be false positives. 

### 6 Thank you. 

### 7 **DR. DANIEL SCHLENK:**Thank you. Any questions 

### 8 from the Panel? Okay, thank you very much. 

### 9 **DR. TERRY QUILL:**Thank you. 

### 10 **DR. DANIEL SCHLENK:**Okay any other public 

### 11 commenters that we have? Okay this concludes our public 

### 12 commenting period. At this point in time, I think it's probably 

### 13 a good idea to maybe go for an early lunch to beat the crowds 

### 14 and let's be back at 1:00 and we'll start the charge questions 

### 15 then. 

### 16 (WHEREUPON lunch was taken) 

### 17 **DR. DANIEL SCHLENK:**Can we take out seats 

### 18 please? Okay let's go ahead and get started. I need to read 

### 19 the charge questions into the record. I believe you've got the 

### 20 remote there yeah. You can -- if you wouldn't mind go ahead and 

### 21 reading question one in. Just state your name Mary that's 

### 22 great. 

### 23 MS. Mary Manibusan: Mary Manibusan. question 

### 24 number one -- please comment on the appropriateness of the 

### 25 overall conceptual approach for prioritizing the EDSP chemical 

### 26 inventory noting subsequent charge questions address issues

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1 associated with specific components of the prioritization 

### 2 approach. In your comments, please address the extent to which 

### 3 EPA's description of the process transparently captures and 

### 4 describes the key technical steps of the prioritization scheme 

### 5 and whether there are other scientific considerations the agency 

### 6 should incorporate into its EDSP prioritization scheme; the 

### 7 robustness of the scientific support for the overall approach 

### 8 and, in particular, the logical sequence of filters, and whether 

### 9 changes to the approach could set priorities more efficiently 

### 10 and effectively. 

### 11 **DR. DANIEL SCHLENK:**Thank you. The lead 

### 12 discussant for question one is Dr. Chambers. 

### 13 **DR. JANICE CHAMBERS:**Thank you. Thank you 

### 14 for the opportunity to start off the discussion here. I think I 

### 15 can probably speak for the whole panel by saying that the EPA 

### 16 should be commended for its efforts to develop a logical 

### 17 prioritization scheme for screening a massive number of 

### 18 chemicals. Clearly it's been a lot of work that's gone into it 

### 19 up to this point and we appreciate that. 

### 20 EPA has incorporated the advice from previous 

### 21 SAPs and this SAP recognizes that and appreciates that as well. 

### 22 This question addresses an overview of the process so we will 

### 23 not be addressing as your sub -- as your parenthetical says we 

### 24 will not be addressing the components of the approach in detail. 

### 25 The SAP appreciates that this effort is still a work in 

### 26 progress and probably the number of questions that arose for

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1 clarification and everything indicate that we do appreciate that 

### 2 much is not really totally finalized yet. 

### 3 The SAP also appreciates that EPA will need to 

### 4 consider both the legislative mandates to test certain chemicals 

### 5 that drive what you do and also the level of effort resources 

### 6 that would be required do a completely through job of screening. 

### 7 And EPA will need to balance its efforts guided by multiple 

### 8 factors, some of which are not scientific factors and therefore 

### 9 are outside the purview of the SAP to recommend. 

### 10 To a certain extent, it appears that EPA is 

### 11 trying to compare and match results from diverse methods that 

### 12 were run independently and more transparency is needed in 

### 13 approaches, methodology and rationale, and again I think that 

### 14 was reflected by the number of questions that we had about 

### 15 different things especially rationale. 

### 16 The SAP appreciates being convened early in 

### 17 this process for comment and while this is still a work in 

### 18 progress there is an opportunity to provide suggestions for 

### 19 changes and approach that perhaps you'll be able to utilize. 

### 20 The SAP understands that the point of this activity is to make 

### 21 an enormous process manageable guided by the best scientific 

### 22 principles. 

### 23 Overall, I think we find the approaches 

### 24 logical and that filters can remove from the possibility of 

### 25 testing those compounds that are unlikely to be systemically 

### 26 active early in the process as well as employing methods early

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1 that are less labor intensive and do not require intact animals. 

### 2 For example, the fiscal chemical factor such 

### 3 as corrosivity would make the compound destructive at the site 

### 4 contract and would preclude systemic distribution and therefore 

### 5 the filter would remove from testing compounds that would not be 

### 6 systemically active. However, the rationale for the cut off 

### 7 levels for some of these filters should be provided in more 

### 8 detail than it is in the present White Paper for better clarity 

### 9 and transparency. 

### 10 The adverse outcome pathway is a logical 

### 11 organizing principle because it relates potential overall toxic 

### 12 response and biological plausibility to a meaningful molecular 

### 13 initiating event relevant to a particular toxicological 

### 14 mechanism and will allow the assessment of internally consistent 

### 15 data within the AOP, when data are accumulated at subsequent 

### 16 levels of screening. You might just say that the terminology 

### 17 introduced by one of the public commenter's initial molecular 

### 18 event does potentially have some merit and might be worth 

### 19 considering. 

### 20 This categorization and organizing principle 

### 21 AOP should assist in the predictions that might be required for 

### 22 different chemicals and different endocrine pathways in the 

### 23 future. And one concern that at least I and at least one other 

### 24 of us here has is the bioavailability and exposure. Those are 

### 25 not well described and it think it's critical that a potential 

### 26 natural exposure levels be assessed, not necessarily measured

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1 just assessed in order to determine whether the compound is 

### 2 likely to reach target organisms and if so then it's molecular 

### 3 targets. 

### 4 The White Paper would benefit from greater 

### 5 clarity and transparency regarding how exposure is being taken 

### 6 into consideration and the prioritization process. I do 

### 7 understand that some of that is driven by the regulations, but I 

### 8 think just better explanation would help those of us in the 

### 9 ivory tower academic institutions to understand what is going 

### 10 on. 

### 11 To a certain extent the water solubility -- 

### 12 excuse me the water stability and the other physico-chemicals 

### 13 filters do address this in terms of environmental stability, but 

### 14 consideration of which of these chemicals are measureable or 

### 15 likely to be measureable in an environmental matrices or food 

### 16 source does seem to make some sense and might give some thought 

### 17 to that. If information about production or use of certain 

### 18 listed chemicals provides no evidence that these chemicals are 

### 19 entering the environment or have the potential to enter the food 

### 20 chain, then the theoretical estrogenicity seems to be a moot 

### 21 point from a practical stand point. I would think those would 

### 22 end up being low priority or off the list for testing. 

### 23 If production or other processes change in the 

### 24 future such that the excluded chemicals are likely to enter the 

### 25 environment, then the screening can be conducted at that point. 

### 26 Again the SAP does understand that regulations could override

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1 exposure considerations. 

### 2 Most of the initial screening techniques can 

### 3 be conducted relatively quickly and efficiently, some 

### 4 computationally and therefore it will not invest animal's lives 

### 5 unnecessarily. Such information will give insights into whether 

### 6 the estrogen receptor activation pathway is likely to be 

### 7 activated and if so whether the concentrations at which a 

### 8 compound is active are of concern. 

### 9 However, a couple of the panelists do have 

### 10 additional concern regarding the sequence of steps presented 

### 11 here for determining the prioritization. That suggestion is 

### 12 going to be made for an alternative as well as suggestions 

### 13 regarding obtaining more chemicals to perform laboratory tests 

### 14 and Drs. Burris and Perkins will address that in just a minute. 

### 15 16 So in conclusion of my remarks here while the 

### 17 methodology presented at this SAP focused on the ER binding AOP, 

### 18 the overall concept and logic behind it makes sense and it 

### 19 should provide a generic approach that would be adapted -- 

### 20 should be adapted to other ED pathways in the future. 

### 21 **DR. DANIEL SCHLENK:**Thank you. Our first 

### 22 associate discussant is Dr. Burris. 

### 23 **DR. THOMAS BURRIS:**So commenting on setting 

### 24 the priorities for the compounds, I think that there is a 

### 25 significant issue using computational models that predict -- 

### 26 that are made to predict receptor binding when the capabilities

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1 are present to measure the actual data. My suggestion is that 

### 2 these HDP assays be utilized as a priority setting compound for 

### 3 progression to the later Tier 1 assays and of course there has 

### 4 been some discussion about replacing the earlier Tier 1 assays 

### 5 with these HTP assays. 

### 6 My suggestion would be screening the entire 

### 7 compound library that is amendable to testing. The risk I 

### 8 believe in using the computational prediction is they're based 

### 9 on clearly defined high affinity binding models and the risk 

### 10 there is that they're not likely to be predictive of compounds 

### 11 that display unexpected modes of binding. So they're basically 

### 12 designed to predict what we already know and we'll clearly miss 

### 13 potential modes of binding that have never been described before 

### 14 and I think that creates a clear risk. 

### 15 The other risk is that low affinity binding 

### 16 molecules may not fit into the models that have been designed 

### 17 for high affinity binding molecules. So that's a risk. So here 

### 18 again is pointing to the issue of with the ability to be able to 

### 19 run those 5000 compounds and generate empirical data the powers 

### 20 is much more impressive. 

### 21 I would suggest that an effective HTP approach 

### 22 would employ both biochemical and a limited number of cell based 

### 23 assays. There have been some presentations combining those 

### 24 assays to make a reasonable prediction of what should go 

### 25 forward. One of the public commenters had a nice model looking 

### 26 at various methods to evaluate whether a computer should go

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1 forward looking at the concordance with EDSP data. 

### 2 I think this is something that the FDA should 

### 3 look at, this type of analysis of positives coming through the 

### 4 HTP assay and the concordance with EDSP. I think that it's 

### 5 critical that the assay be designed or the selection procedure 

### 6 be designed so as to minimize the number of false negatives that 

### 7 is clearly a risk. 

### 8 When I did some statistical analysis on the 

### 9 data that was presented today, I thought the filter was quite 

### 10 good, at least with the one model that was used where no false 

### 11 negatives were detected. Approximately 44 percent of the 

### 12 compounds that were detected in the HTP assay were positive in 

### 13 the EDSP. I think it's unreasonable to expect that there be 

### 14 complete concordance between the HTP assays and especially the 

### 15 in vivo EDSP assays. I think that clearly the tools are there 

### 16 and it can be used to set the priorities. 

### 17 One issue that I'm not absolutely clear on is 

### 18 the availability of the 5000 compounds within the compound 

### 19 universe. I think that the computational models that are 

### 20 available could be used to prioritize what compound should be 

### 21 accessed first if there is limited availability of resources to 

### 22 obtain those compounds. For example if some of these compounds 

### 23 have to be synthesized that, of course, will take time and the 

### 24 computational models could be used to set priorities on what 

### 25 compounds need to be synthesized and then tested first. 

### 26 And I believe that is the end of my comments.

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1 Oh one more comment. Within the series of data that will be 

### 2 generated from the HTP there will clearly be various levels of 

### 3 activity, and so I believe in setting the priority in the active 

### 4 compounds from the HTP assays. It's critical to define the 

### 5 potency and the efficacy of those compounds as a priority to go 

### 6 into the in vivo EDSP assays. 

### 7 The other point that should be considered is 

### 8 potential exposure. When comparing two compounds especially at 

### 9 low affinity, one that has potential high exposures is what I 

### 10 would consider higher priority than one that has low exposure 

### 11 and low potential to ever reach those concentrations in the 

### 12 environment. 

### 13 **DR. DANIEL SCHLENK:**Thank you. Dr. Meeks. 

### 14 **DR. BETTE MEEKS:**Well first of all just to 

### 15 echo a few of Jan's remarks. I think you've put an awful lot of 

### 16 effort into this and it is really appreciated and we recognize 

### 17 that it is a moving target. I really think that the Expert 

### 18 System has been thoughtfully developed and well communicated in 

### 19 the context of the organizing construct of the Adverse Outcome 

### 20 Pathway as a basis to anchor prediction or observation of early 

### 21 key events to adverse outcomes traditionally considered in 

### 22 regulatory risk assessment and we certainly need that in the 

### 23 early stages. 

### 24 In essence, depending upon the proximity of 

### 25 these key events to the adverse outcome of interest, it also 

### 26 provides a basis for assessing confidence or uncertainty in the

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1 outcome of various approaches to prioritize chemicals for 

### 2 testing and I think that's probably a recurring theme that 

### 3 you'll hear from me that I think that we can weight different 

### 4 input to setting up a prioritization scheme, but we need to be 

### 5 very clear about that. And it's often -- I think we can set 

### 6 those criteria a priori in terms of what we're waiting most. 

### 7 Currently unspecified in the conceptual 

### 8 approach are target capture rates for the initial phases of the 

### 9 prioritization. So what's at least a preliminary goal? What 

### 10 would be considered a reasonable proportion initially for 

### 11 testing? I think that's kind of a rather critical aspect to 

### 12 determine the required degree of discrimination for the purpose 

### 13 at hand. So again, it comes down to really understanding what's 

### 14 our endgame here? Where are we trying to get to? 

### 15 So in my view as well, the overall conceptual 

### 16 approach does not yet fully address the stated programmatic goal 

### 17 which is to determine the order in which to screen chemicals for 

### 18 endocrine activity. And I heard that and I wrote it down and I 

### 19 said okay, that's what we're working for. This would require 

### 20 relative ranking of the candidate chemicals for screening. So 

### 21 in my view this would be facilitated through consideration of 

### 22 incorporation of a broader range of potentially more 

### 23 discriminating surrogates of, for example, potential for 

### 24 exposure. And as Jan has mentioned, to a small degree some 

### 25 parameters related to potential for exposure have been addressed 

### 26 in the existing filters, though they're not explicitly described

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1 as such. 

### 2 And again we keep hearing reference to 

### 3 exposures so to contextualize this from my perspective in terms 

### 4 of what are the most important roads to pursue, I kind of need 

### 5 to understand what this nebulous taking account of exposure 

### 6 actually consists of. So, in addition, I think that because of 

### 7 the way the system has been developed, they've also not 

### 8 necessarily been introduced in a logical sequence because 

### 9 they've been variously included in filters for, for example 

### 10 physical chemical properties, predicated principally on 

### 11 testability. So for example, exclusions based on chemical 

### 12 reactivity, for example chemicals that hydrolyze in the 

### 13 environment. 

### 14 And then of course, one of the simpler filters 

### 15 from an exposure perspective not produced or used in the U.S. is 

### 16 only considered prior to the introduction of testing rules. So 

### 17 again, I think it would be helpful to kind of overlay this with 

### 18 what are the really important considerations from the exposure 

### 19 perspective. 

### 20 I think that more systematic consideration of 

### 21 potential for exposure through introduction of information on 

### 22 parameters expected to be most discriminating at earliest stage 

### 23 from an exposure perspective is really advised. These 

### 24 parameters in my experience provide much greater potential for 

### 25 relative ranking of the candidate chemicals. For example, the 

### 26 fact that a significant proportion of the relevant substances

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1 with unknown binding potential fall into one use category class 

### 2 indicates that this avenue could net considerable benefit. So 

### 3 again using the use category information to bound the estimates 

### 4 of exposure, so that's it. 

### 5 **DR. DANIEL SCHLENK:**Thank you. Dr. Perkins. 

### 6 **DR. EDWARD PERKINS:**Well I would like to echo 

### 7 the sentiment of the others, I think it's a great start. It's a 

### 8 very complicated process. In general, I think it's fairly 

### 9 clearly outlined. 

### 10 There are a number of improvements that people 

### 11 have mentioned that I would reiterate, perhaps moving HTP 

### 12 testing results up earlier to compensate for some of the 

### 13 failings or drawbacks of just computational models. It's clear 

### 14 from some of the evidence that you're getting capturing more 

### 15 false positives than false negatives, is more important. 

### 16 Along the same lines, I think the program 

### 17 needs to put more effort into ensuring people that this is a 

### 18 prioritization and ranking, it's not saying that these are 

### 19 endocrine disrupting chemicals. It's just a priority for 

### 20 further testing. And in that context, I think expectations do 

### 21 need to be somewhat managed of what sort of false positive rate 

### 22 would be acceptable. It's going to be very difficult to develop 

### 23 absolutely predictive models and absolutely predictive assays. 

### 24 But there's got to be some acceptable reduction in total numbers 

### 25 as long as you're not excluding potentially positive ones. 

### 26 So, I think in those veins, the Expert System

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1 analysis may be more valuable in prioritizing chemicals that you 

### 2 need to go further get for testing that may have specific 

### 3 effects. I'm a little concerned that in the broader space, that 

### 4 you're only able to get 30 to 40 percent currently. I know 

### 5 that's because of sort of programmatic limitations at the 

### 6 moment. But if the current program scheme is to be in effect 

### 7 then it would really, I think, benefit to put more effort into 

### 8 actually acquiring these things. Or synthesizing things that 

### 9 the Expert System analysis says may be problematic and putting 

### 10 those as a priority. 

### 11 Along with the physical chemical and exposure 

### 12 filtering, I think chemicals such as fragrances, are going to be 

### 13 causing a problem. One the HTP assays can't really deal with 

### 14 that at the moment. There's also going to be respiratory type 

### 15 inhalation exposures for that or just through dermal exposures. 

### 16 So the route of exposure is going to be different perhaps from 

### 17 some other things. So I think that's going to be a problem and 

### 18 the physical chemical logic rules may be challenged with that. 

### 19 And again to reemphasis this idea of ranking 

### 20 chemicals, I think we have a lot of the data, a lot of the 

### 21 potential for ranking things either based on potency or activity 

### 22 or exposure, a combination of exposure and physical chemical 

### 23 properties and effects based. So I think another priority would 

### 24 be how you can use these categories, aggregate them, to get a 

### 25 better ranking of potent estrogen chemicals that are going to be 

### 26 high exposure levels.

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1 I really commend the program for trying to 

### 2 integrate new approaches in there, but also I think that it's a 

### 3 little narrow in that a lot of other areas of information are 

### 4 being excluded for providing background information on some of 

### 5 the chemicals that are being used or attached. So some 

### 6 component of, I think, background literature for chemicals 

### 7 should be also included, at least in the Expert System analysis 

### 8 to support some of the conclusions and perhaps acquired data 

### 9 that hasn't been through EPA programs, as part of the weighting. 

### 10 11 And I guess finally, coming to this sort of 

### 12 ultimately, you're going to need some sort of a weight of 

### 13 evidence or aggregate analysis to make sense of this. I think 

### 14 there needs to be a very clear, transparent way that you do 

### 15 this, with the pluses and the minuses to that as a final segment 

### 16 for prioritizing or passing things on. 

### 17 I have one last comment. On the AOP thing, I 

### 18 think it's a great organizing principle. One caution I have in 

### 19 the document, it's only put down for fish species. There are 

### 20 detailed differences between human and fish, in between one can 

### 21 be indicative of the other but some of the details are 

### 22 different. So, I think it also would be good for you to present 

### 23 both human and fish side by side to show what the steps are, 

### 24 what's common between them and how you can extrapolate between 

### 25 the different model systems more directly because as people have 

### 26 said, and they will continue to say, the context in which you

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1 are doing the assay is very important. 

### 2 So a tumor cell line versus a fish ovary or 

### 3 fish liver is going to be very different. So by framing the 

### 4 question very narrowly and providing the sort of information 

### 5 space you're dealing with, and explicitly saying we know there's 

### 6 these differences and here's how we're handling them. I think 

### 7 it will help circumvent a lot of just basic confusion about the 

### 8 program. 

### 9 **DR. DANIEL SCHLENK:**Okay. Thank you. Dr. 

### 10 Shultz. 

### 11 **DR. TERRY SCHULTZ:**I concur with the comments 

### 12 that were said by my colleagues on the panel before me. I would 

### 13 like to address one point which will be discussed in detail 

### 14 later on and that is the ability to put together a second 

### 15 generation of filters that would assist you in reducing the 

### 16 inventory you have. You list Phys-Chem properties but I believe 

### 17 that there are other molecular properties or even biological 

### 18 properties or chemical properties that would certainly help you 

### 19 to do those types of things and I encourage you to look at that. 

### 20 Thank you. 

### 21 **DR. DANIEL SCHLENK:**Thank you. Other Panel 

### 22 members? Dr. Keiser. 

### 23 **DR. MICHAEL KEISER:**First I agree with 

### 24 everything that has been brought forward already. I wanted to 

### 25 add one extra point because one concern that has come up is the 

### 26 idea that with some of the predictive computational models, we

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1 might be missing out on chemotypes or examples of chemicals that 

### 2 would be non-intuitive or don't match ones we've seen so far. 

### 3 And to that point, we'll go into more detail later, but I will 

### 4 point out there there's some additional techniques that could be 

### 5 added in for cases where we have unknown categorizations to help 

### 6 address that. 

### 7 And then the final point I wanted to make 

### 8 right now is again specifically at green, with the idea of 

### 9 assigning confidences, weightings or other numerical values, to 

### 10 some of the predictions and to the different approaches that we 

### 11 have in hand, and that in fact in most cases data sets do 

### 12 already exist to do this. It's simply a question of putting 

### 13 together a transparent, clear and open process for combining it. 

### 14 And the advantage of course of this, is then these different 

### 15 data sources, either computational or HTP, may be brought 

### 16 together in a way that the biases or limitations of them are not 

### 17 necessarily aligned and so you have orthogonal approaches that 

### 18 can reinforce each other. Thank you. 

### 19 **DR. DANIEL SCHLENK:**Thank you. Any other 

### 20 comments from the Panel? Dr. McManaman. 

### 21 **DR. JAMES McMANAMAN:**I also agree with 

### 22 everything that's been said so far. I would like to urge the 

### 23 EPA to think about clarifying the language regarding physical 

### 24 properties used for discrimination -- discriminating compounds. 

### 25 26 In particular, as I indicated in my question

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1 in the beginning of this session was that by not defining 

### 2 polymers and ionic substances well, I think that you would 

### 3 eliminate many of the compounds that would activate the 

### 4 alternative ER pathway such as proteins and peptides, because 

### 5 they are both polymeric and ionic at neutral pH. So if we could 

### 6 just get a little more clarity on that, it would be great. 

### 7 **DR. DANIEL SCHLENK:**Okay. Thank you. Any 

### 8 other comments from the Panel on Question 1? Okay, let me go 

### 9 back to the Agency, any questions or clarification that you have 

### 10 for any of the responses that you received. 

### 11 **MS. MARY MANIBUSAN:**No we appreciate the 

### 12 Panel's consideration and comments, and we'll look forward to 

### 13 the written comments. Thank you. 

### 14 **DR. DANIEL SCHLENK:**Okay. All right, let's 

### 15 move on to Question 2. If we can get that up on screen and if 

### 16 you wouldn't mind again reading from the italics would be great 

### 17 at the number. 

### 18 **MS. MARY MANIBUSAN:**Question Number 2. 

### 19 Whether there are additional chemical characteristics that 

### 20 should be considered (e.g. volatility), whether there are 

### 21 additional data sources for each parameter identified (e.g. 

### 22 chemaxon for pKa values, EpiSuite for half-life values, et 

### 23 cetera), and how to consider multiple data sources in a weight 

### 24 of evidence approach. 

### 25 **DR. DANIEL SCHLENK:**Do you want to keep 

### 26 going, there's actually -- next slide in.

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1 **MS. MARY MANIBUSAN:**Sorry. Whether the 

### 2 exclusion criteria cut offs and the confidence that that 

### 3 application of the exclusion criteria will reasonably identify 

### 4 those substances that are untestable and/or unable to trigger 

### 5 the molecular initiating event of the ER pathway. Also, please 

### 6 discuss options for evaluating cases where uncertainty in a 

### 7 chemical's specific parameter range overlaps a p-chem filter cut 

### 8 off. Please consider in your comments the following exclusion 

### 9 criteria: pKa values less than 2 (<2) or greater than 11 point 

### 10 5 (>11.5); hydrolysis half-life of less than 40 (<40) days and 

### 11 charged species of 99 percent or greater at pH7. 

### 12 **DR. DANIEL SCHLENK:**Okay, thanks. Our lead 

### 13 discussant on this is Mark Cronin. 

### 14 **DR. MARK CRONIN:**Thank you. Mark Cronin. As 

### 15 a note of order, the question that describes this is Section 

### 16 2.2.2 and I think that should be 2.2 as a note of order. I've 

### 17 compiled together comments from the associate respondents and 

### 18 also some of the discussions and submitted material. And whilst 

### 19 the question is all labeled 2.1, I will deal with it 

### 20 sequentially. There're a number of sub-questions there so, 

### 21 perhaps if we could back onto the first slide. Thank you. 

### 22 I'm going to make some general statements 

### 23 which really relate to the first paragraph, not yet the bullet 

### 24 points. And we agree that as the Agency expands its universal 

### 25 structures considered for prescreening, it's important to remove 

### 26 those from consideration on the reasons for phys-chem properties

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1 falling outside of the main of testing. So therefore, exclusion 

### 2 criteria looks on as a very positive addition to the EDSP 

### 3 screening and testing scheme. Its agreement that those 

### 4 chemicals that are too acidic, too basic, too reactive or 

### 5 unstable, charge species or too corrosive need not be 

### 6 considered. I'll come back to those and put some more detail on 

### 7 that. 

### 8 To support this hypothesis however, it will be 

### 9 necessary to demonstrate this, in other words, the use of 

### 10 exclusion criteria by in vitro testing of selected model 

### 11 substances to justify their exclusion. 

### 12 The study described in Section 2.2 of the 

### 13 documents and the Agency's presentation yesterday is important, 

### 14 well informed, we agree it demonstrates that exclusion criteria 

### 15 could be very powerful to reduce rationally the number of 

### 16 compounds needed for testing. And as far as I'm concerned, the 

### 17 first time that such comprehensive approach has been formulated 

### 18 and applied to screening and entering, it could be much widely 

### 19 applied and I would be interested to see that, for instance in 

### 20 the European context. 

### 21 As stated by the Agency yesterday, the 

### 22 principles depending on the approach are well founded in the 

### 23 EDSTAC approach. Therefore, it's a logical understanding that 

### 24 compounds with the appropriate physico-chemical properties were 

### 25 not reactive or may not be suitable for in vitro testing. It 

### 26 may however, be helpful to note that at least three distinct

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1 reasons, yes we're lumping a lot of things together here, to say 

### 2 that something can be excluded. And I'll just give our point of 

### 3 view on those. 

### 4 So for instance some compounds are inactive 

### 5 systemically in whole organisms. This has been known in the 

### 6 pharmaceutical industry for a long time, particularly following 

### 7 oral dosing. For instance, compounds that are not susceptible 

### 8 to passive transports, in other words, cross concentration 

### 9 gradients due to molecular dimensions, those that are too 

### 10 isolable or show poor with regard to pharmaceutical substances, 

### 11 this is ADME properties are likely to be unactive due to low 

### 12 bioavailability. Some of these properties, at least, are 

### 13 captured by the exclusion criteria, in other words, looking at 

### 14 molecular size and ionization. 

### 15 Second, some compounds will be degraded 

### 16 rapidly in the environment. This means that they will not 

### 17 themselves be bioavailable and we see the inclusion criteria of 

### 18 the half-life. With regard to HTP, each in vitro assay will 

### 19 have cutoffs, in other words, solubility and may have its own 

### 20 applicability domain. This criterion must however, be treated 

### 21 with caution whilst it may not be possible for in vitro testing 

### 22 of the compound, it may still capture or elicit an in vivo 

### 23 response. 

### 24 This general group of concepts is well 

### 25 established, although precise cut offs may be lacking. So with 

### 26 regard to pharmaceuticals, there are some very well established

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1 rules of thumb. Lipinski rules of five is the most well 

### 2 established, that's been around for about 15 years. And that 

### 3 identifies compound with poor solubility. There are other rules 

### 4 of thumb for other rotate parameters and perhaps those could be 

### 5 developed as well. And I'll include more text in the report. 

### 6 They hypothesis governing the exclusion, the 

### 7 use of exclusion criteria in the context of the AOP philosophy 

### 8 is that the MIE will not be triggered unless a concentration 

### 9 representing what is sometimes called point of departure, is 

### 10 reached. It may be assumed that cutoffs indicate that none of 

### 11 the substance or worse a tiny proportion is likely to reach the 

### 12 site of action and it's a good assumption to apply these 

### 13 criteria, or place at least, these chemicals at lower priority. 

### 14 With regard to Section 2.2.2, exclusion of 

### 15 polymers, mixtures and undefined substances, the panel agrees 

### 16 that exclusion of polymers from the EDSP is justified. Such 

### 17 compounds are typically high molecular weights and effectively 

### 18 inert. The assumption that they will not interact with the ER 

### 19 is justified due to their size. There is also little evidence 

### 20 they will end some systemic circulation and sufficient 

### 21 concentration to promote an effect. 

### 22 And I take one point, I was going to repeat 

### 23 the comments made previously by Dr. McManaman about to avoid 

### 24 confusion with regard to definition of a polymer, this should be 

### 25 clearly stated, so in other words, manmade substances repeating 

### 26 subunits. And they're probably a lot better definitions that I

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1 can come up with off of the top of my head. 

### 2 With regard to mixtures, if they are 

### 3 constituted of defined chemical structures then these, in other 

### 4 words, the individual chemical structures should be placed back 

### 5 into the screening process. The panel agrees that doing so 

### 6 after single substances is being considered is a pragmatic 

### 7 solution. This will enable the single, hence probably simpler 

### 8 to deal with at the time, compounds to be dealt with most 

### 9 efficiently and rapidly. It should be recognized however, that 

### 10 there may be compounds of concern within a mixture, and the 

### 11 single substances should not be discounted. 

### 12 With regard to undefined substances, the panel 

### 13 agrees it's not possible to assess these substances using the 

### 14 exclusion criteria proposed or indeed in the ES or in the in 

### 15 vitro assays potentially. 

### 16 Generally speaking, better and clearer 

### 17 definitions are required to many terms used in the report. For 

### 18 instances, polymers we've already discussed, charge species, and 

### 19 any reference to specific chemical domains. We'll supply 

### 20 appropriate references for the material above. 

### 21 I'll now move on, if I may, to the first 

### 22 bullet point, whether there are additional chemical 

### 23 characteristics that could be considered. We agree volatility 

### 24 is a logical extension to the above mentioned exclusion rules. 

### 25 In vitro testing of highly volatile substances often gives 

### 26 results that are less active and hypothesized because

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1 concentrations needed to elicit the response must compensate for 

### 2 loss. Possible descriptors for that or cutoffs could be the 

### 3 Henry's law, constant or calculated vapor pressure. 

### 4 Some of the characteristics that may be 

### 5 considered, for instance, with regard to poor in vivo 

### 6 bioavailability, I've mentioned the Lipinski rules or 

### 7 conservative adaption thereof, they're not perfect the Lipinski 

### 8 rules, it must be said. However, we do appreciate that some of 

### 9 the properties considered within the Lipinski rules have already 

### 10 been included in the current set of criteria. Also issues, or 

### 11 the possibility of including indicators of rapid metabolism or 

### 12 clearance in vivo. Other parameters solubility with particular 

### 13 reference to the applicability of the HTP and in vitro assays, 

### 14 and we've also had mention of LogP cutoffs which covers that to 

### 15 some extent. 

### 16 Potentially other measures of stability or the 

### 17 fact that compounds may be too unstable to test, for instance 

### 18 oxidation in the air, if that's the significant route to 

### 19 exposure. And in future, with regard to in vitro testing, there 

### 20 may be interest in using some of the information that's now 

### 21 coming from the expanding science of what's sometimes called 

### 22 quantitative in vitro in vivo extrapolation. And there's some 

### 23 very nice work that we can cite for instance from the University 

### 24 of Utrecht in the Netherlands, and other places Hamner and so 

### 25 forth. 

### 26 The models themselves, we're not suggesting

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1 they give cut off criteria, but the way they look at other 

### 2 factors may be relevant. For instance, some of these approaches 

### 3 are attempting to identify and model the properties of chemicals 

### 4 that infect in vitro concentrations, volatility, absorption to 

### 5 cells, and so forth. So these may provide models and further 

### 6 input into development of P-chem filters. 

### 7 Other characteristics the Agency may consider 

### 8 in excluding chemicals, I think Dr. Schultz has already 

### 9 mentioned by analogy, include electrophilic and nucleophilic 

### 10 reactivity, especially soft electrophilic reactivity as related 

### 11 to covalent protein binding. Similarly, chemicals acting as 

### 12 redox cyclers may be excluded from screening and indeed there 

### 13 are a number of ways in which those could be screened out, using 

### 14 structured approaches. And we can provide references for all of 

### 15 the above material. 

### 16 I'll now move on to the second bullet point on 

### 17 the screen, whether there are additional data sources. There 

### 18 are indeed, as widely known, many possible sources of predicted 

### 19 values for the descriptors. However, there should be some 

### 20 rationale to the selection of predictor models. And I think we 

### 21 may have already had a comment along this line already. 

### 22 For instance, in such a scheme it should 

### 23 really be preferable to have freely available, open access 

### 24 models both that are transparent, well documented, with 

### 25 definable and defined applicability domains, as well as being 

### 26 demonstrably robust. The SAP agrees methods chosen so far are

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1 robust, within reason, freely available and good examples of the 

### 2 state of the art thereof. Therefore, they are appropriate when 

### 3 used carefully and are appropriate to calculate cutoffs. 

### 4 There are a number of other methods to 

### 5 calculate pKa, degradation, et cetera. And what I would propose 

### 6 here, with the Chair's acceptance, I would add later on a list 

### 7 of those rather than -- I didn't have it to hand at the moment 

### 8 and there is a European Chemical Agency's document that may be 

### 9 of use. I'm sure you are familiar with it. 

### 10 Should a number of predictive algorithms be 

### 11 utilized, some issues must be recognized. A strategy needs to 

### 12 be adopted to use such data, simply calculating more and more 

### 13 values isn't necessarily helpful and doesn't necessarily 

### 14 increase accuracy or the quality of the output. Multiple data 

### 15 sources are often algorithms based on the same experimental 

### 16 data. Since the model's often fitting exercises, you're not 

### 17 actually gaining anything from it. You're just getting a 

### 18 different model from the same data. 

### 19 Using the worse-case scenario to set the 

### 20 cutoffs reduced the probability of excluding an active chemical. 

### 21 However, at this time it will increase the probability of 

### 22 evaluating a chemical that could be included. 

### 23 The panel agrees that should multiple data 

### 24 with which predictions for compound be evaluated, a weight of 

### 25 evidence approach may be appropriate. This may be based on the 

### 26 relative performances of the model. It may, for instance, be

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1 dictated by the sensitivity and specificity and productivity of 

### 2 the particular models. 

### 3 And if we go to the next slide, I'll deal in 

### 4 turn with each of three phys-chem properties noted there. So 

### 5 I'll start with the consideration of pKa values of less than 2 

### 6 or greater than 11.5. 

### 7 SAP agrees that it is likely that compounds 

### 8 with pKa values of less than 2 or greater than 11.5 are likely 

### 9 to trigger the MIE of the pathway. There is no evidence that 

### 10 they associated with the effect. And not only do we have the 

### 11 stated criteria, of it not being able to bind, but also they're 

### 12 unlikely to be bioavailable due to ionization. And again, 

### 13 highly acidic and basic compounds are usually intrinsically 

### 14 corrosive and unsuitable for testing. 

### 15 Perhaps it's possible to use, as a suggestion, 

### 16 possibly use the physico-chemical exclusion criteria twice. 

### 17 Firstly, I think this is intended to mean to eliminate the 

### 18 obvious untestable substances and a second stage within a 

### 19 chemical category. So for instance all chemicals outside of 

### 20 those pKa limits, could be eliminated and then within a category 

### 21 for instance, within a phenolic category, a refined set of pKa 

### 22 based criteria could be used to further prioritize for testing. 

### 23 Hydrolysis half-life of less than 40 days is a 

### 24 cutoff. The Panel agrees that highly degraded compounds are 

### 25 unlikely to cause in vivo effect following exposure through 

### 26 environmental waters. This is due to a rapid decrease in

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1 concentration or elimination. However, the recommendation of 

### 2 less than 40 days seems rather long and needs to be justified if 

### 3 it's to be used. Indeed the presentation yesterday suggested 

### 4 that there was little difference between less than one day and 

### 5 less than 40 day half-life, but public perception will be much 

### 6 greater about the difference in terms of their understanding of 

### 7 it. Therefore, one recommendation could be to restrict it to 

### 8 less than one day, particularly for multiple exposures relating 

### 9 to lipophilic type compounds. So I think there we need much 

### 10 greater clarity and explanation of where this figure has come 

### 11 from in terms of the less than 40 days. 

### 12 Moving on to charge species, 99 percent of 

### 13 greater pH 7. We agree that compounds are likely to be charged 

### 14 are greater than 99 percent are unlikely to be bioavailable in 

### 15 vivo. It would be good to be, again, to have some clarity over 

### 16 the precise pH there and why that was considered. Is that 

### 17 relating to pH in environmental waters or the in vitro test 

### 18 systems, and consideration that, for instance, strong acids are 

### 19 ionized in the stomach and will be naturally absorbed. However, 

### 20 it is agreed that the value of 99 percent ionization of the 

### 21 compound in test system is appropriate. The Panel or the Agency 

### 22 may wish to consider the possibility of combining the criteria 

### 23 for pKa in charge as there seems to be considerable overlap as 

### 24 shown in Table 2.2.1. 

### 25 The definition of a charge species is another 

### 26 example where greater clarity in the definition is required.

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1 And yesterday we had a comment regarding methylanilines and it 

### 2 may be that certain species need to be removed from that 

### 3 definition. 

### 4 And lastly with regard to the comment in the 

### 5 main paragraph of this question, also please discuss options for 

### 6 evaluating cases where uncertainty of the chemical's specific 

### 7 parameter range overlaps the P-chem filter. And I'm sorry I 

### 8 should have asked before now for clarification on that. 

### 9 What I assume that meant was that if a 

### 10 compound had a range of parameters, for instance from different 

### 11 methods or there was uncertainly or 95 percent confidence 

### 12 limits, if that range straddles a cut off. Yes, that's what I 

### 13 assumed. Okay. In such a case, the precautionary approach 

### 14 would be to assume or could be to assume that it does not fully 

### 15 meet the criteria for exclusion although this is a good example 

### 16 of an area where more work needs to be placed into it. 

### 17 There was some other general comments which I 

### 18 think address all of Question 2, and again, I've tried to 

### 19 capture some of the discussion as we've gone through. As with 

### 20 all models, the model cutoffs are only as good as the date from 

### 21 which they're modeled. The criteria being applied in the 

### 22 context we are binding analyzed and I think I'm summarizing a 

### 23 comment from Dr. McManaman yesterday and he's welcome to correct 

### 24 me if I'm wrong. So the criteria and the data should be clearly 

### 25 defined and acknowledged that other alternative pathways of ER 

### 26 binding exist. Some compounds could be excluded that could

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1 activate further, and when I say they could be excluded by the 

### 2 current cutoffs, it could activate estrogen receptor. The need 

### 3 here is to define differently with regard to different MIEs and 

### 4 domains. 

### 5 Second general comment, we also agree that it 

### 6 is also relevant to consider the criteria for the corrosivity. 

### 7 And the list of structure that is given in Section, I think it's 

### 8 2.2.3.4, is comprehensive and of course may overlap with 

### 9 ionization. 

### 10 In terms of defining the alerts, it may be 

### 11 better to define them in more detail. They're just alerts which 

### 12 could have mitigating factors so that's an issue that those 

### 13 alerts could be defined or if they are just going to be used as 

### 14 they are. That could be done on paper, electronically in terms 

### 15 of smart strings, other technologies such for instance as the 

### 16 definition of the alerts and the OECD (Q)SAR toolbox. 

### 17 With regard to expert opinion used to apply 

### 18 the criteria, I think it's great, the Panel believes it's great 

### 19 to use expert opinion. Clearly there may be time constraints 

### 20 and again I wonder whether there's a possibility to prioritize 

### 21 the prioritization so obviously, excludable compounds require 

### 22 less attention of the experts as opposed to those that may be on 

### 23 or near because of criteria. Of course this could in part at 

### 24 least be performed with weight of evidence based approaches to 

### 25 refine further confidence. So if for instance, three out of 

### 26 three exclusion criteria pass then there would be confidence

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1 than if it was only a single criterion. 

### 2 Other comments, it would be helpful to have 

### 3 statistics on the relative performance of the cutoffs. I think 

### 4 there was some allusion to it yesterday about some performance 

### 5 statistics helping defining the domain of applicability of 

### 6 approach and also where it may be failing so areas where a 

### 7 better definition may be required. 

### 8 And lastly in terms of cutoffs, which I should 

### 9 have probably put further up, is the issue of developing further 

### 10 cutoffs for exposure. And I think that was probably dealt with 

### 11 in Question 1. 

### 12 So in conclusion, the cutoffs are logical. 

### 13 They're developed, those proposed are on the whole appropriate 

### 14 but need better definition and there should be consideration of 

### 15 other properties and the limitations at times on the data which 

### 16 they have developed. And as with the other questions, full 

### 17 references will be provided, as required, for the text. 

### 18 **DR. DANIEL SCHLENK:**Thank you Dr. Cronin. 

### 19 Dr. Schultz, anything to add? 

### 20 **DR. TERRY SCHULTZ:**I agree with what was 

### 21 stated. I have nothing else to add. 

### 22 **DR. DANIEL SCHLENK:**And Dr. Ehrich? 

### 23 **DR. MARION EHRICH:**This is Marion Ehrich and 

### 24 my comments were included in the statement made by Dr. Cronin. 

### 25 **DR. DANIEL SCHLENK:**Thank you. Other Panel 

### 26 members? Dr. Perkins.

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1 **DR. EDWARD PERKINS:**I have one comment on the 

### 2 hydrolysis half-life cutoff. That this should be revisited in 

### 3 the context of -- that may be appropriate for a single point 

### 4 source where there is only a single omission of that. However, 

### 5 when you have a chronic continuous source then you're still 

### 6 going to be exposed to a chemical even if it's degrading rather 

### 7 rapidly, the closer you are to the source point. And this can 

### 8 be proved especially critical for sensitive life stages which 

### 9 may only last for a few hours to days within an organism as it's 

### 10 spawning or developing within these critical systems. So, I 

### 11 would suggest to revisit that and the logic of it because it may 

### 12 not be protective of what is really happening under chronic 

### 13 exposure conditions. 

### 14 **DR. DANIEL SCHLENK:**Any other panel members? 

### 15 Yes, Dr. Jacobs. 

### 16 **DR. MIRIAM JACOBS:**Miriam Jacobs. I would 

### 17 just like to endorse the last comment. 

### 18 **DR. DANIEL SCHLENK:**Okay. Anyone else? 

### 19 Question 2. Okay, let me at this point go to the Agency. Do 

### 20 you have any questions or clarification? 

### 21 **DR. RAY KENT:**No, we don't have any 

### 22 additional comments or clarifications necessary. Thank you very 

### 23 much for your comments. 

### 24 **DR. DANIEL SCHLENK:**Great. Thanks. Okay 

### 25 since the coffee's not here yet, we should go ahead and move 

### 26 forward to Question 3 if that's okay. And again, maybe we can

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1 just do 3.1 for now and then take a break for 3.2, go that 

### 2 route. 

### 3 **MS. MARY MANIBUSAN:**Great. So Question 3, ER 

### 4 Expert System Development 3.1. Please comment on the approach 

### 5 used to develop and build ER Expert System Version 2 (ESv2) 

### 6 through chemical testing and effect-based category and read 

### 7 across methods to provide a scientifically defensible approach 

### 8 to predict ER binding potential for a larger number of chemical 

### 9 groups, particularly with regard to defining new groups from 

### 10 within the Mixed Organics and Mixed Phenols. Please indicate 

### 11 any considerations unique to ESv2 that indicate the approach 

### 12 used to develop ER Expert System Version 1 (ESv1) in the SAP, 

### 13 2009 reference needs to be modified. 

### 14 **DR. DANIEL SCHLENK:**Oops, excuse me. Dr. 

### 15 Madden has a statement to make. 

### 16 **DR. SHARLENE MATTEN:**No, not a statement. I 

### 17 just got an email from the beyond and the Panel members should 

### 18 be very close to their mike and speak loudly like Mary 

### 19 Manibusan. 

### 20 **MS. MARY MANIBUSAN:**Am I too harsh? 

### 21 **DR. SHARLENE MATTEN:**No the person is very 

### 22 appreciative of your vocal quality and loudness. So, be like 

### 23 Mary instead of being like Mike. 

### 24 **DR. DANIEL SCHLENK:**Okay, Dr. Jacobs. You're 

### 25 the lead discussant for this one. 

### 26 **DR. MIRIAM JACOBS:**Miriam Jacobs. Can you

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1 hear me loud and clear? Thank you very much. I'm going to 

### 2 present the comments from this working group. And before 

### 3 addressing Question 3.1, we agreed together to give a brief 

### 4 overview and lead in so I'm going to start with reading that. 

### 5 The ER Expert System Version 1, the ESv1, is 

### 6 in effect-based Expert System which predicts relative ER binding 

### 7 affinity for food-using inert ingredients and antimicrobial 

### 8 pesticides. The ESv1 was built using data from rainbow trout ER 

### 9 binding and liver slice gene activation assays, as was the 

### 10 subsequent ESv2. It's a transparent and flexible and expandable 

### 11 logical rule-based decision tree that encodes mechanistic 

### 12 understanding with respect to both the chemical and biological 

### 13 aspects of the ER binding and ER agonists stimulation of 

### 14 vitellogenin production in the liver slices. 

### 15 A positive hit or active classification is any 

### 16 statistically significant signal above background at any 

### 17 concentration up to solubility of cytotoxicity, whichever is 

### 18 reached first. Based on the training sets of representative 

### 19 chemicals, an ER binder is defined as a chemical having an RBA 

### 20 compared with the endogenous ER-alpha ligand 17beta-estradiol or 

### 21 greater than 0.00001 percent, and as a non-binder as having an 

### 22 RBA of less than 0.000001 percent. It's therefore very 

### 23 sensitive to chemicals with low potencies, those which some of 

### 24 the HTP ER assays, for example, could not detect, and provides 

### 25 mechanistic information that cannot always be deduced from the 

### 26 higher to in vivo tests.

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1 The applicable chemical domain was expanded to 

### 2 include nonfood use and inert substances that remained 

### 3 unclassifiable by ES Version 1 which contained 223 discrete 

### 4 substances, SDWA chemicals and fragrances, primarily mixed 

### 5 organics and mixed phenols. Selected chemicals from these 

### 6 groups were then tested and the results modified the ESv1 rule 

### 7 set to extend the chemical coverage, i.e. ESv2. 

### 8 This is a robust approach and the Agency have 

### 9 made a very good start at coming up with the yes-no nodes on a 

### 10 decision tree. Indeed the Agency should be congratulated in 

### 11 putting together a well thought out and consistent approach. 

### 12 There is a strong philosophy behind the approach, i.e. 

### 13 attempting to expand the current ESv1 through rational analysis 

### 14 of chemical groups and directed testing. 

### 15 And here we're going to start having a little 

### 16 bit of repetition from some of the earlier points and I think 

### 17 it's just going to happen again and again with all the 

### 18 questions. However, the output of an Expert System is only as 

### 19 good as the inputs. Future considerations may include making 

### 20 some yes-no decisions more important than others and this would 

### 21 recommend waiting. 

### 22 The platform in which the software is 

### 23 presented is developed from a proprietary system. And while 

### 24 this is a robust and proven system, the Agency could consider 

### 25 other platforms, e.g., KNIME Work Flows, which are open access 

### 26 and would offer greater flexibility. And possibility of

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1 developing complex structural alerts, for example, for grouping, 

### 2 as well as links through a wide variety of (inaudible) tools and 

### 3 databases. 

### 4 The coverage of each ESv1 is given in terms of 

### 5 percentages. This number is useful but it may be illustrative 

### 6 to show the coverage graphically. For example, the 

### 7 distributions of chemicals and coverage in terms of two 

### 8 dimensional or three dimensional plots of descriptors. This 

### 9 could have the advantage of: (A) being simpler to comprehend; 

### 10 and (B) illustrating where there may be large areas of the 

### 11 chemical universe that have not been covered in this approach. 

### 12 And other tools that could be used for example would include 

### 13 tools like Spotfire or commercial tools. 

### 14 Regarding Question 3.1. The level of 

### 15 confidence in a prediction based on an alternative method, for 

### 16 example in vitro or in silico, is often centered on the summary 

### 17 experience with the method and the robustness of the data which 

### 18 it has provided. Increasing confidence in a prediction is 

### 19 related to having a transparent mechanistic probable rationale 

### 20 for the prediction and increasing the weight of evidence 

### 21 supporting the prediction. There is growing agreement that the 

### 22 mechanistic basis for prediction is best provided by an adverse 

### 23 outcome pathway, which is done in the case of ER binding leading 

### 24 to potential reproductive toxicity. The weight of evidence is a 

### 25 combination of the number of events that comprise a particular 

### 26 AOP, the relevance of each event to the final in vivo endpoint

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1 in question, the number of methods used to assess each event, 

### 2 the relevance of each method, the particular event and the 

### 3 reliability of the databases supporting each method. 

### 4 In the case of ER binding, potentially leading 

### 5 to reproductive toxicity, the rainbow trout ER binding and liver 

### 6 slice GA assays are highly relevant. Moreover, using them in 

### 7 tandem increases confidence and predictions. As the ER ES 

### 8 training set is expanded, confidence will increase. 

### 9 A second aspect of confidence in a prediction 

### 10 is that in the perceived consequence of the prediction being 

### 11 incorrect, being wrong, the net result is that there is a 

### 12 greater confidence in accepting a positive or toxic prediction 

### 13 than accepting a negative or non-toxic prediction. One can take 

### 14 this argument one step further. For example, if both one 

### 15 butanol and one octanol were tested and shown to be negative in 

### 16 both the rainbow trout ER binding and liver slice GA assays, 

### 17 there should be a greater confidence than on hexenol would also 

### 18 be a negative chemical. However, extending that prediction to 

### 19 112 dihydroxy dodecane, another aliphatic alcohol, but one with 

### 20 two polar groups which can be configured 10.2 angstroms apart, 

### 21 should be made with lesser confidence. 

### 22 The weak point in the approach is how 

### 23 similarity is used to develop the chemical groups. Confidence 

### 24 may vary with the rationale and structural boundaries of the 

### 25 chemical group on which the prediction is based. Relevant, 

### 26 current and emerging approaches that should be considered in the

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1 building of ES Version 2 and also ES Version 3, include naive 

### 2 basion classifiers, statistical similarity methods, such as the 

### 3 similarity ensemble approach, and with that we can get 

### 4 references -- the Kaiser references. 

### 5 The second two approaches in particular are 

### 6 less susceptible to the (Q)SAR limitations described on Page 24 

### 7 of Appendix B and may defensibly be applied across the diverse 

### 8 structures with widely varying physio-chemical properties. This 

### 9 is referring to Appendix B, Page 27. They characterize the 

### 10 inventories of interest. By contrast, the current ESv Versions 

### 11 1 to 3 approaches must rely on an exact match for chemicals, 

### 12 such as MO and MP that lack general rules and this is referring 

### 13 to Appendix B, Page 34. And this alone may not scale well. 

### 14 The 14 new groups added during expansion of 

### 15 ESv1 and ESv2 did not completely cover all of the 2009 mixed 

### 16 phenols and mixed organics. The additional groups or sub-groups 

### 17 within those currently proposed are likely to be found as the 

### 18 additive process continues and this will mean further 

### 19 modification and this is stated in the White Paper. 

### 20 Furthermore, however, as the new data becomes 

### 21 available it will become important to ask what performance 

### 22 statistics can be assessed per node. When used as an augmenting 

### 23 or second pass analysis, additional chemical informatics and 

### 24 systems pharmacology computational methods may increase the 

### 25 recovery rates of compounds that would otherwise remain unknown 

### 26 binding potential or false negatives.

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1 More generally, it would be sensible to 

### 2 subject ES Version 2 to quantitative performance testing. 

### 3 Approaches include, for example, analysis of blind out group 

### 4 chemicals not explicitly included within the model but within 

### 5 the model domain; and (2) cross fold analysis for sensitivity, 

### 6 specificity, and positive and negative predictive value by 

### 7 automated data subdivision and model rebuilding; and (3) 

### 8 parallel analysis and testing of the new chemicals. Such 

### 9 analysis would be consistent with the earlier SAP 

### 10 recommendations given in Appendix B and quantify the model's 

### 11 dependence on known chemistry. And that's the answer to 

### 12 Question 3.1. 

### 13 **DR. DANIEL SCHLENK:**Thank you. Dr. Denslow. 

### 14 **DR. NANCY DENSLOW:**All of my comments were 

### 15 incorporated and I have no further comments. 

### 16 **DR. DANIEL SCHLENK:**Oh, Dr. Jacobs has 

### 17 something else to add. 

### 18 **DR. MIRIAM JACOBS:**Sorry, I thought were just 

### 19 doing 3.1. I haven't gone on to 3.2 yet. 

### 20 **DR. DANIEL SCHLENK:**That's correct. We're 

### 21 only doing 3.1, yes. Okay? Dr. Shultz. 

### 22 **DR. TERRY SCHULTZ:**I'll hold my comments 

### 23 until after we finish the entire question. 

### 24 **DR. DANIEL SCHLENK:**Okay. Dr. Keiser? 

### 25 **DR. MICHAEL KEISER:**I agree with everything 

### 26 that has been stated so far. I'll pull the microphone a little

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1 closer here. A few additional points just to add. One, 

### 2 reiterate that the current ES as a decision system is both 

### 3 defensible and robust. And my comments mostly focus on ways in 

### 4 which it may be extended not simply in terms of coverage but 

### 5 also in terms of predictive ability into chemical classes that 

### 6 may be encountered in the future or those that are currently in 

### 7 the unknown binding potential category. 

### 8 And so currently there are 576 of those that 

### 9 have come at least through the current proposal that remain in 

### 10 that category. Given the motivation to minimize the false 

### 11 negatives and indeed that more new compounds are expected per 

### 12 year as mentioned in Appendix B, then some of the second pass 

### 13 chemical informatics approaches that have been mentioned before 

### 14 would fit. 

### 15 I wanted to add one additional one that has 

### 16 come up from some discussion today and yesterday, which is the 

### 17 idea of using data from screens and this would borrow from the 

### 18 high content screening approaches. There's some published 

### 19 information that we can put in the written form of these 

### 20 comments that might be used as an additional augmentation to the 

### 21 Expert System. 

### 22 Additionally, it might be worth revisiting 

### 23 some virtual screening and docking approaches and I was glad to 

### 24 hear by Dr. Judson that there's some discussion there about 

### 25 collaborations going forward. 

### 26 So then of course, the idea is not to replace

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1 human decision making or human judgment but rather to augment it 

### 2 and that speaks to the transparency concern. Many of these 

### 3 approaches that have been brought up and are suggested here are 

### 4 not traditional (Q)SAR approaches and as a result some of them 

### 5 go beyond it and have the ability with the algorithms and other 

### 6 information in the literature and open source approaches, to be 

### 7 queried in terms of transparency. 

### 8 And then finally the one additional point I 

### 9 would like to make is first to commend the EPA on providing open 

### 10 access to the Expert System and given the system such as it is, 

### 11 it might make sense, if possible, to make the data sets or the 

### 12 code underlying it as available as possible to analyses such as 

### 13 those already mentioned but one additional example would be 

### 14 random deletion of rule sets, of known correct rule sets from 

### 15 the Expert System and based on that random deletion, application 

### 16 of that now restricted model to the data that you have and 

### 17 asking what the stability of predictions are in the face of 

### 18 random deletion as one way to model uncertainty going backwards 

### 19 in that case instead of going forward, if additional data are 

### 20 hard to come by at this point. Thank you. 

### 21 **DR. DANIEL SCHLENK:**Thank you Dr. Keiser. 

### 22 And Dr. Cronin. 

### 23 **DR. MARK CRONIN:**No further comment. Mark 

### 24 Cronin. 

### 25 **DR. DANIEL SCHLENK:**Thanks. And Dr. Safe? 

### 26 **DR. STEPHEN SAFE:**I think I'll wait until the

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1 end. 

### 2 **DR. DANIEL SCHLENK:**Okay. Everybody wants 

### 3 coffee I guess. Dr. Burris? 

### 4 **DR. TOM BURRIS:**Nothing to add. 

### 5 **DR. DANIEL SCHLENK:**And Dr. Ehrich? 

### 6 **DR. MARION EHRICH:**My comments have already 

### 7 been spoken by Dr. Jacobs. 

### 8 **DR. DANIEL SCHLENK:**All right. Other panel 

### 9 members which I think we covered everybody there. Just wanted 

### 10 to make sure I didn't miss anybody. Any other panel members 

### 11 want to comment on 3.1? Okay. We go to the Agency. Do you 

### 12 have what you need for that particular question? 

### 13 **MS. MARY MANIBUSAN:**Yes. We got that 

### 14 covered. 

### 15 **DR. DANIEL SCHLENK:**All right, well let's go 

### 16 ahead and take a break. We'll do 3.2 when we get back. Let's 

### 17 be back at 2:25. 

### 18 (WHEREUPON a break was taken) 

### 19 **DR. DANIEL SCHLENK:**Okay. Let's go ahead and 

### 20 start with 3.2. If you can read that into the record that would 

### 21 be great. 

### 22 **MS. MARY MANIBUSAN:**Question 3.2. Building 

### 23 from the Expert System Version 1 training set, please comment on 

### 24 the level of scientific confidence that a chemical is unlikely 

### 25 to initiate the ER AOP, if the in vitro assay Training Set data 

### 26 shows no activity. Please comment on the extent to which the

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1 level of confidence may vary by chemical category. 

### 2 **DR. DANIEL SCHLENK:**Thank you. Dr. Jacobs. 

### 3 **DR. MIRIAM JACOBS:**Well I'm going to start -- 

### 4 Miriam Jacobs, sorry. I'm going to start by just flagging up a 

### 5 discussion we've had during the break. It's also going to be a 

### 6 discussion that will come up again and again through the various 

### 7 questions regarding the estrogen receptor AOP or an estrogenic 

### 8 Adverse Outcome Pathway because binding with the ER is one of 

### 9 the molecular initiating events that contributes to downstream 

### 10 protein signaling and is certainly a major MIE for an estrogenic 

### 11 AOP that could have reproductive and/or carcinogenic 

### 12 consequences. 

### 13 However, all of the tests that were being seen 

### 14 here describe or deal with ER binding or with the HTP assays 

### 15 also, but there are other relevant MIEs that contribute to the 

### 16 downstream protein signaling including membrane ERs, enzymes, 

### 17 sulfur transfer raises, other estrogen receptors, ER related 

### 18 receptors, NFkB, we've just had this discussion in the break, 

### 19 map kinases. 

### 20 And this is an area that we really feel needs 

### 21 more discussion and exploration and it will come up again and 

### 22 again. And perhaps this question can be refined to address 

### 23 estrogenic pathways not only those mediated first by the 

### 24 estrogen receptor. It doesn't follow that a chemical is 

### 25 unlikely to initiate the ER AOP if the in vitro assay training 

### 26 set data shows no activity.

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1 This would also affect the extent to which the 

### 2 level of confidence may vary by chemical category. One example 

### 3 might be the phthalates, which they not only fit into an ER AOP 

### 4 but also to an estrogenic or steroidal AOP to which the ER MIE 

### 5 belongs. 

### 6 Another point is that the group felt that at 

### 7 the moment it is not possible to answer a question relating to 

### 8 unknowns. We need to have measures of confidence and these may 

### 9 vary a lot particularly according to the size of the categories 

### 10 and there is therefore a need for better definition of 

### 11 categories. Categories need to be correctly defined and be 

### 12 defensible. And there needs to be a clear rule set. Could a 

### 13 protocol be defined so that the prediction confidence can be 

### 14 quantified per category? 

### 15 And then also in -- oh this is more for 

### 16 Question 4, so I'm going to leave this point. And that's it. 

### 17 Thank you. 

### 18 **DR. DANIEL SCHLENK:**Dr. Denslow. 

### 19 **DR. NANCY DENSLOW:**My comments are included 

### 20 in the comments just said. 

### 21 **DR. DANIEL SCHLENK:**Okay. Thanks. Dr. 

### 22 Schultz. 

### 23 **DR. TERRY SCHULTZ:**The panel realizes that 

### 24 the reason for using the Adverse Outcome Pathway was so you 

### 25 could identify a singular molecular initiating event or 

### 26 initiating event leading to a particular outcome. I think the

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1 question it's raising is, are there other initiating events that 

### 2 would be of importance leading to the same outcome? That is are 

### 3 there other issues involved, molecular initiating events that 

### 4 relate to endocrine disruption by way of estrogenic activity. 

### 5 That's sort of the situation. 

### 6 Obviously the importance of the AOP as you 

### 7 have used it is that it allows you to form the chemical category 

### 8 when you mix your molecular initiating events and then you have 

### 9 difficulty in forming your categories. So if you're going to 

### 10 build a category approach to your testing an assessment, and you 

### 11 have to base it on a single initiating event, but if you want to 

### 12 address the issue of am I making the chemicals safe from ER 

### 13 related endocrine disruption, then you have to be able to 

### 14 address other possible initiating events. And so we realize 

### 15 that this is a good start because you've identified the most 

### 16 prominent initiating event, but you should not ignore that there 

### 17 are other possible initiating events that could follow part of 

### 18 the pathway, at least at the higher levels of organization. 

### 19 To go back to the category approach that you 

### 20 used in defining mixed phenols, and in that scenario, the 

### 21 question that becomes on that is why not mixed anilins or more 

### 22 specifically the fact that you identified chemicals that have 

### 23 the probability to bind to the A site, why was there not equal 

### 24 emphasis put on chemicals that bind to the B site? That's all I 

### 25 have. 

### 26 **DR. DANIEL SCHLENK:**Okay. Dr. Keiser.

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1 **DR. MICHAEL KEISER:**First just to say I agree 

### 2 with what's been stated before. One thing in the current 

### 3 decision path, literature evidence is taken occasionally into 

### 4 consideration and one nice subset or single source that might be 

### 5 considered to be added for this literature evidence will be the 

### 6 Chembl database, C-H-E-M-B-L, an open source, freely available 

### 7 database that reflects current scientific literature. That 

### 8 might also catch cases that would be otherwise be hard to find 

### 9 by a manual review of literature for known estrogenic binders. 

### 10 Secondly, regarding the issue of the AOP, 

### 11 whereas some of these other pathways or other potential 

### 12 initiating events might be less likely and indeed the current 

### 13 one does encapsulate the primary one as has just been mentioned, 

### 14 there are additional sources that might specifically be used for 

### 15 this approach, in addition to expert manual curation. One such 

### 16 source would be work done at Stanford recently by Atul Butte, 

### 17 looking at network analyses to find potentially unexpected or 

### 18 less appreciated initiating events in these outcome pathways. 

### 19 And a second one, also coming from that is 

### 20 work now going on at Mt. Sinai and we'll be happy to provide 

### 21 further information in the written form. 

### 22 And then thirdly, with respect to specific 

### 23 chemical categories and confidence, as mentioned before, right 

### 24 now it's a bit hard for the panel to assign confidences to 

### 25 specific ones of the categories without having seen some 

### 26 numerical retrospective work on the predictive ability of the

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1 model at those categories. So if you imagine that this decision 

### 2 tree, if you see it as a tree and you start at the root of the 

### 3 tree with your question and progress down the tree to the 

### 4 leaves, it would be assigning as we were talking about 

### 5 weightings or confidences, as best we can, even if they're only 

### 6 preliminary to the particular leaves of those trees. Thank you. 

### 7 **DR. DANIEL SCHLENK:**Okay. Dr. Cronin. 

### 8 **DR. MARK CRONIN:**Mark Cronin. No further 

### 9 comment 

### 10 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 11 **DR. STEPHEN SAFE:**I don't talk in terms of 

### 12 leaves and trees and I'm trying to -- I want to say some things 

### 13 but I want to get them right. And I look at what you're doing 

### 14 from an endocrine perspective and the fact that I view all of 

### 15 the compounds you're looking at, even the groups, although less 

### 16 so for the groups, as selective receptor modulators. And so 

### 17 these selective receptor modulators depend on some context and 

### 18 all sorts of different things. They may act through the 

### 19 estrogen receptor binding DNA or not binding DNA. And there's 

### 20 lots of evidence for that. 

### 21 If you knock out the DNA binding domain of the 

### 22 estrogen receptor, and knock that in, have an estrogen receptor 

### 23 that can't bind DNA in a mouse, it's functional in a lot, but 

### 24 not all estrogenic responses are ongoing. So what you're trying 

### 25 to do I think from an endocrine point of view is to have some 

### 26 confidence that what you're doing will predict. And then when I

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1 look at the assays you're doing, you're really, except for the 

### 2 mammalian one hybrid assay, you're really only doing two assays. 

### 3 4 Estrogen receptor binding which I think is 

### 5 essential but will depending on how you do it, and the context 

### 6 and you've shown this yourself, you're going to miss some low 

### 7 binders. Or you're going to have a lot of inconsistency, and 

### 8 that's problem. You only can get over that problem by using a 

### 9 number of estrogen receptor binding assays. And I think you've 

### 10 done that to some extent and I think that's good. 

### 11 The other problem is the estrogen 

### 12 transactivation assays. Now every time you do an ERE assay, and 

### 13 we discussed this in the group, what you're looking at is an 

### 14 artificial system where you have a frog estrogen responsive 

### 15 element inserted into a Luciferase or whatever reporter gene 

### 16 system. And that tells you how well you're going to active a 

### 17 frog gene, in a different context. 

### 18 And so I was initially and still am a little 

### 19 bit, worried about repeating this same type of assay but again 

### 20 if you do it in a different cell context then maybe it's telling 

### 21 you something because you're going to get some variability and 

### 22 you should get variability if all of the compounds you're 

### 23 looking at are selective receptor modulators. 

### 24 So I guess what I would try to emphasize or 

### 25 recommend, number one if you're so stuck on these assays and I 

### 26 know why you are because they're sensitive. And that makes a

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1 lot of sense. You want a sensitive assay to pick up a signal. 

### 2 But the more of those assays you do, then you can use the 

### 3 differences, compound dependent differences between the assays 

### 4 as maybe a prognostic sort of indicator. Not a mechanistic 

### 5 indicator because it's not, it's a prognostic indicator. And we 

### 6 discussed this before, it's really the same thing as when you do 

### 7 the gene array on a woman's tumor and that array has certain 

### 8 peaks that dictate the success of treatment. 

### 9 So in a sense if you're going to do the same 

### 10 assay all the time, in a different context, try to see if you 

### 11 can get some prognostic features about it that will say this 

### 12 fingerprint of assay positives or negatives is typical of this 

### 13 type of weak binder. Because those are your problematic ones, 

### 14 the strong binders are going to come through all the time. 

### 15 The other alternative to the prognostic 

### 16 approach that I see is to -- there are many assays out there 

### 17 that will detect not at a high efficacy, but that will detect 

### 18 sub-nanomolar concentrations of weak ER agonists. A lot of them 

### 19 are membrane ER signaling pathways. But look at them -- that's 

### 20 an alternative if you don't want to be stuck in the ERE 

### 21 activation mode, you might want to consider another relevant 

### 22 assay and just see how it goes. And I guess I was surprise that 

### 23 you hadn't done it but I sort of understand why. Because you 

### 24 want sensitivity. 

### 25 **DR. DANIEL SCHLENK:**Okay. Dr. Burris. 

### 26 **DR. THOMAS BURRIS:**I have just one comment to

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1 add. That if the EPA decides to go after more nonclassical 

### 2 estrogen pathways that aren't mediated by the classical binding 

### 3 of the compounds to ER, that the computational models have to be 

### 4 revisited because they are based on a lot of the physical 

### 5 chemical properties of compounds that bind to ER and in 

### 6 particular the one that I've been worried about is the one with 

### 7 the charge at pH 7. Clearly if you're not binding to the ligand 

### 8 binding domain of ER, charge isn't going to be something that 

### 9 you can eliminate compounds with. That's all. 

### 10 **DR. DANIEL SCHLENK:**Okay. Dr. Ehrich. 

### 11 **DR. MARION EHRICH:**No further comment. 

### 12 **DR. DANIEL SCHLENK:**Okay. Other Panel 

### 13 members? Dr. Nagarkatti. 

### 14 **DR. PRAKASH NAGARKATTI:**So I wanted to bring 

### 15 this up in response to Question 5 but now that a lot of people 

### 16 have raised similar issues about the comparison of the tests, I 

### 17 thought I would bring this up right now. 

### 18 And as Dr. Safe said, that you are primarily 

### 19 relying on ER binding and activation of EREs and you also 

### 20 mentioned that the results from both of these assays if they 

### 21 were to be positive in both of these, would be used as a 

### 22 criteria for classifying this compound as active or inactive. 

### 23 And I think the drawback there is that we know mechanistically 

### 24 that there are a lot of compounds which might activate ER, but 

### 25 not activate ERE pathway, but then go through the nonclassical 

### 26 pathway of activation of AP1 or SP1 or NFkB pathways or they

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1 might directly activate kinases phosphatases and so on. 

### 2 So I think if you don't take that into 

### 3 consideration therefore you -- if the ultimate goal is to look 

### 4 at modelation of some physiologic functions or toxicity, that 

### 5 you might find these alternative pathways also to contribute 

### 6 towards those modelations of physiologic functions or toxicity 

### 7 that you may not be able to catch that. 

### 8 So I don't know whether to welcome that or 

### 9 whether we should call what you are looking at is only the 

### 10 classical pathway and that you're not right now looking 

### 11 alternative pathways or that you need to let us know whether 

### 12 you're also looking at that and -- but in this -- what you 

### 13 presented, you are only trying to focus only on the classical 

### 14 pathway because that might really help us focus on saying that 

### 15 okay, we can do everything but we are trying to narrow down to 

### 16 the classical pathway and that's what we are trying to focus on 

### 17 right now. 

### 18 But otherwise it causes so much confusion 

### 19 about what are the typically -- how do we define going about the 

### 20 endocrine disrupting chemicals. 

### 21 **DR. DANIEL SCHLENK:**Okay. Any other 

### 22 comments? Yes, Dr. Waters. 

### 23 **DR. KATRINA WATERS:**Hi this is Katrina 

### 24 Waters. I wanted to expand on something that Dr. Keiser 

### 25 mentioned earlier in terms of uncertainty and model predictions. 

### 26 And the very last sentence of commenting on the extent to which

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1 the level of confidence can vary by chemical category. 

### 2 If the categories are going to be used to 

### 3 assign some level of guilt by association for the unknown 

### 4 binding compounds, then the size of the category is going to be 

### 5 a big factor in the consideration of the confidence you have in 

### 6 your predictions. So, very, very small categories don't have 

### 7 enough sampling in order to be able to truly estimate false 

### 8 discovery rates or errors in the confidence in that. 

### 9 And likewise, very large categories that have 

### 10 a very broad range of binding affinity predictions are going to 

### 11 be really unreliable in terms of your confidence of predicting 

### 12 things that are going to be positive or negative for the unknown 

### 13 binders. So you might consider some unsupervised informatics 

### 14 types approaches to cluster the large categories to try to get 

### 15 them down into smaller sizes that have enough sample to have 

### 16 some confidence but yet aren't too big to incorporate the wide 

### 17 range of properties that they contain that really add to the 

### 18 uncertainty. 

### 19 **DR. DANIEL SCHLENK:**Any other comments? 

### 20 Yes, Dr. McManaman. 

### 21 **DR. JAMES McMANAMAN:**Okay one last comment 

### 22 about alternatives. 3.2 says please comment on the level of 

### 23 scientific confidence that a chemical is unlikely to initiate 

### 24 the ER AOP if the in vitro training set data shows no activity. 

### 25 26 From what I understood from the presentation

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1 and what I just finished reading, the ER training set looks like 

### 2 it's mainly built on binding to the ER receptor -- the rainbow 

### 3 trout receptor. Binding is only one component of that 

### 4 activation process. The other component is binding to the DNA. 

### 5 So even if we're talking about the classical ER pathway, you're 

### 6 only looking at one aspect of that. So therefore, you could get 

### 7 a compound that does not affect binding of estradiol to the 

### 8 receptor, that's not an antagonist. But it still could affect 

### 9 the interactions at the level of the ER binding to the DNA 

### 10 elements. So that's something that seems to be missing in the 

### 11 training set. 

### 12 **DR. DANIEL SCHLENK:**Okay. Anyone else that 

### 13 would like to throw in on that? Okay it seems like a lot of 

### 14 Panel discussion regarding alternative pathways in terms of 

### 15 what's also out there besides just the ER and I guess you guys 

### 16 are focused on ER so, I think those written comments will 

### 17 actually have a lot of material about alternative pathways that 

### 18 are present that could probably be used in future studies. But 

### 19 I guess I would also again like the Panel to know that they are 

### 20 really focusing this one sort of very specific entity here and 

### 21 they really need your comment on that one specific entity. 

### 22 I think they recognize that there are other 

### 23 entities out there and other pathways that are there, but at 

### 24 this particular point in time, they want your help on this one 

### 25 entity at this point. So, if you could focus your comments on 

### 26 that, I think they would be very appreciative in that particular

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1 regard. Just my own little thoughts there. Any other comments 

### 2 from the Panel for that? Yes, Dr. Ehrich. 

### 3 **DR. MARION EHRICH:**Marion Ehrich. Relating 

### 4 to that comment, I think that just saying it in your document 

### 5 that you are dealing with the primary pathway I think would be 

### 6 helpful and that would answer a lot of the concerns that were 

### 7 here at the table. 

### 8 **DR. DANIEL SCHLENK:**Okay with that, we'll go 

### 9 back to the Agency. Any questions or clarification? There's a 

### 10 lot thrown at you there so anything that you have specific 

### 11 questions about? 

### 12 **DR. STEPHEN BRADBURY:**Thanks and a couple of 

### 13 the Panel matters brought it up and Dr. Schlenk reinforced it 

### 14 and perhaps the document could have been written clearer, but 

### 15 building from the 2009 SAP to this one, we do acknowledge that 

### 16 there are multiple ways that the estrogen signaling pathway 

### 17 could be perturbed sufficiently to cause sex change in fish or 

### 18 whatever adverse outcome you want to describe. 

### 19 The specific effort right now is to look at 

### 20 and people use different words, but whether or not a chemical 

### 21 has the potential to bind to the estrogen receptor, I think Dr. 

### 22 Burris called it the classical pathway or somebody called it the 

### 23 primary pathway. But, binding to that nuclear receptor doing a 

### 24 binding assay and the charge question was about the training set 

### 25 data, so binding to the rainbow trout liver site is solid prep. 

### 26 And then based on different decision criteria,

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1 whether or not to use a liver slice to see if in fact you're 

### 2 getting the gene turning on, you're getting expression -- you're 

### 3 getting a result of that binding, it's actually playing out. So 

### 4 those assays aren't designed to pick up different mechanisms. 

### 5 It never was intended and never meant it to be intended. 

### 6 What the question is getting at is for that 

### 7 specific molecular initiating event if you look at binding up 

### 8 the solubility, and you've looked at gene expression up to the 

### 9 point the cells are getting sick and nothing happens, how 

### 10 confident can we be or not be that that chemical's -- where is 

### 11 it? And if you can't have, and it doesn't mean that we know 

### 12 nothing about what it might do in another pathway because we 

### 13 didn't look at, but for that specific interaction that question 

### 14 was getting at -- I'm going to be overly simple, but if you got 

### 15 no response, is that as same -- do we have the same thoughts 

### 16 about that structure as the structure for which we did see 

### 17 something happen. 

### 18 If everything is the same, it sort of begs the 

### 19 point, why do it? So that's what that question is trying to get 

### 20 at. Otherwise, what is it that you would want beyond nothing 

### 21 happening to get a sense of how do you relate that to a chemical 

### 22 for which something did happen, in the context of this very 

### 23 specific -- and the I reason I'm kind of bringing this up is I 

### 24 think you've touched on a very important point that the feedback 

### 25 will be important because yes there are many different 

### 26 initiating events and depending upon where you are in that

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1 pathway, you could be assimilating a lot of different ways to 

### 2 get to a certain place on the pathway. 

### 3 So I think it is important to get clarity as 

### 4 where is your physical model of providing empirical data? Where 

### 5 is it on that pathway? And we weren't trying to talk about the 

### 6 relationship of these structures to a membrane receptor. We 

### 7 weren't testing that. We never intended -- we may have 

### 8 inadvertently picked that up and that would important to know. 

### 9 But yes, by definition these chemicals don't tell us anything 

### 10 about those initiating events because the assays weren't 

### 11 designed to pick up whether or not they could initiate that way. 

### 12 So I realize that the verbal comments are 

### 13 supposed to try to capture what's going to be in writing and at 

### 14 least me, I don't know for the whole team, I'm a little confused 

### 15 right now as to where we are at. 

### 16 **DR. DANIEL SCHLENK:**Let's go to Dr. Jacobs 

### 17 since you're the lead discussant you get the job of educating 

### 18 Dr. Bradbury with it. 

### 19 **DR. MIRIAM JACOBS:**We, the Panel really felt 

### 20 and we've stated therefore that we consider that it is a 

### 21 particularly sensitive ER in vitro test and particularly that 

### 22 using the rainbow trout, we are binding a liver slice, gene 

### 23 expression assays in tandem increases the confidence in 

### 24 predictions. And we've stated that, and that's clear. What our 

### 25 discussion and our questions have grown around how the next ES 

### 26 version 2 is being built and also version 3 and the information

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1 that we've been hearing on the high through put assays and the 

### 2 knowledge -- the understanding that we have of the different 

### 3 estrogenic mechanisms going on in those assays as well. So 

### 4 that's where a lot of our discussion has really started to come 

### 5 from. 

### 6 **DR. DANIEL SCHLENK:**Does anyone else want to 

### 7 chime in on that? I think that again, the question is are you 

### 8 confident? Are you confident if you get no response? I guess 

### 9 that's the bottom line. So is the Panel confident that if 

### 10 there's no response that -- that's there on its own so I see a 

### 11 lot of shaking heads that they're not confident. Dr. Sandy. 

### 12 **DR. MARTHA SANDY:**So I have a question to 

### 13 other Panel members. Is the human ER the same as the rainbow 

### 14 trout ER? 

### 15 **DR. DANIEL SCHLENK:**I think Nancy would like 

### 16 to answer that. 

### 17 **DR. NANCY DENSLOW:**Nancy Denslow. They're 

### 18 not the same. They're 60 percent similar and while they're 95 

### 19 percent similar in the DNA binding domain, in the ligand binding 

### 20 domain they're not, they're about 60 percent and in other parts 

### 21 they're only 19 percent depending on what part of the estrogen 

### 22 receptor you're looking at. So and there's the alpha estrogen 

### 23 receptor and then there's the beta which is again, even in same 

### 24 organism, a very similar to DNA binding domain but then very 

### 25 different in the ligand binding domains, again around 60, 70 

### 26 percent similarity. So they're not identical either. So

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1 estrogen receptor alpha doesn't necessarily report on what 

### 2 another soluble estrogen receptor beta would do in the same 

### 3 organism. 

### 4 **DR. DANIEL SCHLENK:**Okay. Yes, Dr. 

### 5 McManaman. 

### 6 **DR. JAMES McMANAMAN:**Yes, I intended my 

### 7 comments to be related to the question and I think that restate 

### 8 them, I think that the training set assays that you have set up 

### 9 will capture those compounds that are bona fide estrogen 

### 10 receptor binders and lead to transactivation. But so you would 

### 11 get those strong ones. I'm not convinced that you'll get the 

### 12 weaker binders and the weaker transactivators because of the 

### 13 complexity of that we just talked about in terms of the 

### 14 particular assay that you're using -- the binding assay that 

### 15 you're using and secondly the transactivation assay that you're 

### 16 using, you may lose response because of interactions with the 

### 17 tissue that are non-predictable. So I still have concern that 

### 18 you won't be picking up the weaker ones and that still may be 

### 19 important. I think that you'll get the robust ones but not 

### 20 necessarily the weaker ones. 

### 21 **DR. DANIEL SCHLENK:**Okay, so there's an 

### 22 estimate of confidence there, or not, lack of I guess. Yes, Dr. 

### 23 Schultz. 

### 24 **DR. TERRY SCHULTZ:**I believe that the way you 

### 25 have set up your series of assays, you're asking the question is 

### 26 the chemical likely to bind to either the A site or the B site,

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1 or both sites in a receptor? And I'm using the rainbow trout 

### 2 receptor as my model receptor because studies that have been 

### 3 done with model chemicals have shown that the binding potential, 

### 4 strong, moderate, weak for classic groups of chemicals, 

### 5 steroids, phenols, parabens, anilins is the relative ranking of 

### 6 the chemicals is the same within those categories. So you're 

### 7 basically saying that I've come up with a biological model and 

### 8 then I've come up with binding constraints within that and my 

### 9 justification for that is this is the most likely way that 

### 10 you're going to get ER related endocrine disruption. It doesn't 

### 11 mean that you've not had other ways of doing that. 

### 12 And from my perspective it doesn't mean that 

### 13 you don't have other ways of binding to that receptor. I have 

### 14 never heard anything from the Agency on the C region of the 

### 15 receptor. And it's kind of ignored as a regular binding site. 

### 16 Clearly the occupitation of that C site has something to do with 

### 17 potency. So as long as the Agency continues to emphasize that 

### 18 and realizes the limitations imposed by that, I don't see any 

### 19 way around -- or other way to move forward without doing those 

### 20 types of situations. If you wait to try to say I have a master 

### 21 battery of assays that account for all possible ways that I can 

### 22 interfere with ER-led endocrine disruption, we're not going to 

### 23 get very far in any reasonable period of time. 

### 24 So as long as you understand those caveats and 

### 25 recognize those things, I don't see a problem. But on the 

### 26 flipside of that, I say this is a good thing because this means

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1 that the Agency has done a good job as they defined it and now 

### 2 the committee is asking to move forward looking into these other 

### 3 scenarios, these other possibilities on that. I think if they 

### 4 hadn't done a good job on the A-B side, we wouldn't be talking 

### 5 about all these other things yet because you hadn't even been 

### 6 able to come close to solving the biggest problems, so let's not 

### 7 worry about the secondary or tertiary problems on that. 

### 8 I think what the Panel was saying, at least 

### 9 from my perspective as a Panel member, I'm saying you've done a 

### 10 good job with this, but let's not forget that there's other 

### 11 issues that you're going to have to address if you want to 

### 12 capture all of this, even within the ER related scenarios. And 

### 13 that's probably because the questions eight and nine, we get 

### 14 into asking about moving into other pathways. Thank you. 

### 15 **DR. DANIEL SCHLENK:**Thank you Dr. Shultz and 

### 16 Dr. Jacobs. Let me just again, if you wouldn't mind making sure 

### 17 that the comments are incorporated into the final written record 

### 18 that would be awesome. Anyone else? Yes, Dr. Safe. 

### 19 **DR. STEPHEN SAFE:**And if we look at the 

### 20 question, I think what we have to understand is that you wanted 

### 21 to find out if -- please comment on the extent to which the 

### 22 level of confidence may vary by chemical category, I don't know 

### 23 that we can do that. All we can say is that if a compound in 

### 24 the assay doesn't bind or activate vitellogenin gene expression 

### 25 in the trout assay, if it doesn't do that, then all we can say 

### 26 is it doesn't do that. And that's what you're asking us and

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1 that's what we can say. If it doesn't do that, it doesn't do 

### 2 that. 

### 3 Now that is sort of an adverse response in the 

### 4 trout context but that's all we can say. However, as more 

### 5 chemicals get fed into this system, it may then become in the 

### 6 future a very useful prognostic assay, even though it may not 

### 7 directly refer to the many adverse responses. So it could 

### 8 develop into a great prognostic assay. But you just need more 

### 9 data. 

### 10 **DR. DANIEL SCHLENK:**Okay. Anyone else? Dr. 

### 11 Keiser. 

### 12 **DR. MICHAEL KEISER:**So coming at this from 

### 13 the perspective of someone a little more on the computational 

### 14 side, who does not necessarily have the same background in some 

### 15 the estrogen receptors specific nuances, I'll simply say that 

### 16 what I'm observing on the Panel is some questions about the 

### 17 remet and the extent of what we mean by AOP and how far out 

### 18 we're going to go with that definition. If we begin to extend 

### 19 to human, clearly there're other questions. If we extend to 

### 20 other sites, there're other questions. But if the definition of 

### 21 ER AOP for this question is indeed focused on these particular 

### 22 in vitro and in vivo outcomes, then it seems that it's much 

### 23 easier for us to say that the unlikelihood of missing something 

### 24 is lower. So, just my two cents. 

### 25 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 26 **DR. EDWARD PERKINS:**Yes, and I want to

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1 reiterate that also that's why I emphasized the definition of 

### 2 the AOP and its relation to the human AOP the critical events in 

### 3 there and the actual outcomes. It's a common reproductive 

### 4 toxicity but the manifestations are different. One can be used 

### 5 to infer effects on another but I think for transparency and to 

### 6 avoid sort of misinterpretations of goals like this, you have to 

### 7 be careful to very, very clearly define what your system is and 

### 8 what the expectations are. Thank you very much. 

### 9 **DR. DANIEL SCHLENK:**Okay. Any other 

### 10 comments? Good discussion. Let me go back Dr. Bradbury to 

### 11 Mary. Do you have any other questions or clarification? Okay. 

### 12 All right. Let's move on. Let me just remind the folks that 

### 13 actually did again verbally provide comments that you need to 

### 14 get those into a written form to Dr. Jacobs here so she can have 

### 15 the fun job of assimilating all of that together in some 

### 16 coherent fashion. Okay. So please do that before the end of 

### 17 the week. Oh, well, the end of the week. Okay so let's move on 

### 18 to Question 4 and if you could start Mary at 4.1, that would be 

### 19 great. 

### 20 **MS. MARY MANIBUSAN:**Great. Question 4. High 

### 21 through put ER binding and ER transactivation Data. 4.1 Given 

### 22 the importance of a well-defined endpoint to determining if or 

### 23 how endpoint data are appropriately used, the panel is asked to 

### 24 comment on: Whether the assays are sufficiently described so 

### 25 that others can reconstruct the assay conditions and data 

### 26 analysis and to what extent additional information would be

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1 useful? Whether EPA has described sufficiently the important 

### 2 experimental conditions that affect the assay measurements, and 

### 3 whether EPA has sufficiently discussed their potential impact on 

### 4 assay results and interpretation of the results? 

### 5 **DR. DANIEL SCHLENK:**Okay. Our lead 

### 6 discussant for that is Dr. Denslow. 

### 7 **DR. NANCY DENSLOW:**Nancy Denslow. This is as 

### 8 far as it goes, okay. So in response to this question I do have 

### 9 this broader statement that I wanted to get into the record so 

### 10 even though we've just discussed this, I'm just going to read 

### 11 it. This is still that we think that you've done an excellent 

### 12 job on the assays that you are using for the specific purpose 

### 13 that you're using it. But I still want to read this one little 

### 14 piece that is going to relate to the discussion that we've just 

### 15 had. 

### 16 So there was considerable discussion among the 

### 17 Panel members concerning the ER AOP that the ER Expert System is 

### 18 hoping to model. If the ER Expert System is supposed to 

### 19 validate chemicals that are estrogen, agonists and antagonists 

### 20 in the broadest sense, then the HTP assays evaluated may not 

### 21 capture the entire set as the assays are specific to only one 

### 22 aspect of estrogen action and that's through the estrogen 

### 23 receptor Alpha and through canonical estrogen response elements 

### 24 in the DNA. Not included are affects through estrogen receptor 

### 25 Beta or the membrane receptor and nongenomic estrogen action. 

### 26 Even if the ER Alpha is the only focus, not all aspects of ER

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1 Alpha transactivation were tested. 

### 2 For example, specific steps in the process 

### 3 that occur after binding of the ligand to the receptor were not 

### 4 necessarily captured by the assays. There are HTP assays 

### 5 available in the Tox21 repertoire that capture these other steps 

### 6 as was indicated in the presentations but these assays were not 

### 7 used in this context. For example receptor dimerization, 

### 8 receptor binding to other response elements, cofactor binding, 

### 9 et cetera. And so the Expert System could be broadened to 

### 10 include these endpoints by using additional HTP assays already 

### 11 available in Tox Cast. And we'll provide references for all 

### 12 those statements. 

### 13 But we are sure that the ER AOP that was 

### 14 defined was much narrower interpretation of estrogen affect and 

### 15 that it was estrogen binding to an ER Alpha which binds to a 

### 16 canonical estrogen response element that turns on vitellogenin 

### 17 synthesis in fish hepatocytes, rainbow trout and the HTP assays 

### 18 that were used cover this AOP. So the ER Expert System as 

### 19 envisioned for the ESv3 has several advantages that expert 

### 20 systems for other endocrine disruption mechanisms currently 

### 21 lack. 

### 22 First the Expert System is based on a very 

### 23 well documented AOP where the downstream effects are at the 

### 24 different levels of biological organization are hardwired and 

### 25 easily scaled. The net result of having a hardwired AOP is that 

### 26 very few events, for example, the rainbow trout estrogen

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1 receptor binding and the liver slice gene activation assay need 

### 2 to be assessed with large numbers of chemicals. 

### 3 Second, the ER binding is a very well 

### 4 established molecular initiating event with two decades of 

### 5 studies supporting its application. To a lesser extent, this 

### 6 gene activation assay is an established key event being examined 

### 7 in a variety of protocols such as yeast mammalian cells, et 

### 8 cetera. More specifically the liver slice assay dating back 

### 9 from 2004 has withstood the scrutiny of time, thus the 

### 10 mechanistic interpretation and the real liability and 

### 11 consistency of data derived from these assays are acceptable for 

### 12 this narrower ER AOP. 

### 13 And so now I want to answer the question. 

### 14 That was just a comment that we want in the record. So the 

### 15 first question was whether the assays are sufficiently described 

### 16 so that others can reconstruct the assay conditions and data 

### 17 analysis and to what extent the additional information would be 

### 18 useful. And so we thought the assays were very well described, 

### 19 but we did have some discussion about these. So and they really 

### 20 do work, the detection system for each was very well 

### 21 established, the endpoint tested. How the assays deal with 

### 22 cytotoxicity was described, the acceptance criteria and known 

### 23 issues that affect the performance of the assays, all were very 

### 24 good. 

### 25 However, there were some criticisms leveled at 

### 26 the description of the assays. Several panel members noted that

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1 the original references given for the assays were difficult to 

### 2 find and in one case, I think a reference was wrong. The 

### 3 primary reference for the BG1 assay was a four page abstract, 

### 4 although a link was also provided to a full SOP. The primary 

### 5 description of the assays needs to be better referenced. 

### 6 There was also considerable discussion about 

### 7 the use of proprietary assays that only one vendor can perform 

### 8 and for which the performance of the assay cannot be tested by a 

### 9 third party. Assay performance and real liability can therefore 

### 10 not be assessed independently of the vendor. 

### 11 Okay, so for the second part, whether EPA has 

### 12 described sufficiently the important experimental conditions 

### 13 that affect the assays measurements and whether EPA has 

### 14 sufficiently discussed their potential impact on assay results 

### 15 and interpretation of the results. There was a lot of 

### 16 discussion. We thought that the descriptions were excellent. 

### 17 However, there was still some question about how or it was not 

### 18 clear how the eight assays were picked from among the 21 assays 

### 19 available from the Tox21 and the criteria for picking the eight 

### 20 assays should be more transparent. 

### 21 There was a worry that the eight assays picked 

### 22 were selected to test basically the same steps in the process 

### 23 and therefore would give the same type of data rather than 

### 24 picking assays that would test different steps in the process 

### 25 and which could then provide additional criteria for deciding 

### 26 that a chemical is not an estrogen.

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1 It might have been better to pick assays that 

### 2 would not necessarily test the exact same step to broaden the 

### 3 information used for grouping chemicals into whether they are or 

### 4 are not an estrogen mimic. For example, a panel of different 

### 5 gene activation assays that recognize different promoter 

### 6 elements may have added more information for the ER Expert 

### 7 System. 

### 8 Also there was discussion that more 

### 9 information could have been provided about the cellular 

### 10 background for each of the transactivation assays. 

### 11 Interpretation of results should be made in terms of the 

### 12 cellular background as estrogen receptors require cofactors for 

### 13 function and these cofactors vary by cell type. 

### 14 The Panel thought that there was insufficient 

### 15 discussion about the potential metabolism of chemicals in the 

### 16 different cellular backgrounds and how this might compare to an 

### 17 in vivo situation. Were all chemicals that can normally be 

### 18 metabolized into estrogen active derivatives metabolized 

### 19 correctly in the cells? 

### 20 And the Panel appreciated the efforts that 

### 21 were put into understanding the effect of the protein 

### 22 concentration on the binding assays. This is very well done. 

### 23 There was no mention of plans however, how to improve these 

### 24 assays, no experimental design for improvement was proposed. 

### 25 And then assays that rely on cell cultures may 

### 26 be subject to bioavailability constraints that media

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1 constituents may impart. These constraints may result in false 

### 2 negatives since protein constituents may bind and reduce 

### 3 bioavailability. Alternatively, protein binding may enhance 

### 4 material uptake, depending on the mechanism of transport from 

### 5 the medium into the cell. 

### 6 Klassen, et al., in 1999 reported that binding 

### 7 the retinoids to B assay, significantly reduce their uptake by 

### 8 keratinocytes. The permeability of mannitol was significantly 

### 9 different in caco-2 cells cultured in fetal bovine serum versus 

### 10 iron supplemented calf serum. And that's a reference by Lentz 

### 11 et al., 2000. Note it seems like some of the effects might be 

### 12 predicted from the physico-chemical characteristics of the test 

### 13 substance and hence could further be scrutinized when and if 

### 14 that compound is tested. And there're some references. 

### 15 The next question, which was to see whether 

### 16 the chemical library purity and analysis, chemical exposure, 

### 17 solvents used, chemical dilutions and plates and plate layouts 

### 18 provides information necessary for data interpretation. In 

### 19 general we were very happy with the results and how this was 

### 20 presented. But there's always considerable discussion and on 

### 21 this point too. While the Panel appreciates that it's difficult 

### 22 to obtain all chemicals at high enough purity, they caution 

### 23 against using chemicals that are not fully characterized. 

### 24 An example of a problem in this regard was 

### 25 provided by a Panel member that described a test of PCBs in 

### 26 Europe. This was the EU Project SysTax where one company

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1 synthesized the test chemicals at 98 percent purity but the 

### 2 impurity was dioxin. That actually had a profound effect on the 

### 3 assays. So depending on what the impurities are, they might or 

### 4 might not change interpretation of the assay and there will be a 

### 5 reference for that. 

### 6 And then finally (d), the adequacy of test 

### 7 concentrations maximum and minimum concentrations tested and 

### 8 cutoffs and whether the concentration cut offs affected some 

### 9 assay results more than others, especially in the context of 

### 10 false negatives. We thought in general these were all adequate. 

### 11 And more discussion on this point too. 

### 12 The Panel appreciated the care that was taken 

### 13 for calculations and rejection of outliers. The very 

### 14 conservative threshold calculation for the lowest effective 

### 15 concentration LEC, could lead to identifying a chemical as 

### 16 nonactive when it is actually active, i.e. increase false 

### 17 negatives. This could explain some of the discrepancies 

### 18 observed among the assays. The use of one concentration at 25 

### 19 micromolars in the binding studies was considered insufficient 

### 20 and that it missed some chemicals. It would be better to have 

### 21 full dose response curves, even for estradiol, as a positive 

### 22 control which in some cases this one had only had two points. 

### 23 And that's it. 

### 24 **DR. DANIEL SCHLENK:**Thank you. Our first 

### 25 associate discussant is Dr. Perkins. 

### 26 **DR. EDWARD PERKINS:**I don't have anything

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1 more to add. 

### 2 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 3 **DR. THOMAS BURRIS:**Nothing more to add. 

### 4 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 5 **DR. STEPHEN SAFE:**Nothing to add. 

### 6 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 7 **DR. TERRY SCHULTZ:**Basically I felt that the 

### 8 high through put screening data that you were using, that the 

### 9 incomplete development of the filtering protocols which were to 

### 10 address false positives and the issue of the limit to test 

### 11 concentrations to address the false negatives, made the Panel's 

### 12 decision here to be too early. I would like to see both of 

### 13 those issues better addressed so that we could more competently 

### 14 comment on the high through put screening as used to help 

### 15 supplement your categories. I'm getting tired, I don't if I 

### 16 made sense there or not. 

### 17 **DR. DANIEL SCHLENK:**You could say it again if 

### 18 you want. Okay. And Dr. Jacobs. 

### 19 **DR. MIRIAM JACOBS:**I agree with what's been 

### 20 said and my comments were included. 

### 21 **DR. DANIEL SCHLENK:**Okay. Other Panel 

### 22 members? It must be 3:30 I think. Okay. Okay, let me go to 

### 23 the Agency. Any questions or clarification? 

### 24 **DR. RICHARD JUDSON:**Richard Judson. I would 

### 25 just like to make one clarification or comment on the preamble. 

### 26 It has to do with the eight assays versus why did we bring the

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1 eight assays here rather than the full complement. And the 

### 2 eight assays were brought forward not because that's what we 

### 3 planned to use to help train the Expert System, it was a subset 

### 4 that we felt like our internal review panel could get through 

### 5 and present an approach. The intention is to bring the complete 

### 6 set of all the assays, merge those together into a consensus 

### 7 model from the HTP. And there was some discussion earlier about 

### 8 Alpha versus Beta. We actually had ER Beta assays so there will 

### 9 be information on both Alpha and Beta and these other events 

### 10 that are not transcriptional or binding captured in these other 

### 11 assays. 

### 12 **DR. DANIEL SCHLENK:**So is that a question or 

### 13 a comment? 

### 14 **DR. RICHARD JUDSON:**It was just a comment. 

### 15 **DR. DANIEL SCHLENK:**Yeah so this is a period 

### 16 of questions, you can't add any more. 

### 17 **DR. RICHARD JUDSON:**Then I retract what I 

### 18 just said. 

### 19 **DR. DANIEL SCHLENK:**Yeah, just to let you 

### 20 know. You're not supposed to do that, so, okay. All right. 

### 21 Let's go ahead and move to 4.2. 

### 22 **MS. MARY MANIBUSAN:**Question 4.2. With 

### 23 respect to data interpretation, please comment on the approach 

### 24 for defining an active chemical, i.e., to initiate the ER AOP, 

### 25 and an inactive chemical, i.e., unlikely to initiate the ER AOP 

### 26 compound and whether the method of data interpretation is

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1 adequately described and if the rationale for the approach is 

### 2 sufficiently presented. Please include in your comments: The 

### 3 adequacy of the approach to generate the data plots; how and 

### 4 what parameters are calculated, et cetera; How the background 

### 5 data were used to establish the control level; Whether the 

### 6 process for identifying assay interference is adequately 

### 7 described. Adequacy of data normalization, outlier 

### 8 identification, curve fitting, background subtractions, and all 

### 9 other data processing and calculation techniques, and 

### 10 appropriateness of the statistical analyses. 

### 11 **DR. DANIEL SCHLENK:**Okay. Dr. Denslow. 

### 12 **DR. NANCY DENSLOW:**Nancy Denslow. A preamble 

### 13 here too. The method of data interpretation defined in Figure 

### 14 3.1.2, the Expert System decision tree base on the rainbow trout 

### 15 ER binding and the liver slice GA knowledge base, which is a 

### 16 2009 SAP and EPA HQ-OPP-2009-0322-0002 and expanded in Figure 

### 17 3.2a.1 is adequate and sufficient. It is consistent with the 

### 18 current thinking by others, e.g. OECD. It has withstood the 

### 19 test of time be it short. Moreover, as demonstrated in screen 

### 20 shots Figures 3.1.3a and 3.1.3b, it is amiable to automation. 

### 21 The supporting information presented from Line 642 to Line 862 

### 22 is sufficient to support the expansion of the Expert System 

### 23 decision tree. 

### 24 Okay, so now let me get to the specific. The 

### 25 adequacy of the approach to generate the data plots, how and 

### 26 what parameters are calculated, et cetera, the approach is clear

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1 and as concise as required detail permits. And the Panel 

### 2 unanimously agreed that the approach was very clear. 

### 3 All right. How the background data was used 

### 4 to establish the control level. The background information 

### 5 permits the unambiguous establishment of control criteria. 

### 6 However, there was considerable discussion on whether DMSO which 

### 7 is weakly estrogenic should be called a negative control. So 

### 8 because it does have a weak estrogenic activity, it's not really 

### 9 negative. 

### 10 Whether the process for identifying assay 

### 11 interference is adequately described. Assay and data 

### 12 interference which is based on the AOP is adequately described, 

### 13 but there was still some question about whether the method 

### 14 described fully how the EPA will deal with assay interferences. 

### 15 Right now the plan appears to be simply to exclude data that 

### 16 comes from the interference and there was no discussed plan for 

### 17 how they were going to fix this. And there was no explanation 

### 18 about how the assay interference could affect false negatives. 

### 19 Okay, (d) was adequacy of data normalization, 

### 20 outlier identification, curve fitting, background subtractions 

### 21 and all other data processing and calculation techniques, and 

### 22 the appropriateness of the statistical analyses. Data analyses, 

### 23 et cetera, meets the currently accepted standards, especially as 

### 24 stated by OECD, and there was one question that was brought up 

### 25 and that was there was no mention of when human intervention is 

### 26 necessary. If this is done in a blind manner, i.e. without the

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1 knowledge of other results for the chemical so that you're not 

### 2 already putting subjective information into there. That's it. 

### 3 **DR. DANIEL SCHLENK:**Thank you Dr. Denslow. 

### 4 Dr. Perkins. 

### 5 **DR. EDWARD PERKINS:**No other comments. 

### 6 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 7 **DR. THOMAS BURRIS:**No comments. 

### 8 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 9 **DR. STEPHEN SAFE:**No comments. 

### 10 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 11 **DR. TERRY SCHULTZ:**No additional comments. 

### 12 **DR. DANIEL SCHLENK:**And Dr. Jacobs. 

### 13 **DR. MIRIAM JACOBS:**Just one really small 

### 14 comment. I agree with everything that was said, but normally 

### 15 one would term the DMSO control as a vehicle control not a 

### 16 negative control. 

### 17 **DR. DANIEL SCHLENK:**Okay. Thanks. Let me 

### 18 just also reiterate this subgroup met together so that's why 

### 19 they're saying no comment so that Dr. Denslow actually read the 

### 20 comments. They've been working very hard together to get that. 

### 21 So, just to let you guys know that. Any other Panel members? 

### 22 Okay. Let me go to the Agency to seek any questions or 

### 23 clarification. 

### 24 **DR. RICHARD JUDSON:**We just have one 

### 25 question. When you write up your report could you include the 

### 26 reference for the DMSO in its estrogenic activity please?

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1 **DR. DANIEL SCHLENK:**Yes. Okay. All right. 

### 2 Let's move on then to Question 5. Oh sorry, I didn't see 4.3 on 

### 3 the bottom there. They snuck it in. 

### 4 **MS. MARY MANIBUSAN:**So Question 4.3. Would 

### 5 you recommend other considerations or approaches to analyzing 

### 6 the high through put data? 

### 7 **DR. DANIEL SCHLENK:**Dr. Denslow. 

### 8 **DR. NANCY DENSLOW:**While data analysis will 

### 9 always be subject to other techniques, the approaches used are 

### 10 transparent, mechanistically probable, allow for a clear 

### 11 statement of the weight of evidence and allow for hypothesis 

### 12 based targeted testing. There may be additional software 

### 13 packages that could help, for example Spotfire, Hierarchal 

### 14 Analyses to aggregate the results by chemical class. 

### 15 **DR. DANIEL SCHLENK:**Okay. Dr. Perkins. 

### 16 **DR. EDWARD PERKINS:**I guess, one of the 

### 17 comments that I'd like to make is that there's significant 

### 18 progress I think in using agglomerative techniques to pull 

### 19 together multiple types of analyses and build relationships from 

### 20 them that might be utilized here. Especially if you want to 

### 21 start to actually rank and prioritize, somehow there will have 

### 22 to be a weighting scheme applied to the HTP assays. 

### 23 **DR. DANIEL SCHLENK:**Thank you. Dr. Burris. 

### 24 **DR. THOMAS BURRIS:**I think my comment's very 

### 25 similar just stating a different way that although each of the 

### 26 individual assays that I've looked at I think are well

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1 controlled and validated, the next step of how to deal with 

### 2 eight or 21 assays and how to integrate all of that date into a 

### 3 decision point, is going to be critical and potentially quite 

### 4 difficult to deal with. But certainly, I would hope that there 

### 5 is some type of validation set that could be used to bring about 

### 6 some level of comfort that the processes would be valid. 

### 7 **DR. DANIEL SCHLENK:**Thank you. Dr. Safe. 

### 8 **DR. STEPHEN SAFE:**No comment. 

### 9 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 10 **DR. TERRY SCHULTZ:**No comment. 

### 11 **DR. DANIEL SCHLENK:**And Dr. Jacobs. 

### 12 **DR. MIRIAM JACOBS:**I have a comment to add 

### 13 for more explanation with respect to hierarchal analysis and 

### 14 cluster analysis of assays. An example that I would like to 

### 15 give, but it also fits in Question 5 so I can give it there 

### 16 instead, rather than repeating. 

### 17 **DR. DANIEL SCHLENK:**Okay we'll hold off until 

### 18 that then. Any other input from the panel? Yes, Dr. Waters. 

### 19 **DR. KATRINA WATERS:**Hi. I just had one 

### 20 comment to make. I think it was pretty clear to the Panel when 

### 21 we were reviewing the documents that there was a lot of 

### 22 transparency in how you presented the data and how you did the 

### 23 filtering and stuff. But I think that it was clear from the 

### 24 public comments this morning that a lot of that is not portrayed 

### 25 when the data is publicly released. And so I think developing 

### 26 some standard protocols and really clear descriptions with those

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1 releases for how this data is changing from one release to 

### 2 another, how the filtering is done so that the replication of 

### 3 this can happen in the larger community, would be really useful. 

### 4 **DR. DANIEL SCHLENK:**Okay. Any other comments 

### 5 for 4.3? Okay now let's move to Question 5. 

### 6 **MS. MARY MANIBUSAN:**Question 5.1. Please 

### 7 comment on the selection of reference chemicals and whether they 

### 8 are sufficient to assess the performance of each high through 

### 9 put assay for ER agonists. How well do the reference chemicals 

### 10 represent the range of potencies needed to establish the 

### 11 reliability and relevance of these assays for use in the EDSP 

### 12 chemical prioritization for the inert ingredients, fragrances, 

### 13 and Safe Drinking Water Act chemicals? 

### 14 **DR. DANIEL SCHLENK:**Thank you. Dr. Jacobs 

### 15 you are lead on this one. 

### 16 **DR. MIRIAM JACOBS:**Okay. Miriam Jacobs. 

### 17 With this question we have been working on it early this morning 

### 18 and though it may not be as perhaps as well integrated as some 

### 19 of the earlier questions, so I apologize for that and we will do 

### 20 our best but there may be a lot more comments at the end. 

### 21 Reference chemicals for test development are 

### 22 as derived from the OCED performance based test guidelines for 

### 23 TG 455 and 457. These are based on validated data. They took a 

### 24 lot of exhaustive discussion and development. There is good 

### 25 representation of a range of substances, negatives, eight 

### 26 inactives, very weak, weak, moderate and strong chemicals. So

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1 the panel supported very much the use of those reference 

### 2 chemicals. 

### 3 The list is very necessary and a strong basis. 

### 4 However, how well they represent the range of potencies, 

### 5 particularly negatives and inactives needed to establish the 

### 6 reliability and relevance of these assays and models, 

### 7 particularly the models that are built or are going to be built 

### 8 from these assays for the use in the EDSP chemical 

### 9 prioritization for the inert ingredients, fragrances and SDWA 

### 10 chemicals is less certain. 

### 11 The panel felt that it should be augmented 

### 12 with data from the literature and particularly to include more 

### 13 inactives, at least to 50 percent, to have an adequately 

### 14 balanced statistical model to calculate sensitivity and 

### 15 specificity to further elucidate. There are two types of 

### 16 evaluation which appear to be confused in the documents and 

### 17 presentations that we've seen so far. 

### 18 Assay validation against reference chemicals 

### 19 and classification performance of the assays to protect activity 

### 20 of the AOP. Here the reference chemicals represent a broad 

### 21 range of estrogen receptor activity, classified from inactive to 

### 22 weakly active to strongly active. However, assay performance 

### 23 was evaluated only on a binary scale of active or inactive which 

### 24 is statistical but not relative to potency. It would be useful 

### 25 to evaluate assay performance and reproducibility to the goal 

### 26 standards set before in estradiol because this is used to

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1 normalize all the raw data in each plate. 

### 2 Assays that do not reproducibly produce high 

### 3 quality data for the goal standard should be removed rather than 

### 4 simply normalized and compared to other assays. Assay 

### 5 sensitivity should then be evaluated separately for each assay 

### 6 using weakly active to moderately active standards to establish 

### 7 the dynamic range of the assay. 

### 8 Statistical evaluation of the performance of 

### 9 the assay requires a different set of reference chemicals 

### 10 because the model is evaluating active versus inactive classes. 

### 11 In this case, size and composition of the truth sets is 

### 12 insufficient because only eight inactive chemicals are included. 

### 13 14 Additionally, many tested chemicals have 

### 15 substantial literature to support classification as active or 

### 16 inactive, which could be used to increase the size of the truth 

### 17 set for the statistical evaluation of the assays. An equal 

### 18 balance of classes is necessary for accurate estimates of 

### 19 sensitivity and specificity so that false negatives can be 

### 20 minimized and capture weakly active chemicals. 

### 21 It's therefore important to conduct a robust 

### 22 literature search on chemicals and chemical families to identify 

### 23 lead compounds. For example, the PAH chemicals discussion that 

### 24 we had yesterday that Dr. Safe was identifying. And it's 

### 25 assumed that the antagonist assay is going to be utilized but 

### 26 there was no specific information provided.

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1 Additional comments with respect to the 

### 2 ToxCast databases that the reference set could really be 

### 3 expanded over 100 including more diverse chemical classed that 

### 4 are known to be ER active in the literature so that's really 

### 5 what I've been saying. 

### 6 So the next question would be 5.2. So perhaps 

### 7 there are. 

### 8 **DR. DANIEL SCHLENK:**Dr. Waters you're our 

### 9 next associate for 5.1 

### 10 **DR. KATRINA WATERS:**My comments are included 

### 11 in that statement. 

### 12 **DR. DANIEL SCHLENK:**Okay. Dr. Safe. 

### 13 **DR. STEPHEN SAFE:**Mine as well. 

### 14 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 15 **DR. THOMAS BURRIS:**No comments. 

### 16 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 17 **DR. EDWARD PERKINS:**My comments are included 

### 18 in the previous statement. 

### 19 **DR. DANIEL SCHLENK:**Dr. Nagarkatti 

### 20 **DR. PRAKASH NAGARKATTI:**My comments are 

### 21 included as well. 

### 22 **DR. DANIEL SCHLENK:**And Dr. Sandy. 

### 23 **DR. MARTHA SANDY:**And mine are included. 

### 24 **DR. DANIEL SCHLENK:**Great. Any other 

### 25 comments from other Panel members? Okay. Let's go to the 

### 26 Agency. Any questions or clarification on the comments?

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1 **DR. RICHARD JUDSON:**Richard Judson. It's 

### 2 just that, could the committee be quite clear on at least a 

### 3 general scheme for increasing negatives that we believe are 

### 4 negatives, to the extent that you can say here are some rules 

### 5 that you would have us follow. That would be appreciated. 

### 6 That's it. 

### 7 **DR. DANIEL SCHLENK:**Is that clear to you 

### 8 guys? Do you know how to answer that? Can you answer that I 

### 9 guess in the written comments. 

### 10 **DR. MIRIAM JACOBS:**From our discussions we 

### 11 felt that there should be a lot of information coming from the 

### 12 high through put on inactives. And that would be able to be 

### 13 used and we've said already also that there should be a lot of 

### 14 information also from the literature and this is really 

### 15 specifically dealing with the model building aspects. Dr. 

### 16 Waters you might have something more to add. 

### 17 **DR. KATRINA WATERS:**Sure. Katrina Waters. 

### 18 So I think there was fairly good and we'll get to this in the 

### 19 later questions, comparison of the inactive compounds between 

### 20 the ES Version 1 predictions and what came through the high 

### 21 through put screening. So I think for statistical evaluation 

### 22 you don't necessarily need the truth set to have the very weak 

### 23 to the moderate to the strong activity when you're really 

### 24 capturing that binary kind of classification. So if you can 

### 25 increase your number of inactive compounds, you can get more 

### 26 confidence in that prediction.

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1 **DR. RICHARD JUDSON:**May I make a statement 

### 2 and see whether this captures what you're -- so what I think I'm 

### 3 hearing is that if we go to the literature and we find a 100 or 

### 4 200 chemicals that have been tested two or three times in in 

### 5 vitro assays in different labs and they're always negative, 

### 6 those could be a reasonable set of negatives to use for 

### 7 comparison. 

### 8 **DR. MIRIAM JACOBS:**Yes but also your own 

### 9 data. 

### 10 **DR. DANIEL SCHLENK:**Pretty clear? Any other 

### 11 questions. Okay, let's move on to 5.2 and read that into the 

### 12 record Mary. Thanks. 

### 13 **MS. MARY MANIBUSAN:**Question 5.2. Please 

### 14 comment on whether the Agency's evaluation of the performance of 

### 15 the HTP ER binding and activation assays has considered and 

### 16 accurately assessed all relevant aspects of the assays. For 

### 17 example, have signal-to-noise ratios, background subtraction and 

### 18 interferences been adequately characterized when describing the 

### 19 strengths and limitations of the assay? 

### 20 **DR. DANIEL SCHLENK:**Okay. Dr. Jacobs. 

### 21 **DR. MIRIAM JACOBS:**Miriam Jacobs. The 

### 22 results and explanations for poor data, we considered that they 

### 23 were not always adequate. For example, there was no 

### 24 documentation provided regarding the algorithm for dealing with 

### 25 the flares and we were sure that you must have one and that 

### 26 would be standard practice but we couldn't see that. The

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1 standard protocol for monitoring assay validation at the 

### 2 contractor sites would be useful and it was unclear if this was 

### 3 going on in practice. 

### 4 We also were initially unclear as to whether 

### 5 the HTP assays were run in-house or outside but this was 

### 6 clarified in an earlier question today that it was by the 

### 7 contractors. 

### 8 The evaluation of performance against 

### 9 reference of the eight assays wasn't evaluated based on data 

### 10 processing thresholds. Sometimes poor performance for single 

### 11 chemicals was blamed on individual artifacts such as flares. 

### 12 The limitations of all assays should be evaluated against 

### 13 reproducible measurement of response to the positive control and 

### 14 limit of detection. Interference from bursts of cell stress or 

### 15 toxicity is used to reduce false positives but will coordinately 

### 16 cause more false positive also. 

### 17 There were too many questions for 

### 18 clarification and they were answered as tweaking of the data 

### 19 using expert knowledge. Unbiased system performance requires 

### 20 more objective evaluation, for example, standard procedures in 

### 21 relation to the data in order to identify new data requirements 

### 22 for assay developments. It's critical to have standard 

### 23 procedure for dealing with manual procedures, specifically that 

### 24 the data be handled in a blinded structure or class. 

### 25 **DR. DANIEL SCHLENK:**Dr. Waters. 

### 26 **DR. KATRINA WATERS:**My comments are included.

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1 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 2 **DR. STEPHEN SAFE:**My comments are included. 

### 3 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 4 **DR. THOMAS BURRIS:**My comments are included. 

### 5 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 6 **DR. EDWARD PERKINS:**My comments are included. 

### 7 **DR. DANIEL SCHLENK:**Dr. Nagarkatti. 

### 8 **DR. PRAKASH NAGARKATTI:**My comments are 

### 9 included as well. 

### 10 **DR. DANIEL SCHLENK:**And Dr. Sandy. 

### 11 **DR. MARTHA SANDY:**My comments are included. 

### 12 **DR. DANIEL SCHLENK:**Other panel members? 

### 13 Anything to add? Okay. The Agency, clear on those comments? 

### 14 **MS. MARY MANIBUSAN:**No additional comments. 

### 15 Thank you. 

### 16 **DR. DANIEL SCHLENK:**Okay. Let's read in 5.3 

### 17 please. 

### 18 **MS. MARY MANIBUSAN:**5.3. Based on analyses 

### 19 in sections 5 and 6, please comment on the comparative 

### 20 performance of the eight high through put assays for detecting 

### 21 ER reference agonists, e.g., do some assays perform differently, 

### 22 and if so to what extent? Was sufficiently detailed information 

### 23 provided explaining the likely reasons for assay differences, 

### 24 when they are observed, e.g., cutoffs, assay interferences, 

### 25 background differences? 

### 26 **DR. DANIEL SCHLENK:**Dr. Jacobs.

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1 **DR. MIRIAM JACOBS:**The Panel felt that the 

### 2 U.S. EPA had provided extremely useful information but that it 

### 3 was far from complete, and it was often preliminary and stated 

### 4 as such. There was evaluation of differences between the eight 

### 5 high through put assays was based on the assumption that they 

### 6 provided redundant measures of the same biological activity when 

### 7 in fact they represent complimentary but distinct components of 

### 8 the mechanism of initiation mode of action for the ER AOP. 

### 9 This appears to be mostly a statistical 

### 10 approach which did not sufficiently describe the effects of 

### 11 thresholds, limit of detection or sensitivity of each assay to 

### 12 detect the range of estrogen receptor activity for the 

### 13 referenced chemicals. The eight assays chosen represent the 

### 14 distinct and yet limited survey potential MIEs for ER adverse 

### 15 outcome pathways. 

### 16 Was repetition really there? They are 

### 17 compared statistically as though they are expected to represent 

### 18 the same biological activity. It's possible that these eight 

### 19 assays and even some of the remaining 13 assays not used, 

### 20 represent additional distinct modes of activation which when 

### 21 combined with the eight, would improve predictions of active and 

### 22 inactive compounds and integration of the asset performance 

### 23 should be considered for future predicted models. 

### 24 It would be relevant to conduct a systematic 

### 25 integrative aggregate assay result to assess the overall utility 

### 26 of the assays. For example, withhold an assay and see how the

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1 data performs and then after analysis of those, to stop working 

### 2 with the poor performance, so to get rid of the redundant 

### 3 assays. 

### 4 This sort of approach is used extensively in 

### 5 the pharmaceutical industry and there are some small case 

### 6 studies that I referred to in the earlier question that might 

### 7 provide useful case study models. 

### 8 For example, there's an endocrine related 

### 9 example in the literature from one of the EU Fifth Framework 

### 10 Programmes, a project called Dysregulation of Endogenous Steroid 

### 11 Metabolism potentially alters neuronal and reproductive system 

### 12 development, effects of environmental plasticizers. So this is 

### 13 not by the ER AOP although some aspects could be considered, and 

### 14 it was for a very small chemical class, but it's an interesting 

### 15 model. This EndoMet as the acronym is called, it provides a 

### 16 case study for the use of a battery approach. The selected 

### 17 chemicals represented different classes, i.e. phthalates, 

### 18 alkylphenols and adipate classes. Their effects were measured 

### 19 on parameters from five different tests selected from 23 in 

### 20 vitro tests using cluster analysis to identify bottleneck 

### 21 potential AOP with steroid metabolism and function. The 

### 22 analyses identified four different pathways for which the in 

### 23 vitro test panel of the five selected assays could be 

### 24 potentially used as an indicator for the classification as a 

### 25 potential human endocrine disruptor. 

### 26 For this example, critical pathways were shown

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1 to include estrogen synthesis and sulphonation, synthesis of 

### 2 sulphate/PAP and that's 3-phosphoadenosine, 5-phosphosulfate and 

### 3 thyroid hormone regulation. 

### 4 The five tests identified were sulfide 

### 5 oxidase source activities, sulfotransferases, sults 101 activity 

### 6 (phonetic), Pap synthese, Pap S activity, aromatase, and the T 

### 7 screen assay. 

### 8 These five tests utilized human or rat cell 

### 9 lines and are considered to be relevant for humans. The initial 

### 10 23 tests included the thyroid as a target, neuronal cell lines, 

### 11 and also included tests from steroid synthesis and metabolism as 

### 12 well as the classical nuclear receptors. And while for this 

### 13 group of tests, there still needs to be further optimization and 

### 14 more substance testing, they have only been tested on very 

### 15 limited class of chemicals. 

### 16 The project does demonstrate how multi-variant 

### 17 analysis can be used for designing specific tests batteries for 

### 18 different chemical classes and such assays could be considered 

### 19 to be more prognostic and methods would be a methodology that 

### 20 would be appropriate for the one under discussion here. And the 

### 21 references for that are Wearing (phonetic) et al 2012 and Song 

### 22 (phonetic) et al 2012. 

### 23 Then there are some additional methods that 

### 24 were touched upon earlier by Dr. Keiser. They're in the Mt. 

### 25 Sinai, there's a group that has been doing analysis of MCF7, 

### 26 gene array pathways and those sorts of references would be

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1 appropriate to include here as well as analytical approaches. 

### 2 **DR. DANIEL SCHLENK:**Thank you. Dr. Waters. 

### 3 **DR. KATRINA WATERS:**My comments are included. 

### 4 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 5 **DR. STEPHEN SAFE:**I just wanted to point out 

### 6 a couple of things. Just based on the question and the 

### 7 comparative performance, again I would emphasize that if an ERE 

### 8 assay is done in a different cell context you would not expect 

### 9 to get concordance. If you did get concordance with every 

### 10 compound inducing relatively the same way in two different cell 

### 11 line, then you would have a redundancy. But you would expect 

### 12 differences for selective estrogen receptor modulators and so 

### 13 the fact that there's not a coordinate response I think is 

### 14 promising and the more compounds you do that the different assay 

### 15 systems or combination of assay systems may turn out in the 

### 16 future to be prognostic. 

### 17 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 18 **DR. THOMAS BURRIS:**I have nothing to add. 

### 19 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 20 **DR. EDWARD PERKINS:**Nothing more to add. 

### 21 **DR. DANIEL SCHLENK:**Dr. Nagarkatti. 

### 22 **DR. PRAKASH NAGARKATTI:**My comments are 

### 23 included. 

### 24 **DR. DANIEL SCHLENK:**And Dr. Sandy. 

### 25 **DR. MARTHA SANDY:**I have nothing else to add. 

### 26 **DR. DANIEL SCHLENK:**Other Panel members?

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1 Okay. Let me go back to the Agency. Any questions or 

### 2 clarification? 

### 3 **MS. MARY MANIBUSAN:**Just to echo a general 

### 4 request and I've heard this mentioned already, if you're citing 

### 5 a reference, please provide that in your written write up 

### 6 please. 

### 7 **DR. DANIEL SCHLENK:**Okay. I think I have 

### 8 that. All right. Well I think we are at a stopping point at 

### 9 this particular point in time. So I thank the panel for the 

### 10 work they've done so far. We will adjourn today and get back 

### 11 tomorrow morning. Do you need to state anything? 

### 12 **DR. SHARLENE MATTEN:**So tomorrow we'll start 

### 13 at 9 o'clock in the morning. If Group 5 still needs to clarify 

### 14 anything, I'm sure you'll add it in the morning. There's 

### 15 opportunities so don't feel that you have to stop. We always 

### 16 encourage more work. So for those who are on the phone or Adobe 

### 17 Connect, we're going to end and we'll resume at 9 o'clock in the 

### 18 morning. 

### 19 **DR. DANIEL SCHLENK:**And just if the panel 

### 20 could meet in the back room in about five minutes that would be 

### 21 great. I have a couple of things to go over. 

### 22 (WHEREUPON the meeting was adjourned) 

### 23 24 25 26

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1 **DR. SHARLENE MATTEN:**Everyone, please take 

### 2 their seats. Or if you're not taking your seat, kibitz out in 

### 3 the hall. This is the third day of the Scientific Advisory 

### 4 Panel Meeting, concerning Prioritization of the EDP -- EDSP 

### 5 Universe of Chemicals with the Purpose of Looking at 

### 6 Methodologies Related to Prioritization. 

### 7 My name is -- oh, I forgot to say who I was. 

### 8 My name is Sharlene Matten. I am the FIFRA Scientific Advisory 

### 9 Panel designated federal official for this meeting. Thank you 

### 10 very much for coming to this meeting; it's been a very enjoyable 

### 11 meeting, fruitful discussion. And with that, I'm going to turn 

### 12 the microphone over to Dr. Daniel Schlenk, who is the Chair of 

### 13 the FIFRA SAP. 

### 14 **DR. DANIEL SCHLENK:**Thanks, Sharlene. As 

### 15 is customary, let's just briefly go around the room and for the 

### 16 record, state your name and your affiliation. As Sharlene 

### 17 mentioned, my name is Dan Schlenk, I'm a professor in the 

### 18 Department of Environmental Sciences at the University of 

### 19 California, Riverside. 

### 20 **DR. STEPHEN KLAINE:**Good morning. I'm Steve 

### 21 Klaine. I'm a professor at Clemson University, Environmental 

### 22 Toxicology. 

### 23 **DR. MARTHA SANDY:**I'm Martha Sandy. I'm a 

### 24 toxicologist with the Office of Environmental Health Hazard 

### 25 Assessment of Cal EPA. 

### 26 **DR. JAMES MCMANAMAN:**Good morning. Jim

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1 McManaman, University of Colorado, professor of reproductive 

### 2 sciences. 

### 3 **DR. PRAKASH NAGARKATTI:**Good morning. I'm 

### 4 Prakash Nagarkatti, vice president for research at the 

### 5 University of South Carolina. I'm sorry; my throat is really 

### 6 bad. 

### 7 **DR. KATRINA WATERS:**I'm Katrina Waters from 

### 8 the Pacific Northwest National Laboratory. 

### 9 **DR. MARK CRONIN:**Mark Cronin from Liverpool 

### 10 John Moores University. 

### 11 **DR. TERRY SCHULTZ:**Terry Schultz, the 

### 12 University of Tennessee. 

### 13 **DR. JANICE CHAMBERS:**Jan Chambers, 

### 14 Mississippi State University. 

### 15 **DR. BETTE MEEK:**And I'm Bette Meek, 

### 16 University of Ottawa. 

### 17 **DR. EDWARD PERKINS:**Ed Perkins, U.S. Army 

### 18 Corps of Engineers. 

### 19 **DR. THOMAS BURRIS:**Tom Burris, Scripps 

### 20 Research Institute. 

### 21 **DR. MICHAEL KEISER:**Michael Keiser, SeaChange 

### 22 Pharmaceuticals. 

### 23 **DR. STEPHEN SAFE:**Steve Safe, Texas A & M 

### 24 University. 

### 25 **DR. NANCY DENSLOW:**Nancy Denslow, University 

### 26 of Florida.

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1 **DR. MIRIAM JACOBS:**Miriam Jacobs, European 

### 2 Food Safety Authority. 

### 3 **DR. DANIEL SCHLENK:**Thank you. To get us 

### 4 started this morning, I believe Mary has a few comments that she 

### 5 would just like to get before you in terms of summing up where 

### 6 things are going and where things have been, and some 

### 7 recommendations in terms of some of the questions that we've 

### 8 been dealing with over the last couple of days. Mary. 

### 9 **MS. MARY MANIBUSAN:**Thank you, Dr. Schlenk. 

### 10 I asked for an opportunity just to provide some clarification to 

### 11 the Panel from yesterday's discussion of multiple MIEs and 

### 12 different modes of action. 

### 13 For clarification, the Agency has specifically 

### 14 brought before this Panel a specific mode of action, with a 

### 15 specific MIE that starts with the ER receptor binding 

### 16 interaction, and then following up in higher organization 

### 17 levels, the gene activation in rainbow trout for the purposes of 

### 18 prioritization. 

### 19 So we would appreciate your clarity and your 

### 20 crystallization, your comments centered on just that, as our 

### 21 primary mode of action. And we recognize, again, that there are 

### 22 other ways to trigger estrogenic effects from endocrine active 

### 23 chemicals, but this is the particular MIE that the Agency would 

### 24 appreciate comment on. 

### 25 Just again, to just reconfirm that we're here 

### 26 for prioritization of chemicals and our task before us is, as

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1 I've discussed, 10,000 plus chemicals of broad chemistries. How 

### 2 do we move forward in terms of smarter, strategic testing and 

### 3 chemical categorization? Those comments from the Panel would be 

### 4 greatly appreciative. 

### 5 And just understand that from the Agency's 

### 6 perspective, it's very important to make sure that while we're 

### 7 moving forward and using computational toxicology, that we're 

### 8 not starting from scratch; we're really starting from where the 

### 9 SAP had provided guidance to the Agency back in 2009, with 

### 10 respect to the ER Expert System, and moving from there and 

### 11 specifically bring before the Panel the newer technologies, the 

### 12 high through put assays and the five that we've demonstrated are 

### 13 comparison between the training set that form the basis for the 

### 14 ER Expert System, and then your thoughts about expanding that 

### 15 chemical domain coverage with the high through put, recognizing 

### 16 again, with the Agency's need to minimize any false negatives 

### 17 and any false positives as we move forward. 

### 18 So again, Chair, thank you for that 

### 19 opportunity to provide that clarification. I want to make sure 

### 20 that we're all centered on the same focus. 

### 21 **DR. DANIEL SCHLENK:**Okay. Thanks. At 

### 22 this point, let's go ahead and read in our next question, which 

### 23 is Question 6.1, I believe. 

### 24 **MS. MARY MANIBUSAN:**Question 6.1: "Please 

### 25 Comment on the Agency's assessment of how high through put in 

### 26 vitro assays performed with respect to expectations of

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1 performance for chemicals within chemical categories when 

### 2 compared with the in vitro training set data used to build the 

### 3 Expert System Versions 1 and 2." 

### 4 **DR. DANIEL SCHLENK:**Our lead discussant 

### 5 for Question 6 is Dr. Perkins. 

### 6 **DR. EDWARD PERKINS:**Thank you. We had a few 

### 7 comments. We think the Agency's assessment of high through put 

### 8 assay performance was clear and able to provide explanations for 

### 9 most instances where assay results were different from the 

### 10 training set. 

### 11 In general, the high through put assays 

### 12 appeared biased towards identifying high-potency, high affinity 

### 13 ligands, while the rainbow trout assays detected low-potency 

### 14 chemicals. Much of this appeared due to use of a high single 

### 15 concentration pre-screen, such as in the receptor binding assays 

### 16 or because a lower concentration was used in the HTP assays than 

### 17 in the low through put rainbow trout assays. 

### 18 As a result, this drove appearance of a high 

### 19 degree of false negatives in the HTP assays. The Panel felt 

### 20 that adjusting dose or concentration exposures would help 

### 21 minimize this. The Panel also felt that Agency expectations is 

### 22 that categories of chemicals should be behave similarly, may be 

### 23 unreasonable, as categories were not really created on 

### 24 effects-based data, but were constructed using more expert 

### 25 opinion. And this is reflected in some of the results that you 

### 26 had. So that's the sum for that question.

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1 **DR. DANIEL SCHLENK:**Okay. Anything to 

### 2 add, Dr. Waters? 

### 3 **DR. KATRINA WATERS:**No. My comments are in 

### 4 that. 

### 5 **DR. DANIEL SCHLENK:**And Dr. Burris? 

### 6 **DR. THOMAS BURRIS:**Nothing to add. 

### 7 **DR. DANIEL SCHLENK:**Okay. Dr. Safe? 

### 8 **DR. STEPHEN SAFE:**Nothing to add. 

### 9 **DR. DANIEL SCHLENK:**Dr. Nagarkatti? 

### 10 **DR. PRAKASH NAGARKATTI:**Nothing to add. 

### 11 **DR. DANIEL SCHLENK:**And Dr. Sandy? 

### 12 **DR. MARTHA SANDY:**Nothing to add. 

### 13 **DR. DANIEL SCHLENK:**Other Panel members 

### 14 for 6.1? 

### 15 (No response.) 

### 16 Okay. Let me go to the Agency. Any questions 

### 17 or clarification that you need for that first part of the 

### 18 question? 

### 19 **DR. MICHAEL HORNUNG:**No questions. 

### 20 **DR. DANIEL SCHLENK:**Great. Okay. Let's 

### 21 go to 6.2 

### 22 **MS. MARY MANIBUSAN:**6.2: Please comment on 

### 23 EPA's approach to comparing the Expert System Training Set data 

### 24 with the high through put data and the interpretations regarding 

### 25 discordant results between the two assay approaches. Please 

### 26 recommend other considerations or approaches to conducting this

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1 comparative analysis. 

### 2 **DR. DANIEL SCHLENK:**Okay. Dr. Perkins? 

### 3 **DR. EDWARD PERKINS:**I think overall, we 

### 4 appreciated the approach the EPA used, going from a fairly 

### 5 simple and clear approach and then trying to bring in more 

### 6 complex ones. As was presented by some of the public comments, 

### 7 other types of analyses can come up with other types of answers, 

### 8 such as pulling into more positives, but covering all the 

### 9 negatives. So it's mostly biased towards false positives. 

### 10 The Panel believes that the assays should be 

### 11 put into context, such as isolated receptor may be give you a 

### 12 slightly different result that a transfectant expression assay 

### 13 versus native receptor MRNA behavior, just because of the 

### 14 cellular context and the MiLu that they're behaving in. This is 

### 15 especially apparent because of the concentration ranges they 

### 16 would be using. Also, the protein concentration that may absorb 

### 17 some of the chemicals, but also the behavior of MRNA initiation, 

### 18 given the different situations. 

### 19 So you shouldn't really expect complete 

### 20 agreement between receptors, transcriptional assays because of 

### 21 that. So you should look for more support between them rather 

### 22 than complete concordance. 

### 23 There appears to be a relatively good 

### 24 concordance for the two methods to predict inactive chemicals, 

### 25 however, more comparison between Tier 1 results and HTP results 

### 26 would be useful since the ESv1 is trained based on these data.

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1 It would be useful to note if these chemicals 

### 2 that were inactive by ESv1 and active by HTP assays were 

### 3 predominately the result of assay interference, such as 

### 4 fluorescents, cytotoxicity, or flash sites. 

### 5 The concordance of positive results appears to 

### 6 be extremely poor in the analysis, and it appears to be the 

### 7 result of two inconsistencies between the method. First, the 

### 8 use of single concentration screening approaches in the high 

### 9 through put assays, such as in the receptor-based assays. It 

### 10 seems to clear even less concentrations necessary to measure 

### 11 responsive nuclear-active chemicals as seen in Tier 1 assays. 

### 12 Also with the high through put assays, where 

### 13 it wasn't done with a single dose screening, the whole 

### 14 concentration range was shifted lower than in the in vitro 

### 15 rainbow trout assays. So there is a natural mismatch there. So 

### 16 as you would expect, a priority to miss some weak binders and 

### 17 things like that. 

### 18 Second, the -- let's see, one, two, three, 

### 19 four, five six -- 10 to the minus 7th, 10 to the minus 6th, E2 

### 20 activity threshold considered as positive by the ESv1 framework, 

### 21 needs to be confirmed as measurable by both Tier 1 assays and 

### 22 HTP assays if you expect them to be concordant. 

### 23 If this activity level is not measurable, then 

### 24 the group should also consider adjusting threshold accordingly. 

### 25 This was also brought up in the public comment that need to 

### 26 reexamine, if this is an overly conservative binding affinity

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1 value or not, compared to other published values, I think. 

### 2 Identification of protein interference on 

### 3 binding was a very good explanation for some of the discordant 

### 4 results and lack of metabolism, I believe, too. The comparative 

### 5 analysis may be more efficiently performed using the larger 

### 6 assay data set, for instance, all 21 of the endocrine -- all 21 

### 7 of the estrogen receptor AOP-related assays that were mentioned 

### 8 but not brought into the analysis. 

### 9 In comparison with the training set, this may 

### 10 enable further assays or combination of assays that might better 

### 11 capture the training results, using a -- take one out and look 

### 12 at different combinations. 

### 13 The Agency should consider analyzing results 

### 14 of combined or separate assays with or without filters and a 

### 15 combination of different filters. Obviously, the minimal amount 

### 16 of manipulation would be ideal. That's very clear that way. 

### 17 With the goal of trying to minimize false negatives, we don't 

### 18 really feel the false positives is a critical issue since this 

### 19 is a prioritization effort and we're expecting that potency or 

### 20 some other approach would fall into play with actually ranking 

### 21 prioritizing efforts. 

### 22 Let's see. Interpretations regarding 

### 23 discordant results highlighted differences in the approaches, 

### 24 but did not address a path forward strategy to fix the problem 

### 25 or validate the HTP assays against the results of the Tier 1 

### 26 assays.

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1 The Agency needs to establish criteria for how 

### 2 accurately the in vitro assay results cross Tier 1 and HTP 

### 3 predict in vivo AOP activity. If it's an AOP, they should 

### 4 either be causally or highly statistically linked to the adverse 

### 5 outcomes. So we need to have that. And one that also falls 

### 6 into earlier comments about very clearly defining molecular 

### 7 events and cellular events, graphically, so we can understand 

### 8 exactly how the assays link between all the different events, 

### 9 closer to the figure that was presented, showing where the 21 

### 10 assays fell within the cellular context. 

### 11 More assays covering other key events in the 

### 12 estrogen receptor, AOP, given if the AOP is very strictly 

### 13 defined in a mechanistic manner, would likely increase 

### 14 confidence that low-affinity binders were not excluded, since 

### 15 higher-level assays of higher levels of organization are likely 

### 16 to be able to detect more complex subtle effects. 

### 17 The inclusion of other assays with increased 

### 18 concentration ranges and/or increasing the concentration range 

### 19 of existing assays would probably help address low-affinity 

### 20 binding chemicals. Additionally, assay-specific weighting of 

### 21 data could also be used to inform prioritization, address 

### 22 potency problems and also address issues of false positives or 

### 23 false negatives. 

### 24 And I think that's all we have for this 

### 25 question. 

### 26 **DR. DANIEL SCHLENK:**Anything to add, Dr.

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1 Waters? 

### 2 **DR. KATRINA WATERS:**I have nothing to add. 

### 3 **DR. DANIEL SCHLENK:**Dr. Burris? 

### 4 **DR. THOMAS BURRIS:**Nothing to add. 

### 5 **DR. DANIEL SCHLENK:**Dr. Safe? 

### 6 **DR. STEPHEN SAFE:**Yes. I just wanted to 

### 7 emphasize the points that Ed made in the sense that since the 

### 8 assays being used are fairly similar, but a little bit different 

### 9 in terms of self-context, one wouldn't expect a complete 

### 10 concordance, but there would be modest concordance. And the 

### 11 lack of concordance shouldn't be a worry because of the nature 

### 12 of the compounds. 

### 13 I would just reiterate what Ed said in terms 

### 14 of the need for, perhaps, adding one or two additional assays 

### 15 that are different than the classical transactivation assay that 

### 16 may be useful for low-affinity binders. 

### 17 **DR. DANIEL SCHLENK:**Okay. Dr. 

### 18 Nagarkatti? 

### 19 **DR. PRAKASH NAGARKATTI:**Nothing to add. 

### 20 **DR. DANIEL SCHLENK:**And Dr. Sandy? 

### 21 **DR. MARTHA SANDY:**Nothing to add. 

### 22 **DR. DANIEL SCHLENK:**Other Panel members? 

### 23 (No response.) 

### 24 Okay. We can go back to the Agency. Any 

### 25 questions or clarification regarding 6.2? 

### 26 **DR. STEVEN BRADBURY:**A couple of thoughts to

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1 maybe get some additional clarity in the Panel's response, and 

### 2 some of it tracks back to some of the discussion yesterday. I 

### 3 can't remember which Panel member talked about comparing apples 

### 4 to apples and oranges to oranges or oranges to apples. It was, 

### 5 in part, around the concentration range used in the various 

### 6 assays in sort of the confounding situation wherein where the 

### 7 rainbow trout assays are running up to solubility or running up 

### 8 to cell intoxication. And as was mentioned by Dr. Perkins, high 

### 9 through put assays may have different concentration ranges. 

### 10 In fact, some of those responses may have been 

### 11 positive if they had been running up to higher concentrations, 

### 12 which then makes it difficult, it seems, and then realizing 

### 13 we're in a world of low-potency compounds. 

### 14 We already know from 2005, 2006, 2009, there 

### 15 aren't high-affinity compounds in this structure space. We're 

### 16 dealing with low-potency compounds. We're dealing with maybe 95 

### 17 percent of the compounds that don't even bind to the receptor. 

### 18 So that's where the action is. The action is being able to be 

### 19 confident nothing is happening and then finding those few 

### 20 needles in the haystack. 

### 21 So what I chased and sort of alluded to but 

### 22 the strategy to understanding how these different assays work 

### 23 and understanding how much variability may be in the context of 

### 24 different cell lines or trout versus mouse or cow or whatever, 

### 25 it seems it's pretty hard to get to the bottom of that without 

### 26 being able to compare apples to apples.

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1 So yes, it will probably be some noise across 

### 2 cell lines or across species, but our biggest variability we're 

### 3 dealing with is variability across chemicals, it seems, at least 

### 4 based on the 2009 SAP, where we were looking at human trout. 

### 5 Yeah, there's noise, but the noise isn't that big compared to 

### 6 what we're trying to figure out across chemical structure space. 

### 7 So I guess my first area of clarification from 

### 8 the Panel would be are we going to learn a lot by comparing 

### 9 assays that are using different concentration ranges or is it 

### 10 important to be first comparing the different context of 

### 11 different cell lines, at least making sure we're looking at same 

### 12 concentration response curve so we can figure out how much noise 

### 13 there may or may not be across these different assay methods in 

### 14 the context of the structure space, which is our big variability 

### 15 question that we got right now. 

### 16 So is getting some advice on what you think 

### 17 the first step should be and then the second step. Because 

### 18 right now it seems like there's lots of things do to, but there 

### 19 isn't clarity on knocking off the biggest areas of uncertainties 

### 20 in a systematic manner. 

### 21 **DR. DANIEL SCHLENK:**Okay. Dr. Perkins, 

### 22 do you want to take a crack at that? 

### 23 **DR. EDWARD PERKINS:**Sure. I mean, that's 

### 24 somewhat addressed in answers to 6.3; however, I think 

### 25 essentially what we're saying is that if you can understand the 

### 26 limitations or the behavior of the assays, they may not be

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1 exactly the same result, but they're likely be at least 

### 2 complimentary. That's probably actually a better strategy to 

### 3 have complimentary assays that are measuring the same thing a 

### 4 few times in a slightly different way than to have several 

### 5 identical measures of the exact same thing. 

### 6 I personally feel that this is more likely to 

### 7 capture diverse effects, and that was the comment about trying 

### 8 to have a few more assays that would cover different aspects of 

### 9 the ER post-MIE, the early key events in there and pair them 

### 10 together. So that would help capture it, thus, to allude to the 

### 11 broader problem of low-affinity binders and the diversity of 

### 12 chemicals versus classes of chemicals. 

### 13 From listening to a lot of the presentations 

### 14 and reading the problems and looking At how some of the results 

### 15 and seeing some of the other high through put focused efforts 

### 16 outside of these presentations, we think that it may be time to 

### 17 really consider, truly pursuing an effects-based categorization, 

### 18 using the HTP assays to classify -- think about classifying the 

### 19 chemicals on effect-based, using the high through put assays as 

### 20 a variable in, along with physico-chemical properties and 

### 21 classification. This may help add a biological aspect to the 

### 22 categorization that doesn't currently appear to be there. 

### 23 Categorization currently appears to be based 

### 24 on expert opinion and on structural characteristics. And if we 

### 25 let biology help decide what the categories are, we may rise and 

### 26 be able to address some of the issues that you're seeing now. I

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1 guess that's about it. Does anyone else have anything to add? 

### 2 **DR. DANIEL SCHLENK:**Yes. Dr. Burris? 

### 3 **DR. THOMAS BURRIS:**I think just to address 

### 4 that question and to reinforce what Ed said, I think diversity 

### 5 in the assays in the assay panel is critical. I think we've 

### 6 seen data to suggest that even a positive result from a single 

### 7 assay can lead to positive results in the Tier 1 in vivo assays. 

### 8 9 In terms of a decision tree, picking up 

### 10 activity in a single assay may be enough to lead to a reason to 

### 11 test those assays further downstream. But in term of 

### 12 prioritization, with a diverse set of assays, clearly compounds 

### 13 that are active in multiple assays would be higher priority than 

### 14 in fewer assays, but I think the idea of being able to cast a 

### 15 net across assays that detect diverse types of activity, 

### 16 targeting ER is absolutely critical. 

### 17 **DR. DANIEL SCHLENK:**Yes. Dr. Waters. 

### 18 **DR. KATRINA WATERS:**So I'll just add to sort 

### 19 of address your comment. You're correct. You're kind of in an 

### 20 odd situation. I don't know that the ToxCast program was really 

### 21 focused, initially, on the identification of weakly active 

### 22 compounds. So because of that, they're screening for the 

### 23 receptor binding is done at the lower level, and that's where 

### 24 you essentially missing data for those that would need the 

### 25 higher concentration to show activity. 

### 26 However, the public commenters yesterday

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1 morning demonstrated, I think, pretty clearly that in the 

### 2 absence of the extra filtering to get rid of the false 

### 3 positives, many of those compounds still do show up positive. 

### 4 And if your goal is to prioritize but not lose anything, I think 

### 5 you could consider, you know, reducing some of the stringency on 

### 6 the filtering of the data and you're still going to identify all 

### 7 of the positive compounds. And then, as the other Panel members 

### 8 said, you can use the results of that data to help rank and 

### 9 prioritize, based on their activity in the multiple assays. 

### 10 **DR. DANIEL SCHLENK:**Okay. Anyone else? 

### 11 Dr. Perkins? Dr. McManaman. I'm sorry. 

### 12 **DR. JAMES MCMANAMAN:**I appreciate your 

### 13 question about the diversity of the chemicals and it seems to me 

### 14 that the high through put assay that you guys have used has the 

### 15 best chance of ranking those chemicals according to whether they 

### 16 are going to be active or inactive. 

### 17 Because it's an amplified assay, you've got 

### 18 multiple EREs, so you would pick up anything that I think would 

### 19 likely to be active, whereas some of the other assays that you 

### 20 would use may not be as sensitive as this one. So I think that 

### 21 this would be -- I would say that if it were me, I would be 

### 22 wanting to use something like this as my first screening to rank 

### 23 my chemicals to find out whether they're active or inactive and 

### 24 then go on further to validate on another assay. 

### 25 I think that there is another control 

### 26 experiment that you might use too, so aligned that you can

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1 eliminate the nuclear localization signal on the receptors and 

### 2 thereby get a specific effect of the nuclear effects of a 

### 3 particular compound by comparing those two. 

### 4 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 5 **DR. EDWARD PERKINS:**I had one final thought 

### 6 -- well, maybe not final -- one thought. That it would be nice 

### 7 to -- one problem is trying to address all chemicals equally 

### 8 across the universe. I think I do feel that in the initial 

### 9 stages, during the filtering, screening of the physical chemical 

### 10 effects where you have exposure that you potentially factor in 

### 11 potential for high exposure to chemicals to identify ones, 

### 12 perhaps, as a class of ones that if they are weak binders, they 

### 13 would cause effects because of the potential high exposure rates 

### 14 and that might help reduce the universe of potential things that 

### 15 you have to examine with very sensitive assays. 

### 16 So exposure potential or production volume 

### 17 might come in to categorizing things that you need to screen 

### 18 with more sensitive assays. 

### 19 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 20 **DR. TERRY SCHULTZ:**In response to the 

### 21 question raised by the Agency, it noted that the action was at 

### 22 the low end of the binding affinity and they want to use the 

### 23 high through put assays to expand their tested chemical universe 

### 24 as the inventories increased. If that's the case, they must 

### 25 adapt the high through put assays to a concentration response so 

### 26 that they could assure that they are focusing on the

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1 low-affinity binders because what came out of the 2009 SAP was 

### 2 the case of doing the full concentration range and the argument 

### 3 for the 0.00001 cutoff. 

### 4 So in order to be consistent and use the high 

### 5 through put data to further define the chemical space as the 

### 6 inventories increase in diversity, they must maintain that 

### 7 continuity. 

### 8 **DR. DANIEL SCHLENK:**Dr. Sandy. 

### 9 **DR. MARTHA SANDY:**Just to follow up on what 

### 10 Ed was saying, I believe when he's talking about high exposure 

### 11 levels, you're also including in that chemicals that you may be 

### 12 exposed to every day, maybe not at a high level, but they're 

### 13 ubiquitous exposures. 

### 14 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 15 **DR. MICHAEL KEISER:**So picking up a moment on 

### 16 Dr. Schultz's point and responding to the Agency, one thing that 

### 17 seems to be coming out is a pattern -- and others can comment if 

### 18 they agree -- is that specifically because we're dealing with a 

### 19 case of low potency compounds and diverse compounds and we 

### 20 expect that space to grow, the idea that has come out is not 

### 21 simply to attempt to choose one or two of the high through put 

### 22 assays as simply indicative, but rather to have several shots on 

### 23 goal as we go through this, and look at several different ones 

### 24 of these assays from either the eight that we've looked at or 

### 25 even potentially up into the 21, but then there should also 

### 26 specifically be a way to prioritize.

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1 There is some discussion of a per assay 

### 2 weighting or ideas might also be borrowed from the analysis of 

### 3 high content screens, where one screen, of course, brings 

### 4 multiple different sorts of data together. This is also picking 

### 5 up some of the ideas from Dr. Safe yesterday, where a pattern or 

### 6 a fingerprint or a combination of differently weighted high 

### 7 through put assays could be collapsed into a single ranking or 

### 8 initial filtering score. And in this case, you might not 

### 9 necessarily have a fully mechanistic rationale for why one assay 

### 10 is rated more highly than the other, but rather as a prognostic 

### 11 approach. 

### 12 So in that case, you would, however, be able 

### 13 to select which assays should not be part of the initial high 

### 14 through put panel, simply by those which are not prognostic of 

### 15 the outcome that you were focused on through this single MIE 

### 16 that the different assays all have different viewpoints onto. 

### 17 **DR. DANIEL SCHLENK:**Any other response 

### 18 from the Panel on this 6.2? Yeah, Dr. Denslow. 

### 19 **DR. NANCY DENSLOW:**It seems to me that you 

### 20 could adopt a strategy where the first assay is one that you're 

### 21 doing, which is great because if it's a positive in that assay, 

### 22 then you know that those chemicals need to be prioritized. If 

### 23 it's a negative, you don't know. So then you do other assays 

### 24 for the ones that are negative that could be a different 

### 25 activity, different type of thing, where you don't have to run 

### 26 all chemicals through all assays.

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1 So depending on -- some of the questions you 

### 2 asked us was how sure were we that your negatives were really 

### 3 negative, and I think that's where we're coming down hard is 

### 4 that we're not sure. We're sure the positives are probably 

### 5 positive and so those are good ranks. Those are good chemicals 

### 6 to then prioritize, but we're not sure that the negatives are 

### 7 really negative, and that's where I think all this other comment 

### 8 is coming from. 

### 9 **DR. STEVEN BRADBURY:**Thank you. And that's 

### 10 where I'm confused. We have one assay that's running up to 

### 11 solubility or running up to cell toxicity. And as the 2009 SAP 

### 12 said and if nothing happens, I think Dr. Safe said, "No 

### 13 activities. No activity." I mean, if you can't get any more in 

### 14 the buffer or if the cell is dying, probably other stuff is 

### 15 happening of that cell before it can screw up that estrogen 

### 16 signaling pathway. 

### 17 So positives with the one assay, the trout 

### 18 assay and no activity with the others, it could still be 

### 19 activity there. Maybe there's a way to jam more into the cell 

### 20 before it gets sick. I don't know. But it's the flip side 

### 21 that's the issue, is that we know from the analysis done 

### 22 already, probably largely due to the cutoffs, it's the negatives 

### 23 from the high through put that we don't have confidence that 

### 24 they're negative. 

### 25 It's not so much the negatives out of the 

### 26 trout that -- I'm not saying there's 100 percent confidence that

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1 nothing is nothing, but there is probably a higher degree of 

### 2 confidence that nothing is nothing with the trout assay just 

### 3 because of the way it was run. 

### 4 The negatives with the high through put in 

### 5 fact may be negatives, but we don't know. And as I think the 

### 6 Panel is indicating, the first sort of thing to come to grips 

### 7 with is that concentration curve that's been used. So that's 

### 8 the part I'm struggling with. And somebody else mentioned, you 

### 9 want to make sure you sweep up the positives. 

### 10 Well, if we use the high through put right now 

### 11 there would be a lot of things that we know are positive in 

### 12 trout we wouldn't be sweeping up because the high through put 

### 13 would be saying negative, but we've got data with the trout that 

### 14 saying positive. And it be solely be -- there's going to be 

### 15 noise across species, I get that, but I bet you a big chunk of 

### 16 that difference is just due to concentration curves, the 

### 17 exposures that are being used. 

### 18 So that's the part of the advice, so far, from 

### 19 the Panel that is confusing. Not to say you're wrong. It's 

### 20 just if you could help explain it so we can understand how to go 

### 21 forward. 

### 22 **DR. DANIEL SCHLENK:**That was Dr. Bradbury. 

### 23 Yeah, I think you might have to wait until the report comes out 

### 24 to get that put together. 

### 25 **DR. STEVEN BRADBURY:**To get where we're 

### 26 trying to get the clarity.

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1 **DR. DANIEL SCHLENK:**I think the Panel has 

### 2 understanding there. Yes? I see a lot of everyone shaking 

### 3 their heads. Dr. Safe? 

### 4 **DR. STEPHEN SAFE:**You know, I think we 

### 5 understand that you're worried about the negatives and that 

### 6 you're not sure they're negative or not. And I think one of the 

### 7 reasons for that is although you're trying to maximize your 

### 8 sensitivity, there are classes of compounds that don't work 

### 9 exactly in the classical way and that's what you're measuring. 

### 10 So the alternative to that, if you want to 

### 11 look at these negatives that you're worried about, would be to 

### 12 think about another assay, and there are a number of them out 

### 13 there that will get nominal or concentrations of weak agonists 

### 14 in a pathway, for example, that activates the membrane estrogen 

### 15 receptor. 

### 16 So I'm not saying do this assay or that assay, 

### 17 but there are alternatives that you might want to consider for 

### 18 the negatives you're worried about. 

### 19 **DR. STEVEN BRADBURY:**And then I'll be quiet. 

### 20 And those are great suggestions, but if you could also help us 

### 21 by being clear on which MIE you're discussing when you do that 

### 22 because again, I think Mary pointed out -- I apologize for being 

### 23 a few minutes late -- we're focused right now on one very 

### 24 specific MIE that nuclear receptor binding domain and not the 

### 25 membrane. But that doesn't mean that we aren't going to move 

### 26 into other MIEs. If you just make sure your comments are crisp,

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1 in terms of this MIE versus a different MIE that may perturb the 

### 2 estrogen-signaling pathway. Thanks. I'll be quiet. 

### 3 **DR. DANIEL SCHLENK:**Thanks. Okay. Anything 

### 4 else on 6.2? 

### 5 (No response.) 

### 6 Okay. Want to read 6.3 into the record? 

### 7 **DR. MARY MANIBUSAN:**6.3: "Given that there 

### 8 may be varying occurrences of false negatives and false 

### 9 positives based on different sources of training set data, 

### 10 please include in your comments options for addressing these 

### 11 uncertainties for different categories in the context of 

### 12 building Expert System Version 3." 

### 13 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 14 **DR. EDWARD PERKINS:**Okay. And you'll have to 

### 15 clarify us. We may not have answered this one exactly the way 

### 16 you'd like either -- or sufficiently, to clear it up. 

### 17 The Panel suggests that the Agency examine 

### 18 effects of over-fitting and overtraining on the data. The 

### 19 potential of weighting data, depending on the source, if the 

### 20 different sources that you're mentioning come from other people 

### 21 or other data sets or if we're just talking about adding to a 

### 22 new one and adding new assays, adding, weighting the data, 

### 23 depending on reliability, how sure you are of the data, whether 

### 24 it's been QC, QA and measures like that will help. 

### 25 Also, one thing that will help is if somehow 

### 26 you can find at least a few core assays that you can at least

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1 compare directly between them to look at a performance-based 

### 2 measure as a control, at least, that's performed exactly the 

### 3 same way, even if the methods change, at least you'll have one 

### 4 thing you can compare across them. And perhaps that would be 

### 5 like the fish liver assay or one of the other assays. So you'll 

### 6 always have some constant comparison between the two spaces. 

### 7 I can see a lot of benefit for pulling in 

### 8 other data sets, but you do have to be careful about how they 

### 9 did them, their proper controls. So the reliability of the 

### 10 assays is a big issue when you look at that, I think, but you 

### 11 can address that by incorporating specific types of controls in 

### 12 there, I think. 

### 13 Also, the Agency should look at the number of 

### 14 assays that are being used in the analysis. Just as we 

### 15 mentioned before, just comparing on a one-to-one basis. It's 

### 16 unlikely that you'll find the perfect homerun assay and that 

### 17 different combinations of assays may be better. And as you grow 

### 18 a dataset, you're going to put in more different chemicals, 

### 19 potentially more high through put assays as the technology 

### 20 evolves. 

### 21 So things will change and you may not have a 

### 22 comparable data set to the others; however, the expansion of 

### 23 high through put assay data with the chemical set, I think would 

### 24 take a priority. However, if you do have some coherent assays 

### 25 and keep standards through them, that might help a lot for some 

### 26 cross comparison.

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1 The other area we think it may be very 

### 2 sensitive to between training set data are different chemical 

### 3 categorization strategies, just by looking at how you're 

### 4 comparing assay results versus chemical classes, that could also 

### 5 alter how you're doing it between data sets. 

### 6 As you add more chemicals in, you're going to 

### 7 probably be expanding data classes or splitting them or fusing 

### 8 them, so that could also cause confusion on the latter analysis. 

### 9 I guess in relation to that, I would personally recommend a 

### 10 coherent framework for making your data transparent and 

### 11 independent of platform, such as big data approaches, where you 

### 12 can use meta-analysis triplets. Things like that. 

### 13 So you can basically do ontology searches 

### 14 throughout your data, no matter if it's chemistry, biology or 

### 15 annotations so that it's independent of whatever software 

### 16 package you're using or whatever analysis package you're using. 

### 17 That will help keep your data available and also be useable for 

### 18 other people to link their databases too, so that they can also 

### 19 provide more information. 

### 20 Some of that has been talked about with 

### 21 bringing in high through put data about putting it all into a 

### 22 coherent format, but I don't know about all the other types of 

### 23 data either. And it will be very important when you continue to 

### 24 evolve the data sets not to change, to come up with a simple 

### 25 standard that you keep coherent across time. 

### 26 We think ESv3 should continue, if that's

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1 continued throughout routes if you continue those cycle of 

### 2 prediction validation at filling the gaps for how well the 

### 3 predictions validate with the Tier 1 assays; however, it wasn't 

### 4 clear how the class definitions for guilt by association are 

### 5 actually being used given that several classes contain chemicals 

### 6 with both inactive and active binding potentials. 

### 7 Specifically focusing on false negatives 

### 8 predicted by high through put positive activity, it could be 

### 9 determined if there are artifacts of assay interference, but it 

### 10 should be positive and it comes out negative. Otherwise, 

### 11 identify how does positive activity is missed by the ESv3 using 

### 12 additional chemical properties or weighting of nodes within 

### 13 decision trees. 

### 14 The Panel suggests that Agency efforts should 

### 15 be focused on using additional HTP assays targeting different 

### 16 effects specifically related to the ER AOP key events in order 

### 17 to add more context and more connectivity to the AOP. Closer 

### 18 concordance between the training set and HTP data sets, now and 

### 19 in the future, should evolve and incorporate more knowledge of 

### 20 the biology that's happening, especially if you use more of an 

### 21 effects-driven comparison, e.g., categorized compounds based on 

### 22 HTP activity and physico-chemical properties to form categories 

### 23 of chemicals. Using that, you'll evolve the categories based on 

### 24 what data you're putting in, but since you're letting the 

### 25 biology, different aspects of biology shape your responses and 

### 26 categories, this may be a more closer effect than what you might

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1 see in the more complex liver slice systems. 

### 2 Finally, we have a suggestion or thought. 

### 3 Since high through put screening has become much more 

### 4 sophisticated over the last several years, it appears that 

### 5 screening of 5,000 chemicals, such as the 5,000 inerts and Safe 

### 6 Drinking Water Act chemicals is relatively inexpensive and 

### 7 rapid, given a few select assays. 

### 8 We feel the concept of high through put assays 

### 9 and effects-based screening can be rapidly extended further by 

### 10 focusing on acquiring the HTP assay results on all chemicals in 

### 11 the space being tested, trying to encompass the 5,000 inerts and 

### 12 SWDA chemicals. 

### 13 If select ER AOP-specific HTP assays are 

### 14 validated on a small scale on what you're comfortable with what 

### 15 they're saying to you, then these could provide critical effects 

### 16 data necessary for prioritization in conjunction with the 

### 17 development of ESv3. 

### 18 So the last comment was a little bit farther, 

### 19 probably, than the question, but we think that right now that 

### 20 ESv2 could take significant advantage in evolving it by 

### 21 incorporating the effects data as part of the decision rules. 

### 22 So that's all I have. 

### 23 **DR. DANIEL SCHLENK:**Dr. Waters? 

### 24 **DR. KATRINA WATERS:**I have no additional 

### 25 comments. 

### 26 **DR. DANIEL SCHLENK:**Dr. Burris?

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1 **DR. THOMAS BURRIS:**No additional comments. 

### 2 **DR. DANIEL SCHLENK:**Dr. Safe? 

### 3 **DR. STEPHEN SAFE:**No comments. 

### 4 **DR. DANIEL SCHLENK:**Dr. Nagarkatti? 

### 5 **DR. PRAKASH NAGARKATTI:**No additional 

### 6 comments. 

### 7 **DR. DANIEL SCHLENK:**And Dr. Sandy? 

### 8 **DR. MARTHA SANDY:**Just to emphasize the last 

### 9 point there of the suggestion that you test all these chemicals 

### 10 and then you get a much better handle on how to categorize them 

### 11 by structure, it would be much closer to a true structure 

### 12 activity relationship that you could derive. And then that 

### 13 information can be used for the larger universes of chemicals 

### 14 that are out there for other programs. We think it's a 

### 15 worthwhile thing to do. 

### 16 **DR. DANIEL SCHLENK:**Any other Panel 

### 17 members? Dr. Chambers? 

### 18 **DR. JANICE CHAMBERS:**Let me just raise a 

### 19 question with that last suggestion of doing all the compounds. 

### 20 I thought we heard that many of them are not available, at least 

### 21 at the purity that you would need to have some confidence in the 

### 22 assay results. So if that's the case, then should there be some 

### 23 modification to that answer that ideally, all of them should be 

### 24 run? But if they're not available, what do you do? 

### 25 **DR. DANIEL SCHLENK:**Yes. Dr. Sandy. 

### 26 **DR. MARTHA SANDY:**That's a very practical

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1 addition, yes. But we would encourage the EPA to look further 

### 2 than just the two contract chemical suppliers they had 

### 3 experience with the HTP assays. But to do a little more digging 

### 4 and look, you could probably find 70 percent of those chemicals 

### 5 at least and test them if possible. 

### 6 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 7 **DR. EDWARD PERKINS:**It does come down to 

### 8 practicality, cost and urgency of screening; however, there is 

### 9 the caveat that if you can't get the chemical, then is it really 

### 10 a problem, I suppose. 

### 11 From earlier ones, at least myself, I try to 

### 12 emphasize that more effort should be put into obtaining 

### 13 chemicals for the testing. We understand the time constraints 

### 14 and budgetary constraints and programmatic constraints, but 

### 15 there are still 30 percent of 10,000, I believe, for ToxCast. 

### 16 So that's still 3,000 different chemicals. There's still a 

### 17 large -- a lot more than 1,700 chemicals out available that you 

### 18 already have. So that space alone is much larger. So it should 

### 19 still be able to get a lot more data by incorporating the 

### 20 effects-based data. 

### 21 **DR. DANIEL SCHLENK:**Any other comments? Let 

### 22 me just remind the Panel that if you made comments in addition 

### 23 to what you already provided to Dr. Perkins, that you provide 

### 24 those in a written format so that he can incorporate this 

### 25 together so that they all go into the final minutes. 

### 26 At this point, let me go to the Agency. Any

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1 questions or clarification regarding the comments? 

### 2 **DR. STEVEN BRADBURY:**I'm baffled. I'm 

### 3 confused, but I think Question 8 will probably be helpful. 

### 4 Maybe then I can synthesize some of the latter discussion 

### 5 because I think some of the latter discussion gets into what 

### 6 we're getting at in the charge questions for Number 8. So I 

### 7 think I'll just defer 'til now and then maybe I'll -- at least 

### 8 me -- I'll get it because I don't get it right now. 

### 9 **DR. DANIEL SCHLENK:**Okay. 

### 10 **DR. STEVEN BRADBURY:**There are a couple of 

### 11 things that may be helpful. There is a discussion about are 

### 12 these effects-based categories in the Expert System. As a point 

### 13 of clarification, they are. They're based on low through put 

### 14 empirical data as opposed to high through put empirical data, 

### 15 but the categories are based on empirical data. 

### 16 So that's one area that I'm just confused as 

### 17 to the recommendation. Then I think Charge Question 8 will get 

### 18 at some of the things we just discussed and that might help sort 

### 19 things out. But if not, then we'll let you know. 

### 20 **DR. DANIEL SCHLENK:**Can someone respond in 

### 21 terms of what effects-based data you're talking about, outside 

### 22 what has already been presented? 

### 23 **DR. EDWARD PERKINS:**I can attempt to. Part 

### 24 of the -- I think for a number of us, a number of the questions 

### 25 were sort of overlapped - felt kind of overlapping, so we 

### 26 probable did talk about things and other things, but I think for

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1 us, the fundamental driver, I believe, was the observation that 

### 2 high through put assays have a lot of -- you can generate a lot 

### 3 of information fairly quickly for a large number of chemicals. 

### 4 I think the general feeling is that this data 

### 5 is not being used in conjunction or with structure to try to 

### 6 understand relationships as much. We appreciate that the 

### 7 initial categories were done on the training set that is a 

### 8 fairly small selection of chemicals. 

### 9 We did note that a lot of the discordant 

### 10 results between categories, which is actually another one, 

### 11 however, we talked about expectations of performance for 

### 12 chemicals within categories as part of this too, so it kind of 

### 13 overlapped. 

### 14 So I think what we're trying to communicate is 

### 15 that we feel that you're missing an opportunity to integrate the 

### 16 data as part of the categorization effort. We're not saying to 

### 17 replace something. We're saying that this is -- in the concept 

### 18 of AOP is that these are different steps within the same 

### 19 continuum, ideally, of the ER AOP, and that measurements along 

### 20 this line should be indicative of what's happening. 

### 21 So if you look at that receptor binders, an 

### 22 early effects stage, along with transcriptional activation and 

### 23 just the feeling that you should be able to incorporate that 

### 24 into the Expert System. Either I missed it or it wasn't quite 

### 25 there. 

### 26 There's really no plan for integration of the

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1 new data that's coming in as part of a logic for categorizing 

### 2 and classes. So that's pretty much where it came from, if that 

### 3 clarifies it. 

### 4 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 5 **DR. MICHAEL KEISER:**I'd like to just put out 

### 6 sort of clarifying questions to the Panel by stating, you know, 

### 7 coming from the perspective of hearing it today and thinking 

### 8 about of "staying back" one way I would phrase it and seeing if 

### 9 people agree. 

### 10 So I have in front of me on my computer, 

### 11 opened up, the schematic diagram of the Expert System. 

### 12 Following the decision rules, each sort of diamond or process in 

### 13 it, many of the rules, as would make a lot of sense, are defined 

### 14 by structural features or occasionally by things such as LogKow, 

### 15 physical property in that case, but in terms of an effect-based 

### 16 decision or logic, one example, perhaps, of a new category of 

### 17 rule that might be added from this now available and 

### 18 increasingly available high through put data, would be the 

### 19 combination or the particulate results that came from a high 

### 20 through put as a node in the decision tree. 

### 21 So to try to be very specifically clear about 

### 22 that, instead of contains a charge or in addition to contains a 

### 23 charge, as one example, or Site 8 contains phenol fragment. 

### 24 Additional one could be Screens 1, 2, and 3 out of HTP Panel 

### 25 show positive above preset threshold as a single decision rule. 

### 26

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1 And I see it looks like it might be some 

### 2 question for clarification on that. 

### 3 **MS. MARY MANIBUSAN:**This is Mary Manibusan. 

### 4 I'm equally confused because I think what you're talking about 

### 5 is empirical data and the Expert System was indeed built in 

### 6 response to empirical data, but what it's based on is a (Q)SAR 

### 7 approach. So all those nodes are based on empirical data. So 

### 8 I'm not quite getting the suggestion of incorporating high 

### 9 through put data into nodes. 

### 10 **DR. MICHAEL KEISER:**The suggestion, as I 

### 11 understand it -- because again, this is only my attempt to 

### 12 clarify from what I understand from the discussion here -- is 

### 13 the steps along the way before we end up at a categorization of 

### 14 chemicals into a single group could contain empirically derived 

### 15 rules along the decision pathway. Maybe someone else might also 

### 16 want to phrase that or clarify. 

### 17 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 18 **DR. THOMAS BURRIS:**I think the issues, at 

### 19 least from my perspective is that those rules are based on -- 

### 20 these (Q)SAR rules are based on what we currently understand 

### 21 about how compounds bind to the receptor, but there are issues 

### 22 that there are compounds that may bind to it that we completely 

### 23 would be unable to predict that bind in the mechanism that is 

### 24 unclear or have a low-affinity binding that does not fit the 

### 25 current (Q)SAR rules. So by generating the actual biological 

### 26 data through an HTP, you would be able to capture those

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1 compounds. 

### 2 **DR. DANIEL SCHLENK:**Would it help the Panel 

### 3 to actually put the scheme up on the screen so that you can 

### 4 direct that or do you need that? Yes? 

### 5 Can we get Version 2? Do you want the Version 

### 6 2 paradigm? 

### 7 In the meantime, any question? Yes. Dr. 

### 8 Hornung? 

### 9 **DR. MICHAEL HORNUNG:**I was wondering if I 

### 10 could try to provide some clarification on the Expert System. 

### 11 **DR. DANIEL SCHLENK:**In terms of? 

### 12 **DR. MICHAEL HORNUNG:**In terms of the 

### 13 categories and the effects. 

### 14 **DR. DANIEL SCHLENK:**Okay. 

### 15 **DR. MICHAEL HORNUNG:**The effects-based 

### 16 categories. 

### 17 **DR. DANIEL SCHLENK:**Okay. 

### 18 **DR. MICHAEL HORNUNG:**Those structure rules 

### 19 are based on the effects that are seen in the binding and the 

### 20 gene activation assay. So a chemical, initially, before any 

### 21 testing is done, is put into a category, but then the testing 

### 22 that gives you the activity or inactivity result, active or 

### 23 inactive result, is then what helps define that category. 

### 24 So if you see within that category, based on 

### 25 the activity of your category that you initially may have set up 

### 26 with 15 chemicals, if it looks like that doesn't appear to be a

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1 category based on the activity, then you go back and try to do 

### 2 refinements. 

### 3 From what I was hearing, I'm not sure if that 

### 4 concept had gotten relayed, I guess. I don't know if that helps 

### 5 clarify. 

### 6 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 7 **DR. MICHAEL KEISER:**Yes. The Panel does 

### 8 appreciate the fact that the existing rule sets and categories 

### 9 are based specifically on the effects in the attempt, indeed, to 

### 10 reflect that. I think maybe a point of confusion is that the 

### 11 actual proposal that I heard at the end of the comments earlier 

### 12 was a) wherever possible, or its pragmatic, to actually do the 

### 13 initial high through put screening, and then -- and this could 

### 14 simply be an augmentation or variant of the current Expert 

### 15 System -- molecules going through the augmented or Expert System 

### 16 that incorporates effect-based rule sets would need to be 

### 17 prescreened. Because as you, I think, rightly, would point out, 

### 18 otherwise, you would not have this screening data as the input, 

### 19 along the decision chain. And it was literally using empirical 

### 20 data that would then be present for a prescreened compound as a 

### 21 decision point within this system. 

### 22 So it's describing a system that is not solely 

### 23 computational and does not solely derive from the chemical 

### 24 structure and thus, you would need a physical instantiation and 

### 25 you need to source it, test it, and put it through the high 

### 26 through put screen and then get the empirical data for that

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1 compound to even put it through the augmented system. 

### 2 So obviously that is a limitation of the 

### 3 system that would be proposed here because it would not have the 

### 4 same breadth of being able to apply it to any chemical 

### 5 structure. You would need to test the chemical. 

### 6 However, where such data are available, if 

### 7 indeed the Agency were to pick up this idea of putting as many 

### 8 compounds as possible through the HTP, in parallel or even ahead 

### 9 of parts of the augmented Expert System, we would actually be 

### 10 interested to see better results, perhaps, coming from it by 

### 11 incorporating that empirical data as an input and not simply as 

### 12 the means to create the categories that are structurally 

### 13 organized in the decision tree. Excuse me. That are 

### 14 structurally tied to the effects that had been observed. 

### 15 **DR. DANIEL SCHLENK:**Dr. Perkins, did you have 

### 16 something that you wanted to add? 

### 17 **DR. EDWARD PERKINS:**The other way that -- 

### 18 excuse me. I'm getting all choked up about it. The other way 

### 19 that this can be beneficial is that the (Q)SAR approach is 

### 20 limited by the data that you put into initially and by trying to 

### 21 add as much high through put data across a large a space as 

### 22 possible. And this goes back to a future question, I think, is 

### 23 that that can help evolve the (Q)SAR. 

### 24 Essentially what we're saying is you can 

### 25 either use this to evolve the (Q)SAR to make it more refined 

### 26 because clearly, it doesn't predicate accurately everything

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1 right now. So by adding more features -- well, if it predicated 

### 2 everything correctly now, we wouldn't have to do all this. 

### 3 To enhance the predictive capability of the 

### 4 ESv3, incorporation of data results, more structure activity 

### 5 relationships and development of the (Q)SAR that forms the basis 

### 6 of that would be a great, great advantage; to allow, as was 

### 7 mentioned, about adding it potentially an empirical testing as a 

### 8 node there that could potentially facilitate a fluid evolving of 

### 9 the rules or adapting to, as you get more and more data, rather 

### 10 than trying to recomputed everything. 

### 11 So it provides a couple of different 

### 12 approaches that are not necessarily incompatible with what 

### 13 you're already doing. So that's why we're suggesting it. I 

### 14 think in our full report we'll try to get more clearer on it, 

### 15 but we understand that this was built on (Q)SAR with structure 

### 16 activity relationships, but somehow we would like to capture it 

### 17 right, but one of the problems is we don't have enough structure 

### 18 activity relationships across the chemical space that we're 

### 19 talking about. That's why you want to expand the list, isn't 

### 20 it? 

### 21 **DR. DANIEL SCHLENK:**Go ahead, Dr. Bradbury. 

### 22 **DR. STEVEN BRADBURY:**Yeah. And I think 

### 23 that's where Question may help sort of -- 

### 24 **DR. EDWARD PERKINS:**As I said, it falls into 

### 25 question, but again, and that's why I said it wasn't directly 

### 26 addressed in the question, but we were talking about

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1 uncertainties in different categories for building ESv3. So 

### 2 it's somewhat related to the question. The question overlaps, 

### 3 so you have to forgive us. 

### 4 Just to avoid the confusion, the real 

### 5 statement is just that we think that an opportunity is there to 

### 6 either incorporate high through put assay data for a larger 

### 7 space of chemicals already -- and I'm sure we'll talk about it 

### 8 more in Question 8 -- either as part of development or 

### 9 refinement of the (Q)SAR approach or as some other type of 

### 10 feature. And we'll try to give more specific instances of that 

### 11 in a fuller report. 

### 12 **DR. DANIEL SCHLENK:**Okay. Let's go back to 

### 13 the Agency. Are we good at this point? Sorry, I got a comment 

### 14 over here from Dr. Schultz. 

### 15 **DR. TERRY SCHULTZ:**The Panel was somewhat 

### 16 divided in the fact that some of the Panel members looked at 

### 17 this very narrowly and said can high through put data be used to 

### 18 expand the chemical categories that define chemicals that have 

### 19 the potential to bind to the A or the B site in the estrogen 

### 20 receptor, which is the basis for the diagram that you had there. 

### 21 22 But in the presentation by the Agency, it kind 

### 23 of opened up Pandora's box and there are other members of the 

### 24 Panel that say, well, wait a minute; if we have these high 

### 25 through put capabilities and we can test thousands of chemicals 

### 26 relatively quickly and inexpensively -- relative is a term --

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1 then perhaps we need to think of it as a paradigm shift where we 

### 2 can do the high through put screening first and then come up 

### 3 with our algorithm based on the fingerprint of the experiential 

### 4 evidence that comes off of the eight or the 21 estrogen 

### 5 binding-related high through put assays. 

### 6 So those are two different things that are 

### 7 being looked at in the Panel and I think that what has come out 

### 8 is that in Question 6, the answer from the Panel has said 

### 9 Agency, you may want to look at this way of doing it and when we 

### 10 get to Question 7, you'll hear the other side of the Panel 

### 11 saying this. And the reason for that is the word "category" is 

### 12 used both in Question 6 and Question 7. 

### 13 So the different groups who were addressing 

### 14 these looked at them in different situations on that. Perhaps 

### 15 after we get through the answers to Question 7, it may be a 

### 16 little bit clearer to the Agency. 

### 17 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 18 **DR. THOMAS BURRIS:**This point may seem a 

### 19 little blunt, but the (Q)SAR is designed to predict ER binding. 

### 20 You have three high through put assays that don't predict it, 

### 21 they show it. And you could run, and they are capable of 

### 22 running the number of compounds that we're considering. So why 

### 23 predict it when you can determine it directly? 

### 24 **DR. STEVEN BRADBURY:**I understand, and some 

### 25 of them I question -- gets back to the conversation we had 

### 26 before on the concentration ranges. So in other words, we have

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1 empirical data that shows 30 percent -- quite a range of what 

### 2 would be predicted as active through the trout assay versus the 

### 3 high through put assay. 

### 4 And, in fact, we would conclude with the high 

### 5 through put assay there are a lot more chemicals that are 

### 6 negative than what the rainbow trout data suggests. 

### 7 I think that previous question was getting at 

### 8 -- and we'll see what your recommendations are -- to use high 

### 9 through put data at full dose response curves or use high 

### 10 through put data at cutoffs because we'll get different answers, 

### 11 the White Papers shows it. You get different answers for the 

### 12 same chemical with the two assays and it may just be because 

### 13 they were run at different concentrations. 

### 14 If our goal is to be confident that "no 

### 15 activity" means no activity for this specific MIE, it will just 

### 16 be helpful to hear from the Panel how to interpret whatever in 

### 17 vitro platform you want to talk about, in terms of what's the 

### 18 concentration range you're testing at; what are the solubility 

### 19 limits, or not? What's the cell toxicity, or not, that should 

### 20 be used? 

### 21 I think the Agency will absorb all the 

### 22 comments and we will use the OECD principles for (Q)SAR 

### 23 validation where the very first principle is a well-defined 

### 24 endpoint. Know exactly what your endpoint is. Know what your 

### 25 bioassay methods are because in my opinion, an in vitro platform 

### 26 or a (Q)SAR system are just models. One is a physical model and

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1 one is a "mathematical model." They're all models trying to 

### 2 recreate reality, which we'll never find. 

### 3 So it's really important to find what your 

### 4 physical model is or your mathematical model is because if you 

### 5 don't do that, you don't know how to interpret the information. 

### 6 So one would be very helpful to hear about not high through put, 

### 7 low through put, but what concentration range should we be 

### 8 looking at. What should be the cutoffs we're using because 

### 9 we're not looking for new drugs, we're looking at chemicals that 

### 10 we know don't do much. 

### 11 The vast majority don't do anything, but we 

### 12 want to find those needles in the haystack. So assuming a 

### 13 chemical isn't one of the needles, just because we may not have 

### 14 tested it high enough, it's something that we just get your 

### 15 advice on how to sort our way through that. 

### 16 **DR. DANIEL SCHLENK:**Okay. Dr. Meek. 

### 17 **DR. BETTE MEEK:**Just to make the point again 

### 18 that some of those aspects do come up in response to Question 7. 

### 19 **DR. DANIEL SCHLENK:**Great. Dr. Jacobs. 

### 20 **DR. MIRIAM JACOBS:**And also to make the point 

### 21 that some of those questions have been addressed in previous 

### 22 questions with respect to the dose response curve and the 25 

### 23 micromolar not being sufficient. 

### 24 **DR. DANIEL SCHLENK:**Okay. Any other 

### 25 comments? Dr. Schmieder, you have something? 

### 26 **DR. PATRICIA SCHMIEDER:**Yes. I was just

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1 going to ask for a clarification to see if I understood Dr. 

### 2 Schultz's comment. And it's addressed to all the Panel too. 

### 3 Does the Panel think that we only use empirical data for the 

### 4 Site A and B chemical groups and not realize that we use for 

### 5 every one of those little groups up there that that is all based 

### 6 on empirical data that is binding in gene activation? And that 

### 7 the whole system is not just based on Site A and B and that, 

### 8 like for instance, the special rules class. 

### 9 We're not saying that we understand where 

### 10 those chemicals interact within the receptor, but that they do 

### 11 interact. Does the Panel understand it different than that? 

### 12 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 13 **DR. TERRY SCHULTZ:**No. The problem you have 

### 14 with the special rules is you cannot explain, from the 

### 15 mechanistic standpoint of the rationale for that. The A and the 

### 16 B site, you have an explanation for that. 

### 17 The special rules are, we think it fits in the 

### 18 receptor because we get activity, but we don't know how that is 

### 19 and that becomes much more difficult to deal with if you're 

### 20 going to be doing a chemical structure-based approached as 

### 21 opposed to the approach suggested by just doing the high through 

### 22 put screening and getting the information and working back from 

### 23 that. 

### 24 **DR. DANIEL SCHLENK:**Okay. Dr. Jacobs. 

### 25 **DR. MIRIAM JACOBS:**Which is where some of the 

### 26 suggestions yesterday might come in helpful, doing 3D docking

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1 studies, which we understood that were going to be conducted on 

### 2 some of the high through put chemical data. And then you would 

### 3 have more information on what's going on within the receptor. 

### 4 **DR. PATRICIA SCHMIEDER:**Another -- 

### 5 **DR. DANIEL SCHLENK:**Hold on a second. Dr. 

### 6 Keiser had something -- no? Okay. Dr. Schmieder. 

### 7 **DR. PATRICIA SCHMIEDER:**Just a point of 

### 8 clarification, are you saying that the docking studies can only 

### 9 be done with high through put data and not the same empirical 

### 10 low through put data? 

### 11 And that the high through put data gives you 

### 12 different mechanistic understanding of where it binds in the 

### 13 pocket, versus low through put empirical data that's also 

### 14 measuring binding and transactivation? 

### 15 **DR. MIRIAM JACOBS:**No. The docking studies 

### 16 can be used across the board. It's just that we heard yesterday 

### 17 from Dr. Judson that there were plans to be using those sorts of 

### 18 tools, particularly with the chemicals from the high through put 

### 19 data or perhaps I misunderstood. 

### 20 **DR. DANIEL SCHLENK:**Okay. Any other 

### 21 comments? I think we've beat this one to death a little bit. 

### 22 So I've been informed that we need to actually 

### 23 implement a fairly longer break than normal to deal with flight 

### 24 changes and check-outs, et cetera. So let's be back at 10:45 if 

### 25 possible. That will give us a nice down time for Question 7 to 

### 26 come up, I hope. So let's adjourn until 10:45, please.

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1 (Whereupon, a brief recess was taken.) 

### 2 **DR. DANIEL SCHLENK:**Let's go ahead and move 

### 3 on to Question 7. Again, let me just reiterate a couple of 

### 4 things that I've heard during the break. If the Panel could 

### 5 please consider their responses in terms of what the Agency can 

### 6 do now with the data that is available and the information that 

### 7 is available now, and prioritize that as top priority. And then 

### 8 in terms of research ideas, you can do that as a separate sort 

### 9 of subsequent component of your comments. That would be greatly 

### 10 appreciated. 

### 11 With that, let's go ahead and read into the 

### 12 minutes Question 7.1, please. 

### 13 **DR. MARY MANIBUSAN:**Question 7.1: "Please 

### 14 comment on the adequacy and efficiency of the category-based 

### 15 approach to select chemicals for testing to expand the training 

### 16 set in terms of covering the approximately 1,700 chemicals not 

### 17 covered in the domain of the Expert System Version 2." 

### 18 **DR. DANIEL SCHLENK:**Thanks. Our lead 

### 19 discussant for that is Dr. Meek. 

### 20 **DR. BETTE MEEK:**Thanks very much. I'm going 

### 21 to just hit the high points here, recognizing, of course, that 

### 22 some of the discussion has evolved in relation to the previous 

### 23 question, but I'm still going to go through what we have because 

### 24 I think it provides, I hope, helpful advice to the Agency, 

### 25 whatever road they consider to take. 

### 26 First of all, there's a bit of background

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1 information in here, understanding the challenge posed by the 

### 2 task. We appreciate what a significant challenge it is. And 

### 3 also, we have some background text on how EPA has proposed to do 

### 4 that and it's really to illustrate, I think, our understanding 

### 5 of what you're proposing to do. And I'm not going to go through 

### 6 that here, but it is a kind of summary of what you're proposing 

### 7 to do. 

### 8 Just to get into the responses, essentially, 

### 9 in theory, examining new chemical categories is an adequate and 

### 10 efficient approach to expand the training sets, and the Panel 

### 11 recognizes and appreciates the significant and thoughtful effort 

### 12 invested, to date, to increase the chemical space. 

### 13 However, the difficultly lies in defining the 

### 14 categories and selecting derivatives to assess them. In the 

### 15 extreme, every chemical is its own category, however, this 

### 16 conservative definition of a chemical category does not allow 

### 17 for reduced testing, even in the screening scenario. 

### 18 Similarly, broadly characterized chemical 

### 19 categories are those defined by simple structural alerts are of 

### 20 little value because of the likelihood of a large number of 

### 21 false positives. So the answer lies then in assessing the 

### 22 tested or screen domain in relationship to the possible 

### 23 structural space of the chemical. 

### 24 So we have provided an example here, which I'm 

### 25 not going to go through, but will include in the written report 

### 26 with a particular class of chemicals, but it's essentially to

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1 encourage the Agency to think of different ways of selecting and 

### 2 prioritizing derivatives for assessing new categories, 

### 3 especially as molecular complexity increases. 

### 4 In terms of where clearly defined new chemical 

### 5 classes can be determined as with those proposed for the Expert 

### 6 System Version 3, the category-based approach to select 

### 7 chemicals for testing is efficient and the proposed Expert 

### 8 System Version 3 expansion does seem sensible; however, for 

### 9 compounds remaining outside of clearly defined groups or the 

### 10 high through put data, the 576 ungrouped compounds, 

### 11 category-based strategies may be inadequate. 

### 12 So Appendix B notes that inventories of 

### 13 concern contain diverse structures with widely varying physical 

### 14 chemical properties and this may confound straightforward 

### 15 categorization. 

### 16 For example, Expert System Version 1, it was 

### 17 observed that there is not sufficient data at this time to 

### 18 further categorize these compounds into subgroups which bind ER 

### 19 and those which do not, nor to hypothesize structural parameters 

### 20 associated with the activity of those compounds. Within such 

### 21 categories, the Expert System version 2 saw only modest 

### 22 increases, such as three percent increase for fragrances. 

### 23 Expert System version 3 may be expected to 

### 24 increase coverage for many of the chemicals with unknown binding 

### 25 potential, but 576 still remain. So one way to address this may 

### 26 be the incorporation of supporting or second pass chemical

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1 informatics and system pharmacology computational approaches, 

### 2 particularly for recalcitrant and diverse unknown binding 

### 3 potential compounds. 

### 4 This would be consistent with earlier 

### 5 recommendations to consider SMILES, SMARTS, or structural 

### 6 similarity approaches in Appendix C. Relevant tools include 

### 7 naive Bayesian classifiers, statistical similarity methods, such 

### 8 as the similarity ensemble approach, and stack neural network 

### 9 classifiers. The second two approaches, in particular are less 

### 10 susceptible to the (Q)SAR limitations described on page 24 of 

### 11 Appendix B. 

### 12 So the recommendation really is to use 

### 13 unsupervised grouping, especially on the remaining 576 chemicals 

### 14 with unknown binding potential, then apply predictive methods on 

### 15 the groups. So this is an ideal test case for these new 

### 16 predictive methods. 

### 17 So noted as well, the uncertainty associated 

### 18 with classifying the universe of chemicals into smaller groups 

### 19 as a basis to select representative chemicals for a training set 

### 20 was stressed by one Panel member. For example, selection of 

### 21 chemicals for opposite ends of the spectrum, for example, in 

### 22 relation to LogKow in a class may result in mixed responses or 

### 23 missing the effects of intermediate chemicals. 

### 24 It was noted that the classification approach 

### 25 clearly introduces biases since the categorization is based on a 

### 26 rather seductive structure and LogKow approach that may be

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1 unrelated to the endocrine -- to the ER effects. 

### 2 It was suggested then that the category's 

### 3 training data set approach be pursued only if constraints such 

### 4 as time and resources prevent high through put screening of the 

### 5 entire chemical space, prior to the Expert System analysis. In 

### 6 this scenario, only chemicals that cannot be obtained would need 

### 7 expert analysis and hence, would not be available for a training 

### 8 set. 

### 9 Immediate expansion to high through put 

### 10 screening using select high through put assays may provide a 

### 11 better solution or classifier for further use in more detailed 

### 12 analysis in the training set. Another comment in relation to -- 

### 13 while the effort is focusing on filling out the unknown binders, 

### 14 it was unclear to what extent the predicted binders were 

### 15 validated so confidence can be derived as more than just active 

### 16 or inactive using measurable thresholds. Specifically, the in 

### 17 vitro assays should be verified to have sufficient sensitivity 

### 18 to measure the cut point for 17B-estradiol, the activity 

### 19 threshold. 

### 20 This is really a reference back to some of the 

### 21 information that was provided in Question 2, the chemical 

### 22 categories in the Expert System should be well defined. This is 

### 23 in terms of the structural characteristics of the category, as 

### 24 well as the relevant physical chemical properties. The 

### 25 properties will include LogKow, but also other relevant 

### 26 descriptors as appropriate; for example, PKa for phenols,

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1 hydrogen bonding and size and shape descriptors for other 

### 2 categories. So that's it for 7.1. 

### 3 **DR. DANIEL SCHLENK:**Okay. Thank you. Dr. 

### 4 Cronin, anything to add? 

### 5 **DR. MARK CRONIN:**No. No further comments. 

### 6 **DR. DANIEL SCHLENK:**Dr. Waters? 

### 7 **DR. KATRINA WATERS:**I have nothing to add. 

### 8 **DR. DANIEL SCHLENK:**Dr. Schultz? 

### 9 **DR. TERRY SCHULTZ:**I think the Panel is 

### 10 basically asking EPA to come up with a clear and transparent way 

### 11 of which they are selecting not only their categories, but also 

### 12 the derivatives within those categories they're going to test so 

### 13 that it is apparent to everyone of how you define your 

### 14 categories, the chemical you are using to cover that category to 

### 15 lead up to why not this chemical or add an additional chemical. 

### 16 I think what came out about methylcholanthrene yesterday was an 

### 17 example of that. 

### 18 **DR. DANIEL SCHLENK:**Dr. Keiser? 

### 19 **DR. MICHAEL KEISER:**Nothing to add. Thank 

### 20 you. 

### 21 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 22 **DR. EDWARD PERKINS:**Nothing to add. 

### 23 **DR. DANIEL SCHLENK:**Other Panel members for 

### 24 7.1? 

### 25 (No response.) 

### 26 Okay. We can go back to the Agency. Any

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1 questions or clarification regarding comments? 

### 2 **MS. MARY MANIBUSAN:**No additional comments. 

### 3 Thank you. 

### 4 **DR. DANIEL SCHLENK:**Okay. Let's move onto 

### 5 7.2, please. 

### 6 **MS. MARY MANIBUSAN:**7.2: "Based on the high 

### 7 through put assay performance (analysis of reference chemicals 

### 8 and comparative analysis with the Expert System Training Set), 

### 9 please comment on the adequacy of the high through put data for 

### 10 advancing the ER Expert System's rules to cover the additional 

### 11 groups of chemicals in Expert System version 3." 

### 12 **DR. DANIEL SCHLENK:**Dr. Meek. 

### 13 **DR. BETTE MEEK:**Right. Clearly, one of the 

### 14 strengths of integrated assessment and testing strategies is the 

### 15 breadth of information that can be incorporated, often from in 

### 16 vitro and high through put methods, which are typically designed 

### 17 to provide information about a single biological property or 

### 18 event that reflects the in vivo toxicology to test chemical or 

### 19 chemical category, which can be interpreted in the construct of 

### 20 the Adverse Outcome Pathway. 

### 21 There are reasons for the variability and 

### 22 results from the in vitro methods as compared to outcomes from 

### 23 in vivo methods and the outcomes of in vivo tests. Vitro tests 

### 24 are largely associated with a single factor or a limited set of 

### 25 factors, while outcomes from in vivo tests are influenced by 

### 26 many factors.

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1 For that reason, the statistical-based 

### 2 acceptance of an in vitro method is less important than the 

### 3 mechanistic relevance of each in vitro method to a given key 

### 4 event and the relevance of the key event to the final adverse 

### 5 effect in question. So all of the high through put methods used 

### 6 in the Agency's assessments are standardized, well-established 

### 7 assays that typically quantify ER binding and gene activation 

### 8 with a reporter system; therefore, all are relevant to RE (sic) 

### 9 binding and gene activation which were used in assessment of the 

### 10 Expert System version 1 and Expert System version 2. And the 

### 11 reason for the variability and results from different in vitro 

### 12 methods is largely a factor of protocol. 

### 13 So a number of the aspects that we've heard 

### 14 about here, including abiotic factors like solubility and 

### 15 volatility, which would largely be controlled, but also 

### 16 biological factors such as cytotoxicity metabolic capability, 

### 17 which are more difficult to standardize. 

### 18 So the importance of weight of evidence is 

### 19 more directed towards accepting the results from any 

### 20 mechanistically transparent integrated approach. The weight of 

### 21 evidence is typically assessed by examining several factors, 

### 22 including the number of key events that comprise the scheme; the 

### 23 relevance of each key event of the final adverse effecting 

### 24 question; the number of methods used to assess each key event; 

### 25 the relevance of each method to a given key event, and the 

### 26 reliability of the data measured or otherwise determined for

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1 each method. 

### 2 So the HTP assay data may be useful to cover 

### 3 additional groups of chemical in Expert System version 3, when 

### 4 used in full dose response format. Single concentration data 

### 5 appear to be inadequate, as in the NVS HTP assays. The White 

### 6 Papers states, 

### 7 "The number of prescreened negatives that 

### 8 would have been binders if run in full dose response mode would 

### 9 argue that the prescreening procedure be reassessed for 

### 10 improvements or that running in a full dose response mode would 

### 11 be more appropriate for prioritization approach." 

### 12 The HTP data can be incorporated, but should 

### 13 be weighted by the observations with a rationale that where HTP 

### 14 data are included as a training set, statistical power grows, 

### 15 but each observation becomes less certain. This is appropriate, 

### 16 but to address weakness, quantified sensitivity should be 

### 17 reflected in leave for category weightings. 

### 18 Second major concern is the false positive 

### 19 increase, but this is acceptable with respect to the 

### 20 programmatic roles. The HTP assay performance against 

### 21 sensitivity to detect weakly active reference chemicals has not 

### 22 been sufficiently evaluated and hence, would not advance the ER 

### 23 Expert System predications for weakly active chemicals. 

### 24 However, incorporation of missed active chemicals, the false 

### 25 negatives from Expert System version 2 should be evaluated for 

### 26 missing chemical properties within the Expert System are the

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1 common points of departure in the rule-based system that are 

### 2 breaking down for these missed chemicals. 

### 3 It was also noted, as we've heard, with 

### 4 additional evolution, it was considered that the high through 

### 5 put screening was significantly enhanced or even replaced the ER 

### 6 Expert System. That's it. 

### 7 **DR. DANIEL SCHLENK:**Thank you. Dr. Cronin? 

### 8 **DR. MARK CRONIN:**No further comment. 

### 9 **DR. DANIEL SCHLENK:**Dr. Waters? 

### 10 **DR. KATRINA WATERS:**I have no additional 

### 11 comments. 

### 12 **DR. DANIEL SCHLENK:**Dr. Schultz? 

### 13 **DR. TERRY SCHULTZ:**No additional comments. 

### 14 **DR. DANIEL SCHLENK:**Dr. Keiser? 

### 15 **DR. MICHAEL KEISER:**I'm the first to say I 

### 16 agree with what's been stated. One clarification I would add is 

### 17 that there has been mention of potentially replacing the system 

### 18 entirely. I, however, would like to state that I think there is 

### 19 value to working in parallel or putting the two systems 

### 20 together, the Expert System with the high through put screening 

### 21 data. I think the data can be combined, and there is some 

### 22 discussion I think we might be about to have about that. 

### 23 But additionally, regardless of combining or 

### 24 integrating the two approaches, the fact that each one has its 

### 25 own source of error, and these errors are not correlated, speaks 

### 26 to the value of including both because you decorrelate error and

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1 have a better chance of compensating for errors in each 

### 2 different approach. 

### 3 Turning away from what has been strictly 

### 4 defined in the question, and what we've just discussed, and to 

### 5 some of the comments from earlier, I first want to preface it by 

### 6 saying this is responding to things brought up in Question 6 

### 7 that pertain to this question and is not meant to supplant or 

### 8 change any of our answers to Question 7 as defined and 

### 9 responded. 

### 10 So with respect to the additional idea of 

### 11 integrating some of the high through put data as input into the 

### 12 decision rule set for the Expert System, the idea behind this, 

### 13 first off, as I think has been brought up, if resources do not 

### 14 allow, then it would probably be within the Agency's remit to 

### 15 decide that it is not an appropriate path, given lack of 

### 16 resources to do the high through put screen at that point as an 

### 17 input. 

### 18 However, if such resources are available, if 

### 19 such screens may be run, I'd like to clarify a few points. One 

### 20 is none of the discussion or points, as I understand it, pertain 

### 21 to the ES TrSet assays. So this was simply with respect to the 

### 22 high through put assays. No changes are proposed in that 

### 23 discussion to the concentration levels, in the RtER assay or to 

### 24 the Vitellogenin assay. Rather, the discussion is simply about 

### 25 the high through put ones. 

### 26 Under that focus with the high through put

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1 assays being run in parallel or even ahead of, depending on 

### 2 timing, changes in the Expert System from version 2 to version 

### 3 3, and additional suggestion that the Agency might want to take 

### 4 under consideration is the ability to incorporate preliminary 

### 5 results from first screen with the high through put data on 

### 6 compounds that are easily available to purchase, as described as 

### 7 almost properties of the molecule. 

### 8 Could we pull up that diagram really quick 

### 9 that we had up earlier in Question 6? 

### 10 I just want to illustrate it visually. Again, 

### 11 this is my attempt just to clarify the question as much as 

### 12 possible. So we have this up here. The idea behind this as an 

### 13 additional suggestion to be taken under consideration is that 

### 14 where you see diamonds in this diagram, where there are specific 

### 15 rules to finding the progression from one decision point along 

### 16 the tree to the next, that an augmentation of this system might 

### 17 incorporate specific diamonds where instead of based on a 

### 18 physical characteristic of the molecule, such as its typology, 

### 19 its chemical structure, the presence of a particular ring 

### 20 system, any of the above, that there may additionally diamonds 

### 21 that could be added to this decision schematic where the diamond 

### 22 would be defined as a fingerprint or other combination of a 

### 23 small number of HTP assay results. 

### 24 So an example of a diamond that I'm just going 

### 25 to make up to be illustrative is a compound, having been through 

### 26 the high through put screen, where the high through put

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1 screening question might be the BG1 assay and the screen 

### 2 might've been done at three concentration points across a range 

### 3 to be written down or defined by the Agency; that a compound 

### 4 that is positive in that screen above a preset threshold that 

### 5 two out of the three points then progresses along a certain 

### 6 branch compared to the other one. 

### 7 So you see in this case, it is not a question 

### 8 of redefining the effects categories, but rather, using the HTP 

### 9 data within the decision-making steps in the diamonds, 

### 10 specifically, to move to categories as a result that downstream 

### 11 of it are not based on chemical structure in a way that a 

### 12 medicinal chemist might interpret, but rather based on the 

### 13 direct groupings coming from the assays, where the end result 

### 14 group -- I'll stop there and open up further comments. Thank 

### 15 you. 

### 16 **DR. DANIEL SCHLENK:**Okay. Dr. Perkins? 

### 17 **DR. EDWARD PERKINS:**No further comments. 

### 18 **DR. DANIEL SCHLENK:**Okay. Any other comments 

### 19 from the Panel on 7.2? Yes, Dr. Jacobs? 

### 20 **DR. MIRIAM JACOBS:**I would just like to 

### 21 specifically endorse what Dr. Keiser was saying, especially in 

### 22 relation to thoughts on replacement of the ESv1. I would really 

### 23 like to see that it's not replaced, but the work goes on in 

### 24 tandem. So I was a bit concerned with the item that was 

### 25 highlighted by the group from Dr. Meek's presentation. 

### 26 **DR. DANIEL SCHLENK:**Okay. Sounds like

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1 there're some suggestions there for some augmentation of the 

### 2 system that's been presented. Any other comments? 

### 3 Yes, Dr. Meek? 

### 4 **DR. BETTE MEEK:**Maybe just to clarify that it 

### 5 may have been the brevity of the presentation in terms of -- but 

### 6 I think I captured the concept of the parallel approaches are 

### 7 always informative. 

### 8 **DR. DANIEL SCHLENK:**Okay. Any other input on 

### 9 7.2? 

### 10 (No response.) 

### 11 Okay. Let me go to the Agency. Any questions 

### 12 or clarification regarding the comments? 

### 13 **MS. MARY MANIBUSAN:**I have just simple 

### 14 question of clarification for Dr. Keiser. Currently, the ER 

### 15 Expert System is built to predict for relative binding affinity 

### 16 to the ER receptor. What you're suggesting, it sound like is to 

### 17 add additional diamonds to capture, perhaps, the downstream key 

### 18 events along the AOP. 

### 19 So the BG1-Luc is gene activation. So 

### 20 exploring the expansion to cover those key events based on high 

### 21 through put information. Is that accurate? 

### 22 **DR. MICHAEL KEISER:**First, I will say, yes, 

### 23 that was the intent that I had had, additionally responding to 

### 24 an implication of that question is that were the desire to keep 

### 25 the Expert System and to be completely focused on binding and 

### 26 not to cover the realm of things such as the Vitellogenin --

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1 well, excuse me -- such as the BG1 HTP. I would then, under 

### 2 that remit, modify my response to say that to focus completely 

### 3 on the binding-specific assays from HTP, such as the Novascreen 

### 4 ones. I want to make sure that this is a clear point. 

### 5 In that case, that if the Expert System only 

### 6 is focused on binding, I still feel that it could be valuable to 

### 7 bring in the HTP binding-focused assays as decision points in 

### 8 the diamonds, as we've described because as I think Dr. Bradbury 

### 9 pointed out, these are all models. Some are physical models, 

### 10 some are computational models. And the advantage of this 

### 11 computational approach is that even incorporating the direct 

### 12 physical model of the HTP into the computational system should 

### 13 still give us additional power compared to using that alone. 

### 14 So I will ask, does that answer your question 

### 15 for clarification? 

### 16 **DR. DANIEL SCHLENK:**Dr. Schmieder. 

### 17 **DR. PATRICIA SCHMIEDER:**Given that the Expert 

### 18 System is based on binding verified by Vitellogenin activation 

### 19 in each group currently, then I'm not sure I understand how you 

### 20 would recommend combining -- would you say that you could 

### 21 combine just BG1, which is just transactivation without knowing 

### 22 the binding part? Or you could do one or the other, but you 

### 23 don't need both, given that it's already built on both, the 

### 24 binding verified by transactivation. 

### 25 **DR. MICHAEL KEISER:**And again, this whole 

### 26 realm of discussion, I do want to just clarify and keep on

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1 record is an additional suggestion as opposed to a 

### 2 recommendation to change the underlying system entirely. 

### 3 So to respond to that, my recommendation on 

### 4 this, if I understand your question correctly, is I am treating 

### 5 the RtER, as confirmed by Vitellogenin, as a gold standard 

### 6 against which the Expert System model is attempting to 

### 7 recapitulate computationally. 

### 8 As we know, it's not perfect by any means, and 

### 9 the full Tier 1 is indeed, but this all about prioritizing 

### 10 forward; it's not meant, at this stage, to replace that. But in 

### 11 so much as the Expert System is basically an attempt at a 

### 12 computational first approximation of that TrSet or RtER in 

### 13 Vitellogenin, then I believe its power could be improved -- and 

### 14 this taking Mary Manibusan's point into consideration too -- 

### 15 that by specifically, where available, incorporating HTP binding 

### 16 data. And we can put BG1 aside for this and not cloud matters, 

### 17 and maybe others can weigh in on specific details of that, but 

### 18 based on my understanding right now, I would say the binding HTP 

### 19 data, where available, could increase the predictive power if 

### 20 integrated as a property, as a diamond, as opposed to an 

### 21 eventual category into the Expert System. 

### 22 **DR. DANIEL SCHLENK:**Okay. Dr. Schultz. 

### 23 **DR. TERRY SCHULTZ:**My interpretation on the 

### 24 question that was asked can high through put assays be used in 

### 25 place of the rainbow trout cytosolic binder and liver slice 

### 26 assay. So the knowledge-based Expert System says I have to have

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1 binding and I have to verify binding with gene activation. 

### 2 Therefore, if you're going to use that analogy that you have to 

### 3 have a high through put screen for binding, followed by a high 

### 4 through put screen for gene activation. 

### 5 We're not asking the question: Are these 

### 6 really key events? That's accepted. Those two key events are 

### 7 relevant to the question that you're asking. What we're asking 

### 8 is, we have different methods for assessing those key events. 

### 9 The methods you're accepting, relevant to the particular key 

### 10 event, a particular high through put relevant, just like the 

### 11 rainbow trout cytosolic is. 

### 12 And then the next question: What is the 

### 13 reliability of the data measured by those events? We think that 

### 14 they're high through put assays can meet those criteria to 

### 15 expand your knowledge-based Expert System, which is basically 

### 16 chemical category formation. But the reliability of the data, 

### 17 because of the incompleteness of the dose response analysis and 

### 18 the incompletion of addressing the questions of the false 

### 19 positives, flash, et cetera, et cetera, means that that's not 

### 20 there yet. Thank you. 

### 21 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 22 **DR. EDWARD PERKINS:**Yes. I'd like to support 

### 23 what Dr. Schultz just said. The main problem with adequacy of 

### 24 the HTP data now, especially in the case of the receptor 

### 25 binding, which is a critical factor, you're using there as this 

### 26 initial prescreen which kicks out a number of low dose effects,

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1 which we also know is stated by Dr. Bradbury. That's a 

### 2 principal area of major concern is low-affinity binders. 

### 3 So immediately there, it's going to kick out a 

### 4 lot of potential assays that would throw out a lot of chemicals 

### 5 that could potentially have useful information and it's probably 

### 6 a source of a lot of the false negatives in the HTP screening 

### 7 assays versus the liver slice assays. 

### 8 As we've talked about in several of the other 

### 9 questions, that really has to be addressed before this is going 

### 10 to be useable and to also reiterate in the previous discussion 

### 11 when we were talking about increasing dose range, we were only 

### 12 referring to the HTP assays, where full dose range was not done. 

### 13 14 We realize you did a great job with the fish 

### 15 liver assays and the full range was done and that's why all the 

### 16 low-affinity binders were found. We would've like to have seen 

### 17 that replicated in the high through put assays, but I think I 

### 18 might get some agreement that unless you can do the full dose 

### 19 testing, the receptor binding assays may not be useful. 

### 20 However, given the caveat of limited funding, the expense of 

### 21 receptor binding assays, you may become much more reliant on 

### 22 transactivation assays and things like that. 

### 23 So again, you're going to have to either add a 

### 24 weighted scheme for using these types of things, given some of 

### 25 either the practical constraints for using them. That might 

### 26 dictate how you're treating some of the assays. You have to be

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1 careful about exactly what the model will predict. So I think 

### 2 that's going to have to factor in to what you're doing. 

### 3 **DR. DANIEL SCHLENK:**Thanks. Dr. Keiser. 

### 4 **DR. MICHAEL KEISER:**I just wanted to thank 

### 5 both the Panel members and I endorse both of those points. 

### 6 Thank you. 

### 7 **DR. DANIEL SCHLENK:**Okay. Anyone else on 

### 8 7.2? 

### 9 Any further questions from you guys, from the 

### 10 Agency? 

### 11 **MS. MARY MANIBUSAN:**No additional comments. 

### 12 Thank you. 

### 13 **DR. DANIEL SCHLENK:**Okay. Let's go for 

### 14 7.3, please. 

### 15 **MS. MARY MANIBUSAN:**7.3: "To the extent there 

### 16 are differences between the Expert system TrSet and high through 

### 17 put data in detecting the ability of chemicals to initiate the 

### 18 ER AOP, especially in the context of minimizing "false 

### 19 negatives" for low potency compounds, please comment on the 

### 20 strengths and limitations of combining data from the different 

### 21 assays to generate training set data for building the structure 

### 22 based rules within the ESv3. 

### 23 What are the strengths and limitations of 

### 24 using training set data from assays that have the same or 

### 25 similar degree of sensitivity?" 

### 26 **DR. DANIEL SCHLENK:**Dr. Meek.

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1 **DR. BETTE MEEK:**Thanks. Again, the 

### 2 discussion has evolved a bit, additionally, I'm going to address 

### 3 what we had previously. So when using alternative methods to 

### 4 assess chemicals for toxicity, the issue of false negatives is 

### 5 always of concern because of the consequences of being wrong. 

### 6 An examination of the data in Table 6.21 

### 7 reveals false negatives are more likely to be an issue for 

### 8 chemicals that are weak or extremely weak binders. Using the 

### 9 relative binding affinity of 0.000001 percent is the cutoff 

### 10 value, appears to minimize the false negatives to an acceptable 

### 11 level. 

### 12 It is appropriate to combine data from 

### 13 different assays with the same or similar degree of sensitivity 

### 14 to generate the training data set for Expert System version 3. 

### 15 One approach is to prioritize a compound for testing via the 

### 16 full ES Training Set, the rainbow trout ER and the Vitellogenin 

### 17 gene expressed liver slice assay as a gold standard if a 

### 18 compound was positive in any two of the HTP assays to which it 

### 19 was subjected. 

### 20 A weakness is that this may increase the 

### 21 false positive rate for the initial prioritization step, but 

### 22 given the key programmatic goal of minimizing false negatives, 

### 23 it seems acceptable. Again, depending on testing resources and 

### 24 encountered false positive rates, the weakness might also be 

### 25 mitigated by adopting, instead, a consensus-based method -- and 

### 26 this has come up previously -- wherein a weighted average across

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1 assay positives contributes to a single aggregate score by which 

### 2 further testing is prioritized. 

### 3 Considering the careful work that has been 

### 4 done to build Expert System versions 1 and 2, it would seem 

### 5 sensible that Expert System version 3 compounds be subject to 

### 6 confirmatory RtER and JA liver slice assay for all positives or 

### 7 borderline cases. 

### 8 The HTP data appeared to have a high false 

### 9 positive rate, but also a very low false negative rate when 

### 10 interference thresholds are ignored. In order to capture the 

### 11 chemicals with low potency, the filtering thresholds should be 

### 12 reevaluated and possibly eliminated if minimizing false 

### 13 negatives is the goal. A combination of assays should be 

### 14 explored, as opposed to individual assay performance. 

### 15 In addition, strategies to augment the HTP ER 

### 16 binding data with at least a single higher concentration for all 

### 17 negative chemicals should be explored in order to synchronize 

### 18 with the higher concentrations used for the ES Training Set. 

### 19 More assays covering other key events in the ER AOP would likely 

### 20 increase confidence that low affinity binders were not excluded. 

### 21 The inclusion of other assays are increased dose range of 

### 22 existing assays would help address the low-affinity binding 

### 23 chemicals. 

### 24 Additionally, assay specific weighting of data 

### 25 could also be used to inform prioritization and address potency 

### 26 problems. That's it.

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1 **DR. DANIEL SCHLENK:**Dr. Cronin? 

### 2 **DR. MARK CRONIN:**No further comment. 

### 3 **DR. DANIEL SCHLENK:**Dr. Waters? 

### 4 **DR. KATRINA WATERS:**I have no additional 

### 5 comments. 

### 6 **DR. DANIEL SCHLENK:**Dr. Schultz? 

### 7 **DR. TERRY SCHULTZ:**One issue that comes up 

### 8 with this is if you look at the Expert System as a series of 

### 9 (Q)SARs and then you start mixing your assays and therefore 

### 10 mixing your endpoints, you end up violating one of the 

### 11 principles of (Q)SAR, of having a well-defined endpoint. 

### 12 If you look at your Expert System as a 

### 13 knowledge-based system and you say I'm gathering knowledge on ER 

### 14 binding and ERG in expression, you can then probably do that 

### 15 under the caveat that I'm building a category that has these 

### 16 types of components. So that becomes a very fine line that 

### 17 you're walking on and I only mention that because during the 

### 18 Agency presentations, a number of times you have said that 

### 19 you've been following the OECD (Q)SAR principles. Then in this 

### 20 question you open up the issue that you're violating one of 

### 21 those. Thank you. 

### 22 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 23 **DR. MICHAEL KEISER:**So responding to the 

### 24 interesting point just brought up about OECD principles and not 

### 25 violated the well-defined endpoint, one point that might 

### 26 partially mitigate that is the endpoint are the RtER and the

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1 Vitellogenin liver slice assay. And hence, in the 

### 2 recommendation from Dr. Meek, the suggestion that HTP data, when 

### 3 incorporated into the structure-based rules training set be 

### 4 confirmed in the positive case by the TrSet assays; 

### 5 specifically, RtER and Vitellogenin, would, at least in the 

### 6 positive case, if not the negative case, keep us focused on the 

### 7 single well-defined endpoint. 

### 8 I'd leave it to the Agency or the rest of the 

### 9 Panel to decide whether the fact that negatives in that example 

### 10 are not progressed to the TrSet assays, does or does not 

### 11 potentially violate the well-defined endpoint. But for 

### 12 positives, if that recommendation is followed, you will remain 

### 13 focused on that endpoint. 

### 14 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 15 **DR. EDWARD PERKINS:**No further comments. 

### 16 **DR. DANIEL SCHLENK:**Other Panel members? 

### 17 (No response.) 

### 18 Okay. Go to the Agency. Any questions or 

### 19 clarification for 7.3? 

### 20 **MS. MARY MANIBUSAN:**No additional comments. 

### 21 Thank you. 

### 22 **DR. DANIEL SCHLENK:**Great. Let's go 

### 23 ahead and move onto Question 8. 

### 24 **MS. MARY MANIBUSAN:**Question 8.1: "Please 

### 25 provide any initial thoughts on how varying empirical and/or 

### 26 computational techniques could be employed to account for

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1 metabolites that may trigger the ER molecular initiating event." 

### 2 **DR. DANIEL SCHLENK:**Our lead discussant 

### 3 is Dr. Schultz. 

### 4 **DR. TERRY SCHULTZ:**Thank you. This was 

### 5 basically a question for the future. It wasn't in response to 

### 6 anything that had been proposed by the Agency and that really 

### 7 opened it up for a lot of diverse thoughts by the Panel. 

### 8 I believe in my written comments I have 

### 9 captured those. My presentation will not be in any order of 

### 10 importance of relevance; it's more of a first come, first serve 

### 11 type of a situation. 

### 12 The Panel acknowledges that the toxicity of 

### 13 chemicals which directly elicit a mode of action such as our ER 

### 14 binders, are more easily explained than those that are mediated 

### 15 by metabolism. So we understand that there's a challenge here 

### 16 that has a higher degree of uncertainty that you have to deal 

### 17 with. 

### 18 If the Agency was to increase the confidence 

### 19 that has identified the complete universe of chemicals, 

### 20 including the daughter products, will require prioritization for 

### 21 further testing and valuation. It will need to improve its 

### 22 capability to do encyclical metabolic simulations in addition to 

### 23 using high through put assays which incorporate metabolism. 

### 24 The Panel noted that new capabilities in the 

### 25 next generation of metabolic simulators that would assist the 

### 26 Agency in its endocrine description screening program,

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1 especially in regard to ER binding Expert System may include the 

### 2 ability to identify the most likely or probable metabolites 

### 3 based on the structure of the parent compound and not just a 

### 4 list of possible metabolites that are generated from the current 

### 5 simulator, such as the OECD toolbox. 

### 6 As a sidebar on that, dealing with the OECD 

### 7 toolbox, because you're doing metabolites of metabolites of 

### 8 metabolites, it is likely that you're finally going to find 

### 9 something that has a ring and an OH group to it, which isn't 

### 10 going to help you very much on that sort of situation. We 

### 11 realize that's the limitation of the capabilities that are 

### 12 currently available in the public domain. 

### 13 In regards to computational techniques for 

### 14 predicting metabolites, the Panel notes that further 

### 15 improvements in metabolic simulation that may assist the Agency 

### 16 within its program would include designing simulators that would 

### 17 predict the percent, such as 90, 75, 25, 10 of the parent 

### 18 compound that has transformed into the most likely metabolites. 

### 19 Whether that's the top three or the top five, we really don't 

### 20 care. The idea here is to get away from a shopping list and 

### 21 focus on the things that are the most likely ones that are going 

### 22 to be formed. 

### 23 The Panel also notes that improving the 

### 24 metabolites may assist the Agency, especially in relation to the 

### 25 exposure filter. If you look at your Figure 2.21, where you 

### 26 have your scenario, it says one of your filters is exposure.

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1 So you can design simulators that might 

### 2 predict the rate of metabolism that would assist you in that 

### 3 sort of a situation, so things that would be rapidly converted, 

### 4 seconds to minutes versus hours versus days, may assist you in 

### 5 refining that exposure filter. 

### 6 I'm looking at metabolism here and its 

### 7 broadest context so that could be metabolism in relation to 

### 8 microbial degradation as opposed to liver or skin or whatever 

### 9 other simulator that you wanted to look at. 

### 10 Additionally, the Panel noted that 

### 11 predications of reactivities such as half-life in water and the 

### 12 exposure filter may also serve as an early indication that 

### 13 metabolism is likely to be of concern. In other words, use all 

### 14 the information that you have to suggest where you might want to 

### 15 put resources early into the issue of metabolism. 

### 16 Predictions of metabolism had been 

### 17 demonstrated to be better if several methods are combined. For 

### 18 instance, there are several studies that demonstrate when you 

### 19 combine predictions that are for rule-based system with those 

### 20 that predict the coverage based on known metabolites of combing 

### 21 those things you tend to do a better job. So you may want to 

### 22 look at that. 

### 23 The Panel suggests the strategies for using 

### 24 predicted stable metabolites may also be investigated. There 

### 25 are at least two possible strategies for this. The first one 

### 26 includes the prediction of metabolites within the categories.

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1 The second is that cyclic procedures where metabolites would be 

### 2 predicted and then run through the Expert System. Each 

### 3 strategy, of course, has its advantages and its disadvantages, 

### 4 but it seems more than likely that the second strategy would be 

### 5 more practical. 

### 6 Again, balancing the fact that you don't want 

### 7 to have a shopping list of metabolites because it's likely 

### 8 you're going to come up with something in there that is going to 

### 9 be important, but the fact that you need to look at more than 

### 10 just the initial metabolite, but you don't have to look at every 

### 11 single possible metabolite. 

### 12 Changing the view a little bit, one Panel 

### 13 member noted that when ER binding takes into consideration the 

### 14 dynamic and plastic characteristic of the ER receptor and the 

### 15 subpocket theory and you can define those by the inergetics and 

### 16 cysteric constraints on that sort of a situation. 

### 17 If you look at the empirical data for that, 

### 18 you find that the vast majority of ER binders, maybe with the 

### 19 exception of the perfluorinated compounds are aliphatic 

### 20 chemicals. 

### 21 Looking again at this A site and the B site, 

### 22 you know that these are basically polar in character, so there 

### 23 is a limited number of hydrogen binding groups, for example and 

### 24 OH groups and amino groups that seem to interact with these 

### 25 subpockets. 

### 26 So it's possible that you could use this

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1 information to design a particular metabolic simulation where 

### 2 you were looking at, for example, oxidation of cyclic compounds 

### 3 as a means of identifying your pro ER binders for the A site and 

### 4 B site. That doesn't assist you with your special cases, but we 

### 5 have difficulty understanding the mechanistic understanding of 

### 6 the special cases. So you really don't have a solid ground of 

### 7 which to anchor that in. 

### 8 For example, you could restrict simulations, 

### 9 cyclic compounds to only those that have uncovered hydrogen 

### 10 substitution carbon atoms in a five or six membered ring system. 

### 11 And then restrict the reactions to only those that lead to 

### 12 oxidation, et cetera, et cetera. 

### 13 So this may provide you a simplified means of 

### 14 getting at your primary metabolism derivatives -- metabolites in 

### 15 relation to your Expert System. The Panel further notes that 

### 16 bench level in vitro assays such as the rainbow trout liver 

### 17 slice are employed in the AOP. That confirms that you have ER 

### 18 binders. The importance of that, of course, is the fact that 

### 19 the trout liver slice is considered to be metabolically 

### 20 competent system. 

### 21 So that you could use chemicals that are 

### 22 negative for ER binding, but positive in the liver slice assay. 

### 23 So a negative, positive response might provide you a means of 

### 24 looking at when metabolism would be important and capturing that 

### 25 type of information that provides you with a second generation 

### 26 of simulators that you could use.

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1 Obviously, taking that logic one step further, 

### 2 the same sort of scenario could be said for the high through put 

### 3 cell-based systems. Some of them incorporate metabolisms so 

### 4 they could be paired with similar endpoints to look for the 

### 5 potential of metabolic activation. Obviously other points in 

### 6 that regard as noted by the Panel members was the potential of 

### 7 any co-culturing factors that may assist in metabolism 

### 8 peroxidases has been used in some of the approaches for protein 

### 9 binding through activation. 

### 10 It must be noted, however, that we're 

### 11 concerned about that the rainbow trout AOP-based prediction of 

### 12 metabolic similarities may not be amenable to mammals, where 

### 13 metabolite stability and viability may be much more important. 

### 14 It was noted that the cytochrome P450 activity varies between 

### 15 species. 

### 16 So the rainbow trout may not be applicable to 

### 17 other species, and the difference, of course, different species 

### 18 have different capacities to inactivate or activate metabolites 

### 19 through Phase II metabolism, so the question of stability and 

### 20 viability becomes important especially when the site of 

### 21 activation would be far away from the site of action. 

### 22 In the case of the rainbow trout assay, you 

### 23 basically were thinking of the site of metabolism was in the 

### 24 hepatocytes and the gene expression of Vitellogenin was also in 

### 25 the hepatocytes. You don't have that comparable system in 

### 26 mammals and that may influence how metabolism can actually be

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1 used in your expert system. 

### 2 The Panel did want to alert the Agency that 

### 3 several reviews on metabolic simulators have recently been done 

### 4 and those specific citations will be added to the document. And 

### 5 that basically captures the main points that were presented to 

### 6 me by the other Panel members. Thank you. 

### 7 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 8 **DR. EDWARD PERKINS:**I second what Terry said 

### 9 and I wanted to add a few more things and a couple of 

### 10 clarifications. 

### 11 I think there's some potential for new work in 

### 12 genome constrain metabolic modeling, tissue-specific modeling 

### 13 that allows you to combine tissue-specific expression of P450s 

### 14 between different species that might help resolve some problem. 

### 15 It can also help understand the type of metabolic enzymes Phase 

### 16 II, I guess, metabolic enzymes that might be in Phase I that 

### 17 might be present and available to act that might help direct 

### 18 some of the other metabolic simulators based on a species 

### 19 context that could address some of the issue of aquatic versus 

### 20 mammalian questions. 

### 21 Also, on point of positive and negative 

### 22 controls, it seems like for ones that you might have a priority, 

### 23 you think there might be a metabolism or potential for 

### 24 metabolism based on a computational approach. If you could 

### 25 develop cell lines that were enriched for metabolism, such as 

### 26 the ATG cell lines, have that cell line, but also try the same

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1 assay in the background of a cell line that does not have 

### 2 metabolism. So that would be a good plus/minus characterization 

### 3 type issue there. 

### 4 If there could be further enrichment of cell 

### 5 cultures that can either have a specified profile of metabolic 

### 6 enzymes or enriched such that you can characterize it and you 

### 7 have kind of a full range of different metabolic capabilities, 

### 8 that would be great. I think there's been a lot of evolution of 

### 9 coculture of different cell types and tissue types that may be 

### 10 amenable to these new developments in microfluidics and I guess 

### 11 three-dimensional matrices for cells that might let you look at 

### 12 effects in the context of metabolism and then look it at without 

### 13 coculture so you can look out without metabolism in certain 

### 14 cases. 

### 15 Another potential area is high through put 

### 16 PBPK modeling, such that aquatic organisms are often goes in 

### 17 through the gills and it's getting into the blood directly. So 

### 18 it goes straight to target points without passing through the 

### 19 liver. So you bypass metabolism. 

### 20 In certain cases metabolism is an issue. In 

### 21 certain cases it may not be an issue, but having that to augment 

### 22 exposure and target sites of concern may help direct when you 

### 23 expand out to new MIEs and potential metabolic areas. 

### 24 Let's see what else was there. I think that 

### 25 was about it for me. Thank you. 

### 26 **DR. DANIEL SCHLENK:**Dr. Chambers.

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1 **DR. JANICE CHAMBERS:**Terry pretty much 

### 2 captured my points, but I really wanted to make that you kind of 

### 3 keep in mind is kinetics, and it kind of touches on what Ed just 

### 4 brought up too, is that many of these metabolic enzymes are not 

### 5 particularly efficient and if you have a system where you're 

### 6 putting in an extraordinarily high dose of the parent chemical, 

### 7 that may not reflect what an intact organism would experience 

### 8 and how much of that active metabolite gets formed. Is it ever 

### 9 a fast enough and high enough level to be effective in the 

### 10 target system? 

### 11 Also again, how quickly does it detoxify and 

### 12 then picking up the PBPK approach of the intact animal in a 

### 13 mammalian system anyway, if it's bioactivated the liver, it's 

### 14 got to get into the blood stream. It could bind into the 

### 15 albumin or it could be detoxified other places and so forth. 

### 16 It's got to go a fair distance to get to a target organ. 

### 17 So if this is all an exercise to prioritize 

### 18 the chemicals to put into the screening, do you really need to 

### 19 have a lot of detailed information about metabolites and all or 

### 20 do you really need to think in terms of whether or not a 

### 21 metabolite would ever reach a critical concentration to trigger 

### 22 a pathway? 

### 23 If that's not the case, then you should 

### 24 prioritize the chemicals which are more active in the parent 

### 25 form or do produce a lot of metabolite in a hurry. 

### 26 **DR. DANIEL SCHLENK:**Dr. Jacobs.

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1 **DR. MIRIAM JACOBS:**I would like to follow up, 

### 2 especially with Dr. Perkins' comments and to reiterate with 

### 3 respect to the relatively recent publications on incorporating 

### 4 metabolizing systems in vitro tests for endocrine activity. So 

### 5 there was a detailed review paper, Number 97, that was published 

### 6 from the OECD in 2008 in the general publication, which pretty 

### 7 exhaustively characterizes the available cell lines, including 

### 8 many of those that were included in the HTP assays, where the 

### 9 data is available on the characterization, and give suggestions 

### 10 on methods for incorporating metabolism. 

### 11 In addition, that paper has been updated 

### 12 recently. It's currently in review and hopefully it will be 

### 13 published during the time of this report. So that could be an 

### 14 additional citation that will be able to be used. But some of 

### 15 the information that comes from that paper is also in relation 

### 16 to discussion we again had yesterday on looking at how to 

### 17 interpret combined computation and empirical testing that needs 

### 18 to be developed, so using principal component multivariates and 

### 19 class analyses, for example -- they gave some examples yesterday 

### 20 -- but also one of the groups at Mount Sinai New York have been 

### 21 doing metro analyses of gene expression of microarray data, such 

### 22 as that used for PBDE 17-estradiol on MCF-7 cells, and this all 

### 23 published, combined with the characterization of the metabolic 

### 24 capacity of MCF-7 cells. And there is freely available 

### 25 reference collection of gene expression profiles from cultured 

### 26 human MCF-7 cells, for example, in the Connectivity Map, which

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1 has data for over 15,000 substances, some of which may be 

### 2 metabolites. It would be a useful resource to explore and it 

### 3 included pattern-matching software for data mining. 

### 4 There are a number of other databases that 

### 5 would be useful to search. There is one under construction at 

### 6 the moment at the JRC on endocrine active substances. Not 

### 7 specifically in relation to metabolism, but there are 

### 8 metabolites included. Thank you. 

### 9 **DR. DANIEL SCHLENK:**Thanks. Any other 

### 10 Panel members? Any questions or clarification from the Agency? 

### 11 **MS. MARY MANIBUSAN:**No additional comments. 

### 12 Thank you. 

### 13 **DR. DANIEL SCHLENK:**Okay. We are at a 

### 14 quarter to 12 right now. Let's go ahead and break for lunch and 

### 15 we're come back and do Question 9. You should beat the crowds 

### 16 in terms of getting out to lunch lines right now. Let's be back 

### 17 at 12:50. Let's do that. 

### 18 (Whereupon, at 11:45 a.m., a 

### 19 luncheon recess was taken.) 

### 20 * * * * * 

### 21 **DR. DANIEL SCHLENK:**Let's go ahead and get 

### 22 started. Okay. Let's read 9.1 into the record. 

### 23 **MS. MARY MANIBUSAN:**Question 9.1: "Please 

### 24 comment on whether the principles and concepts used to develop 

### 25 the ER Expert System are generally applicable for any 

### 26 category-based prioritization system for other molecular

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1 initiating events, e.g., other AOPs for perturbing estrogen, 

### 2 androgen receptor, and thyroid hormone systems." 

### 3 **DR. DANIEL SCHLENK:**Lead discussant on 

### 4 that is Dr. Schultz. 

### 5 **DR. TERRY SCHULTZ:**Thank you. The comments 

### 6 from the Panel are prefaced by a boiler plate that you have 

### 7 heard a number of times, basically demonstrating that the Panel 

### 8 understands the context that you have presented this in the ER 

### 9 Expert System based on rainbow trout cytosolic binder and then 

### 10 gene activation in the liver slice on that. 

### 11 Basically, we feel that this is a scientific 

### 12 and sound and transparent means of doing targeted testing, which 

### 13 subsequently will provide data that increases the weight of 

### 14 evidence that, in turn, increases confidence in the chemical 

### 15 categories and predications to which they're based upon. So we 

### 16 do think that this is a way of identifying your more likely 

### 17 candidates for further evaluation. 

### 18 This work, if taken in total, as clearly moved 

### 19 from the principle's concept discussed in 2009 into a proof of 

### 20 concept stage. However, with that said, there is clearly some 

### 21 things that have been introduced during this SAP that will need 

### 22 to be reexamined, and I don't think we need to discuss that 

### 23 anymore since we spent an amount of time on Questions 6 and 7 to 

### 24 kind of address that. 

### 25 However, since the Expert System is only used 

### 26 in prioritization, we don't feel it really needs to predict

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1 higher-level effects. That we're happy with the fact you 

### 2 developed an Expert System on lower-level, mechanistically 

### 3 relevant endpoints because its prioritization and other things 

### 4 can be captured later. 

### 5 So in that regard, the ER-mediated fish 

### 6 reproduction of permanent AOP, as described in the SAP can be 

### 7 used as a road map for other molecular initiating events related 

### 8 to other scenarios involved in endocrine disruption; however, 

### 9 the Agency needs to be more explicit and include more details on 

### 10 key events that can be used as assay points, especially in 

### 11 providing more detail in an AOP on the species of concern and 

### 12 the model assays where those assays on based on human, rat, 

### 13 mouse, frog, fish, whatever the scenario is, so that it is 

### 14 transparent and the relationships between each one of the assays 

### 15 -- the endpoints of those assays within the context of the AOP 

### 16 is well-documented, scientifically feasible and defensible. 

### 17 In other words, you're basically applying the 

### 18 principles of the Adverse Outcome Pathway and the principles of 

### 19 (Q)SAR within OECD context to make those. The better those 

### 20 cases made, the less likely that we're going to have issues in 

### 21 accepting the initial things that are put forward. 

### 22 With that said, there are several issues, 

### 23 especially weighting to expanding the test chemical space to 

### 24 cover more structurally diverse chemical inventories that was 

### 25 presented in the context of the ER binding AOP pathway that has 

### 26 been used during this SAP that will need to be resolved, and the

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1 Panel strongly encourages the Agency to move forward quickly to 

### 2 resolve these issues and ER pathways so that protocols and 

### 3 schemes that you devise can be readily transferred to other MIE, 

### 4 AOP-based Expert Systems. 

### 5 The solution proposed by the Agency to expand 

### 6 test chemical spaces and using high through put new screening 

### 7 test data ToxCast and Tox21 to inform our chemical spaces, 

### 8 followed by a strategic experimental testing protocol, is still 

### 9 preliminary. 

### 10 However, this has significant promise and if 

### 11 placed before or in combination with the Expert System analysis, 

### 12 can also be used as part of development of new MIE AOPs 

### 13 associated with endocrine disruption. 

### 14 Again, emphasize that issues identified by the 

### 15 Panel relating to chemical selection strategies needs to be 

### 16 developed. So a protocol for selecting chemicals with that 

### 17 inventory testing is very transparent and has a rationale that 

### 18 can be justified so we don't end up again saying, well, we need 

### 19 to test one more chemical or we need one more assay. 

### 20 The Panel noted that one challenge for moving 

### 21 forward to other molecular initiating event AOP-based Expert 

### 22 System is the choice of the molecular initiating event. This 

### 23 appears to be a very critical issue that the Agency should 

### 24 emphasize why the ones that they're selecting as their first 

### 25 cuts are the most likely to get the biggest bang for the buck 

### 26 and where the limitations are so we don't end having to go over

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1 this territory again, but there many ways of interfering with AR 

### 2 endocrine receptor or thyroid. In the case of thyroid, it's 

### 3 going to be much more important because they seem to be a lot 

### 4 less consistence on what's more important. There seems to be a 

### 5 lot more on that. 

### 6 As further noted by some of the Panel members, 

### 7 a second challenge is identifying the truly positive chemicals 

### 8 that you could use for the development of the AOP initial 

### 9 development of the Expert System and to validate the assays and 

### 10 components associated with that. 

### 11 In that context, what will be in the Paper was 

### 12 specific examples of talking about in the chem, EMBL-14 

### 13 database, which demonstrates that there's a lot more information 

### 14 related to ER-related chemicals and pathways than there is for 

### 15 AR. And in the case of thyroids disruption, it's smaller and 

### 16 it's subdivided into a number of different pathways or 

### 17 mechanisms. So that's going to be a real challenge to the 

### 18 Agency. 

### 19 The Panel agrees that the principles and 

### 20 concept used in developing the ER binding Expert System are 

### 21 generally applicable to performing category-based prioritization 

### 22 for other MIEs leading to endocrine disruption-related final 

### 23 endpoints. That's basically the summary of that first part. 

### 24 The Panel also believes that there are growing 

### 25 agreement on what issues can reduce the uncertainty that will be 

### 26 presented as these new concepts are brought forward, and these

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1 include, obviously, a transparent mechanistic plausible or 

### 2 probable scenario which is best met by a high-quality Adverse 

### 3 Outcome Pathway that is used to define the system, the 

### 4 weight-of-evidence that could be applied to that and the ability 

### 5 to do hypothesis-based testing, especially with rapid, 

### 6 inexpensive assays. 

### 7 Note: We said rapid, inexpensive assays. It 

### 8 doesn't mean they have to be high through put, but we certainly 

### 9 would encourage you to start making the effort into that 

### 10 paradigm shift. 

### 11 As far as the AOP, remember, it's a framework 

### 12 that comprises events at different levels of biological 

### 13 organization and other key dimensions such as gender, life 

### 14 stage, which may be much more critical in the way that you deal 

### 15 with thyroid disruptions because of the different endpoints that 

### 16 thyroid disruption effects. And we need to identify the causal 

### 17 relationships to the final adverse effect that's under 

### 18 consideration. So we're back to following the best principles 

### 19 for developing to the Adverse Outcome Pathway. 

### 20 Issues we see that will be critical to the 

### 21 Agency as they put forth new Adverse Outcome Pathways with which 

### 22 to base their prioritization schemes on is the ability to 

### 23 separate events that control toxic potency from events that 

### 24 report symptoms. I think that you've done an excellent job in 

### 25 your ER example because you identified binding and then gene 

### 26 activation, and those things happen early on and they can be

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1 identified. 

### 2 While the Adverse Outcome Pathway will need to 

### 3 be linked to events at high levels of biological organization, 

### 4 they will not necessarily have to be measured or evaluated 

### 5 because you're using it in a prioritization scheme. 

### 6 You will need to attain acceptance on the key 

### 7 events included in the Adverse Outcome Pathways, especially 

### 8 those at lower levels of biological organization because those 

### 9 are the ones that you're actually going to be generating data 

### 10 for. You're going to have to attain agreement on the methods 

### 11 used for investing those key events and you're going to have to 

### 12 get agreement on the in vitro data that's used a standard for 

### 13 verifying the method/data or the key events, or in that case, 

### 14 the whole Adverse Outcome Pathway. And I think that one of the 

### 15 successes that you've had in your ER binding example is you were 

### 16 able to demonstrate that very, very clearly and concisely in the 

### 17 2009 SAP. And that is basically the answers from the Panel for 

### 18 9.1. 

### 19 **DR. DANIEL SCHLENK:**Thank you. Dr. Meek, 

### 20 anything to add? 

### 21 **DR. BETTE MEEK:**I have nothing to add. 

### 22 Thanks. 

### 23 **DR. DANIEL SCHLENK:**Dr. Burris? 

### 24 **DR. THOMAS BURRIS:**Nothing to add. 

### 25 **DR. DANIEL SCHLENK:**And Dr. Perkins? 

### 26 **DR. EDWARD PERKINS:**I agree with everything

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1 that was said and I just had one comment to add and it may apply 

### 2 to 9.2 or 9.3 is that in discussion with people from the Panel 

### 3 it seems clear that it's going to be hard to avoid -- get a 

### 4 perfect system to include all potential chemicals that may cause 

### 5 an effect on any one pathway. So I think it's very necessary 

### 6 that EPA clarify what rate of false negatives they'll be able to 

### 7 tolerate in reducing the space of chemicals that they're looking 

### 8 at. Associated with each filter, there's a certain uncertainty 

### 9 that you may be filtering out, something may cause effects down 

### 10 the line. 

### 11 As we come to learn more about mechanisms such 

### 12 as methylation status and epigenetics causing subsequent 

### 13 generations to have effects, these types of information may need 

### 14 to be built in on how you may capture false negatives in other 

### 15 AOP strategies or do you need a garbage bin that just searches 

### 16 for general toxicity. So that was one comment I had. 

### 17 **DR. DANIEL SCHLENK:**Thanks. Dr. Jacobs. 

### 18 **DR. MIRIAM JACOBS:**In addition to the 

### 19 comments that have been presented, I would just like to 

### 20 reiterate again how important it is to go back to the literature 

### 21 and see what's there as well. And with respect to the androgen 

### 22 receptor, there are a couple of useful models and there is one 

### 23 particularly that has been developed by a Swiss group, based on 

### 24 three-dimensional automated docking techniques that includes the 

### 25 androgen receptor and some thyroid receptor endpoints, and many 

### 26 other endocrine-related receptor endpoints as well. But

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1 particularly, I think the androgen receptor one will be worth 

### 2 exploring as you start developing your pathways. And then the 

### 3 Danish group and Danish EPA have also published large, 

### 4 reasonably large keys over the androgen receptor. 

### 5 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 6 **DR. MICHAEL KEISER:**General comment on the 

### 7 question itself. First, I'll say I agree with everything that 

### 8 has been stated. Picking up on Dr. Jacobs point, there will be, 

### 9 especially as you move forward to additional systems, situations 

### 10 where a category-based approach, as we've discussed today and 

### 11 yesterday, is going to run into limitations. 

### 12 So one example of a way to address that would 

### 13 be some of these other models or systems that you could apply to 

### 14 molecules that by the category-based approach still remain in an 

### 15 unknown binding potential regime, in addition to the chemical 

### 16 informatics techniques talked about yesterday and some of the 

### 17 ideas that came up around the time of Questions 6 and 7, all of 

### 18 those would be appropriate approaches that you might evaluate 

### 19 for cases where the category-based approach does not cover the 

### 20 chemical domains that you're working in. 

### 21 **DR. DANIEL SCHLENK:**Thanks. Dr. Safe. 

### 22 **DR. STEPHEN SAFE:**I've got just a few 

### 23 additional comments. It always sort of worries me when you have 

### 24 definitions that you seem to be married to. MIE; I would ask 

### 25 Nancy, "What's MIE?" And AOP and all these things. Really, all 

### 26 you're doing is looking at binding and nothing to do with an

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1 adverse anything and rather than continue on that for the 

### 2 androgen receptor, you know, why don't you just call it an 

### 3 androgen ligand assay system, which would be ALAS, or something 

### 4 that like that, that really reflects what you're doing. 

### 5 Because, you know, there's no Adverse Outcome Pathway by -- 

### 6 well, Vitellogenin, maybe, but you're not really doing that. 

### 7 That's just a minor little thing. 

### 8 The other thing, of course, with the androgen 

### 9 receptor is that from an environmental and an exposure point of 

### 10 view, you're going to be looking for anti-androgens. So the 

### 11 assay systems you're going to be looking for, I mean, you can 

### 12 think of a number of them, but remember, it's going to be an 

### 13 anti-androgen assay and it would be a nice way to look at it or 

### 14 have a system to look at it where you don't have to add 

### 15 something where it's already activated and you can look for 

### 16 deactivation in one assay system, rather than inducing with 

### 17 androgen and then inhibiting with an anti-androgen because 

### 18 you're going to be looking, primarily for anti-androgens. 

### 19 **DR. DANIEL SCHLENK:**Thanks. Any other 

### 20 Panel members? Dr. McManaman. 

### 21 **DR. JAMES MCMANAMAN:**I agree with everything 

### 22 that has been said. I want to make a last pitch, in terms of 

### 23 reading it into the minutes, is that in some of these other 

### 24 systems you may not be dealing with receptor binding. You may 

### 25 be dealing with activation of systems that involve the receptors 

### 26 that are activated by other kinds of hormones, meaning, peptide

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1 hormones, in which case the structure function activity that you 

### 2 would use to define the ER or the AR or the thyroid may not be 

### 3 applicable and you might have to go try additional approaches to 

### 4 define their activities. 

### 5 **DR. DANIEL SCHLENK:**Okay. Dr. Keiser. 

### 6 **DR. MICHAEL KEISER:**I just wanted to add one 

### 7 thing, following up on that comment. Going forward especially, 

### 8 as you move into systems like thyroid and beyond and as the 

### 9 field advances, you will begin to encounter cases where a single 

### 10 binding event is not, in a sense, the atomic initiating event -- 

### 11 and I think we're touching on that today -- it is not 

### 12 necessarily something that needs to be immediately addressed and 

### 13 changed, but it should be in the minutes and in the mind for the 

### 14 future to think about how this approach can be extended to 

### 15 multi-target or multi-system initiation events where you have to 

### 16 consider that to be the event that might be comprised of 

### 17 individual units that are needed in concert to follow the 

### 18 pathways in question. 

### 19 **DR. DANIEL SCHLENK:**Okay. Any other 

### 20 comments on 9.1? 

### 21 (No response.) 

### 22 Okay. Let me go to the Agency. Any questions 

### 23 or clarification for the responses? 

### 24 **MS. MARY MANIBUSAN:**No additional comments. 

### 25 Thank you. 

### 26 **DR. DANIEL SCHLENK:**Okay. Let's go ahead

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1 read in 9.2 then. 

### 2 **MS. MARY MANIBUSAN:**9.2: "What lessons have 

### 3 been learned from the development of the ER-focused 

### 4 prioritization model that will facilitate more efficient and 

### 5 effective development of an effect-based prioritization model 

### 6 for the androgen pathway?" 

### 7 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 8 **DR. TERRY SCHULTZ:**As was stated during this 

### 9 Panel meeting, it appears that the primary AR path we're leading 

### 10 into conception will be similar to the ER pathway and that the 

### 11 seminal event would be receptor binding, though it be agonist 

### 12 versus antagonist. In the downstream events, especially at 

### 13 higher levels of biological origin will largely be a cascade of 

### 14 events that do little to mediate the final outcome. 

### 15 If this assumption is true, the scheme 

### 16 developed for the ER pathway is directly applicable to the AR 

### 17 pathway, as long as we consider the fact that we're not looking 

### 18 at antagonists, we're looking at agonists. 

### 19 What we've learned from the work on the ER 

### 20 pathway and the associated chemical groups defined by the 

### 21 knowledge-based Expert System includes the importance of having 

### 22 a well-developed AOP, and an example that has been presented by 

### 23 the Panel is that AR-binding alluding to fish growth inhibition. 

### 24 So you're having a different apical endpoint that you're 

### 25 looking at in this case. 

### 26 It's also demonstrated the importance of being

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1 able to demonstrate the typical responses that observed at the 

### 2 levels of biological organization. So in the context of the AR 

### 3 scheme, that would be AR binding, altering the growth, the most 

### 4 susceptible cell types, leading to malformation and dysfunction 

### 5 in the most likely organs or organ systems in following the 

### 6 Adverse Outcome Pathways. 

### 7 So feeling at least part of the Panel members 

### 8 is the importance of having the Adverse Outcome Pathway provides 

### 9 you with the opportunity of linking the high through put or in 

### 10 vitro tests that you're going to use to the regulatory outcome 

### 11 that you are actually being charged with protecting. 

### 12 Thirdly, the importance of having developed 

### 13 and standardized assays for in vitro and/or in vivo for which 

### 14 data can be gathered in a systematic manner. Many of the 

### 15 problems you have in using Adverse Outcome Pathways is you run 

### 16 into identifying a key event and there's no assay that's 

### 17 developed that actually addresses that event as needed within 

### 18 the confines of the adverse outcome. 

### 19 And fourthly, of course, the importance of 

### 20 being able to apply the mechanistic information and data to 

### 21 define chemical categories that can be developed into 

### 22 structure-based Expert System, which in turn can be validated by 

### 23 hypothesis-based testing. So again, back to the principles, you 

### 24 bind the best principles for an AOP and the best principles for 

### 25 (Q)SAR. 

### 26 **DR. DANIEL SCHLENK:**Dr. Meek?

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1 **DR. BETTE MEEK:**I have nothing further. 

### 2 Thanks. 

### 3 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 4 **DR. THOMAS BURRIS:**I just want to mention one 

### 5 technical aspect of the androgen receptor that is important as 

### 6 these assays are being developed, especially in comparison to 

### 7 the ER assay Panel. 

### 8 The GAL4 assay that's typically used in the 

### 9 field as a GAL4 DNA binding domain fused to the ligand-binding 

### 10 domain of the nuclear receptor, I just want to point out that 

### 11 the androgen receptor does not work in that assay. I assume 

### 12 that through ToxCast there may already be an AR assay that's 

### 13 using that's a modified version of that. 

### 14 So one is restricted either using the intact 

### 15 androgen receptor or you can completely replace the androgen 

### 16 receptor DNA binding domain with the Gal-4 DNA binding domain so 

### 17 that the chimeric receptor has the amino terminus followed by 

### 18 the yeast transcription factor DNA binding domain, then the 

### 19 androgen LBD. That will work. This is just from a significant 

### 20 amount of experience working on this receptor. You wouldn't be 

### 21 able to predict that a priori, but it's been seen by a number of 

### 22 groups that this is needed. But a GAL assay can be used with 

### 23 some modifications. 

### 24 **DR. DANIEL SCHLENK:**Okay. Dr. Jacobs. 

### 25 Sorry, I skipped Dr. Perkins down there. 

### 26 **DR. EDWARD PERKINS:**No further comments.

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1 **DR. DANIEL SCHLENK:**Dr. Cronin? 

### 2 **DR. MARK CRONIN:**No further comment. 

### 3 **DR. DANIEL SCHLENK:**Now Dr. Jacobs. 

### 4 **DR. MIRIAM JACOBS:**There are a couple of 

### 5 androgen receptor transactivation assays in validation; one is 

### 6 the Japanese ones that they currently have to test a few more 

### 7 substances, so they're reasonably close to being developed into 

### 8 a test guideline, hopefully. And then there's another one being 

### 9 developed for validation in the European Commission, the POLM 

### 10 assay. So I think looking at these assays a bit further down 

### 11 the line will be very helpful. 

### 12 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 13 **DR. MICHAEL KEISER:**So speaking to what 

### 14 lessons have been learned, a general comment, I think one thing 

### 15 that has come up has been -- especially in the computational 

### 16 side -- but additionally on the HTP side. 

### 17 As you move forward through this process over 

### 18 the course of years, whatever the current techniques are will 

### 19 remain a moving target. I recognize the need of the Agency to 

### 20 have a core at the center of that to direct the efforts, such as 

### 21 the Expert System, for instance, with the category-based 

### 22 approach. 

### 23 So speaking generally, going onto these other 

### 24 systems, it will be in years afterwards, it should always be 

### 25 important that in addition to the core, for whatever parts of 

### 26 your inventory you are unable to cover, you have in place

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1 protocol system or approach to fill in whatever other 

### 2 technologies might be still rather cutting edge, but will be a 

### 3 way to get some initial grip on parts of your inventory that you 

### 4 don't have coverage from you core. But of course, you should 

### 5 keep the core as the basis and guiding effort and then when 

### 6 things are not covered by it, turn to some of these other 

### 7 current approaches, whatever they may be at that point in time. 

### 8 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 9 **DR. STEPHEN SAFE:**Nothing further. 

### 10 **DR. DANIEL SCHLENK:**Any other comments 

### 11 from the Panel. Yes, Dr. Denslow. 

### 12 **DR. NANCY DENSLOW:**The in vitro assay for 

### 13 androgen receptor that's based on the Blah System, works in my 

### 14 hands. So I think it should work. 

### 15 **DR. DANIEL SCHLENK:**Okay. Thanks. One 

### 16 comment I had, I hadn't heard anybody mention the AR Spigen 

### 17 (phonetic) relationship, where that is. I know OECD has 

### 18 actually been developing that and that would seem to be a 

### 19 comparable AOP, if you will, in terms of if we use the 

### 20 Vitellogenin for ER and then the Spigen model for the AR. I 

### 21 don't know. Dr. Jacobs, you probably have some insight there. 

### 22 **DR. MIRIAM JACOBS:**It was never developed 

### 23 further as a test guideline, but there is a guidance document 

### 24 available for it. 

### 25 **DR. DANIEL SCHLENK:**So that may be 

### 26 something, as a parallel system. Any other comment on 9.2?

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1 Okay. Let's go to the Agency. Any questions 

### 2 or clarification for 9.2? 

### 3 **MS. MARY MANIBUSAN:**No additional comments. 

### 4 **DR. DANIEL SCHLENK:**Okay. Can we go ahead 

### 5 and read 9.3 in? 

### 6 **MS. MARY MANIBUSAN:**9.3: "What will be the 

### 7 challenges in developing a prioritization using in vitro methods 

### 8 and computer-based simulations for the thyroid hormone system?" 

### 9 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 10 **DR. TERRY SCHULTZ:**The Panel feels that 

### 11 developing any computational Expert System to prioritize testing 

### 12 for a thyroid hormone system mediators may need to consider a 

### 13 variety of potential molecular initiating events which generally 

### 14 have less data to model than just ER or even AR. 

### 15 The Panel feels that this increase complexity 

### 16 and data limitation may lead, at least initially, to weaker 

### 17 categorization power. While mediated receptor binding is 

### 18 generally agreed to be the primary initiating event for ER and 

### 19 possibly for AR-related endocrine disruptor, TR binding may not 

### 20 be the primary means of chemical alteration for thyroid hormone 

### 21 systems. Thus, the scheme developed or the ER pathway as 

### 22 described in this SAP may not be directly applicable to the 

### 23 thyroid disruptors. 

### 24 Thyroid hormone homeostasis in mammal systems 

### 25 is important in developing the central nervous system, general 

### 26 growth of individuals and play a role in metabolism. So you

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1 already have multiple AOPs because you may have multiple final 

### 2 adverse outcomes that you're trying to deal with. 

### 3 Therefore, it would be greater burden on the 

### 4 Agency to justify why the AOP that they select to begin work on 

### 5 is likely to be the most important one. That is, is the AOP 

### 6 will have the greatest chemical space for active compounds as 

### 7 you want to put your initial effort where you're going to get 

### 8 the most coverage of the chemical universe. 

### 9 Thyroid disruptions stimulations are 

### 10 inhibitions -- disruptor stimulation inhibitions of thyroid 

### 11 through multiple mechanisms as a pathway was demonstrated in the 

### 12 FIFRA SAP consultation we had in 2011. It was entitled, 

### 13 "Integrated Approaches to Testing and Assessment Strategies for 

### 14 Using New Computational Molecular Tools." 

### 15 We strongly advise the Agency to revisit that 

### 16 and build on the work that was presented there and reviewed by 

### 17 an expert panel. Obviously what came out of that was an Adverse 

### 18 Outcome Pathway for the molecular initiating event was PX 

### 19 activation of the PXR receptor and the CAR receptor in liver 

### 20 ligand to the final adverse effect of impairment of neural 

### 21 development in children. I'm listing this as a specific example 

### 22 where the Agency has always made a ready-made progress in that. 

### 23 To demonstrate the complexity of that, it was 

### 24 proposed that the activation of PXR and CAR leads to up 

### 25 regulation of hepatic Phase I and Phase II enzymes and hepatic 

### 26 transporters. The up regulation of these enzymes and

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1 transporters results in increased metabolism of T4 and the 

### 2 resulting decrease and circulating tissue T4 and tissue T3. So 

### 3 we're already seeing an Adverse Outcome Pathway that has been 

### 4 proposed by the Agency in a different context that is much more 

### 5 complicated than the pathways that we have demonstrated with the 

### 6 ER. 

### 7 Taking this one step further, of course, it 

### 8 should be noted that the activation of PXR and CAR and the 

### 9 indirect stimulation of enzymes is only one pathway. Thyroid 

### 10 disruptors can exert their action directly on thyroid by 

### 11 stimulating and inhibiting thyroid receptors or stimulating and 

### 12 inhibiting thyroid enzymes that inactivate the thyroid hormone 

### 13 or displace it from binding proteins in the blood. 

### 14 Thyroid receptors Alpha 1, Alpha 2, and Beta 1 

### 15 are also possible molecular initiating events, but they are 

### 16 unlikely to be the most important molecular initiating event. 

### 17 Ligand binding domains of those receptors impact by recruitment 

### 18 and release of co-activation and core reflective factors in the 

### 19 receptor and our potential key events. 

### 20 Lastly, T3 and T4 binding, competitive 

### 21 inhibition, paraprotein characters, thyroxin with carriers, 

### 22 several are mentioned are also possible MIEs. I won't go 

### 23 through the whole thing, but the point is that the Panel is 

### 24 aware that there is a lot more mechanisms involved with the 

### 25 thyroid. So the Agency needs to be clear to define that they 

### 26 are aware of this, which one they select, why they selected it

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1 to move forward on that type of a situation. 

### 2 The Panel recommends that the Agency explore 

### 3 all the possible molecular initiating events, establish a 

### 4 priority list of pathways and summarily move that as time and 

### 5 resources permit. It was also noted that because the pathways 

### 6 are more complex, have different apical endpoints that assays 

### 7 that may be included and established in the Expert System may be 

### 8 very different in design than those that were used from the ER 

### 9 and AR binding systems. 

### 10 For example, human neural progenitor cell 

### 11 systems exposed to potential thyroid disruptors, and then in 

### 12 vitro measurements of proliferation, migration and 

### 13 differentiation are all in vivo aspects that may or may not be 

### 14 low-throughput or high through put, but could be used to measure 

### 15 possible key cellular events that were demonstrated in the 

### 16 Adverse Outcome Pathways. 

### 17 It is likely that ex vivo assays such as the 

### 18 zebra fish larvae or the xenopus larvae, which studied very 

### 19 early stages in development, may also be relevant for screening 

### 20 for thyroid disruptors for higher level of effects. Zebra fish 

### 21 assay basically evaluates the impairment of T4 synthesis, while 

### 22 the xenopus assay evaluates agonist and antagonist activities. 

### 23 In summary, the Panel feels that moving 

### 24 forward into the thyroid area is going to be a much greater 

### 25 challenge to the Agency than moving into the AR realm and that 

### 26 they're going to be a greater burden of proof on transparency

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1 and mechanistic plausibility, et cetera, et cetera, and why the 

### 2 tests are selected in order to build the Expert System. Thank 

### 3 you. 

### 4 **DR. DANIEL SCHLENK:**Dr. Meek? 

### 5 **DR. BETTE MEEK:**Nothing further. Thanks. 

### 6 **DR. DANIEL SCHLENK:**Dr. Burris? 

### 7 **DR. THOMAS BURRIS:**Nothing further. 

### 8 **DR. DANIEL SCHLENK:**Dr. Perkins? 

### 9 **DR. EDWARD PERKINS:**I just wanted to second 

### 10 what Terry said and I happen to put together a slide right 

### 11 during his talk. This sort of outlines everything that Terry 

### 12 was talking about and the problems that you might run into. 

### 13 It's a very complex system and what's also not included in there 

### 14 and it was alluded to binding to something, it just doesn't 

### 15 necessarily indicate effects, is a feedback control system that 

### 16 also is in there. 

### 17 So at some point, whether what tier it's at 

### 18 that has to be understood, at what point do you exceed the 

### 19 threshold for compensation? 

### 20 But one of things that I wanted to point out 

### 21 in the challenges that you have is currently, there's only a few 

### 22 high through put assays that I'm aware of that ToxCast is using 

### 23 right now and that's the thyroid receptor binding assays and 

### 24 assays for the liver cellular transporters. So a number of 

### 25 assays that cover these different potential molecular initiating 

### 26 events really haven't been developed. So there is a lot of work

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1 going on there. I know people are working on that. 

### 2 From my understanding, one of the most 

### 3 reliable indicators of disruption of thyroid metabolism is 

### 4 decreases in serum T3 and T4 concentrations and there have been 

### 5 assays in them using zebra fish embryo larvae, non-animal 

### 6 models, so to speak, that have been adapted for high through put 

### 7 areas, but these are all new tests, so they're going to require 

### 8 a lot of validating and things like that. 

### 9 Some of the other areas of integrating more 

### 10 complex organisms, more fish larvae or frog embryo tests may 

### 11 have to be adapted to more high through put methods because they 

### 12 have very nice phenotypes, very easy to assay and they integrate 

### 13 multiple MIEs. So we're possible to sort of capture complexity 

### 14 without expanding your universe of potential assays, which is 

### 15 going to be a problem in the future, especially if everything is 

### 16 defined very narrowly. You're going to have to have a universe 

### 17 of AOPs to capture the universe of chemicals. 

### 18 One consideration would be how can you do a 

### 19 high through put assay that would use a more complex integrative 

### 20 assay such as an embryo test in a more facile manner and use 

### 21 that as a screen for either a prescreen for sensitivity to 

### 22 something or a general screen. 

### 23 The slide right before that, Slide 4 -- can 

### 24 you go back one? There we go. This is just to illustrate one 

### 25 result with several different chemicals. You have more direct 

### 26 effect chemicals that are causing, potentially, through a really

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1 recognized MIE and then you have some that are more indirect, 

### 2 like PFOS and PFOA. Some of the embryo assays, they don't 

### 3 really see those because they're not able to get some of the 

### 4 more subtle effects. 

### 5 Many of the other in vitro mammalian assays 

### 6 don't either, but more complex ones like alternative species 

### 7 like frogs looking at morphogenesis and also embryo 

### 8 developmental slides can start capturing that. And those could 

### 9 be more consistent with the actual AOP, with the actual adverse 

### 10 outcome. So that's something that may be a consideration where 

### 11 you have a complex and subtle effects you want to capture at the 

### 12 same time. So that's all I have. Thank you. 

### 13 **DR. DANIEL SCHLENK:**Great. Dr. Cronin. 

### 14 **DR. MARK CRONIN:**No further comment. 

### 15 **DR. DANIEL SCHLENK:**Dr. Jacobs. 

### 16 **DR. MIRIAM JACOBS:**This is probably 

### 17 unnecessary, but I just want to flag up the thyroid DRP work 

### 18 that was led by the U.S. EPA in 2006 and then the current 

### 19 ongoing scoping work on the thyroid again. It's going on now 

### 20 under the auspices of the OECD, but just to acknowledge that the 

### 21 U.S. EPA are playing a very significant role. 

### 22 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 23 **DR. MICHAEL KEISER:**No further comment. 

### 24 **DR. DANIEL SCHLENK:**And Dr. Safe. 

### 25 **DR. STEPHEN SAFE:**No further comment. 

### 26 **DR. DANIEL SCHLENK:**Any other Panel

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1 members? 

### 2 (No response.) 

### 3 Okay. Does the Agency have any questions or 

### 4 clarification regarding 9.3? 

### 5 **MS. MARY MANIBUSAN:**No additional comments. 

### 6 Thank you. 

### 7 **DR. DANIEL SCHLENK:**Okay. So this 

### 8 concludes the questions that have been proposed in terms of the 

### 9 charge questions. What is sort of customary at this point is 

### 10 that we have one last movement around the Panel to present final 

### 11 comments, maybe issues that you feel that were not addressed in 

### 12 the questions. This is your opportunity to get them on the 

### 13 record so it can be included into the written document. 

### 14 I'll go ahead and start to my left with Dr. 

### 15 Klaine. 

### 16 **DR. STEPHEN KLAINE:**I have no other comments. 

### 17 **DR. MARTHA SANDY:**I wanted to thank the EPA 

### 18 staff for a very clear documents and presentations. Thank you. 

### 19 **DR. JAMES MCMANAMAN:**I also want to thank the 

### 20 EPA for nice presentations and bearing with us in our questions. 

### 21 **DR. PRAKASH NAGARKATTI:**I want to thank EPA 

### 22 as well. I have no additional questions or comments. 

### 23 **DR. KATRINA WATERS:**I have no further 

### 24 comments. 

### 25 **DR. MARK CRONIN:**I have just a couple of 

### 26 comments. Firstly, thank you again for some very, very nice

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1 work. I would emphasize the importance to the European Union 

### 2 issues with a number of pieces of legislation that have gone 

### 3 through or are going through, and being enacted at the moment. 

### 4 And I would encourage this approach, particularly based around 

### 5 the AOP and categories to be publicized, certainly outside of 

### 6 this forum, as possible. One aspect is through the OECD and the 

### 7 advisory group on molecular biology. 

### 8 I would also commend a number of novel 

### 9 approaches. They've been covered, we needn't go over them 

### 10 again, and I would just suggest, from a personal point of view, 

### 11 we shouldn't get too hung up over how we use categories, 

### 12 definitions of categories and so forth. The category approach 

### 13 is there to group chemicals together to get information within 

### 14 that group and to apply that information. It's been used fairly 

### 15 flexibly within the European Union for reach and I can see 

### 16 things here that we can take and possibly cross over. I would 

### 17 just mention that. Thank you. 

### 18 **DR. DANIEL SCHLENK:**Dr. Schultz. 

### 19 **DR. TERRY SCHULTZ:**Yes. I would like to 

### 20 congratulate the Agency in making a step out into the realm of 

### 21 actually discussing the paradigm shift. We've been hearing 

### 22 about this for a number of years, mostly in conceptual papers 

### 23 with boxes and arrows. The seeing, the effort to make the high 

### 24 through put screening data applicable to the real world 

### 25 questions that you are faced with and also to match that up with 

### 26 the efforts that you have done over the past decade when these

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1 types of data weren't available. 

### 2 I think this is a very good start. You have 

### 3 forced us to ask some really tough questions and I think that's 

### 4 a good mix of a Panel served well. Thank you. 

### 5 **DR. DANIEL SCHLENK:**Dr. Chambers. 

### 6 **DR. JANICE CHAMBERS:**I'll just say that 

### 7 having sat through the previous SAPs on this topic, you've made 

### 8 a lot of progress and I commend you for the progress that you've 

### 9 made. 

### 10 **DR. BETTE MEEK:**I really think that it's 

### 11 exciting that what you're doing is really exciting, as you're 

### 12 kind of moving this forward to have pragmatic application. I 

### 13 think you face a number of challenges as the methodology 

### 14 evolves, and we saw that, you know, at this meeting. 

### 15 I just have one small suggestion in terms of 

### 16 so that the Panel in the future can kind of position this as a 

### 17 component of a larger agenda. It would be really helpful -- I 

### 18 recognize that you're learning while doing, but it would be 

### 19 really helpful to contextualize it from the perspective that 

### 20 this is one AOP for one aspect; we are looking at other aspects. 

### 21 22 That, and also, if you can be thinking about 

### 23 those other components we talked about, like exposure, where 

### 24 those might fit in and we recognize that that's going to change 

### 25 over time, but it's really just to kind of force some thinking 

### 26 at the very outset to provide you with some of the more

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1 discriminating other potential influences, recognizing your 

### 2 regulatory constraints, of course. 

### 3 **DR. DANIEL SCHLENK:**Dr. Perkins. 

### 4 **DR. EDWARD PERKINS:**I'd like to thank the 

### 5 Panel and their insights into the problems and also EPA and all 

### 6 the hard work they've done to try to develop something useful 

### 7 out of the high through put screening efforts and all the work 

### 8 that's gone into this. 

### 9 I agree totally with what Bette said. There 

### 10 was some vision given on what this context is and how this is 

### 11 serving as a model for development of future ones, but given the 

### 12 complexity of the entire universe of chemicals and sort of the 

### 13 universe of AOPs, I know it's a relatively new concept applying 

### 14 in a regulatory context, but it builds on modes of action and 

### 15 pathways. So it's not totally foreign. 

### 16 Some ideas or concepts on how this approach 

### 17 may be applied in parallel: Are there going to be 10,000 AOPs 

### 18 to analyze, 10,000 chemicals? Or are you going to find you've 

### 19 got two -- that could potentially can bog you down so much in 

### 20 developing protocols to so accurately define things initially, 

### 21 when you're really just trying to prioritize for things that can 

### 22 potentially cause problems. So some thought might be needed on 

### 23 just how much specific prioritization is needed. 

### 24 I think the ideas on trying to identify and 

### 25 prioritize of sort of relative hazard weighting of different 

### 26 pathways and mechanisms is one good route to do that, but that's

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1 something I'm looking forward to seeing as you evolve and see 

### 2 what kind of strategy to expand this. 

### 3 You don't want to expand it infinitely, you 

### 4 want to have some management set as a goal to have 10, 20, 100 

### 5 AOPs to act as a pre-filter for everything. Conceptually, it's 

### 6 potentially possible, given a pathway-based approach with using 

### 7 sort of the ToxCast as it's evolving. But other than that, I 

### 8 think everything went nicely. 

### 9 **DR. DANIEL SCHLENK:**Dr. Burris. 

### 10 **DR. THOMAS BURRIS:**No other comments. 

### 11 **DR. DANIEL SCHLENK:**Dr. Keiser. 

### 12 **DR. MICHAEL KEISER:**First, I would like to 

### 13 join the others in thanking the EPA, the Agency, and also the 

### 14 Panel, too, for this, and especially the very clear 

### 15 presentations. 

### 16 Additionally, I'd also like to thank the 

### 17 public commenters for a lot of hard work that obviously went 

### 18 into comments and the data and information sent to us. And just 

### 19 more generally, I'm quite excited about this direction. I'm 

### 20 looking forward -- and I like that it was phrased earlier by Dr. 

### 21 Bradbury, this idea of physical and computational and other 

### 22 models that contribute to the prioritization step. 

### 23 So computational, Expert System, physical, 

### 24 things like HTP that then eventually take us to higher levels of 

### 25 testing and confirmation as we move forward. So to that point, 

### 26 just to reiterate, that I think it's very important with these

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1 models that we assign weighting or confidences to them so that 

### 2 we can take them all together and find out what sorts of 

### 3 aggregate priorities we might want to take from them. Thank 

### 4 you. 

### 5 **DR. DANIEL SCHLENK:**Dr. Safe. 

### 6 **DR. STEPHEN SAFE:**No further comment. 

### 7 **DR. DANIEL SCHLENK:**Dr. Denslow. 

### 8 **DR. NANCY DENSLOW:**I really like the approach 

### 9 that you're taking. I think the high through put assays are 

### 10 really there and I would second what Dr. Meek said that, you 

### 11 know, where I got bogged down I think was in the statement that 

### 12 we're looking at all estrogenic activity and you're not really; 

### 13 you're looking at one specific piece. So just focusing it 

### 14 smaller made a difference. Thank you. 

### 15 **DR. DANIEL SCHLENK:**Dr. Jacobs. 

### 16 **DR. MIRIAM JACOBS:**I would like to thank the 

### 17 U.S. EPA and the Panel and the audience as well, as everyone has 

### 18 done before me. I really appreciated the presentations and the 

### 19 critical eye that was presented by the U.S. EPA on their 

### 20 analysis of the work in the documents, and the public 

### 21 commenters, the work that was presented was really helpful and 

### 22 useful to see. 

### 23 I think we got a little bit stuck this 

### 24 afternoon, a bit circular. I think that's really because we, 

### 25 perhaps, the Panel had been looking much more holistically at 

### 26 the estrogenic activities because biology isn't linear. I mean,

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1 there are other factors coming in and there are other factors 

### 2 that are coming into play with high through put assays, 

### 3 especially for example. 

### 4 So with respect to that also, I want to echo 

### 5 Bette's comments on the ways forward. And thank you. Thank you 

### 6 very, very much. 

### 7 **DR. DANIEL SCHLENK:**Well, final comments 

### 8 from my own personal evaluations of the meeting. Again, the 

### 9 Agency, as Dr. Chambers mentioned, having seen this kind of move 

### 10 through the ranks over the years, I think, without a doubt, 

### 11 every person that I know and have talked with in the Panel is 

### 12 very positive in terms of the direction that you're moving. I 

### 13 think this is the way to go. 

### 14 There are challenges, obviously, and I think 

### 15 that came out in some of our discussions. Hopefully that will 

### 16 be beneficial to you and not something that hinders you in your 

### 17 progress, moving forward. But I look forward to the next 

### 18 meetings with additional data. As scientists, you know, we're 

### 19 always asking for more data. So it would be very interesting to 

### 20 see what the next round brings. So I'm very on pins and needles 

### 21 for that. So I think that will be very exciting. 

### 22 At this time, I would like to thank my fellow 

### 23 Panel members for doing a fantastic job of meeting together and 

### 24 actually going above and beyond the call of duty, in terms of 

### 25 putting together your responses. I think you've really done a 

### 26 good job in trying to beat out the best out of these questions

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1 as best as you can. So I'm appreciative of that. 

### 2 I'm thankful to the Agency for the 

### 3 presentations that you made and the hard work that went into 

### 4 those presentations. Obviously, the tremendous amount of work 

### 5 that's gone forth, not only in the high through put areas, but 

### 6 also the low through put areas. I love that. 

### 7 The public commenters, thank you for your 

### 8 comments throughout the week. Obviously, you heard, they were 

### 9 definitely influencing some folks' opinions as well. So those 

### 10 words were not wasted. Thank you for that. 

### 11 I would also like to thank Sharlene and the 

### 12 FIFRA SAP staff for getting us here and providing all of the 

### 13 materials that we had, through numerous emails and physical 

### 14 copying and change of flights and all the other good stuff that 

### 15 has to happen through that process. So we're very grateful for 

### 16 that. 

### 17 With that, I'll turn it over to the Agency for 

### 18 final word. 

### 19 **MS. MARY MANIBUSAN:**On behalf of Agency, I 

### 20 would like to extend our thank you and appreciation. Over the 

### 21 last three days we've received excellent and very constructive 

### 22 and very specific recommendations and advice on how to move 

### 23 forward. Your positivity and your energy is very, very helpful 

### 24 in supporting the Agency as we look forward to the next decade 

### 25 of EDSP. 

### 26 I'm a timeline person, so I think about

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1 timelines as I've talked with you. We recognized where we've 

### 2 been. We know where we are today and we know where we'd like to 

### 3 go. I believe all of us can share in this moment that this will 

### 4 be the new inflection point on our timeline. This is quite 

### 5 revolutionary. We're very excited about moving forward, 

### 6 accomplishing our mission very effectively and efficiently. And 

### 7 those are the things that will bring us forward in 2013. 

### 8 For the subsequent SAPs that will be coming, 

### 9 there will be some reiterating themes that the Agency will be 

### 10 asking from this SAP Panel, in terms of recognizing the Agency's 

### 11 goals and programmatic accomplishment in terms of what is the 

### 12 question we're seeking to answer. 

### 13 What is the tolerated uncertainty that we're 

### 14 willing to accept in making that decision and how do we anchor 

### 15 our decisions by toxicity pathways and biological mechanisms of 

### 16 understanding? But doing this in a very, very transparent way 

### 17 so that we're bringing our entire community together; not only 

### 18 our scientific community, but the public community and our 

### 19 stakeholders at large, together, as we move forward because the 

### 20 endocrine program certainly is indeed a community-based program. 

### 21 So I would like thank the public commenters as 

### 22 well for their insightfulness and taking the time to provide 

### 23 their intelligent information and comments before this Panel. 

### 24 I'd like to extend my appreciation for our designated federal 

### 25 official, Dr. Sharlene Matten, for organizing us, making us fit 

### 26 within the timeframe and accomplish, again, our very large goal

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1 in front of us. So thank you. Thank you very much, and I look 

### 2 forward to 2013. 

### 3 And our next meeting will be focused on some 

### 4 operational aspects of data reviews for the Tier 1 assay and 

### 5 battery performance as we bring before the SAP our analysis of 

### 6 the List 1 chemicals that we had issued test orders on, and then 

### 7 moving forward again on finishing up our task of validating the 

### 8 Tier 2 methods so that we can make timely decisions on chemicals 

### 9 that would advance for additional testing and then coming back 

### 10 to the SAP with our -- follow our evaluation of the complete 

### 11 data set of Tier 1 data, the publicly available information, 

### 12 making sure we tie in all the literature that speaks to per 

### 13 chemical, as well as our 158 information that gives us 

### 14 information already on reproductive development effects, chronic 

### 15 effects, any repeat studies that will bring to bear in our 

### 16 weight-of-evidence determinations. 

### 17 So thank you in advance for those SAPs as 

### 18 well. Dr. Bradbury. 

### 19 **DR. STEVEN BRADBURY:**Just real quick so you 

### 20 can all catch your planes. I just wanted to thank the Panel 

### 21 again and Sharlene and the staff that helped put this together, 

### 22 and the public comments. I want to also thank our colleagues in 

### 23 the Office of Research and Development working with us, as well 

### 24 as colleagues in the Office of Water, and also some of the 

### 25 comments made by the Panel in terms of we're like a little fish 

### 26 in the big ocean, and there are a lot of other fish in the

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1 ocean. 

### 2 So collaboration across the globe in how these 

### 3 new concepts and new technologies are brought to bear and we'll 

### 4 try to do it at SAP meetings to make sure we're providing 

### 5 context in what we're trying to take on, but also want to 

### 6 acknowledge how we're all collectively tapping into real neural 

### 7 nets, biological neural nets, as well as the physical and 

### 8 mathematical models we're using. 

### 9 So I just want to acknowledge that want to 

### 10 keep reaching out through the SAP and through other venues to 

### 11 make sure and try to bring everybody's ideas together as we go 

### 12 forward. So thanks again. Chairman Schlenk, turn it back to 

### 13 you. 

### 14 **DR. DANIEL SCHLENK:**All right. We'll turn it 

### 15 over to Dr. Matten to close us out. 

### 16 **DR. SHARLENE MATTEN:**I'd like to thank all of 

### 17 the members of the Panel for their very intensive work for 

### 18 staying within our requirements for the Federal Advisory 

### 19 Committee Act and preparing, doing so very well in preparing for 

### 20 this meeting and articulating your points. I hope the report 

### 21 will be as clear as we've ended in putting things in the right 

### 22 organizational structure to make it very simple for the Agency 

### 23 to understand all the different points. 

### 24 I appreciate the Agency and its different 

### 25 components, Office of Pesticide Programs, Office of Research and 

### 26 Development, Office of Science and Coordination Policy, and any

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1 other offices that have contributed to the good and welfare of 

### 2 developing this program; all of the members of the public who 

### 3 did very well in providing their comments in written form in a 

### 4 timely way so they could be distributed and encompassed by the 

### 5 Panel, and the presentations that we were able to provide. 

### 6 We are trying to move towards less paper, so 

### 7 perhaps, there was a little confusion, probably because I was 

### 8 trying to do multiple things, but we have resolved those things 

### 9 for future. I think the Panel really thought the memory sticks 

### 10 with which we are trying to provide presentations in advance, so 

### 11 I appreciate my colleagues for being able to move forward with 

### 12 that. This is our second time using them. 

### 13 14 15 16 17 18 I would like to thank my office group, first and foremost, Laura Bailey, the executive secretary; my other colleagues who are designated federal officials, Joe Bailey and Fred Jenkins, the entire administrative team, Shirley Percival, Joyce Coates, and Don Woods and the contractors from EnDyna , who do a lot of the meeting logistics and travel logistics. 

### 19 I'd like to thank the presenters from EPA for 

### 20 really responding well to comments to really make their 

### 21 presentations very precise and informative. I think that we all 

### 22 tried to appreciate all of the hard work that everyone has done. 

### 23 So I thank you very much from me, personally. For those who 

### 24 were the direct contacts in the material, it was all done 

### 25 extremely well, on time, early and I can say that I appreciate 

### 26 that quite a lot.

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1 So with that, before I adjourn officially, I'd 

### 2 like the Panel members to meet in the room adjacent to this. 

### 3 Thank you very much. So I'll close the meeting. 

### 4 (Whereupon, at 1:55 p.m., the 

### 5 FIFRA SAP meeting was adjourned.) 

### 6 * * * * * 

### 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

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363:25 371:8,8,9 484:14 betalactamase 107:3 110:12,13,22 115:9,14 116:8 116:8 118:16 119:3 122:15 139:21 betalactamases 120:26 beta-estradiol 97:26 beta-lactamase 98:3 bets 174:11 Bette 214:16,16 229:18,25 230:3,3 231:13 310:14

391:15,15 430:17 433:20 439:13 446:4 452:1 472:21 479:1 486:5 491:10 492:9 better 12:17 13:5 24:24 26:3 34:18 86:23 89:10 122:8 195:25,26 197:24 210:8,15 228:15 237:14 243:12 256:10 261:11 293:8 295:10 300:16 305:8 306:8 314:25 322:26 323:16 330:11 331:7,14 344:10 366:5 367:1 369:20 370:13 398:10 403:2 413:17 417:10 425:10 437:11 443:1 458:17,21 468:19 Bette's 495:5 Betty7:15,15 beyond 18:24 19:3 36:8 69:17 187:20 197:23

202:21 209:16 295:1 333:17 341:5 355:20 476:8 495:24

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www.transcriptionetc.com Page 11 BG-1 98:16 257:5 BG1 98:19 100:4 111:12 115:7,14 118:4,16 119:4 139:17,21 144:23 156:16 157:17 158:22 159:11 160:23 161:13,14 164:2 171:4 190:3 268:9 284:19 285:16 366:3 445:1 447:1,21 448:16 BG1s 120:26 BG1-Luc245:2 446:19 bias 105:24 biased 394:12 396:9 biases 317:16 436:25 bid 131:15 big 91:21 93:3 96:12 100:24 109:6,23 111:19,20 120:19 139:5 155:24 169:16 183:18 186:13 199:10 289:16 352:5,16 402:5,14 410:15 413:10 414:11 498:26 bigger229:15 biggest 186:8,20 360:6 402:2,19 469:25 bill 236:23 241:20 bin 241:15,15 473:15 binary 177:8 178:1,18 378:23 381:24 bind 15:4 33:10 51:17 57:1 65:7 72:12 79:18,18

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51:14 54:15,18,21 56:7,12,15,25 57:5,16,18,21 60:10 61:20 62:4,20,24 63:13,14,22 64:1,11,21 64:22 65:16,16 67:24 68:2,4 69:1,14 70:16 71:8 71:22 72:25 73:2,13 74:18 80:6,11,23,26 81:2 84:19 86:17 89:25 95:18,18,21 96:2,2,4,18 97:5 112:26,26 115:22 118:3,18 123:12,13 128:3 130:15,17,19,23 132:2 137:21 138:19 140:2 141:21 143:10 144:10,22 145:1,8,10,25 146:3,9 146:18,25 147:2,4,14 148:2,3,18 149:7,11,26,26 150:8,11,21,25 151:13,19 152:4,10,17,22 153:5,8 153:15 154:6,6,9,11,12,16 155:11,12,14,23,25,25 156:1,13,15 157:5 158:7,9,10 159:9,10 160:10,11 160:17 161:12,12,24,25 162:21,21,25 163:15,22 164:4,8,23,26 165:24 166:11,14,18 167:25 170:10 171:8,8 172:3 175:18,25 176:18 180:13 180:17 181:5,6,16,16,16,17,18,18,21 182:5,12,19 183:25,26 184:2,9 185:2 188:17 189:5,7 196:14 196:16 197:7,18 198:3 199:1,1 202:19 204:10 206:4 210:1 216:25 220:17 223:4 226:12 227:19 238:10 245:26 252:14,15 258:7 265:4 267:1 268:7,15,23 269:4,4,5 274:5 276:17,19 277:9,13 277:21 279:25 280:1,18,23 281:6,14 283:11 285:6,19 288:4,5 307:17,26 308:9,11,13,15,17 313:1 325:11 329:22,26 333:8 334:6,9,13 336:23 337:4,5,16 338:26 340:7 343:8,14 347:19,19,21 348:4,9 350:2,7,8,19 352:4,10 353:2,3,4,7,9 354:23,24,25 355:3,7 356:22 357:19,19,24,25 358:14 359:3,8,13,15 362:21 364:3,8,8,15 365:1 365:3 367:22 368:3,6 369:19 371:10 372:15 382:15 392:15 394:15 397:26 398:3 399:20 404:23 406:22 411:24 415:6 422:24 423:19 428:19 431:6 432:14 435:24 436:2,14 440:7,9 446:15,25 447:6,18,22,24 448:15,18 449:1,1,3,25 450:19,21 452:9 453:16,22 454:14 457:1 459:13 459:23 460:22 461:9 468:25 470:20 471:25 472:15 474:15,26 475:24 476:10 477:11 478:3 479:9,16,16,18 482:17,19 484:13,17,20 485:9 486:14,23 bindings 33:15 51:14 152:10 binding-focused447:7 binding-related428:5 binding-specific447:3 binds 95:11,14 205:26 226:5 267:6 364:15 432:12 binning 243:5 bioactivated464:13 bioactive 137:11 164:16 bioactivity 224:10 bioassay 58:9 429:25 bioavailability 63:13 65:19 156:2 305:24 321:12

324:6 367:26 368:3 bioavailable 321:17 327:12 328:14 biochemical 308:22 biod 265:9 biologic 185:6 biological 24:11 30:15,21 46:3 47:6 50:19 55:6

57:9 59:5 60:9,21 205:7 221:22 254:4 259:19,20 265:26 268:19,24,25 275:26 278:16,22 305:12 316:17 334:12 359:7 364:24 385:6,18 403:21 422:25 439:17 440:16 471:12 472:3,8 477:13 478:2 497:15 499:7 biologically44:17 51:21 244:10,12,19 biologist 6:10,10 biology7:22 58:11 59:2 89:11,13 140:10 184:11

255:15 403:25 414:14 415:20,25,25 490:7 494:26 biomarkers 250:9 Biomedical 275:1,3

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www.transcriptionetc.com Page 12 BioReliance 279:22 bipyridines 200:19 bisphenol 97:25 107:3 109:24 116:11 273:5 bisphenols 113:9 bit22:8 38:21 40:19 42:7 44:6 50:1 52:22 53:3

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273:3 Blah481:13 blamed 383:11 blank 133:1 blend 34:20 blind 339:3 373:26 blinded 105:1,4 383:24 block 124:18 blood463:17 464:14 484:13 blown97:22 blue 98:7 110:25 111:3 115:10 116:23,24 117:11 blues 118:13 blunt428:19 blush 171:12 board 262:16 279:14 432:16 body 257:17 bog492:19 Bogart247:9 bogged494:11 boiler467:6 bona 358:9 bond 61:8 226:15 bonding 196:19 438:1 border 120:21 borderline 119:26 453:7 Borger267:8 268:19 Borgert253:4,5 borrow251:23 340:17 borrowed408:2 bottleneck 386:20 bottom 66:5 75:22,26 95:17 110:15 119:12 143:21 150:19,26 151:8 153:7 155:1,9 163:26 165:10 166:13 172:11 174:3,9,12 189:19 198:2 201:26 357:9 375:3 401:25 bought 113:21,21,21 bound 159:18 185:12,12 204:14 313:3 boundaries 32:15 68:21 86:5 337:24 boundary 147:10 196:26 bounds 24:5 147:8 bovine 144:14,16 148:20 151:8 153:6,20 154:3,10 155:19 163:25,25 277:10,12,15,16,19,21 368:9 box 32:24 84:14 166:7 180:21 188:19 199:21

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205:11 251:5 490:23 Bradbury 8:24 9:11,13,14,15 16:14,24,26 17:9

25:13 28:24 38:3,10,19 48:15 218:13 227:6,6 233:14 263:20 354:12 356:18 362:10 400:26 409:9 410:22,25 411:19 419:2,10 426:21,22 428:24 447:8 450:1 493:21 498:18,19 branch 68:18 194:1 445:6 branched73:16 199:15 branches 67:15 breadth425:4 439:15 break 38:22 52:14 91:3 138:12 142:11,12 190:7

205:10 223:2 247:3,5 333:1 342:16,18 343:5,18 432:23 433:4 466:14 breakdown 192:12 breaking442:2 breakout 82:5 192:2 277:14 brevity446:5 bridged 196:1 197:19,24 brief5:15 88:7 247:12 277:24 334:3 433:1 briefed 3:15 briefly 8:23 91:24 213:19 282:18 390:15 bright 111:13,17 112:8,14 bring 12:12 20:18 22:16,26 23:5 47:20 48:4 53:20

93:1 94:2 108:4,18 131:25 140:20 143:4 144:3 152:21 162:14 184:25 187:11 225:17 239:1 247:26 252:2 350:14,17 370:26 371:5 376:5 393:11 396:5 447:7 497:7 498:5,15 499:11 bringing 17:1 23:2 36:12 47:16 108:3,19 124:19 125:6 277:22 355:23 414:21 497:17 brings408:3 495:20 broad 63:9 143:14 352:10 378:20 393:1 broaden 367:2 broadened 364:9 broader 10:13 14:12 64:19 232:5 311:22 314:3

363:9 403:11 broader-based 88:19 broadest 363:20 458:7 broadly49:3 219:2 434:18

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www.transcriptionetc.com Page 13 broken 193:7 199:23 brominated 202:23 208:14 bromine74:6 bromophenols 201:13 brought20:24 29:10 33:11 75:5 152:25 285:8

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77:13 78:26 79:2 218:8 334:8 353:2 356:26 378:7 378:7 422:5 426:15 446:15 447:23 473:14 Bulgaria 85:25 bullet26:15 40:9 159:16 319:23 323:22 325:16 bullets 40:7 bunch 104:3 107:9 111:15 184:18 burden483:3 485:26 Burgas 85:25 burning 142:10 Burris 7:24,24 124:5,6,6 125:18,19,19 135:21,22

214:20,20 225:18,19 290:11,12,22,25 291:14,21 292:2,11,14 307:14,22,23 342:3,4 349:25,26 354:22 370:2,3 374:6,7 375:23,24 380:14,15 384:3,4 388:17,18 391:19,19 395:5,6 400:3,4 404:2,3 416:26 417:1 422:17,18 428:17,18 472:23,24 479:3,4 486:6,7 493:9,10 burst 109:6,23 127:8 bursts 110:10 383:14 butanol 337:15 Butte 346:16 button 264:6 269:19 butylbenzenethiol 158:11,12,22,26 159:9,15 butylthiol 159:19 buy 102:16,16 125:13,14 126:2 131:9 228:11 Bye 212:7 bypass 129:3 463:19 B-mode 225:21 B-type74:16 85:6 C C 359:14,16 436:6 caco-2 368:9 Cal 390:25 calculate 106:9,11 257:2 326:3,5 378:14 calculated 226:8,13 324:3 372:4,26 calculates 75:8 calculating 326:12 calculation43:1,7,8 75:7 226:17,25 369:14 372:9

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88:21 161:7,8 177:3 187:14 188:4,26 189:10,11 190:12 191:26 193:13 199:4 203:23 227:17 240:3 267:3 268:13 314:24 344:9,11,11 345:9 346:23 346:25 347:1 352:2,6,9,14 359:6 370:15 394:1,22 394:23 403:25 412:11 415:22,23,26 419:12,15 420:7,10,12 423:13,16 424:8 425:12 427:1,18 434:9,14,19 435:2,21 437:22 438:2,11,12,14 445:8,10 458:26 467:15 478:21 490:5,11,12 categorization 29:15 166:19 305:20 393:3 403:17

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486:24 cell-based 61:24 461:3 cell-free 96:18 center 1:13 17:6 91:7 93:11,22 229:14 233:9,13

480:20 centered 336:16 392:20 393:20 centers 87:7

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67:1 80:19 81:5 89:4 180:11,12 282:26 296:4 339:11 414:14 chemists 46:1 230:22,26 chemotypes 317:1 Chen256:22 chief6:5 8:1 children483:21 chime46:24 357:7 chimeric479:17 chlorhexidine 153:25 chloride43:25 chlorinated 201:2,10 202:23 208:15 chlorine 201:10

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328:15,26 356:3 392:19 401:1 402:19 410:26 class 35:21 78:3 81:26 90:19 131:12 143:15 146:12 146:13,15,16,22,24 147:6,7,7,10 149:2,3,5 150:7 159:6,17 173:22 177:8,17,21,25 178:4 179:10,13 179:14 180:18,19 187:15,23 190:9 191:23 192:19 192:20 193:6,14,20 199:22 201:17,25 228:26 259:18 313:1 375:14 383:24 386:14 387:15 406:12 415:4 431:8 434:26 436:22 465:19 classed 380:3 classes 70:19,24 78:14,24 89:13 138:17 139:10 143:14,19 159:7 164:25 177:3,7,20 179:8 181:3 187:4 188:7 189:17,18 192:2,4 193:2,14,21,22 198:10 204:3 226:20 340:5 379:10,18 386:17,18 387:18 403:12 411:8 414:4,7 415:5 421:2 435:5 classic 133:7 359:4 classical 89:12 128:26 350:2 351:10,13,16 353:5

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334:15 378:19 379:15 381:24 386:24 403:21 436:24 classifications 276:15,16 classified 158:1 179:23 378:21 classifier437:11 classifiers 338:2 436:7,9 classify 178:6 403:18 classifying 350:22 403:18 436:18 clean 104:14 120:7 cleanest 107:19 Cleaning 283:2 clear 10:14 12:2,7 18:3 25:19 27:26 30:15 48:17

94:16 128:7 156:4 165:6 185:10 205:18 218:8,8 218:10 220:26 221:25 230:7 236:14,25 239:6 248:5,17 259:3 260:8 262:17 272:9,11,16 273:19 285:10 297:15 298:10 299:13 308:14 309:17 311:5 313:13 315:14 317:13 334:1 344:12 356:24 366:18 372:26 373:2 375:10 376:20,23,26 381:2 381:7 382:10 384:13 394:8 396:5 397:10 398:16 411:21 412:16 415:4 421:21 438:10 447:4 473:3 484:25 489:18 493:14 499:21 clearance 324:12 clearer 323:16 354:14 426:14 428:16 clearly 137:11,12 160:20 241:21 248:21 250:22

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93:12 94:2 112:2 131:2,15 133:7 136:15 139:16 233:8 291:17 293:26 309:3 315:11 324:21 335:9 346:19 361:12 381:11 390:10 407:17 409:3,8 421:1,7 425:10 445:13 495:1,2 497:8 498:9 commend 239:24 293:13 315:1 341:9 490:8 491:8 commendable 272:18 commended 303:16 commending 262:24 comment 3:24 4:2 85:16 124:1 127:11 139:26 185:16 206:21 211:21 213:12 233:17,26 261:4,4 261:19 263:25 268:1 272:5 275:17 296:2,5 302:24 304:17 310:1 315:17 325:21 329:1,4,23 330:5 332:1,17 333:4 341:23 342:11,23,26 347:9 349:26 350:11 352:21,22 353:21 354:4 360:21 362:24 365:14 370:14,25 371:13,14,23 374:14,19 376:8,10,12,20 377:7 382:14 384:19 392:24 393:25 395:22 397:25 403:7 404:19 407:17 409:7 416:18 427:13 431:2 433:14 437:12 439:9 442:8 451:19 454:2 466:24 473:1,16 474:6 476:7 480:2 480:14 481:16,26 488:14,23,25 494:6 commented 233:15 commenters 211:20 250:20 260:15 302:11 308:25

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485:6 486:13 487:10,19 488:6,11 complexity 358:13 435:3 482:15 483:23 487:13

492:12 complicated 71:2 86:3 181:2 313:8 484:5 complimentary 19:21 385:7 403:2,3 compliments 34:12 component 68:26 181:6 248:13 315:6 353:3,4

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221:13 248:15 303:1,24 385:7 454:16 470:10 491:23 499:25 composition 77:12 379:11 compound48:18 63:7,8 102:4 110:15,17,24 111:12 112:12,14 113:22 118:24 137:11 141:21 164:17 182:24 203:8 207:18 224:12 257:12 270:17 272:14 288:16 292:4,6 305:3 306:1 307:8 308:2,7 309:18,20 321:22 322:3 326:24 328:21 329:10 349:3 350:22 353:7 360:23 368:14 371:26 388:10 406:3 424:20 425:1 444:25 445:3 452:15 452:18 457:3,18 compounds 5:21 6:11 30:26 38:14 42:12 45:10

48:6,12 64:19 84:1 100:17 101:26 102:9,11 103:6 111:15,25 112:24 113:8,20,26 114:4,15 116:1,4 118:10,15,23,25 119:4,11,17,17 120:3,26 122:6 123:3,22 125:21,25 126:3,5 133:12 136:4,10 171:26 172:7,8 181:3,24 184:26 185:2,4,9,26 186:13 192:9,14 202:21 205:5,5,6,24 208:11,14 208:15 216:17,23,25 217:3 220:14,16 228:1,6,11 228:13,16,19,20 230:23 231:5,7,9 263:12 270:9 270:15 271:12 272:13 273:16 280:10,12 281:1,19 288:6 293:6 304:25 305:5 307:24 308:10,19

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96:22 97:1,21 98:5,6 99:10,23 100:13,16 106:9 106:17 107:2,7,10,21,21 109:11,12,14,15 110:9 116:3,14 119:8 129:14 130:24,25 136:20 144:19 144:25 145:6,16 146:20 150:23 152:23 153:11 155:23 156:24 157:4,8,9,18 161:3 162:8 167:3 169:16 173:6,9,10,26 174:5,7,8,11,13,23,24 175:3,15 176:6 208:25 209:1 243:16 245:9 259:21 263:9 270:16 279:20,26 280:20,24,26 281:5 321:8 322:8,21 328:1 334:17 367:22 369:8 369:15,18 394:15,16,20 396:15,16 397:8,14 399:18,18 401:5,9 402:9,12 404:25 406:25 407:2 410:7,16 428:26 429:18 430:7 441:4 443:23 445:2 453:16 464:21 concentrations 33:2 62:10 63:19,26 109:7,21,26 118:7,7 141:20 148:25 150:16 151:19 157:21 158:7 162:24 163:7 168:5 169:9,23,25 171:22 172:6,20,26 173:16 175:23 176:11 273:3 279:19 280:9,15,16 307:7 310:11 324:1 325:4 349:18 369:7,7 370:11 397:10 401:11 411:13 429:13 453:18 487:4 concentration-response 105:10 concept 11:17 13:12 32:1,1 186:3 221:19 222:4

252:18 254:11,21 307:18 416:8 420:17 424:4 446:6 467:19,20 470:20 492:13 conception477:10 concepts 11:24,25 14:1 29:14 218:17,22 223:15,23

256:9 321:24 466:24 470:26 492:16 499:3 conceptual 55:4 56:17 66:14,24 221:21 252:7

302:25 311:7,15 490:22 conceptually 249:25 250:6 289:18 493:5 concern41:10 42:8 61:13 79:23 84:4 195:20 201:3

234:21 263:7 292:2 305:23 307:8,10 316:25 323:10 341:2 358:17 435:13 441:18 450:2 452:5 458:13 463:22 468:11 concerned 120:2 129:5 169:5 185:3 291:14 314:3

320:16 445:24 461:11 concerning 124:7 135:23 244:3,3 363:17 390:4 concerns 152:25 241:7 242:4,17 253:12 256:4

354:6 concert476:17 concise 373:1 concisely472:16 conclude429:4 concluded4:18 235:7 concludes 225:11 233:5 302:11 489:8 conclusion73:4 234:26 278:25 289:5 296:13,15

301:16,24 307:16 331:12 conclusions 235:14 315:8 concordance 118:14,14 154:20 155:11,13 165:26 166:5 169:21 172:15 173:3 174:19 243:6 258:14 286:24,25 294:4,21 309:1,4,14 388:9,9 396:22,24 397:5 400:10,10,11 415:18 concordant 397:22 concrete 180:3 197:1 concur 316:11 concurrent 107:14 conditions 332:13 362:25 363:2 365:16 366:12 conduct5:25 247:26 251:10,14 379:21 385:24 conducted 145:18 306:25 307:3 432:1 conducting 395:26 CONFERENCE 1:13 confidence 15:16,22 23:26 24:2,2,3,4 25:4,6 61:22

89:26 106:11 107:24 139:6,8 159:17 178:4 179:9 184:23 193:17,22 216:11,20 217:3,5,20 218:1 219:18,19,24 220:10 222:7,11,21 223:17 249:3,5 250:16 251:15 252:1 253:21,26 255:3 310:26 319:2 329:11 330:25,26 336:15,18 337:7,8,9,12 337:17,21,23 342:24 343:1 344:2,8,13 346:23 347:26 352:1,5,8,11,16,23 356:23 358:22 360:22 381:26 399:14 409:23,26 410:2 417:21 437:15 453:20 456:18 467:14 confidences 90:16 317:9 346:24 347:5 494:1 confident49:22 81:22 85:26 178:17,19 179:10,13 195:7 196:5 244:5 355:10 357:8,8,9,11 401:19

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www.transcriptionetc.com Page 21 429:14 configured 337:20 confines478:18 confirm 35:10 51:13,23 263:4 confirmation 113:16 135:3 137:20 147:13,26 148:14 493:25 confirmatory453:6 confirmed 28:26 397:21 448:5 455:4 confirms 460:17 Conflict 3:15 conformational 210:6 confound 272:12 435:14 confounding 58:16 401:6 confused 171:21 231:23 239:15 356:14 378:16

409:10 419:3,16 422:4 confusing 93:24 410:19 confusion 54:7 316:7 322:24 351:18 414:8 424:10

427:4 500:7 congratulate490:20 congratulated 335:10 Congress 297:18 298:8,8,9,15 conjugate40:19 41:6,7,19,23 42:2,19 conjunction416:16 420:5 Connect389:17 connection 254:24 connectivity415:17 465:26 connects 13:18 consensus 136:16,18 233:1 371:6 consensus-based452:25 consequence 95:16 97:3 177:5 337:10 consequences 55:24 343:12 452:5 conservative 106:20,20 259:1,2,5 286:1 324:7

369:14 397:26 434:16 conserved 18:26 consider2:6 34:19 44:1 90:3 124:11 138:25 237:25

248:14 250:10 252:23,26 261:10 262:8,14 277:18 279:2 304:4 310:10 318:23 319:8 325:7 328:22 330:6 335:24 349:21 352:13 356:20 397:24 398:13 403:17 405:5 411:17 433:5,25 436:5 476:16 477:17 482:12 considerable 313:2 328:23 363:16 366:6 368:20

373:6 consideration 31:18 128:8,25 129:6,14 170:4

202:9 280:7 306:6,14 311:21 312:20 318:12 319:26 327:5 328:18 331:14 346:4 351:3 352:5 444:4,13 448:14 459:13 471:18 487:18 488:10 considerations 25:9 237:22 240:12 265:8,8 303:5

307:1 312:18 333:11 335:19 375:5 395:26 considered 87:10 164:13 209:26 238:15 276:9,21

285:26 292:18 310:7,21 311:10 312:16 318:20 319:25 320:6 323:6,23 324:5,9 328:16 337:26 346:5 369:19 382:15,22 385:23 386:13 387:9,18 397:20 442:4 460:19 considering4:21 35:18 37:22 51:8 185:11 188:19

277:18 305:19 428:22 453:3 consistence470:4 consistency 135:18 180:17 262:21 365:11 consistent 32:25 56:23 130:18 140:4 185:23

275:25 278:12 279:1 305:14 335:11 339:9 372:17 407:4 436:4 488:9 consistently 136:22,23 consists 4:19 312:6 consortium 282:19 constant40:16,24,26,26 41:1,3,4,5 324:3 413:6 constants 41:21 constituents 368:1,2 constituted 323:3 constitutes 75:19 constrain462:12 constraints 330:19 359:8 367:26 368:1 418:13,14

418:14 437:3 450:25 459:16 492:2 constrictive 166:19 construct 100:22 251:5 310:19 439:19 constructed 394:24 construction466:5 constructive496:21 consultant264:10 consultation 3:20 47:17 483:12 consulting 283:3 consume 238:11 contact4:1,15 contacts 500:24 contain 352:17 415:5 422:14 435:13 contained 68:14 335:3 containing 80:3 contains 69:11 189:22 234:2 421:22,22,23 contaminants 21:4 299:2 contaminate 32:26 contamination 113:5 209:8 content 141:5 340:18 408:3 context 11:10 12:21 15:12 16:13 48:25 54:26 56:17

58:3,8,10 60:7,9 91:19,20 92:17,17,18 123:19,21 126:5 130:11 140:2,5 142:22 144:12 146:15 182:4,25 183:10 185:6,13 215:25 216:13 222:6 255:18 270:8 278:19 297:10 310:19 313:20 315:26 320:20 322:7 329:22 332:3 347:17 348:5 348:17,20 349:10 355:22 361:4 364:7 369:9 388:8 396:11,14 399:10 401:23 402:10,14 412:11 415:17 451:18 458:7 462:19 463:12 467:8 468:15 468:19,25 470:11 478:2 484:4 492:10,14 499:5

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www.transcriptionetc.com Page 22 contexts 25:25 123:9 contextualize 312:3 491:19 continually 270:7 continue 14:20 67:21 76:21 165:14 204:8,14 224:5

271:3 272:3 277:21 315:26 414:23,26 415:1 475:1 continued 14:25 415:1 continues 62:26 230:14 338:18 359:17 continuing 21:7 165:10 188:4 continuity407:7 continuous 332:5 continuum420:19 contract93:20 229:9,11,12 230:22 305:4 418:2 contractor 279:23 383:2 contractors 383:7 500:17 contrary 286:22 contrast281:22 338:10 contribute 143:11 343:15 351:5 493:22 contributed 2:21 167:2 500:1 contributes 343:9 453:1 contribution 250:12 control 62:8,13 91:16 93:19 96:13 126:19,23 206:2

241:4 369:22 372:5 373:4,5,7 374:15,15,16 383:13 405:25 413:2 471:23 486:15 controlled 376:1 440:15 controls 126:25,25 207:1 413:9,11 462:22 convened 304:16 convening 247:16 conversation48:22 428:25 conversations 225:26 conversion 164:16 converted458:3 convince 139:7 convinced 358:11 cooperativity 181:9,11,21 182:26 183:2 coordinate 388:13 coordinately 383:15 coordination 3:19,20 16:16,17 147:2 499:26 copy 239:8 copying496:14 core412:26 480:20,25 481:4,5 484:18 corner 142:20 151:16 Corps 7:21 214:19 391:18 correct 84:6 123:10 129:12 132:13,15,15,15 134:25 137:23,24 140:15 164:18 168:1,9,12,17 168:17,18 179:2 185:21 186:16 244:1 329:23 339:20 341:14 404:19 correctly 86:21 112:10 234:10 288:15 296:20

297:8,11 344:11 367:19 426:2 448:4 correlated442:25 correlates 24:13 correlation 30:20 65:17 66:1 129:19 194:2 280:13

280:22 281:3,8,9 286:23 correlational 254:18 corresponding 115:2,14 corrosive 32:11 40:4,12 43:11,17,20,24 44:3 50:25

217:15 265:14,18 320:5 327:14 corrosively 265:16 corrosivity 32:17 81:14 305:3 330:6 corticosterone 117:13,14 Cosmetic 18:9 cost 31:15 231:4,5 418:8 cough 236:19 Council 247:8 296:4 Counsel 3:21 Counselm 283:1 count 136:17 270:1 countries 238:23 261:26 counts 104:26,26 couple 11:25 25:9 41:24 43:18 74:21 78:26 84:8

85:8 86:14 89:1 102:13 115:9,15,16 126:15 132:9 138:21 145:22 150:3 151:2 152:16,17 161:19 175:10 203:3 210:19 220:16 229:14,18 237:6,9 237:10 283:20 284:25 300:7 307:9 354:12 388:6 389:21 392:8 400:26 419:10 426:11 433:3 462:9 473:22 480:4 489:25 course 10:1 12:4 16:21 17:4,8 18:25 30:12 87:24 144:16 190:5 207:15 211:9 235:15 237:5 238:19 248:11 249:9 250:3 251:8 252:3,8 253:24 255:14 256:16 262:11 263:25 265:22 294:6 308:3 309:23 312:14 317:14 330:8,23 340:26 408:3 433:21 459:3 460:18 461:17 475:8 478:19 480:18 481:4 484:7 492:2 covalent 325:11 cover 34:7,22 53:1,17 54:11,17,20 59:10,15 65:11

74:12 77:19,22,24 79:1,5,13 82:23 143:15,16 188:16,18 189:10 191:19 193:6 196:4,10 198:7 205:1 220:8 240:16 338:15 364:18 403:8 438:14 439:10 441:2 446:20,26 468:24 474:19 480:26 486:25 coverage 33:12,15,24 34:6,25 35:21 53:10 68:19

73:25 74:11 75:4,16 77:3,5,10 78:17,19,20 79:3,7 79:7,10,14,15,16 82:7 83:1 84:5 190:23,23 191:22 192:11,18,21 194:1 196:7 197:16,23 201:26 202:2,6,26 204:7 220:7 223:19 335:7 336:4,6,7 340:4 393:15 435:24 458:20 481:4 483:8 covered 39:23 59:17 61:15 62:1 65:11 78:25 82:25 191:20 200:9 204:3 208:2 222:16 336:11 342:9 342:14 433:17 481:6 490:9

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www.transcriptionetc.com Page 23 covering 19:22,24 36:21 39:15 82:10 94:6 193:12

240:20 396:8 399:11 433:16 453:19 covers 54:14 83:10 188:21 190:26 203:22 324:14 cow401:24 co-activation484:18 co-chair 225:2 co-culturing461:7 CPU 227:16 crack 209:10 402:22 crazy209:18 create 99:13 177:4 425:12 created 237:4 394:23 creates 308:14 creating 273:2 credence 197:16 Creosol 265:17 cresol 81:10 crisp 411:26 criteria 14:3 15:14 29:17 32:8,9,17,24 37:16 39:13

40:15 41:17 43:10 44:15,25 45:3 46:11,19,21 47:14 49:13 68:21 74:15 117:3 121:24 135:23 147:2 166:22 196:26 217:17 226:26 231:3 270:6 278:5,17 285:3,12 288:2 290:2 292:10 293:9 311:6 319:2,3,9 320:2,10,14 321:13,17 322:7,13 323:14 324:10 325:1 327:11,16,22 328:22 329:15 329:21,24 330:6,18,23,26 350:22 354:26 365:22 366:19,25 373:5 399:1 449:14 criterion42:15 43:6,6,9 44:11 47:13 50:2 321:20

331:1 critical 3:12 20:21,22 21:13 22:2,17 24:1 25:20,25

26:7 28:20,25 34:9 50:8 51:22 182:18 246:19 265:11 270:21 271:22 272:2 275:22,22 305:25 309:5 310:4 311:11 332:8,10 362:2 376:3 383:22 386:26 398:18 404:5,16 416:15 449:25 464:21 469:23 471:14,20 494:19 criticisms 365:25 Cronin7:1,1 45:7,8,8,18,18,21 214:10,10 319:13

319:14,14 331:18,24 341:22,23,24 347:7,8,8 391:9,9 438:4,5 442:7,8 454:1,2 480:1,2 488:13 488:14 489:25 Crop 279:12 CropLife 247:20 260:5 275:16 279:4 282:25 CropScience 260:2 279:15 cross 126:18 133:26 209:7 321:8 339:5 399:2

413:26 490:16 crosstalk 128:16,19 cross-agency 16:7 17:2 93:9 crowds 302:13 466:15 crude 256:18,19 CRYSTAL 1:15 crystalized 210:2 crystallization 392:20 culture 162:10 cultured 368:9 465:25 cultures 367:25 463:5 curation 346:15 curious 87:5 89:5 90:2 136:2 138:1 141:1,5 177:2 177:7 179:18,22 225:21 270:2 273:12 294:3,9 current 17:17 19:15 23:16 27:8,22 28:7 29:12

31:21 44:11 204:3 233:20 235:2,5 237:2,8,20 239:6 242:7 244:2 289:21 298:21 299:9 314:6 324:10 330:2 335:13 337:26 338:10 340:2,9 346:2,7,12 372:18 422:25 424:14 457:4 480:18 481:7 488:18 currently 20:5,13 87:16 187:8 230:10 238:10

242:18 311:7 314:4 338:17 340:6,8 364:20 373:23 403:22,23 422:20 446:14 447:19 457:12 465:12 480:6 486:21 curve 64:6 97:24 105:12,13,22 106:3 107:4,8,13,18 109:13 110:3 119:9 138:26 139:3 146:1,2,9,10,19 148:3,3 150:10 151:14,19 153:5,8,16,16 158:7,10 158:13,14 160:11,16,17 161:4 177:6 293:8 372:8 373:20 402:12 410:7 430:22 curves 62:9 104:7,8 113:1 146:3,4,6 150:8,8,19,24 151:13 152:2,11 154:3 160:26 161:2,5,25 163:9 163:11 173:24 177:1 179:18 369:21 410:16 429:9 curve-fitting 104:4 custom 131:8 customary 390:15 489:9 cut 14:11 75:10 100:23 145:14 174:13 200:15

257:25 258:26 259:7,23 266:24 268:11,16,23 278:4,17 305:6 319:2,7 321:25 325:1 329:12 369:8 437:18 cutoff44:6 50:16 51:2 65:7 66:21 67:26 68:25

89:25 106:14,18 107:17,22 150:10,17,25 151:12 151:17,20 152:12,15 153:7 154:22,23,23 155:16 155:23 157:4 173:9,26 174:22 175:15 177:23 199:3 256:15 327:24 332:2 407:3 452:9 cutoffs 36:4 45:25 50:18,19,23 167:3 255:24

321:19 322:10 324:2,14 326:3,20 329:20 330:2 331:3,8,10,12 369:8 384:24 409:22 429:10 430:8 cuts469:25 cutting 239:5 481:2 cycle 200:6 415:1 cyclers 325:12 cyclic 83:19 201:2 459:1 460:2,9 cyclohexane 200:1 cyclohexanol 85:9,11 Cyclohexanones 199:8 200:12 cysteric459:16

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www.transcriptionetc.com Page 24 cytochrome461:14 cytosol 65:15,16 145:4 162:21,23 171:1 180:16 cytosolic 64:2 65:20,25 66:4 145:4 148:19,19 149:19 167:25 170:20,22 171:8 238:10 448:25 449:11 467:9 cytostatic 112:24 cytotoxic 109:5 110:15 112:24 cytotoxicity 61:24 98:10,11 107:15 108:4 109:5 110:12,17,21 128:23 133:12,12 145:17 156:25 162:9 272:8,9,11 334:17 365:22 397:4 440:16 C-H-E-M-B-L 346:6 D d 369:6 373:19 daltons 32:3 Dan2:14 5:8,17 87:3 213:16,22 260:4 390:17 dancing 253:23 Daniel 5:10 8:17 9:12 16:14 37:4,24 38:19,25 45:6

46:7 47:7 48:3 49:9,24 50:13 51:5 52:12,18 83:12 84:21 85:13 87:3,20 88:4 91:2,6 122:13,25 123:2 123:26 124:5,12 125:17 126:11 127:26 130:20,26 132:17 133:14 134:10,22 135:21 137:4,16 139:13 139:24 140:13,18 141:13 142:8,13 167:19 170:7 171:17 175:8 176:12,22 180:25 183:15 185:15 186:22 188:8 204:17 205:21 206:7,11 208:8 209:9,24 210:17,24 211:5 213:17 215:3 225:8 227:24 229:5,16 230:19 231:10,20 232:20 233:4 235:19,22 239:10 246:26 247:6 253:2 258:24 259:26 263:14 264:3 269:17 274:14,26 279:7,11 282:8,11 290:10 293:18 295:24 302:7,10,17 303:11 307:21 310:13 313:5 316:9,21 317:19 318:7,14,25 319:12 331:18,22,25 332:14,18,24 333:14,24 339:13,16,20,24 341:21,25 342:2,5,8 342:15,19 343:2 344:18,21 345:26 347:7,10 349:25 350:10,12 351:21 352:19 353:12 354:8 356:16 357:6,15 358:4,21 360:15 361:10,25 362:9 363:5 369:24 370:2,4,6,17,21 371:12,15,19 372:11 374:3,6,8,10,12,17 375:1,7,15,23 376:7,9 376:11,17 377:4,14 380:8,12,14,16,19,22,24 381:7 382:10,20 383:25 384:1,3,5,7,10,12,16,26 388:2,4,17,19,21,24,26 389:7,19 390:12,14 392:3 393:21 394:4 395:1,5,7,9,11,13,20 396:2 399:26 400:3,5,17,20,22 402:21 404:2,17 405:10 406:4 406:19 407:8,14 408:17 410:22 411:1 412:3,13 416:23,26 417:2,4,7,16,25 418:6,21 419:9,20 421:4 422:17 423:2,11,14,17 424:6 425:15 426:21 427:12 428:17 430:16,19,24 431:12,24 432:5,20 433:2,18 438:3,6,8,18,21,23 439:4,12 442:7,9,12,14 445:16,18,26 446:8 447:16 448:22 449:21 451:3,7,13,26 454:1,3,6,22 455:14,16,22 456:2 462:7 463:26 464:26 466:9,13,21 467:3 472:19,23,25 473:17 474:5,21 475:19 476:5,19 476:26 477:7 478:26 479:3,24 480:1,3,12 481:8 481:10,15,25 482:4,9 486:4,6,8 488:13,15,22,24 488:26 489:7 490:18 491:5 492:3 493:9,11 494:5 494:7,15 495:7 499:14 Danish474:3,3 data 15:16 20:2,8 21:13 22:1,16 23:2,15 27:14,15

27:18 28:17 31:3,5 33:1 34:19,26 43:20 54:17,20 60:5 62:1 66:7 83:7,10 86:9 90:3 91:11,14,15,17 91:21 92:1,2,3,4,10,10,13,16,17,23 93:6,19 96:13 97:4 98:24,25 103:16,19,20,21,24 104:5,6,10,13 104:13,14,15,16,17,18,19,21,24 105:2,7,16,26 106:1,1 108:4,4,8,13,13 111:23,23,26 113:6 114:12 115:2,2 117:5 119:1 120:6,10 121:7,10,11 121:12,17 122:5,6,9 124:10,23,25 125:8,16 127:23 129:8,10 130:18 134:18 137:7,8 138:21 142:19,24 143:7,12 144:4 152:6 155:20 158:2 161:21 162:25,26 166:24 167:5,7 168:4 171:6 173:13,14,19,23,24 174:17,26 175:6 176:4 177:16,17 178:8,9,9,12 183:21 188:21 189:2,3,3 189:24,26 190:6,19,24,26 191:1,11,12,13,20,21 191:21,25 192:12,24 194:26 195:1,5 198:6 202:3 203:14 206:4,5,5 209:3 220:4,15,21,25 222:19 224:13 227:1 232:15,16 233:1 234:3,17 240:5 242:5,6,10 243:14,21,21 244:15 245:21,22,24 251:5,8,24 252:2 254:13,14 255:18 256:17,26 257:1,5 259:5,24 260:24 261:25 262:1,14,14,17 263:10,12 266:24 267:7,15,24 269:14 270:14 271:11,15,24 272:3,8,11 273:9,12,13,15,15,18,21 274:4 276:14,18 279:17 280:8 281:18,23 282:5 282:16,16 283:9,10,18,21,25,25 284:1,12,18,23 286:1,7,7 288:9,10,25,26 289:5,6,13,15 290:5 291:5 293:3,5 294:8,13,24 295:4,7,13,13 305:15 305:15 308:1,19 309:1,9 310:1 314:20 315:8 317:11,15 318:21,23 325:17 326:12,14,16,18,23 329:24 331:15 334:8 336:17 338:20 339:7 340:17 341:11,16,19 342:25 343:26 352:24 354:25 356:4 361:9 362:21,23,25 365:11,16 366:23 368:18 370:8 371:23,26 372:3,5,7,9,13,25 373:3,11,15 373:19,21,22 375:6,8 376:22,25 377:1,23 378:12 379:1,3 382:9,22 383:9,18,21,21,24 386:1 394:2 394:24 395:23,24 396:26 398:6 399:21 404:6,24 405:6,8 407:5 408:4 410:13 412:9,18,19,21,22,23 413:8,22,23 414:2,5,7,10,11,14,17,21,23,24 415:18,24 416:16,21 418:19,20 419:14,14,15,21 420:4,16 421:1,18 422:5,6,7,9,26 424:18,20,26 425:6,11,20,21 426:4,9 427:6,17 429:1,6,9,10 431:3,6 432:2,9,10,11,13,19 433:6 435:10,17 437:3 439:9 440:26 441:2,4,12,14 442:21,21 443:11 444:5 445:9 448:16,19 449:13,16,24

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364:7 dimerizes 95:12 dimers 133:6 diminishing 125:13 dioxin 369:2 direct5:23 43:1,7 46:12 48:7,11 245:9 249:14

423:4 445:13 447:11 462:17 463:22 480:20 487:25 500:24 directed 18:9 83:25 236:23 335:14 440:19 direction 68:18 246:23 248:25 493:19 495:12 directive 56:23,24 directly42:26 128:6 315:25 351:1 361:7 413:1

426:25 428:23 456:13 463:17 477:16 482:22 484:10 Director 6:24 7:16 9:14 16:15 263:21 directors 263:21 disadvantages459:3

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351:20 disruption7:23 8:7,10 9:8 16:23 245:19 268:21 345:4,13 359:10,22 364:20 468:8 469:13 470:15 471:16 487:3 disruptions 471:15 483:9 disruption-related 470:22 disruptor 1:4 2:8 18:20 122:2 386:25 482:19

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485:11,20 dissemination 262:10 dissociation40:16,24,26 41:3,4,5,8,21 distance464:16 distances 227:13 distinct278:19 320:26 385:7,14,20 distinction 37:16 distributed 80:8 500:4 distribution 188:25 189:18 305:4 distributions 336:7 diverse 56:26 65:16 185:26 304:11 338:7 380:3

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374:15,26 DNA 95:14,18,21 181:6,9,18,21 183:26 185:13 186:12 347:19,19,21,23 353:4,9 357:19,24 363:24 479:9,16,16,18 dockers 210:20 docket 1:7,9 4:8,16 139:1 211:12 213:8,8 261:15

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267:25 320:13 376:21 378:16 489:18 494:20 dodecane 337:19 dodecyl 177:15 dodecylbenzwnesulfonic 161:24 dodecyltrimethylammonium 43:25 doing 9:6,9 12:26 23:26 24:7 25:2 26:18 32:14

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96:4 182:5,14,14 291:16 343:9,16 364:23 404:11 445:10 446:17 477:12 dozens 105:6 dr2:1,14 5:8,10,22 6:2,8,13,19,23 7:1,5,11,15,20

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37:10,15,19 54:4 77:20 78:15,21 79:8,26 208:1 215:14 296:7 297:13,20,24,25 298:2,5 299:4,20 300:2 377:13 416:6 drive 1:15 215:21 281:8 304:5 driven 306:7 driver420:1 drivers 57:9,12 215:12 driving 190:15 191:4 226:24 drop 63:3 155:14 dropped 162:8,24 271:13,14 drove 394:18 DRP488:17 Drs 307:14 drug 8:4,7 18:8 drugs 6:17 430:9 ducks 225:15 due 27:16 73:6 74:5 152:15,24 153:11 172:6 173:15 175:2,5 321:9,11 322:19 327:12,26 394:14 409:22 410:16 Duluth 103:2 108:20 132:1 169:12 232:17 265:24 duration44:7 duty495:24 dwellers 75:26 dye43:5 dyes 138:24 dying409:14 dynamic 99:3 379:7 459:14 dysfunction478:4 Dysregulation 386:10 E earlier 53:12 57:3 66:19 78:17 95:25 101:23 110:12 127:9 131:2,19 132:10 137:17,20 144:11 144:19 145:7 148:5 152:19,25 158:9 162:16 165:25 167:13 168:14 169:24 171:7 176:26 180:11,12 187:3 191:17 208:13,16 216:5 259:20 277:6 278:2 285:2 289:26 294:7 295:3 308:4 313:12 335:16 339:9 351:25 371:7 377:19 383:6 386:6 387:24 399:6 418:11 424:11 436:4 443:5 444:9 493:20 earliest 312:22 early 30:19 66:8 88:19 135:5 215:13 302:13

304:16,26,26 310:20,23 370:12 377:17 403:9 420:22 458:12,15 471:26 485:19 500:25 earth 232:9 easier 296:14 361:23 easily 67:14 213:18 266:21 290:3 364:25 444:6

456:14 easy70:23 127:22 272:23 487:12 EC 257:2,6 ECCVAM98:20 114:25 134:6 243:2 echo 310:15 313:6 389:3 495:4 eclectic 267:6

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eco 265:7 ecological 20:14 263:8 Ecology 142:15 ecosystem 5:26 ecotoxicologist 5:25 ecotoxicology 25:11 ECVAM 244:24 Ed7:20 214:18 227:26 307:20 391:17 400:7,13

404:4 407:10 464:3 edge 187:24 481:2 EDP 390:4 EDSP 17:26 20:21 22:4 23:8,10 26:19 31:25 33:26

34:7 35:9 37:17 39:10 54:2 76:14 77:3 197:9,25 198:14,18 202:20 203:7,26 213:6 232:4 233:21 234:20 235:1,2,5,7 236:4 247:25 248:2,4 254:23 254:26 255:1 274:3 280:17 281:6 282:6,16,20,24 288:26 290:13 291:2,17,26 292:4 294:13 295:13 296:21 298:7 299:11,26 300:9,10,12 301:17,26 302:25 303:6 309:1,4,13,15 310:6 320:2 322:16 377:11 378:8 390:4 496:25 EDSPR279:25 EDSP21 21:9 24:16 36:25 EDSTAC 18:20 28:10,15 32:1 38:11 39:24 320:23 EDSTAC's 20:22 educated 240:19 educating 356:17 EdWARD7:20 214:18 227:26 232:21 313:6 332:1

361:26 369:26 374:5 375:16 380:17 384:6 388:20 391:17 394:6 396:3 402:23 406:5 412:14 418:7 419:23 425:17 426:24 438:22 445:17 449:22 455:15 462:8 472:26 479:26 486:9 492:4 effect41:15 53:12 57:19 60:18 63:23 72:17,21 73:1

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32:12 55:5 56:8,26 57:10,26 64:17 159:1 167:3,4 234:7 252:15 257:18 299:8 314:3,23 362:5 364:23 368:11 385:10 386:12,18 392:22 399:16 403:7 406:2,10,13 412:18 415:16 416:15,21 420:22 423:13,19 424:9 425:14 436:23 437:1 445:8 449:26 463:12 468:1 471:16 473:9,13 485:20 486:15 488:4,11 498:14,15 effects-based 29:15 35:19 60:7,8 82:20 236:15 394:24 403:17 416:9 418:20 419:12,21 423:15 effects-driven415:21 effect-based 333:6 334:6 403:19 421:15 424:16

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98:23 136:26 209:21 210:23 223:3 225:2 236:5,8 246:8 303:25 304:5 310:16 313:17 314:7 354:19 398:19 418:12 420:16 434:11 437:13 471:9 481:5 483:7 490:23 efforts 9:18,26 10:13 14:13,25 21:7 28:13 159:26

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349:20 350:25 388:7 EREs 182:6 350:19 405:18 ERG454:14 Erich 214:6,6 err 127:18 error287:14,19 442:25,26 errors 352:8 442:25 443:1 ERs 123:6 343:16 ERTA 135:8 ER-alpha 144:9,10,22,23 151:7 153:4,9 160:15 163:24,25 334:20 ER-focused477:3 ER-interaction 60:13 ER-led 359:22 ER-mediated 82:21 468:5 ER-related470:14 ES 243:26 323:14 335:3 337:7 338:1,1 339:2 340:2

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46:25 47:3,24 48:10,10 49:15 56:14 57:7 60:10 60:16 88:26 89:7,14 145:9 171:14 182:4 217:18 219:25 220:12 223:4 224:23 239:25 246:9 252:8 252:9,14 254:2,9 255:8,11 289:19 305:12,17 306:2 316:17 319:5 321:9,14 322:17 343:9 344:25 345:3,8 355:7 365:4 399:6 435:3 456:1 466:26 468:7 469:21,22 482:13 483:14,18 484:15 484:16 485:3 486:25 490:7 molecule41:16 138:2,3 178:25 444:7,18 molecules 87:8 308:16,17 424:15 474:14 moment294:1 300:10 314:6,14 326:7 344:7

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73:2,26 74:4,5,25 75:2 84:13 87:24 90:11 115:26

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215:6 303:22 334:4 over-fitting412:18 ovotestes 55:21 owed 196:20 oxidase 387:5 oxidation 324:18 460:2,12 oxidative 109:19 127:8 oxidizing44:4 oxygen 61:4 74:18 226:6 o'clock 389:13,17 P pace 27:8,22 28:3,23 Pacific 6:25 214:8 391:8 package414:16,16 packages 266:16 375:13 page 50:2 241:19 274:23 299:23 338:6,9,13 366:3

436:10 PAH 379:23 paid 257:10 pair76:18 119:10 403:9 paired461:4 pairs 104:7 Pandora's427:23 panel 1:1 2:4,11,15,16,16,23,24,25 3:4,7,14 4:2,5

4:13,25 5:4,8,14 6:1,7,12,18,22 10:12,17 16:20

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234:9 238:20 245:16 246:15 249:21,23 250:3,11 252:6 256:22 271:12 272:25 273:6 274:8 275:21 277:26 280:6,7,23 284:11,16 299:17,23 305:8 306:4 330:14 338:19 465:5,11,15 470:11 500:6 papers 206:3 227:9 429:11 441:6 490:22 Paperwork233:24 para 165:21 199:18 parabens 226:10 359:5 paradigm423:6 428:1 471:10 490:21 paragraph 319:23 329:5 parallel 146:10 339:8 425:8 442:19 444:1 446:6

481:26 492:17 parameter40:17 106:4,12,13 107:12 162:20

318:21 319:7 329:7 parameters 48:24 49:4 59:3,4 107:24,24 127:17 136:13 144:7 157:2 251:5 311:25 312:22,24 322:4 324:12 329:10 372:4,26 386:19 435:19 paraprotein484:21 paraquat200:19 para-cresol 81:11 para-substituted 194:5 parent 63:7,8 145:20 149:18,23 161:11 163:21 164:13 167:11 207:18 224:12 457:3,17 464:6,24 parenthetical 303:23 park 257:8,19 parse 68:13 76:8 78:8,23 parsed 68:18 part 6:4 20:23 57:21 65:7 87:9 104:11 113:19 129:25 132:24 138:17 165:19 177:19 178:3,12 182:19 190:20 195:4 198:9 206:13 210:9 232:17 241:18 266:2 275:22 293:14 315:9 330:23 345:17 357:21 366:11 395:17 401:5 408:13 410:8,18 416:21 419:23 420:12,16 421:1 427:8 447:22 469:12 470:23 478:7 partial 113:10 116:12 135:26 136:4,26 137:9 164:1 partially454:26 participant 3:17 participating 10:8 participation4:26 218:4 particular 24:9 33:11,14 38:4 46:16 47:2,5,5,13

50:19 56:22 67:20 68:24 69:18 75:9 89:23 90:1,4 90:26 112:12 131:14,18 135:25 141:18 184:6 196:25 215:8 271:7 303:8 305:13 317:26 324:12 327:2 336:25 337:2 338:5 342:12 344:26 347:6 350:6 353:24,26 358:14 361:21 389:9 392:23 406:3 434:26 436:9 444:19 449:9,10 460:1 particularly 5:21 43:14 100:6,10 101:11 208:14

321:6 328:8 333:9 344:9 356:21,21 378:5,7,12 432:18 436:2 464:5 473:23 474:1 490:4 particulate421:19 parties 282:21 partly 131:26 132:2 135:15 partners 121:9 126:2 134:5 parts 12:13 189:11 261:26 262:12 357:20 425:9

480:25 481:3 party 366:9 pass 38:3 137:23 289:25 330:26 338:23 340:12

435:26 passing 315:16 463:18 passive 321:8 Pat35:1 52:21 66:20 67:7 74:15 76:23,24 84:20

86:7,7 88:12,12 89:9 90:5,5,18 144:11 145:6 148:16 158:9 179:4,6,6 180:4,4 185:18 188:14 197:10 205:11 206:20,20 208:18 210:4 225:24 227:7,22 228:22 229:19,23 231:25 232:2,18 path 90:2 346:3 398:24 443:15 477:9 pathway7:9 13:13,13,16,20,25 15:18,20,21,23,24 18:3 19:24 29:4,23,25 30:2 36:6 42:10 46:13,22 47:22 48:5,14,21 49:13 52:23,26 54:26 55:10,11

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92:12,14 95:6 96:9,12 129:25 185:21 186:4 223:22 225:1 239:3 242:18 246:5 250:12 255:10 255:14 269:26 305:22 307:20 329:25 343:23 346:11,18 349:19 350:2,26 351:5,11 353:14,17 353:23 360:14 385:15 386:22,26 387:26 469:2 470:14,16 471:21 472:7 474:2 476:18 478:6,15 484:5 485:4,5,16 492:15,26 497:15 pathway-based493:6 Patlewicz 234:9 PATRICIA 225:24 228:22 229:22,26 232:18

430:26 432:4,7 447:17 pattern 164:23 407:17 408:5 pattern-matching466:3 Pat's 85:4 PBDE465:22 PBPK463:16 464:12 PCBs 368:25 peaks 349:8 peer 3:1 11:2,5 29:10 218:2,3 235:2,6 238:22 251:7

252:3 257:5 260:12,14 261:1 273:21 290:8 peer-review 10:21,22,23 pejorative 296:19 pentachlorophenol 201:8 pentylaniline 157:13,15 pentyl-cyclohexanone 154:1 people 5:6 9:24 97:10 98:23 103:23 104:18 116:25 134:6 209:16,22 211:18 212:1 216:12 224:1 238:19 289:23 297:26,26 301:1,13 313:10,17 315:25 350:15 354:20 412:20 414:18 421:9 473:2 487:1 peptide46:12,18 47:6 475:26 peptides 318:4 perceived 74:17 337:10 percent 35:4 42:16,26 43:2,8,9 51:2,3,14,15 65:12

78:13,15,17,19,21,22,24 79:3,7,7,10,19,20,21,22 79:26 80:22 82:14,25 102:20,20,21,22,24 103:15 105:8 106:10 107:7,11 113:10,11,11,14,15 118:16,18 125:10,21 126:3,4,8,16 133:7,8 146:6 146:7 147:22 154:19 155:3,6 166:6,16,20,21 169:21,22 171:25 172:1,2,2 174:18,18,24,24,25 189:23,25,25 190:1,8,9,9,15,16,18,19,22,23,25 191:2,12,12,13 192:10,17,18,19,19,21,22 198:6 204:5,7 208:3 222:20 228:3,15,16,19 229:20,24 230:1 231:4,23,24 232:10,12 264:23 274:9,9 281:4 282:22 286:19,25 287:7 288:10,26 289:13 291:25 293:10 309:11 314:4 319:11 328:12,14,20 329:11 334:21,22 357:18,19,20,21,26 369:1 378:13 401:17 409:26 418:4,15 429:1 435:22 452:9 457:17 percentage79:25 82:14 83:1 180:2,22 192:9

228:24 229:4 percentages 82:10 191:4 336:5 perception 328:5 Percival 500:16 perfect75:23,24 121:21 122:7 127:23 225:5 324:7

413:16 448:8 473:4 perfectly 220:19 perflourinated 81:4 perfluorinated 81:3 83:26 459:19 perfluoro 192:14 202:21 203:5,8 perform 147:14 197:6 275:23 307:13 366:7 384:21 performance 22:18 92:8 114:22 135:2,20 220:10

243:18 246:2,14 251:6,9 267:14,22 271:23 272:1 272:7 275:7,12,12,20 276:24 277:3,19,25 278:10 278:11,13,21 279:1 331:3,4 338:21 339:2 365:23 366:8,9 377:8,22 378:19,22,25 379:8 382:14 383:8,10,19 384:20 385:22 386:2 388:7 394:1,8 420:11 439:7 441:20 453:14 498:5 performances 326:26 performance-based 245:3,5 413:1 performed 168:21 285:10 286:12 330:24 393:26

398:5 413:2 performing470:21 performs 386:1 period4:2 94:19 124:1 185:17 202:11 209:2

213:12 233:7 261:4,4,12 265:11 302:12 359:23 371:15 periodically 11:9 Perkins 7:20,20 214:18,18 227:26,26 232:20,21

307:14 313:5,6 331:26 332:1 361:25,26 369:25 369:26 374:4,5 375:15,16 380:16,17 384:5,6 388:19,20 391:17,17 394:5,6 396:2,3 401:8 402:21,23 405:11 406:4,5 412:13,14 418:6,7,23 419:23 425:15,17 426:24 438:21,22 445:16,17 449:21,22 455:14,15 462:7,8 465:2 472:25,26 479:25,26 486:8,9 492:3,4 permanent2:15 6:1,7,12,18,22 468:6

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www.transcriptionetc.com Page 67 permanganate44:3 permeability 368:8 permit485:5 permits 373:1,5 peroxidases 461:8 peroxyoctanoic44:9 person 134:21 232:8 249:22 333:21 495:11 496:26 personal 226:1 490:10 495:8 personally 242:26 403:6 414:9 500:23 perspective 75:13 89:4 208:4 241:8,8 242:1 271:18

280:2 291:9 312:3,15,19,23 347:14 359:12 360:9 361:13 393:6 421:7 422:19 491:19 pertain 238:4 443:7,20 pertaining 200:10 perturb 95:26 412:1 perturbations 48:20 272:15 perturbed 354:17 perturbing467:1 pesticide 7:13 9:14 10:12 16:4 17:3 20:2 27:13

31:1 37:13 230:10,17 231:17 237:18 263:21 297:14,16 499:25 pesticides 3:1 334:8 pesticide-related 3:2 PFOA 202:16,18 203:10 488:2 PFOS 202:16,18 203:10 488:2 pH40:11 41:14 42:6,9,13,16 43:1,2,9 44:22,24

45:14 50:22,23,24,25,25,26 71:20,20,21 156:25 162:20,23,24 163:1,3,14,19 167:3 201:12 318:5 328:13,16,17 350:7 Pharma214:23 pharmaceutical 321:6,10 386:5 pharmaceuticals 8:2 321:26 391:22 pharmacological 256:7 257:7 pharmacologist 6:20 pharmacology7:26 8:3 253:4 338:24 436:1 Pharmacy7:2 phase7:8 24:17,22,26 25:1,16 93:26 94:1,1 135:9

245:22 281:17 282:5 283:26 461:19 462:15,16 483:25,25 phases 93:26 311:8 phenol70:26 71:6 72:6,18 160:6 421:23 phenolic 265:16 327:21 phenols 71:4,12 72:1,2 73:21 75:23 76:1 80:10

84:24 85:2,5 160:5 184:5 189:14 191:3 198:10 201:5,10,20 203:13 204:6 333:10 335:5 338:16 345:20 359:5 437:26 Phenolsulfonphthaleins 74:1 phenones 73:9,15 phenotypes487:12 philosophy 322:7 335:12 phone 38:26 39:2 212:7 389:16 phones 5:4 phonetic 387:6,21,22 481:17 phos 74:23 phosphatases 351:1 phosphoric41:24 43:21 74:14 phosphorus 74:19 photocopy 274:19 photon 104:26 phrase421:8 422:16 phrased493:20 phrases 297:23 pHs 75:9 phthalates 85:8,10 344:3 386:17 physical 32:15 36:2 48:1 101:25 236:12 264:14,15

265:23,25 266:23 312:10 314:11,18,22 317:23 350:4 356:4 406:9 421:15 424:24 429:26 430:4 435:13 437:24 444:18 447:9,12 493:21,23 496:13 499:7 physically 108:23 physicians 235:23,26 236:1 physico-chem 39:18 49:16 51:9,12,26 53:13 80:15

80:18 physico-chemical 29:13 31:19,26 39:7 45:25 47:11

228:7 320:24 327:16 368:12 403:20 415:22 physico-chemicals 306:12 physiologic 351:4,6 physiological 201:12 257:23 280:14 physiologically 257:23 281:26 physio-chemical 338:8 phys-chem 316:16 319:26 327:4 pH7 319:11 pick 137:14 155:20 157:11 188:16 195:2 197:21

205:2,5 254:7 258:2 288:19 349:1 355:4 356:11 367:1 405:18 425:7 picked 174:22 287:22 356:8 366:18,21 picking 118:9 155:18 358:18 366:19,24 404:9

407:15 408:4 464:12 474:8 picture 107:23 252:6 300:23 pie 196:18,18 piece 66:25 69:22 93:7 255:21 363:14 494:13 pieces490:2 pier235:4 pink72:12 73:13 165:16 195:23 pins495:20 pipeline 104:22 pit75:22 pitch475:22 pituitary 19:24 pK50:18

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42:19 75:1 162:15,16 163:18,18 178:9 201:11,22 264:20,20,21 318:22 319:9 326:5 327:5,8,20,21 328:23 437:26 pKas41:25 42:22,24,24 43:5 pKs 51:3 place 56:2 57:2 72:18 129:20 223:5 253:12 261:25

322:13 356:2 448:25 480:26 placed 275:9 323:4 329:16 469:11 places 222:6 324:24 464:15 plan 17:15,18 20:18 21:6,9,9,18,20,22 22:4,5,9,12

22:16,26 23:4,10,12,13,14,24 24:16 25:13 95:4 138:8 236:16 248:16 373:15,16 420:26 planes498:20 planned 22:13 108:17 260:23 371:3 plans 21:12 184:26 367:23 432:17 plasma 280:9 plastic459:14 plasticizers 386:12 plate 99:10,21,24 104:25 111:14,14,17,17 112:13 126:17,18,23,26 127:3 159:2 209:16 271:14,14 368:17 379:1 467:6 plates 94:23 99:12,25 102:26 103:14,14 104:26 105:3,9 111:11 112:4,4 114:3 126:21 161:5 209:16 368:17 platform 335:22 414:11 429:17,25 platforms 15:8 335:25 plausibility 55:17 305:12 486:1 plausible44:17 51:21 55:16 58:13 471:1 play71:17 75:20 88:2 155:24 167:3 169:16 187:23 191:19 203:9 230:11 256:13 398:20 482:26 495:2 played 156:2 playing 153:3 355:3 488:21 plays 154:12 157:3 187:16 please 3:24 4:1,16 5:3 17:25 39:1,2 122:26 134:22 187:1 206:17 213:10,14 236:18 247:4,6,18,23 264:6 275:14 302:18,24 303:2 319:5,8 329:5 333:4,10 342:23,26 352:22 360:21 362:16 371:23 372:2 374:26 377:6 382:13 384:17,19 389:5,6 390:1 393:24 395:22,25 412:10 432:26 433:5,12 433:13 439:5,9 451:14,19 455:24 466:23 plot 65:15 119:7,7 plots 97:21 107:2,25 109:10 280:9 336:8 372:3,25 plotting 119:14 plugged 84:18 plus 114:8 281:19 393:1 pluses 315:15 plus/minus 463:2 pocket225:26 226:2,4 432:13 point5:14 8:18 19:19 23:19 28:1 30:19 37:5 40:23 45:1 48:15 50:6 60:16 61:2,4,5 66:17 67:2,9,10 67:19 69:12 70:9,12 71:22 73:22 74:1,3,20 89:9 90:8 92:16 99:14 104:2 112:13 117:5 118:9 119:10 124:2 125:4,13 126:10 127:21 129:10 133:5,25 135:1 142:9 146:19 153:19 179:23 182:18 184:14 185:15 193:18 195:2,19 196:4 202:14 215:8 216:1,14 217:22 218:13 219:22 220:20 222:4,17 225:9,13 233:13 234:13 238:4 239:22 244:9,11,14 246:7 247:3 250:7 253:15 254:20,23,23 255:2,7 256:1,25 266:5,8 267:16 276:12,20 286:20,21 294:16 299:16 300:6,14 301:18 302:12 303:19 304:20 306:21,21,25 310:7 316:13,25 317:3,4,7 319:9 321:2 322:9,22 323:22 325:16 332:3,7,19 337:22 341:8,20 344:6,16 347:25 353:24,25 355:9,19,24 368:21 369:11 376:3 388:5 389:8,9 393:22 407:16 417:9 418:26 419:12 424:10,17,21 427:13 428:18 430:17,20 432:7 437:18 443:16 444:15 447:4 448:14 454:24 454:25 462:21 474:8 475:9 479:10 481:7 484:23 486:17,18,20 489:9 490:10 493:25 497:4 pointed 222:14 297:8 411:22 447:9 pointing 81:23 308:18 points 56:20 60:14,15,24 61:9 62:6 104:6 119:15 132:9 139:5 151:25 160:18 175:12 183:6 185:21 237:12 250:22 256:3 301:25 319:24 335:16 340:1 369:22 400:7 433:21 442:1 443:19,20 445:2,5 447:7 451:5 461:5 462:5 463:18 464:2 468:10 499:20,23 point's49:7 polar46:14 337:20 459:22 policy 3:6,20 16:16,17 263:18 264:2 269:2,6,7,13

282:17,19 283:4 289:18 296:10,12 300:11 499:26 Pollution 8:20 10:14 16:4 17:3 230:17 POLM480:9 polycyclic 205:24 polycyclics 206:4 polymer 32:3 46:19 322:24 polymeric46:10 318:5 polymers 40:2 46:14,15,24 47:1,5 70:9 78:2 318:2

322:15,16 323:18 polynuclear206:6 pool 33:23 poor 148:2 289:6 321:10 322:3 324:5 382:22

383:10 386:2 397:6 population 18:13 35:12 55:24 56:6 170:26 243:25

297:21,26 299:5 populations 18:16 portal 31:23 32:11 portion 84:14 portrayed 376:24

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330:20 335:26 possible 32:19 175:7 180:15 237:1 245:21 250:8

321:21 323:13 324:2 325:18 327:15 341:11,12 344:7 345:14,17 359:21 385:18 418:5 424:12 425:8,22 432:25 434:22 444:12 457:4 458:25 459:11,26 484:15,22 485:3,15 487:13 490:6 493:6 possibly 54:17 202:3 215:6 265:2 327:16 453:12

482:19 490:16 post249:19 posted 211:19 213:8 posting 211:18 post-MIE403:9 Potassium44:3 potatoes 68:20 potencies 220:14 259:16 334:23 377:10 378:4 potency 15:5 216:24 217:3 256:5,6,13,20,23,25 257:8,19,21,24 258:12 259:20 268:20,22 277:20 292:12 310:5 314:21 359:17 378:24 398:19 399:22 407:19 451:19 453:11,25 471:23 potent 15:3 88:17 97:12,18 119:12 130:9 137:9,11 141:26 152:18 184:9 216:19 314:25 potential 15:18 19:9 30:7 32:24 33:23 36:23 37:9

51:14 54:15,18,21 56:8,25 67:25 68:2,9 70:16 71:8,10 80:6,11,26 81:2 86:17 99:7 111:4 149:7 163:22 177:12 182:13,20 183:11 188:17 193:24 197:26 198:3 201:11 204:10 207:19 215:16 240:12 259:9 261:22 278:3 290:13 298:17 299:19 299:22 305:11,25 306:19 308:13 310:8,9,11 311:23,25 312:21,24 313:1 314:21 333:8 336:24 338:26 340:7 346:11 354:21 359:3 363:3 366:14 367:15 385:14 386:21,25 406:11,13,14,16 412:19 427:19 435:25 436:3,14 450:4 461:5,6 462:11,23 463:15,23 473:4 474:15 482:13 484:19 485:11 486:25 487:14 492:1 potentially 57:24,25 69:7 100:16 111:26 112:19 135:26 148:3 153:18 192:12 198:5 200:17 203:16 207:24 209:17 305:18 311:22 313:25 323:15 324:16 337:4 346:17 376:3 386:11,24 406:10 407:25 413:19 426:7,8 442:17 450:5 455:11 487:26 492:19,22 493:6 potentials 415:6 potently 129:26 POTOMAC 1:14 power238:1 259:16 441:14 447:13 448:13,19

482:17 powerful 320:15 powers 308:19 practical 102:13 103:4 125:11 208:10 306:21

417:26 450:25 459:5 practicality418:8 practically 241:15 practice 262:8 382:26 383:3 pragmatic 323:6 424:12 491:12 pragmatically 217:6 Prakash 6:13,14 128:1,24 129:11 171:18 172:5,18 173:12,18 174:15 175:1 214:4,4 350:14 380:20 384:8 388:22 391:3,4 395:10 400:19 417:5 489:21 preamble 370:25 372:12 precautionary 329:13 precedent9:1,9 precedents 85:1 precipitation 158:13,14 precise 321:25 328:16 500:21 precision 145:14

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457:14 prediction 33:9 90:10,16,17 122:10 249:4 250:17

251:16,22 274:9 288:22 289:9 308:8,24 310:20 336:15,18,20,21,22 337:9,10,12,13,18,25 344:13 381:26 415:2 458:26 461:11 predictions 46:2 59:1 61:12 78:16 79:18 82:6,13

96:6 233:26 234:6 284:21 286:22 293:26 294:11 294:13,13 305:21 317:10 326:24 337:7 341:17 351:25 352:6,10 356:24 381:20 385:21 415:3 458:16,19 predictive 7:2 8:3 28:18 89:26 216:21 219:9

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487:21 prescreened424:17,20 441:7 prescreening 319:25 441:9 prescriptive 26:11 presence 264:17 265:1 444:19 present 141:2 142:17 253:14 264:8,24 271:7 284:2

294:11 305:8 308:1 315:22 334:2 353:18 371:5 424:20 462:17 489:10 presentation 35:23 36:26 37:5 44:14 52:19 53:2

66:19 76:13 83:15,24 87:17 91:7 94:14 135:16 142:14 156:23 180:12 188:12,13 211:6,13 213:12 215:5 218:5 233:5 274:17,18 296:18,24 320:13 328:3 352:26 427:22 445:25 446:5 456:9 presentations 4:10,21 5:4 14:9,17 16:1,7 36:1

66:23 211:3,12,24,26 215:11 225:11,20 238:25 260:19 261:14 308:23 364:6 378:17 403:13,16 454:18 489:18,20 493:15 494:18 496:3,4 500:5 500:10,21 presented 52:19 131:5 195:5 213:7 215:18,22

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269:18 275:1 279:8 282:11 295:25 presenters 37:8 215:7 275:18 500:19 presenting 91:21,24 233:12 preset421:25 445:4 president 6:14 391:4 press 249:24 269:19 pressure 324:3 presumably 294:7 presuming 262:3 pretty77:22 79:9,15 81:22 85:21 89:18 97:18 99:4 111:24 130:8 139:5 166:5 179:10 180:10,22 181:1 191:4 195:17 207:3 226:7 228:13 239:26 243:1 297:15 376:20 382:10 401:25 405:1 421:2 464:1 465:6 prevent437:4 preventing 142:6 Prevention 8:21 10:14 16:4 17:3 230:17 previous 11:16,20 68:11 73:20 158:11 171:19

272:1 278:9 303:20 380:18 429:7 430:21 433:22 450:10 491:7 previously 3:25 32:14 322:23 452:3,26 pre-filter493:5 pre-screen 394:15 primarily 5:19 20:26 40:7,16 76:11 116:2 242:19

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408:22 453:2 prioritizing 1:4 2:7 5:12 9:19 28:8 33:26 61:14

213:5 254:3 302:25 314:1 315:16 398:21 435:2 448:9 priority 33:7 37:26 38:5,6,8,17 52:10 216:15

223:10,11 258:3 261:19,21 262:5,11 263:3,13 289:22 306:22 308:2 310:3,5,10 313:19 314:10 314:23 322:13 397:16 404:13 413:24 433:7 462:22 485:4 private 264:11 pro460:3 probabilities 245:14 probability 326:20,21 345:23 probable 115:21 336:19 375:10 419:26 457:2

471:2 probably 15:17,19 19:7 52:14 77:22 79:1 102:16 107:18 109:8 166:14 170:11 198:6 199:6 202:17 232:10 243:8 244:16 247:13 283:16 289:7 302:12 303:15,26 311:2 322:26 323:7 331:9,10 353:18 360:13 399:19 402:1 403:2 409:4,14,22 410:1 414:7 416:19 418:4 419:3 443:14 450:5 454:14 481:21 488:16 500:7 problem 25:12 85:19 111:9 113:25 125:10 133:11 138:7 142:5 176:20 181:2 186:9,20 273:5 292:20 301:9 314:13,17 348:8,8,11 359:25 368:24 398:24 403:11 406:7 418:10 431:13 449:23 462:14 487:15 problematic 314:9 349:13 problems 139:2 248:22 249:11 360:6,7 399:22

403:14 426:17 453:26 478:15 486:12 492:5,22 procedure 296:10 309:5 383:23 441:9 procedures 243:15,22 271:18 383:20,23 459:1 proceed 200:23 221:3 proceeded 235:8 process 10:16,21,22,23 11:2,6 17:23 26:17 29:19

30:5,23 31:4,11 35:6,8,17,24 40:23 50:8 51:8,23 76:21 91:26 93:18 98:21 104:9,14 108:1 110:7,9 115:21 145:23 176:25 178:23 179:1 181:4,14 182:6,21 188:5 225:23 230:7 232:11,11 236:10 237:19 238:21 240:10,18,23 241:8 242:10,14 244:6,26 245:4,10,17 246:7,13 253:19 255:18 256:11 263:13 265:4 271:17,20 273:1 284:7 303:3,22 304:17,21,26 306:6 313:8 317:13 323:5 338:18 353:4 364:2 366:22,24 372:6 373:10 421:12 480:17 496:15 processed 143:7 146:4 processes 114:25 233:16 306:23 376:6 processing 22:1 92:4 372:9 373:21 383:10 procure74:22 125:11 207:13 procured 113:20 117:3,7 118:5 119:5 produce 148:6 149:15 151:21 162:4,26 163:3 177:25 235:4 379:2 464:25 produced 35:14 95:14 135:17 161:24 230:5 231:14

242:7 312:15 produces 95:15 146:22 182:13 producing 167:4,11 231:18 product301:8 302:3 production 21:1 33:3 230:16 299:7 306:17,23

334:14 406:16 productivity 327:1 products 249:7 262:2 283:3 456:20 profession7:12

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408:10,14 program 1:5 2:8 5:13 7:10 9:19 10:12 11:12,20 16:4 17:10,14,17,23 18:10,22 19:8 20:14,20 21:5 21:26 25:20 28:4,9,25 29:1 32:5 75:8 85:17,21 86:1 92:25,26 93:3,8,9,11 97:9 98:21 101:19 102:10 103:19 113:19 121:17 124:15 129:23 131:7,10 184:17 204:24 213:6 216:6,17 236:4,5 236:21 242:1 260:21 262:13 263:21 269:23 271:23 274:3 283:6,14 298:9 313:16 314:6 315:1 316:8 404:20 456:26 457:16 497:20,20 500:2 programmatic 25:10,17 215:25 311:16 314:5

418:14 441:20 452:22 497:11 programmers 85:25 Programmes 386:10 programs 9:14 17:4 35:10 92:23 98:21 248:19

262:9 315:9 417:14 499:25 progress 76:20 108:25 117:7 118:11 248:6 289:24

303:26 304:18 347:3 375:18 483:22 491:8,8 495:17 progressed455:10 progresses 445:5 progression 125:7 308:3 444:15 project 17:18 95:4 128:21 368:26 386:10 387:16 projected 22:19 projector 239:22 proliferation 95:16 184:1 485:12 prominent 345:16 promiscuous 138:2,2 promise40:20 469:10 promised 202:7 promises 209:12 promising 233:20 388:14 promote 218:18 221:8 236:2 262:2 322:21 promoter 122:16 367:5 pronounced 234:10 proof467:19 485:26 proper413:9 properly 234:17 235:7,17 properties 29:13 31:20,26 32:16 36:3 39:7,18

47:11,12 48:2 49:15 51:9,12,26 58:20 60:15,19 62:7 63:10 217:14 219:13 221:14 228:7 236:13 264:14,15 265:18,21,23,25,26 266:4,5,7 312:10 314:23 316:16,17,18,18 317:24 319:26 320:24 321:11,12 324:9 325:3 327:4 331:15 338:8 350:5 352:17 403:20 415:12,22 435:14 437:24,25 441:26 444:7 property41:15 101:25 266:1 421:15 439:17

448:20 proportion 119:18 128:26 141:12 229:19 311:10

312:26 322:11 proposal 28:5 340:9 424:11 proposals 131:11,15 propose 197:14,20 208:15,22 326:5 proposed 3:2 20:5 21:2 29:10 30:22 36:4 197:22 198:12,13 199:12 202:2 233:19 234:3 254:25,25 323:14 331:13 338:17 367:24 425:3 434:3 435:5 435:7 443:22 456:6 469:5 483:24 484:4 489:8 proposing 23:22 208:12 236:14 434:5,6 proprietary 133:18 234:20,22 335:23 366:7 proprieties 265:26 protect 18:1 378:19 protected 269:3 protecting478:11 protection 1:12 6:5 215:15 249:8 297:12 protective 332:12 protein 57:20 63:13,18,26 64:3,5,10 65:25,26

95:15,19 130:16 133:3 144:12,14 148:17,18,23 151:9,10 153:2,5,11,23,26 171:10 252:14 272:15 325:11 343:10,16 367:21 368:2,3 396:16 398:2 461:8 proteins 55:20 144:16 156:3 176:16 318:4 484:13 protocol 103:2,7 116:2 121:8 169:12 228:5,11

344:13 383:1 440:12 469:8,16 481:1 protocols 94:10 365:7 370:9 376:26 469:2 492:20 proton40:21 41:2,9 42:4 prototype 240:23,25,25 prove 139:10 proved 332:8 proven 335:24 proverbial 240:21 provide 3:26 11:7 16:12 18:23 35:21 36:11,25 104:18,21 232:17,19 240:19 247:14,21 260:17 266:13,15,22 269:12 271:3 276:4 304:18 307:19 312:24 325:5,14 333:7 346:20 362:13 364:11 366:25 386:7 389:5 392:10 393:19 394:8 414:19 416:15 418:23 423:10 437:10 439:17 455:25 460:13,23 467:13 491:26 497:22 500:5,10 provided 2:21 4:9,11,12 33:24 34:8 141:7 240:9

263:18,23 264:10 266:1 268:19 305:7 331:17 336:18,22 366:4 367:9 368:25 379:26 382:24 384:23 385:2,6 393:9 418:23 434:24 437:21 provides 2:26 3:5 21:21 56:17,18 222:7 234:10

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20:3 24:6 26:17 33:1,3 67:3 211:20 213:12,13,13 218:1,4,4 225:12 233:7,7 247:17,19 249:7 261:3 261:15,22 262:21,26 263:11,25 266:21 267:15,25 269:15 271:18 272:3,4 302:10,11 305:17 308:25 328:5 376:24 396:6 397:25 404:26 457:12 493:17 494:20 496:7 497:18,21 498:22 500:2 publically 242:13 271:16 272:21 273:9 274:13 publication 238:22 465:6 publications 94:2 465:3 publicized490:5 publicly 284:18 376:25 498:11 published 18:25 133:25 205:26 206:3 238:19

245:17 249:23 340:18 398:1 465:5,13,23 474:3 pull75:18 76:5,16 89:19 184:13 194:13 201:16

253:14 339:26 375:18 444:8 pulled 84:17 197:9 256:26 pulling 396:8 413:7 purchase444:6 pure78:3 purify 230:23 purities 228:21 purity 102:17,20,22,24 228:14,15 230:23 368:16

368:22 369:1 417:21 purple 68:16 165:16 purplish-pink 143:24 purpose 33:17 55:2,3 75:10 219:8 220:1,2 233:22

236:14 237:7 241:1 242:7 243:23 244:13 246:11 251:17 258:10 311:12 363:12 390:5 purposes 26:2 38:5 40:21 119:14 236:3 251:19

392:17 pursue 160:2 207:7 312:4 pursued437:3 pursuing 180:7,21 227:15 403:17 purview 304:9 push 158:7 162:24 264:6 pushed 38:5 187:19 196:24 pushing 168:5 172:16 197:5 put 10:2 18:21 19:21 21:2,8 24:18,23 25:3 28:12

28:17,20,23 29:16 31:16 32:7 33:6 34:4,9,11,16 34:19,21,21,26 36:11,17 54:17,20 67:14 83:7,9 85:1,20 91:11,14 92:9,16,17 93:1 94:8,13,16 98:24 99:1,3,4,9,26 101:23 102:26 103:5,24 104:5 112:20 113:1,1,2 119:15 121:16 124:21 125:3,8 131:11 134:7 142:18,19,23 143:1,6,13 144:5 151:11 155:4 159:25 162:15 165:8,17 166:1,4,6,9,12,16,20,25,25 167:14,17 168:1,6 169:10 175:13 182:3,7 183:19 185:6,13 188:20 188:21 189:2,9,24,26 190:16,17,18,19,24,26 191:2,11,12,14,20,25 192:7,16,18,21,23 194:25 198:5 202:1,2,3,5,5 203:13,16 204:20 205:14 206:17 208:11,17,19,20 209:6,8,14,15,21,22 211:14,15 219:2,2 220:4,18,24 224:13 226:9 228:8,23 229:8 230:15 231:6,26 232:2,2,15,16,23 233:1 236:5,22 238:9,13 241:11 243:20 244:26 246:18 249:16 251:3,4 255:7 265:13 269:8 274:5 275:8 276:11 278:3 279:17,21,24 280:22 281:2 281:10,13 282:16 295:15 296:26 300:7,10 310:15 313:17 314:7 315:19 316:14 320:6 331:9 340:19 345:24 357:1 362:21 367:21 370:8,14 375:6 377:9 381:12,21 384:20 385:5 393:12,15,25 394:7,11,17 395:24 396:11 397:9,12 401:9 403:15,19 405:14 406:23,25 407:5,21 408:7,14 409:23 410:4,10,12,24 413:18,19,23 414:21 415:8 416:3,8 418:12 419:13,14 420:2 421:5,18 421:20 422:9 423:3,21 424:13,25,26 425:1,20,21 427:6,17,25 428:2,5,20 429:3,5,9,10 430:6,7 431:22 432:2,9,10,11,13,18 435:10 437:4,9,10 439:7,9,16 440:5 442:5,20 443:11,16,22,25,26 444:5,26,26 446:21 448:16,24 449:3,4,10,14 450:17 451:17 456:23 458:15 461:2 463:15 464:18 468:21 469:6 471:8,21 478:9 483:7 485:14 486:10,22 487:6,11,19 490:24 492:7 494:9 495:2 496:5,6 498:21 puts 213:21 putting 38:6 187:22 188:26 254:15 280:1 298:15

314:9 317:12 335:11 374:2 414:21 415:24 425:7 442:19 464:6 495:25 499:21 PX483:18 PXR483:19,24 484:8 pyrethroids 35:20 pyridines 200:16,18 204:22 p-chem 82:24 157:2 167:7 178:9 217:6,9 218:8

319:7 325:6 329:7 P-fluor 83:26 84:15 P-par92:14 P-phos 83:26 84:15 p-toluene 158:5 p.m 91:4 501:4 P450 140:25 461:14 P450s462:13 Q QA 273:18 412:24 QC 103:13,15 108:13 120:10 412:24

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25:13 31:2,20 37:8 44:16 51:16 54:19 59:3,10 60:4 65:10 67:20 72:9,14 84:23,24 86:15 87:4,12 88:26 89:18,21,21 90:15 95:25 117:16 120:13 124:7,20 125:20 127:7 128:1 129:8,12,20 133:16 134:13 135:10,22 137:26 140:23 141:16 146:11 146:14 153:12 154:9 159:12 168:22,23 169:2,8 171:19,20 172:5 174:16 175:16 176:14 177:2,12 179:7,16 180:24 183:12,13,21 184:25 187:3 188:11 193:16 194:2,6 202:11 206:21 207:8,15 207:19 208:10 216:2,7 219:19 221:16 227:5 230:4 231:13 244:13 258:22 264:13 266:2,3,25 269:10 275:7 277:18 279:15 287:15 291:13 292:7 292:19 294:23 295:18 296:7 302:21,23 303:12,22 316:4 317:12,26 318:8,15,18 319:15,19 329:5,18 331:11 332:19,26 333:3 334:3 336:14 337:1 339:12,23 342:12,22 343:22 344:7,16 345:1,21 347:3 350:15 354:24 355:6,13,19 357:7,12 358:7 358:25 360:20 361:21 362:18,20 363:8 365:13,15 366:17 368:15 371:12,22 373:13,24 374:25 375:2 375:4 376:15 377:5,6,17 380:6 382:13 383:6 386:6 388:6 393:22,23,24 394:5,26 395:18 399:25 402:15 404:4 405:13 406:21 416:19 417:19 419:3,17 422:2 423:7 425:22 426:23,25 426:26 427:2,2,8 428:8,10,12,12,15,25 429:7 430:18 432:25 433:3,12,13,23 437:21 440:5,24 443:4,6,7,8 444:9,11 445:1,7 446:14,24 447:14 448:4,24 449:5,7,12 454:20 455:23,24 456:5 461:19 466:15,23 474:7 476:18 497:12 questionable 116:10 148:3 questioning 178:19 questions4:16,20 5:5 9:23 12:7,8,10,22 13:9 14:4

14:6,7,8 16:1 37:5 38:20 45:6 52:3,13 67:9 71:23 83:12,17 88:5,7 90:9 91:2 94:11 122:1,12,13,20 124:2 126:15 131:2,23 137:19 140:14,20 142:10 146:18,22 148:4 152:6,20 154:4 155:26 167:20 168:25 169:19 170:3,9 176:22,25 177:18 180:18 180:25 183:17 184:15,16,17 185:17 186:23 188:9 194:3 204:17 207:15 210:24 211:13 213:18 215:8 215:26 216:4,4,9 217:1,1,9 218:6,20 219:8 223:12 225:10,13,14,16,18 227:10,25 229:5,18 233:5 235:18,20 239:10 246:26 247:1,2,21 248:22,24 250:14 253:2 258:24 260:1 261:11 264:4,12 269:17 274:14 275:10 279:7 282:8 289:9 290:10 293:19 295:25 298:2 300:8 302:7 302:14,19,26 303:26 304:14 318:9 331:16 332:20 335:18 343:7 354:9,11 356:25 360:13 361:16,19 361:20 362:11 370:23 371:16 374:22 377:19 380:26 381:19 382:11 383:17 389:1 392:7 395:16 395:19 400:25 409:1 419:1,6,24 421:6 430:21,22 439:1 446:11 449:18 450:9 451:9 455:18 462:20 466:10 467:23 474:17 476:22 482:1 489:3,8,9,12 489:20,22 490:25 491:3 495:26 queue 138:7 quick 20:19 45:9 50:16 88:7 101:14 116:5 137:19

137:19 140:23 143:3 144:3 244:22 444:8 498:19 quicker 17:19 23:19 34:5 179:17 quickly 84:1 120:11 143:4 146:17 180:10 223:19

242:23 290:17 307:3 420:3 427:26 464:11 469:1 quiet411:19 412:2 Quill 295:26,26 296:1,3 302:9 quinoline 163:3 quinolines 200:16,18 204:22 quite 9:6 17:7 21:16 30:1 41:13 50:3 85:24 96:21

98:10,26 99:3 102:14 108:20 109:4 130:4 144:14 169:3 187:5 192:8 208:12 244:5,5 248:23 260:6 309:9 376:3 381:2 420:24 422:8 429:1 493:19 497:4 500:26 quote 28:15 254:1 quotient257:3 Q)SAR7:8 26:8,9 28:16 55:1 57:20 58:5,6,7 82:22

88:10,19 124:7,11 207:20 218:24 241:3 248:9 250:4,25 284:22 285:22 330:16 338:6 341:4 422:6,20,25 425:19,23,25 426:5,15 427:9 428:19 429:22,26 436:10 454:11,19 468:19 478:25 Q)SARs454:9 R R 87:22 106:5 RAAship 16:5 radar 199:8 radioligand 96:19 rainbow 33:19,20 69:2 73:10,12 145:12,13 150:8

167:24 168:10 170:9,12,19 173:23,24 195:22 220:15 232:15 267:1,1,18 268:3,7 269:4 279:26 280:18,22 281:1,10,13 284:22 285:22 288:13

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132:25,26 197:4 198:26 291:26 random 341:14,15,18 range 17:7 58:20 62:23 63:9 69:18 70:20 81:25

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313:21 452:21 453:9,9 458:2 473:6 rated408:10 rates 294:10,21 311:8 338:25 352:8 406:13 452:24 ratio 98:8,12 110:14,16 rational 240:10 335:13 rationale 10:25 47:16 56:19 88:9 131:3,24 141:1

141:11 218:7 259:9 265:12,13 266:23 270:2 304:13,15 305:6 325:20 336:19 337:24 372:1 408:9 431:15 441:13 469:17 rationales 97:8 rationally 320:15 ratios 382:17 raw 104:19,24 121:10,11,12 256:17 257:1 259:24

379:1 Ray 36:1 38:24 39:4 45:12,20,26 46:15,23 49:20

50:6,21,21 66:18 80:18 162:15 332:21 RBA 67:26 154:18 155:7,8 165:9,9 227:3 244:11

334:19,22 RBAs 165:2 reach 67:22 69:19 151:25 306:2 310:11 322:11

464:21 490:15 reached 90:4 173:2 239:7 322:10 334:18 reaching499:10 reactions 460:11 reactive 32:11 40:4 43:11 44:1 49:2 201:14 217:16

265:5 320:4,25 reactivities 458:11 reactivity40:9,12 43:11 45:3 49:18 186:13 312:12

325:10,10 read 29:14 35:18 64:18 101:2 107:25 115:3 116:21

119:7 203:20,20 236:26 240:4 261:17 264:14 267:8 284:16 302:18 333:6 342:20 363:10,13 374:19 382:11 384:16 393:22 412:6 433:11 466:22 477:1 482:5 readily 137:15 469:3 reading 242:12,25 249:23 284:11 301:24 302:21

318:16 334:4 353:1 403:14 475:23 readout 100:6,19,19,20 110:13 144:24 156:16,17

156:18 readouts 94:25 100:13,14 109:3,13,18,24 ready 28:13 104:13 114:10 115:24 136:11 ready-made483:22 read-across 221:16 real 50:15 94:19 113:13 116:24 120:25 121:16

125:11 137:2 145:15 156:3 184:11 218:22 301:10 365:10 366:9 427:4 470:17 490:24 498:19 499:6 realistic 75:10 reality430:2 realize 191:1 223:21,26 294:8 345:14 356:12 431:4

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18:6,22 19:11 21:9,21,23 22:18 23:10,17 24:1,12 26:7 28:1,25 30:19,21 32:20 33:15 37:8,10 39:11 44:16 47:11,20 51:22 52:3,3,8 53:25 55:7 56:4 57:23 58:2,22 68:26 69:12 71:17 75:20 76:18,19 81:22 88:17 91:13 92:10 95:6 96:8 97:5,10 103:4 103:4 104:4,20 105:23 106:15,15 108:22 111:3,8 111:18 112:8,14,18,20 113:16 116:17,20,25 117:17 118:23 119:12,24,26 120:12,22 121:19 123:9,17,18 125:15 126:5 127:22 128:21 129:10 129:26 130:7 131:2 135:12 136:25,25 137:2 139:5 140:9 155:10 156:4 158:21 160:5 170:14 175:11 176:10 178:6,19 179:13 180:18 181:24,25 184:16,19 185:3,5,11 191:4 196:10 199:5 200:8 200:17 201:26 203:9 206:24 207:1,4 212:3

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142:1 144:9,21 145:4,5 170:13,16,20,21,23 171:5 343:17,18 361:15 367:12 387:12 396:20 406:1 475:25 484:11,14,17 receptor-alpha 170:11,14 receptor-based 397:9 recess 52:17 433:1 466:19 recipients 282:20,21 recognize27:8 39:4 44:6,22 50:11 137:1 248:8

278:18 310:16 353:22 359:25 367:5 392:21 480:19 491:18,24 recognized 39:9 50:22,23 134:26 272:8 323:9

326:11 488:1 497:1 recognizes 303:21 434:11 recognizing48:1 393:15 433:21 492:1 497:10 recombinant 64:4,7 65:20,22,23,26 66:4 144:9

148:12,15,18 149:18 recommend 239:4 252:26 261:2,13 278:24 304:9

335:21 348:25 375:5 395:26 414:9 447:20 recommendation 19:2,6 20:22 22:21 28:19 38:18

49:21 262:15 269:9 277:2 278:11 328:1,7 419:17 436:12 448:2,3 455:2,12 recommendations 3:5,7 16:24 18:21,22,23 29:25

36:4 37:1 52:4 83:25 238:26 247:22 249:15 260:25 262:7 276:4,6 279:3 339:10 392:7 429:8 436:5 496:22 recommended 38:12 39:25 249:15 recommends 485:2 recomputed426:10 reconfirm 392:25 reconsider 257:25 reconstruct 362:25 365:16 record 134:23 234:2 302:19 342:20 360:17 363:9

365:14 382:12 390:16 412:6 448:1 466:22 489:13 recount20:20

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321:18,26 322:14,24 323:2,12 324:5,19 329:4 330:3,17 333:9 354:1 368:24 457:1 461:6 468:5 regarding 3:2 306:5 307:10,13 317:23 329:1 336:14 343:7 346:10 353:14 382:24 395:24 398:22 400:25 419:1 439:1 446:12 489:4 regardless 442:23 regards457:13 regime474:15 region 111:16 359:14 regions 111:17 register 32:8 39:26 registrants 27:14 230:10 231:18 282:22 registration 30:26 31:11 237:19 regression 59:20 regular 359:15 regulate 298:13 regulated 27:5 215:18 regulation 244:11 387:3 483:25,26 regulations 3:14 306:7,26 regulatory 3:3,6,6 7:18 18:6 25:25 26:1,14 28:13

29:6 33:16 35:10 53:16,17 56:19 58:20 59:3,10 59:11,15 61:13 65:10 124:17 134:5 218:20 219:14 221:16 232:4 233:10,13,22 236:3 263:18 263:19,19 264:1,2 267:10 270:8 282:12 283:4 296:9,11 310:22 478:10 492:2,14 reinforce 317:18 404:4 reinforced 354:13 reiterate 203:25 239:16 313:11 340:2 362:1 374:18

400:13 433:3 450:10 465:2 473:20 493:26 reiterating 184:14 497:9 rejection 369:13 relate 215:24 217:17 221:23 319:23 345:4 355:21

363:14 related4:5 131:1 171:9 227:9 262:6 264:1 268:12

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445:22 457:24 458:7 460:15 465:15 466:7 relationship 19:14 59:2,4 64:21 65:1 73:13 74:26

89:16 151:8,11 153:23,26 201:23 205:8 220:11 222:12 224:10 227:2 356:6 417:12 434:22 481:17 relationships 63:2 159:18 164:24 222:19 240:3

375:19 420:6 426:5,16,18 468:14 471:17 relative 33:25 64:22 89:25 107:8,9 130:17 256:19

256:25 257:3,8,19 259:15 268:15,23 280:10 311:20 312:25 326:26 331:3 334:6 359:5 378:24 427:26 446:15 452:9 492:25 relatively43:16 97:11,12 109:7,14 118:6 129:26

177:6 195:19 243:24 307:3 388:10 396:23 416:6 427:26 465:3 492:13

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377:25 representative 196:7 197:10,13 200:21 334:18

436:19 representatives 76:18 134:4 203:3 236:22 represented 195:23 386:17 representing 219:10 322:9 represents 68:5 reproducibility 114:18 119:2 378:25 reproducible 113:12 116:13 146:3 233:26 234:6

234:18 383:13 reproducibly 379:2 reproduction 20:10,11,15 55:24 58:13 468:6 reproductive 6:10 8:16 56:8 336:24 337:5 343:11

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73:11 78:3 84:11 86:2 89:12 91:3 97:13 99:1 103:15 116:14,16 118:16 119:4,11,13,21 123:7 127:1,24 128:24 129:21 142:20 143:24,26 146:2 151:16 152:11 153:1,7,15,17 155:22 157:21 162:6 163:4,16 165:9 168:20 169:3 171:2 173:19 177:18 179:7,23 188:11,19 193:15 194:16 195:24 200:5 215:3 218:3 221:2 222:19 230:12 246:23 247:9 253:26 260:1 264:4 271:1 275:2 284:9 291:13 293:22 317:8 318:14 342:8,15 346:23 347:13 350:17 351:10,17 354:19 356:15 362:12 371:20 373:3,15 375:1 389:8 402:15,18 410:10 411:23 416:19 419:8 426:1,17 439:13 448:18 466:14,16 486:10,23 487:23 499:14,21 rightly424:17 right-hand 35:16 rigor26:16 ring 187:20 196:1,3,18,18 197:17,24 199:16,16,17

200:1,2 444:19 457:9 460:10 rings 72:17 201:10 rise403:25 risk 5:26 7:16,18 12:18,24,25,26 19:13 24:3,4

25:11,15 31:3 56:5 58:12 215:18,21 216:18 217:14 262:3,4,23 263:8 298:12 308:7,9,14,15,17 309:7 310:22 risks 261:22 risk-based 248:12 Riverside 5:19 213:23 390:19 RNA 95:14,19 100:22,25 157:16 268:8 road 24:15 54:23 83:4 142:20 251:12 289:26

433:25 468:7 roadmap 251:13 252:26 roads 312:4 robot99:26 robotic 209:22 robots 94:24 robust 39:17 51:1 103:24 236:14 325:26 326:1

335:8,24 340:3 358:19 379:21 robustness 59:19 303:7 336:17 Rockville 93:22 role 154:12 155:24 156:2 167:4 169:16 187:16,23

263:26 482:26 488:21 roles441:20 rolled 74:20 rolling 103:16 roman 66:15 189:21 room 5:14 111:18 212:4 238:19 389:20 390:15

501:2 root72:10 347:2 roster 211:14 rotate 322:4 rotely 81:5 86:18 Rotroff271:9,11 274:8 278:1 285:1,3,5,24 286:23

287:8,17,25 288:19 290:18 rough 68:17 179:19 roughly 39:22 198:21 round495:20 rounds 131:10 route 314:16 324:18 333:2 492:26 routes415:1 row99:10 165:10 166:7,8 225:15 rtER 64:4 443:23 448:5,12 453:6 454:26 455:5 rule 59:9 109:1,21 202:14 335:6 341:14,14 344:12

421:17,25 424:8,16 443:12 rules 59:13,14,22 60:1 65:8 66:21 83:18 85:17

191:7 197:1 220:8 222:21 226:23 230:6 312:16 314:18 322:1,1,3 323:24 324:6,8,9 338:12 381:4 416:21 421:12,13 422:15,19,20,25 423:18 426:9 431:8,14,17 439:10 444:15 451:22 455:3 rules-based 67:7 rule-based 232:22 334:11 442:1 458:19 run 50:10 62:5 63:15 68:7 70:3,24 72:22,24 75:22

75:26 76:17 85:10 93:20 94:5,21,23,24 96:20,21 96:22,23,25 98:4,4,5,16,17 99:2,8,14,15,17 100:4 100:5,10,12,14,15 101:1,4,6 103:18 110:24 114:3 114:8,13,19,20 116:2 125:14 130:5,13,14,14,15 130:24 131:10,22 132:10 139:20 149:21 152:23 152:26 157:7 162:5,19 176:10 184:18 198:21 220:3 229:13,22 231:6 232:16 249:12 250:26 283:13 295:12 304:12 308:19 383:5 408:25 410:3 417:24 428:21 429:13 441:8 443:19 444:1 459:2 474:11 478:15 486:12 running74:26 111:11 121:9 139:17 145:25 163:7

164:8 178:13 186:24 279:24 282:24 401:7,7,11 409:10,11 428:22 441:10 runs 139:21 152:23 251:6 S S 87:22 387:6 SAB 28:10 SAB/SAP22:22 38:11 39:24 safe 8:5,5 18:14 26:24 31:9 35:11 37:10,15,19

49:10,25,26,26 54:4 77:20 78:15,21 122:14,15,21

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392:2 salicylates 69:4 226:10 sample 118:5 119:5,6 141:4 352:15 sampled 117:4 samples 95:2 102:20 113:21 117:2,6,7 118:5

232:16 sampling 352:7 Sandy 6:2,3 213:26,26 229:6,7,7 231:11,12,12

357:11,12 380:22,23 384:10,11 388:24,25 390:23 390:23 395:11,12 400:20,21 407:8,9 417:7,8,25 417:26 489:17 SAP 1:1,8 2:6 4:18 5:11 9:5 10:3 11:9,23 14:11,12

14:14,16 16:20,22 20:18,24 22:16,24,26 23:3,5,7 26:18 28:10,25 29:3,25 33:11 36:12 38:4 53:4 54:8 66:15 70:8 142:22 161:22 211:23 227:21,23 233:15 234:2,4,25 235:12,14,16 236:16 240:8 251:12 261:5 271:1,13 285:12,15 287:23 290:9 290:19 303:21,25 304:3,9,16,20 306:26 307:17 325:26 327:7 333:12 339:9 354:15 372:16 390:13 393:9 402:4 407:1 409:11 467:21 468:6,26 472:17 482:22 483:12 496:12 497:10 498:5,10 499:4,10 501:5 SAPs 11:20 12:13 22:13 233:16 234:24 248:3

260:23,24 303:21 491:7 497:8 498:17 sat491:7 satisfied 3:14 satisfy 283:22 286:8 save 124:1 239:5 saw 61:17 68:11 79:22 137:16 150:2 152:17 157:4 161:20 162:9 164:26,26 167:1 169:20 174:26 208:16 223:2 267:7 295:3 435:21 491:14 saying 37:18 50:7 84:6 123:19 168:9,10 174:21 186:16 204:23 205:4 224:21 251:20 253:8,23,25 254:17 266:11 296:22 297:4 303:15 313:18 316:5 351:14 354:4 359:7 360:8,9 374:19 380:5 402:25 407:10 409:26 410:13,14 411:16 416:15 420:16 420:17 425:24 428:11 431:9 432:8 443:6 445:21 469:18 says 39:7 109:21 110:7 115:5,8,10,11 116:15 119:26 127:2 162:7 289:24 292:10 297:15,18 299:15,21 303:23 314:9 352:22 448:26 457:26 scale 38:7 338:13 378:23 416:14 scaled 364:25 scales 221:22 scenario 326:19 345:20 434:17 437:6 457:26 461:2

468:13 471:2 scenarios 360:3,12 468:8 scenes 9:22 schedule 30:26 38:21 scheduled 235:13 schematic421:11 444:21 scheme 204:20 218:21 221:11 236:13 277:5 290:25

303:4,6,17 311:4 314:6 320:3 325:22 375:22 381:3 423:3 440:22 450:24 472:5 477:15 478:3 482:21 schemes 469:3 471:22 Schlenk 2:14 5:8,10,17 8:17 9:12 16:14 37:4,24

38:19,25 45:6 46:7 47:7 48:3 49:9,24 50:13 51:5 52:12,18 83:12 84:21 85:13 87:3,3,20 88:4 91:2,6 122:13,25 123:2,26 124:5,12 125:17 126:11 127:26 130:20,26 132:17 133:14 134:10,22 135:21 137:4,16 139:13,24 140:13,18 141:13 142:8,13,16 167:19 170:7 171:17 175:8 176:12 176:22 180:25 183:15 185:15 186:22 188:8 204:17 205:21 206:7,11 208:8 209:9,24 210:17 210:24 211:5 213:16,17,22 215:3 225:8 227:24 229:5,16 230:19 231:10,20 232:20 233:4 235:19 235:22 239:10 246:26 247:6 253:2 258:24 259:26 263:14 264:3 269:17 274:14,26 279:7,11 282:8 282:11 290:10 293:18 295:24 302:7,10,17 303:11 307:21 310:13 313:5 316:9,21 317:19 318:7,14 318:25 319:12 331:18,22,25 332:14,18,24 333:14 333:24 339:13,16,20,24 341:21,25 342:2,5,8,15 342:19 343:2 344:18,21 345:26 347:7,10 349:25 350:10,12 351:21 352:19 353:12 354:8,13 356:16 357:6,15 358:4,21 360:15 361:10,25 362:9 363:5 369:24 370:2,4,6,17,21 371:12,15,19 372:11 374:3,6,8,10,12,17 375:1,7,15,23 376:7,9,11,17 377:4,14 380:8,12,14,16,19,22,24 381:7 382:10 382:20 383:25 384:1,3,5,7,10,12,16,26 388:2,4 388:17,19,21,24,26 389:7,19 390:12,14,17 392:3 392:9 393:21 394:4 395:1,5,7,9,11,13,20 396:2 399:26 400:3,5,17,20,22 402:21 404:2,17 405:10 406:4,19 407:8,14 408:17 410:22 411:1 412:3,13 416:23,26 417:2,4,7,16,25 418:6,21 419:9,20 421:4 422:17 423:2,11,14,17 424:6 425:15 426:21 427:12 428:17 430:16,19,24 431:12,24 432:5,20 433:2,18 438:3,6,8,18,21,23 439:4,12 442:7,9,12,14 445:16,18,26 446:8 447:16 448:22 449:21 451:3,7,13,26 454:1,3,6,22 455:14,16,22 456:2 462:7 463:26 464:26 466:9,13,21 467:3

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www.transcriptionetc.com Page 82 472:19,23,25 473:17 474:5,21 475:19 476:5,19 476:26 477:7 478:26 479:3,24 480:1,3,12 481:8 481:10,15,25 482:4,9 486:4,6,8 488:13,15,22,24 488:26 489:7 490:18 491:5 492:3 493:9,11 494:5 494:7,15 495:7 499:12,14 Schmieder 35:1 36:6,20 51:10 52:19,21 76:24 86:7

86:7 88:12,12 89:9 90:5,5,18 165:25 169:26 171:7 179:6,6 180:4,4 185:18 186:23 188:13,14 206:20,20 208:18 210:4 218:24 225:24,24 228:22 228:23 229:22,23,26 232:18 430:25,26 432:4,6,7 447:16,17 Schmieder's 232:3 school 6:21 7:2 Schultz7:5,6 37:24,25,25 49:9,11,11 83:13,14

84:23 85:15 167:20,21 168:9 169:4 170:6 204:18 204:19,19 205:16 206:22 211:8 214:12,12 316:11 325:8 331:19,20 339:22 344:22,23 358:23,24 370:6,7 374:10,11 376:9,10 391:11,11 406:19,20 427:14,15 431:12,13 438:8,9 442:12,13 448:22 448:23 449:23 454:6,7 456:3,4 467:4,5 477:7,8 482:9,10 490:18,19 Schultz's407:16 431:2 science 3:19 8:3 9:8 10:15,18,20,24 11:1,4,17,22 16:17 31:3 217:24 219:8 236:2 247:15,23 269:7 269:13 279:12 324:21 499:26 sciences 5:18 6:24 275:1,3 390:18 391:2 science-based 268:16 scientific 1:1 2:4,6,15,23,24,25,26 3:4,9 5:8 10:12 10:16 26:7,15 127:15 133:5 149:6 213:3,4,4 215:21,22,24 219:4 225:22 248:1 249:3,5 250:16 251:15 262:16 263:18 303:5,7 304:8,21 342:24 346:7 352:23 390:3,8 467:11 497:18 scientifically 39:17 44:16 217:21 238:16 333:7

468:16 scientist7:22 scientists 17:2 236:1 248:7 281:20 495:18 scope 26:23 134:9 270:11 296:16,21 301:26 scoping 246:8 488:19 score 126:25 408:8 453:1 scores 90:1 Scott233:9,11,12 235:21 249:21 scour232:9 scratch 104:20 393:8 scratching 265:7 screen 18:11 21:17 25:18 37:14 52:11 67:16 129:18 130:6,9,24 185:8 199:14,20 237:4,5,15 239:7 244:17 266:6 311:17 318:15 325:17 372:19 387:7 406:17 408:3 423:3 424:26 434:22 443:16 444:5,26 445:1,4 449:3,4 487:21,22 screened 32:19 134:17 237:20 274:2 325:13 screening 1:4 2:8 5:12 12:23 18:1,10,20 19:8,8,15 19:17,18,20 24:20 27:23 30:11 31:18,25 32:4,7 33:7 34:1 35:9 37:17 38:5,7 91:14 92:16,18 112:21 130:1 133:9 135:6 187:8 208:11 213:6 230:13,16 231:6 236:24 237:13,16,18,22,26 238:3,6,14,14 239:5 240:10 241:13 243:20 244:26 245:25 246:10 253:20,24 255:1,22 256:14 258:4 262:13 264:15,20 266:23 289:19 295:4 297:19 300:1,25 301:12,22 303:17 304:6 305:16 306:25 307:2 308:6 311:20 320:3,18 323:5 325:12 340:18,23 370:8,14 381:21 397:8,13 404:22 405:22 406:9 416:3,5,9 418:8 424:13,18 428:2 431:22 434:17 437:4,10 442:5,20 445:1 450:6 456:26 464:18 469:6 485:19 490:24 492:7 screens 283:8 284:18 286:10 340:17 408:3 421:24

443:19 screw409:15 Scripps 7:25 391:19 Scripts 214:20 scrutinized 368:13 scrutiny 365:9 SDWA 32:25 77:23 78:5,11,14 82:4 202:26 335:4

378:9 se 12:1 252:10 Sea214:22 SeaChange 8:2 391:21 search 68:21 379:22 466:5 searches414:13 473:15 seat 390:2 seats 247:6 302:17 390:2 second 24:22 34:14 36:16 40:7 72:18 73:8 86:25

88:26 118:14 127:6 137:25 159:16 165:10 169:7 179:16 190:21 192:2 213:3 242:24 316:14 321:15 325:16 327:18 330:5 337:9 338:5,23 340:12 346:19 365:3 366:11 397:18 402:17 432:5 435:26 436:9 441:18 459:1,4 460:25 462:8 470:7 486:9 494:10 500:12 secondary 55:23 70:21 142:3 360:7 secondly 300:19 302:3 346:10 358:15 seconds 39:5 458:4 secretary 500:14 section 6:6 18:15 35:24 62:2 80:12 83:9 180:11

275:20,20 278:1 284:16 288:1 296:8 319:15 320:12 322:14 330:7 sections 54:21 384:19 sector 264:11 secure47:24 seductive436:26 see 9:25 10:26 11:6 12:15 14:17,23,25 19:4 20:8

31:8 32:12 37:8 38:16 39:6 46:5 52:15,26 54:21

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245:22 408:13 416:7,13 433:15 435:6 436:19 437:10 483:4 484:26 selected 34:11 91:26 114:15 117:23 141:8 143:14 143:15 205:23 218:9 266:7,8 270:3 320:10 335:5 366:22 386:16,19,23 484:26 486:2 selecting 189:10 204:21 205:19 222:19 434:14

435:1 438:11 469:16,24 selection 219:18 270:2,6 309:5 325:20 377:7 420:8

436:20 469:15 selective 123:4,20,23 256:9 347:16,17 348:23

388:12 selector 140:5 self282:24 self-context400:9 self-proliferation 95:20 self-rebinding 96:24 138:10,13 seminal477:11 send 103:17,18 300:25 sends 103:21 senior7:22 sense46:3 47:14 48:24,25 59:5 81:5 88:22,24 89:3 154:8 160:2 173:3 195:26 217:12 239:3 296:19 306:16 307:18 315:13 341:11 349:1,9 355:21 363:20 370:16 400:7 421:13 476:10 sensible 339:1 435:8 453:5 sensitive 128:18 133:11 153:10,18 163:14 243:26

244:19 291:15 332:8 334:23 348:26 349:1 356:21 405:20 406:15,18 414:2 sensitivity 136:7 149:19 174:2 244:18 249:10

251:10,19 276:25 277:12 278:10 289:8 291:23 293:24 294:4 327:1 339:5 349:24 378:14 379:5 379:19 385:11 411:8 437:17 441:16,21 451:25 452:13 487:21 sent493:18 sentence 351:26 sentiment 313:7 separate45:1 123:13 194:11 248:18 398:14 433:8

471:23 separated 71:5 163:9 185:13 269:8 separately 379:5 September 23:11 39:26 sequence 303:8 307:10 312:8 sequencing-based 100:19 sequentially 319:20 Seri 196:14 series 16:22 49:13 59:24 61:6 62:24 65:22 66:3

67:8,9 71:23 73:21,23,24 74:11 75:3,18 85:6 87:17 90:9 98:6 99:10 138:20 183:23 195:24 225:20 226:9 283:19 310:1 358:25 454:8 serum 280:9 368:9,10 487:4 serve 2:11 456:10 458:12 served491:4 serves4:22 196:5 serving 2:15 258:10 492:11 session 196:23 318:1 set 34:17 36:18 40:14 45:2 57:15 59:8 61:20 64:19

68:3 69:5,10,13 70:1,11 73:10,23 74:15,24 87:13 90:20 91:21,25 94:3 98:24,25 100:9,9 101:18 104:5 105:21 108:7,7,11 111:26 114:15 121:24 124:24 125:7 130:8,13 131:23 133:2 134:13 137:3 141:9 142:19,24,26 143:6,8,13,25 144:1 145:23 146:11 147:7,7 150:7 152:22 154:15,18 155:2,7,13,20,25 156:12,17 158:1 164:12 165:1,3 165:7,14 166:1,2,7,9,17,21,24 167:2,13,18 171:22,25 172:19,21 173:3,5 179:9 181:18 183:20 188:22 193:26 195:22 197:1,3,12,15,23 201:1,8 220:15 222:19 228:18 229:15 231:4 232:5 243:9,10 249:16,17 254:16 255:24 256:21

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249:12 317:11 334:18 341:11,14,14 379:11 412:21 413:8 414:5,24 415:18 424:8,16 434:10 setting 136:12 210:18 216:15 223:10,11 258:3

261:19,21 262:5,11 263:3,13 289:22,26 290:2 293:9 307:23 308:2 310:3 311:4 settle 68:13 seven 62:2 66:16,16 98:21 105:15,24 108:3,16,19 110:6,7 112:22 116:22 274:20 278:1 281:7 288:9 seventh 259:1,21 Seventy-seven 198:4 sex 55:22,23 354:17 shadow 111:22 shaking 117:23 357:11 411:2 shape 146:9 148:2 415:25 438:1 share 231:25 244:23 269:22 497:3 shared 272:21 sharing 60:20 Sharlene 2:1,2 5:10 9:20,24 211:10,11 213:1,2

263:16 274:16,22,25 333:16,21 389:12 390:1,8 390:14,16 496:11 497:25 498:21 499:16 sheet 81:14 Sheets43:20 shift296:5 428:1 471:10 490:21 shifted 64:8 184:2 397:14 shifts 201:11 shipped 102:26 Shirley 500:16 shopping457:20 459:7 short43:16 75:4 150:20 223:20 238:9 240:2

372:19 Shortly 18:19 21:5 shot 67:16 shots 372:20 407:22 shoulder 153:8 show28:2 65:21 68:13 97:20,22 108:26 145:24 146:5 150:8 165:16 192:2 195:21 201:2 249:22 276:12 280:9 292:4 315:23 321:10 336:6 404:25 405:3 421:25 428:21 showed 39:5 85:5 87:1 115:17 143:22 148:16 152:20 158:9,18 165:25 171:7 187:18 196:14 224:12 showing 63:7,20 119:2 121:15 153:20 154:24 160:10 161:4 399:9 shown 89:24 122:5 138:21 151:4 163:3 225:20

234:18 277:8,16 289:4 328:24 337:15 348:6 359:3 386:26 shows43:8 118:4,4 137:12 143:21 147:17 150:20 152:8 155:1 166:7 194:13 280:12 342:26 343:26 352:24 429:1,11 shuffled 99:16 112:4 Shultz 258:25,26 316:10 339:21 360:15 sic440:8 sick 126:22 355:9 409:20 side 24:12 30:25 35:16,17 56:3,5 58:18 62:15 74:6

78:3 86:26 87:1 96:9 116:11 124:21 127:18 153:17 159:20 169:12 175:24 176:4,5 208:20 220:3 226:11,12,14 249:14,14,19,19 315:23,23 360:4 361:14 409:20 428:10 480:16,16 sidebar457:6 sides 194:22 sidetrack 86:14 sieve 258:6 sieves 29:19 sifted 163:11 Sigma 102:6 228:12 signal 99:18 133:9 138:12 161:16 162:4,12 182:8

244:10 278:22 334:16 349:1 406:1 signaling48:21 182:14 223:21 269:26 270:4

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266:11,15 335:22 375:12 414:15 466:3 solely410:14 424:22,23 solid 273:3 354:25 460:6 solubilities 62:7 solubility 61:23,26 103:3,6 145:7,16 156:25 160:12 172:22,23 173:14,24 174:3,17 175:3 188:1 197:4 208:23 220:3 306:11 321:19 322:3 324:12 334:17 355:8 401:7 409:11 429:18 440:14 solubilize 103:8 solubilized 102:25 soluble 62:9 103:8,9 114:1 228:9 358:2 solution 12:10 197:6 201:15 323:7 437:11 469:5 solutions 14:8 62:22 solve 13:10 112:9 301:9 solved 113:25 138:6 213:18 solvents 368:17 solving 360:6 somebody 104:19 121:7 126:9 133:19 196:20

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