FIFRA SCIENTIFIC ADVISORY PANEL (SAP) OPEN MEETING

REEVALUATION OF THE HUMAN HEALTH EFFECTS OF ATRAZINE: REVIEW OF NON-CANCER EFFECTS AND DRINKING WATER MONITORING FREQUENCY DOCKET NUMBER: EPA-HQ-OPP-2010-0481 UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL ONE POTOMAC YARD (SOUTH BUILDING) 2777 SOUTH CRYSTAL DRIVE ARLINGTON, VIRGINIA 22202 SEPTEMBER 14, 2010 8:40 A.M.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 2

1 FIFRA SCIENTIFIC ADVISORY PANEL 2 MEETING 3 SEPTEMBER 14, 2010 4 DR. JOSEPH BAILEY: We'll get started 5 here in just a few minutes as soon as everyone gets 6 settled. 7 Okay, good morning, everyone. I'm Joe 8 Bailey, and I want to welcome everyone to this FIFRA 9 Scientific Advisory Panel meeting. This is our third 10 meeting this year on atrazine, and the topic for this 11 particular meeting is the Review of Non-Cancer Effects 12 and Drinking Water Monitoring Frequency. 13 I particularly want to welcome the Panel 14 and thank them for agreeing to participate in this 15 meeting. And I just have a word, a few quick routine 16 administrative remarks to make. I am serving as the 17 DFO for the meeting, and I will be serving as liaison 18 between EPA and the Panel. 19 And this is a Federal Advisory Committee 20 within the Federal Advisory Committee Act, which means 21 that the recommendations and advice that the Panel 22 provides to EPA is purely recommendations and advice on 23 science issues, and all regulatory matters are strictly 24 the Agency decisions, of course, taking into account 25 the recommendations and advice that the Panel gives.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 3

1 As far as the ethics regulations go, we 2 kind of review the contradict disclosure information 3 that the Panel members have provided to ensure that all 4 of our ethics regulations are complied with. 5 There is tomorrow on the Agenda, 6 beginning I believe about mid-morning, an opportunity 7 for public comment. That's one of the requirements for 8 FACA held meetings is to provide an opportunity for 9 public comment. 10 We do have a number of commenters who 11 have requested time on the Agenda to hear their 12 remarks. If there is anyone else who wishes to give 13 remarks during the public-comment opportunity tomorrow, 14 please let me know or one of the other people here from 15 the SAP office. If you have not made arrangements 16 prior to the meeting for public comments, we will ask 17 that you limit your comments to five minutes on the 18 Agenda. 19 As usual, we have a Public Docket 20 created for this meeting, and the number should be at 21 the top of everyone's Agenda. All of the background 22 documents that have been provided to the Panel so far 23 are in that document. 24 Some of them are sensitive materials 25 that you must contact the docket and sign anFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 4

1 affirmation of nondisclosure as to those; but 2 everything else should be available in the docket, and 3 that will include all of the slide presentations that 4 are made by EPA or public commenters. They are 5 enclosed in the docket very shortly after that 6 presentation, so they will be available as well. 7 And I think that covers everything I 8 wanted to bring up. Again, thank you all for coming 9 this morning, and I will now turn the mic to Chair of 10 this meeting, Dr. Steve Heeringa. Thank you. 11 DR. STEVEN HEERINGA: Thank you very 12 much, Joe, and welcome everyone to this 4-day meeting 13 of the FIFRA Science Advisory Panel on the topic of Re14 Evaluation of the Human Health Effects of Atrazine: 15 Review of Non-Cancer Effects and Drinking Water 16 Monitoring Frequency. 17 As so indicated, I am Steve Heeringa of 18 the University of Michigan. I am currently in for a 19 short period as the chair of the FIFRA Scientific 20 Advisory Panel. 21 I will be serving as Chair of these 22 proceedings with no specific substantive expertise to 23 contribute; but we have a very highly qualified Panel 24 of experts, and so we look forward to this meeting and 25 I thank them in advance and probably throughout thisFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 5

1 meeting for their contributions. 2 But at this point, let's meet the Panel, 3 beginning here on my left with Ken Portier. 4 DR. KENNETH PORTIER: Good morning, I'm 5 Ken Portier, Director of Statistics at the American 6 Cancer Society National Office in Atlanta. I am a 7 biostatistician and member of the current Panel. 8 DR. JANICE CHAMBERS: I'm Jan Chambers; 9 I'm a Professor in the College of Veterinary Medicine 10 in Mississippi State University on pesticide 11 toxicology, neurotoxicology and metabolism expertise, 12 and I'm a member of the current Panel. 13 DR. CAREY POPE: I'm Carey Pope; I'm a 14 Professor of Toxicology at Oklahoma State University; 15 I'm a neurotoxicologist, and I'm a member of the 16 permanent Panel. 17 DR. JOHN BUCHER: I'm John Bucher. I'm 18 the Associate Director of the National Toxicology 19 Program at NIEHS. I'm a member of the current Panel. 20 DR. DANIEL SCHLENK: Good morning, my 21 name is Dan Schlenk; I'm a Professor of Environmental 22 Toxicology at the Department of Environmental Sciences, 23 University of California Riverside. My expertise is in 24 fate and effects of pesticides on aquatic organisms, 25 and I'm a member of the current Panel.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 6

1 DR. RICHARD GREENWOOD: I'm Richard 2 Greenwood, a current Professor of Environmental Science 3 at the University of Portsmouth in the UK. My 4 expertise is in the area of pharmacokinetics and 5 toxicology. 6 DR. KANNAN KRISHNAN: My name is Kannan 7 Krishnan; I'm Professor of Occupational and 8 Environmental Health from the University of Montreal in 9 Canada. My expertise is in pharmacokinetics and 10 cytotoxicology. 11 DR. MOIZ MUMTAZ: Hi, I'm Moiz Mumtaz, 12 ATSDR, CDC. I'm interested in chemical mixtures. 13 DR. NELSON HORSEMAN: Nelson Horseman 14 from the University of Cincinnati. I'm a physiologist 15 and endocrinologist, specific expertise in mammary 16 gland biology. 17 DR. JAMES McMANAMAN: I'm Jim McManaman, 18 I'm from the University of Colorado; I'm Professor of 19 Obstetrics and Gynecology, and I'm an expert in mammary 20 gland biology. 21 DR. KATHERINE ROBY: I'm Kathy Roby from 22 the University of Kansas Medical Center in the 23 Department of Anatomy and Cell Biology, and my area of 24 expertise is female reproduction. 25 DR. BARRY DELCLOS: Barry Delclos fromFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 7

1 the FDA National Center for Toxicological Research and 2 work in the Department of Toxicology there. 3 DR. SANDRA LEGAN: Good morning. I'm 4 Sandy Legan from University of Kentucky; I'm a 5 Professor of Physiology, and my expertise is female 6 reproduction and control of the LH surge. 7 DR. WESLEY STONE: I'm Wes Stone with 8 United States Geological Survey; I'm a hydrologist, 9 work with chemical transport. 10 DR. RICHARD COUPE: Richard Coupe, also 11 with U.S. Geological Survey from Mississippi. 12 DR. HERBERT LEE: Herbert Lee, 13 Department of Applied Mathematics and Statistics and 14 Associate Dean for Graduate Studies in Research in 15 Baskin School of Engineering. I'm at the University of 16 California Santa Cruz; I'm a statistician. 17 DR. BETTE MEEK: And I'm Bette Meek. I'm 18 at the University of Ottawa interchange from Health 19 Canada. I have a background in toxicology, but I've 20 worked in regulatory risk assessment in Health Canada 21 for a rather long time. 22 DR. SUSAN AKANA: Susan Akana from the 23 University of California San Francisco; I'm a research 24 physiologist. I specialize in hypothalamic-pituitary25 adrenal axis and its interaction with energy balance.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 8

1 DR. PENELOPE FENNER-CRISP: Penny 2 Fenner-Crisp; I'm a private consultant, and I live in 3 Charlottesville, Virginia. My area of expertise is 4 toxicology and regulatory risk assessment like this 5 over here in the U.S. 6 DR. ELLEN GOLD: I'm Ellen Gold, I'm a 7 Professor of Epidemiology and Chair of the Department 8 of Public Health Sciences at the University of 9 California at Davis, and I have an interest in women's 10 reproductive health, endocrinology. 11 DR. SHELLEY HARRIS: Good morning, I'm 12 Shelley Harris; I'm a scientist at Cancer Care Ontario 13 and Associate Professor at the University of Toronto, 14 and I'm an epidemiologist with expertise in pesticide15 exposure substances. 16 DR. JOHN BAILAR: I'm John Bailar, 17 retired from the University of Chicago. I am a 18 physician, a Ph.D. statistician, mostly an 19 epidemiologist, and I'm currently a Scholar in 20 Residence at the National Academies here in Washington. 21 DR. GERALD LEBLANC: Good morning, I'm 22 Gerry LeBlanc, and I'm a Professor and Head of the 23 Department of Environmental and Molecular Toxicology at 24 North Carolina State University, and my area of 25 expertise is endocrine toxicology and I'm a member ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 9

1 the permanent Panel. 2 DR. STEVEN HEERINGA: Again, thank you, 3 everyone on the Panel, obviously a very diverse and 4 deep set of scientific expertise that the Staff of 5 FIFRA SAP has again assembled for us. I acknowledge 6 their role in this, as well. 7 At this point, I'd like to open the 8 proceedings. I would like to turn to Dr. Steven 9 Bradbury, who is Acting Director of the Office of 10 Pesticide Programs at EPA. 11 DR. STEVEN BRADBURY: Chairman Heeringa. 12 I want to welcome the Panel members to this week's 13 meeting, as well as the public, who will be 14 participating tomorrow in our proceedings. We want to 15 thank you first for all your hard work in leading up to 16 the meeting. 17 There's a lot of information I know you 18 have been reviewing, and we appreciate the effort that 19 you've invested thus far and the investment of time and 20 your expertise during the proceedings this week and as 21 you prepare the Report in the coming months. 22 The activities of a peer-review Panel 23 such as the Panel that you're on is really critical to 24 the business of the Agency in ensuring that we got the 25 best possible scientific advice in the decision-makingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 10

1 that we have to do. It's really critical to have this 2 kind of peer-review process in place. 3 It's also important to ensure that 4 there's an opportunity for the public to provide you 5 information and insights as we proceed in evaluating 6 whatever scientific issues we have before us; it 7 supports our regulatory decision-making. 8 I just want to reaffirm the very 9 important role we play in the work of the Environmental 10 Protection Agency, in particular in the work of the 11 Pesticide Program as we not only deal with some 12 scientific issues around the pesticide atrazine but 13 more broadly around our risk-assessment approaches and 14 specific scientific challenges we take on as we go 15 forward. 16 Science is always evolving and, hence, 17 our regulatory approaches need to evolve with the 18 science. And some of the topics that we've been 19 discussing around atrazine in the past and will be 20 discussing this week and into the future tap into a 21 number of areas that are interesting from a scientific 22 perspective and also during reporting for informing 23 where we are with the science on atrazine in 24 particular. 25 Some of the discussions we've had withFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 11

1 over the last several meetings have included 2 epidemiology and how we should be trying to advance our 3 ability to use epidemiology studies in conjunction with 4 external toxicology studies and problem-formulation 5 stuff through the various stages of doing a risk 6 assessment. 7 And we'll be getting some very important 8 advice on some non-cancer issues regarding atrazine 9 during this week's meeting, which will be very helpful; 10 but it will also give us insights more broadly in terms 11 of how to use epidemiology studies as we advance our 12 risk-assessment approaches. 13 We'll also be spending some time with 14 you discussing toxicity pathways and looking at 15 different intermittent pathways and how different Modes 16 of Action can give us insights into adverse outcomes 17 and try to understand those relationships; it's very 18 important for understanding atrazine and its Mode of 19 Action and understanding adverse outcomes and how they 20 relate to different --across different levels of 21 biological immunization, very critical to ensure we 22 have the most up-to-date information around atrazine as 23 we take a look at risk assessment. 24 But those insights will be more broadly 25 applicable as we try to take advantage of the NASFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 12

1 report on 21st Century toxicology testing and 2 integrative assessment approaches on risk assessments 3 and how it to look across different levels of 4 biological immunization and understand Mode of Action 5 that are mechanisms of action, which in turn is 6 critical for improving our ability to look at dosimetry 7 in our risk assessments and to better define what's the 8 appropriate dosimetric to use for different Modes of 9 Action at different levels of biological organization 10 and how to link this information together, both in 11 terms of the dose, delivered dose, perhaps from 12 exposure but also the duration of exposure, and our 13 understanding of Mode of Action and how that relates to 14 the timing of exposure. 15 Again, very important for understanding 16 the science around atrazine and informing our current 17 risk assessment and whether or not we need to revise 18 the current risk assessment with the insights that we 19 got from previous peer reviews and future peer reviews 20 will give us some insights more broadly as we advance 21 on risk-assessment techniques. 22 And then, of course, looking at 23 dosimetry and Mode of Action and understanding risk 24 potential from epidemiology studies or experimental 25 toxicology studies, and how do you relate thatFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 13

1 information to environmental exposures. 2 In this case with atrazine, how to take 3 a look at drinking-water concentrations and 4 understanding how different changes in space and time 5 of atrazine concentration in the drinking-water sources 6 relate to that understanding we have about dosimetry 7 and temporal exposure with those and Mode of Action and 8 adverse outcomes, and how do you bring that all 9 together so that we can take a look at what's going on 10 out in the world and be confident that the current use 11 of atrazine or any pesticide down the road is safe in 12 the context of our risk assessment, what we understand 13 about dosimetry, and how that relates to temporal and 14 spatial exposure on the pesticide question, in this 15 case atrazine. 16 So a number of really important issues 17 that are very central to understanding the science on 18 atrazine, but also very informative more broadly if we 19 take a look at our risk-assessment tools in general. 20 The peer reviews that we've had thus far 21 and the ones that we'll have, the peer review we've 22 having this week and the peer review that we'll have in 23 2011 are very important in taking a look at our 24 regulatory position with atrazine and ensuring that the 25 current state of the science is still reflective ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 14

1 what we knew about atrazine back in 2003. 2 It has been about seven years' time 3 since we did the re-registration decision for atrazine, 4 and there has been a lot of epidemiology studies that 5 have come over that time frame along with a number of 6 experimental toxicology studies done over that time 7 frame. And those studies are very important as we take 8 a look at now about five, six, seven years of intensive 9 drinking-water monitoring data that's been accumulated 10 as a condition of re-registration of atrazine. 11 And so in 2010, looking back at 2003 and 12 many years of monitoring now, the Agency felt it was 13 time to sit back, take a look at the state of the 14 science both in terms of experimental toxicology, 15 epidemiology, dosimetry in the context of sampling 16 designs for drinking water which are including making 17 sure we've got the most current science and that's 18 where we stand. I want to thank you very much at this 19 time for the group that met here in February. 20 What I would like to do now is turn over 21 the mic to Dr. Tina Levine, who is the Division 22 Director for our Health Effects Division to introduce 23 the other members of our team that have prepared for 24 the risk assessment. 25 We have had a number of folks fromFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 15

1 across multiple Divisions in the Pesticide Program 2 participating in this effort, as well as colleagues 3 from our Office of Research and Development and from 4 NIEHS who have been involved in our efforts to date, 5 and I will turn it over to Dr. Levine to introduce the 6 team members and acknowledge their contributions 7 DR. STEVEN HEERINGA: Thank you very 8 much, Steve. 9 DR. TINA LEVINE: Thank you very much. 10 I'd also like to extend my welcome to the Panel and 11 echo Steve's appreciation for your time and efforts. 12 Your feedback is an important component of improving 13 the scientific foundation on regulatory decisions. 14 I would also like to thank Joe Bailey 15 and the SAP Support Team for their help in making sure 16 these meetings run smoothly and putting together these 17 wonderful Panels. 18 Let me acknowledge the OPP Team: Ms. Anna Lowit; Ms. 19 Mendez who we're very happy to see back, even though we 20 tried to keep her away; Chester Rodriguez; John 21 Liccione; Marquita King; Jessica Kidwell as well as 22 many other toxicologists in HED, along with Nelson 23 Thurman and Mary Frankenberry from the Environmental 24 Fate and Effects Division, who contributed to the 25 current Issue Paper.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 16

1 I would also like to acknowledge the help of 2 Melanie Briscoe from PRD, who has provided a 3 significant amount of support to this effort. 4 And finally, I would like to thank the ORD Team: Ralph 5 Cooper; Susan Law, Tammy Stoker; Danielle Roddell; and 6 Jerry Goldman. Our colleagues at ORD are vital to this 7 ongoing re-evaluation, and we truly appreciate their 8 time and talents. 9 As Joe mentioned and Steve talked about, we 10 have conducted three SAP reviews this year. In 11 February we presented a Draft Framework for 12 incorporating epidemiology and human incident data into 13 our risk assessments. The February meeting included 14 two case studies involving atrazine. One of these case 15 studies looked at epidemiology studies involving 16 developmental outcomes. The same epidemiology study 17 included in the February case study will be discussed 18 today in the larger context of the total weight of 19 evidence across the non-cancer epidemiology database. 20 The second atrazine case study discussed in 21 February involved an ongoing collaborative project with 22 the principal investigators of the Ag Health Study. To 23 keep the Panel and the public up-to-date on that 24 collaborative project, we provided a short update in 25 the Appendices of the Issue Paper.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 17

1 In preparation for April, we reviewed 2 approximately 100 experimental toxicology studies that 3 were published involving atrazine, that were published 4 since 2003, and considered a wide variety of toxic 5 effects including immunotoxicity, neurotoxicity and 6 effects on steroidogenesis. 7 At the April meeting we placed a strong 8 emphasis on Mode of Action. This involved updating and 9 reaffirming the key events related to the hypothalamic10 pituitary-gonadal axis, what we typically call the HPG 11 axis, which were previously supported by the SAP in 12 2000. 13 The April Issue Paper also described emerging 14 data on the effects of atrazine on the hypothalamic15 pituitary-adrenal axis, what we call the HPA axis. At 16 that meeting, the Panel agreed with us that the HPG 17 axis remains the major pathway for atrazine toxicity. 18 With respect to the HPA axis, we heard from you that 19 these new studies provided a biologically plausible 20 hypothesis, but that a little work needed to be done to 21 clarify existing uncertainties. 22 At the April meeting, we also presented some 23 proposed approaches for evaluating drinking-water 24 monitoring data. As you'll see from today's 25 presentation, we've taken your advice on usingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 18

1 monitoring datasets based upon more intensive sampling, 2 and we'll present a general framework for developing a 3 monitoring program tailored to the endpoints we are 4 discussing this week. 5 This week's meeting brings the topics from 6 February and April together in a single integrated 7 analysis. We'll be discussing a wide array of topics 8 such as non-cancer epidemiology, statistical approaches 9 for evaluating drinking-water monitoring and 10 pharmacokinetics. We will also hear from Dr. Sue 11 Fenton, formally of EPA and now with NIEHS, about her 12 work on atrazine and the developing mammary gland. 13 The literature review contained in the Paper 14 is current up to July 15. There have been new atrazine 15 studies published since July 15. These new papers are 16 not in the Agency's Issue Paper and will not be 17 discussed this week. We expect to hold another SAP on 18 human health effects of atrazine in 2011 to cover 19 remaining issues regarding non-cancerous risk 20 assessment and cancer epidemiology. The 2011 meeting 21 has not been scheduled. When it is scheduled, we will 22 review all studies that become available from this past 23 mid-July until approximately two months prior to the 24 meeting. 25 We look forward to your thoughtfulFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 19

1 deliberations over the next few weeks, and now I'd like 2 to turn the microphone over to Dr. Anna Lowit, who will 3 begin our scientific presentations. Thank you. 4 DR. STEVEN HEERINGA: Thank you. Dr. 5 Levine. 6 Dr. Lowit. 7 DR. ANNA LOWIT: Good morning. I would 8 like to reiterate Steve and Tina's appreciation for all 9 of your time and thank you for coming. Some of you are 10 here for your third atrazine this year, and some of you 11 are on your first. 12 A couple of years ago, the NRC published 13 a couple of new reports, NAS reports. One of those 14 reports, science and physicians' reports, encourages 15 the Agency to do a couple of things and one of those in 16 particular is to make our risk assessments more useful 17 and to have more utility. 18 And the primary way to do that is 19 through problem formulation by better linking the 20 purpose and scope of the risk assessment more in line 21 with risk-management goals and needs. 22 And as you will see from the next few 23 days with atrazine, this is really an excellent case 24 study of doing that sort of thing: of focusing on 25 problem formulation across a broad spectrum ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 20

1 scientific issues, and to think about the needs of the 2 Risk Managers as it relates to the drinking-water 3 monitoring, and to align those things in a way to 4 answer two straightforward although very complex 5 questions: do we need to do any risk assessment; and 6 to evaluate the drinking-water monitoring frequency. 7 So all those questions may be relatively 8 straightforward. 9 As you can tell from the complexity of 10 this Panel, it's a very complex set of issues. 11 You'll hear a combination. In order to 12 align the risk assessment with the risk-management 13 needs, we have a very multi-disciplinary interactive 14 team, as you will see from the presentations. 15 You will see a discussion of hazard 16 assessment around sensitive life stages, endpoints, 17 duration of exposure, but also a very detailed analysis 18 of statistics around drinking-water exposure. 19 But then at the end of the week, we'll 20 take all of those topics and will try to overlay them 21 on top of each other and think about them in an 22 integrative way of how to take durations of exposure, 23 Mode of Action, life-stage sensitivity and think about 24 drinking-water exposure and monitoring and how those 25 things come together to help think about the drinking-FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 21

1 water monitoring frequency question. 2 Prior to the 2010 review, atrazine has 3 been through the previous risk assessment has been 4 through a significant amount of peer review, starting 5 with the SAP 1988 on mammary gland tumors in rats. 6 That was followed up about 12 years later with the 2000 7 SAP which you heard Tina and Steve refer to, where the 8 key events and Mode of Action were evaluated. 9 And at that time it was really related 10 to development of the mammary gland tumor. One of the 11 outcomes of that meeting was that although those 12 mammary gland tumors in the adult rat may not be 13 relevant for human, the reproductive and developmental 14 findings from atrazine related to the Mode of Action 15 certainly are relevant for human. 16 In 2003 there was an evaluation of 17 prostate cancer epidemiology, and we anticipate in 2011 18 when we do our SAP review on cancer epidemiology that 19 we will build on that 2003 analysis. 20 You have heard quite a bit from Dr. 21 Levine. We have in this year, this is our third 22 meeting. And not to reiterate her points, but I think 23 it's worth noting that the meetings are really intended 24 to build on each other; that the February meeting was a 25 very broad meeting about framework, of how to thinkFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 22

1 about bringing epidemiology and experimental toxicology 2 data together into an analysis for our risk assessment 3 and to really focus on problem formulation in bringing 4 those things together. 5 We did at that time have two atrazine 6 case studies, one on developmental epidemiology 7 studies, which you will hear Dr. Christensen talk 8 about. The other one is a collaborative effort with 9 principal investigators at the Agricultural Health 10 Study. It's a project we're very excited about that's 11 moving on at a nice, steady pace that I expect you will 12 hear more about in 2011. 13 In April we focused entirely on 14 experimental toxicology data in drinking-water 15 analysis. And so today and this week, we are going to 16 bring all of it back together and talk about it in one 17 integrative week. 18 So to focus a little bit on April, just 19 to lay some of the groundwork of how we got to what's 20 in the Issue Paper and the presentations you'll hear 21 today, one of the important outcomes of the April 22 meeting was a reaffirmation of the key events related 23 to the HPG axis. Certainly we're going to I think 24 probably talk a great deal about LH, luteinizing 25 hormone, in the next few days.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 23

1 The other important part that was 2 discussed at the April meeting was emerging data around 3 the HPA axis, and as Dr. Levine indicated, it was the 4 consensus of the Panel that although they're very 5 plausible biological hypotheses, there are still some 6 existing uncertainties in those data. And as a result, 7 the current Issue Paper in our plan right now is to 8 focus on the HPG axis as it relates to selecting 9 endpoints for risk assessment. 10 Another important point that came out of 11 the April meeting as it relates to endpoint selection 12 and thinking about durations of exposure for risk 13 assessment is that the April Panel provided the 14 conclusion that seemingly effects from corticosterone 15 and ACTH were not relevant for risk assessment. 16 And I guess the last important point 17 from April around the hazard assessment brings us to 18 the focus on LH; that at that meeting we reviewed close 19 to 100 studies on many different effects and that even 20 as of right now we're unaware of any studies around 21 immunotoxicology, neurotox, steroidogenesis or a 22 variety of other things that provide in vivo doses and 23 those relevant things eliciting attenuation of LH. And 24 that's important from a risk-assessment point of view, 25 because you want to be regulating at the most sensitiveFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 24

1 level. 2 Also in April on the drinking-water 3 monitoring side, we provided a few examples of how to 4 think about evaluating data, and we proposed some 5 modeling approaches. We did hear strongly from the 6 Panel that we needed to be using datasets that had 7 higher frequency monitoring, and that has been 8 certainly you'll hear a lot about that today. 9 Okay, so how does that move us into what 10 we're going to do this week? We have reviewed a number 11 of non-cancer epidemiology studies that includes the 12 studies that were evaluated for the February meeting, 13 along with a number of other outcomes. 14 The feedback that we got from the Panel 15 in April on our reviews on how to think about those 16 studies, how to think about those studies in respect to 17 thinking of our risk assessment, was very helpful as we 18 broadened that analysis to add another influence. 19 As Dr. Levine said, Dr. Suzanne Fenton 20 will be here first thing in the morning to give her 21 presentation on her own work on the development of the 22 mammary gland. So you will not hear a presentation 23 from the Agency today on that, though she will be 24 presenting her own data. 25 There are reviews of both Dr. Fenton'sFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 25

1 work and the newer study submitted by Syngenta, which 2 is one of the Coder studies, are contained in Appendix 3 A. Particularly the Rayner and the Coder studies, the 4 effects that are seen are very high doses at around 100 5 milligrams per kilogram, which is something about 50 6 times higher than the value that we have estimated for 7 our point of departure for LH. 8 The other study out of the Fenton lab is 9 a mixture study. It is what we often call the Enoch 10 study. It is unique among those mammary gland 11 development studies, in that the doses are quite low 12 and they actually include a dose lower than the LH 13 endpoint. 14 We do have some concerns about the 15 design of that study. We have concerns about how the 16 design affects the interpretation. We also have 17 concerns about how the mammary glands are evaluated and 18 how we evaluate the different dose-responses. So we 19 are really looking forward to the comments from the 20 Panel on the study design and interpretation of dose21 responses there. 22 Okay. So the current Issue Paper is, as 23 far as we know, and we believe this to be true, the 24 literature review is up-to-date as of July 15th. That 25 analysis, the new studies are from January 30th to JulyFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 26

1 15th, which is essentially our cutoff from the February 2 meeting -- excuse me, our cutoff from the April meeting 3 our cutoff from the April meeting up until the cutoff 4 for the current meeting. 5 All of those reviews are contained in 6 Appendix A, and for those of you who were here in April 7 and you looked at Appendix A and it seemed a lot of it 8 seemed the same. Well, it was actually intentional. 9 We wanted to, partly as a tool for transparency, partly 10 because we knew there would be some new members on the 11 Panel, we decided to include all of the reviews from 12 both April and the new ones in September. But 13 hopefully we had a table at the beginning, so you were 14 able to figure out what was the new part. 15 One of the really important parts of the 16 new Issue Paper is our proposals for dose-response 17 assessment. As we move out of caudal formulation into 18 a, sort of a new analysis phase, we have reviewed many, 19 many new experimental toxicology studies. 20 We've reviewed quite a few epidemiology 21 studies. And as you'll hear from Dr. Christensen, our 22 view is that those epidemiology studies, although very 23 informative for human relevance and qualitative 24 characterization, don't provide the quantitative 25 support for using in a risk assessment, so that ourFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 27

1 proposal is to rely on the animal data. 2 We heard you loud and clear also in 3 April to think about doing internal dosimetry and doing 4 benchmark dose analysis, so you will certainly hear 5 about that today. 6 Another area that you'll hear from 7 Nelson Thurman and Mary Frankenberry is around the 8 drinking-water exposure analysis. That will be another 9 area where certainly the Panels from April will hear 10 that we heard your advice loud and clear and that we 11 have moved forward with that. We're proposing a 12 framework of how to think about the water monitoring. 13 Later in the afternoon we're going to 14 have two presentations on two what I like to think of 15 as integrative topics, the first one being the analysis 16 for the FQPA safety factor, and that's an integrative 17 analysis because it by statutory requirement has to 18 include both hazard and exposure together, and we'll 19 see an evaluation. 20 It is important to remember that that 21 analysis is still going on. We have not proposed an 22 update to the FQPA factor used back in 2003 in the red, 23 and we are going to wait to propose that factor until 24 there are some important toxicology studies ongoing and 25 until the drinking-water analysis is much furtherFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 28

1 along. 2 But we are soliciting comment from all 3 of you on the important scientific factors around 4 drinking water and hazard to think about as we move 5 forward in the coming months to complete that analysis. 6 The second multidisciplinary area, which 7 is really the crux of a lot of this, is pulling it all 8 together in one bucket, I guess, for lack of a 9 scientific word, is the implication of the toxicology 10 in the Mode of Action on the critical duration of 11 exposure. 12 And that is a very important part of 13 this whole re-evaluation, the reason being is that 14 critical duration of exposure then goes into the 15 drinking-water analysis to determine the averaging 16 time, and you'll see some from some of the 17 presentations we will rely on. 18 As we move forward in the next few 19 months and the next year, we do have some issues that 20 we will be talking with the SAP on. We will be having 21 an SAP on human health in 2011. As Dr. Levine noted, 22 it has not been scheduled yet. But at that meeting, we 23 will pick up our external toxicology literature review 24 from July 15th forward; so things that have come out in 25 the last month will be included in that 2011 review.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 29

1 And we will follow up with proposals for 2 the duration of exposure. That's important as it 3 relates to drinking water monitoring frequency. We 4 will discuss with you our more complete analysis around 5 life-stage sensitivity as it relates to the FQPA 6 factor. And obviously, we will have a very detailed 7 analysis of drinking water at that time. 8 We'll also be talking extensively about 9 cancer, particularly epidemiology, as we await the 10 findings of a new study from the Agricultural Health 11 Study on atrazine. And at that point, we hope that 12 we'll be able to do an integrated weighted analysis 13 around cancer doing the epidemiology and the toxicology 14 more in light in the way that we've done the non-cancer 15 for this meeting. 16 So with that, this will sort of give you 17 a bird's eye view of the presentations. They are 18 largely intended to build on each other. We'll start 19 with non-cancer epidemiology, which is in my mind a 20 continuation of problem formulation. As we largely 21 focused on that in April and on experimental 22 toxicology, this is our epidemiology review in thinking 23 about how to use those data in risk assessment. 24 That presentation will be followed by 25 Dr. Chester Rodriguez, who will have a fairly lengthyFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 30

1 presentation on our proposals for dose-response 2 assessment, and many of you were here; you gave us some 3 recommendations, and we certainly took them to heart 4 and have done quite a lot of work on that. 5 After that, you will hear -- the end of 6 Dr. Rodriguez's presentation will talk about how to 7 think about the possible dosimetrics to link them to 8 drinking-water monitoring. So the natural presentation 9 after that is actually the presentation on drinking10 water monitoring. 11 After Nelson and Mary give their 12 presentation, I will come back and give the two 13 integrative presentations around the sensitivity, the 14 life-stage sensitivity, the FQPA factor and the 15 implications, pulling it all together to think about 16 durations of exposure. 17 And just on a personal and a 18 professional note, Dr. Elizabeth Mendez is not giving a 19 presentation today, but she has been instrumental in 20 every single one of them. And we are glad she is back, 21 even if it's just for a couple of hours. 22 DR. STEVEN HEERINGA: Thank you, Dr. 23 Lowit. 24 And just for the Panel and for the 25 audience, too, in terms of the proceedings today, weFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 31

1 have this set of presentations by the Scientific Staff 2 of the EPA that are scheduled for today and then 3 another tomorrow morning to start from Dr. Fenton. 4 There's plenty of time in today's schedule I believe 5 for exchange on questions and clarification. 6 It's not going to be an intense day. 7 Proceedings will become much more intense and time8 limited as we move into the period of public comment 9 and Charge Questions later on; it's just the way the 10 meeting is structured, and we probably can't change 11 that. 12 But at this point before we move on to 13 Dr. Christensen, are there any questions or forwarding 14 questions or clarification for Dr. Lowit based on her 15 discussion of the proceedings from February and April 16 in terms of this meeting? 17 Okay, Dr. Christensen. 18 DR. CAROL CHRISTENSEN: Thank you, and 19 good morning. Again, my name is Carol Christensen, I'm 20 an epidemiologist with the Health Effects Division, and 21 over the next several minutes I'll be reviewing with 22 you our evaluation of the atrazine non-cancer 23 epidemiology literature. 24 So to provide a little bit of background 25 about how we got here, previous our discussions we'veFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 32

1 had with the Panel with reference to epidemiology data, 2 as well as review the purpose of our current 3 evaluation. 4 I'll touch upon in this presentation our 5 methodology or the way in which we selected these 6 studies included in our review, and I'll briefly 7 summarize those studies. Mainly we're focusing on the 8 results of those investigations and some of the major 9 strengths and limitations. Our full analysis is of 10 course presented in the written materials. 11 Looking across this database, we 12 discussed the information available to inform our, our 13 informal causal inference, including a discussion of 14 other non-causal explanations for the associations 15 observed. In synthesizing and integrating across both 16 the observational and the experimental datasets to 17 inform risk assessment, I will conclude with our 18 summary of the two, the two data streams and our 19 proposed next steps. 20 So by way of background as has been 21 mentioned, in February of this year EPA presented its 22 Framework for incorporating epidemiologic research into 23 our pesticide resistance process, and at that time we 24 articulated our attention to explicitly consider these 25 kinds of observational epidemiologic data in the publicFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 33

1 formulation phase of risk assessment. 2 Very briefly, as has been mentioned and 3 just to be very clear, several studies related to both 4 fetal and perinatal outcomes were presented to the 5 Panel in February of this year and for the sake of our 6 comprehensive evaluation they are also included this 7 morning as well as the written materials. 8 And as Anna just mentioned, this review 9 reflects our assessment of the non-cancer epidemiology 10 literature as our evaluation of atrazine potential for 11 carcinogenic effects will occur in 2011. 12 So the purpose of our review again of 13 course is to identify non-cancer epidemiology studies 14 to inform risk assessment; so looking at investigations 15 of atrazine exposure in association with several 16 different health outcomes within the human population. 17 Our evaluation included assessment of the strengths and 18 limitations of each study individually. 19 This information is presented in 20 Appendix B2, as well as synthesizing and integrating it 21 across the observational epidemiology databases in 22 Section 3.0. So the results of the non-cancer epi 23 studies have been integrated with the experimental 24 database to formulate our overall conclusions and 25 proposed next steps in Section 4. Of course, theseFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 34

1 will be modified by certain criteria and scientific 2 judgment as we look forward to the Panel in February of 3 this year. 4 So again back in February, the Panel 5 provided several very helpful comments to the Agency as 6 far as elements to consider for good-quality 7 epidemiology studies that are to be used to inform our 8 risk-assessment process. This wasn't by any means 9 exhaustive, but some of the key points mentioned by the 10 Panel are clearly articulating whether the study is 11 hypothesis generating or hypothesis testing in nature; 12 of course, the assessment of the validity and 13 reliability of the exposure assessment; and outcome 14 ascertainment was identified as a key factor. 15 The measurements of potentially 16 confounding variables, particularly when those 17 variables are measured in the same way between 18 comparison groups is also mentioned. And sample size 19 and statistical power, particularly the statistical 20 power to look at sub-analyses, sensitivity analyses -- 21 for example, the influence of a third variable -- or 22 effect modification in the association under study was 23 also mentioned. 24 So, again, by no means exhaustive. The 25 Panel's recommendations were certainly very, veryFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 35

1 helpful in the completion of our evaluation about these 2 non-cancer health effects. 3 So our literature review methodology, 4 the way in which we identified these studies, of course 5 looking for evaluations of non-cancer adverse health 6 outcomes in the open literature. We went to common 7 major/minor databases: Pubmed, Science, limited use of 8 Google Scholar. 9 We identified search criteria, 10 delineated in Appendix B1 of that material, and as well 11 as we utilized some citation mapping available through 12 Web of Science, we had hand searching of key reference 13 points and so on. 14 So we identified several hundred studies 15 initially that could potentially be included in this 16 review; however, upon review of both the title abstract 17 and the full text realized that not all of them were 18 appropriate to this question. 19 For example, some of them were really 20 exposure-only studies that did not measure association 21 with the non-cancer health outcomes, so they were 22 excluded. Several studies did not measure atrazine and 23 triazine specifically, so of course were excluded from 24 our consideration of the question today. 25 There were a handful that reflectedFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 36

1 essentially case reports of acute pesticide poisoning, 2 and all very important in the literature review and one 3 instance in which the full typed document was not 4 available and several in which the studies were not in 5 fact original research but reflected editorial or human 6 opinion pieces. 7 So applying those criteria, we 8 identified 19 studies of non-cancer health effects for 9 our epidemiology literature review. So very briefly, 10 in contrast to the studies brought to the Panel's 11 attention in February of this year, the 19 studies 12 included in our lit review: several different study 13 designs, cohort case control, master case control, as 14 well as the ecologic studies presented back in 15 February. 16 In addition, it's notable that several 17 of these investigations took place within ongoing long18 term epidemiology studies: the Agricultural Health 19 Study; the Ontario farm family study; the Study for 20 Future Families; now they are using a vested or hybrid 21 study on childhood deficiencies and good control 22 device. 23 However, looking across the database, in 24 many instances exposures have been most limited, 25 particularly for our consideration of these inFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 37

1 quantitative risk assessments. 2 So these 19 studies can generally fall 3 into the following categories of male and female 4 reproductive health, fetal and perinatal outcomes, as 5 well as respiratory health. And the next several 6 slides kind of delineate the study results by major 7 health effects topic area. 8 So with regards to female reproductive 9 health, we identified three studies, all of which took 10 place within the Agricultural Health Study. Very, very 11 briefly, the Ag Health Study is a long-term, 12 prospective, cohort investigation of private and 13 commercial pesticide applicators in Iowa and in North 14 Carolina. 15 The study also asked spouses of licensed 16 pesticide applicators to participate in this study, and 17 in many instances the spouses are female; so that 18 comprised largely the sample population for these 19 investigations of female reproductive health hazards. 20 Some authors published an increase -- 21 they observed an increased odds of long and missed 22 menstrual cycles in association with atrazine exposure 23 to atrazine or lindane users - and it should be quite 24 clear in this investigation authors were not able to 25 make an estimate of atrazine only in association withFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 38

1 menstrual-cycle characteristics; they grouped atrazine 2 or lindane users and compared that to non-users of any 3 pesticide. 4 In addition, a similar group of authors 5 a few years later published a study in which they 6 observed a delay in the timing of menopause in 7 association with ever use of atrazine over a lifetime; 8 and I should clarify that exposure was assessed by a 9 questionnaire, and the exposure metric of ever/never 10 use over a lifetime was used in these studies. 11 And excuse me, lastly, authors published 12 an elevated odds of gestational diabetes among women 13 who reported ever using atrazine over a lifetime who 14 also reported engaging in direct application of 15 pesticides during the first trimester of pregnancy. So 16 I want to be very clear that this observation was only 17 among women who reported using any pesticides during 18 the first trimester of pregnancy. 19 So looking across these three 20 investigations of female reproductive health outcomes, 21 there are several strengths as well as limitations, 22 these studies did measure atrazine exposure on an 23 individual level versus an aggregate level, all taking 24 place within the Ag Health Study, which is a large, 25 relatively highly exposed sample and is appropriate toFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 39

1 investigate this question. 2 However, as I mentioned, particularly 3 with respect to menstrual-cycle characteristics, 4 authors were not able to evaluate atrazine 5 specifically. In addition, the measurement of timing 6 of exposure is somewhat limited, again using that 7 ever/never exposure metric over the course of a 8 lifetime. 9 The frequency of assessment of menopause 10 was minimal in that investigation. Menopause is not a 11 one-time event. It occurs sometimes over several years 12 and, as the authors acknowledge, a more frequent 13 assessment of the occurrence of menopause would have 14 aided that investigation. 15 And the potential for residual 16 confounding by physical activity, both the menstrual17 cycle paper and the menopause paper used occupational 18 physical activity; but authors acknowledged the 19 potential for residual confounding by physical activity 20 as well. 21 So overall, these three studies we feel 22 are supportive of a hypothesis that atrazine may affect 23 the hormonal review in such a way as to possibly 24 influence reproductive health outcomes. 25 So with regard to male reproductiveFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 40

1 health; we just found one study. Authors looked at 2 semen parameters in association with urinary 3 concentration of atrazine mercapturate. They never 4 calculated purely for atrazine. 5 Authors reported an eleven-fold elevated 6 odds of poor semen quality in association with atrazine 7 use, and authors defined "poor semen quality" as "sperm 8 concentration below the population median". However, 9 as you know, the observations are significantly 10 elevated but with a very large confidence interval, 11 indicating lack of precision with regard to the small 12 sample size. 13 In additional analysis within the study, 14 authors reported that individuals with more than one 15 different pesticide analyte measured in the urine were 16 more likely to have "poor semen parameters", which were 17 lack of sperm concentration, morphology and motility. 18 So in this study, the use of biomarkers 19 of both atrazine exposure and semen parameters is 20 considered a strength. The analytic techniques, both 21 the statistical and the laboratory-based techniques, 22 are sufficient to maximize the information gained from 23 this evaluation. 24 However, it does reflect a relatively 25 small sample size for some of the comparisons served,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 41

1 and it's been noted subsequent to the publication of 2 this study that atrazine mercapturate, indeed likely 3 underestimates total atrazine exposure, researchers 4 with CDC laboratories suggest the measurement of 5 additional urinary metabolites would be fruitful for 6 future epidemiologic evaluations. And the potential 7 for confounding by other pesticides or other 8 environmental exposures is also possible. 9 So overall this one study that we 10 identified in reproductive health is suggestive of a 11 possible association; however, we need replications to 12 fully inform the nature of this association. 13 Several studies looked at fetal and 14 perinatal outcomes, two studies within the Ontario farm 15 study. The long-term investigation looked at 16 miscarriage or spontaneous abortion. Across these two 17 studies, authors recorded a 20 to 40% non-communicative 18 elevated odds of miscarriage in association with 19 herbicide and/or atrazine use. 20 These two studies, the strength of these 21 two studies include the fact that to be eligible to 22 participate in the Ontario farm family study, both male 23 and female partners in the pregnancy had to live and 24 work on the site, so it is a highly exposed population 25 to investigate this question. And as a case controlFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 42

1 study, design is highly efficient and it significantly 2 reduces the possibility of recall bias. 3 However, limitation, the main limitation 4 is the essentially probabilistic nature of the exposure 5 assessment. In this evaluation, participants were 6 asked individually whether they engaged in a farm 7 activity that could have included a pesticide 8 application and separately the specific use of 9 pesticides was assessed at the farm level, not at the 10 individual level. 11 So authors assumed that those who 12 reported engaging in a pesticide activity and reported 13 use of specific pesticides were in fact the same 14 people; but it's essentially a probabilistic method of 15 exposure assessment, as acknowledged by the authors. 16 So largely because of this method of 17 exposure assessment and the fact that both the male and 18 female partners were living and working on the farm, 19 it's very difficult to isolate the influence of either 20 male or female exposures specifically in relation to 21 this fetal outcome. Also, the period of recall for 22 birth characteristics or pregnancy characteristics is 23 quite lengthy in some instances. 24 So overall, we feel these investigations 25 reflect initial evaluations of this question. BothFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 43

1 studies published over ten years ago. However, there 2 are several major limitations to consider in the 3 incorporation of these data into our assessment 4 process. 5 Similarly, we identified several 6 epidemiology investigations about birth defects in 7 association with atrazine exposure. And just to note 8 for the Panel, many of these studies or several of 9 these studies were proffered in February of this year: 10 for instance, the study by Mattix, et al., which is an 11 ecologic study we discussed back in February but very 12 briefly. 13 These authors, again using an ecologic 14 study, compared the rates of abdominal-wall defects in 15 the State of Indiana to the U.S. as a whole, reported 16 that the rates were higher in Indiana as compared to 17 the U.S. in an area of high use of atrazine. 18 Authors also reported they observed a 19 statistically significant correlation between monthly 20 concentration of atrazine in surface water and monthly 21 rates of abdominal-wall defects in the State of 22 Indiana. 23 Another study that looked at abdominal 24 wall or a type of abdominal wall defect specifically 25 was published recently in this year. In February ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 44

1 this year Waller, et al., reported a statistically 2 significant 40% to 60% increased odds of gastroschisis. 3 Gastroschisis is one of the two major types of 4 abdominal wall defects in association with maternal 5 residence being close to an area of a high atrazine 6 USGS monitoring site. Authors define "high" as being 7 greater than the EPA limit. 8 Authors in this study also reported a 9 positive long-term exposure response relationship 10 between maternal distance from the high USGS monitoring 11 site and the gastroschisis. 12 Two other studies, also both of these 13 brought to the SAP in February of this year, looked at 14 atrazine exposure in association with several different 15 types of birth defects, so not just abdominal wall 16 defects. Winchester, et al., in 2009 reported a 17 statistically significant correlation between 11 and 22 18 different birth defects that they were able to evaluate 19 in association with the estimated time of conception 20 during the spring months versus all other seasons of 21 the year. 22 I won't recite the 11 to 22 here, but I 23 will note that the authors did not observe a 24 significant difference between the rates of 25 omphalocele, which is a second major type of abdominalFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 45

1 wall defect, in association with the estimated time of 2 conception in the spring. 3 In a second evaluation, authors reported 4 a 20% increase of lumen abnormalities among women who 5 resided closer versus further away from corn and 6 soybean fields. So atrazine exposure was estimated by 7 proximity of maternal residence to corn and soybean 8 fields in this study. 9 These authors also looked at several 10 different types of birth defects, and I will just note 11 that they observed a non-significant 50% increased odds 12 of abdominal cavity defects -- not quite the same as 13 abdominal wall defects or gastroschisis or omphalocele, 14 but I'm trying to kind of tie together across the 15 studies for you. 16 So the evaluation of these birth defect 17 investigations, several of which are hypothesis18 generating to suggest associations in a similar 19 direction; however, the ecologic nature of these 20 studies or these exposure surrogates which were not 21 validated are significant limitations to this dataset 22 under reporting of birth defects which we examined back 23 in February and has been given consideration in the 24 observational work on this type of outcome and 25 certainly after, after here.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 46

1 So in conclusion, several of the 2 hypothesis-generating studies suggest that atrazine may 3 play a role in developmental outcomes; however, there 4 are several uncertainties, largely having to do with 5 the study design and the method of exposure assessment. 6 We also identified a number of 7 investigations that had adverse birth outcomes. The 8 effects were small for gestational age. Two studies 9 which looked at this question reported no association 10 when they modeled atrazine exposure over the entire 11 pregnancy period; however, when authors were able to 12 isolate atrazine exposure during specific periods of 13 the pregnancy, two investigations reported that a 20% 14 and 50% increased odds respectively if the third 15 trimester overlapped a period of high atrazine use, 16 essentially that spring/summer period in which atrazine 17 is typically used at peak levels. 18 And for the sake of completeness, I'll 19 note an ecologic study published over ten years ago 20 which reported a significant correlation between 21 residents in a county in which high atrazine levels 22 were measured on USGS sites with intrauterine growth 23 retardation. 24 Intrauterine growth retardation is 25 exactly an effect of that gene, but we suspected theyFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 47

1 are probably measuring a very similar outcomes on 2 grouping them here together. 3 Some of these same authors also looked 4 at preterm-delivery and low-birth-weight adverse birth 5 outcomes in association with atrazine exposure, three 6 of which reported no significant association. One 7 study by Savitz, et al., in 1997 is within the Ontario 8 farm family study, that same study I mentioned with 9 reference to miscarriage. 10 In that same study authors reported a 11 two- to fourfold increased odds of the male partner who 12 was exposed in the preconception period. Again, this 13 is utilizing that probabilistic method of exposure of 14 living on the farm site. 15 And Villanueva also reported a 30% 16 increased odds of preterm delivery if the first 17 trimester overlaps the period by half a season, 18 essentially the spring-type period, and that was based 19 upon a sub-analysis. 20 So for low birth weight, three 21 investigations reported no association; and that same 22 Villanueva study also looked at low birth weight, 23 reported a non-significant 20% increased odds of low 24 birth weight in association with atrazine exposure. 25 And I should note both Villanueva and the Ochoa-AkuFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 48

1 study were able to measure atrazine as treated in 2 water, so getting a little bit closer to atrazine 3 exposure and water monitoring. 4 So the strength of these studies, again, 5 the two that I just mentioned, were fine measures of 6 atrazine exposure, i.e., atrazine in drinking water, 7 are somewhat consistent for the outcomes of small for 8 gestational age. Looking across these studies, both 9 the main analyses and the sub-analyses kind of give a 10 hint to critical windows of exposure. Those studies 11 are supposed to overlap at first trimester or third 12 trimester, for example. 13 However, there is still likely exposure 14 measured in air, keeping the challenges of measurement 15 exposure for this outcome, and the possibility of 16 unmeasured confounding other environmental effects that 17 can probably play a role in the association between 18 atrazine and birth outcomes. 19 So in conclusion, you can see it is just 20 a possible association with small for gestational age; 21 however, the evidence for preterm delivery and low 22 birth weight are more limited. 23 And finally, we identified one 24 investigation within the Ag Health Study. Every 25 atrazine in association with wheeze, wheeze had someFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 49

1 association associated with asthma and considered to be 2 sort of a tighter health outcome to try to measure via 3 questionnaire. Authors reported a statistically 4 significant 20% increased odds in association with ever 5 use of atrazine over a lifetime sorry, no, I'll 6 correct myself. 7 One of the strengths actually of this 8 investigation is that authors asked participants about 9 both atrazine use or all pesticide use and episodes of 10 wheeze within a similar recent time period. So this 11 comes up, these data were collected in a follow-up 12 questionnaire within the Agricultural Health Study, so 13 the questions were asked the last year or the last year 14 that you engaged in pesticide application, what 15 pesticide did you use and how many episodes of wheeze 16 did you experience. So that was a strength of the 17 study. 18 In addition, I have given you large 19 amounts of data collected in this cohort. Authors were 20 able to control for corn and grain dust and the 21 association between atrazine and these. 22 And collaborative -- that's essentially 23 a hypothesis-generating study -- atrazine was not among 24 the a priori hypotheses identified by the authors, and 25 at this time there is no biological mechanism proposedFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 50

1 to inform the nature of the observed association. 2 So this really reflects the initial 3 investigation. There are other studies and other work 4 ongoing on atrazine in specific and pesticides in 5 general within the Ag Health Study. 6 So looking across all of these data, 7 what does it say or how can it help us inform the 8 nature of the association or inform our ability to make 9 causal inference? 10 As we stated in the written materials, 11 at this time EPA cannot conclude that the associations 12 identified in the epidemiological database are indeed 13 causal in nature. The reason for this? Largely 14 because other non-causal explanations cannot be ruled 15 out or eliminated at this time. 16 As I suggested over the course of the 17 presentation, we made several biases, including the 18 possibility for exposure measurement error leading to 19 exposure. Misclassification is likely across these 20 studies; however, we feel in general the nature of this 21 misclassification would most likely be non22 differential. 23 With respect to the potential for 24 confounding measurement error, there are some 25 instances, particularly the measurement of physicalFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 51

1 activity in the recurrent count outcomes, as well as 2 the measurement of other pesticides and other 3 environmental exposures in the analysis of these 4 particular associations that would have enhanced the 5 precision of the respective studies. 6 In addition, some of the studies, as 7 well as the sub-analyses reported within the studies 8 that are relevant to our question of use of this data 9 in atrazine risk assessment. Our sample size is 10 relatively small, relatively low; cannot rule out the 11 possibility of chance in some of our observations. 12 So synthesizing within the active 13 database of these crops to the experimental toxicology 14 database, what can we learn from this evaluation? It 15 was notable that among the comparatively stronger 16 studies within the observational epi database, there is 17 some support from the toxicology database, as we note 18 on the next slide. 19 Just within the epidemiology database, 20 investigations of female reproductive cycle 21 functioning, timing of menopause were comparatively 22 stronger studies in our opinion. Our reasons for this 23 are the authors had a priori hypotheses regarding their 24 definition of hormone-reactive pesticides, which did 25 include atrazine, so they sort of came in with prior,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 52

1 prior knowledge or prior ideas about the nature of the 2 association. 3 The fact that in other methodological 4 work, the self-reported menstrual-cycle characteristic 5 was found to be relatively reliable, enhancing 6 measurable outcome. 7 As well within the Ag Health Study, 8 accuracy of the self-report information regarding very 9 specific chemicals have been shown to be relatively 10 higher on or at the top. 11 I'm looking at male reproductive 12 outcomes, the study by Swan, et al., in 2003. It had 13 biomarker exposure. As I mentioned, both statistical 14 and laboratory analytic methods were good. 15 And looking at adverse birth outcomes, 16 the two studies that measured atrazine exposure in 17 treated water, so closer to the actual exposure 18 profile, resulted in a relatively consistent estimate 19 for this outcome. I recall a 20% to 50% increased odds 20 in association with atrazine use if the third trimester 21 overlaps the period of higher atrazine use. 22 In addition, these studies were able to, 23 as I just mentioned, look at different periods of the 24 pregnancy, so inform a little bit better the timing of 25 the exposure, and in general the reporting of birthFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 53

1 characteristics or birth weight, the many factors that 2 go into defining this outcome have been shown to be 3 relatively reliable as well. 4 So as I mentioned, there is some 5 consistency in the experimental observations, as we've 6 heard with the female reproductive effects, male 7 reproductive effects, semen parameters and small for 8 gestational age. For example, the observation of 9 reduced pup weight in the toxicology data. 10 However, for several of the reasons that 11 I articulate here and identified throughout the 12 presentation, we feel these epidemiologic data are not 13 sufficient quality to include in our quantitative 14 assessment of atrazine. The lack of an exposure 15 response. 16 Measurement for many of these studies 17 used the ever/never use of atrazine over a lifetime or 18 atrazine concentration above or below the limit of 19 detection; plus they extended the exposure metric; lack 20 of individual-level exposure measurement in some of the 21 studies; and lack of validation for use of surrogates 22 for individual exposure is also a factor within our use 23 in quantitative risk assessment. 24 As noted, the use of the atrazine 25 mercapturate biomarker has been shown to underestimateFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 54

1 total atrazine exposure; other biomarkers are now 2 recommended for epidemiology studies. The ever/never 3 reporting of birth defects and the lack of precision in 4 most sample size were some main questions as well as 5 the sub-analyses were also important factors supporting 6 that statement. 7 So in conclusion, we feel the use of 8 these non-cancer epidemiology results will support and 9 inform our hazard characterization. As Dr. Lowit 10 mentioned, our use of these data are to be qualitative 11 and not quantitative in nature at this time. 12 These data do provide support for the 13 human relevance and the critical effects found in 14 graphs; however, on the biological plausibilty, we 15 cannot link the influence of atrazine on the 16 hypothalamic-pituitary-adrenal axis specifically to the 17 recorded outcomes and the classification of the data. 18 So for these reasons and others, we will 19 continue to rely upon the external toxicology data in 20 our quantitative risk assessment. 21 So that concludes my presentation. I'll 22 be happy to take any clarifying questions. I did, 23 however, want to of course acknowledge the good work of 24 many different people on the atrazine team for the 25 review and specific integration of these data, and IFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 55

1 particularly acknowledge my colleague, Dr. Tinelle 2 Logdall, who is here with us from our Office of 3 Research and Development, who also helped respond to 4 these clarifying questions. Thank you very much. 5 DR. STEVEN HEERINGA: Thank you, Dr. 6 Christensen. 7 At this point, I'll put it out to the 8 Panel for questions of clarification in Dr. 9 Christensen's presentation or really the content of the 10 Issue Paper and other supporting material that we've 11 had. We got our epidemiology studies to consider. Are 12 there any questions of clarification from the Panel? 13 Dr. Bucher? 14 DR. JOHN BUCHER: I'm John Bucher. This 15 isn't so much clarification, but I was wondering if EPA 16 has ever attempted any kind of exposure reconstruction 17 for any of these studies. 18 DR. CAROL CHRISTENSEN: No. We have 19 reviewed what's available to us in the published 20 literature; we've not engaged in that kind of original 21 work ourselves at this point in time. 22 DR. STEVEN HEERINGA: Dr. Harris? 23 DR. SHELLEY HARRIS: Thanks for your 24 presentation. I just had a question on how many people 25 reviewed the individual epidemiologic studies? Was itFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 56

1 one person that reviewed all of them, or was there a 2 team? 3 DR. CAROL CHRISTENSEN: No, as I 4 mention in my acknowledgments, it was certainly a team 5 effort, both in the selection or the application of the 6 exposure criteria and the selection of the individual 7 studies, the 19 studies that I just went over. 8 And it was a team effort in both the 9 review of the individual studies and some of this 10 across the database and in the tox database. Typically 11 how it worked one person would sort of draft the 12 initial review, and many others would review and 13 provide comment; we had several meetings along the way. 14 So it was certainly a group effort. 15 DR. SHELLEY HARRIS: Was there any 16 thought given to developing some kind of scoring system 17 for those papers, the ones they reached and passed the 18 inclusion criteria? 19 DR. CAROL CHRISTENSEN: No, we did not 20 apply any kind of quantitative criteria for the 21 selection of these. 22 DR. STEVEN HEERINGA: Any other 23 questions or clarifications or exploration in regard to 24 the logic of the work that's been done? 25 Dr. Portier.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 57

1 DR. KENNETH PORTIER: Dr. Bucher, this 2 is kind of an observation. As I was reading Section 3 3 in the Appendix, I kept thinking, "Well, you're telling 4 me what's really not good about these epi studies". 5 It would have been nice to see or to 6 think about what criteria would have represented a 7 really good, strong environmental epi study. I can 8 think of a lot of occupational studies where you get a 9 lot of good quantitative information that myself and a 10 number of other people at ACS, our epidemiologists have 11 this discussion all the time: 12 Well, what would make a good kind of 13 broad population study that would provide strong 14 evidence for the kind of risk assessment we're doing 15 here? And I don't get that in the report. I get a 16 feeling of what's not there, not what you would have 17 liked to have seen. 18 DR. STEVEN HEERINGA: Dr. Christensen, I 19 don't know if there's an answer to that. Dr. Portier 20 admitted that himself. But, feel free. 21 Any other questions or clarification 22 this morning? 23 Well, I think that again the pace today 24 will be a little more leisurely, which is good, 25 actually. It probably is a good pace, and so let'sFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 58

1 take a break and at this point let's give ourselves 2 about 25 minutes, reconvene at 25 after 10:00. Thank 3 you. 4 (WHEREUPON, a recess was taken.) 5 DR. STEVEN HEERINGA: Welcome back, 6 everyone, to the second half of our first morning 7 session. To be precise, Science Advisory Panel meeting 8 on the Re-Evaluation of the Human Health Effects of 9 Atrazine: Review of the Non-Cancer Effects and 10 Drinking Water Monitoring Frequency". 11 Prior to the break we had heard from Dr. 12 Christensen in sort of an overview on the epidemiologic 13 assessment that had been done based on literature 14 review, and at this point I think we're to hear from 15 Dr. Chester Rodriguez on Proposed Updates to the Dose16 Response Assessment. 17 DR. CHESTER RODRIGUEZ: So thank you 18 very much. 19 So like he was saying, I'm going to be 20 talking about the Proposed Updates to the Dose-Response 21 Assessment for atrazine. Just to give you a little 22 background and to put in context, you know, what I'm 23 going to be talking about. 24 In the previous risk assessment to 25 support up here in the red, the approach was based on aFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 59

1 NOAEL/LOAEL. The previous study was a 6-month rat 2 laboratory study, and the key event was hormone 3 attenuation. This current re-evaluation examines new 4 science and more sophisticated approaches. 5 That includes internal dosimetry and 6 benchmark of the dose model, two of the recommendations 7 from the April 2010 report. 8 So this is what the outline looks like. 9 I'm first going to be talking about the support for 10 internal dose-response assessment. Then I'm going to 11 be moving on to the temporal aspects of plasma 12 triazines, which will then lead to a bit to a 13 comparison of LH attenuation studies of different 14 repeated dose and durations. 15 Then I'm going to move on to propose a 16 daily steady-state area under the curve as an internal 17 dosimetric. And finally, I will conclude with 18 benchmark dose modeling and some indications for water 19 monitoring. 20 So there is good reason to actually do 21 an internal dose-response assessment. In a report 22 published by the National Research Council, it was 23 cited that the dose at a target site of the internal 24 dose is the ultimate determinant of risk. 25 As we move on to apply a Mode of ActionFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 60

1 approach to risk assessment, we need to be paying 2 attention to the pharmacokinetics, because that will 3 determine what internal dosimetry would that in turn 4 will lead to the Mode of Action of the key events which 5 ultimately would lead to the observed toxicity. 6 So my presentation, then, is mostly 7 concentrated on this part, the pharmacokinetics in 8 dealing with internal dosimetry. Now, talking 9 specifically about atrazine, okay, atrazine is quickly 10 and extensively metabolized as soon as it enters the 11 body through the oral route. 12 It first undergoes a first round of 13 dealkylation, mediated by the cytochrome of the P450 14 enzymes. And the result of that is the deviated two, 15 two mono-dealkylated metabolites. One is the 16 deethylatrazine, DEA; the other one is deisopropyl17 atrazine, DIA. 18 So the difference basically is that you 19 remove, okay, this ethyl group for this metabolite, and 20 then you remove this isopropyl group for the other 21 metabolite. 22 Another round of cytochrome P450 of 23 dealkylation then leads to the ultimate metabolite of a 24 Diaminochlorotriazine, or DACT. 25 So atrazine can also undergo aFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 61

1 glutathione conjugation by which that single chlorine 2 on the triazine ring gets substituted with a 3 glutathione residue, okay, and is conjugated, actually 4 can become part of this pathway as well. 5 So the fast metabolism of atrazine as a 6 parent chemical leads to a very short half-life in 7 vivo. 8 Now, there are no direct half-life 9 measurements for atrazine; but we can get a sense of 10 how short the half-life is from pharmacokinetic 11 metabolism studies. 12 For example, in a report by McMullin in 13 2003, it was reported that after dosing rats by oral 14 gavage with 90 mg/kg of atrazine, 30 minutes post dose 15 the parent chemical accounted for only 4% of total 16 plasma chlorotriazines, whereas that was really at more 17 than 50%. 18 Twenty-four hours post dose, the parent 19 chemical was very detectable in plasma, whereas that 20 actually was more than 98% of total chlorotriazines. 21 So these certain indications are very transient levels 22 of atrazine as a parent chemical. 23 There is another reason why there is 24 support for moving to an internal dose, dose-response 25 assessment. And that is that at least some of theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 62

1 metabolites are also active in attenuating LH. It has 2 been demonstrated by at least two different groups that 3 that actually has intrinsic activity at intramolar 4 levels of atrazine. 5 DEA and DIA are presumed to be active at 6 the base on the intact chlorinated structure, the 7 single chlorine. Now, glutathione conjugates are 8 presumed to be inactive in this process; however, the 9 only disposition studies that we have are actually 10 based on core of the radiolabel, a radiolabel that is 11 actually from the triazine ring. Therefore, 12 glutathione conjugates need to be included in the 13 internal dosimetry. 14 So in summary, in addition to just being 15 a good thing to do to do an internal dose-response 16 assessment, we have a very short in vivo half-life for 17 the parent chemical atrazine, and the activity of the 18 chlorinated metabolites are actually good reasons to 19 use internal dosimetry in dose-response assessment. 20 So based on this, then, we're actually 21 proposing to use an internal dosimetric that is based 22 on all triazine species without having to distinguish 23 between the parent and the active metabolites. We feel 24 that this dosimetric is conservative in case none of 25 the metabolites are active. Like I said before, it'sFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 63

1 actually convenient because the only pharmacokinetic 2 data that we have is actually based on this position of 3 a core of the radiolabel; that is, they don't 4 distinguish between the parent and the metabolites. 5 So now moving on, I'm going to be 6 talking then about the temporal aspects of plasma 7 triazines. For this, we paid careful attention to the 8 results of this study from the Cooper Lab, where they 9 dosed rats with a single dose of atrazine via oral 10 gavage, and the single dose was as high as 300 mg/kg. 11 And the result of the high single dose was actually 12 more this attenuation of the LH. 13 In contrast, when they tested in much 14 lower dose of 6 or lower and say 50 mg/kg/day given 15 once per day over 3 days, they almost got complete 16 attenuation with the exception of the 6-hour time 17 point. And as you can see from the results of these 18 studies, there was no NOAEL. There is no real dose19 response, because you get incomplete attenuation of the 20 LH response. 21 So from this study, then, we can 22 conclude that the effect is not governed by peak levels 23 -- that is Cmax -- and that it represents really a 24 repeated dosing effect. 25 So based on these two proposals that weFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 64

1 should be using total triazines for the reasons I cited 2 before and the additional observation that the 3 attenuation is not really a single dose effect, we are 4 proposing then to use as dosimetric the area under the 5 plasma concentration time curve that includes all 6 triazine species. 7 And the rationale for this is that this 8 dosimetric is really the product of concentration times 9 duration and this is a hypothetical view of a repeated 10 dosing event. So the area under this curve, then, 11 would be our proposed dosimetric. 12 So what does the plasma profile looks 13 like for triazine for repeated daily dosing? For that, 14 we turned our attention to Thede of the 1987 study that 15 was based on a C14 radiolabel of a triazine ring. And 16 as you can see, this is the triazine ring right here, 17 and all the carbons are actually carbon 14; so you have 18 the radiolabel at three different places. 19 So female rats were dosed daily for 10 20 days with a wide range of doses that included 1, 3, 7, 21 10, 50 and 100 mg/kg/day of atrazine. But actually 22 more importantly, though, plasma levels, were actually 23 monitored very frequently. 24 I think it was nearly every 24 hours. 25 And it included the elimination phase. So by far, thisFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 65

1 study provides the most thorough plasma concentration 2 time profile for triazine from repeated dosing with 3 atrazine, and we just want to make that clear. 4 So now this is what the plasma profile 5 looks like. And as you can see, then, dosing started 6 time 0, and plasma levels were actually monitored 7 almost every 24 hours. So one of the things that we 8 noted right away from this profile is that the plasma 9 levels do not change much up to 96 hours; that is after 10 4 days. 11 And that is for up for the dose groups 12 1, 3, 7 and 10. For the high dose groups of 50 and 13 100, plasma levels do not change much starting at 72 14 hours; that's 3 days. And they stay pretty much 15 constant during this period of continuous daily dosing. 16 They also monitor the elimination phase 17 for each of these dose groups. This is where daily 18 dosage stopped, and these plots here represent the 19 elimination phase. 20 So I am getting more evidence of this 21 notion of pseudo-steady state, because that's really 22 what we're talking about. This quad actually looks 23 like this. When you repeat a dosing, it goes up and 24 down, up and down, and they build up until you reach 25 what we are terming pseudo-steady state.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 66

1 So we wanted to get more evidence for 2 that. We came across this new study by Stoker, et al., 3 where pregnant Wistar rats were dosed daily with either 4 5 or 25 mg/kg/day of atrazine either for 3 or 7 days. 5 So basically we're comparing a 3-day exposure to a 7- 6 day exposure. And plasma levels were analyzed for 7 chlorotriazines at the end of the dosing period. 8 So these are the two groups right here. 9 So for this group, then, daily dosing took place from 10 gestation day 18 through 20. For the other group, 11 daily dosing was done from gestation day 14 through 20. 12 So as you can see from the different columns, the 13 individual chlorotriazines actually vary for the two 14 exposure groups, as you can see for that for DACT, for 15 DIA and DEA. But the sum of all of these in the last 16 column, this is total chlorotriazines. 17 Now, this is the 3-day exposure group. 18 This is the 7-day exposure group. As you can see, the 19 plasma levels actually should remain the same. And if 20 you want to take into account the variability, these 21 two numbers compared to these two numbers will be the 22 same. So basically there is no difference between the 23 3-day and the 7-day exposure groups. And that is 24 consistent with this notion of pseudo-steady state of 25 the plasma levels or triazines.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 67

1 So now I've been talking about pseudo2 steady state. What does it mean exactly? Well, for 3 pseudo-steady state plasma levels, plasma levels 4 actually increase upon repeated dosing until you get up 5 into a mean value, and then after that plasma levels 6 should remain pretty much the same so that they 7 oscillate around the mean value. So the magnitude of 8 this oscillations actually depends on the dose rate. 9 So the more frequent you dose, the 10 smaller the oscillation. And the oscillation will be 11 the smallest when you have an IV infusion, where this 12 would just be a line that would go like this. So as 13 you now, once you stop dosing them, you have an 14 elimination phase. But the critical aspect of pseudo15 steady state, at least for us, is that once you reach 16 these levels, plasma levels will remain fairly 17 constant, regardless of how long this repeated dosing 18 continues. And that is a powerful statement, at least 19 for us, because of the next slide coming up. 20 So now moving through the slide then, 21 I'm going to talk about the comparison of different 22 studies of different repeated dosing durations. So at 23 this point, then, we came up with a hypothesis. We 24 hypothesized that the level of LH attenuation will be 25 similar for studies that achieve the same level ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 68

1 pseudo-steady state plasma levels of triazines. 2 So that's the hypothesis, and in order 3 to proceed in investigating this hypothesis, what we 4 did is we compiled all these studies where daily dosing 5 with atrazine was done for at least four days, because 6 after four days you seem to get these pseudo-steady 7 state plasma levels. 8 We expressed the attenuation effect as 9 percent control to account for the difference in rat 10 strains and inter-lab variability. We only examined 11 doses that were less or equal to 30, because after that 12 the level of the effect does not really change; you 13 seem to get a plateau after that. 14 So we concentrated on the dose range 15 after 30 mg/kg/day atrazine. 16 So these are the studies that we 17 complied. At first and foremost, we had a Cooper, et 18 al., a brand-new study, doesn't publish. It is a 4-day 19 study in duration. The mode of oral dosing was by 20 gavage, and the atrazine doses that were evaluated was 21 as follows: 1.5, 3.12, 6.25, 12.5 and 25. And the 22 NOAEL of 3.12 was established with a LOAEL of 6.25. 23 We also came across this McMullin 2004 24 study with a repeated daily dosing duration was for 5 25 days. And the doses evaluated were 0 of course, 30,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 69

1 100 and 300. We only examined, like I mentioned 2 before, only the 30 of it, because the 100 and 300 were 3 high and they don't provide any additional information, 4 where this here is close to a plateau. There was no 5 NOAEL identified in this study. 6 We also looked at a summary of a 2001 7 study, which actually was for 1 month and covered doses 8 of 0, 2.5 and 5. We only looked at these two doses, 9 2.5 and 5, because of the same reasons that I cited 10 before. And the NOAEL for this study was actually 5. 11 Last but not lest, we looked at the critical 6-month 12 study that was used in the last risk assessment. 13 So the duration of this study is 14 actually 6 months, and the doses that were evaluated 15 were as follows: 0, 1.8, 3.65 and 29.4. The NOAEL and 16 LOAEL were 1.8 and 3.65. I just want to make a note of 17 that. because that was a critical study that was used 18 in the last risk assessment for a point of departures, 19 I think. 20 So when we plotted all these studies as 21 percent control well, actually, before getting to 22 that, let me just say a few things about the 4-day 23 study, because we're actually proposing to use it as a 24 critical study. 25 Basically, it was aimed at identifyingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 70

1 the NOAEL or LOAEL for the effect following repeated 2 daily dosing with atrazine. They used rats that were 3 regularly cycling. And the effect was evaluated over 4 the course of one full estrous cycle, which in the rat 5 is 4 consecutive days. 6 So dosing was performed via oral gavage 7 once per day beginning at 900 hours on the day of 8 vaginal estrus, and it continued on the day of diestrus 9 I and II, and it ended on the day of proestrus, at 10 which point the effect on LH was analyzed, evaluated. 11 So when we compared this 4-day study to 12 the other studies, they differed drastically. 13 Integration, this is what we saw. First of all, 14 though, this is the dose-response for the 4-day study 15 at the 1800-hour time point. That is the peak of the 16 LH surge. And like I said before, the NOAEL was set at 17 3.12 and the LOAEL 6.25 at this time point. 18 So then this is what we saw when we 19 compare the four different studies that actually differ 20 drastically, like I said before, in repeated daily 21 dosing durations. The thick line is the 4-day study, 22 and as you can see once again, you have a well-defined 23 dose-response. 24 But actually we were ecstatic when we 25 saw this, because basically you cannot differentiateFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 71

1 the NOAELs of the study. They're hard to tell, even 2 though, like I said before, they differ drastically in 3 repeated daily dosing durations. 4 And actually more importantly, I should 5 say that the single dose that we examined from the 6 McMullin 2004 5-day study was nearly on top of the 29.4 7 dose group for the 6-month study. So to me, this is 8 very remarkable that these studies are so similar. 9 So now moving on, so we covered, then, 10 the support for doing internal dose-response 11 assessment, the temporal aspects of plasma triazines, 12 and I just talked to you about comparing different 13 studies of different repeated daily dosing durations. 14 So now we're going to move on to what we're proposing 15 as an internal dosimetric. and that's a daily steady16 state area under the curve for total triazines. 17 So this is summary of the internal 18 dosimetric, of the internal dosimetry. I suppose that 19 it has to be based on total triazines. And the reason 20 for that is that we have a very short in vivo half-life 21 for atrazine as a parent chemical. 22 I also talked about the activity of the 23 chlorinated metabolites and the uncertainty about 24 glutathione metabolites. From this, then, we are 25 actually proposing to use the area under the plasmaFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 72

1 concentration time curve as the dosimetric, based on 2 the observation that the effect is not really a single3 dose effect, a Cmax effect, but a repeated dosing 4 effect. 5 So the grounds, then, for selecting this 6 dosimetric is that it accounts for levels as well as 7 duration of exposure. And when you add to that the 8 steady-state condition strongly associates with the 9 effect of attenuation of the ledge. 10 And when you include, then, the 11 following repeated daily exposure, you get what we call 12 pseudo- steady state of the plasma levels of triazines 13 by the fourth day in the rat. So when you add all of 14 this up, we propose an internal dosimetric that will 15 based on the daily steady-state area under the curve. 16 So what would this dosimetric look like 17 graphically? It would actually be one of these little 18 triangles that you see in the steady-state area of the 19 plasma profile. So it will be the area under each of 20 these rectangles. 21 But this is not really rectangles. What 22 they are, I actually try this always, and that leads me 23 to the next slide. For this analysis, we used a 24 classical trapezoidal rule, okay, in a non25 compartmental analysis, and we also used the linearFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 73

1 elimination phase assumption. 2 So the way you do this analysis, by the 3 way, if you've never done one of these, is that you 4 estimate the area under the curve to the last time 5 point. And we have a software package that will do 6 that for us. 7 Then you take these three data points, 8 which represents the elimination phase in your product, 9 and from the slope of this line, you can estimate the 10 elimination rate constant. So then you have the area 11 to the last time point; you have an estimate of the 12 remaining area, which will be from here all the way to 13 infinity. 14 With the assumption of linear kinetics 15 then, this area for the remaining part, is actually the 16 ratio of the plasma time point here, or the elimination 17 rate constant. So basically if you want to get an 18 estimate of the area from zero all the way to infinity, 19 you basically add these two up, and that is the basis 20 of the so-called trapezoidal rule in compartmental 21 analysis. 22 So we did this for each of those groups. 23 The one that I am showing you is for the 1 mg/kg/day of 24 the dose group; but we did them all. 25 Now, when we plotted the AUC for totalFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 74

1 triazines as a function of the atrazine dose -- excuse 2 me -- what we saw was a very linear relationship. I 3 mean, I could not believe that this was real data, 4 actually, after being so linear. 5 The dash line represents the 95% 6 confidence interval. 7 When we also plotted the pseudo-steady 8 state plasma levels as a function of atrazine dose, we 9 also saw a very nice linear relationship. 10 I should mention that the pseudo-steady 11 state of the daily area under the curve is just the 12 product of the steady-state serum levels times twenty13 four. So, these two plots are pretty much the same. 14 Except that they differ by only 24. 15 But the take-home message from this is 16 that we're seeing a linear pharmacokinetics within the 17 dose range of 1 all the way to 100. That suggests to 18 us that there are no dose-dependent changes in the 19 pharmacokinetics or total triazines that may preclude 20 the use of the daily area under the curve as internal 21 dosimetric. 22 So now, now I'm going to move on to 23 talking about benchmark dose modeling. This part of 24 the work, by the way, was done by Joanie Shione of our 25 group. And then I'm going to talk a little bit aboutFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 75

1 the implications on water monitoring. 2 So Joan, she did the same thing, okay, 3 that I did by compiling all the studies that could be 4 analyzed by benchmark dose model, and they started 5 studies and they came up with once again, the Cooper 6 4-day study was on top of the list on the basis that it 7 has a well-defined dose-response curve. 8 There was also a 1-month study by 9 Danelle, et al., a 1-month study by, authored by 10 Morseth, and the critical study that was used in the 11 last active risk assessment. I should, I should point 12 out that we use the Benchmark Dose Software, the latest 13 version. 14 The models that were analyzed were those 15 - okay, that are used for continuous data, like the 16 effect that we're seeing, less attenuation. These 17 other models that were evaluated -- exponential, Hill, 18 power, polynomial, linear -- the details of the 19 analysis that includes, okay, the basic criteria are 20 all in Appendix C of the Issue Paper. 21 But I'm going to talk -- but the main 22 point of this presentation, by the way, will be on the 23 Cooper of the 4-day study, since we're proposing it to 24 use it as a critical study. So like I've been 25 mentioning throughout, this study has a well-definedFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 76

1 dose-response relationship. 2 I guess you can see from the plot that I 3 showed you before, the 1800 of the time point where the 4 search of the LH actually takes place. And this study 5 also had less data variability when compared to the 6 other datasets. 7 So as for the selection of the benchmark 8 response, BMR, this is a critical issue and we'd like 9 to get input from the Panel on this. As to the BMR, it 10 is selected generally on the basis of biological and/or 11 statistical rounds. 12 I don't know what happened. Sorry, I 13 don't know what is going on there. 14 So in the absence of information 15 regarding the level of LH attenuation that could be 16 considered associated with an adverse effect, in the 17 absence of this information, then we use a BMR that's 18 based on one standard deviation from the control mean. 19 So that's a default approach in the absence of any 20 other information, and we would appreciate to get 21 feedback on that from the Panel. 22 So, BMD modeling was performed based on 23 the external dose of atrazine. The best-fit model for 24 that was exponential. The same analysis was then based 25 on steady state of the triazine levels, which you canFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 77

1 derive from the linear regression analysis that I 2 showed you before. And the best-fit model for that was 3 the Hill model. And the details of all this analysis 4 is in Appendix C of the Issue Paper. 5 So these are the results. When you do 6 the analysis based on the external dose of atrazine, 7 you come up with a BMDL of 1.96 mg/kg/day of atrazine. 8 When you do it based on steady-state levels of all 9 triazines, you come up with a BMDL of 0.65 mg/L. 10 Just keep in mind that the units of this 11 are different. And when you do the analysis based on 12 the daily steady-state area under the curve for total 13 triazine, you come up with a BMDL of 15.56 mg/L-h, 14 that's sort of the units of the area. 15 So these steady state dosimetrics, by 16 the way, can be converted back to an atrazine exposure 17 based on the linear regression analysis that I showed 18 you before. And if you do that, you come up with a 19 dose of atrazine of 1.86, and you get the same results 20 for these two because they're pretty much the same, 21 like I mentioned before; they just differ by 24. So 22 these dosimetrics, then, may be used to establish a 23 point of departure, and we'd like to get input from the 24 Panel regarding this analysis. 25 Now so as of the whole point behind thisFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 78

1 work, one of the main points I'm just saying is to try 2 to refine the drinking water monitoring frequency. 3 Now, this is a hypothetical water 4 chemograph. It's not pretty-looking; I mean, it's 5 hypothetical, but this is realistic based on the shape. 6 So how will you use, then, the results of our analysis 7 to analyze something like this? 8 Well, on the next slide, we came up with 9 two approaches that you can use. First, you can use a 10 drinking water of the rolling average value for a time 11 period of concern, and that can be compared to a point 12 of departure that is based on external dose of 13 atrazine. 14 This is in line with the current 15 approach, actually, that's in place right now. It's 16 set to the time period; it's actually 90 days. So 17 you're seeing a 90-day rolling average. 18 As a second approach, though, you can 19 compare an average daily concentration of triazines to 20 steady-state levels of triazines, or you can even 21 compare the area under this water chemograph over a 22 period of concern and you can compare that to a daily 23 steady-state area under the curve. 24 So those are two possible approaches 25 that we came up with, and we'd like to get input fromFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 79

1 the Panel whether that's good or bad, I guess. 2 So given the linear relationship between 3 steady-state triazine levels and external dose of 4 triazine, the potential levels of concerns can be 5 related back to an atrazine exposure. So the 6 observation that we're seeing, linear kinetics, is a 7 very good thing that will simplify the analysis. 8 So this is just a summary slide, a 9 conclusion slide, if you will. So I talked to you 10 about the basis of doing an internal dose-response 11 assessment that includes all plasma triazines, parent 12 as well as metabolites. 13 I talked about the temporal aspects of 14 plasma triazines and the effect, LH attenuation. Both 15 of these actually support the use of a daily steady16 state area under the curve for triazines. We use 17 benchmark dose modeling that was based on steady-state 18 dosimetrics of total triazines as well as external dose 19 of atrazine. 20 And we feel that our analysis will give 21 very valuable perspectives for refining water 22 monitoring frequency, and of course we'd like to get 23 feedback from the Panel on the impact of this analysis. 24 So with that, I will conclude my 25 presentation. Thank you.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 80

1 DR. STEVEN HEERINGA: Thank you, Dr. 2 Rodriguez. 3 At this point, I'd like to open it up to 4 the members of the Panel for any questions or 5 clarification either on Dr. Rodriguez's presentation or 6 the corresponding material in the Issue Paper. 7 Dr. Bailar? 8 DR. JOHN BAILAR: There's a great deal 9 of information here. It's a kind of information that 10 I'm not really familiar with in a direct personal way, 11 so I may have missed something. 12 But I am very much impressed by the 13 regularity of the dose-response curve, even at the 14 lowest positive dose, which was not statistically 15 significantly different from the control. 16 This suggests to me that an approach 17 might be developed that is closely related to how we 18 deal with carcinogens, which I know a good bit more 19 about; that is, in the process of considering 20 regulation, to consider what would be an de minimis 21 risk and then proceed from that, rather than from what 22 appear to be the NOAEL and the LOAEL. 23 DR. STEVEN HEERINGA: Thank you, Dr. 24 Bailar. 25 Others?FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 81

1 Dr. Schlenk. 2 DR. DANIEL SCHLENK: Yeah, just a point 3 of clarification. Are you through this presentation 4 moving away, then, from a PBPK model? Is that kind of 5 the idea that I'm getting, because it seems that you're 6 kind of going with this AUC sort of thing, and I'm just 7 curious. Can you just fill me in a little bit more on 8 why you are deciding to go away from the PBPK? 9 DR. CHESTER RODRIGUEZ: Okay. So the 10 reason why we're moving away from a PBPK model is 11 because there is no reliable PBPK model. So the ideal 12 approach here would actually involve a PBPK model; but 13 that's not the only pharmacokinetic analysis that you 14 can do. 15 You can use non-compartmental analysis, 16 which is what we use, and actually derive actually very 17 useful information from that. So you don't have to 18 have a full PBPK model to actually make use of 19 pharmacokinetic information. 20 DR. DANIEL SCHLENK: Just to follow up; 21 so, again, will you be pursuing a PBPK model in the 22 future, or is that sort of a dead end, I guess? 23 DR. CHESTER RODRIGUEZ: If one becomes 24 available absolutely, because, you know, when you have 25 a PBPK model you can extrapolate across differentFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 82

1 routes of exposure, across different species, high2 dose, low-dose, absolutely. So if one actually becomes 3 available, absolutely we'll use it. 4 DR. STEVEN HEERINGA: Dr. Lowit? 5 DR. ANNA LOWIT: Yes, just one point I 6 have on the timing issue. Dr. Rodriguez has actually 7 reviewed the PBPK models and our reviews of that are in 8 the Appendix. There's a timing issue just to keep in 9 mind. One of the things that we always like to say is 10 that because of the nature of atrazine we're always 11 cognizant of what is going on in the literature, and we 12 always keep up with the literature. 13 But there's a goal within 2011 to create 14 whether or not to make a choice around the drinking 15 water monitoring and whether we need to do any risk 16 assessment, and it appears extremely unlikely in that 17 time frame for that existing PBPK model to be upgraded, 18 and peer-reviewed for that matter, to use. 19 So the hope is to have a less 20 sophisticated but still informative approach to looking 21 at internal dosimetry. 22 DR. STEVEN HEERINGA: Dr. Chambers and 23 Dr. Lee. 24 DR. JANICE CHAMBERS: I'm kind of 25 confused about one of the things you alluded to severalFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 83

1 times. You're talking about a dose-response curve on 2 the Cooper, et al., study of 2010 actually in slides 56 3 and 57 4 DR. CHESTER RODRIGUEZ: Yeah. 5 DR. JANICE CHAMBERS: and I just don't 6 see where you're saying dose-response with those 7 standard errors or standard deviations. What dose8 response have you. 9 DR. CHESTER RODRIGUEZ: What dose10 response? 11 DR. JANICE CHAMBERS: There's no-effect 12 and there's effect, and it just looks like the no13 effect levels are all together with the levels of 14 standard errors and the effect levels, all of it like 15 they're not different from one another. So where is 16 the dose-response curve? 17 DR. CHESTER RODRIGUEZ: The dose18 response is actually based on that 4-day study. I'm 19 sorry, I guess I'm not following your question closely. 20 Are you confused about why you compare so well to the 21 other studies? 22 DR. JANICE CHAMBERS: Look at graph 56 23 so I can see it. 24 DR. CHESTER RODRIGUEZ: Okay, give me a 25 second.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 84

1 Okay, it's this one; 56, yeah. 2 DR. JANICE CHAMBERS: Oh, okay, all 3 right. I don't quite see what you're talking about is 4 the dose-response there, because the standard errors 5 suggest that there's the 3.12 and the low was all the 6 same statistically, and the 6.25 above is all the same, 7 right? 8 DR. CHESTER RODRIGUEZ: No, actually. 9 Based on the statistics that was done on this, the 3.12 10 excuse me, the 6.25 is actually very different from the 11 control. So that's why the NOAEL was set at 3.12 and 12 the LOAEL at 6.25. And so these statistics that was 13 done I believe was on a 0.05 confidence okay, p-value 14 if you will. But the benchmark dose modeling is based 15 on one standard deviation from the control mean. 16 Okay, does that answer your question? 17 DR JANICE CHAMBERS: No, not at all. 18 Let me try again. 19 DR. ANNA LOWIT: Dr. Chambers, let me 20 try. I think it may be helpful to look at slide 57 21 again, because that's the overlay of the four studies 22 on top of each other. I think if you look at the 23 pattern, you sort of look from the bend in Cooper data 24 around the doses around six and to the left, and then 25 six and higher.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 85

1 I can see your point, actually; as you 2 think about below six, there across those four studies 3 there's actually across four studies it's remarkable 4 that similar dose is that low; but I think it's in the 5 nature of this kind of data that you see those kind of 6 error bars. But it is also clear from this plot that 7 the dark black line from the Cooper data has much 8 stronger dose-response characteristics than did the 9 other studies. 10 But then if you look at you start to 11 get in the medium doses because we haven't really 12 plotted the really high ones on the graph, it's six to 13 higher; you can see it, you can really begin to see a 14 plateau. And if you continue the if Dr. Rodriguez had 15 continued to plot the doses greater than 30, because 16 several of these studies have that, you would see that 17 the plateau is almost completely flat at or around the 18 25% attenuation. 19 DR. CHESTER RODRIGUEZ: Right. And 20 actually, one of the reasons why, I mean, I didn't 21 include the higher doses is because you will not get to 22 see them at this region in the low dose, which I think 23 is critical. When you have different studies that 24 differ up dramatically in dosing but yet the NOAELs and 25 the LOAELs are hard to distinguish, that to me isFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 86

1 remarkable and 2 DR. STEVEN HEERINGA: Dr. Bailar? 3 DR. JANICE CHAMBERS: I'm sorry. 4 Dose-response implies to me that you get 5 more response with higher doses and all, and everything 6 from 6.25 across there with those standard errors looks 7 like it's the same and the things that you're looks 8 like the dose-response curve below that, you're saying 9 is all NOAELs and therefore not an effect. So I really 10 just don't understand. 11 DR. STEVEN HEERINGA: Dr. Bailar and Dr. 12 Portier on this one, and then we'll switch. 13 DR. JOHN BAILAR: Oh. This PowerPoint 14 reinforces my concern about the use of NOAELs. There 15 are, what, six points there that are labeled NOAEL? 16 Every one of them was below control. 17 What's the probability that would occur 18 by chance alone, even though the individual points may 19 not be statistically significantly different than 20 control? I think there's, you know, a real indication 21 that there's something going on in that area that's 22 labeled NOAEL. 23 DR. STEVEN HEERINGA: Dr. Portier and 24 then-- 25 DR. KENNETH PORTIER: Well, I justFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 87

1 wanted to respond to Dr. Chambers' point. I think what 2 EPA is doing here is model-fitting and then join a 3 conclusion from the model-fitting that there's a dose4 response pattern, and this is given in Figures 5.9 and 5 5.10 in the white paper. 6 So they're not doing pair-wise 7 comparisons, you're right. If you look at the 25 and 8 you look at the 15, they overlap and you say there's no 9 difference. But when you step back and actually fit a 10 curve to this; there is a significant fit. 11 The Hill curve fits, and the Hill curve 12 suggests that a small change in dose produces a small 13 change in response, and I think that's what they're 14 basing their risk assessment on a more model-fitting 15 exercise rather than a dose-comparing, and that's why 16 they're trying to get away from this NOAEL, you know, 17 kind of making this point, this dose is significantly 18 different from this other dose. The model says a small 19 change produces a small response, and the Hill model is 20 the one that they are basing it on. 21 DR. CHESTER RODRIGUEZ: Thank you. 22 DR. STEVEN HEERINGA: Dr. Legan. 23 DR. SANDRA LEGAN: Thank you, Sandra 24 Legan. 25 With regard to the comments that haveFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 88

1 been made and a couple of other things that I'm aware 2 of about LH surges, I'd like to just ask a couple of 3 questions. 4 Yes, there's a large degree of 5 variability around these data points, and that is 6 probably almost certainly due to the fact that these 7 data were taken from one time point, the 1800-hour time 8 point. And that is generally over a group of animals, 9 the peak time of the LH surge under these photoperiodic 10 conditions. 11 However, it's important to remember that 12 if you look at any LH surge data in any species, the 13 peak time at the peak is very variable in terms of the 14 levels. So this is simply the nature of the data; 15 that's our problem, especially with small changes in LH 16 peaks that you're talking about in terms of their 17 attenuation at low doses of atrazine or any common 18 disruptor. 19 In having said that, though, I'm a 20 little bit concerned with the fact that you see a 21 plateau, as you've pointed out, at higher levels. And 22 no dose there, shown at least up to 30 mg/kg/day, has 23 suppressed the amplitude of the LH surge more than 24 about, what, 40%, 50%, let's say; is that right? Maybe 25 60% at most. It turns out that physiologically, well,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 89

1 only maybe 20% of the LH-surge total amplitude is 2 required for fertility in the female rat. 3 So the LH surge is a very large release 4 of pituitary hormone that is overkill as far as the 5 fertility of the animal is concerned. So I think we 6 need to keep that in mind, because as one of the 7 previous Panel members mentioned, the Hill equation or 8 the analysis is going to talk about very small changes 9 in the amplitude of a physiologic hormonal release, and 10 these small changes in amplitude will have essentially 11 no effect on the reproductive function of the female 12 rat. Even down to 50% or 60% of the loss of this 13 released hormone, they'll still ovulate their full 14 complement of ova, and they will be fertile. So I 15 don't-- 16 The other thing is that if you're 17 talking about at doses of 30 milligrams and beyond, out 18 to 100 I think was on the subsequent graphs with the 19 linear relationships for the benchmark dose analysis. 20 If there's no further suppression of the 21 LH surge -- and as Dr. Chambers pointed out so well, it 22 plateaued if they're all the same after that, that I'm 23 not sure I understand how more atrazine exposure could 24 be detrimental, even to the rat. 25 I mean, there's no dose, all the way outFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 90

1 to 100 mg/kg. Well, that I can see from attenuating 2 the LH surge, is going to affect the fertility of the 3 rat. 4 And then having said that, the last 5 comment is if you have a plateau beyond 30 mg/kg and 6 you didn't use the third--you didn't use those two 7 higher doses in the analysis, the first analysis of the 8 dose-response that we just saw slide 55 or 56; but 9 those doses are added on to the benchmark analysis. 10 And they're not part -- I don't think they're part of 11 the dose-response, what dose-response you have up to 12 30. 13 So could you address those issues a 14 little bit, especially the latter one? 15 DR. STEVEN HEERINGA: Dr. Cooper, I see 16 you've come to the microphone. I assume that you've 17 been singled out to address this. 18 DR. RALPH COOPER: As soon as the 19 questions get hard and somebody throws something at me. 20 Ralph Cooper, USEPA. 21 I think some of the points you make are 22 right spot-on; but what I think is a little confusion 23 here and Chester put this slide up, they stopped in the 24 previous slide. I don't know how you can go back to 25 the one we had where you did your analysis where youFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 91

1 stopped at 30 and you have the plateauing take place, 2 okay? Then you're focusing on, if you look at that, it 3 stopped at 30. If you look at the data that we 4 submitted right here, just stop here a moment -- you 5 see we stopped at 75. 6 What you're seeing there is I don't know 7 what the difference is between 6.25 and 25; but as you 8 increase the dose beyond 75 -- and that's what I think 9 this slide that Chester brought up with the blocks on 10 it shows that 100 dose does bring it down statistically 11 different. 12 If you compared the 100 to the 12you 13 would see it was lower to the point where the higher 14 doses are even right at baseline to the analyst. So 15 you can get back down to baseline with the higher 16 doses. 17 In this 4-day exposure, though, that's 18 the pattern that was observed. And I guess it's mostly 19 where it was shallow dose-response that's there, it's 20 just that it was locked off in his analysis. And 21 there's been -- 22 SPEAKER: Well, have we seen is that 75 23 through their data? 24 DR. RALPH COOPER: That right there is 25 the same.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 92

1 SPEAKER: 25. 2 DR. RALPH COOPER: Oh, I'm sorry, it's 3 25. We don't have it on here now. 4 SPEAKER: So there wasn't any data like 5 that in the Issue Paper, either. 6 DR. RALPH COOPER: Yeah, 75 isn't here. 7 We eliminated it. 8 SPEAKER: In this one. 9 SPEAKER: Figure 5.9, Figure 5.9 shows 10 the 75 dosimetric. 11 DR. CHESTER RODRIGUEZ: Yeah. So we 12 were actually concentrating on doses that were below 13 30. And the rationale for that is that that was the 14 highest dose in the 6-month study. 15 So just to be able to compare studies, 16 then we concentrated on that, on the range. And we 17 also felt like the higher doses, I mean, the human 18 relevance actually goes down, you know, because it's 19 unlikely that a human would get exposed to 50 or 100 20 mg/kg. So I think this is more relevant in the context 21 of human health of the risk assessment. 22 DR. STEVEN HEERINGA: Okay. Now we go 23 to Dr.-- 24 DR. RALPH COOPER: I didn't hear his 25 answer.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 93

1 DR. STEVEN HEERINGA: Oh, sure. 2 DR. RALPH COOPER: The second part of 3 your question, Dr. Rodriguez. These studies were done 4 with young adult female rats that have amplitude, LH 5 surge amplitudes at, in our lab anyway, at 20, 27, 30 6 milligrams per mil, and that's somewhere probably. as 7 you mentioned, the 80% excess in the amount of LH 8 that's, quote, unquote, "needed" for ovulation. So the 9 females will in this particular instance, you wouldn't 10 anticipate there would be any disruption of ovulation. 11 We chose the 75 as our highest dose 12 based on previous work that showed that 2 weeks of 13 dosing with 75, they stopped cycling. So, again, it is 14 this cumulative effect kind of thing that we were 15 working with, that in one cycle you may not see an 16 adverse outcome but continued dosing may eventually 17 manifest itself. 18 The second thing is that this is perhaps 19 in the 2000 SAP, the concern about atrazine and LH was 20 brought up because of the extended exposure bringing 21 about premature reproductive senescence. 22 And you know in the middle-aged female 23 that amplitude of that LH peak is not at 30. It's 24 down; it could be as low as 10. So now a small 25 decrease might actually drop them below the ovulatoryFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 94

1 quota. So that's what the framework that we were 2 working is in those studies. 3 So you're correct in saying that I 4 wouldn't anticipate an adverse outcome of a single5 cycle exposure like this. And we're looking at that, 6 because one of the things is as you raise up the dose, 7 you saw fewer animals showing the expected proestrus 8 here. So there's something going on there but we just 9 don't know what, we haven't got the values yet. 10 But these lower doses here are just, as 11 we said in my thing, predictors for changes that are 12 occurring that are, that bode some type of adverse 13 outcome if dosing was extended. 14 DR. STEVEN HEERINGA: Dr. Legan, follow15 up? 16 DR. SANDRA LEGAN: Thank you. 17 I agree with your comments about the 18 fact that you might not see this effect over a single 19 cycle. Have you done in regard to what you said just 20 now, have you done a comparison of when the LH surge 21 actually drops below a certain amplitude, like below 22 about an 80% suppression, and the timing of when the 23 cycles stop, because in order for the effect on the LH 24 surge to be meaningful in terms of the treatment or the 25 effect of atrazine on it, that has to coincide.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 95

1 They can't just miss cycles when the LH 2 surge is suppressed after 75. What was it, 2 weeks at 3 75 mg/kg, I mean those things, the timing has to be 4 just right to be able to make that conclusion. 5 DR. STEVEN HEERINGA: Dr. Lowit. 6 DR. ANNA LOWIT: I think there was one 7 more piece of your question that hadn't been answered 8 yet, the part around when we did our benchmark dose 9 modeling as part of what's in Appendix C. The 75 mg/kg 10 dose was used in that analysis, so even though it's not 11 on this graph, the 75 dose was used in those 12 calculations. 13 DR. SANDRA LEGAN: It's on slide 62 and 14 63, 50 and 100 were used, not 75 I think; am I right? 15 And I don't know where -- that was part of the 16 question, why you're including those and where I guess 17 those doses were in the total atrazine where you 18 labeled disappearance data. 19 DR. RALPH COOPER: But the doses -- 20 DR. STEVEN HEERINGA: Dr. Cooper? 21 DR. RALPH COOPER: The doses that were 22 submitted in that internal report that's in the Docket 23 had the 75, and it had the full day of proestrus 24 changes of the characterization of surge in all of the 25 dose groups at all the time points, and this one wasFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 96

1 pulled out of that quota dataset; so I don't know. 2 DR. ANNA LOWIT: Dr. Legan, I think it's 3 important to keep in mind there are two sets of 4 quantitative calculations here. There is the set of 5 calculations that Dr. Rodriguez has the lead on around 6 pharmacokinetics, particularly with the Thede study, 7 looking at--what place--around slide 60 that that 8 pseudo-steady state graph. 9 Then there is a separate set of 10 calculations, primarily done by Dr. John Liccione, that 11 are contained in Appendix C that are benchmark dose 12 modeling estimate calculations, and they're important 13 distinctions, because the T-study is looking at plasma 14 concentrations over a wide range and then the benchmark 15 dose modeling is intended to derive a point of 16 departure for assessment purposes. They're certainly 17 interrelated as you think about using the internal 18 dosimetrics, but they are two separate sets of 19 calculations. 20 But in the benchmark dose modeling, the 21 75 dose was used? 22 DR. STEVEN HEERINGA: Figure 5.9 on the 23 Issue Paper shows that one. 24 Dr. Krishnan, and then Dr. Greenwood. 25 DR. KANNAN KRISHNAN: Just a couple ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 97

1 clarifying questions, too. This relates to slide 63. 2 Can I see slide 63, please? Yeah. 3 I was looking at the model part of it. I 4 just want to make the point that zero dose doesn't 5 correspond to zero AUC here, it's like that's suggested 6 by the equation more rather than the details and 7 origin. So is it like it takes a 0.7 mg/kg or 8 something to have zero AUC, or am I misreading 9 something here? 10 SPEAKER: I have no idea, actually. 11 DR. CHESTER RODRIGUEZ: I think you're 12 right, actually. One way of actually doing this thing 13 of integration is to force it through zero. I didn't 14 do that. I just felt like, you know, we may lose some 15 information when you force it to zero, and I just 16 wanted to get a sense of what the Y intercept would be 17 without modifying the data too much by forcing it 18 through the origin. 19 DR. KANNAN KRISHNAN: And the second q 20 of clarification is that the total dose or the 21 equivalence that you calculated, was it always adding 22 milligrams to milligrams, or in any of the cases would 23 you do an equivalence based on millimoles? In other 24 words, if you have data on the metabolites, one would 25 do millimoles using their molecular weights, add themFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 98

1 up, and then once you have total millimole then 2 multiply it with the atrazine molecular weight to have 3 the atrazine equivalence. 4 So my question is has any attempt of 5 calculations and computation based on the molecular 6 weights of the individual metabolites and atrazine 7 done, or was it just simply adding milligrams all the 8 time straight? 9 DR. CHESTER RODRIGUEZ: It was simply 10 adding actually milligrams. We just felt that the 11 molecular weights, you know, are so similar, and what 12 you're really looking are actually radiolabeling 13 equivalence. So it really doesn't matter what the 14 species is as long as it has the radiolabel. So that 15 was the basis for just adding milligrams. 16 DR. STEVEN HEERINGA: Dr. Greenwood, do 17 you want to talk? 18 DR. RICHARD GREENWOOD: I may be missing 19 something here, but if you're giving the same dose for 20 4 days, given that you've got an almost perfect 21 regression line between dose and area under the curve, 22 is there really that particular area under the curve? 23 I mean, I may be missing something here; 24 but it's easier to measure the dose than the area under 25 the curve, so why would you use area under the curve?FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 99

1 I mean, you said it's more useful; but I don't see why 2 if you're going to give the same dose 4 days. 3 DR. CHESTER RODRIGUEZ: Right, so 4 DR. RICHARD GREENWOOD: Sorry. 5 DR. CHESTER RODRIGUEZ: No, that's okay. 6 That goes back to the figure that I showed you before, 7 what a single high dose had a very modest effect, 8 whereas a much smaller dose, but even for at least 3 9 days, had a very pronounced effect. So that actually 10 suggests to me at least that duration of exposure is 11 actually critical, okay? And on that basis, then the 12 area under the curve is justified as an internal 13 dosimetric. 14 DR. STEVEN HEERINGA: That would be 15 slide 46 that shows that. 16 Dr. Lowit? 17 DR. ANNA LOWIT: Adding to what Dr. 18 Rodriguez said, I think the observation you've made 19 about the linearity of the lines at steady state, 20 because I think what Dr. Rodriguez basically just added 21 to that was that steady state is the required event for 22 these linear lines. We've asked ourselves that 23 question, too, Dr. Greenwood, and I think there's a 24 couple of ways to think about this. It's certainly 25 something that we'd like to hear your feedback on.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 100

1 From a simplicity standpoint, it would 2 appear from those lines that doing the sort of matching 3 of rolling-average water concentration to an 4 administered dose of atrazine is actually a pretty 5 reasonable thing to do; that's what those graphs would 6 suggest. 7 As we think about, as you hear Nelson 8 Thurman's presentation later, one of the important 9 questions that we'll be asking in the coming months is 10 the adequacy of the current monitoring data. And to 11 understand peaks that we don't have in the current 12 dataset, data peaks, we have peaks in certain datasets; 13 but had you had more monitoring, theoretically they 14 could have been higher. 15 Something that we've talked about is 16 that these area-under-the-curve metrics may help 17 provide conceptual help interpretation of some of that 18 work that Nelson will be doing in the coming months. 19 And so by using the AUC metrics in those 20 I don't know the right term, because Nelson is not 21 sitting here to tell me the right term; but as we move 22 through those statistical analyses to evaluate that, 23 the AUC metric may help us interpret missing piece in a 24 theoretical way easier than will be rolling averages 25 that can be a little bit hard to get your mind aroundFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 101

1 what exactly are the rolling average of a week or two 2 weeks or three months, or six months for that matter. 3 So that's something to think about. 4 DR. STEVEN HEERINGA: Dr. Akana. 5 DR. SUSAN AKANA: I'm interested in the 6 idea of the pseudo-steady state, and this is a 7 measurement of all the radiolabeled core. But is it 8 correct that there are different bio efficacies of 9 atrazine versus some of its metabolites like that? 10 DR. CHESTER RODRIGUEZ: To my knowledge, 11 there is no information on the relative activities of 12 the metabolites compared to the parent. So in the 13 absence of that information, I think it's justified to 14 use total plasma triazines. 15 DR. SUSAN AKANA: I'm not sure, but I 16 thought I recollected SAP to earlier this year that 17 that had a very different effect on the HPA axis and 18 some of the other metabolites and atrazine itself. So 19 I'm not pointing to LH. It may have some other effects 20 on other hormone systems that are not being recognized. 21 DR. STEVEN HEERINGA: Dr. LeBlanc. 22 DR. GERALD LEBLANC: Gerry LeBlanc. 23 Continuing on that line concerning 24 pseudo-steady state, based upon the information we see 25 and I think it's reasonable to assume that a pseudo-FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 102

1 steady state atrazine is not predominant triazine and 2 that among the dealkylated products, that is the 3 dominant triazine. I was wondering if you know 4 anything about GSH conjugates, whether to what 5 proportion they exist as pseudo-steady states? 6 DR. CHESTER RODRIGUEZ: I can say that 7 based on mass-balance pharmacokinetic studies, 8 glutathione conjugates can account up to 30% of the 9 metabolism, and of course you'd only have one, right? 10 You have at least four glutathione conjugates. They 11 are presumed to be not active because of the lack of 12 the chlorine on the triazine ring. But, actually, 13 there is no information actually to support that that 14 I've seen. 15 So by using, then, total triazines we 16 feel we are being conservative, just in case some of 17 the metabolites are active or not active. But in the 18 absence of relative activity information, I think this 19 is the best approach. 20 DR. STEVEN HEERINGA: Dr. Bailar. 21 DR. JOHN BAILAR: I'd like to hear a 22 little more about why you consider this a conservative 23 approach. I can imagine situations in which it might 24 be quite the opposite. 25 DR. CHESTER RODRIGUEZ: Quite theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 103

1 opposite in what sense, I mean? 2 DR. JOHN BAILAR: In the sense that if 3 it's a single complement of the total that's the bad 4 actor, you could be diluting that effect by throwing in 5 all the others. 6 DR. CHESTER RODRIGUEZ: Right, that is a 7 possibility. But in the absence of that information, I 8 think this is the best approach. This is the current 9 state of the science. 10 DR. JOHN BAILAR: I think that's, you 11 know, a reasonable position, but I'd be happier if you 12 said something more about it. 13 DR. CHESTER RODRIGUEZ: You have a very 14 good point, thank you. 15 DR. STEVEN HEERINGA: Dr. Lowit. 16 DR. ANNA LOWIT: I think it's important 17 to keep in mind there have been a couple of comments 18 now about the metabolites that there are normal 19 metabolites, and there is very little dose-response 20 data for anything but the administered dose to 21 atrazine. 22 There's...Susan Mollis, who sits behind 23 me, has done some outcome data with some of the 24 metabolites. There is certainly some DACT data out of 25 the McMullin lab and there has been a couple of otherFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 104

1 groups. But it's, it's sparse, the dose-response is 2 generally very poor. So we don't want to over3 interpret that data. 4 DR. STEVEN HEERINGA: Dr. LeBlanc? 5 DR. GERALD LEBLANC: I don't know if it 6 needs to be said, but just to follow up on John's 7 comment, you referred to your approach being a 8 conservative approach, and I see it as a conservative 9 approach. I mean, you're looking at total triazine 10 internal dose without any consideration of which 11 component is toxic, which component isn't toxic; so it 12 seems reasonable to me, as well. 13 DR. STEVEN HEERINGA: Additional 14 questions or clarification for Dr. Rodriguez's 15 presentation before we conclude? 16 I think Dr. Portier has one question. 17 DR. KENNETH PORTIER: Oh, I have the 18 naive question of the day. When you talk about liter 19 there, is that the liter plasma internal dose or is 20 that liter water administered dose? It's not clear in 21 the documentation and you switched back and forth in 22 the discussion, so I got lost. 23 SPEAKER: Right. 24 DR. STEVEN HEERINGA: Dr. Rodriguez. 25 DR. CHESTER RODRIGUEZ: Are you talkingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 105

1 about the units? 2 DR. KENNETH PORTIER: On the -- 3 DR. CHESTER RODRIGUEZ: Yeah. So these 4 are liters of blood, right, so that's actually the 5 units of the area under the curve are units of 6 concentration times time, right? It's easy to account 7 levels, that's more or less duration, and that's why 8 you see that hour at the end. But, yeah, we're talking 9 about plasma levels. 10 DR. STEVEN HEERINGA: Dr. Lowit. 11 DR. ANNA LOWIT: At this point in time, 12 we have not actually taken any of the internal 13 dosimetric data and linked it to any of the water data; 14 so I'm sorry if we weren't explicit in some of those 15 metrics. We haven't actually made that connection yet. 16 DR. STEVEN HEERINGA: Okay. Well, it's 17 been I think a productive start, and we've got a long 18 way to go. What I'd like to do at this point unless 19 there are additional questions from Panel members for 20 Dr. Rodriguez, Dr. Lowit or Dr. Cooper, why don't we 21 take a lunch break, and we're scheduled to return here 22 at 1:15. 23 So we'll see everybody back at 1:15 for 24 two additional sessions this afternoon, and again I 25 think that the pace of these sessions will be veryFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 106

1 favorable. We'll have time to ask questions and make 2 sure everybody is clear on the content and look forward 3 to the afternoon. 4 Everybody, see you at 1:15. 5 (WHEREUPON , a luncheon recess was taken.) 6 DR. STEVEN HEERINGA: Good afternoon, 7 everyone. Welcome back to the afternoon session of our 8 first day of our FIFRA Science Advisory Panel Meeting 9 on the topic of the Re-Evaluation of the Human Health 10 Effects of Atrazine: Review of Non-Cancer Effects and 11 Drinking Water Monitoring Frequency. 12 At this point we are in the process of 13 hearing the formal scientific presentations that 14 accompany the Issue Paper, and we heard this morning 15 from Dr. Christensen and Rodriguez, and I think at this 16 point we are going to be turning to the issues related 17 to the evaluation of water-sampling strategies and the 18 frequency of monitoring, and I think Nelson Thurman and 19 Mary Frankenberry. 20 MR. NELSON THURMAN: Okay, so we're 21 transitioning from toxicity to exposure, and the reason 22 we're focusing on the drinking water is that for 23 atrazine drinking water is the major contributor to 24 aggregate exposure of human health. So it's important 25 for us to have a reliable estimate of that drinking-FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 107

1 water exposure. 2 And while I'm the one talking here, Mary 3 Frankenberry was instrumental in helping put all the 4 report together and putting this together; so it's a 5 team effort, so it's not just the guy talking. 6 Some of the conditions of re7 registration for atrazine included some monitoring 8 programs. One focused on community water systems for 9 human health, and the requirement was to monitor those 10 community water systems that through quarterly Safe 11 Drinking Water Act monitoring were above a certain 12 concentration. Those are considered to be the more 13 vulnerable systems for monitoring. 14 The program was designed to provide an 15 exposure estimate for a 90-day period of concern. So 16 there was weekly monitoring during the time frame when 17 you most expect to find atrazine in the waters, 18 generally beginning sometime in April and running 19 through August more or less, depending on the time and 20 the area. 21 There was a second monitoring program 22 that's focused on ecological exposure and particularly 23 impacts of atrazine on aquatic plant communities. That 24 monitoring tend to be up toward the headwater streams. 25 You're going to hear me refer to information andFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 108

1 lessons we've learned from both of those programs. 2 We've had, this is the second SAP that has focused on 3 the human-health part of the community water-system 4 monitoring and how that relates to the human health 5 assessment. Most of this focus of that SAP and this 6 has been on the frequency of monitoring: how frequently 7 do we have to monitor to capture the exposures of 8 concern. 9 There were two other SAPs, one in 10 December of 2007 and a followup in May of 2009, that 11 looked at the ecological effects exposure. The focus 12 in that one was primarily more of a spatial rather than 13 temporal, but there was some sampling frequency 14 involved in that, but it was more or less looking at 15 what contributes to vulnerable systems. 16 And so you'll hear me refer to both, but 17 those are the SAPs that we have addressed before. 18 And so as a quick recap of some of the 19 monitoring issues that we raised at the SAP. Like I 20 mentioned, the original monitoring design looked at was 21 based on providing exposure estimates for a 90-day 22 period of concern. It looked at weekly sampling during 23 that. 24 You've heard some of the presentations, 25 and we may end up with a different duration of concernFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 109

1 as a result of our deliberations, and it leads to the 2 question: Weekly sampling was adequate for a 90-day 3 duration of concern. If we have a shorter duration, 4 how adequate is that existing monitoring? Do we need 5 more monitoring, or can we provide some type of 6 confidence bounds or possibly even a safety factor that 7 could account for the differences, based on the 8 monitoring? 9 And I will point out that these 10 questions, where we're focusing on atrazine, these 11 questions have broader implications for other 12 pesticides as well. How much can we derive from 13 existing less frequent sampling, and how frequently 14 does sampling have to be to adequately characterize the 15 various exposures? 16 The main points we heard from the 17 September SAP regarding monitoring, the biggest one 18 was, well, to design a study you really need to know 19 the duration of concern, because honestly once you know 20 that it's easy to design a study, given whatever 21 confidence bounds you need. 22 For shorter durations, the estimates of 23 the peak exposures become more critical. For longer 24 duration, obviously it's less critical. We haven't 25 determined what that duration of concern is yet, butFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 110

1 we're looking at approaches that will work fast once we 2 have that in place. And we realize that one approach 3 may work better for short duration of exposure and 4 other approaches may be adequate for longer duration, 5 so we may be looking at more than one approach, 6 depending on where we end up. 7 The SAP was concerned that some of the 8 presentations we were looking at community water 9 systems that had weekly sampling intervals, and the 10 concern was those weekly sampling intervals may be 11 missing some short-term peaks and may be excluding out 12 some of the variability and providing a biased 13 representation of the actual concentration profiles. 14 The best set of more intensively sampled 15 data, which would include either daily or near daily 16 sampling during the time when we are likely to find 17 atrazine, is actually for ambient waters not 18 necessarily associated with the source water, the 19 community water system. Some of them we are looking 20 primarily at Heidelberg College's data. 21 Some of them are on rivers and streams 22 that are large enough to support a community water 23 system, if they don't. Some of them, like some of the 24 atrazine ecological exposure, are in the headwaters and 25 generally farther upstream than most of the communityFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 111

1 water systems, although there are some smaller systems 2 that are not too far off from that. 3 So we'll talk a little bit more about 4 what we're looking at in that regard and our proposal 5 for how we think we would use that. And one of the 6 other things, the SAP recommended combining a 7 regression-based model such as USGS's watershed 8 regression on pesticides with creating random function 9 models. 10 They also suggested looking at extreme 11 value theory. We've taken a closer look at some of 12 those approaches, and we're going to follow up on that. 13 We provided some information in the background paper on 14 that. 15 For today, we're going to focus on three 16 main issues that we're bringing for you related to 17 monitoring, and I want to point out that I can't count 18 that should be Questions 4.1, 4.2 and 4.3 up there. 19 So we're briefly going to touch on a 20 framework for monitoring studies based on 21 recommendations from the SAP and some of the lessons 22 we've learned. 23 We're going to talk to you a little bit 24 more about what intensively sampled datasets we think 25 we can use and how we propose looking at them, and thenFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 112

1 we're going to follow up with some of the approaches 2 interpreting monitoring data based on the feedback we 3 have from the April SAP. 4 So to begin with, this is, I guess when 5 you work on it for a while and work on some of these, 6 some of this starts to seem like common sense; but it's 7 probably a pretty good idea to capture down in writing 8 so that we have a chance to take a look at it. 9 We're looking and what we provide is a 10 framework for what we would describe more as targeted 11 monitoring study. You are not likely to be able to 12 monitor everywhere a pesticide occurs. So what we want 13 is to have a monitoring study that focuses on the area 14 where the pesticide is most likely to be found. 15 Models like WARP or other spatial data 16 layers can be used to identify vulnerable sites, not 17 just in pesticide use but also folding in some of your 18 hydrologic soil, weather factors that would drive 19 exposure. 20 In the same manner you're not 21 necessarily likely to sample throughout the year, you 22 may not need to sample throughout the year; but what 23 you want to do is target your intensive sampling during 24 the period where you're mostly likely to find the 25 pesticide in water, which is going to be at or aroundFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 113

1 the time when it's likely to be applied and then for 2 several, depending on the mobility and the half-life of 3 the pesticide, for a period of time afterwards. 4 For something like atrazine, which is a 5 pre-emergent herbicide, it's pretty easy to define that 6 time frame. For other pesticides, it may be a little 7 more difficult; but it is a way to focus that 8 monitoring. 9 The sampling frequency needs to be based 10 on the toxicological exposure duration of concern, 11 sampling more frequently if you've got a short duration 12 and less frequently if longer duration. What you want 13 to see here is you need to balance the cost of the 14 study with the needed accuracy that you need. 15 And one of the reasons we're looking 16 closely at the intensively sampled data is we want to 17 be able to, depending on your sampling frequency, can 18 we provide some type of confidence bounds that you're 19 likely to get in a monitoring study or, alternatively, 20 can we provide some type of a safety factor that can be 21 used to address with less frequent monitoring. 22 One of the recommendations came out in 23 the last SAP was consider using auto-samplers to 24 collect the data for exposure periods of interest. 25 We're seeing more use of auto-samplers; it's still notFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 114

1 commonly used for at least the pesticide monitoring we 2 see, but we do see it being used a little bit more 3 often as the technology improves. 4 There are a couple of ways you could do 5 that. One is to use it to fill in for events in 6 between your regularly sample intervals. For instance, 7 you might have a flow triggering, so if you have a 8 runoff event that it triggers increase in flow. 9 Another way may be to use auto-samplers to collect 10 regular intervals over your time period of concern so 11 that you end up with a time-integrated average over 12 that time period. So there's a couple of ways of 13 looking at that. 14 And the idea, the concept of possibly 15 using auto-samplers to integrate over the time period 16 of concern plays into what Dr. Rodriguez was talking 17 about in terms of looking at how do we relate 18 monitoring to an area under the curve approach where we 19 may not need to capture every single peak, but what we 20 need to do is capture what that exposure is over that 21 duration of concern. 22 Now, targeted monitoring isn't new, and 23 I think what we've seen with atrazine is a pretty good 24 example both in the community water system and in the 25 ecological exposure, a pretty good example of how thatFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 115

1 can be applied. 2 For the ecological exposure monitoring, 3 we used WARP to identify the watersheds that were most 4 vulnerable for atrazine exposure, based on WARP 5 estimates. The dark-blue watersheds you see here were 6 the ones that were the most vulnerable based on WARP. 7 And then what we did was have a 8 spatially balanced random selection process to identify 9 candidate watersheds for monitoring. And the results 10 of that study and through some of the followup 11 monitoring that is going on now are helping us better 12 define those vulnerable areas and how to target and 13 pinpoint those areas. 14 For the community water systems, the 15 approach was a little bit different, in that the 16 community water systems were identified based on Safe 17 Drinking Water Act monitoring on a quarterly basis. 18 The interesting thing is that most of those community 19 water systems that were identified -- and they're shown 20 as dots on the, on the map -- happened to fall into 21 that most vulnerable tier of watersheds; so that's more 22 reinforcement in terms of identifying vulnerability 23 based on something like WARP. So it does show that 24 there's ways to target the monitoring and that would be 25 more efficient in the way that where we have to target.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 116

1 Both the models also targeted in time 2 with more intensive monitoring during the time period 3 coinciding with when atrazine was likely to be applied, 4 in this case to corn or sorghum, and continuing mostly 5 through the summer months in that regard. 6 For the ecological exposure monitoring 7 study, a subset of those sites included auto-samplers 8 to complement the regular 4 days' grab samples that 9 were taken at those sites, and that subset of 10 monitoring sites -- and they're not shown on there, but 11 they spread from Ohio, Indiana, Illinois and Missouri - 12 - that subset of sites are one of the subsets that 13 we're looking at because it provides daily or near 14 daily monitoring during the actual atrazine use area. 15 And that leads us to the second point, 16 which is the need for a set of intensively sampled 17 datasets. And we agree with the SAP's recommendation 18 that this is important. 19 For community water systems, the most 20 intensively sampled monitoring data we can find is 21 basically the weekly samples that we see that Syngenta 22 has been doing for the last several years with 23 community water systems here, and then there are a few 24 other precursor programs that also sampled weekly 25 during that regard.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 117

1 We do know of some monitoring data that 2 is sampled more intensively. Heidelberg College, which 3 is now Heidelberg University, has been doing intensive 4 sampling in several streams of various sizes in Ohio 5 for a number of years, and so that provides a wealth of 6 data over time in one location where at least during 7 the likely times, times you're likely to find atrazine, 8 you have daily or near daily monitoring. 9 As I mentioned, the atrazine ecological 10 exposure monitoring program also had its subset of 11 sites that included daily or near daily sampling, 12 ranging primarily from April through the end of August. 13 That was spread out more over from Ohio westward to 14 Missouri and I think actually into Nebraska as well. 15 So it covers a little broader cross-section of the 16 atrazine use area. Some of those will have from two to 17 five years of data in that regard. 18 We think these datasets are critical for 19 determining confidence bounds in monitoring estimates 20 that help us to evaluate less frequent monitoring, and 21 what we're proposing to do is to use those datasets to 22 simulate different sampling frequencies to evaluate 23 what kind of confidence bounds we get in that with the 24 idea of possibly saying if you have, depending on what 25 your duration of concern is, a sampling frequency of XFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 118

1 days or X weeks might have a safety factor involved 2 associated with that. 3 We also think those are important if 4 we're looking at ways of estimating exposure in between 5 monitoring data points, and so this is a way for us to 6 evaluate how well those work. 7 As I've been talking about, we mentioned the Heidelberg 8 monitoring data and the atrazine ecological exposure 9 monitoring. Those are monitoring data for streams and 10 rivers. 11 We don't really have the same type of 12 daily or near daily monitoring for reservoirs. We're 13 looking at a couple of approaches: one, the streams and 14 rivers may use as a surrogate. We'd expect them to be 15 able a little more flashy in nature than what you'd see 16 in reservoirs. 17 The other is to use a model, pesticide 18 root zone model exposure analysis modeling system, 19 which is what we use to estimate our drinking water 20 exposures for reservoirs. It gives us daily 21 concentrations over typically 30 years of weather data. 22 So that's something we are considering as an 23 alternative for the reservoirs. 24 One issue that was raised at the last 25 SAP is whether drinking water treatment smooths outFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 119

1 those atrazine peaks so that treated water would be 2 less variable than the source water. What we know 3 about atrazine is that your conventional drinking water 4 treatment, which would be a process of sedimentation, 5 flocculation and chlorination, does not remove atrazine 6 from the water. 7 In general, you need an activated 8 charcoal treatment to remove atrazine from the water. 9 You can have, and activated charcoal can be used to 10 remove things like odor, it can remove things like 11 other organics other than atrazine. So the 12 effectiveness depends on how much you need and whether 13 you're targeting atrazine or something else. 14 What we did is we went back and looked 15 at 44 of the community water systems that had detects 16 of total chlorotriazines of 15 ug/L, and there's 17 nothing magical about that other than we wanted to 18 have a cutoff of looking at the more vulnerable systems 19 based on higher exposures, and that just seemed to be a 20 cutoff that worked given the time we had. So don't put 21 too much weight in that actual number; it was just to 22 help us get a manageable set of community water system 23 data. 24 We compared the paired source and 25 treated water samples for that, and what we found isFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 120

1 that for a third of those community water systems, 2 there was no difference in concentrations between the 3 source water and the treated water. For roughly half 4 of the systems, we saw some reduction in total 5 chlorotriazines as it went from source to treated 6 water; but it wasn't a complete removal. And roughly 7 about a sixth of the sites did we see complete removal 8 of the triazines as a result of treatment. 9 This plot here is an illustration of one 10 of the sites where the total chlorotriazines were 11 similar in both the source water, which you see in 12 blue, the blue and blue dots, and the treated water, 13 which is the magenta dots. So the bottom line, as you 14 see, it very much follows the same pattern. 15 The bottom line is that if we focus on 16 source water, it emphasizes, first of all, the 17 importance of protecting source water. Secondly, it 18 removes another potential source of variability that 19 you would have to account for whenever you're doing the 20 analysis. 21 But thirdly, it also reflects the type 22 of community water systems we see right here. So we 23 believe it's reasonably protective without being an 24 overkill. In those systems where the treatment does 25 knock out atrazine altogether, those can be addressedFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 121

1 separately in the risk assessment. 2 For terms of looking at variability, 3 though, this gives us a chance to use some ambient 4 water data and provide what we think is a reasonable 5 assessment of the type of variability you might see. 6 In the background document, we propose 7 assessing the uncertainty in the sampling frequency by 8 matching the weekly community water systems with the 9 more intensively sampled monitoring datasets. 10 And I want to try to explain what we 11 mean, because this is a comment we've wrestled with and 12 we still are asking ourselves: how well is this going 13 to work? But the idea is that for the community water 14 systems you have what we describe as a chemograph 15 shape, which looks at the number, the duration, the 16 spacing, the magnitude of your peaks or spikes that 17 occur during the monitoring season. 18 And the question we have is: can we 19 provide some general characterization of chemograph 20 shape or some classifications of chemograph shape where 21 we might be able to match a community water system with 22 weekly samples to one of the more intensively sampled 23 datasets. 24 And the idea is that if we can more 25 closely match these monitoring datasets based onFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 122

1 chemograph shapes, it may give us a bit more confidence 2 in analyzing the uncertainty bounds in that. 3 I do want to caveat this. We can start 4 with an intensively sampled dataset where you've got 5 daily or near daily samples, and we can create a 6 smoother chemograph based on, for instance, weekly 7 sampling. But you can't necessarily start with weekly 8 sampling and build in your daily peaks in between. 9 What we can do is that we can use that 10 intensively sampled dataset to provide some confidence 11 bounds of good exposure estimates we might see in 12 between. 13 So when we're talking about that, that's 14 where our thinking is. It is not to take a 7-day 15 sample and create this out of air but to provide some 16 confidence bounds in what we may have missed with the 17 sampling frequency, in this case the weekly sampling. 18 So we've done some preliminary analysis 19 on the intensively sampled Heidelberg and ecological 20 exposure monitoring datasets. We provided that 21 analysis in Appendix D2. I just want to touch on a few 22 things. I just point out what we did in this, we were 23 focused on that April to August time frame when we have 24 the most intensive sampling going on, and just for a 25 preliminary analysis we sampled those chemographs at 7-FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 123

1 day fixed intervals. 2 So we had seven separate fixed-interval 3 samples on that. And that's just to give us a feel for 4 whether we can work with that. If we were to do 5 further analysis then what we would really need to do 6 to simulate the way Syngenta did the monitoring on this 7 is to do bootstrapping analysis within weekly sampling 8 intervals, so it could occur at any time within that 9 regard. 10 Then what we also did is we compared the 11 number of spikes, the maximum detections. We also 12 looked at maximum 4-day average and the number of days 13 when those 4-day averages exceeded 20 to 40 ug/L. 14 These were numbers we just picked to work with. 15 These are not, once we determine the 16 magnitude and duration of concern, that's what we'll 17 ultimately work with; but this was just to give us an 18 idea of how we might work with that. 19 The numbers exceeding that period - as 20 a matter of fact, let me just go to the next slide. 21 Our thinking here is, particularly if 22 you're looking at something we know is not that one 23 peak exposure where you so you may have one day that 24 triggers the effect but it may be the period has to go 25 long enough until you get to your, as Dr. RodriguezFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 124

1 explained, in a pseudo-steady state, then you probably 2 need more than one day to do that. So that's why we 3 are looking at the days where you might be exceeding 4 your, your, the threshold value, whatever it turns to 5 be. 6 This is a sampling from one year from 7 the Maumee River, part of the Heidelberg monitoring. 8 It's a relatively simple chemograph as far as those go. 9 You've got one large short-duration peak that's pretty 10 much driving your exposure, and you have a few smaller 11 peaks. 12 Just to get you oriented, the blue line 13 you see here is the daily concentrations measured at 14 this site. The dashed lighter-blue line are your 15 rolling 4-day averages. The red arrows point to the 16 peaks or spikes that we've identified in that 17 monitoring dataset. 18 Now if we are to take sampling at weekly 19 sampling intervals -- and what you see here, the red 20 dots show the weekly sampling intervals for one of the 21 seven potential intervals. The red line you see there 22 is just to help to see the shape of the chemograph. 23 What you do see is you start to see a little bit of 24 data-smoothing going here; you see some of the smaller 25 peaks are cut off, some of the valleys in between thoseFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 125

1 peaks are cut off, you get some smoothing. 2 If the sampling is timed right, though, 3 and this timing happened to hit your peak exposure, you 4 still get a shape that is fairly reflective of the 5 actual chemograph shape; however, if you were to take 6 those samplings, start those sampling intervals a 7 couple days later, you're likely to miss this short8 duration peak altogether, and in this regard, this is 9 what's driving your exposure. So if we were looking at 10 a short duration of concern, this would be missed 11 altogether. 12 And so this is one of the questions we 13 were asking in terms of doing this analysis: Can we 14 provide some type of a confidence bound around the 15 exposure estimates you get from weekly sampling; or 16 another way is some estimate of what's the probability 17 of missing the peaks of a certain duration of certain 18 exposure concentration? 19 I'm going to move to a little bit more 20 complex chemograph. This happens to be when you've got 21 two fairly large very short-duration spikes and a 22 number of smaller short-duration spikes that hit in 23 between, do not, you know, for now these actual 24 concentrations measured here are higher than what we've 25 seen in any of the community water system monitoring.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 126

1 So we're not really concerned at this point about the 2 magnitude of the exposures; what we are concerned about 3 is how well we capture the actual exposure involved in 4 that. 5 In this particular area, you start 6 seeing, the more short-duration spikes you get, you 7 start seeing, even when you start capturing some of 8 these, there is still a lot of really small spikes, 9 peaks and valleys that are missed. And if you time it 10 right, it is possible even with 7-day intervals, which 11 we tend to think is a very robust monitoring data for 12 most pesticide, even 7-day intervals you can miss every 13 one of them, every one of those spikes. 14 This is the reason why we want to look 15 at more intensive monitoring datasets to get a feel for 16 what confidence we might have in exposure estimates 17 from less frequent sampling. 18 And I also point out that this is a good 19 example of one of the reasons that I think the April 20 SAP recommended that when you start looking at exposure 21 estimation methods, you need to look at methods that 22 have the possibility of estimating exposures that are 23 greater than the maximum that you measure. And so 24 these are some of the things that are driving where 25 we're going now.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 127

1 So if we take a look at preliminary 2 analysis just to kind of sum up that section, the 3 chemograph shape, and particularly the duration of 4 frequency of the peaks and how much of an overlap we 5 get between peaks, whether they're separate or closer 6 together, it's very critical to sampling analysis that 7 we do. 8 As we look at this even with the more, 9 with 7-day fixed intervals, you can start, you can see 10 the effect of the data-smoothing, which comes back to 11 reinforce what the April SAP said and expressed as a 12 concern. 13 All that said, we believe that with this 14 analysis supports more strongly than before the need 15 for using intensive monitoring with daily sampling so 16 that we can get the expected during that expected 17 exposure period; so it's a critical to evaluate less 18 frequent monitoring samples and strategies in that 19 regard. 20 I want to close with an update on 21 approaches we've been looking at for estimating 22 exposures between sampling points, because we still 23 have weekly sampling. We've got a lot of years of 24 monitoring with weekly sampling. And we may find that 25 that weekly sampling interval is adequate if we canFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 128

1 provide some means of estimating exposures in between, 2 how best to estimate the exposures in between that. 3 We have a lot of pesticide monitoring 4 data for other pesticides where the intervals are even 5 longer, and a lot of monitoring 2-week intervals are 6 pretty good. A lot of NACWA monitoring tends to be in 7 2-week and some of the times you do get weekly, but 2- 8 week is more common. 9 One of the things that we understand and 10 the comment made by the SAP is the common methods we 11 use for interpreting between sampling intervals, which 12 is a linear interpretation or a stairstep-type 13 approach. They're likely to underestimate the peaks, 14 especially for the short-duration exposures. 15 We presented some ideas on using 16 artificial neural networks as a way of estimating 17 exposures. We haven't given up on that. It's possible 18 that they may still be too complicated for easy use, 19 but it hasn't been something we've given up on. We've 20 looked at some of the other recommendations in that 21 time frame. 22 The April SAP suggested looking at 23 extreme value theory. From what we've read and looked 24 at, it works best where we have a lot of measurements 25 over long periods of time.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 129

1 We did find a paper by Huang and 2 Batterman that used both deterministic and still3 casting modeling to generate 1,000 years of data, and 4 then provided somewhat characterizing potential 5 exposures based on that. That may have some 6 application to what we're doing. It may also be 7 complex, but we are taking a look at that. 8 As far as kriging methods, kriging is 9 generally used for geospatial assessments, although it 10 does lend itself to temporal assessments because you 11 still have a, you have a similar autocorrelation-type 12 approach. It assumes a stationary mean and variance 13 that's critical. 14 The SAP recommended that we might 15 estimate the correlation structures across the pooled 16 systems, and your other recommendation which we're 17 looking at in more detail because we think there is 18 some real promise there is to combine it with a 19 regression model such as WARP. 20 So we've been looking at that. And 21 we've been looking at some of the USGS modeling 22 efforts, and there's some promise there that we're 23 following up on. 24 We've done some exploratory kriging 25 analyses using some of the intensive monitoring datasetFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 130

1 since we put out the background paper. And by "we", I 2 mean primarily Dr. Jim Hetrick and EFED did some of 3 this analysis, so I want to give him credit for doing 4 this work. I get to just be the talking head in this 5 regard. 6 I'm not going to try through the next 7 slide or two document what we did, but I want to use 8 what we've done to illustrate where our thinking is in 9 terms of next steps. 10 So we did a variogram analysis on log 11 transform data, and we found that the Gaussian 12 spherical models provide the best description of the 13 semi-variance structure. This is an illustration of 14 one year, but we looked over multiple years. 15 For those of you who may not be familiar 16 with the variogram analysis, the range refers to the 17 temporal scale where you have an autocorrelation within 18 the concentrations. So once you get beyond the range, 19 then we don't see a temporal relationship; it's more of 20 a random process. 21 But we did see in all the years, we saw 22 a strong temporal autocorrelation; it ranged from 35 to 23 83 days. And once again, this is using more 24 intensively sampled data than I think Dr. Lee looked at 25 with the weekly sampling at the last SAP.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 131

1 We then estimated a time series using 2 one-dimensional, ordinary-point kriging, and this is 3 what you see here. 4 I'm going to step on to the next one, 5 because we then used a Gaussian sequential simulation 6 to assess the uncertainty associated with the missing 7 data. And we're only showing you the 50th, the 75th 8 and the maximum in this particular slide, which is I 9 think the one graph that does show up in the handouts 10 in that regard. So some of the lower percentiles, you 11 don't see plotted on here. 12 The conditional simulations generally 13 trace the actual monitoring data, and we would like the 14 fact that they do provide us a means of estimating 15 confidence bounds of data. 16 One issue that's critical not just to 17 kriging but to other exposure estimation methods is how 18 much information you lose when you go from frequent to 19 less frequent monitoring data, and what I'm going to 20 show you is a series of kriging data that started with 21 roughly the 4-day sampling, roughly weekly sampling and 22 then biweekly sampling, and you can see that by the 23 time you get to biweekly, this exposure profile really 24 didn't look anything like this. 25 At some point, that information lossFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 132

1 impacts not only our capabilities of estimating short2 term exposures, but it also impacts how well we can 3 estimate long-term exposures as well. So that's one of 4 the concerns we have as we move forward. 5 So to kind of wrap up what we're looking 6 at in terms of exposure estimation options, one of the 7 things we've talked about is the kriging looked 8 interesting, but we may get more information if we were 9 to do some type of co-kriging your concentrations with 10 something like daily stream flow. That may be helpful 11 in that regard. 12 The conditional simulations that are 13 based on monitoring data structure, particularly in 14 percentiles or some temporal structure, are very 15 critical in terms of providing some type of confidence 16 bounds that we can use to assess the monitoring 17 frequency. 18 We're looking very closely not just at 19 some of the updates of the WARP model. At the 2009 SAP 20 on atrazine ecological exposure, the SAP recommended 21 that we explore developing a corn-belt version of the 22 WARP model that incorporates more of the data, the 23 information available on a much more detailed scale 24 than on the national scale model. 25 I know there's efforts in USGS now onFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 133

1 looking at that corn-belt version of WARP, and so 2 that's something we are keeping our eyes on and keeping 3 in touch on with USGS, because we believe it has 4 applications not just for the ecological exposure but 5 also for community water-system assessments. 6 Even now, the WARP provides percentile 7 estimations that can be used in combination with 8 conditional simulations and we think there is some 9 promise there. One thing that came out, I think we may 10 have referenced this in the April SAP but we've taken a 11 little bit more look at this and talked to the person 12 in USGS who developed the SEAWAVE model, which was a 13 way of combining WARP with or, they're looking at 14 combining WARP with this seasonal variability model. 15 They use this model to assess trends 16 in assay concentrations over years, taking into account 17 the seasonal variability you get in rainfall and 18 runoff. They are looking at the potential of combining 19 this with WARP to provide more detailed monitoring 20 estimates. 21 One thing I will point out is that is 22 probably a little longer-term effort than we may be 23 looking at for a 2011 turnaround, so part of what we've 24 got to consider as we go forward is what can we do in 25 the immediate future.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 134

1 So I promise this is the last slide. 2 So for drinking water monitoring portion 3 in this SAP, our questions are focusing on these main 4 issues. We've proposed a general framework for 5 designing a monitoring study that could be used to 6 estimate drinking-water exposures for range of exposure 7 durations of concern. 8 We've also made a proposal of what we 9 would like to do in terms of using intensively sampled 10 monitoring datasets to evaluate both various sampling 11 frequency strategies as well as other exposure 12 estimation methods. And we've updated our 13 considerations in terms of methods for estimating 14 exposure from less frequently sampled monitoring data. 15 So at this point I'm going to open 16 things up for questions. 17 DR. STEVEN HEERINGA: Thank you very 18 much, Nelson. 19 Questions or clarification from the 20 Panel? And first, Dr. Lee. 21 DR. HERBERT LEE: It's Herbert Lee, I 22 got two sets of questions. The first set is about the 23 variogram estimation you put up. What was the raw data 24 used to estimate that variogram? 25 MR. NELSON THURMAN: I think that wasFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 135

1 actually one of the ecological exposure monitoring 2 profile sites. So it was one that had the 3 DR. HERBERT LEE: It was daily data? 4 MR. NELSON THURMAN: Daily data. 5 DR. HERBERT LEE: Looking at things like 6 the Heidelberg datasets, those don't look like Gaussian 7 correlation functions to me from ones I've seen, 8 certainly not a stationary Gaussian correlation 9 function itself. They're a lot less smooth. So I'm 10 surprised to hear you say that Gaussian looks 11 appropriate. 12 DR. NELSON THURMAN: Okay, and let me 13 clarify that we only looked at a small group. In fact, 14 I'm pretty sure I only provided Jim with the ecological 15 exposure datasets. 16 DR. HERBERT LEE: Okay. 17 MR. NELSON THURMAN: So that may very 18 well change when you look at the Heidelberg. 19 DR. HERBERT LEE: My second question is, 20 what do you mean by chemograph matching? 21 DR. NELSON THURMAN: What I mean is -- 22 let me see if I can go back to that. I mean, naively I 23 thought that we could just say number of peaks with the 24 peaks might be a way of doing this; but what you soon 25 learn is that, what I've learned by this exercise isFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 136

1 when you start taking a look at weekly sampling, you 2 cut out a lot of the smaller peaks. 3 So I'm looking at more of a generalized 4 shape-type matching now rather than and probably more 5 generalized shape in terms of what is the duration of 6 your high exposure and whether there, how much overlap 7 do you see in that. 8 DR. HERBERT LEE: It seems like an 9 important thing to take into account there is the fact 10 that you may be missing peaks altogether when you're 11 matching those. But it's something that can be done. 12 DR. NELSON THURMAN: We learned that 13 very quickly, and if you have any suggestions on how we 14 might do this better, we're open to that, because the 15 more we looked at that, the more we saw holes in that 16 approach. 17 DR. STEVEN HEERINGA: Dr. Bailar. 18 DR. JOHN BAILAR: You're proposing to 19 collect some very extensive data, which will be 20 subjected to a quite sophisticated analysis. I'm 21 interested in the use of the output of that. 22 I see two broad categories of possible 23 use: one is what you could call scientific, trying to 24 understand the relationship between exposures and 25 health outcomes.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 137

1 The second has to do more with 2 monitoring and surveillance over the very long run, and 3 I can see a use here of these data in deciding what 4 kind of monitoring and surveillance will be needed 5 during a long period of what might be regulation. Do 6 you see further uses of these data in the first of 7 those, the scientific category? 8 DR. NELSON THURMAN: Yes, I do see, and 9 I do want to clarify. We're proposing using extensive 10 data that has already been collected so we're 11 DR. JOHN BAILAR: Yes. 12 DR. NELSON THURMAN: In that regard, but 13 yes. And what we're proposing to do is take what can 14 we learn from that data. Our focus more is on what you 15 talk about the second part: how much monitoring, how 16 frequently do you have to monitor to do that; but at 17 the same time, there are scientific lessons that we're 18 going to learn from this that have a broader 19 application. 20 And the other thing I want to point out 21 is that we're actually requiring the registrant to 22 collect the data, so it's not EPA out there. 23 DR. JOHN BAILAR: Right, I understand 24 that and appreciate it; but I think this document might 25 be improved by having some further explanation of theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 138

1 intended uses of these data and how you expect these 2 uses to play out over the years to come. 3 I have a second question that has to do 4 with sampling strategy. I understand how less frequent 5 sampling may miss the peaks; but still, if you have a 6 lot of samples, the peaks should be represented with 7 their frequency in the general population of possible 8 samples. So it's not clear to me why over a large 9 dataset you would miss the 1% kind of peak; it ought to 10 show up in 1% of your samples. Am I being clear? 11 DR. NELSON THURMAN: Yes, you are, and 12 one thing I want to say at this point, what we're 13 focusing on at this point, we're looking at individual 14 community water systems. So the concern is: is there 15 exposure on those community water systems that may be 16 exceeding what we, what would be determined to be a 17 level of concern in a year-by-year basis. 18 For the 90-day exposure period for those 19 sites we haven't had an exceedance in any of these 20 community water systems, but we've been looking closely 21 at that. If you look at this regard, our focus has 22 been on and once again, this is a condition of the re23 registration, because our assessment in general is, and 24 what you've explained, we have not been seeing 25 concentrations in general that have exceeded what we'reFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 139

1 seeing at our existing level of concern; but at the 2 same time, we haven't seen some of the monitoring data 3 that is sampled more intensively. 4 So what we wanted to do as a condition 5 is have Syngenta go to those community water systems 6 that had higher estimate exposures from quarterly 7 samples, three of which are not likely to be in the 8 atrazine use period, and do more intensive sampling to 9 see whether that, those exposures are higher and how 10 much higher they are. 11 So that's where we have been going with 12 that. But our focus has been on the individual 13 community water system method. 14 DR. JOHN BAILAR: I think this aspect of 15 the sampling could also use some further explanation. 16 I'm not objecting to it. 17 DR. STEVEN HEERINGA: Other questions 18 and clarifications? 19 Dr. Lee, did you get all of your 20 questions in? 21 Yes, Dr. Krishnan. 22 DR. KANNAN KRISHNAN: In some of the 23 slides like 83 and other places, when you refer to TCT, 24 yes, it's atrazine as well as some of its 25 transformation products; and what other triazines areFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 140

1 also included and what's the approximate percentage? 2 Is there some idea of the range that you can give or 3 infer from historical data? 4 DR. NELSON THURMAN: All right. First 5 of all, it includes atrazine, simazine and the chloro6 degradates of -- 7 DR. KANNAN KRISHNAN: DIA, DEA and 8 DR. NELSON THURMAN: Yes. So it 9 includes the chloro-degradates as well. I've been 10 working with basically the total TCT. The monitoring 11 data particularly in the last few years has not 12 analyzed for the individual components as well as the 13 total chlorotriazines, so we could go back and make 14 that estimate. One of the things we find is that it 15 tends to vary, depending on the timing and such and how 16 much degradation has occurred in there. 17 So it's not an easy straightforward, 18 "Here's what the percentage is". It's more of a range 19 over time. But we have not done that, but we have a 20 capability of doing that. 21 DR. KANNAN KRISHNAN: Also what I am 22 trying to understand is the atrazine assessment if it 23 is based on atrazine numbers at the end of the day, 24 would that be compared with the TCT monitoring data or 25 one liter atrazine, or would you add atrazine plus itsFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 141

1 metabolites, if you can clarify that? 2 DR. NELSON THURMAN: We're focusing on 3 the TCT. 4 DR. STEVEN HEERINGA: Dr. Portier. 5 DR. KENNETH PORTIER: Maybe this thought 6 is not fully formed, but I'll ask it anyway. You 7 mentioned that in moving forward in designing a 8 monitoring plan, we needed to know about the dose and 9 duration, right, the area under the curve and how long. 10 It strikes me there's another aspect of sampling that 11 you haven't talked about, and that's the tolerance for 12 uncertainty in a decision. 13 You know, we use the phrase 14 "monitoring", and monitoring usually means at some 15 point I want to be able to answer the question kind of 16 with certainty that we have exceeded a certain level. 17 So we monitor to see when we exceed, right, and then 18 you can implement some kind of intervention. 19 An alternative is to measure and then be 20 able to come up with a probabilistic statement that 21 says, "Given what I've seen in these monthly 22 measurements, my chances of having exceeded this 23 criteria is such-and-such", right? 24 Now you haven't monitored, you haven't 25 measured that exceedance; you've measured something,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 142

1 and through some kind of modeling or estimation 2 procedure you were able to make a probabilistic 3 statement. 4 Within the EPA scheme of things, is that 5 kind of second statement about a probabilistic chance 6 of exceeding, does that fit into the regulatory scheme? 7 Is that something that's thought about when you're kind 8 of designing the sampling scheme? 9 And the reason I'm talking about it is 10 because I think this is a key example of where you may 11 never be able to monitor enough, measure enough, to be 12 able to say with certainty that you've exceeded. 13 But you may be able to answer a 14 probabilistic statement with enough sampling to be able 15 to be confident that you exceeded a certain likelihood 16 of the event happening. I hope I'm clear enough. 17 DR. NELSON THURMAN: Yeah, and actually 18 we've talked about that in terms of that as an 19 approach. We didn't flesh it out much in the 20 background document and obviously didn't talk about it 21 too much other than, you know, one of the things I made 22 very brief mention and probably a lot briefer than it 23 should be in terms of our focus has been on: can we 24 try to find confidence bounds or some type of safety25 factor approach which plays in easier to what we'veFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 143

1 been doing at FQPA. 2 But the other option is what's the 3 likelihood that we actually missed something of this 4 magnitude and this duration. 5 So it's something we've talked about in 6 some of the approaches, and I think extreme value is an 7 example of one where it may be easier to use it that 8 way. 9 So we have thought about that; we just 10 didn't flesh it out too much in that. 11 DR. STEVEN HEERINGA: Yes, Dr. Coupe. 12 DR. RICHARD COUPE: I'm Richard Coupe. 13 I just wanted to follow up a little bit on the 14 discussion we had the question before when you were 15 talking about mixtures of total chlorotriazines, and 16 this kind of brings up a little something I was going 17 to talk about later is that you'd have trouble going 18 back I think and looking at the total chlorotriazine 19 totals in historical data, because simazine was such a 20 big factor in days gone by and we no longer use 21 simazine. And so you wouldn't be able to do the ratios 22 very well. 23 The point that I wanted to bring up is 24 though, is that sometimes we seem to think that this is 25 all -- no offense to statisticians and whatnot -- butFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 144

1 it's not the same dataset every year. Things change 2 through time with market forces, and pesticides come 3 in, come out. 4 Sometimes it's not really easy to use 5 historical data. It's what you have right now which 6 you need to start with, but you need to always keep in 7 mind that these things are changing and the use 8 patterns and how you do it, adjuvants change; a lot of 9 things change that could mean every year you have a 10 different set of conditions out there that lead to an 11 exposure problem. 12 So you need to sample every minute of 13 every day. 14 DR. STEVEN HEERINGA: Any comments or 15 questions at this point? 16 I think if they come up, we'll have the 17 opportunity, of course, to address them again. Thank 18 you, Nelson and Mary, for this. 19 At this point, I think we're really 20 right on schedule with the program, which as I said is 21 an easy thing to do today; it'll be more difficult in 22 days to come. Forewarned is forearmed, I guess. 23 At this point in time, I think we return 24 back to Dr. Lowit for a presentation on Scientific 25 Considerations and Potential Sensitivity of Infants &FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 145

1 Children and Implications of the Mode of Action on 2 Water Monitoring Strategy. 3 DR. ANNA LOWIT: Well, if you'll give us 4 a minute, we're going to do some Musical Chairs. 5 DR. STEVEN HEERINGA: You can certainly 6 take your time. 7 DR. ANNA LOWIT: Ralph? I think it's 8 settled. I have a question for Dr. Heeringa. My 9 slides are, I have basically two short presentations, 10 about 10 or 12 slides. There's a natural stop in the 11 middle. I can either just do them all, or I can stop 12 in the center; either one. 13 DR. STEVEN HEERINGA: Why don't we stop 14 in the center and take a short break? No matter what 15 you do, we're going to be out of here early today, and 16 I don't want to drag this on unnecessarily today; but 17 why don't we go ahead and take a break in the middle 18 just for questions on it? 19 DR. ANNA LOWIT: Okay. Well, it's not 20 that many; it probably won't take that long. 21 So I did ask some members of the team to 22 come back up, because neither of these presentations 23 are heavy in data; they're more conceptual 24 intentionally. And so the people who know the answers 25 to the detail questions can be close at hand when thoseFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 146

1 come up. 2 Joe? Joe? Can you help me with working 3 the slides? It's like 20 of them or something. 4 So there are two presentations, and 5 they're coming up and we'll stop in between; but 6 they're both integrated, which is where we thought we 7 could sort of put them together. 8 DR. STEVEN HEERINGA: Go ahead and put 9 them together 10 DR. ANNA LOWIT: It doesn't matter. 11 DR. STEVEN HEERINGA: You're making a 12 convincing argument. 13 DR. ANNA LOWIT: Okay. Both topics have 14 both a hazard-assessment component and an exposures15 assessment component, and largely the issues as you 16 think about evaluating life stages and sensitivity are 17 not too far removed from how you think about the 18 critical duration. A lot of the same points come up, 19 particularly on the water. So it's moderately logical 20 to do this together. 21 So I'll start with the FQPA analysis. 22 FFDCA, as amended by the FQPA, or what should read the 23 Food Quality Protection Act, makes some unique 24 requirements of the Agency. And one of those is to pay 25 special attention to infants and children by thinkingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 147

1 explicitly about the availability of information on the 2 toxicology and the exposure of a particular pesticide. 3 And this relates to the FQPA safety 4 factor, the 10X factor. And for those of you who are 5 not familiar with our regulation it's a unique statute, 6 in that the Congress requires that we apply an extra 7 factor and that that factor can be removed in the event 8 that you have sufficient information for toxicology and 9 exposure. 10 At the present time, we have not 11 proposed a new FQPA factor. We have not revised the 12 factor from the red. We are reserving that decision 13 pending things that are still ongoing. 14 The chapter in the paper, in the Issue 15 Paper, has a lot of detailed information about 16 experimental toxicology studies, a short summary of 17 epidemiology which you heard from Dr. Christensen this 18 morning and is really missing in the water section 19 largely because, as you have heard from Nelson and 20 Mary, a lot of that work is still ongoing and still 21 being worked out. 22 So what I'm going to do in just these 23 few slides is instead of digging into the details of 24 the data, to talk to you more about the way we'll think 25 about the FQPA analysis and the kinds of things thatFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 148

1 we'll think about. And so the question we're asking 2 the Panel is more around the framework of thinking 3 about those things, are we on target with the right 4 things, are we missing some things, that sort of 5 information. 6 So with respect to hazards, I'm going to 7 separate this. The bulk of this is going to be the 8 hazard considerations, because we've just spent, you 9 know, a good 45 minutes talking about the water so that 10 we don't have to duplicate that. So we'll circle back 11 around to hazard. 12 With respect to the hazard 13 considerations, the important issue to consider is the 14 availability of data to assess critical life stages. 15 And as I'll talk about a little bit more in detail in a 16 couple of minutes, there are some key studies that are 17 still ongoing that are pending. 18 But at the end of the day, we're going 19 to consider everything that we have available to us: 20 we're going to think about Mode of Action; we're going 21 to look at the animal toxicology database, the human 22 relevance of that data; we're going to look at the 23 dose-response relationships; and we're going to think 24 about the epidemiology findings. 25 So as we consider all the informationFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 149

1 with respect to the Mode of Action, as I very quickly 2 gleaned over this morning, the key events involving the 3 neuroendocrine Mode of Action for atrazine have been 4 well established. 5 In 2000, the SAP supported those. It 6 starts with the effect of the pulsatile release of 7 GnRH, leading to changes in LH attenuation and 8 ultimately in the rat to mammary tumors, but in the 9 human we think more about development and reproductive 10 outcomes. 11 As you heard from Dr. Christensen this 12 morning, there is epidemiology data that is relevant 13 for thinking about infants and children. And we think 14 that this information provides qualitative evaluation 15 of the human relevance of some of the animal findings. 16 I think in particular, as you heard Carol talk about, 17 they're small for gestational age because we certainly 18 see changes in pup weight in the animal toxicology 19 base. 20 The two Farr studies on the menstrual 21 cycle effects as it relates, I think it's easy to think 22 about changes in LH attenuation affecting menstrual 23 cycle and, maybe to a little bit lesser extent, the 24 semen. 25 On the animal data, we did a fairlyFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 150

1 extensive evaluation about it in the Issue Paper, which 2 is really the beginning of what will become a very 3 extensive sensitivity analysis. 4 We have right now a number of studies on 5 specific life stages: those from gestational exposure, 6 lactational, early postnatal and peripubertal. And 7 there are also two very recent high-quality tissue 8 dosimetry studies in fetuses and lactating pups, and 9 both of those studies actually come from EPA labs. One 10 of them is out of the Stoker lab. 11 The second one is a brand-new study, 12 which is a collaborative effort between Tammy Stoker of 13 NHEERL and one of the OPP analytical labs, so I think 14 that's a case where you collaborate across the Agency 15 to get very high-quality data. 16 We are putting a lot of focus on LH 17 attenuation as you've heard today, because it is well 18 documented with respect to dose and across lots of 19 different time courses from one day all the way out to 20 many months of exposure and, as Dr. Rodriguez 21 discussed, both the plasma concentrations and the LH 22 data we have. And you can correlate those very highly. 23 The LH attenuation is the most sensitive 24 endpoint of the database. No other high-quality study 25 provides endpoints lower than those from LHFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 151

1 attenuation, and that includes outcome data related to 2 delayed puberty onsets among other things, in addition 3 to the more standard guideline kind of studies we see 4 on systemic toxicity, such as dogs and rats and 5 everything else. 6 There is strong biological plausibility 7 with respect to LH attenuation, which makes it a good 8 endpoint for thinking about assessing human risk. 9 Okay. But one of the points of why we 10 are reserving the decision or a proposal around the 11 FQPA factor is there are three experimental toxicology 12 studies that are still ongoing -- and I mean ongoing, 13 still in the lab, animals still being exposed. 14 Two of these are at the ORD lab, the 15 first two of the bullets on the slide. The first one 16 is looking at hormonal changes and outcomes from 17 gestational exposure, and this will provide a nice 18 dataset. Right now there isn't a strong dataset 19 looking at hormonal exposure from gestation. 20 The ORD labs are also looking at 21 behavioral changes in male rats from gestational 22 exposure. The SAP report from 2000 on the key events, 23 as that Panel thought about the human relevance for 24 developmental and repro outcomes, there's a statement 25 in that report that neurobehavioral findings turn outFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 152

1 to be a sensitive endpoint. So ORD is looking at that 2 to see if that turns out to be true. 3 And then the third bullet is a study 4 being conducted by Syngenta, which I expect that or I 5 assume they'll talk about tomorrow, although I don't 6 know. But they're looking at the latent effects in 7 adults from both gestational/lactational exposure, and 8 that study is actually a direct result of some 9 uncertainty the Agency had identified in 2003. 10 And the statement is actually in the 11 Charge Question that one of the in the previous risk 12 assessment the Agency had identified multi-life 13 exposure studies as an area of uncertainty. 14 As I talked about a minute ago we have 15 gestational exposure, lactational, peripubertal, a very 16 isolated, but none that cover multiple life stages. 17 And as you think about people who don't move or a farm 18 family, let's say, that lives in Iowa and does that for 19 many years, that would be a multi-life stage exposure. 20 So the study that Syngenta's conducting 21 is, I believe, intended to fill that need of multi-life 22 stage. I expect them to talk about it tomorrow, so I'm 23 not going to spend a lot of time on it. We just 24 thought we'd let you know sort of how we saw the 25 status. It's a very complicated study. It hasFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 153

1 multiple cohorts and multiple subsets. They're also 2 looking at recovery, which is very nice. 3 As of right now, as of July the 15th, 4 excuse me, two of the subsets have been submitted, 5 what's called Cohort I, Subset A, and Cohort II, Subset 6 D, which is essentially the gestational through 7 lactation and a postnatal through necropsy. So a great 8 deal of that study is still ongoing. I believe we'll 9 probably hear more about it tomorrow. 10 We are reserving our larger view of that 11 study, simply because so much of it is not in; but we 12 have made one observation so far that in the animals in 13 those two subsets that we've seen that there's a lack 14 of effect on LH. 15 The dose is less than 50 mg/kg, and as 16 you saw from Dr. Chester Rodriguez's presentation this 17 morning, that's actually a relatively unusual finding, 18 because even some of the other Syngenta data show 19 effects on LH at doses much lower than 50. 20 So that would be the hazard, the things 21 we'll think about for hazard: Mode of Action, dose22 response, epidemiology, human relevance. The other 23 part of the FQPA analysis is, it would be the drinking 24 water exposure. 25 And as you heard Nelson and Mary'sFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 154

1 presentation, there's quite a bit of work to do in this 2 area. Part of it is because they're waiting for the 3 tox team to tell them what the duration is, and part of 4 it is it's just a very difficult issue that we're 5 working through. 6 But we are asking Questions 4.1 to 4.3, 7 and the answers to those will help us think about the 8 FQPA analysis. 9 So in the coming months, the Agency is 10 going to work, we're going to work towards completing 11 the scientific analysis for the FQPA factor. And what 12 we'd like from you in Question 5 is to think about 13 those factors I just talked about: the Mode of Action, 14 dose-response, human relevance, the findings and the 15 epidemiology, along with the thinking that will be the 16 responses to Questions 4.1 to 4.3: are we missing some 17 factors; does that seem like the right set of things to 18 think about; that sort of stuff. 19 Okay, so that's the FQPA issues. So if 20 we move on to thinking about putting it all together in 21 the water monitoring strategy, if we take a step back 22 and go back to the beginning. 23 The current drinking water program, 24 monitoring program that Syngenta conducts as a 25 requirement of registration that the Agency requires,FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 155

1 right now they're doing weekly monitoring during the 2 application and roughly the growing season, which is 3 roughly the spring to the summer, and biweekly for the 4 rest of the year. 5 In the last risk assessment, the Agency 6 conducted 90-day rolling averages of monitoring data, 7 and those averages were derived from interpolating 8 between the weekly monitoring points. 9 And I think the issue of interpolating 10 has been addressed in other comments; I'm just trying 11 to make sure everyone is on the same page of what was 12 done in the last assessment. So these 90-day rolling 13 averages were then compared against a level of concern 14 derived from the 6-month Morseth LH data. 15 So the question is: in this matching of 16 the 90-day rolling average to 6-month Morseth study, 17 given the current knowledge of atrazine, particularly 18 its temporality of the toxicology, should the critical 19 duration of exposure be revised and, if so, how? 20 That's the essence of this question. 21 So if we take another step back, it's 22 important to just say explicitly that the atrazine 23 database is lacking in human-specific information that 24 we can use to quantitatively extrapolate between rats 25 and humans.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 156

1 Because of that, because of the lack of 2 real chemical-specific quantitative information, what 3 we have to do is to infer generic knowledge across 4 multiple disciplines. And so that's what we have done 5 and what we are asking for feedback on, because I 6 expect and I hope that there are more things that we 7 haven't considered, and that is what we ask from you. 8 So what we've done is we've looked at a 9 couple of different things. We're looking at the 10 empirical effects from animal studies and also 11 toxicodynamic and toxicokinetic information. 12 So if you go back to the toxicology 13 dataset for atrazine, there are a number of endpoints. 14 Delayed puberty onset is one of the major ones 15 throughout the developmental and reproductive dataset. 16 And in the rat, those studies are 4 days or longer; but 17 in humans, as we know, puberty occurs over a long 18 period of time. 19 So linking the 4-day exposure in the rat 20 from the LH study to the puberty in humans that lasts a 21 long period of time, there's a little bit of a mismatch 22 there. 23 As it relates to prostatitis, another 24 one of the endpoints measured in the atrazine database, 25 in the rat, exposure to the dam, when the dam wasFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 157

1 exposed to atrazine there's an inhibition of prolactin, 2 transmission in the milk. 3 And it is this milk exposure to the pup 4 that affects the development of the TIDA neurons in the 5 offspring. And it is those TIDA neurons that in turn 6 cause the effect of the prostatitis in the male pups, 7 but it is derived from exposure to the dam. 8 In humans, prolactin plays a role in 9 development and maintenance of the prostate; but the 10 critical periods of development and the hormonal 11 involvement is far less known, particularly the 12 temporality of that. 13 Okay. So as we think about LH 14 attenuation, as the Cooper data have shown that we saw 15 from Dr. Rodriguez earlier, a single day of dosing is 16 not sufficient at low doses; at extreme high doses, you 17 may see some attenuation but not too much. 18 And to reach its maximal effect, we see 19 that at or around pseudo-steady state, which we believe 20 at the lower doses occurs around 4 days in the rat, and 21 that there is a very nice matching of this LH 22 attenuation beginning at 4 days to the pseudo-steady 23 state tissue levels also after 4 days. But the 24 question here is matching, matching this 4 days of 25 exposure from the LH in the pseudo-steady state in theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 158

1 rat to the humans. 2 Pharmacokinetically, we much prefer to 3 use a PBPK model, as was alluded to earlier. McMullin, 4 there is a McMullin model out that we have reviewed 5 that we find has some important shortcomings. It does 6 not do a very good job of capturing the rapid kinetics 7 of atrazine and under-predicts the plasma 8 concentrations of the chlorotriazine metabolites. 9 Dr. Rodriquez did do a series of 10 calculations looking at and taking the elimination-rate 11 values from the Thede study and doing some allometric 12 scaling to see if that can't help inform durations of 13 exposure that would be relevant to humans. 14 So I won't do his calculations justice, 15 because he did them much better justice this morning; 16 but in essence what he has done is taken the 17 elimination-rate constants from the lower dose groups, 18 which are pretty constant across the 1- to 10-mg group, 19 and performed allometric scaling for an average female 20 body weight of 60 kilograms. 21 And he also assumed that there would be 22 three to five half-lives required to get steady state. 23 And if you look at this, the last column in here, you 24 see that the values range from something in the order 25 from about two-and-a-half weeks to about a month, maybeFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 159

1 four or five weeks, which all, despite the 2 uncertainties in the calculations, are all shorter than 3 the 90-day rolling averages currently being used. 4 So we also asked ourselves: 5 What is known about the LH surge in 6 humans that might inform critical periods of duration? 7 Dr. Mendez, who is not here now but was here this 8 morning, did some research into this area and found 9 that information from the pharmaceutical literature 10 might give us a qualitative handle on thinking about 11 windows of susceptibility in the human as it relates to 12 the menstrual cycle. I won't go into this in detail; 13 if someone wants to ask questions, they can ask Dr. 14 Cooper. 15 But our look at that literature is that 16 you can conceive from that IVF literature two different 17 periods of possible susceptibility. First would be the 18 follicular phase of the menstrual cycle, or in other 19 words approximately the first half of the female cycle, 20 or more specifically the second half of the late 21 follicular cycle, which would only last four or five 22 days. 23 It's very important as you think about 24 these IVF drugs and how they would relate the atrazine, 25 you have to be very careful not to put too much weightFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 160

1 on the absolute findings. IVF drugs are very potent. 2 They're given for very specific reasons. Most of them 3 are given by injection, they're not oral. 4 So there are some uncertainties around 5 making those comparisons. But we think qualitatively 6 they do suggest that the follicular phase, maybe even 7 the late follicular phase, is a potential window of 8 susceptibility. 9 So we have tried to think about this in 10 a multidisciplinary way, allometric scaling from the 11 pharmacokinetics; we've thought about the relevance of 12 the LH from rats to humans as it relates to the 13 menstrual cycle. 14 We've also thought about the 15 experimental toxicology data and the outcomes you see: 16 the delayed puberty, the prostatitis and how you would 17 relate those windows in the rat to the windows in the 18 human, and there really is no absolute finding. You do 19 see a range of possible values from that analysis of 20 just a few days, from four or five days up to something 21 in the order of for four or five weeks, maybe 22 approximately a month. 23 There are a couple of things you can 24 take from that. First, all of those are shorter than 25 the 90-day rolling average being used right now. ThereFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 161

1 may be other things we haven't thought of that are as 2 long as the 90-day rolling average. If there are, we 3 are looking for that feedback. 4 Another thing you can take from that is 5 that there is a lack of precision around the estimate, 6 and there is going to be a lack of precision in the 7 rat-to-human extrapolation. So what we'd like to do is 8 to have a multidisciplinary approach that thinks about 9 this from multiple points. 10 So as we think about answering Charge 6, 11 the Question No. 6, we'd like for you to comment on our 12 analysis -- I mean our preliminary conclusions -- but 13 we are also hoping that you have some alternatives and 14 some additional things for us to think about. 15 I think that may be the last slide in 16 this set, but both of these areas that I went over just 17 now, the FQPA analysis for the life stage sensitivity 18 and also the critical duration of exposure, not only 19 are they important as we think about the water 20 monitoring; but as we think about the 2011 SAP, they 21 will be the most likely two major areas on the hazard22 assessment side that we address at the next meeting. 23 DR. STEVEN HEERINGA: Thank you, Dr. 24 Lowit. 25 At this point, members of the Panel, anyFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 162

1 questions of clarification on the material? 2 Dr. Fenner-Crisp. 3 DR. PENELOPE FENNER-CRISP: Most of the 4 studies that are still ongoing both in the Agency and 5 outside are focusing on the non-adult life stages. In 6 order to make conclusions about whether or not the 7 younger life stages are more sensitive than the adult, 8 do you have enough adult data against it to compare? 9 DR. ANNA LOWIT: I believe so, yes. 10 DR. PENELOPE FENNER-CRISP: For all of 11 the endpoints of concern? 12 DR. ANNA LOWIT: Yeah, I think that, I 13 mean, atrazine has a very strong database from really 14 top to bottom. I think when you add these datasets in, 15 they provide a very solid package from which to make 16 those findings, yes. 17 DR. PENELOPE FENNER-CRISP: What's the 18 timeline for finishing up the undone studies? Are they 19 going to be available by the time, your target time for 20 completing your reassessment? 21 DR. RALPH COOPER: Actually, there's two 22 papers in draft form at the moment, one addressing the 23 female and one addressing the male, wherein the dams 24 were treated from gestation day 14 through 21, they 25 were allowed to give birth, and then we followed theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 163

1 offspring out, looking at a number of different 2 measures from body weight, most of your male 3 reproductive parameters. 4 There was some behavioral observations 5 in there, and we've gone on to look at some of the 6 parameters in the female that we look at typically in 7 cyclicity in that. 8 That study was run in four blocks, so 9 some of those offspring are getting up there in age and 10 we're waiting for them -- as a matter of fact, the 11 controls are undergoing reproductive senescence at the 12 moment. So this was a gestational only exposure; 13 that's the one that EPA was responsible for, or ORD. 14 And my answer to your question now is 15 imminent. 16 DR. PENELOPE FENNER-CRISP: That's good 17 news. 18 DR. RALPH COOPER: I hope that these two 19 papers are going through internal clearance within the 20 next couple of weeks, if I'm not overestimating how 21 hard those people are working. 22 DR. ANNA LOWIT: You can ask Syngenta 23 the same question on their study tomorrow. 24 DR. PENELOPE FENNER-CRISP: I will. 25 And the other question I have is, areFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 164

1 you open to the possibility there may be more than one 2 critical duration of exposure to have to decide what 3 kind of monitoring strategy you may have? 4 DR. ANNA LOWIT: We're open to your 5 feedback. 6 DR. PENELOPE FENNER-CRISP: Or you 7 wouldn't answer this question back in April. Is this 8 BMR going to be applied to all risk assessments from 9 acute through chronic, unlike the current one which has 10 a different dataset driving the acute? 11 DR. ANNA LOWIT: Just make sure we have 12 the terms right. The BMR is the response, make sure we 13 have the acronyms right. The BMR is the Benchmark 14 Response, which is the magnitude of the value in the 15 benchmark dose. So I think -- 16 DR. PENELOPE FENNER-CRISP: It 17 represents a particular dataset. 18 DR. ANNA LOWIT: Yeah, so the benchmark 19 dose estimates that are in Appendix C, I believe, focus 20 on LH. As you accurately have said, the last risk 21 assessment, there were different endpoints for 22 different durations of exposure. 23 I think, I believe that what Dr. 24 Rodriguez has very elegantly shown in his overlay of 25 the temporal, the plasma data and the LH data is that IFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 165

1 think we need to rethink the durations of the risk 2 assessment, because if steady state or pseudo-steady 3 state around plasma levels are driving what the 4 responses are, particularly around LH, I think we have 5 to take a step back and look at those standard 6 durations that we normally use and maybe do something 7 or focus as it relates to the atrazine Mode of Action 8 from the toxicology data. 9 DR. STEVEN HEERINGA: Yes, Dr. Meek. 10 DR. BETTE MEEK: This is a really simple 11 question. It's just you mentioned that you weren't 12 proposing any change in the FPQA factor -- FQPA, sorry 13 -- at this time; but it was applied in the previous 14 assessment, the tenfold factor was applied? I just 15 want to make it clear. 16 DR. ANNA LOWIT: In the previous 17 assessment, it's in the Charge Question if you look at 18 the very beginning of the Charge Question. The 19 previous assessment for those systems included in the 20 community water system, the 10X was reduced to 3X, 21 because the drinking water monitoring was sufficient to 22 evaluate the 90-day rolling average. 23 DR. STEVEN HEERINGA: Additional 24 questions at this point? 25 I have one while others are thinking;FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 166

1 it's for Nelson. The auto-samplers, they're 2 accumulating samplers; they're not specific aliquot3 type 4 MR. NELSON THURMAN: Yes, that's true. 5 DR. STEVEN HEERINGA: Yes, Dr. Schlenk. 6 DR. DANIEL SCHLENK: I just had a 7 question. Maybe I should have asked it during the epi 8 presentation, but there were I guess some birth defects 9 that were associated with some of the epi studies, and 10 in reading the background documents, I guess there 11 wasn't a Mode of Action association with that. 12 I'm curious with the gonadotropin13 releasing hormone antagonist, has there ever been any 14 association with any birth defects associated with that 15 just to see if there's a common Mode of Action through 16 that pathway, and if that has or has been evaluated, 17 just-- 18 DR. ANNA LOWIT: Well, I can add I'll 19 add the first part, and Ralph can add the second part. 20 To my knowledge that with respect to the 21 experimental toxicology database, there is some 22 standard rat in vivo developmental studies, and none of 23 those abdominal-wall defects have been seen. And they 24 can be seen in the rat, and they weren't. And some of 25 those doses in those studies are very high. But IFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 167

1 think Ralph had something to add. 2 DR. RALPH COOPER: Well, I was just, 3 with the GnRH antagonist, you can get pregnant, if I 4 understood the question. 5 DR. DANIEL SCHLENK: No, I'm just saying 6 with those particular compounds; I'm just saying 7 experimentally if you use those in a lower dose 8 obviously, they're very, very potent -- but could you 9 potentially generate a system that would allow you to 10 look at that potential endpoint and whether or not 11 those gut-wall issues are related to LH reduction or 12 not, if anybody's ever done that. 13 DR. STEVEN HEERINGA: Yes, Dr. Horseman, 14 you look like you're -- 15 DR. NELSON HORSEMAN: This might relate 16 to the previous question. As I understand, all these 17 toxicology studies in gestation have been done in late 18 gestation. Has there been work done earlier in 19 gestation to look at outcomes of the pups? 20 DR. RALPH COOPER: To my knowledge, 21 early gestation alone actually, for a completely 22 different reason there was a study where the animals 23 were exposed for a little bit of different time on the 24 atrazine and they looked at outcome, and it was body 25 weight and blood pressure and these kinds of things.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 168

1 I don't believe that work has been 2 published yet. It was actually done by another group 3 in RTP. But the Syngenta study that Anna showed up 4 there would be one where you would expose throughout 5 gestation; they started on gestation day 0, right? 6 And then there's multi-gen studies where 7 they do if there is a multi-gen on this, and I don't 8 know, I don't believe there was any such signals in 9 that study about effects in the pups there of that 10 nature. 11 And then of course there is the rat 12 developmental study that's required wherein the animals 13 are dosed from gestation day 6 to 16 or 6 to 20 I 14 guess, depending on the study; but 6 to 16 probably 15 given the age of the study, and they, for 16 teratogenicity there. 17 DR. STEVEN HEERINGA: Okay. What I 18 would like to suggest, Dr. Lowit, is that we take a 15- 19 minute break, and I know you have one wrap-up 20 presentation. 21 But talking with Joe Bailey, I think one 22 thing that would be productive, given that we have the 23 time today, we can't move on, unfortunately. I think 24 it would be nice to cover this in a little bit of time 25 this afternoon.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 169

1 But what we can do is I'd like to go 2 sort of systematically through the Charge Questions, 3 not to discuss them but just to make sure that we 4 understand exactly what you're asking. And so we might 5 just click them off and say clear enough and we'll move 6 on; but we'll do that afterwards, and that'll benefit 7 our discussants, too, to make sure that, again, we 8 don't want to get into a discussion where we might sort 9 of influence your thinking, because I think that but 10 just to make sure that we understand the question 11 correctly and what you may be driving at there. 12 So we'll do that after the break. Let's 13 plan to reconvene at five minutes after 3:00. 14 (WHEREUPON, a recess was taken.) 15 DR. STEVEN HEERINGA: Okay, welcome 16 back, everyone. We'll wrap up our first day of the 17 FIFRA SAP meeting. 18 At this point, I mean, I turn the floor 19 back to Anna Lowit for the last of her presentations; 20 but I also think she had one point she wanted to 21 address from a break conversation we just felt that 22 should be brought out in the public discussion here, 23 too. 24 DR. ANNA LOWIT: And Dr. Krishnan and I 25 had a conversation; he approached me at the breakFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 170

1 asking for clarification around how the total 2 chlorotriazines, i.e., the TCTs, are handled in the 3 exposure assessment. I thought it made more sense for 4 Nelson to do that than it did for me. 5 DR. STEVEN HEERINGA: Dr. Cooper is not 6 here, so it's Nelson. 7 MR. NELSON THURMAN: Sure. The water 8 monitoring data measured atrazine, simazine and each of 9 the chloro metabolites. And for what we were providing 10 in terms of exposure estimates has been on the sum of 11 the total chlorotriazines. 12 So that's how it's measured. If we find 13 at some point one metabolite is more toxic than the 14 other, we can go back and break that out, if necessary. 15 At this point, it makes sense just to do exposure 16 estimates on total chlorotriazines. Does that answer 17 your question? 18 DR. KANNAN KRISHNAN: Yeah. Thanks. 19 Based on I think where we are headed, I think that was 20 a critical question. I wanted to be sure that I I 21 know I had heard you almost around 2:30, but so I 22 wanted be sure that I have correctly get that 23 classification sort of thing. 24 DR. ANNA LOWIT: So just so we close 25 that loop, there is an implicit assumption there thatFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 171

1 the Chloro metabolites are equipotent to parent 2 atrazine, and in the absence of really robust data to 3 do something more quantitative, it seems like a fairly 4 reasonable thing, particularly what's little - we know 5 a little bit about DACT and equal molar potency seems a 6 pretty reasonable assumption. 7 DR. STEVEN HEERINGA: Yes, Dr. Horseman. 8 DR. NELSON HORSEMAN: One tiny little 9 question, because I think it will come up tomorrow. 10 Hydroxyatrazine, which is apparently a bacterial or 11 plant metabolite, is it in any of these TCT 12 measurements? 13 MR. NELSON THURMAN: I was going to look 14 for Mary Frankenberry, who's dealt in it. I don't 15 recall that it is. 16 DR. KANNAN KRISHNAN: In the list of 17 components of TCTs listed in the document, it wasn't 18 listed. 19 DR. STEVEN HEERINGA: That was Dr. 20 Krishnan. 21 I guess if we can confirm -- I mean, if 22 we get information to the contrary, we'll 23 MR. NELSON THURMAN: Okay, yeah. If 24 it's not a chlorotriazine, then it would not have been 25 included, so .FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 172

1 DR. STEVEN HEERINGA: Dr. Lowit, I think 2 if we could have your last presentation, and then I 3 would like to go through this step of just walking -- 4 DR. ANNA LOWIT: Okay, it's just a 5 couple slides to pull it all back together; there are 6 just three or four slides. 7 Just to go back to the very beginning, 8 we've covered a lot of very diverse topics today and 9 we'll have one more in the morning before the public 10 comments with Dr. Suzanne Fenton. We heard a very 11 detailed presentation from Carol Christensen on non12 cancer epidemiology today, and we'll have a couple of 13 questions on that. 14 We are proposing to continue to 15 emphasize LH attenuation in any future risk assessment, 16 as we believe this is a biologically plausible key 17 event in the Mode of Action for atrazine. As we see 18 the database, it's the most sensitive endpoint and 19 appears relevant for reproductive and developmental 20 outcomes, which are throughout the database. 21 We have conducted a number of series of 22 calculations since the April meeting around internal 23 dosimetry and benchmark dose analysis that you heard 24 about from Dr. Rodriguez. 25 And then drinking water, we have updatedFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 173

1 the approaches. We provided some examples that you 2 heard from Nelson quite a bit ago. 3 The last two-part presentation -- I 4 don't know why that text is red -- that I gave a little 5 bit ago integrating the exposure and the hazard, 6 thinking about life stage sensitivity and the critical 7 duration of exposure. And both of those are components 8 for which we're still working and we're looking for 9 your feedback on, and will be a large bit of the focus 10 in 2011. 11 So thinking about the Next Steps around 12 non-cancer, pending the outcome of this meeting, we 13 anticipate a number of things in the coming months. We 14 anticipate selecting one or more or a range for that 15 matter of critical durations of exposure. And so 16 pending SAP -- put that S up there before you ask me 17 about the multiple ones. So certainly, we're thinking 18 about that. 19 And we'll be further developing the 20 drinking water analysis as you heard from Nelson, and 21 we'll be completing the FQPA analysis. We do expect an 22 SAP on these issues, along with any new studies that 23 come out in the literature, including those that have 24 come out since July the 15th, and we'll talk about 25 those in 2011.FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 174

1 Also in 2011, we'll move back to cancer 2 and cancer epidemiology. We are expecting sometimes in 3 the coming months a new study from the Agricultural 4 Health Study on cancer, and that will be integrated 5 along with other cancer epidemiology studies in the 6 experimental toxicology database on cancer, and we'll 7 also cover that at the 2011 meeting. 8 And I think that's it, yep. 9 DR. STEVEN HEERINGA: Okay. Thank you 10 very much. 11 At this point, what I would appreciate 12 and I think it would be useful; we often do it 13 concurrently just but I think since we have a little 14 time, if we could bring up the Charge Questions and 15 just step through those. 16 I don't think we'll spend a lot of time. 17 But if you would, Dr. Lowit, just sort of try to 18 paraphrase essentially what in the intent of that 19 question is. Most cases it will be very obvious, but I 20 think there are some times where we've had situations 21 where what you intended and what our people read into 22 it were slightly different. 23 So if you could do that now, it 24 certainly will prepare us for tomorrow afternoon or 25 Thursday morning as we begin to address the ChargeFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 175

1 Questions. 2 So, again, you can make this as short 3 and sweet as you'd like. If you think it's very clear 4 what you're asking for, just . 5 DR. ANNA LOWIT: Well, we always think 6 it's clear; but sometimes it turns out to not be that 7 way. So this is probably a very good idea. And I've 8 asked the major leads on each particular subsection to 9 come up; so if I misspeak on our question for that or 10 if there's anything to add, we'll do this as a team 11 effort. 12 The epidemiology, we have two sub13 questions. The first one is largely on our reviews and 14 that to give us feedback on whether or not our reviews, 15 particularly in the Appendix, do an adequate job of 16 identifying the major strengths and limitations of 17 those studies. So I think the source of that 18 information is predominantly in the Appendix. 19 DR. STEVEN HEERINGA: Dr. Gold and her 20 discussants are prepared to address that, I'm sure. 21 Sure, if you'd like to. 22 DR. ELLEN GOLD: So one thing we were 23 wondering is if you want comments on each specific 24 study or if you want more generic kind of comments 25 about strengths and limitations that might not haveFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 176

1 been touched on. 2 DR. ANNA LOWIT: I think it depends; I 3 think that's largely up to you. We're not going to 4 tell you how to answer the question. There are 5 multiple ways to cut it. 6 DR. STEVEN HEERINGA: Right. I think we 7 have the detail and that will be in the written report, 8 and the question is whether we go individually through 9 the studies; but we can discuss that more, but I think 10 the intent of that question is quite clear. 11 DR. ANNA LOWIT: Go ahead. 12 DR. STEVEN HEERINGA: Dr. Christensen. 13 DR. CAROL CHRISTENSEN: Yeah, this is 14 Carol Christensen, so just very briefly. I guess 15 specifically with those five evaluations that I had 16 sort of characterized as being somewhat stronger across 17 the database; so comments individually on those studies 18 in particular would be helpful. 19 In my slide presentation and also within 20 the written evaluation, sort of identified those on 21 female/male reproductive health and small for 22 gestational age. 23 These are the ones that we are 24 characterizing as being relatively stronger across the 25 database. So it's the two Farr studies, the State ofFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 177

1 Illinois and the Villanueva - I'm sorry, it's Ochoa2 Akuna. It's in my slide presentation. 3 DR. STEVEN HEERINGA: So from this 4 morning, there are five studies in particular that you 5 would appreciate individual comments, okay. I think 6 there are 14 other studies total that you considered? 7 Maybe Question 8 DR. ANNA LOWIT: Okay, Question 1.2 has 9 two subparts, and admittedly there is some overlap 10 between A and B. But they're intended to get at 11 specific sections in the Issue Paper. The latter part 12 of Section 3 describes the utility of the epidemiology 13 database as it relates to risk assessment, and that's 14 what's in Part A. 15 And then, Part B focuses primarily on 16 what's in Section 4 of the Issue Paper, which is 17 thinking about bringing the strengths or similarities - 18 - the strengths, the differences and the uncertainty in 19 both of the experimental toxicological and the 20 epidemiology databases together in one integrated 21 analysis. 22 So admittedly, there is some overlap 23 between A and B; but they are separate sections of the 24 Paper, so we had separate sub-questions. 25 DR. STEVEN HEERINGA: John, are youFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 178

1 fairly comfortable with Part 1.2? 2 Dr. Gold? 3 Okay, thanks. So, Question 2.1. 4 DR. ANNA LOWIT: Okay, the large 5 subheading of Question 2 relates to mammary gland 6 development, and there are also two parts here. 7 The first one relates primarily to the 8 two Rayner papers and the Coder paper, which is one of 9 the Syngenta-supported papers. The Rayner, the two 10 Rayner publications come out of the Fenton lab. 11 And we find as we look at those there 12 are some similarities; but there are also some 13 differences, and we are looking for some feedback on 14 what could lead to some of those, the differences in 15 those findings, and similarities for that matter. 16 DR. STEVEN HEERINGA: Dr. McManaman, 17 fairly comfortable with those questions as you 18 understand them? 19 DR. JAMES McMANAMAN: Yeah. 20 DR. STEVEN HEERINGA: Good, thank you. 21 Yeah, a confident response, that's good. I like that. 22 DR. ANNA LOWIT: Question 2.2 is 23 explicitly about the Enoch mixture paper. As I noted 24 very briefly in my introductory remarks, if you look at 25 our review in Appendix A, we have some concerns aboutFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 179

1 that study and are having some trouble interpreting 2 that study. 3 And so that's what Question 2.2 is 4 getting at: not only a review of the strengths and 5 limitations, but in your view how the study design 6 impacts the interpretation. So it's predominantly a 7 mixture question, but I think some of the mammary gland 8 development issues are also implicit in that. 9 DR. STEVEN HEERINGA: Dr. Mumtaz? 10 DR. MOIZ MUMTAZ: That should be good. 11 You want to focus only on the mammary glands, is that 12 right, Anna? 13 DR. ANNA LOWIT: Just the Enoch paper. 14 DR. MOIZ MUMTAZ: Yeah, right. And then 15 look at the experimental design. I'm comfortable with 16 that. 17 DR. STEVEN HEERINGA: Good, thank you, 18 sir. 19 Question 2.3. 20 DR. ANNA LOWIT: Yes, and then there is 21 one more as it relates to mammary gland development. 22 It is thinking about integrating the mammary gland 23 development papers into the weight of the evidence for 24 atrazine. 25 We are, as you heard earlier, choosingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 180

1 to emphasize more of the LH attenuation and use the 2 mammary gland development more in hazard 3 characterization as opposed to hazard identification, 4 and we're looking for feedback on that proposal. 5 DR. NELSON HORSEMAN: If Jim and Moiz 6 make it easy, it will be easy. 7 DR. STEVEN HEERINGA: Okay, Dr. 8 Horseman. 9 Okay, Question 3.1. 10 DR. ANNA LOWIT: I keep forgetting to 11 advance the slides. 12 Okay. Then we have a series of smaller 13 questions intended to get at particular components of 14 the tissue dosimetry and the proposed updates to the 15 dose-response assessment. And we had hoped that we had 16 written them so that there was a logical order that one 17 would build on the other, but I'm sure there's a good 18 bit of overlap. 19 The first one has to do with simply the 20 non-compartmental analysis conducted by the Agency that 21 you heard from Dr. Rodriguez this morning to estimate 22 internal dose of atrazine and its metabolites. 23 DR. STEVEN HEERINGA: Dr. Greenwood? 24 DR. RICHARD GREENWOOD: No, I think 25 that's clear. Obviously, there will be some overlapFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 181

1 with other things; but I think that that's clear, thank 2 you. 3 DR. ANNA LOWIT: So I think that the 4 difference between A and B, A is more the actual 5 analysis and the mathematical procedure behind the 6 analysis, and Part B is a related question asking about 7 our interpretation of what is Figure 5.5 in the Issue 8 Paper which we saw a couple of times today, which is 9 the figure from the Thede paper of the tissue dosimetry 10 data of the ramping up, and the values stay fairly 11 constant until they stop dosing and they're eliminated 12 and our interpretation of what that figure shows. 13 DR. STEVEN HEERINGA: We're set there; 14 Dr. Krishnan indicates that we're all set on that one, 15 too, in terms of our understanding of what you're 16 after. 17 DR. ANNA LOWIT: Okay. And then we have 18 two sub-questions on the benchmark dose analysis. The 19 first one is akin to the first one of 3.1; it's more of 20 a technical review of the actual analysis, the things 21 contained in Section 5.3 in Appendix C. 22 And then the Part B of that one is more 23 of a conceptual subpart around establishing a benchmark 24 response as it relates to calculating benchmark doses. 25 So one is a technical mathematical question, and theFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 182

1 other one is mostly conceptual. 2 DR. STEVEN HEERINGA: Parts A and B, Dr. 3 Meek, are you, and then Dr. Roby? 4 Okay, great. Okay. I guess 4.1, Dr. 5 Lowit. 6 DR. ANNA LOWIT: Okay. Then we have the 7 next one in that wait a minute. Now, I'm confused. 8 The slides are like out of order or something. 9 DR. STEVEN HEERINGA: I think I jumped 10 you over. 11 SPEAKER: 3.2 is 2. 12 DR. STEVEN HEERINGA: Yeah, we did 3.1.A 13 and B, and I think we did not do we did C and then I 14 jumped ahead here. So, Dr. Akana on 3.1.C. 15 DR. ANNA LOWIT: So we, the Agency, we 16 need to fix these slides. C is missing from the slides. 17 Okay, so it's just out of order. Okay. 18 So this subpart, whichever number it is 19 I'm moving too fast to figure out, but this is a 20 subpart question. I think it's the one after Figure 21 5.5, but it doesn't matter I guess for right this 22 second. 23 We do have a sub-question around our 24 interpretation of what is Figure 5.5, which was 25 discussed at length this morning after Dr. Rodriguez'sFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 183

1 presentation, and it is the overlay plot of the LH 2 attenuation data from I believe it's four studies -- 3 the new Cooper data; the Morseth 6-month data; and a 4 couple of other studies -- and that the analysis that 5 went into that plot and our interpretation of it. 6 DR. STEVEN HEERINGA: Yeah, we have this 7 question as 3.1.C, and Dr. Akana is our lead 8 discussant. 9 DR. ANNA LOWIT: Okay, we'll have to fix 10 these tonight, the slide numbering. 11 So I believe that covers all the dose12 response, even though they're out of order. And I'll 13 let Nelson cover his. 14 MR. NELSON THURMAN: Okay, Question 4.1 15 is basically focusing on what we propose as the 16 framework for the monitoring study design; so more or 17 less wanting to get your feedback on strengths, 18 weaknesses, anything else you'd want to recommend on 19 that. 20 DR. STEVEN HEERINGA: So Wesley Stone on 21 Part A, are you okay with that, Wesley, and then on 22 Part B 23 MR. NELSON THURMAN: Okay, and Question 24 4.2 is 25 DR. STEVEN HEERINGA: Oh, Dr. Coupe onFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 184

1 4.1; yes, I'm sorry. 4.2, I'm sorry. 2 MR. NELSON THURMAN: Okay. 4.2 is 3 basically questions relating to our plan to use the 4 intensively sampled monitoring data. 5 And the question on chemograph shapes, 6 we already heard some question about what we're 7 intending, and I hope I clarified that a little bit. 8 But that is something the more we put together, the 9 more difficult the concept and application is. So 10 we're interested in your feedback on that. 11 And then the second's on the strengths 12 and weaknesses of the data that we propose using. 13 DR. STEVEN HEERINGA: I think Wes is 14 indicating he's good with Part A. 15 DR. WESLEY STONE: On Part B, can you 16 say that again in terms of what you're looking for? 17 MR. NELSON THURMAN: On Part B, we're 18 looking for the strengths and weaknesses of the 19 datasets themselves. The Heidelberg data, the 20 ecological exposure monitoring data. And then I 21 touched on it briefly, but the idea of using prism 22 exams for reservoirs, as opposed to the -- 23 DR. WESLEY BROWN: Okay, so it's about 24 the data themselves, because it wasn't clear. It's 25 using them for something in particular or just usingFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 185

1 them in general or-- 2 MR. NELSON THURMAN: Well, using them 3 for evaluating the sampling frequency and the model 4 estimation. 5 DR. STEVEN HEERINGA: And there is a 6 Part 4.3, too. 7 MR. NELSON THURMAN: Yes, there is, and 8 4.3 is what we had recommend, their update on base 9 basically how we might estimate the exposure 10 estimation, what methods we'd use to estimate exposures 11 in between. 12 DR. STEVEN HEERINGA: Dr. Lee indicates 13 that he comprehends and we addressed that. 14 Question 5, then, I guess we go back. 15 DR. ANNA LOWIT: Questions 5 and 6 are a 16 little different than I think all the other questions; 17 they're more conceptual and a little bit more esoteric 18 than some of the other ones that are far more 19 technically oriented. 20 The first, Question 5 on the scientific 21 considerations for potential sensitivity, that was the 22 first half of my presentation this afternoon. And what 23 we're asking you to provide us feedback is on the sorts 24 of scientific factors we should think about as we move 25 forward with an updated FQPA 10X analysis, keeping inFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 186

1 mind that we're going to be that there's particular 2 focus under FQPA to infants and kids, and we'll be 3 thinking about things like that I had in my slide: the 4 drinking water, sufficiency of the drinking water data; 5 the Mode of Action; the epidemiology; the human 6 relevance; all the things that were in my slides and 7 that are in that chapter of the Issue Paper. So it's 8 predominantly a bigger-picture question around the 9 factors to think about. 10 DR. STEVEN HEERINGA: Carey, Jan, Bette, 11 Penny, are you okay with that in terms of the way it's 12 okay. 13 DR. ANNA LOWIT: And then Question 6 is 14 the one that's intended to bring everything together as 15 we think about the critical windows of exposure or the 16 duration of exposure for moving forward with a risk 17 assessment or as we think about the monitoring 18 frequency. 19 We've thought about it in a number of 20 ways I talked about earlier, and we're asking you to 21 not only comment on what's in our Paper that we've 22 already done and the sorts of things that we're 23 thinking about, but also to provide alternatives and 24 additional things to think about, because this will be 25 one of the areas that we put a good bit of emphasis onFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 187

1 in the coming months. 2 DR. STEVEN HEERINGA: Dr. Bucher, 3 associate discussants? Do you feel comfortable with 4 it? 5 Dr. Coupe? 6 Okay. I think it appears that we're all 7 set there. Anything from the Panel? I appreciate 8 taking the time to do that. I think that hopefully 9 will be helpful. 10 Okay, at this point in time, in the 11 Agenda we have completed the scientific presentations 12 from the EPA. We'll have an opportunity first thing 13 tomorrow morning to I think Dr. Lowit and the team, if 14 you have anything that occurs to you overnight you want 15 to present, you'll have an opportunity first thing. 16 But we'll also have a presentation tomorrow morning by 17 Suzanne Fenton of NIEHS. 18 And after that presentation and our 19 questions of clarification, we'll move to the period of 20 public comment. If there's anyone in the audience who 21 wishes to make a public comment, please see Joe Bailey, 22 the Designated Federal Official, to sign up for a time. 23 One other comment I guess, public 24 comment I think will take a substantial share certainly 25 of the late morning and afternoon, I believe. ThereFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 188

1 will be a number of presentations, and several of them 2 will be fairly lengthy and in-depth. 3 So we'll spend the time we need to focus 4 on that. But for public commenters, slides, make sure 5 that they are brought to Joe or to the SAP Staff to be 6 loaded up, hopefully before your presentations, or if 7 you have handout material see that they get to Joe also 8 so that he can arrange to have them copied and 9 distributed. 10 There is one handout that the Panel has 11 received. It's a preliminary view and statistical 12 analysis of data associated with Cooper, et al., study 13 by Dr. Silken and I think this may be relevant to some 14 of the public comment tomorrow; but just note that the 15 Panel has received it, and a copy of this will also go 16 into the Docket. 17 Okay, at this point, are there oh, Dr. 18 Lowit. 19 DR. ANNA LOWIT: Just one issue about 20 the first thing in the morning, we're still working to 21 get Dr. Fenton's slides. Our hope is that by 8:30, we 22 have 30 copies of them. They may appear while she's 23 talking if we don't get them in time, but we're doing 24 our best. 25 DR. STEVEN HEERINGA: That's fine; weFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 189

1 understand. 2 DR. ANNA LOWIT: She just came back from 3 Spain a couple of days ago, so 4 DR. STEVEN HEERINGA: I'll have to use 5 that one, just came back from Spain. 6 SPEAKER: For you it's Russia. 7 DR. STEVEN HEERINGA: Yeah, Russia. 8 Okay. And just a note from Joe Bailey, too, we will 9 start tomorrow morning at 8:30. Barring any other 10 questions from the Panel this afternoon again, you'll 11 have a chance tomorrow morning first thing, too, and 12 probably throughout the session tomorrow -- but I think 13 we'll bring the afternoon's proceedings to a close. 14 Panel members, if we could just meet 15 briefly in the breakout room just to discuss any sort 16 of small group sessions that you may want to plan in 17 preparation for addressing the Charge Questions, 18 probably later tomorrow afternoon or certainly first 19 thing Thursday morning. 20 So, if not, good afternoon, everybody, 21 and we'll see you 8:30 tomorrow morning. 22 (WHEREUPON, the MEETING recessed for the day at 3:32 23 p.m.) 24 25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 190

1 CAPTION 2 The foregoing matter was taken on the date, and at the 3 time and place set out on the Title page hereof. 4 It was requested that the matter be taken by the 5 reporter and that the same be reduced to typewritten 6 form. 7 Further, as relates to depositions, it was agreed by 8 and between counsel and the parties that the reading 9 and signing of the transcript, be and the same is 10 hereby waived. 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 191

1 CERTIFICATE OF REPORTER 2 COMMONWEALTH OF VIRGINIA 3 AT LARGE: 4 I do hereby certify that the witness in the foregoing 5 transcript was taken on the date, and at the time and 6 place set out on the Title page hereof by me after 7 first being duly sworn to testify the truth, the whole 8 truth, and nothing but the truth; and that the said 9 matter was recorded stenographically and mechanically 10 by me and then reduced to typewritten form under my 11 direction, and constitutes a true record of the 12 transcript as taken, all to the best of my skill and 13 ability. 14 I further certify that the inspection, reading and 15 signing of said deposition were waived by counsel for 16 the respective parties and by the witness. 17 I certify that I am not a relative or employee of 18 either counsel, and that I am in no way interested 19 financially, directly or indirectly, in this action. 20 21 22 23 24 MARK REIF, COURT REPORTER / NOTARY 25 SUBMITTED ON SEPTEMBER 14, 2010FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 192 0 0 65:6 68:25 69:8 69:15 168:5 0.05 84:13 0.65 77:9 0.7 97:7

1 1 64:20 65:12 69:7 73:23 74:17 158:18 1% 138:9 138:10 1,000 129:3 1.2 177:8 178:1 1.5 68:21 1.8 69:15 69:16 1.86 77:19 1.96 77:7 1:15 105:22 105:23 106:4 10 64:19 64:21 65:12 93:24 145:10 10:00 58:2 100 17:2 23:19 25:4 64:21 65:13 69:1 69:2 74:17 89:18 90:1 91:10 91:12 92:19 95:14 10-mg158:18 10X147:4 165:20 185:25 11 44:17 44:22 12 21:6 145:10 12.5 68:21 12you 91:12 14 2:3 64:17 66:11 162:24 177:6 15 18:14 18:15 87:8 119:16 168:18 15.56 77:13 15th 25:24 26:1 28:24 153:3 173:24 16 168:13 168:14 18 66:10 1800 76:3 1800-hour 70:15 88:7 19 36:8 36:11 37:2 56:7 1987 64:14 1988 21:5 1997 47:7 1-month75:8 75:9

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25% 85:18 27 93:5 29.4 69:15 71:6 2-week128:5 128:7

3 3 57:2 63:15 64:20 65:12 65:14 66:4 99:8 177:12 3.0 33:22 3.1 180:9 181:19 3.1.A182:12 3.1.C 182:14 183:7 3.12 68:21 68:22 70:17 84:5 84:9 84:11 3.2 182:11 3.65 69:15 69:16 3:00 169:13 3:32 189:22 30 61:14 68:11 68:15 68:25 69:2 85:15 88:22 89:17 90:5 90:12 91:1 91:3 92:13 93:5 93:23 118:21 188:22 30% 47:15 102:8 300 63:10 69:1 69:2 30th 25:25 35 130:22 3-day 66:5 66:17 66:23 3X165:20

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5 5 66:4 68:24 69:8 69:9 69:10 154:12 185:14 185:15 185:20 5.10 87:5 5.3 181:21 5.5 181:7 182:21 182:24 5.9 87:4 92:9 92:9 96:22 50 25:5 63:14 64:21 65:12 92:19 95:14 153:15 153:19 50% 45:11 46:14 52:19 61:17 88:24 89:12 50th 131:7 55 90:8 56 83:2 83:22 84:1 90:8 57 83:3 84:20 5-day 71:6

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89:12 62 95:13 63 95:14 97:1 97:2 6-hour63:16 6-month59:1 69:11 71:7 92:14 155:14 155:16 183:3

7 7 64:20 65:12 66:4 66:5 122:25 72 65:13 75 91:5 91:8 91:22 92:6 92:10 93:11 93:13 95:2 95:3 95:9 95:11 95:14 95:23 96:21 75th 131:7 7-day 66:18 66:23 122:14 126:10 126:12 127:9

8 8:30 188:21 189:9 189:21 80% 93:7 94:22 83 130:23 139:23

9 90 61:14 78:16 900 70:7 90-day78:17 107:15 108:21 109:2 138:18 155:6 155:12 155:16 159:3 160:25 161:2 165:22 95% 74:5 96 65:9 98% 61:20

A abdominal 43:23 43:24 44:4 44:15 44:25 45:12 45:13 abdominal-wall 43:14 43:21 166:23 ability11:3 12:6 50:8 able 26:14 29:12 37:24 39:4 44:18 46:11 48:1 49:20 52:22 92:15 95:4 112:11 113:17 118:15 121:21 141:15 141:20 142:2 142:11 142:12 142:13 142:14 143:21 abnormalities 45:4 abortion 41:16 absence 76:14 76:17 76:19 101:13 102:18 103:7 171:2 absolute 160:1 160:18 absolutely 81:24 82:2 82:3 abstract35:16 Academies 8:20 accompany 106:14 account2:24 66:20 68:9 102:8 105:6 109:7 120:19 133:16 136:9 accounted61:15 accounts 72:6 accumulated

14:9 accumulating 166:2 accuracy 52:8 113:14 accurately164:20 achieve 67:25 acknowledge 9:5 15:6 15:18 16:1 39:12 54:23 55:1 acknowledged 39:18 42:15 acknowledgments 56:4 acronyms 164:13 across 11:20 12:3 15:1 16:19 19:25 32:11 32:15 33:21 36:23 38:19 41:16 45:14 48:8 50:6 50:19 56:10 66:2 68:23 81:25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 195

82:1 85:2 85:3 86:6 129:15 150:14 150:18 156:3 158:18 176:16 176:24 ACS 57:10 Act2:20 107:11 115:17 146:23 ACTH 23:15 Acting9:9 action11:16 11:19 12:4 12:5 12:9 12:13 12:23 13:7 17:8 20:23 21:8 21:14 28:10 59:25 60:4 145:1 148:20 149:1 149:3 153:21 154:13 165:7 166:11 166:15 172:17 186:5 activated119:7 119:9 active 51:12 62:1 62:5 62:23 62:25 75:11 102:11 102:17 102:17 activities 9:22 101:11 activity 39:16 39:18 39:19 42:7 42:12 51:1 62:3 62:17 71:22 102:18 actor 103:4 actual 52:17 110:13 116:14 119:21 125:5 125:23 126:3 131:13 181:4 181:20 actually 25:12 26:8 30:9 49:7 57:25 59:20 61:3 61:20 62:3 62:9 62:11 62:18 62:20 63:1 63:2 63:11 64:17 64:21 64:22 65:6 65:22 66:13 66:19 67:4 67:8 69:7 69:10 69:14 69:21 69:23 70:19 70:24 71:4 71:25 72:17 72:22 73:15 74:4 76:4 78:15 78:16 79:15 81:12 81:16 81:16 81:18 82:2 82:6 83:2 83:18 84:8 84:10 85:1 85:3 85:20 87:9 92:12 92:18 93:25 94:21 97:10 97:12 97:12 98:10 98:12 99:9 99:11 100:4 102:12 102:13 105:4 105:12 105:15 110:17 117:14 135:1 137:21 142:17 143:3 150:9 152:8 152:10 153:17 162:21 167:21 168:2 acute 36:1 164:9 164:10 add24:18 72:7 72:13 73:19 97:25 140:25 162:14 166:18 166:19 166:19 167:1 175:10 added90:9 99:20 adding97:21 98:7 98:10 98:15 99:17 addition 36:16 38:4 39:5 49:18 51:6 52:22 62:14 151:2 additional 40:13 41:5 64:2 69:3 104:13 105:19 105:24 161:14 165:23 186:24 address 90:13 90:17 113:21 144:17 161:22 169:21 174:25 175:20 addressed 108:17 120:25 155:10 185:13 addressing162:22 162:23 189:17 adequacy 100:10 adequate 109:2 109:4 110:4 127:25 175:15FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 196

adequately109:14 adjuvants 144:8 administered 100:4 103:20 104:20 administrative 2:16 admitted57:20 admittedly 177:9 177:22 adrenal 7:25 adult 21:12 93:4 162:7 162:8 adults 152:7 advance 4:25 11:2 11:11 12:20 180:11 advantage 11:25 adverse 11:16 11:19 13:8 35:5 46:7 47:4 52:15 76:16 93:16 94:4 94:12 advice 2:21 2:22 2:25 9:25 11:8 17:25 27:10 Advisory 2:1 2:9 2:19 2:20 4:13 4:20 58:7 106:8 affect39:22 90:2 affecting149:22 affects 25:16 157:4 affirmation 4:1 afternoon 27:13 105:24 106:3 106:6 106:7 168:25 174:24 185:22 187:25 189:10 189:18 189:20 afternoon's 189:13 afterwards 113:3 169:6 Ag16:22 37:11 38:24 48:24 50:5 52:7 against155:13 162:8 age 46:8 48:8 48:20 53:8 149:17 163:9 168:15 176:22 Agency2:24 9:24 10:10 14:12 19:15 24:23 34:5 146:24 150:14 152:9 152:12 154:9 154:25 155:5 162:4 180:20 182:15 Agency's 18:16 Agenda3:5 3:11 3:18 3:21 187:11 aggregate 38:23 106:24 ago 19:12 43:1 46:19 152:14 173:2 173:5 189:3 agreed 17:16 agreeing2:14

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162:25 alluded

82:25 158:3 alone 86:18 167:21 already137:10 184:6 186:22 alternative 118:23 141:19FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 197

alternatively 113:19 alternatives 161:13 186:23 altogether120:25 125:8 125:11 136:10 am 2:16 4:17 4:18 5:6 8:17 65:20 73:23 80:12 95:14 97:8 138:10 140:21 ambient110:17 121:3 amended

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141:15 142:13 163:14 164:7 170:16 176:4 answered95:7 answering161:10 answers 145:24 154:7 antagonist166:13 167:3 anticipate 21:17 93:10 94:4 173:13 173:14 anybody's 167:12 anyone 3:12 187:20 anything102:4 103:20 131:24 175:10 183:18 187:7 187:14 anyway93:5 141:6 apparently171:10 appear80:22 100:2 188:22 appears 82:16 172:19 187:6 Appendices 16:25 Appendix 25:2 26:6 26:7 33:20 35:10 57:3 75:20 77:4 82:8 95:9 96:11 122:21 164:19 175:15 175:18 178:25 181:21 applicable 11:25 application 38:14 42:8 49:14 56:5 129:6 137:19 155:2 184:9 applications 133:4 applicators 37:13 37:16 applied

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169:25 approaches 10:13 10:17 11:12 12:2 17:23 18:8 24:5 59:4 78:9 78:24 110:1 110:4 111:12 112:1 118:13 127:21 143:6 173:1 appropriate 12:8 35:18 38:25 135:11 approximate 140:1 approximately 17:2 18:23 159:19 160:22 April 17:1 17:7 17:13 17:22 18:6 22:13 22:18 22:21 23:2 23:11 23:13 23:17 24:2 24:15 26:2 26:3 26:6 26:12 27:3 27:9 29:21 31:15 59:7 107:18 112:3 117:12 122:23 126:19 127:11 128:22 133:10 164:7 172:22 aquatic 5:24 107:23 area 6:4 6:23 8:3 8:24 27:6 27:9 28:6 37:7 43:17 44:5 59:16 64:4 64:10 71:16 71:25 72:15 72:18 72:19 73:4 73:10 73:12 73:15 73:18 74:11 74:20FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 199

77:12 77:14 78:21 78:23 79:16 86:21 98:21 98:22 98:24 98:25 99:12 105:5 107:20 112:13 114:18 116:14 117:16 126:5 141:9 152:13 154:2 159:8 areas 10:21 115:12 115:13 161:16 161:21 186:25area-under-thecurve 100:16 argument146:12 arrange 188:8 arrangements 3:15 array 18:7 arrows 124:15 articulate 53:11 articulated

32:24 articulating 34:10 artificial 128:16 ascertainment 34:14 aspect67:14 139:14 141:10 aspects 59:11 63:6 71:11 79:13 assay 133:16 assembled9:5 assess 131:6 132:16 133:15 148:14 assessed38:8 42:9 assessing121:7 151:8 assessment7:20 8:4 11:6 11:23 12:2 12:17 12:18 13:12 14:24 18:20 19:20 20:5 20:12 20:16 21:3 22:2 23:9 23:13 23:15 23:17 24:17 26:17 26:25 29:23 30:2 32:17 33:1 33:9 33:14 33:17 34:12 34:13 39:9 39:13 42:5 42:15 42:17 43:3 46:5 51:9 53:14 53:23 54:20 57:14 58:13 58:16 58:21 58:24 59:10 59:21 60:1 61:25 62:16 62:19 69:12 69:18 71:11 75:11 79:11 82:16 87:14 92:21 96:16 108:5 121:1 121:5 138:23 140:22 146:15 152:12 155:5 155:12 161:22 164:21 165:2 165:14 165:17 165:19 170:3 172:15 177:13 180:15 186:17 assessments 12:2 12:7 16:13 19:16 37:1 129:9 129:10 133:5 164:8 associate 5:18 7:14 8:13 187:3 associated

49:1 76:16 110:18 118:2 131:6 166:9 166:14 188:12 associates 72:8 association 33:15 34:22 35:20 37:22 37:25 38:7 40:2 40:6 41:11 41:12 41:18 43:7 44:4 44:14 44:19 45:1 46:9 47:5 47:6 47:21 47:24 48:17 48:20 48:25 49:1 49:4 49:21 50:1 50:8 52:2 52:20 166:11 166:14 associations 32:14 45:18 50:11 51:4 assume 90:16 101:25 152:5 assumed

42:11 158:21FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 200

assumes 129:12 assumption73:1 73:14 170:25 171:6 asthma49:1 Atlanta 5:6 atrazine 2:10 4:14 10:12 10:19 10:23 11:8 11:18 11:22 12:16 13:2 13:5 13:11 13:15 13:18 13:24 14:1 14:3 14:10 16:14 16:20 17:3 17:14 17:17 18:12 18:14 18:18 19:10 19:23 21:2 21:14 22:5 29:11 31:22 33:10 33:15 35:22 37:22 37:23 37:25 38:1 38:7 38:13 38:22 39:4 39:22 40:3 40:4 40:6 40:19 41:2 41:3 41:19 43:7 43:17 43:20 44:5 44:14 45:6 46:2 46:10 46:12 46:15 46:16 46:21 47:5 47:24 48:1 48:2 48:6 48:6 48:18 48:25 49:5 49:9 49:21 49:23 50:4 51:9 51:25 52:16 52:20 52:21 53:14 53:17 53:18 53:24 54:1 54:15 54:24 58:9 58:21 60:9 60:9 60:17 60:25 61:5 61:9 61:14 61:22 62:4 62:17 63:9 64:21 65:3 66:4 68:5 68:15 68:20 70:2 71:21 74:1 74:8 76:23 77:6 77:7 77:16 77:19 78:13 79:5 79:19 82:10 88:17 89:23 93:19 94:25 95:17 98:2 98:3 98:6 100:4 101:9 101:18 102:1 103:21 106:10 106:23 107:7 107:17 107:23 109:10 110:17 110:24 113:4 114:23 115:4 116:3 116:14 117:7 117:9 117:16 118:8 119:1 119:3 119:5 119:8 119:11 119:13 120:25 132:20 139:8 139:24 140:5 140:22 140:23 140:25 140:25 149:3 155:17 155:22 156:13 156:24 157:1 158:7 159:24 162:13 165:7 167:24 170:8 171:2 172:17 179:24 180:22 ATSDR6:12 attempt98:4 attempted55:16 attention 32:24 36:11 60:2 63:7 64:14 146:25 attenuating 62:1 90:1 attenuation 23:23 59:3 59:13 63:12 63:16 63:19 64:3 67:24 68:8 72:9 75:16 76:15 79:14 85:18 88:17 149:7 149:22 150:17 150:23 151:1 151:7 157:14 157:17 157:22 172:15 180:1 183:2 at--what96:7 AUC 73:25 81:6 97:5 97:8 100:19 100:23 audience 30:25 187:20 August107:19 117:12 122:23 authored75:9 authors 37:20 37:24 38:4 38:11 39:4 39:12 39:18 40:1 40:5 40:7FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 201

40:14 41:17 42:11 42:15 43:13 43:18 44:6 44:8 44:23 45:3 45:9 46:11 47:3 47:10 49:3 49:8 49:19 49:24 51:23 autocorrelation 130:17 130:22autocorrelationtype 129:11 auto-samplers 113:23 113:25 114:9 114:15 116:7 166:1 availability 147:1 148:14 available 4:2 4:6 18:22 32:12 35:11 36:4 55:19 81:24 82:3 132:23 148:19 162:19 average 78:10 78:17 78:19 101:1 114:11 123:12 155:16 158:19 160:25 161:2 165:22 averages 100:24 123:13 124:15 155:6 155:7 155:13 159:3 averaging28:15 await 29:9 aware 88:1 away 15:20 45:5 65:8 81:4 81:8 81:10 87:16 axis 7:25 17:10 17:11 17:15 17:15 17:17 17:18 22:23 23:3 23:8 54:16 101:17

B B1 35:10 B2 33:20 background

3:21 7:19 31:24 32:20 58:22 111:13 121:6 130:1 142:20 166:10 bacterial 171:10 bad79:1 103:3 Bailar8:16 8:16 80:7 80:8 80:24 86:2 86:11 86:13 102:20 102:21 103:2 103:10 136:17 136:18 137:11 137:23 139:14 Bailey2:4 2:8 15:14 168:21 187:21 189:8 balance 7:25 113:13 balanced115:8 Barring189:9 Barry 6:25 6:25 bars 85:6 base 62:6 149:19 185:8 based18:1 31:14 47:18 58:13 58:25 62:10 62:20 62:21 63:2 63:25 64:15 71:19 72:1 72:15 76:18 76:22 76:24 77:6 77:8 77:11 77:17 78:5 78:12 79:17 83:18 84:9 84:14 93:12 97:23 98:5 101:24 102:7 108:21 109:7 111:20 112:2 113:9 115:4 115:6 115:16 115:23 119:19 121:25 122:6 129:5 132:13 140:23 170:19 baseline 91:14 91:15 basic 75:19 basically 60:18 66:5 66:22 69:25 70:25 73:17 73:19 99:20 116:21 140:10 145:9 183:15 184:3 185:9 basing87:14 87:20 basis 73:19 75:6 76:10 79:10 98:15 99:11 115:17 138:17FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 202

Baskin7:15 Batterman 129:2 become 18:22 31:7 61:4 109:23 150:2 becomes 81:23 82:2 begin 19:3 85:13 112:4 174:25 beginning3:6 5:3 26:13 70:7 107:18 150:2 154:22 157:22 165:18 172:7 behavioral 151:21 163:4 behind 77:25 103:22 181:5 believe 3:6 25:23 31:4 74:3 84:13 120:23 127:13 133:3 152:21 153:8 157:19 162:9 164:19 164:23 168:1 168:8 172:16 183:2 183:11 187:25 benchmark 27:4 59:6 59:18 74:23 75:4 75:12 76:7 79:17 84:14 89:19 90:9 95:8 96:11 96:14 96:20 164:13 164:15 164:18 172:23 181:18 181:23 181:24 bend84:23 benefit169:6 best9:25 102:19 103:8 110:14 128:2 128:24 130:12 188:24 best-fit76:23 77:2 Bette 7:17 7:17 165:10 186:10 better12:7 19:19 52:24 110:3 115:11 136:14 158:15 beyond 89:17 90:5 91:8 130:18 bias 42:2 biased 110:12 biases 50:17 bigger-picture 186:8 biggest109:17 bio 101:8 biological 11:21 12:4 12:9 23:5 49:25 54:14 76:10 151:6 biologically 17:19 172:16 biology6:16 6:20 6:23 biomarker 52:13 53:25 biomarkers 40:18 54:1 biostatistician 5:7 bird's 29:17 birth 42:22 43:6 44:15 44:18 45:10 45:16 45:22 46:7 47:4 47:20 47:22 47:24 48:18 48:22 52:15 52:25 53:1 54:3 162:25 166:8 166:14 bit21:20 22:18 31:24 48:2 52:24 59:12 74:25 80:18 81:7 88:20 90:14 100:25 111:3 111:23 114:2 115:15 122:1 124:23 125:19 133:11 143:13 148:15 149:23 154:1 156:21 167:23 168:24 171:5 173:2 173:5 173:9 180:18 184:7 185:17 186:25 biweekly 131:22 131:23 155:3 black 85:7 blocks 91:9 163:8 blood105:4FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 203

167:25 blue 120:12 120:12 120:12 124:12 BMD 76:22 BMDL 77:7 77:9 77:13 BMR 76:8 76:9 76:17 164:8 164:12 164:13 bode 94:12 body 60:11 158:20 163:2 167:24 bootstrapping 123:7 bottom 120:13 120:15 162:14 bound125:14 bounds 109:6 109:21 113:18 117:19 117:23 122:2 122:11 122:16 131:15 132:16 142:24 Bradbury 9:9 9:11 brand-new68:18 150:11 break 58:1 58:11 105:21 145:14 145:17 168:19 169:12 169:21 169:25 170:14 breakout189:15 brief 142:22 briefer 142:22 briefly32:6 33:2 36:9 37:11 43:12 111:19 176:14 178:24 184:21 189:15 bring4:8 13:8 22:16 91:10 143:23 174:14 186:14 189:13 bringing22:1 22:3 93:20 111:16 177:17 brings 18:5 23:17 143:16 Briscoe 16:2 broad19:25 21:25 57:13 136:22 broadened24:18 broader 109:11 117:15 137:18 broadly10:13 11:10 11:24 12:20 13:18 brought36:10 44:13 91:9 93:20 169:22 188:5 BROWN184:23 Bucher5:17 5:17 55:13 55:14 55:14 57:1 187:2 bucket28:8 build21:19 21:24 29:18 65:24 122:8 180:17 bulk 148:7 bullet152:3 bullets 151:15 business 9:24

C C14 64:15 calculated

40:4 97:21 calculating 181:24 calculations 95:12 96:4 96:5 96:10 96:12 96:19 98:5 158:10 158:14 159:2 172:22 California5:23 7:16 7:23 8:9 Canada6:9 7:19 7:20 cancer5:6 8:12 18:20 21:17 21:18 29:9 29:13 172:12 174:1 174:2 174:4 174:5 174:6 candidate 115:9 capabilities 132:1 capability140:20 capture 108:7 112:7 114:19 114:20 126:3 capturing126:7 158:6 carbon64:17 carbons 64:17FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 204

carcinogenic 33:11 carcinogens 80:18 Care 8:12 careful 63:7 159:25 Carey 5:13 5:13 186:10 Carol 31:18 31:19 55:18 56:3 56:19 149:16 172:11 176:13 176:14 Carolina 8:24 37:14 case 13:2 13:15 16:14 16:14 16:17 16:20 19:23 22:6 36:1 36:13 36:13 41:25 62:24 102:16 116:4 122:17 150:14 cases 97:22 174:19 casting129:3 categories 37:3 136:22 category 137:7 caudal 26:17 causal 32:13 50:9 50:13 cause 157:6 caveat122:3 cavity45:12 CDC 6:12 41:4 Cell 6:23 center6:22 7:1 145:12 145:14 central 13:17 Century12:1 certain 34:1 61:21 94:21 100:12 107:11 125:17 125:17 141:16 142:15 certainly 21:15 22:23 24:8 27:4 27:9 30:3 34:25 45:25 56:4 56:14 88:6 96:16 99:24 103:24 135:8 145:5 149:17 173:17 174:24 187:24 189:18 certainty 141:16 142:12 chair 4:9 4:19 4:21 8:7 Chairman 9:11 Chairs 145:4 challenges 10:14 48:14 Chambers 5:8 5:8 82:22 82:24 83:5 83:11 83:22 84:2 84:17 84:19 86:3 87:1 89:21 chance 51:11 86:18 112:8 121:3 142:5 189:11 chances 141:22 change 31:10 65:9 65:13 68:12 87:12 87:13 87:19 135:18 144:1 144:8 144:9 165:12 changes 13:4 74:18 88:15 89:8 89:10 94:11 95:24 149:7 149:18 149:22 151:16 151:21 changing144:7 chapter 147:14 186:7 characteristic 52:4 characteristics 38:1 39:3 42:22 42:22 53:1 85:8 characterization 26:24 54:9 95:24 121:19 180:3 characterize 109:14 characterized 176:16 characterizing 129:4 176:24 charcoal 119:8 119:9 Charge 31:9 152:11 161:10 165:17 165:18FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 205

169:2 174:14 174:25 189:17 Charlottesville 8:3 chemical 6:12 7:9 61:6 61:15 61:19 61:22 62:17 71:21 chemicals 52:9 chemical-specific 156:2 chemograph78:4 78:21 121:14 121:19 121:20 122:1 122:6 124:8 124:22 125:5 125:20 127:3 135:20 184:5 chemographs 122:25 Chester 15:20 29:25 58:15 58:17 81:9 81:23 83:4 83:9 83:17 83:24 84:8 85:19 87:21 90:23 91:9 92:11 97:11 98:9 99:3 99:5 101:10 102:6 102:25 103:6 103:13 104:25 105:3 153:16 Chicago 8:17 childhood36:21 children 145:1 146:25 149:13 chlorinated 62:6 62:18 71:23 chlorination 119:5 chlorine 61:1 62:7 102:12 chloro 140:5 170:9 171:1 chloro-degradates 140:9 chlorotriazine 143:18 158:8 171:24 chlorotriazines 61:16 61:20 66:7 66:13 66:16 119:16 120:5 120:10 140:13 143:15 170:2 170:11 170:16 choice 82:14 choosing179:25 chose 93:11 Christensen 22:7 26:21 31:13 31:17 31:18 31:19 55:6 55:18 56:3 56:19 57:18 58:12 106:15 147:17 149:11 172:11 176:12 176:13 176:14 Christensen's 55:9 chronic 164:9 Cincinnati 6:14 circle 148:10 citation 35:11 cited59:23 64:1 69:9 clarification 31:5 31:14 55:8 55:12 55:15 57:21 80:5 81:3 97:20 104:14 134:19 162:1 170:1 187:19 clarifications 56:23 139:18 clarified184:7 clarify17:21 38:8 135:13 137:9 141:1 clarifying 54:22 55:4 97:1 classical 72:24 classification 54:17 170:23 classifications 121:20 clear 27:2 27:10 33:3 37:24 38:16 65:3 85:6 104:20 106:2 138:8 138:10 142:16 165:15 169:5 175:3 175:6 176:10 180:25 181:1 184:24 clearance 163:19 clearly34:10FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 206

click 169:5 close 23:18 44:5 69:4 127:20 145:25 170:24 189:13 closely80:17 83:19 113:16 121:25 132:18 138:20 closer45:5 48:2 52:17 111:11 127:5 Cmax 63:23 72:3 Coder 25:2 25:3 178:8 cognizant82:11 cohort36:13 37:12 49:19 153:5 153:5 cohorts 153:1 coincide 94:25 coinciding116:3 co-kriging132:9 collaborate 150:14 collaborative 16:21 16:24 22:8 49:22 150:12 colleague 55:1 colleagues 15:2 16:6 collect113:24 114:9 136:19 137:22 collected49:11 49:19 137:10 College 5:9 117:2 College's 110:20 Colorado 6:18 column66:16 158:23 columns 66:12 combination 20:11 133:7 combine 129:18 combining111:6 133:13 133:14 133:18 comes 49:11 127:10 comfortable 178:1 178:17 179:15 187:3 coming4:8 9:21 19:9 28:5 67:19 100:9 100:18 146:5 154:9 173:13 174:3 187:1 comment3:7 3:9 28:2 31:8 56:13 90:5 104:7 121:11 128:10 161:11 186:21 187:20 187:21 187:23 187:24 188:14 commenters 3:10 4:4 188:4 comments 3:16 3:17 25:19 34:5 87:25 94:17 103:17 144:14 155:10 172:10 175:23 175:24 176:17 177:5 commercial 37:13 Committee 2:19 2:20 common35:6 88:17 112:6 128:8 128:10 166:15 commonly 114:1 communities 107:23 community 107:8 107:10 108:3 110:8 110:19 110:22 110:25 114:24 115:14 115:16 115:18 116:19 116:23 119:15 119:22 120:1 120:22 121:8 121:13 121:21 125:25 133:5 138:14 138:15 138:20 139:5 139:13 165:20 comparatively 51:15 51:21 compare 70:19 78:19 78:21 78:22 83:20 92:15 162:8 compared38:2 43:14 43:16 66:21 70:11 76:5 78:11 91:12 101:12 119:24FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 207

123:10 140:24 155:13 comparing66:5 71:12 comparison 34:18 59:13 67:21 94:20 comparisons 40:25 87:7 160:5 compartmental 72:25 73:20 compiled68:4 compiling75:3 complement 89:14 103:3 116:8 complete 28:5 29:4 63:15 120:6 120:7 completed187:11 completely 85:17 167:21 completeness 46:18 completing154:10 162:20 173:21 completion35:1 complex20:4 20:10 125:20 129:7 complexity20:9 complicated 128:18 152:25 complied3:4 68:17 component15:12 104:11 104:11 146:14 146:15 components 140:12 171:17 173:7 180:13 compounds 167:6 comprehends 185:13 comprehensive 33:6 comprised37:18 computation 98:5 conceive 159:16 concentrated60:7 68:14 92:16 concentrating 92:12 concentration 13:5 40:3 40:8 40:17 43:20 53:18 64:5 64:8 65:1 72:1 78:19 100:3 105:6 107:12 110:13 125:18 concentrations 13:3 96:14 118:21 120:2 124:13 125:24 130:18 132:9 133:16 138:25 150:21 158:8 concept114:14 184:9 conception 44:19 45:2 conceptual 100:17 145:23 181:23 182:1 185:17 concern 78:11 78:22 86:14 93:19 107:15 108:8 108:22 108:25 109:3 109:19 109:25 110:10 113:10 114:10 114:16 114:21 117:25 123:16 125:10 127:12 134:7 138:14 138:17 139:1 155:13 162:11 concerned88:20 89:5 110:7 126:1 126:2 concerning101:23 concerns 25:14 25:15 25:17 79:4 132:4 178:25 conclude 32:17 50:11 59:17 63:22 79:24 104:15 concludes 54:21 conclusion 23:14 46:1 48:19 54:7 79:9 87:3 95:4 conclusions 33:24 161:12 162:6 concurrently 174:13 condition 14:10FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 208

72:8 138:22 139:4 conditional 131:12 132:12 133:8 conditions 88:10 107:6 144:10 conducted16:10 152:4 155:6 172:21 180:20 conducting152:20 conducts 154:24 confidence 40:10 74:6 84:13 109:6 109:21 113:18 117:19 117:23 122:1 122:10 122:16 125:14 126:16 131:15 132:15 142:24 confident13:10 142:15 178:21 confirm

171:21 confounding34:16 39:16 39:19 41:7 48:16 50:24 confused82:25 83:20 182:7 confusion 90:22 Congress 147:6 conjugated

61:3 conjugates 62:7 62:12 102:4 102:8 102:10 conjugation 61:1 conjunction 11:3 connection105:15 consecutive 70:5 consensus 23:4 conservative 62:24 102:16 102:22 104:8 104:8 consider 32:24 34:6 43:2 55:11 80:20 102:22 113:23 133:24 148:13 148:19 148:25 consideration 35:24 36:25 45:23 104:10 considerations 134:13 144:25 148:8 148:13 185:21 considered

17:4 40:20 49:1 76:16 107:12 156:7 177:6 considering80:19 118:22 consistency53:5 consistent48:7 52:18 66:24 constant65:15 67:17 73:10 73:17 158:18 181:11 constants 158:17 consultant8:2 contact3:25 contained18:13 25:2 26:5 96:11 181:21 content55:9 106:2 context13:12 14:15 16:18 58:22 92:20 continuation 29:20 continue 54:19 85:14 172:14 continued70:8 85:15 93:16 continues 67:18 continuing101:23 116:4 continuous 65:15 75:15 contradict3:2 contrary 171:22 contrast36:10 63:13 contribute 4:23 contributed

15:24 contributes 108:15 contributions 5:1 15:6 contributor 106:23 control 7:6 36:13 36:13 36:21FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 209

41:25 49:20 68:9 69:21 76:18 80:15 84:11 84:15 86:16 86:20 controls 163:11 convenient63:1 conventional 119:3 conversation 169:21 169:25 converted77:16 convincing146:12 Cooper16:5 63:8 68:17 75:5 75:23 83:2 84:23 85:7 90:15 90:18 90:20 91:24 92:2 92:6 92:24 93:2 95:19 95:20 95:21 105:20 157:14 159:14 162:21 163:18 167:2 167:20 170:5 183:3 188:12 copied 188:8 copies 188:22 copy 188:15 core 62:10 63:3 101:7 corn 45:5 45:7 49:20 116:4 corn-belt 132:21 133:1 correct49:6 94:3 101:8 correctly 169:11 170:22 correlate 150:22 correlation 43:19 44:17 46:20 129:15 135:7 135:8 correspond

97:5 corresponding 80:6 corticosterone 23:14 cost113:13 Council 59:22 count 51:1 111:17 county46:21 Coupe 7:10 7:10 143:11 143:12 143:12 183:25 187:5 couple 19:12 19:13 19:15 30:21 88:1 88:2 96:25 99:24 103:17 103:25 114:4 114:12 118:13 125:7 148:16 156:9 160:23 163:20 172:5 172:12 181:8 183:4 189:3 course 2:24 12:22 32:10 33:13 33:25 34:12 35:4 35:23 39:7 50:16 54:23 68:25 70:4 79:22 102:9 144:17 168:11 courses 150:19 cover 18:18 152:16 168:24 174:7 183:13 covered

69:7 71:9 172:8 covers 4:7 117:15 183:11 create 82:13 122:5 122:15 created

3:20 creating111:8 credit130:3 criteria 34:1 35:9 36:7 56:6 56:18 56:20 57:6 75:19 141:23 critical 9:23 10:1 11:21 12:6 28:10 28:14 48:10 54:13 67:14 69:11 69:17 69:24 75:10 75:24 76:8 85:23 99:11 109:23 109:24 117:18 127:6 127:17 129:13 131:16 132:15 146:18 148:14 155:18 157:10 159:6 161:18 164:2 170:20 173:6 173:15 186:15FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 210

crops 51:13 cross-section 117:15 crux 28:7 Cruz 7:16 cumulative 93:14 curious 81:7 166:12 current5:7 5:12 5:19 5:25 6:2 12:16 12:18 13:10 13:25 14:17 15:25 18:14 23:7 25:22 26:4 32:2 59:3 78:14 100:10 100:11 103:8 154:23 155:17 164:9 currently 4:18 8:19 159:3 curve 59:16 64:5 64:10 71:16 72:1 72:15 73:4 74:11 74:20 75:7 77:12 78:23 79:16 80:13 83:1 83:16 86:8 87:10 87:11 87:11 98:21 98:22 98:25 98:25 99:12 105:5 114:18 141:9 cut124:25 125:1 136:2 176:5 cutoff 26:1 26:2 26:3 26:3 119:18 119:20 cycle 39:17 51:20 70:4 93:15 94:5 94:19 149:21 149:23 159:12 159:18 159:19 159:21 160:13 cycles 37:22 94:23 95:1 cyclicity 163:7 cycling70:3 93:13 cytochrome 60:13 60:22 cytotoxicology 6:10

D D2 122:21 DACT 60:24 66:14 103:24 171:5 daily 59:16 64:13 64:19 65:15 65:17 66:3 66:9 66:11 68:4 68:24 70:2 70:20 71:3 71:13 71:15 72:11 72:15 74:11 74:20 77:12 78:19 78:22 79:15 110:15 110:15 116:13 116:14 117:8 117:8 117:11 117:11 118:12 118:12 118:20 122:5 122:5 122:8 124:13 127:15 132:10 135:3 135:4 dam 156:25 156:25 157:7 dams 162:23 Dan 5:21 Danelle 75:9 DANIEL5:20 81:2 81:20 166:6 167:5 Danielle 16:5 dark 85:7 dark-blue 115:5 dash 74:5 dashed 124:14 data 14:9 16:12 17:14 17:24 22:2 22:14 23:2 23:6 24:4 24:24 27:1 29:23 32:1 32:18 32:25 43:3 49:11 49:19 50:6 51:8 53:9 53:12 54:10 54:12 54:17 54:19 54:25 63:2 73:7 74:3 75:15 76:5 84:23 85:5 85:7 88:5 88:7 88:12 88:14 91:3 91:23 92:4 95:18 97:17 97:24 100:10 100:12 103:20FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 211

103:23 103:24 104:3 105:13 105:13 110:15 110:20 112:2 112:15 113:16 113:24 116:20 117:1 117:6 117:17 118:5 118:8 118:9 118:21 119:23 121:4 126:11 128:4 129:3 130:11 130:24 131:7 131:13 131:15 131:19 131:20 132:13 132:22 134:14 134:23 135:3 135:4 136:19 137:3 137:6 137:10 137:14 137:22 138:1 139:2 140:3 140:11 140:24 143:19 144:5 145:23 147:24 148:14 148:22 149:12 149:25 150:15 150:22 151:1 153:18 155:6 155:14 157:14 160:15 162:8 164:25 164:25 165:8 170:8 171:2 181:10 183:2 183:3 183:3 184:4 184:12 184:19 184:20 184:24 186:4 188:12 database 16:19 32:11 33:24 36:23 50:12 51:13 51:14 51:16 51:17 51:19 56:10 56:10 148:21 150:24 155:23 156:24 162:13 166:21 172:18 172:20 174:6 176:17 176:25 177:13 databases 33:21 35:7 177:20 dataset45:21 96:1 100:12 122:4 122:10 124:17 129:25 138:9 144:1 151:18 151:18 156:13 156:15 164:10 164:17 datasets 18:1 24:6 32:16 76:6 100:12 111:24 116:17 117:18 117:21 121:9 121:23 121:25 122:20 126:15 134:10 135:6 135:15 162:14 184:19 data-smoothing 124:24 127:10 date 15:4 Davis 8:9 day 31:6 63:15 66:6 66:10 66:11 70:7 70:7 70:8 70:9 72:13 95:23 104:18 106:8 123:1 123:23 124:2 140:23 144:13 148:18 150:19 157:15 162:24 168:5 168:13 169:16 189:22 days 19:23 22:25 63:15 64:20 65:10 65:14 66:4 68:5 68:6 68:25 70:5 78:16 98:20 99:2 99:9 116:8 118:1 123:12 124:3 125:7 130:23 143:20 144:22 156:16 157:20 157:22 157:23 157:24 159:22 160:20 160:20 189:3 de 80:20 DEA 60:16 62:5 66:15 140:7 dead81:22 deal 10:11 22:24 80:8 80:18 153:8 dealing60:8 dealkylated

102:2 dealkylation 60:13 60:23 dealt 171:14FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 212

Dean 7:14 December 108:10 decide 164:2 decided

26:11 deciding81:8 137:3 decision 14:3 141:12 147:12 151:10 decision-making 9:25 10:7 decisions 2:24 15:13 decrease 93:25 deep 9:4 deethylatrazine 60:16 default76:19 defect43:24 45:1 45:16 defects 43:6 43:14 43:21 44:4 44:15 44:16 44:18 45:10 45:12 45:13 45:22 54:3 166:8 166:14 166:23 deficiencies 36:21 define 12:7 44:6 113:5 115:12 defined

40:7 defining53:2 definition51:24 degradates 140:6 degradation 140:16 degree 88:4 deisopropyl 60:16 delay 38:6 delayed

151:2 156:14 160:16 Delclos 6:25 6:25 deliberations 19:1 109:1 delineate 37:6 delineated

35:10 delivered12:11 delivery 47:16 48:21 demonstrated62:2 Department5:22 6:23 7:2 7:13 8:7 8:23 departure 25:7 77:23 78:12 96:16 departures 69:18 depending 107:19 110:6 113:2 113:17 117:24 140:15 168:14 depends 67:8 119:12 176:2 derive 77:1 81:16 96:15 109:12 derived

155:7 155:14 157:7 describe 112:10 121:14 described17:13 describes 177:12 description 130:12 design25:15 25:16 25:20 42:1 46:5 108:20 109:18 109:20 179:5 179:15 183:16 Designated

187:22 designed107:14 designing134:5 141:7 142:8 designs 14:16 36:13 despite 159:1 detail 129:17 145:25 148:15 159:12 176:7 detailed20:17 29:6 132:23 133:19 147:15 172:11 details 75:18 77:3 97:6 147:23 detectable 61:19 detection 53:19 detections 123:11 detects 119:15 determinant59:24 determine 28:15 60:3 123:15FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 213

determined

109:25 138:16 determining 117:19 deterministic 129:2 detrimental 89:24 developed80:17 133:12 developing18:2 18:12 56:16 132:21 173:19 development 15:3 21:10 24:21 25:11 55:3 149:9 157:4 157:9 157:10 178:6 179:8 179:21 179:23 180:2 developmental 16:16 21:13 22:6 46:3 151:24 156:15 166:22 168:12 172:19 deviated60:14 deviation 76:18 84:15 deviations 83:7 device 36:22 DFO 2:17 DIA 60:17 62:5 66:15 140:7 diabetes 38:12 Diaminochlorotria zine 60:24 diestrus 70:8 differ70:19 71:2 74:14 77:21 85:24 differed70:12 difference 44:24 60:18 66:22 68:9 87:9 91:7 120:2 181:4 differences 109:7 177:18 178:13 178:14 different11:15 11:15 11:20 11:20 12:3 12:8 12:9 13:4 23:19 25:18 33:16 36:12 40:15 44:14 44:18 45:10 52:23 54:24 59:13 62:2 64:18 66:12 67:21 67:22 70:19 71:12 71:13 77:11 80:15 81:25 82:1 83:15 84:10 85:23 86:19 87:18 91:11 101:8 101:17 108:25 115:15 117:22 144:10 150:19 156:9 159:16 163:1 164:10 164:21 164:22 167:22 167:23 174:22 185:16 differential 50:22 differentiate 70:25 difficult42:19 113:7 144:21 154:4 184:9 digging147:23 diluting103:4 direct38:14 61:8 80:10 152:8 direction 45:19 Director 5:5 5:18 9:9 14:22 disappearance 95:18 disciplines 156:4 disclosure 3:2 discuss 29:4 169:3 176:9 189:15 discussant183:8 discussants 169:7 175:20 187:3 discussed16:17 16:20 18:17 23:2 32:12 43:11 150:21 182:25 discussing 10:19 10:20 11:14 18:4 18:7 discussion 20:15 31:15 32:13 57:11 104:22 143:14 169:8 169:22 discussions 10:25 31:25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 214

disposition 62:9 disruption93:10 disruptor 88:18 distance 44:10 distinctions 96:13 distinguish62:22 63:4 85:25 distributed

188:9 diverse 9:3 172:8 Division 14:21 14:22 15:24 31:20 Divisions 15:1 docket3:19 3:25 4:2 4:5 95:22 188:16 document3:23 36:3 121:6 130:7 137:24 142:20 171:17 documentation 104:21 documented

150:18 documents 3:22 166:10 dogs 151:4 dominant102:3 done 14:6 17:20 29:14 30:4 56:24 58:13 66:11 68:5 73:3 74:24 84:9 84:13 93:3 94:19 94:20 96:10 98:7 103:23 122:18 129:24 130:8 136:11 140:19 155:12 156:4 156:8 158:16 167:12 167:17 167:18 168:2 186:22 dosage 65:18 dose 12:11 12:11 25:12 25:20 27:4 58:15 59:6 59:14 59:18 59:23 59:24 61:14 61:18 61:24 63:9 63:10 63:11 63:14 63:18 64:3 65:11 65:12 65:17 67:8 67:9 68:14 71:5 71:7 72:3 73:24 74:1 74:8 74:17 74:23 75:4 75:12 76:23 77:6 77:19 78:12 79:3 79:17 79:18 80:14 82:2 83:7 83:9 83:17 84:14 85:4 85:22 87:3 87:12 87:17 87:18 88:22 89:19 89:25 91:8 91:10 92:14 93:11 94:6 95:8 95:10 95:11 95:25 96:11 96:15 96:20 96:21 97:4 97:20 98:19 98:21 98:24 99:2 99:7 99:8 100:4 103:20 104:10 104:19 104:20 141:8 150:18 153:15 153:21 158:17 164:15 164:19 167:7 172:23 180:22 181:18 183:11 dose-comparing 87:15 dosed63:9 64:19 66:3 168:13 dose-dependent 74:18 dose-response 26:16 30:1 58:20 59:10 59:21 61:24 62:15 62:19 70:14 70:23 71:10 75:7 76:1 79:10 80:13 83:1 83:6 83:16 84:4 85:8 86:4 86:8 90:8 90:11 90:11 91:19 103:19 104:1 148:23 154:14 180:15 dose-responses 25:18 doses 23:22 25:4 25:11 64:20 68:11 68:20 68:25 69:7 69:8 69:14 84:24 85:11 85:15 85:21 86:5 88:17 89:17 90:7 90:9 91:14 91:16FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 215

92:12 92:17 94:10 95:17 95:19 95:21 153:19 157:16 157:16 157:20 166:25 181:24 dosimetric 12:8 59:17 62:21 62:24 64:4 64:8 64:11 71:15 71:18 72:1 72:6 72:14 72:16 74:21 92:10 99:13 105:13 dosimetrics 30:7 77:15 77:22 79:18 96:18 dosimetry 12:6 12:23 13:6 13:13 14:15 27:3 59:5 60:3 60:8 62:13 62:19 71:18 82:21 150:8 172:23 180:14 181:9 dosing61:13 63:24 64:10 64:13 65:2 65:5 65:15 65:23 66:7 66:9 66:11 67:4 67:13 67:17 67:22 68:4 68:19 68:24 70:2 70:6 70:21 71:3 71:13 72:3 85:24 93:13 93:16 94:13 157:15 181:11 dots 115:20 120:12 120:13 124:20 Dr 2:4 4:10 4:11 5:4 5:8 5:13 5:17 5:20 6:1 6:6 6:11 6:13 6:17 6:21 6:25 7:3 7:7 7:10 7:12 7:17 7:22 8:1 8:6 8:11 8:16 8:21 9:2 9:8 9:11 14:21 15:5 15:7 15:9 18:10 19:2 19:4 19:4 19:6 19:7 21:20 22:7 23:3 24:19 24:19 24:25 26:21 28:21 29:25 30:6 30:18 30:22 30:22 31:3 31:13 31:14 31:17 31:18 54:9 55:1 55:5 55:5 55:8 55:13 55:14 55:18 55:22 55:22 55:23 56:3 56:15 56:19 56:22 56:25 57:1 57:1 57:18 57:18 57:19 58:5 58:11 58:15 58:17 80:1 80:1 80:5 80:7 80:8 80:23 80:23 81:1 81:2 81:9 81:20 81:23 82:4 82:4 82:5 82:6 82:22 82:22 82:23 82:24 83:4 83:5 83:9 83:11 83:17 83:22 83:24 84:2 84:8 84:17 84:19 84:19 85:14 85:19 86:2 86:2 86:3 86:11 86:11 86:11 86:13 86:23 86:23 86:25 87:1 87:21 87:22 87:22 87:23 89:21 90:15 90:15 90:18 91:24 92:2 92:6 92:11 92:22 92:23 92:24 93:1 93:2 93:3 94:14 94:14 94:16 95:5 95:5 95:6 95:13 95:19 95:20 95:20 95:21 96:2 96:2 96:5 96:10 96:22 96:24 96:24 96:25 97:11 97:19 98:9 98:16 98:16 98:18 99:3 99:4 99:5 99:14 99:16 99:17 99:17 99:20 99:23 101:4 101:4 101:5 101:10 101:15 101:21 101:21 101:22 102:6 102:20 102:20 102:21 102:25 103:2 103:6 103:10 103:13 103:15FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 216

103:15 103:16 104:4 104:4 104:5 104:13 104:14 104:16 104:17 104:24 104:24 104:25 105:2 105:3 105:10 105:10 105:11 105:16 105:20 105:20 105:20 106:6 106:15 114:16 123:25 130:2 130:24 134:17 134:20 134:21 135:3 135:5 135:12 135:16 135:19 135:21 136:8 136:12 136:17 136:17 136:18 137:8 137:11 137:12 137:23 138:11 139:14 139:17 139:19 139:21 139:22 140:4 140:7 140:8 140:21 141:2 141:4 141:4 141:5 142:17 143:11 143:11 143:12 144:14 144:24 145:3 145:5 145:7 145:8 145:13 145:19 146:8 146:10 146:11 146:13 147:17 149:11 150:20 153:16 157:15 158:9 159:7 159:13 161:23 161:23 162:2 162:3 162:9 162:10 162:12 162:17 162:21 163:16 163:18 163:22 163:24 164:4 164:6 164:11 164:16 164:18 164:23 165:9 165:9 165:10 165:16 165:23 166:5 166:5 166:6 166:18 167:2 167:5 167:13 167:13 167:15 167:20 168:17 168:18 169:15 169:24 169:24 170:5 170:5 170:18 170:24 171:7 171:7 171:8 171:16 171:19 171:19 172:1 172:1 172:4 172:10 172:24 174:9 174:17 175:5 175:19 175:19 175:22 176:2 176:6 176:11 176:12 176:12 176:13 177:3 177:8 177:25 178:2 178:4 178:16 178:16 178:19 178:20 178:22 179:9 179:9 179:10 179:13 179:14 179:17 179:20 180:5 180:7 180:7 180:10 180:21 180:23 180:23 180:24 181:3 181:13 181:14 181:17 182:2 182:2 182:3 182:4 182:6 182:9 182:12 182:14 182:15 182:25 183:6 183:7 183:9 183:20 183:25 183:25 184:13 184:15 184:23 185:5 185:12 185:12 185:15 186:10 186:13 187:2 187:2 187:5 187:13 188:13 188:17 188:19 188:21 188:25 189:2 189:4 189:7 draft 16:11 56:11 162:22 drag145:16 dramatically 85:24 drastically70:12 70:20 71:2 drinking2:12 4:15 14:16 20:25 28:4 29:3 29:7 30:9 48:6 58:10 78:2 78:10 82:14 106:11 106:22 106:23 106:25 107:11 115:17 118:19FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 217

118:25 119:3 134:2 153:23 154:23 165:21 172:25 173:20 186:4 186:4 drinking-water 13:3 13:5 14:9 17:23 18:9 20:2 20:6 20:18 20:24 22:14 24:2 27:8 27:25 28:15 30:8 134:6 drive 112:18 driving124:10 125:9 126:24 164:10 165:3 169:11 drop 93:25 drops 94:21 drugs 159:24 160:1 due 88:6 duplicate 148:10 duration 12:12 20:17 28:10 28:14 29:2 64:9 68:19 68:24 69:13 72:7 99:10 105:7 108:25 109:3 109:3 109:19 109:24 109:25 110:3 110:4 113:10 113:11 113:12 114:21 117:25 121:15 123:16 125:8 125:10 125:17 127:3 136:5 141:9 143:4 146:18 154:3 155:19 159:6 161:18 164:2 173:7 186:16 durations 20:22 23:12 30:16 59:14 67:22 70:21 71:3 71:13 109:22 134:7 158:12 164:22 165:1 165:6 173:15 during3:13 9:20 10:22 11:9 38:15 38:17 44:20 46:12 65:15 107:16 108:22 110:16 112:23 116:2 116:14 116:25 117:6 121:17 127:16 137:5 155:1 166:7 dust49:20

E earlier 101:16 157:15 158:3 167:18 179:25 186:20 early 145:15 150:6 167:21 easier98:24 100:24 142:25 143:7 easy 105:6 109:20 113:5 128:18 140:17 144:4 144:21 149:21 180:6 180:6 echo 15:11 ecologic 36:14 43:11 43:13 45:19 46:19 ecological 107:22 108:11 110:24 114:25 115:2 116:6 117:9 118:8 122:19 132:20 133:4 135:1 135:14 184:20 ecstatic 70:24 editorial 36:5 EFED 130:2 effect34:22 46:25 63:22 63:24 64:3 68:8 68:12 70:1 70:3 70:10 72:2 72:3 72:3 72:4 72:9 75:16 76:16 79:14 83:12 83:13 83:14 86:9 89:11 93:14 94:18 94:23 94:25 99:7 99:9 101:17 103:4 123:24 127:10 149:6 153:14 157:6 157:18 effectiveness 119:12 effects 2:11 4:14FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 218

4:15 5:24 14:22 15:24 17:5 17:6 17:14 18:18 23:14 23:19 25:4 31:20 33:11 35:2 36:8 37:7 46:8 48:16 53:6 53:7 54:13 58:8 58:9 101:19 106:10 106:10 108:11 149:21 152:6 153:19 156:10 168:9 efficacies 101:8 efficient42:1 115:25 effort9:18 15:2 16:3 22:8 56:5 56:8 56:14 107:5 133:22 150:12 175:11 efforts 15:4 15:11 129:22 132:25 either42:19 66:3 66:4 80:5 92:5 110:15 145:11 145:12 elegantly 164:24 elements 34:6 elevated38:12 40:5 40:10 41:18 eleven-fold

40:5 eliciting23:23 eligible 41:21 eliminated 50:15 92:7 181:11 elimination 64:25 65:16 65:19 67:14 73:1 73:8 73:10 73:16eliminationrate 158:10 158:17 Elizabeth 30:18 Ellen 8:6 8:6 175:22 else 3:12 4:2 119:13 151:5 183:18 emerging17:13 23:2 emphasis 17:8 186:25 emphasize 172:15 180:1 emphasizes 120:16 empirical 156:10 enclosed4:5 encourages 19:14 endocrine 8:25 endocrinologist 6:15 endocrinology 8:10 endpoint23:11 25:13 150:24 151:8 152:1 167:10 172:18 endpoints 18:3 20:16 23:9 150:25 156:13 156:24 162:11 164:21 energy7:25 engaged

42:6 49:14 55:20 engaging38:14 42:12 Engineering7:15 enhanced51:4 enhancing52:5 Enoch 25:9 178:23 179:13 ensure 3:3 10:3 11:21 ensuring9:24 13:24 enters 60:10 entire 46:10 entirely 22:13 environmental 5:21 5:22 6:2 6:8 8:23 10:9 13:1 15:23 41:8 48:16 51:3 57:7 enzymes 60:14 EPA 2:18 2:22 4:4 9:10 18:11 31:2 32:21 44:7 50:11 55:15 87:2 137:22 142:4 150:9 163:13 187:12 epi 33:22 51:16 57:4 57:7 166:7 166:9FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 219

epidemiologic 32:22 32:25 41:6 53:12 55:25 58:12 epidemiological 50:12 epidemiologist 8:14 8:19 31:20 epidemiologists 57:10 epidemiology 8:7 11:2 11:3 11:11 12:24 14:4 14:15 16:12 16:15 16:16 16:19 18:8 18:20 21:17 21:18 22:1 22:6 24:11 26:20 26:22 29:9 29:13 29:19 29:22 31:23 32:1 33:9 33:13 33:21 34:7 36:9 36:18 43:6 51:19 54:2 54:8 55:11 147:17 148:24 149:12 153:22 154:15 172:12 174:2 174:5 175:12 177:12 177:20 186:5 episodes 49:9 49:15 equal 68:11 171:5 equation 89:7 97:6 equipotent171:1 equivalence 97:21 97:23 98:3 98:13 error 50:18 50:24 85:6 errors 83:7 83:14 84:4 86:6 esoteric 185:17 especially 88:15 90:14 128:14 essence 155:20 158:16 essentially 26:1 36:1 42:4 42:14 46:16 47:18 49:22 89:10 153:6 174:18 establish 77:22 established

68:22 149:4 establishing 181:23 estimate 37:25 52:18 73:4 73:9 73:11 73:18 96:12 106:25 107:15 118:19 125:16 128:2 129:15 132:3 134:6 134:24 139:6 140:14 161:5 180:21 185:9 185:10 estimated25:6 44:19 45:1 45:6 131:1 estimates 108:21 109:22 115:5 117:19 122:11 125:15 126:16 133:20 164:19 170:10 170:16 estimating 118:4 126:22 127:21 128:1 128:16 131:14 132:1 134:13 estimation126:21 131:17 132:6 134:12 134:23 142:1 185:4 185:10 estimations 133:7 estrous 70:4 estrus 70:8 et43:10 44:1 44:16 47:7 52:12 66:2 68:17 75:9 83:2 188:12 ethics 3:1 3:4 ethyl 60:19 evaluate 20:6 25:18 39:4 44:18 100:22 117:20 117:22 118:6 127:17 134:10 165:22 evaluated21:8 24:12 25:17 68:20 68:25 69:14 70:3 70:10 75:17 166:16 evaluating10:5 17:23 18:9 24:4 146:16FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 220

185:3 evaluation4:14 21:16 27:19 31:22 32:3 33:6 33:10 33:17 35:1 40:23 42:5 45:3 45:16 51:14 106:17 149:14 150:1 176:20 evaluations 35:5 41:6 42:25 176:15 event 39:11 59:2 64:10 99:21 114:8 142:16 147:7 172:17 events 17:9 21:8 22:22 60:4 114:5 149:2 151:22 eventually93:16 ever/never38:9 39:7 53:17 54:2 everybody 105:23 106:2 106:4 189:20 everyone 2:5 2:7 2:8 4:12 9:3 58:6 106:7 155:11 169:16 everyone's 3:21 everything4:2 4:7 86:5 148:19 151:5 186:14 everywhere 112:12 evidence 16:19 48:21 57:14 65:20 66:1 179:23 evolve 10:17 evolving10:16 exactly46:25 67:2 101:1 169:4 examined45:22 68:10 69:1 71:5 examines 59:3 example 34:21 35:19 48:12 53:8 61:12 114:24 114:25 126:19 142:10 143:7 examples 24:3 173:1 exams 184:22 exceed 141:17 exceedance 138:19 141:25 exceeded123:13 138:25 141:16 141:22 142:12 142:15 exceeding 123:19 124:3 138:16 142:6 excellent19:23 Except74:14 exception 63:16 excess 93:7 exchange 31:5 excited

22:10 excluded35:22 35:23 excluding110:11 excuse 26:2 38:11 74:1 84:10 153:4 exercise 87:15 135:25 exhaustive 34:9 34:24 exist 102:5 existing17:21 23:6 82:17 109:4 109:13 139:1 expect18:17 22:11 107:17 118:14 138:1 152:4 152:22 156:6 173:21 expected94:7 127:16 127:16 expecting174:2 experience 49:16 experimental 12:24 14:6 14:14 17:2 22:1 22:14 26:19 29:21 32:16 33:23 51:13 53:5 147:16 151:11 160:15 166:21 174:6 177:19 179:15 experimentally 167:7 expert6:19 expertise 4:22 5:11 5:23 6:4 6:9 6:15 6:24FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 221

7:5 8:3 8:14 8:25 9:4 9:20 experts 4:24 explain 121:10 explained124:1 138:24 explanation 137:25 139:15 explanations 32:14 50:14 explicit105:14 explicitly 32:24 147:1 155:22 178:23 exploration 56:23 exploratory 129:24 explore 132:21 exponential 75:17 76:24 expose 168:4 exposed

38:25 41:24 47:12 92:19 151:13 157:1 167:23 exposure 8:15 12:12 12:12 12:14 13:7 13:14 20:17 20:18 20:22 20:24 23:12 27:8 27:18 28:11 28:14 29:2 30:16 33:15 34:13 37:22 38:8 38:9 38:22 39:6 39:7 40:19 41:3 42:4 42:15 42:17 43:7 44:9 44:14 45:6 45:20 46:5 46:10 46:12 47:5 47:13 47:24 48:3 48:6 48:10 48:13 48:15 50:18 50:19 52:13 52:16 52:17 52:25 53:14 53:19 53:20 53:22 54:1 55:16 56:6 66:5 66:6 66:14 66:17 66:18 66:23 72:7 72:11 77:16 79:5 82:1 89:23 91:17 93:20 94:5 99:10 106:21 106:24 107:1 107:15 107:22 108:11 108:21 110:3 110:24 112:19 113:10 113:24 114:20 114:25 115:2 115:4 116:6 117:10 118:4 118:8 118:18 122:11 122:20 123:23 124:10 125:3 125:9 125:15 125:18 126:3 126:16 126:20 127:17 131:17 131:23 132:6 132:20 133:4 134:6 134:11 134:14 135:1 135:15 136:6 138:15 138:18 144:11 147:2 147:9 150:5 150:20 151:17 151:19 151:22 152:7 152:13 152:15 152:19 153:24 155:19 156:19 156:25 157:3 157:7 157:25 158:13 161:18 163:12 164:2 164:22 170:3 170:10 170:15 173:5 173:7 173:15 184:20 185:9 186:15 186:16 exposure-only 35:20 exposures 13:1 36:24 41:8 42:20 51:3 108:7 109:15 109:23 118:20 119:19 126:2 126:22 127:22 128:1 128:2 128:14 128:17 129:5 132:2 132:3 134:6 136:24 139:6 139:9 146:14 185:10 expressed68:8 127:11 extend 15:10 extended53:19 93:20 94:13 extensive 136:19 137:9FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 222

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J JAMES 6:17 178:19 Jan 5:8 186:10 JANICE 5:8 82:24 83:5 83:11 83:22 84:2 84:17 86:3 January25:25 Jerry 16:6 Jessica 15:21 Jim 6:17 130:2 135:14 180:5 Joan 75:2 Joanie 74:24 job 158:6 175:15 Joe 2:7 4:12 15:14 16:9 146:2 146:2 168:21 187:21 188:5 188:7 189:8 John 5:17 5:17 8:16 8:16 15:20 55:14 55:14 80:8 86:13 96:10 102:21 103:2 103:10 136:18 137:11 137:23 139:14 177:25 John's 104:6 join 87:2 JOSEPH2:4 judgment34:2 July 18:14 18:15 25:24 25:25 28:24 153:3 173:24 jumped 182:9 182:14 justice 158:14 158:15 justified99:12 101:13

K Kannan6:6 6:6 96:25 97:19 139:22 140:7 140:21 170:18 171:16 Kansas 6:22 KATHERINE 6:21 Kathy 6:21 Ken 5:3 5:5 KENNETH5:4 57:1 86:25 104:17 105:2 141:5 Kentucky 7:4 key 17:9 21:8 22:22 34:9 34:14 35:12 59:2 60:4 142:10 148:16 149:2 151:22 172:16 kids 186:2 Kidwell 15:21 kilogram

25:5 kilograms 158:20 kinds 32:25 147:25 167:25 kinetics 73:14 79:6 158:6 King15:21 knew14:1 26:10 knock 120:25 knowledge 52:1 101:10 155:17 156:3 166:20 167:20 known 157:11 159:5 kriging129:8 129:8 129:24 131:2 131:17 131:20 132:7 Krishnan 6:6 6:7 96:24 96:25 97:19 139:21 139:22 140:7 140:21 169:24 170:18 171:16 171:20 181:14FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 234

L lab 25:8 63:8 93:5 103:25 150:10 151:13 151:14 178:10 labeled

86:15 86:22 95:18 laboratories 41:4 laboratory 52:14 59:2laboratorybased40:21 labs 150:9 150:13 151:20 lack 28:8 40:11 40:17 53:14 53:19 53:21 54:3 102:11 153:13 156:1 161:5 161:6 lacking155:23 lactating150:8 lactation 153:7 lactational 150:6 152:15 large 38:24 40:10 49:18 88:4 89:3 110:22 124:9 125:21 138:8 173:9 178:4 largely29:18 29:20 37:18 42:16 46:4 50:13 146:15 147:19 175:13 176:3 larger16:18 153:10 last11:1 23:16 28:25 49:13 49:13 66:15 69:11 69:12 69:18 73:4 73:11 75:11 90:4 113:23 116:22 118:24 130:25 134:1 140:11 155:5 155:12 158:23 159:21 161:15 164:20 169:19 172:2 173:3 lastly38:11 lasts 156:20 late 159:20 160:7 167:17 187:25 latent152:6 later 21:6 27:13 31:9 38:5 100:8 125:7 143:17 189:18 latest75:12 latter90:14 177:11 Law16:5 lay 22:19 layers 112:16 lead59:12 60:4 60:5 96:5 144:10 178:14 183:7 leading9:15 50:18 149:7 leads 60:23 61:6 72:22 109:1 116:15 175:8 learn 51:14 135:25 137:14 137:18 learned

108:1 111:22 135:25 136:12 least 61:25 62:2 67:15 67:18 68:5 88:22 99:8 99:10 102:10 114:1 117:6 LeBlanc 8:21 8:22 101:21 101:22 101:22 104:4 104:5 ledge 72:9 Lee 7:12 7:12 82:23 130:24 134:20 134:21 134:21 135:3 135:5 135:16 135:19 136:8 139:19 185:12 Legan 7:3 7:4 87:22 87:23 87:24 94:14 94:16 95:13 96:2 leisurely 57:24 lend129:10 length182:25 lengthy29:25 42:23 188:2 less 68:11 75:16 76:5 82:19 105:7 107:19FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 235

108:14 109:13 109:24 113:12 113:21 117:20 119:2 126:17 127:17 131:19 134:14 135:9 138:4 153:15 157:11 183:17 lesser149:23 lessons 108:1 111:21 137:17 lest69:11 let's 5:2 57:25 58:1 88:24 152:18 169:12 level 24:1 38:23 38:23 42:9 42:10 67:24 67:25 68:12 76:15 138:17 139:1 141:16 155:13 levels 11:20 12:3 12:9 46:17 46:21 61:21 62:4 63:22 64:22 65:6 65:9 65:13 66:6 66:19 66:25 67:3 67:3 67:5 67:16 67:16 68:1 68:7 72:6 72:12 74:8 74:12 76:25 77:8 78:20 79:3 79:4 83:13 83:13 83:14 88:14 88:21 105:7 105:9 157:23 165:3 Levine 14:21 15:5 15:9 19:5 21:21 23:3 24:19 28:21 LH7:6 22:24 23:18 23:23 25:7 25:12 59:13 62:1 63:12 63:20 67:24 70:10 70:16 76:4 76:15 79:14 88:2 88:9 88:12 88:15 88:23 89:3 89:21 90:2 93:4 93:7 93:19 93:23 94:20 94:23 95:1 101:19 149:7 149:22 150:16 150:21 150:23 150:25 151:7 153:14 153:19 155:14 156:20 157:13 157:21 157:25 159:5 160:12 164:20 164:25 165:4 167:11 172:15 180:1 183:1 LH-surge 89:1 liaison 2:17 Liccione 15:21 96:10 licensed37:15 life 20:16 146:16 148:14 150:5 152:16 161:17 162:5 162:7 173:6 life-stage 20:23 29:5 30:14 lifetime 38:7 38:10 38:13 39:8 49:5 53:17 light 29:14 lighter-blue 124:14 likelihood

142:15 143:3 likely40:16 41:2 48:13 50:19 50:21 110:16 112:11 112:14 112:21 112:24 113:1 113:19 116:3 117:7 117:7 125:7 128:13 139:7 161:21 limit 3:17 44:7 53:18 limitation42:3 42:3 limitations 32:9 33:18 38:21 43:2 45:21 175:16 175:25 179:5 limited

31:8 35:7 36:24 39:6 48:22 lindane 37:23 38:2 line 19:20 67:12 70:21 73:9 74:5 78:14 85:7 98:21 101:23 120:13 120:15 124:12FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 236

124:14 124:21 linear72:25 73:14 74:2 74:4 74:9 74:16 75:18 77:1 77:17 79:2 79:6 89:19 99:22 128:12 linearity 99:19 lines 99:19 99:22 100:2 link 12:10 30:7 54:15 linked 105:13 linking19:19 156:19 list75:6 171:16 listed 171:17 171:18 lit36:12 liter 104:18 104:19 104:20 140:25 literature 18:13 25:24 28:23 31:23 33:10 35:3 35:6 36:2 36:9 55:20 58:13 82:11 82:12 159:9 159:15 159:16 173:23 liters 105:4 little 17:20 22:18 31:24 48:2 52:24 57:24 58:21 72:17 74:25 81:7 88:20 90:14 90:22 100:25 102:22 103:19 111:3 111:23 113:6 114:2 115:15 117:15 118:15 124:23 125:19 133:11 133:22 143:13 143:16 148:15 149:23 156:21 167:23 168:24 171:4 171:5 171:8 173:4 174:13 184:7 185:16 185:17 live 8:2 41:23 lives 152:18 living42:18 47:14 loaded 188:6 LOAEL 68:22 69:16 70:1 70:17 80:22 84:12 LOAELs 85:25 location 117:6 locked 91:20 log130:10 Logdall 55:2 logic 56:24 logical 146:19 180:16 long7:21 36:17 37:21 67:17 98:14 105:17 123:25 128:25 137:2 137:5 141:9 145:20 156:17 156:21 161:2 longer109:23 110:4 113:12 128:5 143:20 156:16 longer-term 133:22 long-term

37:11 41:15 44:9 132:3 loop 170:25 lose 97:14 131:18 loss 89:12 131:25 lost104:22 lot9:17 14:4 24:8 26:7 28:7 30:4 57:8 57:9 126:8 127:23 128:3 128:5 128:6 128:24 135:9 136:2 138:6 142:22 144:8 146:18 147:15 147:20 150:16 152:23 172:8 174:16 lots 150:18 loud27:2 27:10 low25:11 47:20 47:22 47:23 48:21 51:10 84:5 85:4 85:22 88:17 93:24 157:16low-birthweight47:4FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 237

low-dose 82:2 lower 25:12 63:14 63:14 91:13 94:10 131:10 150:25 153:19 157:20 158:17 167:7 lowest80:14 Lowit 15:18 19:2 19:6 19:7 30:23 31:14 54:9 82:4 82:5 84:19 95:5 95:6 96:2 99:16 99:17 103:15 103:16 105:10 105:11 105:20 144:24 145:3 145:7 145:19 146:10 146:13 161:24 162:9 162:12 163:22 164:4 164:11 164:18 165:16 166:18 168:18 169:19 169:24 170:24 172:1 172:4 174:17 175:5 176:2 176:11 177:8 178:4 178:22 179:13 179:20 180:10 181:3 181:17 182:5 182:6 182:15 183:9 185:15 186:13 187:13 188:18 188:19 189:2 lumen 45:4 lunch 105:21 luncheon 106:5 luteinizing22:24

M magenta 120:13 magical 119:17 magnitude 67:7 121:16 123:16 126:2 143:4 164:14 main 42:3 48:9 54:4 75:21 78:1 109:16 111:16 134:3 Mainly32:7 maintenance 157:9 major 17:17 32:8 37:6 43:2 44:3 44:25 106:23 156:14 161:21 175:8 175:16 major/minor 35:7 male 37:3 39:25 41:22 42:17 42:20 47:11 52:11 53:6 151:21 157:6 162:23 163:2 mammary6:15 6:19 18:12 21:5 21:10 21:12 24:22 25:10 25:17 149:8 178:5 179:7 179:11 179:21 179:22 180:2 manageable 119:22 Managers 20:2 manifest93:17 manner112:20 map 115:20 mapping35:11 market144:2 Marquita 15:21 Mary 15:23 27:7 30:11 106:19 107:2 144:18 147:20 171:14 Mary's 153:25 mass-balance 102:7 master36:13 match 121:21 121:25 matching100:2 121:8 135:20 136:4 136:11 155:15 157:21 157:24 157:24 material 35:10 55:10 80:6 162:1 188:7 materials 3:24 32:10 33:7 50:10 maternal 44:4 44:10 45:7 mathematical 181:5 181:25 Mathematics 7:13 matter82:18FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 238

98:13 101:2 123:20 145:14 146:10 163:10 173:15 178:15 182:21 matters 2:23 Mattix43:10 Maumee 124:7 maximal 157:18 maximize 40:22 maximum

123:11 123:12 126:23 131:8 may 20:7 21:12 39:22 46:2 74:19 77:22 80:11 84:20 86:18 93:15 93:16 97:14 98:18 98:23 100:16 100:23 101:19 108:10 108:25 110:3 110:4 110:5 110:10 110:11 112:22 113:6 114:9 114:19 118:14 122:1 122:16 123:23 123:24 127:24 128:18 129:5 129:6 130:15 132:8 132:10 133:9 133:22 135:17 136:10 138:5 138:15 142:10 142:13 143:7 157:17 161:1 161:15 164:1 164:3 169:11 188:13 188:22 189:16 maybe 88:24 89:1 141:5 149:23 158:25 160:6 160:21 165:6 166:7 177:7 mcmanaman 6:17 178:19 McManaman 6:17 178:16 McMullin 61:12 68:23 71:6 103:25 158:3 158:4 mean 67:2 67:5 67:7 74:3 76:18 78:4 84:15 85:20 89:25 92:17 95:3 98:23 99:1 103:1 104:9 121:11 129:12 130:2 135:20 135:21 135:22 144:9 151:12 161:12 162:13 169:18 171:21 meaningful 94:24 means 2:20 34:8 34:24 128:1 131:14 141:14 measurable 52:6 measure 35:20 35:22 38:22 48:1 49:2 98:24 126:23 141:19 142:11 measured34:17 40:15 46:22 48:14 52:16 124:13 125:24 141:25 141:25 156:24 170:8 170:12 measurement 39:5 41:4 48:14 50:18 50:24 50:25 51:2 53:16 53:20 101:7 measurements 34:15 61:9 128:24 141:22 171:12 measures 48:5 163:2 measuring47:1 mechanism

49:25 mechanisms 12:5 median40:8 mediated60:13 Medical 6:22 Medicine 5:9 medium 85:11 Meek 7:17 7:17 165:9 165:10 182:3 meet5:2 189:14 meeting2:2 2:9 2:10 2:11 2:15 2:17 3:16 3:20 4:10 4:12 4:24FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 239

5:1 9:13 9:16 11:9 16:13 17:7 17:16 17:22 18:5 18:20 18:24 21:11 21:22 21:24 21:25 22:22 23:2 23:11 23:18 24:12 26:2 26:2 26:3 26:4 28:22 29:15 31:10 31:16 58:7 106:8 161:22 169:17 172:22 173:12 174:7 189:22 meetings 3:8 11:1 15:16 21:23 56:13 Melanie 16:2 member5:7 5:12 5:15 5:19 5:25 8:25 members 3:3 9:12 14:23 15:6 26:10 80:4 89:7 105:19 145:21 161:25 189:14 Mendez 15:19 30:18 159:7 menopause 38:6 39:9 39:10 39:13 39:17 51:21 menstrual 37:22 39:16 149:20 149:22 159:12 159:18 160:13 menstrual-cycle 38:1 39:3 52:4 mention 56:4 74:10 142:22 mentioned16:9 32:21 33:2 33:8 34:9 34:18 34:23 39:2 47:8 48:5 52:13 52:23 53:4 54:10 69:1 77:21 89:7 93:7 108:20 117:9 118:7 141:7 165:11 mentioning75:25 mercapturate 40:3 41:2 53:25 message 74:15 met14:19 metabolism

5:11 61:5 61:11 102:9 metabolite 60:19 60:21 60:23 170:13 171:11 metabolites 41:5 60:15 62:1 62:18 62:23 62:25 63:4 71:23 71:24 79:12 97:24 98:6 101:9 101:12 101:18 102:17 103:18 103:19 103:24 141:1 158:8 170:9 171:1 180:22 metabolized

60:10 method 42:14 42:16 46:5 47:13 139:13 methodological 52:3 methodology 32:5 35:3 methods 52:14 126:21 126:21 128:10 129:8 131:17 134:12 134:13 185:10 metric 38:9 39:7 53:19 100:23 metrics 100:16 100:19 105:15 mg/kg61:14 63:10 90:1 90:5 92:20 95:3 95:9 97:7 153:15 mg/kg/day 63:14 64:21 66:4 68:15 73:23 77:7 88:22 mg/L 77:9 mg/L-h77:13 mic 4:9 14:21 Michigan 4:18 microphone 19:2 90:16 middle 145:11 145:17 middle-aged

93:22 mid-July 18:23 mid-morning3:6 mil 93:6 milk 157:2 157:3FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 240

milligrams 25:5 89:17 93:6 97:22 97:22 98:7 98:10 98:15 millimole 98:1 millimoles 97:23 97:25 mind29:19 77:10 82:9 89:6 96:3 100:25 103:17 144:7 186:1 minimal 39:10 minimis 80:20 minute 144:12 145:4 152:14 168:19 182:7 minutes 2:5 3:17 31:21 58:2 61:14 148:9 148:16 169:13 miscarriage 41:16 41:18 47:9 misclassification 50:19 50:21 mismatch156:21 misreading97:8 miss 95:1 125:7 126:12 138:5 138:9 missed 37:21 80:11 122:16 125:10 126:9 143:3 missing98:18 98:23 100:23 110:11 125:17 131:6 136:10 147:18 148:4 154:16 182:16 Mississippi 5:10 7:11 Missouri 116:11 117:14 misspeak 175:9 mixture 25:9 178:23 179:7 mixtures 6:12 143:15 mobility 113:2 mode 11:18 12:4 12:13 12:23 13:7 17:8 20:23 21:8 21:14 28:10 59:25 60:4 68:19 145:1 148:20 149:1 149:3 153:21 154:13 165:7 166:11 166:15 172:17 186:5 model 59:6 75:4 76:23 77:2 77:3 81:4 81:10 81:11 81:12 81:18 81:21 81:25 82:17 87:18 87:19 97:3 111:7 118:17 118:18 129:19 132:19 132:22 132:24 133:12 133:14 133:15 158:3 158:4 185:3 modeled 46:10 model-fitting 87:2 87:3 87:14 modeling24:5 59:18 74:23 76:22 79:17 84:14 95:9 96:12 96:15 96:20 118:18 129:3 129:21 142:1 models 75:14 75:17 82:7 111:9 112:15 116:1 130:12 moderately146:19 Modes 11:15 12:8 modest99:7 modification 34:22 modified34:1 modifying97:17 Moiz 6:11 6:11 179:10 179:14 180:5 molar 171:5 molecular 8:23 97:25 98:2 98:5 98:11 Mollis 103:22 moment91:4 162:22 163:12 monitor 65:16 107:9 108:7 112:12 137:16 141:17 142:11 monitored64:23FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 241

65:6 141:24 monitoring2:12 4:16 14:9 14:12 17:24 18:1 18:3 18:9 20:3 20:6 20:24 21:1 24:3 24:7 27:12 29:3 30:8 30:10 44:6 44:10 48:3 58:10 59:19 75:1 78:2 79:22 82:15 100:10 100:13 106:11 106:18 107:7 107:11 107:13 107:16 107:21 107:24 108:4 108:6 108:19 108:20 109:4 109:5 109:8 109:17 111:17 111:20 112:2 112:11 112:13 113:8 113:19 113:21 114:1 114:18 114:22 115:2 115:9 115:11 115:17 115:24 116:2 116:6 116:10 116:14 116:20 117:1 117:8 117:10 117:19 117:20 118:5 118:8 118:9 118:9 118:12 121:9 121:17 121:25 122:20 123:6 124:7 124:17 125:25 126:11 126:15 127:15 127:18 127:24 128:3 128:5 128:6 129:25 131:13 131:19 132:13 132:16 133:19 134:2 134:5 134:10 134:14 135:1 137:2 137:4 137:15 139:2 140:10 140:24 141:8 141:14 141:14 145:2 154:21 154:24 155:1 155:6 155:8 161:20 164:3 165:21 170:8 183:16 184:4 184:20 186:17monodealkylated 60:15 month 28:25 69:7 158:25 160:22 monthly43:19 43:20 141:21 months 9:21 18:23 28:5 28:19 44:20 69:14 100:9 100:18 101:2 101:2 116:5 150:20 154:9 173:13 174:3 187:1 Montreal 6:8 morning2:7 4:9 5:4 5:20 7:3 8:11 8:21 19:7 24:20 31:3 31:19 33:7 57:22 58:6 106:14 147:18 149:2 149:12 153:17 158:15 159:8 172:9 174:25 177:4 180:21 182:25 187:13 187:16 187:25 188:20 189:9 189:11 189:19 189:21 morphology40:17 Morseth75:10 155:14 155:16 183:3 mostly8:18 60:6 91:18 112:24 116:4 182:1 motility 40:17 move 24:9 26:17 28:4 28:18 31:8 31:12 59:15 59:25 71:14 74:22 100:21 125:19 132:4 152:17 154:20 168:23 169:5 174:1 185:24 187:19 moved27:11 moving22:11 59:11 61:24 63:5 67:20 71:9 81:4 81:10 141:7FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 242

182:19 186:16 multidisciplinary 28:6 160:10 161:8multidisciplinary 20:13 multi-gen 168:6 168:7 multi-life 152:12 152:19 152:21 multiple 15:1 130:14 152:16 153:1 153:1 156:4 161:9 173:17 176:5 multiply 98:2 Mumtaz 6:11 6:11 179:9 179:10 179:14 Musical 145:4 myself 49:6 57:9

N NACWA128:6 naive 104:18 naively135:22 NAS 11:25 19:13 national 5:6 5:18 7:1 8:20 59:22 132:24 natural 30:8 145:10 nature 34:11 41:12 42:4 45:19 50:1 50:8 50:13 50:20 52:1 54:11 82:10 85:5 88:14 118:15 168:10 nearly64:24 71:6 Nebraska 117:14 necessarily 110:18 112:21 122:7 necessary 170:14 necropsy 153:7 neither 145:22 Nelson6:13 6:13 15:22 27:7 30:11 100:7 100:18 100:20 106:18 106:20 134:18 134:25 135:4 135:12 135:17 135:21 136:12 137:8 137:12 138:11 140:4 140:8 141:2 142:17 144:18 147:19 153:25 166:1 166:4 167:15 170:4 170:6 170:7 171:8 171:13 171:23 173:2 173:20 180:5 183:13 183:14 183:23 184:2 184:17 185:2 185:7 networks 128:16 neural 128:16 neurobehavioral 151:25 neuroendocrine 149:3 neurons 157:4 157:5 neurotox 23:21 neurotoxicity 17:5 neurotoxicologist 5:15 neurotoxicology 5:11 newer 25:1 news 163:17 NHEERL150:13 nice 22:11 57:5 74:9 151:17 153:2 157:21 168:24 NIEHS 5:19 15:4 18:11 187:17 NOAEL 63:18 68:22 69:5 69:10 69:15 70:1 70:16 80:22 84:11 86:15 86:22 87:16 NOAEL/LOAEL 59:1 NOAELs 71:1 85:24 86:9 86:14 no-effect83:11 non 50:21 72:24 172:11 non-adult162:5 non-cancer2:11 4:15 11:8FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 243

16:19 18:8 24:11 29:14 29:19 31:22 33:9 33:13 33:22 35:2 35:5 35:21 36:8 54:8 58:9 106:10 173:12 non-cancerous 18:19 non-causal 32:14 50:14 non-communicative 41:17 non-compartmental 81:15 180:20 nondisclosure 4:1 none 62:24 152:16 166:22 non-significant 45:11 47:23 non-users 38:2 normal 103:18 normally 165:6 North 8:24 37:13 notable 36:16 51:15 note 30:18 43:7 44:23 45:10 46:19 47:25 51:17 69:16 188:14 189:8 noted28:21 41:1 53:24 65:8 178:23 nothing119:17 noting21:23 notion65:21 66:24 NRC 19:12

O objecting139:16 observation 38:16 53:8 57:2 64:2 72:2 79:6 99:18 153:12 observational 32:16 32:25 33:21 45:24 51:16 observations 40:9 51:11 53:5 163:4 Obstetrics 6:19 obvious 174:19 obviously 9:3 29:6 109:24 142:20 167:8 180:25 occupational 6:7 39:17 57:8 occur 33:11 86:17 121:17 123:8 occurred140:16 occurrence 39:13 occurring94:12 occurs 39:11 112:12 156:17 157:20 187:14 Ochoa 177:1 Ochoa-Aku 47:25 odds 37:21 38:12 40:6 41:18 44:2 45:11 46:14 47:11 47:16 47:23 49:4 52:19 odor 119:10 offense 143:25 office 3:15 5:6 9:9 15:3 55:2 Official 187:22 offspring157:5 163:1 163:9 oh84:2 86:13 92:2 93:1 104:17 183:25 188:17 Ohio 116:11 117:4 117:13 okay 2:7 24:9 25:22 31:17 60:9 60:19 61:3 72:24 75:2 75:15 75:19 81:9 83:24 84:1 84:2 84:13 84:16 91:2 92:22 99:5 99:11 105:16 106:20 135:12 135:16 145:19 146:13 151:9 154:19 157:13 168:17 169:15 171:23 172:4 174:9 177:5 177:8 178:3 178:4 180:7 180:9 180:12 181:17 182:4 182:4 182:6 182:17 182:17 183:9 183:14 183:21 183:23 184:2FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 244

184:23 186:11 186:12 187:6 187:10 188:17 189:8 Oklahoma 5:14 omphalocele 44:25 45:13 one-dimensional 131:2 ones 13:21 26:12 56:17 85:12 115:6 135:7 156:14 173:17 176:23 185:18 one-time 39:11 ongoing16:7 16:21 27:24 36:17 50:4 147:13 147:20 148:17 151:12 151:12 153:8 162:4 onset 156:14 onsets 151:2 Ontario 8:12 36:19 41:14 41:22 47:7 open 9:7 35:6 80:3 134:15 136:14 164:1 164:4 opinion 36:6 51:22 OPP 15:18 150:13 opportunity3:6 3:8 3:13 10:4 144:17 187:12 187:15 opposed

180:3 184:22 opposite 102:24 103:1 option143:2 options 132:6 oral 60:11 61:13 63:9 68:19 70:6 160:3 ORD 16:4 16:6 151:14 151:20 152:1 163:13 order 20:11 68:2 94:23 158:24 160:21 162:6 180:16 182:8 182:17 183:12 ordinary-point 131:2 organics 119:11 organisms 5:24 organization 12:9 oriented124:12 185:19 origin97:7 97:18 original 36:5 55:20 108:20 oscillate 67:7 oscillation 67:10 67:10 oscillations 67:8 others 54:18 56:12 80:25 103:5 165:25 Ottawa7:18 ought 138:9 ourselves 55:21 58:1 99:22 121:12 159:4 outcome 34:13 42:21 45:24 48:15 49:2 52:6 52:19 53:2 93:16 94:4 94:13 103:23 151:1 167:24 173:12 outcomes 11:16 11:19 13:8 16:16 21:11 22:21 24:13 33:4 33:16 35:6 35:21 37:4 38:20 39:24 41:14 46:3 46:7 47:1 47:5 48:7 48:18 51:1 52:12 52:15 54:17 136:25 149:10 151:16 151:24 160:15 167:19 172:20 outline 59:8 output136:21 outside 162:5 ova 89:14 overall 33:24 39:21 41:9 42:24 overestimating 163:20 overkill 89:4FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 245

120:24 overlap 48:11 87:8 127:4 136:6 177:9 177:22 180:18 180:25 overlapped

46:15 overlaps 47:17 52:21 overlay20:20 84:21 164:24 183:1 overnight187:14 overview58:12 ovulate 89:13 ovulation 93:8 93:10 ovulatory 93:25

P p.m 189:23 P450 60:13 60:22 pace 22:11 57:23 57:25 105:25 package 73:5 162:15 page 155:11 paid63:7 paired 119:24 pair-wise 87:6 Panel 2:1 2:9 2:13 2:18 2:21 2:25 3:3 3:22 4:13 4:20 4:23 5:2 5:7 5:12 5:16 5:19 5:25 9:1 9:3 9:12 9:22 9:23 15:10 16:23 17:16 20:10 23:4 23:13 24:6 24:14 25:20 26:11 30:24 32:1 33:5 34:2 34:4 34:10 43:8 55:8 55:12 58:7 76:9 76:21 77:24 79:1 79:23 80:4 89:7 105:19 106:8 134:20 148:2 151:23 161:25 187:7 188:10 188:15 189:10 189:14 Panels 15:17 27:9 Panel's 34:25 36:10 paper 15:25 16:25 17:13 18:13 18:16 22:20 23:7 25:22 26:16 39:17 39:17 55:10 75:20 77:4 80:6 87:5 92:5 96:23 106:14 111:13 129:1 130:1 147:14 147:15 150:1 177:11 177:16 177:24 178:8 178:23 179:13 181:8 181:9 186:7 186:21 papers 18:15 56:17 162:22 163:19 178:8 178:9 179:23 parameters 40:2 40:16 40:19 53:7 163:3 163:6 paraphrase 174:18 parent61:6 61:15 61:18 61:22 62:17 62:23 63:4 71:21 79:11 101:12 171:1 participants 42:5 49:8 participate 2:14 37:16 41:22 participating 9:14 15:2 particular2:11 10:10 10:24 19:16 51:4 93:9 98:22 126:5 131:8 147:2 149:16 164:17 167:6 175:8 176:18 177:4 180:13 184:25 186:1 particularly 2:13 25:3 29:9 34:16 34:19 36:25 39:2 50:25 55:1 96:6 107:22 123:21 127:3 132:13 140:11 146:19 155:17 157:11 165:4 171:4 175:15 partly26:9 26:9FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 246

partner 47:11 partners 41:23 42:18 passed 56:17 past10:19 18:22 pathway17:17 61:4 166:16 pathways 11:14 11:15 pattern 84:23 87:4 91:18 120:14 patterns 144:8 pay 146:24 paying60:1 PBPK81:4 81:8 81:10 81:11 81:12 81:18 81:21 81:25 82:7 82:17 158:3 peak 46:17 63:22 70:15 88:9 88:13 88:13 93:23 109:23 114:19 123:23 124:9 125:3 125:8 138:9 peaks 88:16 100:11 100:12 100:12 110:11 119:1 121:16 122:8 124:11 124:16 124:25 125:1 125:17 126:9 127:4 127:5 128:13 135:23 135:24 136:2 136:10 138:5 138:6 peer 12:19 12:19 13:20 13:21 13:22 21:4 peer-review 9:22 10:2 peer-reviewed 82:18 pending147:13 148:17 173:12 173:16 PENELOPE 8:1 162:3 162:10 162:17 163:16 163:24 164:6 164:16 Penny 8:1 186:11 people 3:14 42:14 54:24 55:24 57:10 145:24 152:17 163:21 174:21 per 25:5 63:15 70:7 93:6 percent68:9 69:21 percentage 140:1 140:18 percentile 133:6 percentiles 131:10 132:14 perfect98:20 performed70:6 76:22 158:19 perhaps 12:11 93:18 perinatal 33:4 37:4 41:14 period 4:19 31:8 42:21 46:11 46:15 46:16 47:12 47:17 47:18 49:10 52:21 65:15 66:7 78:11 78:16 78:22 107:15 108:22 112:24 113:3 114:10 114:12 114:15 116:2 123:19 123:24 127:17 137:5 138:18 139:8 156:18 156:21 187:19 periods 46:12 52:23 113:24 128:25 157:10 159:6 159:17 peripubertal 150:6 152:15 permanent5:16 9:1 person56:1 56:11 133:11 personal 30:17 80:10 perspective 10:22 perspectives 79:21 pesticide 5:10 8:14 9:10 10:11 10:12 13:11 13:14 15:1FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 247

32:23 36:1 37:13 37:16 38:3 40:15 42:7 42:12 49:9 49:14 49:15 112:12 112:14 112:17 112:25 113:3 114:1 118:17 126:12 128:3 147:2 pesticides 5:24 38:15 38:17 41:7 42:9 42:13 50:4 51:2 51:24 109:12 111:8 113:6 128:4 144:2 Ph.D 8:18 pharmaceutical 159:9 pharmacokinetic 61:10 63:1 81:13 81:19 102:7 Pharmacokinetical ly158:2 pharmacokinetics 6:4 6:9 18:10 60:2 60:7 74:16 74:19 96:6 160:11 phase 26:18 33:1 64:25 65:16 65:19 67:14 73:1 73:8 159:18 160:6 160:7 photoperiodic 88:9 phrase 141:13 physical 39:16 39:18 39:19 50:25 physician 8:18 physicians 19:14 physiologic 89:9 physiologically 88:25 physiologist6:14 7:24 Physiology7:5 pick 28:23 picked 123:14 piece 95:7 100:23 pieces 36:6 pinpoint115:13 pituitary 89:4 pituitary-adrenal 17:15 pituitary-gonadal 17:10 place--around 96:7 placed 17:7 places 64:18 139:23 plan 23:7 141:8 169:13 184:3 189:16 plant 107:23 171:11 plasma59:11 61:16 61:19 63:6 64:5 64:12 64:22 65:1 65:4 65:6 65:8 65:13 66:6 66:19 66:25 67:3 67:3 67:5 67:16 68:1 68:7 71:11 71:25 72:12 72:19 73:16 74:8 79:11 79:14 96:13 101:14 104:19 105:9 150:21 158:7 164:25 165:3 plateau 68:13 69:4 85:14 85:17 88:21 90:5 plateaued89:22 plateauing91:1 plausibility 151:6 plausibilty54:14 plausible 17:19 23:5 172:16 play 10:9 46:3 48:17 138:2 plays 114:16 142:25 157:8 please 3:14 97:2 187:21 plenty31:4 plot76:2 85:6 85:15 120:9 183:1 183:5 plots 65:18 74:13 plotted

69:20 73:25 74:7 85:12 131:11 plus 53:19 140:25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 248

point 5:2 9:7 23:10 23:16 23:24 25:7 29:11 31:12 55:7 55:21 58:1 58:14 63:17 67:23 69:18 70:10 70:15 70:17 73:5 73:11 73:16 75:11 75:22 76:3 77:23 77:25 78:11 80:3 81:2 82:5 85:1 87:1 87:17 88:7 88:8 91:13 96:15 97:4 103:14 105:11 105:18 106:12 106:16 109:9 111:17 116:15 122:22 124:15 126:1 126:18 131:25 133:21 134:15 137:20 138:12 138:13 141:15 143:23 144:15 144:19 144:23 161:25 165:24 169:18 169:20 170:13 170:15 174:11 187:10 188:17 pointed

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pre-emergent 113:5 prefer158:2 pregnancy 38:15 38:18 41:23 42:22 46:11 46:13 52:24 pregnant66:3 167:3 preliminary 122:18 122:25 127:1 161:12 188:11 premature 93:21 preparation 17:1 189:17 prepare 9:21 174:24 prepared14:23 175:20 present18:2 147:10 187:15 presentation 4:6 17:25 24:21 24:22 29:24 30:1 30:6 30:8 30:9 30:12 30:19 32:4 50:17 53:12 54:21 55:9 55:24 60:6 75:22 79:25 80:5 81:3 100:8 104:15 144:24 153:16 154:1 166:8 168:20 172:2 172:11 173:3 176:19 177:2 183:1 185:22 187:16 187:18 presentations 4:3 19:3 20:14 22:20 27:14 28:17 29:17 30:13 31:1 106:13 108:24 110:8 145:9 145:22 146:4 169:19 187:11 188:1 188:6 presented16:11 17:22 32:10 32:21 33:4 33:19 36:14 128:15 presenting24:24 pressure 167:25 presumed62:5 62:8 102:11 preterm

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19:19 19:25 22:3 29:20 88:15 144:11problemformulation 11:4 procedure 142:2FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 250

181:5 proceed

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132:15 170:9 proximity 45:7 pseudo 67:1 67:14 72:12 101:25 pseudo-steady 65:21 65:25 66:24 67:3 68:1 68:6 74:7 74:10 96:8 101:6 101:24 102:5 124:1 157:19 157:22 157:25 165:2 puberty151:2 156:14 156:17 156:20 160:16 public 3:7 3:9 3:16 3:19 4:4 8:8 9:13 10:4 16:23 31:8 32:25 169:22 172:9 187:20 187:21 187:23 188:4 188:14 publication 41:1 publications 178:10 public-comment 3:13 publish68:18 published17:3 17:3 18:15 19:12 37:20 38:5 38:11 43:1 43:25 46:19 55:19 59:22 168:2 Pubmed 35:7 pull 172:5

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38:9 49:3 49:12 questions 20:5 20:7 31:5 31:9 31:13 31:14 49:13 54:4 54:22 55:4 55:8 55:12 56:23 57:21 80:4 88:3 90:19 97:1 100:9 104:14 105:19 106:1 109:10 109:11 111:18 125:12 134:3 134:16 134:19 134:22 139:17 139:20 144:15 145:18 145:25 154:6 154:16 159:13 162:1 165:24 169:2 172:13 174:14 175:1 175:13 178:17 180:13 184:3 185:15 185:16 187:19 189:10 189:17 quick 2:15 108:18 quickly60:9 136:13 149:1 quite 21:20 25:11 26:20 30:4 37:23 42:23 45:12 84:3 102:24 102:25 136:20 154:1 173:2 176:10 quota 94:1 96:1 quote 93:8

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56:17 reading57:2 166:10 reaffirm

10:8 reaffirmationFIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 253

22:22 reaffirming17:9 real 63:18 74:3 86:20 129:18 156:2 realistic 78:5 realize 110:2 realized35:17 really9:23 10:1 13:16 19:23 21:9 21:23 22:3 25:19 26:15 28:7 35:19 50:2 55:9 57:4 57:7 61:16 63:23 64:3 64:8 65:21 68:12 72:2 72:21 80:10 85:11 85:12 85:13 86:9 98:12 98:13 98:22 109:18 118:11 123:5 126:1 126:8 131:23 144:4 144:19 147:18 150:2 160:18 162:13 165:10 171:2 reason28:13 50:13 59:20 61:23 71:19 81:10 106:21 126:14 142:9 167:22 reasonable 100:5 101:25 103:11 104:12 121:4 171:4 171:6 reasonably120:23 reasons 51:22 53:10 54:18 62:18 64:1 69:9 85:20 113:15 126:19 160:2 reassessment 162:20 recall 42:2 42:21 52:19 171:15 recap 108:18 received188:11 188:15 recent49:10 150:7 recently 43:25 recess 58:4 106:5 169:14 recessed189:22 recite 44:22 recognized

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133:10 referred104:7 refers 130:16 refine 78:2 refining79:21 reflect40:24 42:25 reflected35:25FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 254

36:5 reflective 13:25 125:4 reflects 33:9 50:2 120:21 regard 39:25 40:11 56:23 87:25 94:19 111:4 116:5 116:25 117:17 123:9 125:8 127:19 130:5 131:10 132:11 137:12 138:21 regarding11:8 18:19 51:23 52:8 76:15 77:24 109:17 regardless 67:17 regards 37:8 region85:22 registrant137:21 registration 107:7 138:23 154:25 regression77:1 77:17 98:21 111:8 129:19regressionbased111:7 regular 114:10 116:8 regularity80:13 regularly 70:3 114:6 regulating23:25 regulation 80:20 137:5 147:5 regulations 3:1 3:4 regulatory2:23 7:20 8:4 10:7 10:17 13:24 15:13 142:6 reinforce 127:11 reinforcement 115:22 reinforces 86:14 reiterate 19:8 21:22 relate 11:20 12:25 13:6 114:17 159:24 160:17 167:15 related

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67:16 remaining18:19 73:12 73:15 remains 17:17 remarkable 71:8 85:3 86:1 remarks 2:16 3:12 3:13 178:24 remember 27:20 88:11 removal 120:6 120:7 remove 60:19 60:20 119:5 119:8 119:10 119:10 removed

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respectively 46:14 respiratory37:5 respond

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147:11 155:19 Richard

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58:17 80:2 81:9 81:23 82:6 83:4 83:9 83:17 83:24 84:8 85:14 85:19 87:21 92:11 93:3 96:5 97:11 98:9 99:3 99:5 99:18 99:20 101:10 102:6 102:25 103:6 103:13 104:24 104:25 105:3 105:20 106:15 114:16 123:25 150:20 157:15 164:24 172:24 180:21 Rodriguez's 30:6 80:5 104:14 153:16 182:25 Rodriquez 158:9 role 9:6 10:9 46:3 48:17 157:8 rolling78:10 78:17 100:24 101:1 124:15 155:6 155:12 155:16 159:3 160:25 161:2 165:22 rolling-average 100:3 room 189:15 root118:18 roughly120:3 120:6 131:21 131:21 155:2 155:3 round60:12 60:22 rounds 76:11 route 60:11 routes 82:1 routine 2:15 RTP 168:3 rule 51:10 72:24 73:20 ruled50:14 run 15:16 137:2 163:8 running107:18 runoff 114:8 133:18 Russia189:6 189:7

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110:14 111:24 113:16 116:16 116:20 116:24 117:2 121:9 121:22 122:4 122:10 122:19 122:25 130:24 134:9 134:14 139:3 184:4 samplers 166:2 samples 116:8 116:21 119:25 121:22 122:5 123:3 127:18 138:6 138:8 138:10 139:7 sampling14:15 18:1 108:13 108:22 109:2 109:13 109:14 110:9 110:10 110:16 112:23 113:9 113:11 113:17 117:4 117:11 117:22 117:25 121:7 122:7 122:8 122:17 122:17 122:24 123:7 124:6 124:18 124:19 124:20 125:2 125:6 125:15 126:17 127:6 127:15 127:22 127:23 127:24 127:25 128:11 130:25 131:21 131:21 131:22 134:10 136:1 138:4 138:5 139:8 139:15 141:10 142:8 142:14 185:3FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 258

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128:14 shorter 109:3 109:22 159:2 160:24 shortly4:5 short-term

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57:18 58:5 80:1 80:23 82:4 82:22 86:2 86:11 86:23 87:22 90:15 92:22 93:1 94:14 95:5 95:20 96:22 98:16 99:14 101:4 101:21 102:20 103:15 104:4 104:13 104:24 105:10 105:16 106:6 134:17 136:17 139:17 141:4 143:11 144:14 145:5 145:13 146:8 146:11 161:23 165:9 165:23 166:5 167:13 168:17 169:15 170:5 171:7 171:19 172:1 174:9 175:19 176:6 176:12 177:3 177:25 178:16 178:20 179:9 179:17 180:7 180:23 181:13 182:2 182:9 182:12 183:6 183:20 183:25 184:13 185:5 185:12 186:10 187:2 188:25 189:4 189:7 Steve's 15:11 Stoker16:5 66:2 150:10 150:12 Stone 7:7 7:7 183:20 184:15 stop 67:13 91:4 94:23 145:10 145:11 145:13 146:5 181:11 stopped

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51:16 51:22 52:16 52:22 53:16 53:21 54:2 55:11 55:17 55:25 56:7 56:7 56:9 57:4 57:8 59:13 61:11 62:9 63:18 67:22 67:25 68:4 68:16 69:20 70:12 70:19 71:8 71:13 75:3 75:5 83:21 84:21 85:2 85:3 85:9 85:16 85:23 92:15 93:3 94:2 102:7 111:20 147:16 148:16 149:20 150:4 150:8 150:9 151:3 151:12 152:13 156:10 156:16 162:4 162:18 166:9 166:22 166:25 167:17 168:6 173:22 174:5 175:17 176:9 176:17 176:25 177:4 177:6 183:2 183:4 stuff 11:5 154:18 sub 175:12 sub-analyses 34:20 48:9 51:7 54:5 sub-analysis 47:19 subheading178:5 subjected136:20 submitted25:1 91:4 95:22 153:4 subpart181:23 182:18 182:20 subparts 177:9 sub-question 182:23 sub-questions 177:24 181:18 subsection175:8 subsequent41:1 89:18 subset116:7 116:9 116:12 117:10 153:5 153:5 subsets 116:12 153:1 153:4 153:13 substances 8:15 substantial 187:24 substantive 4:22 substituted

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148:9 168:21 188:23 Tammy 16:5 150:12 tap 10:20 target59:23 112:23 115:12 115:24 115:25 148:3 162:19 targeted112:10 114:22 116:1 targeting119:13 TCT 139:23 140:10 140:24 141:3 171:11 TCTs 170:2 171:17 team 14:23 15:6 15:15 15:18 16:4 20:14 54:24 56:2 56:4 56:8 107:5 145:21 154:3 175:10 187:13 technical 181:20 181:25 technically 185:19 techniques 12:21 40:20 40:21 technology114:3 temporal 13:7 13:13 59:11 63:6 71:11 79:13 108:13 129:10 130:17 130:19 130:22 132:14 164:25 temporality 155:18 157:12 ten 43:1 46:19 tend107:24 126:11 tends 128:6 140:15 tenfold

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174:8 176:3 177:13 178:21 179:3 180:25 181:1 186:14 188:25 Thede 64:14 96:6 158:11 181:9 themselves 184:19 184:24 theoretical 100:24 theoretically 100:13 theory111:11 128:23 therefore 62:11 86:9 there's 9:17 10:4 31:4 57:19 80:8 82:8 82:13 83:11 83:12 84:5 85:3 86:20 86:21 87:3 87:8 88:4 89:20 89:25 91:21 94:8 99:23 114:12 115:24 119:16 129:22 132:25 141:10 145:10 151:24 153:13 154:1 156:21 157:1 162:21 166:15 168:6 175:10 180:17 186:1 187:20 There's...Susan 103:22 they'll 89:13 152:5 they're 23:4 71:1 77:20 83:15 87:6 87:13 87:16 89:22 90:10 90:10 96:12 96:16 115:19 116:10 127:5 128:13 133:13 135:9 145:23 146:5 146:6 149:17 152:6 153:1 154:2 155:1 160:2 160:3 166:1 166:2 167:8 177:10 181:11 183:12 185:17 thick 70:21 third2:9 19:10 21:21 34:21 46:14 48:11 52:20 120:1 152:3 thirdly120:21 third--you90:6 thorough65:1 thoughtful 18:25 threshold124:4 throughout4:25 53:11 75:25 112:21 112:22 156:15 168:4 172:20 189:12 throwing103:4 throws 90:19 Thurman 15:23 27:7 106:18 106:20 134:25 135:4 135:12 135:17 135:21 136:12 137:8 137:12 138:11 140:4 140:8 141:2 142:17 166:4 170:7 171:13 171:23 183:14 183:23 184:2 184:17 185:2 185:7 Thurman's 100:8 Thursday 174:25 189:19 thus 9:19 13:20 TIDA 157:4 157:5 tie 45:14 tier 115:21 tighter 49:2 time-integrated 114:11 timeline 162:18 Tina 14:21 15:9 21:7 Tina's 19:8 Tinelle 55:1 tiny 171:8 tissue 150:7 157:23 180:14 181:9 title 35:16 today 16:18 22:15 22:21 24:8 24:23FIFRA SCIENTIFIC ADVISORY PANEL 09/14/10 CCR#15732-5 268

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